{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "991b7857",
   "metadata": {},
   "source": [
    "# Lesson 14 activity solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ead7758",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Import the required libraries and load the weather dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c338227c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e58ac9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weather_condition</th>\n",
       "      <th>wind_strength</th>\n",
       "      <th>temperature_c</th>\n",
       "      <th>rainfall_inches</th>\n",
       "      <th>humidity_percent</th>\n",
       "      <th>pressure_hpa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.13</td>\n",
       "      <td>48.8</td>\n",
       "      <td>1016.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Snowy</td>\n",
       "      <td>Gale</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>89.6</td>\n",
       "      <td>1009.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainy</td>\n",
       "      <td>Strong Wind</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1003.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Light Breeze</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.62</td>\n",
       "      <td>49.3</td>\n",
       "      <td>1006.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Calm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>38.6</td>\n",
       "      <td>1016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  weather_condition wind_strength  temperature_c  rainfall_inches  \\\n",
       "0             Sunny  Light Breeze            8.2             0.13   \n",
       "1             Snowy          Gale            1.6             0.29   \n",
       "2             Rainy   Strong Wind            7.3             0.01   \n",
       "3            Cloudy  Light Breeze           21.6             0.62   \n",
       "4             Sunny          Calm           12.0             1.09   \n",
       "\n",
       "   humidity_percent  pressure_hpa  \n",
       "0              48.8        1016.5  \n",
       "1              89.6        1009.4  \n",
       "2             100.0        1003.3  \n",
       "3              49.3        1006.9  \n",
       "4              38.6        1016.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the weather dataset\n",
    "url = 'https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/weather.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c931b63",
   "metadata": {},
   "source": [
    "## Exercise 1: Humidity Differences Among Weather Conditions\n",
    "\n",
    "**Objective**: Determine if humidity is significantly different among different weather conditions.\n",
    "\n",
    "**Background**: \n",
    "The weather dataset contains humidity measurements for different weather conditions (Sunny, Rainy, Cloudy, etc.). We want to know if these weather conditions are associated with significantly different humidity levels.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. **Explore the data**:\n",
    "   - Check how many unique weather conditions are in the dataset\n",
    "   - Calculate the mean and standard deviation of humidity for each weather condition\n",
    "   - Create a boxplot showing the humidity distribution for each weather condition\n",
    "   - Based on the visualization, do you think the humidity levels differ significantly?\n",
    "\n",
    "2. **Choose an appropriate statistical test**:\n",
    "   - Since we're comparing humidity across multiple groups (weather conditions), what test should we use?\n",
    "   - Hint: Consider ANOVA (Analysis of Variance) or Kruskal-Wallis test\n",
    "   - State your null and alternative hypotheses\n",
    "\n",
    "3. **Check assumptions** (if using ANOVA):\n",
    "   - Are the sample sizes roughly equal across groups?\n",
    "   - Use a visualization or test to check if the data appears roughly normally distributed\n",
    "   - Check for equal variances across groups (you can use a simple visual check or Levene's test)\n",
    "\n",
    "4. **Perform the statistical test**:\n",
    "   - Conduct the chosen test using scipy.stats\n",
    "   - Report the test statistic and p-value\n",
    "   - Using α = 0.05, determine whether to reject or accept the null hypothesis\n",
    "\n",
    "5. **Interpret the results**:\n",
    "   - What does your test result tell you about humidity and weather conditions?\n",
    "   - If you rejected the null hypothesis, which weather conditions appear to have different humidity levels? (Use the boxplot and descriptive statistics to support your conclusion)\n",
    "   - What are the practical implications of this finding?\n",
    "\n",
    "**Bonus**: \n",
    "- If you found a significant difference, perform post-hoc pairwise comparisons to determine which specific weather conditions differ from each other\n",
    "- Consider whether Bonferroni correction should be applied to the pairwise comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94539f74",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecc7624b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique weather conditions:\n",
      "['Sunny' 'Snowy' 'Rainy' 'Cloudy']\n",
      "\n",
      "Number of unique conditions: 4\n"
     ]
    }
   ],
   "source": [
    "# Unique conditions\n",
    "print(\"Unique weather conditions:\")\n",
    "print(df['weather_condition'].unique())\n",
    "print(f\"\\nNumber of unique conditions: {df['weather_condition'].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17ce762e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        mean        std  count\n",
      "weather_condition                             \n",
      "Cloudy             67.741121  12.334473    107\n",
      "Rainy              85.812676  10.342104     71\n",
      "Snowy              79.673529  10.793273     34\n",
      "Sunny              52.985621  14.754123    153\n"
     ]
    }
   ],
   "source": [
    "# Calculate statistics for each weather condition\n",
    "humidity_stats = df.groupby('weather_condition')['humidity_percent'].agg(['mean', 'std', 'count'])\n",
    "print(humidity_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce18f76d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplot\n",
    "plt.title('Humidity Distribution by Weather Condition')\n",
    "sns.boxplot(df, x='weather_condition', y='humidity_percent')\n",
    "plt.xlabel('Weather Condition')\n",
    "plt.ylabel('Humidity (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543d2731",
   "metadata": {},
   "source": [
    "Based on the boxplot, there appear to be differences in humidity across weather conditions, particularly between Sunny and Rainy conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86eeee7",
   "metadata": {},
   "source": [
    "### Choose test and state hypotheses\n",
    "\n",
    "**Test choice**: One-way ANOVA\n",
    "\n",
    "1. **Null hypothesis**: All weather conditions have the same mean humidity\n",
    "2. **Alternative hypothesis**: At least one weather condition has a different mean humidity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb99bd0",
   "metadata": {},
   "source": [
    "### Check assumptions\n",
    "\n",
    "#### 1. Sample size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d99dbac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample sizes:\n",
      "weather_condition\n",
      "Sunny     153\n",
      "Cloudy    107\n",
      "Rainy      71\n",
      "Snowy      34\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check sample sizes\n",
    "print(\"Sample sizes:\")\n",
    "print(df['weather_condition'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6151deb",
   "metadata": {},
   "source": [
    "#### 2. Normality (Shapiro-Wilk test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3d534ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Shapiro-Wilk test for normality:\n",
      "\n",
      "  Sunny: W=0.9719, p-value=0.0032\n",
      "  Snowy: W=0.9480, p-value=0.1070\n",
      "  Rainy: W=0.9423, p-value=0.0027\n",
      "  Cloudy: W=0.9916, p-value=0.7537\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nShapiro-Wilk test for normality:\")\n",
    "print()\n",
    "\n",
    "for condition in df['weather_condition'].unique():\n",
    "    condition_data = df[df['weather_condition'] == condition]['humidity_percent']\n",
    "    stat, p_value = stats.shapiro(condition_data)\n",
    "    print(f\"  {condition}: W={stat:.4f}, p-value={p_value:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c04a73",
   "metadata": {},
   "source": [
    "#### 3. Equal variances (Levene's test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fea459ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Levene's test for equal variances:\n",
      "  - Statistic: 7.5783\n",
      "  - p-value: 0.0001\n"
     ]
    }
   ],
   "source": [
    "groups = [group['humidity_percent'].values for name, group in df.groupby('weather_condition')]\n",
    "levene_stat, levene_p = stats.levene(*groups)\n",
    "\n",
    "print(f\"\\nLevene's test for equal variances:\")\n",
    "print(f\"  - Statistic: {levene_stat:.4f}\")\n",
    "print(f\"  - p-value: {levene_p:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfdf72a",
   "metadata": {},
   "source": [
    "### ANOVA Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a476c685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "One-way ANOVA Results:\n",
      "  - F-statistic: 120.1495\n",
      "  - p-value: 0.000000\n",
      "\n",
      "Using α = 0.05:\n",
      "  Reject null hypothesis\n",
      "  Conclusion: Humidity IS significantly different among weather conditions\n"
     ]
    }
   ],
   "source": [
    "f_stat, p_value = stats.f_oneway(*groups)\n",
    "alpha = 0.05\n",
    "\n",
    "print(f\"\\nOne-way ANOVA Results:\")\n",
    "print(f\"  - F-statistic: {f_stat:.4f}\")\n",
    "print(f\"  - p-value: {p_value:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9051ebb0",
   "metadata": {},
   "source": [
    "### Kruskal-Wallis H Test as a non-parametric alternative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ac315f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Kruskal-Wallis H Test Results:\n",
      " - H-statistic: 185.5009\n",
      " - p-value: 0.000000\n"
     ]
    }
   ],
   "source": [
    "kruskal_stat, kruskal_p = stats.kruskal(*groups)\n",
    "\n",
    "print(f\"\\nKruskal-Wallis H Test Results:\")\n",
    "print(f\" - H-statistic: {kruskal_stat:.4f}\")\n",
    "print(f\" - p-value: {kruskal_p:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a897aa54",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "The ANOVA test shows a highly significant difference in humidity among weather conditions (p < 0.001). Looking at the descriptive statistics and boxplot:\n",
    "\n",
    "- Rainy days tend to have higher humidity levels\n",
    "- Sunny days tend to have lower humidity levels\n",
    "- Cloudy days fall in between\n",
    "\n",
    "**Practical implications**: Weather condition is a strong predictor of humidity, which makes sense meteorologically. Rainy conditions are associated with moisture in the air, while sunny conditions typically have drier air. This relationship is important for weather forecasting and understanding atmospheric conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b639c86d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pairwise t-tests with Bonferroni correction:\n",
      "Number of comparisons: 6\n",
      "Adjusted alpha: 0.008333\n",
      "\n",
      "Sunny vs Snowy: t=-9.9623, p=0.000000 ***\n",
      "Sunny vs Rainy: t=-16.9094, p=0.000000 ***\n",
      "Sunny vs Cloudy: t=-8.4775, p=0.000000 ***\n",
      "Snowy vs Rainy: t=-2.8065, p=0.005991 ***\n",
      "Snowy vs Cloudy: t=5.0566, p=0.000001 ***\n",
      "Rainy vs Cloudy: t=10.1925, p=0.000000 ***\n"
     ]
    }
   ],
   "source": [
    "# Bonus: Post-hoc pairwise comparisons with Bonferroni correction\n",
    "from itertools import combinations\n",
    "\n",
    "conditions = df['weather_condition'].unique()\n",
    "comparisons = list(combinations(conditions, 2))\n",
    "\n",
    "print(\"Pairwise t-tests with Bonferroni correction:\")\n",
    "print(f\"Number of comparisons: {len(comparisons)}\")\n",
    "print(f\"Adjusted alpha: {alpha / len(comparisons):.6f}\\n\")\n",
    "\n",
    "for cond1, cond2 in comparisons:\n",
    "    group1 = df[df['weather_condition'] == cond1]['humidity_percent']\n",
    "    group2 = df[df['weather_condition'] == cond2]['humidity_percent']\n",
    "\n",
    "    t_stat, p_val = stats.ttest_ind(group1, group2)\n",
    "    significant = \"***\" if p_val < alpha / len(comparisons) else \"\"\n",
    "\n",
    "    print(f\"{cond1} vs {cond2}: t={t_stat:.4f}, p={p_val:.6f} {significant}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65c7a15",
   "metadata": {},
   "source": [
    "## Exercise 2: Testing for Association Between Weather Condition and Wind Strength\n",
    "\n",
    "**Objective**: Determine if there is a significant association (non-random relationship) between weather condition and wind strength.\n",
    "\n",
    "**Background**: \n",
    "If weather conditions and wind strength are independent, we would expect days to be randomly distributed across all combinations of weather condition and wind strength. However, certain weather conditions might be more likely to occur with certain wind strengths (e.g., rainy days might be associated with stronger winds). We can use a chi-square test of independence to test this.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. **Explore the data**:\n",
    "   - Check the unique values in both `weather_condition` and `wind_strength` columns\n",
    "   - Count how many days fall into each category for both variables\n",
    "   - Create a contingency table (crosstab) showing the count of days for each combination of weather condition and wind strength\n",
    "   - Use `pd.crosstab(df['weather_condition'], df['wind_strength'])` to create this table\n",
    "\n",
    "2. **Visualize the relationship**:\n",
    "   - Create a heatmap of the contingency table to visualize the distribution of days\n",
    "   - You can use `plt.imshow()` or `sns.heatmap()` if seaborn is available\n",
    "   - Based on the visualization, do certain weather conditions appear to be associated with certain wind strengths?\n",
    "\n",
    "3. **State your hypotheses**:\n",
    "   - **Null hypothesis**: Weather condition and wind strength are independent (days are randomly distributed across combinations)\n",
    "   - **Alternative hypothesis**: Weather condition and wind strength are associated (days are NOT randomly distributed)\n",
    "\n",
    "4. **Perform a chi-square test of independence**:\n",
    "   - Use `scipy.stats.chi2_contingency()` on your contingency table\n",
    "   - This test compares the observed frequencies to expected frequencies if the variables were independent\n",
    "   - Report the chi-square statistic, p-value, and degrees of freedom\n",
    "\n",
    "5. **Interpret the results**:\n",
    "   - Using α = 0.05, determine whether to reject or accept the null hypothesis\n",
    "   - If you reject the null hypothesis, what does this mean in practical terms?\n",
    "\n",
    "6. **Draw conclusions**: Are weather condition and wind strength independent or associated?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4a2ff8",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94ae7b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique weather conditions:\n",
      "['Sunny' 'Snowy' 'Rainy' 'Cloudy']\n",
      "\n",
      "Unique wind strengths:\n",
      "['Light Breeze' 'Gale' 'Strong Wind' 'Calm' 'Moderate Wind']\n"
     ]
    }
   ],
   "source": [
    "# Factor levels exploration\n",
    "print(\"Unique weather conditions:\")\n",
    "print(df['weather_condition'].unique())\n",
    "print(f\"\\nUnique wind strengths:\")\n",
    "print(df['wind_strength'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "038eb4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Weather condition counts:\n",
      "weather_condition\n",
      "Sunny     153\n",
      "Cloudy    107\n",
      "Rainy      71\n",
      "Snowy      34\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Wind strength counts:\n",
      "wind_strength\n",
      "Light Breeze     121\n",
      "Moderate Wind     98\n",
      "Calm              66\n",
      "Strong Wind       55\n",
      "Gale              25\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count days in each category\n",
    "print(\"\\nWeather condition counts:\")\n",
    "print(df['weather_condition'].value_counts())\n",
    "print(\"\\nWind strength counts:\")\n",
    "print(df['wind_strength'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ae624c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Contingency Table:\n",
      "wind_strength      Calm  Gale  Light Breeze  Moderate Wind  Strong Wind\n",
      "weather_condition                                                      \n",
      "Cloudy               14     7            43             27           16\n",
      "Rainy                15     5            22             16           13\n",
      "Snowy                 5     4            13             10            2\n",
      "Sunny                32     9            43             45           24\n"
     ]
    }
   ],
   "source": [
    "# Create contingency table\n",
    "contingency_table = pd.crosstab(df['weather_condition'], df['wind_strength'])\n",
    "print(\"\\nContingency Table:\")\n",
    "print(contingency_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204a27f7",
   "metadata": {},
   "source": [
    "### Plot contingency table heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "415c86a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s3t7eCvW2b98OmUyGrl274sWLF/LN3t4elSpVUmiefr/fOCkpCS9evICfnx+EELh48WK+Y+vSpQvMzc3l+/Xq1QMAfP7559DR0VEoT0tLw5MnT3KN4c2bN3jx4gUaNWqE5OTkHN1kOjo6GDJkiHxfT08PQ4YMQWxsLM6fPy8vDwsLy9HUX1BZU0TfvHkDANDX14eW1ru3VWZmJl6+fAkTExNUqVIFFy5ckD8uKCgIjo6O2Lhxo7zs2rVruHLlisJ4JwsLC1y/fh13794tUHyDBw9WGNfw5ZdfQkdHB/v27QMAaGlpoWfPnti9e7f8NQDAxo0b4efnB1dX1w+e//1xF8C77oSyZcuifPnyqFq1KqysrOTdCtkHpG7duhXVqlVD1apVFd6LgYGBAJDnezE+Ph4vXrxAkyZNcP/+fcTHxyt1PbK6MwCgUaNGyMzMxMOHDwEAhw4dQlxcHLp3764Qk7a2NurVq5ej+wZ4d03/S0JCAgDA1NQ0X3Fm/fuMHj1aoXzMmDEAkGOskru7Oxo1aiTft7GxQZUqVXD//n2Fc9avXx9169ZVqPehbsGC2LdvH7S1tTFixIgcsQshsH//foXyoKAguLm5yfe9vb1hZmamEHtutLW15WOlZDIZXr16hYyMDNSpU0fhdy3LZ599BktLS/l+1vXKep7o6GhcunQJffr0Ufi8atasGdzd3fPz0uXv7RMnTgB4956vXbs29PT04OvrK+9WyzpmYGCAOnXq/Od5iyL20NBQLFu2DK6urtixYwfGjh2LatWqoWnTpgqfyf/F1dUVLVq0UCjbunUrGjVqBEtLS4Xfq6CgIGRmZuKvv/5S6vU+ffoUV69eRe/evRWm6Tdp0kRh/Fd+ffHFFwr7jRo1+s/3X2mgMQlS3bp1ERQUlGN7/00GAHfv3oUQApUqVYKNjY3CdvPmTcTGxsrrPnr0CH379oWVlZW8b7ZJkyYAoNSXUvny5RX2s36BnZycci1/f4zM9evX0bFjR5ibm8PMzAw2NjbyBCJ7DI6OjjkGyFauXBkACm3GRWJiIoB/v/hkMhkWL16MSpUqQV9fH2XKlIGNjQ2uXLmiEG9WYrJz504kJycDeJeUGBgYyPv4AWDGjBmIi4tD5cqV4eXlhXHjxuHKlSv5jq9SpUoK+yYmJnBwcFC4Hr1798bbt2/l4xVu376N8+fPo1evXv95fk9PT1hYWCgkQVnjGCRJgq+vr8IxJycn+fvh7t27uH79eo73Yda/2fvvxVOnTiEoKAjGxsawsLCAjY2NfKmAj3kvZv1+ZL3nshLRwMDAHHEdPHhQISbgXVJerly5/3xeMzMzAFBIQj/k4cOH0NLSQsWKFRXK7e3tYWFhIU/o8npdWa/t/d+lhw8f5ng/APjgOLOCePjwIRwdHXMkg1kDkQsSe17WrVsHb29v+fg8Gxsb7N27N9f3xH/922fF9THXqEGDBpAkKdffBwsLC7i7uysc8/HxydeA+KKIXUtLC0OHDsX58+fx4sUL7Nq1C61atcLRo0cVBpb/l9z+qLp79y7+/PPPHL9TQUFBAJDj9yq/rzf770deZR9iYGAgH6f0/vPl5/2n6UrdLDaZTAZJkrB//35oa2vnOJ6VjWdmZqJZs2Z49eoVvv76a1StWhXGxsZ48uQJ+vbtm+vAzrzk9jwfKs9q3YmLi0OTJk1gZmaGGTNmwM3NDQYGBrhw4QK+/vprpWIoLNeuXYOtra38C3D27NmYMmUK+vfvj5kzZ8LKygpaWloYOXJkjnh79+6NBQsWYOfOnejevTs2bdqETz75ROEvwMaNGyMiIgK7du3CwYMH8dNPP2Hx4sVYsWIFBg4cqJLX4O7ujtq1a2PDhg3o3bs3NmzYAD09PXTt2vU/H6ulpQVfX1+cPn1aPuX//TWO/Pz88PPPPyMtLQ1nz55Fhw4d5MdkMhm8vLywaNGiXM+dlUBHRESgadOmqFq1KhYtWgQnJyfo6elh3759WLx4sUrei1nvuaxz/fLLL7C3t89R7/0WT0CxxfBDzMzM4OjomGMK+H95v7XrQ/7rdRVnBY19w4YN6Nu3Lzp06IBx48bB1tZWPsEhIiJCZc+jDGtra1StWhUnT55EYmIirly5gqlTp8qP+/n54eTJk3j8+DEePXqU79a7ov73tba2Rrt27dCuXTv4+/vj+PHjePjwYa5rOWWX24w1mUyGZs2aYfz48bk+JuuPoixF+Xrzei4qhQmSm5sbhBBwdXXN8aZ839WrV3Hnzh2sW7cOvXv3lpfnNnsqvx/iygoLC8PLly+xfft2NG7cWF4eGRmZa/2nT5/mmGZ9584dACiUlWTDw8MRERGh0CW2bds2BAQEYM2aNQp14+LiUKZMGYUyT09P1KxZExs3bkS5cuXw6NEjLF26NMfzWFlZoV+/fujXrx8SExPRuHFjTJs2LV8J0t27dxEQECDfT0xMRHR0NFq3bq1Qr3fv3hg9ejSio6OxadMmtGnTJkfrY14aNmyI/fv3Y/fu3YiNjVWYCePn54dJkyZh3759ePv2rcKUfzc3N1y+fBlNmzb94Hvojz/+QGpqKnbv3q3wl2Vu3V0f+17M6uqxtbWV/3WrKp988glWrVqF8PBw+Pr6frCus7MzZDIZ7t69K295Ad5Nx46Li8vXF1Vu58ytqzY/6+4oc12dnZ1x+PBhvHnzRqEVKatLvCCx52bbtm2oUKECtm/frhDf+wmJMrLiKug1ytKwYUP8/PPPOHjwIDIzMxWWtPDz88PmzZvls75UtR6YqmLPTZ06dXD8+HFER0fD2dm5QL9jbm5uSExMVNnvVNbrvXfvXo5j2csK6/upNNCYLrb86tSpE7S1tTF9+vQc2bgQAi9fvgTwb1b9fh0hBH744Ycc58xKSOLi4lQaa24xpKWlYfny5bnWz8jIkE8pzaq7cuVK2NjYoHbt2vJyVUzzf/jwIfr27Qs9PT2MGzdOIebs13Xr1q159uH36tULBw8exJIlS2BtbY1WrVopHM/698hiYmKCihUr5pjqnZdVq1YpTOEPCQlBRkZGjufp3r07JEnCV199hfv37+dY9+lDsj7k582bByMjI9SoUUN+rG7dutDR0ZEvhfD+F0LXrl3x5MkTrF69Osc53759i6SkJAC5vw/i4+MRGhqa43HGxsYf9T5s0aIFzMzMMHv27FyXPijoejAAMH78eBgbG2PgwIGIiYnJcTwiIkL++5WVwC5ZskShTlZrW5s2bZR+/tatW+PMmTP4559/5GXPnz9XGAeXF2V+x1u3bo3MzEwsW7ZMoXzx4sWQJCnHe6+gcntf/P333wgPDy/Q+RwcHFCjRg2sW7dOoYvu0KFDuHHjRr7P07BhQ2RmZuL777+XD2XI4ufnh8TERCxfvlyl64F9bOzPnj3LtV5aWhqOHDmi0N1bkM/7rl27Ijw8HAcOHMhxLC4uDhkZGfk+F/BuOIWnpyfWr18vH+YAAMePH8fVq1cV6hoZGSkdL71TKluQZs2ahQkTJuDBgwfo0KEDTE1NERkZiR07dmDw4MEYO3YsqlatCjc3N4wdOxZPnjyBmZkZfv/991z7ZbOSjxEjRqBFixbQ1tZWqs86L35+frC0tESfPn0wYsQISJKEX375Jc9mVkdHR8ybNw8PHjxA5cqV8euvv+LSpUtYtWqVwkDl3r174/jx4/lurr1w4QI2bNgAmUyGuLg4nD17Fr///rs8nvcHwn/yySeYMWMG+vXrBz8/P1y9ehUbN25EhQoVcj13jx49MH78eOzYsQNffvlljoXi3N3d4e/vj9q1a8PKygrnzp3Dtm3bMGzYsHzFnpaWhqZNm6Jr1664ffs2li9fjoYNG6Jdu3YK9WxsbNCyZUts3boVFhYWSn0BZ63/Eh4eDn9/f4VuKCMjI1SvXh3h4eGwsLBQuO1Nr1698Ntvv+GLL77AsWPH0KBBA2RmZuLWrVv47bff5GupNG/eHHp6emjbti2GDBmCxMRErF69Gra2toiOjlaIpXbt2ggJCcGsWbNQsWJF2Nraygd954eZmRlCQkLQq1cv1KpVC926dYONjQ0ePXqEvXv3okGDBjm++PPLzc0NmzZtwmeffYZq1aqhd+/e8PT0RFpaGk6fPo2tW7eib9++AIDq1aujT58+WLVqlbyr+Z9//sG6devQoUMHhVbB/Bo/fjx++eUXtGzZEl999RWMjY2xatUqODs7/+e4Njc3N1hYWGDFihUwNTWFsbEx6tWrl+t4k7Zt2yIgIACTJk3CgwcPUL16dRw8eBC7du3CyJEjFQZkf4xPPvkE27dvR8eOHdGmTRtERkZixYoVcHd3V/jSVMacOXPQpk0bNGzYEP3798erV6+wdOlSeHh45PucWX8EhIeHy/89s1SuXBllypRBeHg4vLy85OuBqcLHxP748WPUrVsXgYGBaNq0Kezt7REbG4vNmzfj8uXLGDlypLwFvEaNGtDW1sa8efMQHx8PfX19+RpleRk3bhx2796NTz75BH379kXt2rWRlJSEq1evYtu2bXjw4EGOFvb/Mnv2bLRv3x4NGjRAv3798Pr1ayxbtgyenp4Kr9fQ0BDu7u749ddfUblyZVhZWcHT07PQb8GlEYpwxlyhyJq+fPbs2VyPN2nSJMc6SEII8fvvv4uGDRsKY2NjYWxsLKpWrSqGDh0qbt++La9z48YNERQUJExMTESZMmXEoEGD5FNw35/um5GRIYYPHy5sbGyEJEnyKfR5TZM/duyYACC2bt36n6/l1KlTon79+sLQ0FA4OjqK8ePHy6fIvz/NNOt1njt3Tvj6+goDAwPh7OyssE7O+3Xz80+fFX/WpqOjI6ysrES9evXEhAkTFJY6yJKSkiLGjBkjHBwchKGhoWjQoIEIDw/PMW36fa1bt5avlZLdrFmzRN26dYWFhYUwNDQUVatWFd99953C1P3cZF3L48ePi8GDBwtLS0thYmIievbsKV6+fJnrY3777Tf5WjfK8vX1la+Nkl3WuimtWrXKcSwtLU3MmzdPeHh4CH19fWFpaSlq164tpk+fLl86QQghdu/eLby9vYWBgYFwcXER8+bNEz///HOO6enPnj0Tbdq0EaampgKA/Jrn9XuS9V7MPmX52LFjokWLFsLc3FwYGBgINzc30bdvX3Hu3Dl5nT59+ghjY2Olr9WdO3fEoEGDhIuLi9DT0xOmpqaiQYMGYunSpQpLV6Snp4vp06cLV1dXoaurK5ycnMSECRMU6gjxbmp1bktB5Paeu3LlimjSpIkwMDAQZcuWFTNnzhRr1qz5z2n+Qrybau3u7i50dHQUPgOyT00X4t1yCaNGjRKOjo5CV1dXVKpUSSxYsEBhiQUh3k3zHzp0aI7YnZ2dRZ8+fXK/gP9PJpOJ2bNnC2dnZ6Gvry9q1qwp9uzZk+dU+dyW60Au079///13Ua1aNaGvry/c3d3F9u3bc32NH+Lo6CgAiFWrVuU41q5dOwFAvh7Y+9QVe0JCgvjhhx9EixYtRLly5YSurq4wNTUVvr6+YvXq1Tn+3VavXi0qVKggtLW1FX5/8novCvHuPTFhwgRRsWJFoaenJ8qUKSP8/PzE999/L/88U/b1btmyRVStWlXo6+sLT09PsXv3btG5c2dRtWpVhXqnT58WtWvXFnp6egrnyet3eOrUqfn6jtB0khAlYBQj/Sd/f3+8ePFC6UGwxUHHjh1x9erVXPvTi9KuXbvQoUMH/PXXXwpTxomISooaNWrAxsYm33cboLyVujFIVLxER0dj7969+ZpSX9hWr16NChUqFNqNZImIVCU9PT3H2KWwsDBcvnw5xy2GqGBK3RgkKh4iIyNx6tQp/PTTT9DV1VVY4LKobdmyBVeuXMHevXvxww8/cNYHERV7T548QVBQED7//HM4Ojri1q1bWLFiBezt7XMs/EgFwwSJ1OL48ePo168fypcvj3Xr1uW65k5R6d69O0xMTDBgwAD873//U1scRET5ZWlpidq1a+Onn37C8+fPYWxsjDZt2mDu3LmwtrZWd3gagWOQiIiIqMjMnTsXEyZMwFdffSVfyiNrQc73DRkyBCtWrFBDhO+wBYmIiIiKxNmzZ7Fy5coc90kFgEGDBmHGjBny/aw1nNSFg7SJiIio0CUmJqJnz55YvXp1rncqMDIygr29vXzLuoWVurAFqZDIZDI8ffoUpqamHPRLREQ5CCHw5s0bODo65uuehgWVkpKCtLQ0lZ9XCJHj+01fXx/6+vq51h86dCjatGmDoKAgzJo1K8fxjRs3YsOGDbC3t0fbtm0xZcoUtbYiMUEqJE+fPpXfbJSIiCgvUVFRKFeuXKGcOyUlBYam1kBGssrPbWJikmOV8qlTp2LatGk56m7ZsgUXLlzA2bNncz1Xjx494OzsDEdHR1y5cgVff/01bt++je3bt6s87vxiglRIsm5SufnYZRiZmP5HbcquQhnj/65EOdTptkDdIZRYQT2Vv78bvdOtlqO6QyiR3iYlYnCL2go3NVa1tLQ0ICMZ+u59AG091Z04Mw2JN9YhKipKoSsst9ajqKgofPXVVzh06BAMDAxyPd3gwYPlP3t5ecHBwQFNmzZFRESEym7PoywmSIUkq9nRyMQUxkyQlGZqaqLuEEokSSf3pm36b7qGfM8VFP8I/DhFMgxDxwCSChMkIb3rEjQzM/vPsULnz59HbGwsatWqJS/LzMzEX3/9hWXLliE1NVV+8+Us9erVAwDcu3ePCRIRERFpnqZNm+Lq1asKZf369UPVqlXx9ddf50iOAODSpUsAAAcHh6IIMVdMkIiIiDSdBECVLVVKnMrU1BSenp4KZcbGxrC2toanpyciIiKwadMmtG7dGtbW1rhy5QpGjRqFxo0b57ocQFFhgkRERKTpJK13myrPpyJ6eno4fPgwlixZgqSkJDg5OaFz586YPHmyyp6jIJggERERUZEKCwuT/+zk5JRjFe3igAkSERGRppMkFXexaf76flxJm4iIiCgbtiARERFpumI8Bqm40vxXSERERKQktiARERFpOo5BUhoTJCIiIo2n4i62UtABpfmvkIiIiEhJbEEiIiLSdOxiUxpbkIiIiIiyYQsSERGRpuM0f6UxQSIiItJ07GJTmuangERERERKYgsSERGRpmMXm9I0/xUSERERKYktSERERJqOY5CUxgSJiIhI07GLTWma/wqJiIiIlMQWJCIiIk0nSSpuQdL8Lja2IBERERFlwxYkIiIiTaclvdtUeT4NxwSJiIhI03GQttI0/xUSERERKYktSERERJqO6yApjS1IRERERNmwBYmIiEjTcQyS0jT/FRIREREpiS1IREREmo5jkJTGBImIiEjTsYtNaZr/ComIiIiUxBYkIiIiTccuNqWxBYmIiIgoG7YgERERaTqOQVIaEyQiIiJNxy42pWl+CkhERESkJLYgERERaTwVd7GVgvYVzX+FREREREpiCxIREZGm4xgkpTFBIiIi0nSSpOJZbJqfILGLjYiIiCgbJkhERESaLmsdJFVuBTR37lxIkoSRI0fKy1JSUjB06FBYW1vDxMQEnTt3RkxMjApeeMExQSIiIqIicfbsWaxcuRLe3t4K5aNGjcIff/yBrVu34vjx43j69Ck6deqkpijfYYJERESk6bIGaatyU1JiYiJ69uyJ1atXw9LSUl4eHx+PNWvWYNGiRQgMDETt2rURGhqK06dP48yZM6q8Ckop9gmSJEnYuXNnoT+Pi4sLlixZUujPU5SunD2NyV/2xGeNPRFUzQanDu/Ls+6SaWMRVM0Gv69bUYQRlhwNalaBSxnDHNuU8SPVHVqxlvH4NFJOfYf0+wflZen39iH1fDBSwuch5e/FSLv5G2TJL9QYZfFwZ18owmb1xp5hTbBvdHOcCR6LN88eyI8nvXiKnYN8ct2enDusvsCLievnz2D2iN4Y2KwmOtdwxN9H9+eo8/j+Xcz5qg96NayCHvXdML5HKzyPfqyGaNWgGHSxDR06FG3atEFQUJBC+fnz55Genq5QXrVqVZQvXx7h4eEf/dILSu2z2J49e4bvvvsOe/fuxZMnT2Bra4saNWpg5MiRaNq0qbrDK9FS3iajQhUPtOzUA9NG9M2z3slDe3Hz8jlY29oXXXAlzO5DJ5GZmSnfv3PrBj7v3Aat26m3Cbg4k715isxnFyAZ2SqUSyb20LXxBPTNgIy3yHh0AmnXN0O/zlBIpeD+Tnl5cecCXAO6wNLFHUKWiRs7luP04uFoOuM36OgbwsjKDi2/V/zSf/DXDtw7sAF2nn5qirr4SH2bDJfKHmjaoTvmjx6Q4/izqAeY1K8Dmnbohs++HAsjY1NERdyGnr6BGqLVHAkJCQr7+vr60NfXz1Fvy5YtuHDhAs6ePZvj2LNnz6CnpwcLCwuFcjs7Ozx79kyl8SpDrQnSgwcP0KBBA1hYWGDBggXw8vJCeno6Dhw4gKFDh+LWrVvqDK/Eq9s4CHUbB32wzouYaCz7bgLmrv4Nk77oUUSRlTzWZWwU9kN+/B7OrhVQv0EjNUVUvInMNKTf2QWdim2QEXVS4ZiOfa339iyg49wEaZd+gkiJh2RoidLKb+RShf1a/aZi/+jmiHt4E2Uq14KkpQ0D8zIKdaIvhsGxThB0DIyKMtRiqVbDQNRqGJjn8U3L5qJWw0D0HjVFXmbv5FIEkRUThbQOkpOTk0Lx1KlTMW3aNIWyqKgofPXVVzh06BAMDEpOQqrWP9f+97//QZIk/PPPP+jcuTMqV64MDw8PjB49Os9+x6tXryIwMBCGhoawtrbG4MGDkZiYKD/u7++vMDIeADp06IC+ffvK92NjY9G2bVsYGhrC1dUVGzduVKjfv39/fPLJJwpl6enpsLW1xZo1az7uRRcjMpkMc7/+H7r2HwqXSlXVHU6JkZaWhp1bt6Brjz6QSsFaIAWRHvEntCwrQtvC9YP1RGYaMmOuQNK3gKRvVkTRlQzpb999rukZ535d4h7eRHzUHTg3bFeUYZVIMpkM508cgaNzBcz4sjv6BXjhm8/b5NoNR8qJiopCfHy8fJswYUKOOufPn0dsbCxq1aoFHR0d6Ojo4Pjx4/jxxx+ho6MDOzs7pKWlIS4uTuFxMTExsLdXX8+G2hKkV69e4c8//8TQoUNhbGyc43j2pjYASEpKQosWLWBpaYmzZ89i69atOHz4MIYNG6bUc/ft2xdRUVE4duwYtm3bhuXLlyM2NlZ+fODAgfjzzz8RHR0tL9uzZw+Sk5Px2WefKfVcxdmWn36EtrYOOvYarO5QSpSD+3YjIT4On3b7XN2hFEuZz69DJD2DjktAnnUyos8hJXw+Us8sgCwuAroePSBpaRdhlMWbkMlwdcsiWFWsDrOyFXOt8/DkLpg6uMK6YvUijq7kiX/1AinJSdjx8zLU9AvAtyGbUTewJRaMGYjr59Q3xqVIFdIYJDMzM4Utt+61pk2b4urVq7h06ZJ8q1OnDnr27Cn/WVdXF0eOHJE/5vbt23j06BF8fX2L7BJlp7Yutnv37kEIgapV899ysWnTJqSkpGD9+vXypGrZsmVo27Yt5s2bBzs7u/88x507d7B//378888/8PHxAQCsWbMG1apVk9fx8/NDlSpV8Msvv2D8+PEAgNDQUHTp0gUmJia5njc1NRWpqany/ez9ssXNneuXseOXVQj5/ShbQZT068Z18G/aAnYOjuoOpdgRqQlIjzwEPY/ukLTy/njRtvGElkUFIC0RGU/OIP32duh59/ngY0qTy5vmI+FpBBqPX53r8cy0FET9fQBVPsk51oZyEjIZAMDHvwXa/v8fhK5VPXH78jkc2LYeHnXU9yVcGpiamsLT01OhzNjYGNbW1vLyAQMGYPTo0bCysoKZmRmGDx8OX19f1K9fXx0hA1BjC5IQQunH3Lx5E9WrV1docWrQoAFkMhlu376d73Po6Oigdu3a8rKqVavmaLEaOHAgQkNDAbxr5tu/fz/69++f53nnzJkDc3Nz+Za9X7a4uXouHHEvX6BHYA0097RHc097xDyNwsr5U9Gzaa3/PkEp9TjqIU4dP4rPPu+r7lCKJVliNJCehLRLa5ByajZSTs2GSHiEzOiz734W776oJB0DaBlaQcu8PHSrdoZ4+xKyl/n7HdZ0lzfNR8yVE2g4JgSGVrn/0ffk/FFkpqWgvG+bIo6uZDK1tIK2jg6c3CorlJdzrYQX0U/UFFURKwbT/D9k8eLF+OSTT9C5c2c0btwY9vb22L59u0qfQ1lq+3OtUqVKkCRJ5QOxtbS0ciRf6enpSp+nd+/e+OabbxAeHo7Tp0/D1dUVjRrlPSB3woQJGD16tHw/ISGhWCdJQe26opZvE4WybwZ1RVC7LmjZiYO187J10y+wLmOLwOat1B1KsaRl7gK9GoMUytLv7YFkaA2dsr55zFITAASELKNIYiyuhBC4snkBoi+GoeHYFTC2KZtn3Ycnd8GhemPom5beQe3K0NXVQ0X36njyIEKh/OnD+7BxKKemqIqWJEmq7S34yHOFhYUp7BsYGCA4OBjBwcEfdV5VUluCZGVlhRYtWiA4OBgjRozIMQ4pLi4uR6tOtWrVsHbtWiQlJcnrnzp1ClpaWqhSpQoAwMbGRmHsUGZmJq5du4aAgHfjIapWrYqMjAycP39e3sV2+/btHIPDrK2t0aFDB4SGhiI8PBz9+vX74OvJa2qjOr1NSsSTR5Hy/ejHj3Dv5lWYmlvCzrEczC2tFOrr6OjCqowtnFxzH/NQ2slkMmzbvB6du/WEjg67gnIj6ehD0lGc1g8tXUg6htAytoUs5TVkL25Ay6ICJF0jiNQ3yHh8GtDShbZl6X7fXdk0D1F/H0D9od9Dx8AIKfHv1obSNTSBtt6/M38SY6Pw8u5F+I5YoqZIi6e3yUl49t7nXeyTKETeugYTcwvYOJRD+77/w6LxX8C9Vn14+vjh4uljOPfXIcz4aZsao6biTK2f8sHBwWjQoAHq1q2LGTNmwNvbGxkZGTh06BBCQkJw8+ZNhfo9e/bE1KlT0adPH0ybNg3Pnz/H8OHD0atXL/n4o8DAQIwePRp79+6Fm5sbFi1apJD8VKlSBS1btsSQIUMQEhICHR0djBw5EoaGhjniGzhwID755BNkZmaiT58+hXotCsPt65cxtk8H+f6Kee+mtzbv8BnGz1mmpqhKrpPHj+LJ4yh07VHy3gvFhSTpQJYQhYynZ4GMt4CuMbTMykPPqw8kvZyTNUqTyLDfAQAnv/9Cobxm32/h3KCtfP/Ryd0wtLSFrbv6xmYURxHXL2PqoE/l+2sXTgMA+LftiuEzl6BeYCsMnjwX29csw8/zp8DRuQLGfb8a1WrWU1PERau4tSCVBGpNkCpUqIALFy7gu+++w5gxYxAdHQ0bGxvUrl0bISEhOeobGRnhwIED+Oqrr+Dj4wMjIyN07twZixYtktfp378/Ll++jN69e0NHRwejRo2Stx5lCQ0NxcCBA9GkSRPY2dlh1qxZmDJlSvanQ1BQEBwcHODh4QFHx5I3ILdG3QY4fPN5vutvPHKhEKMp+RoHBOHBi7fqDqPE0ffqJf9Z0jeFnns3NUZTfHVYnXMBvdy4dxoK905DCzmaksfTxw+/X3r6wTpNO3RH0w7diygiKukkUZDR0qVEYmIiypYti9DQUKVvmpeQkABzc3PsOnsfxiamhRSh5qpok/tsQfqwqu1nqTuEEqtl3w7qDqHE6uWT93gpylty4hv0algF8fHxMDMrnHXAsr6LDNsHQ9LN2VNSUCL9Ld7uGlqosasbB1LkQiaT4cWLF1i4cCEsLCzQrh0XYiMiopKLXWzKY4KUi0ePHsHV1RXlypXD2rVrOSCXiIiolOE3fy5cXFwKtE4TERFRccQWJOWV3ltnExEREeWBLUhEREQaji1IymOCREREpOGYICmPXWxERERE2bAFiYiISNNJ/7+p8nwaji1IRERERNmwBYmIiEjDcQyS8tiCRERERJQNW5CIiIg0nCRBxS1IqjtVccUEiYiISMNJUHEXWynIkNjFRkRERJQNW5CIiIg0HAdpK48tSERERETZsAWJiIhI03GhSKUxQSIiItJ0Ku5iE+xiIyIiIip92IJERESk4VQ9SFu1SwYUT2xBIiIiIsqGLUhEREQaji1IymOCREREpOk4i01p7GIjIiIiyoYtSERERBqOXWzKYwsSERERUTZsQSIiItJwbEFSHhMkIiIiDccESXnsYiMiIiLKhi1IREREGo4tSMpjCxIRERFRNmxBIiIi0nRcKFJpbEEiIiIiyoYtSERERBqOY5CUxwSJiIhIwzFBUh672IiIiIiyYYJERESk4bJakFS55VdISAi8vb1hZmYGMzMz+Pr6Yv/+/fLj/v7+Oc79xRdfFMZlUAq72IiIiKjQlCtXDnPnzkWlSpUghMC6devQvn17XLx4ER4eHgCAQYMGYcaMGfLHGBkZqStcOSZIREREmk6N0/zbtm2rsP/dd98hJCQEZ86ckSdIRkZGsLe3V2GAH49dbERERBqusLrYEhISFLbU1NQPxpGZmYktW7YgKSkJvr6+8vKNGzeiTJky8PT0xIQJE5CcnFyo1yM/2IJEREREBeLk5KSwP3XqVEybNi1HvatXr8LX1xcpKSkwMTHBjh074O7uDgDo0aMHnJ2d4ejoiCtXruDrr7/G7du3sX379qJ4CXligkRERKThCmuaf1RUFMzMzOTl+vr6udavUqUKLl26hPj4eGzbtg19+vTB8ePH4e7ujsGDB8vreXl5wcHBAU2bNkVERATc3NxUFrOymCARERFRgWTNTPsvenp6qFixIgCgdu3aOHv2LH744QesXLkyR9169eoBAO7du8cEiYiIiAqPBBW3IH3kiG+ZTJbneKVLly4BABwcHD7qOT4WEyQiIiINp86VtCdMmIBWrVqhfPnyePPmDTZt2oSwsDAcOHAAERER2LRpE1q3bg1ra2tcuXIFo0aNQuPGjeHt7a2yeAuCCRIREREVmtjYWPTu3RvR0dEwNzeHt7c3Dhw4gGbNmiEqKgqHDx/GkiVLkJSUBCcnJ3Tu3BmTJ09Wd9hMkIiIiDSeGtdBWrNmTZ7HnJyccPz4cRUEpHpMkApZWoYMOhkydYdR4hjqaas7hBJp5MRe6g6hxPIp+98DTSl3dkYG6g6hREqSpak7BPoAJkhEREQaTp1jkEoqJkhEREQajgmS8nirESIiIqJs2IJERESk4STp3abK82k6tiARERERZcMWJCIiIg33rgVJlWOQVHaqYostSERERETZsAWJiIhI06l4DJJKF50sppggERERaThO81ceu9iIiIiIsmELEhERkYbjNH/lsQWJiIiIKBu2IBEREWk4LS0JWlqqa/YRKjxXccUEiYiISMOxi0157GIjIiIiyoYtSERERBqO0/yVxxYkIiIiomzYgkRERKThOAZJeUyQiIiINBy72JTHLjYiIiKibNiCREREpOHYgqQ8tiARERERZcMWJCIiIg3HQdrKYwsSERERUTZsQSIiItJwElQ8Bgma34TEBImIiEjDsYtNeexiIyIiIsqGLUhEREQajtP8lccWJCIiIqJs2IJERESk4TgGSXlMkIiIiDQcu9iUxy42IiIiomzYgkRERKTh2MWmPLYgEREREWXDFiQiIiINxzFIymOCREREpOlU3MVWCu40wi42IiIiouzYgkRERKTh2MWmPLYgERERUaEJCQmBt7c3zMzMYGZmBl9fX+zfv19+PCUlBUOHDoW1tTVMTEzQuXNnxMTEqDHid5ggERERabisaf6q3PKrXLlymDt3Ls6fP49z584hMDAQ7du3x/Xr1wEAo0aNwh9//IGtW7fi+PHjePr0KTp16lRIVyL/2MVGRESk4dTZxda2bVuF/e+++w4hISE4c+YMypUrhzVr1mDTpk0IDAwEAISGhqJatWo4c+YM6tevr7KYlcUWJCIiIioSmZmZ2LJlC5KSkuDr64vz588jPT0dQUFB8jpVq1ZF+fLlER4ersZI2YJERESk8QprJe2EhASFcn19fejr6+eof/XqVfj6+iIlJQUmJibYsWMH3N3dcenSJejp6cHCwkKhvp2dHZ49e6a6gAuALUhERERUIE5OTjA3N5dvc+bMybVelSpVcOnSJfz999/48ssv0adPH9y4caOIo1UOW5CIiIg0XGGNQYqKioKZmZm8PLfWIwDQ09NDxYoVAQC1a9fG2bNn8cMPP+Czzz5DWloa4uLiFFqRYmJiYG9vr7J4C6JACZJMJsO9e/cQGxsLmUymcKxx48YqCYyIiIiKt6yp+8qSyWRITU1F7dq1oauriyNHjqBz584AgNu3b+PRo0fw9fVVdbhKUTpBOnPmDHr06IGHDx9CCKFwTJIkZGZmqiw4IiIi+njqnMU2YcIEtGrVCuXLl8ebN2+wadMmhIWF4cCBAzA3N8eAAQMwevRoWFlZwczMDMOHD4evr69aZ7ABBUiQvvjiC9SpUwd79+6Fg4NDqVhNk4iIqCQrrEHa+REbG4vevXsjOjoa5ubm8Pb2xoEDB9CsWTMAwOLFi6GlpYXOnTsjNTUVLVq0wPLly1UXbAEpnSDdvXsX27Ztk/clUvF17Vw4fl8bjHs3ruDV8xhMXhIK36at5ccXTRqBI7t/VXhMrQYBmLliS1GHWuwtmD0D38+dpVBWsVJlnDp/TU0RFV/hv63E7dOH8OrxfejoGaBstZrw7zcG1uUqAADevonDiQ1L8eDiKSQ8j4aRuRUq1W+KRr2+goGxqZqjV6/r589g17rluH/zKl4/j8H4RWtQL7CVQp3H9+/ilx9m4cb5M8jMyEC5CpUxbuFq2DiUU1PUxcOls6ex6aeluH39Ml7GPsPs4F/QuFkb+fE1P87Fkb07EPvsCXR0dVHFowYGj54Ej+p11Bh16bBmzZoPHjcwMEBwcDCCg4OLKKL8UTpBqlevHu7du1eiEiR/f3/UqFEDS5YsUXcoRSrlbTJcK3ugWcce+G5kv1zr1G4QiJGzfpDv6+rqFVV4JU6Vau7YtvtP+b62Duc45ObR1bOo1aYHHCp7QZaZib/WLcavkwdi4Io90DMwQuLLWCS+ikXAgPEoU74i4mOf4sCyqUh8FYuOE39Ud/hqlfo2GS6VPdC0Q3fMHz0gx/FnUQ8wqV8HNO3QDZ99ORZGxqaIirgNPX0DNURbvLxNTkLFqp5o07knJg3rneO4k2tFjPp2HhydXJCa+ha/hYZgdL/O2HL4PCytyqgh4qLFe7EpT+lP+OHDh2PMmDF49uwZvLy8oKurq3Dc29tbZcEBQN++fbFu3ToAgI6ODsqVK4cuXbpgxowZMDDI34fC9u3bc8RZGtRp1BR1GjX9YB1dPT1YlbEtoohKNh0dHdjaqXdWRUnw2cyfFPbbjJ6DH3v44dm96yjv6QMbl8roNGmp/LilQ3k06T0Kf3w/DrLMDGhpl97Es1bDQNRqGJjn8U3L5qJWw0D0HjVFXmbv5FIEkRV/vk2awbdJszyPN2/7qcL+8ImzsGfbBkTcuo46fk0KOzwqgZT+JMoaZd6/f395mSRJEEIU2iDtli1bIjQ0FOnp6Th//jz69OkDSZIwb968fD3eyspK5TFpiqvnTqNHE3eYmFmget2G6DX8G5hZ8Hrl5n7EPXhXdoa+gQHq+NTDpGmzUM6pvLrDKvZSk94AAAxNzPOuk/wGekYmpTo5+i8ymQznTxxBh77/w4wvuyPy1jXYlS2Pjv2H5eiGow9LT0vDrl/XwcTUDBWreqo7nCKhzjFIJZXSC0VGRkbm2O7fvy//f2HQ19eHvb09nJyc0KFDBwQFBeHQoUMAgJcvX6J79+4oW7YsjIyM4OXlhc2bNys83t/fHyNHjpTvu7i4YPbs2ejfvz9MTU1Rvnx5rFq1Sn48MDAQw4YNUzjH8+fPoaenhyNHjhTKa1SH2g0DMPq7pZi9ehv6jZyMq+fCMfXL7pyJmItaderix5CfsHn7H5i/aCkePXyA9i0DkfjmjbpDK9aETIbDq2ajnHst2LhUzrVOcvxrnNocghotuxZxdCVL/KsXSElOwo6fl6GmXwC+DdmMuoEtsWDMQFw/p95bMpQUp44dQLMaTgj0csBvoSuwOHQ7LKys1R1WkcjqYlPlpumU/nPN2dm5MOLIt2vXruH06dPyOFJSUlC7dm18/fXXMDMzw969e9GrVy+4ubmhbt26eZ5n4cKFmDlzJiZOnIht27bhyy+/RJMmTVClShUMHDgQw4YNw8KFC+WLXm3YsAFly5aV30wvu9TUVKSmpsr3sy+/Xhw1adVR/rNLZXe4VHbHwNb1cPXsKdSoz/Ws3te0eUv5zx6e3qhVpy5qe1bErh3b0LN37uO7CDgYMgPPH97F5ws25Xo8NTkRW6cNQZnybmjYc1iudegd8f9rzvn4t0DbXoMBAK5VPXH78jkc2LYeHnXUu2ZMSVCrXkOE7jqOuNcv8cdv6/HtyP5YtfUQLK1t1B0aFUMFutVIREQEhg8fjqCgIAQFBWHEiBGIiIhQdWxye/bsgYmJCQwMDODl5YXY2FiMGzcOAFC2bFmMHTsWNWrUQIUKFTB8+HC0bNkSv/322wfP2bp1a/zvf/9DxYoV8fXXX6NMmTI4duwYAKBTp04AgF27dsnrr127Fn379s0za54zZ47CcutOTk6qeOlFysHJBWaW1oh+9EDdoRR75hYWcHOrhMj799QdSrF1MGQG7v0Thh5z1sOsTM6xW6nJifhtykDoGRqj0+Rl0NYpfeMElWFqaQVtHR04uSm2xJVzrYQX0U/UFFXJYmhkjHLOFeBZwwcTZi+FtrYO9mzdoO6wioSEf7vZVLKp+wUVAaUTpAMHDsDd3R3//PMPvL294e3tjb///hseHh7ybi9VCwgIkN/DpU+fPujXr598LFRmZiZmzpwJLy8vWFlZwcTEBAcOHMCjR48+eM73B5NLkgR7e3vExsYCeDflsFevXvj5558BABcuXMC1a9fQt2/fPM83YcIExMfHy7eoqKiPfNVF78Wzp3gT9wqWNhy0/V+SEhPxIPI+7Owc1B1KsSOEwMGQGbgTfhjdZ6+FhX3O6eepyYn4dcoAaOnq4tNvl0NHL/fbE9C/dHX1UNG9Op48UPxj9OnD+6V+in9ByWQypKWl/ndFKpWU7mL75ptvMGrUKMydOzdH+ddffy1f+EmVjI2N5csK/Pzzz6hevTrWrFmDAQMGYMGCBfjhhx+wZMkSeHl5wdjYGCNHjkRaWtoHz5l9VpskSQq3TRk4cCBq1KiBx48fIzQ0FIGBgR/sXszrDsbq9DY5CU8fRcr3nz15hIhb12BqbgFTc0tsCvkeDYLawLKMLaKjHuDnRTPhUN4VtRsEqDHq4mnapK/RvFUblHMqj5hn0Zg/ewa0tbXRsctn6g6t2Dm4fAZuHN+DzlOCoWdojMRXzwEA+sam0NU3eJccTR6A9NS3aDt2AVKTE5GanAgAMDK3gpa2tjrDV6u3yUl49t7vbOyTKETeugYTcwvYOJRD+77/w6LxX8C9Vn14+vjh4uljOPfXIcz4aZsaoy4ekpMS8eThv9cu+vFD3L1xFaYWljC3sMT6kEVo0LQlytjYI+71S2zf+BNexEQjoFV7NUZddLQkCVoqHDekynMVV0onSDdv3sy1+6p///5Fss6QlpYWJk6ciNGjR6NHjx44deoU2rdvj88//xzAu78I7ty5A3d39496Hi8vL9SpUwerV6/Gpk2bsGzZMlWEX6TuXr+ECf07yfd/WjAVANC03WcYOmUeHty5gSO7f0VSQgKsbO1R07cJeg37Grr8az6Hp08e44v+vfD61UtYl7FB3fp+2HfkBMqU4diF7C7uezdJYtM3imvRtB45G97NOuHZvet4evsyAGDlwOYKdb74+TAs7Epva0jE9cuYOujf6ehrF04DAPi37YrhM5egXmArDJ48F9vXLMPP86fA0bkCxn2/GtVq1lNTxMXHrWuXMKJXO/n+0jmTAQCtOnbH2BkL8fD+XezfsQXxr1/CzNIK1bxqInjTXlSoVE1dIRcpzmJTntIJko2NDS5duoRKlSoplF+6dAm2tkXTNdOlSxeMGzcOwcHBqFSpErZt24bTp0/D0tISixYtQkxMzEcnSADkg7WNjY3RsWPH/35AMePt0wB7r8bkeXzmyl/zPEaKVq3dqO4QSoxv9t764HFn73r/Wae08vTxw++Xnn6wTtMO3dG0Q/ciiqjkqFWvIU7eeZXn8dnB64swGtIESidIgwYNwuDBg3H//n34+fkBAE6dOoV58+Zh9OjRKg8wNzo6Ohg2bBjmz5+Pixcv4v79+2jRogWMjIwwePBgdOjQAfHx8R/9PN27d8fIkSPRvXv3fC9KSUREVNxwJW3lSUIIocwDhBBYsmQJFi5ciKdP3/2l4+joiHHjxmHEiBEaddEePHgANzc3nD17FrVq1VLqsQkJCTA3N8fW8HswMind95cqiDrOluoOoURafKJw1iIrDXzKmqk7hBLLzoh/QBZEUmICWtRyQXx8PMzMCuf9l/VdFPj9EegYGqvsvBlvk3B0bNNCjV3dlG5BkiQJo0aNwqhRo/Dm/xfJMzXVrAQgPT0dL1++xOTJk1G/fn2lkyMiIqLiREt6t6nyfJruo9b117TEKMupU6cQEBCAypUrY9s2zg4hIqISTlJxtxgTpHdq1aqFI0eOwNLSEjVr1vzgRb5w4YLKglMXf39/KNnzSERERBokXwlS+/bt5Wv8tG/fXqPGGREREWk6TvNXXr4SpKlTp8p/njZtWmHFQkRERFQsKH2rkQoVKuDly5c5yuPi4lChQgWVBEVERESqIxXCf5pO6QTpwYMHyMzMzFGempqKx48fqyQoIiIiInXK9yy23bt3y38+cOAAzM3N5fuZmZk4cuQIXF1dVRsdERERfTRO81devhOkDh06AHg3TbBPnz4Kx3R1deHi4oKFCxeqNDgiIiL6eFxJW3n5TpCy7nTv6uqKs2fPokyZMoUWFBEREZE6Kb1QZGRkZGHEQURERIWE0/yVl68E6ccff8TgwYNhYGCAH3/88YN1R4wYoZLAiIiIiNQlXwnS4sWL0bNnTxgYGGDx4sV51pMkiQkSERFRMaMlSdBSYbOPKs9VXOUrQXq/W41dbERERCULu9iUp/Q6SERERESaLl8tSKNHj873CRctWlTgYIiIiEj1OM1feflKkC5evKiwf+HCBWRkZKBKlSoAgDt37kBbWxu1a9dWfYRERERERSxfCdKxY8fkPy9atAimpqZYt24dLC0tAQCvX79Gv3790KhRo8KJkoiIiAqMY5CUp/Q6SAsXLsTBgwflyREAWFpaYtasWWjevDnGjBmj0gCJiIjo43AWm/KUHqSdkJCA58+f5yh//vw53rx5o5KgiIiIiNRJ6QSpY8eO6NevH7Zv347Hjx/j8ePH+P333zFgwAB06tSpMGIkIiKijyAVwqbplO5iW7FiBcaOHYsePXogPT393Ul0dDBgwAAsWLBA5QESERERFTWlEyQjIyMsX74cCxYsQEREBADAzc0NxsbGKg+OiIiIPh6n+StP6QQpi7GxMby9vVUZCxEREVGxoHSClJSUhLlz5+LIkSOIjY2FTCZTOH7//n2VBUdEREQfT0t6t6nyfJpO6QRp4MCBOH78OHr16gUHB4dS0cxGRERUkrGLTXlKJ0j79+/H3r170aBBg8KIh4iIiEjtlE6QLC0tYWVlVRixEBERUSEpBY0+KqX0OkgzZ87Et99+i+Tk5MKIh4iIiDTInDlz4OPjA1NTU9ja2qJDhw64ffu2Qh1/f395N2DW9sUXX6gp4ncKdKuRiIgI2NnZwcXFBbq6ugrHL1y4oLLgiIiI6OOpcwzS8ePHMXToUPj4+CAjIwMTJ05E8+bNcePGDYUlggYNGoQZM2bI942MjPL9HBUqVMDZs2dhbW2tUB4XF4datWoVaAKZ0glShw4dlH4SIiIiUh91zmL7888/FfbXrl0LW1tbnD9/Ho0bN5aXGxkZwd7evkDxPHjwAJmZmTnKU1NT8eTJkwKdU+kEaerUqQV6IiIiItIsCQkJCvv6+vrQ19f/4GPi4+MBIMd45o0bN2LDhg2wt7dH27ZtMWXKlP9sRdq9e7f85wMHDsDc3Fy+n5mZiSNHjsDFxSU/LyWHAi8Uef78edy8eRMA4OHhgZo1axb0VERERFSICquLzcnJSaF86tSpmDZtWp6Pk8lkGDlyJBo0aABPT095eY8ePeDs7AxHR0dcuXIFX3/9NW7fvo3t27d/MI6sXi1JktCnTx+FY7q6unBxccHChQuVeGX/UjpBio2NRbdu3RAWFgYLCwsA7/r4AgICsGXLFtjY2BQoECIiIipZoqKiYGZmJt//r9ajoUOH4tq1azh58qRC+eDBg+U/e3l5wcHBAU2bNkVERATc3NzyPF/WYtWurq44e/YsypQpU5CXkSulZ7ENHz4cb968wfXr1/Hq1Su8evUK165dQ0JCAkaMGKGywIiIiEg1pELYAMDMzExh+1CCNGzYMOzZswfHjh1DuXLlPhhvvXr1AAD37t3L1+uLjIxUaXIEFKAF6c8//8Thw4dRrVo1eZm7uzuCg4PRvHlzlQZHREREH09LkqClwi42Zc4lhMDw4cOxY8cOhIWFwdXV9T8fc+nSJQCAg4NDvp/nyJEjed4G7eeff873ebIonSDJZLIcU/uBd3192QMiIiKi0m3o0KHYtGkTdu3aBVNTUzx79gwAYG5uDkNDQ0RERGDTpk1o3bo1rK2tceXKFYwaNQqNGzeGt7d3vp5j+vTpmDFjBurUqaOy26ApnSAFBgbiq6++wubNm+Ho6AgAePLkCUaNGoWmTZt+dEBERESkWpKk2pW0lTlXSEgIgHeLQb4vNDQUffv2hZ6eHg4fPowlS5YgKSkJTk5O6Ny5MyZPnpzv51ixYgXWrl2LXr165T+w/6B0grRs2TK0a9cOLi4u8tHrUVFR8PT0xIYNG1QWGBEREZV8QogPHndycsLx48c/6jnS0tLg5+f3UefITukEycnJCRcuXMDhw4dx69YtAEC1atUQFBSk0sCIiIhINdS5knZRGDhwIDZt2oQpU6ao7JwFWgdJkiQ0a9YMzZo1U1kgREREVDjU2cVWFFJSUrBq1SocPnwY3t7eOcZKL1q0SOlz5nua/9GjR+Hu7p5j1Uzg3aqYHh4eOHHihNIBEBEREX2MK1euoEaNGtDS0sK1a9dw8eJF+ZY1I05Z+W5BWrJkCQYNGqSwIFQWc3NzDBkyBIsWLUKjRo0KFAgREREVDnVO8y8Kx44dU/k5892CdPnyZbRs2TLP482bN8f58+dVEhQRERGROuW7BSkmJibX9Y/kJ9LRwfPnz1USFBEREamOpo9BCggI+ODA8aNHjyp9znwnSGXLlsW1a9dQsWLFXI9fuXJFqRUviYiIiFShRo0aCvvp6em4dOkSrl27luMmtvmV7wSpdevWmDJlClq2bAkDAwOFY2/fvsXUqVPxySefFCgIIiIiKjyaPs1/8eLFuZZPmzYNiYmJBTqnJP5rBaf/FxMTg1q1akFbWxvDhg1DlSpVAAC3bt1CcHAwMjMzceHCBdjZ2RUoEE2TkJAAc3Nz3Hv8Aqa5DGynD9PVVvo+ygTgxpOcs0wpf8wM8x5CQB9Wyd5E3SGUSAkJCbCzNkd8fHyuE6BU9Rzm5uYYvOEf6Bmp7t8pLTkRqz6vW6ixq8K9e/dQt25dvHr1SunH5rsFyc7ODqdPn8aXX36JCRMmyFfGlCQJLVq0QHBwMJMjIiIiKjbCw8Nz9Hrll1ILRTo7O2Pfvn14/fo17t27ByEEKlWqBEtLywI9ORERERU+Te9i69Spk8K+EALR0dE4d+5cgVfXLtBK2paWlvDx8SnQExIRERGpkrm5ucK+lpYWqlSpghkzZqB58+YFOmeBEiQiIiIqOSQJ0NLgaf6hoaEqPycTJCIiIg2npeIESZXnUqXz58/j5s2bAAAPDw/UrFmzwOdigkREREQlWmxsLLp164awsDBYWFgAAOLi4hAQEIAtW7bAxsZG6XMqNZc6PT0d/fv3R2RkpNJPREREROqRNUhblVtxMnz4cLx58wbXr1/Hq1ev8OrVK1y7dg0JCQkYMWJEgc6pVIKkq6uL33//vUBPRERERFQY/vzzTyxfvhzVqlWTl7m7uyM4OBj79+8v0DmVXo2vQ4cO2LlzZ4GejIiIiIpe1hgkVW7FiUwmy/V+sbq6upDJZAU6p9JjkCpVqoQZM2bg1KlTqF27NoyNjRWOF7Qpi4iIiAqHpt+sNjAwEF999RU2b94MR0dHAMCTJ08watQoNG3atEDnVDpBWrNmDSwsLHD+/HmcP39e4ZgkSUyQiIiIqEgtW7YM7dq1g4uLC5ycnAAAUVFR8PT0xIYNGwp0TqUTJA7QJiIiKlm0JAlaKmz2UeW5VMHJyQkXLlzA4cOHcevWLQBAtWrVEBQUVOBzFviOoGlpabh9+zYyMjIK/OREREREBXX06FG4u7sjISEBkiShWbNmGD58OIYPHw4fHx94eHjgxIkTBTq30glScnIyBgwYACMjI3h4eODRo0cA3k2xmzt3boGCICIiosKjVQhbcbBkyRIMGjQIZmZmOY6Zm5tjyJAhWLRoUYHOrfRrnDBhAi5fvoywsDCFO+QGBQXh119/LVAQREREVHiyBmmrcisOLl++jJYtW+Z5vHnz5jnGS+eX0mOQdu7ciV9//RX169dXWCjKw8MDERERBQqCiIiISFkxMTG5Tu/PoqOjg+fPnxfo3Eq3ID1//hy2trY5ypOSkordyppEREQEaEGSD9RWyYbi8X1ftmxZXLt2Lc/jV65cgYODQ4HOrXSCVKdOHezdu1e+n5UU/fTTT/D19S1QEERERETKat26NaZMmYKUlJQcx96+fYupU6fik08+KdC5le5imz17Nlq1aoUbN24gIyMDP/zwA27cuIHTp0/j+PHjBQqCiIiICo+mLhQ5efJkbN++HZUrV8awYcNQpUoVAMCtW7cQHByMzMxMTJo0qUDnVjpBatiwIS5duoS5c+fCy8sLBw8eRK1atRAeHg4vL68CBUFERESkLDs7O5w+fRpffvklJkyYACEEgHe9Wy1atEBwcDDs7OwKdG6lEyQAcHNzw+rVqwv0hERERFS0VH3/tOJ0LzZnZ2fs27cPr1+/xr179yCEQKVKlWBpaflR5y1QgiSTyXDv3j3ExsbmuAlc48aNPyogIiIiUi1JUu3q18Wli+19lpaW8PHxUdn5lE6Qzpw5gx49euDhw4fypqwskiQhMzNTZcERERERqYPSCdIXX3whn8nm4ODAqf1ERETFnKYO0i5MSidId+/exbZt21CxYsXCiIeIiIhI7ZReB6levXq4d+9eYcRCREREhSBrkLYqN02XrxakK1euyH8ePnw4xowZg2fPnsHLyyvHEt/e3t6qjZCIiIg+ivT//6nyfJouXwlSjRo1IEmSwqDs/v37y3/OOsZB2kRERKQJ8pUgRUZGFnYcREREVEg0eR2kwpKvBMnZ2Vn+819//QU/Pz/o6Cg+NCMjA6dPn1aoS0RERFQSKT1IOyAgAK9evcpRHh8fj4CAAJUERURERKqjzkHac+bMgY+PD0xNTWFra4sOHTrg9u3bCnVSUlIwdOhQWFtbw8TEBJ07d0ZMTIyKr4JylE6QssYaZffy5UsYGxurJCgiIiJSHUmSVL7l1/HjxzF06FCcOXMGhw4dQnp6Opo3b46kpCR5nVGjRuGPP/7A1q1bcfz4cTx9+hSdOnUqjEuRb/leBykrUEmS0LdvX+jr68uPZWZm4sqVK/Dz81N9hERERFRi/fnnnwr7a9euha2tLc6fP4/GjRsjPj4ea9aswaZNmxAYGAgACA0NRbVq1XDmzBnUr19fHWHnP0EyNzcH8K4FydTUFIaGhvJjenp6qF+/PgYNGqT6CImIiOijFKdB2vHx8QAAKysrAMD58+eRnp6OoKAgeZ2qVauifPnyCA8PL/4JUmhoKADAxcUFY8eOZXcaERFRKZeQkKCwr6+vr9DDlJ1MJsPIkSPRoEEDeHp6AgCePXsGPT09WFhYKNS1s7PDs2fPVB5zfik9Bmnq1KlMjoiIiEqQrHuxqXIDACcnJ5ibm8u3OXPmfDCOoUOH4tq1a9iyZUsRvOqPo/S92ABg27Zt+O233/Do0SOkpaUpHLtw4YJKAiMiIqLiLSoqCmZmZvL9D7UeDRs2DHv27MFff/2FcuXKycvt7e2RlpaGuLg4hVakmJgY2NvbF0rc+aF0C9KPP/6Ifv36wc7ODhcvXkTdunVhbW2N+/fvo1WrVoURIxEREX0ELUlS+QYAZmZmCltuCZIQAsOGDcOOHTtw9OhRuLq6KhyvXbs2dHV1ceTIEXnZ7du38ejRI/j6+hbuhfkApVuQli9fjlWrVqF79+5Yu3Ytxo8fjwoVKuDbb7/NdX0kIiIiUi91DtIeOnQoNm3ahF27dsHU1FQ+rsjc3ByGhoYwNzfHgAEDMHr0aFhZWcHMzAzDhw+Hr6+v2gZoAwVoQXr06JF8Or+hoSHevHkDAOjVqxc2b96s2uiIiIioRAsJCUF8fDz8/f3h4OAg33799Vd5ncWLF+OTTz5B586d0bhxY9jb22P79u1qjLoALUj29vZ49eoVnJ2dUb58eZw5cwbVq1dHZGSkws1siYiIqJh4b2C1qs6XX/nJDQwMDBAcHIzg4OCPCEq1lG5BCgwMxO7duwEA/fr1w6hRo9CsWTN89tln6Nixo8oDJCIiIipqSrcgrVq1CjKZDADk9005ffo02rVrhyFDhqg8QCIiIvo4WpCgpUyzTz7Op+mUTpC0tLSgpfVvw1O3bt3QrVs3lQZFREREqiOpuItNpd11xZTSXWwAcOLECXz++efw9fXFkydPAAC//PILTp48qdLgiIiIiNRB6QTp999/R4sWLWBoaIiLFy8iNTUVwLt7q8yePVvlARIREdHHyZrmr8pN0ymdIM2aNQsrVqzA6tWroaurKy9v0KABV9EmIiIijaB0gnT79m00btw4R7m5uTni4uJUERMVkgWzZ8DOTE9ha1DbU91hlUhLvp8HK2MdTBg3Wt2hFDuXzp7G+CHd0b6hOxpWtsJfh/YqHF/z41z0aFEPQdXLoWUdV3zVpyOuXz6npmiLl3NnTmJYvy5oWrsSvJ1McfTPPxSOCyEQ/P0sBNauCJ+KNhjUvS0eRt5TU7TF14J5c9Cgvg9sLE1R3tEWXTp3wJ3bt9UdlloV1kramkzpBMne3h737uX8hTx58iQqVKigkqCo8FSp5o6rdx/Jt90Hw9QdUolz4fxZrP15NTw8vdUdSrH0NjkJFat6YvS383M97uRaEaO+nYd1f5zE8s374FDWCaP7dcbrVy+KONLi5+3bZFSp5oWJsxbmejw0ZDE2ha7AlNlLsPGPYzA0NMIXn3dEakpKEUdavJ346zi++HIojp88gz37DyEjPR2ftG6OpKQkdYemNoV1s1pNpvQstkGDBuGrr77Czz//DEmS8PTpU4SHh2Ps2LGYMmVKgQN5/vw5vv32W+zduxcxMTGwtLRE9erV8e2336JBgwYFPi8p0tHRga2d+m7+V9IlJiZiSP/eWLJsBRbO55i73Pg2aQbfJs3yPN687acK+8MnzsKebRsQces66vg1KezwirVGAc3RKKB5rseEENiwZjkGDR+HgBafAAC+W7IKAbXccPTAHrRq/2mujyuNdu/9U2F/1Zq1KO9oi4sXzqNho5w9IES5UTpB+uabbyCTydC0aVMkJyejcePG0NfXx9ixYzF8+PACB9K5c2ekpaVh3bp1qFChAmJiYnDkyBG8fPmywOeknO5H3IN3ZWfoGxigjk89TJo2C+Wcyqs7rBJj/KjhaNaiFfwDg5ggqUB6Whp2/boOJqZmqFiV3b0f8uTRA7yIjUH9RgHyMlMzc3jVqIPLF/5hgvQBCfHxAABLSys1R6I+WlBtt1hpWAcp311skZGRAABJkjBp0iS8evUK165dw5kzZ/D8+XPMnDmzwEHExcXhxIkTmDdvHgICAuDs7Iy6detiwoQJaNeunfx5f/rpJ3Ts2BFGRkaoVKmSfEXvLMePH0fdunWhr68PBwcHfPPNN8jIyAAA7NmzBxYWFsjMzAQAXLp0CZIk4ZtvvpE/fuDAgfj888+RlJQEMzMzbNu2TeH8O3fuhLGxsfz+cyVNrTp18WPIT9i8/Q/MX7QUjx4+QPuWgUgsoa+nqP2+9VdcvnQR385gYvSxTh07gGY1nBDo5YDfQldgceh2WFhZqzusYu3F8xgAgHUZW4VyaxtbvIyNUUdIJYJMJsO4MSPh69cAHp5Mwin/8p0gubm5wdXVFf3798eGDRvw/PlzuLu7o27dujAxMfmoIExMTGBiYoKdO3fKlw3IzfTp09G1a1dcuXIFrVu3Rs+ePfHq1SsAwJMnT9C6dWv4+Pjg8uXLCAkJwZo1azBr1iwAQKNGjfDmzRtcvHgRwLtkqkyZMggLC5Of//jx4/D394exsTG6deuG0NBQhecPDQ3Fp59+ClNT0xyxpaamIiEhQWErbpo2b4l2HT+Fh6c3AoKaY9O23YiPj8OuHdv++8Gl3OPHUZg4bhRW/bweBgYG6g6nxKtVryFCdx1HyK9/ol7jQHw7sj9ev3yu7rBIA40cPhTXr1/D+o1b1B2KWnEMkvLynSAdPXoUffr0wf379zFo0CCUL18elSpVwpAhQ7BlyxbExBT8LxgdHR2sXbsW69atg4WFBRo0aICJEyfiypUrCvX69u2L7t27o2LFipg9ezYSExPxzz//AACWL18OJycnLFu2DFWrVkWHDh0wffp0LFy4EDKZDObm5qhRo4Y8IQoLC8OoUaNw8eJFJCYm4smTJ7h37x6aNHk3BmLgwIE4cOAAoqOjAQCxsbHYt28f+vfvn+trmDNnDszNzeWbk5NTga9HUTG3sICbWyVE3ucsmP9y+eIFPH8eC/8GPrAx04eNmT5OnfgLq0KWwsZMX94ySfljaGSMcs4V4FnDBxNmL4W2tg72bN2g7rCKtTI2dgCAly9iFcpfPo+Fta2dOkIq9kaOGIZ9+/bgwKFjKFeunLrDUSutQtg0Xb5fo7+/P6ZNm4awsDC8fv0ahw4dQvfu3XHz5k307dsXjo6O8PDwKHAgnTt3xtOnT7F79260bNkSYWFhqFWrFtauXSuv4+3976whY2NjmJmZITb23YfFzZs34evrC+m9tLZBgwZITEzE48ePAQBNmjRBWFgYhBA4ceIEOnXqhGrVquHkyZM4fvw4HB0dUalSJQBA3bp14eHhgXXr1gEANmzYAGdn51yXOACACRMmID4+Xr5FRUUV+FoUlaTERDyIvA87Owd1h1LsNfYPxMl/LuF4+Hn5VrNWHXT5rAeOh5+Htra2ukMs0WQyGdLS8m49JqBseReUsbXD3yfD5GWJbxJw9dI5VK9VV32BFUNCCIwcMQy7d+3AnwePwsXVVd0hUQmk9CBtADAwMEBgYCAaNmyIgIAA7N+/HytXrsStW7c+KhgDAwM0a9YMzZo1w5QpUzBw4EBMnToVffv2BQCFhSmBd+OSsm6cmx/+/v74+eefcfnyZejq6qJq1arw9/eXJ31ZrUdZBg4ciODgYHzzzTcIDQ1Fv379FBKw9+nr60NfX1+5F1zEpk36Gs1btUE5p/KIeRaN+bNnQFtbGx27fKbu0Io9U1NTuHsojl8wMjaCpZV1jvLSLjkpEU8eRsr3ox8/xN0bV2FqYQlzC0usD1mEBk1booyNPeJev8T2jT/hRUw0Alq1V2PUxUNyUiIePbgv338S9RC3rl+BuYUlHMo64fMB/8OqpQtQ3tUNZZ1cEPz9TNjYOSDw/2e10Tsjhw/Fr1s2Yev2XTAxNcWzZ88AvFuvz9DQUM3RqYckSXl+fxX0fJpOqQQpLS0NZ86cwbFjxxAWFoa///4bTk5OaNy4MZYtW5YjwfhY7u7u2LlzZ77qVqtWDb///juEEPJ/uFOnTsHU1FTetJo1Dmnx4sXyWP39/TF37ly8fv0aY8aMUTjn559/jvHjx+PHH3/EjRs30KdPH9W9ODV4+uQxvujfC69fvYR1GRvUre+HfUdOoEwZG3WHRhrk1rVLGNGrnXx/6ZzJAIBWHbtj7IyFeHj/Lvbv2IL41y9hZmmFal41EbxpLypUqqaukIuN61cuYkDX1vL9BTMmAADafdoDsxavRL8vR+FtcjJmfDMCbxLiUdPHFyG/bIc+x8UpWLUyBADQvKm/YvlPoejVp2/RB0QlUr4TpMDAQPz9999wdXVFkyZNMGTIEGzatAkODh/fPfPy5Ut06dIF/fv3h7e3N0xNTXHu3DnMnz8f7dvn76/K//3vf1iyZAmGDx+OYcOG4fbt25g6dSpGjx4NLa13PYmWlpbw9vbGxo0bsWzZMgBA48aN0bVrV6Snp+dI8CwtLdGpUyeMGzcOzZs3L/F92KvWblR3CBrljz+PqjuEYqlWvYY4eedVnsdnB68vwmhKFh/fRrgSlfesUkmSMHTsZAwdO7kIoyp53qYLdYdQ7Ej/v6nyfJou3wnSiRMn4ODggMDAQPj7+6NJkyawtlbNtFwTExPUq1cPixcvRkREBNLT0+Hk5IRBgwZh4sSJ+TpH2bJlsW/fPowbNw7Vq1eHlZUVBgwYgMmTFT9ImjRpgkuXLsHf3x8AYGVlBXd3d8TExKBKlSo5zjtgwABs2rQpz8HZREREpHkkIUS+Uu2kpCScOHECYWFhOHbsGC5duoTKlSujSZMm8oTJxkbzump++eUXjBo1Ck+fPoWenl6+H5eQkABzc3Pce/wCpmZmhRihZtLVLg1zJFTvxpPit7xESWFmqPvflShXlew/bqmX0iohIQF21uaIj4+HWSF9T2R9F60KuwFDk5xL1BTU28Q3GOzvXqixq1u+W5CMjY3RsmVLtGzZEgDw5s0bnDx5EseOHcP8+fPRs2dPVKpUCdeuXSu0YItScnIyoqOjMXfuXAwZMkSp5IiIiKi4KQ3dYqpU4D/TjY2NYWVlBSsrK1haWkJHRwc3b95UZWxqNX/+fFStWhX29vaYMGGCusMhIiKiIpTvFiSZTIZz587Ju9hOnTqFpKQklC1bFgEBAQgODkZAQMB/n6iEmDZtGqZNm6buMIiIiD6aqle/LgWz/POfIFlYWCApKQn29vYICAjA4sWL4e/vDzc3t8KMj4iIiKjI5TtBWrBgAQICAlC5cuXCjIeIiIhUjAtFKi/fCdKQIUMKMw4iIiIqJKq+f1ppmGdcGl4jERERkVIKdC82IiIiKjnYxaY8tiARERERZcMWJCIiIg3He7EpjwkSERGRhmMXm/LYxUZERESUDVuQiIiINByn+SuvNLxGIiIiIqWwBYmIiEjDcQyS8pggERERaTjOYlMeu9iIiIiIsmELEhERkYaTpHebKs+n6diCRERERJQNW5CIiIg0nBYkaKlw5JAqz1VcsQWJiIiICtVff/2Ftm3bwtHREZIkYefOnQrH+/btK59pl7W1bNlSPcH+PyZIREREGi5rDJIqN2UkJSWhevXqCA4OzrNOy5YtER0dLd82b978ka/647CLjYiISMNJ//+fKs+njFatWqFVq1YfrKOvrw97e/uPCUul2IJEREREahcWFgZbW1tUqVIFX375JV6+fKnWeNiCREREpOEKa5p/QkKCQrm+vj709fWVPl/Lli3RqVMnuLq6IiIiAhMnTkSrVq0QHh4ObW1tVYSsNCZIREREVCBOTk4K+1OnTsW0adOUPk+3bt3kP3t5ecHb2xtubm4ICwtD06ZNPzbMAmGCREREpOEkFU/zzxqDFBUVBTMzM3l5QVqPclOhQgWUKVMG9+7dY4JEREREhaOwutjMzMwUEiRVefz4MV6+fAkHBweVnzu/mCARERFRoUpMTMS9e/fk+5GRkbh06RKsrKxgZWWF6dOno3PnzrC3t0dERATGjx+PihUrokWLFmqLmQkSERGRhlP3vdjOnTuHgIAA+f7o0aMBAH369EFISAiuXLmCdevWIS4uDo6OjmjevDlmzpypsi67gmCCRERERIXK398fQog8jx84cKAIo8kfJkhEREQaTt0LRZZETJCIiIg0nJb0blPl+TQdV9ImIiIiyoYtSERERBqOXWzKYwsSERERUTZsQSIiItJw6p7mXxKxBYmIiIgoG7YgERERaTgJqh03VAoakJggERERaTpO81ceu9iIiIiIsmELEhERkYbjNH/lsQWJiIiIKBu2IBEREWk4TvNXHhMkIiIiDSdBtTPPSkF+xC42IiIiouzYgkRERKThtCBBS4X9YlqloA2JCVIha7/0FLT1jdUdRonz6xf11R1CiRTUb6G6Qyi5kuLUHUGJ1WfSl+oOoURKS05Udwj0AUyQiIiINBzHICmPCRIREZGmY4akNA7SJiIiIsqGLUhEREQajitpK48tSERERETZsAWJiIhI06l4Je1S0IDEBImIiEjTcYy28tjFRkRERJQNW5CIiIg0HZuQlMYWJCIiIqJs2IJERESk4TjNX3lsQSIiIiLKhi1IREREGk5S8TR/lS4ZUEwxQSIiItJwHKOtPHaxEREREWXDFiQiIiJNxyYkpbEFiYiIiCgbtiARERFpOE7zVx4TJCIiIg3HWWzKYxcbERERUTZsQSIiItJwHKOtPLYgEREREWXDFiQiIiJNxyYkpbEFiYiISMNJhfCfMv766y+0bdsWjo6OkCQJO3fuVDguhMC3334LBwcHGBoaIigoCHfv3lXhFVAeEyQiIiIqVElJSahevTqCg4NzPT5//nz8+OOPWLFiBf7++28YGxujRYsWSElJKeJI/8UuNiIiIg2n7mn+rVq1QqtWrXI9JoTAkiVLMHnyZLRv3x4AsH79etjZ2WHnzp3o1q3bx4ZbIGxBIiIiogJJSEhQ2FJTU5U+R2RkJJ49e4agoCB5mbm5OerVq4fw8HBVhqsUJkhEREQaTiqEDQCcnJxgbm4u3+bMmaN0bM+ePQMA2NnZKZTb2dnJj6kDu9iIiIg0XSHNYouKioKZmZm8WF9fX4VPol5sQSIiIqICMTMzU9gKkiDZ29sDAGJiYhTKY2Ji5MfUgQkSERGRhlP3NP8PcXV1hb29PY4cOSIvS0hIwN9//w1fX1+VPY+y2MVGREREhSoxMRH37t2T70dGRuLSpUuwsrJC+fLlMXLkSMyaNQuVKlWCq6srpkyZAkdHR3To0EFtMTNBIiIi0nDqnuZ/7tw5BAQEyPdHjx4NAOjTpw/Wrl2L8ePHIykpCYMHD0ZcXBwaNmyIP//8EwYGBqoLWklMkIiIiKhQ+fv7QwiR53FJkjBjxgzMmDGjCKP6MCZIREREGo63YlMeEyQiIiJNxwxJaZzFRkRERJQNW5CIiIg0nKqn5qvyXMUVW5CIiIiIsmELEhERkYZT9zT/kogJEhERkYbjGG3lsYuNiIiIKBu2IBEREWk6NiEpjS1IRERERNmwBYmIiEjDcZq/8pggERERaToVz2IrBfkRu9iIiIiIsmMLEhERkYbjGG3lMUHSYC/P7cbLc38gLS4GAGBg4wzbxr1gVqkuMt4mICZsHd7cP4/0+FjoGJnDrGoD2Pv3hbaBiZojL56SEt/gh/kzcXj/H3j18jmqeVTHxJnz4VWjtrpDK7YyHp9GxsNj0HbwgW6F5gCA9Hv7IIuPhEhLBLT0oGVWFjrOgdAyKqPmaIuXjJjzyIg+A+0y3tAt1wgAkHp3B0TSU4V62tYe0HXyV0OExcfFHasR+fdhxD2JhLaeAeyr1EC9nqNgUdY1R10hBPbP/hJRl06i+bgf4Fq3qRoippKg2CVIz58/x7fffou9e/ciJiYGlpaWqF69Or799ls0aNBA3eGVKLqmNrBvOhD6VmUBAK8vH8TDX79FpcErAAikv3kJx6Ah0LdxRnp8DB7vXYKMNy/h3GWqegMvpiaPGYq7t29g3tLVsLVzwB+/b0H/z9piT9g52Dk4qju8Ykf25ikyn12AZGSrUC6Z2EPXxhPQNwMy3iLj0QmkXd8M/TpDIUns9QcAWXIMMl9eh2RgneOYtrU7dOzr/lugpVuEkRVPT6+fg0eL7rCp6AmRmYF/Nv2AvbMGo+viXdA1MFKoe3XvL6VjGejs2ISktGL3adS5c2dcvHgR69atw507d7B79274+/vj5cuX6g6txDGr4guzSvWgb10O+tblYB/YH1p6hkh+chMGtq5w6ToNZlV8oW/lCBPXmrAP7I+EO2cgZJnqDr3YSXn7Fof27cLYybPgU78hnF3dMGzsJJR3qYDN61erO7xiR2SmIf3OLuhUbAPoGCgc07GvBS3z8tAysICWiQN0nJsAaQkQKfFqirZ4EZlpSH94CDpOAYC2fs4Kkg4kXeN/N229og+ymGkzeSWqBHSAlVNFWLtUhf/Q75D4IhrP799QqPci8hau/LEO/l/OVFOkVJIUqwQpLi4OJ06cwLx58xAQEABnZ2fUrVsXEyZMQLt27fDgwQNIkoRLly4pPEaSJISFhQEAwsLCIEkSjhw5gjp16sDIyAh+fn64ffu2/DHTpk1DjRo18Msvv8DFxQXm5ubo1q0b3rx5AwBYv349rK2tkZqaqhBfhw4d0KtXr0K/DoVByDIRd+0YZOkpMCrnnmudzJQkaOkbQdLSLuLoir/MzAxkZmZCX1/xC8vAwBAX/glXU1TFV3rEn9CyrAhti5xdHO8TmWnIjLkCSd8Ckr5ZEUVXvKU//gtaZi7QNnXK9Xjm6ztIuboGqbc2I/1pOIQsvYgjLP7SkhMBAAYm5vKy9NS3OPLDeDQcOAlGlqWvO1cqhP80XbFKkExMTGBiYoKdO3fmSE6UNWnSJCxcuBDnzp2Djo4O+vfvr3A8IiICO3fuxJ49e7Bnzx4cP34cc+fOBQB06dIFmZmZ2L17t7x+bGws9u7dm+M8xd3bmPu4NucTXP2uFR7vXQLnrtNgYOOco15GcjxiT2yAda02aoiy+DM2MUWN2vUQsmQeYp9Fv3t//L4Fl87/jecxMeoOr1jJfH4dIukZdFwC8qyTEX0OKeHzkXpmAWRxEdD16MHEHEDm67sQb59Dx6F+rse1LStD17kZ9Cp2gI5tLWS+vo30h4eLOMriTchkOL12Luyr1IRV+Ury8vC182FfpQZcfALVGJ36ZN2sVpWbpitWCZKOjg7Wrl2LdevWwcLCAg0aNMDEiRNx5coVpc/13XffoUmTJnB3d8c333yD06dPIyUlRX5cJpNh7dq18PT0RKNGjdCrVy8cOXIEAGBoaIgePXogNDRUXn/Dhg0oX748/P39c32+1NRUJCQkKGzFgX4ZJ1QashIVByyDdZ22iNo1HynPHyrUyUxNQuSmSTAo4wy7Jr3VFGnxN2/paggh0KRWJVR3scKGNSFo06ELtLRKwSdFPonUBKRHHoJu5faQtPIe4qht4wm9GgOh59kLkoEV0m9vh5BlFGGkxY9Ie4P0Jyeg69wsz2unU8YD2mbloWVoDW2rKtAtHwRZ/H3IUtk9meXkT7PwKuoemo5aIC97cPYYnlz7G359v1FjZFTSFLtB2p07d0abNm1w4sQJnDlzBvv378f8+fPx008/5Zmc5Mbb21v+s4ODA4B3rUDly5cHALi4uMDU1FShTmxsrHx/0KBB8PHxwZMnT1C2bFmsXbsWffv2hZRH2jxnzhxMnz5dmZdaJLS0deWDtI0cK+Pt09t48fd2lPtkFAAgMzUZkRsnQFvfEM6fTYekXezeEsVGeZcK+GX7ASQnJyHxzRvY2tlj1JDeKOf84W6k0kSWGA2kJyHt0pr3SgUyEx4hM/oc9P2+gSRpQdIxgKRjABhaQde0LFL/XgjZy9vQtvFQW+zqJkt+DmS8Rdrt394rFchMeorMF1ehX/2LHIPYtYzs3tVKjQf0zVHanfzpOzy8cBztpq+DibW9vPzJtb+REBOF0L6+CvUPfT8K9tVqod30tUUcadHjGG3lFctvQwMDAzRr1gzNmjXDlClTMHDgQEydOhUnTpwA8G6aZpb09Nz733V1/53ZkZXUyGSyXI9n1Xn/eM2aNVG9enWsX78ezZs3x/Xr17F37948Y54wYQJGjx4t309ISICTU+5jCNRJCAGR+e6aZaYmIXLDN5B0dOHSbSa0dDjYMz+MjIxhZGSM+LjXOHX8CMZO5oDPLFrmLtCrMUihLP3eHkiG1tAp65vHLDUBQJT6FiQt03LQq9JNoSz90VFIBhbQsa2V67UTb18AACRdoxzHShMhBE6tmY3If46g3fRQmNmVUzhes8NAVGvaWaFs65iO8O07Hs61/YswUipJimWClJ27uzt27twJGxsbAEB0dDRq1qwJAAoDtlVt4MCBWLJkCZ48eYKgoKAPJjz6+vo5BvCqW/SRn2BasS70zG2RmZqMuGtHkfTgMmx7zv3/5OhryNJT4dxxAjJTk5GZmgwA0DEy53iQXJwMOwwhBFzdKuFh5H18P3MSXCtWRsfPSubA/cIg6ehD0lGc1g8tXUg6htAytoUs5TVkL25Ay6ICJF0jiNQ3yHh8GtDShbZlRfUEXUxI2nqQDLNN69fSgaRtAC1Da8hS4yF7fQdaZs6AtgFEyktkPDkJydgRWoalb9Dx+07+NAv3Tu5Di/E/QtfAGMmv3yWOekYm0NE3gJFlmVwHZpuUcciRTGksNiEprVglSC9fvkSXLl3Qv39/eHt7w9TUFOfOncP8+fPRvn17GBoaon79+pg7dy5cXV0RGxuLyZMnF1o8PXr0wNixY7F69WqsX7++0J6nsGQkxSFq5zxkJL6Clr4xDO1c4dpzLkzdaiPxwSUkP7kFALi9THHcUdURG6BnYZ/bKUu1NwnxWDxnGp5FP4G5hSWat26Pkd9MzdEaSXmTJB3IEqKQ8fQskPEW0DWGlll56Hn1gaRnrO7wijVJ0kLmm8fIeH4ZkGVA0jWBloUbdOzqqDs0tbtx8FcAwB/T+imU+/9vFqoEdFBDRMUPb1arvGKVIJmYmKBevXpYvHgxIiIikJ6eDicnJwwaNAgTJ04EAPz8888YMGAAateujSpVqmD+/Plo3rx5ocRjbm6Ozp07Y+/evejQoUOhPEdhcmo3Ns9jJi414P0tZ78oo1W7zmjVrvN/VyQF+l7/trBJ+qbQc+/2gdr0Pv1KHeU/S3qmCvv0ryFbrxXJY6h0kcT7A3ooh6ZNm8LDwwM//vijUo9LSEiAubk5PL7eBW19/mWsrF+/yH2aM31YzS5z1B1CyZUUp+4ISqw+k75UdwglUlpyIkL71Ed8fDzMzApnHbCs76JrkbEwVeFzvElIgKerbaHGrm7FqgWpOHn9+jXCwsIQFhaG5cuXqzscIiIiKkJMkPJQs2ZNvH79GvPmzUOVKlXUHQ4REVGBcYy28pgg5eHBgwfqDoGIiEglVL36NVfSJiIiIiqF2IJERESk8djJpiy2IBERERFlwxYkIiIiDccxSMpjgkRERKTh2MGmPHaxEREREWXDFiQiIiINxy425bEFiYiIiCgbtiARERFpOOn//1Pl+TQdW5CIiIiIsmGCREREpOmkQtjyadq0aZAkSWGrWrWqal5XIWIXGxERkYZT9zR/Dw8PHD58WL6vo1P804/iHyERERGVaDo6OrC3t1d3GEphFxsREZGGy5rmr8pNGXfv3oWjoyMqVKiAnj174tGjR4XzQlWILUhERERUIAkJCQr7+vr60NfXVyirV68e1q5diypVqiA6OhrTp09Ho0aNcO3aNZiamhZluEphCxIREZGGkwrhPwBwcnKCubm5fJszZ06O527VqhW6dOkCb29vtGjRAvv27UNcXBx+++23or4MSmELEhERkaYrpFHaUVFRMDMzkxdnbz3KjYWFBSpXrox79+6pMCDVYwsSERERFYiZmZnClp8EKTExEREREXBwcCiCCAuOCRIREZGGU+MySBg7diyOHz+OBw8e4PTp0+jYsSO0tbXRvXt3lby2wsIuNiIiIio0jx8/Rvfu3fHy5UvY2NigYcOGOHPmDGxsbNQd2gcxQSIiItJwBZma/1/ny68tW7ao7omLEBMkIiIijafam9WqdsR38cQxSERERETZsAWJiIhIw6mzi62kYgsSERERUTZMkIiIiIiyYRcbERGRhmMXm/LYgkRERESUDVuQiIiINJyk4mn+ql0yoHhiCxIRERFRNmxBIiIi0nAcg6Q8tiARERERZcMWJCIiIg0nQbU3BykFDUhMkIiIiDQeMySlsYuNiIiIKBu2IBEREWk4TvNXHluQiIiIiLJhCxIREZGG4zR/5TFBIiIi0nAco608drERERERZcMWJCIiIk3HJiSlsQWJiIiIKBu2IBEREWk4TvNXHhMkIiIiDcdZbMpjglRIhBAAgMzUZDVHUjIlvklQdwglkshIVXcIJVdmmrojKLHSkhPVHUKJlPY2CcC/3xeFKSFBtZ+pqj5fcSSJoviXKYUeP34MJycndYdBRETFXFRUFMqVK1co505JSYGrqyuePXum8nPb29sjMjISBgYGKj93ccAEqZDIZDI8ffoUpqamkIphW2RCQgKcnJwQFRUFMzMzdYdTYvC6FRyvXcHx2hVccb52Qgi8efMGjo6O0NIqvDlTKSkpSEtTfQupnp6exiZHALvYCo2Wllah/UWgSmZmZsXuQ6Mk4HUrOF67guO1K7jieu3Mzc0L/TkMDAw0OpEpLJzmT0RERJQNEyQiIiKibJgglVL6+vqYOnUq9PX11R1KicLrVnC8dgXHa1dwvHZUUBykTURERJQNW5CIiIiIsmGCRERERJQNEyQiIiKibJggERHRBz179qxIbodBVJwwQSIiojytXr0aderUwblz55gk5eLzzz/H2rVr1R0GFQImSESkEWQyWb7KSDm9e/eGiYkJBg8ejPPnzzNJes+LFy+gq6uLkSNHYuvWreoOh1SM0/yJCokQoljeh08TyWQy+b2sLl26hLS0NJiZmaFq1apqjqxkS0tLg56eHmQyGWrUqAEdHR2EhITAx8enUO8dVpJERUVh4cKFWLt2LVavXo0uXbqoOyRSEb7DSUFWvhwTE4PY2Fg1R1MyZF2ze/fu4dSpUzh9+jTevn0LSZLYglEEhBDyL+tJkyahS5cu6NevH+rVq4fRo0fjzp07ao6w5NLV1QUAPH36FPPmzcOlS5cwYcIEtiQByMjIAAA4OTmhQ4cOaN26NXr37o0//vhDzZGRqjBBIgWSJGH79u1o0aIFatSogdGjRyM8PFzdYRVbWa1E27dvxyeffIL+/ftjzJgx8PHxwdOnT/lXdhHIaqVbtGgRfvrpJ6xduxbXr19H//79sXLlSiQkJKg5wpJLkiTs2LEDlStXxsmTJ9GpUyfcvXsXgwYNKvVJko7Ou3u9T5o0Cd9++y1ev34NHR0ddO/end1tmkIQvefmzZuifPnyYsGCBeL7778XXl5eon379uLPP/9Ud2jFgkwmy1H2119/CRMTE7Fy5UqRkZEh9u3bJyRJEgsXLlRDhKVX165dxeLFi4UQQmzbtk1YWFiI5cuXCyGESEtLU2NkJVdsbKyoXLmymDNnjrzs2bNnolq1aqJ69erin3/+EZmZmWqMUL02bNggjI2NxcmTJ8WbN2/E33//Lfr37y9MTU3Ftm3b1B0efSQmSKWcTCZT+NK/c+eOGDJkiHw/PDxcNG7cWLRt25ZJkhDiwYMHQgghMjMz5ddt4cKF4osvvhBCCPHo0SPh5OQkhg4dKn8Mv5xVLyMjQ2H/9evXwtPTUxw7dkyEh4cLExMTsWLFCiGEEKmpqWLWrFkiPDxcHaGWaC9fvhSVK1cW27dvF0L8+15+8uSJcHBwEC1atBAnT57M9Q+H0mDq1KmiWbNmCmV37twRnTp1EsbGxmLfvn1qioxUge3/BEmScPToUXz99ddYsGAB0tLS5Mfq16+PuXPnIi4uDitXrizV/et79uyBq6srjhw5otB1FhkZiZSUFDx+/Bh+fn5o1aoVli5dCgDYvn07lixZgszMTHWFrXFkMhm0tbUBABs2bAAAWFhYICgoCF988QUCAgIQHByMIUOGAADevHmDw4cP49y5c2qLuaQyMzNDZmYmwsLCALwbk5SRkQE7Ozt4eXnh4MGDmDhxIlJTU9UbqJrY2dnhzp07ePz4sbysUqVK6Ny5M5KTk9GmTRscPnxYjRHSx2CCVMpJkoQ///wTQUFBuHDhAn777Tfs3LkTmzdvltfx9fXFggULcO/ePWzevBlJSUlqjFh9KlasiJ49e6J79+44evSofOyLj48PoqKiUK9ePbRo0QIrV64E8G4Q55EjRxAVFaWQdFLBvT9bbdq0aejduzcGDBgAAGjTpg2sra3h6emJdu3aAQBevnyJXr16IS0tDV9++aXa4i4JxP+PJ0pISIBMJoNMJoOOjg4mTZqErVu3YtGiRQDejb3R1taGp6cnTpw4gdDQUBgYGKgz9EKX12QLb29vWFhYIDQ0FNHR0fLycuXKoVu3bli1ahX8/f2LKEpSNR11B0BFLyMjQz7A8OHDh7h58yZCQkIwZMgQnDt3DvPmzcPKlSuhra2Nrl27AgDq1auHNWvWwNbWFsbGxuoMX22qVq2KqVOnQkdHB59++il+++03BAUFoUWLFggODkZKSgp69+4NAEhMTMScOXOwfft2HDt2DIaGhmqOvuR7PzkaMWIENm3ahObNm8u/2IOCgnD//n2sXbsWlStXRuXKlZGSkgIACA8Ph7a2NjIzM+WtT6RIkiT88ccfWLp0KeLi4tCrVy+0a9cOPXr0QGRkJObPn49bt26hbt26uHDhAjZs2IAxY8bA0dFR3aEXKvHeLMmsREhXVxfjxo1DgwYN0LlzZ2zatAlxcXHo2LEjHBwcMH/+fNjZ2WHAgAGQJEnhM5dKEDV38VER2rx5s8L+zZs3hbu7u3Bzc1MYX3T27FnRpUsX0ahRI7F169aiDrPYu3Pnjujbt6+wtLSUX7fHjx+LihUriho1aghXV1fRokUL4ejoKC5cuKDmaDXPyJEjhbm5uXj48KFYt26d8PPzUzh+7do1sXTpUvHdd9+J9evXy8crpaenqyPcEuPMmTPCyMhITJo0SXz22WfC29tb9O7dW0RERIi0tDSxadMm4eHhIWrWrCnq1KkjLl68qO6QC937Y6smTpwoTE1NRWBgoDAyMhKNGzcWjx8/FkIIsWDBAhEQECAkSRJVqlQR3t7e8vFapXV8liZgglRK3L17V5QpU0YEBgbKyy5evCgGDhwozMzMcsy4OnfunOjevbvw8vISO3bsKOJoi7+bN2/Kk6T9+/cLIYSIiYkRv/32m5g6dar49ddfRWRkpHqD1EA7d+4UZmZm4vLly0IIIVavXi2cnJxEcnLyBxOg7IO6SdH9+/fF9OnTxbx58+Rla9euFX5+fqJnz57i5s2bQoh3kxOSkpLEmzdv1BWqWkRHR4vWrVuLixcvitTUVPHw4UPh5uYmfHx8xKNHj4QQ7yYKhIeHi9OnTzMp1xBMkEqJ1NRUsXv3buHh4SGCgoLk5devXxdDhgwR5cuXF2vWrFF4zJkzZ0S/fv3kM7dKo6y//iIjI8Xt27fFlStX5MfeT5KyWpL416Jq5XY9s/5qF0KIY8eOicqVK4vk5GR5/fHjxxdZfCXRDz/8IH777Tf5/r1794SPj48oW7asmD9/vkLdrCSpT58+4ty5c0UdqlocPXpUYebpwoULRfXq1UXLli1FTEyMvDw6Olq4ubmJevXqiYiIiBznYVJe8jFBKkXS0tLEnj17RJUqVUTTpk3l5VevXhVDhw4VVapUET///LPCY1JSUoo6zGIj68t5586dwsvLS7i4uAh3d3cxcuRIeZ2sJMnW1pZTelXs/fV1Xr9+nWuifufOHWFnZycePnwohBCiTZs2wtzcnF9OuZDJZOLRo0eib9++4tatWwrHvvvuO1G2bFnRqlUreYtIlvXr1wsPDw8xePBgkZqaWpQhF7kZM2aI+vXrKyTmZ86cEeXLlxfW1tbi7t27Qoh/35vPnj0TlStXFhUrVhRRUVFqiZkKDxOkUiLrFz41NVX88ccfOZKkK1euiKFDhwpPT0/54nokxL59+4SJiYkIDg4Wd+7cEcHBwUKSJPm6R0IIcevWLdG5c2fh6uoqkpKS2IqkAu9fwxkzZoiGDRsKW1tb0alTJ7Fx40Z518WdO3eEhYWFuHDhgvj0009F1apV5X/9M0n6V26LOZ4+fVqsXbtWvr9gwQLh5eUlxowZkyNJ2rx5c6nrMr5x44a8ZfLChQvC1tZWtGvXTrx+/VoI8e979MmTJ6Jr1658v2kgJkilUHJycq5J0tWrV0WfPn2Ej4+PiIuLU2OERS+3L5Dnz5+Lzp07y8dnPX36VLi4uIjmzZsLY2NjMWDAAHndO3fuiKdPnxZZvKXFtGnThK2trdiyZYuIiIgQVatWFbVq1RK3b98WQgjx5s0b4ebmJiwsLBSSI479yCkqKko0bdpU/PPPPyItLU107dpV1KhRQ2zYsEFe57vvvhM1a9YUo0ePzpEkabqdO3fKW4G3b98uJEkSv/76q3j79q0Q4t3kFWtra9GxY0d5kpT9c4NJkmZhgqTBsv7COXfunFi9erX46aef5IMt80qSrl+/LqKjo9USr7pkfchFRUWJDRs2iNWrV4vHjx+L1NRUsWTJEnHnzh0RExMjPD09xZAhQ0RKSoqYPHmykCRJ9OzZU83RayaZTCaioqJEnTp1xO7du4UQ727pYmRkJFavXi2E+PffrUmTJqJatWrypIjJUe4OHTokHB0d5Qn/33//LT7//HPRsGFDsX79enm92bNnCx8fHzFkyBCF8V6aKutz0sPDQ9SrV09e3r17d2FhYSG2bt2qkCTZ2NiIzp07i5cvX6olXio6TJA0VNYv/e+//y4cHR1F7dq1RePGjUWZMmXEiRMnhBBCvH37Vvzxxx/Cw8ND1KlTR53hqk3Wl+y1a9dE9erVxeeff64wyDfr+A8//CCaNm0qnj17JoQQIiQkRNSpU0dUqFChVHyJqMOTJ0+Et7e3yMzMFLt27RImJiYiJCRECCFEUlKSWL9+vXj58qV4/fq1/N+JydGHjR07Vr5EghDv/njq1q1bjiRp8uTJonHjxgqDkjVV1nvnn3/+Eba2tuLbb7+VH+vdu7cwMTFRSJLOnTsnJEkSEyZMUEu8VHSYIGmI3Ma9hIWFiTJlyohVq1YJId799SNJkjA0NBR79+4VQrxLkn7//Xfh4+Mj/9AsLbKu2bVr14SlpaWYPHmyiI+Plx/ftWuX2LVrlxBCiBEjRojatWvLj40bN07MmTNHPkaBPk5u79/Xr18LFxcX0a9fP2Fubi5PjoR4928WEBAgDh06JC8rzTdNzS7rWrx580YkJSXJy5OTk4WPj49o2rSpfALGhQsX5EnS+91tL168KNqg1SwuLk589dVXws/PT+G+fX369MmRJN28eZPdaaUAEyQNkPVhGBsbK86ePSvOnj0rhHh3I8Wsv4YeP34sypcvL/r16yd69+4t9PX1xbFjx4QQ72aqlbZ1TbK8fPlSNG7cWAwbNkyhfO7cuUKSJPmXcFhYmDA1NRVt27YVXbt2Febm5uLGjRtqilqzvJ/YREdHi9evX4vY2FghxLuWOwsLC9G9e3chxLtE6u3bt6JNmzaiRYsW/JL6gMjISFGtWjUxfvx4+bpRmZmZYt26dcLDw0OEhobKE9OLFy+Kzz//XHh6eootW7aoM+wis3HjRjF+/HgRHR0tH7sWHh4u7O3tc7QO9e3bV1hYWIj169crzORji6VmY4JUwmV9uVy/fl00aNBAtGzZUnTs2FEI8a4p+PTp0yIhIUHUq1dPDB48WAghxMmTJ4UkSUKSJHHgwAG1xV4c3LhxQ7i5uYmjR4/Kr2VISIjQ1dUVwcHBolmzZqJNmzZiw4YNYseOHaJly5aiW7du8i8c+jjvtxzN/L/27j6uxvv/A/jrdKNWnYqkG6mQu2Sxb24nSdaaoUIhrC/ZRInlZkiTZSS01tz1K5vcFL5isyZMKqJEhIrcRG4qFUkl3b5/f/iea51mZt+N0837+Xh4PDrXuc7V+1yu0/U+n8/78/n4+9OwYcPI1NSUrK2t6cSJE1RWVkZeXl6koaFBrq6uNGvWLLK2tiYzMzPhpsYtRy8XGxtL6urqZGlpSUZGRhQWFkY1NTVUVVVFdnZ29P7770u1Lp07d47c3Nxa/Gi1+vp6evz4MRkbG5NIJCJvb28KCAgQCq+3bNlC8vLywhdIibFjx0rNIcdaPk6QmrGGXUSampq0bNkyys3N/d236rNnz5KFhYVQoJ2RkUHOzs60aNGiVt8KsnPnTpKXl5e6Ud+7d49OnjxJRC9G9tnY2NCgQYPo+vXrVFtb26rnhnpTli9fTlpaWvTjjz/SyZMnadiwYaSsrExPnjyhe/fuUVRUFA0fPpymTZtGy5cv54Ls11BaWkrOzs40ffp0iouLoy5dupC7uzsdO3aMCgsLSVtbm3x9faVe05qu7b1795JYLCYPDw/y8PCgXr160ZUrVygvL4+mTZtGI0aM+F3ZASfjrYucrNeCY/87kUiEx48fw93dHZ988gm+/vprGBoaQl5eXmr16UePHiEtLQ21tbUAgD179qC8vBx+fn7o1auXrMJvEoyNjaGgoICDBw8CeLEwpYGBASwtLVFfXw8zMzNMnDgRdXV1UFVVhby8PJSUlGQcdctSUFCA+Ph4REZGwt7eHqWlpbh8+TI2bNgADQ0NdOzYEZMmTUJcXBx27NgBf39/KCgooK6ujhcA/S/J5728vFzYpq6ujtWrVyM6Oho1NTVITExEhw4d4OfnB29vb3z66aeIjIxEYmKi8JrWdG07Ojpi9uzZAICFCxfC1tYWkyZNQkREBMRiMerq6hAfHw8Awt9OOTk5qb+trGXjBKmZKygoQH5+PsaPHy/1wZWsPk1EGDlyJBwcHPDuu+9iwIABCA4OxurVq6GioiKrsJsMY2NjaGhoICIiArm5uRCJRMJzknOYnZ0NY2NjiMViWYXZojS8Tp8+fQoAyMrKgpmZGWJjYzF58mSsWbMGc+bMwbNnzxAUFITc3FypaxoA5OXl337wTZScnBzu3r0LJycnLF26VNjetWtXBAYGIiQkBHV1dfDx8cG2bdtw584dREREICcnBzExMUIC0JLt378f4eHhuHfvHgBAUVER/fr1Q25uLp48eYLg4GAsXboUhYWFyMjIwMmTJ7FmzRo8e/ZMKhGXXIesFZBxCxb7m3bv3k0KCgpCF9HLmoArKiooJiaGDh48SEFBQXT9+vW3HWaTtn//fmrTpg1NmzaNMjMzhe2lpaW0aNEiatu2LWVkZMgwwpbJ19eXli9fTgUFBeTo6Eje3t4kFospNDRU2CcrK4vs7e3p2LFjMoy0ecjJyaFJkyaRiYkJWVhY0KlTp6isrIzy8vLI0dGRNm3aJLX/t99+S3Z2dkLXe0tWV1dHHh4eJBKJyNHRUWq03tSpU6VGqObm5tKRI0eoXbt21L17d+5Wa8U4QWrmTp8+TcrKyrR///4/3EdSbMxerra2lrZu3UoKCgrUs2dPmjFjBs2aNYtGjx5Nurq6dOHCBVmH2CI0vNH88ssvZGhoSGlpaURE5OnpSSKRiObOnSvsU1ZWRqNGjSJbW1u+Sf0JyfkpLy+n1NRUsrS0JAMDA/r000/p7t279OOPP5K2tvbvamoaFmm3BjExMeTs7ExisZimT59ODx48oMrKShoxYgQtWbJEat/CwkKhnpNHS7ZOIqL/tlezZunBgwd47733MGjQIISEhMDIyAjAi24ISXfRggULoKioiDVr1kh1ITFpZ8+eRWBgIG7dugWxWIyhQ4fCzc0NJiYmsg6tRdm+fTvu3r2L+vp6+Pn5CdsdHBxw8eJFDB06FO3bt0d6ejpKSkqQlpYGRUVF1NfXc/fGX7B27VocPnwYmZmZCA8Px9atW/HOO+9g586dUFNTk3V4b1XDv4d5eXk4ffo05s2bBz09PYwdOxb6+vq4ePEiZs6ciffeew91dXVCF27Dn1nrwglSC3DgwAG4uLjA2dkZS5YsgampKQDg2bNnWLVqFSIjI3Hs2DF0795dxpE2ffzH8M2RnNvu3bvj5s2bcHJywt69e6X2CQgIQEZGBqqrq9GrVy/4+vpCQUEBtbW1XJD9mhpew1evXsWOHTuwYcMGdO7cGffu3cOuXbswbtw4GUcpe0VFRfDx8UF6ejquX78OZWVleHh4wNfXV9ahsSaCE6QWoL6+HmFhYfD09ISJiQkGDx4MZWVlPHjwACkpKThy5Aj69esn6zCbhYbfNBv+zP43N2/eRElJCTp27Ah9fX1hu62tLS5cuIA9e/bA2tr6lUkpJ61/XeNrNyEhAevXr8elS5eQmJiILl26yDC6N+/PPruSa6qyshLnz5/H999/j4iICPTv3x9nz559i5GypowTpBYkNTUV69atw82bNyEWizFkyBC4ubmhW7dusg6NtUKSIfnV1dUoKirC3r17MWbMGOH5AQMG4MmTJ4iIiMDAgQMhJyfHSenf9KrzV1BQAEVFRWhpab3lqN6uhuegvLwcampqLz0vjbdFR0fD3t4eCgoKfB0yAJwgtTj8bZs1BaGhofDy8kJoaKgwtURsbCxycnKgqKgo7Pevf/0LFRUV+OGHH4Qkib2a5OZ9+fJlFBUVobi4GB999BHU1dVlHZrMNaxT27VrF86ePQsfHx/o6ur