diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 000000000..ec0ccb4fe Binary files /dev/null and b/.DS_Store differ diff --git a/.idea/DataScienceGuidedCapstone.iml b/.idea/DataScienceGuidedCapstone.iml new file mode 100644 index 000000000..88012e042 --- /dev/null +++ b/.idea/DataScienceGuidedCapstone.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 000000000..105ce2da2 --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 000000000..d6f60f111 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,6 @@ + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 000000000..0d0d51796 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 000000000..35eb1ddfb --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 000000000..27c4a1c33 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,168 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + { + "associatedIndex": 4, + "fromUser": false +} + + + + { + "keyToString": { + "ModuleVcsDetector.initialDetectionPerformed": "true", + "RunOnceActivity.ShowReadmeOnStart": "true", + "RunOnceActivity.TerminalTabsStorage.copyFrom.TerminalArrangementManager.252": "true", + "RunOnceActivity.git.unshallow": "true", + "RunOnceActivity.typescript.service.memoryLimit.init": "true", + "codeWithMe.voiceChat.enabledByDefault": "false", + "com.intellij.ml.llm.matterhorn.ej.ui.settings.DefaultModelSelectionForGA.v1": "true", + "git-widget-placeholder": "master", + "junie.onboarding.icon.badge.shown": "true", + "node.js.detected.package.eslint": "true", + "node.js.detected.package.tslint": "true", + "node.js.selected.package.eslint": "(autodetect)", + "node.js.selected.package.tslint": "(autodetect)", + "nodejs_package_manager_path": "npm", + "settings.editor.selected.configurable": "com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable", + "to.speed.mode.migration.done": "true", + "vue.rearranger.settings.migration": "true" + } +} + + + + + + + + + + + + + + + + 1779231074418 + + + + + + + + + + + + + file://$PROJECT_DIR$/Notebooks/03_exploratory_data_analysis.ipynb + 43 + + + + + \ No newline at end of file diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb index a52eb6c24..fd81ea132 100644 --- a/Notebooks/02_data_wrangling.ipynb +++ b/Notebooks/02_data_wrangling.ipynb @@ -120,19 +120,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:07.319713Z", + "start_time": "2026-06-09T23:20:07.292275Z" + } + }, "source": [ "#Code task 1#\n", "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n", - "import ___ as pd\n", - "import ___ as plt\n", - "import ___ as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "import os\n", "\n", "from library.sb_utils import save_file\n" - ] + ], + "outputs": [], + "execution_count": 142 }, { "cell_type": "markdown", @@ -142,8 +147,23 @@ ] }, { - "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:07.375422Z", + "start_time": "2026-06-09T23:20:07.346529Z" + } + }, + "cell_type": "code", + "source": [ + "# the supplied CSV data file is the raw_data directory\n", + "ski_data = pd.read_csv('/Users/ayanrobinson/Documents/GitHub/DataScienceGuidedCapstone/raw_data/ski_resort_data.csv')" + ], + "outputs": [], + "execution_count": 143 + }, + { "metadata": {}, + "cell_type": "markdown", "source": [ "There are some fundamental questions to resolve in this notebook before you move on.\n", "\n", @@ -154,39 +174,106 @@ ] }, { - "cell_type": "markdown", "metadata": {}, - "source": [ - "## 2.5 Load The Ski Resort Data" - ] + "cell_type": "markdown", + "source": "## 2.5 Load The Ski Resort Data" }, { - "cell_type": "code", - "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [ - "# the supplied CSV data file is the raw_data directory\n", - "ski_data = pd.read_csv('../raw_data/ski_resort_data.csv')" - ] - }, - { "cell_type": "markdown", - "metadata": {}, - "source": [ - "Good first steps in auditing the data are the info method and displaying the first few records with head." - ] + "source": "Good first steps in auditing the data are the info method and displaying the first few records with head." }, { + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:07.514549Z", + "start_time": "2026-06-09T23:20:07.400666Z" + } + }, "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "#Code task 2#\n", "#Call the info method on ski_data to see a summary of the data\n", - "ski_data.___" - ] + "ski_data.head" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 144 }, { "cell_type": "markdown", @@ -204,214 +291,85 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": { - "scrolled": true + "scrolled": true, + "ExecuteTime": { + "end_time": "2026-06-09T23:20:07.639475Z", + "start_time": "2026-06-09T23:20:07.519285Z" + } }, - "outputs": [], "source": [ "#Code task 3#\n", "#Call the head method on ski_data to print the first several rows of the data\n", - "ski_data.___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2.6 Explore The Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.1 Find Your Resort Of Interest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your resort of interest is called Big Mountain Resort. Check it's in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 4#\n", - "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n", - "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n", - "#transpose method, but you can access this conveniently with the `T` property.\n", - "ski_data[ski_data.Name == ___].___" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's good that your resort doesn't appear to have any missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.2 Number Of Missing Values By Column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Count the number of missing values in each column and sort them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 5#\n", - "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", - "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", - "#Order them (increasing or decreasing) using sort_values\n", - "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", - "missing = ___([ski_data.___.___, 100 * ski_data.___.___], axis=1)\n", - "missing.columns=[___, ___]\n", - "missing.___(by=___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.6.3 Categorical Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 6#\n", - "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", - "ski_data.___(___)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", "\n", - "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", - "* Is `Region` always the same as `state`?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.3.1 Unique Resort Names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 7#\n", - "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", - "ski_data['Name'].___.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have a duplicated resort name: Crystal Mountain." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 8#\n", - "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", - "(ski_data[___] + ', ' + ski_data[___]).___.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 9#\n", - "#Concatenate 'Name' and 'state' and count the values again (as above)\n", - "(ski_data[___] + ', ' + ski_data[___]).___.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A: 1** Your answer here" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "ski_data.head(10)" + ], "outputs": [ { "data": { + "text/plain": [ + " Name Region state \\\n", + "0 Alyeska Resort Alaska Alaska \n", + "1 Eaglecrest Ski Area Alaska Alaska \n", + "2 Hilltop Ski Area Alaska Alaska \n", + "3 Arizona Snowbowl Arizona Arizona \n", + "4 Sunrise Park Resort Arizona Arizona \n", + "5 Yosemite Ski & Snowboard Area Northern California California \n", + "6 Bear Mountain Sierra Nevada California \n", + "7 Bear Valley Sierra Nevada California \n", + "8 Boreal Mountain Resort Sierra Nevada California \n", + "9 Dodge Ridge Sierra Nevada California \n", + "\n", + " summit_elev vertical_drop base_elev trams fastEight fastSixes \\\n", + "0 3939 2500 250 1 0.0 0 \n", + "1 2600 1540 1200 0 0.0 0 \n", + "2 2090 294 1796 0 0.0 0 \n", + "3 11500 2300 9200 0 0.0 1 \n", + "4 11100 1800 9200 0 NaN 0 \n", + "5 7800 600 7200 0 0.0 0 \n", + "6 8805 1665 7140 0 0.0 0 \n", + "7 8500 1900 6600 0 0.0 1 \n", + "8 7700 500 7200 0 0.0 0 \n", + "9 8200 1600 6600 0 0.0 0 \n", + "\n", + " fastQuads ... LongestRun_mi SkiableTerrain_ac Snow Making_ac \\\n", + "0 2 ... 1.0 1610.0 113.0 \n", + "1 0 ... 2.0 640.0 60.0 \n", + "2 0 ... 1.0 30.0 30.0 \n", + "3 0 ... 2.0 777.0 104.0 \n", + "4 1 ... 1.2 800.0 80.0 \n", + "5 0 ... 0.4 88.0 NaN \n", + "6 2 ... 1.5 198.0 198.0 \n", + "7 1 ... 1.2 1680.0 100.0 \n", + "8 1 ... 1.0 380.0 200.0 \n", + "9 0 ... 2.0 862.0 NaN \n", + "\n", + " daysOpenLastYear yearsOpen averageSnowfall AdultWeekday AdultWeekend \\\n", + "0 150.0 60.0 669.0 65.0 85.0 \n", + "1 45.0 44.0 350.0 47.0 53.0 \n", + "2 150.0 36.0 69.0 30.0 34.0 \n", + "3 122.0 81.0 260.0 89.0 89.0 \n", + "4 115.0 49.0 250.0 74.0 78.0 \n", + "5 110.0 84.0 300.0 47.0 47.0 \n", + "6 122.0 76.0 100.0 NaN NaN \n", + "7 165.0 52.0 359.0 NaN NaN \n", + "8 150.0 54.0 400.0 49.0 NaN \n", + "9 NaN 69.0 350.0 78.0 78.0 \n", + "\n", + " projectedDaysOpen NightSkiing_ac \n", + "0 150.0 550.0 \n", + "1 90.0 NaN \n", + "2 152.0 30.0 \n", + "3 122.0 NaN \n", + "4 104.0 80.0 \n", + "5 107.0 NaN \n", + "6 130.0 NaN \n", + "7 151.0 NaN \n", + "8 150.0 200.0 \n", + "9 140.0 NaN \n", + "\n", + "[10 rows x 27 columns]" + ], "text/html": [ "
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" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 146 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's good that your resort doesn't appear to have any missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.2 Number Of Missing Values By Column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count the number of missing values in each column and sort them." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:08.269689Z", + "start_time": "2026-06-09T23:20:08.194233Z" + } + }, + "source": [ + "#Code task 5#\n", + "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n", + "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n", + "#Order them (increasing or decreasing) using sort_values\n", + "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n", + "missing = pd.concat(\n", + " [ski_data.isnull().sum(),\n", + " 100 * ski_data.isnull().mean()],\n", + " axis=1\n", + ")\n", + "\n", + "missing.columns = ['count', '%']\n", + "\n", + "missing = missing.sort_values(by='count', ascending=False)\n", + "#missing = pd.concat(ski_data([ski_data.sum().isnull(), 100 * ski_data.mean().isnull()], axis=1)\n", + "#missing.columns=[___, ___]\n", + "#missing.___(by=___)" + ], + "outputs": [], + "execution_count": 147 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6.3 Categorical Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:08.529907Z", + "start_time": "2026-06-09T23:20:08.448255Z" + } + }, + "source": [ + "#Code task 6#\n", + "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n", + "ski_data.select_dtypes(include='object')" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " Name Region state\n", + "0 Alyeska Resort Alaska Alaska\n", + "1 Eaglecrest Ski Area Alaska Alaska\n", + "2 Hilltop Ski Area Alaska Alaska\n", + "3 Arizona Snowbowl Arizona Arizona\n", + "4 Sunrise Park Resort Arizona Arizona\n", + ".. ... ... ...\n", + "325 Meadowlark Ski Lodge Wyoming Wyoming\n", + "326 Sleeping Giant Ski Resort Wyoming Wyoming\n", + "327 Snow King Resort Wyoming Wyoming\n", + "328 Snowy Range Ski & Recreation Area Wyoming Wyoming\n", + "329 White Pine Ski Area Wyoming Wyoming\n", + "\n", + "[330 rows x 3 columns]" + ], + "text/html": [ + "
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\n", "
" - ], + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 148 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n", + "\n", + "* Is `Name` (or at least a combination of Name/Region/State) unique?\n", + "* Is `Region` always the same as `state`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3.1 Unique Resort Names" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:08.722703Z", + "start_time": "2026-06-09T23:20:08.595443Z" + } + }, + "source": [ + "#Code task 7#\n", + "#Use pandas' Series method `value_counts` to find any duplicated resort names\n", + "ski_data['Name'].value_counts().head()" + ], + "outputs": [ + { + "data": { "text/plain": [ - " Name Region state summit_elev vertical_drop \\\n", - "104 Crystal Mountain Michigan Michigan 1132 375 \n", - "295 Crystal Mountain Washington Washington 7012 3100 \n", - "\n", - " base_elev trams fastEight fastSixes fastQuads ... LongestRun_mi \\\n", - "104 757 0 0.0 0 1 ... 0.3 \n", - "295 4400 1 NaN 2 2 ... 2.5 \n", - "\n", - " SkiableTerrain_ac Snow Making_ac daysOpenLastYear yearsOpen \\\n", - "104 102.0 96.0 120.0 63.0 \n", - "295 2600.0 10.0 NaN 57.0 \n", - "\n", - " averageSnowfall AdultWeekday AdultWeekend projectedDaysOpen \\\n", - "104 132.0 54.0 64.0 135.0 \n", - "295 486.0 99.0 99.0 NaN \n", - "\n", - " NightSkiing_ac \n", - "104 56.0 \n", - "295 NaN \n", - "\n", - "[2 rows x 27 columns]" + "Name\n", + "Crystal Mountain 2\n", + "Alyeska Resort 1\n", + "Brandywine 1\n", + "Boston Mills 1\n", + "Alpine Valley 1\n", + "Name: count, dtype: int64" ] }, - "execution_count": 11, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], + "execution_count": 149 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have a duplicated resort name: Crystal Mountain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "ski_data[ski_data['Name'] == 'Crystal Mountain']" + "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?" ] }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.118672Z", + "start_time": "2026-06-09T23:20:09.028007Z" + } + }, + "source": [ + "#Code task 8#\n", + "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n", + "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 150 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.284942Z", + "start_time": "2026-06-09T23:20:09.175434Z" + } + }, + "source": [ + "#Code task 9#\n", + "#Concatenate 'Name' and 'state' and count the values again (as above)\n", + "#(ski_data[___] + ', ' + ski_data[___]).___.head()\n", + "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 151 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1." + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "**A: 1** Your answer here NO, after further soring data we can see there are multiple resorts in states like OHIO" + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.428462Z", + "start_time": "2026-06-09T23:20:09.382853Z" + } + }, + "source": [ + "ski_data[ski_data['Name'] == 'Crystal Mountain']\n", + "\n", + "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()\n" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Alyeska Resort, Alaska 1\n", + "Snow Trails, Ohio 1\n", + "Brandywine, Ohio 1\n", + "Boston Mills, Ohio 1\n", + "Alpine Valley, Ohio 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 152 + }, { "cell_type": "markdown", "metadata": {}, @@ -571,14 +1294,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.604975Z", + "start_time": "2026-06-09T23:20:09.560696Z" + } + }, "source": [ "#Code task 10#\n", "#Calculate the number of times Region does not equal state\n", - "(ski_data.Region ___ ski_data.state).___" - ] + "(ski_data.Region != ski_data.state).sum()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(33)" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 153 }, { "cell_type": "markdown", @@ -589,12 +1328,20 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.709321Z", + "start_time": "2026-06-09T23:20:09.662632Z" + } + }, + "source": [ + "ski_data['Region'].value_counts()" + ], "outputs": [ { "data": { "text/plain": [ + "Region\n", "New York 33\n", "Michigan 29\n", "Sierra Nevada 22\n", @@ -604,46 +1351,44 @@ "New Hampshire 16\n", "Vermont 15\n", "Minnesota 14\n", - "Montana 12\n", "Idaho 12\n", + "Montana 12\n", "Massachusetts 11\n", "Washington 10\n", - "Maine 9\n", "New Mexico 9\n", + "Maine 9\n", "Wyoming 8\n", "Utah 7\n", - "Oregon 6\n", "Salt Lake City 6\n", "North Carolina 6\n", + "Oregon 6\n", "Connecticut 5\n", "Ohio 5\n", - "West Virginia 4\n", "Virginia 4\n", - "Mt. Hood 4\n", + "West Virginia 4\n", "Illinois 4\n", + "Mt. Hood 4\n", "Alaska 3\n", "Iowa 3\n", - "Missouri 2\n", + "South Dakota 2\n", "Arizona 2\n", + "Nevada 2\n", + "Missouri 2\n", "Indiana 2\n", - "South Dakota 2\n", "New Jersey 2\n", - "Nevada 2\n", "Rhode Island 1\n", - "Maryland 1\n", "Tennessee 1\n", + "Maryland 1\n", "Northern California 1\n", - "Name: Region, dtype: int64" + "Name: count, dtype: int64" ] }, - "execution_count": 13, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ski_data['Region'].value_counts()" - ] + "execution_count": 154 }, { "cell_type": "markdown", @@ -654,17 +1399,39 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.826086Z", + "start_time": "2026-06-09T23:20:09.779170Z" + } + }, "source": [ "#Code task 11#\n", "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n", "#group that by 'state' and perform `value_counts` on the 'Region'\n", - "(ski_data[ski_data.___ ___ ski_data.___]\n", - " .groupby(___)[___]\n", + "(ski_data[ski_data['Region'] != ski_data['state']]\n", + " .groupby('state')['Region']\n", " .value_counts())" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "state Region \n", + "California Sierra Nevada 20\n", + " Northern California 1\n", + "Nevada Sierra Nevada 2\n", + "Oregon Mt. Hood 4\n", + "Utah Salt Lake City 6\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 155 }, { "cell_type": "markdown", @@ -682,15 +1449,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:09.928523Z", + "start_time": "2026-06-09T23:20:09.887493Z" + } + }, "source": [ "#Code task 12#\n", "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n", "#the number of unique values in each\n", - "ski_data[[___, ___]].___" - ] + "ski_data[['Region', 'state']].nunique()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Region 38\n", + "state 35\n", + "dtype: int64" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 156 }, { "cell_type": "markdown", @@ -715,31 +1500,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:10.256950Z", + "start_time": "2026-06-09T23:20:09.994940Z" + } + }, "source": [ "#Code task 13#\n", "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n", - "fig, ax = plt.subplots(___, ___, figsize=(___))\n", + "fig, ax = plt.subplots(1, 2, figsize=(12,8))\n", "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.Region.value_counts().plot(kind=___, ax=ax[0])\n", + "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n", "#Give the plot a helpful title of 'Region'\n", - "ax[0].set_title(___)\n", + "ax[0].set_title('Region')\n", "#Label the xaxis 'Count'\n", - "ax[0].set_xlabel(___)\n", + "ax[0].set_xlabel('Count')\n", "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n", - "ski_data.state.value_counts().plot(kind=___, ax=ax[1])\n", + "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n", "#Give the plot a helpful title of 'state'\n", - "ax[1].set_title(___)\n", + "ax[1].set_title('State')\n", + "ax[1].set_xlabel('Count')\n", "#Label the xaxis 'Count'\n", - "ax[1].set_xlabel(___)\n", + "ax[1].set_xlabel('Count')\n", "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n", - "plt.subplots_adjust(wspace=___);\n", + "plt.subplots_adjust(wspace=0.5);\n", "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n", "# as the importance of easy-to-read and informative figures is frequently understated\n", "# and you will find the ability to tweak figures invaluable later on" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 157 }, { "cell_type": "markdown", @@ -766,103 +1568,440 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.6.3.5.1 Average weekend and weekday price by state" + "##### 2.6.3.5.1 Average weekend and weekday price by state" + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:10.353660Z", + "start_time": "2026-06-09T23:20:10.294765Z" + } + }, + "source": [ + "#Code task 14#\n", + "# Calculate average weekday and weekend price by state and sort by the average of the two\n", + "# Hint: use the pattern dataframe.groupby()[].mean()\n", + "#ski_data.head()\n", + "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n", + "state_price_means.head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " AdultWeekday AdultWeekend\n", + "state \n", + "Alaska 47.333333 57.333333\n", + "Arizona 81.500000 83.500000\n", + "California 78.214286 81.416667\n", + "Colorado 90.714286 90.714286\n", + "Connecticut 47.800000 56.800000" + ], + "text/html": [ + "
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AdultWeekdayAdultWeekend
state
Alaska47.33333357.333333
Arizona81.50000083.500000
California78.21428681.416667
Colorado90.71428690.714286
Connecticut47.80000056.800000
\n", + "
" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 158 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:10.552529Z", + "start_time": "2026-06-09T23:20:10.388801Z" + } + }, + "source": [ + "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n", + "# Compare the index order you get from\n", + "# state_price_means.index\n", + "# with\n", + "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n", + "# See how this expression simply sits within the reindex()\n", + "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n", + " .sort_values(ascending=False)\n", + " .index)\n", + " .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n", + "plt.xlabel('Price ($)');" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 159 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:10.741128Z", + "start_time": "2026-06-09T23:20:10.707759Z" + } + }, + "source": [ + "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Object `states` not found.\n" + ] + } + ], + "execution_count": 160 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.6.3.5.2 Distribution of weekday and weekend price by state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:10.768195Z", + "start_time": "2026-06-09T23:20:10.755184Z" + } + }, + "source": [ + "#Code task 15#\n", + "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n", + "#specify 'state' for `id_vars`\n", + "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", + "#call the resultant price column 'Price' via the `value_name` argument,\n", + "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", + "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']],\n", + " id_vars='state',\n", + " var_name='Ticket',\n", + " value_vars=['AdultWeekday', 'AdultWeekend'],\n", + " value_name='Price')" + ], + "outputs": [], + "execution_count": 161 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:10.809767Z", + "start_time": "2026-06-09T23:20:10.768841Z" + } + }, + "source": [ + "ticket_prices.head()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " state Ticket Price\n", + "0 Alaska AdultWeekday 65.0\n", + "1 Alaska AdultWeekday 47.0\n", + "2 Alaska AdultWeekday 30.0\n", + "3 Arizona AdultWeekday 89.0\n", + "4 Arizona AdultWeekday 74.0" + ], + "text/html": [ + "
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0AlaskaAdultWeekday65.0
1AlaskaAdultWeekday47.0
2AlaskaAdultWeekday30.0
3ArizonaAdultWeekday89.0
4ArizonaAdultWeekday74.0
\n", + "
" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 162 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:11.200830Z", + "start_time": "2026-06-09T23:20:10.848862Z" + } + }, "source": [ - "#Code task 14#\n", - "# Calculate average weekday and weekend price by state and sort by the average of the two\n", - "# Hint: use the pattern dataframe.groupby()[].mean()\n", - "state_price_means = ski_data.___(___)[[___, ___]].mean()\n", - "state_price_means.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "#Code task 16#\n", + "#Create a seaborn boxplot of the ticket price dataframe we created above,\n", + "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n", + "#This will use boxplot's x, y, hue, and data arguments.\n", + "plt.subplots(figsize=(12, 8))\n", + "sns.boxplot(x='state', y='Price', hue='Ticket' , data=ticket_prices)\n", + "plt.xticks(rotation='vertical')\n", + "plt.ylabel('Price ($)')\n", + "plt.xlabel('State');" + ], "outputs": [ { "data": { - "image/png": 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" }, + "metadata": {}, "output_type": "display_data" } ], - "source": [ - "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n", - "# Compare the index order you get from\n", - "# state_price_means.index\n", - "# with\n", - "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n", - "# See how this expression simply sits within the reindex()\n", - "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n", - " .sort_values(ascending=False)\n", - " .index)\n", - " .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n", - "plt.xlabel('Price ($)');" - ] + "execution_count": 163 }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?" + "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n", + "\n", + "* disregard State completely\n", + "* retain all State information\n", + "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n", + "\n", + "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.6.3.5.2 Distribution of weekday and weekend price by state" + "Thus we currently have two main questions you want to resolve:\n", + "\n", + "* What do you do about the two types of ticket price?\n", + "* What do you do about the state information?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type." + "### 2.6.4 Numeric Features" ] }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "source": "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features." + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "#Code task 15#\n", - "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n", - "#specify 'state' for `id_vars`\n", - "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n", - "#call the resultant price column 'Price' via the `value_name` argument,\n", - "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n", - "ticket_prices = pd.melt(ski_data[[___, ___, ___]], \n", - " id_vars=___, \n", - " var_name=___, \n", - " value_vars=[___, ___], \n", - " value_name=___)" + "#### 2.6.4.1 Numeric data summary" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:11.392816Z", + "start_time": "2026-06-09T23:20:11.366094Z" + } + }, + "source": [ + "#Code task 17#\n", + "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n", + "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n", + "#will be useful again\n", + "ski_data.describe().T" + ], "outputs": [ { "data": { + "text/plain": [ + " count mean std min 25% 50% \\\n", + "summit_elev 330.0 4591.818182 3735.535934 315.0 1403.75 3127.5 \n", + "vertical_drop 330.0 1215.427273 947.864557 60.0 461.25 964.5 \n", + "base_elev 330.0 3374.000000 3117.121621 70.0 869.00 1561.5 \n", + "trams 330.0 0.172727 0.559946 0.0 0.00 0.0 \n", + "fastEight 164.0 0.006098 0.078087 0.0 0.00 0.0 \n", + "fastSixes 330.0 0.184848 0.651685 0.0 0.00 0.0 \n", + "fastQuads 330.0 1.018182 2.198294 0.0 0.00 0.0 \n", + "quad 330.0 0.933333 1.312245 0.0 0.00 0.0 \n", + "triple 330.0 1.500000 1.619130 0.0 0.00 1.0 \n", + "double 330.0 1.833333 1.815028 0.0 1.00 1.0 \n", + "surface 330.0 2.621212 2.059636 0.0 1.00 2.0 \n", + "total_chairs 330.0 8.266667 5.798683 0.0 5.00 7.0 \n", + "Runs 326.0 48.214724 46.364077 3.0 19.00 33.0 \n", + "TerrainParks 279.0 2.820789 2.008113 1.0 1.00 2.0 \n", + "LongestRun_mi 325.0 1.433231 1.156171 0.0 0.50 1.0 \n", + "SkiableTerrain_ac 327.0 739.801223 1816.167441 8.0 85.00 200.0 \n", + "Snow Making_ac 284.0 174.873239 261.336125 2.0 50.00 100.0 \n", + "daysOpenLastYear 279.0 115.103943 35.063251 3.0 97.00 114.0 \n", + "yearsOpen 329.0 63.656535 109.429928 6.0 50.00 58.0 \n", + "averageSnowfall 316.0 185.316456 136.356842 18.0 69.00 150.0 \n", + "AdultWeekday 276.0 57.916957 26.140126 15.0 40.00 50.0 \n", + "AdultWeekend 279.0 64.166810 24.554584 17.0 47.00 60.0 \n", + "projectedDaysOpen 283.0 120.053004 31.045963 30.0 100.00 120.0 \n", + "NightSkiing_ac 187.0 100.395722 105.169620 2.0 40.00 72.0 \n", + "\n", + " 75% max \n", + "summit_elev 7806.00 13487.0 \n", + "vertical_drop 1800.00 4425.0 \n", + "base_elev 6325.25 10800.0 \n", + "trams 0.00 4.0 \n", + "fastEight 0.00 1.0 \n", + "fastSixes 0.00 6.0 \n", + "fastQuads 1.00 15.0 \n", + "quad 1.00 8.0 \n", + "triple 2.00 8.0 \n", + "double 3.00 14.0 \n", + "surface 3.00 15.0 \n", + "total_chairs 10.00 41.0 \n", + "Runs 60.00 341.0 \n", + "TerrainParks 4.00 14.0 \n", + "LongestRun_mi 2.00 6.0 \n", + "SkiableTerrain_ac 690.00 26819.0 \n", + "Snow Making_ac 200.50 3379.0 \n", + "daysOpenLastYear 135.00 305.0 \n", + "yearsOpen 69.00 2019.0 \n", + "averageSnowfall 300.00 669.0 \n", + "AdultWeekday 71.00 179.0 \n", + "AdultWeekend 77.50 179.0 \n", + "projectedDaysOpen 139.50 305.0 \n", + "NightSkiing_ac 114.00 650.0 " + ], "text/html": [ "
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" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 167 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have." + "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:12.617678Z", + "start_time": "2026-06-09T23:20:12.553817Z" + } + }, + "source": [ + "#Code task 20#\n", + "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n", + "#Hint: don't forget the transpose will be helpful here\n", + "ski_data[ski_data['SkiableTerrain_ac'] > 10000].T" + ], "outputs": [ { "data": { "text/plain": [ - "0 82.424242\n", - "2 14.242424\n", - "1 3.333333\n", - "dtype: float64" + " 39\n", + "Name Silverton Mountain\n", + "Region Colorado\n", + "state Colorado\n", + "summit_elev 13487\n", + "vertical_drop 3087\n", + "base_elev 10400\n", + "trams 0\n", + "fastEight 0.0\n", + "fastSixes 0\n", + "fastQuads 0\n", + "quad 0\n", + "triple 0\n", + "double 1\n", + "surface 0\n", + "total_chairs 1\n", + "Runs NaN\n", + "TerrainParks NaN\n", + "LongestRun_mi 1.5\n", + "SkiableTerrain_ac 26819.0\n", + "Snow Making_ac NaN\n", + "daysOpenLastYear 175.0\n", + "yearsOpen 17.0\n", + "averageSnowfall 400.0\n", + "AdultWeekday 79.0\n", + "AdultWeekend 79.0\n", + "projectedDaysOpen 181.0\n", + "NightSkiing_ac NaN" + ], + "text/html": [ + "
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39
NameSilverton Mountain
RegionColorado
stateColorado
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vertical_drop3087
base_elev10400
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fastSixes0
fastQuads0
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triple0
double1
surface0
total_chairs1
RunsNaN
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LongestRun_mi1.5
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Snow Making_acNaN
daysOpenLastYear175.0
yearsOpen17.0
averageSnowfall400.0
AdultWeekday79.0
AdultWeekend79.0
projectedDaysOpen181.0
NightSkiing_acNaN
