{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**loading the dataset**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Control delimiters, rows, column names with read_csv (see later) \n", "df = pd.read_csv(\"loans.csv\", engine='python')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How does it look like ?**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# show all columns\n", "pd.set_option('display.max_columns', None)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_dcollections_12_mths_ex_medmths_since_last_major_derogpolicy_codeapplication_typeannual_inc_jointdti_jointverification_status_jointacc_now_delinqtot_coll_amttot_cur_balopen_acc_6mopen_act_ilopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utilchargeoff_within_12_mthsdelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_120dpd_2mnum_tl_30dpdnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75pub_rec_bankruptciestax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limitrevol_bal_jointsec_app_earliest_cr_linesec_app_inq_last_6mthssec_app_mort_accsec_app_open_accsec_app_revol_utilsec_app_open_act_ilsec_app_num_rev_acctssec_app_chargeoff_within_12_mthssec_app_collections_12_mths_ex_medsec_app_mths_since_last_major_deroghardship_flaghardship_typehardship_reasonhardship_statusdeferral_termhardship_amounthardship_start_datehardship_end_datepayment_plan_start_datehardship_lengthhardship_dpdhardship_loan_statusorig_projected_additional_accrued_interesthardship_payoff_balance_amounthardship_last_payment_amountdisbursement_methoddebt_settlement_flagdebt_settlement_flag_datesettlement_statussettlement_datesettlement_amountsettlement_percentagesettlement_term
01040017NaNNaN140001400014000.036 months12.69469.63CC2Receiving Dock Worker9 yearsMORTGAGE40000.0Not Verified2015-10-01Charged OffnNaNNaNdebt_consolidationDebt consolidation166xxPA17.070.0Jun-20011.0NaNNaN5.00.0584890.015.0f0.00.06057.7900006057.794091.511556.410.0409.8773.7766Oct-2016469.63NaNJul-20180.0NaN1IndividualNaNNaNNaN0.00.0119776.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6500.0NaNNaNNaN4.023955.02167.090.00.00.0141.0172.03.03.01.03.0NaN3.0NaN0.03.03.08.08.06.03.08.03.05.0NaN0.00.02.0100.0100.00.00.0123292.029809.06500.025992.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
11050463NaNNaN100010001000.036 months9.1731.88BB2Portfolio Manager1 yearMORTGAGE80000.0Verified2015-10-01Fully PaidnNaNNaNcredit_cardCredit card refinancing949xxCA12.510.0Oct-19673.0NaN22.09.01.0763437.232.0w0.00.01021.7300001021.73999.9921.740.00.000.0000Feb-201627.85NaNFeb-20170.0NaN1IndividualNaNNaNNaN0.00.053994.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20500.0NaNNaNNaN4.05999.012866.037.20.00.0188.0575.04.04.03.04.0NaN1.0NaN0.03.03.06.016.09.06.020.03.09.00.00.00.03.0100.00.01.00.080788.053994.020500.060288.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
21056254NaNNaN130001300013000.036 months6.89400.75AA3mailhandler10+ yearsRENT70000.0Not Verified2015-10-01Fully PaidnNaNNaNdebt_consolidationDebt consolidation751xxTX21.720.0Oct-19960.0NaNNaN16.00.01111324.423.0w0.00.014425.91541314425.9213000.001425.920.00.000.0000Oct-2018401.38NaNJan-20190.0NaN1IndividualNaNNaNNaN0.0316.093918.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN45600.0NaNNaNNaN10.05870.028646.026.90.00.0227.060.07.05.03.07.0NaN7.0NaN0.07.08.08.08.08.010.010.08.016.00.00.00.04.0100.00.00.00.0141771.043988.039200.042191.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
31013860NaNNaN560056005600.036 months6.24170.98AA2Managing Director4 yearsRENT49000.0Source Verified2015-10-01Fully PaidnNaNNaNdebt_consolidationDebt consolidation968xxHI2.450.0Dec-19880.0NaNNaN4.00.0340241.58.0w0.00.06051.3444766051.345600.00451.340.00.000.0000Aug-20172635.62NaNAug-20170.0NaN1IndividualNaNNaNNaN0.00.03402.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8200.0NaNNaNNaN0.0851.03898.046.60.00.0NaN322.026.026.00.026.0NaNNaNNaN0.03.03.03.06.00.04.08.03.04.00.00.00.00.0100.033.30.00.08200.03402.07300.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
41018431NaNNaN110001100011000.060 months12.29246.31CC1RTI Administrator4 yearsMORTGAGE86000.0Not Verified2015-10-01Fully PaidnNaNNaNdebt_consolidationDebt consolidation388xxMS24.352.0Nov-20011.02.0NaN12.00.0606268.924.0w0.00.014008.06937314008.0711000.003008.070.00.000.0000Aug-20185894.86NaNFeb-20190.0NaN1IndividualNaNNaNNaN0.00.0331363.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8800.0NaNNaNNaN3.027614.01083.081.90.00.0167.0160.010.010.02.023.02.06.02.00.02.03.02.05.013.04.09.03.012.00.00.00.01.087.550.00.00.0393082.081179.06000.0118795.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " Unnamed: 0 id member_id loan_amnt funded_amnt funded_amnt_inv \\\n", "0 1040017 NaN NaN 14000 14000 14000.0 \n", "1 1050463 NaN NaN 1000 1000 1000.0 \n", "2 1056254 NaN NaN 13000 13000 13000.0 \n", "3 1013860 NaN NaN 5600 5600 5600.0 \n", "4 1018431 NaN NaN 11000 11000 11000.0 \n", "\n", " term int_rate installment grade sub_grade emp_title \\\n", "0 36 months 12.69 469.63 C C2 Receiving Dock Worker \n", "1 36 months 9.17 31.88 B B2 Portfolio Manager \n", "2 36 months 6.89 400.75 A A3 mailhandler \n", "3 36 months 6.24 170.98 A A2 Managing Director \n", "4 60 months 12.29 246.31 C C1 RTI Administrator \n", "\n", " emp_length home_ownership annual_inc verification_status issue_d \\\n", "0 9 years MORTGAGE 40000.0 Not Verified 2015-10-01 \n", "1 1 year MORTGAGE 80000.0 Verified 2015-10-01 \n", "2 10+ years RENT 70000.0 Not Verified 2015-10-01 \n", "3 4 years RENT 49000.0 Source Verified 2015-10-01 \n", "4 4 years MORTGAGE 86000.0 Not Verified 2015-10-01 \n", "\n", " loan_status pymnt_plan url desc purpose \\\n", "0 Charged Off n NaN NaN debt_consolidation \n", "1 Fully Paid n NaN NaN credit_card \n", "2 Fully Paid n NaN NaN debt_consolidation \n", "3 Fully Paid n NaN NaN debt_consolidation \n", "4 Fully Paid n NaN NaN debt_consolidation \n", "\n", " title zip_code addr_state dti delinq_2yrs \\\n", "0 Debt consolidation 166xx PA 17.07 0.0 \n", "1 Credit card refinancing 949xx CA 12.51 0.0 \n", "2 Debt consolidation 751xx TX 21.72 0.0 \n", "3 Debt consolidation 968xx HI 2.45 0.0 \n", "4 Debt consolidation 388xx MS 24.35 2.0 \n", "\n", " earliest_cr_line inq_last_6mths mths_since_last_delinq \\\n", "0 Jun-2001 1.0 NaN \n", "1 Oct-1967 3.0 NaN \n", "2 Oct-1996 0.0 NaN \n", "3 Dec-1988 0.0 NaN \n", "4 Nov-2001 1.0 2.0 \n", "\n", " mths_since_last_record open_acc pub_rec revol_bal revol_util \\\n", "0 NaN 5.0 0.0 5848 90.0 \n", "1 22.0 9.0 1.0 7634 37.2 \n", "2 NaN 16.0 0.0 11113 24.4 \n", "3 NaN 4.0 0.0 3402 41.5 \n", "4 NaN 12.0 0.0 6062 68.9 \n", "\n", " total_acc initial_list_status out_prncp out_prncp_inv total_pymnt \\\n", "0 15.0 f 0.0 0.0 6057.790000 \n", "1 32.0 w 0.0 0.0 1021.730000 \n", "2 23.0 w 0.0 0.0 14425.915413 \n", "3 8.0 w 0.0 0.0 6051.344476 \n", "4 24.0 w 0.0 0.0 14008.069373 \n", "\n", " total_pymnt_inv total_rec_prncp total_rec_int total_rec_late_fee \\\n", "0 6057.79 4091.51 1556.41 0.0 \n", "1 1021.73 999.99 21.74 0.0 \n", "2 14425.92 13000.00 1425.92 0.0 \n", "3 6051.34 5600.00 451.34 0.0 \n", "4 14008.07 11000.00 3008.07 0.0 \n", "\n", " recoveries collection_recovery_fee last_pymnt_d last_pymnt_amnt \\\n", "0 409.87 73.7766 Oct-2016 469.63 \n", "1 0.00 0.0000 Feb-2016 27.85 \n", "2 0.00 0.0000 Oct-2018 401.38 \n", "3 0.00 0.0000 Aug-2017 2635.62 \n", "4 0.00 0.0000 Aug-2018 5894.86 \n", "\n", " next_pymnt_d last_credit_pull_d collections_12_mths_ex_med \\\n", "0 NaN Jul-2018 0.0 \n", "1 NaN Feb-2017 0.0 \n", "2 NaN Jan-2019 0.0 \n", "3 NaN Aug-2017 0.0 \n", "4 NaN Feb-2019 0.0 \n", "\n", " mths_since_last_major_derog policy_code application_type \\\n", "0 NaN 1 Individual \n", "1 NaN 1 Individual \n", "2 NaN 1 Individual \n", "3 NaN 1 Individual \n", "4 NaN 1 Individual \n", "\n", " annual_inc_joint dti_joint verification_status_joint acc_now_delinq \\\n", "0 NaN NaN NaN 0.0 \n", "1 NaN NaN NaN 0.0 \n", "2 NaN NaN NaN 0.0 \n", "3 NaN NaN NaN 0.0 \n", "4 NaN NaN NaN 0.0 \n", "\n", " tot_coll_amt tot_cur_bal open_acc_6m open_act_il open_il_12m \\\n", "0 0.0 119776.0 NaN NaN NaN \n", "1 0.0 53994.0 NaN NaN NaN \n", "2 316.0 93918.0 NaN NaN NaN \n", "3 0.0 3402.0 NaN NaN NaN \n", "4 0.0 331363.0 NaN NaN NaN \n", "\n", " open_il_24m mths_since_rcnt_il total_bal_il il_util open_rv_12m \\\n", "0 NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN \n", "\n", " open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi total_cu_tl \\\n", "0 NaN NaN NaN 6500.0 NaN NaN \n", "1 NaN NaN NaN 20500.0 NaN NaN \n", "2 NaN NaN NaN 45600.0 NaN NaN \n", "3 NaN NaN NaN 8200.0 NaN NaN \n", "4 NaN NaN NaN 8800.0 NaN NaN \n", "\n", " inq_last_12m acc_open_past_24mths avg_cur_bal bc_open_to_buy bc_util \\\n", "0 NaN 4.0 23955.0 2167.0 90.0 \n", "1 NaN 4.0 5999.0 12866.0 37.2 \n", "2 NaN 10.0 5870.0 28646.0 26.9 \n", "3 NaN 0.0 851.0 3898.0 46.6 \n", "4 NaN 3.0 27614.0 1083.0 81.9 \n", "\n", " chargeoff_within_12_mths delinq_amnt mo_sin_old_il_acct \\\n", "0 0.0 0.0 141.0 \n", "1 0.0 0.0 188.0 \n", "2 0.0 0.0 227.0 \n", "3 0.0 0.0 NaN \n", "4 0.0 0.0 167.0 \n", "\n", " mo_sin_old_rev_tl_op mo_sin_rcnt_rev_tl_op mo_sin_rcnt_tl mort_acc \\\n", "0 172.0 3.0 3.0 1.0 \n", "1 575.0 4.0 4.0 3.0 \n", "2 60.0 7.0 5.0 3.0 \n", "3 322.0 26.0 26.0 0.0 \n", "4 160.0 10.0 10.0 2.0 \n", "\n", " mths_since_recent_bc mths_since_recent_bc_dlq mths_since_recent_inq \\\n", "0 3.0 NaN 3.0 \n", "1 4.0 NaN 1.0 \n", "2 7.0 NaN 7.0 \n", "3 26.0 NaN NaN \n", "4 23.0 2.0 6.0 \n", "\n", " mths_since_recent_revol_delinq num_accts_ever_120_pd num_actv_bc_tl \\\n", "0 NaN 0.0 3.0 \n", "1 NaN 0.0 3.0 \n", "2 NaN 0.0 7.0 \n", "3 NaN 0.0 3.0 \n", "4 2.0 0.0 2.0 \n", "\n", " num_actv_rev_tl num_bc_sats num_bc_tl num_il_tl num_op_rev_tl \\\n", "0 3.0 8.0 8.0 6.0 3.0 \n", "1 3.0 6.0 16.0 9.0 6.0 \n", "2 8.0 8.0 8.0 8.0 10.0 \n", "3 3.0 3.0 6.0 0.0 4.0 \n", "4 3.0 2.0 5.0 13.0 4.0 \n", "\n", " num_rev_accts num_rev_tl_bal_gt_0 num_sats num_tl_120dpd_2m \\\n", "0 8.0 3.0 5.0 NaN \n", "1 20.0 3.0 9.0 0.0 \n", "2 10.0 8.0 16.0 0.0 \n", "3 8.0 3.0 4.0 0.0 \n", "4 9.0 3.0 12.0 0.0 \n", "\n", " num_tl_30dpd num_tl_90g_dpd_24m num_tl_op_past_12m pct_tl_nvr_dlq \\\n", "0 0.0 0.0 2.0 100.0 \n", "1 0.0 0.0 3.0 100.0 \n", "2 0.0 0.0 4.0 100.0 \n", "3 0.0 0.0 0.0 100.0 \n", "4 0.0 0.0 1.0 87.5 \n", "\n", " percent_bc_gt_75 pub_rec_bankruptcies tax_liens tot_hi_cred_lim \\\n", "0 100.0 0.0 0.0 123292.0 \n", "1 0.0 1.0 0.0 80788.0 \n", "2 0.0 0.0 0.0 141771.0 \n", "3 33.3 0.0 0.0 8200.0 \n", "4 50.0 0.0 0.0 393082.0 \n", "\n", " total_bal_ex_mort total_bc_limit total_il_high_credit_limit \\\n", "0 29809.0 6500.0 25992.0 \n", "1 53994.0 20500.0 60288.0 \n", "2 43988.0 39200.0 42191.0 \n", "3 3402.0 7300.0 0.0 \n", "4 81179.0 6000.0 118795.0 \n", "\n", " revol_bal_joint sec_app_earliest_cr_line sec_app_inq_last_6mths \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " sec_app_mort_acc sec_app_open_acc sec_app_revol_util \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " sec_app_open_act_il sec_app_num_rev_accts \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " sec_app_chargeoff_within_12_mths sec_app_collections_12_mths_ex_med \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " sec_app_mths_since_last_major_derog hardship_flag hardship_type \\\n", "0 NaN N NaN \n", "1 NaN N NaN \n", "2 NaN N NaN \n", "3 NaN N NaN \n", "4 NaN N NaN \n", "\n", " hardship_reason hardship_status deferral_term hardship_amount \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "\n", " hardship_start_date hardship_end_date payment_plan_start_date \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " hardship_length hardship_dpd hardship_loan_status \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " orig_projected_additional_accrued_interest hardship_payoff_balance_amount \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " hardship_last_payment_amount disbursement_method debt_settlement_flag \\\n", "0 NaN Cash N \n", "1 NaN Cash N \n", "2 NaN Cash N \n", "3 NaN Cash N \n", "4 NaN Cash N \n", "\n", " debt_settlement_flag_date settlement_status settlement_date \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " settlement_amount settlement_percentage settlement_term \n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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7499957605NaNNaN150001500015000.036 months13.56509.47CC1Driver4 yearsRENT55000.0Not Verified2018-12-01CurrentnNaNNaNdebt_consolidationDebt consolidation171xxPA17.150.0Oct-20120.0NaNNaN9.00.0727740.014.0w14316.2214316.221001.991001.99683.78318.210.00.00.0Feb-2019509.47Mar-2019Feb-20190.0NaN1IndividualNaNNaNNaN0.00.021229.00.02.01.03.08.013952.085.01.01.04778.061.018200.01.00.02.04.02359.02457.069.70.00.074.054.012.08.00.035.0NaN8.0NaN0.02.05.02.02.04.07.010.05.09.00.00.00.02.0100.050.00.00.034632.021229.08100.016432.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
74999635399NaNNaN800080008000.036 months6.46245.05AA1Realtor2 yearsMORTGAGE100000.0Not Verified2018-12-01CurrentnNaNNaNdebt_consolidationDebt consolidation231xxVA9.901.0Sep-20010.021.0NaN13.00.01764651.729.0w7389.007389.00692.18692.18611.0081.180.00.00.0Feb-2019245.05Mar-2019Feb-20190.0NaN1IndividualNaNNaNNaN0.03323.0326631.01.02.00.00.031.013286.057.02.03.02173.054.034100.00.03.00.03.027219.012434.026.00.00.082.0206.02.02.02.02.063.017.021.00.04.05.05.011.03.010.024.05.013.00.00.00.02.082.840.00.00.0381832.030932.016800.023370.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNDirectPayNNaNNaNNaNNaNNaNNaN
7499978452NaNNaN400004000040000.060 months16.14975.71CC4Director of Operations3 yearsMORTGAGE157630.0Verified2018-12-01CurrentnNaNNaNcredit_cardCredit card refinancing972xxOR16.611.0May-20040.018.0NaN11.00.01576156.721.0w39562.2839562.281155.051155.05437.72717.330.00.00.0Feb-20191208.85Mar-2019Feb-20190.0NaN1IndividualNaNNaNNaN0.00.0474781.01.04.01.01.05.059310.038.00.00.09029.043.027800.00.05.00.01.043162.09453.059.80.00.071.0175.027.05.02.027.0NaNNaNNaN0.03.04.03.04.06.06.013.04.011.00.00.00.01.095.266.70.00.0542479.075071.023500.0100389.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
74999821664NaNNaN700070007000.036 months8.19219.97AA4Bill Poster10+ yearsMORTGAGE42000.0Verified2018-12-01CurrentnNaNNaNotherOther707xxLA6.490.0Oct-20030.0NaNNaN7.00.0689853.122.0w6654.436654.43456.87456.87345.57111.300.00.00.0Feb-2019243.27Mar-2019Feb-20190.0NaN1Joint App70000.04.35Verified0.0440.0114983.00.00.00.00.049.00.0NaN1.01.01048.053.013000.00.00.00.01.016426.01620.064.00.00.0127.0160.08.08.04.08.0NaNNaNNaN0.03.05.03.03.013.05.05.05.07.00.00.00.01.0100.033.30.00.0232200.06898.04500.00.07445.0Jul-20030.04.06.059.80.06.00.00.058.0NNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
74999924316NaNNaN150001500015000.060 months17.97380.66DD1Document4 yearsRENT110000.0Not Verified2018-12-01CurrentnNaNNaNdebt_consolidationDebt consolidation950xxCA13.280.0Mar-20110.0NaNNaN10.00.01343972.313.0w14685.5914685.59746.34746.34314.41431.930.00.00.0Feb-2019380.66Mar-2019Feb-20190.0NaN1IndividualNaNNaNNaN0.00.024340.00.02.00.02.016.010901.054.01.01.03313.063.018600.01.01.00.03.02704.03131.073.70.00.068.092.07.07.00.07.0NaN16.0NaN0.04.07.04.04.03.08.010.07.010.00.00.00.01.0100.075.00.00.038908.024340.011900.020308.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " Unnamed: 0 id member_id loan_amnt funded_amnt funded_amnt_inv \\\n", "749995 7605 NaN NaN 15000 15000 15000.0 \n", "749996 35399 NaN NaN 8000 8000 8000.0 \n", "749997 8452 NaN NaN 40000 40000 40000.0 \n", "749998 21664 NaN NaN 7000 7000 7000.0 \n", "749999 24316 NaN NaN 15000 15000 15000.0 \n", "\n", " term int_rate installment grade sub_grade \\\n", "749995 36 months 13.56 509.47 C C1 \n", "749996 36 months 6.46 245.05 A A1 \n", "749997 60 months 16.14 975.71 C C4 \n", "749998 36 months 8.19 219.97 A A4 \n", "749999 60 months 17.97 380.66 D D1 \n", "\n", " emp_title emp_length home_ownership annual_inc \\\n", "749995 Driver 4 years RENT 55000.0 \n", "749996 Realtor 2 years MORTGAGE 100000.0 \n", "749997 Director of Operations 3 years MORTGAGE 157630.0 \n", "749998 Bill Poster 10+ years MORTGAGE 42000.0 \n", "749999 Document 4 years RENT 110000.0 \n", "\n", " verification_status issue_d loan_status pymnt_plan url desc \\\n", "749995 Not Verified 2018-12-01 Current n NaN NaN \n", "749996 Not Verified 2018-12-01 Current n NaN NaN \n", "749997 Verified 2018-12-01 Current n NaN NaN \n", "749998 Verified 2018-12-01 Current n NaN NaN \n", "749999 Not Verified 2018-12-01 Current n NaN NaN \n", "\n", " purpose title zip_code addr_state \\\n", "749995 debt_consolidation Debt consolidation 171xx PA \n", "749996 debt_consolidation Debt consolidation 231xx VA \n", "749997 credit_card Credit card refinancing 972xx OR \n", "749998 other Other 707xx LA \n", "749999 debt_consolidation Debt consolidation 950xx CA \n", "\n", " dti delinq_2yrs earliest_cr_line inq_last_6mths \\\n", "749995 17.15 0.0 Oct-2012 0.0 \n", "749996 9.90 1.0 Sep-2001 0.0 \n", "749997 16.61 1.0 May-2004 0.0 \n", "749998 6.49 0.0 Oct-2003 0.0 \n", "749999 13.28 0.0 Mar-2011 0.0 \n", "\n", " mths_since_last_delinq mths_since_last_record open_acc pub_rec \\\n", "749995 NaN NaN 9.0 0.0 \n", "749996 21.0 NaN 13.0 0.0 \n", "749997 18.0 NaN 11.0 0.0 \n", "749998 NaN NaN 7.0 0.0 \n", "749999 NaN NaN 10.0 0.0 \n", "\n", " revol_bal revol_util total_acc initial_list_status out_prncp \\\n", "749995 7277 40.0 14.0 w 14316.22 \n", "749996 17646 51.7 29.0 w 7389.00 \n", "749997 15761 56.7 21.0 w 39562.28 \n", "749998 6898 53.1 22.0 w 6654.43 \n", "749999 13439 72.3 13.0 w 14685.59 \n", "\n", " out_prncp_inv total_pymnt total_pymnt_inv total_rec_prncp \\\n", "749995 14316.22 1001.99 1001.99 683.78 \n", "749996 7389.00 692.18 692.18 611.00 \n", "749997 39562.28 1155.05 1155.05 437.72 \n", "749998 6654.43 456.87 456.87 345.57 \n", "749999 14685.59 746.34 746.34 314.41 \n", "\n", " total_rec_int total_rec_late_fee recoveries \\\n", "749995 318.21 0.0 0.0 \n", "749996 81.18 0.0 0.0 \n", "749997 717.33 0.0 0.0 \n", "749998 111.30 0.0 0.0 \n", "749999 431.93 0.0 0.0 \n", "\n", " collection_recovery_fee last_pymnt_d last_pymnt_amnt next_pymnt_d \\\n", "749995 0.0 Feb-2019 509.47 Mar-2019 \n", "749996 0.0 Feb-2019 245.05 Mar-2019 \n", "749997 0.0 Feb-2019 1208.85 Mar-2019 \n", "749998 0.0 Feb-2019 243.27 Mar-2019 \n", "749999 0.0 Feb-2019 380.66 Mar-2019 \n", "\n", " last_credit_pull_d collections_12_mths_ex_med \\\n", "749995 Feb-2019 0.0 \n", "749996 Feb-2019 0.0 \n", "749997 Feb-2019 0.0 \n", "749998 Feb-2019 0.0 \n", "749999 Feb-2019 0.0 \n", "\n", " mths_since_last_major_derog policy_code application_type \\\n", "749995 NaN 1 Individual \n", "749996 NaN 1 Individual \n", "749997 NaN 1 Individual \n", "749998 NaN 1 Joint App \n", "749999 NaN 1 Individual \n", "\n", " annual_inc_joint dti_joint verification_status_joint acc_now_delinq \\\n", "749995 NaN NaN NaN 0.0 \n", "749996 NaN NaN NaN 0.0 \n", "749997 NaN NaN NaN 0.0 \n", "749998 70000.0 4.35 Verified 0.0 \n", "749999 NaN NaN NaN 0.0 \n", "\n", " tot_coll_amt tot_cur_bal open_acc_6m open_act_il open_il_12m \\\n", "749995 0.0 21229.0 0.0 2.0 1.0 \n", "749996 3323.0 326631.0 1.0 2.0 0.0 \n", "749997 0.0 474781.0 1.0 4.0 1.0 \n", "749998 440.0 114983.0 0.0 0.0 0.0 \n", "749999 0.0 24340.0 0.0 2.0 0.0 \n", "\n", " open_il_24m mths_since_rcnt_il total_bal_il il_util open_rv_12m \\\n", "749995 3.0 8.0 13952.0 85.0 1.0 \n", "749996 0.0 31.0 13286.0 57.0 2.0 \n", "749997 1.0 5.0 59310.0 38.0 0.0 \n", "749998 0.0 49.0 0.0 NaN 1.0 \n", "749999 2.0 16.0 10901.0 54.0 1.0 \n", "\n", " open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi \\\n", "749995 1.0 4778.0 61.0 18200.0 1.0 \n", "749996 3.0 2173.0 54.0 34100.0 0.0 \n", "749997 0.0 9029.0 43.0 27800.0 0.0 \n", "749998 1.0 1048.0 53.0 13000.0 0.0 \n", "749999 1.0 3313.0 63.0 18600.0 1.0 \n", "\n", " total_cu_tl inq_last_12m acc_open_past_24mths avg_cur_bal \\\n", "749995 0.0 2.0 4.0 2359.0 \n", "749996 3.0 0.0 3.0 27219.0 \n", "749997 5.0 0.0 1.0 43162.0 \n", "749998 0.0 0.0 1.0 16426.0 \n", "749999 1.0 0.0 3.0 2704.0 \n", "\n", " bc_open_to_buy bc_util chargeoff_within_12_mths delinq_amnt \\\n", "749995 2457.0 69.7 0.0 0.0 \n", "749996 12434.0 26.0 0.0 0.0 \n", "749997 9453.0 59.8 0.0 0.0 \n", "749998 1620.0 64.0 0.0 0.0 \n", "749999 3131.0 73.7 0.0 0.0 \n", "\n", " mo_sin_old_il_acct mo_sin_old_rev_tl_op mo_sin_rcnt_rev_tl_op \\\n", "749995 74.0 54.0 12.0 \n", "749996 82.0 206.0 2.0 \n", "749997 71.0 175.0 27.0 \n", "749998 127.0 160.0 8.0 \n", "749999 68.0 92.0 7.0 \n", "\n", " mo_sin_rcnt_tl mort_acc mths_since_recent_bc \\\n", "749995 8.0 0.0 35.0 \n", "749996 2.0 2.0 2.0 \n", "749997 5.0 2.0 27.0 \n", "749998 8.0 4.0 8.0 \n", "749999 7.0 0.0 7.0 \n", "\n", " mths_since_recent_bc_dlq mths_since_recent_inq \\\n", "749995 NaN 8.0 \n", "749996 63.0 17.0 \n", "749997 NaN NaN \n", "749998 NaN NaN \n", "749999 NaN 16.0 \n", "\n", " mths_since_recent_revol_delinq num_accts_ever_120_pd num_actv_bc_tl \\\n", "749995 NaN 0.0 2.0 \n", "749996 21.0 0.0 4.0 \n", "749997 NaN 0.0 3.0 \n", "749998 NaN 0.0 3.0 \n", "749999 NaN 0.0 4.0 \n", "\n", " num_actv_rev_tl num_bc_sats num_bc_tl num_il_tl num_op_rev_tl \\\n", "749995 5.0 2.0 2.0 4.0 7.0 \n", "749996 5.0 5.0 11.0 3.0 10.0 \n", "749997 4.0 3.0 4.0 6.0 6.0 \n", "749998 5.0 3.0 3.0 13.0 5.0 \n", "749999 7.0 4.0 4.0 3.0 8.0 \n", "\n", " num_rev_accts num_rev_tl_bal_gt_0 num_sats num_tl_120dpd_2m \\\n", "749995 10.0 5.0 9.0 0.0 \n", "749996 24.0 5.0 13.0 0.0 \n", "749997 13.0 4.0 11.0 0.0 \n", "749998 5.0 5.0 7.0 0.0 \n", "749999 10.0 7.0 10.0 0.0 \n", "\n", " num_tl_30dpd num_tl_90g_dpd_24m num_tl_op_past_12m pct_tl_nvr_dlq \\\n", "749995 0.0 0.0 2.0 100.0 \n", "749996 0.0 0.0 2.0 82.8 \n", "749997 0.0 0.0 1.0 95.2 \n", "749998 0.0 0.0 1.0 100.0 \n", "749999 0.0 0.0 1.0 100.0 \n", "\n", " percent_bc_gt_75 pub_rec_bankruptcies tax_liens tot_hi_cred_lim \\\n", "749995 50.0 0.0 0.0 34632.0 \n", "749996 40.0 0.0 0.0 381832.0 \n", "749997 66.7 0.0 0.0 542479.0 \n", "749998 33.3 0.0 0.0 232200.0 \n", "749999 75.0 0.0 0.0 38908.0 \n", "\n", " total_bal_ex_mort total_bc_limit total_il_high_credit_limit \\\n", "749995 21229.0 8100.0 16432.0 \n", "749996 30932.0 16800.0 23370.0 \n", "749997 75071.0 23500.0 100389.0 \n", "749998 6898.0 4500.0 0.0 \n", "749999 24340.0 11900.0 20308.0 \n", "\n", " revol_bal_joint sec_app_earliest_cr_line sec_app_inq_last_6mths \\\n", "749995 NaN NaN NaN \n", "749996 NaN NaN NaN \n", "749997 NaN NaN NaN \n", "749998 7445.0 Jul-2003 0.0 \n", "749999 NaN NaN NaN \n", "\n", " sec_app_mort_acc sec_app_open_acc sec_app_revol_util \\\n", "749995 NaN NaN NaN \n", "749996 NaN NaN NaN \n", "749997 NaN NaN NaN \n", "749998 4.0 6.0 59.8 \n", "749999 NaN NaN NaN \n", "\n", " sec_app_open_act_il sec_app_num_rev_accts \\\n", "749995 NaN NaN \n", "749996 NaN NaN \n", "749997 NaN NaN \n", "749998 0.0 6.0 \n", "749999 NaN NaN \n", "\n", " sec_app_chargeoff_within_12_mths sec_app_collections_12_mths_ex_med \\\n", "749995 NaN NaN \n", "749996 NaN NaN \n", "749997 NaN NaN \n", "749998 0.0 0.0 \n", "749999 NaN NaN \n", "\n", " sec_app_mths_since_last_major_derog hardship_flag hardship_type \\\n", "749995 NaN N NaN \n", "749996 NaN N NaN \n", "749997 NaN N NaN \n", "749998 58.0 N NaN \n", "749999 NaN N NaN \n", "\n", " hardship_reason hardship_status deferral_term hardship_amount \\\n", "749995 NaN NaN NaN NaN \n", "749996 NaN NaN NaN NaN \n", "749997 NaN NaN NaN NaN \n", "749998 NaN NaN NaN NaN \n", "749999 NaN NaN NaN NaN \n", "\n", " hardship_start_date hardship_end_date payment_plan_start_date \\\n", "749995 NaN NaN NaN \n", "749996 NaN NaN NaN \n", "749997 NaN NaN NaN \n", "749998 NaN NaN NaN \n", "749999 NaN NaN NaN \n", "\n", " hardship_length hardship_dpd hardship_loan_status \\\n", "749995 NaN NaN NaN \n", "749996 NaN NaN NaN \n", "749997 NaN NaN NaN \n", "749998 NaN NaN NaN \n", "749999 NaN NaN NaN \n", "\n", " orig_projected_additional_accrued_interest \\\n", "749995 NaN \n", "749996 NaN \n", "749997 NaN \n", "749998 NaN \n", "749999 NaN \n", "\n", " hardship_payoff_balance_amount hardship_last_payment_amount \\\n", "749995 NaN NaN \n", "749996 NaN NaN \n", "749997 NaN NaN \n", "749998 NaN NaN \n", "749999 NaN NaN \n", "\n", " disbursement_method debt_settlement_flag debt_settlement_flag_date \\\n", "749995 Cash N NaN \n", "749996 DirectPay N NaN \n", "749997 Cash N NaN \n", "749998 Cash N NaN \n", "749999 Cash N NaN \n", "\n", " settlement_status settlement_date settlement_amount \\\n", "749995 NaN NaN NaN \n", "749996 NaN NaN NaN \n", "749997 NaN NaN NaN \n", "749998 NaN NaN NaN \n", "749999 NaN NaN NaN \n", "\n", " settlement_percentage settlement_term \n", "749995 NaN NaN \n", "749996 NaN NaN \n", "749997 NaN NaN \n", "749998 NaN NaN \n", "749999 NaN NaN " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What are the total number of loans?**" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "750000" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How many columns does the dataset have?**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "146" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How may columns contain missing data?**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "miss_values_count = df.isnull().sum(min_count=1)\n", "miss_values_count = miss_values_count[miss_values_count != 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What is the count of missing data for each of this column?**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of columns with missing values: 74\n", "Null value count per column: \n", " - For column name 'id' there aren 750000 missing valuess.\n", " - For column name 'member_id' there aren 750000 missing valuess.\n", " - For column name 'emp_title' there aren 61669 missing valuess.\n", " - For column name 'emp_length' there aren 54976 missing valuess.\n", " - For column name 'url' there aren 750000 missing valuess.\n", " - For column name 'desc' there aren 749986 missing valuess.\n", " - For column name 'title' there aren 11781 missing valuess.\n", " - For column name 'dti' there aren 835 missing valuess.\n", " - For column name 'inq_last_6mths' there are 1 missing values.\n", " - For column name 'mths_since_last_delinq' there aren 382775 missing valuess.\n", " - For column name 'mths_since_last_record' there aren 627757 missing valuess.\n", " - For column name 'revol_util' there aren 659 missing valuess.\n", " - For column name 'last_pymnt_d' there aren 909 missing valuess.\n", " - For column name 'next_pymnt_d' there aren 295358 missing valuess.\n", " - For column name 'last_credit_pull_d' there aren 18 missing valuess.\n", " - For column name 'mths_since_last_major_derog' there aren 552182 missing valuess.\n", " - For column name 'annual_inc_joint' there aren 689705 missing valuess.\n", " - For column name 'dti_joint' there aren 689705 missing valuess.\n", " - For column name 'verification_status_joint' there aren 692145 missing valuess.\n", " - For column name 'open_acc_6m' there aren 52540 missing valuess.\n", " - For column name 'open_act_il' there aren 52539 missing valuess.\n", " - For column name 'open_il_12m' there aren 52539 missing valuess.\n", " - For column name 'open_il_24m' there aren 52539 missing valuess.\n", " - For column name 'mths_since_rcnt_il' there aren 74494 missing valuess.\n", " - For column name 'total_bal_il' there aren 52539 missing valuess.\n", " - For column name 'il_util' there aren 153788 missing valuess.\n", " - For column name 'open_rv_12m' there aren 52539 missing valuess.\n", " - For column name 'open_rv_24m' there aren 52539 missing valuess.\n", " - For column name 'max_bal_bc' there aren 52539 missing valuess.\n", " - For column name 'all_util' there aren 52662 missing valuess.\n", " - For column name 'inq_fi' there aren 52539 missing valuess.\n", " - For column name 'total_cu_tl' there aren 52540 missing valuess.\n", " - For column name 'inq_last_12m' there aren 52540 missing valuess.\n", " - For column name 'avg_cur_bal' there aren 32 missing valuess.\n", " - For column name 'bc_open_to_buy' there aren 9089 missing valuess.\n", " - For column name 'bc_util' there aren 9415 missing valuess.\n", " - For column name 'mo_sin_old_il_acct' there aren 23463 missing valuess.\n", " - For column name 'mths_since_recent_bc' there aren 8576 missing valuess.\n", " - For column name 'mths_since_recent_bc_dlq' there aren 577966 missing valuess.\n", " - For column name 'mths_since_recent_inq' there aren 86405 missing valuess.\n", " - For column name 'mths_since_recent_revol_delinq' there aren 502543 missing valuess.\n", " - For column name 'num_tl_120dpd_2m' there aren 31530 missing valuess.\n", " - For column name 'percent_bc_gt_75' there aren 9163 missing valuess.\n", " - For column name 'revol_bal_joint' there aren 696039 missing valuess.\n", " - For column name 'sec_app_earliest_cr_line' there aren 696039 missing valuess.\n", " - For column name 'sec_app_inq_last_6mths' there aren 696039 missing valuess.\n", " - For column name 'sec_app_mort_acc' there aren 696039 missing valuess.\n", " - For column name 'sec_app_open_acc' there aren 696039 missing valuess.\n", " - For column name 'sec_app_revol_util' there aren 696953 missing valuess.\n", " - For column name 'sec_app_open_act_il' there aren 696039 missing valuess.\n", " - For column name 'sec_app_num_rev_accts' there aren 696039 missing valuess.\n", " - For column name 'sec_app_chargeoff_within_12_mths' there aren 696039 missing valuess.\n", " - For column name 'sec_app_collections_12_mths_ex_med' there aren 696039 missing valuess.\n", " - For column name 'sec_app_mths_since_last_major_derog' there aren 732082 missing valuess.\n", " - For column name 'hardship_type' there aren 745844 missing valuess.\n", " - For column name 'hardship_reason' there aren 745844 missing valuess.\n", " - For column name 'hardship_status' there aren 745844 missing valuess.\n", " - For column name 'deferral_term' there aren 745844 missing valuess.\n", " - For column name 'hardship_amount' there aren 745844 missing valuess.\n", " - For column name 'hardship_start_date' there aren 745844 missing valuess.\n", " - For column name 'hardship_end_date' there aren 745844 missing valuess.\n", " - For column name 'payment_plan_start_date' there aren 745844 missing valuess.\n", " - For column name 'hardship_length' there aren 745844 missing valuess.\n", " - For column name 'hardship_dpd' there aren 745844 missing valuess.\n", " - For column name 'hardship_loan_status' there aren 745844 missing valuess.\n", " - For column name 'orig_projected_additional_accrued_interest' there aren 746723 missing valuess.\n", " - For column name 'hardship_payoff_balance_amount' there aren 745844 missing valuess.\n", " - For column name 'hardship_last_payment_amount' there aren 745844 missing valuess.\n", " - For column name 'debt_settlement_flag_date' there aren 740517 missing valuess.\n", " - For column name 'settlement_status' there aren 740517 missing valuess.\n", " - For column name 'settlement_date' there aren 740517 missing valuess.\n", " - For column name 'settlement_amount' there aren 740517 missing valuess.\n", " - For column name 'settlement_percentage' there aren 740517 missing valuess.\n", " - For column name 'settlement_term' there aren 740517 missing valuess.\n" ] } ], "source": [ "print(f\"Number of columns with missing values: {miss_values_count.shape[0]}\")\n", "if miss_values_count.shape[0]:\n", " print(\"Null value count per column: \")\n", " for name, miss_vals in miss_values_count.items():\n", " p = miss_vals > 1\n", " print(f\" - For column name '{name}' there are{'n' if p else ''} \"\n", " f\"{miss_vals} missing values{'s' if p else ''}.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How has the average interest rate of a loan varied over time?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If it is a variable rate, the variation of the rate would be determined with the table of payments and not in the table of loans, which is our sample; since the 90's it is rare to use a variable rate. If it is a fixed rate, there is no variation in the rate and it would be int_rate. As for variation of any value it would be necessary to define if it is the programmed one or the real one; the first one is with financial formulas and there are many, the second one is from the data, normally from the payments.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What is the distribution of sub-grades, and can we easily segment borrowers based on prime and sub prime loans**" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16, 6))\n", "sns.countplot(x=\"sub_grade\", data=df, order = df['sub_grade'].value_counts().index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But can we easily segment borrowers based on prime and sub prime loans ? \n", "Maybe we need less/more categories ?\n", "I would segment them depending on if they pay their loan back " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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sub_gradeint_rateloan_amnt
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
0A134251.05.5449170.3890305.315.325.326.116.4634251.015778.9509809849.3578211000.08400.013000.020000.040000.0
1A225190.06.5879430.3816016.006.196.676.997.0725190.014552.6488699554.5643061000.07500.012000.020000.040000.0
2A326431.07.0623730.2660376.006.837.217.217.5926431.014292.6648639544.5634171000.07000.012000.020000.040000.0
3A433057.07.5570790.2838096.007.357.397.848.1933057.015257.6542039961.0103781000.08000.012000.020000.040000.0
4A534741.08.1188800.2859786.007.967.978.468.8134741.014407.3896849448.6395451000.07200.012000.020000.040000.0
5B143870.09.0754050.6820176.008.399.439.4410.3343870.014550.1493059491.4964161000.07500.012000.020000.040000.0
6B242643.09.8926380.5100486.009.499.9310.4710.7242643.014847.6584679750.6945501000.07200.012000.020000.040000.0
7B341821.010.4949230.5456226.009.9910.4211.0611.3941821.014366.0983729434.3446641000.07000.012000.020000.040000.0
8B446010.011.1080600.3356506.0010.9010.9911.4411.8046010.014328.0379279387.6702261000.07000.012000.020000.040000.0
9B549638.011.8441070.4625376.0011.4911.5311.9912.9849638.014274.1997069438.9124591000.07000.012000.020000.040000.0
10C150421.012.6742810.4726926.0012.6112.6212.7913.5650421.014868.1417469538.4445581000.07500.012000.020000.040000.0
11C243310.013.5259620.5258456.0013.4913.4913.5914.4743310.014893.2475189528.6729021000.07450.012000.020000.040000.0
12C342908.014.0827120.5124396.0013.9914.0714.0815.0242908.015515.4324379502.9422681000.08000.014000.021000.040000.0
13C443154.014.9423080.6362356.0014.4914.9915.0516.1443154.015772.8321829479.3324421000.08500.014000.021000.040000.0
14C540569.015.9111660.6288756.0015.5916.0116.0216.9140569.015884.0654199564.0534991000.08450.014400.021525.040000.0
15D125053.016.9620460.7256186.0016.2916.9917.4717.9725053.015663.6021639352.8010721000.08325.014400.021000.040000.0
16D223256.018.0047230.6916846.0017.9918.0618.4518.9423256.015631.3198749398.0994001000.08000.014400.021600.040000.0
17D320643.018.9552240.8097446.0018.9919.0319.4219.9220643.016024.1607329436.4562611000.09000.015000.022250.040000.0
18D417112.019.8196620.9709016.0019.9920.0020.3920.8917112.016005.1440519351.0793531000.09175.015000.022000.040000.0
19D514683.021.0759081.3508076.0021.4521.4521.8522.3514683.016716.4084329438.1902361000.010000.015000.024000.040000.0
20E18793.021.6068831.8293796.0019.9922.7422.9123.408793.016125.0398049578.7694711000.09000.015000.023000.040000.0
21E27513.022.5407332.1449856.0020.7523.8823.9924.377513.016362.6314399718.3517121000.09000.015000.024000.040000.0
22E37218.023.3773502.2741836.0021.1824.7424.8525.347218.017393.8210039418.3363171000.010000.016000.024000.040000.0
23E46285.024.3431812.2466866.0021.9725.4925.8226.316285.017965.5887039295.0162711000.010975.016600.025000.040000.0
24E57036.025.5141051.9817316.0025.2926.3026.7727.277036.018276.1903078944.3327531000.012000.017350.025000.040000.0
25F13563.026.2258972.7740356.0023.1328.6928.7228.723563.019065.4013478867.2034851000.012000.018000.025000.040000.0
26F22333.026.7843032.7947996.0024.1126.4929.6929.692333.018874.8821269168.8304901000.012000.018000.025000.040000.0
27F31877.027.6990202.50781723.9924.9926.9930.1730.171877.019339.6377209038.3273041000.012000.018125.026000.040000.0
28F41525.028.2760002.4944256.0025.8827.4930.6530.651525.018940.0655748977.0558911000.012000.018000.025600.040000.0
29F51442.028.9095632.03532125.7826.5730.7430.7530.751442.020080.5825249452.2588741000.012500.019000.028000.040000.0
30G11264.029.5825401.7468866.0028.1830.7930.7930.791264.020690.9018999086.7327841000.014000.020000.028000.040000.0
31G2715.029.5609791.6405596.0028.1430.8430.8430.84715.018973.8461549553.0730151000.012000.017675.026600.040000.0
32G3613.029.8375041.5559916.0028.3430.8930.8930.89613.020049.7553029058.3338941800.012900.018425.027300.040000.0
33G4550.030.3091641.3885606.0029.9630.9430.9430.94550.020732.3181828969.4981181000.013000.019787.528287.540000.0
34G5512.030.6228130.77506228.9930.9930.9930.9930.99512.020290.0390629246.0739492000.012212.519750.028000.040000.0
\n", "
" ], "text/plain": [ " sub_grade int_rate \\\n", " count mean std min 25% 50% 75% max \n", "0 A1 34251.0 5.544917 0.389030 5.31 5.32 5.32 6.11 6.46 \n", "1 A2 25190.0 6.587943 0.381601 6.00 6.19 6.67 6.99 7.07 \n", "2 A3 26431.0 7.062373 0.266037 6.00 6.83 7.21 7.21 7.59 \n", "3 A4 33057.0 7.557079 0.283809 6.00 7.35 7.39 7.84 8.19 \n", "4 A5 34741.0 8.118880 0.285978 6.00 7.96 7.97 8.46 8.81 \n", "5 B1 43870.0 9.075405 0.682017 6.00 8.39 9.43 9.44 10.33 \n", "6 B2 42643.0 9.892638 0.510048 6.00 9.49 9.93 10.47 10.72 \n", "7 B3 41821.0 10.494923 0.545622 6.00 9.99 10.42 11.06 11.39 \n", "8 B4 46010.0 11.108060 0.335650 6.00 10.90 10.99 11.44 11.80 \n", "9 B5 49638.0 11.844107 0.462537 6.00 11.49 11.53 11.99 12.98 \n", "10 C1 50421.0 12.674281 0.472692 6.00 12.61 12.62 12.79 13.56 \n", "11 C2 43310.0 13.525962 0.525845 6.00 13.49 13.49 13.59 14.47 \n", "12 C3 42908.0 14.082712 0.512439 6.00 13.99 14.07 14.08 15.02 \n", "13 C4 43154.0 14.942308 0.636235 6.00 14.49 14.99 15.05 16.14 \n", "14 C5 40569.0 15.911166 0.628875 6.00 15.59 16.01 16.02 16.91 \n", "15 D1 25053.0 16.962046 0.725618 6.00 16.29 16.99 17.47 17.97 \n", "16 D2 23256.0 18.004723 0.691684 6.00 17.99 18.06 18.45 18.94 \n", "17 D3 20643.0 18.955224 0.809744 6.00 18.99 19.03 19.42 19.92 \n", "18 D4 17112.0 19.819662 0.970901 6.00 19.99 20.00 20.39 20.89 \n", "19 D5 14683.0 21.075908 1.350807 6.00 21.45 21.45 21.85 22.35 \n", "20 E1 8793.0 21.606883 1.829379 6.00 19.99 22.74 22.91 23.40 \n", "21 E2 7513.0 22.540733 2.144985 6.00 20.75 23.88 23.99 24.37 \n", "22 E3 7218.0 23.377350 2.274183 6.00 21.18 24.74 24.85 25.34 \n", "23 E4 6285.0 24.343181 2.246686 6.00 21.97 25.49 25.82 26.31 \n", "24 E5 7036.0 25.514105 1.981731 6.00 25.29 26.30 26.77 27.27 \n", "25 F1 3563.0 26.225897 2.774035 6.00 23.13 28.69 28.72 28.72 \n", "26 F2 2333.0 26.784303 2.794799 6.00 24.11 26.49 29.69 29.69 \n", "27 F3 1877.0 27.699020 2.507817 23.99 24.99 26.99 30.17 30.17 \n", "28 F4 1525.0 28.276000 2.494425 6.00 25.88 27.49 30.65 30.65 \n", "29 F5 1442.0 28.909563 2.035321 25.78 26.57 30.74 30.75 30.75 \n", "30 G1 1264.0 29.582540 1.746886 6.00 28.18 30.79 30.79 30.79 \n", "31 G2 715.0 29.560979 1.640559 6.00 28.14 30.84 30.84 30.84 \n", "32 G3 613.0 29.837504 1.555991 6.00 28.34 30.89 30.89 30.89 \n", "33 G4 550.0 30.309164 1.388560 6.00 29.96 30.94 30.94 30.94 \n", "34 G5 512.0 30.622813 0.775062 28.99 30.99 30.99 30.99 30.99 \n", "\n", " loan_amnt \\\n", " count mean std min 25% 50% 75% \n", "0 34251.0 15778.950980 9849.357821 1000.0 8400.0 13000.0 20000.0 \n", "1 25190.0 14552.648869 9554.564306 1000.0 7500.0 12000.0 20000.0 \n", "2 26431.0 14292.664863 9544.563417 1000.0 7000.0 12000.0 20000.0 \n", "3 33057.0 15257.654203 9961.010378 1000.0 8000.0 12000.0 20000.0 \n", "4 34741.0 14407.389684 9448.639545 1000.0 7200.0 12000.0 20000.0 \n", "5 43870.0 14550.149305 9491.496416 1000.0 7500.0 12000.0 20000.0 \n", "6 42643.0 14847.658467 9750.694550 1000.0 7200.0 12000.0 20000.0 \n", "7 41821.0 14366.098372 9434.344664 1000.0 7000.0 12000.0 20000.0 \n", "8 46010.0 14328.037927 9387.670226 1000.0 7000.0 12000.0 20000.0 \n", "9 49638.0 14274.199706 9438.912459 1000.0 7000.0 12000.0 20000.0 \n", "10 50421.0 14868.141746 9538.444558 1000.0 7500.0 12000.0 20000.0 \n", "11 43310.0 14893.247518 9528.672902 1000.0 7450.0 12000.0 20000.0 \n", "12 42908.0 15515.432437 9502.942268 1000.0 8000.0 14000.0 21000.0 \n", "13 43154.0 15772.832182 9479.332442 1000.0 8500.0 14000.0 21000.0 \n", "14 40569.0 15884.065419 9564.053499 1000.0 8450.0 14400.0 21525.0 \n", "15 25053.0 15663.602163 9352.801072 1000.0 8325.0 14400.0 21000.0 \n", "16 23256.0 15631.319874 9398.099400 1000.0 8000.0 14400.0 21600.0 \n", "17 20643.0 16024.160732 9436.456261 1000.0 9000.0 15000.0 22250.0 \n", "18 17112.0 16005.144051 9351.079353 1000.0 9175.0 15000.0 22000.0 \n", "19 14683.0 16716.408432 9438.190236 1000.0 10000.0 15000.0 24000.0 \n", "20 8793.0 16125.039804 9578.769471 1000.0 9000.0 15000.0 23000.0 \n", "21 7513.0 16362.631439 9718.351712 1000.0 9000.0 15000.0 24000.0 \n", "22 7218.0 17393.821003 9418.336317 1000.0 10000.0 16000.0 24000.0 \n", "23 6285.0 17965.588703 9295.016271 1000.0 10975.0 16600.0 25000.0 \n", "24 7036.0 18276.190307 8944.332753 1000.0 12000.0 17350.0 25000.0 \n", "25 3563.0 19065.401347 8867.203485 1000.0 12000.0 18000.0 25000.0 \n", "26 2333.0 18874.882126 9168.830490 1000.0 12000.0 18000.0 25000.0 \n", "27 1877.0 19339.637720 9038.327304 1000.0 12000.0 18125.0 26000.0 \n", "28 1525.0 18940.065574 8977.055891 1000.0 12000.0 18000.0 25600.0 \n", "29 1442.0 20080.582524 9452.258874 1000.0 12500.0 19000.0 28000.0 \n", "30 1264.0 20690.901899 9086.732784 1000.0 14000.0 20000.0 28000.0 \n", "31 715.0 18973.846154 9553.073015 1000.0 12000.0 17675.0 26600.0 \n", "32 613.0 20049.755302 9058.333894 1800.0 12900.0 18425.0 27300.0 \n", "33 550.0 20732.318182 8969.498118 1000.0 13000.0 19787.5 28287.5 \n", "34 512.0 20290.039062 9246.073949 2000.0 12212.5 19750.0 28000.0 \n", "\n", " \n", " max \n", "0 40000.0 \n", "1 40000.0 \n", "2 40000.0 \n", "3 40000.0 \n", "4 40000.0 \n", "5 40000.0 \n", "6 40000.0 \n", "7 40000.0 \n", "8 40000.0 \n", "9 40000.0 \n", "10 40000.0 \n", "11 40000.0 \n", "12 40000.0 \n", "13 40000.0 \n", "14 40000.0 \n", "15 40000.0 \n", "16 40000.0 \n", "17 40000.0 \n", "18 40000.0 \n", "19 40000.0 \n", "20 40000.0 \n", "21 40000.0 \n", "22 40000.0 \n", "23 40000.0 \n", "24 40000.0 \n", "25 40000.0 \n", "26 40000.0 \n", "27 40000.0 \n", "28 40000.0 \n", "29 40000.0 \n", "30 40000.0 \n", "31 40000.0 \n", "32 40000.0 \n", "33 40000.0 \n", "34 40000.0 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['sub_grade','loan_amnt','int_rate']].groupby('sub_grade').describe().reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Visualise a low-dimensional split in the dataset when the target variable is the grade?**" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [], "source": [ "collist = df.columns.tolist()\n", "# you can now select from this list any arbritrary range\n", "df1 = df[collist[0:1]]\n", "# or remove a column\n", "collist.remove('sub_grade')\n", "# now select\n", "df1 = df[collist]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**As far as we are considering the grade and not the interest rate we are going to drop the latest**" ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [], "source": [ "df1 = df1.drop(columns = ['int_rate'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**We transform the grades to numeric values**" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [], "source": [ "df1['ascii'] = [ord(x) for x in df1['grade']]" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [], "source": [ "df1 = df1.drop(columns=['grade'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will first plot the Pearson correlation heatmap and see the correlation of independent variables with the output variable `grade`. We will only select features which has correlation of above 0.5 (taking absolute value) with the output variable." ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "#Using Pearson Correlation\n", "cor = df1.corr()" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask = np.zeros_like(cor, dtype=np.bool)\n", "mask[np.triu_indices_from(mask)] = True\n", "plt.figure(figsize=(12,10))\n", "sns.heatmap(cor,\n", " vmin=-1,\n", " cmap='coolwarm',\n", " annot=False,\n", " mask = mask);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, no features are highly correlated with the output variable `grade`, named `ascii` here to be able to respresent it. Hence we can't have insights from Pearson correlation. However this is not the end of the process. We can still handpick the explenatory features." ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of categorical variables : 36\n", "Unnamed: 0 int64\n", "id float64\n", "member_id float64\n", "loan_amnt int64\n", "funded_amnt int64\n", "funded_amnt_inv float64\n", "term object\n", "int_rate float64\n", "installment float64\n", "grade object\n", "sub_grade object\n", "emp_title object\n", "emp_length object\n", "home_ownership object\n", "annual_inc float64\n", "verification_status object\n", "issue_d object\n", "loan_status object\n", "pymnt_plan object\n", "url float64\n", "desc object\n", "purpose object\n", "title object\n", "zip_code object\n", "addr_state object\n", "dti float64\n", "delinq_2yrs float64\n", "earliest_cr_line object\n", "inq_last_6mths float64\n", "mths_since_last_delinq float64\n", " ... \n", "sec_app_open_acc float64\n", "sec_app_revol_util float64\n", "sec_app_open_act_il float64\n", "sec_app_num_rev_accts float64\n", "sec_app_chargeoff_within_12_mths float64\n", "sec_app_collections_12_mths_ex_med float64\n", "sec_app_mths_since_last_major_derog float64\n", "hardship_flag object\n", "hardship_type object\n", "hardship_reason object\n", "hardship_status object\n", "deferral_term float64\n", "hardship_amount float64\n", "hardship_start_date object\n", "hardship_end_date object\n", "payment_plan_start_date object\n", "hardship_length float64\n", "hardship_dpd float64\n", "hardship_loan_status object\n", "orig_projected_additional_accrued_interest float64\n", "hardship_payoff_balance_amount float64\n", "hardship_last_payment_amount float64\n", "disbursement_method object\n", "debt_settlement_flag object\n", "debt_settlement_flag_date object\n", "settlement_status object\n", "settlement_date object\n", "settlement_amount float64\n", "settlement_percentage float64\n", "settlement_term float64\n", "Length: 146, dtype: object\n", "\n", "Sum of null values in each feature:\n", "-----------------------------------\n", "Unnamed: 0 0\n", "id 295358\n", "member_id 295358\n", "loan_amnt 0\n", "funded_amnt 0\n", "funded_amnt_inv 0\n", "term 0\n", "int_rate 0\n", "installment 0\n", "grade 0\n", "sub_grade 0\n", "emp_title 20400\n", "emp_length 20036\n", "home_ownership 0\n", "annual_inc 0\n", "verification_status 0\n", "issue_d 0\n", "loan_status 0\n", "pymnt_plan 0\n", "url 295358\n", "desc 295348\n", "purpose 0\n", "title 7834\n", "zip_code 0\n", "addr_state 0\n", "dti 168\n", "delinq_2yrs 0\n", "earliest_cr_line 0\n", "inq_last_6mths 1\n", "mths_since_last_delinq 144176\n", " ... \n", "sec_app_open_acc 287070\n", "sec_app_revol_util 287222\n", "sec_app_open_act_il 287070\n", "sec_app_num_rev_accts 287070\n", "sec_app_chargeoff_within_12_mths 287070\n", "sec_app_collections_12_mths_ex_med 287070\n", "sec_app_mths_since_last_major_derog 292362\n", "hardship_flag 0\n", "hardship_type 293455\n", "hardship_reason 293455\n", "hardship_status 293455\n", "deferral_term 293455\n", "hardship_amount 293455\n", "hardship_start_date 293455\n", "hardship_end_date 293455\n", "payment_plan_start_date 293455\n", "hardship_length 293455\n", "hardship_dpd 293455\n", "hardship_loan_status 293455\n", "orig_projected_additional_accrued_interest 294215\n", "hardship_payoff_balance_amount 293455\n", "hardship_last_payment_amount 293455\n", "disbursement_method 0\n", "debt_settlement_flag 0\n", "debt_settlement_flag_date 286344\n", "settlement_status 286344\n", "settlement_date 286344\n", "settlement_amount 286344\n", "settlement_percentage 286344\n", "settlement_term 286344\n", "Length: 146, dtype: int64\n" ] } ], "source": [ "count = 0\n", "for y in subset.columns:\n", " if subset[y].dtype == np.object:\n", " count+=1\n", "print(\"number of categorical variables : \" + str(count))\n", "\n", "print(f\"{subset.dtypes}\\n\")\n", "print(f\"Sum of null values in each feature:\\n{35 * '-'}\")\n", "print(f\"{subset.isnull().sum()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Things worth knowing for predictive model**\n", " - the data covers 110909 loans funded by the platform in 2018. \n", " - Number of columns with missing values: 74\n", " - 36 columns have categorical data\n", " - the data set is pretty imbalanced : the proportion of G loans is not even 10000 whereas the C1 loans are at least 50000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cleaning the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we had observe, some columns like annual_inc, int_rate, etc. may be much useful for building our model but on the other hand, some columns like id, member_id, etc. will not be helping. So we hand pick some columns." ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "APPLICANT_NUMERIC = ['annual_inc', 'dti', 'loan_amnt', 'installment']\n", "APPLICANT_CATEGORICAL = ['application_type', 'emp_length', 'home_ownership', 'addr_state', 'term']\n", "CREDIT_NUMERIC = ['acc_now_delinq', 'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy',\n", " 'bc_util', 'delinq_2yrs', 'delinq_amnt',\n", " 'open_acc', 'pub_rec', 'revol_util',\n", " 'revol_bal', 'tot_coll_amt', 'tot_cur_bal', 'total_acc', 'total_rev_hi_lim',\n", " 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats',\n", " 'num_bc_tl', 'num_il_tl', 'num_rev_tl_bal_gt_0', 'pct_tl_nvr_dlq',\n", " 'percent_bc_gt_75', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit',\n", " 'total_il_high_credit_limit', 'all_util',\n", " 'il_util', 'total_bal_il', 'total_cu_tl']\n", "TARGET = ['grade']\n", "\n", "df = pd.read_csv(\"loans.csv\", usecols = APPLICANT_NUMERIC +APPLICANT_CATEGORICAL + CREDIT_NUMERIC + TARGET)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We didn't selected columns like 'title' and 'emp_title' are text which cannot be one-hot encoded / label encoded as they have arbitrary categorical text and very less unique data for each of their categories." ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Rows with null/NaN values: 197840\n", "Columns with null/NaN values:\n", "loan_amnt False\n", "term False\n", "installment False\n", "grade False\n", "emp_length True\n", "home_ownership False\n", "annual_inc False\n", "addr_state False\n", "dti True\n", "delinq_2yrs False\n", "open_acc False\n", "pub_rec False\n", "revol_bal False\n", "revol_util True\n", "total_acc False\n", "application_type False\n", "acc_now_delinq False\n", "tot_coll_amt False\n", "tot_cur_bal False\n", "total_bal_il True\n", "il_util True\n", "all_util True\n", "total_rev_hi_lim False\n", "total_cu_tl True\n", "acc_open_past_24mths False\n", "avg_cur_bal True\n", "bc_open_to_buy True\n", "bc_util True\n", "delinq_amnt False\n", "num_accts_ever_120_pd False\n", "num_actv_bc_tl False\n", "num_actv_rev_tl False\n", "num_bc_sats False\n", "num_bc_tl False\n", "num_il_tl False\n", "num_rev_tl_bal_gt_0 False\n", "pct_tl_nvr_dlq False\n", "percent_bc_gt_75 True\n", "tot_hi_cred_lim False\n", "total_bal_ex_mort False\n", "total_bc_limit False\n", "total_il_high_credit_limit False\n", "dtype: bool\n", "Dropping bad rows...\n", "Rows with null/NaN values: 0\n", "\n", "Int64Index: 552160 entries, 41131 to 749999\n", "Data columns (total 42 columns):\n", "loan_amnt 552160 non-null int64\n", "term 552160 non-null object\n", "installment 552160 non-null float64\n", "grade 552160 non-null category\n", "emp_length 552160 non-null object\n", "home_ownership 552160 non-null object\n", "annual_inc 552160 non-null float64\n", "addr_state 552160 non-null object\n", "dti 552160 non-null float64\n", "delinq_2yrs 552160 non-null float64\n", "open_acc 552160 non-null float64\n", "pub_rec 552160 non-null float64\n", "revol_bal 552160 non-null int64\n", "revol_util 552160 non-null float64\n", "total_acc 552160 non-null float64\n", "application_type 552160 non-null object\n", "acc_now_delinq 552160 non-null float64\n", "tot_coll_amt 552160 non-null float64\n", "tot_cur_bal 552160 non-null float64\n", "total_bal_il 552160 non-null float64\n", "il_util 552160 non-null float64\n", "all_util 552160 non-null float64\n", "total_rev_hi_lim 552160 non-null float64\n", "total_cu_tl 552160 non-null float64\n", "acc_open_past_24mths 552160 non-null float64\n", "avg_cur_bal 552160 non-null float64\n", "bc_open_to_buy 552160 non-null float64\n", "bc_util 552160 non-null float64\n", "delinq_amnt 552160 non-null float64\n", "num_accts_ever_120_pd 552160 non-null float64\n", "num_actv_bc_tl 552160 non-null float64\n", "num_actv_rev_tl 552160 non-null float64\n", "num_bc_sats 552160 non-null float64\n", "num_bc_tl 552160 non-null float64\n", "num_il_tl 552160 non-null float64\n", "num_rev_tl_bal_gt_0 552160 non-null float64\n", "pct_tl_nvr_dlq 552160 non-null float64\n", "percent_bc_gt_75 552160 non-null float64\n", "tot_hi_cred_lim 552160 non-null float64\n", "total_bal_ex_mort 552160 non-null float64\n", "total_bc_limit 552160 non-null float64\n", "total_il_high_credit_limit 552160 non-null float64\n", "dtypes: category(1), float64(34), int64(2), object(5)\n", "memory usage: 325.2 MB\n", "None\n" ] } ], "source": [ "# We order our grade category so order of grades doesn't appear random in graphs\n", "grade_categories = [g for g in \"ABCDEFG\"]\n", "df[\"grade\"] = df[\"grade\"].astype(\"category\", categories=grade_categories, ordered=True)\n", "\n", "# Sanity check that we're working with cleaned data\n", "bad_rows = df.isnull().T.any().T.sum()\n", "if bad_rows > 0:\n", " print(\"Rows with null/NaN values: {}\".format(bad_rows))\n", " print(\"Columns with null/NaN values:\")\n", " print(pd.isnull(df).sum() > 0)\n", " print(\"Dropping bad rows...\")\n", " df.dropna(axis=0, how='any', inplace=True)\n", " print(\"Rows with null/NaN values: {}\".format(df.isnull().T.any().T.sum()))\n", " \n", "print(df.info(null_counts = True, memory_usage = \"deep\", verbose = True))" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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loan_amntterminstallmentgradeemp_lengthhome_ownershipannual_incaddr_statedtidelinq_2yrs...num_bc_satsnum_bc_tlnum_il_tlnum_rev_tl_bal_gt_0pct_tl_nvr_dlqpercent_bc_gt_75tot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limit
411312400036 months757.51B1 yearMORTGAGE80000.0WA11.800.0...11.011.029.07.0100.09.1501190.0167271.0137400.0121695.0
41135800036 months257.39B6 yearsRENT55000.0CO10.160.0...4.013.06.04.095.725.027933.021907.07800.017349.0
411361997560 months453.27C1 yearMORTGAGE92000.0GA19.050.0...4.09.036.06.083.050.0406013.0211797.012300.0217689.0
411392400060 months603.85D3 yearsRENT72500.0CA22.890.0...5.07.09.07.0100.080.073333.064376.017300.041233.0
411412000060 months461.96C8 yearsMORTGAGE62000.0NC7.140.0...6.010.05.06.090.916.7188975.014423.018800.06914.0
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5 rows × 42 columns

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" ], "text/plain": [ " loan_amnt term installment grade emp_length home_ownership \\\n", "41131 24000 36 months 757.51 B 1 year MORTGAGE \n", "41135 8000 36 months 257.39 B 6 years RENT \n", "41136 19975 60 months 453.27 C 1 year MORTGAGE \n", "41139 24000 60 months 603.85 D 3 years RENT \n", "41141 20000 60 months 461.96 C 8 years MORTGAGE \n", "\n", " annual_inc addr_state dti delinq_2yrs ... \\\n", "41131 80000.0 WA 11.80 0.0 ... \n", "41135 55000.0 CO 10.16 0.0 ... \n", "41136 92000.0 GA 19.05 0.0 ... \n", "41139 72500.0 CA 22.89 0.0 ... \n", "41141 62000.0 NC 7.14 0.0 ... \n", "\n", " num_bc_sats num_bc_tl num_il_tl num_rev_tl_bal_gt_0 pct_tl_nvr_dlq \\\n", "41131 11.0 11.0 29.0 7.0 100.0 \n", "41135 4.0 13.0 6.0 4.0 95.7 \n", "41136 4.0 9.0 36.0 6.0 83.0 \n", "41139 5.0 7.0 9.0 7.0 100.0 \n", "41141 6.0 10.0 5.0 6.0 90.9 \n", "\n", " percent_bc_gt_75 tot_hi_cred_lim total_bal_ex_mort total_bc_limit \\\n", "41131 9.1 501190.0 167271.0 137400.0 \n", "41135 25.0 27933.0 21907.0 7800.0 \n", "41136 50.0 406013.0 211797.0 12300.0 \n", "41139 80.0 73333.0 64376.0 17300.0 \n", "41141 16.7 188975.0 14423.0 18800.0 \n", "\n", " total_il_high_credit_limit \n", "41131 121695.0 \n", "41135 17349.0 \n", "41136 217689.0 \n", "41139 41233.0 \n", "41141 6914.0 \n", "\n", "[5 rows x 42 columns]" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import itertools\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors\n", "import matplotlib.ticker as ticker\n", "\n", "sns.reset_orig()\n", "fig = plt.figure(figsize=(15,15))\n", "ax = fig.add_subplot(111)\n", "cax = ax.matshow(df.corr(), cmap=plt.cm.Blues)\n", "fig.colorbar(cax)\n", "\n", "ax.set_xticklabels(df, rotation=90)\n", "ax.set_yticklabels(df)\n", "ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n", "ax.yaxis.set_major_locator(ticker.MultipleLocator(1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we can take a look at the distribution of some values by loan grade for some features of interest using \"violin\" charts." ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "image/png": 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2RmBgILKysqCkpISuXbvC0NAQK1as+MfEfU9MTMStW7cQFhaGzMxMqKiooEOHDhgxYgSWLFkiNZFgfZ4xPz8ft27dgo+PDz58+AB1dXXo6+tj3bp1DfGYEtHQ0ECrVq2Qm5uLT58+STzP398fDg4OiI6ORkFBAZo3bw4tLS388MMPMDIyqpNXyrt372BnZ4dnz54hKysLJSUlaN68Obp3747x48djyZIlco97zHPi4uKgoqKCv/76C/fu3YOmpiZCQ0OhpqbGWp/nz5+T/EO3bt2Cvr4++Y2iKLi5ueHhw4eIi4tDYWEhWrduDR0dHSxYsADff/+9XM9O9z+aBw8ekL/fvn0rcm5ubi5u374Nf39/pKamQiAQ4LvvvsPIkSNhbGzc6FvSs7OzSf0+fPgAoVCI7777DqNHj8by5cvRo0cPkfM3bdoET09P8vfdu3dx9+5daGhoIDo6WuTcN2/e4P79+3j+/Dk+fvyI0tJSNG/eHD179sSECROwePFihe2CFQqFuHbtGoDq3U2mpqYylduwYQMiIiLA4/GQkZGBgQMHAqhetKVDpgQFBcHFxQW3bt1Cfn4+OnTogJUrV4rku8rNzYW1tTV5jxRFoVOnTvj++++xcuVKqfkUCwsLce/ePbi7uyMtLQ1qamrQ1dXFunXrpM6v+vr6KCgowMKFC7F//36UlJSIhbWdN28eAGDJkiW1epmyQXvA9+3bF71794ampiZOnTpF5uJff/1VrExycjIxUu3du1dqXrAff/wRcXFxmDhxIi5cuECOUxQFHx8fPH78GDExMcjPz0dVVRVatWoFLS0tzJw5E0ZGRmjSRNSvytramuQqCA0NlTmMTV3vV5OAgABcu3YNcXFxoCgKffv2xZw5c7BgwQKoqMgvhhcVFZG5JjU1FZWVlaR/rlq1SiELIi4uLrhz5w7evXsHVVVVDBgwAPPmzcPMmTNF5gYfHx/Sr86fP49JkyaxXk8oFGLcuHHIyckRk5sbmpCQENjZ2SE6Ohr5+fnQ1NRE7969MXXqVPz8888S5w+ges6xt7fH8+fPkZ2djbKyMjJeTZw4ET///LPYePXmzRvMnTsXQHV7Kysrw5UrVxAYGIicnBw0b94curq6WL58eZ3yHLq4uCAjIwMA8Ouvv8ocQ3/WrFk4ffo0MjIy8OLFC9Ifgf/vI3379oWtrS0OHToET09PCIVCdOvWDebm5kSWrqqqgqurK7y9vfHq1SuS+6JNmzbQ1tbGTz/9JHX+zM7Oxs2bN+Hv74+MjAw0b94co0ePlnl8fv36NW7fvo3w8HDk5OSgWbNmZOfqokWL0LRpU5muwyE7ddX1aYqLi+Hs7AxXV1ekpqaiqKgI3377LfT19bF69WqJMk9+fj7s7e3h6emJDx8+oKysDJ06dcLYsWOxevVqos8ydXcaWpZk6rECgQBOTk5wdXVFfHw8SkpK0LJlS/B4PEyZMgXz5s2Tu/08e/YMkZGRAKrn1NoMwTSbN2/GkydPkJ6ejps3b2L16tUiYwld/6tXr2LQoEGwtLTE06dP8fnzZ3Ts2BH6+vowMTGROt7XRT/+8OEDJk6cCAB48uQJ1NXVceXKFfj7++Pjx4/Q1NSEjo4OlixZgnHjxkm8d2lpKe7evQsvLy8kJyejvLwc3377LUaNGoWVK1dK1Tf5fD6cnZ3h5OSE5ORkVFVVYeDAgVixYgWpW12QtHZD6+/r1q3D5s2bcf/+fTx48ACJiYmoqqpC9+7dYWRkBGNj4wbfoaahoYGePXsiLi6OjPM1CQgIgKurK2JiYpCbmws+n49WrVqhf//+mDFjBubMmcMqW5SXl+PevXvw9PTEu3fvUFFRgdatW2PQoEEwMjISk2fotRgaMzMzmJmZYcSIEbh9+3atz/Lp0yc0a9YMzZs3R+fOnSWeR7dhaTprWVkZtm/fjqqqKpiZmSnU0UlHRwedO3dGRkYGPDw8iMzPhqurK4DqXf+1GQcFAgHc3Nzg4uKCuLg4FBUVEflx9uzZEnVt5hrYnDlzYG1tDXd3d6SmpkJJSQl9+/bF3LlzMX/+fJHvLO86lFAohKOjIxwdHfHu3TsIhUJ07doVRkZGWLFiBats1BBjKBOKouDg4EDW5Zo1a4ZBgwZh4cKFmDJlisi5zs7O2LFjBwDg9u3bEuWpiooKjB49GsXFxdi2bRtMTEzqXD958fPzg729PWJjY1FQUABNTU3weDzMmDED8+bNk7oDPTo6Gk5OToiMjMSnT59QXl6OFi1aoE+fPpg0aRIWLlwoNh4x58K4uDikpaXh2rVrCAkJQW5uLlq1agU9PT2sWrVK5rmKiZ2dHfLz8wFUr/d26tRJpnILFy7EuXPnUFBQgJiYGJH1dXqdR1dXFxcvXoSFhQWePn0KJSUl9OjRA4cPHyZjN5/Px8OHD+Hj44PXr1+joKAATZo0Qdu2bTFkyBDMnz9fqjySkZGBGzdukDXyli1bYty4cVi/fr1MzxEdHY27d+/i+fPnyMvLg7q6Ong8HoyMjGr9nhzV1Mv4JBQK4ezsDAAwNDQkO11mzpyJGzduEC9YaTlHhEIhNm/eLLJwBQBRUVGIiorC3r17MXjwYPzyyy8oKCggv3/8+BF2dnYICwuDk5OTmPIXGxuLTZs2icU9zczMhIuLC1xcXLB8+XLs3Lmz1oWDukBRlNhiJFCdjPTy5ct49OgR7t27p/A4z/b29rCwsEBFRYXIcTqZuq2tLQ4ePKjQ3B9CoZB8v++++07mRdumTZvC29tb6kJjbGwszM3NiWErLy9PZEdVVlYW1qxZg4SEBJFyHz9+hKurK1xdXfHLL79g69atYhN8WVkZtmzZAl9fX5HjL168wIsXLxAUFAShUCjTs/xbsLe3x759+0Rion/8+JEIEpcvX2Y1uLq5ucHMzEysXb19+xZv376Fra0tzp8/Dz09vQZ/Bmn4+/tj48aNIvXk8/lITk5GcnIy7t+/j3379mHRokViZevzjK9evcKaNWvIbgCgWvFyd3eHl5eX1IVWRZOfn08EA7bFbT6fj507d4okjgaqk0MGBQUhKCgITk5OOHv2rFxGAFrIpShK5Did9Dw2Nhb29va4e/euzMKKJObOnYt79+6hpKQEfn5+xEmhJnSc8Y4dO4oIpYWFhdiwYQPCw8NFzs/JyYG3tze8vb0xe/ZsHDx4UOELWX5+fti2bRvJC0CTmpqK1NRUODg4YOvWrcQr9mtDJxEvKSkROZ6SkoKUlBTcv38fu3btqlObPn78ODEGMWEm4ra3t4eNjY1CcpXFxsYSxXn69Oky52/S09NDRESE1PZ/4cIF2NjYkL/T09NFHDv8/f2xZcsWsfeYmJiIxMRE2NnZ4dSpU6zzZWJiIn755RcRpb+0tBQ+Pj7w8/PD8uXLZXqOhiAnJweBgYEAqmU9oHreHzFiBMLCwmBvb4+1a9eKyVU9e/Yk+aHc3Nwktp+kpCTExcUBAObMmUOOFxUVYf369WJ9lq6Tn58f/Pz84OHhgXPnztU7pIOi7nft2jVcv35d5FhMTAxiYmLg6OiI69evyxXe+MWLF/j111/FQr0wxw8LCwupzjO1YW5uLtK2gep8JmFhYXj06BEsLS3JosS4cePQpk0bfP78GW5ubhKNT2FhYWSxaPbs2XWumzzw+XyYmZmRxSKagoICREZGIjIyEjY2Nrh48aKYAYeiKBw9ehQ3btwQuy6dSDkqKoqMV5Kc4J4/f46dO3eKjAP5+flkntmzZw+WLVsm13M9fPgQQHX+sgULFshV9uzZs+jQoQPat2/P+rtAIMD69etFdrMkJyejZ8+eAKoNRyYmJmIyN1Atj2dlZcHT0xPGxsbYvXu32DnPnj2DqampyPxXVlaGhw8f4smTJ8QwLglLS0ucP39eRM7g8/lk/rC1tcWVK1fQtWtX6S+CQ2bqq+u/ffsWGzZsQFpamsjxjIwMODk54dGjRzhy5AiZT2giIiLw22+/icjVwP/LIk5OTrh48aKIU5E0+Hw+1qxZI+Z1nZeXh9DQUISGhuLevXu4efOmXHl2aDkTAKtuIQk1NTXMmzcPp06dQklJCXx8fETmPJqsrCzs3btXJKcm/Q4ePnyI06dPk50jNcvVVT+miYuLg7m5ucj6C5/PJ/Pfpk2bWBft3r59i3Xr1iEzM1Pk+IcPH2Bvb0+cItnGvvz8fKxbtw6xsbEixyMiIhAREUHymDYElZWVMDExQXBwsMjx+Ph4xMfHw93dHXfu3PlqaQJqylGS1i2AamcnWod79OgRrl27JmKY+PLlC4yNjREfHy9SLicnB76+vvD19YWDgwMuX76sMAPbli1bsGXLllrzjaampgKAiHNiTY4ePYrk5GSMHj0aS5YswenTpxVSR5rp06fj2rVr8PDwkBjqsLS0FP7+/lBSUsKMGTOkGp/y8vKwYcMGREVFiRzPzc2Fv78//P39cf/+fZw9e1aiDFhYWIj58+eLfTNahvT29saVK1fqlMu0rKyMta3Tax6enp6wsbERaQsNNYbSCIVCbN++nYT1BqoNpnS7nj59Oo4fP04W+KdMmQJzc3OUlpbC1dVVovHJ19cXxcXFaNKkSYPnPqYpKyvD1q1bxdpIQUEBGctsbGxw6dIlMcOsQCDAX3/9hfv374tdNz8/n5R3dHTE3bt3Jeq2/v7++P3331FeXk6O5ebmwsPDA56enjh8+LDcugItf6qrqxMnK1lQUVHB5cuX0aVLF4ltg8/nw8TEBC9fviTH0tLS0L17dwDVeraJiQlSUlLEymZkZCAjIwNubm7YsGEDNm7cKHZOQEAANm/eLJIvrry8HA4ODvDw8JDaNoRCIY4dOyamE/D5fDx79gzPnj3D/fv3cenSpVrz1v3XqZfVJSQkhAgWzMZL/5/2uJEG3QGmTp0KR0dHBAUF4cyZM2jZsiUA4NSpU/j111/RtGlTnDhxAsHBwfD29iYLF6mpqWJKcmpqKtasWUM8dLZt2wYvLy+EhobC2tqaDE43b97E33//XZ9XIBEfHx88ePAA48ePh42NDcLCwvDw4UOytTczM1Mkl8TatWsRFRVFGv6wYcOIAU6atwgTT09P/Pnnn6ioqEC3bt1w8uRJBAYGIjAwECdOnECXLl1QUVGBbdu2wd/fn5Sj70MvCNN1iYqKkmmRIDk5mSx2y+vFWZsAZ29vj7Zt2+LatWsIDg7GhQsXiLBaWlqKVatWISEhARoaGti6dSs8PT0RHh6O+/fvE0Xm6tWruHr1qti1zc3NiQC3cOFCPHr0CGFhYbC2tsaQIUPg6uoqIuzXxtWrV1nfY81F/sYiPz8f+/btQ48ePWBpaYmQkBD4+Phg48aNUFZWBp/Px549e8QMbiEhIfjjjz9QUVGB/v374/z58wgJCUFgYCBOnTqFHj16oKCgAGvWrGGdEL4WpaWl2L59OyoqKqCtrQ0rKysEBAQgMDCQLC5RFIWDBw+KeZPV5xk/f/6M1atXIy8vD61bt8aBAwcQGBgIf39/7N27F82aNYO1tfVXew/nzp0j33DChAliv+/evZu0yQULFsDJyQkRERFwd3fH+vXroaqqiuDgYPz+++9ihiRJeHh4wNLSEhRFwcDAADdv3kRAQAACAgJw69YtjB8/HkD1uMdUGOo67g0ZMoTsvpHUv6qqqohRfPbs2US5FgqFZFFZRUUFv/zyC1xdXREeHo6HDx9i2bJlUFJSgouLCw4ePCjT8wPA/v37ERUVhWHDhgGo9jKnn4UmOjqaJKRu164d9u/fDz8/P4SGhuLixYvo378/BAIBjh07hnv37sl8b0URGhqKrVu3oqSkBB06dMChQ4cQEBCAkJAQWFpaok+fPqiqqsL+/fuJAApUG5WioqKIt+DChQsRFRUlotQ4OTkRw5OhoSHu3LmDwMBABAQEwNramhi909LScP78eYU8D/Pdy7pARVPb3GRjYwMDAwO4urrC398ff/31F/ECfvnyJdavX4+SkhL06NEDf//9NwIDAxESEoILFy6gf//+KCkpwYYNG4ihhYZWCDMyMqChoYHdu3fD398fgYGBOHToEFq3bg0rKyuZn0NTU5MsktPcvXsXUVFR2LlzpxxvpBpnZ2dUVVVBSUlJRD6g5b6MjAyRnbd7xUSjAAAgAElEQVRM6IW1qKgoiQmRaUNBy5YtRRbUzM3NER4eDiUlJZiYmODhw4cIDQ2Fp6cnTpw4QZQTb29vheR1UNT9rl+/TnZwhoaGwt3dHUuXLgVQ7bSwdetWmeuUnp6OVatWITc3Fx06dMCBAwfI+HHjxg3o6emhsrISZmZmIjKePJSWlsLGxgYDBgyAlZUVQkND4ezsTGRXf39/HDhwgJyvqqoKIyMjANUKvqQk4PQCrZaWllSPd0WyZ88e0p7Gjx+Pu3fvIiwsDB4eHti4cSPU1NSQkpKClStXEhmWxsHBgSiZEyZMwN27d8m8bmVlhZEjRwKoXgS+ePGixDps374dzZo1g4WFBenHBw4cIOPLsWPHpHp716SsrIzsJtXV1ZW6WMeGlpaWRMMTUG38DQsLw+rVqxEQEAA3NzccOHCA3Of3339HQkICVFVVsXXrVri7uyMsLAzu7u7466+/iAHi1q1bYmNbdnY21qxZg8LCQnzzzTc4evQogoOD4evrix07dkBJSUmqN72VlRVxcBk7dizZ4e7j44O9e/eidevWSEpKgomJSa2LnRyyUx9dv7CwEKtXr0ZaWho0NDSIPh4cHIzz58+je/fuqKysxM6dO/H+/XtSLj09nTh0tWvXjuhrAQEBOHr0KNq3b4+SkhJs2rQJeXl50NPTQ1RUFMzNzck1auqxN27cQGhoKJSVlfHbb7+JtN1Vq1YBqN7dLO+iNh2O8JtvvhHbFV4bzF08bI4OAHDkyBFkZWVh8eLFcHd3R0hICE6ePIlvv/0W5eXl2LhxIxITE0XK1Fc/ptm1axeEQiH+/PNPPH36FEFBQTh+/DhZLD9//jwxHNB8+vQJK1euRGZmJtq2bYt9+/bh6dOnCAsLw+3btzFmzBgIBAIcOHCAVX7fvHkzYmNjoaysjHXr1uHJkycIDQ3FhQsX0KtXL1y5ckW2l1sHbGxsEBwcjLlz58LJyQlhYWG4f/8+8aR/8+aNXPJXXSgrKyPfs+Zcefz4cbJusXTpUjx48AChoaHw8vLCmTNn0LdvXwAg8zaTkydPIj4+HhoaGti7dy+8vLwQFhYGZ2dnsogcFhYmkgbBzc1NRI42NzdHVFSU1DbDhjR5Oisri8grNXfp0/j7++PevXto0aIFDh061CD5YmgZJzU1VWLoPR8fH5SVlWHo0KFSnShpI01UVBSUlJSwaNEiODs7Izw8HM7Ozli4cCGA6j5vampKQhPWxNLSEgkJCVi5ciVcXV3JGhWtawUFBYl8Z3nWod68eYPg4GBMmjQJ9+7dQ0hICO7du4fhw4cDqDY813QWbKgxlCYnJwcuLi4YMmQImd/t7e2JLvD48WORtVMNDQ3i8OTp6SnxPdJy4IgRI76aYeD3338nhqdp06bBzs4O4eHhcHd3x5o1a6CiooKEhASsWrVKTF6xtrYmhicjIyPY2toiODgYT58+xZUrVzB06FAA1YZCaePR1q1b0bp1axw5coSsydDrUhRF4cCBAygqKpL5mXJzc4kzw6hRo6RGDWBDW1tbqlHy1atXePnyJTZu3IjAwEA8fPgQFhYWaNasGQQCATZs2ICUlBRoaGjAzMwMjx8/RlhYGFxdXbFr1y4yL124cEFsXkpOToapqSlKS0vRuXNnnDp1CqGhofD29sZvv/0GPp8vdd3l7NmzRCeYMmUKbGxsEB4eDm9vb5iZmaFFixZ4/fo11q5dK+bEzlEDqh789ttvFI/Ho0aNGkVVVlaK/DZ37lyKx+NRhoaGlEAgECu7Y8cOisfjUTwejzI1NaWEQqHI71evXiW/a2trU8nJyWLXmD9/PsXj8ShjY2OR4xs3bqR4PB6lpaVFxcbGipWrqqqiTE1NKR6PR/Xr149KSEggv4WFhZH7BgcHS3x2+py///5b4nNt3LiRteyPP/5I8Xg8SldXV+y56fJLly6VeG82KioqqLFjx1I8Ho+aPHkylZ+fL3ZOXl4eNWHCBIrH41Hjxo2jKioqRH43NDSkeDwedfbsWbnu/fTpU/LMFy5ckKusJOi68Hg8KiQkhPWcM2fOkO8cExPDes7BgwcpHo9HDRo0iPr06RM5/vLlS6pfv34Uj8ejjh49KlauvLycWrBgAalDzXdCf6exY8dKrLu875FZVt7vL61Ojo6O5DkmTJhAFRUViZU9cuQIOefly5fkeFVVFWkz8+bNo8rLy8XKFhQUkHPWrVsnV53Pnj1L7puenl6vMr6+vuR4VlaWWLn09HSqf//+FI/Ho6ysrBT2jBYWFqQdxsXFiZWNjY2ltLS05H5OGrrc1q1bqeLiYrF/X758oT58+ED5+fmRsY/H41EzZsygSktLRa4VGhpKfme+AyZeXl7knCdPnrDWpea499NPP1E8Ho8yMjISG1coiqIEAgH1ww8/UDwejxo5cqTY79LGPeb3Zs4zlpaWZH4oLi4WK+fn50fKvXv3jhx3cHAgxz09PVnfgbW1NTnn1atXrOdIYunSpRSPx6N27Ngh9hs99o8YMYJKS0sT+720tJTMazo6OlReXp5c964Jcz5zdHSUeq5AIKBmzJhB8Xg8ysDAgLUPFRUVUbNmzaJ4PB6lp6cnNpbQ39jc3Fys7PTp0ykej0f9+OOPYvICRVFUZWUlNW3aNDJOMWHOMdHR0bI8OkVRFPXnn3+Scm/evJG5nCTevn1LrjdixAiqpKSE9Tz6PUybNo0qLCwU+720tJS8x4ULF4r8du7cOSKbhIWFiZVNTEykhgwZQurx4sULkd9HjBhB8Xg86s8//5RY95pl5IH+RjXlrtLSUmro0KEUj8ej1q5dy1o2Pz+fjIXXr19nPWfKlCli9f/48SOZr48cOcJaLi0tjRowYADF4/GoPXv2iPx248YN8uzMPsUc65jvRJH3GzNmjIjsQXP69GlyTlBQEDn+5csXcvzatWsiZdatW0f658ePH8WuWVlZSa1YsYL0oaqqKta6s3HgwAFy31mzZrGOqdu3bydtMykpiRyPjY0lZR89eiRWrqKigho2bBjF4/GoGzduyFwn5vfx8vKSuRxFUdSzZ89I2Z07d4rJ2hRFUYGBgUQmqPkN6XY4f/581vfI5/PJOVOmTBH57fXr1yL6S2Jiolh5Z2dncs79+/dlfq6XL1+Scvv375e5XG0w26yk/hsZGUnOuXPnDus54eHh5JyLFy+K/Ea3nyFDhoi0H5rQ0FDyPXg8HvXlyxfy26dPn8jYsX37dtZ7v3//nho8eDDF4/GoM2fOyProHLVQH12flo8HDhxIRUZGiv3+4cMHMjbs3LmTHKfHOl1dXSolJUWs3MuXL8n4a2lpSY4z9Z2a0PLXrl27WJ/TzMyMyDZsz8KGQCAgbXb58uUylWFSWVlJ5pqasgD9HDWfkSY1NZXS1dWleDwetWbNGpHf6qMfp6enk/sOHDiQVa9hymQ153K6nw8fPpxKTU0VKysQCKgNGzaQNsXUuZ48eSJ1jPn8+TM1ceJEmeXamkjSYWi5ncfjUYcPHxYrV15eTtZYZs6cKdc95ZHDKYqi/v77b1YZoLCwkIyBu3fvZi2bnZ1NaWtrUzwej9q0aZPIb/r6+hSPx6POnz/PWtbY2Fji89X1fdeGQCCgVq9eTa7Ptt6Tl5dHGRgYUDwej3rw4AE5znxPdYFtvW/y5MkUj8ejTpw4wVpm7dq1Im3z559/png8cd319u3b5NrW1tas17p+/brEts5cA7t165ZY2dzcXPKd2eZraetQzLbONhYy2/qsWbNEfmuIMZSiRNdOFy5cKLYOIxAIqF9//ZWMSUz5NzAwkJT18/MTu3ZhYSE1aNAgisfjUQ4ODjLXiTmXsOli0mCOjwcPHmQ95/Hjx+Qc5jqkQCAg7X3lypWssmtpaSk1ZswYisfjUT/99JPIb8x2raenR2VmZoqVZ7ZPDw8PmZ8rJCSElDt16pTM5WqDuc7zxx9/sJ7DXNN5+PAh6zlMnaFmn6JlihEjRrC+E2bZmmNKSkoKmectLCxY782USeTRc/6L1Hnn05cvX+Dt7Q2g2sO7ZmxZWbxgaX755RcxLwZmaKtJkyaxehPRsSqzs7PJsc+fP+PJkycAqnMKaWtri5VTVlaGubk5VFVVQVEUbG1tpdavrvzyyy+sx+kwO8XFxSRWen3x9/cn72H79u2suWnatm1LPJ0/fvzIum27LjCt5orO+9OqVSvW2J0URcHOzg5AtVeApLilmzZtQrNmzcDn80VCILq5uYGiKGhoaLBuzVRTU4OZmZmCnuKfg6R8KszQTx8+fCD/DwoKIn9v3bqV1cuhVatWJK+Rr6+vXF68ioTpaVAzJBEAdOnSBVeuXIG9vb1IaJf6PCNFUXB3dwdQvbuGLU60tra2THnTauPRo0fQ1dUV+zd8+HBMmDABa9asITt99PX1YW1tDXV1dZFr0F4dnTt3lhi6a9KkSWT3Dt3HpCEUCjF+/HjMnTsXpqamrGHqmjRpQsZ0RY15c+bMgZKSEsrLy1lDHzA97Zn5BOh3MHz4cLH40TRLly4lO6/Ytr3XhdevX+PVq1cAAFNTU9awQOrq6vjrr78AVHs/1vRcbEiioqKI5/HmzZtZQ8I2b96c5AgqLCwUC2clCT6fj2nTpmHOnDkwNTVljUWvoqJC2p2i2gjTm0zRc9O4ceNIUmgmL168IB7/mzZtYg2HoK6ujk2bNgGo3g337t078hv9Tg0NDVl3a/Xq1Usst8XXIioqCklJSQAgFqpBXV2dhL/09/cXC7cDVH8DencYmyfmy5cvyc5S5q6qsrIyrFq1ClOnTpX47F27diVttr7tR5H327RpE2s4ql9//ZW0yZrhmdlgymwmJias3psqKipkJ9WHDx8QEhJS63XZMDMzEwlvTLN9+3aoqKiAoiiRsUlbW5uEZWMbE/z8/FBUVARlZWWyS6qhoXf6aWpqYvfu3aye0mPGjCHtzNnZmezaqqiogJGREWbPng1TU1PWkDaqqqrES1va9588eTJrTiZ6NzAgKnPVBnOHlrQdTAKBACUlJVL/SUJSGFsVFRWSi1eSTKOrq0vGd+Z74fP5RDdbuHAhaS9MRo4cKZKHiomjoyMqKyuhrKxMcjzUpHfv3pg/fz4Axc3b/3Xqo+tTFIXHjx8DqNbT2HY1dO7cGfPnz4eOjg7ZXVdUVERCuxobG5NdpkwGDRqEadOmYdiwYTJ7X/P5fAAQC+NHY2pqiuvXr8PBwUHmnRWFhYUk0gAdsUUeVFRUyHMzQ9sx6d69O2sexW7dumHFihUAgMDAQFK+vvoxkzFjxrDqNQYGBqQtMMevwsJCMrcvXbqUNR9VkyZNSPL5vLw8Efmdltu7deuGxYsXi5Vt3bp1g+YLVFJSYg3rp6amRtYi5Bmva8Ln88XG4eLiYmRnZyM4OBhbt27FpUuXAFS/A2YYx6KiIqxYsQLTp0/HypUrWa//7bffkrG15rxE68iS2v+ePXtw8+ZNcv+vwaFDh0hfNzIyYl3v2bt3L3JycjBlyhS5wnzVhenTpwOojuZRk8LCQgQFBUFZWVniPEVDzz8DBgyQqGuvWrWK5LGRtA7ZqlUr/Pzzz2LH27Vrh8GDBwOoe3tUUlJi7UtqampETk9PTxf5rSHG0Jrs2bNHbExv0qQJzMzMoKSkRHJO0owePZqkGGDTKzw9PcHn89GsWTOJso2iob9/u3bt8Mcff7CeM23aNBKd5v79+yQdRklJCX766SfMnDkTa9euZX2P6urqZH1bmvw5a9YskXDwNJLW/GqDKX9KC41fVVWlcPlTU1MTxsbGMDIyIrsUa8LUmZnvpaioiMgnxsbGrO9k0qRJGDNmDOt17ezsIBQKoa6uLnH+ofPm0edzSKbOOZ9cXV3JIMQWL3LmzJk4duwYKisrYWtrK6JoMVFWVmYVbJjx07W0tFjL0ovodD0AIDIykoSKkrSwCFQrbXp6eggNDRVJzqcoVFVVJSYsZD4bMw5nfaCfgU4GK4nx48dDTU0NFRUVePbsmUIGYmZMYjo3k6KgJ+aaJCYmEgPDgAEDJA5kSkpK6NevH2JjY0W2jtPhDUaMGCG2QE8zZMgQklD3fwVJSoikNskMA8Hj8SS+50GDBgGoVnqio6O/2gTPZMiQIVBRUUFVVRVWrlyJhQsXwtDQEEOGDCGLR2PHjhUrV59nTEhIIEKYtMS7kydPbvDJqHfv3hgzZgymTJkiMfcWHR5k4MCBKCsrk3gtHR0dREZGIjo6GhRFSRUimzRpgg0bNkj8XSgU4v3790TAoSgKAoGgTjGqmXTt2hW6urqIjIyEu7u72GI1rdAyY+gXFxeTcAoDBw6UKgANHjwYGRkZYvG66wpznpHWPwYOHIhu3bohLS0NERERJJRBQ8Osn7S5U09PD+3bt0dubi6ePXvGqhjVpGnTpsTYwoZQKERCQgIJcyopdIK8MNstU05QBAMGDGA9Lut4wpRrIiMj0bdvX3z69IkYd2obT77mAgGNk5MTgOpQF2xt5Mcff4STkxOEQiHu37+P3377TeycOXPmwMfHB69evUJaWprI4hStUHbp0oUYIgGgR48e2L59u8R6lZeXIyYmhrSb+rYfRd5v8uTJrMebNm2K0aNHw83NjSSrl8azZ8+IbNuvXz+J7apHjx7Q0NBAaWkpIiMjWec8abRo0YKElKtJu3btMGjQIMTExIjVec6cOTh9+jSCgoLw5csXkXBw9ILiqFGjFJLLTRbouW7MmDFSQ/5MmzYNzs7O4PP5iI2NJaFEpI1XAoEACQkJJHSkNLlXkszVqlUrqKqqorKyUi49gJIxFG5AQABxmJHEs2fPWBfMJY1t2trarA59NMXFxYiMjISysjKqqqpE+sWLFy+Ica+2sY126GFCz0+dO3eGmpqaxPZPL8jl5OSIjS8c8lMfXf/t27dET5O0BgBAzJgYERFB+hRbLiMaecPm6+npISEhAb6+vjA2NsbcuXMxduxYMiZ16dIFXbp0keuaTKe3uuYHpY04kvr21KlTJcrLhoaGOHfuHAQCASIiIjBlypR668dMJPX3pk2bokWLFvj8+bOILhEdHU2+HR1emI127doR/ToyMpIsJtLy09ixYyXqHRMmTECTJk0aJCeztHwk9HFpulNt7Nu3D/v27av1vO7du+PixYsiDk6dOnWSuJANVMu4L168IPWrKZcMHz4c/v7+uHPnDrKzszFjxgwYGBiQubpv374kbF9DQ1EUDh06RMKs8ng8WFhYiJ3n6OgILy8vEnqzoZk+fTouXbqEtLQ0xMXFicjpnp6eqKyshIGBgcQcj0C1EZkOTSZNlwKq5Y+3b98iISEBnz9/FnOSGzBgAMlvVJP6tscuXbpIdGChj5eWlkIoFJJ1voYYQ5l06tSJrLXUpGvXrujZsyeSkpLw/Plzkhe5SZMmmDlzJqysrODt7Y2KigoR4xUtf06YMOGr5Wqj5U9DQ0Op88K0adPw9OlTFBUVIT4+HlpaWmjRooVUA3tVVRXevHlD1p6k6R+Sxm9m+5Wn/cg65rq5uUnVoYBq+YANSfKnnp6e1LzyX758wfPnz8nfzPfy7Nkz8ndt8mdQUJDYcVr+pB3JJM1r2tracHFxQVJSEmt/5qimzsYnR0dHANUDBUVRePPmjdg5gwcPRlRUFPGCZYuP2rx5c9aOyRQ6JA0WNRMxAhDJ0VNbXPnevXsjNDRUrrw+stKyZUuJwiLzeRUlPNFKcPfu3SVOVEC1Uax79+5ISEhg9UyuC0xBTVEe6zSSOi4zce3hw4dx+PDhWq/F/M70/2tLStyzZ8//KeOTpPcpqU0yvV7YPJLYkKc/1cUzhqmgMceADh06YMuWLTh+/DgKCwtJLPOWLVti1KhR+P777zFx4kSxxJ71eUZm3hJpixxsns/y8sMPP+DIkSPk74qKCjx//hynT5/GixcvkJWVhW7dukmcnIuLi4mw4uXlJVNelOLiYhQVFcns0fnlyxcEBgbi/fv3SEtLQ2pqKpKSksTygMi6gFYbc+bMQWRkpNiCJ517REVFRSSJdUZGBvEuunnzpkhsc0koan6g24qGhgbrriImvXv3RlpaWoPMTZKg6/fNN9/UmkekV69eyM3NrdMckp+fj6CgICQmJiI1NRVpaWlISkoSEYAV1T5qzk2KTEIvKUEwczyR5J1Vk8YYT+SlrKyMLAoPHTpULJ42UO2Z1rZtW+Tn58PBwQEbNmwQ85Q3NDREq1at8OXLF7i5uRGPbqFQKLKLVNLckJSUhIiICCQnJyM1NRUpKSlIS0sj/RpQXPup7/3atWsnsZ0AIB79WVlZIgo+G0yZh/Z2rw1JebWk0bNnT6nzcvfu3RETEyPW92fPno0zZ86gsrIST548ITtQiouLST4HWXKIKgKKosiz19ZXmHoC23ibn5+PwMBAJCUlITU1lcxpshqMpCmftPFJHj2AOTaz7fBWBNLaLE1cXByioqJIf0hOTsaHDx9E+gLz/8x3K20clvS96PaflpYmMS9ITWiZiKPu1EfXZ0YlkScXErMc266nurJ+/XoEBgYiPT0d4eHhJKdfv379MG7cOEycOBFDhgyR65rMnc2FhYV1qteXL18ASO53PB5PYlnme6XHvPrqx0ykjV+03sjs58x7s0UVkXbv8vJysntLWr9VV1dHx44dxXL3KgJ5n1cRKCkpQUNDA23atMHAgQMxfvx4zJw5U+qOvrS0NISHh4vMSykpKSILrjXruWPHDsTGxqKgoIDogMrKyhg8eDDGjh2LyZMnS3T4VSR8Ph9mZmbE2ah3796wsrIS222dnp5O8u7u379faq4YRdG/f3/06tULSUlJePz4sYjxiZZPa9u9nZ2dTd69LOuQNB8/fhRrfw3ZHqXN88z1S6Zs2hBjKJPa5LXu3bsjKSlJTP6cO3curKysUFJSAj8/P+Lg+enTJ2I4+FryJ71uAsj3/bOyssQ2W3z69AnBwcFi8ifTmVLa95dlzU+e9sOUPxtqbVQW+TM2NhYxMTEi8mdmZqZM8mdddGtar4+Li5NZ/mTrzxzV1Mn4FB8fT8LKZGZm1roNVpoXbLNmzWq9nzwL1MwwO2xhQ5jQO14kJUiuD2xhhRoS+rnZwgDVRNHPzRw8mYKnLNS2A0KSx0Bdkgkzy9ATg6RdTzRsIZP+zcjbLuv7nmuDKVzL6q3OXKSuKZybmJhAS0uLJEqvrKxEYWEhPD094enpCVVVVSxbtgxbt24l76I+z8hUNqWNZQ3RjtTU1GBgYIDhw4fDxMQE4eHhsLCwQGlpKWvYCGm7fKRRXFxcq/GJz+fj1KlTsLW1FRtX1NTUoK+vD6FQSLyBFMX06dNx4MAB8Pl8eHl5kXCKtKdTTQ+1hm7PslyntnkJaNi5SRLyzCH0OfLUr6ysDCdPnoS9vb3Ywq26ujoMDAxQWlqK6OhoOWotHebclJ6eLtVrvya1zU2SFgbq0l7ovinreKKhoQFlZWURA0hD4+HhQeoZHByM4OBgqefn5OTAx8dHbJdf06ZNSfJdd3d3YnyKiIgg4UzZlMTU1FQcOnQI/v7+YspSu3btMGrUKJFr1BdF3K82+YL+naIoVFRUSD3/a41dsta5prdk586dMXz4cERERMDd3Z0Ynzw9PVFRUQENDQ2Ju8AUTVlZGekbtY1nzOdljmelpaU4ceIEHB0dxcYrDQ0NjBkzBsXFxYiJiZF6fUXrAr169YKSkhIoihILicPE0NCQ1bP0+vXrOHbsmNR7SPPUffnyJY4cOSLiYUrTsWNHGBgYwNXVVeydMcc2aW1MkrNhY87d/1Xqq+vTRhVANl2fhlmutvFIHtq3bw9nZ2dcvXoVLi4uZLEqPj4e8fHxuHLlCng8Hg4cOCBxx2JNNDQ00KlTJ2RmZpJdy/KQlJREdgpJWviSpj8w3yut1yqyr0hzZpXnOrKUkae9NJRu3tBrN4cPH65XGPasrCwcPnwYXl5eYk4LrVu3hr6+Pl6/fs06N/Tu3Ruurq64fPky3N3dkZeXB4FAgJiYGMTExMDS0hJ6enqwsLBoMAen/Px8rF+/nuy009LSwrVr18QMS0KhEDt27EBJSQl+/PFHTJo0qUHqw8aMGTNw7tw5eHh4kJ1meXl5CA8PR9OmTWuVY5h9QB75g01Hb8j2KG/fBhpmDGVS23hPv8+a8kW/fv3Qv39/xMfHw83Njegd7u7uEAqFaNOmjcSQaoqG+R3r+v0LCwtx9OhRPHz4UGxnvaamJkaNGoWcnBwSyUUSdfnG0qipU0tizpw5IlFnaE6dOlVr1A5pRvfnz5/jyJEjePnypdhvXbp0wZgxY1hDWDLTw0hrY5LmFU7+VCx1GtUcHBzqVIbNC1bRMDt6SUmJVCWKbhjyCreKCpWnSORZDKzrc0uiQ4cO6NKlCz58+ICwsDC5yh47dgx+fn4YOXIktmzZIpPFGxCt+7Vr1+QOLdOqVSvk5ubWut1U0aGa/m3QCsA333zDuhW1vjC9AiTFO68JfV6TJk1Y28uoUaMwatQoFBcXIyQkBKGhoQgKCkJaWhoqKythZWWFyspK7NmzB0D9npHpBSKtLTVkO2ratCnOnDmD2bNn49OnTzh58iR69+6NiRMnipzHVObWrFlDcoMogq1bt5J8DgMGDMD48ePB4/HQp08f9OrVCyoqKjh16pTCjU8tW7aEoaEhPD094e7ujnnz5pHdVwDEhB/muGFubi5TyDhFQY/RshgBFT1Gy0JDziEURWHDhg2kfw0aNAjff/89aSM9e/aEsrIyDh48qFDjE3MXYEhIiFz5ZmbPno0WLVpg4sSJEvM3skH3MxUVFbJwJyuyjicCgeCrGp6A//eAlwdbW1vWEJNz5syBnZ0dEhIS8P79e/Tp04d4wero6Ijlg8nLy8OSJUuQk5MDZWVlfP/999DT00Pfvn3Rp08f4mk/c+ZMhRifFHU/ZkgmNuixQFVVVWajD1Bt/JOW76c+yFpnNoeE2bm/VhoAACAASURBVLNnIyIiAuHh4cjNzUX79u1JDP6JEyfKZNhWBOrq6iQsU23jGVNBZBoDTU1NERoaCqA6lMa4cePA4/HQu3dvMl7t37+/VuOTomnZsiUGDhyIuLg4hIWFobi4+KuFkklMTISxsTFKS0uhpqaGSZMmQUdHh4zjdOgdtrB5TFmtLrKSuro6Pn/+jLFjx+LatWv1fBIOWaivrs8cs+TRm5nlysrK6hzOjo3mzZtjy5Yt2LJlC+Lj4xEcHIyQkBA8e/YMFRUVSEhIwMqVK+Hq6soarYWNUaNGwdHREenp6XKHeqTHGACsOR4B6WMyc3yj9an66sf1gXnvx48fy2XEkHWMAP6bunlxcTGWLl2KDx8+QElJCePGjcPw4cPJvETvKF20aJHEheFvvvkGe/bswa5du/Dy5UvS/qOjo1FVVYXnz59jxYoVePz4sUyOcvKQkpKCNWvWkF3zY8eOxZkzZ1jvk5mZSUL7Ojk5kZDPkqB3bNWMEFIXaONTeno6Xr16hUGDBsHDwwMCgQDjx4+v1RmT+TzyyB9fSz6qLw0xhtLUNk/Q8iebkWDOnDmIj4+Hn58fSkpKoKmpSfSKGTNmKNwQIwnmd6zt+7MZqqqqqrB69Wq8ePECQLUeO3r0aNLPe/TogSZNmuCPP/6o1fikaDp37kzSAgQEBIDP5yt0fpbGy5cvsWLFClRWVkJDQ4PIn3369AGPx0Pbtm1RVVXFanxi6talpaUS6yxpXmnWrBmKi4thZGQkd7hfDnEkx/iQAJ/PJ17lI0aMwNu3b6X+oxPt0V6wDQ2dJB6oVpSkQf/OHByZns6S4rjLukj+NaGfITU1VWr8eT6fT3YnMd9VfaGTL+bk5NTqEU1TVVUFDw8PpKSkwMvLSy5PJmayuNqS5bFtKaXL1+apJs2y/1+Ablf5+fkNsguDGVKDTjRfG3S/7dKli9SdCc2bN8eUKVOwb98+eHl5wd7enrR5Ozs7MsnU5xmZY4e0ttTQ7ahNmzYkPAEA7Nq1S2xLdMuWLYlQXJc+I4no6GhieFqyZAmcnZ3x22+/YcaMGeDxeKzJxxUJbWAKDw/Hly9f8OTJE1RWVkJTU1PMAMcMd6fIdyALdFspLS2tNRwW29zU0ND3ysnJEfFAZYNu67LOISEhIcTwZGJiAkdHR2zatAnTpk1Dnz59SD9WdBvp06cPiWH/9OlTmT2RXr58iffv3yM6OrpWOaIm9HusqqqSO+wZc15rzPGkJmlpaWSng7Gxca1y34gRIwBUL6yxhecbNmwYWaCj4+jTYUDZvOWsrKzIeHblyhVcvHgRq1evxrhx40T6iKLaj6Lul5eXJ1WZTk5OBlB7+F+g/jKPrNQWyohul2x1nj59Opo1awaBQABfX18UFRV99ZAnQHW0BFllPGb/pr9tQEAAWRRet24d7O3tsXHjRkydOrVBxytZoXegVFRUwNnZ+avd19LSkhie7O3t8ffff2P58uUiubwqKipY5aj6jm10+a89b/9XUYSuz/zm0qJivH79GqdOnYKDgwPKyspkLhcSEoKzZ8/C2dm5Tt+9f//+WL16Na5fv47Q0FDiZFJSUlLrYjcTZi4sWUI50wgEAty9excAWOVVGmnvgJ5DgP+Xx77WXMFGfe6tpqZGdsBIGyMEAsFXDUn9T+Hu3bvknZ46dQpXr17FmjVrMH78eJH5WJZ5qUmTJtDR0YGpqSnu3LmDwMBAMq9kZ2cTnU5RxMfH4+effyby4IIFC3Dp0iWFG7gUQe/evUmoSw8PDwD/71DBDOMuiY4dO5KITbXpD8x2/jX1PUWhqDGURlb5k83AP3PmTCgrK6OiogJBQUHIzs4mO2S+pvzZvHlzYqCUdR0a+P/v7+HhQQxP27Ztw927d7F+/XpMnjwZvXr1IiEQG0v+pHW0wsJCPH78+Kvd9/Tp06isrESLFi3g7OyM48ePY+nSpRg5ciSZNyS9k/qu1dHlOflTMchtfHr69CkxvsjSmenQGwBYrZGKRldXl3RMT09Piefl5OQQD+uhQ4eS40yvHUmNWFEJ6NmoSw4c4P89vCsqKkh8fTZ8fX2JcYr53PVlyZIlxKvg8OHDUg1gNHfu3CGLcwsXLpRqSKhJ//79idAizahZUlICAwMDGBoa4sSJE+Q47Qn2/PlzicZEOmfNfxk66btAIICfn5/E8x49eoShQ4fCyMiINRyLJLS0tMgWW1rIk8anT5+IMFEzt9Hly5cxc+ZMLFq0iLWstrY2jI2NAVQr1nT/rs8z9unThyh83t7eEssGBATU+mz1Zdy4cWRMLigoEDFGAdVjC/2sISEhUj0LTUxMMHr0aKxYsaLWyZS5U0XSuxcKhSSRMP13zbrVlXHjxqFNmzaoqqqCn58faUdTp04VC93Rtm1bsm386dOnEp9NKBTCyMgIY8eOxbZt2+pcNybM9iqtrb969YoI4LLGFlYEzPpJUzwjIiKQn58PQHwOkfQdmW1k8eLFrOfQXpeAYgU4elEsPz8flpaWtZ5PURROnjxJ/pbUpiVB9zFA+twUFBQEHR0dTJ8+Hb6+vgCqQ1oMHDgQgOLHk/r0MUdHR/JN2IxDNaHlPoqiJMp99Fjl4+ODkJAQFBQUQFVVFdOnTxc7l24/3bt3lxg64927dyQHTn1zaSrqfkKhUMSznUlpaSlx1Bk+fHitdWL2T2nt6t27d9DR0cGUKVPqtFstJyeHJMuuSUZGBsn5wlbn5s2bY8KECQCqZc2goCBUVlaiffv2MDAwkLsu9YHuh0FBQVKNzrSeoKqqisGDBwOQbbyqrKwk41VDJL6Xxrx584gjxYkTJ+QykMsa3pgN+r0MGzZMYm4QZntnvhdtbW2ys0Ha2EbvWq4J3f6Tk5OlPu/x48cxfPhwzJ07t8FyYv0XUISu379/f+LNLem7AtXJyS9dugQLCwuoqKhg6NChZL6SVu7+/fs4f/48zp8/T86XNM9lZGRg2bJlGD16NKuOrKmpiT/++IM4QTLzTtXG8OHDya6le/fuici60rhw4QJpy8uWLZPogCltvqfnAnV1dVKH+urH9UFXV5d8A2n3zsjIwNChQzFp0iTcunWLHKd1cz8/P4ljVURERK07o/4XocffNm3asMpJQHWeEdqRkylHv3jxAosWLcKIESPw7t07sXJt27bFrl27yN/ytP/aSE1NxapVq4jOvXnzZtLXJdG5c2dERUVJ/WdiYkLOp4/t379fIXWm87U+efIE2dnZiIyMhIaGBgwNDWst26pVK+L0VpsRj5Y/evbsKXPkn8aiIcdQmuTkZImG5bdv3xJDPFtu62+//Zbk7fb19SV17N69e73yUMkLc63F19dX6i5N+vtraGgQg6cs8mdZWRnZdf+15c+lS5eStnrw4EG5cj/XR/6k195Hjx4tMRckU/5kjn/Dhw8n60F10a3p7xkXFyfVqXTv3r3Q19fHTz/9xIXdk4LcxidamVVTUyO7XaTRt29fEvdTkhesImnbti2JDXvv3j1iPWYiFAqxf/9+VFZWQklJieQJAaq9OaUZr8rLy3HlypUGqv3/77ySxXjDxNDQkHgeHjt2jNVw9vnzZyJgtm3bliwSKIJOnToRj4d3795hzZo1IjE2a+Lp6Unq0rFjR6xatUqu+6moqJC4yYGBgRKt76dPn0ZeXh4yMzPRv39/cnzOnDlQVVVFRUUFDh06JFZOKBTi8OHDdVoEpQUqeb/hP5GJEyeS8D4nTpwgi85M8vPzcfbsWZSWliI3NxcDBgyQ+foaGhpEsfX19YWLi4vEcwUCAQ4ePEgmWqayC1S/93fv3iE6OlqigZheONPQ0CD9pb7PSHs9enp6su76S09PF1GuGhIzMzMiFDx+/FhMQFywYAGAauPU8ePHWa/h5eWFoKAg5OXloVu3brUuWtPjJQC8f/+e9Zxz586J7Gyr2TfqOu4B1YuGdDg1FxcXovhLWiSn30FiYiKuX7/Oes6tW7eQmJiIT58+oU+fPnLVR1L/HzRoEEkmevHiRVYPm/LyclhYWAConmO/prfW8OHDSYiUM2fOsCoOJSUlZLzU0NAgChqNpGeXpY2cPHmSKB2VlZUKM0D98MMPZFHZ2toa586dk3huVVUV9u/fTwTYmTNnyh23XF9fnyQBP3fuHKsnX0lJCY4fP47y8nJkZmaS+gHATz/9BKB6Jx8dsoxJTk5OnWQQWXZ1syEUCsnuit69e2PQoEG1lpk6dSrx/Hvw4AGr8kX3z7i4ONjY2ACoXnhiSypN1/3Tp0+sckVJSQn+/PNP8nd9515F3u/vv/9mXSg7duwYUU4WLlxYa5169uxJDD63b99GfHy82DmVlZU4dOgQKioqkJaWJtKu5OHIkSNiCq1AIICFhQWEQiFUVVVFvP2Z0N81JCSEyNBGRkZyORcpAvqdlpSUiDli0ISGhpKwLNOmTSPh65h1ZRuvKIrCsWPHSMjFry3raWho4ODBg1BWVkZZWRmWL18udZEeqHa4sba2xv+xd9/xUVXp48c/907PTJJJIwFCCn2xou5iF1EUYVkVlAUL9oo/tuiKq677xbqra1vXgh1FwSCgKALSixSBCNJDSSCd9DqZen9/TOZKTAIJBELgeb9e88pk5s69Z+beuXPOfc55zltvvXXE2w19Ls1lWCgpKWlQnz54GaPRqP+ezZo1q8l0hRkZGaSlpTW57ZEjR+q/IxMnTmzynJKRkcHUqVOprKzEaDQes9SUp4K2aOsfvM+//vrrJlMEFRYW6un9Bg8ejMlkolOnTnrQf/LkyU1ejNyxY4d+EengesjB392Dj5G4uDh27txJSUkJn376aZP1i+zsbP2c3JrUeQDPPPMMERER+P1+HnjggUMGjAKBAO+88w5vvvkmEPzsQnMfNuXHH39s8oLZnj17+PTTT4FfRp3C0bePj0ZcXJx+gX7GjBl66rSDhdrXLpeL7OzsBnWKUP3nwIEDTdbV3G53mwXKOprQ+a+ioqJRZgsIfjZPPPGEfmwffPx37tyZn3/+mYqKCv2Y+bVQ+xgaH/9Hel3D4/Hwl7/8hZKSEiDYRn3wwQcP+zpFUbDb7Ye8HRy8Cj3WVinAQueUffv28eabb6JpGoMGDWpxqvFQO3P79u3Njob8+OOP9Y4+v76ecbSOxXWoY30OhWDd6oUXXmj0uMfj4dlnnwWC9Z/hw4c3+fpQ/XPp0qUsXrwYOL6jnkJC+7+kpKTZ89XChQv1jofXX3+93oH/cO3l0DXs0Od8vOufTqeTiRMnoigKFRUV3HzzzYftdO5yuZg0aVKrRgb/Wui3PTMzs8mAW35+foPP+uDPJSwsTA/Yf/bZZ03Oh7px48Ym29zwy/70+XxMnDixybT3mzZtYtasWZSXl+N0Oo9bOuyOqFXBp8LCQv3i6hVXXNHiNGmh4M6hesG2pUcffZSIiAi8Xi+33347H3zwAdnZ2ZSVlbF27VruvPNOvTfCHXfc0WAS8oiICL2Bv3jxYiZOnEhmZibFxcUsXryY0aNHs3379sPmfD1SoQvHO3fuZOPGjZSVlbXoxGI2m3nqqaeA4I/lqFGjmDNnDkVFRRQVFTFnzhxGjRql9xp4/vnn23w+kXHjxumVzlWrVnHNNdfwxhtvsGnTJkpKSjhw4ADLly9n/PjxjB8/Xh8++d///veIJg8dN26cPsT/4Ycf5t///jcZGRmUlZWxZcsWJkyYoF/0P/fccxs0UFJSUvReM19//TXjxo1j8+bNlJeXs3HjRu69914WL158RBdMQvtwxYoVZGdnNxnMOJwDBw4wffr0w96O9XwDFouFJ554Agj2ernhhhv46quvKCwspLCwkAULFnDrrbfqx9XDDz/c6mH0jzzyiH6R4NFHH+Wxxx5j9erVFBQUUFFRQWZmJl999RWjR4/WR4zcdNNNjUaFjBw5EqfTqc/XMGXKFPbu3UtpaSk7duzgmWee0S+i3nTTTfoP/NG+x3vvvZeUlBR9u++//z55eXkUFxfz1VdfMWbMmOPWSy86OrrBSJ2JEyc2SIFz5ZVXMnDgQCD4A/zggw+yfv16ysrK2Lt3L2+99ZY+F1RUVBTjxo077DYvuugiPUD1zDPPMHv2bAoKCigsLGTFihXcf//9eiM75NdpeY70vBcSqnCGetp37ty52fz5N910kz665KWXXuLxxx9ny5YtlJeXs3PnTv71r3/pOcNTUlK49dZbW1yOg9/L+vXr2b17d4Pv/z//+U9MJhPl5eWMHj2atLQ0CgoKKC0tZenSpdx00036d3rChAkN0gQea4qi8H//938YDAaKioq48cYbmTlzJoWFhZSUlLBw4UL99w+CPXx+HSgIvfc1a9aQmZmpNzgPHj3yj3/8g++++07/fi1dupS77rqLDz/8sMG62irNp9Fo5PXXX9d/K9544w1GjBjB9OnT2blzJ+Xl5WRnZzNr1ixGjhypB0JOO+00Jk6c2OrtqarKP//5T1RVpbS0lFGjRjFt2jTy8vIoKipi2bJl3HrrrXrw4MEHH2xwkXTMmDF6kHLChAm88cYb+u/IvHnzGD169BH9phzcq/K7776jtLSUysrKw75u5cqVek+vw004H2KxWPT0JGVlZU2O9EtKStJHzoVGnDYXMA6NmnG5XNx7772sX7+e0tJS9u/fz/Tp0xkxYkSD3oJHe+y01fZUVSUjI4Obb76ZVatW6b9FjzzyCFOnTgVg9OjR+v4+nCeffBKLxYLL5eLmm2/mww8/1I+NNWvWcPfdd7Nq1SogeByFelK2hsFg4IcffuDuu+8mPT2dsrIyNm3axL333qs3lMePH0+nTp2afP3FF19MTEwMLpdL3+9t0fhftWpVi+pEoRz65513nn68zpw5k/vvv58NGzZQXl7Ovn37eOutt7j//vsJBALExcU16PV98Citxx9/nHnz5lFYWEhBQQFLlizhzjvvbNChxO/3H3aurLZ28cUX8+KLL2I2mykqKuKee+7hjjvuYMaMGezYsYPS0lLy8vJYu3YtL774IldccQUvvPACdXV1GAwG7rjjjlbX1UKfS25uLuPHj2fz5s163WHy5Mlce+21DToY/vp78ac//Yn4+Hi8Xi933XUXn376KYWFhXp9d+zYsc32jE1OTtY7uK1du5YxY8awZMkSSktLycnJIS0tjdtvv53a2lqMRmOD/Slapy3b+g899BAxMTF6e/yzzz4jPz+fwsJC5s+fz9ixYykvLycsLIzx48fr65owYQJWq1WvK3399dcUFRWRm5vLjBkzuPvuu/F6vcTFxTXovHjw71zoQlBo/udQ5oMVK1bwwAMPsG7dOoqLi8nNzWXevHncfffdaJpGWFiYHrxpqeTkZN5++23Cw8OpqanhnnvuYdy4cSxcuJDc3FwqKirYu3cvaWlpjBw5kldffRVN00hJSWHSpEmNRun/2l/+8hfeffddvW0xc+ZMbr31Vmpra3E6nTzyyCMNlj+a9vHReuyxx3A4HPr3/O233yYrK4vS0lLWr1/PAw88oKfZ/f3vf9+gLTdgwAD99+Ltt9/mH//4B7t27aKsrIzVq1dzyy23sGXLluPemeFEEDr/BgIB7rvvPlavXk1JSQnZ2dl8/fXX3HDDDQ3mLT74/BsXF6d/rl988QV///vf+fnnnyktLSU7O5sZM2box1Dnzp31Ttwhoe/VokWLKCwsbHHKr7S0NH3u0yFDhnDjjTdSU1NzyNuJIDk5Wa+XhTpEtGbO2D/+8Y96UPWFF15g4sSJ7Nixg4qKCnbs2MHEiRP1dmb//v31DA1tpS2uQ/3asT6HQrD+OX/+fB544AF+/vlnysrKWL9+PXfccYeewnnChAnN/iYNHjyYsLAwysrK9HZFc4Gq1li6dGmL6p+hgO+gQYP0Dv6TJ0/mz3/+M5s2baKiooI9e/bw6quv8uc//xkIDnr461//qm/r4Pbyww8/zKJFizhw4AD5+fksWLCAW265pUFKw2MxJcbhDBkyhKeffhqj0UheXh633nor9957L19//TU7d+6krKyMnJwcVq1axfPPP8+gQYN45ZVX8Hq9mEwm7r///lZvM3T+y8jI4JFHHmH79u2UlZWxZ88e3n//fa677roGnWZ/fS7529/+htPppK6ujrFjx/LFF19w4MABCgsL+eyzz7jrrrua7fTar18/PQvK4sWLGTt2LCtXrtTbhVOmTOGee+7B6/VisVjaLGPOyar5Ma9NmDVrlh7ta0nqlZChQ4fywgsvUFtby6xZs/jLX/7SulK2Urdu3fjggw948MEHKSoq4sUXX+TFF19stNydd96pX2g92OOPP85tt91GeXk5n3/+uX4xCoIX6B599FFWrFjRbDqVozFgwADee+89amtr9Z6bn3zySbMXUg921VVX8eyzzzJx4kT279/f4GQWEhYWxtNPP92iocOtZTQaeeONN3j11VeZPHkyRUVF/O9//2u2p3mfPn148cUXj7jHVVRUFB9++CEPPPAAWVlZfPjhh40uYEIw3cf//ve/Br0JIHgBpaKigs8//5yFCxc26lk2cOBAvF5vi+ewChkwYACbNm1i27ZtXHnllZhMJn766adWTXaYlZXFk08+edjlxo4de8yHEw8dOpTKykqeffZZcnNzmTBhQqNlFEVh3Lhxeu+A1nA6nUydOpXx48ezfft2Zs2axaxZs5pcVlVV7rjjjkYNrdB6XnvtNR588EHKysr0ESS/dvnll/OnP/2pzd6jxWLhgw8+4J577mHv3r289NJLDUYVqarKhAkTmuzNcyyMHDmSWbNmsX79enJzc/nvf//LY489pr+Hl19+mYcffpilS5eyaNGiJlNjxMbG8vbbbxMfH3/Y7fXq1Yt77rmHd999l5KSkiZ/dMPDw7nxxhv17+e+ffv0kWdwdOc9CH7HU1NT9fz3w4cPb3bEltls5t133+XBBx/k559/ZsaMGU2mp0pJSeG9995r9SSwAwYM4LvvvqOgoEBvrCxatIjExETOOuss/ve///HXv/6V4uLiBqMnQoxGI3/961+5+eabW7XdtjBgwABeeeUVHnvsMQoLC/n73//eaBmz2cxjjz3W5MiHAQMGsHTpUvbv36/3ll69ejVnnnkmt9xyC1OmTKGwsLDJOoDT6eTaa6/Ve0bt27dPDxIera5du/LFF18wYcIEVq9ezdatWw95fv3973/PxIkTj7jn0oUXXsjLL7/M3//+d4qLi/nnP//Z5HKhCvvBDAYD7777Lvfeey9bt25t8jf08ccfb3LE7qFER0fTu3dvMjIy+PTTT/n0008ZOHAgkyZNOuTrQt8NRVFa1Yi78cYb9brTtGnTmgxAXHvttXoQJyIiotnR2GPHjuX7779n69atpKenN/nd6Nu3L8nJycyfP599+/ahadoRpxpsq+3FxcUxbNgwPvzwQ+64445G67jmmmv0jg8t0bdvXyZNmsSf/vQnKioq+Pe//82///3vRssNGzasye9uS5x55pkkJCQwd+7cJus+t99+ux4EaIrRaGTYsGF88sknaJpG9+7dWzRa7nBCc6MczgUXXKAHVZ5++ml8Ph/ffvstS5Ys0YNnB+vZsyf//e9/GwTSzznnHEaPHs20adMoKChoVF+AYP1z+PDh+gXcrKysZlPRHSu///3v6dmzJ8899xw//vgjq1at0oOPTTEYDAwaNIiHHnroiOrd48eP54cffiA3N5fFixfrPYsPdv755wPBDgi/znThcDj46KOPuOeee8jNzeXZZ5/VezNDcBTzI4880uQxDfDnP/+Zuro6Jk+ezJYtW5q8gGG1WnnhhReOa8rak01btvXj4uJ4//33ue+++zhw4ABPP/10o9RY4eHhvP766w16yvfq1Yu33nqL8ePHU1BQwKOPPtpoe506deK9995rMJn46aefTlhYGLW1tTz11FM89dRTPPTQQ/y///f/uO+++9i2bRuLFi1q9nxgt9t54403iImJafH7DjnvvPOYOXMmTz75JGvXrm2yXRmiqirXXXcdjz32WIPyN2XgwIGkp6fz8ssvN0gJDBAfH8+kSZMalfdo28dHIzk5mffff5+HHnqI4uJiXnvtNV577bVGy11++eUNvv8hzzzzDC6XiwULFpCWltZoNOSYMWP0Dk6nkhtvvJG5c+eybt06tm7dyu23395omdTUVM444wxmz55Nfn4+Ho9HHw30+OOPs3fvXjZu3MjMmTObnJMnNjaWd955p9EIogEDBjBnzhyWLVvGpZdeSteuXZs8///awZ005s2b16L0+k2NSmgP11xzDVu3bkXTNCIjI5tNwdwUs9nMpEmTGDduHBs3bmx0LTHkoosu4j//+c8hUxAeiba4DtWUY30OvfDCCwGarV889NBDjB49utnX22w2rr76ambNmoWmaZx99tnNpmhrjabOnU25+uqr9e/Of/7zHx555BEWL17M3LlzmxyBetppp/H66683aGtedtllDBs2jDlz5rB///4mRwp26tSJK664gqlTp+LxeMjPz28w397xMGrUKPr27cuzzz7Lpk2bWLZs2SGnfTGZTFx11VWMGzdOn/6gNf72t7+xYcMGfUBFU6OUBg4cqA8i+HX9MyYmho8++oh7772XoqIifcBGiMVi4c9//nOj39iQJ554Aq/Xy5dffsn69eu56667Gi1jt9t55ZVX2mwk8cmq1cEnCF7EaM1J2OFwMGTIEGbOnNlsL9i2duaZZzJv3jymTJnCokWLyMzMxOv1kpCQwHnnnccf//jHBiOeDta3b19mz57Ne++9x7JlyygoKMDhcNC/f3/uvPNOzjvvvMOmuDhSl1xyCU899RSffPIJubm5hIeHtypv+Y033sj555/P5MmT+eGHH8jPz8dgMJCYmMjll1/OH//4x2N6gjKZTDz66KOMGTOGWbNmsXbtWjIzM6msrERVVWJjYzn99NO55pprGDx48FH/4Hbv3p3Zs2czffp05s+fBMIwXQAAIABJREFUT0ZGBtXV1TgcDvr06cPw4cMZMWJEk72kQj3UQzmnt27dSmVlJcnJyVx//fXcdtttjS4MtsRDDz2k9/otLy8nOjqagoKCFk0qfqIaPXo0F110EZMnT2b16tXk5eXh9Xrp1KkT5513Hrfcckuz36eWSEpKIi0tje+++45Fixaxbds2iouL8Xg8hIWFkZSUxG9/+1tGjRp1yDRoF1xwAXPmzGHy5MmsWrWKnJwcvF4vUVFRnH766Vx33XVcffXVbf4eExMT+fLLL0lLS+Obb74hKysLg8HAWWedxf33309ycvJxCz4pisLEiRO57rrr8Hq9fPLJJwwfPlzvxeVwOJg0aRILFy7kq6++YtOmTZSVlWEymUhNTWXQoEHceuuth20QH+zhhx/mtNNOY+rUqWzbto2amhp9v11yySXcdNNNOJ1O0tLSqK6u5vvvv2+Qs/loz3sQvEgSauAe7oJJXFwc06ZN45tvvuHbb79l27ZtVFRUYLVa6dWrF1dffTVjxow5bG/UpowaNYri4mJmzJhBUVERTqeTgoICEhMTgWDl6Pvvv+eTTz5h6dKlevq9Ll26cOGFFzJ69Ogjqpi1lSFDhtC/f38+/vhjVq5cqU+wmZiYyMUXX8zo0aObrcyHejF//fXXlJSU4HQ6ycvLIzo6mn/84x+cddZZpKWlsWPHDmpra7Hb7SQnJ3PZZZcxZswYLBYL06ZNw+128/3337dZ8AmCF2k+/vhjPZ3dpk2byM3Npba2FqvVSteuXTn33HMZOXLkEacrO9jQoUM599xzmTx5sv45ejweYmJiOOecc/T8+02JjY1l6tSpzJgxg1mzZpGZmYmmaZx22mn6fGytDT5BMA3gs88+S3p6Oj6f77A95srLy/UG4O9+97tW1R369etHv3792LZtGxs2bCAjI6PRSJyhQ4fy3HPP4fV6GTJkSLMpU8LCwvjss8/44IMP9GBPIBDQc+sPHTqU6667jtWrVzN//nzKy8tZt25ds5/v4bTl9iZMmEC/fv345JNP2LVrF0ajkX79+jF69Ogj6m1+wQUXMH/+fD777DOWLFnCvn37cLlcREVFccYZZ3DjjTceVUplRVF45ZVXOPfcc0lLS2Pfvn3YbDbOPvtsbrvtNv3iwKFce+21+kWn9kh5EmKxWHj55Ze5/vrrSUtL46effqKsrAyn00lqaip/+MMfGD58eJPn+YkTJ9K/f3+mT5/Ojh07cLlcOBwOkpKSGDhwIGPGjMFoNDJ16lS8Xi8LFiw47sEnCLZXPv30UzZv3szcuXNJT08nKyuLqqoqLBYL0dHR9OrViwEDBjB48GB9jsojERcXx4wZM3j33XdZvHgxeXl5aJpGdHQ0ffv25brrrmPIkCFMnTqVNWvWsH37drKzsxvUfXv06MFXX33F559/zty5c9m/fz9ms5lzzjmHBx54ALvd3mzwSVVVHn/8cYYNG8bUqVNZt26dnn6qa9euXHTRRdx2220duq59ImjLtv4f/vAH+vXrx9y5c/nkk09YuHAhWVlZeDweunTpwqWXXspdd93V5G/LRRddxPz58/noo49YtmwZOTk5BAIBkpKSuOKKK7jjjjsazZMSHR3NO++8w8svv8zOnTsxGo1UVFQAwfbpm2++yZw5c/Q0gOXl5VitVr0st912W7OjOlsiKSmJTz75hPXr1/PNN9+Qnp5Obm4udXV1hIeHk5qayvnnn8/111/f4ouiffv25amnnuKNN95g+fLl1NbWkpSUxJAhQ7j55pubrasfTfv4aPXv35958+bx+eefs3jxYjIzM6mpqSEiIoIzzjiD66+/vtl5i6xWK//973+ZP38+U6dOJSMjg7q6Onr06MGYMWO44YYbWpQK8mRjNpv58MMPmTx5Mt99951+XSsiIoKePXsyZMgQRowYwfbt25k9ezZ1dXUsX75cH8UUHh7OZ599xvTp05k7dy4ZGRlUVVVht9tJSkri8ssvZ+zYsU2OKnnqqacwGo0sW7ZMrze63W59zuamlJWVdeh5s6+55ho9jdfgwYNbndIvNjaWzz//nG+++YZvvvmGbdu2UVVVRVxcHH369GHkyJFcccUVbRr4DTlW16GO9TnUYDDw5ptv8vHHHzNz5kyys7NxOBycc8453HnnnQ3m1G3Otddeq/+GtWf902638/bbb7No0SJmzJjBzz//THl5OVFRUfTs2ZNrr72WoUOHNnlcvfzyywwYMIBZs2aRkZGB2+3G4XCQmprK5ZdfzujRo3G5XHzxxRcEAgG+//77Nh891xJnnnkmaWlpbNiwge+//5709HT2799PdXU1VquVmJgY+vTpw+9+9zuuuuqqFnVobk5iYiKzZs1i0qRJLFu2jPz8fBRFISYmhn79+jFixAiuvPJK3nnnHTZu3Eh6ejolJSUNgqD9+vVj9uzZTJkyhQULFpCdnY3NZuO3v/0t48aNO2SWIpPJxHPPPcd1113HtGnT+OmnnygqKkJVVbp168Yll1zCbbfddlwz1nRUitaWM3sLIYQQQgghTknbtm3j+uuvR1EUFi1adFQBDyGEOBWFgtn333//Mc8YI4QQJ4M1a9Zw2223YTKZWL58eZPzyAoh2k/bh9uFEEIIIYQQp5zZs2cDwdFyEngSQgghhBDHWqj+OXDgQAk8CXECkuCTEEIIIYQQ4qjk5+frKU9C8/cJIYQQQghxrGRlZfHdd98BHNEc5EKIY69tZ7gTQgghhBBCnBJWrVrFxo0bCQQCfPnll5SXl5OUlNTs/IpCCCGEEEIcjcWLF+vzIqWlpeFyuejbty+XXHJJexdNCNEECT4JIYQQQgghWq24uJjXX39d/99oNPL0009jNEoTQwghhBBCtL38/HxeffVV/X+z2cwzzzyDoijtWCohRHMk7Z4QQgghhBCi1Xr37k3Xrl2xWCycfvrpTJo0iQsuuKC9iyWEEEIIIU5Sffv2JT4+HqvVSv/+/fnoo48488wz27tYQohmKJqmae1dCCGEEEIIIYQQQgghhBBCCHFykJFPQgghhBBCCCGEEEIIIYQQos1IQnaBpmkEAqfmADhVVU7Z9y6OPznexPEkx5s4nuR4E8fb0R5zqqrI3ACi1U6WdpOcszs22X8dl+y7jk32X8cm+6/jkn3XftqizSTBJ0EgoFFaWtPexTjujEaVqCg7lZW1+HyB9i6OOMnJ8SaOJznexPEkx5s43trimIuOtmMwSPBJtM7J0G6Sc3bHJvuv45J917HJ/uvYZP91XLLv2ldbtJkk7Z4QQgghhBBCCCGEEEIIIYRoMxJ8EkIIIYQQQgghhBBCCCGEEG1Ggk9CCCGEEEIIIYQQQgghhBCizUjwSQghhBBCCCGEEEIIIYQQQrQZCT4JIYQQQgghhBBCCCGEEEKINiPBJyGEEEIIIYQQQgghhBBCCNFmjO1dACHE8efz+SgvL9NvZWVl1NW58Pl8+P3++r/B+4qioKoqqmrAYDCgqioGgwGLxYLFYsFsttTft2Kz2XA4wgkPD8dud6CqEt8WQgghhBBCNLZ162ZKS0u45JKB7V0UIYQQQghxDEjwSYiTWHV1FTk52WRn7ycnZz85OdkUFuZTVVV1zLetKAphYXbCw8OJioomNjau0S06OkYCVEIIIYQQQnRgfr+fbds207fvaZhMpha9xu128/rrL+F2u0lOTiUpKfkYl1IIIYQQQhxvEnwS4iTg9/spKMhn//59ZGfv0/+Wl5c1/yJFRTHaUI02VJMNRTWDqgYfJ/gXRQkuq2lAADQNrf6+FvBBwIcW8B9034vmd6P5PWiaRk1NNTU11RQU5DdZBKPRSKdO8cTHdyY+PoGEhATi4zvTqVO8BKaEEEIIIYToABYvXsCUKR8xaNBgbrvt7ha9ZvPmjbjdbgD27NklwSchhBBCiJOQBJ+E6GACgQB5eTns3buHzMw9ZGbuJSdnP16vt8nlVZMd1erEYHVisDhRLRGopjAUgwUlFFxqY5oWCAagfG4Cvjo0Xy0BTzUBbw0BT03wr7cGn89HXl4ueXm5jdZhMBiIjY2jU6d44uI60blzF7p0SaRr10SczqhjVnYhhBBCCCFEy23btgWAlSuXc8MNY7Db7Yd9zbp1a/X7mZl7ufzyY1Y8IYQQQgjRTiT4JMQJzuPxsGvXTrZu3cyuXTvJysrE43E3XlA1YrA4MdiigoEmaxQGixPF0LLUF21JUVQUoxWMVgxENrmMpgUIeGsJuKsIeCrxe6rq71cR8Nbg9/spLCygsLCg0WvDwux07RoMRHXv3pPevfuSkNBZAlJCCCGEEEIcZ5mZewDweNysWrWCwYOHHHJ5j8fDxo3pjV4vhBBCCCFOLhJ8EuIEEwgE2Lcvi23bNrN162YyMnY0HtWkGjFYozHaYjCERWOwRqOawztU8EVRVAxmBwazA+jc4DlNC6B5Xfg91cFglKc6GKCqKyfgqaa2toZdu3aya9dOli5dBIDDEU7Pnr3p3bsPvXr1ITW1R4tzzgshhBBCCCFar6yslLKyUv3/pUsXcuWVVx+yXbJ162bq6lygmiDgJTc3G4/Hg9lsbrTsjh3byM3NZtCgqzpUW0cIIYQQQkjwSYgTgsfjYdu2LaSnr2fjxg1UVJQ3eF4x2jA6EjDa4zHaYlEt4SjKyTsfkqKoKGY7qtkOxDd4Tgv46wNRFfjryvDVFuF3lVJdXcXGjRvYuHEDACaTiZ49e9Onz2/o0+c39OjRC4vF0g7vRgghhBBCiJNTaNSSanIQ8LnIyclm166d9O7dt9nXrF8fTLlndnbHW7EPv7+O/fuz6Nmzd4PlAoEA//vfq1RVVRIb24mzzup/7N6IEEIIIYRocxJ8EqKd1NbWkp6+jg0b1rFly88NU+mpRoz2BEyOBIz2BFRLhPT0q6eohmBKQWsUkAIEA1J6IKq2CF/NAbxeN9u3b2X79q1AcA6plJTu9OjRi549e9GjRy9iYmLlcxVCCCGEEOII7d0bDD4Z7Z1AUfCU7WHJkoXNBp98Ph/p6esBMEd2I+CpwledR2bm3kbBp/37s6iqqgTghx+WSfBJCCGEEKKDkeCTEMeRx+Nh06afWLNmJRs3/oTP90s6PcUYhimiK6bwbhjtnVBUQzuWtGNRVAPGsFiMYbHAb9A0jYC7El/tAXw1B/DVFOL3udizZxd79uzi+++Dr4uMdNKjRy+6d+9B9+49SEnp0aIJkoUQQgghhDgVBAIBMjP3kpraHVVtnHkhNPLJYIvBEBaDp2wPP/64hptuGkt4eESj5bdv30JtbQ2K0YohLA6DLQZfdR5ZWXsbLbt162b9fnr6emprawkLC2vDdyeEEEIIIY4lCT4JcYxpmsb27Vv54YflbNjwIy6XS39OtURgikjGFJGIwRolo3DaiKIoGKyRGKyRWKJ7BYNRnmr8rmJ8tcX4XcX4XWVUVJSTnr6O9PR1+msTEjrTvXtPUlN70KNHL5KTUzAa5VQphBBCCCFOPXPnfkNa2ufccMNohg+/vsFzmqaRmRkMGhnCYoLz0Vqj8dWVsnLlMq65Znij9a1bF0y5Z4rohqKoGG3RuPllBNXBtm7dot/3er2sW7eGyy4b1IbvTgghhBBCHEtyRVWIY6SqqpKVK5exZMkiCgvz9ccVUxjmyBTMkSmoVqcEnI4DRVEwWMIxWMIxO1MB0AI+/K5SfK4S/K4S/LUlBLzVFBTkU1CQz6pVKwAwGk2kpKTSo0cvPWVfdHSM7DchhBBCCHFS0zSNZcsWA7Bq1YpGwacDBwqpqakGRcVgcQJgju6FK28tS5Ys4uqrhzUYLeX3+9mwIdjpyxSRBARHTAHk5+dSV1eH1WoFghkjMjK2B5d1puItz2TVqhUSfBJCCCGE6EAk+CREG9I0jYyMHSxZspB169bg8/mCT6jGYMDJmYohLE4CFycARTVitHcK5qevF/DVBQNRrlJ9hJTP52H37gx2787Ql3M6o+jRo2d9MKo3KSndsVgs7fE2hBBCCCGEOCaysjIpLCwAIC8vl4KCfBISOuvP6yn3rFF6ynBzZDKugnQKC/PZvn0rp512hr78zp3bqa6uQjFY9Dq4arKhGMPQfLVkZe2lb99+AOzenYHX60Ux2rB1OgtveSY7dmyjuLiI2Ni44/L+hRBCCCHE0ZHgkxBtoLKygpUrl7N8+WLy8/P0xw3WaMzRvTBHJqMYTO1YQtESqtGKGt4VU3hXgMbp+mqL8deVUV5exoYN6/Sem6qqkpSUQq9efejVqze9evUhOjqmPd+KEEIIIYQQR2Xt2lUN/v/pp/UNUun9Mt9TtP6YYjBhdqbiKc1g8eIFDYJP69atAcAUkYii/DIiyhgWg7eyYfApNN+T0ZGAarZjtMfjqylk1aoV/OEPIw5b9kAg0OQcVUIIIYQQ4viR4JMQRygQCLB58yaWL1/MTz9twO/3B59QDJidKZije2G0SQCiIzt0ur5gMMpXW0zA5yIray9ZWXtZsGAuADExsfTq1Yc+fX5D376/oVu3bu35VoQQQgghhGixQCCgB59CgZ+fftrQIPgUmqfJ8Ks2jyW6J57SDNavX8sTTzzCb397PuedN6BRyr0QgzUab2V2g3mfQsEnkz0h+NeZqgefhg+//pCZJHbvzuCll57nkksGcssttx/hJyCEEEIIIY6WBJ+EaAVN09i3L5M1a1axZs0PlJWV6s8ZbDGYo3rKKKeTXJPp+jw1+GqL8NUW4a8twl9XTklJMSUlxaxZ8wMAERGRnHnmGfTo0ZuePfuSmNhNemMKIYQQQogT0u7dGZSWloBqwtbld1Tt+oaMjB1UVVUSHh6B3+8nKysToFGHO4M1CktsP9wlO8jJySYnJ5tZs6YHn1RNGO3xDZcPC74+K2svADU11fp9oyMYfDJHJOHKW0d+fh579+6hR4+eTZbb6/XywQfvUFfnYvnyxYwadRNms7ltPhQhhBBCCNEqEnwSogUKCvJYvfoH1qz5gYKCfP1xxWDG7OyOOaoHBquzHUso2pNqtmM22zE7UwDQ/F58rhJ8NQfw1x7AV1tcn5pxJStXrgTAarXRo0cvevXqTc+evenRoxdhYWHt+C6EEEIIIYQICo16MkUkYrBEoFqjCNSVsWnTT1x88WXk5eXi8bhBNaJaIhq93pbQH0tcP3yVuXgq9+GrLgAtEOyoVz8/VIjBGkzbV1hYQE1NNdu3b0XTNFRLBKopWD9WDCZMEd3wVmSxatXyZoNP33wzi7y8XADcbjfbtm3h7LPPabPPRQghhBBCtJwEn4Q4hD17djNt2qdkZOz45UHFgCm8KyZnCiZHl0aNJyEUgwmTIwFTfU9NLeDH7yrBV1OIr+YAPlcxdXUutm79ma1bfw6+RlHo3LkLKSndSUlJJSWlO8nJqVit1vZ8K0IIIYQQ4hTj9/v58cfg/EzmyGQATOFdcdeV8dNPG7j44st+me/JGt1g/qaDqQYL5qjumKO6E/B78LtKMYbFNl7OaEE1Owh4giOetm3bAoCxPuVeiNmZirciizVrVjFmzFiMxoaXM3Jy9vPtt18F12mJIOCuJD19vQSfhBBCCCHaiQSfhGhCaWkp06d/zqpVK+ofUTA6EjBHpmCK6CZp9USrKKqhQao+TQvgryuvnzOqCL+rmICnmry8XPLycvXjTlEUEhI6k5KSSnJyKikpqSQlpWK329vz7QghhBBCiJPYjh3bqKysQDGYMTo6A8ERUO6iLWzevBGPx6PPz2QMa9kct6rBjOpIaPZ5gy2GgKeavXv3/DLf06+WNzoSUIxWqqur+PnnjZxzznn6c4FAgA8/nITf78cYnoglujc1+xazceN6AoG7m0x3HQgEUBTlkPNHCSGEEEKIIyfBJyEO4na7mTv3G+bMmR1MIwGYnN2xxZ+lp3wQ4mgpiorRFo3RFo0lpjcAAV8dflcJflcpPlcpflcJms9Ffn4e+fnBtI8hcXHxJCenkJycQlJS8BYVFSUNZyGEEEIIcdR+SbmXpI9qMlijUYw23G4X27dv/WXkk61lwafDMdhi8FbsY8OGH+vTnCuN5oZSFBVzZArukh189NG7FBTkM2jQYKxWKwsXzmfPnt2gmgjr8lsUgwVUExUVFezdu5uePXs3WFd+fh4TJz5B//7nct99D7XJexBCCCGEEA1J8EmIelu2/MwHH7xNaWkpAIawOGydz200ga4Qx4JqtKKGd8UU3lV/LOBz4XeV4neV4a8rxe8qJeCtoaiokKKiQtavX6svGx4eTlJSCt26JdOtWxLduiXRuXNXmWBZCCGEEEK0mM/n0+uYpvqUexAckW+KSMRTuosff1xNdvY+oO2CT0ZbcN6nzMy9+noVQ+N6rCWmL97KHCorK/jiiyl8++0sBg68koUL5wFgSzhb7zRoCu+Ct2If6enrGwWfZs+eictVy6pVK7jsskH07duvTd6HEEIIIYT4hQSfhAC2bdvCq6++iM/nRTGFYUvojykiWUaSiHalGm1NBKTc+OvKgjdXKf66MgLuSqqqqti6dbOepgRAVVUSErrQrVs3EhOTSEzsRteu3YiL69Rk6hEhhBBCCHFq27LlZ2pqalCMVj1ldIgpPBh8Wr16JX6/H8VgQTW1TTpogzW6wf/GZlL0qWY74b1+j6ciC3fRVmpqqpgz5+vgOsLiMEf1alDeYPBpHaNG3aQ/XlR0gDVrfskqkJb2Of/4xzPS9hNCCCGEaGMSfBKnvN27d/Haa8HAkzE8EXu3i1BU+WqIE5NqtKA6EhrkwNcCfvzu8voRUuX1AalyAn4PeXk55OXlsHbtan15s9lC165d6dq1G127JtKlSyJduyYSExMrQSkhhBBCiFOYnnIvMllPuRditMeDasTv9wFgsEW3WcBGMZhQLREE3JXB7R9ifihFNWCJ6oHZmYq3Mht30VYCvjrCup7foDym8C6gqHoa686duwAwb963BAIBDLYY/HXl7Nmzi/T09Zx77m/b5L0IIYQQQoggucIuTmmZmZm8+OJzuN1ujPYE7N0uRlEN7V0sIVpFUQ0YbTENUkRqmobmc+nBKL+7gkBdOX53BR6Pm8zMvXpakxCz2UKXLl3p0qUrCQmd6dy5CwkJXUhI6Czp+4QQQgghTnLl5WWkp68DwHxQyr0QRTVgcnTBW7kfaLuUeyEGW0ww+KQYMNhiD7t8cA6o5CbLCqAYzBjDOuGrKSA9fR3Dhl1LZWUFy5YtBsAafza+6gLcxVv58supnH32ORgM0hYUQgghhGgrEnwSp6yCgnyeeeYpampqMNhisSddKoEncdJQFAXFFIZqCgv2+qynaQECnmr8deUE3BX43RX46yoIeCrxeNxkZe0lK2tvo3XFxMQSH9+Z+Ph44uM7k5CQQKdOCXTqFI/RKD8lQgghhBAdWSAQ4L333qKurg6DNbrZ4I8pIlEPPrX13LjGsDi85ZkYHfFt1i4zRSTWB5/WM2zYtSxYMA+v14vBFoPRHo/BFo2nbBd5ebn88MNyLr308jbZrhBCCCGEkOCTOEWVlBTz/PNPU15ejmqNwp5yOYrB1N7FEuKYUxQVgyUCgyWiweN6UMpdUR+UqiLgriTgrkQLeCguLqK4uIitW3+9PoXY2Dji4xP0oFTwb2diYmIlMCWEEEII0QEsXDiPLVt+BsVAWOKFzabTMzqCqezQNAxhbRt8Mkf1AC2AKTyxzdZpCk/Elb+ePXt2UVhYwMKF8wGwxPYLdtYymLHEnUZdwU/MmjWd88+/6JAj/v1+PzU11URERLZZGYUQQgghTlZyVVCcciorK3jxxWcpKSlGNYfjSBmEapCUYuLU1jAo1U1/XNM0NL+bgLsSv6eKgLuKgCd483uq0AI+iooOUFR0IHjB4iCqquqBqU6d4unUKYG4uE7ExsYRGxtLWJhdJnYWQgghhGhn+/fv44svPgPAlnAOBmvzgRXVaMGePAg0P6rR1qblUBQVS0yfNl2narZjsEbjryvlzTdfpba2BtUcgSnil/quJboP7uKdlJaWsHDhfIYOHd5oPYFAgDVrfmDmzOkUFx9g3Li/8NvfDmjTsgohhBBCnGwk+CROKS5XLf/5z/MUFOSjmMJwpFyBarS2d7GEOGEpioJitKIarRjtnRo8F5xXqo6ApzI4UioUlHJXEvBUEwj4OXCgkAMHCptct81mIyYmjtjYOGJiYoiJiSU6Ola/73RGSd59IYQQQog2kp6+Hq/XzZVXDtQf83g8TJr0Bj6fD2N4V8zRvQ67HpMj/hiWsu2ZIhLx15Wyb18WAJa4fg06QCmqAWv8mbhy1/Dtt7MwmYx065ZMt27JhIWFsXFjOl9+OY2cnP36ayZPfp/f/KYfDkf48X47QgghhBAdhgSfxCnD4/Hw+uv/Yd++LBSDJRh4Mtvbu1hCdFjBeaVsqCYbRnvDixDBwJSrfoRU9S8jprw1BDzVaH43LpeLnJz9DRryv15/VFQ00dExxMTE6IGpqKgYoqKicDqjiIx0Smo/IYQQQojD2LFjG6+//hIAH330HgMGXMBFF13Gjz+uIScnG8VoJazr+SflqHRTeCJ1B4Ij9BWjDXNkSqNlzM5U3MXbqampYMqUj/XHHY5wqqurgv+oJqyx/fBUZFFVVcFnn03mvvsearSukpJiNm/exPnnX4TVKh0dhRBCCHHqkit24pQQCASYNOkNtm/fCqoJe8qgRnPeCCHaTjAwFYZqCmsUmALQAr76QFRN8K+3hoC3loCnBs1bS8BXi6YFKC0tobS0hN27m99ORESEHoiKiIgkIiKC8PBIIiMjiYiIJDLSidMZhcPhQFXVY/zOhRBCCCFOLH6/n08//Sj4j2rE5XKxdOlili5drC8T1vX8kzYjhGp1opocBLzVWGJ/g6I2HlmvKCqO1Ctwl2TgryvDX1eG5q0NBp4UA5aYPlhi+6EaLRgdCVTv/Z5Vq1ZwwQUXc+aZZ+vryc7ez4svPktlZQV8KJBEAAAgAElEQVTLly/h4Yf/jt0uHR6FEEIIcWqS4JM46WmaxuTJ77N+/Y+gqNiTLsVoi27vYglxSlNUIwZLJAZL03MK6Cn9QkEpb31QyltDwOsKBqe8LjRNo6KigoqKisNu02AwEBnpJDLSSXR0NNHRsfqoqmDKvxgiI52S6k8IIYQQJ5UlSxaSk7MfxWAmvNdwAu5KPGV78FTsA82POaYPpvCu7V3MY0ZRFMISL8BXU4glunezy6lGG7b4s/T/A/Xznqrm8AaBOWNYLJaYPrhLdvDxx+/x3HP/wWazkZm5h5deep6ammoA9uzZxb/+9TR/+9sTRERIx0chhBBCnHok+NRBPfroo3z99dc8/PDD3Hvvve1dnBPazJlpLF26CFAIS7wIkyOhvYskhDiMg1P6NUfTNDS/m4C3Npjiz1eHVn8LNPjrQvO78fv9+kiqzMw9zW7X6YwiOjqaqKhoPcVfMGgVWT+6yklERISMohJCCCE6gFO93VRVVcnMmV8AYO10FupBc3naOp+H312OwRbbzqU89oz2To3mLz0c1WBBDYtr8jlr/Fl4K3MoKSnmyy+nMmDAhbz88r+oq3NhsMVgS+hPzf6V7N+fxQsv/B+PPvokUVHSAVIIIYQQpxZF0zStvQshWqe6upqLL76Ybt264XK5WLBgwVHl5vb7A5SW1rRhCU8MmqYxf/4cpk79FABbl99hacEEukKIk48W8NcHo1wEfK76UVS1B42sqkXz1gIt+0lUFAWHI5zw8AjCw0N/g/cdDgd2e+hmb/D/8ZqfymhUiYqyU1ZWg88XOC7bFKcuOd7E8dYWx1x0tB2DQToRnOyk3QQff/w+S5YsQLVGEd5jCIoix31b8VbnU5O1GEVRMJlMeDwejPZ47EmXoRhM+N2VVGcuRPO5iIuL5557HsBoNOH3+/D7/bjdbkpLSyguLqK0tJiSkhI6d+7KiBGjcDqdTW5TfnM7Ltl3HZvsv45N9l/HJfuufbVFm0lGPnVAc+bMwe/38+STTzJ27FhWrlzJJZdc0t7FOqHs3bubadOmsHPndiDYy+9UCzxpAT8Bn6u9i3HKU422JvPKi+NLUQ0oZjuqufmc+5oWQPO5Cfh+CUYFvLXBYFV94Co0skrTNKqqKqmqqmxVOaxWqx6ICgWlIiIiCQ8PP2i+qgg9PaDNZjspJ/4WQgghjodTvd20b18mS5cuBCCs87nHNPB0IrU9jlf92+TojNnZA0/5nmDgydEFe9IlKGrwMovBEoGj+1XUZC6iqKiQ55//v8OuMyNjBz/+uIqRI0dzxRVX6SPtfT4fGzb8yA8/LMdqNXPppVfQr98ZMhJfCCGEECc0CT51QDNmzGDAgAEMGDCA1NRUpk2bdko1og6lqOgA06dPZe3aVcEHFAPWuNOxxJ3WvgU7jrSAj9rcNXgrc0Dzt3dxhGLAFJFIWNfz9YaoODEpivpLqj9bTLPLhYJUmr+OQP3fYNCq/r7fc9DNrd8HqKuro66ujpKS4haVyWQy6YGoyEhnfSrAKKKioomOjqn/G43FcnJOEC6EEEIcjVO53aRpGlOmfIymaZgikzHa44/Ndk7EtsdxrH9bO/cn4KtFNTuwJZzbKOhlMDtwdB9Mbc5q/O4KUNRgEFBRUBQDiikM1WQP3oxW3KUZuFwlTJnyEStXLmPEiFHs3LmdFSuWUln5yxyna9eupXPnLlx99TAuuuhSzGYzAIFAgLq6Onw+bzBFtQagoaoq4eER0qlJCCGEEMeVXAntYPbs2cOmTZt45ZVXABgxYgSvvfYahYWFxMcfmwZFR1BTU8Ps2TNYuHA+Pp8PAJMzFVuns5od6aBp2onTQGpDtTmr8FZmt3cxRIjmx1uxj1otQFjihe1dGtFGFIMJxWBCNYe3aHlNC6D5vQcFpLz1f90E/J7gaCp/fQCrfnQVmg+v10txcRHFxUWHXL/d7iAmJrb+FkNcXBzdunXBZAojIiKSyMgobLbm588SQgghTjanervpxx/XkJGxAxQDtoRz9Mfbug10QrY9jmP9W1EM2JMuDW0YLeBrvIzBjD35ssOsyBBM3+dMwVO6m7rCjWRl7eWVV/71yyJGG+aoHhDw4S7bQ35+Hh9//B5ffDEFs9mMy1WHx+NudhPh4RH06NGLnj170bNnb+x2B3l5OeTm5pCXl8OBAweIjY0jJSWV5ORUUlK643Q6CQQCeL0e3G4PXm+wQ5XBYMRgUDEYjJjN5uOWWloIIYQQHYvM+dTB/Pvf/+bLL79k5cqVWCwWDhw4wMCBA3nwwQd56KGHjmidHTF3+a+9+uqLbNy4AQCjPQFrQn+MtuYndNU0jerM7/HXtmz0gTi2hgwZwogRI5g5cybz589HTktCNKaaHBgjk4LBKW+tnh6QJi5yNMVqtRIZGUVYmA2DIXiBIPRdC/WC/fX/h/Pr7+rJ0pv2UOegk+U9tiVFUTAaVXy+gJy/j5LZbGbkyD/Ss2fv9i7KCU3mfBItcaq3mz744B2WL1+CJeY32DoHg08nUhtI6v8NGcLicKQO1usZAa8LV0E63op9GB0JmKN7YQrvqqdO1Pxe3GV78JTsIOA9dsek0WjUO3ceitVqIzw8AofDjlo/+svr9eJy1VJbW4Pf78fhCMdud2AwGAi+zUPXqX5d59I0DUVRDnGshB5vfV3tSOt3B5flUOsIlh2MRgM+n5/Qyw633dZ+Lw79+bR8Ha3V0m125Hq01Hc7DovFyg03/JHu3Xvqj8m8QR2X7Lv2JXM+nWJ8Ph+zZ8/myiuvxO1243a7sVqtDBgwgOnTp/PAAw9gMJyac8uUlZUAYE04F0tMnxZWajpuxedkM2LECBITExkxYgTz5s1r7+IIcUJSTDZs8Wc3boj7PQS8NQQ8NfhcJfhri/DVHOCXBnhQMOVf/nEssRDiSOzcuZ033/xARisKcRSk3XRQhxKj5VfPnBhtIKn/H5pqsmHvdhFa4gVNztWlGExYY/tiiemNv64cBQUMRhTVhKKa6tP7/bKvtYAff10pnvIsPKUZDddltGGKTMJoj8dXXYinbLc+Oq4lgSeAujoXdXUuig4xWL+uru6wo/mFEKIt7Ny5jbff/khPSSqEaD8SfOpAli5dSnFxMTNnzmTmzJmNnl+yZAlXXnllO5Ss/RmNJgDcRVtQFAVzdK9DTqirKAqO1MGSdu8EMXPmTL3nY1v01joRmSK6Sdo9ccRCafv8rlICvtrgyKf6m+ZzEfDUEPDVgiY9gYTo6IYO/YMEnoQ4StJuOsivRma0dRvoSNsex6P+36Hq3/Vp90I0TcNbmY23PBOjozNmZyqKwdTgJX53Fe6SnXirsgEFRa0PPhmCwadQoFEBNM2P31WK5m+clk/zufCU7MRTksGvOy8JIURHNHz49RJ4EuIEIWn3OpD777+fjIwMXnjhhUbPjR8/njPOOIP333+/1evtSOkjmpORsYMPP5xEfn4eAKo5HGv82ZgiunXood1H4oSc9PcwQg3OkzLwdBwnPBYdR2i+Bc1XR8AfmufJTcBXh+avC/711RHwudC8wcdaQlEUoqKiiY2NJSEhnrCwcCIjnURGRhEVFUVkpBO73Y7NZsNstpxy50dxbEgqBHG8Sdo9cTjSboJvvpnFl19OQzWHE95zGIp6bEZ6HWnb45jW/zt4/dvvrsKVvw5f9UEj1lUj5sgUzNE9g3M+Fe/AW5XT6nUbDAZSUlLp0aM3drudvLxccnNzKCjIw+fzYTQaSUxMIjk5ldTUVGJi4vB6vXg8wRGEHo+nPv2YAVU1YDAYMJvNhIeH16fdi8DhcOD1eqmtraG2Nph2z+fzER4eTkREJA5HeKM5ovx+P36/H4PBgKqqJ2UdVepLHZvsv45N9l/HJfuufUnavVNIcXExK1as4M4772TAgAGNnh86dCjTpk0jOzubbt26tUMJ21fv3n159tmXWLZsMV999SWVlRXUZq/AEBaHrfO5GG0x7V3E40ZRjdi7XYwW8BPwudq7OC0S8LoIeGtQTXZU08nV21s12o5ZY1+cGLTAwUEkN5rfjeb31N+C9wMH3Q/9be0oJVVVcTqDQaSoqGiiomKIiooiOjqGmJhYYmJicTqjMBgMUkETQghxypJ2U9CVVw5h4cL5lJeX4S7ZgTXutGOynSNtexzL+v/xrH9rmoanbDeqyY4pvEszywTwlO7C764IpsNDBaV+pJLRhmq2Bz8HoxV3yU7qiraAFsBoNHLxxZexc+d28vPz8JTtDqbEO8iZZ57NlVcOweFwUFfnwuWqo67Ohc/nqw/qaWhaMNiXmNiN5OTUJkcD+P1+yspKcTqjGgWGjoTRaMRmsxHTwma4wWA46VNhCiGEEKciCT51ELNmzcLn8zFs2LAmn7/++uv5/PPPSUtL4+GHHz7OpTsxGI1GrrjiKi688BK++2428+Z9i6e2iOq9C3CkDMJo79TeRTyuFNWAwexo72K0SLCcce1dDCF0mhYIBpT01Hb1o5B8oVFJLj3gRKBlufCbYjQaiYiI1HuChodHEB4eQWRkZP2IpeDN6XTicISjqtJLXwghhDgUaTcF2Ww2Ro26mXff/R91RVswO1NRTWHHbHutbXucLPV/T+kuXPnrALB1Pg9LTJ8Gz2taIJiasGJfq9Z72mlnMHbsnSQkdEHTNHbu3M6SJQtZt24NBoOBiy++jMGDh9ClS2KbvA+DwUBsbMffH0IIIYQ4sUjavQ5i6NChqKrKt99+2+wy11xzDRUVFSxdurRVuU07UvqI1igtLeXDD99h8+ZNKKoJR/fBGKxR7V0sIUQ707QAmtdFwFdLwFvzS4CpwTxKdbQm573BYKhPNRKOw+HA4XAQFmbH4XBgtzuw2+04HOH19x364xbLsUl9JyOfxPEkx5s43iTtnjgUaTf9QtM0nn32KXbvzsAUmYK920XtXaSTSsBTQ+Xubxt0RLLGn4U17nQgODq+JnslvqocVIOBqwZfg8lkwu/34fP5cbvdlJaWUFJSRHFxMR6Pm8hIJzfdNJYBAy5sso7o9bqJjnbgcvnlN7eDkfpSxyb7r2OT/ddxyb5rX23RZpLgk+hwjajWcLvd/Oc/z5ORsQPFaMPR/aoOMxpICNFymqYFU9o1mjMpOEop4HUF//pcaD43LQksGQwGoqKicTqjcDqdRESERiMF/4ZGKjkc4YSFhZ1QuemlgiaOJznexPEmwSfRXjpiuykzcy8TJz6Opmk4UgefctkgjhVN06jZvxRfVR49e/bmtNPO4OuvZ8D/Z+/Oo+Sq7zvvv2/d2quru6t639Xqbq0ILQgEAmxWQwAbAwbbxNiesecZ5xl7TpxlEj9zJp7kZJzMczIzmZlMZp4sTkKcmAQC2MQGbLMKhIRAaEdLt5bW1vtae93l+aNaJTXdEgJ1q9XS53VOnVtdde+t361qqe+vPvf7+wGBquUEq64ieeQ1rGQPXq+Pb33r11i1as2595dMEg6Hz1nprr+585c+u/lNn9/8ps9v/tJnN7c055PIhwgEAvzqr/4m3/ve73LsWDfJwy9TsvBOPN7La14hkctZYQ6BicqkXBKnGCalCvMFWCncfJqPWqkUi8WJxyuKcyYV7hfmUorH40SjpRrmTkREROa11taFfOITt/Haay+RPvkOJW13A5AfP05u+CB2qp9Qwzr8pZfv/FdAYW7OXAozNDMjYeRHD2ONn8Dr9fK1r32D+voGgsEg//APf0e2fze54YO4VnqiP/rvWLbsqnPuzzAMSkp0kaSIiIhcXhQ+yWUvEinhN37jO/z+7/8OAwP9JA+/QknrnRimb66bJnLFc133jLmVkjj55MTwd8niY4Uh8M5POByhtLSU0tKyiVsp5eWxiXmTChVM5eUxSkvLFCyJiIjIFeFzn/sCW7ZsIpUaJtn9GnZqCNc+fX6VOvYWZnvssh4hItX9Blayh0jLrfii9dOu47oudmYIOz2MnRnBzgzjZEcxgzGCNVfjDRfmRHKsDOmT7wJw//0PUV/fAMA993yGQCDA449/H9dKEwqF+PVf/w4dHYunfT0RERGRy53CJ7kixGJxfvM3/z2///u/w/h4odMVabkVw2POddNELmunhsNzcuMTtyROPjGxLNxwP7x02ufzEY9XUlFRqFQqBEkxYrE4sVisGCj5fAqVRURERM5UWlrKgw8+zA9+8NdY4ycAKC8vZ/36m9m3by9dXQdIHdtISesdGMbld3GOk09jJXsAyPTvOmv4lD6xmdxw15THrWQPiYM9eKMNhGpWkunfg2tnaWpq5p57PjNp3dtvv4uSklI2bnydBx54mAULFs78AYmIiIjMEwqf5IpRW1vHb/zGd/iDP/g9MsleUkffINx882XZwRK5mAoBU7YQLmXHsXNjOLnExP1xcPLn3N4wDMrLY1RUnAqXpi6j0eglNaeSiIiIyHxy222forv7CNlshrvv/hRtbUtxXYO+vl7+w3/4LTKpfrL9ewhWn3t4uPkoP368eN9O9WMl+6bMfWWlBsgNd2EYBkuXLqepqYXm5haqq2vYsOFVNmx4FWv8OOMT+zIMg6997Rt4vVO/Ulm37gbWrbthdg9KREREZB5Q+CRXlAULFvKrv/ob/NEf/SH58WOkjm8i3HCDvtQWOQ+OlS1WMNnZ8dPVTNlxXCd3zm1jsTjV1TVUVVVTWVk16RaLxaftuIuIiIjIzDBNcyIsmTxxd3V1DY899i/48z//UzJ9O/CW1OINV55zX66dB1wM039xGn+B8uNHAQiFQqTTaTIDuyk5I3xyXZd0z3sA3HTTJ/n6139l0vaLFi3hnns+wz/90z+wZcsmAO6++z5aW9su0hGIiIiIzE/6tk+uOEuXXsW/+Te/yv/8n/+F/Mgh0h4vobprFUCJAK5j4+QSheql7Bh2trB0cmO49rkDpni8gpqaWmpr66iurqW2tpbq6lqqq2vw++fHlxMiIiIiV5obb/wEO3a8x+bNb5E6tpFo2y+ddX5c13UY73oe17EoXfQZDM/MfqXg5JIYvtCMjU7h2nmsRGHIva9//f/mT/7kv2KNn8DODGMGYwBYiRPYqT68Xh8PPPDwtPupq6vnm9/8NkeOHOLIkcOsX3/zjLRPRERE5HKm8EmuSGvWrOUb3/gm//t//09yQwcwPH5CtavmulkiF00hZBrDzoxiZ0dxsqeW44B71u3Ky2PU1tZRU1NLTU3tRMhUR3V1DYFA4OIdgIiIiIjMCMMw+MpXvs6BA/sZGhok3fMu4Ybrp13XSvbh5MYBsNNDU4avuxD5xEmSh18mULGEUN01M7ZPXIeqqmquueZarrvuejZvfotM/x4iTTcWqp56twNwxx2foqLi3FVfLS2ttLS0zkjbRERERC53Cp/kinXjjTfj8Tj8yZ/8CdmB3Rimj2DV8rlulsiMcl2nUMmUGcHJjkyETSPnDJmCwRB1dXXU1tZTV1c/sSwEToFA8OIegIiIiIjMukikhH/9r7/JH/7h75Eb7iJQdRWmv2TKevmxY8X7VnpwZsOnifmUcqNHCNaumZGRKfLjhfauWVMY6eLee+9n8+a3yI8ewa65Gjs1gJMZJhQK8elPP3DBryciIiIipyl8kivaPffcw+DgCD/84Q/I9G7D8PgIVCya62aJfCyOlcHOjGBnhnEyI4X72VFw7WnXD4cjNDQ0Ul/fSENDAw0NTdTXNxKLxTQMpYiIiMgVZsmSZSxZsoz3399NfvQI5gcuzHNdtxjmANjpwRl9/VP7c600TnYMM1j2ods4Vpbc0H4cK02oZvWk4QJd18GaCLTWrFkLFCqXVqxYyc6d28n278ZK9AJwzz2foaQkOqPHIyIiInKlU/gkV7z77rufRCLJc889Q/rkFuzMMMGaq/F4Q3PdNJFpua47Uc00jJ0ZKizTw7hWetr1/f4ADQ2NNDY20djYRENDYVlerpBJRERERE5bt279RPjUPWVUCCczgptPFn+eyfDJdR3s9HDxZyt58pzhk5NPkR14n+xwJzhW4UHDQ7hu7en2pfpx7RyRSAkdHYuLj9977/3s3Lmd3HAXAGVl5dx11z0zdiwiIiIiUqDwSQR46KHPk8/neeGFfyY33Elu9DDBqhUEKhZjeMy5bp5cwVzXwcmOYaeHsDJD2OlC4FTsZH9AdXUNTU3NNDW1FJdVVdV4PDMzabOIiIiIXL7Wrr2Ov/3b7xcucMqOYQZKi8+dqnpatGgJ+/fvxcklcKwsHu+Fz/vpfKBaP5/oIVCxZMp6ruuQPvkuueFOcB0A6urqOXnyBLnBffjLFuANF+ZtOjVE4KpVazDN0326JUuW0dbWTldXJwD33/+QhpYWERERmQUKn0QoTLL7xS8+xjXXXMvf/d3fcPjwQTK975EbOkCwbg2+aKMqRGTWFYKmUaz0EHZ6EDs9hJ0ZmXbYPJ/PR2NjM83NC2hpaaG5eQGNjc2EQqrYExEREZGPJxotZfnyFezYsa0w9F71iuJzp8Kcm2++heHhYfr7e7HTQ3iidRf8ulaqUEVVVlbG6OgoVrIX13UwjMkXUOUG95Mb2g8UQrBPf/oBVqxYyZ//+Z/y5puvkzq+iWjbL4HhKbb31JB7pxiGwac//SB//Mf/L7W1dXzyk7ddcPtFREREZCqFTyJnWLRoCd/97n9i48YNPPnkDxkZGSbV/TpmqBJ/rBVfaTMer66Kk5nh5FNYqQHsVH9hmRmeNmgKBkM0N7ewYEErLS2tLFjQSl1dw6QrOEVEREREZsK6devZsWMbudHDBKquwjAMnHwKOzOEYRisWrWGXbt2TIRPg/hmIHyyM0MA3HDDzWzY8CrJZAI7PYg3XDVpvdzIIQAefviL3HffZ4uPP/rol9mxYxvj46NkB/bgK23EySfw+XysWLFyyuutXn0Nv/3b/4Gamnq8Xn0tIiIiIjIbdJYl8gEej4ebbvoka9eu4yc/+RHPP/8c+fQA6fQA6RPv4I3W4S9bUKiGOmNCW5FzcV0XJzuClezDSvVjpfpx86kp64VCoYmAaSELFiyktbWV6upaDZsnIiIiIhfFmjXX4vX6sLJjONkRzGCsWEXU1tZBaWkZCxe2sXnzRqwZmvfJnqh8amtrZ3Cwny1bNmMleiaFT3ZmBDszhGmaU6qVSkqi/PIvf5X/83/+B5n+XdjZMQCWL19x1iH1li69akbaLiIiIiLTU/gkchbBYJCHHvo8t912J2+99QabNr3JkSOHscZPYI2fAMPEV9qEP9aON1KtYflkEtd1sDOFsMlO9hbCJjs7aR3DMGhqaqGjYxFtbYtoa2tT0CQiIiIicyocDrNy5SrefXcLudEjhIKx4nxPp4awa21tA8D+kPDJsTLkx46RHz2ClR4gVLuGQLxj0jquY2NnR4r7TSYTbNmymXyih+AZw/6dqnq6+urVRKOlfND116/nrbfeYPv2reRHDwOwevXaKeuJiIiIyMWh8EnkQ8Rice655zPcc89nOHHiOJs2vcmmTRvp7T1JfvQw+dHDePwl+Mvb8McW4vGF57rJMgdcO4+VnhhCL9mPlR4Ax5q0TiAQoKNjER0dS+joWMzChe2ao0lERERELjnr1t3Iu+9uIT9yhGDlcqxkL3A6zFmwoBXDMHCtNE4+NaUPlE/0kO3fPbGdW3w8O/A+/lj7pAv3CnOcOpSURKmsrGLZskLgZKcHcO08hunDdR1yI4cBuPHGT0zbZsMw+MpXvsZ3/p89ZDMZDMNg9eprZuotEREREZGPSOGTyEdQX9/Agw8+wgMPPMzBg128/vrLbNq0kUwmQaZvO5m+HXij9QRi7Xij9VMmyJXLg+u6OLlx7NTAROA0UOg0n9GxhsJcTR0di1myZCnLly9n9eoVJBI5LMuZm4aLiIiIiJyHVatWEwgEyGYTZPp3gutQU1NHXV09AIFAkIaGRo4dO4qdHpwUPjlWhuSRV4tzmba0tLJ27XX8+MdPk8+NF4fyO+VU9VRraxuGYVBdXUNlZRUDA/1YqT580QasZB+ulSIcjrBq1ZqztruiopJHHn6Uv/3b77N48VLKyspn4d0RERERkfOh8EnkYzAMg7a2dtra2nn00S/z9tubeP31V9i/fy/W+HGs8eMYvjCBWDv+WJuqoeY5185hpQcLYVNqYOIqzNyU9Sorq+joWER7+2I6OhbT1NRcHELP6/Xg8/mAqduJiIiIiFxKAoEgq1evZdOmN8kO7AUKQ+6dWbHU2trGsWNHsdKD+Eqbio/nRg6Ba9PU1My3vvXr1NTUAnDoUBdbt75DbrSb0LTh00Kg0NdavnwFr732MlaiB1+0oTjk3rp1N0ycU5/d7bd/irq6Ohoams65noiIiIjMLoVPIhcoEAhy8823cPPNt3DixHFee+1l3njjNRKJcTJ9O8j07cQXbcAfb8dbUqdqqEuc6zo4mdFiRZOVHsTJjk5Zz+v1sWBBK+3tHbS1ddDevph4PD4HLRYRERERmXnr1q1n06Y3OVXd/8Eh7Fpb29iw4VXs9FDxMdd1yQ0dAODOO3+pGDwBrF17PVu3vkN+7CihmpXFx62J7U/NIwUUw6d8soegY5Ef6wZg/fqbP7TdhfDq6o94tCIiIiIy0xQ+icyg+voGvvjFx3jooc/zzjubeeWVX7B//17y48fIjx/DMAP4ylrwly/ADFVOunJQLr7C8HkJ7PQgdnqwUN2UHioOEXKmqqoa2tvbWbiwg/b2DpqbF+D16r9QEREREbk8rVixklAoTDqdoqQkSkfH4knPL1xYCIvs9CCu62IYBlayFyc3TigU4vrr109af9WqNZimiZ0dxc6MYgbLcB2reKHXqcongKVLrwLAyYyQHToAjkVVVc2UNoiIiIjIpUvfnIrMAr/fz/r1N7N+/c0cO3aUV199iU2b3mB8fJzc0H5yQ/vx+CL4yhbgK2vGDMYURF0Ek4bPSw9gpwZx7eyU9UKhEK2tbbS1dUzc2iktLZuDFouIiIiIzGCNaKIAACAASURBVA2fz8fatdexYcOrrFq1pjic9ClNTS14vV4sK4eTS2AGosWqp/XrP0EgEJy0fiQS4aqrrmb79vfIj3VjBldMVE25lJfHiMVOjyJQWlpKc/MCursPk+nbAcCNN96sPpOIiIjIPKLwSWSWNTY28aUvfZUvfOFL7Nmzi7feeoOtW7eQySTJDuwmO7AbwxfGF23EV9qIN1yN4THnutmXBSefxkr1YSV7sZJ9Zxk+z0tLywJaW9tYuLCd1tY2amvrpnSuRURERESuNI888ihlZeXceecvTXnO6/XS3LyAgwc7sdODGB4v+bGjANx66x3T7m/t2nVs3/4eubGjBKtXTDvk3inLl6+gu/swOBZwfkPuiYiIiMilQ+GTyEXi9Xq5+upVXH31KrLZLNu2bWXTpjfZtWs7uVyqWBGFx4uvpB5vtB5fpBaPPzLXTZ8XikPopfonAqc+nNz4lPWqqqrPqGjqoLm55UMnLRYRERERuRKVlpbx8MNfPOvzra1txfDJySUAl46OxTQ1NU+7/po1a/mrv/LgZIaxs+PY6cGJ/Sycsu7y5Vfx/PPPAdDevmjS/FEiIiIiculT+CQyBwKBAOvW3cC6dTeQy+XYs2cnW7e+y7Zt7zA6Okp+rJv8WDdpwOOP4i2pxRupxRupweMNzHXzLwmuncfOjmKl+icCp35cKzNpHcMwaGpqYfHipSxZspSOjsWUlZXPUYtFRERERC4vCxe28dJLYKUHcPIpAG699c6zrl9SEmXp0qvYvXsH+bHuM8KnqZVPixYtKcwRZdvcdNMnZucARERERGTWKHwSmWN+v59Vq65h1aprcJyvc+hQF9u3v8fu3Ts5eLATJzdObmi8OH66xx/FDMUxQxV4QxWYoTiG5/L8p+y6Lq6dxcklcLJj2NkR7MwoTnYUJ5+csr7X62XBgoV0dCyeCJuWEImockxEREREZDacCo3s1AAAkUgJ11677pzbXHvtdezevYPcyMHiSAXTVT4FAkE+/ekH6Ozcz/XX3zTDLRcRERGR2XZ5fmMtMk95PJ7icHAPPvgIqVSKvXv3sGfPTnbv3smJE8dxcuM4uXHyo0cmtjLwBKKYwdikm+ENzosJeV3XxbXS2Nmx4rE5uURhCL1cApz8WbctKyujtbWdRYsW09GxmAULFuL3+y9i60VERERErlx1dfUEg0EymcIIBDfffMuHno+vWXMtf/M3f4mTHQOgsrKKaLR02nUfeODhmW2wiIiIiFw0Cp9ELmHhcJg1a9ayZs1aABKJcQ4d6uLQoYMcOtTFwYNdjIwM42THcLJjZwRSYJhBzGA5nmAZZqAcM1iOGSjDMC/+/EauncfJJ3BySZx8cmKZwM4WwiZc+5zbl5fHqK2to6GhiYaGRhoaGmlsbKKkJHqRjkBERERERD7I4/GwYMFC9u7dA8Ctt97+oduUlZWzePHS4jbTDbknIiIiIvOfwieReaSkJMqKFatYsWJV8bHh4SG6u4/Q3X2Yo0eP0N19hJ6ek7h2BivZA8meSfswfBE8vhAebxjDF8LjLdwM0w+GBwwPxsQSwwO44LrgOoCLO3HfdSxwLFzXPn3fyeNaWRw7i2tlC0PmWZlzVi9BodNaVVVNTU0tNTV1VFfXUF1dTVVVDVVV1apmEhERERG5RC1c2M7evXtYtuwqamvrz2ubtWvXnRE+TR1yT0RERETmP4VPIvNcLBYnFouzcuXq4mPZbJbjx49y7NipWzfHjnUzOjqKm09i55Ocu9Zo5kUiJVRWVlFZWUllZTWVlZXU1NRRU1NLZWUVXq/+OxIRERERmW/uuute0uk0d999z3lvc8011/GDH/wVAAsXqvJJRERE5HKkb3tFLkOBQICFC9tZuLB90uNjY2P09p5kZGSYkZFhhodPLYfIZDJYloVtW1iWPbG08Hg8eDweTNM8Y2kSCAQIBAL4/YHi/WAwRGlpKSUlUUpKokSjhWUsFicUCs3RuyEiIiIiIrOlvLycr3716x9pm3g8zr333s/Jk8fp6FgySy0TERERkbmk8EnkClJaWkpp6fST+YqIiIiIiFwsjzzy6Fw3QURERERmkWeuGyAiIiIiIiIiIiIiIiKXD4VPIiIiIiIiIiIiIiIiMmMUPomIiIiIiIiIiIiIiMiMUfgkIiIiIiIiIiIiIiIiM0bhk4iIiIiIiIiIiIiIiMwYhU8iIiIiIiIiIiIiIiIyYxQ+iYiIiIiIiIiIiIiIyIxR+CQiIiIiIiIiIiIiIiIzRuGTiIiIiIiIiIiIiIiIzBiFTyIiIiIiIiIiIiIiIjJjFD6JiIiIiIiIiIiIiIjIjFH4JCIiIiIiIiIiIiIiIjNG4ZOIiIiIiIiIiIiIiIjMGIVPIiIiIiIiIiIiIiIiMmMUPomIiIiIiIiIiIiIiMiMUfgkIiIiIiIiIiIiIiIiM0bhk4iIiIiIiIiIiIiIiMwYhU8iIiIiIiIiMutc1+V73/uP/Pt//5vkcrm5bo6IiIiIzCKFTyIiIiIiIiIy63p7T7Jv3/scO9bNe++9e17buK7LM888ycsv/3yWWyciIiIiM0nhk4iIiIiIiIjMus7OA8X7GzduOK9tDhzYx7PPPsXjj/8l6XRqtpomIiIiIjNM4ZOIXDZc18WyLLLZDKlUikwmQz6fx3GcuW6aiIiIiMgV78zwaefObYyNjX3oNu+88zZQONc/eLBz1tomIiIiIjPLO9cNEBH5oGw2y8jIMKOjI8Xl+Pg4iUSCROL0MplMkMlksCwLy8pjWdZZ92kYBqZpYpomXq8Xr9eLaXrPuG/i8/nx+/0EAgH8fj8+X+F+IBAgFAoTDkcIh8MTtwihUJhgMEggECQYDOL3+/F4lOmLiIiIyJUpkRjnnXfe5oYbbiIQCEx5vqurED6ZBti2zdtvv8Udd9x11v25rss772wu/nzgwH6WL7965hsuIiIiIjNO4ZOIXFSWZTE0NMjgYD+Dg4MMDg5Muo2MDJNOp2f8dU9VRRUqo7Izvv9TCmFVcCK88uHzFZZ+v59wOIhhmPj9AUKhUDG0CgaDhEJhSkpKKCmJEo2WEo2WEg6HFWaJiIiIyLxg2zb/9b/+Z7q6DjA8PMQDDzw86flMJsPRo0cAuL4xwptHk2zc+Po5w6dDh7oYHBwo/nxm5ZSIiIiIXNoUPonIjHNdl9HREU6cOE5Pz8lJt/7+3vMaBs/nMSjxe4j6PZT4TSJ+D2Gvh7DPQ8hXWIa9HgJeA6/HwDQMTA/FpccwcFwX26GwdAtLxwXbKfx85tJyXSwH8raL5bjkHZe87ZJzXPK2Q8ZyyVgfWNoOOdslZ7vFdmez2RkLtwzDoKQkSnl5OfF4JfF4BfF4nHi8goqKCuLxSioqKvF69V+5iIiIiMytn/zkR8XKpm3btk4Jnw4d6sJ1XUoDHm5sLGHj0SRdXZ309JyktrZu2n1u2VKoeqoKe+lPWXR17cdxHF2gJSIiIjIP6BtLEbkgrusyMNDP4cOHOHLk9G10dPSs23g9UBYwKQ+alAe8lAXN4s9Rv4dowCRgGhiGcYGtM8C8wF2cB8ctBFangqicXQivrEk3CqHWxPPZM4KrrO2StRxS+cItmXfI2i6u6zI+Psb4+BhHj3ZPf4SGQXl5jMrKqkm3iopCMFVZWYXf75/9N0FERERErliHDh3k2WefKv58+PBBRkZGKC8vLz52KphqKvUTDZi0xwMcGMry1ltvTAmqoNDPOBU+3dJSwrP7RkmlUpw4cZzGxqZZPiIRERERuVAKn0TkI0mlUnR1HaCr6wCdnQc4ePAAyWRyynoGEA+ZVIa9VIS8xWVFyEs04MFzwcHSpcNjGPhNA/8MBl2W45KeCKLGsjajWbu4HM2c+tkh77gMDw8xPDzEgQP7pt1XNFpKZWUlFRVV1NTUUltbV7xFo6UzEPKJiIiIyJUql8vxZ3/2J9i2zbLKICMZmxOJPDt3buPmm28prndqyLym0sKFUStrQhwYyrJx4wY++9nPTTkn7e4+Qn9/Lz6PweKKII2lKQ6N5Ojs3K/wSURERGQeUPgkImflOA7Hjx+jq+sABw920tm5nxMnjuO67qT1TANqIj7qSnzURX3Ul/ioKfHiNzUcxsfl9RhEAybRgEltiW/adVzXJZl3GMnYDGdsRjIWIxn79C1rk7NPV08dOnRwyj7C4TA1NXXU1zfQ3NxCU1MLzc0tRKOls32IIiIiInIZePLJv+fEieOU+D18ZlEZm44nOZHIs2PH6fDJdd1JlU8ASyuD+D0GfX29dHUdoL190aT9btmyCYD2eICA10NzqZ9DIzkOHNjHLbfcfvEOUEREREQ+FoVPIlI0PDxEV1cnBw920tV1gMOHD5LJZKasFwuaNJX6J24+akp8eD2qnrnYDMOgxG9S4jdpnCYrcl2XtOVOhFGFYGoobTGQthlMWYxmbVKpFIcOdXHoUBdvvnl62/LyGE1NLTQ1NdPY2ERDQyN1dQ0EAoGLd4AiIiIicknbvXsHP/vZ8wA8sLiciN9kUTzIq0cS7Ny5Hdu2MU2T/v4+xsZGMQ2om7iwym96WFoVZHtvmo0bN0wKn1zX5Z13CkPuLa8KAtBUVgitToVYZ+rt7eHZZ5/immuuY+3a62b1mEVERETk/Ch8ErlCnQodDh7s5ODBQvgwPDw0ZT2/adAQ9dEY9dNU5qOp1E/JTI4vJ7PGMAzCPoOwz0N9dGr1VN52GcpYDKQs+pIWPYk8Pck8Q2mbkZFhRkaG2blz26T9VVVV09BQCKOqqqonzTHl801foSUiIiIi88/Ro938zd/8BQ899HmWLl0+5XnHcfjrv/4LAK6tC7OoohASNZT6CHs9pNIpOjv3s3jx0mJgVFfiw2eevmhtVU2I7b1pNm3ayKOPfgWvt/AVxfHjxzh58gSmAYsn9nuqYurkyROMj49NqtT/+79/nG3b3mXjxg2sWXMtjz32L4nH47PwroiIiIjI+VL4JHIFsCyLo0e7OXjwQLGy6eTJE1PWM4DqiJfGUj+NUR+NpX6qI97Lan4mOc1nGtREfNREfCyvOv141nLonQijepN5+pIWfSmLVN6hr6+Xvr5e3nvvnSn7Ky+PUVFRSUVFJfF4nFisgng8TjxeQSwWp7w8hmkquBQRERGZD/75n5/lwIF9/P3f/w2/93v/ecqcTLt27aCvr5egaXBX2+kgyGMYtMcD7OhLs337e5PCp1MB0imt5QFK/B4SyQR/9mf/i0cf/Qrl5eXFqqf2eICgtzCUd9jnoTLsZSBl0dV1gFWrrgGgr6+X7du3Trw2bN26hT17dvHII1/k1lvvxOOZfijwzZs38uSTP+RXfuXf0tbWMQPvmIiIiIicSeGTyGVobGyM/fvf58CBfXR1dXL48EHy+fyU9WJBs1DVVOqnIVqYsyng1TxNV7qA10NzmZ/msslfDiRydiGISubpT1kT80wVbnnHLVZLTTcUCoDH4yEeryhWS526VVVVU11dSywWm/KlhoiIiIhcfLlcjm3b3gWgu/sIBw7sY9GiJZPWeeWVXwCwqjY8pQ+xqKIQPu3cuY1HHnmUzs79ADR+IHwyPQa3LYjy3P5RNm/eyI4d7/Hgg58vzve0rDI0af3mUj8DKYsDB/YXw6eXXnoR13XpiAf41MJSfrRvhGPjaR5//Pvs37+PX/mVfzvl+BzH4cknf0h/fx8//vEzfPvb/+7jvlUiIiIichYKn0QuAwMD/ezb9z779u1l//73p61qCnoNGqN+Gkt9xcqmiIbPk4/g1PxSC2OT531yXZdU3ikEUVmbsazNaNZmLOsU749nbWzHYWCgn4GB/mn37/cHqKmpoba2jpqaOmpqaqmvb6ChoZFQKHwxDlFEREREKFQ1nTn360sv/WxS+DQ8PFQMp9bWTz1Pa48FMCgEV729PXR3HwGgqXTqMM3X1keoK/HxzwdGOT6e5u/+7q+BQhXTksrgpHWbSn1s7aEYZmUyGV577RUArm+IUFvi41+tqeTt4yl+2jnKpk1vct99n6WpqXnSfnbv3kF/fx8AO3a8x8jIMOXlsfN/g0RERETkQyl8EpmHRkZGeP/9XezZU7hN92V+dcRLS5mfplI/jaV+KkKmhs+TWWEYBhG/ScRv0nCWdRzXJZlzJqqlJldNDWUsRtI2uVyWo0e7OXq0e8r28XgFDQ2NNDQ0Ul/fSGNjM42NjQQCwWleTUREREQuxKnKo9ZyP4dGcmzZsokvfvHLlJeXA/D666/gOA7NpX5qIlMDpYjfpLHUx9GxPD/+8dPYtk2J30N5cPqL3xpL/fxfayp592SKnx8cI225tMUChH2TK6pOVeYfPNiFZVls3LiBdDpFPGTSHi9cIOUxDK5vjHBoJMuegQw/+9lP+drXvjFpP6eqtqBQBfXGG69x332f/ZjvloiIiIhMR+GTyDyQTCbZt28Pe/bsYvfuXZw4cWzS8x4D6kt8tJT7aSkL0FLmn9JRE5lLHsMgGjCJBswpw/kB2I7LcMZmMG0xmLIYTFsMpCz6UxbjOYehoUGGhgbZuXN7cRvDMKiurqWpqXni1kJzcwuVlVUavk9ERETkY8rn87z3XqGq6fbWKC92jXF0LM9rr73E/fc/hOM4vPrqSwBcO03V0ykd8SBHx/K8+ebrQGG+p3Odo3kMg2vrIyyrDLK7P8PSyqkXGVWGvQS9BplclqNHj/Dzn78AFKqePnih3fqmCHsGMmzc+AYPP/xFSkvLgELV1qnjW98YYeOxJK+//ir33nu/ziFFREREZpDCJ5FLUDab5cCBfcXKpsOHD+K6bvF5A6gr8bEw5mdheYDmMr/mapJ5zfQYVIa9VIa9UDH5uXTeoT9VmGuqL2nRl8rTm7RI5Bx6e0/S23uyOCk1QCgUoqmphZaWBTQ3L6C5uYX6+kb8/qmhl4iIiIhMtmfPLtLpFFG/h6ZSP+saIhwdG+GVV37Bffd9lp07tzM0NEjIa7C8KnTW/SyKB3j58HixHzPdkHvTifhNrmuITPucxzBoKvVzYCjLj3/8NCdOHMPvMVhdOzUEa56Y1/b4eJ6XX/45n/3s54DJVVu3LYjyzokUvb0n2b9/L4sXLz2vNoqIiIjIh1P4JHIJyGazdHUdYN++99m7dw+dnfuxLGvSOpUhbyFsigVoLZ86BIXI5Srk89Bc5p9SMZXI2fQmLXoTeXoSeXqShYAqnU6zf/9e9u/fW1zX4/FQV1dfrI5qbGymubmF8vKYrnAVEREROcOpIfeWVQbxGIWA6fnOMYaHh9i69Z1iJdOq2jA+8+znUXVRHxGfh2TeAQqVTzOheSJ82rr1HQBW14YITnMhnmEYrG+M8OT7I7z00s+49977MU2T1157GShUbQW8Hq6qDrG1J8Xrr7+i8ElERERkBil8EpkDyWSCrq5O9u7dw75973PoUBe2bU9apzTgoa08UAybys4yPrrIlarEb1LiN2mLBYqP2Y5Lf8qiJ5Hn5EQodTKRJ205HD9+jOPHj7Fp05vF9aPRKA0NTdTV1VNf30BdXT11dQ3E4xV4PAp4RURE5MpiWRZbt24BKFY1eT0G19SFeb07wY9+9BTHjh0FYG3d2Yfcg0KVUkc8wLbeNB6gPnp+lU8fpukDFyStO0uVFBSO4cWuMcbGRtm8eSPRaCmDgwOTqrauqQuztSfF229v4ktf+iqh0NmPa3BwgJ6ekyxbdpUuYBIRERH5EAqfRGZZOp3i8OFDHDp0kMOHuzh06CB9fb1T1iv1e1hQHmBBeWEovXjIVIdG5CMyPQa1JT5qS3ysmnjMdV3Gsg49yYkKqURh2L6BlMX4+Dh79+5h7949k/bj9/upra2jtraeurp6amvristzfSEhIiIiMp/t3buHZDJJxOehpfx0yHNdfYQN3QmOHu0GYEGZn+rIh4dJSyqDbOtN01Dqw2/OzIU9jVEfBuAC7bEAVedoh+kxWNcQ4eeHxnnxxZ9SUVEJTK7aair1URnyMpDOsnnzW9xyy+1T9pNMJvnnf36Wn/3seSwrz1e/+q+49dY7ZuR4RERERC5XCp9EZoht2/T29nDsWDfHjh0tLnt7e6ZdPxY0WVDuZ0FZIXCKBRU2icwGwzAoC5qUBU0WV5yeuDpvu/Sl8vQnLfpThTCqP2UxlLbI5XJ0dx+hu/vIlP2VlZXT0NBIQ0PTxLJwPxI5+1W3IiIiIvPBB4fcO6UsaLKkMsj7AxkA1taf38U4yyqDPLSknIYZGnIPIOAtDMl8ZDTH+sYPP/9aWx/h1SMJursP0919uPDYGVVbhlGo7Hrx4Bivv/7ypPDJsixeeeXnPPvsUyQSieLjzz77FOvX30wgcLoCX0REREQmU/gk8hHl83l6e09y4sRxTp48wYkTx4s3y8pPu015wKQ+6qM+6qMh6qM+6tecTSJzzGcaNET9NEQnfxliOy4jGbsYSA2mC8uBlEUi7zA6OsLo6Ah79uyatF0sFp8mlGpUpZSIiIjMC7Zt8+67k4fcO9P1DRHeH8gQ8XlYVjn1+ekYhsGq2pk/F3pkWYzhtEVL+YeHP2Gfh9W1Id4+kQKgZZqqrVW1IX5+aKw4NPrw8BA7d25n167tjI6OAlAV9nJHa5Tnu8YYGRnmF794gXvvvX/K63V1dRKJhKmtrZ+BIxURERGZvxQ+iZyFZVn09JwsVjAdP36U48eP0dfXi+u6027j8xjURLxUR3zURLzUlPiojXiJ+DVfk8h8YXoMKsJeKsJT/0Sm8w4DaYv+pEVvMk9f0qIvlWcs6zA8PMTw8BC7du2YtE08XkF9fQNVVTVUV9dQXV09cb9awZSIiIhcMvbvf5/x8THCXg8LyqdWKi2MBXj0qhjlQW9xyLq5UhowKQ2cfx/r+saSYvh07TRVWyV+k0XxIHsHM/zBH/zu5Od8Hm5rjbKmNozpMcjaLk/vHeEnP/kRt9xyx6Tq982bN/Knf/rf8Xq9fOMb3+Laa6//mEcoIiIiMv8pfBI5w5Ejh3nttZfYt+99Tp48gW3b064XMA2qwl6qwl4qI4VlTcRHedCcNDyFiFxeQj4PTT4/TR8YOiadd+hPWfSdEUj1JS3Gcw5DQ4MMDQ1Ou7+SkiiVlVVUVlZSWVlFRcXp+zU1dRrKRURERC6KRCLBiy8+D8DSyiCmZ/o+zdLzrHi61FSFvdzeGmUwZU1b1QWFyq69g4VhBWsiXjriATriQZrL/HjPeD9W1oR4oztBXzLJ888/x+c+9wUADh7s5M///E+BwoWM/+t//TGPPfYvuP32u2b56EREREQuTQqf5IqXzWZ58803eOWVX3DwYOek5wKmQXWkECxVT1Q0VYW9RP0ezc8kIkUhX2HugeayyaFUKu/Ql8wzmLYZTlsMZU4vU3mHRGKcRGKcw4cPTtmnYRjU1NTS1NRMY2MzTU0tNDY2UVVVjcejYTtFRETkwo2OjvDCCz/h5Zd/RiZTCF5W1szPgOnD3NISPefzbfEA37q2ipDXQ/QcVVUew+D21ig/3D3Miy/+lDvuuBvHsfnv//2PyOfzLIoHKAuabDmR4vHHv8/IyAgPPvjIOfuPvb09lJeXEwgEz7qOiIiIyHyj8EmuWMeOHeWJJ17hpZdeIpUqDMFgGoUr/a6uCVFX4qMsYCpkEpGPLezzsKA8wILyqc9lLIfhjM1IxmY0YxXvj2RshjMWaculp+ckPT0n2bJlc3G7aDTK1VevZuXKNaxYsZJwWEP3iYiIyLnt3buH//E//guu61JaGiUcjhAKhdm/fy/5fGHe2pqIl9sWRGmNXbmV1x+cC+psllYGaYz6ODae5Z/+6QmOHDnMyMgw1WEvDy+LETANon6Tlw+P8+MfP83w8BBf+MKXKCmZHICNjY3xxBN/y5tvvk5VVQ3f+c7vUFFRORuHJiIiInLRGe7ZJq+RS8pjjz3G22+/PemxaDTKsmXL+OY3v8l11133sfdt2w5DQ8kLbeK80d/fx1NPPcGmTW8WH4sFTdbWh1lTG6ZE8zOJyBxzXZdEzqE3mac3adGbyNOTtOhP5rHO+KttmiaLFi1h1ao1rFq1pjixtdfrIRaLMDycxLKcOToKuVLo900utpn4nYvHI5imqkgvR+o3TTU0NMR3v/tbjI2NTft8U6mPTzZHWVQR0IV3H8HB4Sx/tf300Mphn4dvrKkkFjp9je+WE0me2z+KC/j9AW6++Rbuuuseqqtr2LDhVZ544gckk4ni+tXVNXznO98lHq+Y9jX1N3f+0mc3v+nzm9/0+c1f+uzm1kz0mRQ+zROPPfYYiUSC7373uwDYts3w8DA//OEPefvtt3n66afp6Oj4WPuer52ojyqRSPDcc0/zi1+8iGVZGBSuWLu2PszCWEBzNYnIJc92XLpHc+wbyrBvMMtAypr0fEtLK+vX38SNN97EwoVNOkGTi0IdArnYFD7JuajfNJllWfzBH/wunZ37qY14+dyyGBnLIZV3Secd4iGTljK/QqeP6a+3D9I1nMU04F+srKClfGrV2IHBDD87NEZPonDeZhgG1dW19PaeBE5XnL3QNcZwxp4SQDmOw759e+jsPEBFRQXt7QsIh8uIREr1uc0jOl+a3/T5zW/6/OYvfXZzayb6TBp2bx4pKSlh1apVkx5bv349N9xwA08//TS/9Vu/NUctu7Tlcjl+8YsXee65Z0ilCp3FtpifTy0soz56fsMqyMeXt10SOXuumyEXoMRv4jPVsb0UmB6D1liA1liAu9tgKG2xbzDDvsEMh0ZyHDlyiCNHDvHEEz9g5cqVXHfdelavvlZD84mIyBVF/abTnnjib+ns3E/Qa/DFq+LEQ7PzFcB8Pee/0PPce9tL+fH+Ua5vjEwbPAF0VARpjwc4OJLjzaMJDgxl6e09ic9jcNuCKDc0RjA9BvVRH9/fNkhfXy9/+Ie/x9e+9g127tzOxo0bGBwcmLLfYDDIypVruPvue1m4sH3Sc5lMhnfffZv+/j6uueY6FJSdBQAAIABJREFUmpqaP/YxioiIiHxcCp/muVAoRCCg4RE+yHEcjhw5xLZtW9mw4dXiyXpNxMvdbaW0xzWR62zL2Q7P7B1h72AGXZwwv3k9sKQiyANLyvHrKvFLSjzk5YbGEm5oLCGZs9nVn2FHb4rusTzbtm1j27ZteL0+Fi9ewtKly1m6dDmtrW2YpoYXFRGRK8uV2G966603+fnPXwDgc0tisxI8zfdz/gs9z62K+Pja6g+fo8kwDNpiAdpiAXqTeQ4MZrmqOkh58PRnUh708i9XVfCX2wbp7e3he9/7j8XngqZBWzxAOu8wlLEZzdhkMhk2b97I5s0bWbRoCXfffR+hUIg33niNd97ZTDabBeCZZ55k0aIl3H77p1i7dh1e79TfA8dxSKVSJJMJvF6v5p0SERGRGaHwaR5xXRfLsor3R0ZGePzxx8nlcjz00ENz3Lq5l81m2bNnF++99y7bt29lZGS4+FxpwMMdraWsrAkVh9dzXZe8o1EnZ8s/vT/CnoHMXDdDZoDlwK7+DI47wkNLy+e6OXIWPtNgdW2I1bUhhtM2u/rS7OxPM5jOs3v3Tnbv3glAIBCgo2MRixcvp7GxiVgsRnl5nFAoOC+/kPP7r6wvEkVE5MOp3wTHjh3l+9///wD4ZHMJiysLF9/NdB9ovp/zz8V5bixocl1DoSo9Z09O7MI+D19eEefxHYOMZh3aYn6urgmzKB6YVKFlOS69CYt3TibZ1Zdh//697N+/9wOv46Ey7KVzKFd8vqSkhGi0FMdxsG0Hx7HJZNKkUqlJ21ZWVk1cuHQVCxe2MTQ0wNGjRzl+/CgnThwnEAhSXV1NVVU11dU1xGJxIpESIpEI4XAE0zTJZDIkEuOMjY2RSiUoKSmlurqGSCQyS++siIiIXGoUPs0jW7ZsYfny5VMe/7Vf+zXa2trmoEWXjp/85Ec888yT5PP54mN+06A9FmBJZZCrqkKTTtZd1+Uv3hugeyw/3e5Ezunuu+/mwQcf5Omnn+bFF1/kSpk6b89Ahj0beua6GXKBstksu3btZNeunXPdlBkRDAZpaGhSADWHDMPA6/VgWc4V8/+hzIzrrrue2277FD6fhkGWmaV+Ezz//HPkclnaYn5ua40C87cPdDHOvS/V89zO4Rydw7mPte1wxmE4M3nbRCJBIpE46zY+A2xgYKCfDRteZcOGV8+67u7dZ39t0zSx7emHYYxEIlRUVOHz+bAsi1wuSyaTIZ/PY5oeTNOLaZp4vV6CwSAez/lX7BuGcR6/H6een3ruOBPnk2e+/pnt8XgMvF4Ty7JxPiQAPrMdHzye6dp4tmO+GOfH5/Pv8YPt+Cj/hj/sGD74fs8Ww6D4+el09+P54OdeV1fPgw8+okpLkcucwqd5ZPny5fzu7/4uUPhPe2xsjNdff53/9t/+G6lUim9/+9tz3MK589prrxSDp8qQl7vaSmmPB/B6znXyoS8q5eN58MEHaWxs5MEHH+SFF16Y6+aIXNEymQxdXQfmuhki8jF0du4nEAhyyy23z3VT5DKjfhOk04VKluVVp0d+KJh/fSCde18cTaU+/tXqSnK2y+GRHBuOJjgyOjm8WlwRYFlliIzl0DWcpWs4iz3NF/FnC54AkskkyWRyppsvIvNMV9cBBgcH+O3f/p25boqIzCKFT/NIJBJhxYoVkx676aabSKVS/MVf/AVf/vKXqaiomKPWza26unp6e08CMJC2eOr9YdrjARZXBFkUDxDxT75ayjAMvr66QsPuzaL5PgTHuTz99NPFqy/P7+q6y8OyyqCG3ZunRjM2h0dzHB7Jcngkx1huHk7KICKXnSVLlnHVVVfPdTPkMqR+E3g8hfmLBlJW8bHZ6ANdjHP+i3HuPd/Pc4+P5Xn3ZBKHQuC4sNyPecaFmCfHLd49meT9gTS2C6YBHsPAYxRGDAl6PQRMg6feHyFtORwZyZGb5vdk32CWfYPZi3hkInI5u/POX5rrJojILFP4dBlYunQpTz75JMeOHbvsO1Fn881vfpudO7cV53saHR1ld3+G3f0ZPAasqglze2uU0sDpEMowDPzm/Lvyb754aGk5nnk8+fC5vPjii7zwwgtXTPB0oRMxy9zoSeTZ0Ztmd3+aoczkq09N06StrYOmphZisRixWPyMZZxgMKRh7OS8eb0eYrEIw8NJrMvtP3wRuaxcSf2ma6+9gS1bNvPWsSTtsQAdFYU5n2a6D3Qxzvln89z7UjzPtR2Xn3aOMpCyuKo6xFVVIUK+qW1zXJf3BzJsPJqke+x0hdLOvgwlfg8rq0NUhr1s7Ulx9ANDLRZ+mngv8y4w9cMrKYmydOkyWlvbGBoa5Nixoxw92k0yWRi2r6ys/ANzPkWK8z75/YGJIf7GGB8fJ5FIUFpamPPp1DaBQJBMJkM2m5kYdi+HaZoTNy9er5dQKEQ4HMHn8006N3Uch2w2i+M4BINBTPP8h+abKzpfmt/0+YmIfDwKny4D7733HqZp0tTUNNdNmTM+n481a65lzZprcRyHQ4cOsm3bu2zb9i7d3UfY2pNiZ1+a9Y0RbmouIei9NDoWlzO/6eHzy+PkbZdE7uzDLsxH41mbkaxNecAkGrj0OzoXqsRvTpozTS5doxmbHX0ptvem6U2evtLZ4/HQ2rqQJUuWs3TpchYtWkwgEJzDloqIiFx8V1K/ad26G3j//d288srPefL9YX7lmipioZnv/l+Mc/7ZPPeei/Ncx3UZTtvEQuYHhkQsBE9Pvj/M7v5CNdnBkRw/7RxlSUWQJZVB0nmHobTNYNqiJ5lnLFv4Etw0TW644SZCoTCbNr3B+Pg4bx47PbSdaZqsXbuOW2+9nXi8EsdxsG0L27bJZDIkk4W5oFKpJKZpsnjxMhobm4oVdKe4rsv4+BiBQGBGziXD4fDH2s7j8RAKhS749UVERGR2KXyaRxKJBNu2bSv+nM/neemll3juuef4/Oc/Tzwen8PWXTo8Hg9tbe20tbXz0EOfp7NzP0888QMOHNjHa90J3jmZ4tYFUdbWhScNRSCzw2cas9LRnUuxkJfmuW6EyIThtMX+oSy7+9McHskVp1D2er2sXLma66+/kRUrVhIKfbzOvYiIyHyjflPBL//yVzhy5BAHD3byxO5hvr66ctaCltk857+Uz71ztsM7J1IsqwpSHjz38edtl229Kd48mmAwbdNS5uczi8qojviAQvD01ETwZJomd975S+zcuY3jx4+xqz/Drv6pwxtGIiXcdtud3HHHXZSXxwD4whe+xI4d23jzzdfo7+9j7dp1fOITt1FefuHDChqGQWlp2QXvR0RERK4MhnsljBl1GXjsscd4++23Jz0WCARobm7mvvvu42tf+xo+n+9j7du2HYaGLu8JP13XZevWd/jHf/w7enoKc0NVhEw+2RLlqqqQqjpEZN6wHZdjY7mJMfcz9J0xlwPA4sVLuemmm/nUp27HsjwaFkJmnYYhkYttJn7n4vEI5iUyxJbMLPWbJhscHOB3fue3SCQSXFMX5rOLCwGE47pkLRe/aeiCvAvw0wOjvHU8SSxo8q/XVE6ZaxgglXfYdCzB5hMpUvnJ/2eZBtzYVMInmkt4dt8IuyaCp29969dZvfoaXNelu/swb7zxGp2dB4jH47S0NFFWVkFlZTXt7YsIBAIX63DlAuh8aX7T5ze/6fObv/TZza2Z6DMpfJJ52Yn6uCzL4vXXX+GZZ55kbGwUgJDXYFVtmLV14eJVZyIil4JTw7L0JvP0JC16E3kOjWRJW6f/dHs8Hjo6FrNq1RrWrVtPRUWlTtDkotLvm1xsCp9krszXftOuXTv4oz/6Hq7rEg+aZCyXtOXgAhGfhxsaI6xriGho8o9oJGPxx5v7sCdOy1rK/Hx1ZQXeM8K8vmSex3cMMZotDElYWVnFXXfdw/LlV/OP//j3bNv2LgB+j0HOcTFNk29+89dYs2bttK+pv7nzlz67+U2f3/ymz2/+0mc3txQ+yYyYr52oC5FOp3n55Z/x6qu/oK+vr/j4gjI/a+vDLKtUNZSIXBy24zKesxnJFG7DGZuRjEVf0qI3aZF3pv6ZjkQiXH31alauXMOKFSspKSmZ9LxO0ORi0u+bXGwKn2SuzOd+03PPPcNTTz1x1ueDpsG6xgg3NESmrd6RqZ7ZO8zWnjQtLa309fWQTqdZUxvis4vLMQyD7tEcP9g5SNpyqamp5aGHvsDatddhmoX313Vd3n13Cz/4wV8xPDw0ETx9mzVrrj3ra+pv7vylz25+0+c3v+nzm7/02c2tmegzXV4Tsfz/7N15mFzVYef97723bu1Vve+bdguEEItkQBJgxGoczAuOPTaeJPZkJvHEM3kz75NnZp5JMpPJJE7eN3FmxlkcZ4gdO4YHE9sYE4NxwCCDwewChNZepN73pbr25d73j2qV1O7WArS6u6Tf53nuc0tVp26fq4uac+7vnnNEzlEgEODuu+/hl37pU/zkJy/y9NM/4o03XuPYTJZjM1k8xjS1IQ8NIZv6uX1DyEOFz8IwFEqJyLlzXZdU3mUylWcqXSjuUwUm08U/x9IFztSEsm2b5uZW2traaWtrZ926Daxfv7F040JERETkbH7hF/4vNm3ajGkaNDfX4TgevF4/r776Ev/0T99jcHCAvcfjvDyQ4N9eXXfBrdm61EYTOd4YTgHwy7/8r0ilknzxi3/C68Mp6oI2tUEPDx+YIue4rF+/gf/wH/4TkUh03jEMw2D79g+yZctW9u59mrVr1/OBD1yyEqcjIiIicl6oRSkXNcuyuOKKK7nssm1MTk6wd++P+clPnmFycoLheJ7h+Py1VLyWQV3QQ23QQ90pW3XAo7nSRYR4tsBoojhqaTSZm3udmzdN3mIsy6Kmppba2jpqamqpqamlpaWV1tZ2GhoaFTSJiIjI+2IYBh/4wCULniDetesGrrtuN6+99gr/+I8PMjIyzOvDSW5eGz37QctMXyxLLFNgS11g0c+nUnm+8dYkfo/BhmofG6v9tETsRft5T/fM4gJXXbWDDRs2AXDffb/CAw/8PT/qjmEY4LiwbduVfP7zv4XP5z9tvQKBAHfc8QtLcIYiIiIiq4vCJ5E51dU13HPPx7n77o8xPj5Gf38f/f299Pf3MTDQx9DQINlCgYHZHAOzuXnfNQ2oDXhoCJ8cKdUY8lDhtzA1UkrkgpPMOYwmToZLI4k8o8n8gkWkT1VZWUV9fQN1dfXU1dVTX99IXV0dtbX1VFZWYpqa/klERESWn2ma7NhxDblclq985S95Zyx92vApnXfwmMa8dY3KQTrv8PU3J8gUXP7NlRbtFd4FZV7oTzCeKj582D+b49njcfyWwSV1fm7qiJRGg/XHshwYT2MYBh/72L8off/WW+9gcLCfZ555CteF3btv5LOf/TU8Ht12ERERkYuTWkEiP8c0TerrG6ivb5i30Gs+n2dkZJjBwQGGhgbm9oMMDQ2QyWQYTRZvPp/Kaxk0hjw0R7y0RGyaI8UpGBRIiax+BcdlOl1gPJVnPHnKlsoTzy4eMhmGQW1tPS0trXNbG62trTQ2NuPz+Zb5DERERETO3RVXXI3H42EsWXy4pj5kz/t8KpXnr14dozFs86tX1Kz4dOSu655zHV4bSpIpFEeivzKYWBA+5R2XN0eSAHz4w3cxMTHOO++8RSKR4I3hFG+Ppri2JcQN7RGe6okBsHPn9bS2tpWOYRgG//JffpZwOEI0WsGtt96x4n9HIiIiIitJ4ZPIOfJ4PKUbyqdyHIepqcnSSKm+vl4GBvoYHBwgm8/TG8vRGzs5UsprGjRFbFoiNh0VXtZU+gjaGvEgslLSeYfxZJ6xuXDpxH4yladwhtnyamvr5oVMLS1tNDe3KGQSERGRshQMBtmy5XLefPN13hlLLwiffjaQIFNwOT6TpS+WW3T00GIS2QI+j7mko6UeOzLN/rE0v3ZlLTXBM9/WKDguL/YnSn/eP5bizg0VBE7pgx0cT5PKu1RVVfOJT9yHaZo4jkNn5xEeeeQfOXBgP8/3JXhlsBhiWZbFPfd8fMHP8ng8/OIvfnLJzlNERESknCl8EnmfTNMsrdGybduVpffz+TzDw0McP36MY8e657YestkMx2eyHJ/J8sJcJ6g+5GHNXBC1psJLxKf1XUSWWsFxmUjlGY7nGE4U9yPxHLHTjGIC8Hq9NDQ00tjYTFNTM42NTTQ2NtPc3EIgsPh6ASIiIiLlaseOa+bCpxQ3rYmU3s8WHF4fTpb+vNjoocUMzeb4yutjbK0P8LFLqpakjgXHZd9wiqzj8tP+OB/dVHnG8gfH08xkCkQixRFJAwP97BtJcl1ruFTm9aHiue3efWNpKmTTNNm0aTP/8T/+Lm+++Qbf+tY3GRwcAGDPnlupq6tfkvMRERERuVApfBI5TzweD62tbbS2trFr1/VAcZTU4OAAx45109l5hMOHDzI4ODC3bkyelweLnZ66oId1lV7WVflYW+mb91SeiJxdOu8wHM8xFM8Vw6Z4cfqY/GlGMlVWVtHU1Dy3tZReV1fXaC0mERERuWhceeV2LMtiJFEcCV47N6ro7dEU6bxLIBAglUotOnpoMW+NJim4xQDIcd0lmX58JJEn6xQbdfuGU9y6NnrGerzYHwdgz57bqKio4Bvf+CqvDCa5tiWEYRhMp/N0TWUAuOGGmxZ83zAMrrjiKrZu3cZzzz1LX99x7rnnE+/7PEREREQudAqfRJaRaZqlQGr37hsBiMVmOHLkEIcOHeTw4YP09R1nbG7qr5cGkxhAU9hmXZWPdVVeOiq8eC3dDBcBcNziukynjmYajueYShcWLe/z+Whr66Ctrb20b2lpIxQKLXPNRURERFafcDjMpZdexttvv8k7Yylu7Ijgui4vDRQfkvvoR+/lpz99jv7+3gWjhxZzZLIY6mQKLmOJPA1h+4zlz0VfLFt6nXNcXh9Osqtt8Xr0x7L0xnJYlsWePbfh9Xp56KEHGEsWZ6NYU+njjeEULnDJJVuor2847c+1LIsPfejm911/ERERkYuFwieRFRaNVrB9+zVs334NAIlEnIMHD3DgwH4OHtzP4OAAg/Ecg/Ecz/eBZUBb1FsKo1ojXqwlnD9dZLWKZwuMJorB7Eg8x3AiV3zy9TQLM9XU1NLevob29g7a2jpob++grq5eI5lEREREzmD79mt4++03OTCW5saOCH2x4mhy27a54Yab8Hp9/MM/zB89tJjpdHF2hxP6YtklCZ96Z4rhU0NDIyMjw7w0kOC61tCio6pOTHN+7bW7qKysnHu9k5/85BleGUzSXuEtTSd4/fULRz2JiIiIyHun8ElklQmFwmzf/kG2b/8gAFNTkxw48A4HDrzNwYPvMDExzrGZLMdmsvz4GHhNg47K4oioNRVeWqLeJV3MV2Q5Oa7LTLrARCrPeLLAWDJXnJYymSeZW3xtJo/HpqWltTSaqaOjg7a2NYTDZ34SV0REREQWuvrqHXz96/czGM8xmcrz0sDJACccjrBz5/V861vzRw8t5ujcqKcT+mI5tjefWx1mMwVCXnPRQOnEyKdPfvJfcv/9X2YqkeDIRIbNtf555WbSBd4ZSwFw++0fKb1/00238JOfPMOB8RSbx/xMpwsEAkF27Ljm3ConIiIiIudE4ZPIKldVVc2uXdeza9f1uK7L6OgIBw68zYED73Dw4H5mZ2c5Opkpde48BrREi0FUR2VxZJTWjJLVJFtwmE4XmE4XmEoXmErlmUgVGE/lmUrlOc1AJgzDoK6unubmVlpb2+ZGNLXT0NCEZVnLexIiIiIiF6hIJMrmzZdy4MB+Xh5IlAKcm2++HYBgMDhv9NDpwqcjE8X+yfr1G+jq6pw3Xd7pTKbyPN45w+GJDLvbQty+vmLe57OZYvvRMAwuuWQLN964h8cff4yfDSQWhE8vDSRwXNi8+VI6OtaU3l+7dj0dHWs4fvwYjx6eBorBmtfrPbe/IBERERE5JwqfRMqIYRg0NDTS0NDITTfdiuM49Pf3ltaLOnLkELHYDMdnshyfyUJv8Xu1AQ+tUZvWqJfWqE1jyNZUfXJe5B2X2UyBWKbATMYhNvc6li0wMxc2JU4zgukE27apq2ugsbGRpqYWWlpaaWlppampBZ9v8ZsbIiIiIrJ0duy4lgMH9vNCfwKXYoC0du260ucf+tDJ0UPJXAXBn3vYLe+4dE8Vw6d77vk4f/Znf8xYMk8q5yz6YFyu4PJ8X5yf9MbJO8UnkV4bSnLL2ui8fkvvXIDV0tJGIBBkz57beOKJf6JrKsNoIkd9qDit30Qyz6tDxRFbt99+57yfZRgGN910C3//9/eTmXvq6cYbNeWeiIiIyFJT+CRSxkzTnFvTZg233fZhXNdlZGSIw4cPcfjwQY4ePczo6AjjqTzjqTz7RopPLXpMaA7btES9tERsWiNeqgPWaedrFznBdV1mMgXGkvl5o5dm0nmm0gXiWYfTDFyaJxgMUVtbV9oaGxtpaGiioaGRmpparcskIiIisoKuvnoH3/jG3+G6xZbdiVFPJ6xbt5729jX09h7jjeEku9rmT3d8fDpL1nGprKzissu2UV/fwOjoCAOzWTZUzx+hNDib5VvvTDGZLgBw6aWX0dfXy+xsjJ7pzLzyJ0ZPbdy4CYC6unquvPJqXn/9VV4aSPDhDRX8tC/Os8dnyTvQ1NTMFVdcveD8rr12Nw899E3S6TRtbe2sWbNuQRkREREReX8UPolcQAzDoLGxmcbGZm68cQ8As7Mxuru76Oo6SlfXUbq7u0gmE/TGcvTGcqXvBjwGLREvLVGbtkhxhFTIq6nMLlau6xLPOowk5tZcSuQZnVt/KXO6efHm2LZNVVU11dU1C/Z1dXXU1NQRCoWW6UxERERE5N2qqKhk06bNHD58kEgkyo4d1877/MTooa9//X5eHUyyszU070G2I5NpALZu3YZhGKxfv5HR0RF6Y7kF4dOjh2eYTBeorKzivvt+mQ9+8Dq+/vX7eeaZp3hnLD0/fJophk8bNmwqvXfrrR/m9ddfZd9wimMzWUYTeQC2bLmcz3723yz6UFMgEODGG2/mySd/wC233KGH8ERERETOA4VPIhe4SCTKtm1Xsm3blQA4jsPIyDDd3V309HTS3d3J8ePHSOXzdE5l6Jw6uTBwld8qTtUXsWmLemmK2Hg0Xd8FJ++4jCXzjMRzDMdzDCdyDMfzp50ez7IsGhoaqa9voKbmxOil2tIopkgkqg68iIiISJnbs+dWDh8+yJ133rXoekjXXbebhx76B8ZTGd4YTnFVU7D02ZG59WhP9EHWr9/Iiy8+T//Prfs0msgxGM9hWRb//b//CZWVlQBs334NzzzzFAfH09y1ycU0DPKOy8Bs8eG5jRs/UDrGJZdsoaWllYGBfkYTeSKRCJ/61K+wc+fuM7ZJ/8W/+DS7d99IW1v7e/wbEhEREZEzUfgkcpExTZOmpmaamprZtet6APL5PH19vXR3Hy2NkhoaGmQqXWAqneLt0eJ0fZYBzXNBVGvUS3vUS4Vfo6PKQcFxiWUKTKTyTKQKjCfzxdfJ4nR5i41lKo6ka6KlpW1u3aXivrGxCY9H//sQERERuZBde+0utmzZSjgcWfTzQCDAXXfdw7e//RCPd86wttJLVcDDZCrPeDKPaZps2XI5cHKkUl8si+MWwySAN+emBd+69YpS8ASwefOlhEJhEok4x6ezrK3yMTibo+AWH66rr28olTUMg1/8xU/yla/8FVdfvYNPfeqXiESiZz0/y7Job+94b385IiIiInJWunsoIng8HtauXcfateu4+ebie4lEgp6errmp+jrp6jrK7OwsfbEcfbEcUFzAN+o1aY54aY7YNIVtmiM2Ea+pkS/LLFdwmc4UmE6fXItpOl1gJlPcxzKLB0wnBINB2to6aG/voK2tg7a2dlpa2vD5fMt2DiIiIiKyupwtxPnIR+5m377X6ew8wncPTfPZK2o4OjfqadOmzQSDxdFQbW3teL1e0tksE8k8dSEbx3VL4dPOndfPO67H4+Gqq7bz3HPP8s5YirVVvtJ6Txs2bFrQ17jqqh38zd98TX0QERERkVVE4ZOILCoUCnHZZZdz2WXFpxVd12V0dITOzqN0dR2hs/MIfX29xLIOsYk0hybSJ79rmzRHbBpCHmoCHmqDxX1YodR74rguyZzDTKbAzFyQNJNx5oKl4sileHbxKfJOZVkW9fUNc+uCNc3bKioqdW1ERERE5F0xTZNf+7XP83u/9x85NpPhhb4EPdPF8Onyy68olfN4PKxZs44jRw7RF8tRF7LpnckykykQCAS48sqrFxx7x45ree65ZzkwnubOjS69c+s9bdy4aUFZQG1ZERERkVVG4ZOInBPDMGhoaKShobE0XV8mk6anp5vjx3s4dqyH3t5jDAz0k8g5HJ3MlJ56PMFrGdQGPFQHLCr9FhU+T3Hvt6j0Wfg9xgXZaXRdl7xTXFsp77hkCi7ZgjO3d8nkXTIFh2SuuCVy81/PZgoUzjRsaY7P5yutu1RbW0dNTS01NbWl1xUVlYsuuCwiIiIi8l41NDRy332/wte+9rc81RPjRGv+8suvnFduw4aNc+FTlquaguybG/W0Y8e1i64pdemllxEIBJhNpeiLZek9ZeSTiIiIiKx+Cp9E5D3z+fxs3nwpmzdfWnovk8nQ39/LsWM9DA0NMjw8xPDwEOPjo2QLLoPx4qLCi/FaBhGvScRrEfFZRLwm4bk/h2yTgG0SnNt81tIHVSdCopzjkiu4pX3WccjkXVJ5h3TeJT23z+RPBkjZgkOuFCy55AtuKWzKn0NwdDaGYVBRUUlVVTU1NTVUVxe32tp6amvrqKurIxQKX5DhnYiIiIisbjfeuIc33niNffteA6C6uobW1rZ5Zdav3wgU133KFVzeGT0x5d7uRY9p2zZXXrmdF154jp/2JYhnHSzLYu3a9efxTERERERkqSh8EpGVofHwAAAgAElEQVQl5fP5WL9+Y6lzeUIul2NsbITh4SFGR0eYmBif2yaYmBhndjZGtuAykSowkSqc9eeYBgQ8Jj6PgccwsEwDy6C0Nw0Dx3VxXCicuneK+4ILBefUvUvB4YzrIi0FwzDw+/34/YG5vR+fr/jnSCRCJBIhHI4QiUSJRKKEw2EqK6uoqqrG49GvbBERERFZfQzD4F/9q1/jd37nt5mdnWXr1isWPBR1on8wmsjz9miKdMGlurqGD3zg0sUOCcD27dfwwgvPcXC8OMV3R8faRUdJiYiIiMjqozuZIrIsbNumubmV5ubWRT/PZDJMTk4wPT3F9PQ0MzPF/fT0FDMz08zOxkgkEsTjs2SzWRwXEjmHxOKDqN43y7Lwer14vT58Ph+BQJBQKEQgECAYDBEMFl8HAgF8Pt9cgFQMknw+H16vD6/Xxra92LaN3++jvr6S2dkMhXOZQ09EREREpIxUVFTy7/7d/8Pjj3+fO+/8hQWfF0fw1zIxMc5TPTEArrtu9xmnhd66dRs+n49Mpjid94YNG09bVkRERERWF4VPIrIq+Hw+mpqaaWpqPmvZbDZLPD5LPD5LOp2mUMiTy+XJ5/MUCif2BUzTxLI8WJaFx2Nhmtbca09ps6wTewvbtvH5fNi2d8lHGXk8JrZtYxhZzv/4KhERERGR5ffzU3L/vA0bNhZnPcg6wOmn3DvB6/VyxRVX8dJLL859/wNLV1kREREROa8UPolI2fF6vaU1j0REREREpDysX7+xFCS1t6+htbX9rN/Zvv2a0nc2btx0XusnIiIiIktH4ZOIiIiIiIiInHenrgt7tlFPJ2zbdhXt7WuoqqrSw2ciIiIiZUThk4iIiIiIiIicdx0dawmHI2SzGa69dtc5fcfn8/E//sf/e55rJiIiIiJLTeGTiIiIiIiIiJx3tm3ze7/3B+Tzeaqqqle6OiIiIiJyHil8EhEREREREZFl0djYvNJVEBEREZFlYK50BUREREREREREREREROTCofBJRERERERERERERERElozCJxEREREREREREREREVkyCp9ERERERERERERERERkySh8EhERERERERERERERkSWj8ElERERERERERERERESWjMInERERERERERERERERWTIKn0RERERERERERERERGTJKHwSERERERERERERERGRJaPwSURERERERERERERERJaMwicRERERERERERERERFZMgqfREREREREREREREREZMkofBIREREREREREREREZElo/BJRERERERERERERERElozCJxEREREREREREREREVkyCp9ERERERERERERERERkySh8EhERERERERERERERkSWj8ElERERERERERERERESWjMInERERERERERERERERWTIKn0RERERERERERERERGTJKHwSERERERERERERERGRJaPwSURERERERERERERERJaMwicRERERERERERERERFZMp6VroCISDlwXZdMJkM8Psvs7CzxeHFLpVJksxkymQzZbHbudZZ8PofjODhOAcdxcF0Xj8ckk8nhuu4ixwdwcRwXcHFdt/TeiZ9/KsMw8Pn8+P0ntgB+v59gMEhFRSWVlVWlvc/nO99/PSIiIiIiIiIiIiIlCp9E5KKXzWaZnp5ienqKqalJJicnmJqamtsX/zw7GyOXy610Vd+TE4FUJBIlEokQDhe3SCRCJBIlHA7PvRcmHI4SDAYxTQ2MFRERERERERERkfdG4ZOIXHBc1yWRiDMzM8PsbGxupFJxX9xizMxMMz09zfT0FMlk4twPbhqYPgvTa2L4LEzbBMvE8BgYlolhFfdYBoYBGAYYzHt9RsZcgVOLzvuOceIkcfMOTt7FzTm4+bkt5+Ck8zipAoV0HgouyWSSZDLJ0NDgOZ2iYRiEw2EqKiqpqqqmqqqaysqqefuqqiqi0QqFVCIiIiIiIiIiIrKAwicRKSv5fJ6pqUnGx0cZHx9nfHyMyckJpqenmJmZIRYrboVC4d0d2DIw/R6sgIUZ8GAFbcwTrwMeTL8Hw2cVwyXjbAnS6uCeCKhShWIglSngZAu4GQcnW8DJFHDn3nMyBdzsXIDluqWgrr+/77THN02zNLVfVVUV1dU11NTUztsqKioVUImIiIjI+9bVdZR0OsWWLZevdFVERERE5BwofBKRVSmdTjMw0M/AQN/c1s/AQD9TU5OLrpm0GMM2Mf0WptcqjlLynXxt+S1Mvwcz4MEMWBges2xCpXNlGAaGbWHaFkS95/Qdt+DgZB3cbIFCKo+TyuOkCzip/Mk/n3jPcZiammRqapKensWPZ1kWtbX1NDQ00NDQSH19I/X1xdd1dfV4PPrfkIiIiIic2cGD+/nTP/0ChUKB//pf/5D16zeudJVERERE5Cx0109EVlwqleTYsW66u7vo7u7i2LFuxsfHTv8F08AKejCDHqyQXXwd8BTDJb+nGDj5PRjmhRUmLQfDMrECJgQ8eCp8py3num4plHLSc8FUMk8hmcdJ5or7VJ5CocDIyBAjI0MLjmGaJnV19TQ2NtHY2ExjYxNNTcXXlZVVF1wYKCIiIiInpVJJfvzjf+bQoQN89KP3snHjBxYtNzg4wJe+9MXSzAYPPfRN/st/+f3TthX7+/v4i7/4Irt338hdd91z3uovIiIiImem8ElEllUul6O39zg9PV10d3fS3d3J8PDQoqOZTL+FFfXiifrwVHixol6ssI3psxRMrDDDMLDmpiQ8HddxiwFUIkchfuqWpRDP4RQcRkaGGRkZ5s0335j33UAgQHNzC01NLafsm6mra8CyrPN9eiIiIiJynsRiMX70o8d5+ukfldZePXDgHf71v/63XHfdrgVl//zP/4RkMomnykc+luXIkUO88carXHXVjgXHLhQK3H//XzM8PMT3vvcdbrjhJioqKpflvERERERkPoVPInLeOE4xXOjqOjo3qqmT3t5ji67HZAY92NV+PFW+4r7Ch+lTyFDODNMojkwL2VA//7MTI6cKs1kKsznys9liKDVbDKhSqRRdXZ10dXXO+55lWTQ0NNHc3ExTUwtNTc2lcMrv9y/j2YmIiIjIuzExMc4TTzzG3r0/JpvNAmBFbMygTW4kyd/8zZcYGRni7rs/hmEYZLNZ/vf//lPGxkYxQzaV1zeTPDJN8tAUDz/8INu2XbXgoaQf/ehxenq6Acjnczz99I+4995PLPu5ioiIiIjCJxFZQolEgu7uTjo7j9DV1Ul391ESicSCcobXwq724an2Y1f7sat8mH79OrqYzBs59fPBVMGhEJ8LpGIn9tnivlBgcLCfwcH+Bcesra2jubmFlpa2uX0rzc2tBAKBZTorEREREfl54+NjPPbYIzz33LOlh9A8VT5Cl1TjbQ4BkHh7guThKR555B8ZGRnms5/9Ne6//6/p7DyCYZtU7m7G9HkIbq4i1R1jaGiQvXt/zJ49t5Z+zsjIMN/97sMAeJtDZAcTPPXUk3zkIx/F51v8IaXR0RFee+1lbr75drzec1sjVURERETOje72ish7NjExzuHDBzly5DBHjhxkYGBhIIBpYFf58NTMBU3VfsygR9PmyWkZlomnwrdgzSnXLU7jl49liyOm5oKpfCyLmykwPj7G+PgYb721b973amvraG1to6WljdbW4tbU1IJt28t5WiIiIiIXnK6uo3z/+9+lvr6R9es3sG7dBurq6jEMg7GxUR577Hs8//zJ0MmuCxC6pBq7PjCvPxC+vBYrbDP7+igvvPAcBw7sZ3p6Ckyo2NmEJ1oMhkzbInRpNfF9YzzyyD9y3XW7CQQCuK7LV7/6FbLZLHZ9gIrrmpj84XESiTg/+cmz3HrrHQvqns1m+eIX/5jh4SESiQS/+IufXJ6/NBEREZGLhMInETknjuPQ19dLZ+fhubDpEJOTEwvKWWG7OKKpprh5KnwYpoImef8Mw8AK2lhBGxpD8z5zMoViKBUrhlH5WIZCLIuTPhlK7dv3eqm8ZVk0NTXT2tpOW1s7bW0dtLW1U1VVrWBURERE5Byk02n++q//N+PjY/Pej0SitLS0cvTo4ZOhU32A0KU1eOtOPyI9sK4CK2Qz8+JQMXgCIlc34K0Pzi+3voJU5zSx2AxPPPEY9977Cfbu/TGHDh0AyyB6dQOGaRDYVEn8jTGefPIH3HzzbZimOe84jz76HYaHhwB46qknufPOjxIMzv9ZIiIiIvLeKXwSkUUlk0m6uo7S2XmEo0cP09V1lHQ6Pb+QUZwyw64N4K0NYNf4NX2erAjTZxVvZvzcDY0ToVR+phhG5Wcy5GeyFHIF+vv76O/v42c/+2mpfCQSoaNjLR0da1mzprivq6tfcLNCRERE5GL3ne98i/HxMcygB19ziNxEmvx0htnZWDEIYi502lKDt/bcpkH2NgSp2tNG/M0xvE0hAmuiC8oYpkFoaw2xF4d54ol/4sorr+ahh/4BgPBlNVjh4uj2wJooiXcmGBsb5dVXX+KDH7yudIze3mM8/vj3i8fzmqRSSZ5++knuuuue9/V3IiIiIiIn6S6xiJDP5+nv751bp6mTrq6jDA0NLihneMzSiCa7LoBd7cfw6Ka8rF4nQqlTn7ItTd83MxdGTc8FUrNZZmdn2b//Lfbvf6tUPhAIzI2M6qC9vbhvbW3D5/Mt9iNFRERELnjd3Z388z8/AUDk6np8c6PS3YJDfrrYxvJUeLFr3v3am56ol8rrW85YxtcSxlPtJzuZ5gtf+H2y2Syeah+BjZWlMobHJLChkuSBSR5//DF27LgWwzAoFAr83d99Bcdx8LWE8bWEiL08wpNP/oDbbvvwadeH6unpBmDt2nXv+pxERERELkYKn0QuMq7rMjo6Qnd3F93dxbDp+PEecrncgrJmyINdE8Cu9eOtCWBVeDUlmZS9U6fv8zWdnL7PLTjFQGoqQ246TX6qGEqlUimOHDnEkSOH5h2joaGR5uZWWlpaaWpqobm5hebm5tPesBARERG5EOTzeb761a/gui6+9kgpeILi2p0nHlY7nwzDILytluln+slms2BCdHvDgr5KcH0FycNT9PR0cfjwQTZvvpQnn3ycY8e6MWyT8FV1mF4L851JZmdnefbZp7n99o8s+HmdnUf4oy/8Pgbwh3/4/9Hc3Hpez09ERETkQqDwSeQCl0gkStPnFcOmLhKJ+IJyhm1iV/tPrtdU5dMUenJRMazivwG72k+ACgBcx6Uwmy2OjpobIZWfzuBkCgwPDzE8PMTrr78y7zi1tXW0tLTR2tpW2jc1NeP1elfitERERESW1BNPPEZfXy+G1yRyRe2K1cNbG8DXGibTHyd0STWeioWj0k2/B39HhHR3jCeeeIyqqmoeeeRhAMLbarHm+juhzVXMvjbK448/xp49t2HbdukYsViMv/qr/4kzt37VAw98nd/+7f+ih/JEREREzkJ3lkUuIK7rMjw8xNGjh+fWajrC4GD/woKmgafSh13tKwVOVthWB0rk5ximgafCV7yZ0XHyfSedL07ZF8sV15KazZKPZXEzBcbHxxgfH+PNN18vlTdNk4aGRtrbO+joWEt7+xo6OtYQjVaswFmJiIiInF4iEef//J8vEwqFuP32O2lvX1P6bHh4iO997zsAhK+ow/St7C2F6AcbyG2oxK49/Uir4KYq0t0x9u17ncnJCbLZLHZ9AP8p60n5OyIkDkwyPT3F88/v5aabbgHAcRy+8pW/YHJyEjNk46Ty7N//Fm+88RpXXbX9vJ+fiIiISDlT+CRSxhzHob+/l8OHD85th4jFZhaUs8I2ds3JUU2eCh+GqaBJ5L0y/R68fg/ehvnvO5kC+ViWfCxD4cSaUjNZnJzD0NAgQ0ODvPTSi6XylZVVc2FUR2mrr2/ENLWWmoiIiKyMRx75Nm+88SoAzz+/l8suu5w777yLSy65jK997W/J53PYDUH87ZEVrmlx5Pqpa3suxhPx4m0OkR1M0Nt7HCyDyNX18x68MyyT4KZK4m+O84MffJ8bbrgJy7J49NHvFNcCtQwqdzWR7p0leWiKBx/8BpdddvlpR7bH47OEQmE93CciIiIXNYVPImUkk0nT3d1VGtV09OhhksnE/EKmURzRVBMozbeu6fNElofps/DWBebdBHFdFyddKAZR05niWlLTGQrxHNPTU0xPT80bJeX1+mhra6OtrYO1a9ezYcMmmptbFEiJiIjIeTcw0M/TTz8JgLchSHY0yf79b7F//1vU1tYxPj4GlkH0qvqyClZCH6giO1jsN4W31OAJLwyNAusqSBycYmxshJ/97AWi0SiPPloc5RW5uh5PhY/gJTbpYzHGxkZ48skfcNdd98w7huu6fO973+bRR7/D1Vd/kM9//rfUhhMREZGLlu5Ii6xSjuMwPDzE8ePH5tZsOszx48dwHGdeOcMysGsD2HWB4r7ah2GpgyOyWhiGgRXwYAU88xbkdnLOyUDqlDWlstkMXV2ddHV18uyzTwMQDAZZt24DGzZsYsOGjWzadAk+38J1DURERETeK9d1efDBr+M4Dt7mEJW7mikkciSPTJHqiRWDJ4rhjRW2z3K01cWuDRD8QBVOrkBgY+WiZQxPcfRTYv8Ejz76bRLJBK7r4l8XJdBRnKLP9JiEL68l9vIIjz32CLt23Uh1dTVQ7L9985t/XwrvXn31Jb797Yf4xCfuW56TFBEREVllFD6JrALpdJrBwX56e49z/HgPx48fo6+vl2w2s6Cs6beKIVONH7s2gKdSU+iJlCPTNvHWBvDWzh8lVZjNlcKo3GSa3GSaZPLkU8cAfn+Aa665jhtuuIn16zeW1ZPHIiIisjq9+ebrxbaGaRDeVguAFbKJXFlP6NIaUj0zUHBPG96sduHLa89aJrChguThKUZGhgHwVPqIXFE3r4yvPYLdNUNmIs3DDz/A5z7378nn8/zt3/5laXplX3uETO8sP/jBo7S2trFz5/VLf0IiIiIiq5zCJ5FlFI/PMjQ0yODgAIOD/QwMFPcTE+OLf8Ey8FT4Tk6jV+vHDHh0o1nkAmUYBp6oF0/UC3PrKLiOS34mQ24iXdzGU6STKfbu/TF79/6YpqZmrr/+Q+zadQOVlVUrfAYiIiJSjvL5PA8++A0AghsrF0xLZ/osQpurV6Jqy8q0rWIAdXAKwzap2Nm0YFYJwzAIX1nH1FN9vPji8+zadT1PPPED3nmnGNxFdzTgb48QD3pIHpri7/7uKzQ0NLF+/YZ5x8lkMvzwh/9EV1cnH//4p2hra1/OUxURERE57xQ+iSyxVCrJ6Ogoo6MjjIwMMTQ0yPBwcZ9IxE/7PcNn4anwYlf58VT68FT6sCK2giaRi5xhGthVfuwqP2wojo7KjadI98RI98cZGhrk4Ycf5Nvffojt26/hnns+Tnt720pXW0RERMrIj370BCMjw5g+i+AlF37IdCahzdXggK8lhBVafHpBu8qPf22UdE+ML37xT3BdF8MyiO5sKk2zHLqshnwsS3YwwZe+9Gf8t//2Baqrq3Fdl5deeoFvfesBJicnADh48B1+9Vd/nWuv3bVs5ykiIiJyvil8EjkHjuOQSqWIx2eJx2eZnZ0tvY7FYoyPjzI6OsrY2Cjx+OwZj2UGPFhzIxs8UW/xdcSL6bOW6WxEpJwZhoG3Loi3Lkj4ygKZvjjpYzFyE2lefvlFXnnlZ+zefQOf+cwv4/dHV7q6IiIissrNzEzz/e9/B4DQ1hpM++JeP9aYW9fpbMJba8j0x3FzDobXpHJ3C3aN/+RxDIPoBxuZ+nEf09NTfOlLf8p99/0KDz/8IEePHgbADHqwgjbZ8RRf/vKX6Onp4hOf+DSWpb6hiIiIlD/DdV13pSsh5+7tt9/mG9/4Bq+88gqTk5PU1dVx3XXX8eu//uu0tb23J90LBYfJycQS13T183hMqqpCTE0lyGbzHD9+jMOHD3LsWBezs7MkEgmSyQTxeJzk3GKz58rwWlhhD1bYiydiY0W8WBEvnrCN4bm4O3Micn7kpzPE35kgO1j8fW5ZFh/60B5+4RfuLS2ELXI+nPr/03zeWenqyEVgKf6bq64OYVlqk12ozkefCcq/3zQ7G2NwsI81a1rxesMYRjHg+OpXv8LevT/GU+Wj6uY2zbzwLmSGEqSPxQhtqSlOm7yIQiLH5FN9uNlC6T3DMgheUk1wUyWYBon9EyQPTQGwefOlfP7zv0U0WsHo6AjvvPMW+/e/zaFDBwiHw9x990fZsWMXtu1blnOUpaH2UnnT9Stvun7lS9duZS1Fn0nhUxl54IEH+MIXvsA111zDPffcQ319Pb29vdx///1MTU3xta99jS1btrzr45Z7J+q9cByHvr4eenqO8sYb+zh8+BCpVOqs3zMsA8NnYXotTJ+F4TUxfRZW0MYK25ghGyvkwbT1pJqIrIzcZJrE/gmyI0kAbNvmxhv3cMstd9DU1LzCtZMLkToEstwUPsmZnK8