+YdLTuLaNu3OZBF8FLUzDDzp/C2KyEBERgdmzZ+PXX3+FjY0NAGDy5Mm4f/8+YmJioKioiE6dOsHc3BxpaWkYOHAgPvroIyQlJcHMzEzG0Tdtks/0gQMH4OHhgR49euDatWvYunUrXF1d8e9//1vWIcpMw0Tn8uXL2LVrF65evQpNTU14eXlBW1v7pX8TGyflnBwxCb4SWpiGH35OjtjbdufOHQQGBqJPnz5CcgS8GFGVmpqKvLw83L59G7169UJgYCCGDRuGs2fPYubMma1+VvfXIRKJkJqailmzZmHt2rWYMWMGLl26hH79+mH8+PGyDk+mJInO559/jpSUFHTq1Ant27fHd999h9raWsybN++VLUmMNcbt2Yyxf4y+vj7mz58PNTU1TJw4EQAwZcoU3Lt3DwkJCUhPT8fu3buRk5ODY8eOoaamBgAQHh4OeXl51NXVyTL8ZuHy5cuwsLDAjBkzcP36dYwbNw5ubm7w9PQEABQWFso4Qtk5dOgQduzYgc2bNyMyMhJpaWn47LPP8PPPPyMkJARFRUUQiUTgyhL2OrgFiTH2jyAitGnTBq6urlBUVMSWLVvQoUMHtG/fHmfOnIGmpiYAYPTo0ejVqxfu3bsnVY8E8PIhEg27iyR1hZKC4/z8fOjq6gIAbGxsMGrUKGzZsgXAiyk/7t+/j1mzZrWqddUkysrKoKGhAT09PaGrLDAwEJWVlfj2228hEong5eUFHR0dGUfKmgNuQWKM/SMk38zbtGkDFxcXzJ49G4aGhjAyMhImJiQilJaWorq6Gj169JBxxE2XnJwc7ty5g/j4eMjLy2P//v1Ys2YNAGDEiBHYv38/VFVVMWHCBISGhgrJ1PHjx3HmzJlWsbZaw1Ygyfp+8vLyqK6uRkVFBQCguroaAODr6ws1NTUcOXIE33//PZ49e/b2A2bNDidIjLF/TOMkydPTE48fP8bEiRNRU1MDkUgEFxcXVFVVYfHixbIOt8l6/vw5Vq9ejRkzZmDFihVwdnYWEsp+/fph7ty5EIvFwvxm+fn58PHxwb59+7BixQqoqqrKMvw3rr6+Xmq+MkmCOGnSJIjFYri5uaGyshJt2rQBADx69Ai2trawsLBAaGgoHj58KLPYWfPBw/wZY39b46HSkkLY6upqREZGYuvWrTA0NERJSQnu3r2LjIwMKCoq8rQUr5CZmYnPPvsMycnJWLx4MQICAoTzmpGRgdDQUISGhsLY2BhisRilpaX4z3/+06omhf32229x6tQpGBgYwMbGBmPGjEFWVhZGjRqFDh06YPHixdDQ0MCGDRugo6ODiIgIiMVi+Pv7Y/78+bIOnzVxnCAxxv6y8+fPw8LCAsCLm1Tv3r0xcuRIqX0aJklRUVH48ssvoaGhIaytxvPNvNrjx48xYcIEPH36FLW1tQgMDIStra3wfEVFBbKysnDu3DkYGRnB3NwcBgYGMoz4zWuYiPv7+yM4OBhjx47FzZs3UVhYiMWLF8PNzQ0PHjzA1KlTcf/+fVRXV8PIyAhHjx6FnJwc+vfvj1WrVmHs2LEyfjesqeMEiTH2l2RnZ2Ps2LGwtbWFoqIiQkJCkJWV9dK1/homScePH8eHH34IeXl5To5eQ01NDcrKynDnzh2sX78eV65cwYYNG6SSpNa6gG96ejqio6Px4YcfYujQobhx4wZCQ0MRFRWFFStW4LPPPgMA3Lp1CwoKCjA0NIRIJIKvry927tyJxMREYWFvxv4IJ0iMsb/k6dOniIyMhI+PD6qrq5GSkoLevXujpqbmd6PSgN9PWMrdai8nOU8PHjwQ6mcMDQ0BAElJSdi8eTMyMzOxbt062NraYtWqVaiuroaPj0+rGrH2yy+/YObMmVBTU0NsbCxMTEwAALdv38bmzZuxZ88eLF++HLNmzRJek5WVhYCAAMTGxuLYsWOtqhuS/e9a31cPxtj/jIigrq4uTLino6OD//u//wMAoaaoscaT8nFy9HuS5Oinn36Cg4MDhg8fjk8++QRLly4FAAwdOhRz5sxB37594ezsjFGjRsHPzw8ODg6tKjkCgHbt2sHW1hb3799Henq6sL1z586YM2cOXFxc4OHhgZ9//ll4TiwWY+jQoUhKSuLkiL02bkFijP2pxl05+fn5ePr0KeLi4rBlyxYMHTpUmItHgluK/prDhw/D2dkZq1evhpWVFWJiYuDr64s5c+Zg48aNAF4UbiclJeHKlSvw9PREz549ZRz1m/VHXYiXLl1CQEAA0tLSsG7dOtjb2wvP3bhxA8eOHYO7u7vU9cczaLO/ihMkxtgrNew6S0lJgYKCAnR0dNCpUyc8evQIO3bswPfffw8rKyvhRv7FF19g9OjRsLS0lGXozcbDhw8xZcoUjB07Fl5eXiguLsZ7770HExMTXLp0Cc7OzlIJaGu42TdMjn7++Wc8fvwY5eXl+OSTTyAWi5GZmYnAwECcO3cOAQEBLy265iSd/S3EGGMvMWXKFDp9+rTwePHixdS2bVsyNDSk9u3b06+//kpERCUlJfTNN9+QqakpWVlZkZ2dHXXs2JFqampkFXqzUF9fT0REOTk5RES0fv16unbtGuXn55OpqSm5u7vTkydPyN3dnUQiEbm6usowWtnx9vYmXV1dMjc3Jx0dHeratSsdOnSIiIguX75Mrq6u1KdPH9qzZ4+MI2UtDdcgMcZ+p7S0FA8fPsTHH3+MtLQ0ZGZmIjo6Gj///DN27doFJycnjBo1Cvv374empiZmzJiBr776CgYGBtDX18ft27ehoKDAa6u9gqTmaMyYMbh+/To+//xz9OjRA7t370bnzp3h7+8PDQ0NdO/eHebm5rhw4QLy8vJkHfZbFRUVhZ07d+Lo0aNITEzEvXv30Lt3b3z++edITExEnz594OHhgS5dukjVHDH2T+AuNsbYSz18+BDz58/HkSNH4Ovri7KyMqxYsQIAUFVVhaVLl2Ljxo2Iiop66UryPJT/5ajBaDU3NzeMGzdOGJYOANOnT8f169dx+vRpAMDChQvRtm1bzJ8/v1XMkN2w5mjVqlU4ceIEjh49CpFIJFxPNjY2KCsrQ2pqKgDg+vXrMDExaZVTHrA3h68mxpgUybpWOjo6CA4OxqhRo7Bw4ULk5OQAeHGDV1JSQkBAAObOnYtp06YhMjJS6hhExMnRHxCJRDh58iRWr14NOTk5fPTRRwB+O++jRo3CgwcPMGXKFEyfPh3h4eFwcnJq8ckRACHB2bRpEwCgsrISeXl5UFRUhIKCAiorKwEAAQEBuH37Ni5fvgwA6N69O+Tk5IRzyNg/gRMkxpgUyU2K/juMf926dZg+fTqio6ORnJwstd5aQEAAXFxchKH+Ei29gPjvys3NxbZt25CQkCAknpLzbmlpiQULFuDBgwcoKipCYmLiSyfhbEkaJjYhISGYO3cusrOzMW3aNJSUlGDhwoUAgHfeeQfAi8SpXbt2EIvFUsfhFiT2T+IuNsYYAOnujfXr1yMjIwObN2+GiooKCgsL4eXlhcOHDyMuLg79+/cXuopqa2shLy/PSdFfdODAAcyePRt2dnZYtmyZsBhtQ8+ePYOKiooMopONkydPIiEhAf/617/w8ccfo7KyEmFhYdi4cSNGjhwJHx8fPHnyBEuXLkV5eTmOHz/OSRF7Y7gNnDEmlRydP38excXF2LFjB3R0dPDVV1+hQ4cOCAkJARHhgw8+wPHjx2FhYSHVldZal734M5JE8urVqygsLERZWRlGjRqFcePGoaqqCosWLYKGhgbmzp2Lbt26AfhteHpLT44aXjPx8fFwcXFBTU0NDh48COBFi5GLiwvU1NTg5+eHqKgotG/fHtra2khMTBS61fi6Y2+ETMbOMcaapEWLFpGJiQnNnz+frK2tSUFBgdzd3amqqoqIiB4+fEiTJ08mkUhEV69elXG0TZ9kKH90dDSZmJhQ7969ydzcnIyNjSkzM5OIiHbv3k0dO3ak+fPnt6pzWllZKfx869YtKi0tpYULF5KamhotXbr0pfvHx8dTWloa1dXVERHxVBLsjeIEibFWSnLzloiLiyN1dXVKSkoiIqLnz5/Tvn37SFlZmebMmSPc0PLy8sjX15dvTq8pKSmJ1NXVKSwsjIiIUlNTSSQSUXBwsLDP7t27SVlZmb744guqrq6WVahvzd69e2nVqlVEROTl5UVdu3aluro6unv3Li1atIgMDQ1p3bp1wv6SBL2h2tratxYva524i42xVsjZ2RnLli1D3759hW2lpaVo37493n33XQCAkpISnJycUFFRgRkzZkBDQwN+fn7Q09PDypUrhfojHq32m9zcXBgZGUnNdH3lyhU4Oztj5syZuH37NiZMmAB3d3fMmzcPwIsuOBcXF8jLy+O999576YK/Lc39+/fh6+uLY8eO4cqVK0J3WadOneDp6Qk5OTmEh4dDTk4O3t7eaNOmze9mD+cZstmbxh23jLVCbdq0gampqdQ2AwMD3LlzB8nJyQBe3LgBYNCgQWjXrh0CAgKwbNkyAL+NUuPk6DeHDx9G586dERcXJ4z0A16sn1ZSUoLCwkJYWVnBzs5OGMYeGRmJr776CgAwceJEoQappXr+/DkAwNvbG5aWlkhKSsK0adNgZmYm7GNoaIjZs2fD0dER27Ztw8qVKwHwyEj29nGCxFgrIpnZeteuXWjTpg02bdqE+Ph41NTU4N1338WkSZOwatUqnDx5UrghtW3bFk5OToiIiMC3337LMxb/gQEDBsDV1RUODg44ceKEcP5Gjx6NJ0+ewNTUFB9++CFCQ0NBL8obkJKSgnv37qGiokLG0b95x44dw3fffSdMgNmnTx8sWLAA3333HdasWYPS0lIALxJzIyMjuLu7w9LSElevXhWSTcbeJv76x1grIumWkHRXhISEoKqqCpGRkRgyZAjc3d0RFBQEDw8PzJ49Gx07dsTmzZtRV1eHr776Cl27dkV2djbGjBkj43fS9LRv3x4bN26EiooKPv74Y/z6668YOnQoTE1NoaioCDU1NWFSyJKSEgQFBWHv3r1ISEho8ZNA/vDDD/D19cXYsWMxbNgwABAWNtbX14e3tzcAwNPTE+rq6gCA6upqbNy4UZhConEXG2NvnIxqnxhjMtSwQPv999+nzp07U3JyMhG9KCJesGABicViMjMzo2HDhgkF2f3796fQ0FCZxNyUSUZVnTp1isLCwkhRUZE0NDQoLi6OiIiys7NpyJAhZGZmRp06dSJra2vq1KkTXbhwQZZhvxVRUVGkoqJCe/fupdLS0pfus2HDBpKTkyM/Pz+6cOECjR49mgYOHCg833hAAWNvA08UyVgr0nDOmIYF1oMGDUJhYSF2796NwYMHA3ixFpuSkhI0NTUBAIsXL8a+ffuQkJAAY2NjWYTfpB08eBDTp0/H3LlzUVxcjIsXL+LSpUs4dOgQPvjgAzx48ADXrl1DSkoK+vTpA3NzcxgZGck67DeqqKgIzs7OmDBhAjw8PITt5eXlyMrKQk1NDd5//30AQHBwMPz9/aGrq4t33nkHycnJraJgnTVhss7QGGNv1vHjx8nf3194LGntIJKeR2bgwIHUtWtXSkpKkhpqfubMGZozZw516NChVbR4/C+ePn1KAwcOpGXLlgnbbt++TZ988gkpKyvTiRMnZBid7BQWFpKpqSkdPHhQ2LZ582aaMGECiUQi6tixIw0ZMkRoIbp48SKdOXNGGMLPU0kwWeIibcZasKqqKuzbtw/79u3DunXrAEBqUU8FBQXU1NQAAFJSUqCrqwtbW1tcvXpVOIaJiQn69u2LM2fOoF+/fm//TTQDNTU1KCgokGoRMjIywooVK9CzZ084OzvjxIkTMoxQdp4+fYpffvkFJ06cwIQJE7BlyxZoa2vj6NGjCA4ORkFBAfz9/QEAffv2xeDBgyEvL4+6ujoeJclkirvYGGvh8vLyEBgYiJSUFDg6OuKLL74AIN3d1vBnLy8vfPPNN5CXlxcKY4kLZP/UxIkTUVZWhqioKGhoaAjbXV1dsWfPHrRt2xY5OTktfvmQxuLi4jB+/HhoaWlBLBYjKCgI5ubm0NLSQklJCUaMGAF7e3v4+fnJOlTGpHALEmMtnL6+PpYsWYL+/fvj4MGDWLt2LQDplqSioiJMmjQJMTExCAkJEb7BS5IiTo5+I/lOWVNTg8rKSmG7vb09ioqKEBQUhLKyMmG7qqoqIiIikJGR0eqSIwCwsbHBjRs3cPz4caSnp2PEiBHQ0tISnheLxdDX15dhhIy9HLcgMdZKFBQU4Ouvv8a5c+fg4OCAJUuWAADy8/Ph5OSEwsJCZGVlcbfGK0ha0g4fPozw8HDk5OSgf//+cHZ2xgcffIDVq1fjwIEDUFdXx4cffojMzEzExsYiOTkZJiYmsg6/SSkqKsL06dNRXFyM06dP88zYrMnhBImxVqRhkjR+/HjMmDEDTk5OePjwIdLT06GoqCisJM9eLiYmBuPHj8fs2bOhpaWFQ4cOQV5eHjNmzMBnn32G6OhoHDp0CJcvX0aHDh2wdu1aqSVdWrvi4mKEh4cjKSkJhYWFOH36NF93rEniBImxVqagoACrV69Gamoqrl27Bn19fVy6dAmKioq8tloDDeuuJH8mnz59inHjxmH48OHw9fUF8KIlZOXKlTh//jwCAwOFiRDLy8uhqKgIJSUl2byBJio9PR2+vr7o2rUr1q9fDwUFBb7uWJPECRJjrVBBQQG++OILFBUV4aeffuLkqBFJ0XpxcTEUFBSEuaDq6uowcOBAODg4YPny5cJ+jx49grW1NWxsbPDNN9/INvhm4MmTJ9DQ0IBIJOKWI9ZkcZE2Y62Qrq4ugoODERMTw8nRS8jJyeHmzZsYMGAAlixZguLiYgBARUUFVFVVkZOTI+xbX18PLS0t2NjY4MqVK8J6d+yPaWpqCqMjOTliTRUnSIy1Um3bthVGsnFyJK2+vh47d+7EnTt3cPPmTfj7++Phw4dQV1fHkiVLsH37dmzYsAFycnLC9Ah5eXkwNDTkEX9/AZ8r1pRxFxtjjL1Eeno6rK2tYWFhAXl5efTs2RNLly6Fjo4ONm7cCC8vL0yaNAl6enqoqKjA7t27kZycDDMzM1mHzhj7B3CCxBhr9RoXZNfX10NeXh5ffvklnj17BhUVFRw+fBiWlpZYvnw5tLS0cPz4cYSEhKCiogKamprw8/NDnz59ZPxOGGP/FE6QGGOtmqTQ+vHjx6itrUWHDh2EhGnbtm0ICwvD8ePHsW3bNuzcuROWlpZYsmQJdHR0hOTp+fPnUFZWlvVbYYz9g7gGiTHWqsnJyeHGjRsYMGAARowYgUOHDuH69esAADc3N6ioqMDf3x/z5s2Dg4MDzpw5g3Xr1qGgoECYGZuH8jPW8nBlJmOsVauvr8f27dtRUFAAsVgMPz8/mJiYoH379li7di2mTp2KpKQkVFdXY/ny5RCJRNixYweUlJTg7+8POTk5LjZmrAXiBIkx1qrJycnB09MTFRUVyM3NRbt27TB58mQsXboUU6dORUVFBU6cOAFLS0tMnz4dPj4+UFJSwoQJE4QRbIyxloc/3YyxVk9PTw+LFy9Gx44dce3aNdy8eRPnzp3DrFmzhGVCxGKxsP/ChQthbGwsm2AZY28FF2kzxth/5efnY/Xq1UhOTsbUqVMxf/58AEBOTg66dOki2+AYY28VJ0iMMdaAZEHf1NRU2NvbY9myZQDAS2Iw1spwgsQYY41IkqSLFy/CxsYGK1eulHVIjLG3jGuQGGOsEV1dXfj4+KBbt244c+YMHj16JOuQGGNvGbcgMcbYH3j48CEAQEdHR8aRMMbeNk6QGGOMMcYa4S42xhhjjLFGOEFijDHGGGuEEyTGGGOMsUY4QWKMMcYYa4QTJMYYY4yxRjhBYowxxhhrhBMkxhhjjLFGOEFirIVLSEiASCTCkydP/tZx/v3vf8PBweEfiak5EIlE+PHHH2UdBmNMRjhBYqyZ2Lp1K8RiMWpra4Vt5eXlUFRUxPDhw6X2lSRFt27dwpAhQ5Cfnw8NDY03HmNYWBjMzc2hpqYGTU1N9OvXD2vWrBGeb4pJlp+fH/r27SvrMBhjTYyCrANgjL0ea2trlJeX4/z58xg0aBAA4NSpU9DV1cXZs2fx/PlzKCsrAwDi4+NhaGiIrl27Anixttib9v3332P+/PkICQmBlZUVqqqqcPnyZWRkZPzlY9XU1EBRUfENRMkYY6+HW5AYayZ69OgBPT09JCQkCNsSEhJgb2+Pzp07IyUlRWq7tbW18HPDLrbt27dDU1MTR48eRa9evaCmpgY7Ozvk5+cLr6+rq4O3tzc0NTWhpaWFxYsX489WJTp06BCcnZ3h5uYGExMT9O7dG5MnT8bXX38N4EVLTUREBH766SeIRCKIRCIkJCTgzp07EIlE2Lt3L6ysrKCsrIzdu3cDAMLDw9GrVy8oKyujZ8+e2Lx5s/D7JK87cOAArK2toaKiAnNzcyQnJ0vFFRYWhk6dOkFFRQWOjo4ICgqCpqamcC5WrlyJS5cuCTFt375deG1xcTEcHR2hoqKCbt264dChQ6/3n8UYa/6IMdZsuLi4kK2trfC4f//+9J///Ifc3d3pyy+/JCKiZ8+ekZKSEm3fvp2IiOLj4wkAlZSUEBHRDz/8QIqKijRy5Eg6d+4cpaWlUa9evcjFxUU47tq1a6lt27YUHR1NWVlZ5ObmRmKxmOzt7f8wtlmzZlHPnj3pzp07L32+rKyMnJ2dyc7OjvLz8yk/P5+qqqro9u3bBICMjY0pOjqacnJyKC8vj3bt2kV6enrCtujoaGrXrp3wviSv69mzJ8XExFB2djZNmDCBjIyMqKamhoiIkpKSSE5OjtatW0fZ2dm0adMmateuHWloaAjnasGCBdS7d28hpmfPnhEREQAyMDCgyMhIunHjBnl5eZGamho9evTor//HMcaaHU6QGGtGwsLCSFVVlWpqaujp06ekoKBAhYWFFBkZScOGDSMiori4OAJAubm5RPTyBAkA3bx5Uzjupk2bSEdHR3isp6dHgYGBwuOamhoyMDB4ZYKUl5dHgwYNIgDUvXt3cnV1pb1791JdXZ2wj6ur6++OIUl0goODpbZ37dqVIiMjpbb5+/vT4MGDpV4XHh4uPJ+ZmUkA6OrVq0RENHHiRPr444+ljjFlyhQhQSIiWrFiBZmbm//u/QCg5cuXC4/Ly8sJAMXGxv7hOWCMtRzcxcZYMzJ8+HBUVFTg3LlzOHXqFLp37w5tbW1YWVkJdUgJCQno0qULDA0N//A4KioqQn0SAOjp6aGwsBAAUFpaivz8fAwcOFB4XkFBARYWFq+MTU9PD8nJybhy5QrmzZuH2tpauLq6ws7ODvX19X/63hoev6KiArdu3YKbmxvU1NSEf6tWrcKtW7ekXvfuu+9KxQBAeC/Z2dkYMGCA1P6NH79Kw2OrqqpCXV1dODZjrGXjIm3GmhETExMYGBggPj4eJSUlsLKyAgDo6+ujU6dOOHPmDOLj4zFixIhXHqdxAbRIJPrTGqPXZWZmBjMzM8yZMwfu7u6wtLREYmKiUBP1R1RVVYWfy8vLAbyoH2qYqAGAvLy81OOG70UkEgHAayVkr+Nl5+mfOjZjrGnjFiTGmhlra2skJCQgISFBanj/sGHDEBsbi9TU1D9NRl5FQ0MDenp6OHv2rLCttrYWaWlpf/lYpqamAF60CAFAmzZtUFdX96ev09HRgb6+PnJycmBiYiL1r3Pnzq/9+3v06IFz585JbWv8+HVjYoy1LtyCxFgzY21tDQ8PD9TU1AgtSABgZWUFT09PVFdX/60ECQDmzZuHgIAAdOvWDT179kRQUNCfTjQ5e/Zs6OvrY8SIETAwMEB+fj5WrVoFbW1tDB48GABgbGyMo0ePIjs7G1paWq+cm2nlypXw8vKChoYG7OzsUFVVhfPnz6OkpATe3t6v9T7mzp2LYcOGISgoCGPGjMGJEycQGxsrtDRJYrp9+zbS09NhYGAAsVgMJSWl1zo+Y6zl4hYkxpoZa2trVFZWwsTEBDo6OsJ2KysrlJWVCdMB/B0LFizAtGnT4OrqisGDB0MsFsPR0fGVrxk5ciRSUlLg5OSE7t27Y/z48VBWVkZcXBy0tLQAAJ9++il69OgBCwsLaGtr4/Tp0394vJkzZyI8PBw//PAD+vTpAysrK2zfvv0vtSC9//772Lp1K4KCgmBubo4jR47g888/F+aLAoDx48fDzs4O1tbW0NbWRlRU1GsfnzHWcononyo8YIyxZuDTTz/FtWvXcOrUKVmHwhhrwriLjTHWoq1fvx4ffPABVFVVERsbi4iICKkJJxlj7GW4BYkx1qI5OzsjISEBZWVl6NKlC+bOnQt3d3dZh8UYa+I4QWKMMcYYa4SLtBljjDHGGuEEiTHGGGOsEU6QGGOMMcYa4QSJMcYYY6wRTpAYY4wxxhrhBIkxxhhjrBFOkBhjjDHGGuEEiTHGGGOsEU6QGGOMMcYa+X/elTNCAC4GfAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.title('Heatmap: Days by Weather Condition and Wind Strength')\n",