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" ] }, - "execution_count": 23, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", - "missing_price.value_counts()/len(missing_price) * 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.6.4.2 Distributions Of Feature Values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 18#\n", - "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n", - "#Try passing it an argument figsize=(15,10)\n", - "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n", - "#It's important you create legible and easy-to-read plots\n", - "ski_data.___(___)\n", - "#plt.subplots_adjust(hspace=___);\n", - "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What features do we have possible cause for concern about and why?\n", - "\n", - "* SkiableTerrain_ac because values are clustered down the low end,\n", - "* Snow Making_ac for the same reason,\n", - "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n", - "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n", - "* trams also may get an amber flag for the same reason,\n", - "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.6.4.2.1 SkiableTerrain_ac" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 19#\n", - "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n", - "ski_data.___[ski_data.___ > ___]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Code task 20#\n", - "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n", - "#Hint: don't forget the transpose will be helpful here\n", - "ski_data[ski_data.___ > ___].___" - ] + "execution_count": 168 }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "**A: 2** Your answer here" - ] + "source": "**A: 2** Your answer here Silverton Mountain Colorodo" }, { "cell_type": "markdown", @@ -1179,36 +2765,73 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:12.978120Z", + "start_time": "2026-06-09T23:20:12.969242Z" + } + }, "source": [ "#Code task 21#\n", "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n", - "ski_data.___[39, 'SkiableTerrain_ac']" - ] + "ski_data.loc[39, 'SkiableTerrain_ac']" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(26819.0)" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 169 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:13.002234Z", + "start_time": "2026-06-09T23:20:12.990535Z" + } + }, "source": [ "#Code task 22#\n", "#Use the .loc accessor again to modify this value with the correct value of 1819\n", - "ski_data.___[39, 'SkiableTerrain_ac'] = ___" - ] + "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819" + ], + "outputs": [], + "execution_count": 170 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:13.016632Z", + "start_time": "2026-06-09T23:20:13.006523Z" + } + }, "source": [ "#Code task 23#\n", "#Use the .loc accessor a final time to verify that the value has been modified\n", - "ski_data.___[39, 'SkiableTerrain_ac']" - ] + "ski_data.loc[39, 'SkiableTerrain_ac']" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(1819.0)" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 171 }, { "cell_type": "markdown", @@ -1226,28 +2849,31 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:13.110604Z", + "start_time": "2026-06-09T23:20:13.030367Z" + } + }, + "source": [ + "ski_data.SkiableTerrain_ac.hist(bins=30)\n", + "plt.xlabel('SkiableTerrain_ac')\n", + "plt.ylabel('Count')\n", + "plt.title('Distribution of skiable area (acres) after replacing erroneous value');" + ], "outputs": [ { "data": { - "image/png": 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\n", 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qenp6aN68eZnWL69vvvkaN2/dkgasfC6urtDW1kbbtm1l2i9evCTz/P79N3W/fxj1XVpaWmjTujXOnDkrDVgAkJubi2PHjqFmzZpoUL9+qWv3DwhE06ZNsWzZUnz77bfQ1dUtsu8062lY57gWy5avwPwFC0sMWADwZbNm2Ld3D4KDgxDz9Amin/yLjRvWQ11dHfXq1ZXp++rVK+kvknxuJ05ATU0NHYs5ctqzZ0/cvu2J52Ix1NTUpI+rV90BoMRvsNaqVQubN21EgL8vnkY/RvSTf+F2/CgA4ItCPgG+/3PZs2cPJCcn49KlyzLrDwkJgVgsxtdffVXmdb0r/4NNQsKLAtPk2c/t27WDnp4e9u8/UOx6PnQ7S/P6z/9EX6N60R/a8o0ZMxoXzp/Fw6gI6b7r0qULvviiQYnzFuW7b7+FhoYGXI8ek9mmrKwseHl5F/qzU5r3pcL69+zZ8+3r9rjMOuPi4hESGoqv3vl5Acr+ugDk22fdu3VDRkYmnI44l2q78pX0ftamTWtoamri1OnTMv38/Pzx5MmTEpdf2v2VlJQsE7Bkar10SRqwAKB+/fqoUaM6jh07JvN+lp6ejjNnz6FNmzYyp7jzl/Gu0Pv3oaWlCSMjo1LV27ZtG2hpacHFxVVmebGxz3Dr1u1SnxoDAP+AAFSpUgVbNm9C7169SvxGeL6wsDAEBgZh+LCh0rYGDRqgTZvWOOIs+3Px008/4uSJ44h4ECb9mRo5cgQaNCj+/Ute7dq3Q1UDA7i4usrsP1VVVbi7X0OrVi3lvrxCKY9kaWlpwcDAQJpsC/P48WMMGz4SllN+x7KlS6Cjo4N//32M3Xv2YPfuPXKvSyyOk7tvfHzBvnFx8QAAAzl/0MrKwMAAcYXUKn4ufju9ikx7UlKSzPOst6fzNN97Mb+rShV9qKqqQhxXcD3PxfnrKf12bty4Cenp6Rg00AJjx4xGTk4OvLy9sXTpcpkvPgDAoIEWeP78OU6ePCXXsk1r1oSb2zFERUVh/vwFeBr9FJlZWWjduhWWL1taYHvj3znkL22TYwyNjYzQq9f3iH7yb6HT379e6F3a2tpwO37sTShYtRpRDx8hIyMDNWvWxJ7dOwvUmJ6eLnM4HQCMjY1QpUoVPHn8qNB1GFQ1KNO63pc/PSsrS6Zd3v1c1fDNfoiV45uxH7KdpXn9529LSdv+22+TsHDBfOw/8DdWrf4LiYmJyMnJxZ9/zPigkGVkbAwAOH/uTKHTc3JyZJ4Xtl9K8v77mLGREVRVVXEvKKDQ/v/+K/sFkrK+LuTdZ4aGVSEWi+X60FSYkt7P8mtMiC9kOwrZtveVdn/lnw4uTJxYdlp+CCnsfVX89kObfpUqyHj+35etkhJltzf/wvn87ZW33vz9UujvDrEYXbt2KXI7inLs2HGoq6lj1KgR2LlzO1RVVREQEIhVq1bj+o0bxc57xNkZy5ctRYP69REZFYXhw4YiMzMTJ06clPbp27cPdmzfhlOnT2Pr1u2Ii4/D69c5GDd2DEaMKP5yB3nlh9VdO3cU2cfAoAoyMjJKXJZShqyePXtAXV0dt0u47cKdO3dw584dqKqqomXLFhj/669YvMgBCfEJOHlKvl/SKMWL3tjYpECbicmbN9D8N4GszDdv6JUqiWT6FfdLWB5JSUkwqVZw/dXefjpPfO9FWRbJySnIyclBNZOC66n+9ghHYlJigWklycnJwY4dO7Fjx07o6emhS5fOmDVrJg4fPoj27TrInGYdOWoMtm3dArfjxzB02HDExMQUu+w+fXpDR0cHEyb+JtP3y2ZNC+1v/PbFJdP23hgWJjExEffvh2HFylWFTheLi37T7fzNN6hRozoGDhoCL6//fqb19Qu/Jq2wX0SJiUlITEzEyFFjCp0nLS21TOsquJ4341ulShXEvfNLQd79nPj2mseaNWqUuK4P2U5A/td/lSpVpMsuzqCBFrh1+zZmz54j066r+2H3CsvfpxMn/YanT4v/eQYK3y9yzFRgnbm5ubCwGISsQr7Z9v633cr6upB3n714kYj27TtARUWlzEGrOPk1GhkX3A4TY2NERz8t0P6u0u6v4jbh/WmJb2sr7H21WrVqyMnJQUpycrH1lbXe/P1S6O+OatWkP5vAmw8jlfUKXlxftWrBkO3s4gJnFxdoaWmhU6eOmGFnh/3796Jzl2+Lfc8+ceIkFsyfh6FDh2DFylUYNGggLly4KHNJx6CBA/H48WP8/vsUmXlFItH7iytA3t+/+e8Fc+faw7eIy13iCwnshVG604WmNWti/jx7pKSk4O+/D8o1T25uLvz9AzBn7ptv9zRv/iUASH/4SvoEK69GjRqiadMmMm0WAwbg1atX0mtYop+++YZgkyay/Xr1+r7A8iRZErlru3nzFr75+psC16kNHjwI6enp8PPzk3s7ipKRkQF/f3/07dtHpi4VFRUMHDgQsbGxH3wvrJcvX+LMmbPYt28/qhoYwMzcXGZ6zNOnsBg4CBJJFk64HUPdunWKXV7+G/b7b4IjR44stH/lypXR63vZsbAYMAA5OTnw9vIucj2XL19Bo0aN8PjxYwQFBRV4FBey8pBfo+zRodHvfHu2JJcuX0bVqlWhpqZa6Przx+VD1xUZGQUAqFO7tuw2yLmf7/r4ICUlRa5v8xVG3u18V1Gv/3y1a73ZlgcRD4pdd15eXoEvcDRp0rjAqfii5O/z91/T1655IDs7G3Vq1y50m94/mlseLl++AlVVVVSvUb3Q9b1/lqCsrwt599lVd3doaWli2NChEIK/fwAyMzPRv5/sdTRt2rSG+XvvMYUp7f4qjaioKMQ+ewYLiwEy7VpaWvjxh77w9fUt9S035K3X19cPGRkZGDRQ9pqvGjWqv7kE5Z1rqaKfRqNevXoyYcbAoAratW1XZB0ZGRlwd7+GDRs2olKlSmjUqGGxdaekpODChYsYPHgwvu/ZE9WqVcOR904h5+XlFbinnbGxMXr3LvnbhfL+/r179y6Sk5PxRcOGRb4m5b2vXoU+ktW4cSOoq6tBXU0dhkaG6NixI4YNHYKc3FxMmDhJJmW/b8yY0fjmm69x5cpVxMTEoFKlShg+/M2Ftvk3MU1LS0N0dDR69+6FmzdvISk5GYmJiTIXpZbGc7EY+/buwZo1jhDHxWHQQAt8+21XLFmyVPoiCQgIRGRkJObPs4e6mjqSU1LQt29vdGjfvsDywsLC8OOPP2Ds2DEICrqH3NzcIt9w1zo6omfPHjjq6oy1juuQnJyMgRYW+L5nTyxevETmovcPsWz5ShxxOgRXV2ds27Yd2ZJsjBs3Fo0bN8IUS6syLXP/vj0ICwtHYFAQXrxIhJmZKSZNnIDo6Gg8elTwtFBcXBwGDRqCw4cP4vixoxg+YhTCw8MLXfb16zeQlZWFLVs2YcuWrahUSRPjxo6BfhX9QvsnJiZi+fJlMDWtiYcPH6F7j24YPXoU9u0/UOTFvQCw+q816Nq1C06ddMPuPXsRFRWFSpUqwdzcHN27d8OsWbOLvLeaj48PkpKSsXLFcqxZuw6vX2djoIUFmjYt/GhbYU6ePIWBFhb4++8D2L1rN/wDAvH6dTZq1KiBr7/+GhcuXMT58+c/eF3+/v7IyMhAm7ZtZK4LkXc/p6enY9GixViz5i84Ozvh8CEnxCfEo06dOmjWtGmx31QrzXbK8/rP16ZNa+mRyOJcvnwF06dbY4adLTy9vFC/fn3YTLfGkyfRUFcv+TYd99/+Yps4cTxcXY8iO/s1oqKi8PTpU6z+aw1mzvwTtWrXwjV3DySnpMDY2AitW7VCenq63F++kdddHx/8ffAgHNeuQcsWLeDl7Y309HRUM6mG9h3aIywsTPpFCaDsrwt599mJEycxbNhQrFixDPXr18Ot255QVVVBm9atERERKf+ZhyIkJydjx85dmDbVCskpyTh37gJq1qgOW1sbPH8uRm5e0d+oLsv+Ko28vDwsXbIMmzdvxIED+3Dw70MQVRJh8u+/Q09PD8uWrSj1MuWt9+XLl3Bctx5zZs/C+vWOOHHiJAwMDGBra4OsrCysdfzvy0jHjh7H2DFjsHHjehw+5AQDAwNMmfI7UlNlf7esXrUSmZmZuOvjA7E4DiYmxphqZYmUlBQEBASWWPuRI87o378fli5djNjY2AKnGC9fvoIff/wBy5YtxZkzZ1CzZk1Mt7aGOC6u2Ot5Afl//6anp8N+3nysX+cIgypV8M+ZM0hIeAFDw6po1rQpqhoaFjg6W5QKHbLWvb0fSlZWFl6+fImIiEhs3rIVhw87FRuwACAkJATfdu2KGXa2MDY2Rnp6OsLCwjHul1/hcf26tJ/djD8wz34u9u7dDU1NTTi7uMLGxrZM9YaEhMDZ2QUz7OxQt24diMViLFjogJ07d0n75ObmYtwvv2LpkiVYsWIZJBIJTp48hbn283Dwb9mLgXft3oOGjRpi1sw/oaenB1VVVdQ0LfxTV1TUQ/Trb4HZs2Zi2dIl0NTURGRkJKbb2Ba4qPFDeHl5YejQ4bCbYYt1jmuhqqqKkNBQ/PLreFy+fKVMy7x12xM//tAXI0eOgK6uLuLj43H9+g04rlsvc4HouxKTkjBk6HD8fWAfjh1zxciRowsNoJFRUZj02/8w888/sGvnTiQlJ+GE2wls37EThw8VfFOMi4vHnLn2mD/PHo0bN0JycgrWb9iIv/5aU+w2xMXFoW/fHzF9ujUm//4/1KhRA6lpaYh+Eg33a9eQnJxS5LxJSckYO24cFsyfh00b1yM9PR0XLlzE5N+n4OLF80XO967c3Fz88ut4TJw4AYMHDYSVlRVycl7j2bNn8PTykn5y/dB1ZWdn458zZ9C71/dYsWKltL00+9npiDOei8WwnDIFf/21CioqKoiOjoar69Fy2055X//Am0+xbm4nSlz3+g0boaWlheEjhmPy5MmIiHiAWbPmoE/fPvhajj/N5enphQ0bN2HI4MEYNXIk1NTUMGjwEHh6emHTps2IeBCBCRPHY0D//hCJRIiPj0dAYKDcR+xLa+bM2fDz9cfoMaMwbtxYqKqq4vlzMe763IW/f4BM37K+LuTdZzk5ORgzZhymWlmi/4D+mDhxAtLS0hASGlpuf11jxYqVSE9Px9gxozFs6FBERkVh1uw5mDXzT7xMKfkO/KXZX6XlduIE0tPTYTXVElu3bkFubg78/PwwZOgw+PgUf/uKD61306bNeJGQgPETxqPfzz8jMzMTnp5eWL5ipcztG+76+GCa9XRYWU7Bnj278eTJY6x1XIfu3bvLjKX3nTsYOnQIfv75J+jr6yMxMQl3797FNGubEn9vA8D1GzcQExMDU1NTrFu/ocDpY2cXFxgZGWLMmNEYPmwonjx5gs2bN6NGjRpv/oJLMUrz+/f4cTfExMRiypTfsXLlCujq6CDhxQuEhISU6neqSo2aZuV/ApyIPlktWrTA+XNn8ONPP3/wLxdF69z5GzgdPoRu3XogMipK0eVUSEddXVC1alV079FT0aWUO3Nzc1z3cMdax3XYuHGTosuhT1CFPpJFRBVPUFAQTp06jenTrTFu3K8lz1CBWVtPw5EjzgxYn4GmTZtgQP/+8PHxxavUV6hfvz6mTJ6M1NRUOL1zby6i8sSQRUSl5rBoEUYMHw4dHR2l/SPR+vr68PL0wj4579lFyi09PQMtW7bAiBHDoaenh1evXuG2pydWrlwlc7d2ovLE04VEREREAlC6WzgQERERKQOGLCIiIiIBMGQRERERCYAXvr9VrVo1pb2Al4iI6HOlo6NT7F/VUCSGLLwJWP5+Pooug4iIiMqgdZt2FTJoMWQB0iNYrdu0K/ejWa1bt1L6GzZ+LjhWyoHjpDw4VspDWcdKR0cH/n4+FfZMFEPWO9LS0pCamlquy8zIyCj3ZZIwOFbKgeOkPDhWyoNjJQxe+E5EREQkAIYsIiIiIgEwZBEREREJQKEhy8rKEmfP/IMH4fcRFOiPPbt3oX79egX62dnawM/XB1GRETjq6oKGDRvKTBeJRFiyeBGC7wUiMiIc+/buQY0a1T/WZhAREREVoNCQ9VWnTti3fz9++rk/ho8YCTV1NTgdPgQtLS1pH8spk/Hbb5Mw194eP/z4E+Lj43HE6TB0dHSkfRwcFqJP3z6YPMUSAwYMhLaONg7s3wdVVR6oIyIiIsVQaAoZNXoMXFxc8eDBA4SG3oeNjR3MzMzQokULaZ+JEydgw4aNOHfuPMLDw2E93QZaWpqwsBgAAKhcuTJGDB+GRYsW48aNmwgOCcHUqdZo3LgxunTpoqAtIyIios9dhTrUo6enBwBITk4GANSqVQvVqlWDh8d1aR+JRAIvL2+0a9cWANCiRXOIRCKZPmKxGGHh4Wj/ts/7RCIRdHV1pY93j4oRERERlYcKdZ+shQvmw9v7DsLDwwEAJibGAID4hASZfvHx8TAzM3vTx9gEWVlZSElJkemTEJ8A47fzv2+qlSXs7GwLtLdu3QoZGRkfvB3vqlevbrkuj4TDsVIOHCflwbFSHso6Vu9eXlQRVZiQtWzpEjRp0hgDLAYWmJaXlyfzXEVFpUDb+1RUCs6Xb+Omzdi+Y6f0ef4dY/39AwS5GZuPj2+5L5OEwbFSDhwn5cGxUh7KOFa6urqKLqFYFeJ04ZLFi9Cr1/cYPGQYnj17Lm2Pi4sHAJgYyx6RMjIyQnzCm2lx8XGoVKkS9PX1ZfoYGhkhIV72CFg+iUSC1NRU6aOi3o6fiIiIlJfCQ9bSJYvRt29fDBk6DNHR0TLTnjx5ArFYjK5d/7uAXUNDA506dZQm7qCge5BIJDJ9TExM0LhRI9xVwlROREREnwaFni5ctmwpLAb0x6/jJyI1NQ3Gb49YvXr1CpmZmQCAXbt2Y+pUKzx89C8ePXqEaVOtkJGRCTe3E9K+TkecsWD+PCQlJSE5KRnz5tsjLCwMN27cUNSmERER0WdOoSHrl3FjAQDHj7nKtE+3sYWLy5u2zVu2QlNTE8uXLYG+vj78/QMwYuQomVN8Cxc6IOf1a2zbthVampq4efMmxtnYIjc39+NtDBEREdE7FBqyapqay9VvzVpHrFnrWOT0rKws2M+bD/t588urNCIiIqIPovBrsoiIiIg+RRXmFg6fKsP2c2FeS7/kjoWIPt6jnKshIiKij4VHsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJgCGLiIiISAAMWUREREQCYMgiIiIiEgBDFhEREZEAGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABQasjp27Ij9+/bAz9cHsTHR6NO7t8z02JjoQh+Tf/+ftM9RV5cC07du2fyxN4WIiIhIhroiV66trYWQ0Ps44uyC3bt2FpjeslUbmefdu3XDmjWrcebsOZn2gwcPYfVfa6TPMzMzhSmYiIiISE4KDVnu7tfg7n6tyOnx8fEyz3v37oVbt2/jyZMnMu0ZmRkF+hIREREpktJck2VkZIQePbrjiJNzgWkDLSwQfC8Q7lcvY/48e+jo6BS7LJFIBF1dXemjpP5EREREpaXQI1mlMXTIYKSmpuHsOdlThcfd3BAdHY24uHg0btQIs2fPRNOmTTB8xKgilzXVyhJ2drYF2lu3boWMjIxyrdtUP73M81Zr17YcK6GS1KtXV9ElkBw4TsqDY6U8lHWstLS0FF1CsZQmZA0fPgxubm7IysqSaT982En6//DwcDx89AgXzp9F8y+/xL3g4EKXtXHTZmzf8d81YDo6OvD384G/fwBSU1PLtW7D9toIFeuXad5oH99yrYVK5sN9rhQ4TsqDY6U8lHGsdHV1FV1CsZTidGGHDh3QoEEDHHZyKrHvvXv3IJFIULeYVC6RSJCamip9pKWllWe5RERERMoRskaMGI7AwCCEht4vsW+jRo0gEokgFsd9hMqIiIiICqfgWzhoo27dOtLn5rXM0axZUyQnJSMmNhbAm0OBP//0IxwWLS4wf+3atTHQYgCuXHVHYmIiGjb8Agvmz8O9e/dw9+7dj7UZRERERAUoNGS1bNkCx466Sp87LFwAAHB2cYWNzZsL0/v37wcVFRWcOHGywPzZ2RJ07twZEyZOgI62NmJjn+HKlStY67gOubm5H2cjiIiIiAqh0JDl6emFmqbmxfY5dOgwDh06XOi02NhnGDR4iBClEREREX0Qpbgmi4iIiEjZMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJgCGLiIiISAAMWUREREQCYMgiIiIiEgBDFhEREZEAGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJQKEhq2PHjti/bw/8fH0QGxONPr17y0x3dFyL2Jhomcfp0ydl+ohEIixZvAjB9wIRGRGOfXv3oEaN6h9zM4iIiIgKUGjI0tbWQkjofcy1ty+yz9Wr7mjZqo30MWbMOJnpDg4L0advH0yeYokBAwZCW0cbB/bvg6oqD9IRERGR4qgrcuXu7tfg7n6t2D4SiQTx8fGFTqtcuTJGDB+GadbTcePGTQDA1KnW8LnrjS5dusDDw6O8SyYiIiKSS4U/3PPVV50QFOiPGzc8sHrVShgaGkqntWjRHCKRCB4e16VtYrEYYeHhaN+ubZHLFIlE0NXVlT50dHQE3QYiIiL6/Cj0SFZJ3N3d8c8//+Dp0xjUqmWOP/+YAVcXZ/Tp+wMkEglMjE2QlZWFlJQUmfkS4hNgbGJc5HKnWlnCzs62QHvr1q2QkZFRrttgqp9e5nmrFRMUqfzVq1dX0SWQHDhOyoNjpTyUday0tLQUXUKxKnTIOnXqtPT/4eHhCAwMwh1vT/To0R3nzp0vcj4VFSAvL6/I6Rs3bcb2HTulz3V0dODv5wN//wCkpqaWT/FvGbbXRqhYv0zzRvv4lmstVDIf7nOlwHFSHhwr5aGMY6Wrq6voEopV4U8XvisuLg5PY2JQr+6bxB0XH4dKlSpBX182xBgaGSEhPqHI5UgkEqSmpkofaWlpgtZNREREnx+lClkGBlVQs0YNiOPiAABBQfcgkUjQtWsXaR8TExM0btQId5UwkRMREdGnQ6GnC7W1tVG3bh3pc/Na5mjWrCmSk5KRlJyMGXa2OHP2LMTiOJibm2H2rJlITEqSnip89eoVnI44Y8H8eUhKSkJyUjLmzbdHWFgYbty4oaCtIiIiIlJwyGrZsgWOHXWVPndYuAAA4Oziitmz56Bx48YYPHgQ9PT0EBcXh1u3PfH75Ckyp/cWLnRAzuvX2LZtK7Q0NXHz5k2Ms7FFbm7uR98eIiIionwKDVmenl6oaWpe5PSRo0aXuIysrCzYz5sP+3nzy7M0IiIiog+iVNdkERERESkLhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJgCGLiIiISAAMWUREREQCYMgiIiIiEgBDFhEREZEAGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBKDRkdezYEfv37YGfrw9iY6LRp3dv6TR1dXXMnTMbVy5fQmREOPx8fbB+vSOqVasms4yjri6IjYmWeWzdsvljbwoRERGRDHVFrlxbWwshofdxxNkFu3ftlJmmpaWF5s2/xLr16xEaGgp9fX04OCzEvr170PeHH2X6Hjx4CKv/WiN9npmZ+VHqJyIiIiqKQkOWu/s1uLtfK3Taq1evMHzEKJk2e/v5OHf2H5jWrImY2Fhpe0ZmBuLj44UslYiIiKhUlOqaLD29ysjNzUXKy5cy7QMtLBB8LxDuVy9j/jx76OjoFLsckUgEXV1d6aOk/kRERESlpdAjWaVRqVIlzJk9G25uJ5CamiptP+7mhujoaMTFxaNxo0aYPXsmmjZtUuAo2LumWlnCzs62QHvr1q2QkZFRrnWb6qeXed5q7dqWYyVUknr16iq6BJIDx0l5cKyUh7KOlZaWlqJLKJZShCx1dXVs3bIZqqoqmD1nrsy0w4edpP8PDw/Hw0ePcOH8WTT/8kvcCw4udHkbN23G9h3/XQOmo6MDfz8f+PsHyAS48mDYXhuhYv0yzRvt41uutVDJfLjPlQLHSXlwrJSHMo6Vrq6uoksoVoU/Xaiuro7t27bCvJY5ho8YWWIIunfvHiQSCeoWk8olEglSU1Olj7S0tPIum4iIiD5zFfpIVn7Aqlu3LgYPGYqkpOQS52nUqBFEIhHE4jjhCyQiIiIqgoJv4aCNunXrSJ+b1zJHs2ZNkZyUjOdiMXbu2I7mzb/E2HG/QE1NDcbGxgCA5ORkZGdno3bt2hhoMQBXrrojMTERDRt+gQXz5+HevXu4e/eugraKiIiISMEhq2XLFjh21FX63GHhAgCAs4sr1qxZi969ewEALl+6KDPfoMFD4OnphexsCTp37owJEydAR1sbsbHPcOXKFax1XIfc3NyPtyFERERE71FoyPL09EJNU/Mipxc3DQBiY59h0OAh5V0WERER0Qer8Be+ExERESkjhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRAMoUsjxv34SBQZUC7Xp6evC8ffNDayIiIiJSemUKWebm5lBVVSvQLhKJUL169Q8uioiIiEjZqZemc6/vv5f+/7vvvsWrl6+kz1XV1NCl8zd4+vRp+VVHREREpKRKFbL27NkFAMjLy8P6dY4y07Kzs/H06VM4LFpcftURERERKalShSwz89oAAC/PW/jhh5+QmJQkSFFEREREyq5UIStfp6++Ke86iIiIiD4pZQpZANC58zfo3LkzjAwNoaoqe/28rd2MDy6MiIiISJmVKWTZ2kyHjc10BAYGIS4uDnl5eeVdFxEREZFSK1PIGjNmNKbb2OLYsePlXQ8RERHRJ6FM98nSEIng4+Nb3rUQERERfTLKFLKcDjvBwmJAOZdCRERE9Oko0+nCSpUqYdSokejSuTPu37+P7NevZaY7OCwql+KIiIiIlFWZQlaTpk0QEhoKAGjUuJHMNF4ET0RERFTGkDVkyLDyroOIiIjok1Kma7KIiIiIqHhlOpLl6upc7GnBoUOHl7kgIiIiok9BmUJWSEiozHMNdXU0a9YUjRo1gqvr0XIpjIiIiEiZlSlkLVzoUGi7na0NdHR0PqggIiIiok9BuV6Tdey4G4YN40XxREREROUastq2bYOsrCy5+3fs2BH79+2Bn68PYmOi0ad37wJ97Gxt4Ofrg6jICBx1dUHDhg1lpotEIixZvAjB9wIRGRGOfXv3oEaN6h+8LUREREQfokynC3ft3CHzXEVFBSbVTNCyRQusW7de7uVoa2shJPQ+jji7YPeunQWmW06ZjN9+m4TpNrZ4+PARpltPwxGnw+jS9VukpaUBABwcFuL773ti8hRLJCUmYf6CeTiwfx969/kBubm5Zdk8IiIiog9WppD16tUrmee5ubmIiorCX6vXwOP6dbmX4+5+De7u14qcPnHiBGzYsBHnzp0HAFhPt0FggB8sLAbg4MFDqFy5MkYMH4Zp1tNx48ZNAMDUqdbwueuNLl26wMPDo/QbR0RERFQOyhSybGztyruOAmrVqoVq1arBw+O/0CaRSODl5Y127dri4MFDaNGiOUQikUwfsViMsPBwtG/XtsiQJRKJIBKJpM95sT4RERGVtzKFrHzNmzfHF180QF5eHiIeRCA4JKS86oKJiTEAID4hQaY9Pj4eZmZmb/oYmyArKwspKSkyfRLiE2D8dv7CTLWyhJ2dbYH21q1bISMj40NLl2Gqn17meau1a1uOlVBJ6tWrq+gSSA4cJ+XBsVIeyjpWWlpaii6hWGUKWYaGhti6dTO+/uorpKS8hIoKoKenh1u3b2PyZEskJiaWW4Hv3/RURUWlxL+PqKJS/N9Q3LhpM7bv+O8aMB0dHfj7+cDfPwCpqakfVvB7DNtrI1SsX6Z5o318y7UWKpkP97lS4DgpD46V8lDGsdLV1VV0CcUq07cLly5ZjMq6lfFdtx5o9mVzNG3WHN2690Rl3cpYsnhRuRQWFxcPADAxlj0iZWRkhPiEN9Pi4uNQqVIl6OvLhhhDIyMkxMseAXuXRCJBamqq9JF/ET0RERFReSlTyPruu28xa/YcREZGStsiIiIwZ649unX7rlwKe/LkCcRiMbp27SJt09DQQKdOHaVpOyjoHiQSiUwfExMTNG7UCHeVMJETERHRp6NMpwtVVVXx+nV2gfbX2dlQVZU/t2lra6Nu3TrS5+a1zNGsWVMkJyUjJjYWu3btxtSpVnj46F88evQI06ZaISMjE25uJwC8+Zaj0xFnLJg/D0lJSUhOSsa8+fYICwvDjRs3yrJpREREROWiTCHr1q3bWOTggCmWVhCLxQCA6tWrY+HCBbh585bcy2nZsgWOHXWVPndYuAAA4OziChsbW2zeshWamppYvmwJ9PX14e8fgBEjR8mc3lu40AE5r19j27at0NLUxM2bNzHOxpb3yCIiIiKFKlPImmtvj717dsPb6zZiY58hLy8PpqY1ERYWhqnTpsm9HE9PL9Q0NS+2z5q1jliz1rHI6VlZWbCfNx/28+bLvV4iIiIioZUpZMXGPkPvPj+ga5cuaNCgPlRUVPAgIkJ6Q1AiIiKiz12pLnz/5puvcc39ivQrk9dv3MCevfuwe89eBAQEwv3qZXTo0EGQQomIiIiUSalC1qSJE3HosFOh95J69eoVDh48hP/9NqnciiMiIiJSVqUKWU2bNin2bw16eFxHixbNP7QmIiIiIqVXqpBlZGSE169fFzn9dU4OqlY1/OCiiIiIiJRdqULW8+diNG7cuMjpTZo0Rlyc+IOLIiIiIlJ2pQpZV69exR8z7FCpUqUC0zQ1NTHDzg6XL18pt+KIiIiIlFWpbuGwbv0G9O3bFzdveGDv3n2IinqIvLw8fPFFA/zyyzioqqph/YaNQtVKREREpDRKFbISEhLQr/8ArFi+DLNnz4KKigoAIC8vD9eueWD2nLlISCj6DzMTERERfS5KfTPSmJgYjBk7Dvr6+qhTpw5UVFTw6NEjpKSkCFEfERERkVIq0x3fASAlJQWBgYHlWQsRERHRJ6NUF74TERERkXwYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJgCGLiIiISAAMWUREREQCYMgiIiIiEgBDFhEREZEAGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAKo8CHL2+s2YmOiCzyWLV0CAHB0XFtg2unTJxVcNREREX3u1BVdQEn6/vAT1NTUpM8bN24E5yNOOP3PP9K2q1fdYWNrJ32enZ39UWskIiIiel+FD1mJiYkyz62spuDRo3/h6eklbZNIJIiPj//YpREREREVqcKHrHdpaGhg0MCB2L5jp0z7V191QlCgP1JevoSXpxdWrFyFFy9eFLkckUgEkUgkfa6joyNYzURERPR5UqqQ1adPb+jp6cHFxVXa5u7ujn/++QdPn8agVi1z/PnHDLi6OKNP3x8gkUgKXc5UK0vY2dkWaG/duhUyMjLKtWZT/fQyz1utXdtyrIRKUq9eXUWXQHLgOCkPjpXyUNax0tLSUnQJxVKqkDVi+HC4u7tDLBZL206dOi39f3h4OAIDg3DH2xM9enTHuXPnC13Oxk2bZY6G6ejowN/PB/7+AUhNTS3Xmg3bayNUrF+meaN9fMu1FiqZD/e5UuA4KQ+OlfJQxrHS1dVVdAnFUpqQZWpqii5dOmPixN+K7RcXF4enMTGoV7foVC6RSIo8ykVERERUHir8LRzyDR82FAkJCbh85Uqx/QwMqqBmjRoQx8V9pMqIiIiIClKKkKWiooJhw4bC1fUocnJypO3a2tqYP88ebdu2gZmZGb76qhP279uLxKSkIk8VEhEREX0MSnG6sGuXLjAzM8MRZ2eZ9tzcXDRu3BiDBw+Cnp4e4uLicOu2J36fPAVpaWkKqpaIiIhISUKWx/XrqGlqXqA9MzMTI0eNVkBFRERERMVTitOFRERERMqGIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJgCGLiIiISAAMWUREREQCYMgiIiIiEgBDFhEREZEAGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgAFTpk2dnaIDYmWuYR4O9boI+frw+iIiNw1NUFDRs2VFC1RERERP9RV3QBJQkLC8ew4SOkz3NycqT/t5wyGb/9NgnTbWzx8OEjTLeehiNOh9Gl67dIS0tTRLlEREREACr4kSwAyMl5jfj4eOkjMTFROm3ixAnYsGEjzp07j/DwcFhPt4GWliYsLAYormAiIiIiKEHIqlu3Lvx8feDleQtbt2xGrVq1AAC1atVCtWrV4OFxXdpXIpHAy8sb7dq1LXaZIpEIurq60oeOjo6g20BERESfnwp9utDP3x/TrKfj4cNHMDY2gvW0aTh10g3duveAiYkxACA+IUFmnvj4eJiZmRW73KlWlrCzsy3Q3rp1K2RkZJTfBgAw1U8v87zVSgiLVL7q1aur6BJIDhwn5cGxUh7KOlZaWlqKLqFYFTpkubtfk/4/LAzw8fGF5+2bGDJkCPz8/AAAeXl5MvOoqKgUaHvfxk2bsX3HTulzHR0d+Pv5wN8/AKmpqeW3AQAM22sjVKxfpnmjfXxL7kTlyof7XClwnJQHx0p5KONY6erqKrqEYlX404XvysjIQFhYGOrWrYu4uHgAgImxsUwfIyMjxCfEF7sciUSC1NRU6YMXyRMREVF5U6qQJRKJ0OCLLxAnFuPJkycQi8Xo2rWLdLqGhgY6deqolGmciIiIPi0V+nTh/Hn2uHjpMmJiYmBkZIjp1tNQWVcXLq5HAQC7du3G1KlWePjoXzx69AjTplohIyMTbm4nFFs4ERERffYqdMiqUaMGtmzehKpVDfDiRSL8/Pzw08/9ERMTAwDYvGUrNDU1sXzZEujr68PfPwAjRo7i6T8iIiJSuAodsiZPsSyxz5q1jliz1vEjVENEREQkP6W6JouIiIhIWTBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgEwZBEREREJgCGLiIiISAAMWUREREQCYMgiIiIiEgBDFhEREZEA1BVdABXNfOCVD5o/+niPcqqEiIiISotHsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgADFlEREREAmDIIiIiIhIAQxYRERGRABiyiIiIiATAkEVEREQkAIYsIiIiIgFU6JBlZWWJs2f+wYPw+wgK9Mee3btQv349mT6OjmsRGxMt8zh9+qSCKiYiIiJ6o0L/geivOnXCvv37ERAQCHV1Ncyc+SecDh/Ct991R0ZGhrTf1avusLG1kz7Pzs5WRLlEREREUhU6ZI0aPUbmuY2NHYLvBaJFixbw9vaWtkskEsTHx3/s8oiIiIiKVKFD1vv09PQAAMnJyTLtX33VCUGB/kh5+RJenl5YsXIVXrx4UeRyRCIRRCKR9LmOjo4g9RIREdHnS6lC1sIF8+HtfQfh4eHSNnd3d/zzzz94+jQGtWqZ488/ZsDVxRl9+v4AiURS6HKmWlnCzs62QHvr1q1kTkOWB1P99HJdXmlUa9dWYetWRvXq1VV0CSQHjpPy4FgpD2UdKy0tLUWXUCylCVnLli5BkyaNMcBioEz7qVOnpf8PDw9HYGAQ7nh7okeP7jh37nyhy9q4aTO279gpfa6jowN/Px/4+wcgNTW1XOs2bK+NULF+uS5TbrVWlXnW6OM9yrEQ5eHj46voEkgOHCflwbFSHso4Vrq6uoouoVhKEbKWLF6EXr2+h8XAwXj27HmxfePi4vA0Jgb16hadyiUSSZFHuYiIiIjKQ4UPWUuXLEafPn0weMgQREdHl9jfwKAKataoAXFc3EeojoiIiKhwFTpkLVu2FBYD+uPX8RORmpoGY2NjAMCrV6+QmZkJbW1tzLCzxZmzZyEWx8Hc3AyzZ81EYlJSkacKiYiIiD6GCh2yfhk3FgBw/JirTPt0G1u4uLgiNzcXjRs3xuDBg6Cnp4e4uDjcuu2J3ydPQVpamiJKJiIiIgJQwUNWTVPzYqdnZmZi5KjRH6kaIiIiIvlV6D+rQ0RERKSsGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAIYuIiIhIAAxZRERERAJgyCIiIiISAEMWERERkQAYsoiIiIgEwJBFREREJACGLCIiIiIBMGQRERERCYAhi4iIiEgA6oougOhd5gOvlHne6OM9yrESIiKiD8MjWUREREQC4JEsKhSPKBEREX0YHskiIiIiEgBDFhEREZEAGLKIiIiIBMCQRURERCQAhiwiIiIiATBkEREREQmAt3Cgcvcht39QFPOBV2BYLQXmtfRLPS9vWUFERIXhkSwiIiIiATBkEREREQmAIYuIiIhIALwmiz4ZiroWTFF/goh/+oiIqGLjkSwiIiIiATBkEREREQmApwuJFEgZb3dBRETy4ZEsIiIiIgEwZBEREREJ4JMJWePGjYWX5y08jIrA+XNn0KFDB0WXRERERJ+xT+KarH79fobDwgWYM2cu7tz1wZgxo3Do4AF89113xMTGKro8ogqHt38gIhLeJ3Ek67dJk+B0xBmHnY4gMjISCxY4IDY2FmPHjlF0aURERPSZUvojWRoaGmjRojk2bd4i0+7hcR3t2rVTUFVEny5lPAqmjDeMVUaf21FOZXwtfIjPbXvLg9KHrKpVq0JdXR0JCfEy7fEJCTAxMS50HpFIBJFIJH2uo6Mj82950hSpQkfzkzhg+MnjWAlPV1f3g5ehpaVV6uV8yLh+SM2f28/T+/uqLGOlTBT1cyUEecaqIm6vEL+3y5PSh6x8eXl5Ms9VVFQKtOWbamUJOzvbAu3+fj6C1EZEb62+r+gKSk8Za1YU7iv5fW77SuDt1dHRQWpqqqDrKAulD1mJiYl4/fo1jI1NZNqNDA0RH59Q6DwbN23G9h07ZdoMDAyQlJRUrrXp6OjA388Hrdu0Q1paWrkum8oXx0o5cJyUB8dKeSj7WOno6EAsFiu6jEIpfcjKzs5GUNA9dO3aBefPn5e2d+3aBRcuXCx0HolEAolEItMmZAJOS0urkAmbCuJYKQeOk/LgWCkPZR2rilyz0ocsANixcyc2rF+HoMAg+Pj6YvToUTA1NcWBvw8qujQiIiL6TH0SIevUqdMwMDCAjY01TExMEB4ejtFjxiEmJkbRpREREdFn6pMIWQCwf/8B7N9/QNFlyJBIJFizZm2BU5NU8XCslAPHSXlwrJQHx0o4KjVqmhX+FTwiIiIiKrPP6yYuRERERB8JQxYRERGRABiyiIiIiATAkEVEREQkAIYsgYwbNxZenrfwMCoC58+dQYcOHRRd0ifNysoSZ8/8gwfh9xEU6I89u3ehfv16BfrZ2drAz9cHUZEROOrqgoYNG8pMF4lEWLJ4EYLvBSIyIhz79u5BjRrVZfro6+tjw4Z1CLsfgrD7IdiwYR309PQE3b5PlZWVJWJjouHgsECmneNUMVSvXh0bN6xHcHAQoiIf4NLF82jevLlMH46V4qmpqeHPP/+Al+ctREVGwPP2TdhMt4aKiopMP47Vx8eQJYB+/X6Gw8IF2LBhI3r17gvvO3dw6OABmNasqejSPllfdeqEffv346ef+2P4iJFQU1eD0+FD0NLSkvaxnDIZv/02CXPt7fHDjz8hPj4eR5wOy/yBUQeHhejTtw8mT7HEgAEDoa2jjQP790FV9b+XyuZNG9GsaTOMGj0Go0aPQbOmzbBxw7qPubmfhJYtW2L0qJEICQ2Vaec4VQz6+vo4eeI4Xr9+jdGjx+Lb77rDYdFivHz5UtqHY1UxWFpOwdgxozHXfh6+/a4blixdhsmTf8f48b/+14djpRC8hYMA/jl9CveCgzF79hxpm8e1qzh//gKWr1ipwMo+H1WrVkXwvUBYDBwMb29vAG/+APiuXbuxectWAG8+tQUG+GHpsuU4ePAQKleujHtBAZhmPR2nTp0GAFSrVg0+d70xesw4eHh4oEGDBrju4Y4ff/oZ/v4BAIA2bVrjn9On0KXrt4iKeqiQ7VU22trauHDhHObMmQvradMQEhqCBQscAHCcKoo5s2ehffv2sBg4qMg+HKuKYf/+vUiIT4DdjD+kbTt3bEdGZgamTZsOgGOlKDySVc40NDTQokVzeHhcl2n38LiOdu3aKaiqz0/+4evk5GQAQK1atVCtWjWZcZFIJPDy8ka7dm0BAC1aNIdIJJLpIxaLERYejvZv+7Rr2xYpKSnSNxgA8PPzR0pKCtq15fjKa9myJbhy5Spu3Lgp085xqjh69foegUFB2L59K4IC/XHxwjmMHDlCOp1jVXHcvXMXnTt/g3r16gIAmjZtgg4d2uPqFXcAHCtF+mTu+F5RVK1aFerq6khIiJdpj09IgImJsYKq+vwsXDAf3t53EB4eDgDSfR+fkCDTLz4+HmZmZm/6GJsgKysLKSkpMn0S4hNg/HZ+YxNjJLx4UWB9CS9eSPtQ8fr364fmXzbHDz/+VGAax6niqFWrFsaOGY0dO3dh44ZNaNW6FRYvWgSJRIKjR49xrCqQTZu3oHLlyrjucQ05OTlQU1PDipWrcOLkSQB8XSkSQ5ZA8vJkz8KqqKgUaCNhLFu6BE2aNMYAi4EFppVlXFRU3puvkP4qbzqVreDPSM2aNbBo0UKMGDkKWVlZRfbjOCmeqqoqgoKCsOLtJQ7BISFo1LAhxo4dg6NHj0n7cawUr3+/fhg0aCAsLaci/MEDNGvWFA4OCyEWi+HqelTaj2P18fF0YTlLTEzE69evYWxsItNuZGiI+PiEIuai8rJk8SL06vU9Bg8ZhmfPnkvb4+LeHFk0MZb9tGVkZIT4t0cd4+LjUKlSJejr68v0MTQyQsLbsYuPi4eRkVGB9RpWrcrxlUOL5i1gbGyM8+fO4snjR3jy+BG+/vo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}, + "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ski_data.SkiableTerrain_ac.hist(bins=30)\n", - "plt.xlabel('SkiableTerrain_ac')\n", - "plt.ylabel('Count')\n", - "plt.title('Distribution of skiable area (acres) after replacing erroneous value');" - ] + "execution_count": 172 }, { "cell_type": "markdown", @@ -1265,8 +2891,15 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:13.172574Z", + "start_time": "2026-06-09T23:20:13.123480Z" + } + }, + "source": [ + "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]" + ], "outputs": [ { "data": { @@ -1276,22 +2909,57 @@ "Name: Snow Making_ac, dtype: float64" ] }, - "execution_count": 31, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]" - ] + "execution_count": 173 }, { "cell_type": "code", - "execution_count": 32, - 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2 rows × 26 columns

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" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 178 }, { "cell_type": "markdown", @@ -1596,18 +3381,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:14.621708Z", + "start_time": "2026-06-09T23:20:13.784896Z" + } + }, "source": [ "#Code task 26#\n", "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n", "#Pass the argument bins=30 to hist(), but feel free to explore other values\n", - "ski_data.___[ski_data.___ < ___].hist(___)\n", + "ski_data[ski_data['yearsOpen']< 1000].hist(bins=30)\n", "plt.xlabel('Years open')\n", "plt.ylabel('Count')\n", "plt.title('Distribution of years open excluding 2019');" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 179 }, { "cell_type": "markdown", @@ -1625,8 +3426,15 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:14.899415Z", + "start_time": "2026-06-09T23:20:14.837560Z" + } + }, + "source": [ + "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()" + ], "outputs": [ { "data": { @@ -1642,14 +3450,12 @@ "Name: yearsOpen, dtype: float64" ] }, - "execution_count": 38, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()" - ] + "execution_count": 180 }, { "cell_type": "markdown", @@ -1660,12 +3466,17 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:14.965414Z", + "start_time": "2026-06-09T23:20:14.938607Z" + } + }, "source": [ "ski_data = ski_data[ski_data.yearsOpen < 1000]" - ] + ], + "outputs": [], + "execution_count": 181 }, { "cell_type": "markdown", @@ -1720,9 +3531,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:15.038077Z", + "start_time": "2026-06-09T23:20:15.000484Z" + } + }, "source": [ "#Code task 27#\n", "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n", @@ -1733,12 +3547,121 @@ "state_summary = ski_data.groupby('state').agg(\n", " resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n", " state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n", - " state_total_days_open=pd.NamedAgg(column=__, aggfunc='sum'),\n", - " ___=pd.NamedAgg(column=___, aggfunc=___),\n", - " ___=pd.NamedAgg(column=___, aggfunc=___)\n", - ").___\n", + " state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n", + " state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n", + " state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n", + ").reset_index()\n", "state_summary.head()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + " state resorts_per_state state_total_skiable_area_ac \\\n", + "0 Alaska 3 2280.0 \n", + "1 Arizona 2 1577.0 \n", + "2 California 21 25948.0 \n", + "3 Colorado 22 43682.0 \n", + "4 Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "0 345.0 4.0 \n", + "1 237.0 6.0 \n", + "2 2738.0 81.0 \n", + "3 3258.0 74.0 \n", + "4 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac \n", + "0 580.0 \n", + "1 80.0 \n", + "2 587.0 \n", + "3 428.0 \n", + "4 256.0 " + ], + "text/html": [ + "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_ac
0Alaska32280.0345.04.0580.0
1Arizona21577.0237.06.080.0
2California2125948.02738.081.0587.0
3Colorado2243682.03258.074.0428.0
4Connecticut5358.0353.010.0256.0
\n", + "
" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 182 }, { "cell_type": "markdown", @@ -1756,27 +3679,41 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:15.144950Z", + "start_time": "2026-06-09T23:20:15.083253Z" + } + }, + "source": [ + "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", + "missing_price.value_counts()/len(missing_price) * 100\n", + "missing_price" + ], "outputs": [ { "data": { "text/plain": [ - "0 82.317073\n", - "2 14.329268\n", - "1 3.353659\n", - "dtype: float64" + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "325 2\n", + "326 0\n", + "327 0\n", + "328 0\n", + "329 1\n", + "Length: 328, dtype: int64" ] }, - "execution_count": 41, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n", - "missing_price.value_counts()/len(missing_price) * 100" - ] + "execution_count": 183 }, { "cell_type": "markdown", @@ -1787,14 +3724,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:15.521624Z", + "start_time": "2026-06-09T23:20:15.512041Z" + } + }, "source": [ "#Code task 28#\n", "#Use `missing_price` to remove rows from ski_data where both price values are missing\n", - "ski_data = ski_data[___ != 2]" - ] + "ski_data = ski_data[missing_price != 2]\n", + "#ski_data = ski_data.drop(missing_price.index, axis=0, inplace=True)" + ], + "outputs": [], + "execution_count": 184 }, { "cell_type": "markdown", @@ -1805,26 +3748,29 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:16.160471Z", + "start_time": "2026-06-09T23:20:15.522871Z" + } + }, + "source": [ + "ski_data.hist(figsize=(15, 10))\n", + "plt.subplots_adjust(hspace=0.5);" + ], "outputs": [ { "data": { - "image/png": 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\n", 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}, + "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ski_data.hist(figsize=(15, 10))\n", - "plt.subplots_adjust(hspace=0.5);" - ] + "execution_count": 185 }, { "cell_type": "markdown", @@ -1849,20 +3795,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:16.407532Z", + "start_time": "2026-06-09T23:20:16.180291Z" + } + }, "source": [ + "import requests\n", + "\n", "#Code task 29#\n", "#Use pandas' `read_html` method to read the table from the URL below\n", + "headers = {\n", + " 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n", + "}\n", + "\n", "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n", - "usa_states = pd.___(___)" - ] + "response = requests.get(states_url, headers=headers)\n", + "usa_states = pd.read_html(response.text)" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kd/z316r17n3rv6_v8fxp4kggs00000gn/T/ipykernel_20070/3712679905.py:11: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n", + " usa_states = pd.read_html(response.text)\n" + ] + } + ], + "execution_count": 186 }, { "cell_type": "code", - "execution_count": 45, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:16.443851Z", + "start_time": "2026-06-09T23:20:16.423777Z" + } + }, + "source": [ + "type(usa_states)" + ], "outputs": [ { "data": { @@ -1870,19 +3844,24 @@ "list" ] }, - "execution_count": 45, + "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "type(usa_states)" - ] + "execution_count": 187 }, { "cell_type": "code", - "execution_count": 46, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:16.489723Z", + "start_time": "2026-06-09T23:20:16.458919Z" + } + }, + "source": [ + "len(usa_states)" + ], "outputs": [ { "data": { @@ -1890,22 +3869,53 @@ "1" ] }, - "execution_count": 46, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "len(usa_states)" - ] + "execution_count": 188 }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:16.519751Z", + "start_time": "2026-06-09T23:20:16.490904Z" + } + }, + "source": [ + "usa_states = usa_states[0]\n", + "usa_states.head()" + ], "outputs": [ { "data": { + "text/plain": [ + " Name & postal abbs. [1] Cities \\\n", + " Name & postal abbs. [1] Name & postal abbs. [1].1 Capital Largest[5] \n", + "0 Alabama AL Montgomery Birmingham \n", + "1 Alaska AK Juneau Anchorage \n", + "2 Arizona AZ Phoenix Phoenix \n", + "3 Arkansas AR Little Rock Little Rock \n", + "4 California CA Sacramento Los Angeles \n", + "\n", + " Established[A] Population [B][3] Total area[4] Land area[4] \\\n", + " Established[A] Population [B][3] mi2 km2 mi2 \n", + "0 Dec 14, 1819 4903185 52420 135767 50645 \n", + "1 Jan 3, 1959 731545 665384 1723337 570641 \n", + "2 Feb 14, 1912 7278717 113990 295234 113594 \n", + "3 Jun 15, 1836 3017804 53179 137732 52035 \n", + "4 Sep 9, 1850 39512223 163695 423967 155779 \n", + "\n", + " Water area[4] Number of Reps. \n", + " km2 mi2 km2 Number of Reps. \n", + "0 131171 1775 4597 7 \n", + "1 1477953 94743 245384 1 \n", + "2 294207 396 1026 9 \n", + "3 134771 1143 2961 4 \n", + "4 403466 7916 20501 53 " + ], "text/html": [ "
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statestate_populationstate_area_sq_miles
0Alabama490318552420
1Alaska731545665384
2Arizona7278717113990
3Arkansas301780453179
4California39512223163695
5Colorado5758736104094
6Connecticut35652785543
7Delaware9737642489
8Florida2147773765758
9Georgia1061742359425
10Hawaiʻi141587210932
11Idaho178706583569
12Illinois1267182157914
13Indiana673221936420
14Iowa315507056273
15Kansas291331482278
16Kentucky[C]446767340408
17Louisiana464879452378
18Maine134421235380
19Maryland604568012406
20Massachusetts[C]689250310554
21Michigan998685796714
22Minnesota563963286936
23Mississippi297614948432
24Missouri613742869707
25Montana1068778147040
26Nebraska193440877348
27Nevada3080156110572
28New Hampshire13597119349
29New Jersey88821908723
30New Mexico2096829121590
31New York1945356154555
32North Carolina1048808453819
33North Dakota76206270698
34Ohio1168910044826
35Oklahoma395697169899
36Oregon421773798379
37Pennsylvania[C]1280198946054
38Rhode Island[D]10593611545
39South Carolina514871432020
40South Dakota88465977116
41Tennessee682917442144
42Texas28995881268596
43Utah320595884897
44Vermont6239899616
45Virginia[C]853551942775
46Washington761489371298
47West Virginia179214724230
48Wisconsin582243465496
49Wyoming57875997813
\n", + "
" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 192 }, { "cell_type": "markdown", @@ -2192,16 +4579,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:17.056370Z", + "start_time": "2026-06-09T23:20:17.011003Z" + } + }, "source": [ "#Code task 32#\n", "#Find the states in `state_summary` that are not in `usa_states_sub`\n", "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n", - "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n", + "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", "missing_states" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 193 }, { "cell_type": "markdown", @@ -2219,28 +4622,33 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:17.312646Z", + "start_time": "2026-06-09T23:20:17.280371Z" + } + }, + "source": [ + "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" + ], "outputs": [ { "data": { "text/plain": [ - "20 Massachusetts[upper-alpha 3]\n", - "37 Pennsylvania[upper-alpha 3]\n", - "38 Rhode Island[upper-alpha 4]\n", - "45 Virginia[upper-alpha 3]\n", - "47 West Virginia\n", + "20 Massachusetts[C]\n", + "37 Pennsylvania[C]\n", + "38 Rhode Island[D]\n", + "45 Virginia[C]\n", + "47 West Virginia\n", "Name: state, dtype: object" ] }, - "execution_count": 52, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" - ] + "execution_count": 194 }, { "cell_type": "markdown", @@ -2251,9 +4659,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:17.457701Z", + "start_time": "2026-06-09T23:20:17.366085Z" + } + }, "source": [ "#Code task 33#\n", "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n", @@ -2262,22 +4673,443 @@ "#value='' #empty string as replacement\n", "#regex=True #we used a regex in our `to_replace` argument\n", "#inplace=True #Do this \"in place\"\n", - "usa_states_sub.state.___(to_replace=___, value=__, regex=___, inplace=___)\n", - "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]" - ] + "usa_states_sub.state.replace(to_replace=r'\\[.*\\]', value='', regex=True, inplace=True)\n", + "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]\n", + "usa_states_sub\n" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kd/z316r17n3rv6_v8fxp4kggs00000gn/T/ipykernel_20070/3541061765.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " usa_states_sub.state.replace(to_replace=r'\\[.*\\]', value='', regex=True, inplace=True)\n" + ] + }, + { + "data": { + "text/plain": [ + " state state_population state_area_sq_miles\n", + "0 Alabama 4903185 52420\n", + "1 Alaska 731545 665384\n", + "2 Arizona 7278717 113990\n", + "3 Arkansas 3017804 53179\n", + "4 California 39512223 163695\n", + "5 Colorado 5758736 104094\n", + "6 Connecticut 3565278 5543\n", + "7 Delaware 973764 2489\n", + "8 Florida 21477737 65758\n", + "9 Georgia 10617423 59425\n", + "10 Hawaiʻi 1415872 10932\n", + "11 Idaho 1787065 83569\n", + "12 Illinois 12671821 57914\n", + "13 Indiana 6732219 36420\n", + "14 Iowa 3155070 56273\n", + "15 Kansas 2913314 82278\n", + "16 Kentucky 4467673 40408\n", + "17 Louisiana 4648794 52378\n", + "18 Maine 1344212 35380\n", + "19 Maryland 6045680 12406\n", + "20 Massachusetts 6892503 10554\n", + "21 Michigan 9986857 96714\n", + "22 Minnesota 5639632 86936\n", + "23 Mississippi 2976149 48432\n", + "24 Missouri 6137428 69707\n", + "25 Montana 1068778 147040\n", + "26 Nebraska 1934408 77348\n", + "27 Nevada 3080156 110572\n", + "28 New Hampshire 1359711 9349\n", + "29 New Jersey 8882190 8723\n", + "30 New Mexico 2096829 121590\n", + "31 New York 19453561 54555\n", + "32 North Carolina 10488084 53819\n", + "33 North Dakota 762062 70698\n", + "34 Ohio 11689100 44826\n", + "35 Oklahoma 3956971 69899\n", + "36 Oregon 4217737 98379\n", + "37 Pennsylvania 12801989 46054\n", + "38 Rhode Island 1059361 1545\n", + "39 South Carolina 5148714 32020\n", + "40 South Dakota 884659 77116\n", + "41 Tennessee 6829174 42144\n", + "42 Texas 28995881 268596\n", + "43 Utah 3205958 84897\n", + "44 Vermont 623989 9616\n", + "45 Virginia 8535519 42775\n", + "46 Washington 7614893 71298\n", + "47 West Virginia 1792147 24230\n", + "48 Wisconsin 5822434 65496\n", + "49 Wyoming 578759 97813" + ], + "text/html": [ + "
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statestate_populationstate_area_sq_miles
0Alabama490318552420
1Alaska731545665384
2Arizona7278717113990
3Arkansas301780453179
4California39512223163695
5Colorado5758736104094
6Connecticut35652785543
7Delaware9737642489
8Florida2147773765758
9Georgia1061742359425
10Hawaiʻi141587210932
11Idaho178706583569
12Illinois1267182157914
13Indiana673221936420
14Iowa315507056273
15Kansas291331482278
16Kentucky446767340408
17Louisiana464879452378
18Maine134421235380
19Maryland604568012406
20Massachusetts689250310554
21Michigan998685796714
22Minnesota563963286936
23Mississippi297614948432
24Missouri613742869707
25Montana1068778147040
26Nebraska193440877348
27Nevada3080156110572
28New Hampshire13597119349
29New Jersey88821908723
30New Mexico2096829121590
31New York1945356154555
32North Carolina1048808453819
33North Dakota76206270698
34Ohio1168910044826
35Oklahoma395697169899
36Oregon421773798379
37Pennsylvania1280198946054
38Rhode Island10593611545
39South Carolina514871432020
40South Dakota88465977116
41Tennessee682917442144
42Texas28995881268596
43Utah320595884897
44Vermont6239899616
45Virginia853551942775
46Washington761489371298
47West Virginia179214724230
48Wisconsin582243465496
49Wyoming57875997813
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" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 195 }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:17.592081Z", + "start_time": "2026-06-09T23:20:17.521407Z" + } + }, "source": [ "#Code task 34#\n", "#And now verify none of our states are missing by checking that there are no states in\n", "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n", - "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n", + "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n", + "\n", "missing_states" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 196 }, { "cell_type": "markdown", @@ -2288,16 +5120,140 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:17.823010Z", + "start_time": "2026-06-09T23:20:17.807179Z" + } + }, "source": [ "#Code task 35#\n", "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n", "#specify the arguments how='left' and on='state'\n", - "state_summary = state_summary.___(usa_states_sub, ___=___, ___=___)\n", + "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n", "state_summary.head()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + " state resorts_per_state state_total_skiable_area_ac \\\n", + "0 Alaska 3 2280.0 \n", + "1 Arizona 2 1577.0 \n", + "2 California 21 25948.0 \n", + "3 Colorado 22 43682.0 \n", + "4 Connecticut 5 358.0 \n", + "\n", + " state_total_days_open state_total_terrain_parks \\\n", + "0 345.0 4.0 \n", + "1 237.0 6.0 \n", + "2 2738.0 81.0 \n", + "3 3258.0 74.0 \n", + "4 353.0 10.0 \n", + "\n", + " state_total_nightskiing_ac state_population state_area_sq_miles \n", + "0 580.0 731545 665384 \n", + "1 80.0 7278717 113990 \n", + "2 587.0 39512223 163695 \n", + "3 428.0 5758736 104094 \n", + "4 256.0 3565278 5543 " + ], + "text/html": [ + "
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stateresorts_per_statestate_total_skiable_area_acstate_total_days_openstate_total_terrain_parksstate_total_nightskiing_acstate_populationstate_area_sq_miles
0Alaska32280.0345.04.0580.0731545665384
1Arizona21577.0237.06.080.07278717113990
2California2125948.02738.081.0587.039512223163695
3Colorado2243682.03258.074.0428.05758736104094
4Connecticut5358.0353.010.0256.035652785543
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" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 197 }, { "cell_type": "markdown", @@ -2322,15 +5278,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-09T23:20:17.967834Z", + "start_time": "2026-06-09T23:20:17.874659Z" + } + }, "source": [ "#Code task 36#\n", "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n", "#'AdultWeekend' on the y-axis\n", - "ski_data.___(x=___, y=___, kind=___);" - ] + "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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state
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Arizona21577.0237.06.080.00.0274771.754540
California2125948.02738.081.0587.00.05314812.828736
Colorado2243682.03258.074.0428.00.38202821.134744
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" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 66 }, { "cell_type": "markdown", @@ -1177,12 +1408,17 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:51:21.218027Z", + "start_time": "2026-06-11T23:51:21.191718Z" + } + }, "source": [ "state_summary_scale = scale(state_summary_scale)" - ] + ], + "outputs": [], + "execution_count": 67 }, { "cell_type": "markdown", @@ -1193,47 +1429,188 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:51:23.435031Z", + "start_time": "2026-06-11T23:51:23.401159Z" + } + }, "source": [ "#Code task 2#\n", "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n", - "state_summary_scaled_df = pd.DataFrame(___, columns=___)\n", + "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns= state_summary_columns)\n", "state_summary_scaled_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 3.5.3.1.1 Verifying the scaling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is definitely going the extra mile for validating your steps, but provides a worthwhile lesson." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First of all, check the mean of the scaled features using panda's `mean()` DataFrame method." - ] - }, - { - "cell_type": "code", - "execution_count": null, + ], + "outputs": [ + { + "data": { + "text/plain": [ + " resorts_per_state state_total_skiable_area_ac state_total_days_open \\\n", + "0 -0.806912 -0.392012 -0.689059 \n", + "1 -0.933558 -0.462424 -0.819038 \n", + "2 1.472706 1.978574 2.190933 \n", + "3 1.599351 3.754811 2.816757 \n", + "4 -0.553622 -0.584519 -0.679431 \n", + "\n", + " state_total_terrain_parks state_total_nightskiing_ac \\\n", + "0 -0.816118 0.069410 \n", + "1 -0.726994 -0.701326 \n", + "2 2.615141 0.080201 \n", + "3 2.303209 -0.164893 \n", + "4 -0.548747 -0.430027 \n", + "\n", + " resorts_per_100kcapita resorts_per_100ksq_mile \n", + "0 0.139593 -0.689999 \n", + "1 -0.644706 -0.658125 \n", + "2 -0.592085 -0.387368 \n", + "3 0.082069 -0.184291 \n", + "4 -0.413557 1.504408 " + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 68 + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], + "source": [ + "##### 3.5.3.1.1 Verifying the scaling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is definitely going the extra mile for validating your steps, but provides a worthwhile lesson." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all, check the mean of the scaled features using panda's `mean()` DataFrame method." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:52:04.265939Z", + "start_time": "2026-06-11T23:52:04.233603Z" + } + }, "source": [ "#Code task 3#\n", "#Call `state_summary_scaled_df`'s `mean()` method\n", - "state_summary_scaled_df.___" - ] + "state_summary_scaled_df.mean()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "resorts_per_state -7.295751e-17\n", + "state_total_skiable_area_ac -4.163336e-17\n", + "state_total_days_open 7.692260e-17\n", + "state_total_terrain_parks 4.599495e-17\n", + "state_total_nightskiing_ac 7.612958e-17\n", + "resorts_per_100kcapita 5.075305e-17\n", + "resorts_per_100ksq_mile 5.075305e-17\n", + "dtype: float64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 69 }, { "cell_type": "markdown", @@ -1251,14 +1628,14 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 4#\n", "#Call `state_summary_scaled_df`'s `std()` method\n", - "state_summary_scaled_df.___" - ] + "state_summary_scaled_df.std()" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1271,14 +1648,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:53:18.099304Z", + "start_time": "2026-06-11T23:53:18.068462Z" + } + }, "source": [ "#Code task 5#\n", "#Repeat the previous call to `std()` but pass in ddof=0 \n", - "state_summary_scaled_df.___(___)" - ] + "state_summary_scaled_df.std(ddof=0)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "resorts_per_state 1.0\n", + "state_total_skiable_area_ac 1.0\n", + "state_total_days_open 1.0\n", + "state_total_terrain_parks 1.0\n", + "state_total_nightskiing_ac 1.0\n", + "resorts_per_100kcapita 1.0\n", + "resorts_per_100ksq_mile 1.0\n", + "dtype: float64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 70 }, { "cell_type": "markdown", @@ -1303,12 +1703,17 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:53:57.728299Z", + "start_time": "2026-06-11T23:53:57.700899Z" + } + }, "source": [ "state_pca = PCA().fit(state_summary_scale)" - ] + ], + "outputs": [], + "execution_count": 71 }, { "cell_type": "markdown", @@ -1319,9 +1724,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:57:04.302476Z", + "start_time": "2026-06-11T23:57:04.214129Z" + } + }, "source": [ "#Code task 6#\n", "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n", @@ -1330,11 +1738,25 @@ "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n", "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n", "plt.subplots(figsize=(10, 6))\n", - "plt.plot(state_pca.explained_variance_ratio_.___)\n", - "plt.xlabel(___)\n", - "plt.ylabel(___)\n", - "plt.title(___);" - ] + "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n", + "plt.xlabel('component #')\n", + "plt.ylabel('explained variance ratio')\n", + "#plt.ylabel(___)\n", + "plt.title('Cumulative explained variance ratio');" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 73 }, { "cell_type": "markdown", @@ -1359,19 +1781,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:58:22.551077Z", + "start_time": "2026-06-11T23:58:22.526834Z" + } + }, "source": [ "#Code task 7#\n", "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n", - "state_pca_x = state_pca.___(___)" - ] + "state_pca_x = state_pca.transform(state_summary_scale)" + ], + "outputs": [], + "execution_count": 74 }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:58:24.570780Z", + "start_time": "2026-06-11T23:58:24.518827Z" + } + }, + "source": [ + "state_pca_x.shape" + ], "outputs": [ { "data": { @@ -1379,14 +1813,12 @@ "(35, 7)" ] }, - "execution_count": 29, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "state_pca_x.shape" - ] + "execution_count": 75 }, { "cell_type": "markdown", @@ -1406,22 +1838,12 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+m5YtW3LffffRpUsXEhISWLt2LcnJyVx44YU899xzADjnSElJISoqiujoaGbNmgVAWloaSUlJXH/99bRp04bBgwfjnGPatGns3r2b3r1707t379AcGJEiQtlDdy6w3MzWAauABc6590JYj4iIVEONGzemY8eOvPee9ydi5syZXHLJJUyaNInFixezdu1aEhISeOKJJwLb1KlTh+XLlzNw4EAALrjgAj788EN69OjB8OHDeeedd/joo4946KGHAJgzZw6ZmZmsW7eOxYsXk5KSwpdffglARkYGU6dOZfPmzXz22WesWLGC0aNH07RpU1JTU0lNTa3iIyJyvJDdtsQ59xkQG6r9i4hI+Cgcdv3Vr37FzJkzufbaa/nnP/9Jt27dAPjhhx/o0qVLYP0bb7yx2PZXX301ANHR0ezfv5/69etTv3596tSpQ05ODsuXL2fQoEFERERw7rnn0qtXL1avXk2DBg3o2LEjzZs3ByAuLo6srCy6d+9eRZ9cJDjV4T50IiIix5mbsYvHFm5ld04e50b+jI8X/pu1a9eSl5dHfHw8l112GTNmzChx27p16xabrl27NgA1atQIvC+cPnLkCM65Uusoun5ERARHjhw5lY8lUilCfZWriIjIceZm7GL8nA3sysnDAV/lgTvvYq4bNJRBgwbRuXNnVqxYwaeffgrAwYMH+eSTT056fz179mTWrFkUFBSQnZ3N0qVL6djxuOv0iqlfvz779u076X2KVCQFOhERqXYeW7iVvPziV6jWadODrE82M3DgQJo0acKrr77KoEGDiImJoXPnzmzZsuWk93fNNdcQExNDbGwsffr04dFHH+W8884rc5uRI0fSr18/XRQh1YKV1c1c3SQkJLj09PRQlyEiIpWs1b0LKOmvkwE7Jvev6nJEToqZramq59Srh05ERKqdpg0jyzVf5HSnQCciItVOSvJFRNaKKDYvslYEKckXhagikepNV7mKiEi1MyC+GUDgKtemDSNJSb4oMF9EilOgExGRamlAfDMFOJEgachVREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYC3mgM7MIM8sws/mhrkVEREQkHIU80AFjgI9DXYSIiIhIuAppoDOz5kB/4G+hrENEREQknIW6h24q8HvgaGkrmNlIM0s3s/Ts7Oyqq0xEREQkTIQs0JnZlcA3zrk1Za3nnHvBOZfgnEto0qRJFVUnIiIiEj5C2UPXDbjazLKAmUAfM/t7COsRERERCUshC3TOufHOuebOuZbAQGCJc25IqOoRERERCVehPodORERERE5RzVAXAOCcSwPSQlyGiIiISFhSD52IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhERCZqZMW7cuMD0lClTmDhxYoW1n5WVRVRUVLF5EydOZMqUKRW2j2CVtd+uXbtWcTUiZVOgExGRoNWuXZs5c+awZ8+eUJcSUitXrjxuXkFBQQgqEfEo0ImISNBq1qzJyJEjefLJJ49blp2dzXXXXUdiYiKJiYmsWLECgOjoaHJycnDO0bhxY15//XUAhg4dyuLFi8u1/xdffJHExERiY2O57rrrOHjwIADDhw/njjvuoHfv3rRu3ZoPPviAW2+9lbZt2zJ8+PDA9vXq1WPcuHG0b9+eSy65hOzsbACmTZvGxRdfTExMDAMHDgysv3nzZpKSkmjdujXTpk0r1g5AWloavXv35qabbiI6OpqCggJSUlJITEwkJiaG559/vlyfT+RkKdCJiEi5jBo1iunTp5Obm1ts/pgxYxg7diyrV69m9uzZjBgxAoBu3bqxYsUKNm3aROvWrVm2bBkAH330EZ07dz6u/e3btxMXFxd4Pffcc4Fl1157LatXr2bdunW0bduWl156KbDsu+++Y8mSJTz55JNcddVVjB07lk2bNrFhwwYyMzMBOHDgAO3bt2ft2rX06tWLhx9+GIDJkyeTkZHB+vXri+1vy5YtLFy4kFWrVvHwww+Tn59/XL2rVq1i0qRJbN68mZdeeomzzjqL1atXs3r1al588UV27NhxsodaJGg1Q12AiIiElwYNGnDzzTczbdo0IiMjA/MXL17M5s2bA9Pff/89+/bto0ePHixdupQWLVpwxx138MILL7Br1y4aNWoU6Okq6sILLwwEMKDYOXobN27kgQceICcnh/3795OcnBxYdtVVV2FmREdHc+655xIdHQ1Au3btyMrKIi4ujho1anDjjTcCMGTIEK699loAYmJiGDx4MAMGDGDAgAGBNvv370/t2rWpXbs255xzDl9//TXNmzcvVm/Hjh1p1aoVAIsWLWL9+vW88847AOTm5rJt27bAcpHKokAnIiJlmpuxi8cWbmV3Th55+QXMzdjFXXfdRfv27bnlllsC6x09epQPP/ywWMgD6NmzJ8888ww7d+5k0qRJvPvuu7zzzjv06NGj3LUMHz6cuXPnEhsby6uvvkpaWlpgWe3atQGoUaNG4H3h9JEjR0psz8wAWLBgAUuXLuUf//gHf/jDH9i0aVOxNgEiIiJKbKdu3bqB9845nn766WJBU6QqaMhVRERKNTdjF+PnbGBXTh4OcA7Gz9nA0s/z+PWvf11syLNv37785S9/CUwX9rJdcMEF7Nmzh23bttG6dWu6d+/OlClTTirQ7du3j/PPP5/8/HymT59e7u2PHj0a6D1788036d69O0ePHuWLL76gd+/ePProo4Hev5ORnJzMX//618DQ7CeffMKBAwdOqi2R8lAPnYiIlOqxhVvJyy9+9WZefgGPLdzKnHHjigW4adOmMWrUKGJiYjhy5Ag9e/YMnI/WqVOnwFWgPXr0YPz48XTv3r3c9fzhD3+gU6dOtGjRgujoaPbt21eu7evWrcumTZvo0KEDZ511FrNmzaKgoIAhQ4aQm5uLc46xY8fSsGHDctcGMGLECLKysmjfvj3OOZo0acLcuXNPqi2R8jDnXKhrCFpCQoJLT08PdRkiIqeNVvcuoKS/EgbsmNy/qss5ZfXq1Tvp3jeR8jKzNc65hKrYl4ZcRUSkVE0bRpZrvoiEhgKdiIiUKiX5IiJrRRSbF1krgpTki0JU0alR75z8VOkcOhERKdWA+GYAgatcmzaMJCX5osB8EakeFOhERKRMA+KbKcCJVHMachUREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMyFLNCZWR0zW2Vm68xsk5k9HKpaRERERMJZzRDu+zDQxzm338xqAcvN7F/OuY9CWJOIiIhI2AlZoHPOOWC/P1nLf7lQ1SMiIiISrkJ6Dp2ZRZhZJvAN8G/n3H9CWY+IiIhIOAppoHPOFTjn4oDmQEczizp2HTMbaWbpZpaenZ1d9UWKiIiIVHPV4ipX51wOkAZcXsKyF5xzCc65hCZNmlR5bSIiIiLVXSivcm1iZg3995HApcCWUNUjIiIiEq5CeZXr+cBrZhaBFyzfcs7ND2E9IiIiImEplFe5rgfiQ7V/ERERkZ+KanEOnYiIiIicPAU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImFOgExEREQlzCnQiIiIiYU6BTkRERCTMKdCJiIiIhDkFOhEREZEwp0AnIiIiEuYU6ERERETCnAKdiIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyIiIhLmFOhEREREwpwCnYiIiEiYU6ATERERCXMKdCIiIiJhToFOREREJMwp0ImIiIiEOQU6ERERkTCnQCciIiIS5hToRERERMKcAp2IiIhImDthoDOz/w1mnoiIiIiERjA9dJeVMK9fRRciIiIiIienZmkLzOwO4LdAazNbX2RRfWBFZRcmIiIiIsEpNdABbwL/Av4M3Ftk/j7n3LeVWpWIiIiIBK3UQOecywVygUFmFgGc669fz8zqOed2VlGNIiIiIlKGsnroADCzO4GJwNfAUX+2A2IqrywRERERCdYJAx1wF3CRc25vZRcjIiIiIuUXzFWuX+ANvYqIiIhINRRMD91nQJqZLQAOF850zj1RaVWJiIiISNCCCXQ7/dcZ/ktEREREqpETBjrn3MMAZlbXOXeg8ksSERERkfII5tFfXcxsM/CxPx1rZs9WemUiIiIiEpRgLoqYCiQDewGcc+uAnpVZlIiIiIgEL5hAh3Pui2NmFVRCLSIiIiJyEoK5KOILM+sKODM7AxiNP/wqIiIiIqEXTA/d7cAooBnwXyDOnxYRERGRaiCYq1z3AIOroBYREREROQnBPMu1CXAb0LLo+s65WyuvLBEREREJVjDn0M0DlgGL0cUQIiIiItVOMIHuTOfcPZVeiYiIiIiclGAuiphvZldUeiUiIiIiclKCCXRj8ELdITPb57++r+zCRERERCQ4wVzlWr8qChERERGRkxPMOXSY2dX8+LivNOfc/MorSURERETK44RDrmY2GW/YdbP/GuPPExEREZFqIJgeuiuAOOfcUQAzew3IAO49lR2b2QXA68B5wFHgBefcU6fSpoiIiMjpKJiLIgAaFnl/VgXt+wgwzjnXFugMjDKziyuobREREZHTRjA9dH8GMswsFTC8c+nGn+qOnXNfAl/67/eZ2cd4z4vdfKpti4iIiJxOgrnKdYaZpQGJ/qx7nHNfVWQRZtYSiAf+U8KykcBIgJ///OcVuVsRERGRn4Rgh1y7AElAL/99hTGzesBs4C7n3HH3t3POveCcS3DOJTRp0qQidy0iIiLykxDMVa7PArcDG4CNwG/M7JmK2LmZ1cILc9Odc3Mqok0RERGR000w59D1AqKccw4CV7luONUdm5kBLwEfO+eeONX2RERERE5XwQy5bgWKnrx2AbC+AvbdDRgK9DGzTP+lZ8aKiIiIlFMwPXSNgY/NbJU/nQh8aGb/AHDOXX0yO3bOLce7alZERERETkEwge6hSq9CRERERE5aMLct+QDAzBoUXd85920l1iUiIiIiQTphoPPvA/cHIA/vEV0GOKB15ZYmIiIiIsEIZsg1BWjnnNtT2cWIiIiISPkFc5XrduBgZRciIiIiIicnmB668cBKM/sPcLhwpnNudKVVJSIiIiJBCybQPQ8swbuZ8NHKLUdEREREyiuYQHfEOXd3pVciIiIiIiclmHPoUs1spJmdb2aNCl+VXpmIiIiIBCWYHrqb/P+OLzJPty0RERERqSaCubFwq6ooREREREROTjA3Fq4F3AH09GelAc875/IrsS4RERERCVIwQ65/BWoBz/rTQ/15IyqrKBEREREJXjCBLtE5F1tkeomZrausgkRERESkfIK5yrXAzC4snDCz1kBB5ZUkIiIiIuUR7LNcU83sM8CAFsAtlVqViIiIiAQtmKtc300IHdMAACAASURBVDezXwAX4QW6Lc65wyfYTERERESqyAmHXM1sFBDpnFvvnFsHnGlmv6380kREREQkGMGcQ3ebcy6ncMI59x1wW+WVJCIiIiLlEUygq2FmVjhhZhHAGZVXkoiIiIiURzAXRSwE3jKz5/Ae+XU78F6lViUiIiIiQQsm0N0DjMR7WoQBi4C/VWZRIiIiIhK8YK5yPQo8579EREREpJoJ5hw6EREREanGFOhEREREwpwCnYiIiEiYK/UcOjP7J95VrSVyzl1dKRWJiIiISLmU1UM3BXgc2AHkAS/6r/3AxsovTSR8jB07lqlTpwamk5OTGTFiRGB63LhxPPHEE5Wy7xEjRrB58+ZKaVtERMJDqYHOOfeBc+4DIN45d6Nz7p/+6yage9WVKFL9de3alZUrVwJw9OhR9uzZw6ZNmwLLV65cSbdu3Spl33/729+4+OKLK6VtEREJD8GcQ9fEzFoXTphZK6BJ5ZUkEn66desWCHSbNm0iKiqK+vXr891333H48GE+/vhj7rrrLjIzM4tts379er799lsGDBhATEwMnTt3Zv369QBMnDiRYcOG0bdvX1q2bMmcOXP4/e9/T3R0NJdffjn5+fkAJCUlkZ6eDkC9evW4//77iY2NpXPnznz99dcAbN++nc6dO5OYmMhDDz1EvXr1qvLwiIhIJQsm0I0F0swszczSgFTgrkqtSiTMNG3alJo1a7Jz505WrlxJly5d6NSpEx9++CHp6enExMRw++238+qrrwLwySefcPjwYWJiYpgwYQLx8fGsX7+eP/3pT9x8882Bdrdv386CBQuYN28eQ4YMoXfv3mzYsIHIyEgWLFhwXB0HDhygc+fOrFu3jp49e/Liiy8CMGbMGMaMGcPq1atp2rRplRwTERGpOicMdM6594BfAGP810XOuYWVXZhIuCnspSsMdF26dAlMd+3alRtuuIH58+eTn5/Pyy+/zPDhwwFYvnw5Q4cOBaBPnz7s3buX3NxcAPr160etWrWIjo6moKCAyy+/HIDo6GiysrKOq+GMM87gyiuvBKBDhw6BdT788ENuuOEGAG666aZKPAoiIhIKwTz6C6AD0NJfP9bMcM69XmlViYSBuRm7eGzhVnbn5NG0YSTtLriYlStXsmHDBqKiorjgggt4/PHHadCgAbfeeitnnnkml112GfPmzeOtt94KDJM6d/zF5GYGQO3atQGoUaMGtWrVCsyvUaMGR44cOW67outERESUuI6IiPz0nLCHzszewLvitTuQ6L8SKrkukWptbsYuxs/ZwK6cPBywKyePf+9tyFtz5tGoUSMiIiJo1KgROTk5fPjhh3Tp0gXwrkgdPXo0iYmJNGrUCICePXsyffp0ANLS0jj77LNp0KBBhdbbuXNnZs+eDcDMmTMrtG0REQm9YHroEoCLXUndCCKnqccWbiUvv6DYvKM/u4A9e/bQufPQwLzo6Gj279/P2WefDXjDoA0aNOCWW24JrDNx4kRuueUWYmJiOPPMM3nttdcqvN6pU6cyZMgQHn/8cfr3789ZZ51V4fsQEZHQsRPlNDN7GxjtnPuyakoqXUJCgiscphIJpVb3LijxrtsG7Jjcv9Ttdu/eTVJSElu2bKFGjap7UMvBgweJjIzEzJg5cyYzZsxg3rx5VbZ/EZHTkZmtcc5VyahmMD10ZwObzWwVcLhwpp4UIaezpg0j2ZWTV+L80rz++uvcf//9PPHEE1Ua5gDWrFnDnXfeiXOOhg0b8vLLL1fp/kVEpHIF00PXq6T5/k2Hq5R66KS6KDyHruiwa2StCP58bTQD4puFsDIREakuqlUPnXPuAzM7F+9iCIBVzrlvKrcskeqtMLQVvco1JfkihTkREQmJEwY6M/s18BiQhneK0NNmluKce6eSaxOp1gbEN1OAExGRaiGYc+juBxILe+XMrAmwGFCgExEREakGgjkzu8YxQ6x7g9xORERERKpAMD1075nZQmCGP30j8K/KK0lEREREyiOYiyJSzOxavCdFGPCCc+7dSq9MRERERIISzEURrYD/c87N8acjzaylcy6rsosTERERkRML5ly4t4GjRaYL/HkiIiIiUg0EE+hqOud+KJzw359ReSWJiIiISHkEE+iyzSzwmC8z+xWwp/JKEhEREZHyCCbQ3Q7cZ2ZfmNlO4B7gN5VblpwOvvrqKwYOHMiFF17IxRdfzBVXXMEnn3wSsnqmTp3KwYMHA9NXXHEFOTk55W4nKyuLN998syJLExERKdMJA51zbrtzrjPQFmjnnOvqnPu08kuTnzLnHNdccw1JSUls376dzZs386c//Ymvv/46ZDUdG+j+7//+j4YNG5a7HQU6ERGpaicMdGZ2rpm9BLztnNtnZheb2f+rgtrkJyw1NZVatWpx++23B+bFxcXRvXt3UlJSiIqKIjo6mlmzZgGQlpZGUlIS119/PW3atGHw4ME45wBo2bIlEyZMoH379kRHR7NlyxYADhw4wK233kpiYiLx8fHMmzcPgIKCAn73u98RHR1NTEwMTz/9NNOmTWP37t307t2b3r17B9rds8c7u+D1118nJiaG2NhYhg4dCsDw4cN5550fH5hSr149AO69916WLVtGXFwcTz75ZGUeRhERESC4IddXgYVAU3/6E+CuyipITg8bN26kQ4cOx82fM2cOmZmZrFu3jsWLF5OSksKXX34JQEZGBlOnTmXz5s189tlnrFixIrDd2Wefzdq1a7njjjuYMmUKAJMmTaJPnz6sXr2a1NRUUlJSOHDgAC+88AI7duwgIyOD9evXM3jwYEaPHk3Tpk1JTU0lNTW1WE2bNm1i0qRJLFmyhHXr1vHUU0+V+dkmT55Mjx49yMzMZOzYsad6qDCzQIgEOHLkCE2aNOHKK6885bZPRlpaWqXuOycnh2effTYwrR5PEZETCybQne2cewv/1iXOuSN4ty4RqXDLly9n0KBBREREcO6559KrVy9Wr14NQMeOHWnevDk1atQgLi6OrKyswHbXXnstAB06dAjMX7RoEZMnTyYuLo6kpCQOHTrEzp07Wbx4Mbfffjs1a3q3YWzUqFGZNS1ZsoTrr7+es88+O6j1K1rdunXZuHEjeXl5APz73/+mWbNmVVpDVVKgExEpv2AC3QEzaww4ADPrDORWxM7N7GUz+8bMNlZEe1L9zc3YRbfJS/jTyn28PPd95mbsKra8cBi1JLVr1w68j4iI4MiRI8ctKzrfOcfs2bPJzMwkMzOTnTt30rZtW5xzmFnQNZe2fs2aNTl69GhgnR9++OG4dSpKv379WLBgAQAzZsxg0KBBgWWrVq2ia9euxMfH07VrV7Zu3Qp4PYsdO3YkLi6OmJgYtm3bxoEDB+jfvz+xsbFERUUFhrQfeeQREhMTiYqKYuTIkYGfw6effsqll15KbGws7du3Z/v27QDs37+/1OHvwmHq9PR0kpKSAPjggw+Ii4sjLi6O+Ph49u3bB8Bjjz1GYmIiMTExTJgwAfCGrLdv305cXBwpKSnHDWGX9LlERE53wQS6u4F/ABea2QrgdeD/q6D9vwpcXkFtSTU3N2MX4+dsYFdOHrVbxHLo8GHueODRQKhbvXo1P/vZz5g1axYFBQVkZ2ezdOlSOnbseFL7S05O5umnnw6EjYyMDAD69u3Lc889Fwh+3377LQD169cPBI2iLrnkEt566y327t1bbP2WLVuyZs0aAObNm0d+fn6Z7ZyKgQMHMnPmTA4dOsT69evp1KlTYFmbNm1YunQpGRkZPPLII9x3330APPfcc4wZM4bMzEzS09Np3rw57733Hk2bNmXdunVs3LiRyy/3fv3uvPNOVq9eHegJnD9/PgCDBw9m1KhRrFu3jpUrV3L++ecDZQ9/l2TKlCk888wzZGZmsmzZMiIjI1m0aBHbtm1j1apVZGZmsmbNGpYuXcrkyZO58MILyczM5LHHHjtuCLukzyUicroL5irXtUAvoCve7UraOefWV8TOnXNLgW8roi2p/h5buJW8fG+03sxocs39fL99LQMvTaRdu3ZMnDiRm266KXDxQZ8+fXj00Uc577zzTmp/Dz74IPn5+cTExBAVFcWDDz4IwIgRI/j5z38e2E/hcN7IkSPp169f4KKIQu3ateP++++nV69exMbGcvfddwNw22238cEHH9CxY0f+85//ULduXQBiYmKoWbMmsbGxJ31RRGFPZqt7F5CXX8BnBY3JyspixowZXHHFFcXWzc3N5YYbbiAqKoqxY8eyadMmALp06cKf/vQn/vd//5fPP/+cyMhIoqOjWbx4Mffccw/Lli3jrLPOAryLVDp16kR0dDRLlixh06ZN7Nu3j127dnHNNdcAUKdOHc4880yg7OHvknTr1o27776badOmkZOTQ82aNVm0aBGLFi0iPj6e9u3bs2XLlqB620r6XCIipz3nXIkvIBE4r8j0zcA8YBrQqLTtyvsCWgIbg1m3Q4cOTsJXy3vmuxYlvFreMz/UpVUr7679r2vzwL8Cx8dq1XFtHviXG3j7ONeoUSO3fv16l5qa6vr37++cc27YsGHuqaeecs45t2PHDteiRYtAW59++ql76qmnXKtWrdz777/vnHNu79697o033nDdunVzDz/8sMvLy3PnnHOO27lzp3POuQkTJrgJEya43Nxc16xZs+PqK7pv55wbNWqUe+WVV5xzzl144YXu66+/ds45t2zZMterV6/AeuvXr3eTJ092zZo1cx9//LG7++673XPPPXdc+zt27HDt2rUrdX+lfS4RkeoGSHcVlJdO9Cqrh+554AcAM+sJTMYbbs0FXqiUdFkCMxtpZulmlp6dnV1Vu5VK0LRhyT0ppc0/XRXtySyUl1/AtoaJPPTQQ0RHRxdblpubG7hI4tVXXw3M/+yzz2jdujWjR4/m6quvZv369ezevZszzzyTIUOG8Lvf/Y61a9dy6NAhwLtSeP/+/YFbsTRo0IDmzZszd+5cAA4fPlzsPn0lKToMPXv27MD87du3Ex0dzT333ENCQgJbtmwhOTmZl19+mf379wOwa9cuvvnmm+OGrI+dLulziYic7soKdBHOucLh0BuBF5xzs51zDwL/U/mleZxzLzjnEpxzCU2aNKmq3UolSEm+iMhaEcXmRdaKICX5ohBVVD3tzskrcf5eV48xY8YcN//3v/8948ePp1u3bhQU/BgEZ82aRVRUFHFxcWzZsoWbb76ZDRs2BC4omDRpEg888AANGzbktttuIzo6mgEDBpCYmBho44033mDatGnExMTQtWtXvvrqqzJrnzBhAmPGjKFHjx5ERPz4s546dSpRUVHExsYSGRlJv3796Nu3LzfddBNdunQhOjqa66+/nn379tG4cWO6detGVFQUKSkpxw1hl/S5REROd+ZKuarQv/I0zjl3xMy2ACOdd84bZrbRORdVIQWYtQTmB9NeQkKCS09Pr4jdSojMzdjFYwu3sjsnj6YNI0lJvogB8T/dW3CcjG6Tl7CrhFDXrGEkK+7tE4KKRETkZJjZGudcQlXsq2YZy2YAH5jZHiAPWOYX9z9U3G1LZgBJwNlm9l9ggnPupYpoW6qnAfHNFOBOICX5IsbP2VBs2FU9mSIiUpZSA51zbpKZvQ+cDyxyP3bl1aCCblvinBt04rVETi+FgVc9mSIiEqyyeuhwzn1UwrxPKq8cEQH1ZIqISPkEc2NhEREREanGFOhCKCIigri4OKKiorjqqqvIyckBTv3h5+XdPisri6iosq9JqVev3knXU959iYiISPko0IVQZGQkmZmZbNy4kUaNGvHMM8+EuiQREREJQwp01USXLl3YtevHB9WX9vDz999/n/j4eKKjo7n11ls5fPgwAO+99x5t2rShe/fuzJkzJ9DOgQMHuPXWW0lMTCQ+Pp558+aVWceJHny+f/9+LrnkEtq3b090dHSgvaysLNq2bcttt91Gu3bt6Nu3L3l53q031qxZQ2xsLF26dFFoFRERqQQKdNVAQUEB77//PldffXVgXkkPPz906BDDhw9n1qxZbNiwgSNHjvDXv/6VQ4cOcdttt/HPf/6TZcuWFbv566RJk+jTpw+rV68mNTWVlJQUDhw4UGotJ3rweZ06dXj33XdZu3YtqampjBs3LhA2t23bxqhRo9i0aRMNGzYMPCnglltuYdq0aXz44YcVedhERETEp0AXQnl5ecTFxdG4cWO+/fZbLrvsssCykh5+vnXrVlq1asUvf/lLAIYNG8bSpUvZsmULrVq14he/+AVmxpAhQwLtLFq0iMmTJxMXF0dSUhKHDh1i586dpdZ0ogefO+e47777iImJ4dJLL2XXrl18/fXXALRq1Yq4uDgAOnToQFZWFrm5ueTk5NCrVy8Ahg4dWjEHT0RERAIU6KrY3IxddJu8hFb3LoCaZzDxlQV8/vnn/PDDD8WGI2vXrh14HxERwZEjRyjtqR4AZlbifOccs2fPJjMzk8zMTHbu3Enbtm1Lbeemm27iH//4B5GRkSQnJ7NkyZJiy6dPn052djZr1qwhMzOTc889N/As0NJqLq02ERERqRgKdFVobsYuxs/ZwK6cPBzgHIyfs4HUz/Yzbdo0pkyZQn5+fqnbt2nThqysLD799FPAe85mr169aNOmDTt27GD79u0AzJgxI7BNcnIyTz/9dCAMZmRklFnjiR58npubyznnnEOtWrVITU3l888/L7O9hg0bctZZZ7F8+XLAC4QiIiJSsRToqtBjC7cWe5wTQF5+AY8t3Ep8fDyxsbHMnDmz1O3r1KnDK6+8wg033EB0dDQ1atTg9ttvp06dOrzwwgv079+f7t2706JFi8A2Dz74IPn5+cTExBAVFcWDDz5YZo0nevD54MGDSU9PJyEhgenTp9OmTZsTfu5XXnmFUaNG0aVLl+OGcEVEROTUWVnDeNVNQkKCS09PD3UZJ63VvQso6WgbsGNy/6ouR0RERCqRma1xziVUxb7UQ1eFmjYsuXeqtPkiIiIiwVCgq0IpyRcRWSui2LzIWhGkJF8UoopERETkp6BmqAs4nRQ+bP2xhVvZnZNH04aRpCRfpIewi4iIyClRoKtiA+KbKcCJiIhIhdKQq4iIiEiYU6ATERERCXMKdCKVxMyKPersyJEjNGnShCuvvLLM7dLT0xk9enRllyciIj8hOodOpJLUrVuXjRs3kpeXR2RkJP/+979p1uzE508mJCSQkFAlty0SEZGfCPXQiVSifv36sWDBAsB7JNugQYMCy1atWkXXrl2Jj4+na9eubN26FYC0tLRAL97EiRO59dZbSUpKonXr1kybNi2w/d///nc6duxIXFwcv/nNbygoKP4UEhEROX0o0IlUooEDBzJz5kwOHTrE+vXr6dSpU2BZmzZtWLp0KRkZGTzyyCPcd999JbaxZcsWFi5cyKpVq3j44YfJz8/n448/ZtasWaxYsYLMzEwiIiL0nFwRkdOYhlxFKtDcjF2B+wzm5RfwWUFjsrKymDFjBldccUWxdXNzcxk2bBjbtm3DzMjPzy+xzf79+1O7dm1q167NOeecw9dff83777/PmjVrSExMBCAvL49zzjmn0j+fiIhUTwp0IhVkbsYuxs/ZQF6+N/TpHIyfs4G4xCR+97vfkZaWxt69ewPrP/jgg/Tu3Zt3332XrKwskpKSSmy3du3agfcREREcOXIE5xzDhg3jz3/+c6V+JhERCQ8achWpII8t3BoIc4Xy8gvY1jCRhx56iOjo6GLLcnNzAxdJvPrqq+Xa1yWXXMI777zDN998A8C3337L559/fvLFi4hIWFOgE6kgu3PySpy/19VjzJgxx83//e9/z/jx4+nWrVu5L2i4+OKL+eMf/0jfvn2JiYnhsssu48svvzypukVEJPyZcy7UNQQtISHBpaenh7oMkRJ1m7yEXSWEumYNI1lxb58QVHR6MzOGDBnCG2+8AXj3ATz//PPp1KkT8+fPL3d7OTk5vPnmm/z2t7+t6FJF5CfKzNY456rkPlTqoROpICnJFxFZK6LYvMhaEaQkXxSiik5vRe8DCAR9H8DS5OTk8Oyzz1ZUeSIiFUqBTqSCDIhvxp+vjaZZw0gMr2fuz9dGMyD+5EOEnJqy7gP47bffMmDAAGJiYujcuTPr168HSr/337333sv27duJi4sjJSWF/fv3c8kll9C+fXuio6OZN28eAFlZWbRt25bbbruNdu3a0bdv30CofPHFF0lMTCQ2NpbrrruOgwcPVuXhEJGfMudc2Lw6dOjgRESCUbduXbdu3Tp33XXXuby8PBcbG+tSU1Nd//79nXPO3XnnnW7ixInOOefef/99Fxsb65xzbsKECa5Lly7u0KFDLjs72zVq1Mj98MMPbseOHa5du3aB9vPz811ubq5zzrns7Gx34YUXuqNHj7odO3a4iIgIl5GR4Zxz7oYbbnBvvPGGc865PXv2BLa///773bRp0yr/QIhIyADprooykm5bIiI/WTExMaXeB3D58uXMnj0bgD59+rB3715yc3OBku/9dyznHPfddx9Lly6lRo0a7Nq1K7Beq1atiIuLA6BDhw5kZWUBsHHjRh544AFycnLYv38/ycnJlfXRReQ0o0AnIj8Zx97YeW7GLq6++uoS7wPoSrggzMyAku/9d6zp06eTnZ3NmjVrqFWrFi1btuTQoUMlbl845Dp8+HDmzp1LbGwsr776KmlpaRXyuUVEdA6diPwkFN7YeVdOHo4fb+zctGO/Eu8D2LNnz8Dj0tLS0jj77LNp0KBBqe3Xr1+fffv2BaZzc3M555xzqFWrFqmpqUHdB3Dfvn2cf/755Ofn61FtIlKh1EMnIj8Jpd3Y+ZXMfay49/j7AE6cOJFbbrmFmJgYzjzzTF577bUy22/cuDHdunUjKiqKfv36cc8993DVVVeRkJBAXFwcbdq0OWGNf/jDH+jUqRMtWrQgOjq6WEAUETkVug+diPwktLp3ASX938yAHZP7V3U5IiK6D52ISHk1bRhZrvkiIj8lCnQi8pOgGzuLyOlM59CJyE9C4Q2cC69ybdowkpTki3RjZxE5LSjQichPxoD4ZgpwInJa0pCriIiISJhToBMREREJcwp0IiIiImFOgU5EREQkzCnQiYiIiIQ5BToRERGRMKdAJyJVql69eiXOHz58OO+8806Z2yYlJaHH/4mIHE+BTqQMZsa4ceMC01OmTGHixIkV1n5WVhZmxoMPPhiYt2fPHmrVqsWdd955Um0+9NBDLF68uKJKFBGRMKBAJ1KG2rVrM2fOHPbs2VNp+2jdujXz588PTL/99tu0a9fupNt75JFHuPTSSyuitErlnOPOO+/k4osvpn///nzzzTeBZY888giJiYlERUUxcuRInHOBZW+//TYdO3bkl7/8JcuWLQPg0KFD3HLLLURHRxMfH09qamqVfx4RkVBSoBMpQ82aNRk5ciRPPvnkccuys7O57rrrSExMJDExkRUrVgAQHR1NTk4OzjkaN27M66+/DsDQoUNL7DmLjIykbdu2gaHEWbNm8etf//qE+/nVr34VaPv5559n8ODBQPGhy9WrV9O1a1diY2Pp2LEj+/btqzbh591332Xr1q1s2LCBF198kZUrVwaW3XnnnaxevZqNGzeSl5dXLPAeOXKEVatWMXXqVB5++GEAnnnmGQA2bNjAjBkzGDZsGIcOHaraDyQiEkIKdKepsWPHMnXq1MB0cnIyI0aMCEyPGzeOJ554olxtpqWlFfujXCgrK4vmzZtz9OjRYvPj4uJYtWoVI0aMYPPmzeXa13PPPRcIM6VJT09n9OjR5Wq3JKNGjWL69Onk5uYWmz9mzBjGjh3L6tWrmT17duD4devWjRUrVrBp0yZat24d6EX66KOP6Ny5c4n7GDhwIDNnzuS///0vERERNG3a9IT7eeGFF3jkkUdYtmwZjz/+OE8//XSxNn/44QduvPFGnnrqKdatW8fixYuJjIysNuFn6dKlDBo0KPB5+/TpE1iWmppKp06diI6OZsmSJWzatCmw7NprrwWgQ4cOZGVlAbB8+XKGDh0KQJs2bWjRogWffPJJ1X0YEZEQ07NcT1Ndu3bl7bff5q677uLo0aPs2bOH77//PrB85cqVxQJfMNLS0qhXrx5du3YtNr9ly5ZccMEFLFu2jF69egGwZcsW9u3bx//P3p2HVVWtDxz/bg7DYVZBURRRnAg4h0EURRHUCqcMp5yuSl71Os+WVhbaoCmmUd1Mfw6lpORsDpkkiFMJyCCYQyqoOIQKyCwc9u8PLjsQMDVFrfV5Hp/L2Wfvtdfe3ut9WcP7tm3blrZt21bZnk6nQ6VSVfnd2LFj/7Q/np6eeHp6PtQzVMXCwoLhw4cTEhKCsbGxcjw8PLxCIHrnzh2ys7Px8fEhKioKe3t7xo0bx4oVK0hLS6NOnTrVbgjo1q0bc+fOxcbGhoEDB1b4rrr72NjYMH/+fDp37sy2bduoU6dOhevOnDlDgwYNaNOmjfIcUBr8TJo0CagY/Gi12r/wlu5ve1wai/ed4WpmPvlFOrbHpQGlaxTvVVBQwPjx44mJicHOzo6goKAKAaeRkREAKpWK4uJigApTsoIgCP9EYoTuH6pDhw7KaFpycjIuLi6Ym5uTkZFBYWEhv/76K+7u7sTGxuLr60vr1q3x9/fn2rVrAISEhODk5IRWq2XQoEGkpKSwfPlyli5dipubmzIqVWbw4MFs3LhR+bxx40YGDx4MVNy5aGZmxrvvvouXlxfHjh1j1apVtGzZEj8/P0aPHq1sFAgKCiI4OFi5/s0336y0rioyMpJevXoBcPz4cby9vXF3d8fb25szZ85U+262x6XRYeEBms7erQQfU6dOZdWqVeTm5irnlZSUcOzYMeLj44mPjyctLQ1zc3M6derEoUOHOHToEH5+ftStW5fNmzfj4+NT7T0NDQ1p3bo1S5YsoV+/fhW+q+4+UDrKZmVlxdWrVyu1Kctyv9HJ7QAAIABJREFUlQFTTQc/2+PSmLP1JGmZ+ciALMOcrScxauTMxo0b0el0XLt2TZn6LQverK2tycnJ+dOdrwCdOnUiNDQUgLNnz3Lp0iVatWr1xJ5JEAThWSMCun8oW1tb9PX1uXTpEkePHqV9+/ZKEBUTE4NWq0WSJCZNmsTmzZuJjY1l5MiRvP322wAsXLiQuLg4EhMTWb58OU2aNGHs2LFMmzaN+Pj4SsHLa6+9xvbt25URlbCwMAYNGlSpX7m5ubi4uPDLL7/g4ODA+++/z88//8z+/fs5ffp0tc9T1bqq8hwdHYmKiiIuLo758+fz1ltvVdlOdcFHVGo+r732GqtWrVLOffnll/n888+Vz/Hx8QDY2dlx8+ZNzp07h4ODAx07diQ4OPi+AR2UTnN//PHHWFlZVThe3X2OHz/O3r17iYuLIzg4mIsXL1Z65qtXrxIdHQ1AdnY2xcXFNR78LN53hvwiXYVj+UU6DhU50KJFCzQaDePGjVNGb2vVqsXo0aPRaDQEBAQoI4z3M378eHQ6HRqNhoEDB7J27VplJE8QBOGfQEy5/oOVjdIdPXqU6dOnk5aWxtGjR7G0tFRGsZKSknjppZeA0inQBg0aAKDVahk6dCgBAQEEBAT86b3q16+Ps7MzP/30EzY2NhgYGODi4lLpPJVKpYxQHT9+HF9fX2UqccCAAdWui6pqXVV5WVlZjBgxgnPnziFJEkVFRVW2U13wsXjfGbbOmFEhsAoJCWHChAlotVolUFq+fDkAXl5e6HSl7fj4+DBnzhw6dux4v1eEs7Nzlbtbq7rPp59+yujRo1mzZg22trYsWbKEkSNHcuDAAeU6Q0NDwsLCmDRpEvn5+RgbGxMeHs748eMZO3YsGo0GfX39Jx78XM3Mr/C58fTSEbdrWQUV3md5H3zwAR988EGl45GRkcrP1tbWyt+1Wq1m7dq1j6W/giAIzyMR0NWQDz/8kG+//RaVSoWenh5fffUVXl5eD91OZGQkhoaGyjq1wMBAevXqRf/+/e97nUqlQqPRcDs7n1t5xRi90JlaZnXI2vEjV8+cxMXFBTs7O5YsWYKFhQUjR47k+PHjqFQqZUSovN27dxMVFcXOnTt5//33SU5O5ueff6ZTp07V9qFs2tXGxkaZbr2XWq1W1s09zNRgVeuqyps7d66y1iwlJQU/P78q26ku+LiamY+NjQ15eXnKd9bW1oSFhVXZzrp165Sfvb29K20IKdOkSROSkpIqHQ8MDCQwMPC+90lISFB+7t27N7179waoENi0adOGn3/+udK1NRn82NYyJu2e91p2XBAEQXg8nuqUqyRJ3SRJOiNJ0m+SJM1+mn15ko4dO8auXbs4ceIEiYmJhIeHY2dn90htVbeT9M8YGxsTtGY3pkM+pU7/+eRfiCH90m/8+MNedAamqFQq6tSpQ2ZmJseOHaN9+/bY2dlx9+5djh07BkBRURHJycmUlJRw+fJlOnfuzKJFi8jMzCQnJ4fjx4+TkZFRbR/69evHnj17qp1uvVfbtm05ePAgGRkZFBcXs2XLlod+7jJZWVk0bNgQuH8wU12QIYKPRzfLvxXGBhU3txgbqJjlL9a4CYIgPC5PLaCTJEkFfAF0B5yAwZIkOT2t/jxJ165dw9raWhlFsra2VtJS/PTTT7i7u6PRaBg5ciSFhYVA6chNWTLbmJgY/Pz8qt14EBUVhbe3Nw4ODvddQF42nagyrUWdbpPIPX2Y4rwsfldZ4ePjg4eHBykpKRgaGmJtbY2BgQEeHh68+eabtGjRAgsLC3bs2MH+/ftxcXFBrVZTr149Jk2axDfffEN2djafffYZZmZmHDp0iHHjxuHp6YmzszPvvfcetWrVol27dtjY2NC0adM/fW8NGzbkrbfewsvLixdffBEnJycsLS0f6e/gjTfeYM6cOXTo0EGZCq2KCD4evwD3hizoq6FhLWMkoGEtYxb01RDg3vBpd00QBOHvQ5blp/IHaA/sK/d5DjDnfte0bt1afh5lZ2fLrq6ucosWLeRx48bJkZGRsizLcn5+vtyoUSMZkKdPny4PGzZMXrp0qbx48WLZ0tJSTk9Pl2VZlqOjo2VfX19ZlmX5vffekxcvXqy0PWLECLl///6yTqeTk5OT5WbNmlW6/8WLF2U9PT25yZu7ZPtyf/SMTOVGE9fJjadvlvPz82VZluWzZ8/KZe85IiJC7tmzp3zkyBHZw8NDTk1NVfp85swZWZZlpc+yLMv29vZKn2VZlm/duiXLsiwXFxfLvr6+ckJCwiO9O1mW5aKiIrlXr17y1q1bH7qNh7XtxBXZe8FPcpM3d8neC36St5248sTvKQiCIPz9ADFyDcVVT3MNXUPgcrnPV4CHX1T2DCufe6vBa4vpXzeToitJDBw4kIULF+Lu7k7Tpk1JT09n69atLFmyhPXr11fK4/ZnAgIC0NPTw8nJiRs3blR73r1rmcpWqNU3N2D06NHEx8ejUqkqbDz49ddfGTNmDD/++CO2trYkJCTQtGlTWrZsCcCIESP44osvmDp1aqX7fffdd6xYsYLi4mKuXbvGqVOnHjrXWVBQEOHh4RQUFPDyyy8/0AaMvyrAvaEYPRIEQRCeK09zDV3lBFl/xBh/nCRJYyRJipEkKSY9Pb0GuvV43Jv+4uqdu4ReMsc9YAyff/45W7ZsURb9l5WXKj9dqqenR0lJCenp6cyYMYPY2FjatGnDpUuXgD/KS0Fpkt2yqgkFBQXVFmaf5d8KtQoyIlZzddVE5Lv53P3tF5pei+Tu3btYWVnh6OhIfn4+ubm5zJ49m+vXr3P+/Hk+/fRToDRnXUJCgpKX7tatW+Tm5uLh4aHc59y5c7i4uBAcHMxPP/1EYmIiPXv2fKRqBMHBwcTHx3P69GlCQkKqzKsmCIIgCP90TzOguwKU3xnQCKiUHVWW5RWyLHvKsuxZt27dGuvcX1U+/UXRrSsU3U5T0l/Ex8djb2+Po6MjKSkplJSUMGHCBHbu3KlUTahVqxaxsbFMmTIFGxsbWrduzZYtW9i1axfZ2dlKeamMjAzq1aunrKfT6XTVlpcKcG9IFykJM2MjVGa1aeg7CPPUKEyKMrGysiI6OloJzH744QesrKzo3LkzKSkp7N69m/DwcEJCQjA3NycsLEzJS9ejRw8sLS0xMDAgOzubNWvW0KtXL0xNTbG0tOTGjRvs3bu3Bt66IAiCIPwzPc0p12ighSRJTYE0YBAw5Cn257Eqn/6ipKiAjP3LKSnM5aqeCpuO7gRMnEfXZUe56/0f8r+bi6Z1W5o3b05+fj7m5ub4+voyZcoULl68SO3atcnLy6N3795IksSWLVvIzs7mzp073LhxA39/f06cOEFaWhqSJFVZXqqkpAQ3NzcuXLhAYWEhVlZWWN3WcSf/Dj4+PnzwwQeoVCoyMjIwNTVFo9EQGxuLqakpZ8+eZc+ePXTp0oVr165Rv359NBqNUnx+7NixWFtbs3LlSrp3705qaipXrlzh+vXrODs74+DgQIcOHWry9QuCIAjCP8pTC+hkWS6WJGkisA9QAatlWU7+k8ueG+XXqxnVb079YaVlqhrWMma4fyvmbD1JfpEOdRM3JH0jTAYtY0bXRswN7MXrr7+Ovb09a9aswdramtTU1Ao1RAEuX77MwIEDadq0KTNnzmTKlCls3ryZadOmVdkfZ2dn4uPj6devH2PGjMHf37/C902bNiU4OJgFCxawYMECtm3bxo0bN/j444+ZM2eOMnKo1WqVNCbl9evXj3nz5rF48WJCQ0OxsrJS0oOYmZmRk5Pzl96nIAiCIAjVe6p56GRZ3iPLcktZlpvJsvzh0+zL43a/9BfVVSNY/vPvT7y8lL+/P19++aVSKeHs2bMV6pOWWb16Nd7e3uTl5TFz5kySkpIwNDQkPT29Ul46KE0I7O/vz7hx43j99dcf9DUJgiAIgvAYiFquT8j9cm/dW42gzNXMfGbMmKHkn4PSsk9ltVWdnJyU0lJQWl6qbLepj48PaWlpVZaXKi4uVnLgjRo1CicnJzw8PHBxceE///lPpcoKOTk5HD16lJs3b7Jw4UI+/PBDJk6ciJ6eHps3b2bKlCmYmppiYWFB9+7dlUTHZZskZs+ejYuLi7Kur8zNmzdp3749u3fvJiUlRcl95+Hh8UjJkgVBEARBKCWV7bR8Hnh6esoxMTFPuxt/WYeFB6oshdSwljFHZnd5bPeRJInp06crxdjbtm1LTk4OQUFB971u/fr1REREsGrVKry9vfn888+pU6cOvXr1Iikpiby8PPT09FCr1Zw7d47BgwcTExPDK6+8wv79+7ly5Yqy7s/c3BwzMzPOnz9P7969+eCDD3jppZeqbUMQBEEQ/i4kSYqVZdmzJu4lRuiegpqqRmBkZMSqVat4++23mTNnzgNft2HDBqU016BBg9iwYUOF74uKihg9ejQajYYBAwZw6tQp+vTpw6lTpyguLmbRokWcPHkSc3Nz5fyuXbuyaNEiXnrppWrbEARBEATh0TzNXa7/WGVJa/u1a07jaZuxrWXMLP9Wjz2Zrb6+Pm+++SY5OTm4u7vz008/Kd+lp6czduxYJa9d3/Fv8/01M44vHExJXhbRJxIwMdLnypUr2NjY0KpVKy5cuIC7uztZWVn4+/uTkJBAeno69evXJyUlhRdffJHdu3dTv359hg0bhqGhIVAavJmbm7Nv3z58fX0BWLp0KTY2NiQkJFBSUoJarX6szy4IgiAI/yRihO4pCXBviLGBiosLe3JkdpcHDub8/PzYt29fhWPLli3DwcGBhQsXVjp/woQJhIaGkpWVVeH4lClTGDZsGPb29oyZ9wVBsyZz9U4h+rVtMbRthdWo/2P8/M/w8/MjPz8fCwsLmjZtSlxcHM2aNePUqVPo6ekxdOhQAOLi4mjXrh1paWkMHz6cf//737Rp04bY2FiMjY3Jzs4mISFB6WNWVhYNGjRAT0+PdevW3be+qiAIgiAI9ydG6B4DSZL417/+xbp164DSTQgNGjTAy8uLXbt2/en1sizzxhtvsHfvXiRJ4p133mHgwIGMHz+ebt26YWFhwaBBg+jRoweDBw9m/vz5HDp0iA8++ICAgAD2799P3bp1qVOnToVyY/lFOvafyWD48OGEhIRgbGxMfHw8EydOJDw8XJnm3DtqCLrCXEoK8ygpyEGS9Mgv0rFsxdfMGzGQ3r17ExISQmpqKhqNhtzcXNLT02nXrh0XL15UUqro6emhUqno3LkztWrVwsPDA1dXVwoKCrh8+TJffvklH3zwARYWFowfP55+/fqxadMmOnfujKmp6ZP7CxIEQRCEv7uaKhr7OP6UFY1/km7evCm7urrKrq6uso2NjWxra6t8LiwsVM4D5H/961+yLMuyqamp7OrqKltbW8s9e/aU9+zZIzdp0kRu1arVfe9lamoqy7Isb968We7SpYtcXFwsX79+Xbazs5OvXr0qb9iwQZ45c6YcEREhW1payl5eXvLNmzdlIyMjeefOnbIsy3JcXJxsZ2cnL1++XDazqCXX7TpKNmrkJJt7vioj6cm1vfrKi9fvkQ0NDWVbW1u5ZcuWsr6+vmxlZSX/+uuvsrOzs9zkzV2yVY+psnHL9rK6qYeMnko28+gp61vayDdv3pTt7e1lb29veceOHfKrr74qt2jRQjYxMZG/+uor2dXVVb5w4YLyTLVr15bT09PliIgIuUOHDnJubq4sy7Ls6+srR0REPM6/KkEQBEF4pgExcg3FSGKE7h5WVlZKrregoCDMzMyYOXNmpfNMTU1JSkoiP790t2qrVq2U2qobNmxg7NixStqO48ePM3XqVPLz88kvUWHy4iQyDKzJKyjE+6VeXD9/CiMjIwIDA+nfvz++vr5ER0cTFhbGqVOncHZ2xtzcHBsbG3755ReKioqYNGkSCxYsQJZl8vPzmTNnDjl3Mrl76hBFty6hy74NKgMs/Eby3syJGBgYcOvWLVSq0s0YL7/8Ml9//TVQmgQ5OSudohsXaPB6CJmHvyUn4QfMGzliZWUFwJ07d2jYsCGrV69mxowZ/Pbbb4SEhNC+fXtCQ0N555132Lt3LxkZGUDplGrt2rUxMTHh9OnT/Pzzz0/wb00QBEEQ/tlEQPcAYmNjmT59Ojk5OVhbWysVEG7fvs3gwYPJz89n9+7d1KtXj5KSEiIjI/nll1+4dOkSLVq0oEWLFvz888/YN3ck7ffb6P47ET3T2si6Yo79tBczcws83LQcOnSITZs2Icsy+/bt4/bt26hUKiZOnEhubi5TpkwhISEBAwMDfv/9d+7evcu1a9cYNmwY+/fvB6A4Iw25pJjinNtIkkRxzm3yrl8kPj6O9u3bK2vVQkJCCAwM5LfffqPuirHcLTFGbe+KnpEppk5+ZMfs4KXOnZR38MYbbzBgwACKi4spKCggLy8PlUrFkiVLWLJkCR4eHvj6+tK4cWMAunXrxvLly9FqtbRq1ara+rKCIAiCIPx1YlPEn5BlmUmTJrF582ZiY2OVgvS6EplbOjW7o44jqwwwtazDjRs3SEtLQ6vVcuPGDerWrcvJkyeVYOt6Zg5FuZlIxhYY27sCEgZ1m2DT9d/ExMQQFhYGQO3atenVqxc6nQ4fHx+MjIwwNFLzzQVj3g76gMJimYKCAkpKSoDSOq3NmzcHQN2sDQZW9kgqfWr5BXL36hkklT7Ozs7k5eUxZcoUAKytrfn8889p3rw5ly+cZdTrgZiZlCZBdnhBS+uOXZk4rK/yHrp3787q1atp3LgxKSkpys5ZIyMjfvzxR06cOMHSpUtJTU3F2toaIyMj9u7dS2JiIps2bSIyMhI/P78a+3sTBEEQhH8SMUL3P+U3E5SlEQEoLCwkKSlJyZ+m0+lQmdWhsLgEQz1DkPSQS3QU2XogZ/7Er7/+yoABA7h8+TJZWVmo1WoMDQ3Jy8vDoJGWwsyf0GVcJSf7JiBTdOsyubUdMDU1pVOnTty9exedTseePXswMzMjKSmJrKw76EpKOLlyBhQXAiADv//+OwCbNm1SniMvObL0W0kP3Z10TGrXQ1+lR4sWLTAxMcHExKTK529qASa/n+T8wp4A9Dr8SaVzxDSqIAiCIDybxAgdpcHcnK0nScvMRwbSMvOZs/Ukp6/dQZZlpbB90JrdWAz9lEzfN5Rrjew0oCvGsGV7iiQVLVq0oFGjRujr/xErm5ubo1KpMNTlIhffBcB29AosO41A0tMnfcObpKeno1KpaNasGdnZ2RQVFVG/fn2GDBmCun5TkEtH4/RMa2PQoCWSoTGyLCNJEm3atMHS0hIDAwP6/3sSJg2aAdDQpR3zRwVQp3Yt6tSpg1qtJiEhQckPV17t2rXp3r37fd9Tt27dKC4uRpIkevfurUyjBgcH/2n1iXtFRkZWKPcVGBjI5s2b//S669evM2jQIJo1a4aTkxM9evTg7NmzD3XvqqSkpODi4gJATEwMkydP/sttCoIgCEJNEQEdsHjfGfKLKuZByy/SceT8LYyMjEhPT2fh2p3M2XqSK7eyuZueqpxn0twL9PQxsLKjBImmTZtWar9hw4bodDpq6zJAlkFSIZcUo6c2Rd+sNmbGRtjY2GBpaYmhoSE6nY4+ffpw9epVhgwZgp5taaCBXIK6iTtF184i381HUpsjyzI6nY7mzZujp6fHhbjDzJs+FpWeRP2Le/m/oIlkZWWRkJDAmTNnKCkpQZIkXFxceOWVV5g7dy5Qmt8uMjISgOTkZH7//XemTp2KVqtl//79FaZRjYyMKCoqYvPmzY80jVpcXFwpoHsQsizTp08f/Pz8OH/+PKdOneKjjz7ixo0bD3x92TT1/Xh6ehISEvJQfRMEQRCEp0kEdMDVKuqqAmQXFCkF6RfMe4fzX43j2prJFKb9qpyjMq2F/aztpT9LknK8Vq1aOHn50WHhAeLPXACgsKCAF9zaoDJUc339TDJ/WomqMIucrExMTU25ceMGt2/fpqSkhPXr13P37l06duxI4Zn/FbnXFZH3axRIpTtV5cJcJElClmWlzFZcXBzz588HIDc3lytXrihBoiRJGBoaYm9vz6RJk1CpVAwbNowePXoou3UBli9fzpQpU4iPjycmJoZGjRpVeC/6+vqMGTOGpUuXVnpnqampdO3aFa1WS9euXZVKFIGBgUyfPp3OnTszcOBAli9fztKlS3Fzc1N2A0dFReHt7Y2Dg0OVo3UREREYGBgwduxY5Zibmxs+Pj7k5OTQtWtXPDw80Gg07NixAygdeXvhhRcYP348Hh4eXL58mVmzZuHi4oJGo1HWLZYXGRlJr169gNKdziNHjsTPzw8HB4cKgV5AQACtW7fG2dmZFStWVGpHEARBEGqKCOgoTdtRFedeo5g5cyZubm7UGbgA25GfYzvqv5i7daPx9M3UH7IQowYtADCzrMN3USfZtWsXgYGBTAr+hostB5GWmY/dxPVIBmpMBy9jyPiZdHvRD11OBiXFd+no3R5vb2+Sk5NRqVTodDrGjRtHcXExPj4+bDx2HpvOI0BPhWSgxsC6MXX7voWhlR2mZuY0b94cAwMD4uLi6NmzJ6NHj8bOzg4TExP8/f1JS0vDzc0NMzMzOnToQGFhIT179qR///7Ex8czcOBAzMzMKgQ27du356OPPuLjjz8mNTVVSRxcXnUVKCZOnMjw4cNJTExk6NChFaYuz549S3h4OFu2bGHs2LFMmzaN+Ph4fHx8ALh27RqHDx9m165dzJ49u9I9k5KSaN26dZV/V2q1mm3btnHixAkiIiKYMWMGpSmA4MyZMwwfPpy4uDhiYmKIj48nISGB8PBwZs2axbVr1+73Xw9Onz7Nvn37OH78OPPmzaOoqAiA1atXExsbS0xMDCEhIdy6deu+7QiCIAjCkyICOmCWfyuMDVQVjhkbqJSNEVB90AfQsJYxC/pqKpTvqm4ad2P05QrHzp07h5eXF2q1mlGjRlFSUsLBgwextbXlTpEec7aeJK9cO7U6DiFz/3KKMtLw6dAeExMTbt++zQsvvIC+vj4GBgb07duX4uJiDAwMaNq0KSYmJuTm5nL16lXMzMyA0uDIx8eH77//noMHD3Lu3DnlHkOGDGHnzp0YGxvj7+/PgQMHKj2zhYWFUoEiMzOTjRs30qJFC/bs2UNMTAx3795FlmV++OEH5ZoBAwYoefBCQ0MrjApC6YiXnp4eTk5ODzyNWkaWZd566y20Wi0vvvgiaWlpShv29vbKer/Dhw8zePBgVCoVNjY2Ss6/++nZsydGRkZYW1tTr149pd2QkBBcXV1p164dly9frvAOBUEQBKEmiV2uoARi9+5yLR+gzfJvxZytJysEacYGqkqBXJl7p3EbTy+dQsyt04pR4wbSYeEBLl+7QVraNb5ev4FNmzah0+kwNTUlJCSEJUuWkNFxOjcz8zHTvMjt/csBMGnRDtW1ZApPR7J3714iIyOZMGECvr6+ODg4EBMTA4BGo8HKygq1Ws3evXsxMzNj+PDhREdHk5CQwHfffceaNWuUqdCyaU+ACxcu4ODgwOTJk7lw4QKJiYncqd2qQkmx7XFpTJ06FXd3d4qLi/H09GTHjh1YWVmRk5PD22+/jaOjI1K5aejy5b2GDh1aaeTPyMhI+blsdK08Z2fnajdOhIaGkp6eTmxsLAYGBjRp0oSCgoJK962q3T9Tvl8qlUpZAxgeHs6xY8cwMTHBz89PuZ8gCIIg1DQxQvc/Ae4NOTK7CxcX9uTI7C6VgrQA94Ys6KuhYa3SXG1VjcqVV92IXi0TA2VHbe6ZI5g4d8Fq1P+xbNsRLl++TNOmTTl8+DBQ9dq+vN+Oc/t8Ag0aNFCO1alTh+3bt1NYWEhRURHbtm3Dxsamyvs7Ojqi1Wq5cuUK48eP56OPPqpUbzYsLAwXFxfc3Nw4ffo0Vm4vVtgFLMswZ+tJolLzadeuHbdu3cLd3R2ADh060L59e1avXs3BgwepXbs23bp1Y+vWrUqtWygd3Sob6frkk0/YsWMH06dPZ9myZVX2G6BLly4UFhaycuVK5Vh0dDQHDx4kKyuLevXqYWBgQEREBKmpqVW20alTJ8LCwtDpdKSnpxMVFUXbtm2rvWd1RAoXQRAE4VkiArqH8GdBX3nVTePKMsooX+6pg5i0bE9+kY7F+84A0K9fP7799lugclDYePpm7kRvQ7qbi4GBAW5ubhw4cIBDhw4RGBjIZ599xpEjRxg1ahQ7d+6kR48eyrU5OTkASJLE4sWL+eyzzygoKGDlypU4OjoqOe8A5syZQ3JyMvHx8fzwww8s//n3KqePF+87g1arpbi4WDkeEhLCxo0bycvL4+eff0ZfX5+wsDBeffVVjh49yuXLpVPOJiYm7N69m5YtW/L555/To0cPPvzwQ1auXElcXFyV71SSJLZt28b+/ftp1qwZzs7OBAUFYWtry9ChQ4mJicHT05PQ0FAcHR2rbKNPnz5otVpcXV3p0qULixYton79+tX+PVanLIWLVqtl7ty5ohKGIAiC8FRJjzIF9bR4enrKZVOKz4OqkhVPC4unqjcuARf/l9S3/PUPM837pDSdvbvKPgNIyXvQWBaxc33FXZ5ubm78+9//JjExURlR6969O2+//TYdO3akSZMmxMTEEBoayq1bt5SduXPnzqVu3boiD5wgCILw3JMkKVaWZc+auJdYQ/cEBbg3rBR4Ld53hrQqplKrmqJ9kLV9NcG2lnGVfQbIM7UlPGoj2+PSlH7duXOHy5cvo1Kpqlx/Vt7z9AuFIAiCIDyrxJRrDXuQHbXlPcw075NSVZ/LqO1d0RUVMGvBZ0BpabQZM2YQGBhYbZmx8jp16sT27dvJy8sjNzeXbdu2KWlMBEEQBEF4MCKgq2EPu7niWVC+z/eSJIm6fd4m7cQBWrRoQcuWLVGr1Xz00UcP1LaHhweBgYG0bdsWLy8vRo0apWywEIQnYdq0aRU23/imoneMAAAgAElEQVT7+zNq1Cjl84wZM5g/fz4LFy58Gt3j6tWr9O/f/6ncWxCE55dYQyc8lA4LD1Q5/dqwljFHZnd5Cj0ShIezadMmNm3axHfffUdJSQlt2rTB0NCQY8eOAaWJtZctW4aXl9dT7qkgCM+7mlxDJ0bohIfysFPGgvCs6dChg1JHODk5GRcXF8zNzcnIyKCwsJBff/2VhIQEJk6cCJQGgC4uLri6utKpUyegdGnBzJkz0Wg0aLVaPvusdMnBTz/9hLu7OxqNhpEjR1JYWAhAkyZNeO+995TSdKdPnwbg4MGDuLm54ebmhru7O9nZ2aSkpODiUlq/ee3atfTt25du3brRokUL3njjjRp9V4IgPD/EpgjhoTwrGzWEvw9JkvjXv/6l5CksLi6mQYMGeHl5sWvXLnbu3MmpU6eqLAf3KGxtbdHX1+fSpUscPXqU9u3bk5aWxrFjx7C0tESr1WJoaKicP3/+fN577z0aNmyIk5MTACtWrODixYvExcWhr6/P7du3KSgoYNiwYZiamnLu3DmGDx/Ol19+SWZmJnfu3OH8+fPs2rWL7du3ExwczP/93/8RHBzMF198QYcOHcjJyUGtVjNo0KAKVVTi4+OJi4vDyMiIVq1aMWnSJOzs7B7LuxAE4e9DBHTCQ6tq964gPCpTU1OSkpLIz8/H2NiY/fv307DhH//96t27N7179/7L9ymfRii3VnM+/XYXN3/9henTp5OWlsbRo0extLTE29u7wnUdOnRg9uzZeHh4sHx5acWW8PBwxo4di75+6T+hderUISEhgUaNGpGXlwfAiBEj+OKLL9BqtQCcP3+eq1ev0rp1a7Zu3aq0PX36dIYOHUrfvn1p1KhRpX537doVS0tLAJycnEhNTRUBnSAIlYiAThCEp6579+7s3r2b/v37s2HDBgYPHqyUo1u7di0xMTF8/vnnBAYGYmFhQUxMDNevX2fRokX079+fyMhIgoKCsLa2JikpidatW7N+/XokSSI2NpbhYyZw/uotJLU5Vj2nUVKvBV9++SXF189x4sQJmjdvzp07d1Cr1dy6dYvQ0FCKiooYM2YMs2fPZtOmTfzwww80aNCArVu3cvXqVSZNmoSxsTFWVlaEhob+aQqexMREhg4diizL1K9fn/nz5/P999+TkZHB+vXrWbRoEeHh4UBp6p+2bdty6dKlCgFmVal/BEEQQKyhEwThGTBo0CA2btxIQUEBiYmJ992QcO3aNQ4fPsyuXbsqTMPGxcWxbNkyTp06xYULFzhy5AhFRUVMmjQJ0+5vUH/EMsy0L5EZ9Q1GDZ3Iv3wK40YvcPLkSdasWUNmZiaRkZF06tSJ999/n3bt2jF8+HB0Oh2TJk3ivffew9nZmUaNGjFgwABcXFyIjo5m0KBBzJs3D0dHR9LS0pR1c+vWrcPX11fpn5ubG6GhoXz77bfo6ekxceJENm7cyG+//UarVq1o1KiRsrZOlmWOHz/O4MGDiY6OfkJvXRCEvxMR0AmC8NRptVpSUlLYsGFDhZJ1VQkICEBPTw8nJyelHjBA27ZtadSoEXp6eri5uZGSksKZM2dISkoifuVMrq6ZRNbRMHTZtzCoaw9AXuZN1q9fj76+PhqNBp1Ox9ixYwFo1KgRt27dYurUqXz55ZcEBwfTqVMnXF1d6dy5M9HR0ZiZmTFhwgQiIiJQq9UsXryYK1euoNFo0NPTU9qqSkREBN7e3qjVajZu3Eh+fj7du3cHwMLCAijdTHHnzp2/9G4FQfhnEFOugiDUuPLr2fKLdGyPS6N3797MnDmTyMhIbt26Ve215auPlJ/mrKoqiSzLODs7o/fqh5XS7TSeuQ3zjHPExsby/vvvK/WLAQIDAwkMDGTHjh2EhobyySefYGZmxsyZM4HSXHafffYZvXv3JjIykokz59Bh4QGu/G5IiaEp73/zg7LO9Pbt2yxdupQ1a9YA4OnpyQ8//IC9vT0xMTHY2dkRFBSkPINarea7774DYOjQoSxdulTp865dux76XQuC8M8gRugEQahRZTWK0zLzkQFZhjlbT2LbtjvvvvsuGo3msd2rVatWpKen80r9HIwNVMi6Yu6mpyLLJejnZ/DB+EEsWrSIzMxMcnJy6NSpE6GhoQBERkZibW2NhYUF5ubmZGdnK+1mZWUpGzfe/+RLLqTnkpaZj2RoDCa1mbzkG7bHpXH79m1++OEHOnbsWKGNgoICAKytrcnJyWHz5s2P7ZkFQfhnEgGdIAg1avG+M+QX6Socyy/SsSY+mylTpjzWexkaGrJ582b2rA6mIGw66d9M4W7ar9haGGF4+AvmDu+Gu7s706ZNo1atWgQFBRETE4NWq2X27Nl8/fXXALzyyits27YNNzc3Dh06RFBQEAMGDMDHx4fTmVBSbqTQqud0fj+0gaE9fenSpQvvvfcezZo1IzAwkLFjx+Lm5oaRkRGjR49Go9EQEBBAmzZtHuh5VCoVbm5uuLi4MGDAAGVH7dOwdu1aJVff4yKqZAjCoxOVIgRBqFFNZ++mqn91JODiwp413Z2/rCafx8zMjJycHKB0OrZ169ZMnz79sd7jQZXffSwIQtVEpQhBEP62bKuoCXy/48+6p/U8Pj4+/Pbbb+Tm5jJy5EjatGmDu7s7O3bsAO5fZcLMzIy3334bV1dX2rVrp2wuqaoqho+Pj7K2EEpz5yUmJiqfs7KyaNKkCSUlJQDk5eVhZ2dHUVERK1eupE2bNri6utKvXz9lRDEwMJDJkyfj7e2Ng4ODMuVcvkpGSkoKPj4+eHh44OHhoVT3EAShaiKgEwShRv3dysc9jecpLi5m7969aDQaPvzwQ7p06UJ0dDQRERHMmjWL3NxcoLTKRFhYGCdPniQsLIzLly8DkJubS7t27UhISKBTp06sXLkSKK2KsW/fPhISEti5cycAo0aNYu3atQCcPXuWwsJCJVkygKWlJa6urhw8eBCA77//Hn9/fwwMDOjbty/R0dEkJCTwwgsvsGrVKuW66tLPlKlXrx779+/nxIkThIWFMXny5Mf/IgXhb0QEdIIg1KgA94Ys6KuhYS1jJKBhLWMW9NU8t9VHavJ58vPzcXNzw9PTk8aNG/Pvf/+bH3/8kYULF+Lm5oafnx8FBQVcunQJ+KPKhFqtVqpMQOnawl69egHQunVrUlJSgNLRt8DAQFauXIlOV7rOccCAAezatYuioiJWr15NYGBgpX4NHDiQsLAwADZu3MjAgQMBSEpKwsfHB41GQ2hoKMnJyX+8t2rSz5QpKipS1hkOGDCAU6dOPZ6XKAh/UyJtiSAINe7vVj7uST5P+RQv6BsStGZ3hXvJssyWLVto1ariiOAvv/xSZSoXAAMDAyRJqnR8+fLl/PLLL+zevRs3Nzfi4+OxsrLipZdeYseOHXz33XdUtY65d+/ezJkzh9u3bxMbG0uXLl2A0qnV7du34+rqytq1a4mMjFSuqS79TJmlS5diY2NDQkICJSUlqNXqh311gvCPIkboBEEQnlHVpXjZHpemnOPv789nn32mBEVxcXGPfL/z58/j5eXF/Pnzsba2VqZoR40axeTJk2nTpg116tSpdJ2ZmRlt27ZlypQp9OrVC5WqdAo6OzubBg0aUFRUpKSDeVBZWVk0aNAAPT091q1bp4wYCoJQNRHQCYIgPKOqS/GyeN8Z5fPcuXMpKipCq9Xi4uLC3LlzH/l+s2bNQqPR4OLiolTFgNJpWQsLC15//fVqrx04cCDr169XplsB3n//fby8vHjppZdwdHR8qL6MHz+er7/+mnbt2nH27FlMTU0f7aEE4R9CpC0RBOEvuXLlChMmTODUqVOUlJTQq1cvFi9ejKGh4dPu2nPvWUnxcvXqVfz8/Dh9+jR6emIcQBAelEhbIgjCc0GWZfr27UtAQADnzp3j7Nmz5OTk8Pbbb1c4r2yNlvBwnoUUL9988w1eXl58+OGHIpgThGeY+F+nIAiP7MCBA6jVamUqTqVSsXTpUlavXs1///tfBgwYwCuvvMLLL79cbb60vLw8XnvtNbRaLQMHDsTLy0tZeL9hwwZlCvDNN99U7ltdHrW/m2chxcvw4cO5fPkyAwYMqLF7CoLw8ERAJwjCI0tOTqZ169YVjllYWNC4cWOKi4s5duwYX3/9NQcOHKg2X9p///tfateuTWJiInPnziU2NhYoneZ78803OXDgAPHx8URHR7N9+3ag+jxqfzd/txQvgvC4XL9+nUGDBtGsWTOcnJzo0aMHZ8+erfLc8gmrH7egoCCCg4OfSNsPS6QtEQThkcmyrKS/qOr4Sy+9pOyK/PHHH9m5c6fyj19ZvrTDhw8rNVxdXFyUpLXR0dH4+flRt25doLTUVVRUFAEBAZXyqO3fv/+JP+vT8ndL8SIIf5Usy/Tp04cRI0awceNGoDSJ9o0bN2jZsuVfbr+4uBh9/ecvPHr+eiwIwlNVPi+ayc27cKJiSaY7d+5w+fJlVCpVhZ2J1eVLq25j1v02bFWXR00QhL+/iIgIDAwMGDt2rHLMzc0NWZaZNWsWe/fuRZIk3nnnnQq7rqH0F8lx48YRExODvr4+n3zyCZ07d2bt2rXs3r2bgoICcnNz2blzJ6+++ioZGRkUFRXxwQcf8OqrrwLw4Ycf8s0332BnZ0fdunWVWYr4+HjGjh1LXl4ezZo1Y/Xq1TX3UhBTroIgPIR786LlWL3Aheu3mfL+pwDodDpmzJhBYGAgJiYmFa6tLl9ax44d+e677wA4deoUJ0+eBMDLy4uDBw9y8+ZNdDodGzZswNfXt4aeVBCEZ1VSUlKlpR4AW7duJT4+noSEBMLDw5k1axbXrl2rcM4XX3wBwMmTJ9mwYQMjRoygoKAAoMISEbVazbZt2zhx4gQRERHMmDEDWZaJjY1l48aNxMXFsXXrVqKjo5W2hw8fzscff0xiYiIajYZ58+Y9wbdQmQjoBEF4YPfmRZMkCauAt1j37UZatGhBy5YtUavVfPTRR5WurS5f2vjx40lPT0er1fLxxx+j1WqxtLSkQYMGLFiwgM6dO+Pq6oqHh4fyG7IgCMK9Dh8+zODBg1GpVNjY2ODr61sh4Co7Z9iwYQA4Ojpib2+vrL0rv0RElmXeeusttFotL774Imlpady4cYNDhw7Rp08fTExMsLCwoHfv3kBpIuzMzEzll84RI0YQFRVVU48OiClXQRAewtXM/ErH9C3qYvnqO5y7Jy9aYGBghbqfxsbGfPXVV5WuV6vVrF+/HrVazfnz5+natSv29vYADBkyhCFDhlS6JicnR/m5f//+9O/f/1EfSRCE50DlpR7HKp3zIHl173dO+SUioaGhpKenExsbi4GBAU2aNFFG8qpaN/wsECN0giA8sCeRFy0vL4+OHTvi6upKnz59+PLLL0VSYkEQFJWXejhy4UYm4+cuUs6Jjo6mdu3ahIWFodPpSE9PJyoqirZt21Zoq1OnTkoZurNnz3Lp0qVK63qhdMStXr16GBgYEBERQWpqqnL9tm3byM/PJzs7m++//x4AS0tLateuzaFDhwBYt25djS8RESN0giA8sFn+rZiz9WSFade/mhfN3Ny8yoLvgiAIUP1Sj+92rGbft1+hVqtp0qQJy5YtIycnB1dXVyRJYtGiRdSvX5+UlBTl2vHjxzN27Fg0Gg36+vqsXbsWIyOjSvccOnQor7zyCp6enri5uSml6zw8PBg4cCBubm7Y29vj4+OjXPP1118rmyIcHBxYs2YNy5Yte3Iv5h6i9JcgCA+l/NSHbS1jZvm3Emk1BEF4Yp6VEniPoiZLf4kROkEQHorIiyYIQk2yrWVMWhXrd2uyBN7z4KmsoZMkaYAkScmSJJVIklQjkasgCIIgCM+fZ6EE3vPgaY3QJQF9gcpb3gRBEARBEP6nbEZALPW4v6cS0Mmy/Cs8u1t/BUEQBEF4doilHn9OpC0RBEEQBEF4zj2xETpJksKB+lV89bYsyzseop0xwBiAxo0bP6beCYIgCIIg/H08sYBOluUXH1M7K4AVUJq25HG0KQiCIAiC8HciplwFQRAEQRCec08rbUkfSZKuAO2B3ZIk7Xsa/RAEQRCE55EkSUqReYDi4mLq1q1Lr169ANi5cycLFy6s9vqUlBRcXFyq/O7dd98lPDz88XZYeOKe1i7XbcC2p3FvQRAEQXjemZqakpSURH5+PsbGxuzfv5+GDf/YBdq7d2969+79SG3Pnz//cXVTqEFiylUQBEEQnkPdu3dn9+7dAGzYsIHBgwcr361du5aJEycCcOPGDfr06YOrqyuurq4cPXoUAJ1Ox+jRo3F2dubll18mP7+0GkNgYCCbN28GYM+ePTg6OtKxY0cmT56sjAAeP34cb29v3N3d8fb25syZM8p9+/btS7du3WjRogVvvPFGzbwMQQR0giAIgvA8GjRoEBs3bqSgoIDExES8vLyqPG/y5Mn4+vqSkJDAiRMncHZ2BuDcuXNMmDCB5ORkatWqxZYtWypcV1BQwH/+8x/27t3L4cOHSU9PV75zdHQkKiqKuLg45s+fz1tvvaV8Fx8fT1hYGCdPniQsLIzLly8/gacX7iVquQqCIAjCc0ir1ZKSksKGDRvo0aNHtecdOHCAb775BgCVSoWlpSUZGRk0bdoUNzc3AFq3bk1KSkqF606fPo2DgwNNmzYFYPDgwaxYsQKArKwsRowYwblz55AkiaKiIuW6rl27YmlpCYCTkxOpqanY2dk9tucWqiYCOkEQBEF4DmyPS1PKX+UX6dgel0bv3r2ZOXMmkZGR3Lp166HaMzIyUn5WqVTKlGsZWa4+U9jcuXPp3Lkz27ZtIyUlBT8/v2rbLS4ufqh+CY9GTLkKgiAIwjNue1wac7aeJC0zHxmQZZiz9SS2bbvz7rvvotFoqr22a9eufPnll0Dpurk7d+480D0dHR25cOGCMnIXFhamfJeVlaVswli7du0jPZPweImAThAEQRCecYv3nSG/SPfHAVnm0uaPmDj0Vb766it69OhR7Vq1Tz/9lJkzZ6LRaNBqtbz66qvKd4MHD0ar1RIVFVXpOmNjY/773//SrVs3OnbsiI2NjTKV+sYbbzBnzhw6dOiATlfar5iYGEJDQx/jUwsPQ7rfkOqzxtPTU46JiXna3RAEQRCEGtV09m7K/t9almWur5+JmUtXLNx7cHFhT+Lj48nOzsbHx6fK683MzMjJyalw7Pr163h5eZGamlrtfXNycjAzM0OWZSZMmICDgwMzZ858XI/1tydJUqwsy541cS8xQicIgiAIzzjbWsbKzwWXEpH09DF376Ecd3Nzw93dna5du+Lh4YFGo2HHjspl08snFH755Zf5/fffcXNz49ChQ8THx9OuXTu0Wi19+vQhIyODlStXYmZmRt26ddm8eTNFRUX4+fnx5ptv0rZtW1q2bMmhQ4cAiIyM/NO0JsKTIwI6QRAEQXjGzfJvhbGBCoCi9FQM6zfH2EDFLP9WyjlqtZpt27Zx4sQJIiIimDFjxn03NuzcuZNmzZoRHx+Pj48Pw4cP5+OPPyYxMRGNRsO8efOYNm0anp6evPbaa/z+++/MmTMHKK1Mcfz4cZYtW8a8efMqtX2/tCbCkyF2uQqCIAjCMy7AvXQDwuJ9Z8gGzIz0WdBXoxyH0qnYt956i6ioKPT09EhLS+PGjRvUr1//T9vPysoiMzMTX19fAEaMGMGAAQOU7wcOHFjh/L59+wJVpzspa6+6tCbCkyFG6ARBEAThGVc+ZUn9Ji2onXelQjAHEBoaSnp6OrGxscTHx2NjY0NBQcFjub+pqWmFz2WpSapLS1KW1iQpKYnvv//+sfVDqJ4I6ARBEAThGXZvypIcK0cu3Mhk/NxFyjnR0dGkpqZSr149DAwMiIiIuO9mh3tZWlpSu3ZtZT3cunXrlNG6RyHSmtQ8EdAJgiAIwjPs3pQlkiRhFfAW3+3YQ7NmzXB2diYoKIgePXoQExODp6cnoaGhODo6PtR9vv76a2bNmoVWqyU+Pp533333kftcVVqTZ5EkScyYMUP5HBwcTFBQ0GNp+3+jks6SJClJAiVJekOSpOUP2LcgSZIeeEuxSFsiCIIgCM+w8ilLypOAiwt71nR3/lbUajUNGjQgOjoaa2trgoODycnJeWxBnSRJ54AbQCfAFogCPGVZzviT6/SBd4AcWZaDH+ReYoROEARBEJ5h5VOWPMhx4cHp6+szZswYli5dWum79PR0+vXrR5s2bWjTpg1HjhwBQKPRkJmZiSzLWFlZKXVyhw0bRnh4+L3N3AGuAcOBpUAQYCFJ0k+SJCX+7z8bA0iStFaSpE8kSYoAPi7fiCRJoyVJ2itJUrV/6SKgEwThuTRt2jSWLVumfPb392fUqFHK5xkzZvDJJ588cHtBQUEEB1f9i7C3t/cj9zMyMpKjR48+8vWCUD5lSZl7U5YIj27ChAmEhoaSlZVV4fiUKVOYNm0a0dHRbNmyRfn3pUOHDhw5coTk5GQcHByUdYc///wz7dq1q+oWU4EPgbqyLK8DPge+kWVZC4QCIeXObQm8KMuyMg8sSdJE4BUgQJbligV3yxFpSwRBeC55e3uzadMmpk6dSklJCTdv3qxQo/Lo0aMVAr6/4q8EZJGRkZiZmf2loFD4ZyufsuRqZj62tYyZ5d+q0i5X4dFYWFgwfPhwQkJCMDb+YwAsPDycU6dOKZ/v3LmjVOOIiorC3t6ecePGsWLFCtLS0qhTpw5mZmaV2pdl+aokSQeAXf871B7o+7+f1wGLyp2+SZbl8osOhwFXKA3m7pv7RYzQCYLwXOrQoYMSaCUnJ+Pi4oK5uTkZGRkUFhby66+/sm/fPtq0aYOLiwtjxoxRkqyGhITg5OSEVqtl0KBBSpunTp3Cz88PBwcHQkL++KW57B/pyMhI/Pz86N+/P46OjgwdOlRpc8+ePTg6OtKxY0cmT55Mr169SElJYfny5SxdulTJxp+amkrXrl3RarV07dqVS5cuARAYGMjkyZPx9vbGwcGBzZs318h7FJ4PAe4NOTK7CxcX9uTI7C4imPsLtsel0WHhAZrO3k1+kY7tcWlMnTqVVatWkZubq5xXUlLCsWPHiI+PJz4+nrS0NMzNzenUqROHDh3i0KFD+Pn5KVU0qiu7Vtbc//5UpfwSydx7vksCmgCN/uy5REAnCMJzydbWFn19fS5dusTRo0dp3749Xl5eHDt2jJiYGLRaLRMnTiQ6OpqkpCTy8/PZtav0F+SFCxcSFxdHYmIiy5f/seHs9OnT7Nu3j+PHjzNv3rwqk6HGxcWxbNkyTp06xYULFzhy5AgFBQX85z//Ye/evRw+fJj09HQAmjRpwtixY5k2bZqSjX/ixIkMHz6cxMREhg4dyuTJk5W2r127xuHDh9m1axezZ89+wm9QEP557k0BI8swZ+tJolLzee2111i1apVy7ssvv8znn3+ufI6PjwfAzs6Omzdvcu7cORwcHOjYsSPBwcF/FtCVdxQo+01yKHD4PufGAf8BdkqSZHu/RkVAJwjCc6tslK4soGvfvr3y2dvbm4iICLy8vNBoNBw4cIDk5GQAtFotQ4cOZf369ejr/7HypGfPnhgZGWFtbU29evW4ceNGpXu2bduWRo0aoaenh5ubGykpKZw+fRoHBweaNm0KwODBg6vt87FjxxgyZAhQuoj68OE//i0PCAhAT08PJyenKu8tCMJfc28KGID8Ih2L951hxowZ3Lx5UzkeEhKi/HLo5ORU4Zc/Ly8vWrZsCYCPjw9paWl07NjxQbsxGXhdkqRESqdUp9zvZFmWDwMzgd2SJFlXd55YQycIwnOlfMZ8KceKrB0/cvXMSVxcXLCzs2PJkiVYWFgwcuRIRo0aRUxMDHZ2dgQFBSnZ6nfv3k1UVBQ7d+7k/fffVwK9suz3UH0G/KrO+SvpnyRJqrLt5ymllCA8L65mVtxT0Hj6ZuW4jY0NeXl5ynfW1taEhYVV2c66deuUn729vSkpqW42tZQsy4Hlfk4ButzvnP99Dir38z5g3/3uIUboBEF4btw7XVJQpwU//vD/7d1/rNV1Hcfx56srjRtcY3TJQgxaQ5FxEVKYoH+I2ryZmhmNTEpHY65wKZUV2Za2OZe45qCYSYk0TWqlkTpTShxtQv5IBAx0hjlIN3J2r/eEA7n33R/nc+EAh8thXPh+P/B6bI5zvuf7+ZzX/YDnvu/3872fz2N0DxhEU1MTQ4cOpaOjg9WrVzNlyhSg+qFcqVR235PW09PDli1bmDZtGrfffjsdHR1UKpXDyjVmzBg2b968e0/L2m8CLS0tdHV17X4+depUli1bBlS3ajqEn+rN7DAdy0vA+AqdmWVj3+mSAcNGsmt7J28PGrn7WFtbG5VKhdbWVmbPnk1bWxujRo1i0qRJAHR3dzNz5kw6OzuJCObOncuQIUMOK1dzczOLFi2ivb2d1tZWJk+evPu1Sy+9lOnTp7N8+XIWLlzIggULmDVrFvPnz2fYsGEsWbLksN7bzBp340WnMe/B9Xt9jhwrS8B4pwgzy0aZV8yvVCoMHjyYiGDOnDmMHj2auXPnFprJzPZXe9vGkV4CRtLzEXHWEel8H75CZ2bZGD6kmX937L+uZhmmSxYvXszSpUvZuXMnEydO5Nprry06kpnVcfnEk4/JZV98hc7MstF7D92+0yW3XdF2TH5Am1nefIXOzKwOr5hvZlafCzozy8qxOl1iZnY4vGyJmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeZc0JmZmZllzgWdmZmZWeYUEUVnaJik/wCvF52jD63AW0WHyIDHqTEep8Z4nBrjcWqMx6kxHqfGjIyIYUfjjbIq6MpO0nMRcVbROcrO49QYj1NjPE6N8Tg1xuPUGPiup08AAAZoSURBVI9T+XjK1czMzCxzLujMzMzMMueCrn/dXXSATHicGuNxaozHqTEep8Z4nBrjcSoZ30NnZmZmljlfoTMzMzPLnAu6fiRpvqRNktZJekjSkKIzlZWkL0h6SVKPJP+mVA1J7ZJelvSqpO8VnaesJN0jaZukDUVnKTNJp0haKWlj+n/u+qIzlZGkgZKekfRiGqdbis5UZpKaJL0g6ZGis1iVC7r+tQIYFxHjgVeAeQXnKbMNwBXAqqKDlImkJuBnwKeBscCVksYWm6q07gXaiw6RgV3AtyLidOBsYI7/TdW1Azg/Is4AJgDtks4uOFOZXQ9sLDqE7eGCrh9FxBMRsSs9XQOMKDJPmUXExoh4uegcJTQZeDUiNkfETmAZ8NmCM5VSRKwC3i46R9lFxJsR8ff0uIvqN+GTi01VPlFVSU8HpP98k3kdkkYAnwF+UXQW28MF3ZEzC3is6BCWnZOBLTXPt+JvvtZPJI0CJgJ/KzZJOaVpxLXANmBFRHic6rsT+A7QU3QQ2+OEogPkRtKfgY/UeemmiFiezrmJ6jTH/UczW9k0Mla2H9U55qsEdtgkDQZ+D9wQEe8UnaeMIqIbmJDuf35I0riI8D2aNSRdAmyLiOclnVd0HtvDBd0hiogL+3pd0tXAJcAFcZyvCXOwsbK6tgKn1DwfAbxRUBY7RkgaQLWYuz8iHiw6T9lFRIekp6jeo+mCbm/nAJdJuhgYCJwo6b6ImFlwruOep1z7kaR24LvAZRGxveg8lqVngdGSPi7p/cAXgT8WnMkyJknAL4GNEfGTovOUlaRhvSsTSGoGLgQ2FZuqfCJiXkSMiIhRVD+fnnQxVw4u6PrXT4EWYIWktZLuKjpQWUn6nKStwBTgUUmPF52pDNIv1VwHPE715vXfRsRLxaYqJ0kPAKuB0yRtlfTVojOV1DnAl4Hz0+fS2nR1xfb2UWClpHVUf7BaERFeksOy4Z0izMzMzDLnK3RmZmZmmXNBZ2ZmZpY5F3RmZmZmmXNBZ2ZmZpY5F3RmZmZmmXNBZ2ZHhaTummUz1koaJenpQ+zjBkkfOFIZy0TS5ZLGFp3DzPLgZUvM7KiQVImIwQ2c15S2YKr32r+AsyLirf7OVzaS7gUeiYjfFZ3FzMrPV+jMrDCSKunP8yStlPRrYL2kQZIelfSipA2SZkj6BjCc6uKvK+v0NUnS06nNM5JaJA2UtETSekkvSJqWzr1G0h8kPSzpNUnXSfpmOmeNpKHpvKck3Zn63SBpcjo+NLVfl84fn47fLOme1G5zytybb2bKtVbSzyU19Y6BpFtT7jWSTpI0FbgMmJ/O/8QR/Ysws+y5oDOzo6W5Zrr1oTqvTwZuioixVPfQfCMizoiIccCfImIB1X1tp0XEtNqGaZu03wDXR8QZVLdteheYAxARbcCVwFJJA1OzccCX0vveCmyPiIlUd5/4Sk33gyJiKvB14J507BbghYgYD3wf+FXN+WOAi1K/P5Q0QNLpwAzgnIiYAHQDV/X2D6xJuVcBsyPiaapbvt0YERMi4p8HG1wzO76dUHQAMztuvJuKmQN5JiJeS4/XA3dI+jHVace/HqTv04A3I+JZgIh4B0DSucDCdGyTpNeBU1OblRHRBXRJ6gQernnv8TV9P5Dar5J0Ytrv81zg8+n4k5I+JOmD6fxHI2IHsEPSNuAk4ALgTODZ6taqNAPb0vk7gd4tpp4HPnWQr9XMbD8u6MysLP7X+yAiXpF0JnAxcJukJyLiR320FVDvhmD10WZHzeOemuc97P3ZuG+/cYB+e8+r7bc79SVgaUTMq9PuvdhzM3Pv+WZmh8RTrmZWOpKGU50CvQ+4A/hkeqkLaKnTZBMwXNKk1L5F0glUpzCvSsdOBT4GvHyIcWak9ucCnRHRuU+/5wFv9V4VPIC/ANMlfTi1GSpp5EHe90Bfq5nZfvyToJmVURvVXwjoAd4DvpaO3w08JunN2vvoImKnpBnAQknNVO+fuxBYBNwlaT2wC7gmInakac9G/Tctr3IiMCsduxlYImkdsB24uq8OIuIfkn4APCHpfelrmgO83kezZcDi9IsV030fnZn1xcuWmJkdgKSngG9HxHNFZzEz64unXM3MzMwy5yt0ZmZmZpnzFTozMzOzzLmgMzMzM8ucCzozMzOzzLmgMzMzM8ucCzozMzOzzLmgMzMzM8vc/wHjHVE1dWn6zgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-11T23:58:49.629994Z", + "start_time": "2026-06-11T23:58:49.498808Z" } - ], + }, "source": [ "x = state_pca_x[:, 0]\n", "y = state_pca_x[:, 1]\n", @@ -1434,7 +1856,20 @@ "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n", "for s, x, y in zip(state, x, y):\n", " plt.annotate(s, (x, y))" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 76 }, { "cell_type": "markdown", @@ -1452,40 +1887,65 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:00:49.127049Z", + "start_time": "2026-06-12T00:00:49.093933Z" + } + }, "source": [ "#Code task 8#\n", "#Calculate the average 'AdultWeekend' ticket price by state\n", - "state_avg_price = ski_data.groupby(___)[___].___\n", + "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n", "state_avg_price.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, + ], "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "state\n", + "Alaska 57.333333\n", + "Arizona 83.500000\n", + "California 81.416667\n", + "Colorado 90.714286\n", + "Connecticut 56.800000\n", + "Name: AdultWeekend, dtype: float64" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" } ], + "execution_count": 77 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:01:09.844832Z", + "start_time": "2026-06-12T00:01:09.748127Z" + } + }, "source": [ "state_avg_price.hist(bins=30)\n", "plt.title('Distribution of state averaged prices')\n", "plt.xlabel('Mean state adult weekend ticket price')\n", "plt.ylabel('count');" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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NnTtP+fPnV926L2vo0CFaunSJataole7tvJdfekklS5ZQu/Yd5ev7z/lQoEDGfx9OTEyMkpKS1LZte91O5VNG9/vkUbt27bR69Rp99tnnZu2FChfW1avmH1tfvmKl+vbto9dfb611635Rk8aNNX/+ArPnnGJiYnT8eJA++/yLVPcX8Z8wk9op2r5dO505c0a9e/c1a7e3Nz/vLl++LDs7OxUsWNAs4Dg7O5v1u3rlihITE7VmzY/67vuFqdZ1L9Bcvnw53ddCRjk7O+vixX8+lZgrVy4VKlQoRTBPzuBrNObSJZUqWTL9PjGXFRMTozff8kh1fWzsPx8tz5L3F1gNt6WQrbiUKqVRI0fo6tWrpmcE7icpKUmBgQf0yfC7n7ipVOl5STL9IMvIv+4z4plnntZzzz1r1ta2TRtdv35dhw8fliSFnbv7iadnnzXv16RJ4xTbi78dn+Ha9uz5Qy/VeSnFc0gdOrTXzZs3tX///gwfR1ri4uIUGBio5s2bmdVlY2Ojdu3a6fz585n6XTaSdO3aNa1fv0Hff79QhQsVkqubmyQpPv5uKPzvfCQr2Wz9PV3/9Um6e9L6fm/ZslW2trYqUbKEDh06lOIrvSuE92q4/Z/9v/rqK6n+IA0JCVHA/v3q3KmT2rZpIwcHB61YuTJFPc8884zOnDmTaj3/DTep1pScrISEBLM2Z2dnNW1q/mkpn78DYevWrcza27ze2mw57tYt7d27V88/X1HHjx9Pta57zwzt3bs3zdfCg2jXrq3ZcutWrWRnZ6e9Pj4PtJ17tm3foXLlyumll+qk2Wfzli0qXLiwcuWyTfUYUzu/03p/QfbGlRtYTYUKzyh37lzKnSu3ihQtohdffFGdO3XUnaQk9ejZSzExaV8l8PDoqpdeqqOtW7cpPDxcefLkUZcunSXJdM89NjZWYWFhatq0ifbs+UOXr1xRTExMigdWM+piRIS+/+5bTZo0RRGRkWrfrq3q16+n8eMnmK4+HDhwUCEhIRo1coRy58qtK1evqnnzpqpVs2aK7QUFBallyxbq1s1Dhw4dVlJSUpq3aSZPmaJGjV7V6lUrNHnK17py5YratW2rxo0aady48WYPE2fGxE8/1/JlP2jVqhWaPXuOEuIT1L17N1Wo8Iz69utv0TYXfv+tgoJO6OChQ7p0KUauri7q1bOHwsLCdPr03dsDx/8OGD17vqtVq1YrISFRoaGh8vf31+XLV/T5Z59q0uSvlZiYoHZt2+q5555LsZ97IaVf3z7atn277txJ0vHjx/Wnv78WL1miKZMnqUrlyvL189PNmzdVvFhx1axVU0FBQVq0KOWnh+7ZsmWLOnXsqJCQUB0/flyVK1VSnz69df7vh4H/a/nyFfryi89VokRx/fnnnyl+YH751STVq1dX635eqwXffqfQ0FDlyZNHbm5ueuWVhho6dNh9f8/Sli1b1bJlC02cOEHr169XqVKlNGjgQEVERpo907R9+w7t27dP3qNG6n/58unQ4cOqXr26OnRoL0lmV5RGjhqtn9b+qLU/rtGixYsVFhamfPny6cknn1Tjxo1Mn0ybN3+BunTprEWLFuqLL740fVqqfPkHex6rRfNmupOYqF27dps+LXX06NEUzwdl1Lx589W6VSt99+0CTZ8xUwcCD8jBwUG13Wtry5Yt2rvXRz//vE7t2rbV4sWLtGD+AgUeOKjExASVLFlSderU0W+//a5NmzZl6P0F2RvhBlbz9d+/o+L27du6du2agoNDNGPmLC1duizdYCNJR48eVf169fSh12A5Ozvr5s2bCgo6oe5vv6Odu3aZ+nl9+JFGjhiu775b8Pe/olfJ03OwRfUePXpUK1as1IdeXipT5klFRETIe/QYzZs339QnKSlJ3d9+RxPGj9dnn01UfHy8fv55nYaPGKklixeZbW/+gm/19DNPa+iQj5U/f37Z2tqqlItbqvsODT2l1q+31bChQzRxwng5ODgoJCREgzwHa+XKVRYdT2p8fX3VqVMXeX04WF9PmSxbW1sdPXZMb7/zrrZs2WrRNv/Y66OWLZrrzTffUL58+RQVFaVdu3ZrytdTTQ/K+vj46ptp09WxQwe99eabypUrl9p36CgfH191695d3qNGavq0qbp586Z+++139endV7//vslsP2vX/qSaNWqoe/du8vQcJFtbW9V60V3nzp3TkCHDtD8gUF093lL37t1ka2urixcj9Kf/nwoMPJBu/aNGjVZiQqIG9O8nJycnHT58RD17vqePP/4o1f4//7xOY8eMVqlSpTQ5ld+WHBkZqebNW2rQoIHq0/t9lSxZUjdiYxV2Nkzbd+zQlStX7zunK1auVNGiReTh0VVdOnfS2bNnNWPGDJUsWfLub+X+W3Jysrq//a68R41Uv359ZW9vrz//9NeAAR9o/a+/mP026ODgYDVt1lyegwbq448/UtEiRXTt2jWdPn1aW/9+2Fm6+6xMu/YdNW7sGH06caLibsVp08ZNGj58pL7/PuPPo/Ts+Z68vAarWzcPJScna/PmLRrlPTrFFamMuvdJLy8vT3V9600N9hykq1ev6sDBg/rhh7tXw5KSkvT2O++qZ88e6tC+nfr37687dxJ14cIF+fj6mgJyRt9fkH3ZlCzlmvGHDgAAOV7bNm00Y8Y0tX69jfz9Ax7pvr0Ge8rLa7Cef76yYi5n/lkxIDVcuQEAA2vz+usqUaKEjgcFKTkpSS9Uf0F9er8vHx/fRx5sgEeFcAMABnYj9oZef721Bg4coLx58yoyMlIrV67S5198ae3SgIeG21IAAMBQ+Cg4AAAwFMINAAAwFMINAAAwlMfygeLixYsrNjbW2mUAAIAH4OTklKHf4v3YhZvixYsrcL+/tcsAAAAWqPZCjfsGnMcu3Ny7YlPthRppXr2pVq3qfX9rKSzH/D5czO/Dxfw+XMzvw5WT59fJyUmB+/0zdOflsQs398TGxurGjRuprouLi0tzHTKP+X24mN+Hi/l9uJjfh+txmV8eKAYAAIZCuAEAAIZCuAEAAIZCuAEAAIZCuAEAAIZCuAEAAIZCuAEAAIZCuAEAAIZCuAEAAIZCuAEAAIZi1XDTrZuHtmz+XSeCjulE0DGtW/eTGjZskO6Y2rVra9PG9ToVGiyfvXvk4dH1UZQKAAByCKuGmwsXLmjip5+qeYuWat6ipf74Y6+++3aBnn766VT7u7m5acnihfLbt09NmjbXtGnTNW7sGLVo0fwRVw4AALIrq/7hzM2bt5gtf/75F+rm4aHqL1TTyZMnU/Tv5tFV4eHh8vYeI0kKCQlR5SqV1bv3+9qwYeMjqRkAAGRv2eaZG1tbW73eurXy5nWUf8D+VPtUr15dO3fuMmvbsWOXqlSurNy5H9s/cA4AAP7F6omgQoUK+mXdT8qTJ49iY2PVo2cvBQcHp9rXuZizoqKjzdqio6NkZ2enwoULKzIyMsUYe3t72dvbm5adnJyy9gAAAEC2YvVwExoaqsZNmil//vxq2aK5pn49Re3ad0wz4CQnJ5st28gm1fZ7BvTvJy+vwSnaq1Wrqri4uFTHlC1b5kEOAQ8oJ89vkZrDLR576c8JWVhJ2nLy/OYEzG/WSOu15FLgporUzJvu2Ef1WjKinHz+Ojo6Zriv1cNNQkKC/vrrL0nSoUOHVLVqFfXs+a6GDBmWom9UZJSKOTubtRUpWlQJCQm6fPlyqtufNn2G5sydZ1p2cnJS4H5/BQYe0I0bN9Ksy98/wIKjQUbl1Pl1K13A4rFhj/CYc+r85hTMb+al91o6FpH+6+xRvpaMKKeev/ny5ctwX6uHmxRsbGRvnyfVVQEBAWrcuJFZW/369XTw0CElJiamOiY+Pl7x8fFZXiYAAMierPpA8dChQ1SrVi25urqqQoUKGjLkY9Vxd9faH9dKkoYNHaKpU6eY+i9avESurq7y9h6l8uXLq0vnznqjS2fNnj3HWocAAACyGateuXEuWlTTvvlaxYoV0/Xr13X8+HG99ZaHdu3eLUkqVry4XEq5mPqHhYWpq0d3jRk9Sm9376aIiAiNHOXNx8ABAICJVcON14cfpbve0zPlg8C+vr5q2qzFwyoJAADkcNnm99wAAABkBcINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFKuGm/79+2nD+l918sRxHToYqG8XzFe5cmXTHePuXlvnw8NSfJUvV+4RVQ0AALKz3NbcuXvt2vp+4UIdOHBQuXPn0pAhH2vZ0h9Uv8EriouLS3fsy3Xr6fr1G6blS5cuPexyAQBADmDVcPNWVw+zZU9PLx05fFCVK1eWn59fumOjoy/p2rVrD7M8AACQA1k13PxX/vz5JUlXrly5b9/ff9uoPHny6GRwsKZO/UZ79/qk2s/e3l729vamZScnpyypFQAAZE/ZKtyM9h4lP799OnHiRJp9IiMj9eFHH+vwocOyz2Ov9u3ba+WK5WrfoVOqV3sG9O8nL6/BKdqrVaua5q2vsmXLWH4QuK+cPL9Fil+1eGzxGtWzsJK05eT5zQmY36yR1mvJpcDN+459VK8lI8rJ56+jo2OG+2abcDNxwng9+2wFtWnbLt1+oaGnFBp6yrQcELBfLqVKqk/v91MNN9Omz9CcufNMy05OTgrc76/AwAO6ceNGiv73+PsHWHAUyKicOr9upQtYPDbsER5zTp3fnIL5zbz0XkvHItJ/nT3K15IR5dTzN1++fBnumy0+Cj5+3Fg1adJYHTp21oULFx94fMD+QJUp82Sq6+Lj43Xjxg3TV2xsbCarBQAA2ZnVr9xMGD9OzZo1U4eOHRUWFmbRNp5/vqIiIiOzuDIAAJATWTXcTJw4QW3bvK533u2pGzdi5ezsLEm6fv26bt26JUkaNnSISpQsoYEDPSVJPXv20Lmwczpx8oTs7OzVvl1bvdaypXr07GW14wAAANmHVcPN2927SZJ+XLPKrH2Q52CtXHm3rVjx4nIp5WJaZ29np5EjR6hEiRK6deuWTp48qa4e3bRt2/ZHVzgAAMi2rBpuSrm43bePp6f5J51mzpqtmbNmP6ySAABADpctHigGAADIKoQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKIQbAABgKFYNN/3799OG9b/q5InjOnQwUN8umK9y5cred1zt2rW1aeN6nQoNls/ePfLw6PoIqgUAADmBVcONe+3a+n7hQr3W6nV1eeNN5cqdS8uW/iBHR8c0x7i5uWnJ4oXy27dPTZo217Rp0zVu7Bi1aNH8EVYOAACyq9zW3PlbXT3Mlj09vXTk8EFVrlxZfn5+qY7p5tFV4eHh8vYeI0kKCQlR5SqV1bv3+9qwYeNDrxkAAGRv2eqZm/z580uSrly5kmaf6tWra+fOXWZtO3bsUpXKlZU7d8qsZm9vr3z58pm+nJycsrRmAACQvVj1ys1/jfYeJT+/fTpx4kSafZyLOSsqOtqsLTo6SnZ2dipcuLAiIyPN1g3o309eXoNTbKdataqKi4tLdR9ly5axoPq7itQcbvHYS39OsHhsZjzqmu/Nb46cq+JXLR77XJ8fs7CStLkUuKkiNfOalh+X8+pRycz7A/6R1mvJpcDN+44tXqN6Vpfz2MjJ5296j6z8V7YJNxMnjNezz1ZQm7bt7ts3OTnZbNlGNqm2S9K06TM0Z+4807KTk5MC9/srMPCAbty4keY+/P0DMlq6GbfSBSwaJ0lhFu4zs6xRs79/wGM3V4/SsYh/6syJc2WtmjPK0vcH/CO98+Pf529qsvv5kd3l1PM3X758Ge6bLcLN+HFj1aRJY7Vt10EXLlxMt29UZJSKOTubtRUpWlQJCQm6fPlyiv7x8fGKj4/P0noBAED2ZfVnbiaMH6fmzZurY6fOCgsLu2//gIAA1atX16ytfv16OnjokBITEx9WmQAAIIewariZOHGC2rVrq379B+jGjVg5OzvL2dlZDg4Opj7Dhg7R1KlTTMuLFi+Rq6urvL1HqXz58urSubPe6NJZs2fPscYhAACAbMaqt6Xe7t5NkvTjmlVm7YM8B2vlyrttxYoXl0spF9O6sLAwdfXorjGjR+nt7t0UERGhkaO8+Rg4AACQZOVwU8rF7b59PD1TftLJ19dXTZu1eBglAQCAHM7qz9wAAABkJcINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFIvCzcqVy5U/f/4U7fny5dPKlcszXRQAAIClLAo3ddzdZWdnl6I9T548erFWrUwXBQAAYKncD9L52WcrmP7/6aef0pUrzqblXLa51KBhA128eDHLigMAAHhQDxRuNv/+m5KTk5WcnKxVK1ekWH/r1i2NGDEqy4oDAAB4UA8Ubl6sXUc2Njby9flDLVq20qVLl0zrEhISFB0draSkpCwvEgAAIKMeKNyEh4dLklzdnngoxQAAAGTWA4Wbfytbtozc3d1VtGhR2drYmK2b8vXUTBcGAABgCYvCzZtvvqHPPp2omJgYRUVFKTk52bQuOTmZcAMAAKzGonAzaOAH+vzzLzRj5qysrgcAACBTLPo9NwUKFNAvv67P6loAAAAyzaJw8+uv61W/fr2srgUAACDTLLotdfqvv/TxRx/qhRdeUFBQkBITEszWL/j2uywpDgAA4EFZFG66vvWmYmNj5V77RbnXftFsXXJyMuEGAABYjUXhprb7S1ldBwAAQJaw6JkbAACA7MqiKzeTJ32V7vrBXh9aVAwAAEBmWRRuChQoYL4Ru9yq8Mwzyp8/v/74Y2+WFAYAAGAJi8JNj569UrTZ2Njo008n6OyZs5kuCgAAwFJZ9sxNcnKy5s2br169embVJgEAAB5Ylj5Q/MQTTyh3bov/FicAAECmWZREvL1HmS3b2NioeLFievXVV7Rq1eosKQwAAMASFoWb55+vaLaclJSkmEsxGjN2nJYvX5Hh7bz44ovq2+d9VapUWSVKFNe77/bUpt9+S7O/u3ttrVm9KkV7vXoNFBIamvEDAAAAhmVRuOnYsXOW7DxvXkcdPXZcy1es1IL58zI87uW69XT9+g3T8qVLl7KkHgAAkPNl6gGZwoULq3y5ckpOTlboqVOKiYl5oPHbt+/Q9u07Hni/0dGXdO3atQceBwAAjM+icOPo6KgJ48epQ4f2srW9+0zynTt3tHr1Go0YMVJxt25laZH/9ftvG5UnTx6dDA7W1KnfaO9en4e6PwAAkHNY9Gmp0aNHqXbt2ur+9juq8GxFVXi2ot55t4dq166tUf952DgrRUZG6sOPPlavXu+rZ6/3FBp6SitXLNeLL76Y5hh7e3vly5fP9OXk5PTQ6gMAANZn0ZWbli1aqNd778vHx9fUtm3bdt269bHmzJ6lYcM+ybIC/y009JRCQ0+ZlgMC9sulVEn16f2+/Pz8Uh0zoH8/eXkNTtFerVpVxcXFpTqmbNkyFtdYpPhVi8cWr1Hd4rGZ8ahrvje/j9tcPSouBW6aLefEubJWzRmRmfcH/COt8+O/529qsvP5kd3l5PPX0dExw30tvi0VFRWdoj06+tID7TwrBOwPVPt2bdNcP236DM2Z+8/Dyk5OTgrc76/AwAO6ceNGmuP8/QMsqsetdIH7d0pDmIX7zCxr1OzvH/DYzdWjdCzinzpz4lxZq+aMsvT9Af9I7/z49/mbmux+fmR3OfX8zZcvX4b7WnRbyj8gQB9+OFh58uQxtTk4OGjw4EEKCHi0k/b88xUVERmZ5vr4+HjduHHD9BUbG/sIqwMAAI+ahb/Eb4yWLFmkAP99OnrsmJScrIoVK+r27Xi98eZbGd5O3rx5VabMk6Zlt9JuqljxOV25fEXh589r2NAhKlGyhAYO9JQk9ezZQ+fCzunEyROys7NX+3Zt9VrLlqn+rSsAAPB4sijcBAUF6eWX66ldu7YqX768bGxs9PPP6/Tj2p906wE+KVWlSmWzX8o3ZrS3JGnFylXy9BysYsWLy6WUi2m9vZ2dRo4coRIlSujWrVs6efKkunp007Zt2y05DAAAYEAWhZv+/fspOipaS5cuM2vv0rmzihQprBkzZ2VoOz4+virl4pbmek9P8weBZ86arZmzZj94wQAA4LFh0TM3Hl3fUkhoSIr2EydPyMOja6aLAgAAsJRF4cbZ2VkRESkf4r10KUbFihXLdFEAAACWsijcnL9wQTVr1kjRXrNmDUVERGS6KAAAAEtZ9MzNsqXLNHbMaNnlttOeP/6QJNV9+WWNGPGJZs+Zm6UFAgAAPAiLws2MmbNUsGBBTZw4Qfb2dpKk27dva8aMmZo+fUaWFggAAPAgLP6r4BMmfqopX0/VU089pVu3bun06dOKj4/PytoAAAAemMXhRpJu3rypgwcPZlUtAAAAmWbRA8UAAADZFeEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYCuEGAAAYilXDzYsvvqiF33+r/QH+Oh8epmZNm953TO3atbVp43qdCg2Wz9498vDo+ggqBQAAOYVVw03evI46euy4ho8YkaH+bm5uWrJ4ofz27VOTps01bdp0jRs7Ri1aNH/IlQIAgJwitzV3vn37Dm3fviPD/bt5dFV4eLi8vcdIkkJCQlS5SmX17v2+NmzY+JCqBAAAOUmOeuamevXq2rlzl1nbjh27VKVyZeXOnXpOs7e3V758+UxfTk5Oj6JUAABgJVa9cvOgnIs5Kyo62qwtOjpKdnZ2Kly4sCIjI1OMGdC/n7y8Bqdor1atquLi4lLdT9myZSyusUjxqxaPLV6juuX7rTnc4rGS5TU/1+fHBx7jUuCmitTM+8j3mzUsr/lRcSlw02w5J86V9Wq+v3/OX3OX/pxghWoyL3PvHZmR+vnx3/M3NdY6P6z1Pc7K71Fa529Wexhz5ejomOG+OSrcSFJycrLZso1sUm2/Z9r0GZozd55p2cnJSYH7/RUYeEA3btxIcz/+/gEW1edWuoBF4yQpzMJ9Zna/1nAsImfVm9Mwvw9XavObmdevNWXH947sev5a63uc1d+jRzG/D2Ou8uXLl+G+OSrcREVGqZizs1lbkaJFlZCQoMuXL6c6Jj4+XvHx8Y+iPAAAkA3kqGduAgICVK9eXbO2+vXr6eChQ0pMTLRSVQAAIDux8kfB86pixedUseJzkiS30m6qWPE5uZQqJUkaNnSIpk6dYuq/aPESubq6ytt7lMqXL68unTvrjS6dNXv2HKvUDwAAsh+r3paqUqWy1qxeZVoeM9pbkrRi5Sp5eg5WseLF5VLKxbQ+LCxMXT26a8zoUXq7ezdFRERo5ChvPgYOAABMrBpufHx8VcrFLc31np4pP+Xk6+urps1aPMyyAABADpajnrkBAAC4H8INAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFMINAAAwFKuHm+7du8nX5w+dCg3Wpo3rVatWrTT7urvX1vnwsBRf5cuVe4QVAwCA7Cy3NXfeunUrjRntrU8+Ga59f/rLw+Mt/bBkkRo0eEXh58+nOe7luvV0/foN0/KlS5ceRbkAACAHsOqVm/d69dKy5Su0dNlyhYSEyNt7jM6fP69u3TzSHRcdfUlRUVGmr6SkpEdUMQAAyO6sFm7s7OxUuXIl7dy5y6x9585dqlGjRrpjf/9towL3+2vFimWqU8c93b729vbKly+f6cvJySnTtQMAgOzLarelChcurNy5cys6OsqsPSo6WsWKOac6JjIyUh9+9LEOHzos+zz2at++vVauWK72HTrJz88v1TED+veTl9fgFO3VqlVVXFxcqmPKli3zgEfzjyLFr1o8tniN6lbZ76PmUuCmtUswNOb34UprfjPz+rWm7PbekZ3PX2t9j7Pye/So5vdhzJWjo2OG+1r1mRtJSk5ONlu2sbFJ0XZPaOgphYaeMi0HBOyXS6mS6tP7/TTDzbTpMzRn7jzTspOTkwL3+ysw8IBu3LiR6hhJ8vcPeJDDMHErXcCicZIUZuE+M7tfazgWkbPqzWmY34crtfnNzOvXmrLje0d2PX+t9T3O6u/Ro5jfhzFX+fLly3Bfq92WiomJUWJiopydi5m1Fy1SRFFR0RneTsD+QJUp82Sa6+Pj43Xjxg3TV2xsrKUlAwCAHMBq4SYhIUGHDh1WvXp1zdrr1asrf3//DG/n+ecrKiIyMqvLAwAAOZRVb0vNnTdP30z9WocOHpJ/QIC6dn1LLi4uWrR4iSRp2NAhKlGyhAYO9JQk9ezZQ+fCzunEyROys7NX+3Zt9VrLlurRs5c1DwMAAGQjVg0369b9okKFCsnTc6CKFSumEydOqKtHd4WHh0uSihUvLpdSLqb+9nZ2GjlyhEqUKKFbt27p5MmT6urRTdu2bbfWIQAAgGzG6g8UL1y4SAsXLkp1naen+aecZs6arZmzZj+KsgAAQA5l9T+/AAAAkJUINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFAINwAAwFCsHm66d+8mX58/dCo0WJs2rletWrXS7V+7dm1t2rhep0KD5bN3jzw8uj6iSgEAQE5g1XDTunUrjRntrW++maYmTZvLb98+/bBkkVxKlUq1v5ubm5YsXii/ffvUpGlzTZs2XePGjlGLFs0fceUAACC7smq4ea9XLy1bvkJLly1XSEiIvL3H6Pz58+rWzSPV/t08uio8PFze3mMUEhKipcuWa/mKFerd+/1HXDkAAMiurBZu7OzsVLlyJe3cucusfefOXapRo0aqY6pXr56i/44du1SlcmXlzp37odUKAAByDqslgsKFCyt37tyKjo4ya4+KjlaxYs6pjnEu5qyo6GiztujoKNnZ2alw4cKKjIxMMcbe3l729vamZScnJ7P/psbR0VH58uXL8LH8m5OD5XnR0n1mdr+PmoO9bY6qN6dhfh+utOY3M69fa8pu50p2Pn+t9T3Oyvl4VPP7MOYqvZ/b/2X1yx3JyclmyzY2Nina0u0vm1Tb7xnQv5+8vAanaA/c7/+gpT58Xx63dgUALMXr1/j4HmfcQ5wrJycn3bhxI90+Vgs3MTExSkxMlLNzMbP2okWKKCoqOtUxUZFRKuZsflWnSNGiSkhI0OXLl1MdM236DM2ZO8+srVChQmn2d3JyUuB+f1V7oYZiY2MzejjIIOb34WJ+Hy7m9+Fifh8uI8yvk5OTIiIi7tvPauEmISFBhw4dVr16dbVp0yZTe716dfXbb7+nOiYgIECNGzcya6tfv54OHjqkxMTEVMfEx8crPj7erO1+iU+SYmNjM9QPlmF+Hy7m9+Fifh8u5vfhysnzm9G6rXpjc+68eXrzjS7q0rmzypcvr9GjveXi4qJFi5dIkoYNHaKpU6eY+i9avESurq7y9h6l8uXLq0vnznqjS2fNnj3HWocAAACyGas+c7Nu3S8qVKiQPD0HqlixYjpx4oS6enRXeHi4JKlY8eJyKeVi6h8WFqauHt01ZvQovd29myIiIjRylLc2bNhorUMAAADZjNUfKF64cJEWLlyU6jpPz5QPAvv6+qppsxYPrZ74+HhNmjQ5xa0sZA3m9+Fifh8u5vfhYn4frsdpfm1KlnJN+6NJAAAAOUz2/GUCAAAAFiLcAAAAQyHcAAAAQyHcAAAAQyHcSOrfv5/Oh4dpzBhvs3avwZ7aH+Cv0JBgrV61Uk8//bSVKsxZvAZ76nx4mNnXgcCAFH2YW8uVKFFC076ZqiNHDik05KQ2/75JlSpVMuvDHFvGz3dvivP3fHiYJk4Yb+rD3FouV65c+vjjj+Tr84dCQ4Lls3ePPAcNlI2NjVk/5thyTk5OGjPGW/v8fBQaEqx1P69VlSpVzPoYfX4f+3BTpUoVdX3rTR09dsysvV/fPnrvvV4aPmKEWrR8TVFRUVq+bOkD/eGux1lQ0AlVqfqC6euVVxub1jG3mVOgQAH9/NOPSkxMVNeu3VS/wSsaM3acrl27ZurDHFuueYvXzM7dzl3ekCT98uuvkpjbzOrXr6+6eXTV8BEjVb9BQ42fMFF9+vTWu+++808f5jhTJn31perVrasBHwzSq40aa+fOXVqxfKlKlCgh6fGY38c63OTNm1fTp3+jjz4eoqtXrpqt69mzh775Zpo2btykEydOaOAgTzk6Oqht2zbWKTaHuXMnUVFRUaavmJgY0zrmNnP69e2j8+cvyHOwlw4cOKBz585pz54/dObMGVMf5thyMTExZuduo0av6vTpv+Tj4yuJuc2s6tVf0G+//a6tW7fp3LlzWr9+g3bu3KUqVSqb+jDHlnNwcFCLFs01fsJE+fn56a+//tKkyVMUFhambt08JD0e8/tYh5uJE8dr69Zt2r17j1l76dKlVbx4ce3cucvUFh8fL19fP9WoUf1Rl5kjlSlTRvsD/OXr84dmzZyh0qVLS2Jus0KTJo118NAhzZkzS4cOBur33zbqzTffMK1njrOOnZ2d2rdrp+UrVkhibrPCn/v+1Msvv6SyZctIkp577lnVqlVT27Zul8QcZ1auXLmUO3du3b5926w97tYt1apZ87GZX6v/hmJreb11a1V6vpJatHwtxbpixe7+5fGoaPO/Th4VFSVXV9dHUl9Otj8wUB8MHKRTp07L2bmoBn7wgdb9vFYNX3mVuc0CpUuXVjePrpo7b76mfTNdVatV1bixYxUfH6/Vq9cwx1moWbOmyp8/v1auXCWJ94asMH3GTP3vf//Trp07dOfOHeXKlUufff6Ffvr5Z0nMcWbFxsbK399fgwYOVHBwiKKiotSmzet6oVo1nT59+rGZ38cy3JQqVVJjx47WG2++lSLd/ltysvkvb7axsUnRhpS2b99h+v+gIMnfP0A+e/eoY8eO2r9/vyTmNjNsbW116NAhffbZ55KkI0eP6pmnn1a3bh5avXqNqR9znHlvdOmi7du3KyIiwqydubXc661bq337durXb4BOnDypihWf05gxoxUREaFVq1ab+jHHlhvwwSBNnvSVAvf7KzExUYcPH9HatT+pUqXnTX2MPr+P5W2pypUqy9nZWZs2btDZM6d19sxp1anjrh7vvquzZ04rKupuoi3m7Gw2rmjRooqKjrJGyTlaXFycgoKCVKZMGUVG3p0/5tZykZGROnky2KwtOCTE9EdmmeOs4eLiorp1X9bSpctNbcxt5o0cOVzTp8/Uz+vWKSgoSGvW/Kh58+ZrQP9+kpjjrHDmzBm179BR5co/rRo1X1TL11rJzs5OZ8PCHpv5fSzDze49e9TwlUZq3KSZ6evAgYP6ce1aNW7STGfOnFFERITq1atrGmNnZ6fatV+Uv39AOltGauzt7VX+qacUGRGhs2fPMreZ9Oef/ipXrpxZW9myZRUefk6SmOMs0qVzJ0VHR2vL1q2mNuY28xwcHZWUnGTWdufOHdnY3v1xxBxnnbi4OEVGRqpAgQKqX7+efvvt98dmfh/L21KxsbE6ceKEWdvNmzd1+fJlU/v8+Qs0YEB/nTr9l06fPq0PBvRXXNwtrV37kxUqzllGjRyh3zdvUXh4uIoWLaJBAz/Q//Ll08q/Lzkzt5kzd958rft5rQYM6K9ffvlV1apWVde33tRHHw8x9WGOM8fGxkadO3fSqlWrdefOHbN1zG3mbN68RR98MEDh4eE6ceKknn/+eb3/Xi8tX77C1Ic5zpz69evLxsZGoaGhKvPkkxo5crhCQ09pxYqVkh6P+X0sw01GzJg5Sw4ODvp04ngVKFBAgYEH9Mabbyk2NtbapWV7JUuW1MwZ01W4cCFduhSj/fv367VWrys8PFwSc5tZBw8eVI+evTRs6FB5DhqosLAwjfIebfbGxBxnTr26deXq6mr6lNS/MbeZM2LESH388Yf6dOIEFSlSVBEREVq85AdNmfK1qQ9znDn58/9Pw4YOVcmSJXTlyhVt2LBRn33+hRITEyU9HvNrU7KUq3GeIAIAAI+9x/KZGwAAYFyEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAAYCiEGwAWc3evrfPhYcqfP3+Gx0yZMlnfLpj/EKvKGl6DPbX5902PfL+Wzo+f71717NkjQ307deqo48eOPPA+HqXVq1ZqzBhva5eBHIpwgxxvypTJOh8eps8+m5hi3cSJE3Q+PExTpky2QmUZZ+kPG0vCRXbDD7EHk9a50rzFa/phyQ+PpIZHcd717PWevvjiq4e2fRgb4QaGEB4ertdbt5aDg4OpLU+ePGrzemudO3fOipUBj0ZMTIzibt2ydhmZljv33b8KdOXKFUP9OQA8WoQbGMLhw0cUHn5ezZs3M7W1aN5c589f0JEjR1P079unt3z27lFoSLA2b/5NLVu2MK2ztbXVpK++lK/PHwoNCdbuXTvUo8e7ZuPv3Tro/f77CtzvryNHDmnihPGmN+bUPPfcs1q1aoVOnjiuE0HHtGnjelWuXFnu7rX19ZTJKlCggM6Hh+l8eJi8BntKktq1a6uNG9br5InjOhAYoBnTp6lIkSKSJFdXV61ZvUqSFHT8aIorVOkdY2rS29c9r7zSULt371RoSLBWrVohNzc3s/Wp3crp2bOH/Hz3prrPKVMmq04dd/Xq2dN07K6urin6vfvO29q6ZbNpuVnTpjofHqa3u3c3tS39YYmGDf3nj4c2btxImzau16nQYPns3aPBnoOUK1cu0/r//e9/+uLzz3ToYKBOBB3TypXL9dxzz6Y5P25ubvpjz259+ulE2djYyM7OTiOGf6IA/z8VEnxCv/6yTu7utU39711hqV+/vnbu2Kbgk0H6YcliFStWzNTH1tZW3t6jdPzYER05ckgjhn8iG5s0S0j3XPnvban8+fPri88/08ED+3UqNFjbtm5Ro0avprrdQoUKav2vv+j7775Vnjx5JKV9/tzvvPu3e3PQrGlT7d69U6dCg7V82Q8qVaqkqc+9c6ZL587y2btHf50OlZTyip69vb1GDP9E/n/66fSpEO3Zs0tvdOlsWv/UU09p8aKFCj4ZpIMH9uubb75W4UKF0p5MGBrhBoaxYuVKdencybTcpUunVP/w4ZAhH6tz504aOmy4Gr7yqubNm69p30xV7dp3fzDZ2trqwoULer93XzVo+IomT/law4YOUatWr5ltp04ddz3x5BPq2LGzBg3yVKdOHdWpU8c065s+bZouXLigFi1eU7PmLTR9xkwlJibI3z9AI0d569q1a6pS9QVVqfqCZs2eI0myt7PXF19+qUaNm+rdHj3lVtpNX//9g+T8+fPq0bOXJOnluvVUpeoLGjXKO0PHmJr09iVJpUqV1Px5c7Vt2zY1adpUS5cu0yfDhqb7PbmfUaO85e/vryVLfjAd+/nz51P02+vjq2eeedr0w6q2e21dunRJtd1flCTlypVLNWpUl4+vn6S7fxV52jdTteDb79Sg4asaMmSYOnXqqIEfDDBtc/Gi71WsWDF19eiuZs1b6MjhI1q5YrkKFiyYYv/PPPOMfv5prX799VcNG/aJkpOTNWXyJNWsWUN9+vbTq42a6Ndf1+uHJYtVpsyTpnGOjo7q0/s9DfhgkNq16yAXl1IaNXKEaX3v999Tl86d5PXhR2rTpp0KFiyo5s2apdj/PemdK/9mY2OjJUsWqUaNGhowYKAaNHxVEz/9NMVfOJekkiVLaO2PaxQSGqIePXvp9u3b6Z4/6Z13qXF0dNQHHwzQoEGeer1NO+XL9z/NmjnDrM+TTz6pVq1eU69e76txk6apbuebqV/r9ddba8RIb9Vv8IqGDh2m2Js3JUnFihXTj2tW6eixY2revKXeestDzkWdNXvOrDTrgrHxV8FhGGtWr9GwoUPk6uqq5ORk1ahRU3369FMdd3dTH0dHR73Xq5c6de6sgID9kqSzZ8+qVs2a8uj6lnx9fZWYmKivJv3zQz0sLEw1a9RQq1av6ZdffjW1X716VcOHj1BSUpJCQkO1ZetW1X35ZS1duizV+lxcSmnW7NkKCb37L9PTp/8yrbt+/bqSk5MVFRVlNubf4ezs2bMaOdJbGzf8qrx58+rmzZu6cuWKJCk6+pKuXbuW4WNMzf321a1bN509e1be3mMkSaGhp/RshQrq379fqtvLiOvXrys+PkFxt+JSHPu/BQUF6fLly6rtXlsbNmxUHffamjNnrnr16ilJqlq1ivLkyaN9+/ZJkgZ+MEDTZ8zUqlWrTcfzxZdfacTw4Zo85Wu99FIdVahQQZWrVFN8fLwkaey48WratKlatmyhH35Yatp39eovaOHC7zV92gzNnnM3SDzxxBNq0+Z1Va9RSxEREZKk2XPmqGHD+urcubM+++xzSXevNgwZ+onOnDkjSfru+4XyHDTQtO2ePXtq+vQZ2rBhoyRpyNBhatCgfprzkJCQkOa58m/16tZVtapVVb9BQ506ddo0B/9VtmwZLV+2VL/99rtG/h1QMnL+pHbepcXe3l7DR4xQYOABSdKgQZ7atWuHqlatqgMH7rbZ2dlpwAcDFRMTk+o2ypYto9atW6lzlze0e/eeFMfTvZuHDh8+Ypp3SRrs5aUA/z9VtmwZ0xzg8UG4gWHEXL6srVu3qVPHDrKxsdHWbVsVc/myWZ+nn35ajo4OWr5sqVm7nZ2d2e0rD4+uevONN+Tq6iIHBwfZ2dnp6NFjZmNOnDyppKQk03JkRKQqPFshzfrmzp2nr778Qh3at9Pu3Xv0y6/rTT/00vJ8xYry8hqsihWfU8GCBWVre/diq4uLi4KDg1Mdk9FjfNB9PVW+vPbvDzQbc++H36Pg6+unOu7u2rPnDz399NNatHiJevd+X+XLl5e7u7sOHz6im3//S75y5UqqUqWK2ZUaW9tccnR0kKODgypXqiQnJycdPXLIbB8ODg568oknTMulSrloxfJl+vyLLzVv3j+fYKpU6XnZ2tpqz+6dZuPt7e11+fIV0/LNmzfNvscREZEqWrSopLu3xUqUKC7/f83hnTt3dPDgIdmkd28qAypWfE4XLlxI94e6g4ODflr7o3766WeN8h5tarf0/ElLQkKCDh78Z55DQkN15coVPfVUeVO4ORcenmawuXs8FZWYmCgfn9SDeaXKlVSnjruCTwalWPfEE08Qbh5DhBsYyvIVKzRh/DhJ0ifDR6RYb2t794eGR7e3dfHiRbN18fG3JUmtWr2m0d7eGjtunAL8A3QjNlZ9+ryvF6pVM+ufmJBotpysZNnapH2nd9LkKVr700969dVX9UrDhvLyGqw+fftr06bUP27s6OioZct+0M6du9R/wEDFXLokFxcXLVv2g+zt7dLcT0aO0ZJ9ZeQHblJSkv770IhdOs8hPQgfHx+99dZbevHFWjp27LiuXbsmX18/ubvXVh13d/n4+Jj62tjYatKkSdqwMeXc3rp9W7a2toqIjFSHDp1SrL929arp/2NiLuliRIReb91ay5Yt140bNyTdvXWZmJioZs1b6M6dJLPx/34INiEhwXzjycmm0Pgw3crAg8Xx8fHavXuPXn31Vc2aPVsXLtw9Vyw5f+4nOTk53ba4v0NpWu53PLY2ttq8eYsmTPw0xbp7V9bweCHcwFC2b98hOzt7SdKOHTtTrD95Mli3bt2Si0upNG/PvFirlvwD/LVw4SJT27//NZ8Zp06d1qlT8zVv3nzNnDFdXTp30qZNm5QQn2D2sKsklS9fXkWKFNHETz/V+fMXJEmVq1Q263Pvh2euXP/8wMzIMf5XRvZ1MjhYzZo2MWt74QXzwHcpJkbFnJ3N2ipWrJjuvhMSEpTLNle6faS7z92MHTtGLVu2MAUZH19f1a37smrUqK75CxaY+h45cljlypXTX3/9leq2Dh8+omLOzkpMTEz303S3bt1St25va8niRVq6dIneeOMtxcbG6siRI8qdO7eKFClquhX2oK5fv66LFyNU/YVq8vO7+6xQrly5VLlyJR0+nPavBUjtXPmvY8eDVLJkyXRvySQlJWnABwM1c8Z0rVy5Qh06dFJERESGzp/Uzru02NnZqUqVKqarNOXKlVXBggUVEhJ637H3HD8eJFtbW7m71zbdlvq3w0eOqGWL5goLC0v1uSI8fnigGIaSlJSk+g0aqn6Dhma3jO6JjY3V7DlzNWa0tzp27KAnnnhCz1esqLe7d1fHjh0kSaf/+ktVKldW/fr1VbZsGX300YeqUqVKpupycHDQhPHj5O5eWy4uLqpZo4aqVKliurUUdi5M+fLl08svv6TChQrJ0cFB4eHhun37tt595x2VLl1aTRo3NnteQ5LOnQtXUlKSGjVqpMKFCytv3rwZOsb/ysi+Fi9arCeeeELe3qNUrlxZtW3TJsUD1Hv3+qhIkSLq17ePnnjiCb3dvbsaNmy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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 78 }, { "cell_type": "markdown", @@ -1503,60 +1963,162 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:10:45.340339Z", + "start_time": "2026-06-12T00:10:45.313536Z" + } + }, "source": [ "#Code task 9#\n", "#Create a dataframe containing the values of the first two PCA components\n", "#Remember the first component was given by state_pca_x[:, 0],\n", "#and the second by state_pca_x[:, 1]\n", "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n", - "pca_df = pd.DataFrame({'PC1': ___, 'PC2': ___}, index=__)\n", + "pca_df = pd.DataFrame({'PC1': state_pca_x[:, 0], 'PC2': state_pca_x[:, 1]}, index=state_summary_index)\n", "pca_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That worked, and you have state as an index." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, + ], "outputs": [ { "data": { "text/plain": [ - "state\n", - "Alaska 57.333333\n", - "Arizona 83.500000\n", - "California 81.416667\n", - "Colorado 90.714286\n", - "Connecticut 56.800000\n", - "Name: AdultWeekend, dtype: float64" + " PC1 PC2\n", + "state \n", + "Alaska -1.336533 -0.182208\n", + "Arizona -1.839049 -0.387959\n", + "California 3.537857 -1.282509\n", + "Colorado 4.402210 -0.898855\n", + "Connecticut -0.988027 1.020218" + ], + "text/html": [ + "
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PC1PC2
state
Alaska-1.336533-0.182208
Arizona-1.839049-0.387959
California3.537857-1.282509
Colorado4.402210-0.898855
Connecticut-0.9880271.020218
\n", + "
" ] }, - "execution_count": 34, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], + "execution_count": 80 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That worked, and you have state as an index." + ] + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:11:18.955359Z", + "start_time": "2026-06-12T00:11:18.921224Z" + } + }, "source": [ "# our average state prices also have state as an index\n", "state_avg_price.head()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "state\n", + "Alaska 57.333333\n", + "Arizona 83.500000\n", + "California 81.416667\n", + "Colorado 90.714286\n", + "Connecticut 56.800000\n", + "Name: AdultWeekend, dtype: float64" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 81 }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:11:23.241181Z", + "start_time": "2026-06-12T00:11:23.209395Z" + } + }, + "source": [ + "# we can also cast it to a dataframe using Series' to_frame() method:\n", + "state_avg_price.to_frame().head()" + ], "outputs": [ { "data": { + "text/plain": [ + " AdultWeekend\n", + "state \n", + "Alaska 57.333333\n", + "Arizona 83.500000\n", + "California 81.416667\n", + "Colorado 90.714286\n", + "Connecticut 56.800000" + ], "text/html": [ "
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PC1PC2AdultWeekendQuartile
state
Rhode Island-1.8436460.761339NaNNaN
\n", + "
" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 86 + }, { "cell_type": "markdown", "metadata": {}, @@ -1853,30 +2521,55 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:11:47.089978Z", + "start_time": "2026-06-12T00:11:47.030484Z" + } + }, + "source": [ + "pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n", + "pca_df['Quartile'] = pca_df['Quartile'].cat.add_categories('NA')\n", + "pca_df['Quartile'].fillna('NA', inplace=True)\n", + "pca_df.loc['Rhode Island']" + ], "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kd/z316r17n3rv6_v8fxp4kggs00000gn/T/ipykernel_20007/2495745839.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n", + "/var/folders/kd/z316r17n3rv6_v8fxp4kggs00000gn/T/ipykernel_20007/2495745839.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " pca_df['Quartile'].fillna('NA', inplace=True)\n" + ] + }, { "data": { "text/plain": [ - "PC1 -1.84365\n", - "PC2 0.761339\n", - "AdultWeekend 64.1244\n", - "Quartile NA\n", + "PC1 -1.843646\n", + "PC2 0.761339\n", + "AdultWeekend 64.124388\n", + "Quartile NA\n", "Name: Rhode Island, dtype: object" ] }, - "execution_count": 40, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n", - "pca_df['Quartile'] = pca_df['Quartile'].cat.add_categories('NA')\n", - "pca_df['Quartile'].fillna('NA', inplace=True)\n", - "pca_df.loc['Rhode Island']" - ] + "execution_count": 87 }, { "cell_type": "markdown", @@ -1896,22 +2589,12 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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5c/3l27ZtIyEhgejo6EBOQ4UL9gidAz4wMwe84Jz7a5DjERERqRK2783ilpc+4XD2SXJyHRHhRu2oSF4b1YWWjcuetLz88ssMHDiQ8PBwf9mjjz5KUlJSoXrdu3fnmmuuITk5+bTtDR8+nEceeYS+ffuSlZVFWNiZx6KGDBnCtGnTCpU1bdqU5cuXU716dbKysmjXrh0DBgygWbNmhepdcMEFzJgxg0mTJhUqb9WqFWlpaZU2oQv2CF1351xH4CrgXjPrdWoFM7vLzFLNLHXv3r0VH6GIiARVcnIy77//fqGyZ599lp///OdBiuj7GI4ePRrUGMoqN89xy0ufsOdgNkeO53I8J48jx3PZcyibW1/65KxG6mbNmsV1113n3169ejV79uyhX79+hep16NCBMz2ffcOGDeTk5NC3b18AoqOjqVmzZpniqlatGtWrVwfg+PHj5OXlFVsvNjaW+Pj4gBLHyiSo0Trnvvb991vgbeDyYur81TmX6JxLbNy4Qp5vKyIilcjQoUOZM2dOobI5c+YwdOjQMx5bcNrvXAvlhG7p1kwOZ5/k1LTNOTiUfZKlW8t2afuJEyfYvn27P1HLy8tj7NixPP3002Vqb/PmzdSrV4+BAwfSoUMHxo8fH9DPdN68ecTHxzN48GB27tzpL9+5cyfx8fG0aNGChx56qMjoXCgLWkJnZrXMrHb+e6AfsD5Y8YiISOXhcnI4vHAhmc8/T+/sbN6dP5/jx48DkJ6eztdff83Ro0fp1q0bHTt25MYbbyQrKwvwRlh+/etf06NHD958801iY2P51a9+Rbdu3UhMTOSzzz6jf//+tGrVyn8dlXOO8ePH065dO+Li4nj99dcBWLRoEcnJyQwePJgf/ehH3HrrrTjnmDp1Kl9//TUpKSmkpKQE5ySdha/2HSEnt/hRuNxcx459R8rUbmZmJvXq1fNvP/fcc1x99dW0aNGiTO3l5OSwZMkSJk2axKpVq9i+fTszZsw47THXXnst6enprF27lh//+MeMGDHCv69FixasXbuWrVu3MnPmTPbs2VOmuCqjYI7QNQGWmtnnwKfAe865fwcxHhERqQSyFi9mS4+efD3+QfZO/TM5L75Em5xcZvbuzck9e5gzZw59+vThd7/7HR9++CGfffYZiYmJTJ482d9GVFQUS5cu5eabbwa8P+QrVqygZ8+ejBw5krlz57Jy5UomTpwIwFtvvUVaWhqff/45H374IePHj2f37t0ArFmzhmeffZYNGzawfft2li1bxujRo2nWrBkfffQRH330UcWfpLN0YcNaRIQXf6uN8HDjgoa1ytRujRo1Ct2PbcWKFUybNo3Y2FjGjRvHK6+8wsMPPxxwezExMXTo0IGWLVsSERHB9ddfz2effXbaYxo2bOifWr3zzjtZvXp1kTrNmjWjbdu2LFmyJOBYKrugLYpwzm0H2gerfxERqXyOLF9Oxv0P4ArepDUvj6tr1WL++vWk3DSEOd/tY+CNN/KPf/yD7t27A95UX7du3fyHDBkypFC7AwYMACAuLo6srCxq165N7dq1iYqK4sCBAyxdupShQ4cSHh5OkyZNSEpKYtWqVdSpU4fLL7+cmJgYABISEkhPT6dHjx7lfCbKV4+LG1E7KpKjJ3JxBQbqzKBOVCQ9Lm5Upnbr169Pbm4u2dnZREVFMWvWLP++GTNmkJqaylNPPRVwe507d2b//v3s3buXxo0bs3DhQhITvVvWTpgwgcsvv5wbbrih0DG7d++madOmACxYsIDWrVsD3lM0GjZsSI0aNdi/fz/Lli3jl7/8ZZk+Z2UUWlf8iYjID5Zzjt2PPV44mfPpU7s2K48cYe3XX5P17bd06NCBvn37kpaWRlpaGhs2bGD69On++rVqFR5hyh+xCQsL87/P387JycG5khcBFKwfHh5OTk5OmT9jZREeZrw2qgtN6kRRq3o4URFh1Koezvl1opg1qgvhYWW/UW6/fv1YunTpGetNnTqVmJgYMjIyiI+PZ9SoUQCkpqb634eHhzNp0iT69OlDXFwczjnuvPNOANatW8f5559fbLtt27alffv2TJ061T9Fu3HjRrp06UL79u1JSkpi3LhxxMXFATBx4kQWLFgAwKpVq4iJieHNN9/kZz/7GW3bti3zuahIwb5tiYiICADZ678gZ9++YvfVCgujc82aPLJzB1c1bkzXrl2599572bp1KxdffDFHjx4lIyODSy+9tEx99+rVixdeeIERI0bw3Xff8fHHH/P000+zadOmEo+pXbs2hw8fplGjso1mBVvLxtEse6g3S7dmsmPfES5oWIseFzc6q2QO4L777mPy5Mn8+Mc/LlQ+cuRIRo4c6d8ePXo0o0ePLnJ8YmKi/95xAH379mXt2rVF6p08ebLQqGy+J598kieffLJIeUntAPz617/2v+/cuTMZGRnF1qvMNEInIiKVwsmMnd6cXwl+UrsO/zt+nCsjImnUoAEzZsxg6NChxMfH07Vr19MmX2dyww03EB8fT/v27enduzd//OMfix39Keiuu+7iqquuCslFEfnCw4ykSxszrFssSZc2PutkDrzbkaSkpJTrCmOgyK1sylv+jYWbNGlSof0Gyk43zFzZJCYmutTU1GCHISIi5SBr8WJ2jR1Hnm+1aokiI/nR2s8rxfMzK5uNGzf6rxmTilXcuTez1RX1nHqN0ImISKVQ8/LLcWca1QkLo3ZKipI5kVMooRMRkUohrEYN6t96KxYVVWIdq1aNhnfdVYFRiYQGJXQiIlJpnPfA/dTq2ROrUaPwjogILCqKpr/5NTXahcaqQ5GKpFWuIiJSaVhEBDFT/8SR5cv57uW/kf2//2GRkdTu04cGw26j2oUXBjtEkUpJI3QiIlKpmBnR3btzwfSXuHTpEi75aCHn/98jSubONedg+yKYdRNMS/T+u33RWTd77NgxkpKS/Ktcw8PDSUhIICEhwX+DZ4Cf/vSntG/f3v/M1awSFsNceeWV1KtXj2uuuSbgGN544w3atGlD27ZtueWWW/zlM2fO5JJLLuGSSy5h5syZp21j7ty5mBn5izHzV7lGR0cHHEdF0gidiIhIVeMc/OtBWPN3OHnUK8vcAulLoMMwuPqPZW765ZdfZuDAgYSHhwPe48DS0tKK1JsyZQp16tQB4Je//CXTpk0r9rFg48eP5+jRo7zwwgsB9b9lyxaefPJJli1bRv369fn2228B+O6773jiiSdITU3FzOjUqRMDBgygfv36Rdo4fPgwU6dOpUuXLv6yVq1akZaWVmkTOo3QiYiIVDVfLi6czOU7eRTWvHpWI3WzZs3iuuuuO2O9/GTOOcexY8dKXLncp08fateuHXD/L774Ivfee68/UTvvvPMA7751ffv2pUGDBtSvX5++ffvy738X/wj5Rx99lAcffJCo0yzQqWyU0ImIiFQ1K54rmszlO3nU218GJ06cYPv27cTGxvrLsrOzSUxMpGvXrrzzzjuF6t9+++2cf/75bNq0iV/84hdl6vNUmzdvZvPmzXTv3p2uXbv6k7Zdu3bRokULf72YmBh27dpV5Pg1a9awc+fOUk3xVgaachUREalq9m8/w/4vy9RsZmYm9erVK1S2Y8cOmjVrxvbt2+nduzdxcXG0atUKgL/97W/k5ubyi1/8gtdff53bb7+9TP0WlJOTw5YtW1i0aBEZGRn07NmT9evXF/u83lNHBfPy8hgzZoz/+a+hRCN0IiIiVU39lqff3+CiMjVbo0YNsrOzC5U1a9YMgJYtW5KcnMyaNWsK7Q8PD2fIkCHMmzevTH2eKiYmhuuuu47IyEguuugiLrvsMrZs2UJMTAw7d+7018vIyPDHlu/w4cOsX7+e5ORkYmNjWblyJQMGDCAUnlKlhE5ERKSq6fZziKxZ/L7ImtD152Vqtn79+uTm5vqTuv3793P8+HHAG71btmwZbdq0wTnH1q1bAe8aun/84x/86Ec/KlVfEyZM4O233y5Sfv311/PRRx/5+9y8eTMtW7akf//+fPDBB+zfv5/9+/fzwQcf0L9//0LH1q1bl8zMTNLT00lPT6dr164sWLCAxMQKeXrXWVFCJyIiUtVclOStZj01qYusCR2HQcvkMjfdr18/li5dCnjPN01MTKR9+/akpKTw8MMP+xO6ESNGEBcXR1xcHLt372bixIkApKamMmrUKH97PXv25MYbb+S///0vMTExvP/++wCsW7eO888/v0j//fv3p2HDhrRp04aUlBSefvppGjZsSIMGDXj00Ufp3LkznTt3ZuLEiTRo0ACAiRMnsmDBgjJ/5srAiptTrqwSExNdKAx7ioiIBENxD4g/re2LvAUQ+7+E+hd5I3ctk88qhjVr1jB58mReffXVs2rnTPr37+9P7ipSdHR0sffMK+7cm9lq51yFDO9pUYSIiEhV1TL5rBO4U3Xo0IGUlBRyc3P996IrDxWdzG3bto1BgwbRpEmTCu03UEroRERE5Jy64447gh3COZd/Y+HKStfQiYiIiIQ4JXQiIiIiIU4JnYiIiEiIU0InIiJShWWdyGLn4Z1knSi6clNChxI6ERGRKijjcAajF44m6fUkBi8YTNLrSdy/8H4yDmecVbvHjh0jKSmJ3NxcwHv0V79+/WjdujVt2rQhPT0dgC+//JIuXbpwySWXMGTIEE6cOFFim4cOHaJ58+bcd999Z+x/zJgxJCQkkJCQwKWXXlroUWQPPvggbdu2pXXr1owePbrYx4Hlmzt3Lmbmf0rEtm3bSEhIIDo6OpDTUOGU0ImIiFQxGYczGPLuEBZnLOZE3gmO5hzlRN4JFmUsYsi7Q84qqXv55ZcZOHCg/5Ylw4cPZ/z48WzcuJFPP/2U8847D4CHHnqIMWPGsGXLFurXr8/06dNLbPPRRx8lKSkpoP6nTJlCWloaaWlp/OIXv2DgwIEALF++nGXLlrF27VrWr1/PqlWrWLx4cbFtHD58mKlTp9KlSxd/mVa5ioiISKXyx1V/JOtkFnkur1B5nssj62QWk1InlbntWbNmcd111wGwYcMGcnJy6Nu3L+DdlLdmzZo451i4cCGDBw8GYMSIEbzzzjvFtrd69Wr27NlDv379Sh3L7NmzGTp0KABmRnZ2NidOnOD48eOcPHmyxHvKPfroozz44INERUWVus9gUUInIiJShWSdyGLZrmVFkrl8eS6PJRlLynRN3YkTJ9i+fTuxsbEAbN68mXr16jFw4EA6dOjA+PHjyc3NZd++fdSrV4+ICO92uDExMezatatoLHl5jB07lqeffrrUsXz11Vd8+eWX9O7dG4Bu3bqRkpJC06ZNadq0Kf379y/2qRpr1qxh586dXHPNNaXuM5iU0ImIiFQh+4/vJyLs9M8VCA8LZ//x/aVuOzMzs9A1azk5OSxZsoRJkyaxatUqtm/fzowZM4q9ds3MipQ999xzXH311bRo0aLUscyZM4fBgwf7p363bt3Kxo0bycjIYNeuXSxcuJCPP/640DF5eXmMGTOGZ555ptT9BZsSOhERkSqkfvX65OTlnLZObl4u9avXL3XbNWrUIDs7278dExNDhw4daNmyJREREVx//fV89tlnNGrUiAMHDpCT48WRkZFBs2bNirS3YsUKpk2bRmxsLOPGjeOVV17h4YcfDiiWOXPm+KdbAd5++226du1KdHQ00dHRXHXVVaxcubLQMYcPH2b9+vUkJycTGxvLypUrGTBgAKHwHHkldCIiIlVIdLVoejTvQZgVnwKEWRg9Y3oSXa30qznr169Pbm6uP6nr3Lkz+/fvZ+/evQAsXLiQNm3aYGakpKQwd+5cAGbOnOm/7q6gWbNmsWPHDtLT05k0aRLDhw/nqaeeAmDChAm8/fbbxcbxv//9j/3799OtWzd/2QUXXMDixYvJycnh5MmTLF68uMiUa926dcnMzCQ9PZ309HS6du3KggULSExMLPW5qGhK6ERERKqY8Z3HEx0ZXSSpC7MwakfWZlziuDK33a9fP5YuXQpAeHg4kyZNok+fPsTFxeGc48477wTgD3/4A5MnT+biiy9m3759/PSnPwUgNTWVUaNGnbGfdevWcf755xe7b/bs2dx8882FpnEHDx5Mq1atiIuLo3379rRv355rr70WgIkTJ6NcNkgAACAASURBVLJgwYIyf+bKwE53D5bKJjEx0YXCsKeIiEgwbNy4sdgL/YuTcTiDp1c9zdJdSwkPCyc3L5eeMT0ZlziOmNoxZY5hzZo1TJ48mVdffbXMbQSif//+vP/+++XaR3Gio6PJyiq6YKS4c29mq51zFTK8d/qrIkVEROQHKaZ2DH/q/SeyTmSx//h+6levX6Zp1lN16NCBlJQUcnNz/QsSykNFJ3Pbtm1j0KBBJd7qJNiU0ImIiFRh0dWiz0kiV9Add9xxTturDHRjYREREREpV0roREREREKcEjoRERGREKdr6ERERKqo7M2bOTjvLU7u3k1k06bUHTSQqEsvDXZYUgYaoRMREali8rKz2Xn3PaTfNITv/v53Dn/wAd/9/e+k3zSEnXffQ16Bpz2U1rFjx0hKSiI3N5ePPvqIhIQE/ysqKop33nkHgP/+97907NiRhIQEevTowdatW0ts89ChQzRv3pz77rvvjP1/9dVX9OnTh/j4eJKTk8nIyPCXd+rUiYSEBNq2bcvzzz9f7PHHjx9nyJAhXHzxxXTp0oX09HTAW+WakJBAdPS5XUByriihExERqWJ2PTCGIytW4LKzITfXK8zNxWVnc2TFCnaNGVPmtl9++WUGDhxIeHg4KSkppKWlkZaWxsKFC6lZsyb9+vUD4J577mHWrFmkpaVxyy238Nvf/rbENh999FGSkpIC6n/cuHEMHz6ctWvXMnHiRCZMmABA06ZNWb58OWlpaXzyySc89dRTfP3110WOnz59OvXr12fr1q2MGTOGhx56CNAqVxEREalEsjdv5sjKlbjjx4vd744f58iKlRzfsqVM7c+aNavYx3jNnTuXq666ipo1awJgZhw6dAiAgwcPFvssV4DVq1ezZ88efyJ4Jhs2bKBPnz4ApKSkMH/+fACqVatG9erVAW8ULi8vr9jj58+fz4gRIwDv6RL//e9/CYWHMCihExERqUIOznsLd/Lkaeu4kyc5MG9eqds+ceIE27dvJzY2tsi+OXPmMHToUP/2Sy+9xNVXX01MTAyvvvoqDz/8cJFj8vLyGDt2LE8//XTAMbRv3555vtjffvttDh8+zL59+wDYuXMn8fHxtGjRgoceeqjYJHLXrl20aNECgIiICOrWres/vjJTQiciIlKFnNy9+/tp1pLk5nJy9zelbjszM5N69eoVKd+9ezfr1q2jf//+/rIpU6bwz3/+k4yMDG6//XZ++ctfFjnuueee4+qrr/YnWIGYNGkSixcvpkOHDixevJjmzZsTEeGtAW3RogVr165l69atzJw5kz179hQ5vrjRuILPhK2stMpVRESkCols2hTCw0+f1IWHe/VKqUaNGmQXs6DijTfe4IYbbiAyMhKAvXv38vnnn9OlSxcAhgwZwpVXXlnkuBUrVrBkyRKee+45srKyOHHiBNHR0Tz11FMlxtCsWTPeeustALKyspg3bx5169YtUqdt27YsWbKEwYMHF9oXExPDzp07iYmJIScnh4MHD9KgQYPSnYgg0AidiIhIFVJ30EDMl1iVxCIjqTdoYKnbrl+/Prm5uUWSutmzZxeabq1fvz4HDx5k8+bNAPznP/8p8mB78K7H27FjB+np6UyaNInhw4f7k7kJEybw9ttvFzkmMzPTf33ck08+6X8MWUZGBseOHQNg//79LFu2jMsuu6zI8QMGDGDmzJmAd91f7969Q2KETgmdiIhIFRJ16aXU6toV8y0QOJVVr06tbl2pfsklZWq/X79+LF261L+dnp7Ozp07C61SjYiI4MUXX2TQoEG0b9+eV1991X+dXGpqKqNGjTpjP+vWreP8888vUr5o0SIuu+wyLr30Uvbs2cMjjzwCwMaNG+nSpQvt27cnKSmJcePGERcXB8DEiRNZsGABAD/96U/Zt28fF198MZMnTz7taGBlYqGwciNfYmKiS01NDXYYIiIildLGjRuLHek6VV52NrvGjOHIipXeAoncXAgPxyIjqdWtK82nTCEsKqpMMaxZs4bJkyfz6quvlun4QPXv35/333+/XPsoTnR0NFlZWUXKizv3ZrbaOZdYEXHpGjoREZEqJiwqihZ/+cv3T4r45hsimzal3qCBZR6Zy9ehQwdSUlLIzc0lPDz8HEVcVEUnc9u2bWPQoEE0adKkQvsNlBI6ERGRKirq0kuJmlD0diFnK/+6tR8S3VhYRERERMqVEjoRERGREKeETkRERCTEKaETERGp4s7lHS/MjLFjx/q3J02axOOPP16oTvv27Qvdl07OnhZFiIiIVEEnsnNY8/4O1n+8i+wjJ4mqFUm7Xs3p0P8CqkWVPT2oXr06b731FhMmTKBRo0ZF9m/cuJG8vDw+/vhjjhw5Qq1atc7mY4hP0EfozCzczNaY2bvBjkVERKQqOJGdw9w/pLLmPzvIPnISgOwjJ1nznx3M/UMqJ7Jzytx2REQEd911F1OmTCl2/2uvvcawYcPo16+f/2a+cvaCntAB9wMbgx2EiIhIVbHm/R0c2ptNbk5eofLcnDwO7c1mzQc7zqr9e++9l1mzZnHw4MEi+15//XWGDBnC0KFDmT179ln1I98LakJnZjHAT4CXghmHiIhIVbL+411Fkrl8uTl5rF+866zar1OnDsOHD2fq1KmFyletWkXjxo258MIL6dOnD5999hn79+8/q77EE+wRumeBB4Hiv1WAmd1lZqlmlrp3796Ki0xEROQHyDnnn2YtSfaRk2e9UOKBBx5g+vTpHDlyxF82e/ZsNm3aRGxsLK1ateLQoUPMmzfvrPoRT9ASOjO7BvjWObf6dPWcc391ziU65xIbN25cQdGJiIj8MJkZUbUiT1snqlYkZnZW/TRo0ICbbrqJ6dOnA5CXl8ebb77J2rVrSU9PJz09nfnz52va9RwJ5ghdd2CAmaUDc4DeZvb3IMYjIiJSJbTr1ZzwiOJTgPCIMNolNT8n/YwdO5bMzEwAPv74Y5o3b07z5t+33atXLzZs2MDu3bvPSX9VWdBuW+KcmwBMADCzZGCcc+62YMUjIiJSVXTofwHb0r4tsjAiPCKMOo2j6NDvgjK3nZWV5X/fpEkTjh496t9euXJlobrh4eFK5s6RYF9DJyIiIhWsWlQEgx9KpEO/C/zTr1G1IunQ7wIGP5R4Vvehk+CoFD8x59wiYFGQwxAREakyqkVF0GVAS7oMaIlz7qyvmZPg0gidiIhIFadkLvQpoRMREfkBOZfPZZXAVIZzroRORETkByIqKop9+/ZVigSjqnDOsW/fPqKiooIaR6W4hk5ERETOXkxMDBkZGehG/BUrKiqKmJiYoMaghE5EROQHIjIykosuuijYYUgQaMpVREREJMQpoRMREREJcUroREREREKcEjoRERGREKeETkRERCTEKaETERERCXFK6ERERERCnBI6ERERkRCnhE5EREQkxCmhExEREQlxSuhEREREQpwSOhEREZEQp4ROREREJMQpoRMREREJcUroREREREKcEjoRERGREKeETkRERCTEKaETERERCXFK6ERERERCnBI6ERERkRCnhE5EREQkxCmhExEREQlxSuhEREREQpwSOhEREZEQp4ROREQCZmaMHTvWvz1p0iQef/zxc9Z+eno67dq1K1T2+OOPM2nSpHPWR6BO1+8VV1xRwdGInJ4SOhERCVj16tV56623yMzMDHYoQbV8+fIiZbm5uUGIRMSjhE5ERAIWERHBXXfdxZQpU4rs27t3L4MGDaJz58507tyZZcuWARAXF8eBAwdwztGwYUNeeeUVAIYNG8aHH35Yqv5ffPFFOnfuTPv27Rk0aBBHjx4FYOTIkdxzzz2kpKTQsmVLFi9ezB133EHr1q0ZOXKk//jo6GjGjh1Lx44d6dOnD3v37gVg6tSptGnThvj4eG6++WZ//Q0bNpCcnEzLli2ZOnVqoXYAFi1aREpKCrfccgtxcXHk5uYyfvx4OnfuTHx8PC+88EKpPp9IWSmhExGREi3/ejl3/PsOOr3aiU6vduJ47nEuH3Q5s2bN4uDBg4Xq3n///YwZM4ZVq1Yxb948Ro0aBUD37t1ZtmwZX3zxBS1btmTJkiUArFy5kq5duxbpc9u2bSQkJPhfzz//vH/fwIEDWbVqFZ9//jmtW7dm+vTp/n379+9n4cKFTJkyhWuvvZYxY8bwxRdfsG7dOtLS0gA4cuQIHTt25LPPPiMpKYknnngCgKeeeoo1a9awdu3aQv1t2rSJ999/n08//ZQnnniCkydPFon3008/5Xe/+x0bNmxg+vTp1K1bl1WrVrFq1SpefPFFvvzyy7KefpGARQQ7ABERqZyeS3uOGV/M4FjOMX9Znsvj4U8fpl3/dkydOpUaNWr493344Yds2LDBv33o0CEOHz5Mz549+fjjj7nwwgu55557+Otf/8quXbto0KCBf6SroFatWvkTMKDQNXrr16/n//7v/zhw4ABZWVn079/fv+/aa6/FzIiLi6NJkybExcUB0LZtW9LT00lISCAsLIwhQ4YAcNtttzFw4EAA4uPjufXWW7n++uu5/vrr/W3+5Cc/oXr16lSvXp3zzjuPPXv2EBMTUyjeyy+/nIsuugiADz74gLVr1zJ37lwADh48yJYtW/z7RcqLRuhERKSItG/T+Nv6vxVK5vJl52azq8Munn/xeY4cOeIvz8vLY8WKFaSlpZGWlsauXbuoXbs2vXr1YsmSJSxZsoTk5GQaN27M3Llz6dmzZ6njGjlyJNOmTWPdunU89thjZGdn+/dVr14dgLCwMP/7/O2cnJxi2zMzAN577z3uvfdeVq9eTadOnfz1C7YTHh5ebDu1atXyv3fO8ec//9l/Dr788kv69etX6s8pUlpK6EREpIiX17/M8dzjJe7Pq5FHsyuaFZry7NevH9OmTfNv54+ytWjRgszMTLZs2ULLli3p0aMHkyZNKlNCd/jwYZo2bcrJkyeZNWtWqY/Py8vzj5699tpr9OjRg7y8PHbu3ElKSgp//OMf/aN/ZdG/f3/+8pe/+KdmN2/eXCjpFSkvmnIVEZEi1u5di8OVuD+PPKL6RJG54PvVrlOnTuXee+8lPj6enJwcevXq5b8erUuXLv5VoD179mTChAn06NGj1HH95je/oUuXLlx44YXExcVx+PDhUh1fq1YtvvjiCzp16kTdunV5/fXXyc3N5bbbbuPgwYM45xgzZgz16tUrdWwAo0aNIj09nY4dO+Kco3HjxrzzzjtlakukNMy5kn9hK5vExESXmpoa7DBERH7w+rzZh2+PfnvaOk1rNeWDwR9UUETnRnR0dJlH30RKy8xWO+cSK6IvTbmKiEgRyTHJRFjJkziRYZH0vqB3BUYkIqejhE5ERIoY3nY4EWElJ3ThFs6trW+twIjODY3OyQ+VEjoRESniwjoX8nTS00SFR1EtrJq/vHp4daLCo3gm+Rla1G4RxAhFpCAtihARkWIlt0jmvYHv8cb/3mDJLu9mwEnNk7jpRzfRqEajIEcnIgVpUYSIiIhIOdCiCBEREREJmBI6ERERkRCnhE5EREQkxCmhExEREQlxSuhEREREQpwSOhEREZEQp4ROREREJMQpoRMREREJcUroREREREKcEjoRERGREBe0hM7MoszsUzP73My+MLMnghWLiIiISCiLCGLfx4HezrksM4sElprZv5xzK4MYk4iIiEjICVpC55xzQJZvM9L3csGKR0RERCRUBfUaOjMLN7M04FvgP865T4IZj4iIiEgoCmpC55zLdc4lADHA5WbW7tQ6ZnaXmaWaWerevXsrPkgRERGRSq5SrHJ1zh0AFgFXFrPvr865ROdcYuPGjSs8NhEREZHKLpirXBubWT3f+xrAj4FNwYpHREREJFQFc5VrU2CmmYXjJZZvOOfeDWI8IiIiIiEpmKtc1wIdgtW/iIiIyA9FpbiGTkRERETKTgmdiIiISIhTQiciIiIS4pTQiYiIiIQ4JXQiIiIiIU4JnYiIiEiIU0InIiIiEuKU0ImIiIiEOCV0IiIiIiFOCZ2IiIhIiFNCJyIiIhLilNCJiIiIhDgldCIiIiIhTgmdiIiISIhTQiciIiIS4pTQiYiIiIQ4JXQiIiIiIU4JnYiIiEiIU0InIiIiEuKU0ImIiIiEOCV0IiIiIiFOCZ2IiIhIiFNCJyIiIhLilNCJiIiIhDgldCIiIiIhTgmdiIiISIhTQiciIiIS4pTQiYiIiIQ4JXQiIiIiIe6MCZ2Z/SGQMhEREREJjkBG6PoWU3bVuQ5ERERERMomoqQdZnYP8HOgpZmtLbCrNrCsvAMTERERkcCUmNABrwH/Ap4EHi5Qftg59125RiUiIiIiASsxoXPOHQQOAkPNLBxo4qsfbWbRzrkdFRSjiIiIiJzG6UboADCz+4DHgT1Anq/YAfHlF5aIiIiIBOqMCR3wAHCZc25feQcjIiIiIqUXyCrXnXhTryIiIiJSCQUyQrcdWGRm7wHH8wudc5PLLSoRERERCVggCd0O36ua7yUiIiIilcgZEzrn3BMAZlbLOXek/EMSERERkdII5NFf3cxsA7DRt93ezJ4r98hEREREJCCBLIp4FugP7ANwzn0O9CrPoEREREQkcIEkdDjndp5SlFsOsYiIiIhIGQSyKGKnmV0BODOrBozGN/0qIiIiIsEXyAjd3cC9QHMgA0jwbYuIiIhIJRDIKtdM4NYKiEVEREREyiCQZ7k2Bu4EYgvWd87dUX5hiYiIiEigArmGbj6wBPgQLYYQERERqXQCSehqOuceKvdIRERERKRMAlkU8a6ZXV3ukYiIiIhImQSS0N2Pl9Rlm9lh3+tQeQcmIiIiIoEJZJVr7YoIRERERETKJpBr6DCzAXz/uK9Fzrl3yy8kERERESmNM065mtlTeNOuG3yv+31lIiIiIlIJBDJCdzWQ4JzLAzCzmcAa4OGz6djMWgCvAOcDecBfnXN/Ops2RURERKqiQBZFANQr8L7uOeo7BxjrnGsNdAXuNbM256htERERkSojkBG6J4E1ZvYRYHjX0k04246dc7uB3b73h81sI97zYjecbdsiIiIiVUkgq1xnm9kioLOv6CHn3DfnMggziwU6AJ8Us+8u4C6ACy644Fx2KyIiIvKDEOiUazcgGUjyvT9nzCwamAc84Jwrcn8759xfnXOJzrnExo0bn8uuRURERH4QAlnl+hxwN7AOWA/8zMz+37no3Mwi8ZK5Wc65t85FmyIiIiJVTSDX0CUB7ZxzDvyrXNedbcdmZsB0YKNzbvLZticiIiJSVQUy5fo/oODFay2Ateeg7+7AMKC3maX5XnpmrIiIiEgpBTJC1xDYaGaf+rY7AyvMbAGAc25AWTp2zi3FWzUrIiIiImchkIRuYrlHISIiIiJlFshtSxYDmFmdgvWdc9+VY1wiIiIiEqAzJnS++8D9BjiG94guAxzQsnxDExEREZFABDLlOh5o65zLLO9gRERERKT0Alnlug04Wt6BiIiIiEjZBDJCNwFYbmafAMfzC51zo8stKhEREREJWCAJ3QvAQrybCeeVbzgiIiIiUlqBJHQ5zrlflnskIiIiIlImgVxD95GZ3WVmTc2sQf6r3CMTERERkYAEMkJ3i++/EwqU6bYlIiIiIpVEIDcWvqgiAhERERGRsgnkxsKRwD1AL1/RIuAF59zJcoxLRERERAIUyJTrX4BI4Dnf9jBf2ajyCkpEREREAhdIQtfZOde+wPZCM/u8vAISERERkdIJZJVrrpm1yt8ws5ZAbvmFJCIiIiKlEeizXD8ys+2AARcCt5drVCIiIiISsEBWuf7XzC4BLsNL6DY5546f4TARERERqSBnnHI1s3uBGs65tc65z4GaZvbz8g9NRERERAIRyDV0dzrnDuRvOOf2A3eWX0giIiIiUhqBJHRhZmb5G2YWDlQrv5BEREREpDQCWRTxPvCGmT2P98ivu4F/l2tUIiIiIhKwQBK6h4C78J4WYcAHwEvlGZSIiIiIBC6QVa55wPO+l4iIiIhUMoFcQyciIiIilZgSOhEREZEQp4ROREREJMSVeA2dmf0Db1VrsZxzA8olIhEREREpldON0E0CngG+BI4BL/peWcD68g9NJHSMGTOGZ5991r/dv39/Ro0a5d8eO3YskydPLpe+R40axYYNG8qlbRERCQ0lJnTOucXOucVAB+fcEOfcP3yvW4AeFReiSOV3xRVXsHz5cgDy8vLIzMzkiy++8O9fvnw53bt3L5e+X3rpJdq0aVMubYuISGgI5Bq6xmbWMn/DzC4CGpdfSCKhp3v37v6E7osvvqBdu3bUrl2b/fv3c/z4cTZu3MgDDzxAWlpaoWPWrl3Ld999x/XXX098fDxdu3Zl7dq1ADz++OOMGDGCfv36ERsby1tvvcWDDz5IXFwcV155JSdPngQgOTmZ1NRUAKKjo3nkkUdo3749Xbt2Zc+ePQBs27aNrl270rlzZyZOnEh0dHRFnh4RESlngSR0Y4BFZrbIzBYBHwEPlGtUIqEgIxXeugum96VZ6pNEmGPHjh0sX76cbt260aVLF1asWEFqairx8fHcfffdzJgxA4DNmzdz/Phx4uPjeeyxx+jQoQNr167l97//PcOHD/d3sW3bNt577z3mz5/PbbfdRkpKCuvWraNGjRq89957RUI6cuQIXbt25fPPP6dXr168+OKLANx///3cf//9rFq1imbNmlXI6RERkYpzxoTOOfdv4BLgft/rMufc++UdmEil9p/HYOY1sO5N2PkprJlF9wbfsXzmE/6Erlu3bixfvpzly5dzxRVXcOONN/Luu+9y8uRJXn75ZUaOHAnA0qVLGTZsGAC9e/dm3759HDx4EICrrrqKyMhI4uLiyM3N5corrwQgLi6O9PT0ImFVq1aNa665BoBOnTr566xYsYIbb7wRgFtuuaUcT4yIiARDII/+AugExPrqtzcznHOvlFtUIpXZ9sXw6V/h5LHvy1wuVzSH5f+czbojF9GuXTtatGjBM888Q506dbjjjjuoWbMmffv2Zf78+bzxxhv+aVLnii4mNzMAqlevDkBYWBiRkZH+8rCwMHJycoocV7BOeHh4sXVEROSH54wjdGb2Kt6K1x5AZ98rsZzjEqm8Vv4/OHm0SHH3C8J5d9NxGoRlER4eToMGDThw4AArVqygW7dugLcidfTo0XTu3JkGDRoA0KtXL2bNmgXAokWLaNSoEXXq1DmnIXft2pV58+YBMGfOnHPatoiIBF8gI3SJQBtX3DCCSFW0b3uxxXHnhZF5NI9bLqj2fVlcHFlZWTRq1AjwpkHr1KnD7bff7q/z+OOPc/vttxMfH0/NmjWZOXPmOQ/52Wef5bbbbuOZZ57hJz/5CXXr1j3nfYiISPDYmfI0M3sTGO2c210xIZUsMTHR5U9TiQTN3wfB1g+L32fh0HEYXPunYnd//fXXJCcns2nTJsLCKu5BLUePHqVGjRqYGXPmzGH27NnMnz+/wvoXEamKzGy1c65CZjUDGaFrBGwws0+B4/mFelKEVFld74Gvlhc77Up4JHS+s9jDXnnlFR555BEmT55cockcwOrVq7nvvvtwzlGvXj1efvnlCu1fRETKVyAjdEnFlftuOlyhNEInlYJz8M9xkPba90mdhUFEdej1EPQcE9z4RESkUqhUI3TOucVm1gRvMQTAp865b8s3LJFKzAyungStB8Anz8OBHdD4Muh6L8R0CnZ0IiJSBZ0xoTOzm4CngUWAAX82s/HOubnlHJtI5WUGLZO8l4iISJAFcg3dI0Dn/FE5M2sMfAgooRMRERGpBAK5MjvslCnWfQEeJyIiIiIVIJARun+b2fvAbN/2EOBf5ReSiIiIiJRGIIsixpvZQLwnRRjwV+fc2+UemYiIiIgEJJBFERcB/3TOveXbrmFmsc659PIOTkRERETOLJBr4d4E8gps5/rKRERERKQSCCShi3DOncjf8L2vdpr6IiIiIlKBAkno9pqZ/zFfZnYdkFl+IYmIiIhIaQSS0N0N/MrMdprZDuAh4GflG5ZUBd988w0333wzrVq1ok2bNlx99dVs3rw5aPE8++yzHD36/fNZr776ag4cOFDqdtLT03nttdfOZWgiIiKndcaEzjm3zTnXFWgNtHXOXeGc21r+ockPmXOOG264geTkZLZt28aGDRv4/e9/z549e4IW06kJ3T//+U/q1atX6naU0ImISEU7Y0JnZk3MbDrwpnPusJm1MbOfVkBs8gP20UcfERkZyd133+0vS0hIoEePHowfP5527doRFxfH66+/DsCiRYtITk5m8ODB/OhHP+LWW2/FOQdAbGwsjz32GB07diQuLo5NmzYBcOTIEe644w46d+5Mhw4dmD9/PgC5ubmMGzeOuLg44uPj+fOf/8zUqVP5+uuvSUlJISUlxd9uZqZ3dcErr7xCfHw87du3Z9iwYQCMHDmSuXO/f2BKdHQ0AA8//DBLliwhISGBKVOmlOdpFBERAQKbcp0BvA80821vBh4or4DkByw3Bza9B4ueYv27L9ApvnWRKm+99RZpaWl8/vnnfPjhh4wfP57du3cDsGbNGp599lk2bNjA9u3bWbZsmf+4Rv+fvfuOr/n6Hzj++tybcbOETDvD10pyswdRErRifyn9KmpU8TVrVVtUjS5aStOl+lNa9UVtpUqRCEUlkYSkNYrYNEgiO3ec3x+pW2kSo42EOs/HI4/mfsY55/O52r6d8T5OThw+fJiRI0cyb948AN566y3atWtHfHw8MTExTJ48mby8PBYvXsyZM2dISkriyJEj9O/fnxdffJG6desSExNDTExMqTalpaXx1ltvsXv3blJSUvjggw/u+Jhz5syhdevWJCcnM2HChL/71lAUxRREAuj1epydnenatevfLvuviI2NfaB1Z2Vl8cknn5g+yx5PSZKku7uXgM5JCPENv6cuEULoKUldIkn3LuM4LPCG9cMh9h04/h0c5GmObgAAIABJREFUXg77Fpa6bN++ffTt2xe1Wo2rqysRERHEx8cDEBoaSv369VGpVPj7+5Oenm667+mnnwYgKCjIdHzHjh3MmTMHf39/IiMjKSws5Ny5c+zcuZMRI0ZgZlaShtHBweGOTd+9eze9e/fGycnpnq6vbDY2NqSmplJQUADADz/8QL169aq0DVVJBnSSJEn3714CujxFURwBAaAoSgsguzIqVxTlC0VRflMUJbUyypMeUvoiWNYFcq9CcS4A3o5GEi8Ww565cOw706W3hlHLY2lpafpdrVaj1+vLnLv9uBCCdevWkZycTHJyMufOnaN58+YIIVAU5Z6bX9H1ZmZmGI1G0zXFxcVlrqksnTp1YuvWrQCsXLmSvn37ms4dOnSI8PBwAgICCA8P5/jx40BJz2JoaCj+/v74+vpy8uRJ8vLy6NKlC35+fvj4+JiGtGfPnk1ISAg+Pj4MHz7c9D38+uuvPPnkk/j5+REYGMipU6cAyM3NrXD4+9YwdUJCApGRkQDs2bMHf39//P39CQgIICcnB4D33nuPkJAQfH19mTFjBlAyZH3q1Cn8/f2ZPHlymSHs8p5LkiTpcXcvAd1EYDPQSFGUH4GvgLGVVP8yoGMllSU9rH75FnQF/P53AgDaeagpMgg+P5gFe+YAEB8fT61atVi9ejUGg4GMjAzi4uIIDQ39S9VGRUXx4YcfmoKNpKQkADp06MCiRYtMgd+NGzcAsLOzMwUat2vfvj3ffPMN169fL3W9u7s7iYmJAGzatAmdTnfHcu5Z6jr4OBRm1YK36oKhmGe7PcmqVasoLCzkyJEjhIWFmS5v1qwZcXFxJCUlMXv2bKZOnQrAokWLGDduHMnJySQkJFC/fn2+//576tatS0pKCqmpqXTsWPKv35gxY4iPjzf1BG7ZsgWA/v37M3r0aFJSUti/fz916tQxvcuKhr/LM2/ePD7++GOSk5PZu3cvVlZW7Nixg5MnT3Lo0CGSk5NJTEwkLi6OOXPm0KhRI5KTk3nvvffKDGGX91ySJEmPu3tZ5XoYiADCKUlX4i2EOFIZlQsh4oAblVGW9BC7cMjUM3eLoihs6GPND6f1NJryI97e3sycOZN+/fqZFh+0a9eOd999l9q1a/+laqdPn45Op8PX1xcfHx+mT58OwNChQ2nYsKGpnlvDecOHD6dTp06mRRG3eHt7M23aNCIiIvDz82PixIkADBs2jD179hAaGspPP/2EjY0NAL6+vpiZmeHn53f/iyJ2vwmbxpQMUQsj6PLAqMN3339JP32SlStX0rlz51K3ZGdn88wzz+Dj48OECRNIS0sDoGXLlrz99tvMnTuXs2fPYmVlhVarZefOnbzyyivs3bsXe3t7oGSRSlhYGFqtlt27d5OWlkZOTg4XL16kZ8+eAGg0GqytrYE7D3+Xp1WrVkycOJHo6GiysrIwMzNjx44d7Nixg4CAAAIDAzl27Ng99baV91ySJEmPPSFEuT9ACFD7ts8DgU1ANOBQ0X33+wO4A6n3cm1QUJCQHkG73xZiloMQM2qU//N2g+pu4cMh86wQbziXeT825ggxy0HM6hsiHBwcxJEjR0RMTIzo0qWLEEKIQYMGiQ8++EAIIcSZM2eEm5ubqchff/1VfPDBB8LDw0Ps2rVLCCHE9evXxfLly0WrVq3ErFmzREFBgXBxcRHnzp0TQggxY8YMMWPGDJGdnS3q1atXppm31y2EEKNHjxZLly4VQgjRqFEjcfXqVSGEEHv37hURERGm644cOSLmzJkj6tWrJ3755RcxceJEsWjRojLlnzlzRnh7e1dYX0XPJUmS9LABEkQlxUt3+7lTD91nQDGAoihtgDmUDLdmA4sfSHRZDkVRhiuKkqAoSkJGRkZVVStVJm1vUJmVf05lDn59qrY9D6vU9VDRHEKjniH1TvP666+j1WpLncrOzjYtkli2bJnp+OnTp/H09OTFF1+ke/fuHDlyhEuXLmFtbc1zzz3HSy+9xOHDhyksLARKVgrn5uaaUrHUqFGD+vXrs3HjRgCKiopK5ekrz+3D0OvWrTMdP3XqFFqtlldeeYXg4GCOHTtGVFQUX3zxBbm5Jb23Fy9e5LfffiszZP3nz+U9lyRJ0uPuTgGdWghxazi0D7BYCLFOCDEd+NeDb1oJIcRiIUSwECLY2dm5qqqVKpNTYwgcCObWpY+rzMDaAdpMrp52PWzyr4Oh4oUV9W10jBtbdvrqyy+/zJQpU2jVqhUGwx8L0FevXo2Pjw/+/v4cO3aMgQMHcvToUdOCgrfeeovXXnuNmjVrMmzYMLRaLT169CAkJMRUxvLly4mOjsbX15fw8HCuXLlyx0eYMWMG48aNo3Xr1qjVatPxhQsX4uPjg5+fH1ZWVnTq1IkOHTrQr18/WrZsiVarpXfv3uTk5ODo6EirVq3w8fFh8uTJZYawy3suSZKkx50iKugR+H3lqb8QQq8oyjFguCiZ84aiKKlCCJ9KaYCiuANb7qW84OBgkZCQUBnVSlVNCDj8FcTNg+xzYKYp6blr9zrYuVZ36x4Oqetg84tl5hua1HSD8bI3SpIk6VGhKEqiECK4KuqqYBwMgJXAHkVRrgEFwN7fG/cvKi9tyUogEnBSFOUCMEMIsaQyypYeMooCQYNKfowGUFQlx6Q/NOsG371cfkBnbi17MiVJkqQKVRjQCSHeUhRlF1AH2CH+6MpTUUlpS4QQfe9+lfSPo1Lf/ZrHkZkFDPoWvuxakruvOLdkWFplBoEDIOC56m6hJEmS9JC6Uw8dQoiD5Rw78eCaI0mPOVcvmPgL/LwJLiaAphb4/gccG1V3yyRJkqSH2B0DOkmSqoGZZUkQ5/uf6m6JJEmS9Ii4l50ipAdErVbj7++Pj48P3bp1IysrC/j7m5/f7/3p6en4+Nx5TYqtre1fbs/91iVJkiRJ0v2RAV01srKyIjk5mdTUVBwcHPj444+ru0mSJEmSJD2CZED3kGjZsiUXL140fa5o8/Ndu3YREBCAVqtlyJAhFBUVAfD999/TrFkznnjiCdavX28qJy8vjyFDhhASEkJAQACbNm26YzvutvF5bm4u7du3JzAwEK1WayovPT2d5s2bM2zYMLy9venQoQMFBQUAJCYm4ufnR8uWLWXQKkmSJEkPgAzoqsnt+f8MBgO7du2ie/fupmPlbX5eWFjI4MGDWb16NUePHkWv1/Ppp59SWFjIsGHD+Pbbb9m7d2+p5K9vvfUW7dq1Iz4+npiYGCZPnkxeXl6F7brbxucajYYNGzZw+PBhYmJimDRpkulZTp48yejRo0lLS6NmzZqmnQKef/55oqOjOXDgQKW8O0mSJEmSSpMBXRUqLtTz06bTLJm0l09GxpCfX0ATDy8cHR25ceMGTz31lOna8jY/P378OB4eHjRp0gSAQYMGERcXx7Fjx/Dw8KBx48YoisJzz/2R3mLHjh3MmTMHf39/IiMjKSws5Ny5cxW28W4bnwshmDp1Kr6+vjz55JNcvHiRq1evAuDh4YG/vz8AQUFBpKenk52dTVZWFhEREQAMGDCgcl6mJEmSJEkmMqCrIsWFetbOTSDph3MU5ukAMFdbMLHLJ8wbuZaiwqJSw5GWlpam39VqNXq9nop29QBQKkjSK4Rg3bp1JCcnk5yczLlz52jevHmF5fTr14/NmzdjZWVFVFQUu3fvLnV+xYoVZGRkkJiYSHJyMq6urqa9QCtqc0VtkyRJkiSpcsiArookbT/HzYxCDHpjqeMGvRF9jhnDn3mFefPmodPpKiyjWbNmpKen8+uvvwIl+2xGRETQrFkzzpw5w6lTpwBYuXKl6Z6oqCg+/PBDUzCYlJR0x3bebePz7OxsXFxcMDc3JyYmhrNnz96xvJo1a2Jvb8++ffuAkoBQkiRJkqTKJQO6KpIad7FMMHeLQW9Ed74mfn5+rFq1qsIyNBoNS5cu5ZlnnkGr1aJSqRgxYgQajYbFixfTpUsXnnjiCdzc3Ez3TJ8+HZ1Oh6+vLz4+PkyfPv2O7bzbxuf9+/cnISGB4OBgVqxYQbNmze767EuXLmX06NG0bNmyzBCuJEmSJEl/n3KnYbyHTXBwsEhISKjuZtw3IQSfjIy563WjPm0rhyclSZIk6R9CUZREIURwVdQle+iqgKIoaGzM73iNxsZcBnOSJEmSJP0lMqCrIj5t6qE2K/91q81U+ETUq+IWSZIkSZL0TyEDuioSENWQGs6aMkGd2kxFDWcNAR0aVlPLJEmSJEl61MmAropYaMzo/UowAR0amoZfNTbmBHRoSO9XgrHQmFVzCyVJkiRJelTJKKIKWWjMCOvuSVh3T5mfTZIkSZKkSiN76KqJDOYkSZIkSaosMqCTpAdEUZRSW53p9XqcnZ3p2rXrHe9LSEjgxRdffNDNkyRJkv5B5JCrJD0gNjY2pKamUlBQgJWVFT/88AP16t19NXNwcDDBwVWStkiSJEn6h5A9dJJUifRGPenZ6ZzPOQ9Ap06d2Lp1K1CyJVvfvn1N1x46dIjw8HACAgIIDw/n+PHjAMTGxpp68WbOnMmQIUOIjIzE09OT6Oho0/1ff/01oaGh+Pv789///heDwVBVjylJkiQ9ZGRAJ0mVQAjBV2lfEflNJP/Z8h+e3vQ0hfpCGrRuwKpVqygsLOTIkSOEhYWZ7mnWrBlxcXEkJSUxe/Zspk6dWm7Zx44dY/v27Rw6dIhZs2ah0+n45ZdfWL16NT/++CPJycmo1Wq5T64kSdJjTA65SlIlmJ84n2+OfUOBocB0TCD44voXZB7PZOXKlXTu3LnUPdnZ2QwaNIiTJ0+iKAo6na7csrt06YKlpSWWlpa4uLhw9epVdu3aRWJiIiEhIQAUFBTg4uLy4B5QkiRJeqjJgE6S/qbf8n9j5S8rKTYWlzlXaCikqGkRL730ErGxsVy/ft10bvr06bRt25YNGzaQnp5OZGRkueVbWlqafler1ej1eoQQDBo0iHfeeafSn0eSJEl69MghV0n6m3ad23XHNDROEU4MGj8IrVZb6nh2drZpkcSyZcvuq8727duzdu1afvvtNwBu3LjB2bNn76/hkiRJ0j+GDOgk6W/K0+WhN+orPK9x1NBtULcyx19++WWmTJlCq1at7ntBg5eXF2+++SYdOnTA19eXp556isuXL9932yVJkqR/BkUIUd1tuGfBwcEiISGhupshSaUcuHSA8THjydfnl3veQm3Btz2+pa5t3Spu2eNNURSee+45li9fDpTkAaxTpw5hYWFs2bLlvsvLysrif//7H6NGjarspkqS9A+lKEqiEKJK8lDJHjpJ+pvC6oRRS1MLhbLDruYqc0JcQ2QwVw1uzwMI3HMewIpkZWXxySefVFbzJEmSKpUM6CTpb1IpKhY/tRhHK0eszaxNx63NrHGv4c7cNnOrsXWPtzvlAbxx4wY9evTA19eXFi1acOTIEaDi3H+vvvoqp06dwt/fn8mTJ5Obm0v79u0JDAxEq9WyadMmANLT02nevDnDhg3D29ubDh06mILKzz//nJCQEPz8/OjVqxf5+eX36kqSJN03IcQj8xMUFCQk6WFVpC8Sm3/dLCbvmSym7p0q9pzfI/QGfXU367FlY2MjUlJSRK9evURBQYHw8/MTMTExokuXLkIIIcaMGSNmzpwphBBi165dws/PTwghxIwZM0TLli1FYWGhyMjIEA4ODqK4uFicOXNGeHt7m8rX6XQiOztbCCFERkaGaNSokTAajeLMmTNCrVaLpKQkIYQQzzzzjFi+fLkQQohr166Z7p82bZqIjo5+8C9CkqRqAySIKoqRZNoSSaokFmoLujXqRrdGZRdASA9e7PHfiN51kuNXc7C3MkdnEDRp7k16enq5eQD37dvHunXrAGjXrh3Xr18nOzsbKD/3358JIZg6dSpxcXGoVCouXrxous7DwwN/f38AgoKCSE9PByA1NZXXXnuNrKwscnNziYqKelCvQ5Kkx4wM6CRJeuQt+/EMc78/RoHOCEBekQGdwUjfxQfp0rVbuXkARTkLwm6lnykv99+frVixgoyMDBITEzE3N8fd3Z3CwsJy77815Dp48GA2btyIn58fy5YtIzY29u8/vCRJEnIOnSRJj7jsfB3vbPsjmLvdsSs51A3tzOuvv14mD2CbNm1M26XFxsbi5OREjRo1KqzHzs6OnJycP+rNzsbFxQVzc3NiYmLuKQ9gTk4OderUQafTya3aJEmqVLKHTpKkR9quY1dRq8pP7FygM7AjXce6cePKnJs5cybPP/88vr6+WFtb8+WXX96xHkdHR1q1aoWPjw+dOnXilVdeoVu3bgQHB+Pv70+zZs3u2tY33niDsLAw3Nzc0Gq1pQJESZKkv0PmoZMk6ZH29cGzvLn1ZwrL6aEDaFbbju/Ht6niVkmSJMk8dJIkSfcsxN2hwnMWaoWIJs5V2BpJkqTqIQM6SZIeaU1r2xHsVgtLs7L/OTM3UzG4lXvVN0qSJKmKyYBOkqRH3uKBwbRt5oKFmQo7jRnWFmrcHKxZPbwldeytqrt5kiRJD5xcFCFJ0iPP2sKMRc8F8dvNQk7+lkstawua17EzpSGRJEn6p5MBnSRJ/xguNTS41NBUdzMkSZKqnBxylSRJkiRJesTJgE6SJEmSJOkRJwM6SZIkSZKkR5wM6CRJkiRJkh5xMqCTJEmSJEl6xMmATpKkKmVra1vu8cGDB7N27do73hsZGYnc/k+SJKksGdBJ0h0oisKkSZNMn+fNm8fMmTMrrfz09HQURWH69OmmY9euXcPc3JwxY8b8pTJff/11du7cWVlNlCRJkh4BMqCTpDuwtLRk/fr1XLt27YHV4enpyZYtW0yf16xZg7e3918ub/bs2Tz55JOV0bQHSgjBmDFj8PLyokuXLvz222+mc7NnzyYkJAQfHx+GDx+OEMJ0bs2aNYSGhtKkSRP27t0LQGFhIc8//zxarZaAgABiYmKq/HkkSZKqkwzoJOkOzMzMGD58OAsWLChzLiMjg169ehESEkJISAg//vgjAFqtlqysLIQQODo68tVXXwEwYMCAcnvOrKysaN68uWkocfXq1fznP/+5az3//ve/TWV/9tln9O/fHyg9dBkfH094eDh+fn6EhoaSk5NT9cGP0QAndsCWibD1JRAGEIINGzZw/Phxjh49yueff87+/ftNt4wZM4b4+HhSU1MpKCgoFfDq9XoOHTrEwoULmTVrFgAff/wxAEePHmXlypUMGjSIwsLCB/tckiRJDxEZ0D2mJkyYwMKFC02fo6KiGDp0qOnzpEmTeP/99++rzNjY2FL/U74lPT2d+vXrYzQaSx339/fn0KFDDB06lJ9//vm+6lq0aJEpmKlIQkICL7744n2VW2wo5vv07/k05VNWHluJQDB69GhWrFhBdnZ2qWvHjRvHhAkTiI+PZ926dab316pVK3788UfS0tLw9PQ09SIdPHiQFi1alFvvs88+y6pVq7hw4QJqtZq6devetZ7Fixcze/Zs9u7dy/z58/nwww9LP0txMX369OGDDz4gJSWFnTt3YmVlVbXBT0EmLHoC1j4PCUsg/nPQF8GSp4iL2UXfvn1Nz9uuXTvTbTExMYSFhaHVatm9ezdpaWmmc08//TQAQUFBpKenA7Bv3z4GDBgAQLNmzXBzc+PEiRMP5pkkSZIeQnLrr8dUeHg4a9asYfz48RiNRq5du8bNmzdN5/fv318q4LsXsbGx2NraEh4eXuq4u7s7DRo0YO/evURERABw7NgxcnJyCA0NJTQ0tNzyDAYDarW63HMjRoy4a3uCg4MJDg6+5/YfunyI8THjMQgDBfoCLNQWFOoL+fLUlwwYMIDo6GisrP7Y6H3nzp2lAtGbN2+Sk5ND69atiYuLw83NjZEjR7J48WIuXryIg4NDhQsCOnbsyPTp03F1daVPnz6lzlVUj6urK7Nnz6Zt27Zs2LABBweHUvcdP36cOnXqEBISAkCNGjWAkuBn7NixQOngx9fX957f1T1bPxyu/QrG4j+OCQFXjsCZqyiBZb+fwsJCRo0aRUJCAg0aNGDmzJmlAk5LS0sA1Go1er3+9yJFmXIkSZIeJ7KH7jEiDAbyDh4ke/Nm/C0sTL1paWlp+Pj4YGdnR2ZmJkVFRfzyyy8EBASQmJhIREQEQUFBREVFcfnyZQCio6Px8vLC19eXZ599lvT0dBYtWsSCBQvw9/c39Urd0rdvX1atWmX6vGrVKvr27QuUXrloa2vL66+/TlhYGAcOHGDJkiU0adKEyMhIhg0bZlooMHPmTObNm2e6/5VXXikzryo2NpauXbsCcOjQIcLDwwkICCA8PJzjx4+Xal96djpjdo8hR5dDvj4fgaDIUATAV2lf0aBzA5YsWUJeXp7pHqPRyIEDB0hOTiY5OZmLFy9iZ2dHmzZt2Lt3L3v37iUyMhJnZ2fWrl1L69atK/xuLCwsCAoKYv78+fTq1avUuYrqgZJeNkdHRy5dulT2+xai3M3pqyz4uXkJzuwpHczdoi+ijd05Vv3vawwGA5cvXzYN/d4K3pycnMjNzb3ryleANm3asGLFCgBOnDjBuXPnaNq0aeU9iyRJ0kNOBnSPieytWzn5RGsujB7D5ZmzMM5+A5GRwdFFn7F//35atmxpCqISEhLw9fVFURTGjh3L2rVrSUxMZMiQIUybNg2AOXPmkJSUxJEjR1i0aBHu7u6MGDGCCRMmkJycXCZ4+c9//sPGjRtNPSqrV6/m2WefLdPOvLw8fHx8+Omnn/D09OSNN97g4MGD/PDDDxw7dqzC5ytvXtXtmjVrRlxcHElJScyePZupU6eWOr80bSnFhnICD6DQUMjys8vp/UxvlixZYjreoUMHPvroI9Pn5ORkABo0aMC1a9c4efIknp6ePPHEE8ybN++OAR2UDHPPnTsXR0fHUscrqufQoUNs27aNpKQk5s2bx5kzZ8o886VLl4iPjwcgJycHvV5fdcHPtZOgtqzwdE+tLY3rO6PVahk5cqSp97ZmzZoMGzYMrVZLjx49TD2MdzJq1CgMBgNarZY+ffqwbNkyU0+eJEnS40AOuT4GsjZt5sqMGYjbhq0EEGCpYcc775Dg1pBXP/yQixcvsn//fuzt7U29WKmpqTz11FNAyRBonTp1APD19aV///706NGDHj163LUNtWvXxtvbm127duHq6oq5uTk+Pj5lrlOr1aYeqkOHDhEREWEaSnzmmWcqnBdV3ryq22VnZzNo0CBOnjyJoijodLpS5/ec34NBGCpsf7GhmN5De/PJx5+YjkVHRzN69Gh8fX1NgdKiRYsACAsLw2AoKa9169ZMmTKFJ554osLyAby9vctd3VpePR988AHDhg1j6dKl1K1bl/nz5zNkyBB2795tus/CwoLVq1czduxYCgoKsLKyYufOnYwaNYoRI0ag1WoxMzN7cMGPjTMY9WUO504tGfpVjDo+Wjgf7OuVuebNN9/kzTffLHM8NjbW9LuTk5Ppu9ZoNCxbtqxSmi1JkvQokgFdFXnrrbf43//+h1qtRqVS8dlnnxEWFnbf5cTGxmJhYWGapzZ48GC6du1K7969y71eFBdz9Y038E5JprGlJXohMFMU/l3DHn8rDUk3b5KSkIBXo0Y0aNCA+fPnU6NGDYYMGcKhQ4dQq9WmHqHbbd26lbi4ODZv3swbb7xBWloaBw8epE2bNhW2/dawq6urq2m49c80Go1p3tz9DA2WN6/qdtOnTzfNNUtPTycyMrLUeaMwlrkHwOszLwAUFGo51yI/P990zsnJidWrV5d73/Lly02/h4eHl1kQcou7uzupqalljg8ePJjBgwffsZ6UlBTT7927d6d79+4ApQKbkJAQDh48WObeKgl+XJpDjTpw/VQ5JxVw9Sk3mJMkSZLuX7UOuSqK0lFRlOOKovyqKMqr1dmWB+nAgQNs2bKFw4cPc+TIEXbu3EmDBg3+UlkVrSStSG5cHEIILBWFDe4efOvhyf/Vb0BcXi4niorYk5dLTbUZ+TGxODg4kJWVxYEDB2jZsiUNGjSguLiYAwcOAKDT6UhLS8NoNHL+/Hnatm3Lu+++S1ZWFrm5uRw6dIjMzMwK29KrVy++++67Codb/yw0NJQ9e/aQmZmJXq9n3bp19/zcf5adnU29eiXBQ3nBTFidMFRKxf86KIqCp73nX67/saQo0GsJWNiActvfHVXmYGkHPT6tvrZJkiT9w1RbQKcoihr4GOgEeAF9FUXxqq72PEiXL1/GycnJ1Ivk5ORkSkuxa9cuAgIC0Gq1DBkyhKKikon47u7upmS2CQkJREZGVrjwIC4ujvDwcDw9PctMINddugx/Gl50NDNjlmtttt+8SabBgIeZGVETJxAYGEh6ejoWFhY4OTlhbm5OYGAgr7zyCo0bN6ZGjRps2rSJH374AR8fHzQaDS4uLowdO5avvvqKnJwcPvzwQ2xtbdm7dy8jR44kODgYb29vZsyYQc2aNWnRogWurq54eHjc9b3Vq1ePqVOnEhYWxpNPPomXlxf29vZ/6Tt4+eWXmTJlCq1atTINhd5uqHYoFiqLcu/VmGkY6DUQc7X5X6r7sVY3AEb8CIHPga0r2NWBkCEw6iA4y0ULkiRJlUYIUS0/QEtg+22fpwBT7nRPUFCQeFRczy0SH+46IXp/+qN47tMY4dnUWzRu3FiMHDlSxMbGCiGEKCgoEPXr1xeAmDhxohgwYIBYsGCBeO+994S9vb3IyMgQQggRHx8vIiIihBBCzJgxQ7z33numegYNGiR69+4tDAaDSEtLE40aNSrVjqzNm8VOL2+hgPi5abNSPzVUKhHX6F/isK+vuPTVciGEECdOnBC33nNMTIzo0qWL+PHHH0VgYKA4e/asqc3Hjx8XQghTm4UQws3NzdRmIYS4fv26EEIIvV4vIiIiREpKyn2/x5ycHCGEEDqdTnTt2lWsX7/+vsu4VzvO7BDBy4NF8PJg4bMFFo8oAAAgAElEQVTMR/h96SeClgeJ1/a+JgxGwwOrV5IkSfpnAhJEFcVV1TmHrh5w/rbPF4D7n1T2EDpxNYfen+6nSG+kSF8yd8qq91zqF53FQXOFPn36MGfOHAICAvDw8CAjI4P169czf/58vv766zJ53O6mR48eqFQqvLy8uHr1aqlztm3bQgXzt27NUNMbjLy05VuOvDsXtVpdauHBL7/8wvDhw9mxYwd169YlJSUFDw8PmjRpAsCgQYP4+OOPGT9+fJnyv/nmGxYvXoxer+fy5cv8/PPP953rbObMmezcuZPCwkI6dOhwTwsw/qqn3J8itE4om09t5kTmCRw1jnRv1B3PmnKoVZIkSXq4VWdAVzZB1h8xxh8XKcpwYDhAw4YNH3SbKsXIrxPJKdSXepgCPVzQeDKgU2c+9NWyNDqaet7eFJ8+jRoY+txzpYZLVSoVRqORjIwMJk2axOHDhwkJCUGr1eLl5YVWqzUNuY4YMQKDwcDAgQMpLCxk586dpr081ba22PfuDW++AYBBCN7PyODHvFzyjEZ2FRaQ69YQPeDo6Ejt2rVJSUkhLy+PV199lStXrmA0Gvnggw+YO3cuaWlppKSkEBQUhJOTEy+88AJ5eXkEBgaa2n7y5El69uxJYWEh8fHx1KpVi8GDB/+l3Qhu5ZqrKvaW9gzwGlCldUqSJEnS31WdiyIuALevDKgPlMmOKoRYLIQIFkIEOzs7V1nj/qrjV3K4lFVYKpjTXb+A7sZFCnQG1u46Suxrr1Hr2HGcY2I5e/UqxuJiojZsZNP69aZdE2rWrEliYiLjxo3D1dWVoKAg1q1bx5YtW8jJyTFtL5WZmYmLi4spuDMYDGW2l3Ic8jwoCoqFBevz8lAr4GRhwTBnZzYIwY2GDXF0dCQ+Pt4UmH3//fc4OjrStm1b0tPT2bp1Kzt37iQ6Oho7OztWr15tykvXuXNn7O3tMTc3Jycnh6VLl9K1a1dsbGywt7fn6tWrbNu2rUrevyRJkiQ9jqqzhy4eaKwoigdwEXgW6FeN7akU13KLMFMrcNs6BKOukMwfFmEsykOVf4MCMzWzXFywVBTeql2HoRfO0+/kSRqqFDJTjuAcFEhERATjxo3jzJkz1KpVki6je/fuKIrCunXryMnJ4ebNm1y9epWoqCgOHz7MxYsXURSlzPZSiqIghKCP0cDp69co1umoZWtLjrMzOfn5tG7ThjfffBO1Wk1mZiY2NjZotVoSExOxsbHhxIkTfPfdd7Rr147Lly9Tu3ZttFqtafP5ESNG4OTkxOeff06nTp04e/YsFy5c4MqVK3h7e+Pp6UmrVq2q+JuQJEmSpMdHtQV0Qgi9oihjgO2AGvhCCJF2l9seeo2cbSnWl56zZln7X9QeMI/gq7/wWsLXWOqKTOda2tigURQ2eXiQZTDwzMYNDG3aBDc3N5YuXYqTkxNnz54ttYcowPnz5+nTpw8eHh689NJLjBs3jrVr1zJhwoRy2+Xt7U1Kaiq9evVi+PDhREVFlTrv4eHBvHnzeOedd3jnnXfYsGEDV69eZe7cuUyZMsXUc+jr62tKY3K7Xr16MWvWLN577z1WrFiBo6OjKT2Ira0tubm59/0uJUmSJEm6N9Wah04I8Z0QookQopEQ4q3qbEtlqW2voXVjJyzUZacIdjyfWCqY+7OaajVRNWqwZPFi07HK3l4qKiqKTz/91LRTwokTJ0rtT3rLF198QXh4OPn5+bz00kukpqZiYWFBRkZGmbx0UJIQOCoqipEjR/L888/fsQ2SJEmSJFUuuZfrA7Cgjz9+DWpiZa7GwkyFtYUaSzMV3rblrza93ZA6dbl+44bpc3R0tGlvVS8vL9PWUlCyvdSt1aatW7fm4sWL5W4vpdfrTTnwhg4dipeXF4GBgfj4+PDf//63zM4Kubm57N+/n2vXrjFnzhzeeustxowZg0qlYu3atYwbNw4bGxtq1KhBp06dTImOo6KiuH79Oq+++io+Pj6meX23XLt2jZYtW7J161bS09Np3bo1gYGBBAYG3leyZEmSJEmSSlPEfWyvVN2Cg4NFQkJCdTfjnh29kE18+g1sLc14ysuVovfnkrn6Gyhna6pbFEtLGu3Yjrmr69+uX1EUJk6caNqMPTQ0lNzcXGbOnFn24vwbcOkwqC35Ou4UMXF7WbJkCeHh4Xz00Uc4ODjQtWtXUlNTyc/PR6VSodFoOHnyJH379iUhIYFu3brxww8/cOHCBdO8Pzs7O2xtbTl16hTdu3fnzTff5KmnnqqwDEmSJEn6p1AUJVEIEVwVdcm9XB8gbX17tPX/2NmgqF8/stauQ1QU0CkKVn6+lRLMQcn+pkuWLGH79u0sX76cXbt2lb1IXwzfvQRHVoHaEhCs/PIa48eOBeDZZ59l5cqVjB492nSLTqdjzJgxJCcnm/LW9ezZk59//hm9Xs+7775Lv3798Pf3N13fvn17Pv74YyIiIiosQ5IkSZKkv0YGdFXIslEj7J/uSfbGTYiCAoJOHCexyR/bH6msrak9ffpfLl8YjeQdOEBuTAyiWIeZovDypEnkFRYSEBBQKqDLyMhgxIgRnEuJg8IsFnawoGWDQtwX5vJbniB12vsob3/Jhas3cHV1pWnTppw+fZqAgACys7OJiooiJSWFjIwMateuTXp6Ok8++SRbt26ldu3aDBgwAAuLkq20dDoddnZ2bN++3RTQLViwAFdXV1JSUjAajWg0mr/83JIkSZL0uJNz6KpY7ddfx3H4cFS2tiiKUvJPjSUab2/c/vc/LBs3vuP9kZGRbN++vdSxhQsX4tGwIS83bcrFsS+S+fUKsr75BlFcTNS69Xz9xRdkZ2eXumfcuHEM6BmFm+VN1j2jYei3hagUhX85qGhRX83Z8bas7m1DZGQkBQUF1KhRAw8PD5KSkmjUqBE///wzKpWK/v37A5CUlESLFi24ePEiAwcO5IUXXiAkJITExESsrKzIyckhJSWFOXPmAJCdnU2dOnVQqVQsX7683P1VJUmSJEm6NzKgqwSKojBgwB+7C+j1epydnenatWu51zqPHEGT/T+iWFpS9913cV+/nk88PQju9TRarZbVq1cDMGrUKDZv3kxsbCy1a9dmyJAh9O3bl9mzZ/Paa68BJdt+TZs2Dd3Vq9jm5mHMzzfVJYTAoqCALkIwf9o0oGSV7JgxY9i5cycz33iLX28Y6b4yn5tFgpwiQWaBILe4ZF7lqvjf6NPtKWbOnEl0dDRnz55Fq9Vy6tQpDh8+TIsWLTh69KgppYpKpUKtVtO2bVvWrVuHpaUlfn5+FBYWcv78eV599VViYmL45JNPGDVqFF9++SUtWrTgxIkT2NjYPJgvR5IkSZIeB1W1aWxl/NzaNP5BunbtmvDz8xN+fn7C1dVV1K1b1/S5qKjIdB0gnnvuOSGEEDY2NsLPz084OTmJLl26iO+++064u7uLpk2blin/em6ROHDqmjidkStsbGyEEEKsXbtWtGvXTuj1enHlyhXRoEEDcenSJbFy5Urx0ksviZiYGGFvby/CwsLEtWvXhKWlpdi8ebMQQoikpCRRz8FBzKxXX9irVGKys7MI1FiJQbVqCRWIQbVqiW/c3ISFSiXq1q0rmjRpIszMzISjo6P4Ze0c4e1iJsSMGmLpvzWiZzMzEdVILcxViNEh5sK9pkpcO5Ui3NzcRHh4uNi0aZP497//LRo3biysra3FZ599Jvz8/MTp06dNz1erVi2RkZEhYmJiRKtWrUReXp4QQoiIiAgRExPzoL42SZIkSXroAAmiimIkOYfuTxwdHU253mbOnImtrS0vvfRSqWuMRUXYaDQkbdvGiYGDEDodjd3dycrKAmDlypWMGDHClLbj0KFDjBs3ntNXb3CzWEWDHhNR1apHfmEx3Xs+TWpKMpaWlgwePJjevXsTERFBfHw8q1ev5ueff8bb2xsrGyvyNfmMXzaeYl0xo8eO5p133kEIQUFODu9nZXHTaGR7Tg6niorIMOixAF5xcaVn+hnUQnD92jXUajVQkt/uyx/Pc2v73HPZRpKvGEj6ry0zYwv5/LCOkIbWOHpoAbh58yb16tXjiy++YNKkSfz6669ER0fTsmVLVqxYwWuvvca2bdvIzMwESoZUa9WqhbW1NceOHePgwYMP+quTJEmSpMeWDOhuU2wopkBfgJ2FHSrlj9HoxMREJk6cSG5uLg7W1swoKEQUF3NDr2fkhvUUFhezdfNmXJydMRqNxMbG8tNPP3Hu3DkaN25M48aNOfjTQSyd3dEX5XFqyQRUNrUQBh3fbtqEQ62aprxta9asQQjB9u3buXHjBmq1muEjh6Mr1KFqrWL7we0IleDCpQvk5ueSmZFJdycnfvw9kDpbXIwe+E2nQ1EUMvR6ThQVsaFJU/qeO2uaqxYdHc3gwYP5NRO8PsnD1Rrae5hhr1Ho72vBwp90hLfrBEpJguSXX36ZZ555Br1eT2FhIfn5+ajVaubPn8/8+fMJDCzZrqxhw4YAdOzYkUWLFuHr60vTpk3L7C8rSZIkSVLlkQEdcDr7NAsSFrDv0j4UFDRmGvo164fBaEAIwdixY9m0aRPWags+Cw3m/WsZCCFwUKs5VVSEBeCgVnM1IwO7kyfx9fVl3759ODs7c/ToUezs7EAIDPpiDHnZqO0csXLzI+f6BSxd3Bk9cRzzZ01h9+7dREREUKtWLTp37szSpUv5V/C/+DXtVzAHTRMNl+ddBgWEXpBTlAOAotHgZm7OdYOBCBsbThUXk67TMd7RiZSCAswUhUYWFuRmZjIvOppp06bh5OTERx99VJJb7r0uLPv8UxIuG8HCluBGFnRp3YSn+o0xvaNOnTrRoEEDXnvtNXbs2IG1tTWRkZFYWlqyY8cO03ULFiww/b5t27Yq+w4lSZIk6XH22Ad0JzJPMPC7geTr8xG/Dz/qinUsS1uG7pSOp5s9TWpqKj6hrTFmZVKz4CYu6pLeO2uVinyjwABE2tqxMTuLY6dO0WfQIM6fP092djYajQYLCwv0ej0aN3/yUndhyLxEbs41QFB07TxOjXywsbGhTZs2FBcXYzAY+O6777CxseHUsVMYCgwg4MzcM4jiPxJB63NK8tltu3LFlNvu25wcBCWrXS7pdbiam6MCOp87i11QENbW1mVfQqe5ZB6tybaPP4IBG6FuAOztUeYyOYwqSZIkSQ+nx36V66z9s8jT55mCuVuKDEVcK7jGiRsnsHR2o2b/BXze1I/N7u78X4OGpuuCra3QAVF2tlioVLhZWFDX2Rkzsz9iZTs7O1BUGAtuIvTFANQdthj7NoNQ1GZMf6EXGRkZqNVqGjVqRE5ODjqdDgcXB5xaOqGpr4Hfdw1T26vReGhQaVS3pr8RHBqKnYUFZsBwBwea/b7NVysbW/ysrbBTq3Fq2BCNRkNKSoopP9ztarnUoVO3ntAgBNTlx/kdO3ZEr9ejKArdu3c3DaPOmzev/N0n7iA2NrbUdl+DBw9m7dq1d73vypUrPPvsszRq1AgvLy86d+5cKUmJ09PT8fHxASAhIYEXX3zxb5cpSZIkSVXlsQ7oruRd4Xjm8QrP64w6kq4fJTvzOjfPpqExFKMTgpNFRaZr2tnaYg40srBEBdS3sEDo9RiNgpxCPe3nx5KjrgHCiCH7CggBihph1GNubYO1vSOWFua4urpib2+PhYUFBoOBnj17knElA6dWTtg0L0npIYwCW29bCs8UYiw0orIp+fqMRiNN/P1RqVTE5eXTzdEJgMU3bjD++nVy1WqO/PILx48fx2g0oigKPj4+dOvWjem/JzKOjIwkNjYWgLS0NH777TfGjx+Pr68vP/zwA05OTlhaWrJt2zYsLS3R6XSsXbuWyMjI+37ver2+TEB3L4QQ9OzZk8jISE6dOsXPP//M22+/zdWrV+/5fqPx7vvpBgcHEx0dfV9tkyRJkqTq9FgHdNcLrmOuMr/jNXm6PGo/PYXM2GUMPZbC0+lnSC4oMJ13NDMjpWmzP25QqTBYWHIuT8HYIIBTGXkUZmcAIHTFaOp7oTa35NpXE8nc+TkUZHHjxg1sbGy4evUqN27cwGg08vXXX6PX6UmblUb2wd+TAush+1C26Vsz5pcEJ0IIatSsiWJuzrHiIj7NvIGiUiEaeXLN1hYLS0sMBgOKomBhYYGbmxtjx45FrVYzYMAAOnfuTMFtz7Ro0SLGjRtHcnIyCQkJ1K9fv9Q7MTMzY/jw4aXmy91y9uxZ2rdvj6+vL+3bt+fcuXNASQ/cxIkTadu2LX369GHRokUsWLAAf39/02rguLg4wsPD8fT0LLe3LiYmBnNzc0aMGGE65u/vT+vWrcnNzaV9+/YEBgai1WrZtGkTUNLz1rx5c0aNGkVgYCDnz59n8uTJ+Pj4lMr5d7vY2FhTDsGZM2cyZMgQIiMj8fT0LBXo9ejRg6CgILy9vVm8eHGZciRJkiSpqjzWAZ2rjSvFhuKKz/d0xbtnKDZ1GlO7/1y8+r3Nmn815ZmaNUls0pQvG7rhoylJqlvLzIx9Xt58M306Fl7tce7zJvbtSwKPBmO+RjHX0KrXVF5zqskTlmpSmjch1dsbP0tLwpo3Jy0tDbVajcFgYOTIkej1euo0DaDZvOG4PF0XVKCyVKGpq6Hh2IZY1rFEbaWmgWcDzM3NSUpKokuXLgwbNoyGHh5Y29jQsUcPLl68iL+/P7a2trRq1YqioiK6dOlC7969SU5Opk+fPtja2pYKbFq2bMnbb7/N3LlzOXv2LJYaS3ad3cXz3z9Pp3WdKDIU4ftvX1asWFFmB4oxY8YwcOBAjhw5Qv/+/UsNXZ44cYKdO3eybt06RowYwYQJE0hOTqZ169YAXL58mX379rFlyxZeffXVMt9HamoqQUFB5X5XGo2GDRs2cPjwYWJiYpg0aRIlKYDg+PHjDBw4kKSkJBISEkhOTiYlJYWdO3cyefJkLl++fMc/J8eOHWP79u0cOnSIWbNmodPpAPjiiy9ITEwkISGB6Ohorl+/fsdyJEmSJOlBeawDOicrJwJdA0ulKLmdlZkVw/0GY/g9MDhRqyE7GoZQqC7bq6eYm2Netw6O//0vq+PPU6grPbSnEkbm7f2YutfPgdGIyMtFFBaSnp1N06tXKdi4kaFDh2I0GtmzZw81nVy5UaSQf7kTxiIXUzkuPVy4vPwyRVeK8Ar0wsHOgRs3btC8eXPMzMwwNzfn6aefRq/XY25ujoeHB9bW1uTl5XHp0iVsbW2BkuCodevWfPvtt+zZs4eTJ0+a6ujXrx+bN2/GysqKqKgoes7vyZR9U0i4msCF3AsYhZFZSbOo3aY2H3zwAVlZWaxatYrGjRvz3XffkZCQQHFxMUIIvv/+e1O5zzzzjCkP3ooVK0r1CkJJj5dKpcLLy+ueh1FvEUIwdepUfH19efLJJ7l48aKpDDc3N9N8v3379tG3b1/UajWurq6mnH930qVLFywtLXFycsLFxcVUbnR0NH5+frRo0YLz58+XeoeSJEmSVJUe64AOYGb4TGpY1MBMKb0QwEptxRP1nqB7k6eY1rk5VuZqFOBT3578n083bmhqYLCwRGVjg6LRYP90T9zXrEFta0uB/k/7kgrBLp9ArAzFtLC24dP6DQDIMhi4bjDwfWYmPgMHsmXzZmxsbIiOjkZxbIhTr9cBMyzrz0BRWyCECusmIdQN8sXe3p4je4+wcOFCACIiIujUqZOpSq1Wi6OjIxqNhm3btmFtbc3AgQPp2LEjKSkpDBo0iNmzZ2Nvb8/UqVMpum1e4OnTp/H09OTFF1+kcavGJCUnUaAvHXwV6AsoCi/iw88+ZOnSpTRt2pSTJ09Ss2ZNcnNzmfb7VmPK73nsgFLbe/Xv39+0Zdgtlr8v5ih5ZaUXqQB4e3uTmJhY7ve4YsUKMjIySExMJDk5GVdXVwoLC8vUW165d3N7u9RqtWkO4M6dOzlw4AApKSkEBASY6pMkSZKkqvbYB3T1bOuxvvt6+jTrg525HWaKGW413JgSNoV5EfNQKSoGhbuz/IVQnvJ2pZGLLbquPbFcv5UmmzfitvJ/NDmwnzqzZqG2swOgXVMXzNV/BDLaa6ewLc4vU/f2nJt0r2HPrkb/YldzL5Jem46Hhwf79u1Dp7898FAAFQgzrsd4k30sh3p165nOOjg4sHHjRoqKitDpdGzYsAFXV9dyn7dZs2b4+vpy4cIFRo0axdtvv82WLVtKXbN69Wp8fHzw9/cn4WgCNi3L32dVb6XH3N2c69evExAQAECrVq1o2bIlX3zxBXv27KFWrVp07NiR9evXs3z5ctO90dHRpp6u999/n02bNjFx4kRTgFqedu3aUVRUxOeff246Fh8fz549e8jOzsbFxQVzc3NiYmI4e/ZsuWW0adOG1atXYzAYyMjIIC4ujtDQ0ArrrIhM4SJJkiQ9TB77PHQAztbOvBr6Kq+Glp23dUuwuwPB7g5/OupU7rXD2niyJvECeoMeATTKvoSZMJS57rubNxnq6AiAKCoiPymJXr168emnn1LDyglFKVkUe0vDiWv5bdVUNMV5mJs74e/vT/fu3dm7dy/vv/8+H374IQBDhw5l/PjxpKen88knnwCQm5vLvHnzUBSF9957D09PT959910+//xztFotOTk5pKamAjBlyhSmTJmCwWjAf7n/Hd+dsY4R/e858KAkUBsyZAj5+fkcPHgQMzMzVq9ezZgxY/j+++85f/48DRo0wNramq1bt7Jp0yb0ej2dO3emY8eOzJkzh4iIiHLrUhSFDRs2MH78eObMmYNGo8Hd3Z2FCxfi7e1Nt27dCA4Oxt/fn2bNmpVbRs+ePTlw4AB+fn4oisK7775L7dq1SU9Pv+Nz/pncCUOSJEl6mCh/ZQiqugQHB4uEhITqbsY9+fW3HKasP0ry+Sy6nN7P4CObsTTo7niP7ZNP0uCjkqDs50s36fXpfgp0fwSCigI1NObsnhSBo61lRcVUGiEEwV8HU2yseOHIte+zaa5/kt1rvih13N/fnxdeeIEjR46YetQ6derEtGnTeOKJJ3B3dychIYEVK1Zw/fp1Zs+eDcD06dNxdnaWeeAkSZKkR56iKIlCiOCqqOuxH3J9UP7lYseaEeEkvPYUr0wfiMbszq9asbGhRlSU6bNX3Rp89UIoTV1tMVMpmKkUQtwd2DAqvEqCOSjpEevk0Qm1oi73vDCqUNfUcvCneHakXTEdv3nzJufPn0etVpc7/6xUGY/QXygkSZIk6WElA7oHzN7KnDpeTbAKCACzike4VRbm2EV1KHUsxN2B7RMiSJz+FCkzOvDNf1vi6Wz7oJtcymj/0diY26D60x8VYVQQRivMHPpj0BXy6tyPADAYDEyaNInBgweXv83Yn7Rp04aNGzeSn59PXl4eGzZsMKUxkSRJkiTp3siArorUW/A+5vXqoVhpSh1XLCxQ2drScMkSVOVsyQUlQaGNZfVMd6xjW4eVXVYSWicUhBnCYIkwmmHIb0z+mTFgsMe55zRO/bSTxo0b06RJEzQaDW+//fY9lR8YGMjgwYMJDQ0lLCyMoUOHmhZYSNKDMGHChFKLb6Kiohg6dKjp86RJk5g9ezZz5sypjuZx6dIlevfuXS11S5L06JJz6KqQsaCArI0byfzyK/QZGahsbLDv9TQO/fph5uxc3c27q96Lt3P44nmEvgbCUHrla1DDWqwbFV5NLZOke7dmzRrWrFnDN998g9FoJCQkBAsLCw4cOACUJNZeuHAhYWFh1dxSSZIedXIO3T+UysoKh759afT9NpomJtA4bg8u48Y9EsEcwPi2QVga65cJ5qzM1Yxp969qapUk3Z9WrVqZ9hFOS0vDx8cHOzs7MjMzKSoq4pf/b+/e43Os/weOv9472MZsDkNCNIVmR8xm5tRKSL6IkGKV5BuR0EFfJX07yS9SSVQm9kUHcioihKaMzBhyakjIabPZxg6f3x/37W5rG3PafS/v5+PRo/u+ruvzud73lW7v+3N9rvdn5062bt3KkCFDAEsC6O/vT1BQEK1btwYsUwtGjhxJQEAAgYGBtifMv//+e0JCQggICODRRx+11XesV68eL7/8sm1pul27dgHwww8/EBwcTHBwMCEhIaSlpZGcnIy/vz8AMTExdO/enQ4dOnD77bfz7LPPluq1UkqVHVq2RJVY5O0+PN+xEW98uxMXJ8tvgezcPEa0b0C7RtUv0VqpookIDz30kK1OYU5ODjVr1iQsLIwlS5awaNEiduzYUeRycCWWvB7WvAGHf+FmFzdcstM4uPMX4uLiadGiBYcPH2bDhg14e3sTGBhIuXzTH8aNG8fLL79MrVq18PPzA2DatGn89ttvbNmyBRcXF06dOkVWVhYPP/wwFSpUYM+ePfTr148PP/yQlJQUzpw5w759+1iyZAlff/01EyZM4OOPP2bChAl88MEHtGzZkvT0dNzd3endu3eBVVQSEhLYsmULbm5uNGzYkKeeeoo6depc+bVQSv0jaUKnLkv/iHrc37Q2cXtPYICI+lWp6F54KTSlSqpChQps376dzMxMPDw8WLFiBbVq/VU4u0uXLnTp0uXKT7B1HiweBhdWO8nOoGWNLOL+25E404ZnnnuRw4cPExcXh7e3NxERBacOtGzZkueff54mTZowdepUAFauXMmgQYNwsT7oVKVKFbZu3Urt2rXJyLAUEe/fvz8ffPABgYGBAOzbt48//viDpk2bMn/+fFvfzzzzDH379qV79+7Url27UPhRUVF4e3sD4Ofnx4EDBzShU0oVogmdumyebi60b3yTvcNQ/yAdO3Zk6dKl9OjRgzlz5tCnTx/WrVsHWG47btq0iffff5/o6Gi8vLzYtGkTR48eZfz48fTo0YM1a9YwduxYfHx82L59O02bNmX27NlIdiabPxrMM9+kkH7e4FNeiPmXBxG1nfhw/Sl+PjKfX7bt5LbbbuPMmTO4u7tz8uRJYmNjybQXJQIAACAASURBVM7OZuDAgTz//PN88cUXLFu2jJo1azJ//nz++OMPnnrqKTzc3fEG3q5/G0f+OMy5ffvIrVCBvCKWgUtMTKRv374YY7jpppsYN24cixcv5vTp08yePZvx48ezcuVKwFL6p3nz5hw8eLBAgllU6R+llAKdQ6eUspOc3DxSMyzFtnv37s3cuXPJysoiMTHxog8kHDlyhPXr17NkyZICt2G3bNnCpEmT2LFjB/v37+fHH38ke+cynlqaxpc9Pdg80JNHg8vx4qpztLzFmR8P5RBR24ltiYnMmDGDlJQU1qxZQ+vWrXn11VcJDw+nX79+5Obm8tRTT/Hyyy/TuHFjateuTc+ePfG77TbmVKlK1KnTvPP9Sm45eYojqalkHDvG3rvuJubDDwusehIcHExsbCz/+9//cHJyYsiQIcydO5e9e/fSsGFDateubZtbZ4xh48aN9OnTh/j4+Ov0X0Ap9U+iI3RKqVKVcT6HN77ZxZebfycnL4/M87msOubOb8nJzJkzh06dOl20fdeuXXFycsLPz8+2HjBA8+bNbbcsg4ODSU5OplK5Q2w/ls3dsyyjWrkGanoKAdUtv2WPnMlh9qzP6Nr9fgICAkhKSmLQoEGsXbuW2rVrs3nzZp5++mk2btyIiNCrVy+CgoLIy87m/0aPJuj8eYyBOq6uuDk5MbJ6dcYcPUrnTfEEeHszcNo03po8ucjPsXr1ap588klSU1PJzc3Fz8+Pjh078uabb+Ll5QVYHqY4c+bMVV9zpdQ/nyZ0SqlSk5dneHD6z+w8coZzOXkAGOCjtfvwvjWUkSNHsmbNGk6ePFlsH/lXH8lfdqmoVUlMrQY0ru7Chkc9CvVz7j8VWXvSh0VbEnj1tddJSkoiISEBgOjoaKKjo1m4cCGxsbG88847eHp6MnLkSACGDRjAf2rVoq1rOTZmnOWDEycAuNOzIv/nfJyF9W5FPDzIWrGCU6dOMXHiRGbMmAFAs2bNWLZsGXXr1mXTpk3UqVOHsWPH2j6Du7s7n3/+OQB9+/Zl4sSJtpiXLFlS4mutlLqx6C1XpVSpWbf3BLuPpdmSuQsys/P4s2YEg595joCAgGt2voYt7+N4lgsbDlveZ+cakv7MJc8YDmW40W7Aq4wfP56UlBTS09Np3bo1sbGxAKxZswYfHx+8vLyoWLEiaWlptn5P/f471XItn+Hr1L9G0Co4OVHN2YUNZ89iMjP5bUYMy5YtIzIyskAfWdY5dj4+PqSnp/Pll19es8+slLoxaUKnlCo1K3YcJeN8bpH7XL18qN/ugWt6vnLlyvHlwqU8twaCPsog+KMM4v5wItfJjYeWeRDQ701CQkIYPnw4lSpVYuzYsWzatInAwECef/55Zs6cCcB9993HggULCA4OZt26dQz19WX4H4d56OABKjsXXOv4jZo1+ejkSbol/0bf9et4+eWXqV+/PtHR0QwaNIjg4GDc3Nx4/PHHCQgIoGvXroSGhpbo8zg7OxMcHIy/vz89e/a0PVFrDzExMbZafdeKrpKh1JXTlSKUUqVm7KIkZm5IpqivHXdXJ/5zrx8Phde99ic2Bg5ugEM/g2sFuKMzeN18xd0deOQRMjb8dMnjyt1+G/UXL77i8/ydp6cn6enpgOV2bNOmTXnmmWeuWf+XI//Tx0qpoulKEUqpf6R7A2vi4epc5D5jIOqO61SgWgTqRkDkcAgbeFXJHEDlXr2RChUueox4eFC5z4NXdZ6LadWqFXv37uXs2bM8+uijhIaGEhISwsKFC4GLrzLh6enJiy++SFBQEOHh4baHS4paFaNVq1a2uYVgqZ2XmJhoe5+amkq9evXIy7Pcgs7IyKBOnTpkZ2czffp0QkNDCQoK4v7777eNKEZHRzN06FAiIiLw9fW13XLOv0pGcnIyrVq1okmTJjRp0sS2uodSqmia0CmlSk2zupWJqF8Vd9eCXz0ers70a1GPmt6FH15wRBWj7sSlkjc4FfMVKoKTuzveV1EQ2RjDt799S8/FPQn/Xzh3f3E3OXk5pJ1PIycnh2+//ZaAgABee+017rzzTuLj41m9ejWjRo3i7NmzgGWViXnz5rFt2zbmzZvHoUOHADh79izh4eFs3bqV1q1bM336dMCyKsby5cvZunUrixYtAmDAgAHExMQAsHv3bs6dO2crlgzg7e1NUFAQP/zwAwCLFy/mnnvuwdXVle7duxMfH8/WrVu54447+OSTT2ztiis/c0H16tVZsWIFv/zyC/PmzWPo0KFXfC2VuhFoQqeUKjUiwtSHmvLM3Q24ycsdV2ehXtXyvPqvxozu1Mje4ZWYuLpSd9YsXGrUQMqXL7jPwwPnSpWoO3sWzp4XH8UrjjGG0etH83Lcy+w6tYuz2Wc5mnGUc1nnqNWwFiFNQ7jlllt47LHH+O6773jzzTcJDg6mbdu2ZGVlcfDgQeCvVSbc3d1tq0yAZW5h586dAWjatCnJycmAZfQtOjqa6dOnk5trmevYs2dPlixZQnZ2Np9++inR0dGF4u3Vqxfz5s0DYO7cufTq1QuA7du306pVKwICAoiNjSUpKcnWprjyMxdkZ2fb5hn27NmTHTt2XNG1VOpGoWVLlFKlysXZiYGt6zOwdX17h3JVXG++mfrLviVt+XJOzY4l58QJnCtVonKvXnjf1xmnvyV6l+OH33/g+4Pfk5mTWWC7Uzkn6o+rT8d6HXm91euAJfn76quvaNiwYYFjf/755yJLuQC4uroiIoW2T506lZ9//pmlS5cSHBxMQkICVatW5e6772bhwoV8/vnnFDWPuUuXLrzwwgucOnWKzZs3c+eddwKWW6tff/01QUFBxMTEsGbNGlub4srPXDBx4kRq1KjB1q1bycvLw93dvcTXT6kbkY7QKaXUFXJyc8O7Sxdu/Xwet6/6Ht/5X1G51wNXlcwBzEyaWSiZuyAnL4fvDnxn23/PPffw3nvv2ZKiLVu2XPF59+3bR1hYGOPGjcPHx8d2i3bAgAEMHTqU0NBQqlSpUqidp6cnzZs3Z9iwYXTu3Bln65O/aWlp1KxZk+zsbFs5mJJKTU2lZs2aODk5MWvWLNuIoVKqaJrQKaWUg/k97feL7ncSJ05lnQJgzJgxZGdnExgYiL+/P2PGjLni844aNYqAgAD8/f1p3bo1QUFBgOW2rJeXF4888kixbXv16sXs2bNtt1sBXn31VcLCwrj77rtp1Ojybqk/+eSTzJw5k/DwcHbv3k2FSzyEotSNTsuWKKWuyu+//87gwYPZsWMHeXl5dO7cmbfffpty5crZO7Qyq8+SPmw/ub3Y/a5OrqzttRbPcp6lEs8ff/xB27Zt2bVrF07FPQiilCpEy5YopcoEYwzdu3ena9eu7Nmzh927d5Oens6LL75Y4LgLc7RUyTx4x4N4uBT9xK+TOBFxc0SpJXOfffYZYWFhvPbaa5rMKeXA9P9OpdQVW7VqFe7u7rZbcc7OzkycOJFPP/2UKVOm0LNnT+677z7at29fbL20jIwMHnjgAQIDA+nVqxdhYWG2ifdz5syx3QJ87rnnbOctro7aP0WHWzvgV9UPN2e3AtudcKKia0Web164zMf10q9fPw4dOkTPnj1L7ZxKqcunCZ1S6vKc2APr3oFVr5G0Zj5NmzQpsNvLy4tbbrmFnJwcNmzYwMyZM1m1alWx9dKmTJlC5cqVSUxMZMyYMWzevBmw3OZ77rnnWLVqFQkJCcTHx/P1118DxddR+6dwdXJl2t3TGBAwAG83b5zFGVcnVzre2pHP7/uc2hVr2ztEpezq6NGj9O7dm/r16+Pn50enTp3YvXt3kcfmL1h9rY0dO5YJEyZcl74vl5YtUUqVTF4ufP1v2LEQ8nIgLwezBSS9HKQ8A5VusR1qjEFEuPvuu21PRX733XcsWrTI9uV3oV7a+vXrGTZsGAD+/v62orXx8fG0bduWatWqAZalrtauXUvXrl0L1VFbsWJFqV2G0lLOuRyDggbxROATZOVmUc6pHM5ORa+yodSNxBhDt27d6N+/P3PnzgUsRbSPHTtGgwYNrrr/nJwcXFzKXnqkI3RKqZJZ9V/YuQhysiwJHdC4Sg6bfjsNMZ3BuvTTmTNnOHToEM7OzgWeTLxQLy0hIYGEhAQOHjzIHXfcUWQNsgvHF6e4Omr/RCKCh4uHJnPqhpZz4gQpX83n1KzZLH3nHVxdXBg0aJBtf3BwMJGRkYwaNQp/f38CAgJsxa7zy8rK4pFHHiEgIICQkBBWr14NWJbKyz9FJD09naioKJo0aUJAQIBtigjAa6+9RsOGDbnrrrv49ddfbdsTEhIIDw8nMDCQbt26cfr06et4RQrThE4pdWnZWbDxI8guWBst6lZnMrINn/14CPZ9T25uLiNGjCA6Opryf6vFVly9tMjISD7//HMAduzYwbZt2wAICwvjhx9+4MSJE+Tm5jJnzhzatGlzvT+pUsqB5GVmcnjECPbeGcXR//6XP99+mw2T3sV3zx5S5s8vcOz8+fNJSEhg69atrFy5klGjRnHkyJECx3zwwQcAbNu2jTlz5tC/f3+ysrIACkwRcXd3Z8GCBfzyyy+sXr2aESNGYIxh8+bNzJ07ly1btjB//nzi4+Ntfffr14+33nqLxMREAgICeOWVV67z1SlIEzql1KWd3ANS+OtCRFjQqzxfJKZze7veNGjQAHd3d15//fVCxxZXL+3JJ5/k+PHjBAYG8tZbbxEYGIi3tzc1a9bkjTfeoF27dgQFBdGkSRP+9a9/XfePqpRyDCY7mwPR0aSt/B5z/jwmM9Py7/PnMOfPc3Tcq5ye+9co3Pr16+nTpw/Ozs7UqFGDNm3aFEi4Lhzz8MMPA9CoUSPq1q1rm3uXf4qIMYbRo0cTGBjIXXfdxeHDhzl27Bjr1q2jW7dulC9fHi8vL7pY12tOTU0lJSXF9qOzf//+rF279rpfo/zK3k1ipVTpc/GwzKErQh1vJxb39YK2L0Drkbbt0dHRBdb99PDw4KOPPirU3t3dndmzZ+Pu7s6+ffuIioqibt26ADz44IM8+OCDhdqkp6fbXvfo0YMePXpc6SdTSjmoM8uWcW73Hsy5cwW231bOje/S0jBZWRx7803LUnsVKlx0msYFFzsm/xSR2NhYjh8/zubNm3F1daVevXq2kbwL0z0cjY7QKaUurWp9KO9T/H4nF7ijyxV1nZGRQWRkJEFBQXTr1o0PP/xQixIrpTj5yaeYzMJL4IWXL895Y/giJQVESF26lPj4eCpXrsy8efPIzc3l+PHjrF27lubNmxdo27p1a9sydLt37+bgwYOF1kEGy4hb9erVcXV1ZfXq1Rw4cMDWfsGCBWRmZpKWlsbixYsB8Pb2pnLlyqxbtw6AWbNmlfoUER2hU0pdmgh0ehu+iIa/rzHq6gENOkG1K3u6rGLFikUu+K6UurGdtyZRfycivFerFm/8+ScfJ23HY8gQbgsNZdKkSaSnpxMUFISIMH78eG666SaSk5NtbZ988kkGDRpEQEAALi4uxMTE4ObmVugcffv25b777qNZs2YEBwfblq5r0qQJvXr1Ijg4mLp169KqVStbm5kzZzJo0CAyMjLw9fVlxowZTJo06dpelIvQpb+UUiX36zL4ZiRknAQnZ8uTraEDIOolcNbfh0qpa+fX0ObkpaVd/CARqkRHU+O5Z0snqMtUmkt/6TewUqrkGnaABvfAyb2QnQE+DSwjdEopdY1VaBVJ2rLltpJIRREPDzzbti29oByYXebQiUhPEUkSkTwRKZXMVSl1jYiAz+1QM0iTOaXUdVP1sceQi82nFcGlalXKNw8tvaAcmL0eitgOdAdK95lepZRSSpUJHo0bU/2Z4Yi7e+GdLi44eXlR56OPHPap09Jml4TOGLPTGPPrpY9USiml1I2qSr9+1Jk6lfJhzcHZGVxdEQ8PKvfpg++iRbj53mrvEB2GzqFTSimllMOqEB5GhfAw8s6fx2Rl4eTpiThp1bW/u24JnYisBG4qYteLxpiFRWwvrp+BwECAW2655RJHK6WUUuqfyKlcOdAalcW6bgmdMeaua9TPNGAaWMqWXIs+lVJKKaX+SXTMUimllFKqjLNX2ZJuIvI70AJYKiLL7RGHUkopVRaJiG2ReYCcnByqVatG586dAVi0aBFvvvlmse2Tk5Px9/cvct9LL73EypUrr23A6rqzy0MRxpgFwAJ7nFsppZQq6ypUqMD27dvJzMzEw8ODFStWUKtWLdv+Ll260KXLla2vPG7cuGsVpipFestVKaWUKoM6duzI0qVLAZgzZw59+vSx7YuJiWHIkCEAHDt2jG7duhEUFERQUBBxcXEA5Obm8vjjj9O4cWPat29PZqZlnebo6Gi+/PJLAL755hsaNWpEZGQkQ4cOtY0Abty4kYiICEJCQoiIiODXX3+1nbd79+506NCB22+/nWefdcwluf6JNKFTSimlyoj0czmcPZcDQO/evZk7dy5ZWVkkJiYSFhZWZJuhQ4fSpk0btm7dyi+//ELjxo0B2LNnD4MHDyYpKYlKlSrx1VdfFWiXlZXFE088wbfffsv69es5fvy4bV+jRo1Yu3YtW7ZsYdy4cYwePdq2LyEhgXnz5rFt2zbmzZvHoUOHrvVlUEXQOnRKKaWUg/tp/0nGLd7B7mOWxeqzsvPIqlib5ORk5syZQ6dOnYptu2rVKj777DMAnJ2d8fb25vTp09x6660EBwcD0LRpU5KTkwu027VrF76+vtx6q6V4b58+fZg2bRoAqamp9O/fnz179iAiZGdn29pFRUXh7e0NgJ+fHwcOHKBOnTrX5kKoYukInVJKKeXA4vaeIHrGRnYcOUNOniEnz5BnDP0+/ZmQVncxcuTIArdbS8rNzc322tnZmZycnAL7jSm+UtiYMWNo164d27dvZ/HixWRlZZW4X3V9aEKnlFJKObCXFyWRlZ1XaHtWdh57vJvx0ksvERAQUGz7qKgoPvzwQ8Ayb+7MmTMlOm+jRo3Yv3+/beRu3rx5tn2pqam2hzBiYmJK+EnU9aS3XJVSSikHdSL9HAdOZhTeYQzHF77F4aN7OHpTJZYvX17sKN27775LrVq1+OSTT8jLy6N69erMmDEDsNxGTUpKol69ejRp0qRAOw8PD6ZMmUKHDh3w8fGhefPmtn3PPvss/fv355133uHOO+8EYNOmTcTGxtKwYcNr9OnV5ZCLDak6mmbNmplNmzbZOwyllFKqVPyZlkXkW6s5n/PXCJ0xhqOzR+LpH0WVZvfy0wtRHNq7k7S0NFq1alVkP56enqSnpxfYdvToUcLCwjhw4ECx509PT8fT0xNjDIMHD8bX15eRI0demw93AxCRzcaYZqVxLr3lqpRSSjmoap5u3OTlDsZw09mT1D1zBLN/E+LkQsWQTtSuXJ6qnm4EBwcTEhJCVFQUTZo0ISAggIULCy+bnr+gcPv27fnzzz8JDg5m3bp1JCQkEB4eTmBgIN26deP06dNMnz4dT09PqlWrxpdffkl2djZt27blueeeo3nz5jRo0IB169YBsGbNmkuWNVHXj95yVUoppRyUiPBGhUPkzJ2K17l0csWJz0/8SXw5L7Jy0nmpc6jtWHd3dxYsWICXlxcnTpwgPDycLl26ICJF9r1o0SI6d+5MQkICAIGBgbz33nu0adOGl156iVdeeYVJkyaxcOFC/Pz8mDJlCgDLly8nJyeHjRs38s033/DKK68UWlniQlkTFxcXVq5cyejRowuVRVHXliZ0SimllIP6c+Ikqn72GcZa9BfAxeRSO/04z61/l4aj2tm2G2MYPXo0a9euxcnJicOHD3Ps2DFuuummS54nNTWVlJQU2rRpA0D//v3p2bOnbX+vXr0KHN+9e3eg6HInF/orrqyJuj70lqtSSinlgM7t2cOpmTMLJHMAt5VzY2dWJs5pZzj23//atsfGxnL8+HE2b95MQkICNWrUKFBO5GpUqFChwPsLpUmKK0tysbIm6vrQhE4ppZRyQKdmfoYpYmQrvHx5zhvDFydPkv7DD+ScPk18fDwHDhygevXquLq6snr16os+7PB33t7eVK5c2TYfbtasWbbRuiuhZU1KnyZ0SimllAPK3LYNcnMLbRcR3qtVi7iMs7Tf/SuBzZoxduxYOnXqxKZNm2jWrBmxsbE0atToss43c+ZMRo0aRWBgIAkJCbz00ktXHPuzzz7LCy+8QMuWLckt4jM4ChFhxIgRtvcTJkxg7Nix16Rv66hkYxGxFQkUkWdFZGoJYxsrIiV+pFjLliillFIO6LcHepGVmHjRY5wqVOCWmTPx8G9cSlH9s7i7u1OzZk3i4+Px8fFhwoQJpKenX7OkTkT2AMeA1sDNwFqgmTHm9CXauQD/AdKNMRNKci4doVNKKaUckNe9nRAPj4sf5OKCeyMt5HulXFxcGDhwIBMnTiy07/jx49x///2EhoYSGhrKjz/+CEBAQAApKSkYY6hataptndyHH3640NO+wBngCNAPmAiMBbxE5HsRSbT++xYAEYkRkXdEZDXwVv5ORORxEflWRIr9A6EJnVKqTBo+fDiTJk2yvb/nnnsYMGCA7f2IESN45513Stzf2LFjmTCh6B/CERERVxznmjVriIuLu+L26sZVqVs3xNm52P3i4UHVxx5DXLRgxWXJzoKUg3AuDYDBgwcTGxtLampqgcOGDRvG8OHDiY+P56uvvrJ9v7Rs2ZIff/yRpKQkfH19bfMOf/rpJ8LDw4s649PAa0A1Y8ws4H3gM2NMIBALTM53bAPgLmOM7T6wiAwB7gO6GmMKPiGTj/4pUEqVSREREXzxxRc8/fTT5OXlceLEiQJrVMbFxRVI+K7G1SRka9aswdPT86qSQnVjcvby4pZPPubgYwMw2dmYc+csO0QQd3cq3nUXVQc8Zt8gy5JzafDdf2DrPBCBvFzIOYeXSaNfv35MnjwZj3wjoitXrmTHjh2292fOnLGtxrF27Vrq1q3Lv//9b6ZNm8bhw4epUqUKnp6ehU5rjPlDRFYBS6ybWgDdra9nAePzHf6FMSb/pMOHgd+xJHMXrf2iI3RKqTKpZcuWtkQrKSkJf39/KlasyOnTpzl37hw7d+5k+fLlhIaG4u/vz8CBA7kwZ3jy5Mn4+fkRGBhI7969bX3u2LGDtm3b4uvry+TJf/1ovvAlvWbNGtq2bUuPHj1o1KgRffv2tfX5zTff0KhRIyIjIxk6dCidO3cmOTmZqVOnMnHiRFs1/gMHDhAVFUVgYCBRUVEcPHgQgOjoaIYOHUpERAS+vr58+eWXpXIdlWPzCAqi/orv8Hny35SrXx/XWrXwbNuWOlOncvP4txAn/Wu8RHLOw4yOkDAHcjIhOwNyz0FeDkxrw9MDHuKTTz7h7NmztiZ5eXls2LCBhIQEEhISOHz4MBUrVqR169asW7eOdevW0bZtW9sqGsUtu3ahO+s/Rcn/MMPZv+3bDtQDal/qI+qfBKVUmXEk/QiTf5nM0FVD+fjAxxgnw4EDB4iLi6NFixaEhYWxYcMGNm3aRGBgIEOGDCE+Pp7t27eTmZnJkiWWH8hvvvkmW7ZsITExkalT/3rgbNeuXSxfvpyNGzfyyiuvFFkMdcuWLUyaNIkdO3awf/9+fvzxR7KysnjiiSf49ttvWb9+PcePHwegXr16DBo0iOHDh5OQkECrVq0YMmQI/fr1IzExkb59+zJ06NC/Pt+RI6xfv54lS5bw/PPPX+erqcoKl8qV8XniCeovXcJt36+kzodTqBDWvNgVIFQRdiyEk/stSdzfZaZSZdcsHnjgAT755BPb5vbt2/P+++/b3l9YUaNOnTqcOHGCPXv24OvrS2RkJBMmTLhUQpdfHHDhl2RfYP1Fjt0CPAEsEpGbL9apJnRKqTIhZnsMnb/uTExSDKsPrear3V+RVSeLgdMHsv7H9bRo0YIWLVoQFxdHXFwcERERrF69mrCwMAICAli1ahVJSUmAZYmjvn37Mnv2bFzyzT+69957cXNzw8fHh+rVq3Ps2LFCcTRv3pzatWvj5OREcHAwycnJ7Nq1C19fX2699VYA+vTpU+zn2LBhAw8++CBgmUS9fv1f3+Vdu3bFyckJPz+/Is+tlLpCv3wG2X8f/LLKOw9b5zJixAhOnDhh2zx58mTbj0M/P78CP/7CwsJo0KABAK1ateLw4cNERkaWNJqhwCMikojlluqwix1sjFkPjASWiohPccfpHDqllMP74dAPfJDwAedzz9u25ZGHm68bCRsTcD7gTIx/DHXq1OH//u//8PLy4tFHH2XAgAFs2rSJOnXqMHbsWFu1+qVLl7J27VoWLVrEq6++akv0LlS/h+Ir4Bd1zNWUf8o/ypK/77JUUkoph3c+rcjN6aO9LC9yMqlRowYZGRm2fT4+PsybN6/IdrNmzbK9joiIIC+vuLupFsaY6Hyvk4E7L3aM9f3YfK+XA8svdg4doVNKObz3E94nK7fw0kHlby/P6YTTpLumk5GbQZUqVUhJSWHDhg20aNECsHwpp6en2+ak5eXlcejQIdq1a8f48eNJSUkhPT39quJr1KgR+/fvt61pmf8vgYoVK5KW9tdfJhEREcydOxewLNV0Gb/qlVJXql4kOJcrfv/NIaUXy3WiCZ1SyqFl52az+/TuIve513EnNy0Xr9u82Hp8K2CpEeXt7Y2Pjw+PP/44AQEBdO3aldDQUAByc3N56KGHCAgIICQkhOHDh1OpUqWritHDw4MpU6bQoUMHIiMjqVGjBt7e3gDcd999LFiwwPZQxOTJk5kxYwaBgYHMmjWLd99996rOrZQqgeZPgFMxNyVdPaD1s6Ubz3WgK0UopRza+dzzhMaGkmeKv6Xh6erJW63fonXt1qUYWUHp6el4enpijGHw4MHcfvvtDB8+3G7xKKX+Zt8qmPcQGGN5ytXF3bK9/WvQfMDF214hEdlsjGl2XTr/G51Dp5RyaOWcy1HPqx77CBkfAwAACHJJREFUU/cXe8z53PME+gSWYlSFTZ8+nZkzZ3L+/HlCQkJ44okn7BqPUupv6t8JI3ZD0nw4sQe8bgb/HuBZzd6RXRM6QqeUcnjLk5czZv0YMnMLF0l3c3ajfd32vN7qdTtEppRSxSvNETqdQ6eUcnj31LuHvnf0xc3ZDWf5aymk8i7l8avqx5gWY+wYnVJK2Z/eclVKlQnDmg7jXt97+d+u/7E3ZS9V3avSs0FPwm8Ox0n0t6lS6samCZ1Sqsy4rfJtvNTiJXuHoZRSDkd/1iqllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXGa0CmllFJKlXFijLF3DCUmIseBA/aO4yJ8gBP2DqIM0OtUMnqdSkavU8nodSoZvU4lo9epZOoaY6qVxonKVELn6ERkkzGmmb3jcHR6nUpGr1PJ6HUqGb1OJaPXqWT0OjkeveWqlFJKKVXGaUKnlFJKKVXGaUJ3bU2zdwBlhF6nktHrVDJ6nUpGr1PJ6HUqGb1ODkbn0CmllFJKlXE6QqeUUkopVcZpQncNicjbIrJLRBJFZIGIVLJ3TI5KRHqKSJKI5ImIPimVj4h0EJFfRWSviDxv73gclYh8KiJ/ish2e8fiyESkjoisFpGd1v/nhtk7JkckIu4islFEtlqv0yv2jsmRiYiziGwRkSX2jkVZaEJ3ba0A/I0xgcBu4AU7x+PItgPdgbX2DsSRiIgz8AHQEfAD+oiIn32jclgxQAd7B1EG5AAjjDF3AOHAYP0zVaRzwJ3GmCAgGOggIuF2jsmRDQN22jsI9RdN6K4hY8x3xpgc69ufgNr2jMeRGWN2GmN+tXccDqg5sNcYs98Ycx6YC/zLzjE5JGPMWuCUveNwdMaYI8aYX6yv07D8JVzLvlE5HmORbn3rav1HJ5kXQURqA/cCH9s7FvUXTeiun0eBb+0dhCpzagGH8r3/Hf3LV10jIlIPCAF+tm8kjsl6GzEB+BNYYYzR61S0ScCzQJ69A1F/cbF3AGWNiKwEbipi14vGmIXWY17EcpsjtjRjczQluVaqEClim44SqKsmIp7AV8DTxpgz9o7HERljcoFg6/znBSLib4zROZr5iEhn4E9jzGYRaWvveNRfNKG7TMaYuy62X0T6A52BKHOD14S51LVSRfodqJPvfW3gDzvFov4hRMQVSzIXa4yZb+94HJ0xJkVE1mCZo6kJXUEtgS4i0glwB7xEZLYx5iE7x3XD01uu15CIdACeA7oYYzLsHY8qk+KB20XkVhEpB/QGFtk5JlWGiYgAnwA7jTHv2DseRyUi1S5UJhARD+AuYJd9o3I8xpgXjDG1jTH1sHw/rdJkzjFoQndtvQ9UBFaISIKITLV3QI5KRLqJyO9AC2CpiCy3d0yOwPpQzRBgOZbJ658bY5LsG5VjEpE5wAagoYj8LiKP2TsmB9USeBi40/q9lGAdXVEF1QRWi0gilh9WK4wxWpJDlRm6UoRSSimlVBmnI3RKKaWUUmWcJnRKKaWUUmWcJnRKKaWUUmWcJnRKKaWUUmWcJnRKKaWUUmWcJnRKqVIhIrn5ymYkiEg9EYm7zD6eFpHy1ytGRyIiXUXEz95xKKXKBi1bopQqFSKSbozxLMFxztYlmIralww0M8acuNbxORoRiQGWGGO+tHcsSinHpyN0Sim7EZF067/bishqEfkfsE1EKojIUhHZKiLbRaSXiAwFbsZS/HV1EX2Fikictc1GEakoIu4iMkNEtonIFhFpZz02WkS+FpHFIvKbiAwRkWesx/wkIlWsx60RkUnWfreLSHPr9irW9onW4wOt28eKyKfWdvutMV+I7yFrXAki8pGIOF+4BiLymjXun0SkhohEAF2At63H17+u/yGUUmWeJnRKqdLike9264Ii9jcHXjTG+GFZQ/MPY0yQMcYfWGaMmYxlXdt2xph2+Rtal0mbBwwzxgRhWbYpExgMYIwJAPoAM0XE3drMH3jQet7XgAxjTAiW1Sf65eu+gjEmAngS+NS67RVgizEmEBgNfJbv+EbAPdZ+XxYRVxG5A+gFtDTGBAO5QN8L/QM/WeNeCzxujInDsuTbKGNMsDFm36UurlLqxuZi7wCUUjeMTGsyU5yNxpjfrK+3ARNE5C0stx3XXaLvhsARY0w8gDHmDICIRALvWbftEpEDQANrm9XGmDQgTURSgcX5zh2Yr+851vZrRcTLut5nJHC/dfsqEakqIt7W45caY84B50TkT6AGEAU0BeItS6viAfxpPf48cGGJqc3A3Zf4rEopVYgmdEopR3H2wgtjzG4RaQp0At4Qke+MMeMu0laAoiYEy0XanMv3Oi/f+zwKfjf+vV9TTL8Xjsvfb661LwFmGmNeKKJdtvlrMvOF45VS6rLoLVellMMRkZux3AKdDUwAmlh3pQEVi2iyC7hZREKt7SuKiAuWW5h9rdsaALcAv15mOL2s7SOBVGNM6t/6bQucuDAqWIzvgR4iUt3apoqI1L3EeYv7rEopVYj+ElRKOaIALA8E5AHZwL+t26cB34rIkfzz6Iwx50WkF/CeiHhgmT93FzAFmCoi24AcINoYc85627OkTlvLq3gBj1q3jQVmiEgikAH0v1gHxpgdIvIf4DsRcbJ+psHAgYs0mwtMtz5Y0UPn0SmlLkbLliilVDFEZA0w0hizyd6xKKXUxegtV6WUUkqpMk5H6JRSSimlyjgdoVNKKaWUKuM0oVNKKaWUKuM0oVNKKaWUKuM0oVNKKaWUKuM0oVNKKaWUKuM0oVNKKaWUKuP+H1M387PdOxvpAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:12:20.361212Z", + "start_time": "2026-06-12T00:12:20.184802Z" } - ], + }, "source": [ "x = pca_df.PC1\n", "y = pca_df.PC2\n", @@ -1929,7 +2612,20 @@ "ax.set_title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n", "for s, x, y in zip(state, x, y):\n", " plt.annotate(s, (x, y))" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 88 }, { "cell_type": "markdown", @@ -1949,9 +2645,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:24:08.672536Z", + "start_time": "2026-06-12T00:24:08.491326Z" + } + }, "source": [ "#Code task 11#\n", "#Create a seaborn scatterplot by calling `sns.scatterplot`\n", @@ -1965,14 +2664,34 @@ "state = pca_df.index\n", "plt.subplots(figsize=(12, 10))\n", "# Note the argument below to make sure we get the colours in the ascending\n", - "# order we intuitively expect!\n", - "sns.___(x=___, y=___, size=___, hue=___, \n", - " hue_order=___, data=pca_df)\n", + "# order we intuitively expect\n", + "sns.scatterplot(\n", + " x='PC1',\n", + " y='PC2',\n", + " size='AdultWeekend',\n", + " hue='Quartile',\n", + " hue_order=pca_df.Quartile.cat.categories,\n", + " data=pca_df\n", + ")\n", + "\n", "#and we can still annotate with the state labels\n", "for s, x, y in zip(state, x, y):\n", " plt.annotate(s, (x, y)) \n", "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained');" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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VSwq4ckl///00sBQqVPCVx39ao4asrKy0fsNGWfwTIi0sLPT48WO5ux813AJw585d+ftfUo8e36pbt676sESJWFOBX+R1j3uVvXv3GW1f8PWVJO3b95/2C74vfOrBvj/+NLptw/fC0+P3xjr+6VW3nDlzGtpy586tObNn6YTnMQUGXFbAlUvavGmDJL1wFfjtO3bEalu8aLHs7e3VoMGXkp6GtA4d2mvfvj/ivVjjqZOeCrhySV7nz2rO7Fk6ffq02rRt/1qzBCQpV65cqla1qnbu3GlY7PG337bpwYMHatXK8bXO+V+FCxdW+XLltHnzllfW2br106ujq1atibPfy7Rp3Ur9+/XVTz/N1++7dxvt++WX5SpeoqSqV/9UpcuU0+49e9SsWVMVK1ZMP4wdpwwZMmj2rJk6feqEjhw+pPbt271yvLVr1+mjjz5SgQL5DW0tHR3l6XlC3t7ehrbE+Py8yIclSmjZ0iU6c+aUggKvKODKJc2aOUOWlpbKnz+fUd/g4GBD4H/m/HkvOTj8+zmvVfNT+fn5GQX+/6pTp7bu3r2rPXv2Gv3+OHv2rIKDg1W1SpWXHps3b14VKlRIm/75gun54/f98aeyZcuqAs/dRjB33k/as3ev5syZrZaOLTRixEh5eXnFOu+DBw9i3bayecsWWVhYqFLlSi+tR5Lat2+n33ft0EW/C4a/m08++SRev0sl6ffdxuOe++cLnGfva3x/3xYoUEDZs2fT5i2/Gp0vKCjojdbXAICE4Eo/AJN16tQpnTp1SpJkaWmp774bru7duqpnzx4aO268oV/+/PmVwc5OK1etMvoPfUJt3LhJlhaWatu2tRYunC9zc3OdOHFSkyZN1oGDB+M81szMTGtWr1TWrFk1ffoMnffy0qNHYTI3N9P2bb/JxsbmleNntreXJO3auf2F+59f48CxZSsNcOqvnj2+1f9Gj9LtO3e0edNm/ThxkuG+6Bd53ePicufuXaPtyH+mYt+9Y9weERn5wvvjY/f75/j/nvefq/DPzpEmTRpt3rRRjx8/1qRJk+V30V9hYWHKkSOHlixeGOs9f/TokWGGwPPOnD0rd/ej6vR1R23evEV169RR7ty5Y01Vjotjy1a6f/+BnjyJ1LVr13Tnudd0+/ZtPXr0SLly54r3+Vq1ailzc3Nt277D6NaL3bufBuKCBQq88cJ2hiD/inv0LS0t1bx5M509e9bw7zEhWjo6auLEH7V8xYpYi/s9ExYWZng9GTNm0OhRIzVq1P907949zZw5XRkyZFDVap8of/782rB+rfz8/HTkiNtLx9y0eYtGjhwhxxYtNOHHiSpUqJDKli2jocOGG/ok1ufnv3LmyKHNmzfKz89Po0aNVmBAoMIfP1bZsmU0Yfy4WOe985/PvyQ9jogw6pfpgw8UFBQU57j29pmVIUMGXbns/8L9GTO9/EtM+8yZJUmjR43U6FEvnnb/33UB1q1br7p16ig4OFgbNm564TEhN2/GbrsR8rSeOL5U7datq/43epR+/mW5Jk2eotu3bysqKlqDBw2Md+i/88/Cms9ERDz9YuvZ+xrf37fPvvy9ceNG7NcSEqJcuV7/8a0AEF+EfgDvhSdPnmjaNBd179ZVRYoUMdp37Ngxbdu2XVOnPL0ne+jQ4fFa8O9F1q5bp7Xr1il16tSqXLmSBjo76+efl+rjT2rE+Z/uokWLqESJEurX30nr128wtD+7tzs+nq2W3qVrNwUGxv0f/KCgIDkPHCRJyp8/nxo2bCjnAU6ysrbS0KHD3+i48PBwWVtbxzo2rv+kJ4ePq1VT9uzZ1LRZC7m7/zvd187uxesTxPWZWLxkiRYumK+SH36oTp2+lp+fn1wPHIh3LefOnjOs3v9f0dHROnTosGrW/FTZs2fTtWvX4zyXmZmZWv6zOOSSxQtf2KdVq5ZGX3wllJWVlZo3a6qTJ0/p7NlzcfatU6eO7O3tNX36jASP09LRUVOmTNL69Rvi/SXKqFGjdOrUaW359emV1Vo1a8ppgLMePHigkydPytX1gGrXqhVn6L937552796j5s2ba+KkyWrV0lFhYeHa8tzV2sT8/DyvXr3PZWtrq85duhn9zvjwFYtsxuX2rVvKkT32YpNGfW7f0e3bt9WmbfsX7g8NffkXFrfvPP3dM3PW7Jfe6uH33JdMWbJk0fhxY3XmzBkVLlxYo0aOMJoi/8yzLxOM2rI8Ddv/DeXPa9a0iQ4fOaJhw4x/l6VNa/vSYxIqvr9vn/27zpIlS6x99v98cQAASY3QD8DkZMmS5YVXVZ5d4XnRPZTr12/Qo0ePNGf2LKVJnUb9+ju90QJLYWFh+vPP/bK2stbSpYtVpEhhBQUFGRYU++/Vumd5IOKx8YJj7du1jXXu/15xemb/fldFRkYqb548r7zH+nkXL/prxoyZ+rL+Fyr5Yck3Pi4wMFDFihcz6lutWtUXroaenGL09E1/9n4+0+4F7/mr7Ny5S4GBgRo1eqSqVK6s0f+slp9YZs2erVq1amrypEnq9E1nw6yFZywtLVWz5qfas2evPv20hnLkyKGlS5dp2/bYVyHHjR2r5s2bacKPE1/4hIv4+Oyzuvrggw80ecrUV/Zt/c/aAs+mfseXo2MLTZkySRs3bTJ80fQqVatWUcMGX6pmrTr/NpqZKU3qNIZNW1vbeN2WsmbtOjVq1FC1a9dS06ZNtGvXLsOtElLifn6e9+zLgf8uPtimTZvXPucff+7X4EEDVa1aVR0+fOSFffbs3avGjb+ShYW5PD1PJOj8fn4X5XfxoooXL6Yff5wYZ19zc3PNnTNbMTExatuug5o2baLRo0bqiJtbrPVB0qVLp8/q1jWa4t+kcWNFRUXpqPvL12qIiYmJ9bu0WLGiKl++vK6+ZB2AhIrv71s/Pz9dvx6sxl99pfnzFxjac+bMqQoVynNPP4C3gtAPwOSsWrlC165d0569e+Xr6ytzc3OVKFFC3bt108OHD7Vo8ZIXHrd9+w6FhYVp4YL5srGxUc9evWOFq7hMnjRR4eHh+tvDQ8HBN5Qli7369O6le/fu6cSJk5IkL6+ntw+0a9tWoQ9DFf74sQICrsjX11f+/pc0fPhQmZmZ6c7du/qsbh1Vr/5JrHHO/3Pva5cu32j9+g2KjHwiPz8/BQYGavKUqRoyZLBy58mt/X+66u69e7K3z6yyZcro0aNHmjJ1mooVK6pxY8dq27Ztuuh/SZGREapWrZqKFSum2XPmvvT1xfe4DRs3afCggRo00Flu7u4qXKiQOnX62mjxsXeBh4eH7ty5q4k/TtDUadP15EmkmjZpouLFE35FNTo6WsuW/awRI75TaGhogp/88CrHjh3X0GHDNWH8OO3auUO/LF8ub28fWVlZ6sMSJdS2XVt5e3lrz569at26lSIjIzVz1uwXBooVK1Zo7NgfVKd2bcP98VeDAnTkiJuat4jf/f6tW7VSWFiYYZHAl8maNatqfvqptm797aV//82bN9O0qVM0wHmgYeG5Bg2+1NQpk3X27FmtWLFSZcuWMTrmzJmzsUKxtbW1Jk78UVOnTlNAwL+LHrrud5WTUz89ePhA+fLl08cfV9PcefNe+RpdXV119epVTRg/TlmzZjVawE9K3M/P8w4cOKjHjx9r7tzZmjt3nlKlslHHDu1ll8Hutc+5cOEiNWrYUEuXLNbsOXN1wvOEbGxsVLlKZe3du1dHjrjp11+3qmmTJlq+/BctXrRYnidO6smTSGXPnl1Vq1bV77/v1q5dL1+0c8iQoVqx/BetWrlC69at17Xr15UxQwYVLFRQJUt+qO7de0iSBg50VqVKFdW6TVuFhIRo/vwFqlK5sqZOmawzZ84a/d3dvn1bEyaMV86cOXTxor9q1a6pdu3aatnPv7x0ET/p6Toh/fv300DnAXJzd1eBAgXk1L+frlwJkKWlxWu/j8+L7+/bmJgYTZ48WVOnTtGSxYu0ctUqpU+fXs7OAxQSEpIotQDAqxD6AZicGTNn6vPPPlPXrl2UNUsWWVtb68aNGzp46KBmzZoj338WinuRP/74U+3ad9DPy5Zq6ZLF6tK1W5yPqXre0b/+kqNjCzVs2EB2dna6ffuO/v77b/Xt52SYChoQEKCRo0arS+dvtGHDOllaWqq/0wCtW7deHb/upB++H6OJEyfoyZMoHTx0UC1btZbH338ZjePm5q6Zs2arRfPmatumjSwsLNSseQu5ublr9uw5uuBzQZ27fKPGX30la2trhYSE6MTJk4anA9y4EaLLly+rY8cOypEjh2JiYnT5yhWN+f4HLVmy9KWvL77HzZv3k9KlTStHxxb69tvu8vQ8oe7f9tDSJYvj9T6+LXfu3FWHjh01etRIzZ41Q48ePdLvv+9Wj297avfuhD+R4Netv2nEiO+0YeMmPXjwINHrXbVqtU6cOKGuXbuqV88esre315MnT3Tx4kVt2bxFS5YuU6aMGVW3Th3t3bvvpVcQN2zcpO++G67WrVvp9927lSbN06vgL5od8yI5cmRXjRrVtXHTq1+no2MLWVpaxnnfv7m5uSwtLY2evV67dm1ZWFioVKlS2vrrlljHVKxUJdYiif369VV4WJgW/PPEh2dGjhqtCRPGac7sWXr48KH+N+b7OBe0eyYmJkbrN2xUv759FBQUpIOHjI9J7M/PM75+furarbuGDB6kRQsX6s7dO9qyeYvmL1ioVStfvnp+XEJDQ9W4SVM5OzupXds2GuDU/+mXkSdPauXKp5+T6Ohofd3pG3Xp0lnNmzVV7969FRX1RNeuXZObu/sLF9p73pEjbvqyQUP169tXY8aMlp2dne7cuSOfCxf02z+Paaz+ySfq07uXpk+fYbRgan+nAdr9+079NG+uGjdpaviy9caNEA3/boRGjRyhokWL6O7de5oxc5amvGKGyYyZs5Q6dWq1at1KPXr00IULPho6dLjqfVFPVf9ZYC8xxOf3rSStXvP0aRk9e/XUooULFBgYqFkzZ6tylcqJWg8AvIxZ9hwOr3fjKgAAMPim09caO/YHfVqzttGz5N91tWrV1C8/L1Odup+/MtgBb8uG9euUKVMm1apd59WdAQBx4ko/AABv4MMSJZQrdy45OfXXrl2/p6jAL0nVqlbVr79uJfADAGCiCP0AALyBxYsXyt7eXn/99ZeGDI3/Y/reFT+MHZfcJQAAgCTE9H4AAAAAAEyU+au7AAAAAACAlIjQDwAAAACAiSL0AwAAAABgot67hfyyZs2q0NDQ5C4DAAAAAPCesLW1VXBwcLKM/V6F/qxZs8rzuEdylwEAAAAAeM+ULVchWYL/exX6n13hL1uuAlf7AQAAAABJztbWVp7HPZItg75Xof+Z0NBQPXz4MLnLAAAAAAAgSbGQHwAAAAAAJorQDwAAAACAiSL0AwAAAABgot7Le/oBAAAAvN/MzMyUPn162draytzMLLnLQQoVHROj0NBQ3b9/XzExMcldzgsR+gEAAAC8VzJlyqTWrVupQP78yV0KTISfn59Wr1mr27dvJ3cpsRD6AQAAALw3LC0tNWiQs0Ifhmr5ihW6dfOWoqOjk7sspFDm5ub6IPMH+rJ+fQ0a5KyRI0fryZMnyV2WEUI/AAAAgPeGvb29Ulmn0oJVi+Tv75/c5cAEXAkI0N2799Sndy/Z22fWtWvXk7skIyzkBwAAAOC9YWH+NAJFRkQkcyUwJc8+TxbmFslcSWyEfgAAAAAATBShHwAAAAAAE0XoBwAAAAC8tipVKutqUIDSp08vSXJ0bKHz584kc1V4htAPAAAAAClEjhzZNXXKZB0/5qFL/n7666ibvh/zP2XMmOGtjL9h/TqNGTPaqM3D45hKlymn+/fvv5UakDCEfgAAAABIAXLnzq2dO7Yrf/786tmrt6pV+0RDhg7Txx9/rK1bf1WGDBmSbGxLy5c/+C0yMlIhISFJNjbeDKEfAAAAAFKA8ePGKjIyUq3btJW7u7uCrl7Vn3/uV8tWrZU9WzYNGTJYknQ1KED1Pv/c6Njz587I0bGFYfu74cN08KCr/Hx95HbkkAYNGmgU7J0HOGnP7l1q1bKl3I4c0iV/P02fPk1Vq1ZR1y5ddDUoQFeDAuTg4BBrev+L1K1bR7t2btdFvwtyO3JIA5z6y8Li3Vvp3hS9/Ouad4zzACc5Ow8wartx44bKlC2fTBUBAAAAwNuRIUMGffppDf04cZLCw8ON9oWEhGjTps1q1LChhg0bHq/zPQwNlZPTAF2/HqxixYpq8qSJCn34UHPn/WTokzdvXjVs2EBdu3ZXVHSUAgODlD9fPnl5eWvylKmSpFu3bilXLoc4x6pRo4ZmzZyhkaNG6+jRv5Q3Tx5NmvSjJGmay/QEvAt4HSkm9EuSl5e3WrZqbdiOiopKxmoAAAAA4O3Ily+vzM3N5XvB94X7L/j6KmPGDPrggw/idb4ZM2Ya/hwYGKj58xeoUaNGRqHfyspKffr20+3btw1tERGRCgsPS9B0/n59+2j2nLlav36DJOnKlSuaNHmKRnz3HaH/LUhRoT8q6gn3igAAAADAf5iZmUl6en99fHz5ZX117dJZefPmla2trSwsLPTw4UOjPoFBQUaB/3WVKlVSpUuXVr++fQxt5uYWSp3aRqltbBT2n5kLSFwpKvTny5dPx495KCLisTw9T2jCjxN15cqVl/a3traWtbW1YdvW1vZtlAkAAAAAierSpUuKjo5WocKFtOv332PtL1iggG7evKn79+8rOjpa+udLgGesrKwMfy5XrqzmzZ2jKVOnaf9+Vz14cF9fffWVunfranRM2KNHiVK7mZm5pk6dqh07d8XaF/74caKMgZdLMaH/uKen+vbrr4sX/WVvn1n9+vbV1l83q2at2rpz5+4Lj+nTu1esdQAAAAAAIKW5c+euDhw4qI4dO2jhwkVG9/Xb29uradMmWrbsZ0lP77PPmjWLYX++fHmVJk0aw/ZHH32kwMAgzZw5y9DmkDNnvOqIjIyUhXnCFuA7c+a0ChQooEuXLiXoOCSOFLN6/59/7teOHTvl5eWlgwcPqX2HjpKkFi1avPSYWbPnqHCRYoafsuUqvK1yAQAAACBRfTdihFJZW2vVyhWqVKmScuTIrk8//VRrVq/SxYsXDffHHz58RJ2+7qiSH36oUqVK6ccfJygiIsJwnkv+l5QzZw591aiR8uTJo87fdFK9L+rFq4aAgACVLVtWDg4OypQxo+G2grhMc5mh5s2byXmAkwoXLqyCBQuqUaOGGjx40Gu9D0iYFBP6/yssLExeXl7Kly/fS/tERETo4cOHhp/Q0NC3WCEAAAAAJB5//0v6on4DXb5yRfN/mqu/jrpr1crlunjxor76qoke/TMdf8z3P+jq1WvatGmD5syZpZ9+mq+wsDDDeX7fvVsLFy7SuHE/aM/uXapQoYKmT58Rrxp+mj9fUdFRct3/h86cOaWc8Zgh4Orqqg4dO6l69U+0c8c2bfvtV3Xr2lWBgYGv90YgQcyy53CISe4iXoe1tbWOHDmklStWyiWeH9C0adPKx/u8ChcpFmuRCgAAAACmzyFnTg0Y4KRp01wUGBSU3OW8sYHOA9StW1e1btNWx44dT+5y3ltxfa6SO4emmCv9o0aOUOXKlZUrVy6VLVtGCxf8pHRp02rdP499AAAAAJ7387IlWrtm1Qv3lS9fTleDAlTyww/fclVvxsVlmpYsXpTcZeAdMmXqNI0cOVply5aN11R7vH9SzEJ+2bNn19w5s5UpU0bdunVbx48fV4OGXynIBL6dAwAAQOJbvXqtFi1aoJw5c8b6P2Orli115swZnT5zJkHntLKyivcj0YC3Ze26dcldAt5hKeZKf4+evVSufAXlzVdA5St8pK7duuvChQvJXRYAAADeUXv27tXNmzfV0tF44efUNjZq1KihVq9eowoVymvTxg3y870gj7+P6ofvxyh16tSGvkfdj6hfv75ycZkmr/NnNXnyRDk6ttD5c2dUp05tHTywX36+Plqw4CelTp1aLVo011H3Izp39rTG/vC9zM3//e+2nZ2dZsxw0bmzp+Xn66MVy39Rvnx5DfufnbdGjRpy3f+HLvh4aeWK5cqS5ekq7M4DnNTSsYXq1ftcV4MCdDUoQFWqVE7aNxFAipdiQj8AAAAQH3Z2dipZsqSKFi2iLVt+leN/Qn+Dhg1kZWWls+fOa9XKFdqxc6fq1K2rb3v0VMWKH2n8uLFG/Xt8213eXt6q98WXmj59piQpderU6vzNN+rRo5fatG2vqlWqaPGihapdq5bate+ovv36q23bNmrQ4EvDeaa7TFPpUqX0dafOatjoK5mZmWn58l9kafnv5NvUqVOrx7fd1KdvfzVt2lw5c+bQqJEjJEnzfpqvrVt/0x9//KnSZcqpdJly8vA4llRvIwATkWKm9wMAAABxKVqsqDr07KpIOxv5P7otc5nJ3j6PcufOrYYNG+i337ZJejq1f+fOXWrbtrU2b9miRYsWS3q6MvrIkaO1ceN6DR02XI8fP5b09PFnP82fbxinYsWPZG1traHDhuvy5cuSpG3bt6t5s2YqVbqsHj16pAsXLujIETdVrVpFW7f+pnz58urzzz9To68aG4J67z595PH3X6pX73Nt27Zd0tPFqocM/fe8S5f9LKf+/SRJjx49Ulh4uKytrRUSEvIW3lEApoDQDwAAgBTv0zq1VPvrFpp//qDCA/+95367/0kVzJtPw78fJZ8LFxQeFq5KlSqqdZu2+n7M/5Q3b141bdLE0N/MzEwWFhbKlSuXfH19JUknT52KNd6jR48MwVySbobcVEBAgOGRaZIUcjNEmT/ILEkqVLCQIiMjdfy4p2H/nTt35efnp0IFC770vMHBN5Q5c+Y3eWsAvOcI/QAAAEjRcuTIrnqdW8nlxJ4X7t94xk1DazTVgLEjdOnYWQUGBurgwUMyNzfXihUrtXjJ0ljHPL/w3/NB/pn/LuYXExOjJ0+e/KdNhnv6X7aqupmZmZ5/fnasRQJjYozWBQCAhOI3CAAAAFK0tl07acWFoy/dv/vCCUXFROuOVbTatW2jtWufrnR++vQZFS5SWJcuXYr1k9gr9Ptc8JGVlZXKlStraMuYMYPy58+foMWpIyMiZGHBf+EBxB+/MQAAAJCiZSmYRyGh91+6PywyQrsvnNBXH1aWXYYMWrduvSRpzty5qlC+vMaPG6sSJYorX768+qxuXY394ftEr9Hf/5J27fpdkydNVMWPPlLx4sU0a+ZMXbt+Xb//vjve5wkIDFSxYsVUoEB+ZcqY0WgRQAB4EUI/AAAAUixzc3NFmMW8st+Ws0dlZ5NG126FKOjqVUnS+fNeatqshfLly6fNmzZq9++7NGjwQAXfuJEktToNcNap06f1889L9dvWX2VmZqb27TvEui0gLitXrpKf30Xt3LFdZ86c0kcfVUiSWvFuypgxg06d9JSDg0Nyl/LOc3RsYXi05Zgxo5O7nGRllj2Hw6t/S5qItGnTysf7vAoXKaaHDx8mdzkAAABIBBNWzNf003vj1bdfoeoa3qVPEleEd5lDzpwaMMBJ06a5KPC5tRsSwtraWu07faviZWooIiaVrM0e69wJVy1f+pMiIiISueJ/jRo5QnZ2dnIeOCjWvowZM2jPnt3KkT27ihYrofv3n85+SZUqlX78cYJKlSypQoUKau/effqmc5d4jVe7di059e+vYsWKKSzskdzdj6pL127xrrdKlcrauGF9rPbq1T+Vr5+fJOmLL+qpb58+yps3j6ysrOTv76+f5i/Qxo2bXnre+LwmGxsbpUuXTosXLZDniRMaPXpMvOt+HXF9rpI7hzIfCAAAACmaxaMIWZiZKyomOs5+GVOn1YNrPOoOb8ba2lqTZy7TtnMO2r3n2doPqVXUoZkmz6ysQX2/TpLgb2Njo1atWqp9h44v3D91yhSdP3deObJnN2o3NzdXeHi4Fi9Zoi/r14/3ePXrf6HJkybpx4kTdfjwYZmZmalo0aKvVfvHn1TXgwf/ht1bt24Z/nz37l3NmDlLvr6+ioyMVJ06teUybapu3rwlV1fXF54vPq8pPDxc4eHhiohI3PU5UiJCPwAAAFK039dvUY3GH+uPK+fi7Nc4bxmtGj3tLVUFU9W+07fads5BXoHGYdIr8ImknGr3dXctWTAr0cetWfNTRUVF6dix47H2dejQXunTp5fL9OmqXbuW0b6wsDANGzZckvTRRx/JLn36V45lYWGh778fo7Fjx2r1mrWGdj+/i69V+82btwwzD/7Lzc3daHvx4iVybNFcFSt+9NLQ/zqv6X3GPf0AAABI0f7Y+4dKRadXoYzZXtqnWo5CCvMK0OXLl99iZTBFxcvUiBX4n/EKfKISZT9NknErV66kkydPxWovVKiQnPr3U79+/RUdHfdsl/gqWbKkcmTPrujoGO3+fac8j3toxfJfVLhw4dc637NzrF27WlWrVomz78cfV1OBAgV01P3lT+RAwhD6AQAAkKLFxMRopNMQ1YjOrG7Fqyt7uoyGfQUzZVPfD2vJ/uI9zfhxSjJWCVMREZPqjfa/rlwOuRQcHGzUZm1trblzZ+uHseMMC1Qmhjy5c0uSnJ2dNH3GTHXo2En37t3Tpo3rlSFDhnif58aNGxo4aLC6du2uLl27yc/votatXaNKlSoZ9UuXLp0u+Hjp8qWL+uXnZRoxYpQOHDyYaK/nfcf0fgAAAKR4T5480bjho5U1a1a1+rqd7HMWVkxMjC55++rHsYP14MGD5C4RJsLa7LGk1K/Yn/hsbGz0+LHxuYcNGyrfC77atGlzoo5lbv702vCMmbO0Y8dOSU+fPnHM4y81aPClVqxYGa/z+PldNLol4Nix48qZI7t6fNtdR4/+eyX/4cOHqvtZPdnaptHHH3+s0aNH6vKVy7Gm/uP1EPoBAABgMoKDgzVj4tTkLgMm7NwJVxV1aPbPPfzGijpY6qzn/iQZ9/bt27KzszNq+7haVRUtWlRffvl0MTszMzNJ0pnTJzVz5ixNmfp6a1gE33g6o+CCzwVDW0REhC5fvqKcOXO+1jmfOXbcU82aNjFqi4mJ0aVLlyRJZ8+eU6GCBdWnd29CfyIh9AMAAABAPC1f+pMmz6wsKadR8C/qYKkGxYM0qO/8JBn3zJmzatbMOCx36dpdNjY2hu0ypUvLxWWqmjRtpkuXXn/9ilOnTis8PFwFCuTXX3//LUmytLRUrlwOCgwMfO3zStKHH5ZQ8I0bcfYxMzOTtbX1G42DfxH6AQAAACCeIiIiNKjv12r3dXfVq/upImJSydrssc567tegvvOT5HF9krTf1VXDhg2RnZ2d7t27J0mxFqbMlOnpehYXLvgarZZfqFAhWVtbKWOGDLJNa6sSJYpLenpVXZLKlCmjmTNc5Niyta5fv66HDx9q+YoVch7orKtXrykwKFA9vv1WkrRt2/Z419ylS2cFBgTK28dbVlbWata0iRp8+aU6d+lq6NO7dy+dOnlKly5flrWVlWrVrqXmzZsZVueXpE5fd1S9L+qpZcvW8X5N+BehHwAAAAASICIi4p/H8iX+o/lexsvLSydPnVLDhg3ifU/9MyuW/6xcuXIZtvfs/l2SlCPn07bUqW1UsGBBWVr+Gw9/+GGcop5EaebM6bKxsZGnp6daOLYyfOEgSUfdj2jduvWaOs3lheNaW1lp5MgRypYtm8LDw+Xj46N27Tvojz/+NPRJkyaNxk8Yp+zZsis8PFx+fr7q07eftm79zdAnU6ZMypsnT4JeE/5llj2HQ0xyF/G2pE2bVj7e51W4SDE9fPgwucsBAAAA8JY55MypAQOcNG2aiwKDgpK7nASpVaumRo0coZq16igmJnljnI2Njc6eOa32HTroyBG3ZK0lLhvWr9PZc2c1evSYJB0nrs9VcudQHtkHAAAAACnAH3/8qeUrVip79mzJXYoqV66sw0cOv7OBv0mTxrrg46VKlSomdynJjun9AAAAAJBCLF68JLlLkCTt379f+/fvT+4yXmr37j3y9DwhSbr/3C0J7yNCPwAAAADApISGhio0NDS5y3gnML0fAAAAAAATRegHAAAAAMBEEfoBAAAAADBRhH4AAAAAAEwUoR8AAAAAABNF6AcAAAAAwEQR+gEAAAAgBciYMYNOnfSUg4NDcpfyznN0bKGrQQG6GhSgMWNGJ3c5yYrQDwAAAAAJYG1trX7dO2vjvMnaNneCNs6brH7dO8va2jpJx+3Tu7f27NmrwMBAQ9uzYPv8T/v27Qz7CxTIr/Xr1+rkieO66HdBbkcOafDgQbK0tIxzrL59+2jrr5vl5+uj8+fOvHbN1tbWGjJksP466ib/i746cviQWrVsadSnfv0vtP/PffK/6Kv9f+5TvXr14n3+vHnzysf7fKwat279TaXLlJOHh8dr124q4v6bBgAAAAAYWFtb6+eZE2V/bqce79krSbKR1MChiCrPnKiOfYcoIiIi0ce1sbFRq1Yt1b5Dx1j7+jsN0J9/7jdsP3jwwPDnyMgn2rB+o06fOa179+6rRPHimjx5oszNzfXjjxNfOp61lZV+27ZdHseOq3Wrli/t9yrzf5qnzPaZ5TxwkPz9Lylz5syytLQw7C9fvpx+mjdXkyZP0a6du1Tvi3qa/9NcNW7SVJ6eJ+I8t6WlpebOma2jR/9ShQrljfaFh4crPDxcERGRr127qSD0AwAAAEA89ejU/mngD/Q2ao8I9FZmSd9+3U4zFyxJ9HFr1vxUUVFROnbseKx99+/dV0hIyAuPu3Lliq5cuWLYDgoKUpXNVVSpYsU4x5sydZqkp9PkX9enn36qypUrqUrVj3X37l1JMpqlIEldu3TRgQMHNXv2HEnS7NlzVKVyZXXt0kU9e/WO8/xDBg+Sr5+vDh06HCv0419M7wcAAACAeKpepmiswP9MRKC3apQtliTjVq5cSSdPnnrhvrHjftCZ0ye1Y/s2tW/fTmZmZi89T968eVXz0xpyc3dPkjqf99lndXXq1Cn17PGtjnn8rYMHXTVq5AjZ2NgY+pQvX06uBw4YHbff1fWVIb5atapq0OBLDR8+IklqNyVc6QcAAACAeEoV8yTO/dav2P+6cjnkUnBwcKz2iZMm69ChQwoPD9fHH3+s0aNGKlOmTJoxY6ZRv62/btaHH34oGxsbLV+xQpMnT0mSOp+XJ3duffTRRwp//Fidu3RVpkwZNWH8OGXIkEEDnAdKkuzt7XUz5KbRcTdDbsre3v6l582YMYOmu0xT7z799PDhwyR9DaaA0A8AAAAA8fTYzFI2ceyPMEuaiGVjY6PHjx/Han8+3J89e06S5NS/X6zQ/22PnrK1TasSxYtrxMjv1OPb7po776ckqfUZc3NzxcRIvXv3Nawz8L8x32vhgvka/t0IhYeHS5JiYmKMDzR7QdtzJk+apM2bt+jo0aNJVrspYXo/AAAAAMTTgRNesnYo8sJ9qRyKyNXzfJKMe/v2bdnZ2b2y3/Fjx5U+fXplzpzZqP3q1Wu6cOGCtvz6q8aPnyBn5wEyN0/aOBh8I1jXr183WljwwgVfmZubK3v2bJKkkJAQ2WcxvqqfOXNm3bxpfPX/edWqVdW333bXlcv+unLZX1OnTJadnZ2uXPaP9WQAEPoBAAAAIN7mLV2um8W/iBX8rR2K6EbxL/TTshVJMu6ZM2dVuHChV/b78MMSCgsL1/3791/ax8zMTJaWlnHe+58Y/v7bQ9myZVWaNGkMbQXy51dUVJSuXbsuSTp27Liqf/KJ0XE1qleXh8exl563YaPGqvtZPcPP5ClT9eDBA9X9rJ527tqVNC8mBSP0AwAAAEA8RUREqGPfIfot3EHhdXsrus63Cq/bW7+FO+jrJHpcn/R0cbvChQsbXe2vW7eO2rRprSJFiihPnjxq07qVhgwZrJUrVxrqaNKksRo2bKCCBQsqd+7catDgSw0bNlRbt/6mqKgoSVKZMmV0wPVPZcuWzXDunDlyqESJ4sqZI4csLCxUokRxlShR3CjAv8rmzVt0584dubhMVaFChVSpUiWNGPmd1qxZa5jav2jxYtWoUV29evZQwQIF1KtnD33yycdauGiR4Tydvu6otWtXG7Z9fX3l7e1t+Ll+/bqio6Pl7e2te/fuvd4bbMK4px8AAAAAEiAiIkIzFyzRzFd3TTReXl46eeqUGjZsoBUrVkqSIiOf6OuOHfS/0aNkbm6uy5evaPKUqVq27GfDcVFRUerVs4fy588vMzMzBQYGatmyn7Vw4b+hOnVqGxUsWFCWlv/Gw4GDBqrlc4/r27P7d0lSs+Yt5Ob2dOX/o+5HtG7dek2d5vLCmh89eqRWrdpo7NjvtWvndt25c0dbf9umSZMmG/p4eBxTj569NGTwIA0aNFCXL1/Wtz16ytPzhKFPpkyZlDdPnjd4995vZtlzOLx8hQQTkzZtWvl4n1fhIsVY5REAAAB4DznkzKkBA5w0bZqLAoOCkrucBKlVq6ZGjRyhmrXqxLnQ3dtgY2Ojs2dOq32HDjpyxC1Za4nLhvXrdPbcWY0ePSZJx4nrc5XcOZTp/QAAAACQAvzxx59avmKlYRG85FS5cmUdPnL4nQ38TZo01gUfL1WqVDG5S0l2TO8HAAAAgBRi8eIlyV2CJGn//v3av39/cpfxUrt37zHcInD/Pb/Pn9APAAAAADApoaGhCg0NTe4y3glM7wcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATBShHwAAAAAAE0XoBwAAAADARBH6AQAAAAAwUYR+AAAAAEgBMmbMoFMnPeXg4JDcpbzzHB1b6GpQgK4GBWjMmNHJXU6yIvQDAAAAQAJYW1urV+euWj5lplZNcNHyKTPVq3NXWVtbJ+m4fXr31p49exUYGGjU7ujYQnv37NZFvws64XlM48b+YLS/aNGi2rhhvfx8L+iYx99y6t8v3mNaW1trz+5duhoUoBIliieoXheXaYbg/fzPn3/sNerXpUtnHTywX36+F+Tx91H973+jlSpVqniNkTdvXvl4n9f5c2eM2rdu/U2ly5STh4dHgmo2RZbJXQAAAAAApBTW1tZaMGmaHm3/S1f2bDC0lyiSVwsmTVO3wQMUERGR6OPa2NioVauWat+ho1F7t25d1b1bN40dO07HPT2VKlUq5cmT27A/bdq0WrN6pY4ccVP9Lxsof/58mu4yTY/CwjR//oJXjjviu+G6fj1YJUqUSHDNo0aN1vjxEwzblpYW2rNnt7Zt225oa9KksYYPGypn50H628NDBfLnl4vLNEnS//43Js7zW1paau6c2Tp69C9VqFDeaF94eLjCw8MVERGZ4LpNDaEfAAAAAOKpa/uOerT9L93xvmTU/my7a7sOmrNkUaKPW7Pmp4qKitKxY8cNbXZ2dhoyeJA6ft1Jhw4dNrT7+PgY/ty0aROlSpVK/Z2efhnh7e2tAvnzq1vXrq8M/TVrfqoaNaqrS9fuql27VoJrfvDggR48eGDYrvf558pgZ6c1a9cZ2iqUL6+/PTy0ecsWSVJgYKC2/PqrypYp88rzDxk8SL5+vjp06HCs0I9/Mb0fAAAAAOKpcrGSsQL/M3e8L6ly8VJJM27lSjp58pRRW/Xqn8jMzEzZsmWT6/4/5OHxl376aa5y5Mhu6FO+fDm5ux81mn2wf7+rsmfPply5cr10vMyZM2vy5Enq07e/wsLCEuU1tG7dUgcPHlJQUJCh7a+//lapkiVV5p+Qnzt3btWuVVP79u2L81zVqlVVgwZfavjwEYlSmykj9AMAAABAPFlERr3R/teVyyGXgoODjdry5M4tc3Nz9e3TW6NGj1G3bt2VMUMGrVm9SlZWVpKkLPZZFBISYnRcyM2bT/dlsX/peNNdpmn58hU6derUS/skRJYsWVSzZk2tWr3aqP3XrVs1efIUbdm8UZcvXZS722EdPuKm2XPmvvRcGTNm0HSXaerv5KyHDx8mSn2mjNAPAAAAAPEUZWXxRvtfl42NjR4/fmzUZmZuLmtra40cOVqurq46ftxTPXr2Vr58+VS1alVDvxjFGB9nZva0Pca4/ZnO33RSunRpNWvW7ESr39Gxhe7fv69du343aq9SpbL69u2j4cO/0+f16uubzl1Vt05t9Y9jscHJkyZp8+YtOnr0aKLVZ8q4px8AAAAA4sn9/GmVKJL3hVP8MxbNK/dziXNl/L9u374tOzs7o7YbwTckST4XfIz63b59Wzlz5njaJ+SGsthnMTou8wcfSJJCQm6+cKxq1aqpXLlyuuTvZ9S+c8d2bdq8Wf37D0hw/a1atdSGjZsUGWm8sN7gQYO0ceMmrVq9RpLk5eWlNGlSa/KkiZoxY+YLv5ioVq2qPvusrr79trukp19iWFhY6Mplfw0ePFRr1q5NcH2mjNAPAAAAAPG0cPnPWjDp6eryzwf/jEXyKnX9j7RwsHOSjHvmzFk1a9bEqO1vj78lSQUKFNC1a9clSRkyZFCmTJkUFPj0vvljx45r6JDBsrKyMgTuGjWq69q16woICHjhWCNHjtLESZMN29myZtXq1Sv1bY+e8vT0THDtVapUVv58+bT6n2D/vNSpbRQdHW3UFh0VLclMZmZmLwz9DRs1loXFvzMqPv/8M/Xq2UONvmqi69evJ7g+U8f0fgAAAACIp4iICHUbPEBnc1krd9/mytejiXL3ba6zuazVfbBzkjyuT5L2u7qqcOHCRlf7L170165dv+v7MWNUoUJ5FSlSRDOmT5Ovr68OHzkiSdq8eYsiIiI03WWaihQponr16qlPn95asHCh4TxlypTRAdc/lS1bNklS0NWr8vb2Nvz4XbwoSbp8+bLhy4WEaN26lY4dPy5vb+9Y+/bs2asOHdrrq0aNlCtXLlX/5BMNGjRQe/bsMXwZ0Onrjlq79t+1AHx9fY3qu379uqKjo+Xt7a179+4luD5Tx5V+AAAAAEiAiIgIzVmySHPe4pheXl46eeqUGjZsoBUrVhra+/brrzH/G61ffl6m6JgYubu5q2279nry5Imkp4/Na9W6rcaPG6udO7bp3r17WrBgodHj+lKntlHBggVlaZmweHjU/YjWrVuvqdNcXtonXbp0+rJ+fY0cNfqF+6f/M4V/8OBBypYtm27fvqU9e/bqx4mTDH0yZcqkvHnyJKg2/Mssew6HF6/eYILSpk0rH+/zKlykGKs8AgAAAO8hh5w5NWCAk6ZNc1Hgc4+OSwlq1aqpUSNHqGatOi9dhO9tsbGx0dkzp9W+QwcdOeKWrLXEZcP6dTp77qxGjx6TpOPE9blK7hzK9H4AAAAASAH++ONPLV+xUtmzZ0vuUlS5cmUdPnL4nQ38TZo01gUfL1WqVDG5S0l2TO8HAAAAgBRi8eIlyV2CJGn//v3av39/cpfxUrt375Gn5wlJ0v33/D5/Qj8AAAAAwKSEhoYqNDQ0uct4JzC9HwAAAAAAE0XoBwAAAADARBH6AQAAAAAwUYR+AAAAAABMFKEfAAAAAAATRegHAAAAAMBEEfoBAAAAIAXImDGDTp30lIODQ3KX8s5zdGyhq0EBuhoUoDFjRid3OcmK0A8AAAAACWBtba3OPbtp8tJ5Gv/zXE1eOk+de3aTtbV1ko7bp3dv7dmzV4GBgZKMg+1/fz744APDcTVq1NBvv/0qH+/zOn3qhBYumK9cuXLFa0xra2vt2b1LV4MCVKJE8QTX/HXHjnLd/4f8fC/o4IH9at68mdH+L76op507tuv8uTPyveCtPbt3qVmzpq88b9GiRbVxw3r5+V7QMY+/5dS/n9H+rVt/U+ky5eTh4ZHgmk2NZXIXAAAAAAAphbW1tSbNm6Ett721zesPQ3uxfDk1ad4MDe7RTxEREYk+ro2NjVq1aqn2HToa2rZu/U1//rnfqN90l2lKlSqVbt26JUnKnTu3li5ZpAULF6l3775Kny6d/ve/0Vq8aIE++/yLV4474rvhun49WCVKlEhwzR06tNewYUM0aPAQnThxUmXLlNHkyRN179497dmzV5J09+5dzZg5S76+voqMjFSdOrXlMm2qbt68JVdX1xeeN23atFqzeqWOHHFT/S8bKH/+fJruMk2PwsI0f/4CSVJ4eLjCw8MVERGZ4LpNDaEfAAAAAOKpfZevteW2t87fDDJqf7bdrnNHLZm3MNHHrVnzU0VFRenYseOGtmfB9plMmTKpWrWqch44yNBWsuSHsrCw0MSJkxQTEyNJ+mn+fC1dsliWlpZ68uRJnGPWqFFdXbp2V+3atRJcc/NmTbVixUpt3fqbJOnKlSsqV76sevXsYQj9bm7uRscsXrxEji2aq2LFj14a+ps2baJUqVKpv9MARUREyNvbWwXy51e3rl0NoR//Yno/AAAAAMRT0Y/Kxgr8z5y/GaRiFcslybiVK1fSyZOn4uzTokVzhYWFafv2HYa2kydPKSoqSq1aOsrc3Fzp0qVTs2ZN5ep6IM7AnzlzZk2ePEl9+vZXWFjYa9VsbW2t8MePjdrCw8JVpkwZWVq++Przxx9XU4ECBXTU/ehLz1u+fDm5ux81mlGxf7+rsmfPFu/bFt4nhH4AAAAAiKdI85g32v+6cjnkUnBwcJx9WrV01OYtvxpd/Q8MDFTrNu00dOgQXfL3k7fXOeXInl09evaK81zTXaZp+fIVOnUq7i8a4rLf9YDatG6lkiVLSpJKlSqlVq1aytraWpkyZTL0S5cunS74eOnypYv65edlGjFilA4cPPjS82axz6KQkBCjtpCbN5/uy2L/2vWaKkI/AAAAAMSTVbTZG+1/XTY2Nnr8n6vmzytfvpyKFCmi1avXGLXb29trypRJWr9+g+rXb6AmTZsrIiJSCxf89NJzdf6mk9KlS6tZs2a/Uc3Tp8/Qn3/u17bfftWVy/5aumSx1q1bL0mKiooy9Hv48KHqflZP9b9soImTJmv06JGqUqVynOeOkfGXK2ZmT9/3Z7cw4F/c0w8AAAAA8eT1t6eK5cv5win+xTPn1Pm/jr/gqDd3+/Zt2dnZvXR/m9atdebMGZ0+fdqo/euvO+rhg4caO268oa1P37465vG3ypUrq+PHPWOdq1q1aipXrpwu+fsZte/csV2bNm9W//4D4lVzeHi4BjgP1OAhQ2Vvn1nBwTfUrl1bPXjwQLdv3zb0i4mJ0aVLlyRJZ8+eU6GCBdWnd+9Y9/s/cyPkhrLYZzFqy/zP0wpCQm7Gq7b3CVf6AQAAACCeli9apsaZiqhY5pxG7cUy59RXmYpoxeKfk2TcM2fOqnDhQi/clyZNGjVs2CDWVX5JSp06tdFVdUmKioqWJJmbvzgOjhw5SnXqfq66n9VT3c/qqX37p08M+LZHT02cOCnBtT958kTXrl1XdHS0vmrUSHv37ovziryZmVmcjz88duy4KlWqKCsrK0NbjRrVde3adQUEBCS4PlNH6AcAAACAeIqIiNDgHv2U/2KoBhatpX7Fa2pg0VrKfzE0yR7XJ0n7XV1VuHDhF17t/6pRQ1lYWGrT5i2x9u3bt09lypSWU/9+ypcvr0p++KFcpk1VQECAzpw5K0kqU6aMDrj+qWzZskmSgq5elbe3t+HH7+JFSdLly5d17dr1eNecP38+NW3aRPny5VWZMmU0b+4cFSlaRBN+nGjo07t3L1X/5BPlzp1bBQsUULduXdW8eTNt2rTJ0KfT1x21du1qw/bmzVsUERGh6S7TVKRIEdWrV099+vTWgoWJ/9QEU8D0fgAAAABIgIiIiKeP5Zv39sb08vLSyVOn1LBhA61YsdJoX+vWrbRz507du3cv1nGHDx9Rr1591LPnt+rZs4fCwsJ07NhxtW3b3rDgX+rUNipYsOBLV9R/maPuR7Ru3XpNnebywv3m5hb6tns3FShQQJGRkTpyxE1ffdVYgYGBhj5p0qTR+AnjlD1bdoWHh8vPz1d9+vYzPOZPevoowrx58hi2Hzx4oFat22r8uLHauWOb7t27pwULFvK4vpcg9AMAAABACjB9+gyNGjlCK1euMpoe3+irJnEe9+vWrfp169aX7ndzc1eOnC9/1F1gYGCs/TY2Nsqc2V5u7i++716SfH199dnnX8RZ26RJkzVp0uQ4+0yd5hLriwUvLy81bdY8zuPwFNP7AQAAACAF+OOPP7V8xUplz54tuUtR5cqVdfjIYR054pbcpbxQkyaNdcHHS5UqVUzuUpIdV/oBAAAAIIVYvHhJcpcgSdq/f7/279+f3GW81O7de+TpeUKSdP8Ftz28Twj9AAAAAACTEhoaqtDQ0OQu453A9H4AAAAAAEwUoR8AAAAAABNF6AcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATBShHwAAAAAAE0XoBwAAAADARBH6AQAAACABrK2t1b1LL82ZulyzJ67WnKnL1b1LL1lbWyfpuC4u03Q1KEC9e/U0aq/3+ee6GhQQq//BA/t1yd9P2bJlS9K68G4j9AMAAABAPFlbW2vG5PmKDiqrrXNv6LcFV7V17g1FB5XVjMnzkzz4h4WFq2fPHrKzs4uzX8WPPlKqVKm0bdt2OTq2SNKa8G4j9AMAAABAPHXq0FXHd0Xpis89o/YrPvd0fFeUvm7fJUnHP3TooEJCQtSnd684+7Vu3UqbN2/Rho2b1KplyyStCe82Qj8AAAAAxFOpYpVjBf5nrvjcU+niVZJ0/KioaE34cZI6deqk7NlfPG3f1tZWDRp8qY2bNuvAgQNKkya1qlZN2rrw7iL0AwAAAEA8xTyxfMV+iySvYdeuXTp77qwGOju/cP9XXzWSv7+/fHx8FB0drV+3blXrVq2SvC68mwj9AAAAABBPZpZPXrE/6q3UMW7cBLVo0VyFChWKta9161bauGmzYXvjxs2qX/8LpU+f/q3UhncLoR8AAAAA4unUeXflLvziRfTyFLHTyXNub6WOo0ePar+rq4YNHWLUXqhQIZUvV04jvhuuK5f9deWyv7b99qtSp06txo2/eiu14d0S99wUAAAAAIDB0l8Wasbk+ZLsjO7tz13YTmU/t1C/QYveWi3jx/+oPbt36eLFi4a21q1byc3NXcO/G2HUt3mzpmrdqpV++WX5W6sP7wZCPwAAAADEU0REhPoN6q6v23dRozpVFPPEQmaWUTp5zk39Bi1SRETEW6vFy8tLmzZvVqdOnSRJlpaWat6sqSZPmSpvb2+jvqtWr1avXj1VvHgxnTt3/q3ViOSXYqf39+7dS1eDAjRmzOjkLgUAAADAeyQiIkILFs9VL+f26j2kjXo5t9eCxXPfauB/ZtKkKTIze/rnzz6rq4wZM2rnzl2x+vn7X9K5c+dZ0O89lCKv9JcuXVrt2rbR2XPnkrsUAAAAAHgrnJwGxGoLCgpS/gL/LuaXK3felx5fp+5nSVEW3nEp7kp/mjRpNHv2TA0aPET37r74+ZjPWFtbK23atIYfW1vbt1QlAAAAAADJL8WF/vHjx2rfvj908OChV/bt07uXfLzPG348j3u8hQoBAAAAAHg3pKjp/V81aqSSH5ZU/S8bxKv/rNlzNH/BQsO2ra0twR8AAAAA8N5IMaE/R47s+v77/6l1m7Z6/PhxvI6JiIhIlsU0AAAAAAB4F6SY0F+qZCnZ29tr184dhjZLS0tVrlxJnb7+WnnzFVB0dHQyVggAAAAAwLslxYT+g4cOqWatOkZtLtOmytfPV3PmzCPwAwAAAADwHykm9IeGhsrb29uo7dGjR7pz506sdgAAAAAAkAJX7wcAAAAAAPGTokN/8xaOGj16THKXAQAAAADvJOcBTtqze1eCjjnqfkRdunROoorejIvLNC1ZvOitj7th/TqNGTP6rY+bGFJ06AcAAACA902FCuUVcOWSVq5Y/lbGuxoUoHqff27YLliggK4GBahs2TJG/bb9tlX+F32V2sbG0GZlZSU/Xx+1bdvmrdSK2Aj9AAAAAJCCtGrZUkuWLlXFih8pZ44cb318Xz8/Xb8erGpVqxra0qRJow8/LKFbt26pwkcVDO1ly5ZV6tSpdeTIkbdeJ54i9AMAAABACpE6dWo1bNhAv/yyXHv37pOjYwuj/b179dTJE8fl431eU6dMVqpUqYz2v2ia+pLFi+TiMu2F4x11fxrWlyxZpKtBAYZtNzc3ValSxdCvUqWKunjRX7t37zFqr1q1iq5euyZ//0uSpJaOjnLd/4cu+l3QAdc/1bFjB6PxsmXLpp/mzdW5s6d15swpLV2yWA4ODi99P0qWLKlTJz3Vt28fSVK6dOk0aeKPOnXSU95e57Ru3RoVL17M0P/Z7Q7NmjXVUfcj8jp/VvPmzpGtra3Rezxjhosu+HjJ87iHunfv9tLxUwJCPwAAAACkEF81aiQ/v4vy87uojZs2qWVLR8O+hg0byNl5gCZOnKQv6n+p4Bs3YoXqhPqifgNJUn+nASpdppxh+/CRI6pY8SNZWFhIkqpVrSo3Nze5uburWlXj0P/sKn+bNq01ZMhg/Thxkmp8WksTfpyoQYMGqkWL5pKk1DY2Wr9+rUJDQ9W0WQs1btxUoaGhWrVyuaysrGLVVqVKZa1bu1pTpk7TzJmzJEnLf1mmLFmyqF37jqr3RX2dOX1G69auUYYMGQzH5cmTR/U+/1wdOnZSh46dVLlyJfXu3cuwf+TIEapWtao6d+6q1m3aqWqVyipVquQbvY/JidAPAAAAAClE69YttXHTJknSn3/ul62trT755GNJUtcunbVm7TqtWr1Gfn4XNWnSZPlcuPBG492+fVuSdP/efYWEhBi23dzcZGtrqzJlSkuSqlSpIjd3d7m5uatUqVJKbWMjKysrlS9XTkcOu0mSnPr30/ff/6CdO3cpICBAO3fu0sKFi9S+XVtJ0ldffaWY6Gg5DxwkLy8v+fr6ymmAs3LmzKmqz80ekKTPP/tMy5Yu0bBh3+mXX56ubVCtWlUVLVpU3bp/q1OnTsnf/5K+/2Gs7t27ry+/rG841tzcXP2dBsjb21t//fWXNm7cpI8/ribp6W0KrVu11Pc/jNWBgwfl5eWlfv0HGL7cSIksk7sAAAAAAMCrFSiQX2XKlFHnLk+nm0dFRWnr1t/UqmVLHTx4SAULFtQvy1cYHXPs2HGjK++Jxd//kq5evaoqVarI29tHH35YQm5u7rp165YCAgL00Ucf6XFEhFKnTq1Dhw8rU6ZMypkzp6ZOnazJkycazmNhYaEHDx5IkkqVKqm8efPqgo+X0VipUqVSnrx5pANPt8uWLaM6dWqrW/ce2rXr3ycTlCpZUra2tjp75pTR8TY2NsqbJ49hOyAgQKGhoYbt4Bs3lPmDDyRJefPmUapUqXTM45hh/927d+Xn5/eG71jyIfQDAAAAQArQulUrWVlZ6fixvw1tZmZmioyMlJ2dXbzOER0TLTOZGbVZWr1eLDzi5qZqVavq/Pnz8vf3161btyRJbu7uqlqtqiIeP1ZAQIACAwOVOXNmSdLAQYPl6XnC6DxRUVFPX4u5uU6dOq3effrGGuvZuSXp8uXLunPnrlq3ctS+ffsUGRkp6ekV/OAbN9S8uWOs4+/fu2f485MnT4z2xcTEyNz86SR4MzPj98YUML0fAAAAAN5xFhYWat68mf435nvV/aye4adO3c8VFBSkpk0ay9fXV+XKlTM6rly5skbbt2/dVpasWQzb5ubmKlqkSJxjR0REyNwidnQ8cthNFSqUV/Xqn+iIm7uh3d3tqKpWqawqVavo8OGn9/PfvHlTV69dU548eXTp0iWjn4CAAEnS6dOnlS9fPt28eTNWn2ezASTp9u07auHYUnny5NFP8+bK0tLyn+PPKIu9vZ48eRLr+Nt37sTnbZa//yVFRESoXPl/30c7Ozvlz58/Xse/iwj9AAAAAPCOq1unjuzs7LR69Rp5e3sb/WzbvkOtW7fSosVL1Kqlo1q1bKn8+fNpoPMAFSlc2Og8hw4fVp3atVW7di0VLFBAEyaMU/r06eMcOyAwUB9//LHs7e2NZhQcPnJEtra2atWypdzd/w39bu5P7+svV7acDj/3qL5pU6epT+9e6tz5G+XPn09FixZVS0dHdevWVZK0edNm3b5zW0uXLlbFihWVK1cuVa5cWd+P+Z+yZ89mVNOtW7fUwrGVChYsoLlzZ8vCwkIHDh7UsWPHtXTJItWoUUMODg6qUKG8Bg8epFKlSsXrfX706JFWr1mrkSO+08cfV1ORIkU03WWaoqOj43X8u4jQDwAAAADvuNatW+rgoUNGV7yf2bF9hz788EP5X/SXy/QZ+u67Ydq1c4dyOjgYFrl7Zs2atVq3foNmzJiujRvXK+BKgI4ccYtz7O+//0HVq38ij7+PavfvOw3tAQEBCggIULp06eT23JX+69evKyjoqlKntjGs3C9Jq1av0cCBg9XSsYX27d2jjRvWy9Gxha5ceXqlPyw8XE2bNldQUJAWL1og1/1/aNrUKbKxsdGDBw9j1RUSEqIWjq1UrGhRzZk9S+bm5mrXvoPc3Y9q2tQpOnTQVfPmzlEuBwfdvBkSvzda0g8/jJX70aNatnSJ1q5Zpb/++kunTp2O9/HvGrPsORxikruItyVt2rTy8T6vwkWK6eHD2B8aAAAAAKbNIWdODRjgpGnTXBQYFJTc5cBExPW5Su4cypV+AAAAAABMFKEfAAAAAAATRegHAAAAAMBEEfoBAAAAADBRhH4AAAAAAEwUoR8AAAAAABNF6AcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATBShHwAAAAAAE0XoBwAAAIB3nPMAJ10NCjD6OeF5LFaf48c85Od7QRvWr1PhwoWTqVq8SyyTuwAAAAAASInKli2jAvnzy+/iRXl6nkjy8by8vNWyVWvDdlRUlOHPvXr2ULduXdXfaYAuXvRX/359tWb1Kn1SvYZCQ0OTvDa8u7jSDwAAAAAJkD17Ns2aOV0VypfT7du3VKF8Oc2aOV3ZsmVL0nGjop4oJCTE8HP79m3Dvi5dOmvmzFnauXOXvL291a+/k1KntlGTJo2TtCa8+wj9AAAAAJAAw4cN1cyZM7V9+3Z5eXlp+/btmjlzpr4bPjRJx82XL5+OH/OQu9thzZs7R7lz55Yk5c6dW1mzZpWr6wFD34iICLm7H1WFCuWTtCa8+wj9AAAAABBPZcuW0alTp2JNmQ8NDdXp06dVpkyZJBn3uKen+vbrrzZt22nQ4CGyt7fX1l83K2PGDMqSxV6SFHLzptExISEhymKfJUnqQcrBPf0AAAAAEE8F8ueXn5/fC/f5+fkpf/58OnHiRKKP++ef+w1/9vKSPDyOye3IIbVo0ULHjx+XJMXExBgdY2ZmFqsN7x+u9AMAAABAPPldvKgCBQq8cF+BAgV08aL/W6kjLCxMXl5eypcvn27cCJEkZbG3N+qTOXNmhdwMeSv14N1F6AcAAACAePL0PKFSpUrJ1tbWqN3W1lYlS5ZMkqv8L2Jtba2ChQrpRnCwrly5ouDgYFWv/olhv5WVlSpXriQPj2NxnAXvA6b3AwAAAEACjBv/o74bPlSnT5+Wn5+fChQooJIlS2rc+B+TbMxRI0do9569CgoKUubMH6h/v75Klzat1q3fIElatGix+vTprYv+l+Tv76++fXorLCxcmzdvSbKakDIQ+gEAAAAgAa5fv64+ffurTJkyyp8/n/76+5gWLFycpGNmz55dc+fMVqZMGXXr1m0dP35cDRp+paCgIEnSnLnzZGNjownjx8rOzk6enifUuk3bWAsO4v1D6AcAAACA13DixIm3Np2/R89er+wzdZqLpk5zeQvVICXhnn4AAAAAAEwUoR8AAAAAABNF6AcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATBShHwAAAAAAE0XoBwAAAADARBH6AQAAAAAwUYR+AAAAAABMFKEfAAAAAAATRegHAAAAgNdkZmb21sbKli2bZs2coTNnTsnP10d7du9SyZIljfo4D3DS8WMe8vO9oA3r16lw4cJvrT68myyTuwAAAAAASEnSpk2r7t26KkeO7IqIjFQqa2sFBV3V/AUL9fDhwyQZ087OTr9u2aQjR9zUrl0H3bx5U3nz5tH9+/cNfXr17KFu3bqqv9MAXbzor/79+mrN6lX6pHoNhYaGJkldePcR+gEAAAAgntKmTatJEydo6dKlCgoKMrTnzJlTkyZO0OAhw5Ik+Pfq2UNXr16T0wBnQ1tgYKBRny5dOmvmzFnauXOXJKlffyedPHFcTZo01ooVKxO9JqQMTO8HAAAAgHjq3q1rrMAvSUFBQVq6dKm6d+uaJON+9lldnTx1SvPnz9Opk57a/ftOtWnT2rA/d+7cypo1q1xdDxjaIiIi5O5+VBUqlE+SmpAyEPoBAAAAIJ5y5MgeK/A/ExQUpBw5sifJuLlz51aH9u3k739Jbdq00y/LV+iH779X8+bNJElZsthLkkJu3jQ6LiQkRFnssyRJTUgZmN4PAAAAAPFgZmamiMjIOPtEREbKzMxMMTExiTq2ubm5Tp06pR9/nChJOnP2rIoULqwOHdprw4aNhn7/HTcpakHKwpV+AAAAAIiHmJgYWVtZxdnH2soqSUL2jRs35ONzwajtgq+vcubI+c/+EElSFnt7oz6ZM2dWyM2QRK8HKQehHwAAAADi6erVa8qZM+cL9+XMmVNXr15LknH//ttDBQoUMGrLnz+/goKeLuZ35coVBQcHq3r1Twz7raysVLlyJXl4HEuSmpAyEPoBAAAAIJ7mL1ioTp06xQr+OXPmVKdOnTR/wcIkGXfBwkUqV66s+vTprbx586pJ48Zq17aNli772dBn0aLF6tOnt+rVq6ciRYpouss0hYWFa/PmLUlSE1IG7ukHAAAAgHh6+PChBg8Zpu7duipHjuyKiIxUKmtrBQVdTbLH9UnSyZMn1blLVw0bOlRO/fspICBAo0b/zyjQz5k7TzY2Npowfqzs7Ozk6XlCrdu0VWhoaJLUhJSB0A8AAAAACfDw4UNNneYi6e0ulLd37z7t3bsvzj5Tp7kYagMkpvcDAAAAwGtjZXy86wj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAScjFZZqWLF4UZ5+j7kfUpUvnt1QRgPcJoR8AAACxuLhM09WgAPXu1dOovd7nn+tqUMBbreVqUIDqff55rPb4hOmU4ov6DbRyxcrkLgOACSL0AwAA4IXCwsLVs2cP2dnZJXcpJu/27dsKCw9/6X5LS560DeD1EPoBAADwQocOHVRISIj69O4VZ78KFcpr08YN8vO9II+/j+qH78coderUkqRvOn2tfXv3GPo+mynwdceOhrZVK1do2NAhb1zvp59+qi2bN+r8uTM6c+aUfv55qfLkyWPY7+DgoKtBAWrYsIE2b9ooP98L2rF9m/Lnz6fSpUtr547tuuDjpZUrlitTpkyG457NKBjg1F+nTnrK2+ucJk6cICsrK0OfL7+sr31798jP94LOnDmltWtWGd6DZ77t3l2exz105swpjR831ijI/3d6/9WgALVv305LlyyW7wVv9e/XV5JUt24d7dq5XRf9LsjtyCENcOovCwuLN37vAJguQj8AAABeKCoqWhN+nKROnTope/ZsL+xTtGhRrVq5Qjt27lSdunX1bY+eqljxI40fN1aSdMTNXUWKFFamjBklSZWrVNatW7dUuUolSZKFhYUqVCgvN/ejb1xvmjSpNX/BQtX/soFatmylmOgYLV60UGZmZkb9BjoP0PQZM/V5vS/0JOqJ5s6ZrREjhmvUqNFq0qSZ8uTNo0GDBhod8/HH1VSoUCE1b9FSPXv11hf16mnAACdJUpYsWTR3zmytWbtWNT6tqebNHbVj5y6jcatWraI8efOoRYuW6t/fSY6OLeTo2CLO1zPQeYB+/323atWuq9Vr1qpGjRqaNXOGFi9Zqk9r1taQIcPk6NhC/fr2eeP3DoDpYp4QAAAAJEnVqlRRg1p1ZG1ppUL58+vWnTvatWuXzp47q4HOznIeOCjWMT16dNfmLVu0aNFiSZK//yWNHDlaGzeu19Bhw+Xl5aU7d+6ocpXK2rFjp6pWqaz58xeoa9cukqQyZUorVapU+uuvv+Ksbc6c2YqOjjJqs7a21r59fxi2d+zYabR/gPNAnTl9UoULF5a3t7eh/aef5svV1VWStHjREs2bN0ctHFvqbw8PSdKa1WtiBfKIyEgNGOCssPBw+fj4aPKUqRo54jtNmjRZWbNkkZWVlXbs2KmgoCBJkpeXl9Hx9+7d03ffjVB0dLR8/fy0d98+ffLxx1q1avVLX/PmLb9qzdq1hu1ZM2do9py5Wr9+gyTpypUrmjR5ikZ8952muUyP8/0D8P4i9AMAALznihctqtG9nRR2/IKC17vrcUSEMhcvrdxFi6vH199o3LgJWr9ujX6avyDWsaVKllTevHnVtEkTQ5uZmZksLCyUK1cu+fr6yt39qKpWqaJDhw6rcOHC+mX5Cn37bXcVLFhQVapU0enTZ/To0aM4a/zfmDE6ePCQUdt3w4cZTW3PkyePBg8aqHLlyipTpkwyN386qTVnzhxGof/c+X8DecjNEEnS+efbQm7qgw8yG4117tw5o3vujx07prRp0ypHjhw6e+6cDh48qD/27dF+V1e5uh7Q9u07dO/ePUN/bx8fRUdHG7ZvBN9Q0WJF43zNJ0+eNNouVaqkSpcubXRl39zcQqlT2yi1jU2cawIg8eXMkUPNmzdTZvvMuhlyUxs2bFTQ1atJOuZR9yPKlStXrPZly37W8O9GSJKcBzipbdu2srOzk6enp4Z/N0I+Pj5JWhfebYR+AACA91j+fHn1Q/d+8p66RjFR/15Jf3zvoWIuXtVHMRmk4iW039VVw4YO0bp1642ONzc314oVK7V4ydJY53521dvNzU1t27ZVpUoVde7ced2/f1/u7kdVpUplVa1SRW5ubq+sM+RGiC5dumTU9jA0VHbp0xu2f162RFevXtOgwUN0/XqwzM3Ntf/PfbK2sjY67smTSMOfY2Ji/ml78m+bYmRubnxLwEvFxCg6OlotW7XRRxUqqEaN6vqmUycNHTJYXzZopICAp086eBL5xPgwxcjcLO47bcMehRltm5mZa+rUqdqxc1esvuGPH8evXrwxS0tLDR82VJFPIrVzxw7duHFDWbJkUYeO7WVlaaXxE340+jwlpi/qNzD6oqto0SJau2a1ftu2TZLUq2cPdevWVf2dBujiRX/179dXa1av0ifVayg0NDRJasK7j3v6AQAA3mPOXXvId94mo8D/vGt/eqhGkdJycZmhunXrqEKF8kb7T58+o8JFCuvSpUuxfiIjn4brZ/f1f/llfUPAd3N31yeffPzP/fzub/w6MmbMoMKFC2v6jJk6dOiwfH19lSERnzpQvHhx2djYGLbLlSunhw8f6uq1a4a2vz08NGXqNH32eT1FREbqiy/qJdr4knTmzGkVKFDghe/1sy8vkPSGDxuqgwcP6Odly3Tjxg1J0o0bN/TzsmU6ePBAoixK+TK3b99WSEiI4adOndry978kN7en/4a6dOmsmTNnaefOXfL29la//k5KndpGTZo0TrKa8O4j9AMAALynMmTIoExRlnoSHvdV4tt7PVShZClt2rxZnTp1Mto3Z+5cVShfXuPHjVWJEsWVL19efVa3rsb+8L2hz7P7+ps2aaIjz0K/m5vqff65bGxs9Ndff7/xa7l7955u376tdu3aKG/evKpWrapGjx71xud9xtrKSlOnTFahQoVUs+anGug8QEuXLlNMTIzKli2jPn16q1SpUsqZI4fq1/9CH2TKpAsXLiTa+JI0zWWGmjdvJucBTipcuLAKFiyoRo0aavDg2GstIGnkzJlTkU8ijW4XeZ63t7eioqOUM0eOJK/FyspKzZo2Naz7kDt3bmXNmlWurgcMfSIiIuTufjTWl3V4vxD6AQAA3lN58+bVY/9rr+x358IVlShYWJMmTdF/FsLX+fNeatqshfLly6fNmzZq9++7NGjwQAX/cwX0mWdX848efbpg37lz53X/wQOdOXNWDx8+fOPXEhMTox49e6lUyZL6Y98e/e9/o/XD2HFvfN5nDh06LH9/f23etEHzf5qnPXv2auo0F0nSgwcPVblSJa1Y/rMOHnTVkMGD9P33P+jPP/cn2viS5Orqqg4dO6l69U+0c8c2bfvtV3Xr2lWBgYGJOg5ernmzptq5Y0ecfXbu2KHmzZsleS316n2u9OnTG265yZLFXpIUcvOmUb+QkBBlsc+S5PXg3cU9/QAAAO+pyMgImVm9+L+Dh8bON/zZ3MJcUVFRCgoKUv4ChWL1PXnypFq3aRvnWN26fRur7cMPS8Wrzhw5Yy9cJklOTgOMtg8ePKRPa9Z+6bGBgYGxzuXm5h6rbd269bHWLpCkKVOnacrUabHafX191bZd+5fW/986JWn06DFG25UqV31p3c9zdXU1PHkAb98HmT8wTOl/meDgYH2Q+YMkr6V1q1b6888/FRwcbNT+31s9zMzMuP3jPceVfgAAgPeUt7ePUhV2eGW/rFVLaYfrH6/sB5i6WzdvKUuWuK+aZ82aVbdu3krSOnLmzKlPPvlYq1atMbTduPH0SRRZ7O2N+mbOnNnwlAq8nwj9AAAA76knT57obNAlpcud/aV9zCzMlbZycf3B1WVAGzZu0hf168fZ54sv6mvDho1JWkerlo66efOm9u7bZ2i7cuWKgoODVb36J4Y2KysrVa5cSR4ex5K0HrzbmN4PAADwHps0Z5YWTpomrXPVg4DrRvvMrSxVqEczjZ0/572eHvyi6fl4PwUFBcnK0kpFihR54WJ+RYoUkYWFhYKuXk2yGszMzNSypaPWr9+gqP88dWPRosXq06e3Lvpfkr+/v/r26a2wsHBt3rwlyerBu4/QDwAA8B57/Pixug4eoKG9+6lY0+oKP3dFioyUdb5sepDaQt/Nny7vRF6FHkjJxk/4UcOGDlGVqlW1c8cOBQcHK2vWrPqifn1ZmFtowo8Tk3T86p98IgcHB8Oq/c+bM3eebGxsNGH8WNnZ2cnT84Rat2mr0NDQJK0J7zZCPwAAwHvu8ePHGjN1kqysrFS8eHFZW1nJf8cl3fzPKuAAnt4W88PYccqZI4eaN2+mDzJ/oFs3b+nnZb8k6RX+Z1wPHHjpQo+SNHWai+HJEoBE6AcAAMA/IiMjdfLkyeQuA0gRgq5e1YyZs5K7DOCVWMgPAAAAAAATRegHAAAAAMBEEfoBAAAAADBRhH4AAAAAAEwUoR8AAAAAABNF6AcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATBShHwAAAABeg6Wlpezs7GRpaZncpQAvxacTAAAAABKgdOnScnRsLgtzC92/f1/p06dXVHSU1q3boJMnTybJmBYWFnJ2HqCmTRrL3j6LbtwI1rp16zV9xkzFxMQY+jkPcFLbtm1lZ2cnT09PDf9uhHx8fJKkJqQMhH4AAAAAiKeWji2UN28eLVq4UI8fPza0p0qVSo6OjipSuJDWrd+Q6OP26tVTHdq3U7/+TvL29lHp0qXkMm2q7j94oMWLlzzt07OHunXrqv5OA3Txor/69+urNatX6ZPqNRQaGproNSFlYHo/AAAAAMRD6dKllTdvHi1fvtwo8EvS48ePtXz5cuXLl1elS5dO9LHLly+n33/frX37/lBgYKC2b98hV9cDKl26lKFPly6dNXPmLO3cuUve3t7q199JqVPbqEmTxoleD1IOQj8AAAAAxIOjY3OtW7cuzj7r1q2To2PzRB/777/+1scfV1P+/PkkScWLF1PFih/pj31/SpJy586trFmzytX1gOGYiIgIubsfVYUK5RO9HqQcTO8HAAAAgFewtLSUhblFrCv8//X48WNZmFvI0tJST548SbTxZ8+Zq3Tp0umA635FRUXJwsJCP06cpC2//ipJypLFXpIUcvOm0XEhISFycHBItDqQ8hD6AQAAAOAVbG1tdf/+/Xj1ffDggWxtbXXv3r1EG/+rRo3UrFlT9erVR94+PipRorjGjPmfgoODtf65NQSeX9RPkszMzGK14f1C6AcAAACAVwgNDVX69Onj1TddunSJvnDeyJHfafbsufp161ZJkpeXlxwcHNSndy+tX79BN26ESJKy2Nvrxo0bhuMyZ86skJshiVoLUhbu6QcAAACAV3jy5ImioqOUKlWqOPulSpVKUdFRiTq1X5JsUqdWdEy0UVtUVJTMzJ9GuitXrig4OFjVq39i2G9lZaXKlSvJw+NYotaClIXQDwAAAADxsG7dBjk6OsbZx9HRUevWJf4j+/bs2au+ffuodu1acnBwUL169dS9W1ft2rnL0GfRosXq06e36tWrpyJFimi6yzSFhYVr8+YtiV4PUg6m9wMAAABAPJw8eVJFixRW+/bttW7dOqNF/VKlSiVHR0f5+1/SyZMnE33sESNGavDggZowfpw++CCzgoODtXzFSrm4TDf0mTN3nmxsbDRh/FjZ2dnJ0/OEWrdpm+i3GiBlIfQDAAAAQDytXbdepUuXVpeuXWVhbqEHDx4oXbp0ioqO0rp1G5Ik8EtP1xQYPXqMRo8eE2e/qdNcNHWaS5LUgJSJ0A8AAAAACXDy5EmdPHlSlpaWsrW1VWhoaKLfww8kFkI/AAAAALyGJ0+eJOpj+YCkwEJ+AAAAAACYKEI/AAAAAAAmKsWE/g4d2mvvnt3y9jonb69z2rp1i2rW/DSZqwIAAAAA4N2VYu7pv3btmsZPmKBLly5Jklq0aKGlSxbrs8+/kI+PT/IWBwAAAADAOyjFhP49e/YabU+cOEkd2rdX+XJlCf0AAAAAALxAign9zzM3N1fDBg2UJk1qeRw7/tJ+1tbWsra2Nmzb2tq+jfIAAAAAAHgnpKjQX7RoUf22dYtSpUql0NBQde7SVRcuXHhp/z69e8nZecBbrBAAAAAAgHdHigr9fn5+qvtZPaVPn15f1v9CM6a7qGmzFi8N/rNmz9H8BQsN27a2tvI87vG2ygUAAABgovLmzasGX9ZXhgwZdPfuXf22bbsuX76c3GUBsaSo0B8ZGWlYyO/UqVMqU6a0unT5RkOGDHth/4iICEVERLzFCgEAAACYspw5csjJqb9u3AjWnj17dOfOHWXMmFEtHZsrS5ascnGZrqCrV5NkbFtbWw0ePFBf1KunDz7IrLNnz2jkqP/p5MmThj7OA5zUtm1b2dnZydPTU8O/G8EaaO+5FPPIvhcyM5O1darkrgIAAADAeyBnjhwaNnyofvppnlasWKHg4GBFREQoODhYK1as0E8/zdOw4UOVM0eOJBl/6pTJqv7JJ+rTt79q16krV9cDWrtmlbJlyyZJ6tWzh7p166rvRoxQ/S8bKCQkRGtWr2Jts/dcign9Q4cOUcWKFeXg4KCiRYtqyJDBqlqlijZv2pzcpQEAAAB4Dzg59dd0Fxc9evTohfsfPXqk6S4ucnLqn+hj29jYqH79LzR23HgdPXpUly5d0tRpLgoICFCHDu0lSV26dNbMmbO0c+cueXt7q19/J6VObaMmTRonej1IOVJM6LfPnFmzZk7XwQP7tW7tapUrW0Zt27bXgYMHk7s0AAAAACYub968unEj+KWB/5lHjx4pJOSG8uTJk6jjW1hYyNLSUo8fPzZqDwsPV8WPPlLu3LmVNWtWuboeMOyLiIiQu/tRVahQPlFrQcqSYu7pdx44KLlLAAAAAPCeavBlfe3ZsydefXfv3q0GDeprzpx5iTZ+aGioPDw81L9fP1244KuQkBA1bvyVypUtK39/f2XJYi9JCrl50+i4kJAQOTg4JFodSHlSzJV+AAAAAEguGTJk0J07d+LV9+7du8pglyHRa+jTt7/MzMzkedxDl/z91Pmbb7R58xZFRUUZ+sTExBgdY2ZmFqsN7xdCPwAAAAC8wt27d5UxY8Z49c2QIYPu3rub6DVcvnxZzZq3UIGChVXho0r6skFDWVlZ6UpAgG7cCJEkZbG3Nzomc+bMCrkZkui1IOUg9AMAAADAK2zbvkN169aNV9/PPvtM27btSLJawsLCdOPGDdnZ2alGjer6/ffdunLlioKDg1W9+ieGflZWVqpcuZI8PI4lWS1496WYe/oBAAAAILlcunRJWbJkVZo0aeJczC9NmjSyt8+iy5cvJ3oNNWrUkJmZmfz8/JQvb16NHPmd/Pwuau3adZKkRYsWq0+f3rrof0n+/v7q26e3wsLCtXnzlkSvBSkHoR8AAAAA4sHFZbqGDR/60sf2pUmTRv2dnDR+/IQkGT99+nQaNnSosmfPprt372rHjp36ceIkPXnyRJI0Z+482djYaML4sbKzs5On5wm1btNWoaGhSVIPUgZCPwAAAADEQ9DVq5ow/kc5OfVXSMgN7d69++mifRkyqG7durK3z6Lx4yfo6tVrSTL+b79t02+/bYuzz9RpLpo6zSVJxkfKROgHAAAAgHgKunpVAwcNVp48edSgQX1lsHu6aN/adRuSZEo/8KYI/QAAAACQQJcvX9acOfOSuwzglVi9HwAAAAAAE0XoBwAAAADARBH6AQAAALw3omNiJEkWltzpjMTz7PP07PP1LiH0AwAAAHhv3LlzR5KUP3++ZK4EpuTZ5+nOndvJXElsfL0FAAAA4L0RFhYmd3d3NWzwpSTp4kV/Rf3znHsgoSwsLZU/fz41bPCl3N3dFRYWntwlxULoBwAAAPBeWb9hoySpYYMGyVwJTIW7u7vhc/WuIfQDAAAAeK/ExMRo3foN+m3bNmXMmEnmZmbJXRJSqOiYGN25c/udvML/DKEfAAAAwHspLCxcYWFXk7sMIEmxkB8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJgoQj8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJgoQj8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJgoQj8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJgoQj8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJgoQj8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJgoQj8AAAAAACaK0A8AAAAAgIki9AMAAAAAYKII/QAAAAAAmChCPwAAAAAAJorQDwAAAACAiSL0AwAAAABgogj9AAAAAACYKEI/AAAAAAAmitAPAAAAAICJIvQDAAAAAGCiCP0AAAAAAJioFBP6e/fupR3bt8nH+7xOnfTUksWLVKBA/uQuCwAAAACAd1aKCf1VKlfWsp9/VoOGX6lV6zaysLTQ6lUrlTp16uQuDQAAAACAd5JlchcQX23btTfadnJy1pnTJ1WqVCkdPXo0maoCAAAAAODdlWJC/3+lT59eknT37t2X9rG2tpa1tbVh29bWNqnLAgAAAADgnZFiQ///Ro/S0aN/ydvb+6V9+vTuJWfnAW+xKgAAAAAA3h0pMvSPHzdWxYoVVeMmTePsN2v2HM1fsNCwbWtrK8/jHkldHgAAAAAA74QUF/rH/vC9Pvusrpo0ba5r167H2TciIkIRERFvqTIAAAAAAN4tKSr0jxv7g+rVq6fmLVooICAgucsBAAAAAOCdlmJC//jx49Sk8Vfq9E0XPXwYKnt7e0nSgwcPFB4enszVAQAAAADw7kkxof/rjh0kSZs2rjdq7+80QOvWrX/RIQAAAAAAvNdSTOjPkTNXcpcAAAAAAECKYp7cBQAAAAAAgKRB6AcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATBShHwAAAAAAE0XoBwAAAADARBH6AQAAAAAwUYR+AAAAAABMFKEfAAAAAAATRegHAAAAAMBEEfoBAAAAADBRhH4AAAAAAEwUoR8AAAAAABNF6AcAAAAAwEQR+gEAAAAAMFGEfgAAAAAATFSCQ3/x4sXUr19fdezYQZkyZjTalzZtWk2bOiXRigMAAAAAAK8vQaG/RvXq2r7tN33VqJF69ewhV9c/VbVqFcN+GxsbtWjRPNGLBAAAAAAACZeg0O/s7KSf5i9Qrdp1VLFSFc2dO0/Lli7Rp59+mkTlAQAAAACA15Wg0F+4cGGtWbPWsD3vp/kaPHioFi74SXXr1kn04gAAAAAAwOuzTEjniIgIpU+f3qhty6+/KjomWvPmztGY739I1OIAAAAAAMDrS1DoP3v2nKpVrarTp08btW/d+pvMzcw1ffq0RC0OAAAAAAC8vgSF/p9/Wa7KlSu9cN+WX3+VJLVt1+bNqwIAAAAAAG/MLHsOh5jkLuJtSZs2rXy8z6twkWJ6+PBhcpcDAAAAADBxyZ1DE7SQn52dnb7p9LXSpk0ba1+6dOleug8AAAAAALx9CQr9nTp9rUqVK73w24kHDx6oYqWK+uabTolWHAAAAAAAeH0JCv1f1v9Cy5eveOn+FStWqsGX9d+4KAAAAAAA8OYSFPrz5Mkj/4v+L93vf9FfefLkeeOiAAAAAADAm0tQ6I+KilLWbFlfuj9rtqyKjo5+46IAAAAAAMCbS1DoP3P2rOp9/vlL939Rr57OnD37xkUBAAAAAIA3l6DQv3TpMnXv3k2dvu4oc/N/DzU3N9c3nb5W165dtGzZz4leJAAAAAAASDjLhHTesWOn5s77ST/88L2GDBmsK1euKCYmRnny5JGtra3mzftJ27fvSKpaAQAAAABAAiQo9EvSxImTtGvX72ratLHy5csnM5nJ/ehRbd78q06cOJEEJQIAAAAAgNeRoNCf2sZGI0eO0Oeffy4rK0sdOnRYI0aM1O07d5KqPgAAAAAA8JoSdE//wIHOcnRsoX379mnLll/1yScfa8KP45OqNgAAAAAA8AYSdKX/iy++kLPzIP26daskadPmLfp1yyaZm5vzqD4AAAAAAN4xCbrSnyNHdh396y/D9okTJ/TkSZSyZcua6IUBAAAAAIA3k6DQb2FhoYiICKO2qKgnsrBI8HqAAAAAAAAgiSUorZuZmWn69GmKePxv8E+VKpUm/jhBjx49MrR16dot8SoEAAAAAACvJUGhf/36DbHaNm3anGjFAAAAAACAxJOg0O80wDmp6gAAAAAAAIksQff0AwAAAACAlIPQDwAAAACAiSL0AwAAAABgogj9AAAA/2fvvuOiuLYAjv92ly5iQY2CvSsqFixg71hjA0vUxN5i7733gj32Ens39t5QQUFjoXdEUESlSJO27w+SjRusLyYonu/n4+eze++dO2dmzXuemTtnhBBCiCxKkn4hhBBCCCGEECKLkqRfCCGEEEIIIYTIoiTpF0IIIYQQQgghsihJ+oUQQgghhBBCiCxKkn4hhBBCCCGEECKLkqRfCCGEEEIIIYTIoiTpF0IIIYQQQgghsihJ+oUQQgghhBBCiCxKkn4hhBBCCCGEECKLkqRfCCG+MWGhIdg2b57ZYQghhBBCiP+AJP1CCPEP9ejRHR9vT1QqlabNyMiI4KAAjhw+pDW2Ro0ahIWGULx4sf86TA3LylW5dPlypu1fCCGEEEL8dyTpF0KIf+jmzZsYGxtjaVlJ01azZg0iIiKwtKyEoYGBpt3GxponT54SEBCYGaECEBERQVJSUqbtXwghhBBC/Hck6RdCiH/I3z+AJ0+eYmNtrWmzsbbm7NlzBAcHY1XdStNubV0LJycnblx3ZOCAAVrzlClThschwRQpUgQAczMztm7ZjK+PF95eHqxbt5Y8efJoxo8eNZLz587QpXNnXG474+vjxfz581AqlQweNJB7v9/hwf3fGTZsqNZ+3lzeX7BgQcJCQ2jRwpYDB/bh7+fD+fNnqVatqtY23bp1xdXlFv5+PmzetJH+/fvh6eH2eU6gEEIIIYT410jSL4QQn4GTkxM2Njaa7zY2Ntx0csLJ+ZamXVdXF6tq1bhx4wZ79+2jc2c7rTm6dOnMrVu3CQ4OBmDLlk3kzJmTDh3t6NK1G0WLFGHdL2u1tilSpAgNGzWg2w89GDzkZ7p0tmfHr9spUKAAHTvZMXfufCaMH0fVqlXeG/+E8eNYt249TZs1JyAggLVrVmseV6huZcXCBfPZtGkLTZs155qjY4YLCUIIIYQQ4sukk9kBCCHE10ipVGJlZUXunDl46O7BTScnZs6YjkqlwsDAgAoVLHB2voVKpaJP714AVK1aFUNDQ27cdCIxMZGxY0ZTuXJl7t27h46ODh07tGf2nLkA1Ktbl3LlylHL2oawsCcADB02gqtXLmFpacn9+/c1cYwaNYa4uDh8fX25edOJEiWK071HT9RqNf7+AQwZMggba2vu3v39ncezbt16Ll68BMCSJcu4euUSxYoWxc/fn969e3Hp8mXWrV8PQEBAIFbVqtGkSeN/7fwKIYQQQojPQ5J+IYT4RK2bN2GAXSuyPb6PKiGK1y1/5GW2AmTLlo3KlS3JkSMHAQEBvHjxAicnZ1auWI6hoSE21rV4/Pgxjx49AuDixUt06WLPvXv3aNKkCfr6+pw4fgKAUqVKEhYWpkn4AXx9fYmKiqJUqZKapD8kJIS4uDjNmIjnEaSmpaJWq/9qi3iO6RuPBbyNh6eX5vOzZ88AMM2TBz9/f0qUKM7p02e0xqfHLEm/EEIIIcSXTpb3CyHEJ7CpWZ3hLWuiPLWchAeXifX9neRru8h1YyuvE+KxsbGhto0NTs63gPSieSEhIdSoXh2b2jbcuHFTM9fuPXv4vm1bDAwM6NLZnmPHjpOQmJjeqVBoJe5/UvytPSUlRatfrYaU5L+3qVEqFe89rpSUZK3xgGYbhUJBhkgU759PCCGEEEJ8GSTpF0KITzC4ux1JjnsytKfEvCDhsQ8tW9hibW2Nk5OTps/JyZn6DepTtUpVbtz8K+m/ePES8fEJ9OzZg4YNG7B33z5Nn6+PL+bm5piZFdC0lSpVihw5cuDr6/evHNu7+Pn5U6WypVbbm28qEEIIIYQQXy5J+oUQ4hPk0VNDWupb+2Jcz2FhUR4Li/I4OTlr2p2cnfmhW1cMDQ24+UbSn5aWxv4DB5g4YTxBQUHcuXNX03fN0RFPT09Wr1pFxQoVqFy5MitXLOfmTScePHjw7x3gW2zZspVGjRrRv38/ihUrSvfuP9CoYcO3rkQQQgghhBBfFkn6hRDiE7wvzY0NeICOji5BQUE8f/5c0+7s7Ez27NkJDAzSekYfYM+evejr67N3776/T0fv3n2Jjo7m8OGD7Nu7m+BHwQwcNPhzHcpHc3F1ZfyEifTv348L58/RsEEDNmzcxOvXr//zWIQQQgghxKdRFDAr+M3cqjE2NsbH25PSZcoRGxub2eEIIb5C21YsIN+NTajfeAb+T/pVbZlw5Daud95dJf/vqltZcfDgfqpZ1dC6UPClW7xoISVLlqR9h46ZHYoQQgghxBcts/NQudMvhBCfwGHjDvQa9cpQyE4ndwGe5ir90Qm/np4eRYsWZey4MRw/fuKLT/gHDhhA+fLlKFq0KL17/YSdXScOHDiQ2WEJIYQQQogPkFf2CSHEJ7jv5s6M7ccY1WsY2SODUCVEk5S3JF6RSUwYPfmj52nX7nuWLlmMu7s7w4aN+PcC/kwqV7Fk8OCBZMtmzKNHwUydNp3de/ZmdlhCCCGEEOIDZHm/EEL8n0qWLEnOnDnw8/MnKioqs8MRQgghhBBfoMzOQ+VOvxBC/J/8/P7bV+cJIYQQQgjxqeSZfiGEEEIIIYQQIouSpF8IIYQQQgghhMiiJOkXQgghhBBCCCGyKEn6hRBCCCGEEEKILEqSfiGEEEIIIYQQIouSpF8IIYQQQgghhMiiJOkXQgghhBBCCCGyKEn6hRBCCCGEEEKILEqSfiGEEEIIIYQQIouSpF8IIYQQQgghhMiiJOkXQgghhBBCCCGyKEn6hRBCCCGEEEKILEqSfiGEEEIIIYQQIouSpF8IIYQQQgghhMiiJOkXQgghhBBCCCGyKEn6hRBCCCGEEEKILEqSfiGEEEIIIYQQIouSpF8IIYQQQgghhMiiJOkXQgghhBBCCCGyKEn6hRBCCCGEEEKILEqSfiGEEEIIIYQQIouSpF8IkaXY29vh6eGW2WEIIYQQQgjxRZCkX4hvUN68eZkzexZON68TGOCHq8sttm/bQp06tTM7tE9yy/kmffv20Wo7duw4derW/2z7KFiwIGGhIVhYlP9scwohhBBCCPFf0cnsAIQQ/62CBQvy29EjxMREM2fuPDw9PdHR0aVBg/rMmzuHevUbZnaI/0hiYiKJiYmZHYYQQgghhBBfBLnTL8Q3Zv68uahR07JVG06ePEVAQCA+Pj5s2LCR1m2+B8DczIytWzbj6+OFt5cH69atJU+ePJo5Ro8ayflzZ+jYsQO3nG/i5enOL2vXkC1bNs2Ygwf2M3vWTKZMnoS720Pu/X6H0aNGasWSPXt2Fi1cwIP7v+Pt5cH+/XspX76c1phmTZty+tRJAvx9cXt4n00bN2jmL1SoELNmziAsNISw0BDg7cv73zUHQFhoCLbNm2uN9/Rww97eDoDbt5wAOH/uLGGhIRw8sP/TT7oQQgghhBCZRJJ+Ib4hOXPmpGHDBmzbtp2EhIQM/TExMQBs2bKJnDlz0qGjHV26dqNokSKs+2Wt1tgiRYpg27w5PX/sRc8fe1GrVk1+/nmI1hg7u07Ex8fTuk0b5sydx8iRI6hXt66mf8ev28iXLx/de/yIbYuWuD10Y/++veTMmROAxo0bsWnTBi5evEiz5i2w79yF+w8eANC3X3/CwsJYtHgJlpWrYlm56luP+X1zfIwWLVsDYN+5C5aVq9K3X/+P3lYIIYQQQojMJsv7hfiGFC1aFKVSiZ+f/zvH1Ktbl3LlylHL2oawsCcADB02gqtXLmFpacn9+/cBUCqVjBg5iri4OAAOHTpMnTq1Wbjwr7k8Pb1Y5rAcgMDAIHr99BN16tTmmqMjtWvbULZsWSpZViEpKQmAWbPn0Lx5c1q1asmuXbsZPmwov/12jCVLl2nm9PDwBCAqKorU1FRiY2OJiIh45/G8b46P8eLFCwAiIyPfux/x73FwWEYOExN69+mb2aEIIYQQQnx15E6/EFmYsbExwwf0YeeKeexcPpee9h3SO9Tqd25TqlRJwsLCNAk/gK+vL1FRUZQqVVLTFhISokn4AcKfPSOPqanWXJ6e2sn1s2fPNI8JVKpYkWzZsuHu9gBfHy/Nn8KFC1G0SBEALCwsuH79xv938H/4HHN8rRwclhEWGsKCBfMy9M2bN5ew0BAcHJa9ZUvxd38+0vJ3b3s8RAghhBDiSyJ3+oXIoszNzdk4bxK6tw+QdPUCAGaFy6JW/0StmjU4c/bs2zdUKFC/5aKA4m/tKSkpWv1qtRqlUvs6YnJKcoYxij/GKJVKwp89o1Mn+wz7iomOBiDhMxTk+9AcaWlpoFBotenq6v7j/X4pQkND+b5tW2bMmKUpcKivr0+779vy+PHjTI5OCCGEEEL82+ROvxBZ1JLJI1GcXkXSk0BNW8IjL1553eanH3tgaGiYYRsTExN8fXwxNzfHzKyApr1UqVLkyJEDX1+/zxbfw4du5Mubl5SUFIKCgrT+vIyMBNJXCrzvNYJJycmoVKr37udDc7x48YLvvsun+V6sWFGMjIw035OT0y9cKJXv38+X6uFDN0JDw2jRwlbT1rJFC8LCnuDm5q5pa9CgAUePHMLTww03twds376VIn+suID0CyFz58zm97uuBPj7csv5plYNh9GjRuJy25nAAD/u3nFl9qyZmr4OHdpz+tRJfLw9uff7HdasXoXp31aFlC5dml9/3Ya3lwc+3p4cOXxIa/8AAwcM4Pe7rri5PWDe3Dno6Px13fpDBRk/FP/7ikra29sxevQoLCwsNEUj7e3tuOV8E0ivgREWGqL5Xr58OQ4c2IePtyfeXh6cOX2SSpUqfczPJYQQQgjx2X1VSX/NmjXZvm0Ld++4ypJKId6jaNGi5EkIJy0pY7G+kH1LUKSlcunieVq2bEGxYkUpWbIkfXr34vixo1xzdMTT05PVq1ZRsUIFKleuzMoVy7l504kHn1AA70OuOTpy585dtm7ZRP369SlYsCBWVtUYN26sJkFatsyBdu2+Z8zoUZQsWZKyZcsyeNBAzRyPQx5Tq2ZN8ufPT+5cud66nw/NcePGTXr99CMVK1SgUqVKLFgwX1NjAOD58+ckJCTQsGED8uTJQ/bs2T/bOfiv7Nu/ny6d/1pR0aWLPXv37dMaY2RkyPoNG2nZqjWdO3dBnaZm86aNKP5YBdGnd2+aNWvKgIGDqVuvAUOHDudxSPpKgVatWtKvX1/GjZ9AnTr16N2nL55eXpq59XT1WLR4MU2aNqd3n74UKlyI5W88VpA/f34OHz7I68TX2Nl3wbZFS/bu3YeOzl8XWmxsrClStAh2dp0ZMWIk9vZ2moT+Y7wvfnh/Ucljx46zbt16vLy8NUUjjx07rinyOGLkKCwrV9V8X71qFU+ePKFly9bYtmjJ6jVrSfnbqhchhBBCiP/KV7W838jIEHcPT/bu28/mTRszOxwhvlhFihRB72UwSW/pS3rxhOBfZxJWrjXTp00lX758vHj5kocPHjJh4iQAevfuy5w5szl8+CBpaWlcvnKFKVOmffY4u/foyYTx41i2dAmmprmJiIjA2fkWz5+nF8xzcnKm/4CBjBwxnCFDBhMbG4uz8y3N9ouXLGHhwgXcvOGIgYEBZuaFMuzjQ3PMnDUbh2VLOXz4IE/Dw5k2bTqVKlbU9KempjJ16jRGjhzB2DGjuXXrNp3sMj6S8KWoYGFB/vzfERgUpGk7dPAQEyeMp2DBgqjVaqysqjNo0BBsrK01Y06dOq01z6jRY3B7eJ/SpUvj7e2NubkZAYGB3L59G0h/bOBP5ubmRERE4Oh4nZSUFELDwrh3756m/80LDI8ePWLq1OmcPnUCIyMj4uPj+emnH3kVE8OgwUM0j40EBPy1QgUgOjqayZOnkJaWhp+/PxcuXqRunTrs3r3no87L++L/mKKScXFxpKamaBVz/PNxiZjoGK12c3Mzflm3Dj//9IKZgYFBHxWjEEIIIcS/4atK+i9fvsLly1cyOwwhvnhPnz4l2cTynf3JOkY4OKzglovLW/tDw8Lo1bvPO7dfusyBpcsctNo2bdrMpk2bNd/flhj/vfp6XFwcU6dNZ+q06e/c1+nTZzh9OmMBNYC7d3+naVPtFT/79x9g//4DHz1HeHg43X7ortVWrnwFre+79+xl956974zxS1C1iiXThvTG+Jk3eq/CeW1bmTy1rPAKDOFlZCQXL17C3q4TCoWCi5cuah6h+FORIkUYN3YMVatWIXfu3Jr6DObmZnh7e7N//wH27N2No+NVrly+woULF7l67RoAJ06coF/fPjg73eDy5StcvHSZ8+fPk5qaCqRfiEhfHl+enDlzvjG3Ob6+vliUL8+t27cz1Il4k7ePT3r9hT88C39G2XJlP/r8vC/+N4tKvsnAwEBTVPJTbNiwkSWLF9GpYwccHa9z/MRJgoODP3keIYQQQojP4atK+j+Vnp4eenp6mu/ZsmXLxGiE+O94e3sTk2sg+koVpKVqdyoUxBe05Lbr1swJTnx2RYsWZeGQ7iSdXs5rdRqvAXBFt1R5KperhImJCXv37WPunNkATJo8JcMc27dtISzsCWPHjefp03CUSiVXLl9ETzf9f0MfurlRq5YNjRo1pG6dOqxbtxbH69fp338gYWFPqFuvAfXq1qVu3TrMnzeHwYMG0KGjHbq6uuzZs4urV6/x89DhvHzxAnNzc/bs2YWeXnrBxMSPKNiYkvy3wpGoUSr+ekLtQwUZ3xf/xxSV/BRLlzlw5OhRGjduTKOGDRk9ehSDBv/MmTNvv/AkhBBCCPFvytJJ/9CfhzB69KjMDkOITDFt+QaWjfkZ9ZXtpMRGAaAyzI6qQQ+mrtvx1gr94us0un9PUi9vA3WaVrs6LRVFZCj9unfGYd1mdP9I4K9cuao1LleunJQuXZpx4ydqlr/XqF49w35iY2M5duw4x44d58TJU+zZvZOcOXMSFRVFYmIi586f59z582zbvh3Ha1cpV7YsKBSYmpoyb/58zWsgK1lqF7Xz9PTEzq4TOjo6773b/z4fKsj4vvjfLCr5rjcaJCUnv7WYY1JSEkpVxvI4AQGBBARsYuPGTaxds5oune0l6RdCCCFEpsjSSf+q1WtYv+GvZ/+zZcvG73ddMzEiIf47bh6e9JqyhJH9elI8X05AzePIeBzm/SLPGGcxhXMZkZoQ+9a+tNfxVC1TjLS0NOo3aJjelqZ9cSAqKpqXL1/SvXs3nj17hrm5GZMmTtQa069fX56FP8Pd3Z00dRptWrciPDyc6Oho7O3tUCmV3P39HgkJCXTs2JGEhAQehz5GoVDy+vVrevfqxa87dlK2TBlGjhiuNffWbdvo3bsXv6xdw6rVa3j1KoaqVaty7949/P0DPuoc/FmQ8e6duyiUSiZPnqhVkPF98b9ZVHLO3Pn4+/uTP/93NGrUiDNnzvLgwQMehzymcOFCWFiU50nYE2Lj4khKSiLk8WPq1KmDi4srSUlJvH79mqlTJnPi5EkePQrBrEABLC0tOXXq1EcdhxBCCCHE55alk/6kpCStf/QJ8a0JDQtjzMwFmR2G+Ld95KqN2Ni3XxhQq9UMGjyE2bNmcuniefwDApg6dTqHD/1VGyEuLo4hQwZRrFgxUlNTuX//Pj16/IharSYmOoYhPw9m+vRpqFQqPL28+PGnXkRGRgEwcuRoJkwYR+/evXBzc2PW7Dls3/bX4yWRkVHY2Xdm6pQpHD50gNTUVNzdPXBx+fiLtB8qyPi++OHDRSVPnjpFi5a2HNi/j5w5czJi5Cj27z/ArFmzmT59Gj9068rTp0+pU7c+uXLlYuWK5eTJk4eXLyM5ffo0S5Yue2vcQgghhBD/NkUBs4Jf5RrfsNAQevfuy5mzZz96G2NjY3y8PSldptw7//ErhBBfmw2LZ1Hozi7SkjI+G6+Xx4wrxtWYu2x1JkQmhBBCCCEyOw/N+CDiF8zIyAgLi/JYWJQHoNAfSy3NzcwyOTIhhMg8Dpt2oVO/R8YOpYo06y78sm33fx+UEEIIIYT4InxVy/stLStx6OBfy01nzkh/zde+/QcYOVIK9gkhvk2e3t4s2HuWUT+OwiDwNoqop6TlLUaceSXGLlrLy5cvMztEIYQQQgiRSb7a5f3/j8xeViGEEP8mpVJJ/bq1KWyWH0+/QG5/wjPxQgghhBDi35HZeehXdadfCCHEu6WlpXH5qmNmhyGEEEIIIb4gX9Uz/UIIIYQQQgghhPh4kvQLIYQQQgghhBBZlCT9QgghhBBCCCFEFiVJvxBCCCGEEEIIkUVJ0i+EEEIIIYQQQmRRkvQLIYQQQgghhBBZlCT9QgghhBBCCCFEFiVJvxBCCCGEEEIIkUVJ0i/+U2GhIdg2b/7Z5z14YD8zZ07/7PO+yy3nm/Tt2+df34+DwzK2bN70r+9HCCGEEEIIkTVJ0i8+CweHZYSFhhAWGsKj4EBcbjszf/48cuTIkdmhfbT/KpEXQgghhBBCiP+KTmYHILKOS5cuM3LUaHR0VJQuVZqly5aQw8SEwUN+zuzQhBBCCCGEEOKbJHf6xWeTlJREREQET5485eq1axw7dpx69etlGJc7dy42b9qIv58P169fo1nTplr9tWrV4uSJ4wQG+PH7XVcmTZyASqXS9BsaGrJihQO+Pl78fteVAQP6Z9iHrq4uUyZP4o6rC36+3pw4fgxr61qfdDyjR43E5bYzgQF+3L3jyuxZM985tn//fly8cB4/X29cXW4xb95cjIyMNP329nZ4erhRv359rl65hK+PF7t27iBfvnyaMUqlkunTp+Hp4Yab2wOmTJ6EQvFJIQshhBBCCCGEFkn6xb+icOHCNGxQn5Tk5Ax9o0aN5PjxEzRu0oxLFy+xevVKcubMCUD+/PnZuWM79+/fp2nT5kycOJmuXbswYvgwzfZTp06hto0Nffr0o2u37thY16JSpYpa+3BYtpTq1a0YNHgIjZs048SJk+zauYNixYp+VPytWrWkX7++jBs/gTp16tG7T188vbzeOT4tLY2p06bRsFETho8YSZ3aNkyZMllrjKGhIYMG9mfosBF06NAJc3Mzpk2doukfOKA/XTrbM3rMWNq160DOnDlpYWv7UfEKIYQQQgghxNvI8n7xfzM3M6N9287kyJGTooWLUM2qKr4+XiiVKgwNDQCYPiPj3fF9+w9w9LffAJi/YCG9e/eicuXKXLlyhR9/7ElYWBiTJqcnw37+/nyX/zsmT5rIMoflGBoa0rVLZ4aPGMk1R0cAho8YxR3X25r5ixQpQrt231PNqgbh4eEArFu/noYN69O5c2cWLFj44WMzNyciIgJHx+ukpKQQGhbGvXv33jl+06bNms8hISEsWryEBfPnMWnSX4m/np4e4ydMIjg4GICt27YzcsRwTX/fvn1ZvXoNp06dBmD8hIk0aFD/g7EKIYQQQgghxLtI0i8+mUqlYvqkeWRTFOH3S1GERL+mbs2CRL2IY/HSRbjevU23rl0pXrw4W7ZszbC9p6en5nNCQgKxsbHkyWMKQKmSJblz567WeBcXF4yNjTErUIAcOXOgr6/PHdc7mv6oqCj8/f013ytWrIBSqeS641WtefT09IiMjPqoYzxx4gT9+vbB2ekGly9f4eKly5w/f57U1NS3jrexsWbY0J8pVao02bMbo1LpYGhogKGhIQkJCQDEx8drEn6A8PBn5MmTB4Ds2bOTP/93uL5x7Kmpqdy//wCFrPEXQgghhBBC/J8k6RefbPL4WYS45OKRb6imLebla1KS1FQt1Rp3D3emTpvOgQP7GDVqJIsXL9HaPiU5Reu7Wq1GqUx/0kShUKBWq7X6/0x61ag/KgFWKpWkpKRg26IlqalpWn1xcXEfdYxhYU+oW68B9erWpW7dOsyfN4fBgwbQoaMdKSna8Zubm7Pj11/ZsXMnixYvISoqihrVa7Bs2RJ0dXU1SX/y3x91eOO4hRBCCCGEEOLfIBmH+CSmpqaYGpbgke+rt/af3xNKz67phfWWLXNg4IABfPfddx89v4+vL1ZW1bTarKysePXqFU+ePCUwMIikpCSqVquq6c+RIwfFixfXfHdzc0NHRwdT0zwEBQVp/YmIiPjoWBITEzl3/jxTp02nk509VlZWlCtbNsM4S8tK6OiomDlzFnfv/k5AQCDf5f/4YwZ49eoVT5+GU61qFU2bSqXKUKtACCGEEEIIIT6FJP3ik7Ro3hq3G+++W56akoauOhe6uro4OTnj4+PDsKEf/8q+7dt/xczMjLlzZlOyRAmaN2vGmNGj2LBhI2q1mvj4ePbs3cfUKZOpU6c2ZcqUYbnDMtLS/rqjHxAQyKFDh1m5woEWLWwpVKgQlpaWDBk8iEaNGn5UHPb2dnTt0pkyZcpQuHBhOnbsSEJCAo9DH2cYGxwcjK6uLr179/pjbAd69Oj+0cf8p82bNzNkyBBsbW0pWaIE8+fNxcTE5JPnEUIIIYQQQog/SdIvPolxNhMSYjNW5H9TUmIaenp6AGzYsJFu3bpiZlbgo+Z/+vQp3Xv8SOXKlTl//iwLFsxjz569LF+xUjNm9uw5ON+6xbatW9i3dze3b9/mwYOHWvOMHDWagwcPMX3aVByvXWHb1s1UqVKFsLAnHxVHTHQM3X7oxm9HD3Pxwjnq1KnNjz/1emtNAHd3D6bPmMmQwYO5fOkCHdq3Z/78BR+1nzetW7+Bg4cOstxhKceOHSU2Lo7TZ8588jxCCCGEEEII8SdFAbOC6g8PyxqMjY3x8fakdJlyxMbGZnY4XyUb69o0sBjIncvP3jmm/VAzBo7o+h9GJYQQQgghhBBfpszOQ+VOv/gkN51uULyyLkrV2wvqmRfPjqev638clRBCCCGEEEKIt5GkX3yyFevm0X5QEYyMdbXai5TJgVUrHdZuWPmOLYUQQgghhBBC/JfklX3ikz10e8Ach5H0/2kopjkKoU5VotB5zV23Gwwfuznjq+mEEEIIIYQQQmQKSfrF/+XRo0dMmTU2s8MQQgghhBBCCPEesrxfCCGEEEIIIYTIoiTpF0IIIYQQQgghsihJ+oUQQgghhBBCiCxKkn4hhBBCCCGEECKLkqRfCCGEEEIIIYTIoiTpF0IIIYQQQgghsihJ+oUQQgghhBBCiCxKkn4hhBBCCCGEECKLkqRfCCGEEEIIIYTIoiTpF0KIr0jBggUJCw3BwqJ8ZocihBBCCCG+ApL0CyHEv8zBYRlhoSEsWDAvQ9+8eXMJCw3BwWHZR80VFhaGZeWqeHl5f+4whRBCCCFEFiRJvxBC/AdCQ0P5vm1bDAwMNG36+vq0+74tjx8//uh50tLSiIiIIDU19d8IUwghhBBCZDGS9AshxH/g4UM3QkPDaNHCVtPWskULwsKe4Obmrmlr0KABR48cwtPDDTe3B2zfvpUiRYpo+v++vN/auhZhoSHUqVOb06dO4u/nw7HfjlCiRHGt/Tdt2oQzp08S4O+L083rjBo5ApVK9S8ftRBCCCGEyGyS9AshxH9k3/79dOlsr/nepYs9e/ft0xpjZGTI+g0badmqNZ07d0Gdpmbzpo0oFIr3zj1+/DhmzpqNbYtWpKSksmzpEk1f/fr1WbVyBZu3bKVBw8aMHz8Re3s7hg8b+nkPUAghhBBCfHEk6RdCiH+JoYEBSuVf/zN76OAhqlevTsGCBTE3N8fKqjqHDx3W2ubUqdOcPn2GwMAg3N09GDV6DOXLl6N06dLv3dfChYtwdnbG19eX1WvWUL16dfT19QEYPmwoq9es5cCBgzx69Ihrjo4sWryE7t27f/6DFkIIIYQQXxSdzA5ACCGyEpVKxdC+/alRxgLFq0SUBnoUrmpJyNMwXkZGcvHiJeztOqFQKLh46SIvIyO1ti9SpAjjxo6hatUq5M6dW3PRwNzcDG/vdxfv8/Dw1Hx+Fv4MgDympoSGhVGpUkUsLS217uwrlSoMDQ0wNDAgITHxc54CIYQQQgjxBZGkXwghPhOlUsna+YtIuXCfwNP7Ne0F8uSlnEUJKlesxN59+5g7ZzYAkyZPyTDH9m1bCAt7wthx43n6NBylUsmVyxfR09V7775TUlI0n9WoAVD8ccFAoVCydOlSTp0+k2G7xNevP/1AhRBCCCHEV0OSfiGE+Ey+b9kalWsAz72CtNrVqWnEBYYxtu8Aeowciu4fCfyVK1e1xuXKlZPSpUszbvxEbt++DUCN6tX/cVxubg8pUaIEQUFBHxwrhBBCCCGyFkn6hRDiM2ldryFP1xx9a59arSbN/ymlS5emfoOGQPrr994UFRXNy5cv6d69G8+ePcPc3IxJEyf+47iWOazg1+1bCQsL4/iJk6SlpVG+fDnKli3LokWL//H8QgghhBDiyyWF/IQQ4jNRpaSBWv3O/pTQF5ibFSA2NpbY2NgM/Wq1mkGDh1CpYkUuXTzPjBnTmT1n7j+O6+rVq/T8sRf16tXl9KkTnDj+G/379ePx48f/eG4hhBBCCPFlUxQwK/juf6FmMcbGxvh4e1K6TLm3/oNbCCH+ic2LlvF09dF39hduU5cFV47g7u7x3wUlhBBCCCEyVWbnoXKnXwghPpOzTo7kq1bu7Z0KBcpSZpLwCyGEEEKI/5Qk/UII8ZkcOnYMZV0LTIoX1GpXqFSUHtiBlTu3ZU5gQgghhBDimyWF/IQQ4jNJTU1l4PjRjBk8FMs2NqS+iEFlZECMnprZ2zfywN0ts0MUQgghhBDfGEn6hRDiM0pOTmb+imUolUpy5sxJQkICCQkJmR2WEEIIIYT4RknSL4QQ/4K0tDRevnyZ2WEIIYQQQohvnDzTL4QQQmRhDg7LCAsNYcGCeRn65s2bS1hoCA4Oyz7b/kaPGsn5c2c+23xCCCGE+Gck6RdCCCGyuNDQUL5v2xYDAwNNm76+Pu2+b8vjx48zMTIhhBBC/Nsk6RdCCCGyuIcP3QgNDaNFC1tNW8sWLQgLe4Kbm7umTU9Pj9mzZvLg/u8E+Pty9MghLC0tNf3W1rUICw2hTp3anD51En8/H479doQSJYoDYG9vx+jRo7CwsCAsNISw0BDs7e0A6N+/HxcvnMfP1xtXl1vMmzcXIyMjzdz29nZ4erhRv359rl65hK+PF7t27iBfvnyaMZaWluzdswu3h/fx8nTn0MEDVKxQ4V87b0IIIURWIEm/EEII8Q3Yt38/XTrba7536WLP3n37tMZMmTyJli1bMnzESJrbtiQwKJjdu3aSM2dOrXHjx49j5qzZ2LZoRUpKKsuWLgHg2LHjrFu3Hi8vbywrV8WyclWOHTsOpNe5mDptGg0bNWH4iJHUqW3DlCmTteY1NDRk0MD+DB02gg4dOmFubsa0qVM0/cbG2dh/4CDt2nekdZvvCQwMZMeO7WTLlu1zniohhBAiS5GkXwghhPgGHDp4iOrVq1OwYEHMzc2xsqrO4UOHNf2Ghob07NmDOXPmcvnyFXx9fRk7dhyJiYl07dJZa66FCxfh7OyMr68vq9esoXr16ujr65OYmEhcXBypqSlEREQQERFBYmIiAJs2bebmTSdCQkK4ceMmixYvoW2b1lrz6unpMX7CJB48eMBDNze2bttOnTq1Nf03btzk8OEj+Pn54efnx7jxEzA0NMTauta/eOaEEEKIr5tU7xdCCCGykDJlytCpW3+MspkQ6OeGnq4uAC8jI7l48RL2dp1QKBRcvHSRl5GRmu2KFi2Cnp4et11cNG0pKSncu3ePUqVKae3Dw8NT8/lZ+DMA8piaEhoW9s64bGysGTb0Z0qVKk327MaoVDoYGhpgaGioea1lfHw8wcHBmm3Cw5+RJ08ezXdTU1PGjh1N7dq1yZsnDyqVCkNDQ8zNzf+fUyWEEEJ8EyTpF0IIIbKIAUPGkLNkKw67QmxCGsXyl6ZPzdw8DvQAYO++fcydMxuASZOnaG2rUCjSP6jVGdrVf2tLSUnRfFaT3qdQvnvxoLm5OTt+/ZUdO3eyaPESoqKiqFG9BsuWLUFXV1eT9CcnJ2tvqFajfGPe5Q7LMDU1Zfr0GTx+HEpSUhLHjx1F948LG0IIIYTISJb3CyGEEFlABYsKmJRoxW7HNGIT0gAIfJqCW4iCYiXLA3D58hV0dfXQ1dXjypWrWtsHBgbx+vVratSooWnT0dGhkmUlfH39PjqOpORklEqVVpulZSV0dFTMnDmLu3d/JyAgkO/yf/fJx1izZg02b9nCpUuX8fHxISnpNaampp88jxBCCPEtkTv9QgghRBbQsWs/9rqoM7SrgZhEXSpXrsy9e/eo36AhkF5Y700JCQn8umMHU6ZMJjIqitDQUAYPHoShgSF79u796DgehzymcOFCWFiU50nYE2Lj4ggODkZXV5fevXtx/vwFqle3okeP7p98jEFBQXTq2JH79x+QPbsxU6dM0awSEEIIIcTbyZ1+IYQQIgvQMzQh/nXGpB/gdYqCfPnyAxAbG0tsbOxbx82bt4BTp06xauVyzp45RbGiRej2Q3eio6M/Oo6Tp05x+coVDuzfh5vbA9q1+x53dw+mz5jJkMGDuXzpAh3at2f+/AWffIyjRo0hR44cnDt7mpUrV7B5yxaeP3/+yfMIIYQQ3xJFAbOCb/8XQhZkbGyMj7cnpcuUe+c/eIQQQoiv0Yix07gZ2ZgnL1My9HWro2bP8t48evQoEyITQgghvm2ZnYfKnX4hhBAiC9i17Re6WifxZz2+P+XLqUMuRbAk/EIIIcQ3Sp7pF0IIIbKAiIgIdq+fyrhBU3EONOb5Kx0szBP5TjeAWZOHZ3Z4QgghhMgkkvQLIYQQWYTLbWfuuLbBxqY2pvny89uxu/j5