+E5R3v2nfvtf5m7/5C1KpYvvAMAyqqqqpra3j6NHDuK5L5U2teGsDK1zTC1N2LMX0TwbAcfF3RAhtrcUKzJ+IJt0fZ/aVYdy8S2VlFbbtZWxsZNHjBYNBPvShm7n11g9TXV2zHKcg75PaS+VN16+86fqVL127laXw6SLy2muv8Uu/9Et8+tOf5nd+53fmfTY5Ocm9995LNBrl+9///rs+djl3ot6tvr5eXnjhOV588XmmpibnfWZ4TOxaP3ZtADPgwfSaGF4L057be80Fi83K8nILDk66cPaCsqqZfkv/lpZBdixFYv84ufF06b0tWy7nlltu54orrsI0dQ1kaahDIMtN4ZOczvnsM0F59pscx+HRR7/D9773baDYDnNzDm5h/m0AX1uYimublq1e5diuf79t2PxMBgBPxelHLOVjWWZeGKQwmyu+YYBd48fbEMSuD5KfyZA6Mk0hXvzcsiwuuWQLjuOQSMRLs3ek02kaGhpZt24Da9euZ926DbS3d2Dbi69hJeef2kvlTdevvOn6lS9du5Wl8Oki8hu/8Ru89tprPPvsswQCC59G++EPf0hnZyef+cxnCIfD7+rY5diJejempiZ58cWf8uKLz9Hbe7z0vmGb2HUBvHUB7LoAnkqfpphYpdy8Q+zVETIDCXD0K6vsmQa+lhDR7Q2ahvI8c12X3GiKZOd0aTo+gNraOvbsuZWdO6+nqkpT8sn7ow6BLDeFT3I657PPBOXXb0okEvzt3/4l+/a9DkBgfQXhK+rAADdToJDIUUjkcbIF/B2RZZm9oazb9cvUhnVyBTL9cUyfhV0XXLAGl+u6ZIcSJI9Mkxs7++wdJ1iWRVVVNbZtY9tevF4vtm3j8/kIhcKEQmHC4eIWCoWJRKJEoxVEo1HC4YjWoXqf1F4qb7p+5U3Xr3zp2q2spegzec5eRFaa67o8//zz7NmzZ9FOFMAdd9yxzLVanfL5PH19vfT0dJW2/v6+k+s1meBrCuFrj+JtDGBwSthUcHEpsw7QRSL28giZgfhKV0OWiuOS6YsTcyD6wYaVrs0Fz67xU1HTSCGRI9UTI3VshvHxMR5++EEefvhBKioq6ehYS0fHGtasWUt7+xoqKipWutrLwuvVQwciIhcK9Znm6+vr5S/+4ouMjAyDaRC5up7AmmixX1RwMTwmngrfvFE47jLc1Cnrdv0ytWENw8DfFin9ebHr4q0P4q0PkptOk5vMYHrM4pTwXhPDY2FYBvnZLLmpDPnJNLmpNIVcgfHxsfdcr1AoXBo55bouhmHgui62bVNZWUV1dQ3V1dVUVdVQUVGJ4zhksxlyuSzZbHHz+wNUVVVRVVVNZWVx7/P5yOfzZDJpMpkMmUyGXC5HKBQiHA6fsb3mui6u62pEv4iIyCql8KkMTE1NkclkaG1tXemqrFpvvvkGjzzyj/T1HSefzy/43K7x4++I4msLY3otXNdl+pl+chPpRY4msjLuuOMO7r33Xr773e/y5JNPcqEPTM0MxBl7pExvPlxAZmameeutN3jrrTdWuirLzu/309Jy/hdWP9u/5XIPwAwDPB6LfL7ABf5r66JUVVXNxz/+KRoaGle6KiJnpD7TSalUki/88e+TTCQwgx4qdjZhV/nLtg+02trIF2sbNpE4/Tm/n1DLNE0c5/TBp23bRCLR0mjFdDo9L6iC4hpYoVCYYPDEE9rGvPZVoVAgl8vh8Xje0wiun2+rLfbf4Nnac6e2l5y5UX9n+s6Jn3Ei5DuXep3pGGf6/FyPudxt2vPx897rMS+k9u6pfwfl3g85VxfS9TufmpqauffeT2gtQVlSCp/KwImneAqF8poTezn94AeP0tPTNe89b0MQ/9oodm1gwWKuIqvRvffeS2trK/feey8//OEPV7o6Ihe8dDpNV9fRla6GyKo3MNDHH//xn690NUTOSH2mk4aHh0kmEhgek+pb2jF95T1dmtrIq49V6SOyvb5447rgUEjkyU2myU2kyU8uDDeLo7IsCsn8gukWzxQ8AeRyOSYnJ5icnDhtmUQiQSJRPlNiioisRp2dR5iYGOc//affW+mqyAVEd+TLQGVlJaFQiMHBwdOWSSaTZLNZKisrl7Fmq8fmzZdy+PDBee9lR5JkR5N4qnz4WiP42yOlEMowDCpvaoWCHnkoB2U9Pce78N3vfrf0VOeZnnC7UPhawpp2b4UU0nnSx2fJjafITaUhd2H/tyYi789HPnL3SldB5KzUZzopGo0W25J5h9l9Y0SvrsfwmKuiD/Re2vWrrY18obRhXdcFx8XNOzhZBzdXwMk6OBkHN1vAyRZwMic3N1PAnQuOnEye2AtDxeMUXNzMmUNfN+tQyGqtDhGR1e7WWy+eKYpleRjuSrfc5Jz85m/+Ji+//DJ79+7F5/Mt+Pyb3/wmf/RHf8SDDz7IlVde+a6OXW4L557O9PQUPT3d9PR00d1dXO8pHp+dV8auDxSn32sJL1i4VVavsl6Y+D8g+TsAACAASURBVF040ZleDZ3q82qZFmuW+VzXJT+ZJtk5Q6Z/Fk7p/1uWRWtrO2vXrmPNmnU0NTUTDIYIhUIEg0F8Pr/m0pfT0iKwstyW4r+5pVg8V1af89lngvLqN/3zPz/Bgw9+A8dx8FR4qdjZjBW2V7pa76ldv2rayKuwDeu6Lk66gGGbGNb8qeZcxyU/kyE/mSE3VRyR5KSLAZJbcJe8X2XbNlVV1VRX11BRUTG35tPJ9Z5yueKaTyfWejqxDwQCZLOZ0jR6mUz6lDWfIoTDESKR4h7mT7uXTqcBl1AoPLeFCIXCeL1eXNcll8vNbVlyuRyW5SEQ8K9I21btpfKm61fedP3Kl67dylqKPpPCpzKxb98+PvnJT/KZz3yG//yf//O8zyYmJvjYxz6G3+/niSeeeNdztpZTJ+rdcF2X8fEx9u9/kxdeeJ4jRw6d/NAy8DWH8DYE8dYFMUOei2au23LmFhyc9IU7lUohlcdJ5jCD9gU9VaTptzB0w2/ZuAWHdO8sqc4Z8tOZ0vsbNmxi587drFu3gdbW9tIC0iLvljoEstwUPsnpnM8+E5Rfv+nQoQP81V/9T2KxGIZtEr2mEV9TaF6Z4qiXAlZwedsB76Zdv1rayMvZhnWyhWKYdJqf5+QKpHtiJDuncRJzax4bYHgtzLkgKj+bO+eA6cSaSeHwyRAnGq0gEomeso9i2965b7i4rovrgm17qKqqJhyOqE99BmovlTddv/Km61e+dO1WlsKni8yXv/xl/tf/+l/ccMMN3HPPPVRVVXH06FG++tWvkkgkeOCBB9i0adO7Pm65daLeq7GxUV544TleeOE5hoeH5n1mBjzYdQG8dQHsWj+m31N8ekyNZxEpU27BIdU5Q+LQFG62eHPHtm2uu243N998G2vWrFvhGsqFQh0CWW4Kn+RMzlefCcqz3zQ5OcFf/uWf09XVCYB/TRTXcSnEcziJHM7cdGnhbbUEN1WtZFUveCdGb52xjOMS3zdGqmsGLANvbaD4wGRDEKvCi5PKkzw6Tbo7hnsOv/+CwRBr165n3br1rF27nrq6OrxeL7Z9YrPxer1YVnmvC1YO1F4qb7p+5U3Xr3zp2q0shU8Xob179/LAAw9w8OBBpqenaWxs5LrrruNzn/sczc3N7+mY5diJej9c16Wnp4s33niVo0cPceTIkdMuTGx4TUzbKi2QavgsTK+J6bOKT5X5LEyvhRnyYAVtDFNhlYisLNdxSXXPkDw4WXqiuLa2jptvvo0bbripNGWJyFJRh0CWm8InOZvz0WeC8u035XI5Hnjg73nmmadOW8awDKo/vOaCHn2/kpx0nqm9A+C4hLbW4GsJLwiiCqk8My8OkZ9IL3oMw2cVHyiau4PT1NTM7bd/hJ07r8d1XRKJOIlEgmQyQTqdprGxifr6Bk2dvEqovVTedP3Km65f+dK1W1kKn2RJlGsn6v068QtseHiSQ4cOcfjwQQ4fPkh3dxfZbObsB1iEGfRghWyssF3aeyJerIitacZE5LxyHZd07yyJdyZwksXpV2pr67j77o+xa9cNeqJVzht1CGS5KXySlVLu/aZXXnmJI0cO0traRCRSRXV1LbW19fz5n/8xXV2d+NdEiO5oXOlqlhUnnSc7msLXEjptf88tOEzvHSB3Sqhk1wUIX1GHXVlcmyw7niL24hBOukAgEORzn/v31NbWsn//27zzzlscOnSw1Efdtm0bt976YbZs2aZgqYyovVTedP3Km65f+dK1W1kKn2RJlHsn6r060y+wbDZLMpkgHo+TTCZIJBIkEnHi8VlmZ4tbIlHcx2IzjI+PkcvlzvjzzKAHK+LFE7GL+2hxM3yWpvcTkfeskMqTPh4j3ROjEC/+HqqoqOS++z7FNddcj2EodJLzSx0CWW4Kn2SlXAj9psX+/XR1HeUP/uB3Aai6uQ272r+SVSwbruMy+c+9FGJZ7LoAFbuaMO357S7XdZl9dZT0sRiBQJAbb9zD008/Weo7+tdF8YS9xN8eBxdaW9v4zd/8bRoa5oeAuVyOnp4uotEIW7du1v9zy5DaS+VN16+86fqVL127lbUUfSaNqRdZhNfrxev1Ull5bvOeu67LzMw0o6MjjI2NMjo6wujoCCMjwwwPD5JIJHCSeZxkntzI/O8aXhNP1IsV9eKp8OGpLG6mRzdERGRxruOSHUqQ6omRHU6Upl8JhyN85CN3c/vtd9DYWK0GmoiIiJzV+vUb2bnzel544Tlm941RdVPrRf9wXG4ihVVx5j5ZqnuGQixbLD+WYvrZASqvb8b0n7zNkjo6TfpYDMMw+Pznf4utW7dx220f5lvfeoCXXnqBdHesVPaaa3byq7/66/h8C8M/27bZtGkzHvURRUREpIwofBJZAoZhUFlZRWVlFZs2bZ73meu6zM7OMjw8yPDwEENDgwwODjA42M/Y2Chu1iE3niY3Pn9ubyts46kqBlF2tR9PtV+BlMhFrJDKk5tIkRtLke6NF+f8n7Nhwyauv/5DXHPNTgKBgG5MiIiIyLvy8Y/fx2uvvUxmIk2mbxZ/e3Slq7Ri0r2zxF4axlPlKwZxizzx62QKJPZPAHDrrR/mZz/7KbPTMaae6afyhhaskE1mOEH8rXEA7rvvl9m6dRsANTW1/MZv/N/ccsvtPPDA1+nrO84nPvFpbr/9zos+9BMREZELi8InkfPMMAyi0SjRaHRBMJXNZufCqH4GBvrp7T3O8eM9TE9PUYjnKMRzZPripfKeSh92jb+41QYwgx51UEQuQG7eIT+TITeRLm1OKj+vTEVFJbt338ju3TfS3NyyQjUVERGRC0F1dTV33XUP3/72Q8TfmsDXHMb4uYdZ8rEsbsHBrrpwp+VzXZfEgWKolJ/KEN83TuTq+gXlEvsncHMO7e0d3HffL3PLLbfzp3/6R4yPjzH14z7CV9Yx++oouHDDDTdx660fXnCMTZs28/u//wWy2Sw+n++8n5uIiIjIclP4JLKCvF4vHR1r6OhYM+/9WGyG48eP0dt7jGPHeuju7mR8fIz8dIb8dIZU1wwApt/Crgtg1wbw1gWwol6FUSJlxHVdnFSe/Ey29O87P50prd10KsMwaGvrYMOGjWzbdhVbt27DsrSek4iIiCyN22//CM8++zTj42MkDk0RvqwG13XJjaZIHpkiO5wEoPLGFrz1wRWu7buXHUvi5l18TaHTlskMxCnM5vB6feRyWVLdM9i1fvwdJ0eC5aYzpLqL/bFPf/ozmKZJY2MTv/u7f8Cf/dkX6O/vI/biMFAMmH7lV/71aftohmEoeBIREZELlsInkVUoGq1g69ZtpakZACYnJ+jsPMLRo0fo7DzC8eM9FNIFMn3/P3t3HiZXed55/3uW2nqp6lW9txBIoAWEEBKy2BGLwWaxiTGYGJsszsQhGU/yMuO8jkPeJJ5kcMbYTpyJmcQ2iS98GY/jwcELBhuMLcAgwKBd6m611GotvS/VXetZ3j+qVFKjHUrdXa3f59Jzneqqc6qe6tOqOs+5z30/E4XsKCNo5gJRdRECdWHsqtAxy0SIyPTzfR83nsUZTeGMZsiO5JZHls87UiwWY8GC81i48HwWLlzEggULCYfn7pXGIiIiMrOCwSD33HMfX/nKIyR2jmBGLFK7xnFG01PWi7/eT81N7SU1znATWUZ/sQ+84wfPfN8nsW0EgFtuuRXDMHjyye8Sf70/Ny9vLITv+0z8uh+ANWvWsnjx0sL21dU1fOYz/x+PPPIwnZ07qa2t44/+6E+wbZ12ERERkbOTjoJESkRNTS2XXbaWyy5bC0A6nWbXrk527NjGjh3b6OzcSSaTIbN/ksz+ydxGpoFdfUSpvtoIVkT/7UXOJN/38VIuzlgadyyDM57OZTaNZ8D1j1rfNE2am1toa5tPe/v8wjIWq5qB3ouIiMjZbNWqy1i8eCnbt29l4o0BIBeUuvrq67j66nV88YsPMzIyzOTWYSouqpvh3p66ZOcYeLnb8dfywbO3lRXMHEzgjKYJhULcdNMtlJWV09Gxky1bNjL28gGqr28nc2CS7GCKYDDI3Xd/9KjXKS+v4L/9t8/y6qsvs2zZRUSjsel4eyIiIiKzks5Ci5SoUCjEkiXLWLJkGQCO47B79y527NhWyI6Kx8dxhlI4QymS+e3MiE2gJoRdEyZQHcauCWEGVLpL5HT5vo+XcHDiGdzxXHDp0NLPesfcJhgM0d4+n/nzz6G9PVdys6WljWAwOM29FxERETmaYRj85m/ez+f++0MEA0FuvPFmrr/+JioqKgH42Md+my9/+X+S2DFCqK2SQNXsLxnnZd1C2fJgMERmMp0Lni0/HDzLZT0NA3DddTcW3u/v//4f8dBDn2ZkZJj4hoNkh3NZYLfe+gFqa48dfAuFQlx11bVn8B2JiIiIlAYFn0TmCNu28+W5zgdyA6j+/r4jSvXtoLd3L17SIb3PIb1vsrCtVRkgUBPOBaRqw9ixEIapuaNEDvGy7uF5mcbSOKMZ3LE0/jEymSB34qaxsYnW1jZaWtpobW2ntbWNhoZGTLN0StSIiIjI2ae9fT5f/tI/EQgEjyoZt3LlalatWsNrr71C/LU+qq9vm9E5ZxMdo0xuGSK2tpFgw7HnckrtGsd3PJqbW7jrrnv58pf/jsTOEUJtFQSqcyWNs4NJskMpbDvALbfcWtg2Go3ywAP/hb/5278sjJ/q6uq55ZbbzvybExERESlxCj6JzFGGYdDQ0EhDQyNXXHE1AOl0it27u9m1q5Pu7i527epiYKAfN57FjWdhTzy38aFyfTVhAnUq1ydnFzfl4Iykc4GmkRTZ0TTepHPMdS3LorGxiebmFpqbW2lqaqG5uYWmpmZlM4mIiEjJikSOnhPpkPvu+y22bNlEciRBsmOUsvOrp7Fnh7lJh8lNg/iuz/iGfmre235URQff80l0jAJw8823snLlKtasWcsrr7xMfEMf1Te0Y5gGk/m5nq6++lqqqqa+n0WLLuCeu3+Tb33r3wD4yEc+puM8ERERkVOgs8kiZ5FQKMwFFyzhgguWFO4bHx+nu7uLrq4Odu3qZNeuTiYnJw+X6+vIrWeW2QTqIrm5o+oi2LHgjF7lKPJu+Z6PO5HNZzIdbl7KPeb6NTW1U+ZlamtrZ968Bk0iLSIiImeVqqpq7rnno3zjG/+byc1DhFoqsMoDhcd938dPuxhB64xWU5jcMlTIQveSDpMbh6i8dN6UdVJ743hJh1isissvvwqAj370t9i8eROTYxMkdo4QnFdGti+BaZq87323H/O1brrpfaRSKQAuvXT1GXtPIiIiInOJzpiJnOWi0SgXX3wJF198CZAbLPb1HaCrq5Ourg46Onawd28PXsIh3RMn3ZPLjjJsM5cVVRfJtZoQhqVyYjL7+L6Pl3JxxtK4Y5lcsGksNzcT3tFl8wzDoKmpmfnzFzB//jnMn7+A9vb5hdr/IiIiIme7q6++jpde+iU7dmwj/no/ZRdUkx1OkR3OXcDmpV3MiE3Z4moiC6KnNU7wXY/UnnihHPixOGNpUrvHAfjQh+7hu9/9NsldY4TaKwjW57K2fN8nuSOX0XTjjTcTCOQCZNFojHvv/Rj//M//i8ktw4VyepdffhX19fOO8Wq548M77viNU34PIiIiIqLgk4i8TW6ummYaG5sL5fqSyQS7dnWyc+cOOjt30tnZQSqVJHMwQeZgIrfhoVJ9tbl5owI1YayywAleSaS4fN/HS7u447nAkjuWwRnPBZr8rHfMbUKhEK2t7bS1tdPWNp/588+hvX0+oVB4mnsvIiIiUjpM0+S3fuv3+Oxn/xuZvgSZvsRR63hJh4lfD5DYNkzZ4hoi5548COWlXcZe2k92MIURMKle14odPToANbFpEHxYteoybrvtgwwODvDzn/+M+Gv91NzUjmGZZPoSOGMZQqEQ69bdOGX7K664mpdfXs/mzRtxhlMYhsH733/Hu/uliIiIiMgUCj6JyElFImUsW7acZcuWA+C6Lr29PezYsZ2dO3NtbGz0cKm+/HZmxM7NG1Ubxq4JY1eHMG1lR8m74/s+XsLBiWcOB5ryy+MFmUzTpLGxiZaWNlpb2wpl8+rq6jFN/U2KiIiInK6mpmbuvvteHn/8X6mvn8e55y7k3HMXct55C2lubuVXv3qRH/zgSYaHh5h4c4DE9uF8JlQM4xhjAieeYeyX+3EnswD4WY/RX+6n5vo2zPDhUxeZ/gSZAwksy+Kuuz4CwN13f5Q333yD0dERJrcMU7G8jsTO3FxP11yzjvLyiimvZRgG99//Cf7szx4knU6zatVlNDe3nKlflYiIiMhZScEnETltlmXlS5It4KabbsH3ffr7++jo2MGuXZ10du7MlepLOqT3TZDeN3F421gwF5CqDmPXhLBjoTNaC15Kl+96uTmZxjO48XyQKZ7FiWfAPbpcHuROJMyb10hLSwvNza20trbR0tJGU1NzodSKiIiIiBTHTTe9j+uvfy+WZR312PXX38TVV1/HL3/5c5566v/mg1CDTG4doez8KiLnxTCDue0y/QnGXjqAn/Wor5/HJz7xB3zta1+lr+8goy/up/qaVgzbxPd9JjYOAnDttTfQ2NgMQFlZGfff/wm+9KXPk9g5glUZKMzjdNNN7ztm3+vr5/E7v/NJfvrTp7nrrnvP0G9IRERE5Oyl4JOIvGuGYdDQ0EhDQyNXXnkNAOl0iu7uXXR1ddDV1UF39y6Gh4dwx3Ll0FLduRrthXJ91WECNSHsmjBWRQDDUEDqbFAolRc/HFhy4xnc8WzhqtdjsW2bhoZGmppaaGpqpqWljZaWFhobmwkGg9P4DkRERETObscKPB0SCARYt+5GrrrqWtav/zk//OF/MDDQz+TmIRLbR3IBqDKbiTcHwIeFC8/nU596kGg0xp/8yZ/yV3/1WSaHJxh/tY/o2kbSe+M4I2nC4Qgf+MCHprzWJZdcypo1l/PKKy8Rf60fgNWr33PceZwA1qxZy5o1a4vzixARERGRKRR8EpEzIhQKs3jxUhYvXlq4b2RkmO7uLnbtyrXu7k4SicRR5fqMgIkdDWLFgtjREHYsiB0NTim3IaXD9338tJvLYprM4k7kWz7g5DvHLpUHUFZWTnNzC83NuSBTbtlCXV39CU90iIiIiMjsEQgEuO66G7n66nW8+urL/OAH36e3t4fEjpHCOmvWXM7v/u4nCxcSNTY28Z//8//D5z//OdL7Jph4a7BQUeHWW+8gGo0e9Tof/ej9bNmykYmJ3Hrve99t0/DuRERERORYdCZXRKZNdXUN1dU1rFy5GgDP8+jvP5gPROUCUnv2dJPNZskOpcgOpaZsbwQtrAobqyyAVWZjlueWVlkAM2JjBE1lTM0A3/fxUi5ewsFNZAtLN+HgTWZxJ50TBpgMw6Cubh6NjU00NTXR2Hg4yBSLxbRPRUREROYIy7JYu/ZK3vOeK3jrrTd46qkn6erq4Pbb7+SDH7zrqOO+xYuX8ju/80n+9//+CsmO3BxO1dU1xy2lF43G+OhHf5uvfvXvWb58Beecc+4Zf08iIiIicmwKPonIjDFNk8bGZhobm7n88qsAcByH/fv3sW/fXvbt62Xfvr309u5lYKAfP+PiDLs4w+ljP6EBZsjCDNuYYSvXQjZGyMIMmrnHQhZGML9O4OiJjuUw3/fxsx5e0sFNOnhJBy/p4qWO/NnBS7knfS7DMKipqaWhoZH6+gYaGhpoaGiiqamZefMaNB+TiIiIyFnEMAxWrLiUFSsuJZPJnLBs8hVXXEV//0GefPK7ANx554cJhULHXX/t2itob59PbW1d0fstIiIiIqdOwScRmVVs26a9fT7t7fOn3J9Opzl4cD8DAwMMDQ0wODjA4OBg/vYgk5MT4JPLwDmFYAiAYRmYETvXwhZmxMYK25hlufus/GOGOTcyb3zXw8t4+BkXL51vGRc/7eJlPLy0e/ixjIuXdMHzT+m5DcOgurqG2tq6o9q8efOoq5unuZhERERE5Cincoz4gQ98CN/3SSQmC3PMnkhLS2sxuiYiIiIi74KCTyJSEkKhEPPnL2D+/AXHfNxxHMbHxxgbG2NsbDR/e5R4PE48Ps7ERJx4PJ5fjpNKpfBdvzD/0IkYIQsrbOUzqI7IngpZGAETwzYwLBPDMjDs3BLTACMXlMEADAMjvzz+C739tnHo31F8H3zHm9qyuealXNzU4aykQ8sTlb47kYqKSqqqqvNlE3PLqqrqKfdFozFMU5lkIiIiIlJ8hmFw550fnuluiIiIiMhpUPBJROYE27apqamlpqb2lNZPp1OMjo4yMjLM6OgIo6OjjI6OMDIyzPDwECMjI4yMDOE4Dn7axUmfWjbVbGcYBhUVlVRWVlJRUfm22xWF+yoqKgoBJmUsiYiIiIiIiIiIyOlQ8ElEzkqhUJiGhkYaGhqPu47v+8TjcUZGhhgfH8tnTk0wMREvZFIlk0kymTSZTJp0OpO/ncFxsnieh+u6eJ6H53n4/qmVsDtVhmEQDocJhcJEIhHC4TCRSBmxWFU+cFRVCCDFYlVEo1EikTJlKImIiIiIiIiIiMgZpeCTiMhxGIZBNBolGo2+6+eybZOqqjKGhydwjlP+zvf9Ke3QfflH8z8f7lswGMyV9RMRERERERERERGZRRR8EhGZJoZhYJomSjwSERERERERERGRuUynQEVERERERERERERERKRoFHwSERERERERERERERGRolHwSURERERERERERERERIpGwScREREREREREREREREpGgWfREREREREREREREREpGgUfBIREREREREREREREZGiUfBJREREREREREREREREikbBJxERERERERERERERESkaBZ9ERERERERERERERESkaBR8EhERERERERERERERkaJR8ElERERERERERERERESKRsEnERERERERERERERERKRoFn0RERERERERERERERKRoFHwSERERERERERERERGRolHwSURERERERERERERERIpGwScREREREREREREREREpGgWfREREREREREREREREpGgUfBIREREREREREREREZGiUfBJREREREREREREREREikbBJxERERERERERERERESkaBZ9ERERERERERERERESkaBR8EhERERERERERERERkaJR8ElERERERERERERERESKRsEnERERERERERERERERKRoFn0RERERERERERERERKRoFHwSERERERERERERERGRorFnugMiInJynueRzWbIZrNkMtnC7Ww2i+NkcRwnv3RxHAfDMLAsK99MTNMiHI5QUVFBRUUF4XAEwzBm+m2JiIiIiIiIiIjIHKTgk4jIGeb7PhMTcSYmhunt7WN0dIyJiTjx+DjxeJxkMpFvyUJLp1P5QFOGbDaD67pF7ZNlWZSXV1BZWUlVVQ3V1dVUV9dQVVVNVVXudnV1DbFYDMuyivraIiIiIiIiIiIiMrcp+CQi8g4lkwlGRkYYHR1hbGyUsbExxsZGGR8/cjlGPD5e1OCRCViGgVVYGlgGhaWJgQ/4+Hg+eICHT8b3SXkeLuC6LuPjY4yPj7FvX+9xX8swDGKxqnwwqroQnDrUYrEYsVgVlZVRbFtfKSIiIiIiIiIiIqLgk4jIUXzfZ3JyguHhIYaHhxgaGircHh4eYnQ0F3BKpVKn9bxBwyBimERMk7CZux02TUKGScgwCOZbwDAJGAa2kQsm2Ri5IFP+tvkuy+VlfZ+055HyPRKex2ShuVNuJzwPz/cL77e7+8TPW1lZSTSaC0ZFo7F8ixZ+rqqqKtxWNpWIiIiIiIiIiMjcpeDTGeL7PqOjoxiGQVVV1Ux3R+Ss5/s+icQkIyMjxONjxOO5snfj47nSd2NjI4yOjhYCLY7jnNLzBg2DctOkzDQpMy3KjEO3cy1impTlA07WLJljKWAYBCyLCk4cAPJ8n6TvMeEeDka9PVCV8D2SnocP+d9p/ISZVJDLpqqoqKSqqoqamlqqq2sKy9raWurq5lFbW6dMKhERkTlOYyYRERERkblLZ/aK7OWXX+brX/86r732GqlUittvv52HH36YT33qUzQ3N/OpT32KcDg8090UmZM8z2NoaJC9e/ewd28PBw7sZ2RkuNAymcxpPV/EMKgwLSosiwrTzN02TcpNiwortwzMkoDSmWAaBuWGRblpAYHjruf7PinfLwSokkcEpQo/5+9LeB6+7+fnuxpn796eY7+2aVJbW0d9/Tzq6xtobGyiqamZpqYm6usblDklIiJSwjRmEhERERGZ+xR8KqKvfOUr/OM//iO+72OaJr7v4/s+ADt27OCZZ55h48aNfOMb3yAYDM5wb0VK3/DwMB0d29m5czu7d3fT27uXVCp5wm1ChpHLSMqXvIvkb5eZJuX5VmZalM+iTKXZzjAMIoZBxDRPuu6RgapDWVQTnsuEm1vGPZdx18X1PAYG+hkY6Ac2T3kOy7KYN6+BpqYWmpqaaW5uobk5dzsSKTtD71JERESKQWMmEREREZGzg4JPRfL888/zla98hebmZv70T/+UK664gksvvbTw+Be+8AU+85nP8MYbb/DEE09w3333zWBvRUqP53ns378vH2zawc6d2xkcHDhqPROotWxqbJsay85lLFmHM5ZsBZRm1JGBqrrjrOP7PpOex3g+EDXmuoy6DiP5peO6HDiwnwMH9h+1bVVVNc3NLflMqRaamnLLmppazFMIjomIiMiZozGTnI7h4SFc16W+ft5Md0VERERE3gEFn4rkscceIxQK8dhjj9He3n7U48uWLePrX/86N9xwA9///vc1kBI5iWw2S0/PHnbu3MbOnbmA08REfMo6BlBn2TQFAjQGAtRZAWKWpYylEmcYRi5gaFk0v63an+/7THgeI/lgVG7pMOK4JHyvMGfX1q1Ts6WCwSCNjc350n25bKmWljYaG5s0t5SIiMg00ZhJTmR0euNKwgAAIABJREFUdIRt27YUWn9/HwCrV6/hrrvupaGh8YTbb9++lWeffZq6uno+/OF7VaZZREREZIbpjFuRbNmyhVWrVh1zEHVIbW0tq1ev5q233prGnonMfp7n0d/fR1dXJ93dnXR1ddLTsxvHcaasZwMNgQDNdjAXcLIDBJXNclYxDINKy6LSsnj7p23K86ZkSB1ajroumUyGnp7d9PTsnrKNZVk0NbXQ2tpGa2sb7e3n0N4+n6qqagwFMUVERIpKYyY5kuu6bN++lddf38DWrZuOymo3AB/YsOEV3njjNdatu5E77vgNKiujhXV832fjxjf5wQ+eZOfO7YX7h4YG+P3f/8+6yEhERERkBulIrEgcxyEQCJx0Pd/3yWQy09Ajkdkpk8mwf38ve/bszgcD9tDTs+eYczWFDYPGfLCpORCg3g4oq0mOK2yaNJpBGt/2Uez5PuOey4hzOFNq2HUZdh2yrktvbw+9vT1TtolGY7S3z2f+/AW0t8+nvf0cGhubVLpPRETkXdCYSRzHYevWzbz22iu8/vqGoyob1Fs2rcEgLYEgzXaAcc/l5ckJ9mQzPPvs06xf/wLvf/8d3HTTLWzc+CZPPfV/2bNnN5Arv31eMERXJs2GDa+QzX6BBx74Y80dJiIiIjJDFHwqkvnz57Np0yaSySSRSOSY60xOTrJp06YTXuknMlf4vs/g4AB79+5h794e9u7NneDv6zuI53lHrW8B9XaABjtAQ8CmwQ4QNS1ln8i7ZhoGVZZNlWWzgFDhft/3iXseQ67DsOMw6DoMOllGXZfx8TE2b97I5s0bC+sHg0Ha2tppa5tPe/t8Wlpy2VIVFZUz8bZERERKjsZMc9/u3bvYvn0rrutOaZ7nMTIyzJtvvkEiMVlYP2wYnBsMc04wSHMgSPhtF/rUmSa3xarZm0nz4uQEg8kk3/3ut3nyye8WqiQEMFgWiXBJpIxy02JPJs2Pxkd58803+NKXPs+nPvUgoVD4uH32PE8XGImIiIicAQo+Fcn73/9+HnnkET772c/yN3/zN4RCoSmPp9NpHnroIcbGxrj//vtnppMiZ4jjOOzbt7eQzbRnz2727t1DMnl0NhNAyDCotwPU2Tb1lk2dHaBKczXJNDMMg6hlEbUsFgQPf2ZnfZ8hJxeIGnAdBh2HISdLJpOhqytXFvJIsVgVra1ttLS00dLSSnNzC83NrVRUVEz3WxIREZnVNGaa21555SW++tV/OOaFZkcqM0zODYVYGAzTHAhgnsIYoC0Y4u5AkB3pFL9KTDDhOIQMg+XhMpZHyogcETyaHwxxW7SaH8ZH2bJlE//zf/4tf/Infzol4Ok4Dm+++QYvvvgCGze+yaWXrub++3+PsrKyd/4LEBEREZEpDN/3/ZnuxFyQTqf5yEc+wtatW6mrq+Piiy/mZz/7GYsWLWLJkiVs2LCBAwcOsGjRIr7zne8c90q/meC6HsPDkydfcY6xbZPq6nJGRiZxnBMPkOSwQxlNu3Z15k/Ed7BnTzfZbPaodU2gxrKptW1qj1iWm6YymqSkeL7PmOsy6DoMOFmGHIdh1yF+gpMrsViM5uZWmptbaWlpyS9bqayMnvG/f32+yXTS35tMt2L8zdXUlGNZynSYbqU8ZoK5MW46U5/ZL7/8Io8++g/4vk+zHSBqWZgYmEZuTGBgEDAM2oJBmuxTCzgdj+P77M9mTjr/64FshqfGR8n4Pueeu5AHH/x/6es7yPr1v+BXv3qRycmJKes3NDTywAN/zPz557zjvp1p+s4tXdp3pU37r7Rp/5Uu7buZVYwxk4JPRTQxMcFf/dVf8cMf/hDXdY96/Prrr+ev//qvqampmYHeHd9cGES9E/oAOzXZbJY9e7rp6NhBZ+dOOjp2MjY2etR6QcNgnh2gzrKps3Ot2rKVzSRzWsbzCvNHDTlOYU6pEwWlKioqaW5uyWdKtRQypqLRWNGCUvp8k+mkvzeZbgo+lbZSHTPB3Bg3nYnP7Bdf/CX//M//iO/7LAmFWVdx5i+0OVX9Tpb/GBsh5fsEgyEymXThsTLT5IJQmEY7wPrJOHHPw7YD3Hffb3HNNetmzXs4kr5zS5f2XWnT/itt2n+lS/tuZin4NEv19/cXrtrzPI/6+npWrVpFW1vbTHftmObCIOqd0AfYsQ0PD9HZuZOurg46OzvYvbsbx5ma1WQCdXZuXqZDrcrS/Ewih2Q8j5F8UGokP6fUsOsy7h19ku2QyspK2tvPob39HNra5jN//nwaG5ux7dOvkKvPN5lO+nuT6abg09xQamMmmBvjpmJ/Zq9f/wL/8i//hO/7LA1FuK6ictaNCYYch++PjZDwPSzg3FCYxaEwbYFgIQMr6Xn8ND7GnmwGgMsvv4qPf/x3CYePP1fUTNB3bunSvitt2n+lTfuvdGnfzSwFn6Qo5sIg6p3QBxhkMhl2795FV1dHIdg0MjJ81HoRw6AxkCuR0RgIMM8OYM+yQaVIKcj6PqOFYFQuIDXsOIwdJygVCoU4//wlLF26jCVLLmT+/HNOaUJsfb7JdNLfm0w3BZ9kpsyFcVMxP7N/8Yvn+frXH8X3fS4MR7imfPYFng6ZcF0OOlnaAkFCxzmW8n2fN5IJfpWYwAeam1u4777fJhqNEQwGCQQC+WXu9ky8V33nli7tu9Km/VfatP9Kl/bdzCrGmOn0L6cWgJNOonoyp3LyUKSYfN+nv7+vEGjq6uqgp2fPUeVODKDOsmkM5DKaGgMBYqaymkSKIWAY1NsB6u3AlPsd32fIcRh0sww6Tq65Dul0mk2b3mTTpjcBKCsrZ+nSC7nyymtYvnwFlmXNxNsQERE5JRozzU2e5/HCC8/x2GP/DMBF4QhXz+LAE0CFZbHwJMdNhmFwaVk5jYEAP4mPsX//Ph5++K+PuW4gEGDp0otYuXIVl1xyKbFY1ZnotoiIiEhJU/DpHVq2bNk73tYwDLZu3VrE3ogcbWJigu7uLrq7u+jq6qSrq4N4fPyo9coMc0qgaZ4dIDCLB44ic5FtGDQEAjQEDgelfN9nyHXozWbozWbYn82SSEzy2muv8Nprr1BdXcPVV1/HNdeso7a2bgZ7LyIicmwaM5U213Xp7NzJgQP76es7wMGDBzl48AD9/QdxHAeA5eEIV83ywNPpagkEuaeqlhcmxulzsji+n2tHrJPNZnnrrTd46603eOwxg4ULz2flylWsXLmaxsamGeu7iIiIyGyi4NM7dLJqhYFAgMbGRizLYmBggMnJXHmGuro6ysrKpqOLcpbwfZ+xsTF6e3vo6dnD7t1d7Nq1i4GBvqPWNYF6O0BjPtDUYAeoNM05NVgUmSsMw6DODlBnB1gRKcfzffodh65Mim2pJCMjw3z/+//Of/zH91i+fAW33voBzj9/8Ux3W0REpEBjptI1OTnBI488TGfnzmM+bgErIuW8p6x8To4lykyTW6JTs5l838cjl7Ee91x2Z9LsyqTpdxw6OnbQ0bGDJ554nPb2c7jqqmtZu/YKKiujM/MGRERERGYBBZ/eoe3bt0/5eXR0lI9+9KMkEgk+85nPcO21106ZJH7Dhg385V/+Jel0mm9+85vT3V2ZA3zfJx6Pc+DAPg4c2M++fb309vawd2/PMTOaAGKmxTzbpiGQCzjVaa4mkZJlGgaNgVzg+D1lFezKpNmcSrAvm+Wtt37NW2/9mpUrV3HXXR+hvb19prsrIiKiMVOJGh8f5+/+7r/T07ObgGHQZAeosiyqLLuwrDBNzLNsXGEYBhZgGQYh06TODrCqrIK469KdSdOdSbMvm6GnZzePP/4Y3/72N7nkkku58sprVS5ZREREzkqGf7LL0eSUPPTQQzz11FM89dRTtLa2HnOdgYEBbr75Zq655hoeeeSRae7h8c2FiXPfidk6aV0qlaKv7yB9fQfo68uVtjh48AAHDuxncnLimNsYQMyyqLVs6u0A82ybeXaAsOrki8x5I67DrxMJtqWT+OROjFx77Tp++7fvxzTDs+rzTeam2fp9KnNXMf7mijF5rpy+Uh4zwdwYN53s/8/o6AgPP/w59u/vJWKY3BGrou5tc1XK8SU9j450Lkt9wD1cqC8Wi3HhhRcTCAQwDBPTNDAMA8MwCQQCXHLJpSxadMFJs8j0nVu6tO9Km/ZfadP+K13adzOrGGMmZT4VybPPPsuaNWuOO4gCqK+v5z3veQ/r16+fxp7JbOP7PuPjYwwM9NPf33dUGxsbPeH2laZJtWVTbdnU2ja1lk2NbWueJpGzVLVls64yyopIGb9KTLArk+b553/GSy/+klvedxu33HI74XB4prspIiKiMdMsNzQ0yMMP/zV9fQcpN00+EK2m2tYpg9MRMU2WR8pYHilj0MmyLZViZzrJ2NgYL774i+Nu96Mf/Qetre2sW3cjl19+FZFIZBp7LSIiInJm6EiySDKZDK7rnnS9Q3XMZe46VB5vaGiAwcHBwnJgoJ/BwX76+/vJZNInfI6wYRArlLWwiFk2Nfmlgkwiciw1ts37olUcyGZ4cXKCg5kMTz757/z858/xG79xN1deeQ2msiFFRGQGacw0e/X1HeThh/+aoaFBKk2TD8ZqiKpM3LtSZwe4qiLA5eUV9GTSDLkuPj4+4PvklsCE59KVTtHb28O//dvX+M53Hufyy69i3bqbaGlpZXJygnh8nPHxcSYn42SzSZLJLMuXX0Jtbd0Mv0sRERGR41PwqUgWLVrEK6+8QkdHB4sWLTrmOq+//jobNmxgzZo1p/389913H6+++mrhZ8MwiEQiLFiwgA9+8IPce++9qiFdRL7vk8lkSCYTJBIJEolJUqkUqVSKdDpFKpUklUqRSCQYHx9jfHyceHyMsbExxsfHyGQyJ32NCtMkalrE8kGlmGUVfla5PBF5p5oCQX4jVs2uTJqXJicYHR3ha1/7Ks8++zT33vsxlixZNtNdFBGRs5TGTLPTvn29fP7zn2N0dIQqy+ID0Woq9HsqGsswWBAKs+AE61xVXsmOdIpNqQSjqRTPPfcszz33LKZp4nnHLzO0cOH5vOc9l7N69VqqqqqK33kRERGRd0FzPhXJj3/8Y/74j/+YqqoqHnjgAa6++moaGxsB6O3t5ac//SmPPvooqVSKr33ta6xdu/a0nv++++5jYmKCv/iLvwDAdV3GxsZ44YUX+M53vsN73/tevvjFL560RvSxzIXa5e+EbZsEAj6bN++gp6eH/ft72bevl/379zE+PnZKV2WeSJlpUmlaRE2TCssiZlpUWrngUqVpYSmDSUTOMNf32ZhMsCE5SSb/db98+Qpuvvn9LF160Tv6zhB5O9XhlummOZ9KVymPmXLPV/rjJts2qagIsmHDm2zatJFt27bQ1dWB67rUWDYfiFVRZirwNFN832dfNsumVIJdmTSHTtaEDIOIaVJmmERMk6Tnsd/JFrYzDIPFi5dy2WVraWhoJBQKEQyGCIWObGFlwc8gHS+VNu2/0qb9V7q072ZWMcZMCj4V0Ze//GW++tWvHvMx3/cJBAL82Z/9Gffcc89pP/d9990HwDe/+c2jHnvsscf427/9W/7u7/6O22+//bSfey4Mok5VJpOho2M7mzdvYuvWTeze3X3C9Q0gaBj5ZhI0DAJHtGB+EHDkQKDMNKlQcGlGOb5P4gRXCMrsV2aa2Po/VDRJz+PVxASbU8nCSYyWllZuvPEWLr/8KkKh0Iz2T0qbBgQy3RR8Km2lOmaC0h43DQ8P8fLL69m6dTMdHTtIp6eWAW+yA7wvWkVkmoITpXi8Pt3HpynPw/F9IqZ5zLHlhOvSlUmzM52i74hA1PEEAgHq6uqpq5vHvHnzqKubR339POrr66murqGyMqrg1Bmk46XSpv1X2rT/Spf23cxS8GkW2rRpE9/+9rd57bXX6O/vB6CxsZHLL7+ce++9l/POO+8dPe+JBlKu63LttdfS1tbGt771rdN+7lIeRJ2M4zh0d3exfftWtm3bys6d28hmpx6YV5gmNZada7ZNtWVRYVqE8gEmZQaUjqzv81x8jF2ZNO8ub01mmgWcGwyxrjKmec6KaNR12JhMsC2dIpv/+i8vr+CKK65i2bKLWLRoMeXl5TPcSyk1GhDIdFPwqfSV4pgp9xylNW7yPI8tWzby3HPP8utfv86RQ/+IYdIaCNAaDNIaCBI1rWkZ95Ty8fpsPj4dd1060yl2Z9KkfB8n37L4hWO+k7Esi6qqaqqra6iuzi0PZUtZloVlWZimhWWZhEJhampqqampobq6lrKyMo2bT0LHS6VN+6+0af+VLu27mVWMMZPmfCqyiy66iIsuumhaX9OyLNauXcuPfvQjHMfBts/O3ZrJZOjv72NgoJ89e7rZsWMbnZ07j5p/qdw0aQsEabUDNAWDJywr4UBuNlgpCT+Nj9GVSZ98RZn1XKAjk8aLj3FDZWymuzNnlJsWa8srWRkpZ0c6yeZkkvjkBM8882OeeebHALS2tnP++Rdw3nnnU1tbS1VVDbFYtGTnyAgGQzoZIiIyy2jMdGbF4+P84hfP8/zzP2NgoK9wf7Md4NxgiKZAkGprarBpusY9pXy8PpuPTyOmyUWRMi6KlB31mO/7uEDC84i7LnHPZSy/jLu5lsTHdV2GhgYZGho87dcPhULU1NQSi1VhGAae5x3VwuEwlZWVVFRUUlkZzd+OUlZWhmmaGPkLPw810zSw7QC2HSAQyDXbtgkEggQCNpZlY9u2srVERERmsbl7xD2LDQ8PU1NTU9TnrKurI5vNMjo6Sl1dXVGfezZ75pkf8atfvcjQ0BCjoyPHXCdsGDQHgrQEgrTlB1oA/z42wk8n49PZXZGiuPnmm7nzzjv53ve+x09+8hPmcgJrVyZN11D/THfjrNLb20Nvbw/PPffsTHelKMLhMC0tbQpAnUGGAbZt4TjujFyvEY3GuOuue2hubp3+FxeRM0Zjpnfmxz9+iu9+99s4jgPkSogvDoW5MFxGtWXx72MjrE9MzHAvz5zpOE7W8WluDqpyw2TS90j7Pul0mgMH9nPgwP5p74tpmth2LhCVu1jqUACLwm2gsE4umGVj5i9CPdkxYjH+hg7153SOl97+uqd6LFus/hbjuUrp+Ptkv++ZPt6Vd0f7r3Rp30F1dTUf+tA9NDY2z3RX3hEFn4qor6+PJ598kt7eXjKZzJQvL8/zyGQyDAwMsGnTJjZv3nxG+lBKX+7vVjab5Vvf+rfjHhBdGinn/FCYGuvoEhK+73P2/KZkrrnzzjtpbW3lzjvv5Omnn57p7ojMaqlUiq6ujpnuhpxhu3fv4otf/F8z3Q0ROQUaM51Zv/jF84XA05qyclZEygsl4s6GMZCOk6dHjWVzZ6wawzDI+j6TnsuE6zHu5YopWoBpGORDQRhGbg6rEddhxHULy2I49Lnx9oonIiIic0VPTw+f//yXZrob74iCT0XS3d3N3XffTTweLwygDMOYchtyB/zRaLTor9/X10c4HKaqqqrozz1b2bbNkiXL2Lr12IPS15OTdKZTNAcCtOQznyqtw1c33RmrzpWXkDmjlMt4nI7vfe97hSs6j/ycmYvOC4ZmXVmTuSbjeex3shzIZjiYzdBfpBMBItPptts+ONNdEJFToDHTmdfU1Mz+/fsA2JCYZNBxuChcRksgMCvGQGf6eH06jpPn+vGp6/tMeh5Jz8XxwcPHI/f/0gM8wPE9Xk1MMuG5THheYXm8+aXM/HYiIiJy+m699Y6Z7sI7puBTkTz66KOMj49z0UUXcdttt/HGG2/wzDPP8NBDD5FOp1m/fj3r169n0aJFPPnkk0V9bdd1efXVV1m5cmXJzsnxThiGwac//edMTEwwMNBHf//htmfPbnp6djPmuYylXbalUwDETIu2YK78XmsgSEj1oeeUGypjmCU6gfHp+MlPfsLTTz89pwNPs3lC57li1HXYmEywLZ066kRBXV09ixadT21tPdXVNfnJpGuIxaqJRCIEg8Gz6vtGTkyTwIrIqdKY6cz75Cc/xauv/ornnnuGzs6duRJxmTRVlsWF4QjnBsNEZ/D9n+nj9TN5nDwXjk993yfl+4y7LuOeW1hOui6TnseE55H0i/9dfugZTdOksjI351Nu7qdKIpFDcz6ZmKYB5OZ7MgzjqPme3v7zoXmfDjXTNAtzQOWC2bmsK8MwsCyrsP2RJfhy6/v4/qHSaz6el/vbsSyr0N7t3FI6Xipt2n+lTfuvdGnflT4Fn4rklVdeIRaL8Y1vfIOKigoWLVrET37yE8455xzWrl3L/fffz5e+9CUeffRRvve973HXXXcV7bW//e1v09/fz5//+Z8X7TlLSUVFBRUVFSxYcN6U+xOJBDt3bmfHjm3s2LGV7u5duWBUKsnmVBIDmGcHmGfb1Fg2NfllRAGpkhUwDN4brcLxfRLe3P1SmsxPDlxpWZSbc/PkSZlpYpfooH42832f3myGt1IJdh9RmqS+fh5Ll17IBRcsYfHipdTWzs15MEREZGZpzHTmBQIBrrjiKq644ip6evbw/PPP8tJLv2Q0lWL95ATrJyeImhatgSCtwdwFeWXTOP4508frZ/I4ebYfn444DnuyaVKeT9b3ccgv/dwy4eXK4h0vO+lItm1TVVVNKBQ6IvhyOAgTCoWpqamlpqaGmpra/MVKtfmsQgPP8/B9D8873EKhMGVlZXO67KWIiIhMpeBTkQwODrJmzRoqKioAWLJkCb7vs2nTJtauXQvAH/7hH/J//s//4fvf//47GkhNTEzw5ptvArm6xiMjI6xfv54nnniC22+/nZtuuql4b2gOKCsrY8WKlaxYsRKAZDLBtm1b2bJlI1u2bOLAgf30OVn6nOyU7SKGSYVpEjQNQoZJ0Di8DLytBQ2DiGlSZlqEDQNTB9Kzgm0YM3pF55kWtSyaAjPdCyk1PZk0L01OMOgeLrZz8cWXcOONt3Dhhct1IkBERM44jZmmV3v7fD7+8d/lwx/+TV5+eT0vvvgLdu3qZNxz2ZpOsjWdBHLz9ywJhVkRmb7AwJk6Xj/bjpPHXZeOdIqOdGrKMd7JVFfXUF8/j7q6eurr51FdXTOlVVZW6thQRERE3jUFn4okEAhQVlZW+LmqqopoNEpHx+FJzm3bZvny5Wzbtu0dvcbWrVu5++67gVy6em1tLQsWLOB//I//wW233fbu3sBZIBIpY+XKVaxcuQqA0dEhenq62L69g97evezb18vg4ABJ3yPperyTOhARwyBiWlSaJpWWRaVpUWmZVJoWUcuizDB1EC8i02rYcXhxMs6ebC7TKRQKcdVV13LjjTfT2Ng8w70TEZGzicZMMyMSibBu3Y2sW3cj2Wya3t5dvPLKa2zZspment0Muw4vJiYY81yuKVfQYaY5vk9nOsX2dIqM7xExzPwFj2bhdtL36Eyn6HMOB5wsy2Lp0otobGwkGAwRCuXaodsVFZXMm9dAbW0dwWBwBt+hiIiInC0UfCqSlpYWuru7p9zX3t7Ojh07ptzn+z6jo6On/fzf/OY331X/5Gi5OU3OYeXK9xTqhqZSKQ4c2Mf4+DiJRIJkMtcmJydJpVKk0ylSqRSpVJJUKkUikSAeH2NiYgLf90n6PknXYdgFske/pkXuarxoPhgVsyxipkXUsolZ1qwu4yAipSXpebyamGBzKolP7oTE9de/lzvu+I3CFeciIiLTSWOmmReJRLjssstYtGgZjuMRj4+zfv0LPPHE42xOJXF8n3UVUVV0mAFjrsPmVJJtqSSpU5yvyjAMlixZxpo1l7Nq1WVUVFSe4V6KiIiInDoFn4rkyiuv5LHHHuNf//Vf+djHPoZhGFx44YU88cQTbNy4keXLlzM8PMzrr79OQ0PDTHdXjiMcDh81d9SpcF2XiYk4Y2NjjI2NMDQ0xNDQIIODAwwNDRaa6/uMuC4jrnvM4FS5aRIzc0GpqnxAqsqyiFl2yU5qKyLTy/F9NiYTvJ6cJJ0/cXHJJau4557fVKaTiIjMKI2ZZp/Kyii33HIb0WgV//Iv/4vt6RSu73NDZQxL44+iyfo+466Lj48PuZaPL014LltSSXqyh+fjrKmp5dprr6e9/Rzi8XHGx8cYHx9nYmKcRGICx/FYseJSVq9eQyxWNSPvSURERORkDN8/xUtq5IT6+/u54447GB0d5cYbb+Tv//7v2bFjB3fccQfRaJQ1a9bw5ptvMjg4yP3338+nP/3pme5yget6DA9PznQ3pp1tm1RXlzMyMlnIfDqTHMfJB6T66e/vZ2Cgn/7+g/nbfSQSiRNuX2maVFt2vllU2zY1lk1kGicIFpHZy/N9dqRTvJKYYCI/gXd7+3w+8pGPsXTphTPcO5nLpvv7VKQYf3M1NeVYlo6hplspj5lgboybTvT/Z8OGV/inf/oyruuyIBjivZUxVWZ4F3zfZ7+TZVsqSVc6TZaTn3q56KKLWbfuJi6++BKsY8yJpe/c0qV9V9q0/0qb9l/p0r6bWcUYMynzqUjmzZvH448/zuc//3nq6uoAuOCCC3jwwQd55JFHePbZZwFYunQpf/AHfzCTXZUZYts2DQ2NNDQ0smzZ1Md832dycoK+vj76+w9y8OAB+voO5tsBJicniXsecS8z5Yo4yGVL1Vo2tbZNnWVTbweosiyVyhA5S/i+z55shpcmJxjOTzRdW1vL/fffz4oVq/E8fRaIiMjsoDHT7LZ69RqCwf/KP/zDF+jOpPnh+Cjvi1apAsNpGnddtqeTbE+lGPcOTyRcVlZGIBDAyM8DbBgGpmkSCARYseJSrrvuBhoaGmew5yIiIiLFpcynIkkmk0QikWM+1tvby8aNG6murmbNmjWYsyxTZS5cwfdOlFL0fHx8nIMH97N//z4OHNjPgQP72bdvL4ODA8dcP2AY1Fs28+wA8wIBGmybqGlp8mCROSTleexIp9iSSjDs5k5slJeXc+utH+Tmm2+hoaG6JD7fpPSV0vepzA3KfCpdpTxmgrlSCxsQAAAgAElEQVQxbjqV/z9bt27mS1/6POl0mkY7wOJQuFAWvMI0NaY4hrjr0p1J05VJsS97uL56OBxhzZq1XHnlNSxadMG7/t3pO7d0ad+VNu2/0qb9V7q072aWMp9mkXvuuYfGxkYeffTRox5rbW2ltbV1Bnolc0U0GiUajXL++Yun3J9MJtm3by979+5h79699PTsZs+e3WQyafY7WfY7WUjl1o0YJo2BAI12gAY7wLyATdCYfYN6ETk+3/c54GTZkkrSmU5x6FraYDDIDTe8l1tv/QDl5RXYtv5vi4jI7KMxU2lYuvRC/ut//TO+8IW/5WAyyUHncDDFBmKWTZVlcXGkjOZAcOY6OoN832fYddmVSbErk2bAcaY8vnTphVx55TWsWnUZoVB4hnopIiIiMrMUfCqS3bt309ioFHmZXpFIhIULz2fhwvML93mex/79vXR376K7u4tdu7ro6dlNMn81XncmDYAB1Fo2DfmAVGO+XJ+uZBSZXcZdl95sJtcyGRL+4at92traue66G1i79irKyspmsJciIiInpzFT6Vi06AIeeuhzvPDCcxw8eICDBw8wMNCP47oMuQ5DrkN3Js37olWcEwzNdHeLzvd9dqZTHHSyOL6PA7i+n7vt+0x43pSSeoZhsHDh+axcuZrVq9dQXz9v5jovIiIiMkso+FQkNTU1jIyMzHQ3RDBNk9bWdlpb27nqqmsByGQy9PTsprNzJ52dHXR1dTA8PMSg6zDoOmwhCUDIMGiwA1MypEKzsOSJyFyV9DwGnSwDTu7/5oFshrg3NbU8FAqxZs3lXHvtDZx77nkKGIuISMnQmKm0NDe38pGPfKzws+u6DA4OcPDgAZ5//qf8+tev8aPxUW6JVrFgDgWg0p7HcxPjdOUv2jse2w5w4YUXsXLlalasWEksVjVNPRQREREpDQo+FcmnP/1pHnzwQT73uc/x8Y9/nLa2tpnukkhBMBg8KkNqeHiIrq5Odu3qoLOzg927d5HOZOjJ5tohNZZFox2kMRCgSdlRIkWR8TyGXZdh18k1J3cF8aR3dA1jy7I499yFLF16IUuWLOO88xYRDJ6dJW5ERKS0acxU2izLoqGhkYaGRpYtu4ivfvXv2bDhFX48PsrNlTHOncXl5XzfxwXsk4xjBpwsT4+PMea5WJbFunU3EYvFCASCBAIBgsEgwWCQsrIyFi1aTDg8e9+ziIiIyExT8KlIfvCDH9DU1MTjjz/O448/TjgcprKy8pgT5RqGwfPPPz8DvRQ5rKamlpqaWlavXgOA4zjs3dtDV1cHnZ076erqoL+/L3+CPMnWdC47KmwYuayoQC4zStlRIseX9rwjAkwuI/nbE8cIMh3S0NBEe3s77e3ncM4553L++TqxISIic4PGTHOHbdt88pOfwjT/gVdeeZmn42O8FzhvFgagsr7P0+Oj9GQztAeCLA5HWBAMTQlE+b7PlnSSX07EcYHa2joeeOC/cN55i2au4yIiIiIlTsGnIvnpT3865edkMkkymTzmusoakdnItm0WLDiXBQvO5YYb3gvA+PhYoVRfR8cOuru7SGWz7M5m2H1EdlSVZdFgB5hnB6izbeosWwEpOask80GmEScfaMpnNSVOEGSqqqqmubmFlpY2WlpaaW1to7W1nUgkMo09FxERmT4aM80tlmXxn/7TH2GaJi+//CI/iY9xE7BwFgWgMp7HD+Oj7MtmAdiTzbAnmyFoGCwKhVkcClNr2/x8Is7OdAqAFStW8olPPEBFRcVMdl1ERESk5Cn4VCQ/+9nPZroLIkUXjcZYuXI1K1euBnLZUXv27Kazcwe7dnXR1dXJwEAfo67LqOuyIz9gA6g0TertALWWTa2dazHTwtSJBClhWd9nyHEYcrMMO7kJt4cdh4R//CBTTU0tzc0tNDe30tLSWritExoiInK20Zhp7rEsi9/7vT/EMExeeumX/CQ+Rtx1ieVLdZuQa4ZBwDCos+yijAd83z9pgDLteTw1PspBJ0s4HOF3fuc/0dOzh5de+iVDQ4NsSSXZkkpiAS65uXM/9KF7uOWW246ZjSciIiIip0fBpyJpaWmZ6S6InHG2bXPeeQs577yFhfvGx8fp7u6iq6uDnp499PTsZmhokLjnEc+k2cXhiXptoDqfGVVnB6i3bWqVJSWz1KTnMug4DDgOg06WQddh1HWPu35dXT0tLW354FILLS2tNDW1UFZWNo29FhERmb00ZpqbTNPkE5/4A0zTZP36F3gxMXHcdcOGwbnBMOeFQrQGglinGYia9FxemZxkZzpJezDEqrJy5tmBo9ZLeh7/MTbCgOtQXl7Ogw9+hnPPXchll63lzjs/zI4dW1m//hds2PAr0uk0VVXVPPDAf+H88xef9vsXERERkWNT8KlIrr/++pOuYxgGtm1TWVlJe3s7N954IzfffPM09E7kzIlGo1x88SVcfPElhfsmJibYuzcXiNq7t4fe3h56e/eSzWYZyJ/M54gsqahpUW/bzMvPITXPtgkqICXTxPN9Rt1cFtOAk2XQcRg8QTZTLBajtXU+bW1ttLS00draRnNzq+Zl+v/Zu7MgOa77zve/zKx97aredzQAggBIAiRIirRk09pIiZpLK0yNJdnjsa9lj8PhF0eMY8aeuB7fCM+DHX6YJ0X48YYtX499rZFsWaJEW5IlUZYEiotIkCCIHei9uvZ9y8z7UNVFQGgQIFno9fuJyMhCVWbhVGd1dZ765fkfAABugT7T7mWapn7zN39Hw8MjOn36Ndm2Ldu25Th273ahUFC1WtHpRmc+Wb9haM7n1wF/QNNe33VzMP20puvo5WpVL9eqasuVJF1sNnSx2dCM16eHQmFNeH2SOgHVPxbyytptRaMx/df/+keamZm9rq1HjtyrI0fu1X/8j5/TmTOndfDgXYpEonf2hwQAALDHGK7rulvdiN3g6NGjkiTnbeb3+GmGYeiTn/yk/uzP/uxONeu22LajbLaypW3YCh6PqUQirFyuonb79o8b3h3HcZRKrWh+fl7z81d05cplXb16WdlsZsPtk5ZHox6Pxr0+jXu9GjAtav/jPXFcVwXbVs5uK9cNmzLttnJ2Wxt9AhiGobGxcc3M7NPs7D7NzMxqenqfBgYGNr3t7xSfb9hMvN+w2frxnksmw7IsLnTZbDu5zyTtjn7TVn5m27atM2dO68c/PqmXXnpehUKh95glaczr1aTXpymvT6MeryzDkOO6Ol2v6flqpXdh0IEDd+nJJ/8PvfTSC/rRj/6t936a8Hh1XzCkk9Wy8ratgYGE/uAP/rsmJnbPiDv+5u5cHLudjeO3s3H8di6O3dbqR5+J8KlPKpWKfv3Xf10XLlzQ7/zO7+hjH/uYJiYmJEkrKyv61re+pb/4i79QIpHQn//5nyuVSunzn/+8zp49qz//8z/XU089tWVt3w2dqHeDD7DtoVwu6erVK7p8+aIuXjyvixcvKJNJ37BdyDA17vVqohtG9atePHafhuMo1w2Z8t2gKWe3VbDtDUMmSfL7/ZqcnNbMzGwvbJqenpF/G02Y/U7w+YbNxPsNm43waefayX0maXf0m7bLZ7bjODp79oxeeOGkXnzxxzdckOaRNO71qezYynXLHo+MjOrTn/4VPfTQI72L0lKpVT3zzFf03HPfUbvd7u0/ODikP/iD/67R0bFNe02bYbscP7xzHLudjeO3s3H8di6O3dYifNpG/vRP/1R//dd/rb/6q7/Sgw8+uOE2r7/+uj7zmc/oN37jN/T7v//7ymaz+shHPqJjx47pL//yLze5xW/ZDZ2od4MPsO0rn8/r4sXzunDhnM6ePaOLFy+o3W5dt43XMDTm8XYCKY9PI16PfAZfIu0Vruuq7DjK2u3eSKZcu7Ou3aRcniT5fH6Nj49rfHxSU1OdsnnT0zMaHBzaVRNL8/mGzcT7DZuN8Gnn2sl9Jml39Ju242e267paWVnWG2+8rjfeeF1nzpxWsfjWqKhIJKpPfvJT+vCHH5fHs/HMAdlsVs8++1V9+9vf1ODgoP7Lf/m/NDg4tFkvYdNsx+OH28Ox29k4fjsbx2/n4thtrX70mZjzqU++/vWv66GHHrppJ0qS7rnnHj3yyCP6yle+ot///d9XMpnUiRMndPr06U1sKbD9DQwM6MSJh3TixEOSpGazqUuXLujs2TN6880zOn/+TdVqNc23mppvNSV1vgSIm5YGPR4NeTwatDxKWh5FLEteRkjtWA3HUcGxOyOY2nZvJFPebqv9NvsNDCQ0MTGpsbFxjY9PaHx8UhMTk0okkrsqZAIAYCehz4SNGIbRPV+b0Ic//Lhc19Xi4oLeeON1ua6jn/3ZDyoUCr3tcySTSf3yL/+aPvWpz8o0zZuGVAAAANg8nJH1SbFYVDR66wlKg8Ggcrlc79/RaFTVavVONg3Y8Xw+n+6++4juvvuInnqqU6ZjYWFe586d0dmzb+rs2TPKZjMqOLYKTVsXm43r9vcbhsKmqYhpKWyaCpqmgoapwDW31++3CKo2jeu6qruuyo6tsuOobNsqO7aKtt05lratxtsMzrUsS6OjY5qYmNT4+GQ3YJrQ2NiEgsHgJr4SAABwO+gz4XYYhqGpqWlNTU2/4319Pt8daBEAAADeDcKnPpmZmdHJkyeVzWaVTCY33CaXy+nkyZOanHxrwtPl5WUNDw9vVjOBXcE0ze7cPLP6yEc+JqnzZcbCwlXNz1/R/PxVzc9f1crKkur1uhquq4ZtK9utF/921oOozmIpYnVCq4hpKmpaClsm5f1uoek6qjmOqs5b66rbXV+zVBxbtz4iUiwW19jYeG8U09jYhCYmJjQ0NMJVrQAA7CD0mQAAAIC9g2/t+uRTn/qU/vRP/1S/+Zu/qf/xP/6H7r333usef/311/XHf/zHKpfL+q3f+i1J0ve+9z29+uqr+sQnPrEVTQZ2lVgspqNH79XRo9f/7tVqVWWzWeVyby2lUlGlUqm7LqpYLKpYLMjuzhdUsx2l3yYV8XVHUoW7I6kipqmQaSn0U6Oo/IbRmwx5p3NcVzXXUcVZX2xVbEe1G0Il+23L4W0kHo8rkRhUMplUIjGokZERDQ+PamRkVMPDIwoEAnfkNQEAgM1FnwkAAADYOwif+uRXf/VX9fzzz+tb3/qWfumXfklDQ0OamJiQ4zhaXl5WJpOR67r6wAc+oM997nNKpVL67d/+bVmWpV//9V/f6uYDu1YwGNLkZEiTk1Nvu53jOCqXy8rns8rn88rnc8pmM70lk+ms6/Wamq6rpm0rd4uRVKakoGkqZHTCqFA3lAoY62ujG1KZ8hmGfIYh7yYHVq7rqum6bwVK3XCp3LvdKYlXdRzdvADejXw+v+LxuGKxmGKxAcVicQ0MDCge7yzrtxOJJKOXAADYI+gzAQAAAHsH3/j1iWVZ+vznP68vfvGL+sIXvqCzZ89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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set()\n", "plt.rcParams[\"figure.figsize\"] = (20,8)\n", "plt.rcParams[\"axes.titlesize\"] = 18\n", "plt.rcParams[\"axes.labelsize\"] = 16\n", "plt.rcParams[\"xtick.labelsize\"] = 12\n", "plt.rcParams[\"ytick.labelsize\"] = 12\n", "\n", "# Create violinplot\n", "plt.subplot(121)\n", "v1 = sns.violinplot(x = \"revol_util\", y=\"grade\", data=df)\n", "v1.axes.set_title(\"Amount of Credit Line Used Relative to Total Credit Available by Loan Grade\", fontsize=20)\n", "\n", "plt.subplot(122)\n", "v2 = sns.violinplot(x = \"acc_open_past_24mths\", y=\"grade\", data=df)\n", "v2.axes.set_title(\"Accts Opened in Past 24 Months by Loan Grade\", fontsize=20)\n", "\n", "# Show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The distribution of the credit line used relative to total credit available by loan grade scores give A grade loans having higher values than the G grade loans. There is an interesting trend on the second graph with distribution of accounts opened in the past 24 months for higher grade loans relative to the lower grade loans. So even if the correlation is much lower, the inner distribution of these accounts oppenings give some major insights." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We started with a model with PCA and one hot encoding on every categorical variables. It created a dataframe of 214 features, 555M+ large. With the same model as below it first gave 30% of good classifcations, with KFold it dropped to a mean of 26.10% (with a standard deviation of 7.89%). So we decided to handpick the data and use the architecure the same way James Andersen did [in his Medium article](https://nbviewer.jupyter.org/github/jamesandersen/aws-machine-learning-demo/blob/master/keras-deeplearning/train-model/lending-club-loan-grades.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We prepare a machine learning model on the data. We first need to split the data into \"X\" for the data that will be fed into the model as input and \"Y\" the column we're trying to predict: loan grade.\n", "\n", "Then we'll split the data by row; 80% will be used to train/validate the model and 20% will be used to test how well the model performs on unseen data.\n", "\n", "Last the target, which is multi-class, has to been one-hot encoded." ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "def split_data(data, continuous_cols, categorical_cols, label_col, test_size=0.2, row_limit=None):\n", " \"\"\"Divide the data in to X and y dataframes and train/test split\"\"\"\n", "\n", " # Subset to get feature data\n", " x_df = data.loc[:, continuous_cols + categorical_cols]\n", "\n", " # Update our X dataframe with categorical values replaced by one-hot encoded values\n", " x_df = encode_categorical(x_df, categorical_cols)\n", "\n", " # Ensure all numeric features are on the same scale\n", " for col in continuous_cols:\n", " x_df[col] = (x_df[col] - x_df[col].mean()) / x_df[col].std()\n", "\n", " # Specify the target labels and flatten the array\n", " y = pd.get_dummies(data[label_col])\n", "\n", " # When requested, limit the amount of data that will be used\n", " # Using entire data set can be painfully slow without a GPU!\n", " if row_limit != None:\n", " rows = np.random.binomial(1, 0.1, size=len(data)).astype('bool')\n", " x_df = x_df[rows]\n", " y = y[rows]\n", " print(\"Using only a sample of {} observations\".format(x_df.shape[0]))\n", " #data = self.lcdata.sample(int(row_limit))\n", " else:\n", " print(\"Using the full set of {} observations\".format(data.shape[0]))\n", " #data = self.lcdata\n", "\n", " # Create train and test sets\n", " x_train, x_test, y_train, y_test = train_test_split(x_df, y, test_size=test_size, random_state=23)\n", " print(\"x_train contains {} rows and {} features\".format(x_train.shape[0], x_train.shape[1]))\n", " return x_train, x_test, y_train, y_test" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "def encode_categorical(frame, categorical_cols):\n", " \"\"\"Replace categorical variables with one-hot encoding in-place\"\"\"\n", " for col in categorical_cols:\n", " # use get_dummies() to do one hot encoding of categorical column\n", " frame = frame.merge(pd.get_dummies(frame[col]), left_index=True, right_index=True)\n", " \n", " # drop the original categorical column\n", " frame.drop(col, axis=1, inplace=True)\n", " \n", " return frame" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using the full set of 552160 observations\n", "x_train contains 441728 rows and 105 features\n", "x_train contains 441728 rows and 105 features\n", "y_train contains 441728 rows and 7 features\n", "x_test contains 110432 rows and 105 features\n", "y_test contains 110432 rows and 7 features\n", "Sample one-hot encoded 'y' value: \n", " grade_A grade_B grade_C grade_D grade_E grade_F grade_G\n", "447861 1 0 0 0 0 0 0\n" ] } ], "source": [ "# Divide the data set into training and test sets\n", "x_train, x_test, y_train, y_test = split_data(df, APPLICANT_NUMERIC + CREDIT_NUMERIC,\n", " APPLICANT_CATEGORICAL,\n", " TARGET,\n", " test_size = 0.2,\n", " row_limit = os.environ.get(\"sample\"))\n", "\n", "# Inspect our training data\n", "print(\"x_train contains {} rows and {} features\".format(x_train.shape[0], x_train.shape[1]))\n", "print(\"y_train contains {} rows and {} features\".format(y_train.shape[0], y_train.shape[1]))\n", "\n", "print(\"x_test contains {} rows and {} features\".format(x_test.shape[0], x_test.shape[1]))\n", "print(\"y_test contains {} rows and {} features\".format(y_test.shape[0], y_test.shape[1]))\n", "\n", "# Loan grade has been one-hot encoded\n", "print(\"Sample one-hot encoded 'y' value: \\n{}\".format(y_train.sample()))" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout\n", "from keras.constraints import maxnorm\n", "\n", "def create_model(input_dim, output_dim):\n", " # create model\n", " model = Sequential()\n", " # input layer\n", " model.add(Dense(100, input_dim=input_dim, activation='relu', kernel_constraint=maxnorm(3)))\n", " model.add(Dropout(0.2))\n", " \n", " # hidden layer\n", " model.add(Dense(60, activation='relu', kernel_constraint=maxnorm(3)))\n", " model.add(Dropout(0.2))\n", " \n", " # output layer\n", " model.add(Dense(output_dim, activation='softmax'))\n", " \n", " # Compile model\n", " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", " return model" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beginning model training with batch size 64 and 35 epochs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", " from ipykernel import kernelapp as app\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", " app.launch_new_instance()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 353382 samples, validate on 88346 samples\n", "Epoch 1/35\n", " - 31s - loss: 0.9299 - acc: 0.6013 - val_loss: 0.4764 - val_acc: 0.8208\n", "Epoch 2/35\n", " - 25s - loss: 0.5274 - acc: 0.7795 - val_loss: 0.4230 - val_acc: 0.8219\n", "Epoch 3/35\n", " - 24s - loss: 0.4681 - acc: 0.8038 - val_loss: 0.3759 - val_acc: 0.8448\n", "Epoch 4/35\n", " - 27s - loss: 0.4437 - acc: 0.8144 - val_loss: 0.3513 - val_acc: 0.8593\n", "Epoch 5/35\n", " - 26s - loss: 0.4300 - acc: 0.8205 - val_loss: 0.3510 - val_acc: 0.8581\n", "Epoch 6/35\n", " - 29s - loss: 0.4197 - acc: 0.8249 - val_loss: 0.3651 - val_acc: 0.8478\n", "Epoch 7/35\n", " - 31s - loss: 0.4119 - acc: 0.8279 - val_loss: 0.3275 - val_acc: 0.8648\n", "Epoch 8/35\n", " - 29s - loss: 0.4055 - acc: 0.8312 - val_loss: 0.3168 - val_acc: 0.8702\n", "Epoch 9/35\n", " - 27s - loss: 0.3998 - acc: 0.8335 - val_loss: 0.3298 - val_acc: 0.8652\n", "Epoch 10/35\n", " - 27s - loss: 0.3958 - acc: 0.8362 - val_loss: 0.3154 - val_acc: 0.8697\n", "Epoch 11/35\n", " - 26s - loss: 0.3931 - acc: 0.8365 - val_loss: 0.3155 - val_acc: 0.8736\n", "Epoch 12/35\n", " - 26s - loss: 0.3868 - acc: 0.8391 - val_loss: 0.3006 - val_acc: 0.8793\n", "Epoch 13/35\n", " - 25s - loss: 0.3839 - acc: 0.8406 - val_loss: 0.3218 - val_acc: 0.8647\n", "Epoch 14/35\n", " - 27s - loss: 0.3805 - acc: 0.8424 - val_loss: 0.3137 - val_acc: 0.8698\n", "Epoch 15/35\n", " - 26s - loss: 0.3773 - acc: 0.8428 - val_loss: 0.3047 - val_acc: 0.8742\n", "Epoch 16/35\n", " - 26s - loss: 0.3746 - acc: 0.8453 - val_loss: 0.2908 - val_acc: 0.8826\n", "Epoch 17/35\n", " - 26s - loss: 0.3735 - acc: 0.8454 - val_loss: 0.3047 - val_acc: 0.8745\n", "Epoch 18/35\n", " - 25s - loss: 0.3701 - acc: 0.8465 - val_loss: 0.2946 - val_acc: 0.8788\n", "Epoch 19/35\n", " - 29s - loss: 0.3694 - acc: 0.8462 - val_loss: 0.2958 - val_acc: 0.8772\n", "Epoch 20/35\n", " - 26s - loss: 0.3673 - acc: 0.8483 - val_loss: 0.3035 - val_acc: 0.8773\n", "Epoch 21/35\n", " - 25s - loss: 0.3650 - acc: 0.8483 - val_loss: 0.2870 - val_acc: 0.8847\n", "Epoch 22/35\n", " - 26s - loss: 0.3648 - acc: 0.8491 - val_loss: 0.2881 - val_acc: 0.8834\n", "Epoch 23/35\n", " - 28s - loss: 0.3640 - acc: 0.8489 - val_loss: 0.3021 - val_acc: 0.8751\n", "Epoch 24/35\n", " - 30s - loss: 0.3612 - acc: 0.8502 - val_loss: 0.3163 - val_acc: 0.8665\n", "Epoch 25/35\n", " - 26s - loss: 0.3624 - acc: 0.8499 - val_loss: 0.2869 - val_acc: 0.8822\n", "Epoch 26/35\n", " - 25s - loss: 0.3596 - acc: 0.8512 - val_loss: 0.2905 - val_acc: 0.8832\n", "Epoch 27/35\n", " - 26s - loss: 0.3563 - acc: 0.8528 - val_loss: 0.2839 - val_acc: 0.8822\n", "Epoch 28/35\n", " - 27s - loss: 0.3570 - acc: 0.8524 - val_loss: 0.3027 - val_acc: 0.8745\n", "Epoch 29/35\n", " - 25s - loss: 0.3568 - acc: 0.8529 - val_loss: 0.3116 - val_acc: 0.8683\n", "Epoch 30/35\n", " - 24s - loss: 0.3550 - acc: 0.8537 - val_loss: 0.2901 - val_acc: 0.8800\n", "Epoch 31/35\n", " - 24s - loss: 0.3542 - acc: 0.8530 - val_loss: 0.2898 - val_acc: 0.8797\n", "Epoch 32/35\n", " - 24s - loss: 0.3531 - acc: 0.8544 - val_loss: 0.2853 - val_acc: 0.8841\n", "Epoch 33/35\n", " - 24s - loss: 0.3541 - acc: 0.8536 - val_loss: 0.2992 - val_acc: 0.8760\n", "Epoch 34/35\n", " - 24s - loss: 0.3541 - acc: 0.8538 - val_loss: 0.2810 - val_acc: 0.8867\n", "Epoch 35/35\n", " - 24s - loss: 0.3550 - acc: 0.8537 - val_loss: 0.3013 - val_acc: 0.8760\n" ] } ], "source": [ "from keras.callbacks import ModelCheckpoint\n", "from keras.models import load_model\n", "\n", "\n", "\n", "model = create_model(x_train.shape[1], y_train.shape[1])\n", "\n", "epochs = 35\n", "batch_sz = 64\n", "\n", "print(\"Beginning model training with batch size {} and {} epochs\".format(batch_sz, epochs))\n", "\n", "checkpoint = ModelCheckpoint(\"lc_model.h5\", monitor='val_acc', verbose=0, save_best_only=True, mode='auto', period=1)\n", "# train the model\n", "history = model.fit(x_train.as_matrix(),\n", " y_train.as_matrix(),\n", " validation_split=0.2,\n", " epochs=epochs, \n", " batch_size=batch_sz, \n", " verbose=2,\n", " callbacks=[checkpoint])\n", "\n", "# revert to the best model encountered during training\n", "model = load_model(\"lc_model.h5\")" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = [16, 5]\n", "plt.rcParams[\"axes.titlesize\"] = 18\n", "plt.rcParams[\"axes.labelsize\"] = 16\n", "plt.rcParams[\"xtick.labelsize\"] = 12\n", "plt.rcParams[\"ytick.labelsize\"] = 12\n", "plt.rcParams['legend.loc'] = 'upper right'\n", "plt.rcParams['legend.framealpha'] = 0.7\n", "plt.rcParams[\"legend.fontsize\"] = 14\n", "\n", "# Plot accuracy\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['acc'])\n", "plt.plot(history.history['val_acc'])\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'valid.'], loc='upper left')\n", "\n", "# Plot loss\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'valid.'], loc='upper left')\n", "\n", "# adjust size\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now see how our train model performs on the \"test\" data that we held out of the training data. We measure the performance using the F1 score.\n", "\n" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", " after removing the cwd from sys.path.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Test Set Accuracy: 88.54% (But results would have been better if we trained on the FULL dataset)\n" ] } ], "source": [ "import numpy as np\n", "from sklearn.metrics import f1_score\n", "\n", "y_pred = model.predict(x_test.as_matrix())\n", "\n", "# Revert one-hot encoding to classes\n", "y_pred_classes = pd.DataFrame((y_pred.argmax(1)[:,None] == np.arange(y_pred.shape[1])),\n", " columns=y_test.columns,\n", " index=y_test.index)\n", "\n", "y_test_vals = y_test.idxmax(1)\n", "y_pred_vals = y_pred_classes.idxmax(1)\n", "\n", "# F1 score\n", "# Use idxmax() to convert back from one-hot encoding\n", "f1 = f1_score(y_test_vals, y_pred_vals, average='weighted')\n", "print(\"Test Set Accuracy: {:.2%} (But results would have been better if we trained on the FULL dataset)\".format(f1))" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "from importlib import reload\n", "\n", "# Confusion matrix\n", "conf_matrix = confusion_matrix(y_test_vals, y_pred_vals).astype(float)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# drawing it \n", "\n", "def fmt_pct(x, pos):\n", " \"\"\"Format as percentage\"\"\"\n", " return '{:.0%}'.format(x)\n", "\n", "classes = [l for l in \"ABCDEFG\"]\n", "title='Confusion matrix'\n", "\n", "correct_mask = np.ones(conf_matrix.shape, dtype=bool)\n", "wrong_mask = np.zeros(conf_matrix.shape, dtype=bool)\n", "for i in range(conf_matrix.shape[0]):\n", " correct_mask[i,i] = False\n", " wrong_mask[i,i] = True\n", " row_sum = sum(conf_matrix[i])\n", " for j in range(conf_matrix.shape[1]):\n", " conf_matrix[i, j] = conf_matrix[i, j] / row_sum\n", "\n", "correct_matrix = np.ma.masked_array(conf_matrix, mask=correct_mask)\n", "wrong_matrix = np.ma.masked_array(conf_matrix, wrong_mask)\n", "\n", "fig,ax = plt.subplots(figsize=(8, 8))\n", "blue_map = colors.LinearSegmentedColormap.from_list('custom blue', ['#f1eef6', '#025a90'], N=10)\n", "blue_map.set_under(color='white')\n", "\n", "red_map = colors.LinearSegmentedColormap.from_list('custom blue', ['#feeddd', '#a83500'], N=10)\n", "red_map.set_under(color='white')\n", "\n", "plot_correct = ax.imshow(correct_matrix,interpolation='nearest',cmap=blue_map, vmin=0.000001, vmax=1)\n", "plot_wrong = ax.imshow(wrong_matrix,interpolation='nearest',cmap=red_map, vmin=0.000001, vmax=1)\n", "\n", "colorbar_wrong = plt.colorbar(plot_wrong, shrink=0.35, orientation='horizontal', pad=-0.11, format=ticker.FuncFormatter(fmt_pct))\n", "colorbar_correct = plt.colorbar(plot_correct, shrink=0.35, orientation='horizontal', pad=0.03)\n", "plt.xlabel('Predicted Values')\n", "plt.ylabel('True Values')\n", "ax.xaxis.set_label_position('top')\n", "ax.xaxis.tick_top()\n", "ax.set_xticklabels([''] + classes)\n", "ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n", "ax.set_yticklabels([''] + classes)\n", "ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n", "colorbar_correct.ax.text(-0.3,0.25,'CORRECT',rotation=0)\n", "colorbar_wrong.ax.text(-0.35,0.25,'INCORRECT',rotation=0)\n", "plt.setp(colorbar_correct.ax.get_xticklabels(), visible=False)\n", "\n", "thresh = conf_matrix.max() / 2.\n", "for i, j in itertools.product(range(conf_matrix.shape[0]), range(conf_matrix.shape[1])):\n", " cell_label = \"{:.1%}\".format(conf_matrix[i, j])\n", " plt.text(j, i, cell_label,\n", " horizontalalignment=\"center\",\n", " color=\"white\" if conf_matrix[i, j] > thresh else \"black\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the very beginning James Anderson's model performed better than my model, however his neural network architecture was almost the same as ours. The first thing he did was to remove any rows with nas from the columns he selected. So he didn't selected the columns where there were too many missing values, unlike me. This created a lighter model.\n", "\n", "Yet, if his results were already pretty good we improved them by removing a few features. However the lower the grade the lower were our classification accuracy. Reaching to 19% for G grade borrowers, which were mainly classified in F and some in E. So the next thing to work on would be to improve features selection." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }