{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Showing the consistency of the implementation of ISIMIP within ibicus with the reference implementation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is meant to demonstrate the consistency of the ibicus implementation of ISIMIP3BASD v3.0.1 (most recent version, in the following referred to as ISIMIP) with the reference implementation provided by Lange 2022: \n",
"\n",
"- Lange, S. (2022). ISIMIP3BASD (3.0.1) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.6758997\n",
"\n",
"The code in the ibicus implementation is based upon [Lange 2019](https://doi.org/10.5194/gmd-12-3055-2019) and the [ISIMIP3b bias adjustment fact sheet](https://www.isimip.org/documents/413/ISIMIP3b_bias_adjustment_fact_sheet_Gnsz7CO.pdf). More details of the implementation of ISIMIP within ibicus can be found in the [documentation](https://ibicus.readthedocs.io/en/latest/reference/debias.html#package-name-debias-isimip-class).\n",
"\n",
"Numerous checks were conducted during and after the development process to ensure consistency. This notebook provides a final validation and demonstration of this consistency on the testing data published with the ISIMIP reference implementation.\n",
"\n",
"The structure of this notebook is as follows. First the ISIMIP reference implementation is pulled from https://zenodo.org/record/6758997/files/isimip3basd-master.tar.gz and run on the testing data. In a second step debiasers are initialised using ibicus and run for all variables included in ISIMIP. Consistency is demonstrated by plotting and comparing the values computed by the reference implementation and the ibicus implementation.\n",
"\n",
"\n",
"## Contents\n",
"1. [ISIMIP3BASD v3.0.1 reference implementation](#bullet-1)\n",
"\n",
"2. [ibicus implementation](#bullet-2)\n",
"\n",
" 2.1. [Helpers](#bullet-21)\n",
" \n",
" 2.2. [tas](#bullet-22)\n",
" \n",
" 2.3. [pr](#bullet-22)\n",
" \n",
" 2.4. [ps / psl](#bullet-22)\n",
"\n",
" 2.5. [rlds](#bullet-22)\n",
" \n",
" 2.6. [sfcWind](#bullet-22)\n",
" \n",
" 2.7. [tasrange](#bullet-22)\n",
" \n",
" 2.8. [tasskew](#bullet-22)\n",
" \n",
" 2.9. [hurs](#bullet-22)\n",
" \n",
" 2.10. [rsds](#bullet-22)\n",
" \n",
" 2.11. [prsnratio](#bullet-22)\n",
" \n",
"3. [Summary](#bullet-3)\n",
" \n",
"\n",
"**References:**\n",
"\n",
"- Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). In Geoscientific Model Development (Vol. 12, Issue 7, pp. 3055–3070). Copernicus GmbH. https://doi.org/10.5194/gmd-12-3055-2019\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import iris\n",
"import numpy as np\n",
"import scipy.stats\n",
"from cf_units import num2date\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. ISIMIP3BASD v3.0.1 reference implementation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We pull v3.0.1 of the ISIMIP reference implementation -- the latest version and the version implemented in ibicus and unpack the code:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2022-08-30 12:25:16-- https://zenodo.org/record/6758997/files/isimip3basd-master.tar.gz\n",
"Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
"Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
"HTTP request sent, awaiting response... 206 Partial Content\n",
"Length: 12112564 (12M), 3167934 (3,0M) remaining [application/octet-stream]\n",
"Saving to: ‘isimip3basd-master.tar.gz’\n",
"\n",
"isimip3basd-master. 100%[++++++++++++++=====>] 11,55M 532KB/s in 5,8s \n",
"\n",
"2022-08-30 12:25:25 (532 KB/s) - ‘isimip3basd-master.tar.gz’ saved [12112564/12112564]\n",
"\n"
]
}
],
"source": [
"!wget https://zenodo.org/record/6758997/files/isimip3basd-master.tar.gz -c\n",
"!tar -xf isimip3basd-master.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now let the ISIMIP reference code run through.\n",
"\n",
"The command below is the standard call for ISIMIP debiasing provided `application_example.sh` inside the unpacked `isimip3basd-master.tar.gz`. Three small modifications were made: \n",
"- The paths to `bias_adjustment.py` and to the data (in `-o`, `-s`, `-f` and `-b`) were adjusted.\n",
"- A flag `--n-quantiles 15000` was added to ensure a high number of quantiles for comparison: this is slower but more exact. With a lower number of quantiles differences between the ibicus implementation and the ISIMIP reference get bigger, due to the ISIMIP linear interpolation being not exact. Those are not directly used, but the number gets reduced to a high isimip default.\n",
"- A flag `2> isimip_output.txt` was added to write `stderr` to file."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking inputs ...\n",
"adjusting at location (lon, lat) ...\n",
"(0, 0)\n",
"(0, 1)\n",
"(1, 0)\n",
"(1, 1)\n"
]
}
],
"source": [
"!python -u isimip3basd-master/code/bias_adjustment.py \\\n",
"--n-quantiles 15000 \\\n",
"--n-processes 5 \\\n",
"--randomization-seed 0 \\\n",
"--step-size 1 \\\n",
"-v hurs,pr,prsnratio,ps,rlds,rsds,sfcWind,tas,tasrange,tasskew \\\n",
"--lower-bound 0,0,0,,,0,0,,0,0 \\\n",
"--lower-threshold .01,.0000011574,.0001,,,.0001,.01,,.01,.0001 \\\n",
"--upper-bound 100,,1,,,1,,,,1 \\\n",
"--upper-threshold 99.99,,.9999,,,.9999,,,,.9999 \\\n",
"--distribution ,gamma,,normal,normal,,weibull,normal,weibull, \\\n",
"-t bounded,mixed,bounded,additive,additive,bounded,mixed,additive,mixed,bounded \\\n",
"--unconditional-ccs-transfer 1,,,,,,,,, \\\n",
"--trendless-bound-frequency 1,,,,,,,,, \\\n",
"-d ,,,1,1,,,1,, \\\n",
"-w 0,0,0,0,0,15,0,0,0,0 \\\n",
"--if-all-invalid-use ,,0.,,,,,,, \\\n",
"-o isimip3basd-master/data/hurs_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/pr_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/prsnratio_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/ps_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/rlds_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/rsds_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/sfcWind_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/tas_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/tasrange_obs-hist_coarse_1979-2014.nc,isimip3basd-master/data/tasskew_obs-hist_coarse_1979-2014.nc \\\n",
"-s isimip3basd-master/data/hurs_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/pr_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/prsnratio_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/ps_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/rlds_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/rsds_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/sfcWind_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/tas_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/tasrange_sim-hist_coarse_1979-2014.nc,isimip3basd-master/data/tasskew_sim-hist_coarse_1979-2014.nc \\\n",
"-f isimip3basd-master/data/hurs_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/pr_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/prsnratio_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/ps_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/rlds_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/rsds_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/sfcWind_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/tas_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/tasrange_sim-fut_coarse_2065-2100.nc,isimip3basd-master/data/tasskew_sim-fut_coarse_2065-2100.nc \\\n",
"-b isimip3basd-master/data/hurs_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/pr_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/prsnratio_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/ps_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/rlds_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/rsds_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/sfcWind_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/tas_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/tasrange_sim-fut-basd_coarse_2065-2100.nc,isimip3basd-master/data/tasskew_sim-fut-basd_coarse_2065-2100.nc \\\n",
"2> isimip_output.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A number of warnings are generated and written to `isimip_output.txt` indicating that the number of quantiles get reduced to a high ISIMIP default. This is not a problem for the values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. ibicus implementation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After having executed the reference implementation we can now come to the ibicus implementation. Let' s import it:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from ibicus.debias import ISIMIP"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### 2.1. Helpers "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first define some helpers to read in the ISIMIP-testing and debiased data and gets the dates (last coordinate). Those are needed for the debiasing below."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Given an iris-cube this returns the dates stored in the last time-dimension\n",
"def get_dates(x):\n",
" time_dimension = x.coords()[2]\n",
" dates = time_dimension.units.num2date(time_dimension.points)\n",
" return dates\n",
"\n",
"get_dates = np.vectorize(get_dates)\n",
"\n",
"# This reads in the testing-data from ISIMIP stored in isimip3basd-master/data\n",
"def read_in_and_preprocess_isimip_testing_data_with_dates(variable, isimip_data_path = \"isimip3basd-master/data/\"):\n",
" \n",
" # Load in data\n",
" obs = iris.load_cube(isimip_data_path+variable+\"_obs-hist_coarse_1979-2014.nc\")\n",
" cm_hist = iris.load_cube(isimip_data_path+variable+\"_sim-hist_coarse_1979-2014.nc\")\n",
" cm_future = iris.load_cube(isimip_data_path+variable+\"_sim-fut_coarse_2065-2100.nc\")\n",
"\n",
" # Extract dates\n",
" dates = {\n",
" \"time_obs\": get_dates(obs),\n",
" \"time_cm_hist\": get_dates(cm_hist),\n",
" \"time_cm_future\": get_dates(cm_future)\n",
" }\n",
" \n",
" # Convert to np.array (from masked-array)\n",
" obs = obs.data\n",
" cm_hist = cm_hist.data\n",
" cm_future = cm_future.data\n",
"\n",
" # Move time to first axis (our convention)\n",
" obs = np.moveaxis(obs, -1, 0)\n",
" cm_hist = np.moveaxis(cm_hist, -1, 0)\n",
" cm_future = np.moveaxis(cm_future, -1, 0)\n",
" \n",
" return obs, cm_hist, cm_future, dates\n",
"\n",
"def read_in_debiased_testing_data(variable, isimip_data_path = \"isimip3basd-master/data/\"):\n",
" \n",
" # Load in data\n",
" debiased_data = iris.load_cube(isimip_data_path+variable+\"_sim-fut-basd_coarse_2065-2100.nc\")\n",
" \n",
" # Move time to first axis (our convention)\n",
" debiased_data = np.array(debiased_data.data)\n",
" debiased_data = np.moveaxis(debiased_data, -1, 0)\n",
" \n",
" return debiased_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2. tas "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean near-surface air temperature -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:20<00:00, 5.00s/it]\n"
]
}
],
"source": [
"variable = \"tas\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage agreement between the Ibicus ISIMIP and the reference implementation is 100.0 %.\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.isclose(debiased_values,debiased_values_isimip))/debiased_values.size\n",
"print(\"Percentage agreement between the ibicus ISIMIP and the reference implementation is %s %%.\"%(pct_agreement*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the ibicus and reference implementation over time at one location:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP reference implementation\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"Raw CM\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the dotted ibicus implementation line lies right above the ISIMIP implementation line.\n",
"\n",
"We can also plot the values against eachother:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A linear regression also shows that the reference implementation values' and the ibicus ones are consistent:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.9999998728613527, intercept=3.637642799958485e-05, rvalue=0.9999999999943137, pvalue=0.0, stderr=1.4705499476000933e-08, intercept_stderr=4.208917712180163e-06)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**We conclude that tas is reproduced well by the ibicus implementation of ISIMIP.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3. pr "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean precipitation -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:16<00:00, 4.14s/it]\n"
]
}
],
"source": [
"variable = \"pr\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.96197140249467 % of all values of the Ibicus implementation are within 1e-6 distance of the reference implementation.\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.abs(debiased_values-debiased_values_isimip) < 1e-6)/debiased_values.size\n",
"print(\"%s %% of all values of the ibicus implementation are within 1e-6 distance of the reference implementation.\"%(pct_agreement*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot over time:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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k+7/+9a/ypJNOkqqqysbGRjlhwgRZX18vX3jhBVlQUCD37dsnNU2Tq1atkq+88sqg/9dDmT73xRB55fF75cI/zJNLf32qDEcTIzQri9ECWCczPFOz2WmsBHZIKXdJKePAQ0DvhDZnAQ+YY70JFAkhqgfpexZwv/n6fuBsU4iFpJSvAmkpPaWUYSnlC+brOPAOUJPF/Mc8K1euZOrUqdhsNi688EJeffXVtPPPP/885557LmVlRk6fkpKSAa+3YsUK/vCHP3DTTTfx/vvvk5+fP2B7p9PJaaedBhipvo877jgcDgcLFixIS9t98sknU1paisfj4VOf+lSfefZMB7506VI++OADtm/fDsCUKVNYsGABiqIwb948TjzxRIQQaWM8/fTT/OQnP2Hx4sUcf/zxRKNR9u7dC8CJJ55IYWEhbrebuXPnsmfPnoyf5bbbbksVGtq3b19q/P549dVXufDCC7HZbFRWVnLcccfx9ttvA8b/paamBkVRWLx4cb8pzC0MgsKNLgRxIUhYu7KPLNkE940H9vV4vx/oncYyU5vxg/StlFI2AEgpG4QQWUcECSGKgE8Av+rn/NXA1QATJ04c/IKf+1f3a5sj/b3Tm/7eXZj+3lea/j6/cvDx+s53wPcyQ9pwALvdnlJlSSmJx+MAHHvssbz88sv861//4tJLL+XrX/86n/3sZ/sdv2fa7J6pvnumEs92ntmkA+9vDCklf/vb35g1a1Za/7Vr1/ZJJ54ptfeLL77Is88+yxtvvIHX600JnoGQAzzcshnTopudBQuhHUoUP4lwANwDL24sDk2y2Wn0fVpB7zutvzbZ9B0SQgg78CBwm5RyV6Y2Usq7pZTLpZTLM1WsG2u89dZb7N69G13Xefjhh/uk6T7xxBN55JFHUvUz2tvbAcPmsX79egAef/xxEokEAHv27KGiooKrrrqKK6+8knfeeWdE5vnMM8/Q3t5OJBLhscceSxUoSjKcdOC9+99+++2pB/m77747aB+Hw5H63J2dnRQXF+P1evnggw948803M7brybHHHsvDDz+Mpmm0tLTw8ssvs3LlyqznbNFNJGGmcxft6NGRrYZpMXbIZqexH5jQ430NUJ9lG+cAfZuEENXmLqMayPbpcjewXUr5yyzbj3mOPPJIbrjhBt5//32OPfZYzjnnnLTz8+bN49vf/jbHHXccNpuNJUuWcN9993HVVVdx1llnsXLlSk488cRUEaQXX3yRn/3sZzgcDvLy8njggQdGZJ5HH300l156KTt27OCiiy7qU270lFNOYevWrakKd3l5efzpT3/CZrNldf0bb7yRr3zlKyxcuBApJZMnT+af//zngH2uvvpqFi5cyNKlS/n973/PXXfdxcKFC5k1axarVq3K2O7Pf/5z6vg555zDG2+8waJFixBC8NOf/pSqqqohpUG3MJhU9wgA26gm5rbSo39UGTQ1urmy/xA4EagD3gYuklJu7tHm48C1GAbxIzB2ASsH6iuE+BnQJqX8ielVVSKl/EaPa14OLJdSXtvj2A+AOcBnpJRZ+fRZqdFHhvvuu49169alFWqy+GhxoPfFb//0P9yhPYIeL+Gf5zzJlDKr7OuhTH+p0QfdaUgpVSHEtcBTGN5Qvzcf+mvM83cBT2IIjB1AGPjcQH3NS/8EeEQIcSWwF/hMj8nWAgWAUwhxNnAK0AV8G/gAeMfUp98hpbx3aH8KCwuLXPCBbxF0PUKeCCKDLWAJjY8kWWW5lVI+iSEYeh67q8drCVyTbV/zeBvGDiRTn8n9TCWTjcTiIHD55Zdz+eWXj/Y0LMYwUc2wabiUCHqgAZg8qvOxyA1WRLiFhcWIsLDlUQDa8RIqnD3Ks7HIFZbQsLCwGBEabaXGC6GR0K04jY8qltCwsLAYETZ75gEghIroqB3dyVjkDEtoWFhYjAhxPWa8EGDryhhCZfERwBIao4TNZkslFPzEJz4x4inQGxsbueCCC5g2bRpz587ljDPO4MMPP6S2thYhBDfeeGOqbWtrKw6Hg2uvvXaAK1pYDMyqridSrxvLVoziTCxyiSU0RgmPx8OGDRvYtGkTJSUl/PrXvx6xa0spOeecczj++OPZuXMnW7Zs4Uc/+hFNTUYZzqlTp6YFzT366KPMmzdvxMa3ODzZZ+vOBBRR46M4E4tcYgmNMcCRRx5JXV0dYKQUOeqoo1iyZAlHHXUU27ZtA4yCTBs3bgRgyZIl3HzzzYARRX3vvemhKi+88AIOh4M1a9akji1evJhjjjkGMATWnDlzUqnRH374Yc4777zcfkiLjzybHdNTr+3tmwdoaXEok1WcxkeZW966hQ/aRzZlxOyS2Vy/8vqs2mqaxnPPPceVV15p9J09m5dffhm73c6zzz7Lt771Lf72t79x7LHH8sorrzB58mTsdjuvvfYaYGRpveSSS9KuuWnTJpYtWzbguBdccAEPPfQQVVVV2Gw2xo0bR3197+wwFhbZk0jaNABnp2XT+Khy2AuN0SJZFKi2tpZly5Zx8sknA0bSvcsuu4zt27cjhEgl2TvmmGO47bbbmDJlCh//+Md55plnCIfD1NbW9skKmw2nnXYaN954I5WVlZx//vkj+tksDk+Ojj/Pf9zG69ry1QM3tjhkOeyFRrY7gpEmadPo7OzkzDPP5Ne//jVf/vKXufHGGznhhBP4+9//Tm1tLccffzxg1MhYt24dU6dO5eSTT6a1tZV77rkn445i3rx5aRX9MuF0Olm2bBk///nP2bx5M//4xz9y8TEtDiP2KGUk847GtL4ZhS0+Glg2jVGmsLCQ2267jVtvvZVEIkFnZyfjx48HjCSBSZxOJxMmTOCRRx5h1apVHHPMMdx6660pO0VPPvaxjxGLxbjnnntSx95++21eeumltHZf/epXueWWWygtLc3Nh7M4rPhQGZ967fO/l/PxWut2wk2F7Fw7cCZki5HFEhpjgCVLlrBo0SIeeughvvGNb/DNb36T1atXo2laWrtjjjmGyspKvF4vxxxzDPv3788oNIQQ/P3vf+eZZ55h2rRpzJs3j5tuuolx48altZs3bx6XXXZZTj+bxeGDRrfHlCuwO+fjtXR0oktBW7NlizuYDJoa/VDHSo1uYZEdB3pfXPGbWbztdQJwTtUPufnUT47U1DKybncb5/72DX54zgIuPmJSTsc6HOkvNbq107CwsBgR9oliFPOREldzb9Owt27hHscvcLV/mPOxLLqxhIaFhcUBo2o6dRThVPIAKO4cvFTvgaLFQpxsW09104s5H8uim8NWaHzU1XIWFkPhQO+HuKqBksClGIWXvNF9IzGtAWktXkyX9CLN2uQWB4fDUmi43W7a2toswWFhgSEw2tracLvdw75GItjGBKWeIs0I8NtUdOxITa//MTWdhbF7ebriczkfy6KbwzJOo6amhv3799PS0jLaU7GwGBO43W5qamqG3T8mHbQLNzWOYoi3E9dyn3uqsOE17nPczkuh7wHzcz6ehcFhKTQcDgdTpkwZ7WlYWHxkiCkeIsKGw10F8Z1UB3Jv05DxKMfb3iPY+gT9VI62yAGHpXrKwsJiZIklEqAk8NoNQ7g30ZTzMfeUHcNafTal0b05H8uim6yEhhDiNCHENiHEDiHEDRnOCyHEbeb5jUKIpYP1FUKUCCGeEUJsN38Xm8dLhRAvCCGCQog7eo2zTAjxvnmt24QQYvgf3cLCYqTQGjcihE55tB2Ad/OPzPmYMVXn/Ph3+d/Cb+R8LItuBhUaQggb8GvgdGAucKEQYm6vZqcDM8yfq4HfZNH3BuA5KeUM4DnzPUAUuBH4Wobp/Ma8fnKs07L6lBYWFjnFbzO8plw+I5WIqqs5H3NS/T/5s+OHJGKW99TBJJudxkpgh5Ryl5QyDjwEnNWrzVnAA9LgTaBICFE9SN+zgPvN1/cDZwNIKUNSylcxhEcK83oFUso3pOH29ECyj4WFxejiN9VS7rwJAEwM5z73lK5qrLZt5vzgAzkfy6KbbITGeKCn0/V+81g2bQbqWymlbAAwf1cwMOPN/gPNAwAhxNVCiHVCiHWWh5SFRe4JRwMA5Dm9CAlurS3nY24sO50/qKcyP/F+zsey6CYboZHJbtA7wKG/Ntn0zZasryWlvFtKuVxKuby8vHyYw1lYWGSLo/5VAIqirYCdd90DFwEbCaKJBD8uquZi5cbBG1uMGNkIjf3AhB7va4DeaSX7azNQ3yZT5ZRUPTVnMY+ejuSZ5mFhYTEKtLkNRYErbxwCO6rMvU1jfMNvcZU/T8xhlZY9mGQjNN4GZgghpgghnMAFwBO92jwBfNb0oloFdJoqp4H6PgEk83JfBjw+0CTM6wWEEKtMr6nPDtbHwsLi4NDuKALAm1eBW2pMieX+QR42FQ2fsv0HNZH7YEILg0GD+6SUqhDiWuApwAb8Xkq5WQixxjx/F/AkcAawAwgDnxuor3npnwCPCCGuBPYCn0mOKYSoBQoApxDibOAUKeUW4IvAfYAH+Lf5Y2FhMcpEY10A+BxuHFLilIGcj7kubzXEX6bY1kI4EqLA4cz5mBZZRoRLKZ/EEAw9j93V47UErsm2r3m8jX7COKWUk/s5vg4rX4CFxZijqPllAPL1GFGliPccU3M+Zlw3dhf3yBP5LB4Kcj6iBVgR4RYWFiNAo8twZPTllSKEHZ3c2zSWdRjaaWGLEo5rg7S2GCksoWFhYXHAtDmLAcj3FlOghZisbc/5mAFhqKOW298j0bIj5+NZGFhCw8LC4oBR44ZNI9/pwSUlNhnL+Zhr3SsBsNnCxMO5t6FYGByWWW4tLCxGlsqO18ABHoeHgLOaei33aeESpk1jnZhEW/6snI9nYWDtNCwsLA6YOpcRQuWyubAJB/pBiNNYHfmP8UKJEonnfjwLA0toWFhYHDCt9iLQ7ShCoSTRzgRyX+7VLzwAFNna8NW9mvPxLAwsoWFhYXHgqJ0oprbbCUiRe2+mtfYFAEQVHS3SlfPxLAwsoWFhYXHAjA9voFiPANDlmcQecp/zTZWGTSOhwJaio3I+noWBJTQsLCwOmH2OamJmeJ1DOEDk3sZwrPZy6rU/Zu00DhaW0LCwsDhgOhQvMWEIjZJYM6WiHU3NreBoFXmp1zUN/5fTsSy6sYTGYYKuS1RNH+1pWHxEEXoAl1m9wCEEmpAk1EROx3yX7lQloYQVp3GwsITGYcLdr+zi47dZHiYWuaFK3cVEtRGAcN50OoWHRI7DwDS6hdIbeatyOpZFN5bQOEwI7X2PkvYNoz0Ni48oDbZSOm1G/im7Ytg0VG249dYGR+o6RyobcEjjERa2dhoHDUtoHCZ8defneNBmVTizyA1BbETshsdUSbQeu0igBnNXalnVdVqEj3zFsGvMDVhVEg4WltA4TPhD0bU8oy1Dt+waFjlAIYIX47vlEA40AfEc2jTiGmynCqezGoDIQYhAtzCwhMZhwmP207kq8VXieu5UBhaHLz78TIvtBiBWMAOAsCN3FS7iqo4QKm5bHkLaed0xL2djWaRjJSw8TJgVehuEQlzVcDtsoz0di48YXYqTdsdMAJw2BwCRHJZgVbsamW7bQ1HcQZ3wEpehnI1lkY4lNA4Tfhq5CVzQHjgXPONHezoWHzE0oRF3VQFQHG4wjrVth/FVORkvLm34cVNiz6cw3sxk/e2cjGPRF0s9dZjwdXkVd8njiAvXaE/F4iOGqqsgdPIx8k3ZbcZ3LKzlzqYRdRTSJrxIbxUenHSJ3KditzCwdhqHAaqm83hBHEdBB2co3tGejsVHjGjCyDk1JbQNAK1wKrRBwFOZszHjqg5CxaW4UN0T+SDWnrOxLNKxdhqHAcFQkImO7djsAeKJ3FdUszi8CMQModFZsBAAt93YaUTV3Nk0bE0b8SlBCqOteGw+pBI1BIlFzslKaAghThNCbBNC7BBC3JDhvBBC3Gae3yiEWDpYXyFEiRDiGSHEdvN3cY9z3zTbbxNCnNrj+IVCiPfNMf4jhCgb/kc/fAi3NzDbsd1IItf8wWhPx+IjRsDcaeiecQDkR5oAsLVuzNmYUVsBMSGwuwqpSvgpsbUQCXTkbDyLbgYVGkIIG/Br4HRgLnChEGJur2anAzPMn6uB32TR9wbgOSnlDOA58z3m+QuAecBpwJ1CCJsQwg78CjhBSrkQ2AhcO8zPfVjht5XyDjVIIQg6iwfvYGExBALhTgDyzVgJh8MojhTJoXd3p7sSXYAtbxxeez4hBSJxaxd9MMhmp7ES2CGl3CWljAMPAWf1anMW8IA0eBMoEkJUD9L3LOB+8/X9wNk9jj8kpYxJKXcDO8zrCPPHJ4QQQAFQP+RPfBgSVBX8imG+6rLnDdLawmJohLoMb6ma0HYA7IWTAejw1uRuzEQUMMrLiqI5qAp0SMvJ42CQjdAYD2m1G/ebx7JpM1DfSillA4D5u2Kga0kpE8AXgfcxhMVc4HeZJiyEuFoIsU4Isa6lJXepDA4VEu178NqM1WA0atUdsBhZAooRlxEtWQyAy268j+fQe8q79xkACuIdFDiNIMI2c8djkVuyERqZfNl6bzz7a5NN36zGE0I4MITGEmAchnrqm5kuIKW8W0q5XEq5vLw89xXExjqu+rdQbGEA7A1vjPJsLHrSGGpEl4e2ATekG/O3+Yz1oC/mB8DT/FbOxmx3lQLg8hRTmTDGC9Wtzdl4Ft1kIzT2AxN6vK+hr1qovzYD9W0yVViYv5sHudZiACnlTimlBB4BrBqPWbCt6CgiivGvbjeNlRajz/7OFk5+9DTufOvx0Z7KAREOtQFQYKYqdzkNFWhEceZszBa3IaBc3gryPSUAtOfQW8uim2yExtvADCHEFCGEE8NI/USvNk8AnzW9qFYBnabKaaC+TwCXma8vAx7vcfwCIYRLCDEFw7j+FlAHzBVCJLcOJwNbh/h5D0ua4t3J3Pz2wlGciUVP6vZtBKHRUfv8aE/lgFA7jZxT5aG9ADjzjfiMNu+knI0ZVQ2bhsfpJr98DgCNDsvJ42AwaHCflFIVQlwLPAXYgN9LKTcLIdaY5+8CngTOwDBah4HPDdTXvPRPgEeEEFcCe4HPmH02CyEeAbYAKnCNlFID6oUQ3wdeFkIkgD3A5SPwN/jI42jrVhMkYpbed6wQMZNHxvVDO0Nrp9NY6cvyZJyGscOIabnzZqrY/y8AfFqMYq+xEOq06oQfFLKKCJdSPokhGHoeu6vHawlck21f83gbcGI/fX4I/DDD8buAu/r2sBiIitbnwWe8Lmx6GcMD2mK0CQrj4arED+1o5qD525Nn5JnySkNNVdz2Frla1zW6x0EcPJ5Cyk19iav5VeCSnIxn0Y0VEX4Y8GzBSanXTb4pozgTi56ETZeQeu+h/T9JRFoBKBRG7imP00hVE1I8ORuzyW248+Z5iigvMtRh7VjZmw8GltA4DGjTutUfbXZL7ztmaHkfACV+iEcyB3YBUBj3A+DxGC6wTd6JORsyZto0vE43hb4SpG6jzlSTWeQWS2gcBlSE30m91mOH+APqI0TIZqzIdS08yjM5MDpclaArOEqMHZPX4QZyG6cxpemfAHjsLmw2BXQPYTU4SC+LkcASGocB02LdhvDKtldGcSYWPekyU7p4Yg2jPJMDI4RESidOj2E4c5mG8Cp/7uIm6tzGLsZlpmEfpwcpCmzI2XgW3VhC4zDgMcexqdf7vTNGcSYWPYmqhnfRPs/k0Z3IASKjrTglOJP1NMyUNQGbL2dj1jmNOA2nzRBQQnppNyPTLXKLVU/jMCAou9UEbfai0ZuIRRreFmMHqOmHdlCaI7KPShnApRi2M0UoIG00uHOXe0rVjMy6yZ1GmzIJh81KWHgwsHYahwET+RA3hp5ZHOLunR8l/I4iABQ1MLoTOUBaXeU0aZU43T12FtKGlsP4kzn+p4FuoWHHS0Ie2rahQwVLaHzEiUXD1Nj2UiQNd8SJXW+O8owskvidRjmYQrVulGdyYESlJCq92O3dios8GWd8cF3OxtzjnAx0q6eWJrbh1ptyNp5FN5bQ+IgTTCi8wgycHiOlwy739FGekUWSuGnT2OuYMEjLMY7aQQnpqiGJg05bQc6GrLNXgFRS9pO4rYqgYtUJPxhYQuMjTjCuodvi5DlLQArabFbuqbFCSbvhXRQfNPHz2MaRaGW2TN8tRcij0VGVszGlHkb0MMm2eJegCT2nqUssDCyh8REn0rafPFsHxUJBwY5TtWwaY4V2h6GecuqHds6kDnsBm5mXdkxgR5O5i9OYG3mNvB4OBF6zuFggfmjbhw4FLKHxEUdr2YFUYhQLiVdPMDny7mhPycKk3WnUhCjUmwdpObaJoxET6aqoaq2NiZH3czZmrX0CmuhOU7IyYNjquuKHtgA+FLCExkecvYULiSoK3uJZJISX7c5poz0lC5OEZqTCqFcO7UJhQg9RLdOzJ8eFj44c2jQalGJUkd89nhl/1GVlus05ltD4iNMS8gNQ4i1Cx0OH8I7uhCxSjOtaD0D8ULffyjALtZ1ph7qUMpptucsFJWQYe48EhaFSox5bh1XOOOdYQuMjjmwytu2lLi9OBC7Nyj01Vmh2GDsMpwyN8kwOjIgQrHccnXZMEXb0HNo0ZqmbqFTbUu8LXWadcHORZJE7rIjwjzj2jvcAKPcVUar50240i9Gl3V4EKng4dI23CT2BFDpqr13FlMQeEmZakVywR6nAbut+fC1ofwOADv+enI1pYWDtNMYIm1s3o+kjf5OtLTRWgKV55YRsFey0HeIxAR8hdNOm4T+EVYbJsqsTehnzw0oJHUpezsZtU/KI20tT75WS+QC0J6I5G9PCwBIaY4D9gf1c8K8LeLXu1RG/dsD0JilwFqDa8gmY1eIsRp/xUaPysS5ytyLPNRHVyAG1KLo17bjfMY4OkbuEhTYZwSl6PL4qlyF1Bx3aoZ3H61DAEhpjgI6oYWfoyEGti9IuI4As35mPR+q4sAyFY4VmuxGnIcShWyM8udNYX3hG2nGbsCPJ3ecaL/YzLrY/9d7rsiE1D53RzgF6WYwEltAYAyRXa/EcrJLyojsAQ2hUJhqpkod27YaPEh3CUN9IIVHV3BmNc0nyuyt7qIoAZkQ/oEzmLhdUkygg4hqfel/auZmZspWYWUXQIndYQmMMkLzxkqu2keRFz0qEtOOyuehyTWafKB28k8XBQXb/vyPqoZn+IvndnaTWpx0POqoJCHdOxpRSEsJOwlmROmYvriGgltBxiKdkORTISmgIIU4TQmwTQuwQQtyQ4bwQQtxmnt8ohFg6WF8hRIkQ4hkhxHbzd3GPc980228TQpza47hTCHG3EOJDIcQHQohPD/+jjx0aOgwj4of19YO0HDpRLYhNeBFCoNuLiAprnTBWqFJrU69D6qGpi4+axvy5oY1px7tcEwiL3DhnqpqGTYnjFt0BLs7Cauq0CQRymI7dwmDQJ4gQwgb8GjgdmAtcKISY26vZ6cAM8+dq4DdZ9L0BeE5KOQN4znyPef4CYB5wGnCneR2AbwPNUsqZ5vVeGsZnHnP4W4wtdcK/e8SvXaNtoUAaN5dPJnCKyIiPYTE8WpXu5JGhQzS+IJIwvk9bqi5MO24XdsiRrSYeCeEWYarDtaljBR4HQnMRVi2bXa7JZtm5EtghpdwlpYwDDwFn9WpzFvCANHgTKBJCVA/S9yzgfvP1/cDZPY4/JKWMSSl3AzvM6wBcAfwYQEqpSylbh/ZxxyadilFIJpQDbaGXVgpNV97KWB1eEUTq+oiPYzF0AsKVeh0JHprxM2FTaDjcvWwawY04yY37a1wqRIRC1DcldazAZeNS8QoJ1QpezTXZPKXGA/t6vN9vHsumzUB9K6U0rLLm76SCMmMfIUSR+f5/hBDvCCEeFUJUZpqwEOJqIcQ6IcS6lpaWLD7i6BIyDeBKtHFEryulZL2YRMxj5JsK+GbSJVzENUtojAUE3SqpoCN37qm5JGCm7ZiQSFetBtwTiQuBlCNvYwjrgADp6U69LhSFD/T5RIWGegirqP69+99sa9822tMYkGyERqbMOL2/Cf21yaZvtuPZgRrgNSnlUuAN4NZMF5BS3i2lXC6lXF5ePvaTwYVjQQBaexj2RoKYqoMSwWs3E7s5i9GFTsKSGWOCIlpx6cbXPZw4NG0agZCRan9yeEfa8ZB3MghQ5cg/wIMhw63W2+tR8qFjhTGnQzg9+vdf/R73rLtztKcxINkIjf1AzzDiGqC3xba/NgP1bTJVWJi/kyGl/fVpA8LA383jjwJL+SgQMGwZCX1kt/MBfwt5tg7KdMMzx6fHEEInHLb0vmOBTuFB0Q0VVSx0aJYqDZj2stbJn0k77lQMM2QsMfJeYTG/YQMsD+1NO15kN1KlH8rp0eNamM5d/x7taQxINkLjbWCGEGKKEMKJYaR+olebJ4DPml5Uq4BOU+U0UN8ngMvM15cBj/c4foEQwiWEmIJhXH9LGvvcfwDHm+1OBLYM7eOOTdqdhuOYpgZH9LrhznYSNpVijK1FedSQ19HgoV2/4aNCHIFNGkIj0TXynnMHg5CZtsPrSa8IOaVzAwCR2Mh+pwECDmPnrBakp/k/P/IkcOimR1d1lYQQbHRPHu2pDMigQkNKqQLXAk8BW4FHpJSbhRBrhBBrzGZPArswjNb3AF8aqK/Z5yfAyUKI7cDJ5nvM849gCIT/ANdIKZN5Fq4HbhJCbAQuBb56AJ99zBA0d9mu+MiuNlscpSSEwFa2BIBIoeG4FlRyk+voR09u5a3dVmXArBEqKsYD0O8dN8qTGR7hUDM2XaEskW6PC5lG6kgOghaDZkp0uy+9nOy+guMA6IwfmlHh4bgZrzXGszZk5UgtpXwSQzD0PHZXj9cSuCbbvubxNozdQqY+PwR+mOH4HuDYbOZ8KKHFjQdts2NkbRrNphtnsdtIG21zFwEQ1EY+15GUkrJN57Gn7hhWXjW2dbJjASklQiQYJwPsAqLaoWm8jUVaKZAJCmPpmQZivikQhkgOPPUiYcNDyiPSbRqB0tXg/79DdqfRGTI+ly5CxLQYLptrkB6jgxXpNQZwRI0cOlFG9gaL1hl5p8oV4+bKN20b0dDIe5RFExp3VMCzdsvlMRtiahwpBJpiqCb1wP5BeoxNOh0FtGulJGpWpR13KcajJRLLgdtt6yYASiLpO/NKj+GB1hY+NHe7XT3crpuCY9fGZQmNMUCbowgAu+Yf0esGg8aDqNRl3EyFUcOWoXbUjug4AF1dzUgBI6/B/mgSThhqG5ti7AJlcN9AzccsETWCLp143ekpQ2r8Rh2XaA5sNX7T1VYUTkk7vspvZIlu9deN+JgHg44elQhr/ZbQsBiAiGmycer+Eb3udp9RN7m0fDoAeslsAPze6hEdB7q31gn90MyhdLAJRQ3xqjiMoLiW/OmjOZ1ho0ZbqJRBvHp69cFYgfHdiyqOER8zGRTp8aW706s1R2PTbbTEsst60BqM0RkeO4kiA7Fw6vWujrHrGGEJjbGAbviVtyv5I3rZpG63wmeoQJwu43c4BwFXHWZyOq86sgGKH1XCAWMlOdE0IMe0sfPwGgpavINJsh2Pkj5/LW8iAGEx8kIjETUSQXh7Pb0clbNIaD7aE+EMvfpy7b1P8Jv77xq84UEi2NntQtzYtGkUZzIwltAYA9hMtZQ2wrl68tpfB6DEdIf0SSOATA2O/Cqmy1wl+RXPiF/7o0jQTOane4yHqy14aJYp9dsLWKstxF2U7smUTCYYjY+8wtLd/j4A+Yn0IL5it4Jdc9AVGTwlS3soTrv7ZzTovyIaHhtK1WgPO0a7/8NRnMnAWEJjDBBQjGp6QmZvNLzq6av4zYbfDNgmYXpl5TuNHUxBwnBFFDn4QsaaDU9qv5KbdNgfNcJmJLjLY3jM2UOHpiE8rkeRugOPM90Rs7zTqOQn/SNf36LZawhad2FN2vFSOllMHbEsBPC6df+hwRPhBW8er+4ZGyns2tzdWZF22QtGcSYDYwmNMYCKsbXXRSKrXD261FnftJ4POwZ++G9yzwDpwGkzhJK92NCbt+bPOMAZ9yVkqgQS8tBMh3GwiUYM1aHPZewC6/J7J44+RFA7mSsasSnp2X/0QiPwLmQfWZUrdNs0vJ7itOMFpVXsS0ykQ9gydUtj+7a/ACAVjb9tHRvJsoNx4x7SEwX4Y2NDkGXCEhqjjKZrSKEidAVdiKxy9TSHm0noCcLqwLrbqB5Ckd2BfB4zXiOcgzo1LS5jxVyUw2ptHyW0DiNX0zgzSj+uH5rCVpMRZsu+6k6Rb2QCCttGfucpo4bLeJ49fXfjdntokhMIycGdMf5CBYq0Y5cu2poeHPE5DgfZaX4nEhI1Nna96SyhMcokK5/lmyEasSwquNUFDZfCna0D624rY5sp7vEw8gpjEJEDVUjQ3Gl0irEZkDTW6HKWACBKZqBIgTt8aNo0wsLGf5RT+xz3mBuPeDzU59yBkt9lGInzbH13FKWKQlwfOGFhMKbil9uocM1msSij2bmXpsbRVw8mTGG4RGsnMoajwi2hMcokhYamGvWik5XQBmK/GQgWjw68hdVlFG8PdVcehmuvq2vkbRqOlncACOSoWttHjZDpVeTMq8ItNVzRseti2R9SSjRiOJS+C4WiYC0Ato6RT/Nd7zHiM3zewj7nTlPXoRInoffvjbbhrX+juOpZXjqPVbMuoM1u46HtH4z4PIdKo8fYna13fZqwIlPPhrGGJTRGmeQXo9BMtxDXBldT7G4ztrEebWBV0E7bOPyOSan3nnwjPqMub/aw5joQIb3bLmMxOPGo4aTgE5KQzGOPb+T/J7nGUKlJFmp9jd2KGXgXcJWM+LhBxYGUNpwOZ59zH7pWG+MOkB593a5/g4DTp6/mvEVng1R4ds8LIz7PoRJVDaeCcp9xn7aGx6ZdwxIao0xSaHSpRpBXODH4TmNXey0A7baBEw9qxHD0UBf53Eb7XKxfGtyGJ4tNiVqVAbPA3mZ4FxVEWhDSPuDKeKySLPU6Q+sbge3MM1xwQ7aRd8G2xVqxycyPrqBvHjBwptu/q26EtHPEpKMo9hRRTTXRyH/Qc5CTbSg4Qzvx6RqLbMbc63rEbYwlLKExyiSFxiRp+Ir3jArtj33mKjUqBn44l8p6qtXu1Uqe3RAgzvDIG9lSajYhCOeghsJHjXavUcDSXjSJAhkjLzo2HxADkVSlvljwmT7nPOaTRU2MfAxEUeRDCvoxdleb2tH+amrEVI1WdStljhmphIDHeCbS6Izz+pbXR3yuQ0FP+MmTKrOFcS/VNo+8u/JIYAmNUSbpZtcpjYdIshLaQDRHTP23UEkMEEmcUHS8Pf7FbocTISW+8Mh/GYs630m97swyjcPhTMiMzXF7SyiQUdzqoVcjPLlQcGfIxpoXMz6P2z/ytoL9romEZOY4hiNC6wHwRzInznx//VPYXPtZXtidtuWM1V8B4C+1G0Z0nkOl3jWOZlmNbdZnAaiNZhfZfrCxhMYo0xUzvEuqhbE1DocGfnjEtTiBRCuF5lY6lOjfO6VNuOnMm5d6LxQFKR3U+uYc6LT70PPr7R/FCFspJY9vqCOaGF1Vw2BoZtSyVxE0KDXUuqYM0mPskRQaC+J9HSucBcYiyO8e+XLLIWFHI7PaK1B1CgBNYX/G8y/ufgMp4JRpR6eOLauZi02r4L22N0d8rkMhrkcRuJhYXIbU7dSP0WJpltAYZbrMnUXIOdV4P4idoj5YjwTGxw13w/5iNVRNBxHH3VunLB3E5MjbHPY7u5Mghtp2jvj1s+XDxg5ueu57PPbW2lGbQzZ4O40I+jwZR8GOJg89m0ZUNdRTNYm++cZc3kKkFIQG+T4PB4fajkdmXhR4yxcA3bVkevOfaBSkjdUzTkg7Pk3XyUuMbuoOb7yOCXo7VR6NIlXQ1TE2C5NaQmOUCQUNm8NMaaT4CA1iCE/GaGyIHAVAsJ/cPqGQH6GoVMfSXTld6HhiI586Ot7DVbgrB8GD2fJB8/uoZWvZu3PgFCujTbN3MgCe/ArK9U4KDsFEj0mhsanqoj7nHEiEVNBzUK+7KL6XcXpmNW6N23BlbuvK/Pdsjm+lyDYNjz19MZUvBfERzv02VHQZoUjGKCnIY6IWpitm7TQsMtBlpo5OeA2Xy2igYaDmKaFRHjN04h2hzLpbf8gQQh5bultikR4hPwfRpuXRrd1j09cV8mARadwAwL4cZPIdSYKmV5vPk0+xHsIxSEDaWCS5y/U5+qqK7HqEfJkgL7h9xMetc1TTrNRkPDc+vg+PrhM0I+57omsaHvse5up9H3utvkW0jnLetGZbITvtC3C63GzSl9OgDJ4OZTSwhMYoEzRXa+PtxkMuERxYaOwP7scu4UZpVNDt6GdF1aEbbiSJ4kVpx9tEBbvcI1+7IdQj308gNno2jTZT87Y7Bym5RxLFzC3kszvZ45hFnb1ykB5jj4CZP2t6pK+x2+H2EZVu2l0j/7miCBIisyHcNW4+CS2P5gwBhy2drYRtUOLo29dt8yJFDqoMDgGdGHZz3h5bMRF9bFbBtITGKBMJtiCkQCmcBUCHZ+ACSfu79lOYsPOKOM5or2dejXSaD26fs7dO2Umin7Kym9s2c9rfTqMz1pl2PK7F+cX6X9AS7r9MbKOtCEUzdxhN6wb8DLmk3swOGhjj3kiFwa3YpcRpt2NTnGhZ5BwbawQixvekQuurgnI4nMSkm1AOVu9O2UVBPxmhi4pKiGkF+DOk49kZMOZpr1zd59zkeB1CUYklRs/zzyG7mJ4wtADL9QY0ogM6uowWWQkNIcRpQohtQogdQogbMpwXQojbzPMbhRBLB+srhCgRQjwjhNhu/i7uce6bZvttQog+iW2EEE8IIcZulZIhkAg34JMaNYqhngirA6929nTtpyk+la0l5wDg72dVHzGLuJT30osWygi+ROZI8q1tW6kL1rEvkK6+2tK2hT9s+gM3v3lzv1l4NRlD0Y1UKB05yGyaLZ1mUJdb3z1qc8iGes8UdOkAIahWm/HJQ6+udZdufBdaJn26zzmbInBKQB15m4ZPb2OSmtkuV+BxUKDrxKJ9v+N7/MYufnx+RZ9zeaYLdFtw9P4PCTQKzSzRU0yhONBCbbQYVGgIIWzAr4HTgbnAhUKI3nmcTwdmmD9XA7/Jou8NwHNSyhnAc+Z7zPMXAPOA04A7zesk5/MpPkKlqDucRQS1AlwlRippGRw4cVp9qA49UcyCAmO3EApkvnkCpkHd5/SlHa/Q/eRpmQ1sydQLvaNpkzuPF/e9yHN7n8vYt0hvoFo3xhxNoZHf8goAjTZbVmnmR4ugcKJL40FVpAWxcegFRCZjjApcmT2kaminIFI74uO2KIXUuTK7jdsUwRK5Hxnvq7bt2vsiAJNtfT2vImWrjGvHRkdFpUudhCLZnWc4uOwdd5kxn0NRaAArgR1Syl1SyjjwEHBWrzZnAQ9IgzeBIiFE9SB9zwLuN1/fD5zd4/hDUsqYlHI3sMO8DkKIPOC/gR8M/aOOTSJaDF26KHUpOKRERvrPJxWIBwgmuviC/hrHSkOPHO3KbNRudRlpSZxl6fJ9n30GtY4J/V4foDOerp5Kvq/wVPDjtT/OmNcnrCg4hSEsIrHRM+q2mKm4dUWjvZ8Ar7GAI96KwxRqe71LaBd5ozyjoRMJNKBIqA5n3tW1yDLanCMfp6EKHdVW1O/5D8Ui2m19nTEazIy7k0om9TlXYC6u2iOj891NeqK5zO/v+DzDFrS3H5vlaJKN0BgP9Hwy7TePZdNmoL6VUsoGAPN3cs84UJ//AX5OeizZIY0WbaFYRvEWVxDXPbR4Mz/QodtzKhKvwDnzVJCCFldZxrYBMyq70JW+0xDChUZm/XnQTPnQe6eRfH/z6ptpjbbyq3d+1bevsBN1mNlH297q9zPkmroeD5PtLWMzDQNASXQnJdJ4iDkUB/IQTPQYiQVxSUmeyLxLCpFPnb6PNc+uYc2za3jog4dGZFwbEYoH8DaL2ccTy2Dz2C18SCmYNL5vwatxUePhHGgenWy3yUDJ6VEjxmmRNBwl9jZu7bfPaJGN0BAZjvXe9/fXJpu+WY0nhFgMTJdS/n2Q/gghrhZCrBNCrGtpGXvbu57osVYmy3aKXBIpHcS0/tUUdaYq6vfRi6iaMB2puwj1kz7Z0WI8uIv09PPlWifefrwyUuqpXr71yferqldx0eyLeGTbI2xp6xV4JOJ4zBoRrY7Sfj9DrolqodQ3bG/dhlGbx2DsdU2kUxh/p3HxOuyHoHqqy+4lpBeQqDki4/nq8ETyKaQz2smm1k385YO/jNDIcSbF+q8/Ml7E0EWsTxLI9lgbQsvD6+y7Cyk0C5T5R2mXnMzjlWc6qVR7Pbh0aAiMvZT52QiN/UDP5W8N0PuT9NdmoL5NpgoL83dS0d5fnyOBZUKIWuBVYKYQ4sVME5ZS3i2lXC6lXF5ePvLb45Gk3VHIe+psiopKKZQx7JH+A+/2m/aOImcVhXYVn66j9VNQKWhmmi3ypZfErFRbccnMN0YyULC391RnrJN8Zz42xcbl8y5HItnYsjF1PqEnQOjMiu1C6g7abaNn06iIvk+lakiNnfHRdaEciAgOVDMVRoEeRhNyTNtgMhFWw6A78Dgy11B5wP8MP2sv5MEzH+S0yafRHj1wI7OUkoQiqMtb3m+bJWZak9475rzgOibomRdZzqolADQfhO+ulJLn9j6Xdp8FzXRCzWWGV6Rn+tFEEmXsSoy9+J1shMbbwAwhxBQhhBPDSP1ErzZPAJ81vahWAZ2mymmgvk8Al5mvLwMe73H8AiGESwgxBcO4/paU8jdSynFSysnA0cCHUsrjh/GZRwxN1/o8YIdKXI+jSg9el4MyGUKJ9+8qWhesw6ML7pB34HMKqmUQPZpZ51nvHgdAUVG6JrHWu5RmpShjn/agsSvrnezNH+tE6F6aA1GK3EbfYI/spYGo+dpZhpAO4uro+Sm02NzY1TKktLF/DNs0XFo7PjN1SH3hCqQgq1K/Ywk9VM842YVPzSwMfuj6Cs8VGxlwS9wldMY6UfUD+4zJsri6vajfNnUlRv6p3jvmVkXgkr5MXSj1GDuNroOw03i17lW+8sJXeGJn92M0meTT5zCcCiryXajB2WzveoeO6Nj6Hg8qNKSUKnAt8BSwFXhESrlZCLFGCLHGbPYksAvDaH0P8KWB+pp9fgKcLITYDpxsvsc8/wiwBfgPcI2U/SSaGWVue/c2jn7oaE7+68lc98J1/GPnP4Z+EdXPNNoQisIuOZlG17i00++3vM+uTkM3Xxesw6flsS9vEW5PPju0KTS5+roPAoRNf/MiT/pNYlNc/erP2wKGEb6pPX3rX9fZRkfAzhO/+n807/oAp+JMuyH95hc+UriQShmgNPhetp9+xGkXDoKOaTgSHtoDY9fttljdT5VuLBBcprtnXD206oTHtTD5MoHXkfkxssW5gH12w85V7CwCwB/zH9CYSfVtmdb/ddx5hqG7PZK+oGtWBH7Xgox9ys1U66L13QOa32BousbP1/0CgG0t3YG8XZ3G4m9SyIigL/Q4+GZ4KzoqT+5+MqdzGipZ1eaUUj6JIRh6Hrurx2sJXJNtX/N4G3BiP31+CPxwgPnUAvOzmHpO2RfYR7GrmCXlS3ir8S1e3P8iZ049EyEymWUyo+sh5puaOUU6SfSo6Z3QElz2n8tI6AmmFk6lLdpGV2Iy7064jPNsCkgPUS2zT0BJ59vYBLjt6frbSrWVJjKrbYKmPrWjV7r1rmgHhbrGeYkn+PNfdNwzfGkeVH5zp+FxeIjIAprtI1+tLVt0IhS48pkWbCMQGt0EdAOxz16dKiRUZdbSCEWDfVykxzKd9jz2atNwFmUOSJ0jd1Ee9rHulSbCb94KFdAWaaPMk9l5Ixtipiv5+Ghtv21q7MZupr59P8uqFgOGS6suuih0ZP5uluaXIiQEDnAnNBhP7HyCnZ1GipOGuvdTx5M7HK/NyGQghGCWBuO0Qh7f8TgXz7k4p/MaClZE+AEQSoSYkD+Bnx73Uy6bdxmqrvabdbbfayh2nhdGxs3xtOPqEXjXFe8ioSc4ruY4yjxlBOIBosFqqgoMt7xJdKIkMpeEDGDDkSHHTkW8CdlPYraoZuhVu3rVKY9Em1gtd7LzxLt5yH0BnSEbdV3dKolQm7GinxjcRohiOnKQ2TQbpJQoSpB5kQ/Zr8+hXhm7X+8YAlUx3Gx95kIhNIrpV4ZDXIuC7sTrzJyV4CuROzh232/47ycb8CXMBUnswFQtycqWTcVH9dtmWtxYwbe1de80/ZEOEDqLYrUZ+3iLStF1N43O3DlxhBNh7nj3DsrVcibHE2jR7p1GwEx7E606JnXszppbCEVPYWv7Vra1j3yt9eEydu+qQ4BgPIjPYawMC5yGTnQoNg4pJSoxFMW4RpXeiejhSphczZ825TR+d+rvePyYP/Fe8H5WBJ8HYIbehOzH9bDOWUWMvka9vUVHowqB3is9ui514uYWPZRI/wwBNDaq85i44Gjuv3wpBZpKa3u3wb5LNYSQ3VuFEzu6NjqpD8KJMJoQaJ5JRGzTCdPV53OOFdwyQKE01HodpSsBCPdTwnSsIhKtLBO1OGyZ5/1A6Vf4hX4h559yLG/NvgWA9siBGcNDZnoQxVR3ZcI56SQAGmR3/rHaNsOL3+ebmLmPTUHqLsJq7r67f9r6J5ojzfy0aRNhrYg9PWJYkhU7853dyR+PmFLC/rrZ2IWdx3Y8lrN5DZVD61s6xggmguQ5jdVigcs0pA0hFbRhFNSZrRseUJvtS2i1d3s7JYVGSiBFndyvnYqj0shTtdZ+LP4MVdMAYloURfY950jqz7V0/XkoEUIKsElJTPWnjkspCethatXJFBYUUlXgZIFsIBHv3hF1COOaonQeM9W9lCRGx5bQbBbeSRQtZKKiINFoi4zNHFT5so1xCWOl6TID0Q61MrkJmSB/gDog/3Xp+fzu+su45oTpHB/bAEBj54Gl5Q+Z2ZtLE/3vWCpLJiClQkOP/31twBBWjnHHZewjhGCC7CIvmJsaFoF4gN9v+j2T3Cu5KfhdOuQ4oj0WfHG/EZ9RHe32hjymNMjTtptY4prGv3b9a8AqnQcTS2gcAMF4kDyHITQKnYXAwAXte5NUZc1XjS+MTbjSivEkhUa+09gx1KsF/ES9CN8kI7WX0+ZF6ydxW2V0M+MyxGNUmOkVYr1yXCXdbV0JDzG01Bc0okaQaIwTCew2BbfHxzvaQtp7JKJLppPId3rpsI2n7SC4Le7q3MVZj51Fa6RbPdcSMD5voauAk6KGvrgxNPYiagGaRDENbiMdfnnEWAVHuwbOcDzWCAkbb4hV/Z4vz3dRkW98T/ILJqJIaAgcWNxUxKxsWRnpv6Z6mUchP+6l0d9tM0hGVk/Ir+q3nyKd5Mp36sOODwklQjTXL6F42gqmijBKj3Q+IfN+zOuRYHTWzJnUUcEkdS4dsQ5erns5R7MbGpbQOAACicAB7TSSUaAv5BluiVO1Ohyyu39Xwnid7zAews3+ACCpLDB2EDO0BqRQ+wQxAQQUB2QoiVkWN27aUDRdf56ctz1hjJVMHZJUt31WfyHVNi5Kicluf3fZbHiclIf3E3SMIyiy8q84ILa2bWVX5y62d3TXa2hvMlaJ0wNbaagw/qb1obEXHAWgColuClcvhnNgJHpo2TQ0YjhEdlls9akfQ9XyaDpAu02XmTq8s+KYftuUeOwcnWiiJdK94w3UvwHAzAHEQpOYQostN+lcajtrAfhM54tctLCQyZqfeA+HFL/DWHQ6q5ekjrk9Pn4z7ses7ziRMk8Z/9r1r5zMbahYQmOYaLpGRI2kHuhJFdJwhIbTbvpma350+t9pzNxyO5tcn6fEa+hqpyWMrX440df43qgU4rf3TUnSVH680UdP1/Un4y4WaW1pnyP5+2Xnx1Ntp9KFqnff/J1mQSFP/jg82IDcp5dO7ozSdhpm3XRX8Wzyy43d2N4xunp3EKHI/BtGy5YBEHIVjeKMhoYudXQSHKW/P3hjoMTnRKo+Wg9QXdhlpgr3uPs3WHt8+bylnUarCKfujXpdxaELKir7r8XuEF4SMjcZivZ07cEuFS7lbT62YCIfFJxKVJGpBV84EUbq9j7R6iunlPBhfScrK45gfdP6MREAagmNYZJ8yPY2hGfKEPv37X/P+M9OCo25CSMOY4vvGCKi+1/SW2i8Z1/Anx2fQpheQRuKjfTomYSGRgxHhloGTlN/HuoVLZ0ca3xCTfscyZ1Gu7e7mNNK9UM0oaV85ttNwZlXOpW50a3kbpPfTVvED0BLuKfQMH57y+cz1QWKbmNv29iM1bCLKNVm2V2P3RC6h5JNI5lgz5flQ6ySdpbJesJdB5YPrMvMv1Y5iLOFz2t45CdjnBp0iKklFJf1X69mQWInbj03JVa3d+wiHi/n3iV/x+X2kmdmgk7eZ7au7eRLFV8i3VHgYwX7eddxJZVBQXu0fUzsnC2hMUySQiP5QPc5fNiErc9O46nap/ju699lu79v2cvkjTcjZtg0nDYXCA1NN55+gXgAu7Cn6hm/rC/kqZJuf+2kT3+mQi3FspFp8do+x8tNN79IMH3FlxQaa+NHAn13GlOUbiHzlu/MtD4R0w2y0O2j3TMzTfDlii1Nxs29tbn7JkpGspf7Cpkc38lENUpjx86cz2U4hHHQnG8I4uKo8Rl0/9hNsNib5ILnHd/JWbUvLC5DVQvwywMTjEEz/1r1ANmgAY52G7FSO0z1ZVeiHUUrwGUfoISqUkgk+xCrIbHTvxstVsbqWUaZ2plmTfikGjiiq9h1gcuVrlKeOW8pD2kfIxEzdkjvt2S3s8slltAYJkn1SNKmIYQg35nfx+U26b2Tyc86eeOtr7ocgKmmD3lyBR+IB8h35iOEQOoadOyhqqDbI2pa3DCghjK4CYaEDWkr7nO8MOEHINwrxUZyxZOIG4FXyc+R/H16qLuOht1VlZofgLdtHYqUFMooEfckpJAZ7SzDYUvbFm56/aa+LsIdRmKBzrbuWlyixagYWK0FsE9eRUNiCvVaZkeB0URKCYqGMMuOus1a0LExWKWtP5IJ9pKpvAfDm1fAe9piug5QaLTZ3SAFiQkDC6vjRSsuXef9BiM7gS26nSP12gH7NPqWEhP6iLtpq7pKc3g/H9e2M9lt3DM1unG/tZmLtzZHEW16Ge789ODDvIJi/l71X6xvnYPL5mJj60ZGG0toDJPkTuPdx//As7+6mpv/sQUb3j47jWQw0wftfVMuJ4VGsiRrqVk2Myk0uuJd2IWX6/+6kUt+9Hv+Er6aS0P3pfpPSBir7a5I39iQsBB0emf1nbcZPBRyptdJbg8aap7jtT2psaFbaOwYd2Gq7TQzbXNSaHTY87DpNty+AvIVkfbZDpQX9r3A37b/rU/+nXjUMOh39Igpabb5sOmCwtIaSkrKCCYqaY+PvSzHEdMnv8isaifK5gHgH6TU71giYqapOSLydlbthRB4bYUkZLiPu/dQCCSC6JoHZz+Fn5JoSy4jEh/PZr/hZdVug4jov+wAgMfuAyFH7LubpD5Yj4bOpIRKVbmR9qd9wrkANIaN729ci4J04LL3fSSvmFRM274dzCqeZe00DmWSKiER6EILNPHntXto8dv62DSSD7ttHX13GkEzdcC0qJFWYHeJkVUlJTSiXcT9Aba+v46iyUtZu+QWFl3cnV2lboLhIdQRS/+S61IHJYHb3td7ymXqzyO9aii3J6JI3U6FGffhN20G7ZFOpLShlHTn7JkXNdJzJIVGm72AmPTicrmZHjRW/r29s4ZL8u/XWxjvdRsP2OYemfRbhYOEnocvv5ASn5NpapCg2jFiu56RIhQxPkuF6ZOftGlED6HcU6mdhuibZrw/LlCNqooHku02GKyjUNcpFgN/v6ZUl6FFq9gb2E1UjRIXCVq9/WfGBVgYMnYlSS3CSFHbVQvAffIaPD5DnV3uLQKgJWR8v12RPcyRDShKX/3Yx90bec7+FSZoRWxt3zrq32dLaAyT5APz+eLPcep3HudXS5uZpzfQ0SviNSU02rf1MYYndwg1UUNP67YbW/2k0PBH2pkjm/j2vFZ+fckyjjhrDd68wlT/IpehGvNH0w3PAfOhNC1D7qUSs3ZyojM9pXpHtAupe9hUfDI2zU5r2PgcraEWHJqdKlv3Q3vf+AuA7oRwMTWMkE6EEIQKDOHSGR0ZL5TkA6a32i9iGkKDPRLXRRN+FN2Fy27DaVe4gPWAHHMlM0PSeDB0FBsPsaTK0O4fnQJAwyG5Gt9ZdnrWfQKOGQAHlLU1Gmlkst5JkX1gATuu0MNKzU9AbWV3p+EMUewcJOeVy1iIBEY4023S3Xa8r7ti4CTV+Bt0tBsLxqiQkCEYF2DKko/x7cQVoM8mpsX4sGN0c6pZQmOYJFcjn4k/C0C+Q6dQ1/s83NpjxkPPH/PTFE433gVNF9G6KZcCMDFiGEKDZsnJrkSEdYlFtE/5ZMY51MQNA2pXZ7pHhT9sPuDdlX365JsJDuO9btxgsJFiPU5RcSkJLY82UxD5Aw1M0INMDnVnri3MNzLxtpgR2CWhTUyVxoNZz58MQGdiZGwJTSFDFdZ7p2Ezc3Ql9K5Uum1f+APm6t3BfH+zXwmMvQC/UMJ44NlMJwqPw3hYHEpZbnurVrNhX4nhtn0gO40Wm4eN6jzyK/qWbO2JogjO0AzV1Jv1bwJwXGjgipLhMqOYVFt0aEJjW/s2vvDMFzJ6MYLhbuvTBJ+XT6eOVdkFQkr8IbMcgeJhp5iZsX9xWSVvlZ7Nno5pwOgbwy2hMUySNo1qxYycnnYGL6sr6eqVsNAf9TOl0PB86L1CCCQjqU39bKFZICZgruADiSBhrZC8osw+6RW60T8YTk9a2GE+lOJFfRMBq1VGnqNOT3pkbCjSygS9i5luPy7djj9sGOg6dI16dTyJCatTbSdK0w230/DEarcXEJJFABSYnlNdoZGpAbDXb9htGoPpDxpVhlGkRIrulWuLzYufHnYBzxyj7xgTGsmo5kLTHuMqMh6Abd7eVZTHLiHzwTq/K/vSvhU+43t8IEIjrAXQ9Ty8LsegbZ8f92MAXq17HQClZMWA7QtMId4W7hqwXW/+b/v/8Xr96+zw78h4fnfnbkoSDjx53Y4p9smr0DUfzTZDhazKGHaReacBcES1QuG+rZS4S3i/1RIahyTBRBCkwhsT/x8AxT4nUvMQSgRS3hdSSjpiHRxRZaxgehvDw6FmbLqgytxhtFd9zLi2qb4IJ7qYI5spUTKvYNTJpyClQoc9PZ12Z9RQ3fgcfW0aXoehAov2Mkb6FQcb1PlUFHhYKvcTChsCoTMRJKSV4Cvu3rXUJJqwS0nALEXZoXjoEIYQmhA0bDdhf/9pHoZCPJGs8ZGuTutSFNSEoW5IBvh1AX6lOyHdEYrxcO69wxttYgHjb1sSM+blcxn/kwMxEB9sOk2h4XZknzLmhE4jDUZT5/BjDVTVz1zasio/MKdiKlJzsqHFyFjgHH/8gO2nBwyPvEBb9q7PUkpe2v8S0H/2gd2de9gVnU/LnEtTx/LdDqTuSWkmHHoHK9T+816dKV/kLv1mZudPT6uaORpYQmOYBOJBpO6myGesDspkOxfyOhI9ZSQPJAKoukpNfg01eTV9hEYk4scrNQpV46HnNW0awXiEhJYgLuOcwXsU2zKrevJcDtCdBOPprprhFuPLNyHYNzakIG6u8rrSg97CqvF58icsYLs6Db9Z9yoS76BSD1Po6HZDVCefQELz0WIGD2p6JLVKUksNm0Yy3cOBkNAThM007n5/bep4XIsjRQI9ZmQJTQoNjRDeHobZE6Jv4NYlLZGxZdMIuI15h8uNSHAvpnqtc+uozWmoBMzMxv7xp2Tdx1axBEUK6oOZ0/kPhi51YsRYrGe3IFnsaaEmLojrMaQUTCzKXLAsiavA2PF1qNm7Be/q3EWdGXC4r6tvMsZwIkxrtBk9Xs6E4m5VXoFdY6IeQA3VAhATElX0L4CdC87mwvi3KXXOprar9oArhh4IltAYJv5gG+V6hAVBI6dNYX4+hWZQXlL/nlSblLhLmF0yu496qsvuoVMvJjzR2GFURgx310hnHQGzNvBd8c+QV5E5nXOB1k6BniARTL+J/GYdDXt+XxfDZOF6LepPOx5P+Fko6xlXVUWjOp6QafuIqJ2cxmaKnN1CIz/Pi9Q9dJnOAIVqHUsShkB0FRhz7RoBV3d/jzk2iu5dU9IJ4UTV+Hs1B5M7iSCr4t1b943Tv0RULaEpNLaERkQ1VJouM9mly+HErkNsbBaozEh3ksq+u9n+EBOPRFXzaYoOTf2TGjMRRAp40fOprNpPLLSz0LyPyjWVCdGBAz0dlYYQbx9CivqX9xu7J6ErbGvoa6De02V8R2/U/8Ukhz913OfxUKlFiGgBpJTE0dnpXtrvONOmz+YNfR4kpgKwuXVzv21zjSU0hok/2olHB5/L+BPmF5dzZ9zwKkq63SaFRrG7mFkls9jbtTfNWBZORJC6E7dZxCbffKBHYoHUgzEoS8j3ZA6g8tgkxTJBLJ6+6vCbSeScJX3jNOzlhp6/zVeTdjwmIyyW+yjNd1Gmx4hqQSO/FgmeVI8jr6BbH1toi1OuR4mFjZVVp+Ki3WYIqCIz4jY8AinJe+q+/T0qBXaZq6xZptvnfn8dMS1GQkCru9uOk1dciaoW0hjMTWqI4ZIIGmqMPNX4TE6Xh4R00ew+dGwaYdP7bkrXpkFadlPicyI1H23h4e00kqtrr7Nv0Gomxs1awWMRQ8BoagG+AfJOAZR6k6k9sne5fWn/S1SpecxMRGny9y80EvEKqqq7SzkrNhvr5QraFLtR91xInEr/ArjQ4+DEwnrK9u9HIEY1yM8SGsOkU4uzW51MZIqxPbcpAl8yn0yvnUaxq5hZxbOQyLTdhhppZrzsJD9uPNTUcUY1sk5HYUponKDs6ld/6ymdyG5tEu329EC9gCmYCt19S4f6nEm33vSysgmh81fxaVx2G5fwKhpqSq3TKSam8l0B5DkVpss24jHj5o8IhQ6XsQIqjZpG5wxxKUOlLdoteGKhfanXzWZNhX/mfQmpuaiLdKb+XmHfnFS78Uo7U7QgLaGxlbRQ6zI+S0Hc+H4oigBpR9UPHZtGWI1jk+D0ZvcAB6jUmzla30V4mLXbk/fVfC07m4jHaaPYYSxmWtQaikv7ehP2pCxaj1OX6O3ZGZo7Y51saN7A6cEmClU7dVpftdZuUw18l+16XL3uR6fII6YHewRKDuxUcK39MT5TfxdTCqewrnHdqCUvtITGMAnGg6C7KfJ2e3FcxxNAD6ER695pzC4xaif0TCcSVwOUyzBuU2+fZ271w4lI6hpnyzf7nYPHaUPqzj51wu1NxpevNNpXLePWQtgk2HvcuElVmNtuqEueE4Yg3BcwHm7zlHRvF19eEW8kltFmZufVRQyXWdzJU2rsbtpGoGxmq+nBVamqqLFuY3ZT0A9ATWEpUsujOdSa+nsV27ptGpW0cZy+i47YwS/EFNfiPLfnuYw3dnueIWBl+cLUsWLCFIXGTknPwehSnKi6G82swZ0NhWXjaFbH08Hw6nAndxqzE9k7WVxuMzyaXNKH2zFA3inAVVCKXbfjJ7vU/q/VvYYmNf7uv5z3Y0vwq619/t97uvbgkCVMKC7q03+VvgOVcCoNUMI+sM1l4+z/5rzoNzlpwqmsbVzL7e/entU8RxpLaAyTaKyVo9lGida91XaaNZ+T6qmkemVvi6DKV0WBs4APOrqN4V02D+9qc3GUGf7XJTFjx6F37k6tnH+X/41+5+C0K8yjAeLp3kHtZk2A/MJxffvYBC5dItW+ZWVXacYuqNkxF+gWGp9IvJF2DcWmoAgfMS1kJFcUKktC6wHIKzBWc4ER+GrVmwV7mqIzaLJ1r9I6Wo0HwZHKHuZqrQS7ttNutl0UfDfVzjtpOX+KnkNUxvv1oc8VT+95mq+8+JWMbphRs8CVt0cqDJtUOHRy3EJYNVSr/dUHz4TXl8eH+jyC+vD+Fx1RQ2jsmnhV1n3muHQWRWP8V2ztoG09hZUE9SI6lOyi3F+uexmvrZB9kSUUqA4SMtInnmhP5x7mRdu4JnFfn/4VZsG15rBx39cV9l/QCmDctPk0yFKOLD2fT8/4NPe8fw93b7w7q7mOJFnd2UKI04QQ24QQO4QQN2Q4L4QQt5nnNwohlg7WVwhRIoR4Rgix3fxd3OPcN83224QQp5rHvEKIfwkhPhBCbBZC/OTAPvqBEdFjuDQbBfndHg9PlX8Z6M5c6Y/6cQgXF/z2HWpbg8wqmcWH7d3qqbgWReoOPOYKqMBhlmJNhFMPcrd34HxEFTJMXE/3rmo31WQFxX0N4XZfMUGZR4un26aRDFScqxk2iik246ZOCo1nCz/X5zrzaUTTu1LpJPxmniuvXSB0G4noga/u6wOtSGkjnignJrv1zB2m4XtiQSFOzU27FqHF9K2PmvW2AcqL8oioxo7nYHtQ7TNVUBndfQPGStnb4//WThlNjoHVJ2OKQC3jZSd5kezdZ4UQ5CkFqMSGld+pyYzVKc/LXiVWt/g62HMFXVw8aFufy47U3FnVCVd1lVfrXmWaXs0pjo1crBg2hoYeqlApJbVdtWixMsIl8/pc492Cs4HuOCJvhrQ/PZlX5eVc20u0bnmJG1fdyJlTz+T2d2/nz1v/POh8R5JBhYYQwgb8GjgdmAtcKISY26vZ6cAM8+dq4DdZ9L0BeE5KOQN4znyPef4CYB5wGnCneR2AW6WUs4ElwGohRPY5DEYQKSVhGedpdTUFJd03eoknH6TSbQiPdeAWPl51fZnW1+5nVvEsPuz4MJX6XFf9LKMWt90wgDsq5iClQru7PCU0jtD6us325E2xmqCSHugUSYSQUqHQnaGehl0BaSfRQ3+eKrRU/gUAzo4YGW33B8zYCN/UPteZr+0nIbQetgRDaLiJUSjjuDK4+w6V9vbtFGqSZXIfmgyktv6NNsOGUzLnTN5Rl9Ipo7TGjBvdXtJtCPc4bZxmM3zuk6u5g0XSDTNTChNb0DCO+vTuvYXAjjpAve2xRkjqIB24vUOrdPe5xH+A4aUSafMb38dpWvaG9GnlebyqL2BT6WmDtnXaFZazG1c4c5BeTza2bKQz1smJrXu4zv1P3ikyFlb1wW4h2hZtI5gI8nb0KEKzzulzjXwzaWhj0BAaizrf6NOmJ9VFPr7j+DNFOx7Dptj4n9X/w6rqVdz13l2DznckyWansRLYIaXcJaWMAw8BZ/VqcxbwgDR4EygSQlQP0vcs4H7z9f3A2T2OPySljEkpdwM7gJVSyrCU8gUA81rvAOkuQAeJmBZDouEUXhy27j/hqcEnKND11EO4PdpOcSJKjWhlY5eP2SWziWrRlMoioceooROn3Xjou+wK6HaiapRAPIBNSpbHNgw4F4fiQe1VKa/Qv558qeLJkDHTrgjKZRBPeE/qWDK6vdBt7FA2lF0EdO805tL3Jn3RZyRLTH7hPTZDD+z2FRDS82lxDqyfzYaOWAclms4y6tB7ZB/tjHUhpUJFXgEepZi4DKUCxspd6fEh5+qG2qxnhb+DQVJoZBq3ocAw1rtKuoXxRL2ZktjYLBiViYDNw35tPK7Coe2O9niMzALDiQrvCDTi1CXVQyjyNbUIXnRex5mx7Eql+vUKOrNQT71e/zqKUPil/6s8M+dH+EoN5UrPncZOv+Hiq8fLqOkRo5HkiIjheZYUNLpn4MeZUBS+U/kbbhFGehy7Yufo8Ufjj/kPatxGNkJjPLCvx/v95rFs2gzUt1JK2QBg/k4+ZQYdTwhRBHwCY4fSByHE1UKIdUKIdS0tI6+WSD5kL+DVtOOKtxinZkvFF/ijfhQ9n9+qH+dvHdNZPX41dmHn8Z2PAxAV8C/9YynPJLceJl8mcAR24o91oWo+1k//yoBzWaFuQyeRyr8E0GovRNddKLa++mYhBF40dL1b0HSZuuJFMcMQK4uM4kBJobEy/E6f6/jMWhD1puvlnE7j4eyw24npPoJk9vgaCi1SsiMxg1eLP2vM0xTGsnMrXh2KXDrna8bqrKF1AwBTwrVp13ig4qfGtQ5y0sLkgyCTWiyumcF8aXmbnMQOQvGqkSKWVK0OwaYB0FJ8AjA8odEiHES1AvRpJ2bdp7yomMlKE9Pd2T1U94lpBLKINl/XtI7JeTMJxPOYOn0W811xFF2htr1b8G9uM2IpXtFvYZK9r7q21Lw/G0KGCrOr/KhBx62aMJ0tTWFUzdBOTMw34qL2do1MBoZsyOZbmukv2NslpL822fQd0nhCCDvwIHCblDJjvL+U8m4p5XIp5fLy8vJBhhs6SZUMSrqra/OUs2jSxtEe7VZPNctJ/Fi9mL2NzXjxceKkE3l8x+NE1Agq8bR8My6nA6eUJPQ4HZFOpO4hP2/g7X+ZNPTiPav3tdl8hGVhf13YJ2tocXbnnuowCzJNNjNvTlZCIM2HtO6gduplfa6xQK0FoMFMKBgp7FYL+ZDIxIHnngqoftDyqCo01hPJ1VQk0UmhrpHn8aKZddD3qAGEFLiqFqVdo6KoGqT9oNo0EnqCxrCxA8u003AEjQeLr8fd12ibSGuGolljFXusgdXiQ1y2oS0OqtxFALQPI46nM9aJ1LwUebNPxy4UBW7qZMFl/5tVe4fwoPaoE/74jsf5+ktfT2sT02K83/I+k+IFrLE9weJxPmar25ioxtjb1h1AuKl1E0WimP8kTqCium+CxdZJRo2aOnO3nnSHH4jlxUGu5SH27jKyB0w0g2n3BPYM1G1EyUZo7Ad6WlRrgN7Wr/7aDNS3yVRhYf5OKp0HG+9uYLuU8pdZzD0nJB/Qa/PPTjte7DXyT/nNlXt7tJ1YzMMZ+Tt5z3kluze8yPmzzqcr3sUTO54AJMfp3T7hbm8+rXoZrc4y/KEWKvQwNdo+BuKdAiNzaE/DYlyLYJP931hCOtJiAtpjYaQUNM68AoB5oTdT0e265sVd0FfwTleNbXiDefPrhd2BhNNlI974/j59hkpcbeM4djEdQwA1+Y2vQb29lEZ9AorNxs6yTxvziDeiaV68xemOA0vEDgpUQVPw4CUtbAw1pvKPZRIarojxt/Hau21RNmFHO4RsGhEBIVmQFr+TDSd3GsqBxq6h55+Kh/czSXZRaB+ey242nJJYi+zhdPHv3f/mP7X/SaU3ByPLbFyPM729k887/sP4knzkjFPZF5/O/h7eU5tbN+MSM7nXdxVOV1+BkKyp0WqqtGZ0DF7QalaxwhdtT9C83dj91+TXIBBjbqfxNjBDCDFFCOHEMFI/0avNE8BnTS+qVUCnqXIaqO8TQHIJexnweI/jFwghXEKIKRjG9bcAhBA/AAqBrwz9o44cyZ1GoSt9p1ET2crH5FYCkRaiapSIGuHziSf5TNke7tDOYUOHm+WVy5lSOIUHtjwAQBXdX1CbIkA6iOsxApF2puodlGsDG3A9NkPF0XOnURr9gFl63zw4SabIRrzxbt1re6QTdBeFHmPX0zHpVEKaseot1jQq6atK2D7BMPw1m1/4Ake3b/s+ptBiP7BVc0SNEBcaZcAkzRDCje2GAI1oQexmWpEqnyHQuhKtOHQ7ec70le8UuY9JWoiGzv7/HiNNSkedyKchQzT6nnxjV+bzdu8GpydqKdLGVjbegQgLG1vo6xE0GLHxJ6DoCg3BoaunImonk/ROCnzZp2MfKk2OGaiiO3lksnjaC/teSLVZ17QOgeD/olfw3Ul/RCgKFeXlRBKVtJr1atqj7dSH6rFFKqkpzuwVNTm8GY8Gnarx/baZZQUGombmYhZr9/GcNFKeuGwuqn3V7A2MIaEhpVSBa4GngK3AI1LKzUKINUKINWazJ4FdGEbre4AvDdTX7PMT4GQhxHbgZPM95vlHgC3Af4BrpJSaEKIG+DaGF9Y7QogNQojPH+gfYDgkbRonhl5MO+4tLCOmFRDUIvhjfgB2JaYhJ6zksaLLeKnVqPd93szzUv/kp9zpXhVTacEb2UsnOq+py5FTTxhwLkujhqtfunoqny7RvyHaKzU0uncaga56ivQElaYh1l08jphmCMSZspUytW9EdYnHeOC1dBrb4gk9ku1FlGIig2ohBybpXbPedzbaVONv1Gjm1HIn9jNdN1bwJwReT/WZLZvJt6cnvYrMv5B34ktp6uU/fyC83/I+9226r9+I3KQRfGYM2iPNfdrF9RhSt2G3d9sDdOE9pGwaqoxhH0ZSSvv4RahaAc3DyD/VIey8ph+F3TF4WvTh0uBZAhj3eFukLbVTfHHfi6k265vWM61oBrXNkrmTjJ1tZb6L+Vo7Ya2TiBphU6th5P7v9kf47+gdGcfyuN34dIE07xVROXDqdjBshlOrStlQ24LUTbtGwcQxt9NASvmklHKmlHKalPKH5rG7pJR3ma+llPIa8/wCKeW6gfqax9uklCdKKWeYv9t7nPuh2X6WlPLf5rH9UkohpZwjpVxs/tw7Un+IoZCMa7D3qumcVz2Dl9UjCOuRVAqMJ+Inw9TjWVqTT9fe95C6ziemfQK3zdiuum3pqxCHFMTRCCXSs+j2R5GZr6qn0OgUTvy2/vMYbbfNpsPWvcoNxvwU6wkKzG1/iT1Gqam+WpdYhGNi3zKZM8wCUK1xPwBKWbdNo1jGEfqBlcxMGkqL3SWMLzRSoCdLY0ZFnCJT/eMom4VbM77GdeoE3J70VA3VRV6kmk9HdOS8px7Y8gA/X/9znt37bMbzdcE6FAkfj+8jLhOpRUaSvPAenL2EaqNrBp0D5B4aCu82v8uftvxpRK6VCV3q6DLMyWr2tTSSlHrt2FU3baGhp6uPyxAuZWguvkPF12PnntxlLK1YyoaWDbRH20noCd5reY8psoKf2n/LylLjPnE77ZynG4XKGkINbG7djEDwSugkGqpPzjiWUrOcJtUIwJVSwefKTgifOzePbzX9N289cgtgGMOTOa4OBofO0mYM0W6WU22vSd8lJG0aIFOeR1LzUZHv5jzxHA+p/03jvu0Uugo5bYrhN35sLD1NyG4xnXZbAVG1i5VyD0WOgVfse6qNOfSMeJZEUmk9MqEIJxrd+vMOYWOHNhV7jbHlLZadHCmNXUdMz6cggzG+ihCKlPjVZCGhbvfReYmdOOSBuQAmhcbRsU2Mc0gUCWEzb1eXsLPPa0TPyklHEVINoeKnqo+OvSrfzvH6TqJ6iKg6MtUEt7QZqed/tPZHfSKAwRAaXt3H67Ejgb52DXe8Ea9M18vbhQM5zPQavfnz1j9zy9u3pFa7I81jOx5DF+DQB04AmIkKvYXVcjfh0MC2ut4k9AQaUZbrtUMecyic0PUMYAiNZCDuFxZ9AV3qvLTvJba0bSGiRqgIejjR9g6zJ3U7lPzZZdTWaQg2sKltExPyJvOnxJmo007KOFa+247UDCGVr6sUd2aXufaS4xYQy5/IA5tivLu3g4kFE+mKd6Vlhc4lltAYBm2m0KjIS7dpuB02vir+ARjpAwCesP2IKnuIokVn8JX4l9hgOvFcMucS8jWFKVp6OmxFOEnIKAkSHCF3ke8bePVZ4EzWCe9ezXpkOwuj/X8B56i7cGvd3k0hNQiahwKPYZfIq5jMywljqzxfb6TQ3detMjHtNDTdS1QaBvhCZ7eQ2uVaTCDLVAz90RgwbAFl7kKKvQ4KdZ1IuAFVV5FKFJ+ZVrwsz4VUjdcl9M3Hnu92sVo3/hcj4UHVFe9ib2AvJ086mfZoO79c/8s+beqD9YQSVbwUN2ISeguNXd7ZdMp0m8/M6Ae4ObDdWZKkquLXG349ItfrSWesk1+u/yUebTrbPdcNuX9hRQ27E9No66EezYZkwOxEObL1u3vTXmAsnILxINs6tlHuqaDSvoBKbyUv7nuRdY2GEmVt8BSuLPsLBT2qaublG+Va60P1bGrdRJV9AgUEmVreN3EoQIESZbU0vK2kdGIvyC7mRbHZmH3Nw7yXfyzX/PkdSp2GVuFg2TUsoTEMOtt349Yl0wIb+pxrFpOB7pTILyeOpKSskqkz5vIf27G83WAIiVkls3C0/ZK1lden9Z+v7ULqxgPzbnlpxliLnswOGd5X/kD3dj8kbHS4pvXbx41EFT2KKsVbWC5rKTCNyAV5Pto14wt8EltwO/vqkPPdDqTW7RGSym4LxBwVaTuZ4VBnFuqJTr2QgpJK2tRK9jlKCcaMh8aMuLFSLRd+TpPGyv9Yte/KWigKd7u+CoxMrMbWNsN2c+6Mc7lkziU8+uGjvNOUHseyr2s/RH18Um7MOK6qJxC9kuKptkLiWcQHDIaUkr2BveQ78nm17lU2NG844Gv25I5376Az3klR5Dy8zuwS+/XE6/WxR11AUO8aUj6wZGqed4s/M+Qxh0Kw2EhDEzCFhkiM4+w7X+eY8cfxRsMbvFb/GpMLpvD+Xp2jZqTbDY9RmhAS3m16l/ZoO1Nb6njbdQ3zKjMv/PLz8nBpxt8woBdiL8o+VrnI6+TOi5eyNPQK8X/dBnDQVFSW0BgG/kQUu27HlaES2ItuI3//nq49CBTud38BxWbDYVM4rkpD2/l8qm0krvUJjlJwEFUMwZJMtT4QhWb51i7TsCilIRBCnv6FxjbXUsKiWxBE9RhVMozLNDDaFMFcYaiH/iguyXiNIrqo1g07iqLbcBR1e0mX6yEQOgl9+IKjIdiK1B1UFxRiUwQKXkJqF61Bw1ZUZGazLSqtoC1h6IW3OTMnfKvwGh5WI7HTSKqm5pbO5ZrF1zDON47vv/H9lIttXIvTGm3hi/JlvswzGcctiO2jSKZH8bf65qIJecDprtuj7YQSIa5YcAUl7hLu2JDZCDscPmj/gEc+fITzZ53PbW23ckXHL4d8DSEE44WxMxzKyji50yhy9R9/NBIUmbESzcFWdvt3s6LjQ76s3keFPoeIGuHtxreZqpfwkP17fKwqXd25WNtMlaqmPK32xFZxT8E1uN2ZhYbD6eJF/RiAYQVKLqwp4oJxTYzvqEcRirXTGMt0SEGnVoJr/II+54rcxpe6tqsWm8yjsqDbPfBztif5dsf3iEWNFdZPEj/mSP8/0/rvcnUHp50g+68ZnCQy4VSkFPjNZ01Ui4KQeJX+V4F24USaOwEpJRGh8gwnp9kDzjJ9GWyOsozXyHc7qDKDoFTdjTu/KHVudtTI5DucpHRJutq2UaElGKcaN8JkGcAe209j2BBUkarjAXC5PLwrDWERdPXNkQVwmjTmMxKpRLa0baHCU83L28J4HV6+uPiL7OrcldqBJN1t10VXcsf43yB1e5+dhltrp7CX0HCY+cN6RvYPh+SDY2bxTK6cfyVrG9byduPg/v+DIaXkR2t/RJGriGuXXMvLYjn78/p+/7Ph6zHDu34oK+NOU1+/KHbgOc0GYnHHKwC817AWVaoo4RKusj/JwnWP4nMYaqb8UDkIwfyZ09P61s39PPvj0wkmgtiEnRcb59I5+/wBx0sa9pewF7c2dPXknmU3cH7s+5R7Kg+aB5UlNIZBMGHW0vD01dtfHnwUMHTf4xNdfCf809S5xJLLODf+PV7fbWy1fXoIj5L+kHD0cGOcP0hgH4DP7QDdRcBM2Jd8UM/JoDpLMi+2BSlUwokwETWCRMdtS9e7/sNl5J86oh/Do6+4gnWqUQ/CLhU89m7Vyt7CowEOyPDcpobwaDaKCksAGKcFicsgTUHDFlPq6bYn5duNNtUic3Lx2UoXdilpDh140sLNbZspCdr4z8O/4bmtTRxbcywCkSr7mXS3fTF2ArNnzkGq+dQF0setdU6m3pZewnda0FAzxg+wEFPywTGpYBLnzTqPck85d26484CuCfBO8zu82/wu1yy+hgJnAXfKT7Ol4sxhXWt9wWVpc82GZvNvWK2MjLNAf2jlRwDwnlnC4KHgedyiX8rjnXNYPc6wUa3tOIZfTri9T7LGsuJi9IRhq5rgmcBstZblEwbWFpyvGUKqXS/B7c1s+xiIKaVGnxLneEtojGlCtayUu9PqZicJ5HcX1oloxewo7/acOGLZChrz5vKHN/aR0HTOj9/I1gkXpvVfEO3Wyz9X9ZVBp1Ia3kWJjBIzV5gB0yAeyu8/8GqOZjxk//LBX1LePx/T3ktr43CvYnkQzolu7dMfwGFTsEljFzVFtuHumRzRbXiUHIjQaJUauxIzKKoy0i/s9B5Du+Kgq8mwE0xQu4PDvhg3Vq5HhNdnvNaeedcQV4vY1zV0N8+edMY62RfYx9Kuej5he4Nf/PU56IqwoHxBH6ExwVXEAlcDU7QgTR3piQg1qaKQbifSXIZBNZY4MA+vPV17UFB46PUAbrubi+ZcxLqmdTQNw8W1J3/f/ne8di9nTjUERTSeSKX0Hypt5SeAVkhtV23WfZoixqIoMOXCQVoeGEr5IqQU7A3V4hAu9HgZ+pHX8EhkBUcUX8A187/CjgbBUdP7Fhkb746xQjNimiZGBE+4bmRZWd9nRPqAhpCp1SbgzCKNSG+mFkrucdxKWSjKnsCeg1LNzxIawyAAhPQi3J6+kan14z+NlMbNVJeooW1id0pmp13hC8sLOX7Xz/nwfUNl0PvGU5Tua5Z40r2zMuF2unDpCiHzAd0RM1VGBTP67bOt7L9xxObx+02/T2XlLO3l7rmSegrrzuQfE3/Q73WOoBaAFr0Cm71bHVamGcbqSI/YkaESUv0oMh+fy7iuz1GAVCK0mSlaynzd3kdR9yks6swnXN43RxZAdYEbqRZkjM4eClvbDQH6u8BlvDn7m9yv3sCHD/wXx44/lk1tm2iNtFIXrMMu4Xb9t5TnOanW4rT2Ss5XoDVS0cN7DaCrYL75uQ+sFNO+wD48spAHXvyALfVdqdXxW41Dj6lIEkqEeHrP05w+5XS8Di+vP/E73rddynGh/wzrerNLFDwxN7s7MqaOy0hrxA9ARX5u83PlOQVCNzQI43U3r7q+wudXjafUEaVt8x6WN2isc32REyr7GvEr8t2cpRsq5XZ9Odc7v0XZuL45p3rydqFh2Fdw9VvWeSAqSkqoUvzkx10E4oFUUHEusYTGMAgIyXaReSVf7EvGaoBDdVGRnx6w8+llEzjX9jJbXnqUh503M82fnil3X353EaFl8Q8YDEflDParE2gxH/qdZv3sfHv/Ng2HTWDrPJ1APJByy1xXfEFam6PDz/Ftx5/x+foPphonDaHQJUvSjk8JG0FRneHhJS2UUhLXOjhDdu+6ZpjpVHZjCNmS8UtS52rHfZpX67+NKE5PVpikxu5nodZMR/DAtu9JI7gareETq5ewYf4NfL3tTGJdRt6t1+peoz5Yj0PNZ934z1I2dTEvx4+kvVelOqcMUqSnC9RkXE0ofmBCY0/XHuZEWtnsvpJ9T9zMrJJZFLoKD0hoPFX7FBE1wtnTz+bdvR1cs7aIxwsuYuXZ1wzreiuUDzhD3UltZ/ap4AMdO3BrgvGJoeesGgplXVuoMr/XXr2aN+xHUF5cwN0F93Huh19jbauHF8RKZs7oXVIICopKuSlyM25RxIeNM4lNPWXQ8QpNw/6ZDO//o9hsfL34V2zHWBwcDGO4JTSGQUJGcNsy57+Z73+eKbrxsPyieI7Z4XVp54vKqvjfBY9xS8NSJMKoodEDl80QMoqUTMgiQM7jtKGFJ7M//CHhRJiAmZp58gCBQse3P8Jtod9x8qSTWdtglMFM1tJI8takL/CCtpjZif6N8a97DFWFr1d0c1uFkfqkKz4876lAImB4EoluFUCl6dffaAaFVRUUdZ/zCj6hvE4FmTOnVhTlUaNFUoGIw2VL2xZKyOPrypPMH1/ICZ/+IuMmz+GeZ6OUecp4ef/L7OnaTyBWRXzyxyhw23FQREwPEtO6hUGdrYJ97vSHzoROQz0YPgAVmpSSvV172RubxQ45nuKG1whEVFZWrWRtw9phqy7+vv3vTC2cSrVewZceeIu8wmJOXPML7I7hxeJUzj2GN6OrCWihjMGRmeiKdZGvS/I9Q3fzHQqOsqmENONBvi+6gKcmGrEogRX/xZrYl7ltRwnPTvt22s46iVAUyj3TmNv5DY7p2sCR1YM/Xj/e9RQAe8jsxJENU8t9tHYYWomDYdewhMYQ0XQNnSgnxTN7pIjyGcTML91b8SV4a/p6mFxy3DxaKeTy+Dfw16TXBpgTMmpcq5qP5lkXDTofn6JyY/xVdFQ2NG+g0/T/l6UDeLbY3ASli2sXX4ti5js6olfNjDyPm7NsrzEx3n8VM5/d9PzQd6Ydd3qNGI+uxPBWzcm8U9vzu1V74fFnABANbsWjQZG7+4H1scbfcbvzDua3ZC60U1w+jn8lTicko6lEdMNhc+tmaqI2jnd9iNthw6YIvnZMBd/Xb2cO43i9/nX2d+2hULUzudiBEIKzhCF023qkAtdRsYn0h47uNexAEX0QHfgAdMQ6CKkh9sZm8+yxj3Je7Dv89Z06VlatpCHUwP7g0DMP7+rcxYaWDZw19Sz8957DzYmfcc9nl1PsG37wZmVFJfUYO8VsH3ItwkGjNg5v9Zxhj5sNrqJK/JqxWOlsy2dWlbGYWrrqBHYpE/GpXayekdmjEOA8+8t8q+UGfum8k+XFg8ehKF7DIWK3bfif6wz5Cnd0/ABFKAclVsMSGkMkmUco4MycQsExbiH7NSNm4dXEEZRlyKM/rTyPn1a/yEuu6/Aq6atxYS8yXuiejN5ZvfG4PVRG8rChsLZxLX7zmeMs7j9O471x53Kt9t9MLZrKaZOMirkltvSxyhwRblEvpG3KJ/q9zsq48UCsVWamHS/VDZtGKDQ8F9dkCpESd7faq9JXBEArIdy6gq1H0GPn5NP5l7aS9kmZS3oKIch3GLrw4brddsY62R/cz7vBo/jrwt+mji+dOYnFtloqOrwEE0ECaoDP628w027sGKoUQ0/dM1bDJ/1UJdLtK/Ei46ERYvgr6eQDuCahcc7yqSyZWMTDb2xnRYWROyy5qxwKj21/DJuwUaOs4neRY3CtuIzZVYPb2gZCCMERZk61bI3hITVg1tLIXbJCAJ9D4DQTY76m3cyxurE4LHA7+HrF27zjXsNx5f0Lg2VyMzEcnKP/lMlz++Zs6039xHNQ6z9JqXbEsOdcXFREkyyn3FVh7TTGIsnEgM0FmQPJuvNPQT4OCj2Zv+QLVxxLPhEmOtPTIrSXGNet0bsozyJVtmKz8RW+Q4l9Jmsb1hI2V+mF9v5vLpdNwaaF0VSVLyz4byL7L6Jz2mfT2oyP7uC7jj9SrvdfLKdcGAKvyz4u7fi4iPHFDQ+jZgJAW9h4sB8f6VbtTcC4UcNKlE7S0y3kT1vJNYmv4B3X/2rtnGR09hAC/Go7a3m78W1UXU3ZM2KhcSyZ1K02s9nt3LvwQR6p/zh2MzbmP9GTqJ5iqJ+2Vht111vD3cJKIU5hL5uG2248RCMH4HGW1Gf/ioepyHdx7dwIDwavILhxI+Wect5qGJrePKEneGLnExxbcywv79D4h3ISy08ZGe+ly/S3EVKm0u0Mhh6tY5nch3uYHlvZ4rXDx+UmyhJO7lfPpmzastS5xSuPZ235uUyYMqvf/i/MvolPxn+Ib+JibAPcg0kK3A7uDb/Azzt/New5u+Z/kqsSX6XYVWPZNEYLKSXP732e1+pe63Ouu5ZGZv/rEiXMJWYZ2DvEvf16RMw+6hMo1+9i2sx0g3ry4VGmxyhwZffv8TrtFNvmsbV9K8FWQ71VGe2bzjzJ0pIYTzu/wduP/hQ0L2pgIQXu9C94yZJPcKrzfsbNWtbPVaC22jCeF/XKIxSvNqJcOx2DR7Rnoq7TEJbe/O7dUrWvOxLYIdL92RfWFPHEtatZPql/z5pqs4Z5tqlEdvl3ccm/L+GKp67gxEdP5FfvGDf1rdrjLKtO35WdsWAc4ZiDKT7DA2qv4+hUtt3JRYaAqw922yr8wku9b0naNar8hg0q0Tb84LU9XXtACv5e9A2EonD0qqN4Q1nC4x9GWVm9krWNQ7NrvNP0Dm3RNj4++QwSG/+P02flDyt1SCZa512NSBSytTU7D6owcfJz702K0+Um2vIJ7Hs/w+36Z5gwdXbq3NwjT+eIa343YOGpGp/Of9sf4WMV2dlqpne+zmrbZj7wLBm8cT9MKTO+a26q2OnfyY6O/lXKI4ElNDIgkdz+7u389O2founpCQXbzZxIywKZt/r5hSXsUI1I0Rc95w04Tu/gIICpXYZB9H11DnnVM/ucz8Qv5S2c1vYButRZZ3qXeEr6d7k9bul8tuQfza8/8NG4/R3+5vwe40PphvPJVWU89a2zGV/cf8BRsRn9viz2ftrxvDwjbUdIHZ79oNbMoyWmfDJ1rHLi0tTr6VpfY/HCmqIBXRZ3Tb4WyE5oNIWaWPPsGuzCzs1H3czyyuXs9O+kQi9gihJmXFm6t9iqqSX80f0zVjXUo0iFpd5uf/uV+j6EhL1N27o7CBW7LV1IC5+RZr9nepeYFuP5vc/znVe/wzN7nhl03nu79iLVYpTJhiOCy+1l8xE/477dhcwrXkZ7tJ2d/p2DXKWbV/a/gkNxULo/yC36L7ikbOSisSdMnUM8Xskuf+2gbaWUhITKu/ZjR2z8gXjOdiaB+DhmlHlw2of2iJytfciX7Y+xypvdLttWUMXD6vG8UXDGcKYKQInPySPuH/Kphkbynflc+fSVQ/o/DxVLaGRAEQpfWPQFdnXu6nOztgT9xovizC63is3Gu4lzUAOzqSs/a8hj2z2mkU13U+DOblXXZq/Ep9bgsXvYG92FlIK8oup+2wtFYdbn7uQtbSZ3vbiDiHTiyRBzMhjzokbq6EbXwrTjRdIwgMfDQ/cE2tS6icd3P4gamkplQbfAKvf5kKb/vF0MPXJ2UmEFUnOxvvG9Adt1xbtY8+wauuJd/Oak33DOjHP4+fE/56XzXyLe+h3umnpnn5Wm3aYQqlxBonk+f9nfwRXqv1PnSoqKydcEDcFuYaWIOFWxXg+VYkPIB+356FLnlrdu4biHj+P/vfD/eGLnE3zn1e/QEOx/9wiwo3UHxXE7C0u7l+RnLqymVPpR6g21ztrG7O0ar9S9wvLK5fy9dTqf1b/HnGM+nXXfwZhZkcfERIKW8OABaUbWAg1vFrnYRoIpDj+vuv4fN8uh5+1adPTHWb/yF8w+IXPOtt4o4xbzQ/UiEp6+wYLZIoSg0T0df2w8vzv1dyhC4cqnrmRXZ/ZxMEPBEhr9cMqkU5hWOI273rsrlYwOoCVm6JztVSv760q5MosZdcdQkzf0/XSs3DCeHSlqsduy+/e8NO1r/Lj9ZBaVLQEkim7Hax84UGhSqY//d/wkFoVe427tTBzjM8c4DESpy8NXGuxUuE5PO16QaMWt62ihoXnrNAQb+K/n/4tC3PyzZT3Vie40Kk67QpG56/N7h57zaK72IScFEzy/76mMdQdiWoz/2/5/XPyvi6ntquVXJ/yKOaXdNpJwzMa+Np2lk4oyXt9z4je4J3Yy3wx9jd3Tu4MMi6avokOtpt6M9NZ0DV1Avpaee8rjMNSSUTXGQx88xJ+2/oljxx/Lb0/6Lf845x9IJP/z5v/0+4CVUlIX2sdp2k7mebs/39zqAv7P8z/Mf+dOavJqsjaG7w/sZ1fnLo4efwz/3txC4ZwT8PhG7qGd73Fylv4hUWIpx4eedEQ7eGTbI8S1eMot9+h4buqD9OaXiZuxC526KecOua/d4WTZGVcOmp06SaHHzn9cN3Bhy/BtGgDPT76Oe6MfY0rhFH536u8AuPKpK2kMjXwJYUto9IMiFNYsWsPOzp08vefp1PE2s3pchenNk4l7Y1/lCdeNfKyrdyn1wckzs9ZWyOzdVT+zbAKhWJzihOG1VSqjeGyD5+i58pgpnOt6k2OU91O1NIZCbNIJ/LvzEgod6Vk8lfKZRHUf67UW7tt0H2/Uv5EWp5CJUCLEtc9fS1SN8tmSK3k9cQSFpVVpbfJN2e0bhq2kqKiQIzq9JKTK4zsfTzv38AcPc8pfT+F7r38Pl83F7R+7nSOqj2DDcw/x5n3f4oPtH7L3jb/xjPPrHFmUOdbjqGmllLhthHBTNKlbqFUWupBqPu0xw6EgGbzXWpzuWVMcrAWgrfFF/nf9/7J6/GpuOfYWjhp/FJMKJvHlJV/mlbpXeHL3kxnH98f8RGSMp6OnMmF2typPCMErM2/gm4HPsKRiBesa12WVffiVOiMnUmWrjYuiD/PJ2SO/yn+78Cogc0DaL9/5Jf/z5v/wuac+xy6/sWL22YaeZmM4PJB3BefGvkve7I/lfKwC3c840Y57iPVFejO5zEd9Z5hoXGVq4VR+d+rvOH3K6ZR7ykdopt1YQmMATp50MlMLp/Lb936b2m3EzPoEk+P96yxfLPoUT2vLCE7OXLFrIMYFDCPWB/Yjs+6zamoJf/P+mKO3Ghlzg3oBbvfgKhyXy0Pgkn+zffENVOQP/YYs8Dj4g/NnHNPx97TjbreHmH8lES3Mz9f/nKufuZo1z6xJ27H15n/e/B92+nfy8+N+TpNyFDeqV1BUli402hTDzXlGbOi+6MVTlvLN0Heo8czl0Q8fTc3l7ca3+cHaHzCtaBr3nnIvj37iUY4efzRv7Gzj+8+3sKr213zx9y/zvy/tZx+VzJie2c7ksCk8kH87z7m+zpTC7lWmy27jWLmHRMzYNUXM2BVnryJVjjzjs/4z8AYOm4PvH/n9NBvNhbMvZGH5Qn7y1k8yrsyT/vnSsxCXK12IzzrqLLZoNRTLZQQSAf6+/e99+vfmlf2vMDF/IvqWDXzB/k+OmVU1aJ+hUlNlOFns7KhNO94cbuYfO//B0oqlbO/Yzpdf+DIA+yuGru4dDi3e6TRSwqzK3JaWBcgvKOEv6sd4v+qcwRsPwBHaO7znvIrGnRsAmFY0jW+s+AY2ZeS9zSyh0Q+d4QSqDmsWrWGHf0fKttEinAgJheX9x0FsqjyLqxNfxTuu/6SB/VFUOJnz6iupsWXvty2EoGn2pfyr4xRseInIgqy3x/OnTuSn5y7Cpgw9702x2oKXKEFPej1yt6JzXrudbxR/jZfPf5mvLvsq65rW8fC2hzNe59W6V/nXrn9x9cKrmWmrYeObz3LczHIcvdRzHsX0EnH3H1zVH2V5TvJcdor9s9nTtYe1DWuJqBG+9/r3qMmr4Y6P3cER1UcghGB3Qytr/rSeQOlCaj/3Htd8+lQK5pzE2lV3ZnReSOI99Xu8XXgKUyrS4xicopCASKDpGiGzHkh5ND2DsavQ2CXq6HzriG9R6TO8rmKxCNubAqg6fP/I7xNMBPnBmz/oo6ZKlhc+Jq/v/3HJhCKO8+2jfNP7LK1Yyl3v3TVg2vqIGuGtxrc4atzRfLPlJH4w89EBP/dwOaokHyEFm/amJ5r809Y/oUmNHxz9A/54+h8pdRv6/jJP0YjPIROXBH7Hq66vML4o9zsbt8fLo+O+Rv6cA9vVlIyfwWPaavZ0Dj84NFssoZGBuKpzxd3P88zt13J81bFMK5zGja/dyO83/Z5mKdA1LwVl/RuaJ9o7OFLZTEXeMNwTy2YwP+zkWO39wdv2YPGpn+MZeQT2jhkUxnOb1C1JntfLOjmLSN6EtONuO/zA8Qeqml+j2F3MZfMuY/W41fxy/S9T9SaShBNhfvDmD5hSOIXPL/g8Wx+6kd9zEzef3HdlO0UzVtiifPCgqd7YbQo/PUrnnrrbycPFI9se4Y5372BfYB83r74Zr8NwBOhsa8J297Gcz9P8/rIVTJ40mXOXT+DXFy/lm2cMHLU7df4RrLjuURzO9Hxj+wpORgojYjscNVy2vTJdRVTg9CGlwpz8ozix5jS+9/gmfvGTb6H8aDyf/t8n+dSdr1PpMdRUz+x5hr988Je0/h82f4AiJaeJuj7zUhTBl4rXck7TnayZ90VaIi38Zetf+rRL8nbj28S0GO073fjDCT6+Yna/bQ+EOUUwUY1T29qdqiYQD/Dotkc5ZdIpTMifwKySWfz6iJ/zxWY4Otr3s+UCMeMU1o6/POuF1wGNJQR//9JqPrlo3OCNB6B6+kK+q36OLdHhG9SzJSuhIYQ4TQixTQixQwhxQ4bzQghxm3l+oxBi6WB9hRAlQohnhBDbzd/FPc5902y/TQhxao/jy4QQ75vnbhPDSQuZBU67wpcn7+UU/8Pc8ae/csfH7mRV9Sr+d/3/sjnwNOgu8l39C4RP1v+KB50/pMqj9dumP1x2Gz6i5NmH1req0M3p072s8/+Dx1qHn5xuKBSVj+PmslvJm//xtONOp5sVsd/wZpURxyGE4LtHfheJ5OY3bk5bJf/mvd9QF6zje0d+j3W7u/hi45k8Oe9WJk1IrzcBkG+qdEq9w6vedvrJp/LPosuIdSzj+X0v8Mctf+T8Weezosqoh767NcSVf36fd7UpfOq0U5hYOnSPskxUeY0Kj8/UPkPA1MuHStPjXwoSnTxUV881oXK+8runuP+NPQSL57Ku5rN89cRp7G5s4993fpULpn2G42v+f3t3HlxVdQdw/PvLSlZCEtYEEpCQyDLsEJUlCMqiY1IVwbKJBdTBIkiHatfRuo8bZdC2ssjSkYJaTN3AolJ0FIxalUVZw1KBIBIgkCYvya9/3Is8s76QhJSX32cmk9zz7rn3/JKX+7vvnHvPzeDJnCf58phzw+Le/L28ffA9yjyxSJ/bKm1DwNB5DCqaz8lTyQxJHMLirYs5WVT5+MymQ5sIIYgHdv2BB/oWMqRL/feLAyR07sn+oq7s8RrvWrNzDQWeAqZ2n3q+7Z5QUk61JDyibnei+6p/1kwGTq/bwPTFFt0smPjIEA7lVey6rG81Jg0RCQQWAqOBrsCtIlJ+isfRQIr7NQN43oe69wEbVDUF2OAu474+HugGjAKec7eDu90ZXvuqfN6IejA0azprB2WzcE88j79xlGcy5rPg6gXEl4WQXpJX7T0BLScvZecNrxHZvPZZP6LwW3oH7KIoquJBsybTEp0BxXdChtW67oUIDgzgjVmDGZb248feSkAAZ4JjKSg5n1jbRbZjdp/ZfPjthzyV8xRv7n2T7D3ZrNi+gptSbqJHix78du2XxMbGM+YnkyrdX1AzpxssoeTCZs+VgAAGT30Ez+khlGkpbSLaMKfvHLSsjI9WP8Wt819n14kywscvIW1gzTOU+ur6snw6FJfyyJZHmPuBM/gbGvTjTyOBYdFsKLyOvrsXMevw/Tx7Sw9+d8dErpj+LFOu6ccLg85y88nlLF2xnD9c+RCtw1szd+NcFn21iLH/GEt+UT5nj2SRktS+sibQu2sqgeHNWbftCLN6z6KguIClW5dWWE9VWZ/7PsWnO7G+xW1MurFufe3VCQ4OJiqwM/mlB5j81mTW565n5faVpLdNp2vc+UPM8YA4pnvm4km6OPdpXKr+HPgEE3bNbvD9+NJ/MgDYrap7AURkFZAJeE9/mgksV+cU8mMRiRGRtkByNXUzgQy3/jLgfeCXbvkqVS0C9onIbmCAiOQC0ar6kbut5UAWcP6i+Hp2yzWD+D5kDxvXvcLRneN4OnI+nU+PIy20+hvEIqJi6NIn44L2GRwezfrS/kRFJdS8cjndMsYxYXMh+RFdqXmqw4Y1IfCfTM7J5pqdLwBwU9FahnneI6RtGsu2L/thvYCyaDZtHshbGybwuCeXM+PWVDlVRFRYPHggLqb2v5tz2sWE8eC1/flmYxjNNZSsBTnEFR1k+X8f5t7YKQy5/VHaNK/fvuz4ll2Y+EVnlsSnUxq+ntOhkFTuLD8kPIpnSsbymXRjTkZ7svr8+KThqjE/5W/Siic2FvHSgk/5Tdm3zGsD8z+bT2hRd5Ye/YRXyvIqTMV/TlBgANOT8hi5fTYL999NaEw/Fn+1hL//exFnaY5HmhFMIWF6ihNBQkzgMEZOn9fgXTQTgjpQ8l0pK0r3MDdvLgCzcvOY9sUK9gd2oG/J59xRuIhkmV3llDzGsSt+BFv357H56Y3cW/hH1gWP4PF7ZxAaVL9/Q1+SRgLgPWp3CCg/SlvZOgk11G2tqocBVPWwiJw7XU0APq5kWx735/LlFYjIDJxPJHToUPszdm93Dr2MXsdCOZKbSqdWURS3GU5iA31cB2jVqg1Hhz5GRv/KzxirExLajEk3ZuIpvQjzLdRgeOdo8g6nkeJegdLsVGtOnu3EgLB5eDhNt4K3aVm0jY1x8whuFUlBaHdKmvdgaLfkKrc5cdBdHNgUwcDUwXVqW1b/znz89Qj2FwSREhMJXM67rVcx9uoR1U4RcaEuH3Ata/ISSfGU0KkwgX4nsukx6sePSo0MDWLmsMsYlnoFvZJjK93OuNEjKIzex5bc74k93InMoiR2hKcSH9af4rAHGdUhrtpPwNf1T+X4d11oGRNLz9BJ5JeE0MbzBbmhSZwJDCOitJDk4n0cC+7FQzdP/+EBWA1peM80zm7szHGZzI6AY4R5dtA+YC/t4poTEhJJXGEsJ/I7Mio1ibS2F+fmvktVl5Ez2PTBPlJUKTmeSKuIFgj134MvNd2NKSJjgZGqOs1dngQMUNWfe63zBvCoqn7gLm8A5gGdqqorIvmqGuO1jROq2kJEFgIfqepKt3wx8CZwwN3HCLd8MDBPVauehhXo16+f5uTkVLeKMcaYckTkU1WtcNWJL6dVhwDv095EoPxNClWtU13do24XFu73c3NFV7etxBraYYwxpgH5kjQ+AVJEpKOIhOAMUpe/1TkbmOxeRZUOnHS7nqqrmw2cm29hCvCaV/l4EQkVkY44A95b3O2dFpF096qpyV51jDHGXAQ1dlqqaomI3A2sAwKBJaq6TUTudF//E0730RhgN3AWmFpdXXfTjwGrReRnOF1PY90620RkNc5geQkwU1XPXX96F/AiEIYzAN5gg+DGGGMqqnFM41JnYxrGGFN7dRnTMMYYYwBLGsYYY2rBkoYxxhifWdIwxhjjM78fCBeRY0DtH8DgiAe+q8fmXAqaYszQNONuijFD04z7QmJOUtUK01/4fdKoCxHJqezqAX/WFGOGphl3U4wZmmbc9RmzdU8ZY4zxmSUNY4wxPrOkUb2/NHYDGkFTjBmaZtxNMWZomnHXW8w2pmGMMcZn9knDGGOMzyxpGGOM8ZkljUqIyCgR+UZEdovIfY3dnoYiIu1F5D0R2SEi20TkHrc8VkTeEZFd7vcWjd3W+iYigSLyuYi87i43hZhjRORlEfna/Ztf4e9xi8gc9729VUReEpFm/hiziCwRkTwR2epVVmWcInK/e3z7RkRG1mZfljTKEZFAYCEwGugK3CoiXauvdckqAeaq6uVAOjDTjfU+YIOqpgAb3GV/cw+ww2u5KcQ8H3hbVdOAnjjx+23cIpIAzAL6qWp3nMczjMc/Y34RGFWurNI43f/x8UA3t85z7nHPJ5Y0KhoA7FbVvapaDKwCMhu5TQ1CVQ+r6mfuz6dxDiIJOPEuc1dbBmQ1SgMbiIgkAtcBi7yK/T3maGAIsBhAVYtVNR8/jxvnmUFhIhIEhOM87dPvYlbVfwHflyuuKs5MYJWqFqnqPpznIA3wdV+WNCpKAA56LR9yy/yaiCQDvYHNQGv3SYm431s1YtMawrM4z7Av8yrz95g7AceApW633CIRicCP41bV/wBP4jzk7TDOE0XX48cxl1NVnHU6xlnSqEgqKfPr65JFJBJ4BZitqqcauz0NSUSuB/JU9dPGbstFFgT0AZ5X1d7AGfyjW6ZKbh9+JtARaAdEiMjExm3V/4U6HeMsaVR0CGjvtZyI85HWL4lIME7C+KuqvuoWHxWRtu7rbYG8xmpfA7gKuEFEcnG6Hq8WkZX4d8zgvK8Pqepmd/llnCTiz3GPAPap6jFV9QCvAlfi3zF7qyrOOh3jLGlU9AmQIiIdRSQEZ8Aou5Hb1CBERHD6uHeo6tNeL2UDU9yfpwCvXey2NRRVvV9VE1U1Gedv+66qTsSPYwZQ1SPAQRFJdYuGA9vx77gPAOkiEu6+14fjjNv5c8zeqoozGxgvIqEi0hFIAbb4ulG7I7wSIjIGp987EFiiqg83bosahogMAjYBX3G+f/9XOOMaq4EOOP94Y1W1/CDbJU9EMoBfqOr1IhKHn8csIr1wBv9DgL3AVJwTR7+NW0QeAMbhXCn4OTANiMTPYhaRl4AMnCnQjwK/B9ZSRZwi8mvgdpzfy2xVfcvnfVnSMMYY4yvrnjLGGOMzSxrGGGN8ZknDGGOMzyxpGGOM8ZklDWOMMT6zpGGMMcZnljSMMcb47H+bB7k4+ij4rwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP reference implementation\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"Raw CM\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against the ibicus one:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems that pr is reproduced quite well and the values of the reference and ibicus implementation are in agreement. This can also be checked through a linear regression:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=1.0000577823976702, intercept=8.037901231378822e-10, rvalue=0.9999993853973769, pvalue=0.0, stderr=4.834882356821364e-06, intercept_stderr=2.2981006288726986e-10)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**pr is reproduced well by ibicus. Some slight differences larger than floating point error exist. This is due to:**\n",
"\n",
"- Randomization: pr includes some randomization between the defined bound and threshold. This can lead to differences.\n",
"\n",
"- The references implementation of nonparametric quantile mapping (preceding the parametric one in step 6), which uses linear interpolation, is inexact and differs from the ibicus implementation of nonparametric quantile mapping. This creates some differences. These decrease with the number of quantiles increasing, however they are slightly bigger than floating point error.\n",
"\n",
"- Accumulation of floating point errors in calculations. Especially floating point errors in the computation of quantiles can lead to slight numerical differences (larger than floating point) if those quantiles are mapped back to values. Similarly the distribution fits in step 6 are just slightly different (within floating point accuracy), meaning that the same values are mapped to slighty different ones (with difference potentially larger than floating point error) when transformed using an (inverse) CDF.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4. ps / psl "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean sea-level pressure -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:21<00:00, 5.47s/it]\n"
]
}
],
"source": [
"variable = \"ps\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage agreement is 100.0 % between the Ibicus and the reference implementation of ISIMIP.\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(\"ps\")\n",
"pct_agreement = np.sum(np.isclose(debiased_values,debiased_values_isimip))/debiased_values.size\n",
"print(\"Percentage agreement is %s %% between the ibicus and the reference implementation of ISIMIP.\"%(pct_agreement*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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YVDT6UExNpNk672Z3YvaJSOHju/VfxuNuHETpRjfH5YpCCPGYEKJKCLG9zViqEGK5EGJf5HdKZPwcIcRGIcS2yO+z2uxzQmR8vxDiLyLy7RRCWIUQz0TGPxFCFLTZ57rIOfYJIa7r1yvvghZ3Ezt2bOGi2TlYTSpJTjtmq4OSeu9giTDqmRnehKNN8EC0edHbSfH3za4N6SajHcp2akUiL3zavmLq22I20pTV5TESUmZQplool8lxn3c44Nm9Aoe5mrGdJBMCLMheAMC/rBPZcnBoVhV//Pg+7l37uyE591AQ0kIcatIz/Y8rRQEsA84/Zux2YIWUchKwIvIeoAb4rJRyFnAd8K82+zwI3AJMivxEj3kjUC+lnAj8Cfgd6MoIuAs4EVgE3BVVSAPN7tXP8q76ba7K0/+RQghucHzApENPD8bpDYAPlJlI9WiiWHRF4Qm64z6Gq3krAOWeUs4r2EDhxz9FC+t5GMGwRki4cZg6908A5CSNBUXjcNPQPHF7W5pZ/+crefqtVTR44veVFAUEPkWQmzyu0znp9nTGJ01glT2VVaWDH9F3qKGYJ3cv4+VdT42aiMJSdylBTQ+sqHCPnNDkbhWFlPJ9oO6Y4YuBJyKvnwAuiczdJKUsi4zvAGyRFUMO4JJSrpX6J+LJ6D7HHOt5YGlktXEesFxKWSelrAeW01FhDQjPVefzB+V6ps0/DQBfyMcZrGNG4+rBOL0BUI8JVUlofR9dURR4Pon7GPm+SD9saxItiZ9yRuhDtm9ZD0BjXQ1ZpjLSujA7AYx16SGydSUf90j+tlQ3+3luQ+9MVy8+u4yFDW/ym1XVLP7tCv75xGM01HRfo2lrWP97FWZP7XLe4pwTMTmK2bV3Z6/k6wt3vf83wkLQpEJR4+FBP/9QEPVPjA2EqGweOb6h3voosqSU5QCR3x0DteEyYJOU0g/kASVttpVExoj8PhI5VghoBNLajsfYpx1CiFuEEBuEEBuqq/umpT2BEC8fkDTOuQnVZCIYDvK5lz/HPeNO5trQT/t0bIP40MJhUkUVdo7exKMrioxw/KVU3rGchJAqX5j8BbYGD3OC9jveq9MrxbrryhGqhzS6VhQFyXqdJFH5Xk8vo5Un1u7lx6//j1p3zzK839pewU/2jOehOc/x7LfP49J5eVx68C52vfDrbvc93KjfhPISs7uctyh7EVIEuK3lVo7s29Ij+fpCcUMJG+veRfMWArD8wLpBO/dQEvVPJHozaQo1jZiV1IA4s4UQM9BNSF+NDsWYJrvZ1tU+7QelfFhKuUBKuSAjo3PnXTxs/GgFC8ObuXCGrvvWlK6hvKWcgFpGky9EU4x4fIP+xed1U6geJjl8NNLIrJpRMPM/02lxH8et+TEJB1+Y/AUkkqy8LWw8rJftqDTnU61asKfN6vIY2Yl6GY81jjm9uBKdmoM/IXHcA1Qc3hj3PpWlB3n4hdeZmeeicK5CisvDbz8/m787v84bypJu959V8RAAWY6ufTALshcgENxvX8R9H7d0Obc/uffV2xBofGfG7aiawtYDbw/auYeSg40HMcskCgMBwoRoCjQNtUhx0VtFURkxJxH5XRXdIITIB14CrpVSHogMlwD5bfbPB8rabBsT2dcEJKGbulrHY+wzYNg2PMSfLA+xsCAVgP8d/B8AwXAZvzE9QtUho4jaQOPVzGwjn7A9v924SdgJaPGHqeZo+3FiIjchl9PzT0faVnPjoe+jhcNUuhsRIky6I7XLY0TLeFT2IH+jA+HtaEJQ0hLfMfyhMEX/uo1HtLtYMv9TfvD+d/nsS5/lgc0PcCT7dD5q6r6PxCY1u538nZFkTWJq6lQqsxN4ZUc9myKKdCDZW1XCR+F9nO6zc9Mpi5kZ8HPEvWPAzzscKGoswhRIISPiKxsp2dm9VRSvojurifx+BUAIkQy8DtwhpfwwOjlinmoWQiyO+B+uje5zzLEuB1ZG/BhvA+cKIVIiTuxzI2MDhicQ4qbGr/DfKfejmkw0BZpYfUT3SzSF6zlX3UBDxcjsTTCS8IQgoEgslvaOZicq09gWV2lsqWnkKYdI0vS5X5zyRVqEl312NwcPHyZwZCUAWdaui+I5zU5M0kSid3OvriUU1njHpDuUD8vYfS/asr/KzaV//4hvNVzF43O+wpP7H+GccedwxpgzeHDLg2znDtJa3iAU7Nqx/alMxSwSWvNQumJR9iJqQ/s4M2ELVf/9Zr+2m43F7Sv/RlAIrlr6IEJRqDKdQ5HiIxg+vlfrUkoONBzgYv82jgT1Qo0jJfIpnvDYp4G1wBQhRIkQ4kbgHuAcIcQ+4JzIe4BvAhOBO4UQmyM/Uf/FrcAjwH7gAPBmZPxRIE0IsR/4HpEIKillHfBLYH3k5xeRsQHjvd3VNAZNnLBIN2+8U/wOAS3AGWPOoCHUxILA39hhnjmQIgwKK958gcfXDN+yDf7mGmyKm2PT4BIRJCr11NZ237zIH9JYw0Q0u36TXpyzGLNi4V7LYtZVqXga9gGQkxi7u11bUjVBRnhXj68D4GBtMwFLAwBVLV0/Pa5/8X42/O06yhu93HhJOk+1vM6CrAXcc9o93LvkXpadv4ygbGZu8rNUHtnX6XF8gSBB6kg0dX9tAJdNvgxNajjHrWdGy8d8tLV31xovB1rWkypmc8qk+QBMSpsPIsS26oE971BT5anCE/Kw3buQXWHdlHncKAop5VVSyhwppVlKmS+lfFRKWSulXCqlnBT5XReZ+ysppVNKObfNT1Vk2wYp5Uwp5QQp5TcjqwaklD4p5ReklBOllIuklAfbnPuxyPhEKeXjA/VHiKKt+h3XOD5mUWHE7HTgfxQmFXLWGD0dxGJtprRh5OZSSCl55bknWPrJDRS//++hFqcdWjjculLQaoswK16Swu3NTNaEsawRkyj1O7o9XpM/hKYGcJj1VYlFtTA7YxbWhENsPFTPBuc8ALKSY8ZHtENYClitjO+V43HbzhUIRb8uW+lbnc7bUlTBuk83kONs5trz9vDEwZ8zNnEsfz7zz61tTE/IOoE8xzheVaew39t5NnltyX5mWbaQG6e8hUmFfHHKF/lAHuTLrrv45apaQuGBWVUEgwFUpZI58uj3aGmG7gd6f8fIbBAVL1FH9jrfqVyarJv4jhtFMVrw+kOMq32fi1IOoyqCUncpn1Z9ymfHf5acBL0g3ZcTXmfGgUeGWNLeIaXk92/v4TsbU/mpvJVXAicMtUjtKPndIvb98VwAap0TaREqqquw3ZwkWyJC8VHW0H2105bachLUelLahETMz5wPlhKu3HML9T79i5rcSeXYtiTaMgmrLbj9Pe8GV1l0tI9JKZ2bgd4/2Mj9yan8OL+Jx3Y/zJzMOTx09kMkWdub38anTaLZ4uVAfed9OSq8giI1gdSEMZ3OOZZb59yK0+wkffx77K1o5Pmn/xn3vj1hb2URQUWQ7jya33HSmPGkhjS2VcafdT8SiSqKbOmgMCUVkwZVLSMjl8JQFBGaAyGemPE46oW6Fe21A68B8JnxnyHHqSuKBHMFie6iIZOxL6z4zx94ZtUmvnRiATNmL+Dm0NPd2rkHk/KgndXufHzBMM1hCULiPOYmnmZPIFWtwbKv+yfPQE0RKH5SOXpDnZ81HykkK5VE0mpXAJ1Xjm1LjsmGxVRPXUXPcyGWmyI3aykoVpM7nbfuwMtYM1ZyYs6JPHvRszx09kOtDyhtmZxaiGKux1/0fqfHOhSw41FDZKfH3w0w2ZbMV2d/lT1NG/jG5De5cv+PeG/FG3HvHy/b6vS4l/SxZ7aOZedPoCEwix2h47uMSFFjEQ5N8Jj6D2qnfolAKJmS5v7rAT+QGIoiQmaijT98cQ4LJuQgpeS1g6+xIGsBuQm5rSGGH2aeyY/Ctw6xpL1jbNEzPJj0JL+6ZCbjg/u4RX2dpi66vw0mzb4gV3jv4LeBK1hXVIe/VrdVu5T2H88Uu4uwEkTWd6+sSxOm4lUUbKlHE87mZsxFoPCodSbhQDlCgsvi6uIoOnmqQkgNU1va86KQpb5SLDINIZ24g7FDIQN+P1N9/8AsFX6/5PdMS5vW6fEKXAUgoKCi8yf+I1UH9RaoyR0VTVdcNfUqxiSO4f2UUu5J/Slfe0+wraR/b957a/RgkGkZR4sVCkUhxz6ZFlk2YsJFe8PBxoOIYAbv53+NzEQrMpxIpbGiGLnsqd9DcVMxF46/ENArmKbaUlHMDdS4A/iCPWvHORy4S/kWq/K+ihCCyslfYpL/Sero/iY5GBTX6L6I75qex/f+X1Cr9OzppGM+nWn2dNyq4HnLRd0es6qlAYAU+9Hw1wRLApNTJmNyFPMu01CFE1XpvBZSlPRs3Uy31ZIbz+W0Eg6FsMqd5JjSyQ57GeP5KOa87SV1vGbLYoJ9AlbV2uUxxyXpJps/mM7tXN6DD0Xm9kxei2rhxwt/zMGmg1TOrCYtwcJdT75Bc2P/xZCEDr0MwJyM9r6hs+z6dW88HH/m/Uhjf/1BGrxjEBPOIl+r4ETtCA3ukZGRbiiKGCw/tBxFKCwdu7R1LMuRhRI+zOPm31F5eOT1pdjszSSYOhmApAQHIGj0Dg/Tk2f7/3jJ8jMuMG8iVLmL/Sl6Ux3nMS08cxP0G9+R5u7TacIVesmNdGt7v8CC7BOw2Q+yyLIBi+jcIdyWglQ9n+NIY8/MBCXFu2ixtDBNtaKIJErblCRpy4qiUtxWN6dNPK/bY45LjITahpo7fWBZZyoAYne2644lY5Zw27zbePfImyyZs4pH/D/gwLrOnfA95YAwYQ+rpB3TfvaUdP263j+wpt/ONZxo9DdS768lJyCYlmElNS0Vc8hKQyj+2mVDiaEoYrD80HIWZC0g1Xb0aTTHmUOT1kiGaKSmZmQsF6N4vV7ODn/AWFU3NWVQz+2m/xAsGbySDV1R0RzEI628d8q/+Hrz9eyr11cYSfb2K55cp64oLnT/vdtjinq9IGCavb0ymJ85n6Ai2W8PkBpnVFC0jIdW0blfIBYfNPoICsG4cecQsBRQrsR2Zu878AwAS8ed2u0xEywJJKrJjLduo+RQ7HImmzQ9Kqw3igLgplk38flJn+e1qpf5hXMOG5qSe3WcWByUoMWoxDPlxC8Q9mewsWlklXOPl6JG3Vz6c+0NpiSHSc3MZ3XwVNzS21okcDhjKIpjONBwgKLGIs4Zd0678WxnNnVaMxcFfsNeUdjJ3sOThqoj/NXyN6Z69BISSaYw16tvQ238dZMGktXaPL6VPZ9Sq55aU3FQr/uTZG0fBhtdUYSUWvyBrldDG1ynAJDhal/raH6WHrvfpKoki/g6u2U69RuuGqlGGy+bKvQs/hML5uE0uwiLjk+PwYCf9MBLOKTK1NSuC/hFGWPLINN6gOZ9HU1ZoWCQgKcIBVNcjvpYCCH46eKfclLOSazILOW12v4ztXq1ahJjlHZPT7BiC4/niGcnIa3n0WXDnQMNepGKJ0M3k5aZj6oInGoKIFsj8IYzhqI4huWHliMQ7cxOoK8oPKEWVJOPkvqR1e2sWqRwjv/3tBTqpo3EnAlM8S9je8rSbvYcHIpqW5DOLbxz5EV+51zGNzU9BDnB2t5Uk+nIRKDwoLKEiuaun8Ka/LpT1GVrvypJt6eT59QjkWyp0+OSL9maDFJlpbXrulDH4i1/EyTMzJzMRH8JdqUev6/9Z2dbWTNv2jOYmDArLn8JwMTM6Ww2pbDW2rGRVunB7SyxvE+qsHfZkKk7zIqZHy/6MQiN2ob3CAZ6VtAwFqFgEJNSyZxQ7BX5JQQJ0cLmys19PtdwY3/DfoQ005x+ASISpHG9pq9QR0IuhaEojmH5oeXMzZzboUZOtlN/Mr094THm7v3bUIjWa2q9kn0yH1eafg2JNjOqolDfg/4GA8kvqr9KSDTQEmyhOtPG21LPWrWb2pfXMCkmUq0ZKOaGbhMfUxvXArGjmhbm6M7p6VnxRQUJIbCJZOr9PavLEwjuJjOsYDfZMTvH4VMUqo7pa/HOvt2EzG7OnNS9fyLKxNQJSJOX3dUdncy7Gi2sVfJJT+iZIzsWhUmFOIWNpY6XKNre+zLrUfZWH8avCFxJsaO6Tsmajyrh5d1vxtw+kjnQsB9XwMHi1KOfW2nWH1gMRTHCONx0mL31ezuYneCoovCYg3jDvX9SGwqC5bv4nPIhaVY921YIwY+sL1JY8uoQSwaNbi8rxNHif9vGjOE5eTLQUVEA5NgzmGLZBt3cTBJ8ulkt2seiLfMzdfNTii1+00yG6iRbbKOlKT4zgaZJPlZTMdtmA2BL1Uu/lBzTe7vkoF5wYGnBKXHLMs6lO37TKp7qsG1rnUKdJcDkzM5DbONFEQoz0mbxliWLDQ2xHfE9YWuNXvo8My/2tc46+6v4Wyayuix2dNhIZm/tXpYESjmFza1jB3KuAUZGYUBDUbRh+aHlAJw99uwO26KK4n+us3jacfWgytVXXCWr+Ivl76REIi/Xlq3F7thAVlPP8wL6m+J6P39T9bLZYxPHss/9CVNNuj3XYepYqmNs0hgaTSEam7uOFllpXwxS4DB3PMbC7IUoQmn9n8bDBEsyzRYPJcXx+XWK65rAUs3YpEkApDt0pVTacDRyKhQMooY+IEUz6/kRcRKdOznQsUZmw6HVCFML01LjT7brisVjTqLF2sKq8r6XID9QpdenmpYRu+tepstGlnkeDaESjhxHTu1GfyO1gTpWeM/FOuuS1vE8l+6rqfIM/+AYQ1G0Yfmh5cxMmxkzIzbDnoEqVDA10BIYWc62j1I+y7nBP+BKSuX9kvf5+rtf58/paTyU+I2hFo3iGjeKRV96Xzv9Wqq9VRQ4dWe23dxxRTEmaSy1JsEq8+Iuj+sLuVGFA0V0/IjnJ+bz0ude4ryC+M0948edQaOqsM7fdbXZKNu3voIQGnOS9M/S2LBeYtx/+GgDpO0Vzay0JTMx+cQe+RPyE/MRCH4hLqS+pb358OSGPwAwOWVy3MfrirmZcwHwlr/Q52MFjugZ9XMyOjeLfc2hf7de3/Vyn883XIg6sqv9kxlfcFRJLm75iISwRmnt8K/2YCiKCGXuMnbU7uCcgo5mJwBVUcl0ZJIb2sAPG345yNL1jUqviQZHAesq1/Pd975LSIbQlBbqW4Y+LC9z0/18xv46mfYszi04F0UofJToREHBonQsy52bkAtCcqix61yKceHtJMjOb77jk8djVsxxy3lCru743lweXw7Nvgo9wuzEPH2/jPQJAJSIo4pmzaFtYPJySuFZccsBemJcqjUbaamjqPbok36jJ8gfFT3EdlLKpB4dszNmpM1AQbDQ/BIVR/oWJbdHcWANm0h3dR62u3D+JST7bbx16MNO54w09jfof7fzzM24bEc/c9b0AixhK+Uew/Q0Ysh0ZPLouY9y0fjOs35znDk0qEEate5r/A8nxlStZJFjOd9a+S3GusZy5ZQrCcsWrmvqPh9hoNkRHssuSxKFSQWk2FKYlzmPgBbEYXbEfMqO1t06r/b3XR43gWpcWv+1mZyapt94raX/iWv+RyIZpGDmWN1xnpcVURTyaOZ1zRHdz3LqmHk9lmdcQj5Z1v007T6a27G7ookyi4bLnNYj/0tXOMwOCp3jeco8hQ018YUTd8ZhLQh0HUAwfto8PMGTOejdjTswMpLRuuNAwwHsmuQm0T4gwFawiKrgGKr8w/86DUURwaSYWJSzqMskpSxnFlVmE7eHvz6IkvWds+v+S43zHVwWF/88958UJBWAgIna0Pso/heYT4VFtDpozxyjF4uL5cgGyEvQk7UOd9HrWtMka8V4gvaCfpMzw56BXVOo1+JrsljjK0eVya2Ng3IS9N4QTW0iXIKNGwAoSI6/ymuUyWnj8VrcyCNHbz6V+zaQaCticj+tJqIsyj8Rj72WdYf7VvfJr1XgMnXdnU8IwUlZJyMJs7p4ZZ/ON1zYW7+fFn8+62bc1W48y2VDhhJ7HE03FBiKogfkOHPwanW0BAIjpik6wPcsd3LImsrsjNmk29NbS1dfHv4h/tDQ1q0qqS0lLFpaFUW090dniiLqgP63mNdp//KWQAgUHw5T3yN1ogghSLBO4n0lI65eDYnBLeTIo0/gVpMVmyZJb1jdOvaBZQImzdVtfadYjE+ZgFdR+Hnj4tbPYvqeJwhb65iZHl/iXrzMz56HUIKEix7r9TFCwSCKUsWcYPeO2xszTLjCYV7b8kyvzzec2Fe/n7A/m3FjC9qNpztUruITPIHhX0HWUBQ9INuZjUaI/9juwOMeOVUuj3hMBIS3NVM32ZoMgFA9NHqGzk/RUFPBI6ZvAuirHGCMawwTkyfiNDtj7mNRLSSaUlHM9ZR1kkvRUl9JilpNmuzfoIMxCYVgqeJwXdcJl1JKmlUPebRPoDNpDoradJ1rDlfhVDtmKcdDVLGWuA/x0X59lfIn9WyCQjA5tX8c2VHmZswFYHL4RbwtvTOT7Kspw6coOFLndzt3+okX4PSMY72nCE0ObFvWgabB10BjoI7FwWqmZ7T3uZnMZqrC4/GLMJ7g8E7iNRRFD4jax7erqXh8fc9UHQwCfj9XBF8kIN2tduuoovia5RncFQeGTLZDdR4eUU8E9NDYKHeffDc/WPCDTvfLtqQw37IB3+4VMbe3NNYSUkPtmhb1BzMSM1FMLRza8lKX88oa3dSqAiXjzHbjQSWdcvSbRTgUwi4Okaf0fDUBR0NkL3G+ifmVrxLWJDvceq+H/op4ipLtzCbFnM4fLSexs6Z3SZrbqkv0Y+Us7Hau1eYgMekC/DSzt25vr843XIg6sr8c2sqYjOQO27db9VD84Z5LYSiKHhA1e/xOXEgz3bfjBFhXVMeflw9dtdnG2nJutT4LyFZFETU9pZiqcDcO3Qf0gNvCclMhilDJSzxaKG52xmwW5SzqdL/8xDzKTCYqvbE1QY01F48iMKXO6Fd55+fpyXM7arpWrtsqihFCUpjU3vdgF07Cmp6wV1FxiBZTgPExwnfjIduZjVW14k9R2dVo4tMdu5hs/ggFlfFJ43t1zK6YmjqHkL2UmubePSAdrNgOwPT0sd3M1LlonL7yeGPbK93MHN5EQ2MfcP0ZRe1YoiXNrq8wa3zDOzvbUBQ9ILqiEOYGWvzx2fY/XLuGCz+4lN0bYj/9DjTVMoXTQ78GaK2GG11R/F5+jjJ7/z599oTyinJMliryE/J6FKpakD6RGjNsEbFlr27Rna4p9qSY23vLrLy5ALyvde37KI104JuW0L58yKRgGWlSb9yzudmLJgRJYzomd8aDIhROzTuVTbYGfh7+Em+/+Qp59p3k27Iwq/H/LeNlTsp0FHMTjWW9KwPuL31XP04XORRtuXB8NgWBIOsPD833pr/Y17APNBtTsmMHGFzRopdwH+5lPAxF0QNcFhdWYeEL1lehZF1c+xwKpVIrk3hz06EBli42dZ4gPpNuq4+uKBLMCahCRageGoaw3tOJu37LRNu2Vv9EvOQn5iFEmKKGipjbg0c+ACA9xhNcX8h0ZKJIO6WerhOkSppLAZiZ2z47OmSbQGXEcb2rWj/G1MzeP/1fPe1qmoKNzJx8kPfq01hjzmRaZs8KF8bLrFRdzuaq1d3MjM02NRmzZiY9Kb5s+Kz8iXhCC9mp1Y2IMtydsatyB/l+yckJsT+r3jS9sGO9r2EQpeo5hqLoAUIIUiwpHFKdtGjxPbWdV/lPNssJ/PVgFodqY5dBOFDt5gfPbsY3ABnfodJNnGfWv9xRZ7YQApclidPMn5BW/Fq/nzNeXhOncsRsbnXMxkt0Zbek5O6Y2z1u3R6ebo+vMVG8CCHIx0le6AOkpjtZNxTX8c6O9jeBLcIFUqEws/1TpJI4kYASwhMI4i7Ry8XMzux9yfoFWQuYmDwRzfo2L9l/hGZuZlp632s8xSIrVTcZbUnsec4HQFm4BSHjL5kCMD7zTDThZ2P58Oib0hsONhUxJ9jEpE4Wt9rYCwCocA/vUuOGoughDmsyn1JApSO+WHUZ9JKXoGBXQrzz9v86bA+GNf715MP8dsdZlOzqe4XOY7FWbOB0i37cto2YUmzJCNWLNoRZoW8ExhES4R7VOYKjfSk2KLFj8nck6E/V6anx2cN7Qr45g8MWqK6vp6rJx/Wv/Yj/e//rrYoDoMZXgUmmdigbnmpJQAjJkboKmloOoEjJ2OTeV3kVQnD1tKs5HCjjuym647y/HdlRUuzJADT2MgkuFC4lVe1Z693LCuaAhDc2/bdX5xxqar21uLUWXvBcRt7sM2LOyU1KQmoqlU3Du96ToSh6SILZAYoftz++p/9fiq/ywYTv84/Ml7l6z2001lW12/7vN1azusbFw+HP0DAAPaw/Sv08v5CfByDZltw6nmxNZr1pIisTLu73c8aLRdMb+/R2RfGaNh4tRvZ1g1/3UaT1s48CIDf/LFpUyabKOv7z5Jch8RPC9r2UHNrVOicpuIkxMeQq9Osrj5qSTaw3ZaPIdExK37KdPzP+M7gsLrak6KuogVIUiRZ9deZs7t3TvVWpYUocORRtOX3KRKYGgmyp2dircw4lnqCH947odb3SrGNJtHcsRwNQ6N9DuhaguXZ4t1c2FEUPSTDbmWI6wNgDT8c1v94TIMVpIee8b3Nz8Hs8tfloduv+7Z9w9YbL+HxqEfeGrqRayejiSL2jtiWA2RYk0ZLYzmGcZE1CMXloaNM3OxjwU1dV2u8yxMLv93Kb9QGg54rCYXZgV11IUz01bl+H7Zm1+hc0enPrT07I1ZPZHv3gFd6w7cIkBSEheKdED4OUUlKrhslSOuaBONP1UuNHwirucBVOtXetSttiN9m5bNJl+MM+XBZXr9ufdodNtWGSkOTb3eN9w2GNBkXF5+xZFJojIYk69QIOUj/s8wyibKjYwOde/hyL/7OYu9fejUkKvmLuvBJuQvZEQuEEaoZ5/m63ikII8ZgQokoIsb3NWKoQYrkQYl/kd0pkPE0I8Z4Qwi2E+NsxxzlBCLFNCLFfCPEXESnkI4SwCiGeiYx/IoQoaLPPdZFz7BNCXNdvV90HXFYXDcJCjdr9Td3nbeFp8VNOaH6PCVPnokw4kwdXH+DD+69lxZsv8J3lbv6pfpFzLrsZgUazp/+/DFPKXmGc6WA7sxPoKwq7qOGS8vtbxw7+8xpSH5iOFh74bO1GT4B/qfMwC3Ovbm5ZagKnWT+gYf8nHbaFgnpDn4FQFAsiiqJE/Q/lZoWfLNb9JB9FnNMlDU00mzTU9NM77JuerpsrSwMB7OIwhb0MjT2WK6ZegSIUJqdM7lNXu67Qj5vIh9a5Pd63yt0EQmJO6LkpcFHuKSDCrCzuf7PsQPDc3ueo8dRw69xb+eNpf+beQzamOzr/PyemZVEXzqRBDm2FhO6I55O6DDj/mLHbgRVSyknAish7AB9wJxArW+pB4BZgUuQneswbgXop5UTgT8DvQFdGwF3AicAi4K6oQhpKnBYnNYqD7c6ObSiPpbHJTbO0Y7fp9X7u/twMLixQmFb/Hu+sWcv2Sh+TL7+bvPQkDlivYey+J/td3tmN7+FQqjr0T062JuNVgijBozbnFzmL/4TOoq65770HuqMpqLDfZCfNOiZmKfDuyHbmsc+UQIW/45J+XaRlaaymRX0ly5GFTVNpNgdZmn8Rl025BFVLpKx+PQBbKg4CUBijflNeQqQnRcVWmk0a49TY5oiekpeQx/dP+D7XTL+mX47XGSbhxKf1/LNRVhsJLjD1/Hq/OHEWqoRVm/r/u9HfaFJjbdlaloxZwtdmfZXEwGRu8t6FZ2Hn5fwTrSZUzYI30Lc6WgNNt99QKeX7wLE9Fy8Gnoi8fgK4JDK3RUq5Bl1htCKEyAFcUsq1Ui9M82R0n2OO9TywNLLaOA9YLqWsk1LWA8vpqLAGHYfZgVD8uH3d+yjqpZ1rg3fgHq9HNozPSOD3159Lys8Occu3fsLzXzuJs6dnkZCQxN+0Szlo7d8EMYAfWO+kyJrdoZpokjWJkJD8VNwE6CaT5+om8H+hm6hoGfh1cHNTAxZLJdn23jlzx6ZPptYk2RfqGEnjDbtRpC3uHtQ9QQjB2JQZJJiS+PkpP0YIwYwQmLRNSE2jYp8esDDD2dH0NCZRX+HU1UfatI6Nvx9Gd1w741rOGtuzcuU9JUlK0sM9751QW6kXnxwT6LnDds74iUzxwZaWgz3ed7DZVbeLen89729JZe/dc6j5940AzMjt3FcmhOBcuQOC5YMlZq/o7do3S0pZDhD53Z3tIA8oafO+JDIW3XYkcqwQ0AiktR2PsU87hBC3CCE2CCE2VFcPbPSAw6Q7sy86/Jtu50b7PSTb24fSCkVhQnYKCwpSW98/br6KXebp/S5vXUuAsHDHND2B3n1LSklV+WG+6n+cKeIwNfUN/S7HsSiHP8RlqiG7l5aSwuR8hBLkcF3Haq4TAhtJGEBdd9+Zv+JfFy5rDQ7ITFlEsVnlYG0tRd4GAGbkdfxfpiflgIQdQn/ImJpeMHBCDgAZYT8JouffrzKhK01HVs9DaxVVxeO8hArRgDfYdZ/0oebDUr2HRnP9eA4WXoVz3mU8ffNixqR2XcXhiJhC0zDvrjxYzuxYfwbZzbau9mk/KOXDUsoFUsoFGRn97xBuS7RY3W5T9/HvpqKVvGr5CRmh7p8WUmwCb0v/Lj/DoRDfDj5MQDZ1WFFEFcVvzX/A29LEkb1b+Krpdd623o591/P9KkcsSqzjaVBUklw9L7ENR0NkZxX9qsO2ED6csv9XE1EKkwqZmDKx9f2s8eeCgLf3b2KnZgHNREHmxA77KUIBzYnfrEe+zcnqmRN/qPEkTmeHyCLcwz4flQG97EdaL8OVp6aPByHZWT00SavxsqZkDbl+M7eO83LBV/6PMz//VU6akNbtflWWGQREgLA2fP0UvVUUlRFzUtSsVNXN/BIgv837fKCszbYxkWOZgCR0U1freIx9hoyoonjD0tFZeSxNQYUamURiYvdhr/8I3MG1R+7qdl5PaGyoZanpIySyg48iWu+pXlFpbHazJjSVqf5l/Dn0efaZ+refQSwOh+0gIN3VdSObzoiGyH5gKuiwbbOST9DaOwXUG86buACA3cXLqfFXYJJpnTqVrdKGFBKzBmP6kEMxFCSYk0D10dxJeffOaGnUzVU5jt6Ff59g0RXTrt3v9Gr/waA50MyW6i2c7nGzuGd5hSRHepY0B5oHQLL+obeK4lUgGoV0HdBl5a6IeapZCLE44n+4ts0+bY91ObAy4sd4GzhXCJEScWKfGxkbUqJ9EprjCNfba5/LDcEfkZSR3+3cdxMv5S1L7DasvaU27OD0sG4i62xFcY+8hDqZwPbSJvIzUvm3/Wq2hQv6VY5YtESqamY6exefEF1RrNHaryCDYQ1NePu1F0V35LkySQ+Br/E9koObmKh1XjhvXLhBfyGzeuXEH0oyCGJSW2iu6Zk9Xa37FIBcZ+/+JzPzdN/d3qbh27dhXfk6NDSedN/G5NO+0KN9z/R8BEBTYPi2LognPPZpYC0wRQhRIoS4EbgHOEcIsQ84J/I+Or8Y+CPwlcj8qLH2VuARYD9wAHgzMv4okCaE2A98j0gElZSyDvglsD7y84vI2JASXVH80B27fERbGjwBLCYNX7j7D8D29At4W57YZ/naUtsSQFH1KJUOPoqIfV2YPDR4glx86DdclbiF/ESBp37gHWv5Zc/qckUyfnuKy+LCJGwEwuWEQkcDC1oaaslSy8kID27cfYJpCutNdipUQXIXXdw85nGR+QNrIh0I0gmjCWio7Zljeb91DEhBuqt3vTcmT1yA1CzsCg7fmk8fln2I0GwszJmH096z0vG1yScDur9wuNJtWqiU8qpONi3tZH5BJ+MbgJkxxn1ATBUspXwM6H1brQHAYdYdUx+KqXQXs7L44F9pzljPxa/YefOyNzttxgOQYtVQPf1bQVI7vI6rLa/yEu2zsgGSLLrp6UuW/xHcXsC00C7S7XOZX38vSQ3l0O3V9Y1P7HOB7a0msJ4ihCADG9Ot71FbVkTWWN1c5m5pwatIEnvRNa4vjEk/keKaJwkBlrRTO59oyYPQQSb1c1OlwcCaOR+a3uOQJZuelB6s10KgOWKW2Y4Hi0nFFM6gyjvklueYSClZfeg9Fni8XFbQnRW+I+GU+VD9PLWRQIjhyMha+w4DnCb9Zv+strjbuRUyhSMWG/X+el7a13Wzm8/UPs7/Ql/rFxmjhOuKmWDeB0Cqtf2Kwqyacah26hQTmyrDnB24D7n4VrZmfZ5/yEv6VY5YFKP/HaMKqzekOfL4VM2gvE1finolGbciMKUMTHG8zjgp+2iUU1c9sNMV/SuXa+7/8iIDTbpDNxNWtfSsgJ3iL8Hex+CCGZobW2Doe7zHoripmCp/DZM8NuZO63kJlaxIntVwrvdkKIoeEl0VhPB12zv5JctF+rIbeGrXU11GNZRlL+WXoS/jC/R8ee32h2LK8qnrbH4dqfN0rI8CINmWwnIxncfLCwDISgmyIz2ZZ7yL8AUHNgLD5NUdnC5r7+tb5aRMotYER3xHww+rW5oQQpLch+P2hvMmHA39nGXrvLJwQaTeUWrekgGXqb/JsES681V0zIbvisTAAfLCvSsmGCXJnEOFSRIKD7+V2Edluo/hbdPt5I3veS7UjJadANTW7u/xvo3+RlqCA58gayiKHhI1Pf3a8iAt3XSHa/AEQPVgN9kpdZeyopMmLFJKylPyeCp8Do2+nt2gpaZx97338vQ7HRvK1LX4sVr189sikRVtSbYlo5q8fD30JL9NeIZn9i/j7ZrfMEaUUV3X0CM5esoCvx6X0JcVRWFKHoqphYqqo2GTgWL9b5zO4N5QMpNzcYX0h4gZ4+Z2Ok+k6Z3bpvShD8VQkZWQDECguWftcw+YMmk0976cOoA16yw0IdlTMzi1yHrCrprdyJCTc6f0LmHWnqebKqvCPU/++fZ73+aily5iR82OXp07XgxF0UOiiuI9ptMS7Pof+5fGb2PXyjg171TyE/J5cmfsMgR/3fRX/lF0E1mmgzQ39yxErsXdyL2he7DufbXDtjklTzHNtKuDIztKsjWZQtMBvmZ6jVyHxu663WiEecb5Y5qL1vdIjp6yQpmFGUufurEVJuvRZOn7jpYVK27QlfeYzP4vMd4dibY5oNnI7yKR7isnXc2itIs4e1L3vaOHG1kper7rdmfPmiN5CSD66LyfEvmbflo6/HpoHyrdxuRwAxdmNfRq/8TMiUjNTG2gZ21mNamxs3YnNd4avvLWV1hxaOC6ARqKooc4TLqiWMl03Ng7nSelZGN4Ei2KRqotlS9P/zJbqrewuWpzu3lrStfwz23/BOAfzl8QKtnUI3lq/QqX+u9mTUvHENyUliIUtalDDkWUJGsSzarCL4NXs3XOneyp00sd3ykv44joXX5DvJRgxaz0zTwUzaVYbZ7QOra8RR9LT+pdhE1f+OM5t/PLk+7tsjDf+NR8Hr3ot1h6UfdoqIkGHjT2MIzTJBpJp2szbXfMT9RDa6u68fUNBWUBN4FQGjNmze/V/sk2gRq20uSJ3QWvM6o8VXhDXm6a+VUmp0zmu6u+y7Lty9CzC/oXQ1H0EJNiwiwsCCWA29d5G1FvMMxPgtfiJUiSNYlLJ15KoiWRx7c/3uqrqGip4I4P7sBl0W+Y98sLqbH07AZd49E4SdnBXwJ34W5q72T8tenrFNs61nmKkmxNpkFVeTT8GbLSPXhCekjparWQw/7+r7waxef3k6xU4lQ6V7TxkOvUcyk2a7rylprG/ho96iT6Nx1MpmcWcsnUMwb9vINFtMhiamP8q81wKERY8TMu2PNooLbMHn8CioQ93fgFj6WqqohbH5zEux880f3kXlIvvVSqMzFbehdpl2wOMUHWofXQpFcUSWR86j2VH876E2ePO5uNlRuRsQtY9AlDUfQCu2LhavMbmA+93+mcek8QFD8SjSRLEg6zg6umXsXKIys59/lz+fPGP/PD1T8kEA7wm1P1pLgPmEQ1PUtAa6k8gILk/4I3sr/maC0cTyDEwWo3UmnpUlFowssLlp8i1KOx8RnWUnzVPfvQ9gR3bQUTTftJ62Mv5AxHBgIF6dedgCUHtvM19e+AXuXVoH9RFRW7Bkoo/iffKk8TYSHwpSzq07ltVgdo6ewP9ewzs27Xe6xx2PiwYmufzt8ZQS1IUDaTZO6+VEdnJCQmUx3KoUrt6EfsioP1+nd2ctMRQo9+jus4mXuX3DsgiZyGougFNpODTYyl3tz5zch7ZAuv2L8PHF2yf33O1/njGX9katpUHt/xOJurN3PXSXcxK0O3+aaqVfgaepZ9aj3yAT8wP8eq8Bz21B514BZveZ9HTfcQkI2d+iiichWYiikPlKIKFbvJzlLLKk4q/nuP5OgJDdLBTpGF1dF9xnpXmBQTKdLCKcpK/D4Pu8qbWGVNI9OSToZj5CW0jQRCIpWNlqlxzy+NrHKdCX03ZSYoWTQFe5ZLsU3qAQYlpt7fyLui1lsLQnKe79NeH0MoCo3k0Cx7pgR3lG/Drkm+UajhMgX50/J9vLppYEJs+9aHcZTisLjYJSxUWTp3mDaELLzHDOBIa7kMVVE5Z9w5nDPuHKo91RxuPswJWScQjDxZ32B9iYJiO3BC3LJsSTyDu/2/wWkKU1e0BRbqMh0qrSBLqSckRZcrCoCKm1axe+c/GJ88nrAWZqNHI8glLIhbip5RH1TxqmESHD0sihODJFsu73udVDb5WVOfzA67mQtyB0pyA4tw4tfiD3WtrtGferP6oUbj3FAdm5VSpKYhlPiecQ816VUGLE0DExVU1qyvrkIpfauqkCoEnnDPMrN3N5Xj9ufjvPBH5GTcjfb0Fl7cVMJlJ+SjKv1bjtZYUfQCp8WBovjwejsve1yp5vAQeu2mWNnHGY4MTsjSFYJZMeMwOXhdzGer85QeyVLut3DEMpEHbA9w6oE/tI6/6Z3Grc47ATp1ZkcVhV8Ns6duD1NTppLlyKLJqvCRd+Cihjw1R1BVD2mWzjPV4yUjcTz1pjBlbo1tR3YiTI3MzZzbdyENYpKmBcjW4o/3b6rWb9C5Wt9LhLuSZtKkKhzsQa2pUJ3uT9ECffORdMa+Oj1c15Z7Rp+Oc3JoN5rsWZBAuecwWiCDiVkuEu1WHr1uAf+8dkG/KwkwFEWvSDQ7mKvuYuKBxzudU+8JIFTdORxPUpnL6qLEks5+0bPS0+mVH3G+bQfL87/F78Jfah3fVtrI+IhlrKvwWNCdYlXeKqakTiHbmU2IWsY1f4rU+hap0hnKkfeQQiPD1PcF7VhXDoq5kcpDO7nR810A5mbM7fNxDWKTLCVSxH/TP2zRP4Su3PhXyZ2Rna/nG2ys6EG5cZ9emWC3paDP549FcU0xABNS+2ZaO2RbhF+BQLjzAJm2eENe3OEaTqMOp1X/HplVhcQukj37gqEoekGiJZEjIomDjjmdzpmw52HusD4KHL0hd3dMp6kJa1PPOogtqX6KG7VnMY8/jTXuPOpaAjTW1/B40y0ssG4GYmdlw9GVzifleqbt1NSpZDmz8MpGHld/SUPtwFTr3O7Uy12kp/R91TIxOQeExL31UZ60TMcizExO7XkZBYP48CXOoFhJwh+KLzG01q+bqbKT++6jmJOtJynuKYvfjPSpST+vLzww2ct1FRswScl0paFPxwk49P4l8VaQPdSkK8vZ9Cz3orcYiqIXOMwOGlQbey2dZ2IWmSawTtGzUeMJ1XRZXOTJfXyp8r4eyfJL6w9Yln0nU9JMnKZspfjgHvYeLmOnHIclSQ8b7c5H8UnFUUWR7chGAldp36bC27VhubTBy0m/XcGeip4lCZaG9PC9LFd3jRG7Z1yq/jf+R9NEttvMTE2biVkZmKcqA0i0uBCKl+Y4WgEDBBv03JzchL7Xtpqfpf+vA6X/i2u+lJIAujM9pRctXOPhiBRYQnYy8qf06Tg5ka9akz8+RbG/Tvf9lBb+oE/njRdDUfQCp9mJUPwEPZ3fID+1nMB6y1QSzYmYlO5NLC6Li3JzCo9Zru6RLAc8dkTKWKYkh/iX5R6CO99gfb2Drwe/g5qtP011Znpymp2YhIk6Xx05zhySrElkOXVTwSY1i4puKnUf2L+XguaNrNha3COZRYMeqphs6/vNI5p05zPXodpKWZjdu6Qng/jI1jwINUBTZXzh02rzblQpSXf2/X/tciRhD1vYpsTX16KuvgrVpCsKja4/zM2NdQT8vh7LVBny0hLOwuVK7vG+bZntidR7aokvamlz5T6kFMzPHfgmY2Aoil7hMDsQwstlJR3bcEZpbPFhtvjiLnrnsrjwqpK1ofifTLRwmIu8rzJVHCIzp4DruYvl6mlsO1zHuDQHvnATFsXSmk1+LEKIVvPTlFT9vNH8g8mWHbjLuy6XYCtaztOWX3P++uvjlhkgt+5dILaTv6dEFcXCpFdBaMzJ6NwcaNB3UiIZ5TUN8eVS7LaNR2rOXpcYPxZFGcsRLb4b+v7iLWgCkFCqdJ1A+vdlp/CPf3+jx/J4gpUkkNhlNn48eLPPAqDMHV9l3uKSj0gLCeYkDWBz+DYYiqIXOM1ONAEr7Gd2Oud7pd9lorY57pthoiWRsGyh0Bu//bWxroqfm59go/tR1pZ/THPWYrbWCH5SdA0/tT5Dna+OFFtKlx/iqPlpaqoeG5/t1ENWr7C+SsbBl7s8/ybnqawIz2O1fzKBUPyO71V2PZSwLwUBozjMDpyaykd2PVlpTqahKAYSe+Tve9gUe5V6LO6wByH7Ht0WJcOSiSbL4wq02BLUTZBWMpHCh9ZJr+9wOMwLLpUVwZ7XkQrLak4L9bzq67EkpukPalUt8ZmejviqyA9qjMsbnHa6hqLoBdEn9DVKhz5MrbytnEa1OSEuRzboUU9BfCwTPyUcZ/ZpTdjJXN/feU/bz8sHXuaklHqmlz3LK8FFkDufen99p2anKFFFNjVFVxQJ5gQcJgf/Np/KyoQLu9z3kN/JjcEfcnfgaraVNsQlM0CV1v7cfSXBnE1ICLLsed1er0HfyIj0pKj2NMQ13xo4TLLsv+i52bQQNjVzYM+GbucW1evJeQWqC6H6aWiI3RisotmNTxHsNwXxdFGW51hagi14FWhy9D1vJ9eqK7W6xu6r40opqcDNPrEEmz22taC/MRRFL4hWkFX8ncdzPxU+myaTJe6n5qjD+5rwd+JuXl/TEqTRpP8L99bt5RS28XPlMZ4MnYtr/mXU++o7dWRHaV1RpOmKQghBtjObRruFfZ6ubcEplZ+wNOEwINm4ryQumQGSA3tQpSlm6fPeMDZNj4ZZkD2vm5kGfSXTrPvbRNmquObbtArywj0LduiK+VMvQgrBf4q7b8eqVejVVCda9IeHqk7MZUX1eo6FMLWwbt3zcctS1qxHBZpS++4XG6vpKwl3pDBnV1R5qtDwkescvArJhqLoBdHmRT/y/CbmdqlpeDwegrh75KMA+JjxNPrjszsGy7ZxsUXv61DcVIw282LO999DjUhmem4iRY1F5Cd0XSYj05FJijWltcAe6H4Km1rJmNqOPS7aclrdQ5Rl/4O/Jv+OxRu+E5fMAJPlZhL70bQ6OU3/wszPMhTFQJPl0kujNPsb4ppfpKZRbZ/db+e/YNbnQKq8V979isLjO4xFA2f2aQCUa7Gj4WpKjh5rVc2Rdtsa/Y2dhqzuq9RNTuOcPavPFouE3OkoYQsVpu6Lce6p0h3fZ4q6Pp83XgxF0Quipqd/KTHbhuNubmC75VqC0h2/6SmiKGaZduLuZIl8LNayj7nc+hYAYRmGJD8/ND3Da/ZfUO0vwR10MzO9c/MYwNfnfp1l5y9r58fIcmYhqea77j91ue/PbZ+lxBxmZdY4nvUsIBhHZU8pJSvEdJQ+9idoS9ShbTiyB55o+fbdtgndzNQJCw82c99vpFEcZgc55kn4Qh/R1NB147D1pnyEyCLNngxAlbsh5rzauqM+ho/d7b97Nzx/Bd988bqY+5WUbARgttb3XvdJSUmEwk7qg9076j89sh2AqRmDE/EEhqLoFdEVxYrwlJi13xu9YX4d/jyS+O3w0ZXH7bZlaKWb49rnw5RL+Lp2U+v7Sl8Rq+xL2Z73hdaOV90pihRbCuOT23dby3Zm06CE+Lz/J3gDsROrNE1SFNQVQ3GSwlOBJWwr7b5WjS+o0SLCWPvx5vHZCZ/lzsV3MjnFSLQbaKIPNM2B7s1JWjiMUDzkyL6X72jL0pQJuK3NfLLm6S7nebRaEkzpZCt6zoevLHYL190WfdWtSic1vl2t42UNJewNlVLUEtuseiCSSpI3vu9tbROsJhI0CPq6L0+yvakSGbYwfu4X+3zeeDEURS+wm/Q+CsmiGn+kx/Vb28v577rDANSHLDzGGUAPFEXkC/gr7TLKHPGFyNa2BFHsuqIyKSb21O/hW9/8IRd++Xtsr9mO3WRnfFLPW27qIbKSYjWRNftjPy3VN9Qx1aQ/2Rxu2YlTNLNzx/Zuj93UUEeyqZrkfkyKS7Wl8sUpX+xziKJB99hNdlQJWY0fdju3qr4MKSAv1LNid93x2YXXAPBIY+fZ4d6WZlzKYcYKQZpD/241eWOvQGq8uglnnpqBai6islTPEXl80+sANKpBtBgO+VJfIzJsZWzexN5fTAQhBFNlNcJ/VClVearwhztmXh9uKkYGMyhM779osu4wFEUviK4o7rAuw9OoJ8gse2stP3l5Oweq3TQ0N2NTG4D4ynfAUUWxT6RRp8UXyTCh/HWmmHdjUSxMT53Onro9ZLpsOK0mttdsZ1rqNFSl5/Hr0RDZk50baXj/oZhzGsv2s8CiL70Dmp/fJv+aWVvu7vbYnppiHGoD6XJwe1ob9A9CCOyaikd2Xzqi1K9HEPmy+v7E3ZZp6dOxkMSuxg2EOwl5LassodkUJtdsJytTN9EctMV2/jrqP0FIOCHvTDyKwopDuqJ498haAKQIU1p3uMN+ze5irGEHLlv/FOEuFtOpNOn3ljpfHRe9dBGXvnIpH5R8AOj1nf666a9UBbZwcrgBkzp4t29DUfSCqKJ4Wp5Ki2alvrqM/7qv50XTTyh98hbsB9/kn3bd0R1vp7VEi+7EKjAVQe2+uPZZ1PA66coh0uxpTE6dzJ76PUgpCYaD7K7bzaz0nvU2jhJNujvZtZOFFU8TCHZ8cisxjeElMYc0SwaqUHk9ewF/9FxIqBs/RY11LFWKDXuyYSYaqQSVdHaYxnQ7r7xZf1JPcfRvyLIQgjlJc7HYtrFrU+zmYdtbJJoQpOadTmYkK7zRH9tcFgpWkagJzpp9EQCrKg9S11BFXXgL9pBuPdh9sGNXP+Hfz7RwXb+tZMNqFj6p+yjeLHoTb8iLJjW+vuLrfGvFt7jk5Ut4eOvDJDUXcEWwZ8VD+4qhKHpBNDx2M2Np1ix8WuLmruB1tCSOx9G4j5WVCTwp9XLh8ZqebCYbFsXCeeY1FB55Oa59bjP/gh2OSaTaUpmSMoXmQDMVLRXsbdhLQAt065/ojGgZj4pJS1nq+z0fHey4ZK90h/GZgox1FTIjbQalTi+rA1O69VNUef2ghEhx9r3Ok8HQYFGcBGT3RfYaqnV7f44SXwHBnvCZiafjV8N8uP2VmNv31urRS5PS80m06g9hKfWxzWVbzGMJKDlMSZ2AkFZKa9byyoo/oykhljgWArA9RnpFiWqlwTS9H65GJxc/Gm6klLyy72Um+DUKiy5jjvNLfFj6EZ6mFjyHbmZy0l3Mu+7hfjtvPHSrKIQQjwkhqoQQ29uMpQohlgsh9kV+p7TZdocQYr8QYo8Q4rw24ycIIbZFtv1FRNSwEMIqhHgmMv6JEKKgzT7XRc6xTwgRO/RgCLAoFhShkqzU4nE3srY0xNPiAmZ8/Sm+ZfstD+x18Z7Q8xLiNT2B7tB+XV3I6qRL4ppf0xIgrHp0RREpwbGnfk/cjuzOSDQn4jA5sLlC2CwW3treMf5clKzHaaomJyGTRTmLKPHsZqy1muLn/g8t3PmNwVOum6syrIOTKGTQ/2SH3eRp3Wcxu+v0lXG22v++o6UTzwUpeMLTia/r0MsATE3Nx6yYsWgCbyelP3xaEzbFhaqoTAqbSdA+4T9NjaiaynVn/BCAosb2nfU0qeEXbhR73/0TUWYEi9GExs7aneyq3825bj8OVeHjT+dwxb4ZPH/oEPeeex4PX3MCacn9k6waL/GsKJYB5x8zdjuwQko5CVgReY8QYjpwJTAjss8DQoiokfxB4BZgUuQneswbgXop5UTgT8DvIsdKBe4CTgQWAXe1VUhDiRACu7Bwifk9zCUf07jvQ07Ks5DktPOD86aSRzUFVv2JJmpSigeXxUWTxcaRcPeXGfD7+UboCTStllRbamvEz566PWyv2U6yNZm8hLxeX1+WM4taXzW3jTnAxdu+QSjY/pEqv/Q1QiYvWQkZLMpeRFiGuW76ds5vep5X3nm302MrlXpuRqY9/r+LwfDCKWy0KMSM+GtLsV33CaTm970XxbEk25LJsk2ige1UNHZUADV+PQgj16WHTodxscsc20eRxiFypP75zktZwC6LlbLwZrJt85iWORZVU/HUts/bqHZXgdAYY+m/B57yFL3e01O7ngKp8O/wvdx72/Vs+tk5nH3+l+HmVXx+8ZQhCdroVlFIKd8Hjs3suBh4IvL6CeCSNuP/lVL6pZRFwH5gkRAiB3BJKddK/dP15DH7RI/1PLA0sto4D1gupayTUtYDy+mosIYMuzmBVUyjVGRwT8MPudWslz6+dF4eH9q+zVnKB3FXjo2SaEkkUaklp2Fjt3Mbaiu5Vn0Hj3STak/FaXaSn5DPnvo9bKvZxsz0mX36QGU7sqloqWDhmATsmpvNO3e32/5k4hWEhCDDnsHczLmYFBMN43O4c8wybv9QY19lbHvwetci/fjJfeuXbTB0BBOnU6nY8AW79kc1+HQzZG5S+oDIcaJrCib7YYp3r+6w7WM1DyGtrT5Ck3DgjdGTwuNpwacGSRP6rXDBxKVIRUMxN3Hh+DNQVYXcUIiQt73fcN/hLQDMDPdf5zy7U/9OvFX0FuGWSXxh3gyUSDOiE045l6z8+HJXBoLe+iiypJTlAJHfUYNzHtA2tbEkMpYXeX3seLt9pJQhoBFI6+JYHRBC3CKE2CCE2FBdPTDNxY/FYUngsEjmrTI7Xwn+GHWu3l1OUQTVt2yhtPCcHtcycllcuGQ5X6jr3v5YTRLTgg8RQiPNpjeOn5I6ha3VWznYeLDXZqcoWc4sKlsqmXbm1Vwlf8srRe0/Kkc8erZqhj0Du8nOnIw5rKtcx4+uWIrDovLkU4+xt+gQGw/Vs/ZAbWvRwFq/Xu45OZIEZTDySLQkIFQfjd6u6yIpjdtRJGQ64wvo6ClzUiYiBRyo6WgGawrWYFfSWh+WMsMexoQ6Ftys9Gg0KgoiTa/XdFbh0bpNV8zQLedNpmlsN6W122+vR/8cJ46JnXTbG/JVPdQ+JEP8tGULl88cFgYUoP+d2bEeYWUX473dp/2glA9LKRdIKRdkZPRfxm9XOE0OnEojW/cV86GczbRZR+u9ZOQWELKrPVcUVhdV5kR+Yflet3Nr3QGESe8eFi2ENyVlCpWeSjSp9TriKUqWI4tqbzUmk+SMKRks317azvcw2f0GAOl2/WnxxOwT2VW7C6vFz1/PsvDL5p/xj38+wGUPfsTfHv0na+//MlLTSGjSl/D9VRDQYPDJDTUiRJj60q1dzpP+UlyahqIMTMzM+HH6TX2raB9VFQoGGCu2ktvmFuIQZlpEx9vH4YYahJBkOnVFkJeQR6KwU2jNJStBDxN3WXLw0778d1GTHuBRmNt587KeUhjUVyfmsIpJOYP87MG5l8VDb/+DlRFzEpHf0fVXCdA2bi4fKIuM58cYb7ePEMIEJKGbujo71rAg0eJkumk/D2q/4rysZhKs7U1MTf6mXq0oAiLALn/3S3V5eC1fsbwA0LqiaNsCdEZa3z7AszNmI5GsKlnFlTnlvBG8gYPbP9bPrWmcJPWCaxkO/cO8KGcREsn/DvyPU089g10XPMtnP3c5T9ywiJsme8lt3Mzza7aQ6fkUMBTFSMZlSwagqovGXQB7rONoktkDJkdBil6frMzdvmVvVW0ttSbIMB39jHkdkzkkXB38Kg1F7wGQFymuKYTgV2fcw91n3dM6Z7yqgNpMY/PR6L/62u0gYVpm31u8RlHH6qsTb9M81DPv6rfj9ge9VRSvAtEopOuAV9qMXxmJZCpEd1qvi5inmoUQiyP+h2uP2Sd6rMuBlRE/xtvAuUKIlIgT+9zI2LDAaXGyX6QzTTnMddb3Omxv9Df2+GaYaEkkiIcl/hXd1ttXavZygnkzAKn2oysKgFxnLmn2tM52jYtTck8hx5nDM3ueYdLEKawIz2dntZ5k5Q6EuZPLAd30BDAvcx6n5J3CfRvuY03pGqadeB5nLF7IkskZLLnmZ/x2zEPcubyCF8UihFQ6baZkMPxJyNRXq0e66TTnC7sxi4H7P6faUlEkpNa90268uEWlQYXkrKNmpARzAig+/Mf0TamJKJncxKMPZ2eNPYt5mUcLTE5UdeWys3hz61iwaSvp4TApdnu/Xc/4jClk1MzC1XQyF8waOAXbG+IJj30aWAtMEUKUCCFuBO4BzhFC7APOibxHSrkDeBbYCbwFfENKGbVX3Ao8gu7gPgC8GRl/FEgTQuwHvkckgkpKWQf8Elgf+flFZGxY4DQ7catmTvHdj++Er3bY3uBv6HFjHt3xJrnb/BBeT9cNTD5MuogfCL2zXHRFkZeQR6Ilsc/+CQBVUbl88uV8Uv4J/mQTv7N9m1V1kXLNzX40kweLYj9acl0o/GHJH5iUMonvr/o+u2r1GPqKlgpeOfgq3/vsWFxmyAnvwiScRrmNEUyaXf9c13q6zplxhg6To/Vvnae2KELBpZloOKbN6a6qEoSQjE85WhE5N1iPVXHTXNO+38MOs27oyM/uvGxO6ni9L8v2Nn3CP1VNSG0Coh/NainmECubX+dJx7M4LP2T7d1fdCuNlPKqTjbF9OJIKX8N/DrG+Aagwx1MSukDvtDJsR4DHutOxqHAaXaiCi8WEWLW9PZmnrAWpjnQ3CvTE8CF4Z/xbNBEV89itW4/TruXAHqoIOjL5r+d9TcyHf2TzHbpxEt5cPODvLD3BeaNPYN9h0qAuTSV7GKSeRc+c3K7+U6zk78v/TtffuPL3PrurWQ5s9hZq5dEnpg8kT+dchUv7t5Ps+y/5brB4JMRsfWbK1ahByfGRsNNpjawDwTSPI7NwWPK1Ox5FARMTTtq7XZaXQT9UOtuoq3lP1rnKSuh8xX49OzxsB0O1OnxOHXeJryigglJl/XbdQDkpqewNu96xi7t+OA51BiZ2b3EYXKgCg//tt1LWoK13TZ30I1E9ijZDo5WkC0VCTR105NiYdm/mGraTpI1CXObAnvzs+aTn9g/oacZjgzOHHsmLx94mS8qb/KK+8s01FQQKttKmrmMjBjlSTIdmTyw9AFUoWJSTHxn/nf45Sm/5EDDAV41b+RIyhQyErP6RT6DoSE1QTfTNMqulUCVYqcq4cQBlSXZkk5QNLQrcX8wqJtIxyUfzZuwRnx2pbJ9s6zMSP/2rhp8zcjUj+Or1GsuvbvxZYSQnJrZv2W+FVXlpJv/TN74af163P5geK1vRhAOs4OAIihd/H8c27W2IdLUpbcriiXmT2jcVwDZ53Y6d0zLdiy2OlJtA2vLvGLKFSw/tJy9eQ4+3XUFJ5c2ssd1JhuVJzk7KXYC08SUiaz44op2Y1WeKv666a+YhIlTsk4ZUJkNBpbUSE+KI9bOEzrDWhipeEnoQcJpbxhHmGpTFbXlxWTn65WSP0SXLzfh6DczNWIuq3K3N5c1az6smsCqtn/Ya4vdbCUlJPGE9cKAW4p0n8hnJsztt+sY7hgril4SdcZOPuMSAB7Y/AA/+/BngO7Iht4rikstK/BteqbLuT9QfsR+x9gB7xG9KHsRBa4CPvJv4h/a51hfqfsoMLnJ6cHK4KZZN3FG/hmEZMiIeBrhJNl0J3ZL0NPpnOrGSoSQjA33b4nxY8lypONR4UjN0cS3hmAFVpHc7uafFa03dXh5u/0/VceA0n2UoU8pZItJn/eBpmAOpVE4pv/qPA13DEXRS6IVZD1BD1JKXtj3Ai/tf4mPyz/u84ri1dzr+XrtF/DFqNoapbbFjybcrY7sgUIIweWTL2drzRam5LipPLiVnMPPIpRAa2hsPChC4den/ZoZaTP6nONhMLRE+7Fk13feKrcsUpQvk/4vCNiWrFw9smlHQPdT+DxuZiofkYOl3bz0RP2zWnNMdWO/1oRd6X7V4zJn4tVqCYc16kP7ybQNP/PQQGIoil7SqihCHkrcJVR59CeaP27441FF0dOop4iPIjc/Dbc/zOq9sbPMmxvruI8/E4zUeRpoTsvTew6fbXmSn5Z/i4wmvWRCNDQ2XlwWF/+96L9cOfXKfpfRYPBQFRWTJqgXnZtrykL6rUXmnjGgskxM1c1fxQ16ilVZTR2HTDYyrO0DOjJT9ZSsA5aj5rJQMMA4ZQ/ZMtjtecYLiUmt5pPNb4GpmYVJo6sEjaEoekk0LLQl2MKnlXoS2Q0zb2BX3S6e2aObjXq6otAd5CqpiWFus7+F+u7PYs4rKz3CJHEIr/S15lAMJIVJhaRYU9iZlskPgl/lx8FLgaNZ2QajjzBOii2dR69VteiZzOnO5AGVozASAus8rH/nDrSYqDdpZLfJgwBIsekPYY3+o2Hn9Y2NNCoKLlP3neLGmiwEFcmb254H4Kwxo2tVbCiKXhL1UXhCHj6t+hSXxcVt825jWuo0tlbrpQ3ibVoURQhBoiURd7CZk9JaEHUH8Pg7Pu3sDqRzvqZnbg606Skq17zMeRymnLe1RfjRM3J7uqIwOH5QsBIIxy7bDeCp3ARArhjYTobRUPCSSKe7nRWHEEJjcnr7xj7RKs5jat9qHasJWqlTVWwp3ecdpRToYcCv+htAmjhleudhwccjhqLoJceuKOZnzkdVVL63QK/TlGhJ7FUbUpfFRVOgCXHB77nR/z1W7O5ofjpY3YJyTJ2ngWZ+1nwqPKXMce7jqza9qb2xohi9ZIWbGOfb1On25ogpNmeACgJGSbAkoEgrnyq6wnDtvR+AKantI/IcZgdIqBRHM6nLGhsRSpCMODrwTc3Qjxe27sclCrCYOze7HY8YiqKXRH0UR5qOUNxUzPwsvSjg4pzFLMlfQn5C72yYUUWxsDCNLJeVNzZ37NU7fdf93OR8DaDPpTri5YQsvafAF5yPo5qaMKEY0UujGCEc1Cm2TrcXWfSIuOx+LJrXGTYlBXdIr8O0WeifyXxX+++fIhQU7OxWj/oo/PteAiDX3H2WwMxILoUQklm20ded0cij6CVRRbGmVI/8iCoKgD+c8Qf84e6bz8ci0ZJIs78ZVRHckbuZJQf/RFPDVlzJRxWC39OEyaEv+wdrRTE1dSp2k53lOXPYWV1PoqXeKMMximkx59MiOw+PbQo0ITUTaY6u60H1B7lSwyI+JRwKsV5LAinIdnbMLzIJBz7taE+K0oBu1s1NOTYTqiOZznQsmiSgCE7KHj1hsVGMFUUvifooNlZtxKbamJ569MNjVa099k9EcVn1FQXA5NmLeTV8Eut3H2zdLqXk/7xfZlO2XkFlsBSFSTExJ2MONZZqfBaNLKPn9ajGotgI0/nDkKNpC4maHLAS421JtmRQqlqpqW/EHarAoaa1q1YQJT3sZXpoXev7baquIPJSYyeOtkV/KNJXSWfO69/SHSMBQ1H0EpvJhkAQ0kLMyZiDWe2kd28PiZqeACbOPplfcyMf1x6Nyqhu9uP2h7DbPJgVs14Vc5CYnzWfI+6DZKU1kZtgKIrRzDh/CZmUdrrdp7lJ7KZVan/hSptJg0myp6SSuaaPyZOxfYNmJZGKNtVsayN1nroq39GWfFcuDjWJMYm9azE8kjEURS9RhNKaeNTW7NRXoopCSonFpDAj18WBw0cb/VXt+oCXLXdi0cpIs6cNqvlnQdYCJJJKT2WPku0MjkPMaTSJzi3XRaZ06pTCQRFljCsbIcJ8eKiEPaYE0hPGxZwn7WM4JFyEIxFSk2v1fi7xKor/O/mb/OLUO0elydXwUfQBp9mJJ+TpV0WRaEkkpIXwhrw4zA6+K/7LgopnCAVLMJktlNd7sEsbAdVPqmlwzE5RZqXPwqSYCGkhI+JplONzTqSlaR/BsIZZ7fi8GdBasCmD8/mckKCfJ1T8Ai3JIcZkxs5xcJgSEIqPJm+QFKeFEmFHkfGHsZ+YM7AFDoczxoqiDzjNTkzCxOz02f12zGh2dnMg0j1s0rn8PnQFByr1BKb12mRulHfSIvyD5p+IYjPZWjvnGTkUoxuHyYFQgjT7YvfNdspyCkO1Mbf1N4UZ+splY9CNEJKp6bF9DjnBBpLUWjyV+/X5Sh4mkTQqVwg9xVAUfcBlcTE9bXprTkV/HRNo9VOMmXsWy8Lns7lcdxwerHZTmO6kzlc36IoCjprZDNPT6CbfWwxAU82BmNu9SpgkMTi3l6xIhJPZrstSmBxbUdgdmbgVhYaAQunB3chwLXZ1YPM8jhcMRdEH7jzpTn5xyi/69ZjRm39Js94kpSDNQY4tQMn+bQB8+/C3uE08TZ2vblCyso/l9LzTMQkTha7BsT8bDE9MDr18R12wY+WAYDiIT5E0Ji8eFFmiZtADNr3gX2f9WJzJ45ECdrWoND91NRPNexmbZDzwxIPho+gDU1On9vsx52bMxWVx8fahtzlz7JkIIXjY+hcc+xsIhS9gSyAfR3IO/rB/0JLt2rIgewEfXvVhv66iDEYeavJkqIc62THar8Ktm0mTehki3lPMqplEzUSz2YtA7dQsGu1JcfcbGykM3EzA9RwzEg1fWzwYK4phhlk1c27Buaw8vBJPpN7/zgk38QvfFeyrcvPT4PUcGa83/hmTOGZIZDSUhEGiRQ/Lrvc0d9hWXrEHgIJgVYdtA0WCkqz/VtM7LZ2ToerRTqdrK7nli5+jRQTijnga7RiKYhhyYeGFeENeVh1ZBUDqzLNZHZ7Fa1v0uHXFWgPAOFfsMEADg4EmM1AJgCz7sMO2qmbdiZ1sHdjudm1JjHR6zKfz+mrZkVLj02ZNQzo30uBvoMBVMBjijXgMRTEMOSHrBLIcWbxe9DoAc/JczBDFTF3/Ez6xfp1AuASB6Lfe2AYGPcUZSTqrVTsmfJZFmgbZc08eNHkyIj6TrKTO+1inOHX/nzLeys8/+jkLsxdyxdQrBkW+kY6hKIYhilC4sPBCPir9iHpfPZlJdh61/pFTQ5+wWiykKlhFjjOnyz6/BgYDSWKKHllUFSNqqNaj+yiyEgbPrDMrfyIAM8d23iciIWIue3DLg2Q4MvjDkj/ELPVh0BFDUQxTPjP+M4RkiHeK9Ubu/8q9k4v8v+a/md/lcNNhxrq6r09jYDBQpNijfbM7+ii0ik8AyBvEe3C2U6/D1NUqOxp6bjfZ+ctZfzH8Ez3AUBTDlMkpk5mQNIE3it4AIGHyadSQRGF6AoebDxv+CYMhJS1SmjupcnWHbfWRysm5KZ13wOtvxiePB2Bi8sRO56RYUzh77Nnct+Q+JqdMHizRjgtGRXhsMBikpKQEn6/zjlzDkf8r/D+aA81s27GNk1IlZ1zswKc2c6H4BS6ri127dg21iKMOm81Gfn4+ZvPoNlmkJOhhpZXmjiHah00pSJ+FJNfghW/Py5zHO5e9Q05C58pJVVT+dOafBk2m44k+KQohxLeBmwEB/FNK+WchxBzgISABKAaullI2RebfAdwIhIHbpJRvR8ZPAJYBduAN4NtSSimEsAJPAicAtcAVUsrinspZUlJCYmIiBQUFIypd3x/ys79hPzkJOSSbk/BV2wgmpGHxVTDWNba1vaPB4CClpLa2lpKSEgoLR3fCoc1sR0pBhamj+cYdbEZojkH/rnWlJAz6Rq9NT0KImehKYhEwB7hICDEJeAS4XUo5C3gJ+GFk/nTgSmAGcD7wgBAiGsv2IHALMCnyc35k/EagXko5EfgT8LveyOrz+UhLG9xKq/2BRbWgKireoBdFVXFkT0aa9D+ZRbEMsXSjDyEEaWlpI25lOhAIIRDSSiDo7rAtqWUreVrHcYORS198FNOAj6WUHillCFgNXApMAd6PzFkORLt8XAz8V0rpl1IWAfuBRUKIHMAlpVwrpZToK4hL2uzzROT188BS0cu7/UhTEqDL7DA58ISOdhILaHoRtv7qf2HQM0bi52igSNO85Lk/7jDeKMAiO2+TajDy6Iui2A6cLoRIE0I4gAuBMZHxz0XmfCEyBpAHHGmzf0lkLC/y+tjxdvtElFEj0MHwKYS4RQixQQixobq6ug+XNPxwmB0EwgFCWgiAQDiAWTWjDFLBNQODzgiRQKmpY7mMCsVBncVwFh9P9PpuI6XchW4KWg68BWwBQsANwDeEEBuBRCBahzjWo5jsYryrfY6V5WEp5QIp5YKMjOFZ5CshQQ8n1DSN2267jZkzZzJr1iwWLlxIUVERAAUFBdTU6FnXQgiuueaa1parTd4mMjIyuPbya7GqVpYtW8Y3v/lNAH7+85+Tl5fH3LlzmTlzJq+++uoQXKHBaMMnUqlWnB3GQ7TgiJGIZzBy6ZMzW0r5KPAogBDiN0CJlHI3cG5kbDLwmcj0Eo6uLgDygbLIeH6M8bb7lAghTEASUNcXmYeaZ555hrKyMrZu3YqiKJSUlOB0dvyyOZ1Otm/fjgxKhBC8+c6b5OXloUktpn/iu9/9Lj/4wQ/YtWsXp512GlVVVYPSr9hg9GIWVsJaS4zxRqYEDg+BRAYDRV+jnjKllFVCiLHA54GT2owpwE/RI6AAXgX+I4T4I5CL7rReJ6UMCyGahRCLgU+Aa4G/ttnnOmAtcDmwMuLH6DV3/28HO8ua+nKIDkzPdXHXZ2fENbe8vJycnJzWm3h+fucJQhdccAFvvvEm88+ZzwvPvMAVV17BWyvfwqJ27sieNm0aJpOJmpoaMjONvtYGA8f4YAke2jv2vUEfAQWs6uBXNjYYOPr6yPmCEGIn8D/gG1LKeuAqIcReYDf6yuBxACnlDuBZYCe6qeobUspw5Di3okdL7QcOAG9Gxh8F0oQQ+4HvAbf3Ud4h54tf/CL/+9//mDt3Lt///vfZtGlTp3OvvPJK/vvf/6KEFHbu2MncE+YCdKkoPvnkExRFYbia4AyOHzzmXCqV9iamcre+4A+kLhwKkQwGiL6ank6LMXY/cH8n838N/DrG+AZgZoxxH7pDvN+I98l/oMjPz2fPnj2sXLmSlStXsnTpUp577jmWLl3aYe7s2bMpLi7mjRfe4LSlp9ES0pf5sRTFn/70J5566ikSExN55plnjOgcgwHHbx1Ds7e9Jbi8SX+fYksaCpEMBohRkZk93LBarVxwwQVccMEFZGVl8fLLL8dUFACf+9znuPOOO3nkpUdoqtdNZrEKmUV9FAYGg4VDsSBEe9NTbam+Qh7nLx8KkQwGCMPbOch8+umnlJXpvnpN09i6dSvjxnVet+mGG27gZz/7GTNmzUBDQxGKERprMCwY79mHqnjRwuHWseqgXucpJSmvs90MRiDGHWeQqaqq4rOf/SwzZ85k9uzZmEym1jDXWOTn5/Ptb3+7NUzWUBIGwwV/wiRCQtDs87aOlUVaoybnzB0iqQwGAsP0NEi43XpJg/PPP5/zzz8/5pzi4uIO86M4TA4WnbKI85fq+37lK1/hK1/5CqDnURgYDDYioQC8UOPzkOSMtEb1NQKQk5g6hJIZ9DfG4+kIIdqnuquIJwODwcRp0j+L9e761jFL1RqElOTajaZaxxOGohgh2Ew2xiSOIdmaPNSiGBgAkO8pBqClemfrWJWwYtVUEp1G1NPxhKEoRhAuqwtV6bx5vIHBYKIkTwGgRthbx8pUJz4tBWFUBTiuMP6bBgYGvcKRpEfr1cujtxFvyI0Jx1CJZDBAGM5sAwODXpFs0f0QLS1HKzan+neSow2VRAYDhbGiMDAw6BXpYT0BVKne0DpWo1gIKUb5mOMNQ1EMEtEy46tWreKiiy6KOefCCy+koaFhQM5/0003sXPnzu4nxkH0WoaC3/zmN72ad/LJJw+EOKMaV6reDrbSmt061qgImm1ThkokgwHCUBTDiDfeeIPk5OQBOfYjjzzC9OnTB+TYg0lvFcVHH300EOKMalJcenXiOvQwWSklUnhIMLuGUiyDAWB0KorHPwOb/q2/Dgf191ue0d8HPPr77S/o732N+vudkWZALbX6+z2RArfNlT0+fVNTE5deeinTp0/na1/7GpqmG3XbNi568sknmT17NnPmzOGaa64B9CS7559/vvU40Sf78vJyTj/99NbGRR988EGHc55xxhls2LChdb8f//jHnHDCCZx99tmsW7eOM844g/Hjx7c2PVq2bBkXX3wx559/PlOmTOHuu++OeS333nsvCxcuZPbs2dx1112Anjg4depUbrrpJmbOnMnVV1/Nu+++yymnnMKkSZNYt26d/qdsaeGGG25g4cKFzJs3j1deeaX13J///Oc5//zzmTRpEj/60Y8AuP322/F6vcydO5err74agEsuuYQTTjiBGTNm8PDDD3c6L/q3klLywx/+sLVx1DPP6P/3VatWccYZZ3D55ZczdepUrr76avpY0f64J9Wu/01DPv0zW+upAaExx7NnKMUyGAiklMfVzwknnCCPZefOne0HHrtQyk+f0l+HAvr7zf/V3/tb9Pfbntffexv09zte0d+7a/T3u9/Q3zdVdDhfLJxOp5RSyvfee09arVZ54MABGQqF5Nlnny2fe+45KaWU48aNk9XV1XL79u1y8uTJsrq6WkopZW1trZRSyuuuu651bttj3nffffJXv/qVfjmhkGxqaupw/iVLlsj169dLKaUE5Btv6PJfcskl8pxzzpGBQEBu3rxZzpkzR0op5eOPPy6zs7NlTU2N9Hg8csaMGa37R8/79ttvy5tvvllqmibD4bD8zGc+I1evXi2Lioqkqqpy69atMhwOy/nz58vrr79eapomX375ZXnxxRdLKaW844475L/+9S8ppZT19fVy0qRJ0u12y8cff1wWFhbKhoYG6fV65dixY+Xhw4fbnTtK9G8TlbGmpibmvOj7559/Xp599tkyFArJiooKOWbMGFlWVibfe+896XK55JEjR2Q4HJaLFy+WH3zwQcz/ZYfP0yhm/mMz5PcfOV9KKeU7e1fLmctmyvtf/NUQS2XQG4ANspP76uiMerr+9aOvVXP79xZH+/e2pPbvnWnt3ydm9fj0ixYtYvz48QBcddVVrFmzhssvv7x1+8qVK7n88stJT08HIDW163IICxcu5IYbbiAYDHLJJZcwd+7cLudbLJbWMiKzZs3CarViNpuZNWtWuzIi55xzDmlpegOaz3/+86xZs4YFCxa0bn/nnXd45513mDdvHqCXHdm3bx9jx46lsLCQWbNmATBjxgyWLl2KEKLdOd555x1effVV7rvvPgB8Ph+HD+ud0ZYuXUpSkp60NX36dA4dOsSYMW0bJOr85S9/4aWXXgLgyJEj7Nu3r1XmWKxZs4arrroKVVXJyspiyZIlrF+/HpfLxaJFi1obSc2dO5fi4mJOPfXULv+Wox0prRyx6AUAN1ZGWvpO/txQimQwAIxORTHEHNsr4tj3UsqY/SRMJlOrmUpKSSCgtyM//fTTef/993n99de55ppr+OEPf8i1117b6fnNZnPr8RVFwWq1tr4OhUI9kvOOO+7gq1/9arvx4uLi1mN2dQ4pJS+88AJTprR3fn7yySft9ldVtZ1cUVatWsW7777L2rVrcTgcnHHGGfh8vg7zjpW5M+I5p0F7QjKBeqEXAtxfvQupmViQP36IpTLob0anj2KIWbduHUVFRWiaxjPPPNPhqXXp0qU8++yz1NbWAlBXpzeDKSgoYOPGjQC88sorBINBAA4dOkRmZiY333wzN954I59++mm/yLl8+XLq6urwer28/PLLnHLKKe22n3feeTz22GOtBQxLS0upqqqK+/jnnXcef/3rX1tv3l11+4tiNptbr7uxsZGUlBQcDge7d+/m448/jjmvLaeffjrPPPMM4XCY6upq3n//fRYtWhS3zAbtsaIgQvrn01f3IRNCPnIS7d3sZTDSMFYUQ8BJJ53E7bffzrZt2zj99NO59NJL222fMWMGP/nJT1iyZAmqqjJv3jyWLVvGzTffzMUXX8yiRYtYunQpTqcT0J+s7733XsxmMwkJCTz55JP9Iuepp57KNddcw/79+/nSl77UzuwEcO6557Jr1y5OOukkQHcYP/XUU6hqfGVG7rzzTr7zne8we/ZspJQUFBTw2muvdbnPLbfcwuzZs5k/fz6PPfYYDz30ELNnz2bKlCksXrw45rx///vfreOXXnopa9euZc6cOQgh+P3vf092dja7d++O989i0IYx4VpskXyKg4okMzTGKN9xHCK6WoqPRBYsWCCj0T1Rdu3axbRp04ZIopHJsmXL2LBhA3/729+GWpRhh/F5OspZj15IiDArvvIa8/+1gALTRfzvyx26HRuMAIQQG6WUC2JtM1S/gYFBrwmq6biFYG/NXhAa411jh1okgwHAMD0ZxKRtYyQDg85IRMMn3Wze/R4Ap4n6bvYwGIkYKwoDA4NeMz5wGDv1bGnRO9tNnnzBEEtkMBAYisLAwKDX1Dtm0KhY2OeuRAsmMnn8zKEWyWAAMBSFgYFB77Floykh6j17cWqp2MxGY63jEcNHYWBg0GuShZ4A2ixLOS1k5E8cr/RpRSGE+LYQYrsQYocQ4juRsblCiI+FEJuFEBuEEIvazL9DCLFfCLFHCHFem/EThBDbItv+IiIpwEIIqxDimcj4J0KIgr7Ia2Bg0L+M8RwCIKBAyGWUOzle6bWiEELMBG4GFgFzgIuEEJOA3wN3SynnAj+LvEcIMR24EpgBnA88IISIrlMfBG4BJkV+zo+M3wjUSyknAn8CftdbeYcLUsrWMhwGBiMdX+q81teZOad0MdNgJNMX09M04GMppQdACLEauBSQQLQgfRJQFnl9MfBfKaUfKBJC7AcWCSGKAZeUcm3kOE8ClwBvRvb5eWT/54G/CSGE7EOW4O/W/Y7ddf2bhTs1dSo/XvTjTrcXFxdzwQUXcOaZZ7J27Vpefvll7rnnHtavX4/X6+Xyyy/n7rvvZt26ddxzzz28+OKLvPLKK1x55ZU0NjaiaRrTp0/n4MGD7Y5bWVnJ1772tdbxBx98kNzcXM4//3xOPfVUPv74Y+bMmcP111/PXXfdRVVVFf/+97+NkhUG/YY1IRf0KuPMzywcWmEMBoy+KIrtwK+FEGmAF7gQ2AB8B3hbCHEf+ool2losD/i4zf4lkbFg5PWx49F9jgBIKUNCiEYgjdaPpo4Q4hb0FQljxw7PhJ89e/bw+OOP88ADDwDw61//mtTUVMLhMEuXLmXr1q3Mnz+/td7RBx98wMyZM1m/fj2hUIgTTzyxwzFvu+02lixZwksvvUQ4HMbtdlNfX8/+/ft57rnnePjhh1m4cCH/+c9/WLNmDa+++iq/+c1vePnllwfz0g2OY1JEGACLJlmYnjLE0hgMFL1WFFLKXUKI3wHLATewBQgBtwLflVK+IIT4IvAocDbQsRyqvvrobJxutrWV5WHgYdBLeHQld1dP/gPJuHHj2tUievbZZ3n44YcJhUKUl5ezc+dOZs+ezcSJE9m1axfr1q3je9/7Hu+//z7hcJjTTjutwzFXrlzZWtdJVVWSkpKor6+Pq8S3gUF/kBnQn9kSQw4ys8YNsTQGA0WfnNlSykellPOllKcDdcA+4DrgxciU59B9GKCvFNo2FMhHN0uVRF4fO95uHyGECd2UVdcXmYeKaAE/gKKiIu677z5WrFjB1q1b+cxnPtNaHvu0007jzTffxGw2c/bZZ7NmzRrWrFnD6aefHve54inxbWDQH1gyZwMQFJONYoDHMX2NesqM/B4LfB54Gv0mvyQy5Sx05QHwKnBlJJKpEN1pvU5KWQ40CyEWR6KdrgVeabPPdZHXlwMr++KfGC40NTXhdDpJSkqisrKSN998s3Xb6aefzp///GdOOukkMjIyqK2tZffu3cyYMaPDcZYuXcqDDz4IQDgcpqmpadCuwcAAIClR75s9xmyYnY5n+ppH8ULERxEEviGlrBdC3AzcH1kB+Ij4DqSUO4QQzwI70U1U35BShiPHuRVYBtjRndjRO+ejwL8iju869KipEc+cOXOYN28eM2bMYPz48e36PJx44olUVla2riBmz55NZmZmzEZG999/P7fccguPPvooqqry4IMPkpOTM2jXYWCQb3Ew3+fjc+qIXOgbxIlRZtzAoIcYn6ejaOEwnzz2fQrP+wbZYycNtTgGfaCrMuNGZraBgUGvUVSVk27+81CLYTDAGN4nAwMDA4MuGTWK4ngzsRkMDcbnyGA0MioUhc1mo7a21viSG/QJKSW1tbXYbLahFsXAYFAZFT6K/Px8SkpKqK6uHmpRDEY4NpuN/Pz87icaGBxHjApFYTabKSw06tAYGBgY9IZRYXoyMDAwMOg9hqIwMDAwMOgSQ1EYGBgYGHTJcZeZLYSoBg714RDpHFPGfBQwGq8ZRud1j8ZrhtF53T295nFSyoxYG447RdFXhBAbOktjP14ZjdcMo/O6R+M1w+i87v68ZsP0ZGBgYGDQJYaiMDAwMDDoEkNRdOThoRZgCBiN1wyj87pH4zXD6Lzufrtmw0dhYGBgYNAlxorCwMDAwKBLDEVhYGBgYNAlhqKIIIQ4XwixRwixXwhx+1DLM1AIIcYIId4TQuwSQuwQQnw7Mp4qhFguhNgX+X3cNUEWQqhCiE1CiNci70fDNScLIZ4XQuyO/M9POt6vWwjx3chne7sQ4mkhhO14vGYhxGNCiCohxPY2Y51epxDijsj9bY8Q4ryenMtQFOg3EODvwAXAdOAqIcT0oZVqwAgB35dSTgMWA9+IXOvtwAop5SRgReT98ca3gV1t3o+Ga74feEtKORWYg379x+11CyHygNuABVLKmYAKXMnxec3LgPOPGYt5nZHv+JXAjMg+D0Tue3FhKAqdRcB+KeVBKWUA+C9w8RDLNCBIKcullJ9GXjej3zjy0K/3ici0J4BLhkTAAUIIkQ98BnikzfDxfs0u4HTgUQApZUBK2cBxft3oVbHtQggT4ADKOA6vWUr5PlB3zHBn13kx8F8ppV9KWQTsR7/vxYWhKHTygCNt3pdExo5rhBAFwDzgEyBLSlkOujIBModQtIHgz8CPAK3N2PF+zeOBauDxiMntESGEk+P4uqWUpcB9wGGgHGiUUr7DcXzNx9DZdfbpHmcoCh0RY+y4jhsWQiQALwDfkVI2DbU8A4kQ4iKgSkq5cahlGWRMwHzgQSnlPKCF48Pk0ikRm/zFQCGQCziFEF8eWqmGBX26xxmKQqcEGNPmfT76cvW4RAhhRlcS/5ZSvhgZrhRC5ES25wBVQyXfAHAK8DkhRDG6WfEsIcRTHN/XDPrnukRK+Unk/fPoiuN4vu6zgSIpZbWUMgi8CJzM8X3NbensOvt0jzMUhc56YJIQolAIYUF3+rw6xDINCEIIgW6z3iWl/GObTa8C10VeXwe8MtiyDRRSyjuklPlSygL0/+1KKeWXOY6vGUBKWQEcEUJMiQwtBXZyfF/3YWCxEMIR+awvRffDHc/X3JbOrvNV4EohhFUIUQhMAtbFe1AjMzuCEOJCdDu2Cjwmpfz10Eo0MAghTgU+ALZx1F7/f+h+imeBsehfti9IKY91lI14hBBnAD+QUl4khEjjOL9mIcRcdAe+BTgIXI/+gHjcXrcQ4m7gCvQIv03ATUACx9k1CyGeBs5ALydeCdwFvEwn1ymE+AlwA/rf5TtSyjfjPpehKAwMDAwMusIwPRkYGBgYdImhKAwMDAwMusRQFAYGBgYGXWIoCgMDAwODLjEUhYGBgYFBlxiKwsDAwMCgSwxFYWBgYGDQJf8Ppqz4fiV8LgYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against the ibicus one:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A linear regression also shows that the reference implementation values' and the ibicus ones are consistent:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.9999999951431144, intercept=0.0004899313935311511, rvalue=0.9999999999776888, pvalue=0.0, stderr=2.9128922281955855e-08, intercept_stderr=0.0029384674434487085)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**ps / psl is well reproduced by ibicus.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.5. rlds "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean surface downwelling longwave radiation -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:20<00:00, 5.03s/it]\n"
]
}
],
"source": [
"variable = \"rlds\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage agreement is 100.0 % between the Ibicus and the reference implementation of ISIMIP.\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.isclose(debiased_values,debiased_values_isimip))/debiased_values.size\n",
"print(\"Percentage agreement is %s %% between the ibicus and the reference implementation of ISIMIP.\"%(pct_agreement*100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against the ibicus one:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A linear regression also shows that the reference implementation values' and the ibicus ones are consistent:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=1.0000000224862213, intercept=-6.843278475798797e-05, rvalue=0.9999999999997392, pvalue=0.0, stderr=3.14927108323425e-09, intercept_stderr=1.043150503623679e-06)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**rlds is well reproduced by ibicus.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6. sfcWind "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean near-surface wind speed -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:59<00:00, 14.92s/it]\n"
]
}
],
"source": [
"variable = \"sfcWind\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"98.03392150897476% of all values are within 1e-3. There is a maximum deviation of 0.0035228729248046875 which is 0.08669363071705068% of the average value\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.abs(debiased_values -debiased_values_isimip) < 1e-3)/debiased_values.size\n",
"max_deviation = np.max(np.abs(debiased_values-debiased_values_isimip))\n",
"print(f\"{pct_agreement*100}% of all values are within 1e-3. There is a maximum deviation of {max_deviation} which is {100*max_deviation/np.mean(debiased_values_isimip)}% of the average value\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against ibicus ones:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A linear regression also shows that the reference implementation values' and the ibicus ones are consistent:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=1.0000021330821292, intercept=-8.191132122803424e-06, rvalue=0.9999999715602359, pvalue=0.0, stderr=1.039985640948038e-06, intercept_stderr=4.5461419994753345e-06)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**sfcWind is reproduced well by ibicus. Some differences larger than floating point error exist. This is due to:**\n",
"\n",
"- Randomization: sfcWind includes some randomization between lower bound and threshold. This can lead to differences.\n",
"\n",
"- The references implementation of nonparametric quantile mapping (preceding the parametric one in step 6), which uses linear interpolation, is inexact and differs from the ibicus ones. This creates some differences. These decrease with the number of quantiles increasing, however they are slightly bigger than floating point error.\n",
"\n",
"- Accumulation of floating point errors in calculations. Especially floating point errors in the computation of quantiles can lead to slight numerical differences (larger than floating point) if those quantiles are mapped back to values. Similarly the distribution fits in step 6 are just slightly different (within floating point accuracy), meaning that the same values are mapped to slighty different ones (with difference potentially larger than floating point error) if transformed with an (inverse) CDF.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.7. tasrange "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily near-surface air temperature range (tasmax-tasmin) -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [01:13<00:00, 18.33s/it]\n"
]
}
],
"source": [
"variable = \"tasrange\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"90.94348950410709% of all values are within 1e-3. There is a maximum deviation of 0.0056781768798828125 which is 0.0753957246673246% of the average value\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.abs(debiased_values -debiased_values_isimip) < 1e-3)/debiased_values.size\n",
"max_deviation = np.max(np.abs(debiased_values-debiased_values_isimip))\n",
"print(f\"{pct_agreement*100}% of all values are within 1e-3. There is a maximum deviation of {max_deviation} which is {100*max_deviation/np.mean(debiased_values_isimip)}% of the average value\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against the ibicus ones:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A linear regression also shows that the reference implementation values' and the ibicus ones are consistent:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.9999986970893849, intercept=1.5534482123769067e-05, rvalue=0.9999999844855442, pvalue=0.0, stderr=7.681236296246608e-07, intercept_stderr=6.3467097659119465e-06)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**ibicus can reproduce tasrange well. Some differences larger than floating point error exist. This is due to:**\n",
"\n",
"- Randomization: tasrange includes some randomization between bound and threshold. This can lead to differences.\n",
"\n",
"- The references implementation of nonparametric quantile mapping (preceding the parametric one in step 6), which uses linear interpolation, is inexact and differs from the ibicus ones. This creates some differences. These decrease with the number of quantiles increasing, however they are slightly bigger than floating point error.\n",
"\n",
"- Accumulation of floating point errors in calculations. Especially floating point errors in the computation of quantiles can lead to slight numerical differences (larger than floating point) if those quantiles are mapped back to values. Similarly the distribution fits in step 6 are just slightly different (within floating point accuracy), meaning that the same values are mapped to slighty different ones (with difference potentially larger than floating point error) if transformed with an (inverse) CDF.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.8. tasskew "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily near-surface air temperature skew (tas-tasmin)/tasrange -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:20<00:00, 5.15s/it]\n"
]
}
],
"source": [
"variable = \"tasskew\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.9942957103742% of all values are within 1e-3. There is a maximum deviation of 0.0015919804573059082 which is 0.3169975187576745% of the average value\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.abs(debiased_values -debiased_values_isimip) < 1e-3)/debiased_values.size\n",
"max_deviation = np.max(np.abs(debiased_values-debiased_values_isimip))\n",
"print(f\"{pct_agreement*100}% of all values are within 1e-3. There is a maximum deviation of {max_deviation} which is {100*max_deviation/np.mean(debiased_values_isimip)}% of the average value\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against ibicus ones:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear Regression:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.9999931206025992, intercept=3.6336887746513113e-06, rvalue=0.9999999241443093, pvalue=0.0, stderr=1.6984569092665668e-06, intercept_stderr=8.60771269891601e-07)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**tasskew is reproduced well by ibicus. Some differences larger than floating point error exist. This is due to:**\n",
"\n",
"- Randomization: tasrange includes some randomization between both upper and lower bound and threshold. This can lead to differences.\n",
"\n",
"- The references implementation of nonparametric quantile mapping using linear interpolation is inexact and differs from the ibicus ones. This creates some differences. These decrease with the number of quantiles increasing, however they are slightly bigger than floating point error.\n",
"\n",
"- Accumulation of floating point errors in calculations. Especially floating point errors in the computation of quantiles can lead to slight numerical differences (larger than floating point) if those quantiles are mapped back to values. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### 2.9. hurs "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean near-surface relative humidity -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:19<00:00, 4.99s/it]\n"
]
}
],
"source": [
"variable = \"hurs\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.57598113781565% of all values are within 1e-2 with a maximum deviation of 0.29793548583984375 which is 0.3875% of the average value\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.abs(debiased_values -debiased_values_isimip) < 1e-2)/debiased_values.size\n",
"max_deviation = np.max(np.abs(debiased_values-debiased_values_isimip))\n",
"print(f\"{pct_agreement*100}% of all values are within 1e-2 with a maximum deviation of {max_deviation} which is {np.round(100*max_deviation/np.mean(debiased_values_isimip), 4)}% of the average value\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against ibicus ones:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear Regression:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.9999991927516926, intercept=6.969084543584358e-05, rvalue=0.9999999728811985, pvalue=0.0, stderr=1.0155429998726147e-06, intercept_stderr=7.894066571529341e-05)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**hurs is reproduced well by ibicus. Some differences larger than floating point error exist. This is due to:**\n",
"\n",
"- Randomization: as other variables hurs includes some randomization between both upper and lower bound and threshold. This can lead to differences.\n",
"\n",
"- The references implementation of nonparametric quantile mapping, which uses linear interpolation, is inexact and differs from the ibicus ones. This creates some differences. These decrease with the number of quantiles increasing, however they are slightly bigger than floating point error.\n",
"\n",
"- Accumulation of floating point errors in calculations. Especially floating point errors in the computation of quantiles can lead to slight numerical differences (larger than floating point) if those quantiles are mapped back to values. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### 2.10. rsds "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean surface downwelling shortwave radiation -----\n",
"INFO:root:obs is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_hist is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"INFO:root:cm_future is a masked array, but contains no invalid data. It is converted to a normal numpy array.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:19<00:00, 4.97s/it]\n"
]
}
],
"source": [
"variable = \"rsds\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.77753270459385% of all values are within 0.1 with a maximum deviation of 1.1534500122070312 which is 0.9207561400338631% of the average value\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.abs(debiased_values -debiased_values_isimip) < 0.1)/debiased_values.size\n",
"max_deviation = np.max(np.abs(debiased_values-debiased_values_isimip))\n",
"print(f\"{pct_agreement*100}% of all values are within 0.1 with a maximum deviation of {max_deviation} which is {100*max_deviation/np.mean(debiased_values_isimip)}% of the average value\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against ibicus ones:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear Regression:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=1.0000023217484482, intercept=-8.485653290790651e-05, rvalue=0.999999989836804, pvalue=0.0, stderr=6.216981478242796e-07, intercept_stderr=9.553003222892304e-05)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**ibicus reproduces rsds well. Some differences larger than floating point error exist. This is due to:**\n",
"\n",
"- Randomization: rsds includes some randomization between both upper and lower bound and threshold. This can lead to differences.\n",
"\n",
"- The references implementation of nonparametric quantile mapping, which uses linear interpolation, is inexact and differs from the ibicus ones. This creates some differences. These decrease with the number of quantiles increasing, however they are slightly bigger than floating point error.\n",
"\n",
"- Accumulation of floating point errors in calculations. Especially floating point errors in the computation of quantiles can lead to slight numerical differences (larger than floating point) if those quantiles are mapped back to values. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### 2.11. prsnratio "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the ibicus ISIMIP implementation:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:root:----- Running debiasing for variable: Daily mean snowfall flux / Daily mean precipitation -----\n",
"WARNING:root:obs is a masked array and contains cells with invalid data. Not all debiasers support invalid/missing values and their presence might lead to infs or nans inside the debiased values. Consider infilling them. For computation the masked values here are filled in by nan-values.\n",
"WARNING:root:cm_hist is a masked array and contains cells with invalid data. Not all debiasers support invalid/missing values and their presence might lead to infs or nans inside the debiased values. Consider infilling them. For computation the masked values here are filled in by nan-values.\n",
"WARNING:root:cm_future is a masked array and contains cells with invalid data. Not all debiasers support invalid/missing values and their presence might lead to infs or nans inside the debiased values. Consider infilling them. For computation the masked values here are filled in by nan-values.\n",
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:08<00:00, 2.07s/it]\n"
]
}
],
"source": [
"variable = \"prsnratio\"\n",
"obs, cm_hist, cm_future, dates = read_in_and_preprocess_isimip_testing_data_with_dates(variable)\n",
"debiaser = ISIMIP.from_variable(variable)\n",
"debiased_values = debiaser.apply(obs, cm_hist, cm_future, **dates)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage agreement is 92.59963492546395 with max deviation 1.0\n"
]
}
],
"source": [
"debiased_values_isimip = read_in_debiased_testing_data(variable)\n",
"pct_agreement = np.sum(np.isclose(debiased_values,debiased_values_isimip))/debiased_values.size\n",
"max_deviation = np.max(np.abs(debiased_values-debiased_values_isimip))\n",
"print(\"Percentage agreement is %s with max deviation %s\"%(pct_agreement*100, max_deviation))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the values over time at location [1,1]:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tXPvhtYwvHs+/j/03N8y6gSp/Fa8sfwVwShZVoTLpk8cACOew6FxXbNEOvdBTyJDAkITSxWXNy/CpPkYUjACgyFNEa7SV8a2fcJnyBNLuMuRiyX9hycs0haIdlEuoluEFw1GEQtgK4w1u4O+emyipnZeWXQ2xSL4sUNXjdut9k1mn9LxN1hBuYQexFE+MQ/+q6gj+aJ+dZaOSQ69bzMe+ixhS92F82Yb2DQwNOFINQ+s+YLayME+5DFK4jjzdCP3xrx+nNdrKVXtexdjisaiKyqHjDuW9te/REm1hRfMKRheNpmjn0wGI2EYvR8wdbNEOHRxqpXOEvrxpOeNLxqMqzqCKIk8RbdE2RrQt5kz1VUyzy5e36J/It/9IY9CgJKBjS5uGUANVgSp8qs8R7ikZxfGRP1BTmZ4e+LoiR6FQ9/asn64IFXKUQw/ULmC/Yf/H19XOhKKa4un8x9qjl72yg1BgBJcb5xIq2y6+rCZYE1fr9H98Fz/Xn89TLoMUriNPh0Nvjbbyj8X/YP/R+8eTngCHjz8cwzZ4a9VbLG9ezoSSCejjZyMQeQ49l+CKdNnSJmgE+abxGyaUdnCpboS+cNw5TIn8HUt0URQ+9h7CJz9P1LQp9XtoijRhSpNKfyU+zUfEiqB4AiyQkwiq6XLozoXnUbtPP+qMIdH1FJOb+ihNJdvxSHEZi23HvmKriXEyN7XbI94KnrQOwCrqmP7UOULnuPv4Y+C3eYc+SGHFmu/SoVzc6PzHO/w4Yfn0yumMKhzFS8tfYnXLaiaUTkB8/hReKYlE0pP0yAVs+Q69ZAIhM8RXdV9x9mtn0xBu4JCxh8TXF3mKaIm2oMfEso2uc0V9JTQpTr16WUCnNuiULFb6K/GpPkJmCE1GOFj5FF9beg5tXEyhMLVyjAO/jKCQm497Ib0MW0g0j6N6ucuGp3lV/03yPESWYUeDjBK1aDEOPWgEaY22dujpl4xELR6Wd+iDFOkmRVujrTy6+FFmj56dEJ0DCCE4fPzhzNswD1OaTv7t7evw2la+bDGX4CZGz3n9HJY3L+f22bdzwJgD4uvdCH1E8wJ+r/0DK9ylkeer5zG/eB6A0oAel82t8lfh1bxErAi60cpfPf/HkI3vp2VTg9dxJB695wlHNYHtaCC97tOBhhKsQwg7/qO0bMjB/Nz4GYaZexRRQc085novpqThS6CjwiXu0Ks/4zj7zTyHPkgRT4r24tCf+PqJpNG5i8PHHx7/e0LJBDjmDrzeYiLJZ97nJLZ4hz6xdCICQYFewMOHPcz+Y/ZPWF+kF9FqtFIeWsn31TmYkS4Z7XkPUPyFU85Y4u/Qcanyd3DoSqCCIyPXs2LIQWnZVF0Q49BVvcftVKGBsHvcJlsoqnF+vLymkxRtLdmW/9p7YOXgtd9StA2XGhdglI4HEpuKAFjyEifX3k5ty+DhSvsDjeFGjv730by16q1sm5IR4nXovegevbX6LXYdumvKOcSTyiaxTek2AIwvGQ8TD8Dnr8hz6LmEMl8Z9x98P08e+STTKqZ1W+8mRZePOZEZkb9h+ioSNzjlaebtfjeQGKFX+Cvwa37CVhhV9/CVHEe7VpqWTW4k0ZtDHxJZg4/cLJlaX7YjAKrf+bwKzSamipWYRu4NWw56h/KMtR/ESizjbf8uh773z3l495dpDhtEcvAJY6BQ3VrNypaVKcXichUu5dJbUrQ+VB8vN06F86afxzETj3EmiH35HN6m1UTCTZvL1H7H4Prm+og9R+zJ0ILk04SKPEVY0kLiPG5349C9hdSZDjVSFvBQF6qjQC8goAfwql7CZhgNiyOUjyhqXZqWPVNqXgRAFT0nRb3SAJGDIS/QqjkJYD02Om9SzX952fs7zFxMIEVamCDWxTn0De0bEIgOh+4vo7BiBCC2ah69us3pqO7N6eUa0qFcpJQ0RBoo85X1eKwjJhzBdftc57x4/fd4zUi+bHEwwW3/9zd/xh+1h5BtXYYyz/8HpateA5wIvTZYS5XfqQ13q1xULO713MnojXPSOudGn3PDaErPM7rrCqcR6n2Od1agtjv64Z7Y4OX1Qw/g/OglmEruDdMtX/8ub3svJdC2GnAolwp/RccTUsNydl7/JKW0bt0OPSaRMbJwZC9b5hbSqXJpM9owbZNyb3n6Bz76drzlExlMV0Teocccugiv5Qj1Y2Sky7DgD+9h/Lr/xpUW60J1VPorAeIcuqb7ODhyM18OPS6tc67yTwZ6d+iqUEHYOakCWFnrcOieWPQSLh7L6/ZMjBz8AaovncHPoxdhFw0HHMolHp0DbPyabRdcx0hRt3U79LZqKv2VSQeW5zLSidAbw40AvUboCZh0ML6Ssfkql8EE16Gvr9qBXSL3Eyrq0o5//hz+MfyKeJdoXaguIUIPmSFUVeE7OYq2NOvQ3YiiN8qlKrQaIWxMI/cuqJVluwGg+5z3XGA2saNYihnNvQRSq3c4L9p7ofpLAaepKMGhTzyQmh8v4Ws5dquudFnTuoZRhYOLboH0kqLufNCMHPrXL+Fd8S7hcG72giTDVu/QXU30qO1Ua5hdyzR0H7VhkSCdWxlwInSv6pQtCiE4TvuAipav0zrnjnXPA71H6J5Yl2jUzD1xoFbN+dzc0ssRG9/lee9V0Jp7AznUcCOTxWrU2EzRhnADFf5OyW/dR3nlUGyUrTtCb60edPw5pFeH7kbo5b4MKJeXLsFrhPMc+mBCnHJpW8pN2gMozasSN/jwXsY3zqXEr9NutBMyQ3HKxa/5ndZ/4Cb1fibVvZnWOdd6xwGgde1K7YLGYmeKTqSXSD4b8LY7fLQ3JqHQNHQvzoxeRsRb0dNuWcGIda/xmvdyPJEGbGnTHGmm1FvasUG4Gf2ju9k9sG6rdeiGZbChfcPgdOiy9yqXxkgfKJdj7sQ7dh8i5GbpcDJs9Q69UHf0VAyjgX3VzyHUlLjB+7czo/2Dbk1FAF7NS9gKI6XkWPlnPhx2SlrnXOlzRt+5ejKpoMUcedTMvVLA4fVzAfAoziVkFo1gjr0ThtJzs1Q2sLZ8Dy6M/gIRKKM12oolrcQb2wjDG79nH8+yrdahr2tfh0QOSsrFiEXQRg+RdJxy8Wbg0Ccfjrdy2zyHPpjgRuhNBUPYM3I3LWVdatV/uYTr5DnOcOhObf/gJEXB0URfqwynNSYR0Bus2IXXG+VSEVwJQLQ992Z1flO2D9BBufiMJmaKJVjh3CtbbPKN5FV7NzSPP3lyrKAKflvNvIpj2biVOvQ1rY5sxeii0Vm2JHPE69B74NAbw434VF9mCd/v3sD32aNE8vK5gwce1eNUq8Q49K5aJFII6kI2JUkidJ/W4dCPEnMZ2ZLeCLrdm/4D9J4U1WMlgUYOzjRsVZ0nG4/qJIvLG+bzL++16E3dh3JnG3pwI9PFclRsmiJNQJdITVHAW0TApxOKbp2NRW7J4hZLuYQbM6NbAJ47D68RJmwbSJl7lWbJsNU7dHCi9GhoPbfq9+KtT0xsmm9dz272Iko7tf13jdBDZohL5d+Z3vBaWudb6Z0I9B6ht5U4TwthvWeZ3WwgEHRyDbriJIuDQ3bhlOjvaAuMyaZZSTFx/Uv8x3slmjTjEXqprzRxow/vZcfQRxhd9fC3ElS3VuNVvfFrezAhnQEX6TQVdcMxd+Pd4SQkskc6J5eQd+g4Dj1stjJTfIOI3fAutA/vYA9lMWWxCF1X9PgcUK/mNNFErAhn6rfwypDz0zrfSo+jKdKbQ3fXG1bucehjmpw6dNdGUVDJB/b2RLSCbJqVFN9VHMDZ0UvRvL7kETrAR/eyU/vc7lVOWwmq26oZWThy0LX9Q/p16Bk79O2OwjvECaoGC48++L69fkCRp4g2VWHf6B3UVe6WsO7rc5byZ/MHlAZ0miPNlHhLEMLhQvyqH3DG0DWoVbSK9CJpaTtliL1RLmVtzvxTs2VtRu9nIPB5yb5Ah0P3GC3spXwJwcaedssKGrwjedveGVVR49UOCVUuAD+bz79GXJ6T8r8DgerW6kHJn0OnTtFeHHpGXaIAy+fge/33wBbm0IUQhwkhvhFCLBVCXJ5kfYkQ4j9CiEVCiK+EEGdtflP7D4WeQkJW8jr0plAUEJT4PRi2gUfpUDF3OfSwFeYw+S4TW9MbQbdnm0PN9FrlEltvWLn3uNcW49Dd91DY8h1PeK4nEJOozSUUBNeyi/ItiiJoDDfiVb34NX/iRpoHXVO6a/lsBZBSOk1Fg5A/h04Ti3pJimYcof/zFLyxp2O3PDnX0atDF0KowD3A4cBU4GQhRFf9yZ8Ci6WUOwCzgVuFEL3Nb8gZFOvFBKNN3K3fSXHtpx0rzCjDPrqO3cXXlBXoGJYRTwKC01gEzpd9ZvQpdmt6Oa3zLfM4Gu291aGHSx2Z3WBXBcgcQHHIeXpwI3Szcju+H7mKhuLk0qTZxPYbnuOf+p8A58Yu9ZbGn7Li+Pxf7NPwDOZWGKE3RhoJmsFBWbIIvUfoQSNI2Apn7tC/dz/efZ3B51tShL4bsFRKuVxKGQWeBI7tso0EioRzlxQCDUDuEb8pUOQpot1sZzuxCiXSqc3XDDNm+T+Zqqyk1O8hakcTJG/dKC9iRfhl4U08XnVJWudbqY0BqXR3Kl3gOstoDnLoE1o/Ajp+lBR/CZ/IKYS09Eo3BxILK4/mQtu5MZsiTcm7BZe8xC4NL2+VEfpgrnCB3meKujRbRl2iANsdjXfYdGDLcugjgc6z1apjyzrjbmA7YB3wBXCxlLLbnSGEOF8I8akQ4tPa2tquq7MGx6EHOTD6Z9ZW7dexwlfMA7Pm8rB1OKUBnagVTaBc3Ag9ZIZo08poI70aV8WOIOi9+7MkVkkiGpdl8G4GBp8VzwY6qlx0M8hsZSFasCaLViVHrT6KT5QdAOfm7safA3z/Ef427dGtkkOPO/RBGqH3VuUS7z3IpKkIYNWH+P55MrBlOfRkYWTXq/5QYCEwAtgRuFsI0S1Uk1I+IKXcVUq5a1VVVYam9h+KPEXOxSCMbo/czUEjrrQYtaMJlEucQzfD7G+8y4zWuWmdb4/IHLxpDLTVYlK0Rg7WwLYK5+nEfYrwBtfxiOdmSjtTVjmC0uAKdlG+AaAp3NS9ZBFACDRV2SqrXFwd9JFFg0s210VvlEuflBYBHjkSb8wfbDEcOk5E3jn9PQonEu+Ms4DnpIOlwApgyuYxsf/hdove7L2DoTXvdKwINjBr2Z/Zx7cScJKTbkQKiY1Fx4WfZ9/Wl9I631J1PLboeVoRgBkbh9UWGJbWcQcSFWGngSheelk2juMi17KubPcsWpUce9Q8yZ/lrYAToSeN1Ja/wyGr/6/XuZRbIta0rqHKX9U9UTxI0FunaJ8pl+8/jO+4+4AtK0L/BJgkhBgfS3SeBLzYZZvVwIEAQoihwGQg91oGU8B16GVqPUq005DoSCs7N77KRN2hh6JWIofeubHoj2XXc0f5lWmdb7UyHFv0Pgiio8ol95zMxOBnQIdD17wBFsptcpJDn1NxEr/Vfo1hG7RGW5NH6DVfMm3jf/HJ6FZHuwxWlUUXbtOPLW3s7kxv3yP0qcfiGTYDYNDMFe3VoUspTeAi4DXga+BpKeVXQogLhRAXxjb7I7CXEOIL4C3gN1LKuv4yenPDdegX2D/muyGHdKwoG8vPx77AXJ8zWDpqd+HQOzUWRbVC2mV603pUIihp/JYWxqYC6Q3pyfIOJD4sdHINrkPXrQiHKJ/gjT2+5xJq9FEs1rajOZbwThqh7/lTHtznHYL4MLeybtHqtupBy59DYmSeLDHaEG5AU7S4EF/aqP4U3z17AE4wNxiQ1ngZKeXLwMtdlv2l09/rgEO67jdYEJfQVcLdOHTDstE1x/lGrUQOXVd0NKERNsPsHXkXaRrAnr2eb3fjIz7Te6/q1GI/GNEc7P9qx7E/7tDNFh7w3MZ7dX5gnyxa1h3Dgt+wE7U0hh3mMFWkpilOusi0JN7cG7zUL4haUWraawZtUxEkOnHDNroNX3ebinqrKkuAlPDggXhV5yl5S+LQt3i4Dv1Cz9NMWP9Kx4qmNZxWdxvb2CsBujUWQYeE7v7B1zg0+J+0zvetOhZT6b0iRpQ4EgGtgdyLnoZGVwKdyhYLKjkycj3fVByYRauS44C6x7ksem/qtn+Auu+Y9d2NjBE1W1VidF2bI5s7WBOikFjdkoxH71NTkZTww8fxnvMGMHg49K0kDukZ7tQiVQ2idObKQo3sGv6AT/xOxNm1sQg65orePeRaNrYZ/DuN860VVXiU3rs/dTWm5ZKDibptop/zma9ThO7x8pUcx6FpjuEbSLxQfja11HN8KmEugGADE2reYAhTtiqBruZoDzTUIEHnRHaypHafhLkUxdFyiUXmg8Wh5yN0OiL0BziURVVHd6wYPoMzK55gScFMwOHQO1e5gFPpErEi2JqPsEzv91GXIdSk1aCJCIQ2Ov83fJHWcQcSc33Oj5zr0FVpcZT6EcWtuVczv14byUp9Us8R+pjdefagd/hUTtmqInSXG+5KUwwmJHDomytCt0yo/hTvn5zZs4PFoecjdJwGIV3RsbRwt5vZsGx0NTmHDk6EHjJDzAy+x4zQBmBWr+fbWX7OarP3ahBVj4l/5eAIuiAaSNGhzict7tbv5J16Azgsq7Z1xbjgV5RbIRpiD19JG4sA3Z2+tBVF6K5OUFcqcTChtwi9MdyYecliuBkePBC8xXiFRiQHZxIkQz5Cj6HIU8S+6kfssOHZjoUbv+ZnrbcxwnLK7pNF6F7NGRS9S9scjo2mx6EvFaOIpEFNaEXDAWgqyD2N8RHmStTOl4/q4SjrVuZXHpc1m1LhmMZHOCv4CE2RJgr1wuTRaDTIzC+uZj9l0dYVoceUP7sGKoMJnZOiXR161IrSZrRlTil5C3ln13s4xrwJrx4YNGWL+Qg9hmJPMWGlFTorGwbr2dlcyBciiC1tTNtMyaE/NvL3LFjTzJw0zlVDCeVq706jg0PPvSk6E8xvWNa5g1UIVqmjaVGKsmdUCjxUehG2ZeILz08ZnaOoDK95h9GieKuK0OOUizJ4KZeeIvQ+16BrXj71zOSL9qVMUL2DhnLJR+gx+DU/C9RJvF95YsfCcftwvPdBNhRsF29e6OrQ/ZqfsBVGUXUMO72yKJ0wWhqb+iMtAJQ0LEzruAOJ9/Q9MEViZ+FR4gOGtOZezfxaZQQ1njGphbkANC9vHzWXx6yDMbaiCD3VdT2Y0FOVS5+7RI0QQxrns8R7Bt5wa75scbBBV3UQVrcuQcOy8WgizjV2o1xUL2EzzIy2uZxiPEs6mC6+ozTauziZ5olx6Gp6DUsDiTAC0UX+92p5L9Ma3siSRakxLTSfqcZiRzo3WYVLDG4d+tbUKepG6IPZofdEuTSEG4A+ROjNaznt6wtZKkfi8RYPmsaivEOPQVd0JrCCfTc82rFw9cdcY95Omd2Qkmv0aQ7lMqX9Y06wXiEdLGMoUb13jXNPwIkqGvy5x6GPtFfj6RINnea5nbcqTs2SRalxctsjHNv6T5oiTakpF2DyF3/mLPWVrUpCNx6hD+akaKcIvaviYp8pl+LhPDzxds6KXobuqxg0HHreocegKzqmEJidP5JgHdPtb/EJqyOSUZI4dCvMS6N/zeHKX+gNti1poBAzDc0Tl0PPRcGo8XIlfjsxaqnRRtCi5J6Wy02Fv+bxioud8rUekmNFLd8yTmzYqoZcbGkRetfWf9ehZzx+zlPAN4FdqKcEXfHkOfTBBl3RqVaG8FrZSR0LpxzJQdYdtAdGdJR3pUiKqopIyxFYtoVXRNDTkMT1SuecFY3zM3gnA4P3lJ0JaYnO8RDrPca0LsiSRamxlqHUe8oJW+EeKZdvDnyYq82ztqoI3X3yHNRJUZk6KdoQbkAVKsXeDAONcDNjm+fxofciAg1L82WLgw26EuPQk9She1Ql5YXvli1OafmAi+Q/ez2PFQ0zTllLabT3QRDeWB16UCtI920MGAxAdJEAviD6CLu1vJ4dg3rAbpGPGGM46pA9ReiauvVy6IO5saizE0+WFC3xlnT0S6SL2m/48epfMd+ehFowIh+hDzZ4VA/lspbja++LL7O+eZ07tTsokMGUF75f9ROxIowMfs4PeLPX81hCY50sJ+Id2uu2Xo+j91Lvyz3hpJGsxW8nXuSXFP8fT5WenyWLUuP88ENMa3sVSNH2H8PwxX/jau3vW1cduhVFIHqdb5vLsGwrPj3MzQm46FNTEUDVFP484nYuN85DFI3JO/TBBl3RMQQEO5XiWe31TBbVaJ0i9GTiXABzx5zLTOOBXs9jotImvFh6741FHi3GofcwzTxbGMU6AlYoYVmrXkmLyL2niV94r+HNiiOBnsvXfKEaRouNWxXlYtiOPlFGSoQ5Bkt2OPRkHHrGCVEAXzFfe7YnjAcdNZ8UHWzQVZ0WJcBzxT+KL4tMPZGDo7eAtyhl8sgdciFxxtfJXrhxyzTQRRRPEiH+rlAVFSElQ5tzj0P/kKm0eYYnLNs3+i5T2+dlyaLUWCeraI1JIPdU5VK391Wca1y21SVFB3OFCziUi3sfdmssSjWhqje01TKl7SOe91xFefX7eQ59sMHl0DtHZ26DiUdTUjZguGPoRrR