    "plt.imshow(contingency_table, cmap='Blues', aspect='auto')\n",
    "plt.colorbar(label='Count')\n",
    "plt.xticks(range(len(contingency_table.columns)), contingency_table.columns, rotation=45)\n",
    "plt.yticks(range(len(contingency_table.index)), contingency_table.index)\n",
    "plt.xlabel('Wind Strength')\n",
    "plt.ylabel('Weather Condition')\n",
    "\n",
    "# Add count labels to cells\n",
    "for i in range(len(contingency_table.index)):\n",
    "    for j in range(len(contingency_table.columns)):\n",
    "        plt.text(j, i, contingency_table.iloc[i, j], ha='center', va='center', color='black')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a2507a",
   "metadata": {},
   "source": [
    "Based on the heatmap, there appear to be some patterns - certain combinations occur more or less frequently than others.\n",
    "\n",
    "### Hypothesis\n",
    "\n",
    "- **Null hypothesis**: Weather condition and wind strength are independent\n",
    "- **Alternative hypothesis**: Weather condition and wind strength are associated\n",
    "\n",
    "### Chi-square test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e8a6e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Chi-square Test Results:\n",
      " - Chi-square statistic: 10.7934\n",
      " - p-value: 0.546705\n",
      " - Degrees of freedom: 12\n"
     ]
    }
   ],
   "source": [
    "chi2_stat, p_value, dof, expected_freq = stats.chi2_contingency(contingency_table)\n",
    "\n",
    "print(f\"\\nChi-square Test Results:\")\n",
    "print(f\" - Chi-square statistic: {chi2_stat:.4f}\")\n",
    "print(f\" - p-value: {p_value:.6f}\")\n",
    "print(f\" - Degrees of freedom: {dof}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3870ffa9",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "With α = 0.05, accept the null hypothesis and conclude that weather condition and wind strength are independent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a525fe",
   "metadata": {},
   "source": [
    "## Exercise 3: Testing for Relationship Between Humidity and Pressure\n",
    "\n",
    "**Objective**: Determine if there is a significant linear relationship between humidity (%) and atmospheric pressure (hPa).\n",
    "\n",
    "**Background**: \n",
    "When working with two continuous variables, we can test whether they are correlated. Correlation measures the strength and direction of a linear relationship. In meteorology, understanding relationships between variables like humidity and pressure can help improve weather predictions.\n",
    "\n",
    "**Tasks**:\n",
    "\n",
    "1. **Explore the data**:\n",
    "   - Check for any missing values in `humidity_percent` and `pressure_hpa`\n",
    "   - Calculate basic descriptive statistics (mean, std, min, max) for both variables\n",
    "   - Create a scatter plot of humidity vs. pressure\n",
    "   - Based on the visualization, do you think there's a relationship? If so, is it positive or negative?\n",
    "\n",
    "2. **Choose an appropriate correlation test**:\n",
    "   - Since we have two continuous variables, we can use Pearson or Spearman correlation\n",
    "   - Pearson correlation measures linear relationships and assumes normality\n",
    "   - Spearman correlation measures monotonic relationships and is more robust\n",
    "   - Check the distributions of both variables (histograms or Q-Q plots) to help decide\n",
    "   - State your null and alternative hypotheses\n",
    "\n",
    "3. **Perform the correlation test**:\n",
    "   - Calculate both Pearson and Spearman correlation coefficients\n",
    "   - Use `scipy.stats.pearsonr()` and `scipy.stats.spearmanr()`\n",
    "   - Report the correlation coefficient (r) and p-value for each test\n",
    "   - Using α = 0.05, determine whether the correlation is statistically significant\n",
    "\n",
    "4. **Interpret the correlation coefficient**:\n",
    "   - What is the direction of the relationship (positive or negative)?\n",
    "   - What is the strength of the relationship? (Hint: |r| < 0.3 weak, 0.3-0.7 moderate, > 0.7 strong)\n",
    "   - Calculate the coefficient of determination (r²) - what percentage of variance in one variable is explained by the other?\n",
    "\n",
    "5. **Draw conclusions**:\n",
    "   - Is there a statistically significant relationship between humidity and pressure?\n",
    "   - Is the relationship strong enough to be practically useful?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c19684",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7af788aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values:\n",
      "Humidity: 0\n",
      "Pressure: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(\"Missing values:\")\n",
    "print(f\"Humidity: {df['humidity_percent'].isna().sum()}\")\n",
    "print(f\"Pressure: {df['pressure_hpa'].isna().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d9120bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Humidity statistics:\n",
      "count    365.000000\n",
      "mean      66.182740\n",
      "std       18.232318\n",
      "min       23.400000\n",
      "25%       53.900000\n",
      "50%       66.800000\n",
      "75%       79.400000\n",
      "max      100.000000\n",
      "Name: humidity_percent, dtype: float64\n",
      "\n",
      "Pressure statistics:\n",
      "count     365.000000\n",
      "mean     1012.346301\n",
      "std         7.437610\n",
      "min       983.700000\n",
      "25%      1007.500000\n",
      "50%      1013.200000\n",
      "75%      1017.600000\n",
      "max      1039.000000\n",
      "Name: pressure_hpa, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "print(\"\\nHumidity statistics:\")\n",
    "print(df['humidity_percent'].describe())\n",
    "print(\"\\nPressure statistics:\")\n",
    "print(df['pressure_hpa'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f7102bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "plt.scatter(df['humidity_percent'], df['pressure_hpa'], color='black')\n",
    "plt.xlabel('Humidity (%)')\n",
    "plt.ylabel('Pressure (hPa)')\n",
    "plt.title('Humidity vs Atmospheric Pressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f493fd50",
   "metadata": {},
   "source": [
    "Based on the scatter plot, there may be a slight negative relationship between humidity and pressure.\n",
    "\n",
    "### Test selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0686c5cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Check distributions\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,4))\n",
    "\n",
    "ax1.hist(df['humidity_percent'], bins=30, edgecolor='black')\n",
    "ax1.set_xlabel('Humidity (%)')\n",
    "ax1.set_ylabel('Frequency')\n",
    "ax1.set_title('Distribution of Humidity')\n",
    "\n",
    "ax2.hist(df['pressure_hpa'], bins=30, edgecolor='black')\n",
    "ax2.set_xlabel('Pressure (hPa)')\n",
    "ax2.set_ylabel('Frequency')\n",
    "ax2.set_title('Distribution of Pressure')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cfa4041",
   "metadata": {},
   "source": [
    "**Hypotheses**:\n",
    " - Null hypothesis: There is no correlation between humidity and pressure (r = 0)\n",
    " - Alternative hypothesis: There is a correlation between humidity and pressure (r ≠ 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc82fc4a",
   "metadata": {},
   "source": [
    "### Correlation tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5094ead0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation:\n",
      "  - r = -0.4710\n",
      "  - p-value = 0.000000\n",
      "\n",
      "Spearman Correlation:\n",
      "  - r = -0.5033\n",
      "  - p-value = 0.000000\n"
     ]
    }
   ],
   "source": [
    "pearson_r, pearson_p = stats.pearsonr(df['humidity_percent'], df['pressure_hpa'])\n",
    "spearman_r, spearman_p = stats.spearmanr(df['humidity_percent'], df['pressure_hpa'])\n",
    "\n",
    "print(\"Pearson Correlation:\")\n",
    "print(f\"  - r = {pearson_r:.4f}\")\n",
    "print(f\"  - p-value = {pearson_p:.6f}\")\n",
    "\n",
    "print(\"\\nSpearman Correlation:\")\n",
    "print(f\"  - r = {spearman_r:.4f}\")\n",
    "print(f\"  - p-value = {spearman_p:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95e5fc6",
   "metadata": {},
   "source": [
    "### Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "719938c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coefficient of determination (r²): 0.2218\n"
     ]
    }
   ],
   "source": [
    "r_squared = pearson_r ** 2\n",
    "print(f\"\\nCoefficient of determination (r²): {r_squared:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9580f9",
   "metadata": {},
   "source": [
    "With an alpha level of 0.05, we reject the null hypothesis for both Pearson and Spearman tests, indicating a significant correlation between humidity and pressure. The Pearson correlation coefficient is negative, suggesting a negative relationship, and its absolute value indicates a moderate strength. The coefficient of determination (r²) shows that approximately 22% of the variance in pressure can be explained by humidity.\n",
    "\n",
    "**Conclusion**\n",
    "\n",
    "Yes, there is a relationship between humidity and pressure. Since almost one quarter of the variation in pressure can be explained by humidity, this relationship is strong enough to be practically useful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d1ccf4",
   "metadata": {},
   "source": [
    "### Extra: scatter plot with regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e4916ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize with regression line\n",
    "plt.scatter(df['humidity_percent'], df['pressure_hpa'], color='black', label='Data')\n",
    "\n",
    "# Fit line\n",
    "coeffs = np.polyfit(df['humidity_percent'], df['pressure_hpa'], 1)\n",
    "poly = np.poly1d(coeffs)\n",
    "x_line = np.linspace(df['humidity_percent'].min(), df['humidity_percent'].max(), 100)\n",
    "plt.plot(x_line, poly(x_line), 'r-', linewidth=2, label='Regression line')\n",
    "\n",
    "plt.xlabel('Humidity (%)')\n",
    "plt.ylabel('Pressure (hPa)')\n",
    "plt.title(f'Humidity vs Pressure\\nr = {pearson_r:.4f}, p = {pearson_p:.6f}')\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}