8zNXa37GtnqNpu72OCtFISXh9WnYJBG1q7nVf2tgoXfIJ3w8+xey2/2bJotQ40HyHIsOZKdojhx6rQ9/aIvTBzJ+DkxR178uuHHrYDBPQ0xvenoC1n3FZ3ZXMtbfHLJ2Sj9AHG3RFR5dBzm+8Nb5M/eoZHtBvRRMipYiRGxkUhZbxPfU9TLPnBgRDL6SRAgz/iLTssqROnS/3tKqHi40UWMGEZddV3cJdRb/MkkWp8UvrYUrDX6EIJa6smQylix/nDv3urS4pOphLFsGhWdzAqmsdumEbfavgGbMHl5f9H7eZJ2KWTsk3Fg026KqOKSQNdERwVrCZkaIOXetU5aJ2r3IB+Hr44ewQeRCrl8dXU/iIomJ70tMNFyg5V4duWxZDRD0FVnvC8pBeSgt9iIb6GT/gZr4t3JYSTwmqklq5UjNaGULT1pUUtbcMysXl0LveK3126P5SFquTAdAsiSWtbgnX3vDOmncImaHeN9yMyDv0GHRFxwQeCZweX9Y07TSOjN6AR1NSNhb5VSeJagtnfW/8qx0NoQgDj0zPSRfKMKPaFqb5LgYGFoKFjKfVPzZh+Z6hd9kj9G6WrEqN9bKckIj0WOECENn9Z5xsXLlVDbhINrRlsKGnpKhh9dGhN61hl+D7PKDfythl/wLIiEff0L6Bi96+iDdWDawURt6hx+Bw6BLD6nC0Loeuq0rKskX3Ua+09Uuu0/6GFWrt+UTNa/CKCMXh3uvQASx0mrXcmgLkRLA2apc69ANbX+DI0EvZMaoHnCBfR5gbek2OqZ1mim4tSCYJPdhg2mYH5ZIsQu9LjmDle1wdvJ437V1oHbIXQEY8ervhPL0GjWAvW25e5B16DG6U8tv26+LLij5/mLv1O9FVJaXmhUu56NEaDlE/xYr2/IgV8Q8lIjSMQHq8eJgAdfqwtN/HQMCMtFOhNFJgJf54/WXkDfzW//ssWZUaV4qHscy6XsvXAkue4x/69Zi9JLa3JESt6OBPinaiXDonRaWUfadctj2Mc3z/xxPWgbSU7QJkNrXIdf6Z0jSbirxDj8H90leJjoYfaQQpIoiuipRliy7lsq5yZ2ZG7sP09Sy6ZWgBbCR40m1FVpKO1com7GiIQtFOwGxLWG7pBbTL3FKGlFKyZ+RumjS9x5JFAFUaBEQE2xzYmzCb2BLKFjtTLp0dqBut98mhB8pZLMfhxcBrZD5X1PUXA51MzTv0GNwv/QHP9+PL1k49nzOMyxMi9GSt/wC2dDn0nvlXK9SEEBJvmk56iN3EmODn6b2JAYLhLWU1lQQLJyYs36ntPQ6O9N4tO5CwJdRRgimj8cfylNjpVE6I/oHoVjT3xW0sGszo3CnamUNPdc+mhbql7GfM5Y/6Q+y0+A4gMw7d1U+P2nmHnhW4X7qVwKE7zrkzh56qscjbvpybtfuRbRt7PI+oXwJAQajn7VxE8NOolqa17UDB4Zitbhz6zJbX+YGV3hi+gYJpRDhbfQVb9v7o7c4U3drq0Ad7hG5KM2nZYtyh94VS+u41brT/j5et3akeeSyQWYTubmtYecolK3Ad9R/NG+PLhi+6k1v1+xzKxY6iCCU+5d5FPOozm9hb/RI7kkhDdEVroaNtbhemx6G3iRLqtKp038aAwG6vIyCCFJgtCcufHnsNpys3ptgrO7CMMFfp/8Cid4eufPcqT3muRY9NudkasKVw6PHGos0Voe9wMkdbf+ZNexdqyvcBMkuK5jn0LMP90r8Wo+LLbMtGwXYoFyt5JOM+6jUVj2XvyF1EisZ226YzQjHOXUmTQxdCxSa3OHQZbkETBn4zsQ5d6H6Cdm7RFaYaYHr4fiTpRGoCiYK5FUXoW0RjkbQ6Wv87R+gppoylhUA5X1sjKSSEP9wMZMaHu/TMQDv03Lr7sgj3S/8rx/Dz2LJvt/spl8yfz+uxxqJkF4amaGiKhuVy6L2UvNmheoC069BHWesoMerSfBcDg3DRGFqFl3DxpITl27XMxWd9CRySHcOSwLKhVXgpIo2pPJMP42zgFCW3ykT7E4O9sciWNra00VUdgUgoW3Sdaden6rSOu24RR/Ee22sr2PPLufx9eFlGc0VdyiWfFM0S3OjNkqk59FQRnl/1o4TWc7t+N0rTyh7P46lbBEAg0pCWXe2ihAa1D+JC/QjLliBs9C43ypTm9zhT5FYduhlu5SzN0ZdJJ1LT0hxUsqVgsDcWuRSLJmKBVTLKpQ+Ukv3VC9yq38d/rD1ZNNqRhM6obNHMUy5Zhfsr/mfRoeWy3aLruU77G5oienw09WpeLDvIDmIZRHpuLGos3tb5ozA9LZcWpSLnHLpsrkZgETASOfQ3J/6OfYy7smRVcshQMz/VneHdvTqule/zGFdSHFzZ/4blCAZ7Y5FLsaiKiqZoSSP0vry/8Mwfc0D0VhbJbVhafpCzLAMO3a1uGegIPacoF8MwqK6uJhweeGWzgBXg9qm3UzBZ5euvv3bsmXoK225r0bx+JUcUHMHB4w+Or+uMayZegyo0lg67iCrNm3QbF6O8Rdw+9XaK9JIet3Nx03RH7CqdbQcKRjTM7dNuJ6D4EuzaZ6jJjKNGZN1Wn8/HqFGj0HWdiH8oB0dvRee23m9sVSco/GxFFPqgT4q6DlwTGprQEno2NoVDN/QSVslhlNBGcdBxyn0pW8xJDl0IcRhwB6ACD0opu5UyCCFmA7cDOlAnpdwvU2Oqq6spKipi3LhxAy64HzSCrGhegR2tYMrwoQghqGuLsK4pxJThxawPriViRdimdJtu++pNOio6rW0lTKgqpNCb+mOtb21mQ0RjiH8YVQU9NyEBeGolIJlYtd2mvL3NiraIgdr6LSWeSkYVdzRiNTXUEQm1UTViLEqWBiZIKamvr6e6uprx48djSYgIFZ00IvTRu/Er7zXsofX+vWwJcDspBzOH7lIsqqKiKmqCA92kKpfVH3GcMpdRopYLvnyGv40bvWWULQohVOAe4HBgKnCyEGJql21KgXuBY6SU04Dvdz1OOgiHw1RUVGRlekrnc7oMqjurQgjn4hckt0tBQUqLMWIjSi/qaqrpaDsoaQy4ALDRMEmtEJgNuEM8un5PHitIJc0dH2AWIISgoqIi/pRnt9dxquaMn0snEtVU0Wtz2JaCVBr/gwku5RLn0GV3Dr0v709f/Ay/1//B6/auvDTGkbPoC4eei41FuwFLpZTLpZRR4Eng2C7bnAI8J6VcDSClTK9rJgmyNQrLddajlI0Qu6GLQtUMFw0IBLa0U9omhEBKGx9R6MVRG1psxF2amXdLaFgitxw6MS5R6dLt2u4byldyLDKbHp3Ea0i01XCq7ije9RqpbfiCB0O/ZHTbl/1pXs4glT7RYEKcclE0VKFuNg69fo/LOSJyA9/K0SwoPQRd0TPi0OMReg4mRUcCazq9ro4t64xtgTIhxBwhxGdCiNNJAiHE+UKIT4UQn9bW1vbN4n6C6wTapS/ujmyhYqE4EToSJcXHpQgFKQTfylGYas964HbsGCKTKeS9jLUbcMRuGqWLXa4fzSVzg6XbcnT0D0Aajkv1Uq9UEMmxJ6L+QioF0cEENyJ3k6IJVS6bwKFHtCJqKKeENirbv8OnejPj0GPOPxfLFpOFpV1vWQ3YBTgSOBT4vRBi2247SfmAlHJXKeWuVVW51f3oRujbbrMTEoFt2/z8ypvZ54DDmTFjBsfufyzVq6oBGDduHHV1Tm24EIJLzr8kHpUapklVVRVHHXUUAI888ggXXXQRANdccw07Tp7MCbNPYO9d9+TFF1/s1S6vjKCTW2JRZqw5SnZ5lPWY7QwXDb3OVR1IWBKkcMtPe7mxq7blj8VXs1yb1PN2WwhSyVkMJsQ5dLF5q1w8K+dwjPI+x6gf8MulZ+FVPZlx6GZ26tDTee6vBkZ3ej0KWJdkmzopZTvQLoR4F9gB+HazWDkA6MyPS+Cpp55iw4b1PPfmB8wYXcbcr+ZSUti94aSgoIBvv/6WULCdccLizdc/Z+TI1G39F15wDif+9GTa17RxxMFHsnHjRhQl9e+qJXQsmVsO3f3x6pr41OwwBbRg5ZBDV1rWcIL+Nq+QHrWgqQrGVlKH7vK7WwLloiu6Q7lsJi2XkiX/5GJtPmcbl/Hg8Gvwqq9m5tDt3KVcPgEmCSHGCyE8wElA19DyBWCWEEITQgSA3YHcqbNLAy7lIpBgGaxfv57RlYUMU5y232EjhlFaXpp03/0P3p//vT4HHZNnnnmGk08+OeV5rNjFNWW7qWiaFo/0U8EWOlaOtQuIWOJXdMkXRLyVfCXHIbOUB0kGtXUthysfAmlEak1ruLPxJ8xo+2AALMs+Nqk1PkfQtQ69M+USp5T68P6W7XEDJ0avZpUcxqeBffFp/j5F6DlXtiilNIUQFwGv4ZQtPiSl/EoIcWFs/V+klF8LIV4FPgdsnNLGTcos/eE/X7F4XUvvG2aAqSOKufroaUnXdUToAongBz/4AXvv+X+8+9GRHHbYYcw6Zha77rxr0n2P+/5x3Hzdzew164d88dUSzr/gx7z33ntJt5Wx83z6yWcoikJ61FOORYwuh95lcS5y6E2VMznPugQ/j/VOLWheNmgjaaMXmd0tBPEIfQuhXFJG6H3h0NUCGimmhDZGBJfQUKL3qfV/oMsW0yq1kFK+DLzcZdlfury+Bbhl85k2sHAjdAlIoTJq1Cje/uQL3nzrbZYu/IgzjzuT+/9xPz886ofd9t1+++1Zu3otr7zwLAcdeliP53nw3vv415NPUF5azlNPPdVrVY/XDiHJrSHRhhYAE+hyo2hmkBGiHmQAciSxaNo2COem7/XGLhzCXZVXE4zm1ufdX9gSOPTOVS66om82tcWSlS9ztPINAslV6+/h9OGH9Gli0UCXLeZUp2hnpIqk+wtdOXQAj8fLfgcczAWnnoAoErz20mtJHboQgtmHzeb2P13Byy/9h7CRWh3xggvO4vs/PZXxxeMIeAp6tcsSHixyqy46VR26akcppS2nhix7Gr/jKHUub5FJHXoOPWL0IzaFksgVuFUumtBQlcSyxTi/3gcOffi3/+RMrZZf8Uv+XP4HvJ7PCQ0CyiW3yNksIoFDN8PMnz8fa9U8KmjCtm2++eobRo0ZlXRfRSgcf8rxXP6LHzNtWs8dnabwxM6X3kdvCz1O0+QKVMttjkp0fIa3jMVyLLIP6nb9BU/LanZTv3D+7i35F2njxrVnsn/wtQGwLPvYEhqLOisqaoq22SiXeXvcw+nRy2nzVPGxZzd8ekHfIvQcrHLZauBE6QIpVDZu3Mg5v76UUNRE0zS23WFbzr7g7JT7DRsxjMPOvYyI1nPU7SYMM2ugyrGIMfZY2/Ut5CKHXjNsNldbp+Pjxd4jNVVnjW9bGuytQz53S2gs6lyHrgo1wYG6/HVf5HMjwks7fsboYSaEV2IKZcvh0LcWCCH4ePlXSEXjsMMOY8quszAsm22GFLK4fjEBv9M0tHLlyvg+bW1tNIRdKVwJEmbPns3s2bMBOPPMMznzzDMBpw69umEdzXZjShmBrvDaIaIihzwkENUKINqE6HKjqGaIkaIObC+5cmk5Ur9O1Nar49K8/H3EVZs9GZ+r2BI49K516F05dE1oKJk08cUwYuVzHKbUUaGUcl3TNVxhnNSnKhdTmtjS7pMNfUGecukE18m6EaYk1tYfi5B70nIBGCM24jGaezyHZsVK/tKM0C3Fm2vxOTJWrti1Dl2RJsUE49IJuYDC+kUcoH4KpMelOgPBc8f+/sSW0CnauQ5dE90pl76+t21WPMYJ6rus8U/md4E/4A1Upe3QpZRErAia0OJ2DBTyDr0TBFAmWlGMdpA2o6LLKbGbUyYB4/vFK2R6d70RtSB2rvQcui1y72ZzOfSu78DyFPO1HIOdQxGfv201U9QVQHpc6u+WncqpkX/1t1k5gcHWWNQSbeGofx/FkoYl8WWd69C7JkUN2+gT3QLw0sxHucj4OZavnI/UHfF6i9J26IZtIJEUeYqc1wNIu+QdegIEIXTs2I0fFn4sofUaobvLq2UVYa3nWaF2hglOETt3LrXTuw1FXT+PXOTQVw4/nPusI1GFmtZj7/LCnVkphw2AZdnHYKtyWd+2nlUtq/iu8bv4sm6USxc99L6+t4jUieChXIswLfoFPinT1nJxE6KFnkJgYEsX8w69E4RQCMuYQxcKNeowQmph2hE6ovcYXY3NHk2XcvHYDkUj05TbHQhEYgJkXSULFCvCKFGLyIBr7G+YlkQIEz3NKPSlMb/hNfbsZ6tyA12rXAzL5vfPf0l1YzCbZqWE+wPUOfHZm9piXx36lFWPc4jyCePkWu6KXImnrQ5TmgnHTwXX8Rfqhd3s7W/kHXonCCFAdETDjgZ6J+2SVGqLseXDRR2+aGOP59BiDjpdysVSvADkVml08icWRVoUEgaZug5/oFFZ+xF7qF92m3+aCpoqeh30vaWga1J0eW07//hoFXO/y62h5C5cyqMz9dG5Dj1ZUrSvDn2H6sc5RP2M1sLxXKBcg698fLdzp4IboccplzyHnh0IKSkiiBptBctgvLWCQrslAw7dKXvsCWGl92aizpCxCzLbGuOdocWGdHT9PKRewBI5GiumxpgLCLSvYbhSgyfNG/u8Jefwa/lQP1uVG+hatljX5ioE5s7TYGe41EVnB+lGzKlmivY1KXr/Ts/xO+sCpKeIj5mGz18OQKiXATbQEaHnOfQsQwiFqeN3x1a9zJnzLkee/gssRe/GoR9xxBE0NTV12s9ZXkMZQb3nGmaXQ0/143DuueeyePHijmPH/u8Lh15YWJjxPumhZw7dlnD99dendaSu2+21116bbl4nLBlxPM/be6Kr3rS2X1WyO1/Y4zarDbkKN0J3E4euQ48YOerQY/Z2jpLjE4sUrXuVi9X38XqGLVBVDT8RZpoLKIk9tbVEei9pde1zKZd8hJ4lCKEiEViKB1SViPAQVQLYbhIw5rFefvllSktLO/aLO+neE4Kay6GniOQffPBBpk7tmPCnx8ocZRrc3UAhosSGeHRtLLKijBYbUa1Qnx36Bx9sXqVD05YgrLQjtQ/H/YSnzX1zKgndX4jaUTyKJ35dNzU385TnWgqbv8myZcmRjENPEOdKUuXSV8pljzV/Y391AaV2HX9VrqescTVAp56T1HAduhuh5zn0LCFOncRu5rbWVs47/SR2nrEzf7j0D8gYkd15wMWjjz7KzJ1n8r3Z3+Oai86n0KjnzDPP5Jlnnokf142U169fz0lHn8gJs09g+vTpSRUZZ8+ezaeffhrf74/X/pkfHPgDDj/sSObNm8fs2bOZMGFCfDjGI488wrHHHsthhx3G5MmT+cMf/pD0vd1yyy3MnDmTGTNmcPXVVwNOg9SUKVM499xz2X777Tn11FN588032XvvvZk0aRLz5s0DoL29nbPPPpuZM2ey00478frLrzjv/ZFH+d73vsdhhx3GpEmT+O3vfkeACNdcdRWhUIgdd9yRU089FYDjjjuOXXbZhWnTpvHAAw8AcPnll3fbzv2spJRcdtllbL/99kyfPp2nnnoKgDlz5jB79mxOPPFEpkyZwqmnntqj8x29cQ7bq9+lHalpqnNLWLmVtOgXGJaR0FSkbPyK3ZUlVDR9nkWrUsN1lAkO3eXQle4zRaN2tM+Uy54b/8le4isivmGcGL2GsvGzAWiKNPW6r8uhF3uK43YMFHLboT98JCx43PnbMpzXi5wbm2jQef3ls87rcLPzenFMqr293nn9jeN8aK3p9XSKlAgkerQZzAhfLvyU6675PR/P/5g1K9fw4vOJMvBfffUV1113Ha+98RrPzXmOa/74u/iIuWR44okn2Gv/A3h2zrMsWrSIHXfcsUd72tvb2XPWLJ5+62kKi4q48soreeONN/j3v//NVVddFd9u3rx5PP744yxcuJB//etf8R8EF6+//jrfffcd8+bNY+HChXz22We8++67ACxdupSLL76Yzz//nCVLlvDEE08wd+5c/vznP8ej5+uuu44DDjiATz75hP/973/cdPUfCLYHEQgWLlzIU089xRdffMG/nnmOd9YKrvjTzfj9fhYuXMjjjzvf30MPPcRnn33Gp59+yp133kl9fT033nhjt+1cPPfccyxcuJBFixbx5ptvctlll7F+/XoAFixYwO23387ixYtZvnw577//fsrPsCi4hlLRgifNG/v4L37CPfrtW4VAV9SKJjj0L5jETuG/sLD0kCxalRrxCN1OXuWiiS4c+iaULV693cvcrZ8FeoBP7W0pLhkHpBmhu1UunjzlklW4glmm6gOhsNOOOzBmwiQUVeGI44/gww8+TNj+7bff5sQTT4xrmovyMbRppSmPP3PmTJ7959Pcc/O9fPHFFxQVFfVoj8fjYf+D9gdg6rSp7Lfffui6zvTp0xPkBw4++GAqKirw+/1873vfY+7cuQnHef3113n99dfZaaed2HnnnVmyZAnffefU8o4fP57p06ejKArTpk3jwAMPRAiRcI7XX3+dG2+8kR133JHZs2cTiURYv3Y9CDjwwAMpKSnB5/Ox3Xbbsa56TVLa6c4772SHHXZgjz32YM2aNfHzp8LcuXM5+eSTUVWVoUOHst9++/HJJ58AsNtuuzFq1CgURWHHHXdM+Cy64tPhJ/M+U9Jub19buTcf2Ntj5GhicHMiakcTGm/q28LsrHyHP7Q+i1alhuvIEzh0NymapPXftM0+O3TDkuiawCMMDlQ+o6i1CYDGcM9VbJ3tywblkhuCG6lw1n87/lb1xNeeQOJrX0ni64KKxNdFQ3s9nRAKEoGpeEHVMdAxVS8yNgKua2OKlBIhRKdyRomUoGkadqx9XEpJNOp8ofvuuy9PvvgEr7/5BqeddhqXXXYZp5+edJ42ALquo8dGWSkCvF4nsacoCqbZEYl0TbB2qz6Rkt/+9rdccMEFCctXrlwZP6Z73GTnkFLy7LPPMnnyZACW1q8jIhup/6Y+YX9VVai06tDM9oTzzJkzhzfffJMPP/yQQCDA7NmzCYd7FjrqiUZJPKea8Fl0hWlLFGGlfWMvHncGj3+5mMu2lgi9ExU1pvFDrvbcyhv1ZwNHZM+wFEjKoXcW51KcARfufdlnDl1KjtxwD6qcRgGl/M1zK5HllRToBRlRLkV6vmwxq3AcoYRYEvTLhfNZs3IVlm3x6vOvsvfeeydsf+CBB/L000/T0OA8htmNqykxahk3bhyfffYZAC+88AKG4Xyhq1atYlhFET847UTOOecc5s+f36tNUnWm5/QkofvGG2/Q0NBAKBTi+eef72bnoYceykMPPURbWxsAa9euZePGjWl8Ih3733XXXXEn+9XnjhxtskodDyZCWui6Hn/fzc3NlJWVEQgEWLJkCR999FF8+87bdca+++7LU089hWVZ1NbW8u6777LbbrulbbOLyRtfYbSyLu0IXVed92RsBbXohp3Ioe/c7jzZzS0+Klsm9YgeG4uE01gEHU6+z2WLtsk+TS+ynVyO7Sni6MifCE06ljJvWd8olwEsW8ztCH2A4VaR+6KNyGiQPXeZzg3X/Javl3zNjN1ncNzxxyVsP23aNK644gr2n70/BgYzpk3lgbvu4rzzzuPYY49lt91248ADD6SgwKk9nzNnDtfdeD2qplBZWsmjjz7au1GxR+Ke5nTus88+nHbaaSxdupRTTjmFXXdNHJV3yCGH8PXXX7Pnnk4HZGFhIY899hiqmt5Uod///vf84he/YMaMGUgpGTqiijufuLvbdkIorKGKHbQizj//fGbMmMHOO+/MQw89xF/+8hdmzJjB5MmT2WOPPeL7dN6uM49+/PHH8+GHH7LDDjsghODmm29m2LBhLFmypNt5e0JReD0eEUk7Uttv0aU841mFaR+Q0XkGIzpz6FJKfhU5h9+ZJ7EvZVm2LDmSNRaZtokqVIQQcfrIkhYaWt+1XFSdH4/5D3VtUX6ge/hCTiDqr6DMV5YW5dK19X8gI/S8Q+8ERSh8suoTomqA/fY/gKenTUEprEJ4Dda3rUdVHAfYmbM944wzOOOMM1hcvxhhFdGiljG2oiAhCr3hhhvi2+5+xGxsEWJq5ZSkNsyZMyf+d1tbGzWt9QBcfsXllPqLEta5GDJkCHff3d3Bdt7m4osv5uKLL+62zZdfdox+feSRR+J/jxs3Lr7O7/dz//33x9etqv2WdowEaWCAl156iS+qHTGzm266iZtuuim+7pVXXkn6frtu59oshOCWW27hllsSpxp2liYGkr7vznhn6Oms2DifbdKM0GuGzOKVNcM4a2uJ0GOUS1vEJGpK9la+YUR7GNg5u8Ylgcuhd06KWtKKR+auuqFpm3hV7yYlRQ1LoqsCjyI5TJkHG0dS5iujNljbu52xJ4hivTjh9UAgT7l0giOVKzAUH1L1sF6WYyueXjtFIVZXLmTvdehE44Jb6UCNRSNigCef9ISI8COTXTq2xVixAY/Z1n1dlmDaEpEBh752/Ik8ZB2+dSRFrY6yvrq2KJdqT/Gw5xZmNb3Yy57ZQSrKxY3C3f9dGqbPHHqkjVMb7ma6tRhNUfiL53Y837yYNuUStsIIBAHd6dfIly1mCQKXQ7fijlkIelVbdLYTFMh2KsyeyyN1GUXJQGhLxjocLZH8YerMM8/sNUrd3OhJhkDHjKsx5gJm1j5HkWhK+8ZWFec73hrq0N3GIoCGpibOU1/maXM/His8I8uWJUeqOnT3ydn9f5MduhFkn9DbjLLXoWkqh0VupHHq6ZT5ymiKNPXadBYxI/g0XyfRszzlkhW4EXggWg/hQrYXq2mxJ2Io6UXoFgKjF/3yoPCDyOALToNDH2h47RBSSXJRKyrLxShKNU/OsLCF0VoIWGknRXdceBVzvf+jxVrQz5ZlH4Zl4PE6n0ttWGFy5BGGFWoMlT2X02YLKSN0kRihb3JStHAIJ5c/yZAiH99XFZbIMYS95ZT5yohYEUJmKB59J0PYCuNVvfEfk3yEniW4EXhYLUCqXuooBkVNO0IPCy9NSjquLH3nHNdDt3NHwVD2YL5A5FTb/H+rzqVV+NKO1BqG78s/zIMxt4KpRZ2TorVtUUBwaMF3bB/8JLuGpUCvlIvoQrlsCoduOhy6piocoXyEVrOIMq9zb/dGu0SsSIJDz5ctZgluBB5VfNiqjw2yHBQtXtfaU4Tu1KL3romoYaCQvrNQYxdvLmmMR4QXmyQVMlIyhnUEzNyZyWnaEinMtCP0xrGHcb919FZRtti5schb/RG/1R7n3NAj/CD0dJYtS45k4lydKZd4hG5bSCn7Trm01nBR+91MNL5FUwU36n+l5NtnKfM5Dr23WvSI6Th0d3B1Xm0xS4g3CNlmRyIUsLF71S8XQuAlzFCz5y47TZoZcei4HHoOTZVxPpskn4cQqNiQwQ9Wf+PA2sdQSP/G1oRAxdo6OPROjUW+pm84Q3udx0dfw+/1X2bZsuSIxJrsEqpc7I4qF/d/QxpY0kIi+5gUbWUv82Mq7AY8qsIx0T+xZvufxB16bxF62Arj1Zz71qN68lUu2YIbgReY9SjhBmYoK1CkFY/Qe9wXgY1CRPQs0xoSXiyRgaRnLPqQAzQ1PB14ZRgtxRCL1cpo2pTSgTWoB/jNBmyR/iDk8YtuYbH3bMytoMqlc2PRy94jOaroGVoKx7HOKs2uYSmQinJxnbYbqVu2Fac5+sShV27DEZ6/8W3pvmiKYKUcTthTHqdceovQo1YUX6whUFO0POWSLbhReEgpxFR8rLeKQVGQyLQidENoNPbCoUtn47Rtin9BOcSh273Yn0vDOP5Zfi6Q/tzM9lGzuNM8HmMriNA7qy3WtUWoLPIyKbSIA8x3smxZcqTSQ4/XoXdKisYd+iZquWiqwuHKxwTWfxyP0HtrLnKTouAMD8knRbOE1atWc/ReR/PrX/+WPffZl0Vr27nk4p9z+L6Hc9ReR8VlZ+fNm8f3vvc9wGnt9/v9GFGDSDjM/jNndDtuTU0Nxx9/PDvssAMnzD6KRR9/krZ0rRK7MIXVs/bJQCKKjpWimmekvZ5Cs2lgDeoB0S5TeXpDaNQs7rGO2yoi9M5li8c2PMQx5hvs0vgKv5CP97JnduA69M6ctGV34tA7JUXdbfrk0OuXcZV5NyOiK/GoCr/TnmDo0qcp1AvRFK33pKgZSaBc8mWLwE3zbmJJQ2Zt3r1hSvkUfrPbb1KuFwhWLl3J/931fe679z5W1AW5+g9/hMIIwUiQC068gM8//5ydd96ZBQucsrb33nuP7bffni8WfEE41MYeO03tdtyf//zn7Lfffvz73//m2w2LCLY7EcbSpUv517/+xQMPPMDMmTPj0rUvvvgi119/Pc8//zzEIiirj5NX+geplWWc4D13otujGx/mi0rSToqq2PiIbPEOXUqZ0Fg0NfoFlm3x/oSL+duaI/kkDZpxoOE6dFOacUduyuSNRV0HYGeEUBMz+ZK3ZRuaKvhB9Cr+OH1XRghBmbesV8olbIUZqjpigLqiD2iEnrMOPRsQQjBi9Aj23XEieqiW6cpG7nz2Ax74632Ypkn9xnoWL17MjBkz2Gabbfj666+ZN28ev/zlL5n3wTwikTAzd+8+Mf7tt9+O67YYqh9/aRG0dkjXAimla0XscdLOIQ7dQxRkcnvWqyNQBFQMsE2poEhH+THdSG3I4odZ4ruW1yKfAMP70bLswpQmEolH8RA2LE6IXMWlU7dFCEEdJUQtG6+WntbPQKEz1RK1o/gVP5ZtxSPzzo1FmxShj9qFWdE7+UnpNuiqwnoqCOqlAJT5eu8WdcsWwflBMQdw2ljOOvSeIun+gkDgD/hpVwrwagEWrWrjjttu499vP0dhSSHX/uLauOzrrFmzeOWVV9B1nYMOOogHH3mQqBHlwKv+nNZ5gLSka+MyATJ3RtDZCJRUA7MH2Jbe8HjhacCVaUdq0VF7cINxMtsnK8vcguA6PI/qob7diSArC734ahdxuvoGEfOQnHPonSPdqBXFr/kTI3SxeTh0y5bYEnRVQVcFhynzKF/XAjuc7ETo4aYe93c7Rd3z51yVixDiMCHEN0KIpUKIy3vYbqYQwhJCnLj5TBw4xOvQhQdDDbCqFQoKCigsLqSuti5BYGrffffl9ttvZ88996SqqorGhkaWL13ONpO7i24deOCB3Hfffc4LK0KwpSltm9wSRzWH6tANtJQc+lBrPaVW/QBblBpmzAmke2Pbw3bifutoIqQ3VHqwwnUyuqLTuvoL7tbvZIy1hnGNH3Kt/ncikdzRDnIRtaJxpx2nX2yzWx16Z8qlLw7drF7A7frdVBhr0VSFi7TnGb/in4AToTdGek+KurkJXc0xhy6cZ/57gMOBqcDJQohuRHFsu5uA1za3kQMFN3JWpAnSZrup27PDjjtyyB6HcPlFlyfojO++++7U1NSw7777AjB1+6lMnrot42V1t+Pecccd/O9//2P69OmceND3Wb4k/SG8wr1Ic4xDTxWLSyGwcyhOP6H9H0D65WsaJsW0Y5kDl8jKBtxo16N6aG/ayDSxgjK/YPk2Z7Bj+H4iuVNUFUfEisQlaV36pXMdetIqlz6ULZrBBnYUy/BhoKuCM6K/4f3p1wHpUS5RKxqP0D2KJ+fkc3cDlkoplwMIIZ4EjgUWd9nuZ8CzwMzNauEAYtz4cTz/3vMUWk34QhG2E0387aGHWNO2Ek3RGFs8Nr6t3+8nEumImm+5+xYaQ420Rf34uhx36NChvPDCCwB8WbsEn+pnXPnYtKRrheL85uYSh65joKVo76/ThmPaNpUDbFMq2DhUVbqRWsHSF/nc91NeaPsPML4fLcsuOkfoy/w7cEL0Nt4bsyPq6kaaKCKSg52yhmVQ4augKdIU/0GypBV35PHGItuIO9G+6KGHR81idvQ2/lAyCV1RqKeEdsX5ISnzltEabU3ZhSqlTChb1BWd9i4TvPoT6XiJkcCaTq+rY8viEEKMBI4H/tLTgYQQ5wshPhVCfFpb27uu8EDDjdDbRSFRvYgNlMUkdWWnMXPJoaAggY09pAM7NE4y0XKJ/T+AiZXe4ETgyT8PAb1KCA8kHvOeAKRf7SCH78S1xmm0q8X9aVbW0TlCr21zApPKQi/lweWcp76E0d6UReu6w7ItTGl2m9OZVJzLtjYpKerKPuiqgqYKDlU+YfT61wHitejNkeak+7qfazxCH+CyxXQcejLv0/WWvR34jZQp2gfdnaR8QEq5q5RyV3ewci7BdehRoRNVC6iXxTEHlUanaGx8XW9NNSo2agYJTvesSg5x6BZKyjLKSnM9VXbu/FhbMrMbWxmyLQ9ZhxPcwh1656To1O/u50/eR/F7VCpalnCF/gR2W+58h9DhKF2H7lIupjS7yedualJUWfke9+q3U2g2oKsKp6uvM221U5vfW3NR2HSKJjpH6LnWKVoNjO70ehSwrss2uwJPCiFWAicC9wohjuuLQdlU6nOdtiINsC1UbCdCl2l0isbWT5KrUm4jpY0QNkrPv3sJUGKUi6nmVpIu1edhCw0ryxUina+hk6NOMivdxiJdmlTSjGXkXlJwc8Lo1HAlQ00M0xxaoHn8EWwffpBW/6hsmtcNbkReqBcmvHYj9PMf/ZT5K1viyzaFQ7fDTWwj1uJRnSj9QuMSnp96B0C8/T+VQ3d/aOIOfYCToukQTJ8Ak4QQ44G1wEnAKZ03kFLGyUYhxCPAS1LK5zM1xufzUV9fT0VFRVaaGlwnVSRbKAxHKBJBBGVO639aETo0UciQFNtIFCe6Vf3p2xTjznMp0ahiodvJnxia9SG0R82scehSSurr6/H5nEfecOzHJV3KRVvzAZ/6fswzzQ8CyccEbgmIc+iqzv2BcwjrFgcBHp+fNgI5lxR1HaWbFHXtt2wLUDCWvMqqIuf72tQql6axh3JYNMC9BUNRFUGbCBBUHP3zuEBXJHli1B0Q3blsMaeSolJKUwhxEU71igo8JKX8SghxYWx9j7x5Jhg1ahTV1dVki1+3pc2G9g3USy8Fipdw1KCseQk1wQ34NT/N3uS8GUC70U5zpJn1ZgkjW5I7O1tKNrRvwKM0Ewm0pmWTlLChfQNe0US4IPX5BwxSsr59Ax48BGu7U0eNwSgRw0Y2dk0NDxx8Ph+jRjkR5hPaYcA/0ldbHDqZK42zGKOP6EcLs484h654qG8LMrbCcViF4Rp+or6AaB4G5A4t6jrwrhy6JS2wbB723MK/6s8B3aFhNolDNzs4dIBDtflM2bAYuKxDQjdFLbo7ILpzY1GuRehIKV8GXu6yLKkjl1Ke2VdjdF1n/PjsVRa0G+2c9MRJFLYfxwkTf8R97yxj2fV7ccZjp3PSlJP41Y6/Srnvs98+yzULrqF96eUs/+OpSbdpbGzgR8+fxD6BPbnrh/cn3SYZTnv4+xwgx3HL2S9l/J42N0zL5vt/P40ZJYfyxAnXd1v/8Z2nEWpsYrur/5MF67rDjuUr0n30FiWj+Kc8hAv1XOl17R/EI3Qzwk3Nl7Ko7CxgV/zhDfxaf4qPmg4GdsmqjZ3RlXJxpXQN2wDFwwmRq9mxYAxE30hUW+yDQ/cvf4W/6g/glQ8DcLzyHjuv2whcRom3BEhNubh2dhbnyjUOfauB++WX2bVo4QaqVCeKjtrRXi8M95H+dc8l2Fby51UrGsQWNn4rszImA511gUkZ7dNfMG0JwkZNUQ7W7qlyBoPkCE6VzwEZ3NiWwSilHhEN9qNV2YcbwaqmSdBWKfY7DkiO2IXJ4UdYV75HNs3rhq5JUdd+Z5iFyj2eO9mrxhnMsamUC5E2Rog6PKpD110lfsZfJv8tfrxiT3HK5qKuEXouJkW3Grhf/j7Wexy17BoeUG/GtE1saffKwboR4D/lvimFnQxvBbYQtJZMy8guW2qEc6SxyAq1oAqDYW3fJV0/d+S53CZPSbouG2ih49E3LdQu4R3tIsY2fdSPVmUfroMM6UM4JXolLaMPAMDr8RDBk3N16HEOXe/SWCQtME2+s0eyXN8uvmxTkqLrxx3LkdEbUH0ODWVpfkJ0XD9lvrLUSdEuHHouli1uNRBCoKDwrrIzH1SeyN+V78UfoXpzCG4VxcP2IVgpPtao5TY7ZFYF4sHGG63LaJ/+QtR29NAtT3Ldd10VGDk0j/MZnE7etCO14pFcw4VUe7fpR6uyD/e6bo84jrui0Ll+vVYbF6vPUlj/RdZsS4Y45eLpXuWiRlqZpX5JI6VArLFok+rQnevX5dD3ZT671XSM5SvzpnboySJ0UzpB4UAg79C7QKCzQZSyuGB35mq7J5R39QT3C0QYmCkoF6N1AwBF4cycc5EMUxZakdE+/YVwrIkjXDAu6fqDV9/GX0V3bj1bcDn0tCP0QDkvaQdRq2+5SovQUbaoVy/gOc9VDDHWAuAlzCX6s5Q0ftnT7gOOeFJU754UjeoVfC9yDcsZ5yyzrfgTSF8cetXSZ3hIvxlddSrL9uMTDqh9LL6+Jz0X98nBnVjkPiEMVGI079C7QEWhXG7EH65hiNKStq6yu/4p7x+wg8m/bCPs1Mn6rMz42RaKqfZPzGif/kLHU0ZyDr3VP4ol9qis9hPEISWnqo6gWvocuslYsREtmjuDrvsDHWV/Km3SHy/z9BQPZ0L4MRYO/V42zesG11F2ayyyTWyhc4n2DD9rvBHY9MYiaYQpFCE8sQj9bt8FXDnhqfj6niiXeGOR1hGhAwPGo+cdehf4bYPdWcCZa67gSuPuBM2LnuCuf8reByOFEmGwwIn6QqXdh2D0BAudSIpjDjTMxtUAVLV+m3T9l6NO5jrzRzkxZNm2LOooQEFBSVcLJ1jPs8ZPmN74Zv8al2W4Dqa+dFdON36LVuboFGmailBUombu0GbQwfkH9ACKUOKvHcqlnUaKeFU7CE1ocT10RSjx7tFMsHTsD/lB9Oo45aKoHiJ2x/VT5nUi9GRBS9fGIjfQy0foWYKlFjKX7Xmp9HT+7TsufQ49tv55ew8sLZB0m6jlCkVlJhjkx8Bv5AaHHopRLrY3eeuQGntMNXPAoZsovCp3QclkKLevhOs8F7PEv1P/GZYDiA9cNhwXEPB0OL5L9X8xsn5uVuxKhc73oUdxarttaSORBNrXc4z6IR/Y0+NTjEzbTLs7uCviHLrmfDa72gs5rP4f8fVlvjJM26TNaOu2b1fKxbUhH6FnCULxUU8BC/y786Vvp7S5OPeL00QkJYcum1YCUBjemJFN5bKFsmh3Wd5sIKQXAGAUjU26fs8V9/Efz+9ywqFbtgRhoooMfkB1H//zHch6bWTv2w5iuNf1mOXP8B/P7wh0+ohOFy8zonlRlixLjrhDVzzxZh0rNji9wT+BYyJ/JGhKVKHGyxb7OiB6m6V/5z79tjiHvrP5OUc2PxFf7zYX1YW6B1nJWv+BAat0yTv0LtClpFzUUxquZghNCSJGPcFdf4v3XojREl1hxRTaPDKzx69aMZQ13tyQcu2NQ28rHMNn9rY5MZPTDDZxlDoXr51BH7uUTJBr8EV71rwe7IhaUTRFIygK2CArCPg7tIIO9DzBy1XnZtG67ujsKD2qh4gViUe9Bn6OV+fypPUrNEWLNxb1pWQRwLIdoQ2XQ3+y+CzOGN7RKDe13KFMP6v5rNu+YTOMIpT4/eEGegM1VzTv0Lug0AoyQ/mWyzZezhntDye0SPcE16G/aO2GEauV7YrWYscpm6WTM7LJwktU5Mg4sDpncHd5c3IOfeWoY7nGPDMuQZpNWFJQJwpRM8k/SJsHWn/Kvk0v9p9hOYCoFcWjePi87GDOM36FX++4vjyaQsTMLTGXzpSLV/Vi2IZTgw74QxvRsLjOPCUhQu+LFjrAwtE/4kLjkjiHrqkqZqdS3ImlExleMJz3qt/rtq87T9TVdsonRbMMQ6/gM7kNfw2cz9tFRyWIGPUE16HPYTqGpzTpNi6HrqmZXWhFMkjAzI2IMRjLD0h/cgkyLXYT5EJS1NAK+FhuC1ryH9ikUFRuKfoNH/n36T/DcgCGbeBRPYQMC6+moCod4m+nW8+xY+OrWbSuOzo7dF3RiViROOUytO1bvq++w9PW/k6ELh099L5SLp310AG2N7/gpOaHIHb/CiGYNXIWH63/qFuyM2JF4vy5c4x82WJ2oQZoEV7eEbuyKrB9+mWLsQjeI0LxAc/dDt3odFcWhDObuVllN1Jmbshon/5CMPZjZReNTrp+2vK/8YH3onhiKZuIc+gZ3tjzCmazSkmeI9hS4Ebos5ffygPaLQnrZpvvs037gixZlhwRK4IqVDRFw6t6iVpRzFiPwbcFe3Bc9I+MEhtRUOMTi/rq0Hdddg+363fHOfRJxrccFXoBOjnlWaNmETSDzN84P2HfsBmOlyxCPkLPOrzSolRpZmRkKZWyMSEZ0xNch3+J/jR63VdJt7EMRxvGk4EeOsBadQxr9DEZ7dNf6MgpJH/KCBVP4G1rp5xIisqm1eylfk7ADGW03zhrNaVGbvyA9hdcjrlWrWKdmpgA/k3l3fylNLUQXTbgPlFAh4KhGZviFZI+dlG+Za73Fyh01KH3lUOPohEVHbTJW+U/5JiSZ8HTUb2227Dd0BW9G+3SNULPly1mGUVGE2OUdTwU+RVHtDydMKqrJ7i/xG/KGYR9yemIxmKnnVyWTsjIJlt4MXJED13buBCAkpblSddvHHkwV5rnYOVA+7+peKgXBShKZsNBfl//a45pfbr3DQcxolYUj+rhlcITeaggMQHqzUEOPWJFEhx6xIrEOfSq4EqKCXKZcT6K0De5yuXd4WdxDRfGX+uq0k3OIqAHmDlsJu9Wv9vNTm+nYTT5ssUsI+ofyXdyOBfbv+KT0sPS1oQQQqAJnflyPBFf8hptI1bOmGmypsRuo8BO3pk20Ah6HPlQERiWdL37mJoLSdGor5Jv5XBIkdNIhb9VXsbL3sP7x6gcQdR2KJegYSXUoAMcFnqJI1r+1e82vFf9Hie/dHI80u4JLkUETqVL1O6I0Ke1z+cI9SP+Zc1GCD0+U3RTOHSXPwfYJrKYC0MPQjixe3jWyFmsbFnJmpaOkctdHXq+bDHLUNQCwkLlv8bO1BZsm3ZjEYCm6PhECCuafMCFr3ExAIFoZoMqhlq1FNmZ8e79hTavoxMuCpNrnYxb9gQLvedhh7s3XQw0LFsihIWeYYPJ4sI9+U5s2Ry6YTkUxqUbfs2PQ4na/FMjXzAjOj/FnpsPX9Z9yZf1X9KShsyC+0QBxBuL3KToq/6j+IlxCePFehQZG3CxCRH64atu4g+d5t0PN6o5wnwLoomy17NGzQLgvbUdtEtXDj1ftphl+GyDgGhnN7GYcrshI5EfD4If6G8RqPkk6XrLcDRc9AzZk9X6JNaquSEWZcR/4FJw6KXb8G9rH6Iy+xSRWruYyeoKAkZ606FcjDOXM9TIjUau/oKr8f+tMoF6b2J+5tFR13Cp7w/9boPbadnexVEmQ+fIN86hx5KibZafyf5m/uf9FZoZ2WTKpU0U06YUxV8vqjqS2do/oDjxHhxbPJYxRWMSHHq3CD2fFM0uiqINlCgtPOn5E7s3/TezCF318ZHclvaC5NFdfYkz81AtTl4hkgpS8ZIrjGZg46cAFLQld3jtI/bmD+YZmBny1v0BQy2gCR9qCimGVDh7w3WcHX60n6zKDUStKLqqc492Oh9WnpCwzqspRIz+v+LaDceRt5u9O/SoHe3GobuUy9TwF0zwNvPz6E+x1cAmNxb9p/Ic/uo7M/5aV0TKqq1Zo2bxyYZPCMUS7/myxRyDUTiWOgo5P3oJSyoOTrtsERyHvpShBP3Jo2nDdqVcM+PQy6wmAjI31P/aYvkBrSBVHbrLoWc/KRosGMlGUYzwZjZB6Znhv+QR7cR+sWlx/eIBnTGZCi4nHYya3Tj03Vrf5KfRv/W7Da5Db4v2Ts91ply8qhfDMuKUy0HGe+zDAl6090YK7yZTLl059FHh7/it/QC0dq98mjVyFhErwsfrPwaSUC5qPimaVahaIZaQvG7PpK1wbEZC+bqi4RVB7GjyMrnChs8B8KYRkXTGULOGApkZbdBfaImJcqkFyRO/Q1f8m8Xes1Ba1w2kWUnh1KFbGYs0rSnaka/l5pdaaAw3csp/T+HlFS/3vnE/w7ANPIrOS9FzOKDxmYR1w6MrmWn3/4CLeIRupBGhd0qK6opOxO6ocrmRc3hx2M+YJKpRbHuTk6Jnr7uKn0X+Gn9dZtVyCB9BuHvua7dhu1GoF/LW6reAJBG6ko/QswqfHUEVBgcqn1Fi1hO1Hc2LdORXvbZklraIsvXvJF1vxfQoPBlKeq7yT2ejkhtzOs3YiC1vikodo2wbHrMOwhC+pOsHEv51H1MhGvBHmzLab1R0OROsZZvdnsZwI5a0koo6DTTcCPZNaxfaCxMpwvfH/ZQjzZv63YZMHbrLTbuNRW7U22T6KAvovOH9Nf5IwyZz6LXKEJrVjvttVeV+7BK9H6q6S3boqs6+o/Zlzpo5mLbZvWwxH6FnF4XRRhAWf/PcyuSmdxMig96g6wG+kiNpLJ6SdP3GEkfURy+oyswo4cEm+2WAACX1TsLXH0kuRRAduhPXm6cS0ooH0qykiHjKCQoNzVPU+8adcNS6O/mV9dBmt8et5kinqqO/EbWiaKqH3xrnsH7ofgnrvJqCYUnsfm4Oi1MuSWRou6JrHXrUisYj9CPkB0wU6/hJ9OeEtXKHcrH6zqH/o+R8Xiw6Kf5aUxSkTC1ncdDYg2iKNDG/Zr7j0JN1iubLFrMDu3g8thCcGf0V1VX7xpNH6UDX/GyghDZ/cunVji7LzC60crMeL7kxhb7JNxQAT6Ai6XqXe8yFTtHWovGEhYriy+zp5q3RP+cG+/TNbo/ruFqj2afPolYUBec69HsSn7YmNX/Ibfo9RKPhfrXB/TyCRu/XdtekqCWtOI1xpvomE4Kf84bYk6hSsMkRumEmcuhDIiu4UXsAszb5YPS9R+yNV/Xyxqo3ukXoilDQhJYvW8wW1JhS4hw5nWjBiBjXmF6E7lE8eJUwRJJHHGVNjsa0l94bKTpjSHQDXvr35koXLR6HO/f4S5KuL171Ot95T8PfsHggzUoKl0P3pjtPNIa6oil8YW9+Dt115Dnh0O0oaqiVhd7zmNSY2L5eYtaxk1hKJJSZZEKmcB15OhF6V8ql8/4nRa7iuwmns4O+Bs2MdlS59NGhX1V/KSe3PRx/XWC1MltdhNme/Kk0oAfYa8RevL7q9QT7XOiqno/QswV/jOc+Qn2fYrMhIbveG7zYTFJWMXzta0nXW26Vi5aZg1lZuDNNZFZ611+we+HQZfkE7reOIqSXDaRZSVGy5nWEsPFHMqM4hoWXsYP8erPb4zrydKo6+huGZYDi53lrb6zixCfKVeNOZHb0NiJq/15z8Tr0NDl010G792PQdBx6RPrw+AL8lT9RFVq1yVouK5SxNOkdndD1FTuzR+QeokN3TrnPQWMPoiHsOPzOSVFwaJd8hJ4luF2cf/T8jeHNC+INGOlA1wtYQxk1pcnHl60rcrh1j7cgI5sUxQMi+2WAABWNDofulckjDlk1hT+bP6RNzzBP0A9o9jqllZo3Mz5/r3WPcIP6wGYfdJ1rEbqtl3CNeSZm1fYJ69zBDpF+nCvaOamZjkPv2lgExGu/T1b+x5DwKm7wXUKdZ8wmqy3e5buAD8uOib926ZeeSnH3G7UfamxmQWcO3bU3X+WSJYgS51H7J+ZPaajajbpQHeVpcrBe3U+r8NLkT944ZMbqZr0ZRg4VRg0qA/PI1hsafCMA8HqSa4xrigBkXLcmm6gvckTQPP7MnhY+GXcBPzcu2ux6NG5Emu2kqGmbzjxO23nK6lqHPqz1C+7W78BqXJNs982Czk48HcolQW0xRoG6lMu52muUhVfyhX8mbRRjWAa2tDdbHXppZC236vfChtRj+Uq8JcwcNhNIHqHnq1yyBD02M3Oe3BYZqKCmvYZhBcmFqLrCo+hoIoISaUq6fkiLozGtZ9hYVBHdACL7DhKgWXd+3Hy+5I/jvuoPWOk7lcq6eQNpVlKEY/RQupSZi9bCCXwlx2/2IR1xyiUNB9afcKPFgsalfOk9m7LQyoT1fhlisqjGSJEL2hzo/Bn0lhSVUiZUucQ59Bjlckj4FtrGHsS2Yg0Boy0euffVoT/c/hMOa3g8/tojo+wmvkG296yndOCYAxPsi++vevIcerbgi3Fdx2jv4TcaqQmm79C9KBQrLUxcm3x8mRnzD5mqLa4p2R1LiM1OAfQFtuXcLJ4U70GUj+M24wSaPdnXnhmyxvke/GlUUXRGVWg5+yqLukmmbipyhXKJz+LUKnjS2h9PYSI91jZyFgdHb6G1MDOZ50zQ2Yn39gNnSueJouvgZfcYpvQS8Pm4sO0+JkcWE7I2zaEvYDuaOlWqhcu2ZVb0DlpHzupxv8PGHcZ+o/ZjeuX0hOX5CD2L8Mcurp/o/0ZrWYRhGwwNDE1rX68nQKvwsLJsr6TrqwPbAqBlMoUeUGPO05bZ59GHtCxAkxJVTX7paOVjuMM6gUbviAG2rDvq/aMA0L2Z1aFPXfcsd+l3YfYT5RKxIllt/49z176R/Mk8DW9JokP3ag4FEzH673pzP4tCvbBXDj1e7qskj9DPU/9LodnIs5UXsFidFtd46WtS9FpxAUsqDo6/1pX0JKFLfaXcfeDdDO+iRJpzSVEhxGFCiG+EEEuFEJcnWX+qEOLz2L8PhBA7bH5TBwZaidM1d5lxDuuLnAs93Qjdp/sxBdT7xyVd76rDqRl2ipZHHA0Jc4Ae23pCnXcEUqa2XxXgwcCyMivN7A/UFjgRpteXvMQyFb6ZcAanRK/E3Mx6NJ0j82xG6e6PiW05t7+/C4deFFzNA/qteGr6T0LXdeJDAkN6degRK5E668qhX6C+QsBqo6ZoGvWyI1+yuTj0AqOBu/Q78VZ/2Kfj5VTZohBCBe4BDgemAicLIaZ22WwFsJ+UcgbwR+CBzW3oQMEbGzP1FWNojjXzDC1IL0L3qR5HfztYk3T9iNZFCElaMgKdURZxjjdQF0VPaNZKMWVqTlqv+5pvfWcwZuOcgTMqBaKmU7ufaRI6WjiSr+S4zd4clWsOfeyGOXztPZMCPdGhexSbUaIOO5KZ5lAm6OzQe6Ncuiqedi1b3CX8APqQSYyx11JhdvDcaTv0N6+B53/i/G1G+R/nsUdth76NJiymilXIUN8GtXsUT05F6LsBS6WUy6WUUeBJ4NjOG0gpP5BSuiN1PgJGbV4zBw7+2OPaMeq7tIYcidhhKabzdIV7oU1en3zaS1SIPnFc68sdCidi50Bi1Aqh9DAOTykezi3mD6n1jRs4m1Jg9DqHQ/dmSFWVtq/kcOVjzM1cttcabY1XTGXVocecS6NvHH+1jsTXJUJXqrbliOgNbCjfrd9scB360MBQgkawx/yQ69C7li128PAqfq/G7MZnOMD6OL5fug69/cv/sm7dagAkktftXWkNdGjEy6IRHBi9lbpRh6T35rog15KiI4HO9UvVsWWpcA7wSrIVQojzhRCfCiE+ra2tTd/KAYQ3drGfoM+hNbgSXdHTLlt0L7Qvy5InT9b4tsHqIbpNBS1G0URzgMYY1v4lZbKHyK2gkr/K46nxZn/iT01gHAD+DOv+x9S8wX2eOzDM5JOn+oo2o40RBU5uIZsO3XUu67xT+ItyUnwYsos4h96PdeidI3RLWoSt1J3QXSkX17G71Sy/VJ/Fr8L8EafwX7sjf5Uuh36h52bOrTsFAEvoXGGcw9rKvTsdZ9PkLHItKZosHEv6zoQQ++M49N8kWy+lfEBKuauUcteqquw3niSDJzaV5FrzZOqIMjQwtNsFn3Lf2AVXkyIh6IgJZcafA5SF1wMQyYGxbhu9wwnRg4O0bUrUEJjZlyqoCTg/Kl5PZh2P1eN/wKGRGzF7yBVkCsu2aDfa4wmz1gynKG1OuBG6ZUgCencX4LPaeVi/iaq1b/SbDa5DrwpUJbxOhvigdiU55XKW8jqqqhIpGc86q8OvpBOhhw2L5rXfcGb0SYK1q+KJT13r+Fw8dpC/6LdRtLpvn0euNRZVA507ZUYB3cSuhRAzgAeBY6WUuTEAsw/w634AljOURqMh7YQodIogQmuTrh8d/JwCmbmjK4k6cqv9WRecLlqUYsI9yRC0rucT5Sym1iZ9SBtQGLEfFX+Xzr3eYBdU8Y0cw+Ys8nB54lyI0F3ncuCGR3nSvqzbel3XKBOt2P0oztVmtBHQAhTFlDB7dOhdKZcuSdG9lcdACKrMGiaK9fH90nHoa9++n7OVF9hX/ZyN61djtGxgofc8tqt5qdNxFMaKDSjhvjWEaYqWUxH6J8AkIcR4IYQHOAlIKLQWQowBngNOk1J+u/nNHDh4Y5/IMdq71Idq0k6IQscFtH3t80nXh4VOX1j0uoo9nP315N2ZAwnFDtJj0aWvhNvE6azybzdQJqXExJr/AuDNUDunKLiGY5T344NK/rPsP5z/+vmbZIvr0OMRehYdejj2Q/etd1de9h3Rbb03UMxx0T/xTVXfOON00G60U6gXUqA5T3s9JUZdh+5SKJ0pF4FCQUwtcmLTe5ysdMwiSMehK0teopxW9ojcw7fqJAx0/m3tQ6iwg0NX/YUcHr2JNaOP6eFIqZFTSVEppQlcBLwGfA08LaX8SghxoRDiwthmVwEVwL1CiIVCiE/7zeJ+hj/GV++jLqI+Upd2QhQ6LrSFJfslXV/tGUeQzGqioaMOPReqXIaGv2WE3cOABm8h/9SOZZ23/5pS0sX6gCPjkOnEooraj7nTcw+EnAfNTzZ8wofrP9ykx2bXgQ8NDEUValYduisi9YU2i/8VHdVtvSdGN/RnHXq70U5AD1AYk5DoqVvU5dC7JUXNIKoUnC3/DcDGUYfxN7PjByodDv33BVdzlf93AKxpDBHVi/iDeQZNlbvEt9EU5/OI9rGMVVf1nKJckFK+LKXcVko5UUp5XWzZX6SUf4n9fa6UskxKuWPs3679aXR/whtrQrmVQzGl2SfKZYMn+bxNW5qIvnDoIYfhMoLZZ7I26kNpEj0kiaWkUmlDZNid2R9Y73OYwkzrkZvGHs7+kVsJeRzN99qQk8B3HWFf4DrwIk8RhZ7CnHDoSkShSO+eH1IVwUOePzO1+sl+s6HNaHMi9JjURk8KlG5029Wh29JGlZJZliMzoRQPY53d8UTd2/duWDafrWpkv2ljuM77d4Z+8xhGrJ27cx26rsBD+s0MX5W8A7w3eBRPTlEuWxW8miOsU+9xSgTT7RKFjkiwIJJc1GhMeDEVdve5hL2hwHAqQs1Q9ifdtCoFBEUP6oVGkJcjZ7BH3XMDZ1QK2Jbzo5Jpx6AIlLFCDseQzpOROzJuczn0Ir0oq0nRhnADfs3PrfW/4qKmm5NuowmJ1Y8Ca0EjSIFe0OHQM6BcOo+ENIWPKytvB6DYamKaWB3frzeHvvHVP/NL+Xd2n1DBNH09assaZO0SFnvPYvTGtzuOo6mUi1aUNFQhkyHXyha3KrgXzXiPM6Ahkwjd3XdGY/KEYEjxYmfY9g/QUuWouIUCyQczDyRUO4inp7pu1cud3vNZ7E+tHT1QmNTgDO7NNEL3BddxovpOnHKpDW56hO46rCK9iCJPUVY10RvCDZT7ynlSO5aFpQcn3eYX2hXMrTih32xoM9oSHHomlEvi32q807W8/TvOVDsqUXr73hvWL2ecqGHmuHLuHn0bt/Mjolohj1kHES3q4NA1VXBc9I98Per7Gb3HznbkI/Qswb0IQrpzgfWFcvmkOHkd+lptBC1K8tFtPUGLc+jZr0OvMlYz1kpexQOAqvFf39Gs1CcNnFEpUB2TYMhUbbG46Wv+rN+P3lKNaZtxR77ZInRPUVYpl8ZwI2XeMp60D2JFRfJr1asp/cqh9yVC75wLce9Tnx3hkNDLAFjDduY24wfdtkmF27VzuKH0KqqKvIwu97OmMUjQO4TrzVOJVHQk9fUYh95XKQhd1bGkhTUAjYF5h94FrvOs11S8qpdSb2na+7oX3EY1udO2pYXSBw69OOhw6Fbbhoz33dyo1SqoU3tWUhymNCKijT1uMxCo0R07M3XooVGz2CdyO01F29AQbkDG2i7qQ33PYbgOvMBTQJGnKKua6A3hBsr95XijDRRqyZtlrrLu5rD19/abDW6E7lN9qEJNq2yx8/foRug+aTDJcArrvIUlrLU78lc9UW2WLZm3soHdxzv5oFnR97lPXkdNs0vTdeLQNYW/6Lex7ap/Zvo2nf3dQdEDEKXnHXoXKEIB6XwsmTQVQccFVxxJUYdufMtQM7nOS08ImM7Fbmc4Sq0/EBQ+2pWeB0ZEfb+mwbh2gCxKDely6BlSLpqvgGo5hCh6nD+HTY/Q/ZofXdEp1AuzqoleH66n3FPC+8p57Ff3RNJtTMVLWPZN3Ko3SClpN9op0AsQQlCgF6TXWNTJobt/N1DO82N+C0CACLuKjkHOPX3vDS9dw53Wdew2zrmWh/htikUQ8xtnJm5FbYeEgKYIvESRfeTB3UBvIEoX8w49CTwxXYlMatCh4yKb0f520vVBxYclfEnX9YRQlTPSrr2gJ8WFgYEqQ/hlz9RPu2c0qzIsFewPTGqdiyplxmJonkg9J6tv4Wlft9kcepvRRpHuVFBlk3KRUtIQbqDYU8bVxhlsqEwu9fzX4ot4pvj0frEhakcxbTNesligF/T4Axdv/Ve6O3RpK3EO3S+DXKB15K96itC/CZfSjo/dJjh5KW3nUzg+ei2fR4Zyv3UUdkmHHJWuKpxl/IbPhp+S6VtNsGMgShfzDj0JfDGHlUkNOnRccB8H9ky6foNSRZOW2TGhM4ee/Tr0Mnsj440VPW6jFmxHqxLe7BN/MsUa71iQmSehfW3V3KD/jUDTt/GEaLmvnPrwplEurgMr9hTTbrTHdbsHEq1Gq+NM9XL+bh1KW8WMpNt5VKXftFzcaNzlzwv0gh6TolEriqZoCbLTLuVSJZuY0foeAL6SIVwVPTu+TaoIPWxY3Fq3GzcUXM7IUqczfHSZ0/38aXMJfzZ/iCwdF99eVQSKALOPA09cvzAQ33feoSdBG45+diYJUeiIGuqU5PrbNlbG0SJAUSjGnbf234zHdNGgFFOjj+txm+F6AEVrYWNrdmvRa7VKbDJr+wewh01nt/A9rCvbLV6Dvm3ZtjT0UT4VHIfutrmn0+7eX2gMO7kNv1LAEBop1JI7qTPbH+Knddf1iw3t0USH3hsFFbWi3ZrD3NeFRKg0nHZ/v9fLOtmJQ0/i0MNNG7j7gXtZuKaRSw/dNr68wG7jKd8NTNz4BpCohw5wp343M1f9NYN32cnWmF/IR+hZg3MhZFKDDh1fXKmZnEMfZa1iZDRzp+yNKdHJHODQw2gEtZ4rdfZe8zwIyXd12U3iCrsdpQ+XuKb72EgZUTTqQnWUeEsYVjBs0yiXaFs8Qnf/zwbt4r6H4vY25vl+ytjGD5JuF9GKaOpDV3M6cJ135wi9px+3iBVJOqcTYKk9mm8mnAGAogj2Vb8GQCBQRWIBQtiweO2ha/nFxqu467Aqjt+pk8q3p4ASzWCW6czELWhcnLCvLVQs2Td3mU+KZhmVtuM4M47QY1HD9uGPk65vFz4MJXM9FrNyGgAtReMy3ndzQyVCQPYsK6tPOgmApfXVA2FSSkwMLaTMzjz5qJutnK6+RkHrcmqDtVT5qyj3lTsVL32c69pqtFKsOw1ZboSeFYcee8rQA2O4wjgbo2pa0u3erPwRd/gu6BcbklEuPTl0wza68eHxBKlUEyYunSOcEkZd0bsVNFzx7y+5rPZQ3tvjQY7ab/fEk6g6d024j9vME7jd/B5KUWIl11Xqz5kztG85hTyHnmUUSUeUKVOH7nLdH/uSKx/UKSU06ZknNt0LIhfq0AtoZXz4ux63GTHteABWtnQT5RxQrNFHEhaZR5lapIlr9b9T3vg5deE6Kv2VlPvKMWyjzx2eXTl0d9lAw80DKJ7RPG4dhChNrlvv1fqfQy/U00+KporQJ4gNjGxZFF9+vXYxkDwh+v7SOg7fYQz7H568YWp0WYBlciS3myeiFifKd2iKQrSPM2bdCD1f5ZIlrFXHAZlTLkIIhNRpFMn1wiVWt8fAdFAQdiottJaek5EDgVbhY4Nv2x63maQ6jmBdW+YlmpsTDWoJ0b449LIx7BT+C0uqDqEu2OHQgT7z6J05dNeRZaP93+XQ9ahgJLUU6Mmd1EFNT3NvsLu07uaA69ADupOITCcp2s2hx56GiwhTREd036o7jrgrf94eMbkydDNH8V7K8xxZ9xB/02/BSzQ+GNrFNfbdHF59R29vLSncH5+BKGrIO/Sk8CCkTok3s+HCABoK5db6pOuGyBpGh5dlbo10OsxkFvU/XJhAWO9ZgqDq3xegSqgLbhwYo1JAke3oMv0+AheaptFIMRFbozbkUC4VPidv0BcePWJFMGyjW1I0Wxx6kV5EZfX/eN93MSWR5E9REU85q+WQpOs2FW407v6wFXoKaTfasVNISkStaDcHHVc2tSbTOubA+PLdpMOhd91+1cYGRouNjPCGUtoVKC7jQHUB3/jOxBNMzP+ElQLCwp/O2+uGfB16ljHOrGaoaWXUVOTCbxtsa36RdF1QeIhomf9IiLKJADQWZV+SVsGiwE59UwAoh1yHbhfRGM3umMHRxrcMtzJ/SlDtKOeq/6Wg6RMM23AidH8sQu+DQ4+3/eu54dDL/eWsL5rOZcb56CXJp2t9PeRILrV/3i82uNF4nEPXCpDI+Ei5ruiJcgEFf6ch14dHk+v3rGi0ODb6J8TuP05pl7L3zzk8cgM3Gz9ED5QmrLvXfx4vVJzV63tLBteWfNlilrBbuIH9QpmrIgIYShGfadsnXddCgGbvmKTreoKuOty8mQNDooUwGBf6pueNtj0ETRlKu5ldud/16hBa1cxHHQppcaX+OAUtHwHOmLRNidBdx12o+uDZ8yhsdBQBsyHQ5Qpz1Wgj+Zc1m0BhcuVMr95/HHqb0YZA4NeciLfA4zj2VInRqBXtJt/gvp4uVlAS7qgqe7z8Z0B3Dn1FnfNZj6tMPW1rRKmPJYzlXutY9EDi56IrSp+1XAaybDHzroutAJdduBj6+msqfLSKFLXPom8cuj9GtfhalvbNps0EKSWWEGwIJK+MiCPYwHChsZTsOvQW4cerZDZPFAA9wC7WQ+xTGoHWORRqZbS2O99pX5qL4hF6uBm+eBo5dDoFekFW9Fwawg2MKRoD7RsZKzYkVIh0xoyG13lTuxuzbXe0wswF5XqCO9zC7cnoPLVoCN1pnqgVjfPtLtyIvVhG8HfSo7G9lSC7R+hjlvyNhwMLCOjdJzTFj1mziPe9v+A30XNQxeEJ6y4O30vpBgN4Nv03GkM8KZqvcskSPAXgy5waAfBKi1KZnGooppnRwSWZm+N+S2Z2G3XMWAdt1NOLjO+Cx9izeR5SbSRqZu+pQiWMlz5EVUIQVQppjZWvvrKgnfPveYkST3GfkqJuJB4JbMukyKM8KQ7PWvu/S7lMW/UPXvf8Bo+a3AUYvgoWyG2I9kOQ7uq4uOhtalHU7p4UdZ3kXHsHtCEdSfqpxpKE9S4a2yMEPCr0RKP6yxjBRv7huQHRZch5i1pGYy8aRqkQr1LL16EPPpRYbYyTyatRwmhE9D7I5xY7pY51hRM3ybZNxfKm5QAMkb08ZWx3NK0jvgeKyYqGHsbV9TOq7HWMia7q075nKy/iCzq5kF8uPJU3+THFwte3CD32hLWhETzSYOOKL7Li0C3boinSRLmvnAXFB3IFP02ZJ6odsheXGD8lom7+5qKuDr03Cd1knaJxBy8TOfRd2p1Gqa4O/fbQ4bww+aaeDSsfz23j/8LN9o+gC8XzbPFpPFZ0bs/7p0C+bHEQo10fxhdK8uRlRKi0+pPX/fYET45w6PM3zgfgwJaVPW9YPp6S4c5g62+z2FxUpxTTqGeeswC4QD6DHvkOv+ZjhT2eFfZQPErZJnHoZV89y4fei/jZ0vMo0gZecbE52owtbcp95azQJvKeN7kWOoBXc5xkX+do9gR3QLQL16G7kgBdEbEiKTn0vcTX+EUHPTp3lJP0dHtCAJqCURqDBuMrkpcTd8bs/Q+j6tBLQU1ko3VV6buWywCWLeY59M0NJUBIpKoCsfvEobut/4WtPTf09Dfm18xHMwLUDPlBr9tOkc7NubxxHbBLzxv3E0JCI6T1bcrT4Z5H8Pqfo8yr8v3wRQBMl8/TEM68asalXEZs/IxaWcpt9qkU6uupDQ/s04tbg17uKyfQvppttNRPCKOaPuF972XY656E4t02qx2uFrqLuEM3M0+KFhLB5+ugY3R/CbQmJkXXf/sZb3oupZFbgZ4rxXZ644fsVFAFPJ6w/Ectf2VIZCXwVi/vrjviZYv5pOjgQ8COYtDUbbltWajCZHTbVxkf06PFviYr3POG/QgpJZ/VzCcUnESosPeod/rCe6EI1mSxW1Qhip++PdXYmpeQ3URFjDctEW2o7QZ1MvMIvSXagiIUfqNdxXKjFUsqHC3m0BJd3ifb+gr36aLcV85xG35PIFoPnJx0Wxmo4ANrGjOVzOWee0O70U6lv+OH1o3WU1X9JG0sijn0t+QeiE4qjGMjTp+H1sm1rW0MYskRbDcseYlmAtYkl+1o1ocQjUZJ3gPeMwr0Akq9pawYgMbAPOWymVFu1FNF96Ro1DSxhSDszaz7FMDjKwWgpiB7dejVbdXUhjaybRj89KzlAjDkmPtAwob27DUX+WljXKhvTzUnmS+BtZ6y1jqe9/yeW8v+zeyW92iONGec3HIn3K+oDbLHxCqmiNXIttCAUy4u/1/uK+fZgpN4vPiclNtGK7bjMvNC2goypwh7QyoOPZgi6R+1o93KEF0H35laARjf5ohqKZ0ayhYZo/ix+UuGTUguFZyAs1+HI/+v2+J3yk7kXl/fOHQhBNMrp/N57ed92j8T5B36ZkazfwIrRPfa52hMG7w9kPkNoqkKUipYMnsc+vwahz//s/Equw/pnUv0jZmJsIuoD2XPobcLjXrfNn3a93vGS0RoRZjFrFLHsnHbU3k9si8ATeGmjI7VGm3FLzVuVu/hxMle/ua5hYrar2iNtvZZ7KsvcCt0yn3lLBKTWV6UOt70aI5riPRDlVJXh+5RPeiKnjRCl1ImbyyK0Rh7yUQnuXq8M4RCUTueLFbUtjG6PBB/Tz1izO4ws/sPnaYKzD5quQDMqJrBsqZl/Z4Izzv0zQ21ADNJ4UA4xp91jSjSgSbAI21K2r7dVOv6jPkb56MR4E/2NUybNr33HdrrKbc1gkZ2JHQN28AWkrAn84EiAGeV3ENIsVlqTeHpkb+hfJuZfGtOATJvLmqLtuG1FXZXvmbauBHc5PsF32m7YEs7ZVTaH2iMNCIQlHpLGRH6htFJniRdlARX8on3xxQsf3Wz2uCOn+ucFIXUAl1ud2UqysVLopP1BQJIKRCdKJefLb+QK5VHNsnuoxse5u7gr/q8/4yqGUgkX9Z9uUl29Ia8Q9/MKLKC+ET3m9Roc26eEc2ZP3YpqjNaWg5AHWsqfLrhM4z2sYyYui+qkoYkQtNKpkfXYlnZcegLahaAsBkp+pYUFbrjXGqbPEwZVsx2wwqZaDUBmQ+Lbom2EFGGMtu8m/EjqgiN3IsV7Q43P5Cliw2hBkq9paiKylXt13Nc899Tbqv6S3jD2oU2b+adtj0hYkWwpNWtUSiVQJc7fi6Vlsun3sQRehXhalQJiuHcK1JK3jGn0l4+dZPsbvaO4Gv6XjY8vXI6AtHvtEveoW9mVEQ24hNBbCvxUTUc+6ij/r7NBQ1JPxv84zfZvr6gLlTH6tZV7BJs58iJaQ4OHro9NUX706z0f2Y/GeZUz0GXkmObMm/kAtjdcCLTO+0HmaV9xejyAm5UnKnvmdaitxltRKIeJlYVoqsKO1bYjGp1EqID6tBjbf8AV4uf8vHQk1JuqxYN43fmudQVp/E0lgG6CnO5SDW1yHXoqSJ0vUt5YWmkGi8WquFUmtW2Rrg+8gNapvxwk+xeWHkUNyjn9Xn/Ik8RE0omsKh2Ue8bbwLyDn0zo6lwKs3CS1e6LRqLMMIFfauLRirYWeLQF2xcAMAvop8wc0xy7Y9u0LwUFk4ENUhzOI1Ra5uRS5ZSMmfNHAqCI1g+5Jg+HWOM7eQMPo/OYOjYySiK4LEi55E7U8qlNdrK9u1f8MPAZwDs6K/hEvFSfN1Awe0SBXjXmEJL6XYpt/XqCgIb2b55Syu7CnO5SDXkwk1Apypb3DGS6CDDo/ej3SpFLRgHwIqaRkAyLo0a9J6gKwJjEzh0gB2G7MDndZ/3a94k79A3M4RWiBR2N4ceMZ0LU+tDHTpACSHKgtmpQ/9sw2dg6/xj5OP4ytN/wtgW5+ZdUttzc9Gcj+bx5U17YK76KOU2i6rr2P/eu1le27to2ormFaxpXcOGlt0RVVPStrczniw5DoB7oqcxbqKjXaOO2BekmnH7f2ukBa8JI0udKHPY5N25JnK+sy7m0KWUWP3cONYQbqDMW4ZtWexofUmlnfpJw6MqPKrfyE6f9J03Toau4+dcpHLoboTuOvAVde1IKeNJ0a45qYDPQ3D1ecyqcnolvJ/+hc+95zKhJHPl1M7Yd+PjvGz/ZJOOMaNyBs2RZla3rt6k4/SEvEPfzCg2WxDCJhJN5APtJqcFfWhzcmnd3qACdl90STYD5lZ/ghkazawZqSO6ZNi+zRET+64+9RzViGlx+bynubgyyKuLUj+OXvbmbdQV3M/lr93d63nnrPkfAOMiVZywc98oLltpQUqFMaVVcQGrHStMvKbOuqb0u19tadNuBnnZ2BdthjMpZ+ywSlYxCXBkARZsXMCZr57Jk9882Sdb04VLuYSCrTzp+RMzGl9Pua1XV3jOmsXSEcdu1qenruPnXBTqhUkdutuM41W9vL2khv3/PId75yyLUzDLivZI2D5gNXOJ/T+KG52a78ViG/4pD2F41aYJjDUHxjJXbhr9NKPKKZvsTx4979A3M8rDznCLcHtiJBmOlVEZBaO67ZMOGillg3f0phnXB7RF21jd9h0HROo5cHhmSdnATk6H5arm1InRu/55Ju0lr1Ef3ZnrF09MWib39nffsU6+ipCwJvoki6p7dqhvLH2ZyRGDn4w1KA14etw2FSrNxQy1olyr/C2+bLqvlgl2CzUN6TeItBvtSCTS9jN5mENXaarCQUVNANwx/w5Of+V0Vreujo+m6w8YlkFLtIVyfzntlsrJ0StYN/LQlNv7dJV/27P4sOCAHgWtGsINPU4b6oqu4+dcBPRAjw5dQeNP/3WGV9z7v6W0hJ3gpiuHHiDCReoL+Buc3Mn/otvxTOk56SXye8CKqv253Dh3k+iSCSUTKNAL+pVHzzv0zYzmsp0ACHVphIjEJEKNwr45ZSFVQnZ9yqku/YWwFWZc+wROiayjuDgzBcpJo5zKgrWtyR36i9/9h0ethWwXLuK2A25kXVOYeS8+AOHEH8OH/vdLVGFw9fCjaFPhiv/dlfKcjeFGvmz5jmWt+7PbUakbZ3rDaa0mf95YR8PIA+LLRmy3B8uNbVgX0zd5cdmLnP3a2XzTkFof3q2t/pHyASNKOmqjf6DMw2tL2qPtXLzzxfz3+P9y9MSj+2xvb2iMOG3/Fb4KQpbCh/Y0ZEnqnohin85B2w3hkXe/pfrtv0KS73BZ0zKOef4YfvjSD+OyAr3BpVy6VrmkSoq+v+59AD5aGmR5bTtXHLEdEdPm73MdDfRtg4llgN7yMUyIPMbXQ46kdd7jNKxezPjKTePPwdFykRIsu+8OXVVUtq/cPvsRuhDiMCHEN0KIpUKIy5OsF0KIO2PrPxdC7Lz5TR0cUDUn8gh3EeKJGLHyK9G339Aj28LUW19z7YfXDphTj5o2t726ns9Xn8eK/d6GQHlG+48M+NEtlVBT4k0npeQ/y/7D7z+4CjM0jov2eYTZk4dx/MhW9v78txifPBzf9j+L5/O5ZyVHRgs44ZDrGe+bxUrzdT5YmVwb/rXlbwOSnUcczMjK0kzfchwvD7uO45sfwTu1Qz+7oqwUQwyhyWjgho9v4Iq5V7CgZgGnvXIar618LelxXM1zr1aaoGy4aurPiK74MU8e8QLnTj+3m4PrCyzb4qklT3HF3CtYuHFhwrrOOi7h1kZmKZ9TInvWY7/lxB3YrqCVEe9eRnjhvxLWrWtbx/lvnI8qVNa3r+eity5KK1J3t0lWhx4yQwl5hBeWvsBdC+7iwNGH8K/3VY4aZ3PurPGctudYXvismZ1bvUzQEosM/F4NENQ1NGC88jtOjz7FBftteof1Dhv+xWfeCzDDm9bdO6NyBt82fptyOtOmolfvIoRQgXuAw4GpwMlCiK5FnYcDk2L/zgfu28x2DhqURJsAeO6bf7C0cWn8EU3Eorjy5l6m/aTA7NZy9o9uw7PfPcsfPvxDvzv1ja1hzr3/TQKf3svvZ0pO3j3zDldF8zDKitAYWcbatrXY0uaruq8449Uz+N3c30FkBNO1S9h32rYIIfjBEYfwg8jveZRYpLrxa278+P+Q0sdPv/8vEIKb9v8NirC5782fJT3nax/fS5Gp8Kv9Zm/Cu4cSu5kCQkweligfu40uichGnljyBKdNPY1XTniFbcu25dJ3LuWuBXd1+17Gl4yHNb+jccyVCcvHjB1PS3Qc6xo2z0PykoYl/OjlH/Gnj//Eaytf47RXTuO8189jzpo5fLrhUz5e72iUlPnKkLXf8A/PjQxp7bnJpazAw29OOZyjjRu4bM3e8Wu5LlTH+W+cT8gM8cDBD3DTvjfxZf2XXPrOpb3KIvSUFIWOktA5a+Zw9QdXs+fwPSkPnsGsyHvcufEsxIbPufjASZzkncd31T8jODpxLJxXU7hMe5KvPnqF7xl/ZMgP72aXsZkFIsnQXjiO/1p7YGzibbdD1Q5Y0mJx/eJNtikZ0mlb3A1YKqVcDiCEeBI4Fuhs0bHAo9L5xj8SQpQKIYZLKZNPS96CsYthMz8M/1z+BP9c/gQVJpgEaBMaqGD3kUO/0X8pddVhDq+4lue+e44XlryDkDpVsg4TjUZRCsAQu5ao0GmKv95IVHhpEg5dMtTeSFh4af7/9u49OKryjOP499lLNtlsCLmQC4mQoBEwWKEgIeKdKAQr0KkCFdR26lAcVFodGa2titNqOzqOLUWtFdGxHTr1Ukutoi0yFR2UixcGBMRWqyCSMLkRkmwu+/SPc9AEiSSQNO7Z5zOTyb5nT3bfX3b3ycl7Lq/bzovtp6J2PGsC3wNgeeONrAuew/JoJSkdB3k86c/4ho6B4xmD9Pk44BvPwcC7THtmGqGYEvUJvlgaU2uHc339DmrnfbH1VH5yFkkjJnPfy7t5fuNObmq9loah6ZyVcSUFGc7vbXTOcCrbc3gx8AnjVlQAQn5sH42SxkGJEPNXM6ltMKcNPbEP8cK9t3B38k46Mrse0XKONvBRDFIOXcUb/xrF7BcrGBn6Du8PDvPI1kcY8fLv2MIVrA9OZkismrub7qSsZTal+eVdHmdkTgoL/H8j/ORSLkq7F4AftqygIPYpt4d/BsB1zQ+TqbXcFb4VgMXNy0nVJu4O38zB8HNEAq8CygHJosNfRSQG363LYy0305y8nu17VnP9vq5HDm36/S94sGkep+rt/LTgzGP+Hs4symR6xUXc+9IuZv3nDhp8SdyV20S7fz83Vg1m10N3sixlIZHQHNbvXcWFK8toxNkBebT3Yp3fBz5hxrKN3H/oJ7zjP50nk6+gOWk/pMGUp6YgsWRU2jilNcY33gryQMMe5p1RiS8rCTJHMDjWxB2BlZQGyogGz+3SXxHhEv8mamIZXDVvAeWj+mai66rscu5sT6fht0upbPsnCyO/AeCy6F+4oG09iyLO9V/mRp+ivH0ji1Od13R+dBXj2t/lptRfAjC7dSXkw6Mb1zH+0r6/CmlPCnoB0PkwhT1AWQ/WKQC6FHQRWYCzBc+wYcd5PPbX3PiJc6heE2a9/3Rq9G2y25+hJpBNUiCfkC/MtHEzj+txrzmnmNc/OMBFB8bgb67jjbCzRZMTjdImITRY6LZbiJICQefojtxoM82SCkHnSnO5LU0c8qUhwXxn/ZZDhNLyKBnk/Atcp0WEwrlMycxlwbnl+NJ2QuT4zxZcMv7nPLVtAxktrxFqe4cPUs8jNzidktQtfJpRSvmpRV3Wv+PSUpa9shuJtbKuaTaj2zfxq4quE/sunrWC99bcS6PP2drLa2mg1p+FPzAEYTiXVVx33P097ND5S3nz4+2U+bseZjrjrCX4N7zFjowykjsaqakqZvCgfMaHL6W9o5x8/7Okpw+hJBxhUHsrNdXFjC0cxsWndb0EQV56KhcMC1LTNJqSHHf4oXYoTe0BSoY47VhNAS0dkS7t1lgzJdkR9sZyCEfTEZSOYCEhxnJNQz2hQAp7srKAWVQe+IzdwTY2pU1BECrrXiB7yHCuLColP30CY07u2f6ca887mWhbB207TsLnC5IXTCZdRlEY2kBjyiBKMiPAVArq32BrUjNVSd2/FwdpKs3+MoblptGwfziB5HxK0iPE9GxKa9eyKzmb3aEclBiLGj9kb3ohl59ayA0Xj4TI2Z/3SRa+zoEN+/n26C9f7G7fKXOYWjqDiX1UzAHOH5nDzE/qCNflUttYTEmu85qEGnKpayr6vB2sz6O++Yt2oD6fhpb6z9tpdcMY1/oxeam9v0hfT8ix9tqKyOXAVFW9xm1fCUxU1es7rfN34B5Vfc1trwWWqOqW7h53woQJunnz5j6IYIwxiUNEtqjqUa+s1pMBvD1A5z/lhcCRF7nuyTrGGGP6UU8K+iagRESKRSQJmAusPmKd1cBV7tEuk4D6RBw/N8aYgXTMMXRVbReR64CXcE5YfExVt4vIQvf+h4EXgOnAB0AT8P3uHs8YY0z/6NHFuVX1BZyi3XnZw51uK7Cob7tmjDGmN+xMUWOM8Qgr6MYY4xFW0I0xxiOsoBtjjEcc88SifntikWrgv8f549lA306lEh8SMXciZobEzJ2ImaH3uYer6lFP3x6wgn4iRGRzd2dKeVki5k7EzJCYuRMxM/RtbhtyMcYYj7CCbowxHhGvBf2Rge7AAEnE3ImYGRIzdyJmhj7MHZdj6MYYY74sXrfQjTHGHMEKujHGeETcFfRjTVjtBSJykoisE5EdIrJdRBa7yzNF5B8istv9njHQfe1rIuIXkbdF5Hm3nQiZB4vI0yKy033NyxMk94/d9/c2EVklIsleyy0ij4lIlYhs67Ss24wicqtb23aJyNTePl9cFfQeTljtBe3ATao6GpgELHJz3gKsVdUSYK3b9prFwI5O7UTI/GtgjaqOAs7Aye/p3CJSANwATFDVMTiX5p6L93I/Dkw7YtlRM7qf8blAqfszD7o1r8fiqqDTacJqVW0FDk9Y7Smquk9V33JvH8T5gBfgZH3CXe0JYNaAdLCfiEghcAnwaKfFXs88CDgXWAGgqq2qWofHc7sCQIqIBIAwzixnnsqtqq8CNUcs7i7jTOBPqhpV1Q9x5peY2Jvni7eC3t1k1J4lIkXAOOBNIPfwTFDu976bBffr4QFgCRDrtMzrmUcA1cBKd6jpURFJxeO5VXUvcB/wMc5k8vWq+jIez+3qLuMJ17d4K+hylGWePe5SRCLAM8CPVLVhoPvTn0TkW0DVV00s7lEB4JvAQ6o6DjhE/A8zHJM7bjwTKAaGAqkiMn9gezXgTri+xVtBT5jJqEUkiFPM/6iqz7qL94tIvnt/PlA1UP3rB5OBGSLyEc5Q2oUi8ge8nRmc9/QeVX3TbT+NU+C9nrsC+FBVq1W1DXgWOAvv54buM55wfYu3gt6TCavjnogIzpjqDlW9v9Ndq4Gr3dtXA3/9f/etv6jqrapaqKpFOK/rK6o6Hw9nBlDVz4BPRGSku2gK8B4ez40z1DJJRMLu+30Kzr4ir+eG7jOuBuaKSEhEioESYGOvHllV4+oLZzLq94F/A7cNdH/6KePZOP9qbQXecb+mA1k4e8V3u98zB7qv/ZT/fOB597bnMwNjgc3u6/0ckJEguZcCO4FtwJNAyGu5gVU4+wjacLbAf/BVGYHb3Nq2C6js7fPZqf/GGOMR8TbkYowxphtW0I0xxiOsoBtjjEdYQTfGGI+wgm6MMR5hBd0YYzzCCroxxnjE/wBaYzWKhrA/NwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_time = 0\n",
"max_time = 100\n",
"lat = 1\n",
"lon = 1\n",
"time = np.arange(min_time, max_time)\n",
"\n",
"plt.plot(time, debiased_values_isimip[min_time:max_time, lat, lon], label = \"ISIMIP\")\n",
"plt.plot(time, debiased_values[min_time:max_time, lat, lon], label = \"ibicus implementation\", linestyle = \":\")\n",
"plt.plot(time, cm_future[min_time:max_time, lat, lon], label = \"raw cm\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values of reference implementation against ibicus ones:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"min_all_values = np.min([np.min(debiased_values), np.min(debiased_values_isimip)])\n",
"max_all_values = np.max([np.max(debiased_values), np.max(debiased_values_isimip)])\n",
"\n",
"plt.scatter(debiased_values_isimip.flatten(), debiased_values.flatten())\n",
"plt.plot([min_all_values, max_all_values], [min_all_values, max_all_values])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear Regression:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LinregressResult(slope=0.9903892593000797, intercept=0.0008416249599787345, rvalue=0.9910176848082406, pvalue=0.0, stderr=0.0005827799956240896, intercept_stderr=0.0001465072709228235)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.stats.linregress(debiased_values_isimip.flatten(), debiased_values.flatten())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"prsnratio includes a large amount of randomization: the metric is not defined on any day without rain (given that it is the ratio of prsn over pr) and in step 2 of the ISIMIP algorithm these values where the metric is not defined are filled with draws from all other available values in a given window (in all three: obs, cm_hist, cm_future). This randomization and random draws are not exactly reproducible using ibicus and they influence all other computed values: the transfer of the climate change signal in step 5, the computation which values are set to bounds and the nonparametric quantile mapping in step 6. However the values of ibicus and the reference implementation nevertheless seem in decent agreement and special care was taken to ensure that the outputs of all steps not including randomzation do agree."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Summary \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results of the ibicus and reference implementation of ISIMIP agree. Some smaller differences do exist -- at points slightly larger than floating point error, in particular for variables that involve randomization. However, these differences are within the small numerical differences that are to be expected in two different implementations of an algorithm involving a large variety of steps.\n",
"\n",
"The results above also only serve as last \"sanity check\" and demonstration for the ibicus implementation of ISIMIP. The authors of the software package took special care that the results do also agree in all intermediate steps. Differences seen in the outputs here were therefore shown to be due to the differences named above (in the variables)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 4
}