+2d2WEIIIYTIRJL0CyGEEFlIWloa1687ZnYYQgghhPhCyDP9QgghhBBCCCFEFiVJvxBCCCGEEEIIkUVJ0i+EEEIIIYQQQmRRkvQLIYQQQgghhBBZlCT9QgghhBBCCCFEFiVJvxBCCCGEEEIIkUVJ0i+EEEIIIYQQQmRRkvQLIYQQQgghhBBZlCT9QgghhBBCCCFEFiVJvxBCCCGEEEIIkUVJ0i+EEEIIIYQQQmRRkvQLIYQQQgghhBBZlCT9QgghhBBCCCFEFiVJvxBCCCGEEEIIkUVJ0i+EEEIIIYQQQmRRkvQLIYQQQgghhBBZlCT9QgghhBBCCCFEFiVJvxBCCPGZHTywn5kzp3/0eGvrWoSFhmBiYvIvRiWEEEKIb5Ek/UIIIcQncnBYxpbNmzI7DCGEEEKID5KkXwghvkGStAohhBBCfBsk6RdCfLMcHJYRFhrCz0MGa7XbNm9OWGjIfxpLWGgIYaEhVK1aRatdT08PN7cHhIWGYG1d67Ptb9q06YwYOeqzzfctMzQ0ZMUKB3x9vPj9risDBvTPMKZDh/acPnUSH29P7v1+hzWrV2FqapphXKVKFTl96iT+fj4c++0IJUoU1+rv2bMHN29cJyjQH8drV+jYscO/dlxCCCGEyBok6RdCfNMSEhIZPHgQOXLkyOxQCA0NpXNne622Fra2xMfFffZ9vXr1ipiYmM8+77do6tQp1LaxoU+ffnTt1h0b61pUqlRRa4yerh6LFi+mSdPm9O7Tl0KFC7HcYVmGucaPH8fMWbOxbdGKlJRUli1doumztbVl1swZrN+wgUaNm7Bj5y4cli3Fxsb6Xz9GIYQQQny9JOkXQnzTrl93JCIigqE/D3nvOCurahw+dBB/P19cXW4xe9ZMDA0NAejd6ycuXjivGfvnSoGffvxR07Z7104mThj/3n3sP3CQ79u2xcDAQNPWpUtnDhw4mGFs/vz5WffLWjzcH+Lm9oCtWzZTsGBBAEqWKIG/nw/t27XTjG/RwpYAf1/Kli0LZFzer1AoGDJ4EDeuOxIY4IfLbWeGDRuq6S9btiz79+/F388XN7cHLFq4ACMjo/ceT1aTN29eKlaoQO7cuTVtRkZGdO3SmVmz53DN0REvLy+GjxiFSqXS2nbvvn1cvnyFR48ecffu70ydOp3GjRtlOIcLFy7C2dkZX19fVq9ZQ/Xq1dHX1wdg0MD+7N9/gO3bfyUgIJANGzZy6vRpBg4c8O8fvBBCCCG+WpL0CyG+aampacxfsIhevXpRoED+t44pW7Ysu3ft5NTp0zRp2pSBgwZTo0Z15s2dA8BNJ2fKlClN7ly5AKhlXYsXL15Qy7omACqVCiurajg533pvLA8fPCQkJISWLVsAYGZWgJo1a3Dw0CGtcYYGBhw4sI+4uDg6dLSjXbsOxMXFsXvXDnR1dfHz92fW7DnMmzcHc3NzvvvuOxYvXsS8+Qvw8vJ6674nTZzA4MGDWb5iBQ0aNmbIkKE8j4jQ7G/Xzh1ER0XTslVrBgwYSN26dZj7x/FndQUK5OfXlQvYOXUADva12D3jZ+pUr4qenh5FixZBX1+fO653NOOjoqLw9/fXmqOChQVbt2zm9i0nfLw9OXRwPwDm5uZa4zw8PDWfn4U/AyDPH48BlCxZChdXV63xLi6ulCpZ8vMdrBBCCCGyHEn6hchiChYsSFhoCBYW5f/xXLecb9K3b5+PHv81vHZMpVJRo0YNGjdqiHG2bACcOXMGdw93xowe/dZtBg0awJGjR9m0aTOBgUG4ut5h6tTpdOrUEX19fby8vIiMjKTWH8/c21jXYv36DVjXSv9eubIl+vr63L59+4Px7d23ny5dOgPQ2d6eS5cu8+LFS60x33//Peq0NEaPGYuXlxd+fn6MHDUac3NzbKzTl3pv3/4rt11cWLVyBStXOPDwwUM2bdr81n1my5aNPn16M3fuXA4cOEhwcDC3XVzYvWcvAO07tMfAwIBhw0fg7e3NjRs3mTxlKp06diBPnjwfPKavmYmJCZvnTyGv0zZSruzg9b3zpF7ZjuGLAKpUKIuBvsEH5zA0NGTPnl3ExcXx89DhtGzZmj59+gGgp6erNTYlJUXzWY0aAIXyr/+rVqvVWuMVCgV/axJCCCGE0CJJvxB/06NHd3y8PbWW5xoZGREcFMCRw9p3XGvUqEFYaAjFixf7R/v8mGS5ZcsWhDwKwtzM7K39165eZvasmYSFhWFZuSpeXt7/KCaAFi1bs2vnro8e7+p6B8vKVb/YZ8X79/iRXYtXMrh8XbrnKkfVYqWpUqEiJYoXY+7c+djZdaJUqVIZtqtUsSL2dnb4+nhp/uzevROVSkWhQoUAcHa+hY21NSYmJpQuXZpfd6T3lyxZEmtrax4+dCM+Pv6DMR4+fJhqVatRuHBh7O3t2LtvX8Z4KlWkaNGiWvF4uD9EX1+fIkWLaMaNGjWGcuXKUqFCxfcW7StVqhQGBgY4Xr/xzn4PTw8SEhI0bS4urqhUKkqUKPHBY/qaDejZFV3nfaS9TtBqV6cmo4p+inU1S5KSkqharaqmL0eOHBQv/lcBvpIlS2Jqasq8+fO5ffs2fv7+mObJWMTvQ/z8fKlRvbpWm5VVNXz9fD95LiGEEEJ8O3QyOwAhvjQ3b97E2NgYS8tK3L37OwA1a9YgIiICS8tKGBoYkJCYCICNjTVPnjwlICDwX4/r3LnzREZGYmdvx/LlK7T6qltZUbJkSQYOGkxaWhoRfyzLfheVSkVqauoH9/ny5csPjnlTcnLyB/edWcYM/pkyUUr8l/+VRBdvXht9E2MWjZjAqCVzuXL1KhMnjGf//gNa2yqVSnbu3MXmLVszzBsaGgqAk5MTP/zwAzVr1sDDw5OYmBicnW9hbV0LG2trnJycPirOyMgoLly8wNKli9HXN+DSpcsYGxtrjVEolTx48JCfhw7LsP2LFy80ny3Kl8fIyIi0tDTy5c1LeHj4W/eZ+Mff53dJv5v89tvJ72rPKqqWKUrS5TNv7UtLeEXNCqXZs3cfU6dMJjIykoiI50wYP460tDTNuNDQUF6/fk3vXr34dcdOypYpw8gRwz85ll9+Wc+6dWt56ObG9evXadq0KS1btKBzl67/9/EJIYQQIuuTO/1C/I2/fwBPnjzVLJMGsLG25uzZcwQHB2NV3UrTbm1di5s3bwKgq6vLlMmTuOPqgp+vNyeOH9N6xZq5uTnbt23Bw/0hfr7eXL50gUaNGlKwYEEOHUxPMr083QkLDcHhLVW9U1JSOHjoMPZ2dhn6unTpzP37D/Dw8MywvP/PVQT169fn9KmTBAX6U7NmDbJly8bqVSvx8/Xm97uu9OvXl4MH9jNz5nTNvH9f3h8WGkK3rl3YvGkj/n4+XL9+jWZNm2qdjzdXLOTKlZO1a1bj6nobfz8fLl44T7vvv/+0H+QzyJ07N1ZmxXl2/V6GPnVaGn5rDjKm/yDmzVtA06ZNsLKqpjXm4UM3SpcpTVBQUIY/ycnJwF/P9bdq1VKT4Ds5O1O3bp0/nud3/uh49+7dR20bGw4ePKiVPP4Vz0OKFSvG8+fPM8Tz6tUrAHLmzImDwzJWrlzFvv37Wb16pVaBwDcFBgaSkJBA3Tq139rv4+ODRXkLTeFCgOrVrUhNTSUgIOCjj+trpPjARQ0FMHv2HJxv3WLb1i3s27ub27dv8+DBQ82Yly9fMnLkaFq3bsWVyxf5+efBzJr96fUQzpw9y7TpMxg0cCCXL12kR/cfGDlqNE5OH/93SwghhBDfHrnTL8QfChcuTKuO35M9hwlPI8KpX78eq9esBcDGxoa1v/yCQqnExsYGR8fr6OrqYlWtGlOmTAXAYdlSChUqyKDBQwgPD6eFrS27du6gcZOmBAYGMW/eHPR09ejQ0Y74+HhKly5FXFw8YWFh9Onbj82bNlKnbj1evYp9553XPXv2MnBAf6yta2n+oW9oaEibNq2ZM3fee49vypRJzJ41h+BHj4iJiWbG9GlUr27FT716ExHxnLFjRlOxYgXcPdzfO8+oUSOZM2ces+fMpXevn1i9eiU1aloTFRWVYay+vgEPHjxkzdq1vHoVS5PGjVi5cjnBj4L5/fd7H/hFPp/uHe14fvbdRfRSEl6TV2lIcHAwh48coVevXlr9a9au5cTxY8ybO4ddu3cTHx9PqZKlqFevLlOmTgPQPNffoX17evVOv1Di5OTE9Gnpfz9u33b56HgvX75ChQqVeBUb+9b+I4ePMGjQQLZu3czixUt58uQJ5ubmtGxhyy/r1vHkyVMWLJhH2JMwlq9YiZ6uLmfPnmba1ClMmjwlw3yvX79mzZq1TJ48maTkZFxcXDE1zU2Z0ul3sY8cPsKY0aNZscKBpUsdMDXNzZzZszl46DDPnz//6OP6Gnk/foZ1ru9IjtReJfFo5zz08hflrroU8fHxDBs2gmGM0PT/sm691vijv/3G0d9+02ozMy+k+ezk5Kz1HcDd3SND26+/7uDXX3f8k0MSQgghxDdG7vSLb17uXLlY9MsKOs8ciZPpaw6mBnMr/gk1atZk0pzp5MiRgwoVLHB2voWzszM2f9y9r1q1KoaGhty46USRIkVo1+57+g8YxO3btwkODmbd+vW4uLjQuXN6UTZzM3NcXFzw8vLi0aNHXLhwkVu3bpGWlqZJmJ8/f0FERITmbu3f+fr6cufuXa13ubdt0waVSsXRo7+9dZs/LVm8lGuOjgQHB5OUlIydXSdmzZ7D9es38Pb2ZuSo0RleM/Y2+/Yf4OhvvxEUFMT8BQsxMjKicuXKbx379OlT1q1fj7u7B48ePWLL1m1cuXqVNq1bf3A/n1NhM3NiHj9775jkF1HpfxcWLUGh0O7z9PSiQ0c7ihUrxpHDhzh39gxjx40h/Jn2nH/ezb91K71gn4eHJzGvXuHm5k7sOxL4d3kZGalZRfB3CYmJdOjQidDQUDZv2sDVK5dYtnQJBgYGvHoVS6dOHWncqBHDhg0nNTWVhMREhvw8jK5du9CoUcO3zumwfAXrN2xg7JjRXL1yiXW/rMX0jyJ9CYmJdPuhOzlz5uTUyRNs2LCe69evM/ktFxCymlVbdqKu8wMKlfY1coWuHik1OrF51/5MikwIIYQQ4uPInX7xTcuePTuz1yxjlc9VIhPiNO2nvO8ysJYtT/Lrs2bzegICAnnx4gVOTs6sXLEcQ0NDbKxr8fjxYx49ekTr1q1QKpVcd7yqNb+enh6RkVEAbNmyhfnz51G/fj0cHa9z8tQpPD3f/vq099mzZy+zZs5g8uSpxMXF0aVLZ06dOv3B4nn3HzzQfC5SpDB6enrce+Nu+6tXrzK8ZuxtPD3/eqVYQkICsbGx5HlHUTKlUsnPPw+hbZs25C+QH309PfT09D6qoN3n9CQinML5chP7RLvewPU5f92N1c1lQlR0NHFxcRQvkbGY3/379+na7Yf37qd//4EZ2ipUqPRRMf79ju6bYmJiMvRHREQwYsTbi/MdPHiIgwe1i04+fPiQYsX/erXbyL8V9lOr1axcuYqVK1e9dU4vLy/s7bu89xiyoufPnzNi4Vrmjh6GyXMfdKNCSc5dmKhcxRg3c+kXW7RSCCGEEOJPkvSLb1r/4UPYGOCklfADhEQ/5+mrSPLnyI0iT3YehzwG0hOtkJAQalSvjk1tG27cSH+eX6lUkpKSgm2LlqSmaj+DHReXPvfuPXu5cvUqjRs3pn69evz88xBmzZrNlq3bPinm3347xswZ0/m+bRtuOjlTs2YNFi9Z8sHt3ky0FX/cyv7zlWB/b3+flOQUre9qtRql8u2LhgYO6E//fn2ZNn0GXl5exMcnMHPmdPR09T64n89p56GDOAwYRey2k2/tV+npEqlK1fxWQrzJ08ubDv2GY2FRnsLm5gQEn8Xb+5+/HUMIIYQQ4r8gy/vFF+3ffO+7jo4O+csU58mryLf2uz72w6pgSWoUKU3ZCn+9897JyZn6DepTtUpVbvxRxM/NzQ0dHR1MTfNkKKz2ZjX7sLAn7Nixk779+rN+/Qa6desGoFnGrVJ9+D/JuLg4Tpw4SefO9nTp0pmgoGBu3vy4yvB/CgoKJikpSWtZvrGxMcWK/bNXD/5dzZo1OHv2HIcPH8HDw5Pg4GCKf+Z9fIzw8HB8EyIxrVwmQ59CpaTUwA4s37LhP49LfF3c3T04fe68JPxCCCGE+KrInX7xQaampowbN4ZGDRuSJ08eoqOj8fDwZOmyZdy5c/ez7efggf24e7gzffrMfzxXWGiI5nN8fDxPw8NxcXFly5atPHyYXlW7YMGCBCe+PeEHcHnsx4QGHdHX0eVx0mtNu5OzMwvmz8PQ0EBTuT8gIJBDhw6zcoUDM2fNxs3Nndy5c1Ontg1169YlPj6ekMchXLp0hYCAAHLmyEHt2rXx8/MD4PHjUNLS0mjSpAkXL14iMTHxvUvg9+zZy9GjhylVqhTr1n96shoXF8eBAweZOmUyUVFRPH/+gjFjRpGWlsbnfANbYFAwrVq2wMqqGlFR0Qzo34+8efPi6+v3+XbykWYsWci4IUOpbNORuFseJL+KJ1vJguiUL8Tsjevw9pV3nQshhBBCiKznq0v6f/yxJ4MGDiBfvnz4+PgwbfpMbt++ndlhZWmbNm5AR1eH4SNGEhz8iLx581KnTm1y5syZ2aG914iRo7h8+Qr6+vqUKF6cH7p34+SJY4waPYaDBw+hUEDaezJcl8d+GOrqkZCcRGraX++0d3Z2Jnv27AQGBhEW9kTTPnLUaEYMH8b0aVPJnz8/kZGR3Llzl/iE9ORdpVQxb+4cChTIT2xsLJevXGHGjPQLHE+fPmXJ0mVMmjgBh2VLOXDwUIZnrt9028UFPz8/ihUrxoEDB9457n1mzJzFwgXz+XX7Nl69esXaX9ZhVsCM1x94Z/unWL58BYULFWL3rp0kJCSwc9duzpw9i0n2z79y40PUajULV6/E0NCQRvXrkzOXCe5Op7i37v5/HosQQgghhBD/FUUBs4Kf8b7ev6tt2zasXLGcSZMmc9vFlR49fqBb1640aNCI0LCwD25vbGyMj7cnpcuU++RK2t8qExMTvDzd6dDRDuf3vGfc3MyMOXNmU6dObdLS0rh85QpTpkzTvM7LwWEZOUxM6N2nr2abmTOnY1Hegk529jg4LKOzvfb752vUtKZQofR32Nt37sLkSZMoXboU7u7ujBw1Gn//d78fPCw0hN69+3Lm7Fmt9uXLl9HC1pYaNa1JTExkwbZfyPGdKVXMi2Oib8Tj6Bdscb3AGZ/fNdts7DAY4xQFjWo3AKBBgwas+2UNU6ZO4+DBQ5QtW5ZZs2ZQrWo1EhITOHXyFDNmziI+Pp7Ro0YyerR28t6xkx1OTs5MnjQR2xa2mBUowLNnzzh85CgODstJSdF+Zv6/YmhoyN07LsyaNZs9e/dlSgxCCCGEEEJkNZmdh35Vz/T379ePPXv3sXvPXvz8/Jg+fSZhYWH07Nkjs0PLsuLi4oiNjcXWtjl6eu8uvrZlyyZy5sxJh452dOnajaJFirDul7UfvZ9p06bj6urKzp27sKxcFcvKVQl740LO+PHjmDlrNrYtWpGSksqypR8uXPc2GzduInv27NSrV5fXr18TF/aMoMgIhh3bhN2uRRx2c2J2s25U+K6wZhsTfUNCAx8B8H3btqxft5bhw0dy8OAhDA0M2LVzB9FR0bRs1ZoBAwZSt24d5s6dA6S/q/vYseNcunRZc1yurncAiI2LY+TIUdRv0Ihp02fwQ7eu9O/XN2PQ/5IKFha0+/57ihQpQsUKFVizOr1q+9mz5/6zGIQQQgghhBD/rq8m6dfV1aVSpYpcvXpNq/3q1WtYWVm9dRs9PT2MjY01f7Jly/ZfhJqlpKamMmLkKOw6dcLTw53fjh5mwoTxlCtXVjOmXt26lCtXjiE//8zDhw/5/fd7DB02AhsbaywtLT9qP69evSIpKZmExAQiIiKIiIggLe2vKvgLFy7C2dkZX19fVq9ZQ/Xq1dHX1//k4/HzS38lXaGCBQFYPn8JeZN1CIl+TmjMS/Y+uI5TsDdNS6XHnd84JzlVBgQFBPLjjz1ZsGAevXr34ey59MS4fYf2GBgYMGz4CLy9vblx4yaTp0ylU8cO5MmTh/j4eBISE0lKStIc159F+1asWImr6x0eP37M+fMXWL9+A23atPnkY/onBg7sz4XzZ9m7dw9GRoa079CJl5HvrnMghBBCCCGE+Lp8Nc/0586dGx0dHZ4/137PdsTz5+TLl/et2wz9eUiGpdXi4+TOlYvW7TpjkiMXTo4XqFrNipo1alCtWlUaNmzA4EEDGTN2HPv3H6BUqZKEhYVpPd8+ePAgAAYNHMDAQYM17bbNm7Nly6b3vpP8bTw8/no3/LPwZwDkMTX9qMc63lzqr3lV3R/P8r98+ZJwN19OdZ+Enr4+KEBXqUOqOo2eZW3IFZtGoK8fLVrYkidPHtq178i9e/c0c5cqVQoPTw8SEhI0bS4urqhUKkqUKKF5vOFtWrVqSb++fShatCjZsmVDpVL9p8t93NzdsW3R6j/bnxBCCCGEEOK/99Xc6f+TWp3xveJ/b/vTqtVrKF2mnOZPlapvXxEgtHXr0Zex8/cTku0HbkbbYtV+EQuXb+XO3bs4LF9B2+/bs3//Acb8eUHlHb+BWq2mceNG5MiRA3VaWoZ3wOvq6H50TG8+5/7nu+UV73g3/PuUKlkSgEch6dX9Bw7oT5fO9kyZNIVxo8dy5OQJnoQ/paAiGwdmrmDS0NGkqdW4u7vz4sVLOne215rvfX//3tUOULVqFX5Zu4ZLl6/Q88deNGtuy8pVq9HV/fhzIoQQQgghhBAf8tUk/S9fviQlJYW8efNptecxNSUi4u13U5OSkoiNjdX8iYuL+y9C/apVsrSkUJVurD2nxC8siYjoFE7dTWOHiznjpy3SjPPx9cXIyAgAXx9fzM3NMTMroOk3McmOQqHgxYsXDP15CC9evCBfPu3fzsKiPABWVtU4fOggNWvWoGuXLsyeNRNDQ0MAWraw1drGtnlzzp/TLs63e9dOJk4Y/1HH17dfH17FxtK+XTs83B8ybtxY4uPjuX3bhcOHjzDu55HkNDYhu5ERrVu34u4dV6pUrkxQcDC7d+/mh25deRQcyP17d9mwYR0+Pj5YlLfA0NCQwYMG4nTzOg/u30OtVlO8ePr76JOTkqhXry4DBwzQxFG9enXCnz1j3NgxREdHExgYREFz8486BiGEEEIIIYT4WF9N0p+cnMyDBw+pV6+uVnu9enVxdXXNpKiynk7dBnL4jTcgmhgpWTO0IFVLGpLbzILy5cvTunUrBg8aqCn4ds3REU9PT1avWkXFChWoXLkyNWvU4OXLl8yYOZtevXrh6emJpWUlbGysARgzehRlypTByMiI3bt2cur0aU6cPElYWBi1a9uwZPEiFAoFbu4eAOT64/WAtaxrERUVpYlPpVJhZVUNJ+dbGY7FJIcJefOmP/pRvnx5NmxYR/t27YiPiyM6OpoOHe04evQ3cufOzeFDByhXtiyLFi7AwMAAU1NTSpUqSZeu3fDy8iZPnjwMHz6MOXPmERUVhaPjdW453+LI4SO8fv2aM2dO0aNHd37dsZOIiAhcXFyYN3cOtWrVIuTxY5KTk+nevRu5c+VCR0eHoMAg8ufPr6kx0Kd3L2z/doFDCCGEEEIIIf6prybpB9iwcSPdunahS+fOlCxZkhkzpmNubs6vO3ZmdmhZhkrfhMSkv5alJySpcQ9KpGvDXLSsU5Tjx44ybuwYdu3ew+QpUwFQKpVs3bEDsyKFOHHyOEePHkahVOLm5s6ZM2dw93Cndu3aLF++Aju7TgBkMzbm4MFDmJmbceToUTZt2szixUuIefWKYsWK0b59O4oVK8ajR+lV862qpz+aYWNdi4MHD2niq1zZEn19fW7ffuNKxR+WOyzj/r27APTo0Z24uHhWrFjFq1evGD1mLF5eXsyYOQvH69cxNzfn8OGDPIuIIDQslNTUVMaMGYePjw/x8fEYZzMmPj6eXbt306GjHXXr1sG8YEESEhPp1bsPxYsVI3/+/AwZMpgrV67Q7YceHD58hB7df2DXrt14eHhSrFgx3NweUL26FRcvXeJ1YiJmZgU4f+4MVlZWLF++4t/5UYUQQgghhBDfrK+mkB/AsWPHyZUrFyNHDidfvnx4e3vTvcePhIaGZnZoWUbq6xgM9ApoEv/kFDVrjz9n7XHo3ziFJZPsiY6O1oz/7rvvmLp0HpdfBtD+4FLS/niOfVmrXpQqX4Z6jRowd+58DuzfS+MmzXBzc2fLlk3MnDkLgNq1bbC3s6ND+/Z/xZCaCqRfTHBycubkyVNUqVyZ8+cvULp0aTrZdabDH1Xzra2tefjQjfj4eK3jeLNQYFhoCBMnTuLM2bPMmzuHokWL4uvjpTVerVYzf8FCfv11B2ZmZhTI/52myn4nO3uyZcvGb0eP4Ox0g8tXrjBj5izOnD6j2V6lUpGYmIi+nh7t27ejfft26Orq4ubmzsuXL+nYyY6tWzYT/iwcJydnbG1tSU1NpXLlqiQkJmrm2bRp8///4wkhhBBCCCHE33xVST/A9u2/sn37r5kdRpZ1YNcvdOi1nN2O2u3f5dJB/cpLK+E3MDBgqsN8HDwvE5uUqDX+1esEfKPDadjLjsNL1nPl6lUmThjP/v0HtMYplUp27tzF5i1bM8Ty58UcJycnfvjhB2rWrIGHhycxMTE4O9/C2roWNtbWODk5ffTxKZRKHjx4yM9Dh2Xoe/HiheZzfHyCVl9cXBzNbVtgY2NN/Xr1GDtmDKNHj6Jly9YolekFCnv0/ImnT59qbZeU9FrzefeePaxcsZwZM2bRpbM9x44d10r4hRBCCCGEEOJz++qSfvHvevjgARXu7GJIsy6cczckOi6V6iXSKJP7MVPGjNMa+32n9vz2xD1Dwv+mTR6ODB3Ym3nzFnD+3BkCAgK09/fQjdJlShMUFPTOOW46OTNr1kxatWqpSfCdnJ2pW7cOVlbV2LT54++OP3z4kLZt2vD8+fNPfj1eamoqjo7XcXS8ztJlDnh5ulO7tg3XrjmSmJiIubkZzs7O79z+4sVLxMcn0LNnDxo2bECHjp0+af9CCCGEEEII8am+qmf6xX9jz87NLJxoh1nMdmoYn+LWodGMHPRDhrcfWDWqy4Nnj947V0paKq8MlURERHD4yBF69eql1b9m7VqsqlVj3tw5WFiUp1ixojRr2pQ5s2dpxnh5eREZGUmH9u25+WfS7+SEbfPmGBgYcPu2y0cf25HDR3gZ+ZKtWzdTo0YNChUqRK1atZg1cwYFCuR/53ZNmjSmT+9eWFiUx9zcHDu7TiiVSvz9A4iLi2Pd+g3MnDEdO7tOFClShAoWFvz044+aGgYAaWlp7D9wgIkTxhMUFMSdO3c/Om4hhBBCCCGE+H/InX7xVpGRUezcvvG9Y5KV734P/ZseJURRqFAhFi1aQpvWrbX6PD296NDRjgnjx3Hk8CEUCgVBwcEcO3Zca5yTszO2zZtz61Z6wT4PD09iXr3iUfCj996xVyjSl96npKYAkJCYSIcOnZg8eSKbN20gW7ZsPH0azvXr13n16t3zxETH0KJFC0aNGoWBgT4BgYEMHvIzPj4+ACxatJgXz58z9OchFC5cmJiYGB4+dGPlqtVYW9fi0MEDlC1nwZ49exk+bCh79+77qHP3Ng4Oy8hhYkLvPn3/7zmEEEIIIYQQ3wZJ+sX/TfGevukX9mo+6ytVJCW9JjQ0lOIlSmUYe//+fbp2++G9++rff2CGtgoVKn0wxjx58gDw7FmEpi0iIoIRI0a9c5uRIzP23XZxoZOd/Xv3df/BA4oVK8a1a4780L2Hpt3aupbm83f58pGcnMyBN95AIIQQQgghhBD/FlneL/5vyS9fYaSr/8FxZbPlxdvb5z+ISFvBggUZMXwYz549w9vb+1/fX5fOndmydSs1alTH3MwsQ3/hwoUZO24Mx4+f4Pnz5/96PEIIIYQQQgghSb/4v+3fvIN2Jaq+d0zRHHkJcfPRvIbvv2JiYoLjtSvUqFGdQYOH8Pr16w9v9A8YGhrSpk1rfv11BxcuXMTe3i7DmNOnTmCSPTurVq9m7ZrVuLrext/Ph4sXztPu+++1xrZq1ZKLF87j7+eLm9sD9u3djaGh4Vv3XbFiRR7c/51hw4YC0KBBA44eOYSnhxtubg/Yvn0rRYoU+fwHLYQQQgghhPjiSdIv/m9u7u4kuz+iUcFyb+03z56bbgUsWeew6j+ODGJiYihWvCRNm9ni5PTuivqfy/dt2+LvH4C/fwCHDh+mc+eMjwJYVKiEbYtWREfH8ODBQ3788ScaNmrCrl27WLlyOVWqVAYgX758rF2zmr379lG/QUM6dbLn1OkzmvoEb7K2rsX+fXtYsnQZK1emn2cjI0PWb9hIy1at6dy5C+o0NZs3bXzr9kIIIYQQQoisTZ7pF//I6iXLsevelfHNm3IvNpywxGiy6epTK0chYoLCGD9o+L9+l/1L0LVrZw4dPgzA5ctXyJYtG3Xr1sHR8XqGsU+fPmXd+vWa71u2bmPOnNkM/flnevfpy3f58qGrq8upU6cJDQ0F0t9g8HfNmzVj5crljB8/kaO//aZpP3XqtNa4UaPH4PbwPqVLl/5PHnMQQgghhBBCfDkk6Rf/2IGdezi4ay9W1aphVrggsTGvmHVjGQkJCZkdmoaDwzI629sxb958Vq9Zq2m3bd6cLVs2YWZe6KPnUiqVNG/ckNaN66JWq3G670XlypXp07c/AKmpqRw7dpwunTu/NelXKpX8/PMQ2rZpQ/4C+dHX0wMgt2luANw9PHB0dOTSxfNcuXqVq1evcfLkKaKjozVzVKlSmSZNGtN/wCDOnDmjNX+RIkUYN3YMVatWIXfu3CiV6Qt6zM3NJOkXQgghhBDiGyNJv/gs1Go1Lq6u4Oqa2aG8U0JCIoMHD2LHzl1aCfSnMDU1ZfPimRgH3OS1668A1Os1G11dXe7ecdGMUygUJCcnkyNHjgxzDBzQn/79+jJt+gy8vLyIj0/g5g1HdFTp/zmmpaXRuUs3qltZUb9+PXr36sWE8eNo1botISEhAAQHBxMZGUXXLvZcvHiR5ORkzfzbt20hLOwJY8eN5+nTcJRKJVcuX0RPV+//OmYhhBBCCCHE10ue6RffjOvXHYmIiGDoz0PeOcbKqhqHDx3E388XV5dbzJ41U1NAb+KE8dy4chGDS+t47XED0tKLE5pWtOGVz12cLpzi56HDuXfvPrGxselL9E8ep1ixYlr7aNiwASkpKSxZvIi1a1ZTuHDhDHFMnjSRZQ5LGTRoINmyZUNfX59WrVpq+l++jMTOvjNFihRh3S9r0dFJv2CQK1dOSpcuzfIVK7l+/QZ+fn7kfMuFByGEEEIIIcS3QZJ+8Vk4OCxjy+ZNmR2GhkqlokXrlkxfNJcZi+dRtHhR1MD8BYvo1asXBQrkz7BN2bJl2b1rJ6dOn6ZJ06YMHDSYGjWqM2/uHADu3v0dk9ymqIyya7bJU7cdCpUOj4+sJuerxyhQs237dtq0bceu3XvIkSMHkydN0oxXKBSUK1eOHDlyMH7CRFatWsPaNdqFDqtUqUyFihVYvXotnTt35fiJ4xgbG1OqZEmtcS9evMDOvgslS5Zg7drVqFQqoqKiefnyJd27d6No0aLUrm3D9OnTPuOZFUIIIYQQQnxNJOkXWU7TFs1ZsmM9+s0qsynyIRte3CfZNBvlKlfE1Cwf7h7ujBk9OsN2gwYN4MjRo2zatJnAwCBcXe8wdep0OnXqiL6+Pvlym5D4LIRc1ZpqtslbrxMp8a9IfOyDQZgnSUmvOXz4CH5+fuzauYvcuXNjZPTXq/bq1a2LiYkJN286MXfObKZNm8KVq9e04nj1Kpa01DQmT5rA/v17aWFry5kzZylfvnyGmCMiIrCz70K5smVZs3oVCoWCQYOHUKliRS5dPM+MGdOZPWfuZzy7QgghhBBCiK+JPNMvPjs9PT2mTpnM99+3xdjYmAcPHjB9xizu379PwYIFuX3LiXXrNzBr1mwAtmzeRJMmjSlvUZHY2Fjy5s3L/Xt3qVuvPv7+AXTo0J5+fftSokRx4uPjuXHjJtOmz+DFixeafY4eNRJb2+YscnCgSmdbFtw/y8mfprDr3jV237vGy4RYktNSUVUrwe9uD+j1Qw/Wrd+gFXelihUpVqwYPbp3Jy4uDki/M69SqShUqBCR0a+I8blL7urNCD+7Pb1fpeL1sxCK9ZtHuPMZIIEFC+ZRu3Zt8ubJQ1xcHIaGhkycNJmYmBhKlipJaGgoP3Tvodlv9uzZ6dC+HatXrwHAz8+P3Xv20C9bH4oWLUr+/PkxNzcnNjYWgJEjR2nF/ezZM+rWa6D57uh4nQYNG2uN+ZRChUIIIYQQQoisQ+70f0McHJYRFhrCggXzMvTNmzeXsNAQHByW/eP9TJk8iZYtWzJ8xEia27YkMCiY3bt2kjNnTs0Yy0oVNZ9r1KxBdHQ0NWpUB6C2jQ3h4eH4+wcAMGjgQFQqJU2aNqd3n74UKlyI5X/EWa1aVcJCQ7h+4yadu3SjZXc7tnvefGdsZx+5oVM4Lzdu3GTihPFafUqlkl27dtO6zfc0bWZL02a2NGnaHJvadQkODubi5atEJKrRz1sIw4KlyVasIno58xG0fSaPds4jztySH374gUoVKzF9+gzaft+eps1siYyMRFdXF0i/iPB3arVa63vVqlX4Ze0aLl2+Qs8fe9GsuS0rV63WzCGEEEIIIYQQH0uS/m9MaGgo37dti4GBgaZNX1+fdt+35fHjx/9oboVCgaGhIT179mDOnLlcvnwFX19fxo4dR2JiIl27dNaMrVChAgqFgvLly5GWmsrBQ4exsbYGwNqmFs7OtzRjly5dRvny5UlNTeXu3d+ZOnU6jRs3wsjIiC6dO+Pm5satW7ewqlUDxxeBb4+Nv5LtYyEP8fDzpWnTJlhZVdO0P3zoRslSJbl79y5BQUFaf5KTk0lITOTgJSfiw0PIXb0Zuao345X3HZKeh6KybMruc9epUaM6m7ds4dKly/j4+JCU9BpTU1PNPnx9fDE3N+e7777TtFWr9lcMANWrV+fx41BWrlzFgwcPCAwMoqC5+f/5qwghhBBCCCG+ZbK8/xvz8KEbhQsXpkULW44cOcrBA/tJTEwkLOwJjx490oxr0KABI4YPpUyZMqSmpXHnzh2mTZtBcHAwgGaZ/oCBg/ixZ09q1qyBt7c3D+7/jp6eHrdd/np9XcOGDcibNw/lypXj+ImTABgZGVGhQgVq1qhOfHw8Hdq3w9TUlObNm5EjRw6WLP1rxcHs2bNQKBRcu3oZfX19zd3yUqVK0bZtGxYsWMiUKZPp06c3aqWCF3ExPI2NIpehMTULleKnqg3JpmfArRAfLPMXZWLDjpTInZ/o6Gj69OkDgIVFedasXcupkycICw2hXfsORERE0Ounn+jRozs/9erDrJnTMTMzIyY6GlOb71Eq4Ml9J8zH7SQs4iW7x33PT/0G0K9vX/r17Uvx4sXQ19cnNTWVXLlyAXDN0RF/f39WrnBg5qzZGBtnZ8L4cVq/UVBgEObmZnzfti337t+nSeNG2Law/ex/F4QQQgghhBBZn9zp/0KFhYa898/7luG/bxl/+fLlKF++HNOmTgGgb7/+GBjos3ffPq1xRkaGrN+wkZatWtO5cxfUaWo2b9qoSbizZ0+vYL9o0WIcr9/k9JkzhIc/48qVq+kTvLFkvbO9PeHh4Vrvkvf398fGxppatWrh6+vHoME/k5yczM6du8iTJw/6+voAGBoaYmqam7S0NNRqNT17/sTixUsBGD58KLq6uhw+chTb5s1RKZUMObqesae2UyxXPgx0dDE1ys7Ao+u4FeKDSqFkeZs++L54wrHL55k5cza6un9d9/L09GLqtBkA7NzxK+fOnqFFC1t0dHQYNLA/Q4eNoEOHTsTFJ6DS1SMpFToMmcBlJxeePosAYNSoMWTPnp2yZcvw8uVL5s9fQFJSEl27dEGhUKBWq+nTtx96evqcPHGcpUsWsWDhIq1zf/bcOTZu3MTcubM5f+4MVlZWLF++4p2/txBCCCGEEEK8i9zp/0JZVq6q+dy2bRvGjhmtVawtMTHxvduHhobSvl07opISMM6VE4VaTdHCRSlQoABhYWF89913FCxYELVaTZUqVenff6BmeT3AqVOnteYbNXoMbg/vU7p0aVJT1YybkV50bsflBBLztqNUaRWB/t5s2ryFli1b0LRpE7Zt/5XcuXLRpEljYl69wtfXTzPf/QcPsbGxpmqVKnTsZI+Pjw++vr6UK1+OhIQEalSvTpB/AM0aNsLAwIDnz1+QJ48pia8TeRSSviKhVs2anD59BlPT3BQvXozwZ89QqNV4Rjzm3pMgGpWoyBmf3wl4Gc6ok1vpVMGaigWKsPH2OWyijThw8CC6ujosWbJYE5e/f3qMVtVrEhMTg729HcsdljF+wiTNKofNW7YwcsRwKlfRXpYP4ObuTu06dbXa9h84qDl33t7eBAQE0r5DR60xfy+0N2fuPObM1b5os2nT5vf84kIIIYQQQgiRkST9X6iIiAjN51evXqFWq7XamjZtwuhRIyldujTh4eEcOHCQFStXkZqailKhwNzcnFeJ8TRv24rCOfMSEReNEgUoFISGhaGjo8vtW048e/YMpVKBi8stEhMTCQ0N4/r1a5ibmWkKzOnq6qJUpi8KOXrkENmMjUl4nR5Hsfx6dKpbmHy5dChZvBB16tYB0pfk129Qn5o1aqCrq0vuXLn+eD1d+kqABw/uM2P6dFQqFefPnSElJQUdHR0sLCxQq9XY2janha0tL7wDUavVmJrmJinxNRPGjydXrpwA5MiRg7379lGqZClSU1N58fwFDfOU5M6TQGJfJ5CSmkraGysOiuTKh+/zJ9ialWe9Q/qbA36/d++Dv0V8fLwm4QcID39Gnjx53jm+SJEijBs7hqpVq5A7d27NuTM3N8Pb2/uD+xNCCCGEEEKIz0WW93+F6tevz6qVK9i8ZSsNGjZm/PiJ2NvbMXzYUAAqVasCgFoBKoWSzruXcD3IE7McpjyKjiBvkYJEx0QDkCdPHu7du49ti1YolSosLMqzZPFSQkNDefQoBKVSycKFixk85GcAVCoVPkEvmbI1DICGlbOzYF84gU9fAwqOHjnK6tVrSElJpVnTppiYmPDkyRNWrloNpFfmB6hVsxYqlQqAtLQ0Tpw8iVKpRKFQkJaSStyTFyTHxfP0d09eR71CnZaGrp4u1apV1SzvT0pKwtHxuuaRA7U6DZezV2hTvHL6d7Sr4itQkF3PgFjvEJ49e5be9pZq+n/35mMJf+xIk8i/zfZtW8iVKxdjx42nVeu2tGrdFgA9Xb0P7ksIIYQQQgghPidJ+r8g5ubmzBo3ka3zl7J13lIWTZ1ByRIlMowbPmwoq9es5cCBgzx69Ihrjo4sWryE7t27U7JkSZL0/7gr736bQjnzkJKWyoGHN1EACclJBL16TrYc6c/kp6Sk8vDhQ3x9fXn+PAKFQsH9B/cpXrw4ycnJLFm6jNVr1tDrpx8BOH78OMXMcxD8LD0Rjo5N5bZXPLmz63Dq9DnGjZ/A2l/WAX+9iq5N2+9xdHTUOoYmTRozecpUAO4/eMDw4SM1xf9e+gajY6jHc48AXFfvISk2nsTIGA53GYtCoaBZ0ybExsYSHp6euPv6+aJSqTAwNGT/jt0k3PLGMm9hdFV/LWQxN8lNIYMcFDHJw5olyzXtlpUq/aPf7O9y5cpJ6dKlWb5iJdev38DPz4+cOXJ81n0IIYQQQgghxMeS5f1fiOaNGtO3+feE7L1A2IsoAPRMsjG3+wBCjbTvWFeqVBFLS0vNnX0ApVKFoaEBPQf24XFsJBUpxsOnwZib5KZNuerpReRQo1QoUavVvP7jLvjDhw81d8QjIp5TokQJThw/xuvXrylbtgwlS45k1MgRmsJ6Xf4oSDe4lQkAujoKfptVnFzGOvh4e6JSqYiOjubs2bO0adMatVrNxIkTMrwOUF9fnymTJwFQ3cqKAH9fdHV1UavV5CxiBkD+auXpfnEzOobp+267bS4A7Tu0B8DLK32pvOO1q4SHh2NmZkaVKpV54PI72RQ6pKWlUTdHEXKb1+BZSBjj+wzh5MnjLFq0gNWr12Jubs7AgQOAvy5Q/OnC+bNs2LiJmJiYT/odo6KiefnyJd27d+PZs2eYm5sxaeLET5pDCCGEEEIIIT4XudP/BcibNy/9WnbAZ81BEv5I+AGSYuLw23qCKubFUf6xFB5AoVCydOlSmjazpWkzWxYtW8MlZ392nfHDrFgTSuZKLwqXkpbKUY9btC1XnTZlrUhTq/lzNXt8alL6mJQUzbypqakATJ02jbNnz6FUKtHV1eVxaCjOt24BsG7dep48eUKdCtkAMDECE4P0hLlX7584cvggOjo6nL9wQbN0vlHDhvTs0QNIr8b/pz8/X7p0meTkZE3irWOgj242Q1JfJ/Hbj5NITUom5XUSobceEOHur6mCb2lZiXlz51DNqga//XaMlORkjh45zPFjR8mVKxcKhQIjhQ7LZy5g1WIHgoOD+emnXlhYWHD+3BkmjB+Hg0N6VfzXr19r/SZ29l3YtXPXJ/+WarWaQYOHUKliRS5dPM+MGdOZPWfuJ88jhBBCCCGEEJ+DJP1fgL7duhN68NI7+1+6eqKvq6v57ub2kBIlShAUFETFyjXQLdyeWYd1WXkyjZm7YngUqq8ZezPYC12VDroqHRKSk9hz/zoAb3uUfcbMmQDcvu3KwEGDuXv3LgDz5y/g0aMQzpw5y+w5cwkMDCIlJZlXr16xbt06xo4bi1qtxummE1ZWVpQrV44J48drntXv07c/L168AKBo0SKUKl1WK8meOGkyFhUqce2aI6nJKRyyH01c+At0sxmSp1wxkmLjSXgeRXJ8IjmKFCA5OZnVa9bSp28/7O3taNiwATNmzqJM2fKMHz+RXr37ULtOPdq0/Z6k5CSWv/F6Q1fXOzRt2pxixUvSomUrFEoFSUlJhIam1yhwcnLGzLwQwcHBJCQmsn//AcqVr6B1ns6cPatVbX/kyFH07tNX893R8ToNGjameIlSNG3aHGfn9DnPnD37zt9YCCGEEEIIIf4NkvR/AYrlK0BsWMQ7+1MSk7QKzi1zWEGnTh0ZPWok7e178TBElyZVjRnQyhSAhKT0O+aFcuQlTa2mw84FdNi5QGtOEx2DDPupWbMmAKVLl8Lc3Jx79x+gUCioW6cOhQoVomzZMvz88xCKFi1KeHg4BgYGGBgY8PChG0+fhtO8eTOSkpJo1bIFZmYFADh86DCPHz/mxo2bAFSsWIHvvsvH1WvXNPsdNXIEly9d4OXLl6BUEPfsJc/c/FCnqan+8w8odVQoVCqM8uRCqatDVFQUz58/58KFi1y4eJElixdh27w5ANdv3GDXzh1YWJRn4sQJVLCwoHHjRtSunV5AsFOnjtSoXp2ePbvjctuZ1atWkpyczI8/9tQ6F7ecb9K3bx/N99GjRuJy25nAAD/u3nFl9qyZ7/tJv2gODsvYsnlTZofxSQoWLEhYaAgWFuUBsLauRVhoCCYmJpkcmRBCCCGEEF82eab/S/ARFeTfdPXqVXr+2ItRo0ZQpXJ56tRQExSexDEn7efPC2b/DoC4JO2l67oqHdRJf6tID8TFxgKwZvVq9PR0CQgMZNWq1Vhb16Jq1aooFArGjhmNSqXi1Ssj9u3bT7VqVenS2R4jIyOSktIfGRg6NL3Sf3x8Ag4ODhgY6GteeZczZy7OnzuHl5enZr8dO3YgJiaG2nVqo1Ao+OHCRlQ6OigUoGOgh56xEfrZjdE3yUZScjKJiYmabZ/9UczvT6VLlwZg3S9rSUlJ0TwysHLFcmrUtCZf3rxMmjSR7/LlIzo6hv0HDuDqeodZM2cQGRnJ/v0HMpyXVq1a0q9fXwYNHoKPtw958+WjfPlyH/qZPsjBYRmd7e2YN28+q9es1bTbNm/Oli2btFYT/D8KFizI7VtONG3WHHd3j38aLrq6uvTr24f2HdpTvFgxEhIS8Pf3Z/fuvRw6fFjrUZF/m6vrHSwrV/3kmgtCCCGEEEJ8ayTp/wL4hAZTpFB+YkKevrU/5MZdgjartNquXr3K1atXWbbxJA6nte/az94ZzuV70aTkd9dqr7d+MjkNslHVIB8DB3XmZWSkVv++/QfY90fSa127HnbdfyZnsSb4PkvFJPAxIcF+9Oz5EwcP7Mfdw53p0/+6271l8yaiY2IYOXIUx4/9RqlSJVGpVDRr1pzQsDCMjY3x8fZk+vQZnDl7VpOQNrdtycOHD2nTpjXLHRyYMnUaKa+TGD9qFEaGRryOekXM43BeJyeRmk0fn8AAypYto9nvm6/lMzQ0ZNXK5QCsX7+B/fsPYG5uzp49uyhQoADFihZl7S/rKF++PKampnTt9oNm22JFizJo4IC3Jv3m5uZERETg6HidlJQUQsPCuHfv3lt/q0+VkJDI4MGD2LFzF9HR0Z9lTkhP0D8nXV1ddu/eSfly5Vm8eAkuri68ehVLtapVGTiwP27ubv/3hQUdHZ1PvmCQnJxMRMS7V8cIIYQQQggh0sny/i/Alt27MOvU8J39hewbs373r2/tC/V3pUR+7QsCKiXYWqZik70ArYpbUiavOZYFijLQoj49TSswZfCoDAn/m6rXtKZ51+k4nDdlzTkVK87o8SgmF+UrVEWlUr1zO0hPvMuVK4uRkRHXr1/H3t4OgGrVqqUf65ZN1K9fn21btwBQsaIFo0eNZN7cObjecWX79l9ZvHQxecwLYJQ7B7mKFyRv+RLkLFEY57t3SE1NRV9fn/79+hEY4Ee3rl0BUCqVlCxZkly5cqWfs0KF6NKlM5s2bdDEZponDwClSpUkTZ3GxQvn8fP1xtXlFmXKlqVYsWIolRn/kzhx4gQGBgY4O91g8aKF2NrafvA8fKzr1x2JiIhg6M9D3juuZcsWXL50gcAAP24532TAgP5a/becbzJ8+DAcHJbh5enO4sULuX3LCYDz584SFhrCwQP7tbYZOGAAv991xc3tAfPmzkFH593XAPv17UOtmjXp3LkL27Zvx93dg0ePHnHk6FFatW5LQEAgAA0aNODokUN4erjh5vaA7du3UqRIEc08fy7Tb9OmNQcP7CfA35eOHTqgUCgYOWI4rq63CQzw4/y5MzRo0OCd8fx9eb+9vR2eHm7Ur1+fq1cu4evjxa6dO8iXL59mG0tLS/bu2YXbw/t4ebpz6OABKlao8K5dCCGEEEIIkSVI0v8FeBkZyfL9v1JmRBeym/+VpBjlzUXpQR3Z63RZk1T93cqlc2hS1JNudaBsIX3qW+gwtlUC86cMZGSPftzfcJjv7j7B8KoPq0dOZ/KwMenPzr9H555D2XpZu+11sprncXo0s22t1W5sbEyZMmXIli0bSqWC79u2xcfHF39/fwoWLEj37j9Qo0YNJowfp9lmypRJ/PLLLwAE/nFcSUlJVKtajc3b97Ft5yH8/P1JSUnBx8eXhIQEwp6kF9rTN9AnT548hIWF0rRpc27eTK8V0KZNK0JDQzWPGDRp0pgCBQpoCggCVKlsCZBe/T9NzdRp02jYqAnDR4ykfLmy70zkw8KeULdeAyZNmkJiYiLz583RvKXgn0pNTWP+gkX06tWLAgXyv3VMxYoVWb/uF347dpzGTZqydJkD48aO0VxQ+dOggQPw9vLGtkUrli9fSYuW6b+VfecuWFauSt9+f10osLGxpkjRItjZdWbEiJHY29tlmO9N7Tu0x9HxOm7u7hn6UlJSSEhIAMDIyJD1GzbSslVrOnfugjpNzeZNG7VqUgBMnjSRzVu2UL9BI65cvUrfvn0YMKA/s2fNoUnTZly5cpVtWzdTrFjRjzqPkH7BadDA/gwdNoIOHTphbm7GtKlTNP3GxtnYf+Ag7dp3pHWb7wkMDGTHju1ky5bto/chhBBCCCHE10aW938hHJ2c8PDxoU+37pRuUwtQ8/jlcxavWkBY2JN3bpecnMyUcYMoVqwoVa2sCb4bxu65jqSlpQFw585d7ty5+0mxpCpzkKbO2B4Vq6Zm7UYA5DDJwSyHhShMs/MoIZK8pYpgVrIoNnVqs33Ldk6dPsWypUsoX748a9esYvSYcezZvROAJYuX4u6RvhQ85tUrFEolBobGuD1KoY5NTRQKSEtNITz8Gfnzf0dqaio3rt/AKFs2ChcqTGpqKidOnsLP35+gP2oFNGvWjMFDhjJr9hzmzJ6FoaEhhQoVZOq06WzfthVILyII4OPji6mpKTdvpt8JDwkJ4cGDhzRu3Ehz3v4uMTGRc+fPc+78ebZt347jtauUK1uWh25un3RuCxTIj32bduQyyUGxwoWJionhzJkzuHu4M2b0aEaPGZthmwH9+3H9+g2WL09/vWBAQCClS5XK8DjCjRs3Wbd+veZ7wYIFAYiMjMywFD46OprJk6eQlpaGn78/Fy5epG6dOuzeveetcRcvVgynP87X+5w6dVrr+6jRY3B7eJ/SpUvj7e2tad+4aTOnT5/RfB84YABr1v7Cb8eOATB33nxsbGzo17cvkyZP4WPo6ekxfsIkTf2Irdu2M3LEcE3/n8Uk/zRu/AQ8Pdywtq7FhQsXP2ofQgghhBBCfG0k6f+CvHjxgkWrVvxf2wYGBhEYGPRZ4lApMz5fPXtnON/l0sEmRwQLftuJ3Yj+bHG/QsLT9DvrxwLuUSRnXg52H0eVhjbs3bePdu07MnfObHLmzMnVq1fp2MmOQwcPcP/BA54+faopVDd8xCjikvUYuiZYs79p3b+jnkUu4uNiCHn8mJTUFEaOHMWmjRvIn/87zbjp02fSr29fDA0NMStQgHPnzjNn9ixOnDjBwEHpS+bLlrPAy9OdHDlyAOnP+586fZKbNxwxMsqGiUl29PX1USgUGBoaau5a/8ne3g6VUsnd3++RkJBAx44dSUhI4HHo448+p0qlkrkTJlNUlY3nF++QGBVEgVp1KGFRkVbNmjN37nwO7N/LuvUbMmxbqlRJzp49p9Xm4uJK3759UCqVmgsV9x88+Oh4vH18tC5wPAt/RtlyZd+zhUKrfsK7FClShHFjx1C1ahVy586teVzC3NxMK+l/cP+vWI2NjSlQID8uLq5ac7m4un5SwcT4+HhNwg8QHv6MPH880gFgamrK2LGjqV27Nnnz5EGlUmFoaIi5uflH70MIIYQQQoivjST9IoPHvnco9l1jAsO1k//2VslsWbKHYfOmMP/u6QxJYDuLmugoVTStWZvmv98hLS0NhUJBcnKyJuGG9OTsTeaFihP/WnsuM1NdsmUzoG/f3gwfNkzT/vdl4m9So+bP3uS3FYZTpCegLyMjSUlOwSRHDrIbG/PixQtu33ahbds26OrqZkj6Y6JjGPLzYKZPn4ZKpcLTy4sff+pFZGTUO2P5uxljxmNy9xEBHn89ppH4Moa0pBS612yEw6kDXLl6lYkTxmcoJqhQKDRvIfjT4sULM9QfePO8/lko8V1SkrXPjxo1SsW7n/YJCAygVMlS7z7AP2zftoWwsCeMHTeep0/DUSqVXLl8ET1dPe1YE+IzbPv3Y1QoMra9T3Ly395IoVZrnaPlDsswNTVl+vQZPH6c/ijI8WNHP3vRQyGEEEIIIb4kkvSLDNauXMDcJcXwNy+Kk6+CHNlUtLJMxOn8Vuo1bcjB4N8zJPwqhZLWZa1Y6vgbTsHeNC9SAadtRwgMDGTTxvV0aN8Orzfu9L4pOTkJpfKvZL5CUQPKFzHA6X4Yt2+70MnOXtPn4+tLqVIl2bRps6Zt4qTJTJo4gSdPnqJWq7l500mrbkFMTAxnzpwl+o/Xu1laVkKpVFCxoqUmqRw+fBht27bRbFOzlo3m85mzZzlz9uz/cyoByJUrJ6Wy5yHA48Zb+wN3nqb/oB+YN28B58+d4dmzcADu3nEld+5cpKSk0LVrV7Zt36650KCnp8eLFy/f+ThCWFgYTZo258L5syiV/7zo4NEjR5kwYTwVLCwyPNevUqnQ09PDwECf0qVLM278RG7fvg1AjerVPzh3bGwsT548pUaN6ty6dUvTblXN6rO9JQGgZs0aTJw0mUuX0gtWmJkVwNTU9LPNL4QQQgghxJdICvmJDJKSkhg7rBe3j4yhYb7LlEjay8KJnTl6cDdlrSrj8yJjjYG6xcpjYmDEUfdb+L98yq/u16lapybe3t6cOHmKrl27vHN/Hg9cyfVHLbXc2VUs6mfGDfdY/L3vYWJiQt68ecmdOzcA27f/ipmZGXPnzKZkiRI0b9aMMaNHsWHDxo++KxwcHIyuri69e/eicOHCdOzYgR49un/6ifpILZs0I9rx3Uvv1alpZE+BoKAgzp47R48ePQAYPORnateuy9y58zE3N8Px2lUsLSthZ9cJY2NjTRHDt0lLS8Pb25uEhAQaNmxAnjx5yJ49+/99DBs3bcbFxZV9+/by048/Ur58OQoXLkybNq05eeIYxYsXIyoqmpcvX9K9ezeKFi1K7do2TJ8+7aPm/2XdOoYMHkTbtm0oUaI4kyZOwMKiPJs2b/7wxh8pKCiITh07UrJkSapUqczqVasyrOoQQgghhBAiq5E7/eKd7t65w907d7TaUhRvT6zbWdTk1iMfYpMSAYhNSsQwT3omf+rkKYYPG0rFihXfuu2LFy+Ii4lgdIsEUnRzY2qiQ6PKJlC5FT07twLSi+3VrGXD06dP6d7jR6ZOmcz582eJiopiz569LF+x8qOPy93dg+kzZjJk8GAmTZyAs/Mt5s9fwKqV/189hQ8xyWZMcuir945JjX+Nvr4+OXPk1LQ5OzsDsHXbNhISE1iyeBEnjh/jyZMnxMTE4OXtzbKlS2jduhUGBgZUq1ZNswLiz+X9y5Y50KP7D4wdMxpPTy+Sk5OpVKkiyckpTJo4gYWLFpOamvrBY0hKSqJL127079eX7t1/YOrUKSQkJuDn68vmLVvx8vJGrVYzaPAQZs+ayaWL5/EPCGDq1OkcPnTgg/Nv3ryF7MbGTJs2lTympvj6+vJTrz6frU4FwKhRY1i0aCHnzp4mNCyMBQsWalX3F0IIIYQQIitSFDAr+PEPzX7ljI2N8fH2pHSZcsTGxmZ2OF+lBZtW4eB77YPjDHR06Zm9HLMnTP3ouZVKJYULFyYyMpLo6Oh/EuYXxbpGTfpXqEvYhdvvHFNihD2DZk/h4YP7LFi4iNWr12QYs2jhAlq1aoVFhYrccr5JNmNjFi9ewtWr12jdqiXjx4+jYcPG+P3xusTbt5xo2qw57u4e5M+fn+uOV9m//wBbtmylZMmSLF68kG3btrN0mcO/efhCCCGEEEJ80zI7D5Xl/eKTPHL3pXDOvB8c17SwBcf3HfqkudPS0ggKCspSCT+A0+1bGFYthUL19v/cTIqYcT/Yn6JFi6JUKvHz9XvrOF8/P3Llyql5Dv3SpUts3/4rQUFBrF6zlpcvX2JtY/3WbX/8sSdhYWFMmjwFP39/zpw9y5KlyxgwoP97iyMKIYQQQgghvm6S9ItPsnfbDjoXq/reMQY6ulQ0yMu93+/9N0F9BeavW02ZYfboZTfSas9dvjhFh/+AkVkJWrbu8N45/kzO/6xd4OnhqdX/LCKCPO8oTFeqZEnu3Lmr1ebi4oKxsTFmBQp80rEIIYQQQgghvh7yTL/4JC8jIzmzbT/9u7Vhs8d1UtXa1eON9QwYUaExi8d+XAG3b8V9t4eMdFjAsF59KWGcC0VqGsrshqgLVeCXK8l4PCqHZTF91Go1NWvVfOvbAkqWKEFkZJTmzQQZXkv4t1fUveltr/3TXETgm3nCRwghhBBCiG+OJP3ik104c46XL14wsl8vnuum4Bv/HBVKLE0KkPY8hnkj0l+fJ7Q9evSIMTP/uhgyZ/F6du97zauE9Asnd/1f4+IdT48ePVm4cBGJiYmasXnz5qVDh/YcOPhpj0z8ycfXl1YtW2i1WVlZ8erVK/mthBBCCCGEyMIk6Rf/l7sud7jrcgdzc3NKFC9OSmoK59w9stzz+P8mfZNCmoT/T4v2P2PH+ELs3bOb+QsWEhLyiNKlyzB1ymSePH3KwoWL/q99bd/+K/369mHunNls3bqNEiVKfPKrDoUQQgghhBBfH0n6xT8SGhpKaGhoZofxlcqYbIdEJHP8WiDK6Ees+2UNuXLlIiIigjNnzrLMwYGoqKj/a0+f41WHQgghhBBCiK+PJP1CZJK4lwHkMq5IZGyqps1QT0FyzGPGDh/x3m1r1rLJ0Na0ma3m8+PHjzEzL6TV7+zsTKvWbf5Z0EIIIYQQQoivilTvFyKTrFg0lYENIrEuo4OJkZKqJXQZaRvP8gUTMjs0IYT4aNbWtQgLDcHExCSzQ/mgrylWIYQQ4nORpF+ITBIZGcXQfp2I+n0xzcyugt8qhvfvQGhYWGaHJoT4RvXo0R0fb09UKpWmzcjIiOCgAI4c1i4kWqNGDcJCQwgPD8eyclViYmL+63A/mavrna8mViGEEOJzkeX9QmSi1NRUzp05wbkzJzI7FCGE4ObNmxgbG2NpWYm7d38HoGbNGkRERGBpWQlDAwMS/niziI2NNU+ePCUgIDAzQ/4kycnJREREZHYYQgghxH9K7vQLIYQQAgB//wCePHmKjbW1ps3G2pqzZ88RHByMVXUrTbu1dS1u3ryZYcm8ubk527dtwcP9IX6+3ly+dIFGjRpqtitdujS//roNby8PfLw9OXL4EEWKFAFAoVAwcsRwXF1vExjgx/lzZ2jQoIFm24IFCxIWGkKLFrYcOLAPfz8fzp8/S7VqVTVj3rf/v8dqb2+Hp4cb9evX5+qVS/j6eLFr5w7y5cv3+U+uEEIIkUkk6RdCCCGEhpOTEzY2fxULtbGx4aaTE07OtzTturq6WFWrxs2bNzNsP2/eHPT09OnQ0Y5GjZsyd9584uLiAcifPz+HDx/kdeJr7Oy7YNuiJXv37kNHJ/1xgr59+zBgQH9mz5pDk6bNuHLlKtu2bqZYsaJa+5gwfhzr1q2nabPmBAQEsHbNas0jCe/b/9sYGhoyaGB/hg4bQYcOnTA3N2Pa1Cn/6BwKIYQQXxJZ3i+EEEJ840qVKkX27MZ4e/tw08mJmTOmo1KpMDAwoEIFC5ydb6FSqejTuxcAVatWxdDQkBs3nTA3N9Oay9zMnFOnTuHl5QXAo0ePNH0//fQjr2JiGDR4CCkpKQBajwcMHDCANWt/4bdjxwCYO28+NjY29Ovbl0mT/0rE161bz8WLlwBYsmQZV69coljRovj5+793/2+jp6dHlSpVePDgAQBbt21n5Ijhn34ShRBCiC+UJP1CCCFEJjl4YD/uHu5Mnz4zU/Zfv7Y1I36yx/hFIKrEGBK/60TgK8iWLRuVK1uSI0cOAgICePHiBU5OzqxcsRxDQ0NsrGvx+PFjHj16lCHp37JlC/Pnz6N+/Xo4Ol7n5KlTeHqmJ+AW5ctz6/ZtTcL/JmNjYwoUyI+Li6tWu4urK53t7UhOSWbjxs0AePwxn23z5mzZsgmAwYMHUbFihf+1d99xVdV/HMdfTEVwrxQHuDJxpThAc5Q7904zc+eqUHOPXLhX5co9c89cuVMBFTURRGUoIqiYuABlCL8//HXrhivXRXw/Hw8fcb7zc895PILP/Z7zPU+d/3FiY2NJTEw0HF+/HkG2bNle4GyKiIikTLq9X0RE5BWaNm0q4WGhjB/vnqzO3X0s4WGhTJs2FYDOXboyceLkNx0iAGVKl2TY5/Ww3jGduGObue+zn6TdP1MoZC8x0VG4urpSydUVT6+jANy4cYPQ0FDat2/Hd9/149ix448dd+Uvq3BxrcS69RsoWrQoO7Zvo2OHLwF48P9NAJ8mKSnJ6NjMDP5VREJCfLJ+ZmZmz5z/cf6Z8P8/AMzN9eeRiIikHvqtJiIi8oqFhYXRqGFD0qZNayhLkyYNjRs15MqVK4ay27dvEx0dbYoQ+bZjG+L2LU5WnnA1iNgbV/i4enVcXFzw9PQ01Hl6elG6VGkAjh479sSxw8OvsmzZcjp36crcuT/Tpk0bAPz9/alQvjyWlslvNIyKiuLq1WuUL1/OqNy5rDP3799/5udp1aolTk5OhIeF4n38GLGxsXTu0pWjx44xdOgQAgPOM3fObODRc/z/9tdmfrNm/QSgzfxERCTVUNIvIiLyip0540tYWDh169YxlNWrW5fw8Kv4+voZytatXcPIkSMMx0e9POjduxdTp0zmwnl/jh/zom3bNob659m9HsDZuSwb1q8jKDAA7+NHGT1qpFGi2779F3xUpyElJ+2i+NgtOHQcbagzs7TCKjGe8uXLUapUSbp17UKpUqUAOH/hAg0bNgBgwvhxhIeF0rNnTwAqV67Mpo3ruRgcyPlzZ1m9+hdq1qxJpUqVCAwMBGDR4sWkT5+e2bNmUrJkSRwdHWjWrCkFCxYAYPacOfTs0Z2GDRtQr15dVqxaSYmSJYiOjsIMs6ee8y1btnDu3HmWr1hJ9+49OXnyFCWKF8fRwYETJ09S/eMa/PjTTAC+69fXqO8/N/MbP34igDbzExGRVENJv4iIyGuwes0aWrdqaThu3bolq1avfma/bt26ctrHh1q167JkyVLGj3OnUMGCRm2etnt90aJFWbliOdt37KBGzZp81b0H5cuXw33sGABKlizJ6FEjuXbeh7Oj2xA0uy/RQacNY+du1IM0OfJiZmbG5cuXCQgMYuWK5WTKlImdO3Ya2lX+qAqlSpdh0aJFANjYpGXuz/PYuvVX7t69h6uLCwvm/0xwcDCDBg8G4Nat27Ro2QpbW1s2rF/Lzh3badumDfHxj57xX7BgIb/t3cvUaVOYO3c2Dk5FGLNvLWSy5aPan9Dss7/P57/dv/+Ahw8TiIuNpX//7/ht1w5WrFiGp5cXXbt2IzQ0FF9fXwCjL2Pg0WZ+AwYOxsfHx7DxX+XKlZ55rURERN4G2shPRETkNVi/bj2DBg4gT548JCUl4excju7de+Lq4vLUfvv27WPJkqUA/DRzFl26dMbF1YXAoCBDm6ftXt+9ezc2btrE/PmPNr27ePESw4aNYP36tQwcNBh7+9zExMQQePkKWe9FEn/rOvevBABgbp2WbJUbE3biIE17DyUsLAxLS0uqeHnyWetWzJ4zl2bNW7B+3Vr+/PMmd+/eZe/efeS2z2uIbcf/vxjIkiULvmdOM3PWbG7dum2o9/c/R5u2nz/2s7f4/DMC0j6g0s9DjMrrf+DM+T/DuWRvzY8L5+Hnd9aoPrd9Xvr2caN4cSeGDhtuVOfq6sLsWTMpXLgI6dPbcf/+AzJnzoyNjQ1r1qwFwH3sGEJCQgDYuWsXHTt1Yf68uU+9TiIiIm8LJf0iIiIvKXPmTLi4ViE29oFhxT3y1i327t1HyxbNMTMzY+++vUTeuvXMsfzP+hsdR9y4QbasWY3Kzv5jN/qIiAgAsmbLRmBQECVLlMDBwYGmTZoY2piZmWFhYUHevHn5/fdDXLkShnO12iQ5O3N73wpu/3GQpPhYrLPZY25phc/lG4SFhQGQkJDAH3/8QeHChZ8ad/78+en/XT/KlPmQLFmyGDbDs7fPzfnz55/5uQsVKkSRmi7M8/s9WV103APs0qTF62oQNnk/oGGzxmxZv4kMGTNw9+7dJ45pb2/PsqVLWbZ8ORMnTeb27duUL1eeqVMnY2VlZdgrID7+XxsDajM/ERFJRZT0i4iIvIRe3w4iV5FqeF20JbNlEmUrZORO5KOEedXq1Ywd8+h5+X++Z/5p4v/9OrvHJKD/3L3+r93uzc3N/v9fc5YvX8GChYuSjR0WFkZ8fDy169TF1dWFz1q1pE6zPuRq0puLm+dBXicARk350aifmZlZsl31/23J4oWEh1/lu/4DuHbtOubm5hzYvxdrK+vn+txtun7JysDHvxHg4q0IKuX/AID9of70r1+bLes3UbpUKYKCggGIi4/H3NzCqF+pUiWxtLRg5MhRhvgbNGjwXPGIiIikFvoaW0RE5AU1ad6GO+lrMWePJX8ExXL0fBx+YeZkyOpAcacS7N9/ACsra6ysrDlw4OAbienMGV+KvF+ES5cuJfv314r2w4cPOXToMD16fY1TqTJY2KRnddADmvccRGxsLM5lyxrGs7S0pGSpkgQEPNqM768xLCz+/hMic+ZMFClShOkzfuDw4SMEBgaSKWPG547ZzMyM9PY5uBf7+F361/gcIU/GrAys1pQi2XJzL60Zffu60bp1K2bPmQPAldAr5MuXFyenYmTJnBlra2tCQkKwsrKiY8cO5MuXj2bNmtKu3eMfLRAREUmtlPSLiIi8oIttBBwAADwnSURBVI8+acIBv8Rk5eG3oEW77iQmJlK1WnWqVque/H3wr8nMWbNwLlsW97FjcHIqhqOjA7Vq1mTM6FEA1KjxCZ06dsDJqRj29va0+P/jB/sPHCTy1i2WLlvG0KFDqFatGoULF2bSpInYpLXhl1WrALhyJYzExERq1KhBlixZSJcuHbdv3yEyMpLPP2+Dg4MDlSq5MmLE8KeFacTW1pY7CQ+eWH/13i06rfuJPBmzMatxN9q51KRRw4a4ufXl11+3AbBt+3b2HzjA2jWr8fX1oXHjRvj5nWXE9yPp2aMH+/ftoWmTJowbN/4lzq6IiMjbR7f3i4iIvKA4kr/vHeBhIlimfbTSHRUV9SZDwt//HE2btWDggP5s3LAeMzMzLoWEsGXLVgDu3rlL3bp16dOnD2nTpiH44kV69OzFhQsXAHB3H4+5mTk//jAdW1tbfHx8aNP2c+7cuQPAtWvXmDxlKoMHDWTa1CmsXbceN7c+dO/Rk9GjRrJv726CgoMZNmwEG9avfa6YY2NjSWNu9fTPdeMKvTb/DEAthxL4zdvIyZOnDPVxcXF07fpVsn7z5s1n3rz5RmXr128w/LxmzVrDhn5/2blrl9HmhCIiIm8zs1y58zz9Ib1UxM7Ojgvn/Sny/gdv/I8wERFJfabMXsP03VmTldtYm9GmlD/fD+ppgqjeTlMWzWbiuX3P1dateA1GdvmG+w+efHeAiIhISmHqPFS394uIiLygQ3s2UL148l+lLV1h7fLZJojo7XV4xx7K5nJ8ZrvMNnbcvRyuhP8VadmyBf5nfd/4vEe9POjcudMbn1dE5F2kpF9EROQFbVq/ivR3dvFVjQTKFEqLS1Fr3OrF4bN/Ln5+bz6Repv9unELdTMXJrtthie2SWNhRY+iVZg3fdYbjOzppk2bSnhYKOFhoYRcCsbT4zDDhw3Fxubxj37II3Xr1WfF8hWmDkNE5J2gZ/pFRERewswZ48mYcTYurpW5FxfLoCmHiI2NNXVYb534+HiG9O7LyGkTOBkbwf4r/sQ/fAiAGWaUy+VI3fc+YOLA77lx44aJozW2b99+3Pr0xcrKkgrlKzB58kRs0qVj0KDBpg4txYqMjDR1CCIi7wyt9IuIiLykO3fusHPHNvbt3aOE/yXcvXsXt07dCVyxi572Ffi6SBW+eb8afQpVgQP+fP15Zy5dumTqMJOJi4vjxo0bhIdfZeOmTWzYsJE6tWsB0KP7V3h6HCYoMIDdu3fx6af1DP1cXCoSHhZK5cqV2LF9G0GBF9iyeSMFCxYwtOnbx43dv+2kWbOmHPXy4Jy/H7NnzcTW1tbQ5tNP67F3z26CAgPw9fVh9aqV2NjYUKFCBUIuBZM9e3ajeIcPH8aG9euSfY6CBQsQHhZKoYIFjcq7du3CUS8PAMzNzZkyeRJenkcICgzg0O8H6NSpo1H7adOmsnDBfL7q1o1TJ73x9fXBfewYLC3/Xmv69+39Xbt2Ye+e3QQGnMf7+FHc3ceSLl26574GIiLyZFrpFxERkRTF84gHnkc8TB3GC3vw4AFWVlYMGNCfenXrMHDQEC5evEjFihX48YcZ3LwZiZeXl6H9gAH9GTlqNDdv3mTC+HFMnTKZRo2bGurz589Pndq1+aJ9BzJmzMjcObPo1asnEyZMJEeOHMya+RNjxrqzY8dO7OzsqFChPGZmZhw9epTLly/TvFlTZs+ZC4CFhQXNmjbB3T35qwuDgoI5fdqHJk2bMGnSZEN5k8aN2bhpM/Ao6b969SrdvupBZGQkzs5lmTRxAhEREWzd+quhj6urC9cjImjRohUOjg7MmT0LXz8/Vq785bHnLDExkWHDhxMaeoV8+fIyzn0sQ4cOYfDgIS93MUREREm/iIiIyPOySZuWJvUbUMShABE3/yRtmjRG9aVLl6ZJk8Yc8fCga5cutGzVihMnTgJw+fJlypcrR7vP2xol/RMmTDQc/zRzJsuXLSVNmjSGu0bMzc351q0P0dHRwKNXDlauXIkJEyBnjhxYWVmxffsOwsLCADh37pxh7F9+WUWrVi0NSX+NTz7BxsaGLVu3Pvbzbdi4kQ5ffmlI+gsUcKRUqZJ8/c23ACQkJDB5ylRD+9DQUMo5O9OgQX2jpP/OnTsMGTKUxMREAoOC2LN3Lx9VrvzEpH/+/AVGY06cNJnx49yV9IuIvAJK+kVERESeQ4uGjWhRpSaR+05wb/tpimXNRIUvSpCjUH4CLpzDwsICKysrdu36jTlz5tKgfn1W/bLSaAwrKyt8ff2Mys6e9Tf8HHE9AoBsWbMSFh4OPEqC/0r4Aa5HRJAt66NXRfqdPcuhQ4fYt3c3Bw4e5ODB39m2bTt37twBYPWatfTv/x1lynzIyZOnaN26FVu3/sr9+/cf+xk3b97CsKFDDO2bNmmCr68vAQEBhjbt2n1Om88+I08ee9KmTYuVlRV+fmeNxjl/4QKJiYlGn6voB0WfeG5dXV34uncvChcuQvr0dlhYWGJjkxYbG5snxioiIs9HSb+IiIjIM1Sv/BGN33cm4Ic1hrK4qBjuBYRikZjE5j2/MXfpYq5fv05CQgIfflgagHZffMm1a9eMxoqLM973ISEhwfBzEkkAmJmbP7YeICkpCfP/1ycmJtKqdRvKOTtTtWoVOnbowMAB/fm0fkNCQ0O5efMmu3fvoVWrloSEXObjj6vTvHnLJ37OiIgIPDw8aNK4MSdPnqJR40Ys/8cu+w0a1Of7ESMYNXo0J7xPEBUdTffu3Sjz4YfGnyn+XzGThLnZ47eSsre3Z9nSpSxbvpyJkyZz+/Ztypcrz9Spk7GyslLSLyLykpT0i4iIiDxD+6YtuDRj/WPrYq5HUiRzTm7evGlI0C9cCODBgwfY2+c2upX/dTnu7c1xb2+mTpvOsWNe1K1bh59/ngfAyl9+YfasmVy9eo2QkBCOe3s/dawNGzcxZPAgNm3ejEP+/GzevMVQV6F8ebxPeLNkyVJDmUP+/C8Ve6lSJbG0tGDkyFEkJT360qNBgwYvNaaIiPxNu/eLiIiIPEXGjBlJc+cB/D8hfZyYEwG4VqxoOI6OjmbO3J8Z+f0IWrRoTv78+Snu5MSX7dvTokXzVxbbhx+WpnfvXpQsWRL73LmpV68uWbNkMbod/8CBg9y7d49vvu7N6tVrnjLaI9u378DOLj3jx7nj4eFhdKfCxUuXKFWyJFWrVqVAAUe++64fpUqVeqnPEBISgpWVFR07diBfvnw0a9aUdu0+f6kxRUTkb0r6RURERJ7CxsaGhJgHT22TGPMA23+9Ym7ixElMmzad3r16cvDAPlauXE7NmjW4fDn0lcV2714UFStUYPmyJRw6dJAB/b9j1KjR7N9/wNAmKSmJNWvWYmFhwdp1j79b4Z+ioqLYvWc3Tk5ObNiwyahu2bLl7NixkzmzZ/Lr1i1kzpzZaNX/Rfj5nWXE9yPp2aMH+/ftoWmTJowbl/ztAiIi8mLMcuXO8+SvrVMZOzs7Lpz3p8j7HxAVFWXqcEREROQtYGFhwYqJMwia8eRVcofWNRmyZgGXLl16c4H9B5MmTiB79ux82aGjqUMREXnnmDoP1Uq/iIiIyFM8fPiQs+GXSZ8v12PrLW3SEJszQ4pM+NOnT89HH1WmadMmLFy4yNThiIiICSjpFxEREXmGiTN/IGOLKmQu6mhUbpszK4W+bsmwKRNMFNnTLVq4gMWLFrJs+Qp+P3TI1OGIiIgJ6PZ+ERERkedgbW1Nh8/aUrnkh1jGJ/LQwpzz4ZeZuWQhkZGRpg5PRERSKFPnoXpln4iIpHq5c+eib58+VK9enSxZMhMREcHOnbuYNn06t27dNnV48paIi4tj7pJFzEW3yYuIyNtDt/eLiEiqli9fPnZs30aBAgXo0bMXlSp9xICBg6hcuTJbtmwmU6ZMj+1nZWX1ZgMVEREReQ2U9IuISKrmPnYM8fHxfNamLV5eXoSFh7N//wFatf6MXO+9x4AB/QE46uXBN998zbRpUznn78ekSY+e0XZ2LsuG9esICgzA+/hRRo8aiY2NjWH8HDlysHTpYoICA/DyPEKTxo056uVB586dDG3sc+dm0cIFBFw4x/lzZ5kzZxbZsmUz1Pft48bu33bSrFlTjnp5cM7fj9mzZmJra/uGzpKIiIikVkr6RUQk1cqUKRPVqlVl8ZKlPHhg/J71GzdusGHDRho2aGAo6/5VN86fO0+dup8yffoPFC1alJUrlrN9xw5q1KzJV917UL58OdzHjjH0+WHGNN7LmZPmLVrQpUs32rZtY5TQAyxcOJ9MmTLRtFkLWn/WBof8+Zkze5ZRm/z581Ondm2+aN+BL9p3oGLFCvTq1fM1nBURERF5l+iZfhERSbUcHR0wNzcnMCDwsfUBgYFkzpyJrFmzAnDkiAdz5s411M+YMY2NmzYxf/4CAC5evMSwYSNYv34tAwcNJm+ePFSpUoU6dT/Fx8cHgH7f9cfjyN+7pFf56CM++OADKrq4Eh5+FYDeX3/LwQP7KFWqFKdPnwbA3Nycb936EB0dDcD69RuoXLkSE1LmpvAiIiLyllDSLyIiqYaZmRmfVP2IZnU/wdLCnLDIe89sD5CU9OhFNqf/n7j/pWSJEjg4ONC0SROjPhYWFuTNm5eCBQoQHx/PmTNnDPWXLl0y2hywcOFChIeHGxJ+gICAAG7fvk3hwoUMSX9oaKgh4Qe4HhFBtv9/GSEiIvKu6NvHjTp1alOzVh2TzL9u7Rr8zvoxYsRIk8z/OijpFxGRVCFDhgwsmDKazJePE3t8CSQ+pFDhsiQlJVGlSmV27tqVrE+hggW5deu24XVrMTExRvXm5uYsX76CBQuT79YeFhZGoYIFHxvL/79LMBz89aWCcRvj8oSEBKP6pKQkzM31FJ6IiLxdsmfPzjdf9+aTTz7mvffe4+bNm/j5+TFv/gIOHz5i6vDeSUr6RUQkVZg+ciAZjiwh9u5NQ1lMwAnunTtO29atmDhpCrdv3zbUZc+enaZNm7B23fonjnnmjC9F3i/CpUuXHlsfGBiIlZUVxYsXN6z2Ozg4GL0RIOBCAPb29uTOncuw2l+4cGEyZsxIwBMeOxAREXkb5cmTh82bNnL37h3GjHXH398fS0srqlWrivvYMVSpWv2NxGFpaZnsy/R3mZYQRETkrZc3b17ycJeEfyT8f7mydipmiQns+HULFSpUIHfuXFSrVo1Vv6zk6rVrTJgw8Ynjzpw1C+eyZXEfOwYnp2I4OjpQq2ZNxoweBUBgUBC///47kyaOp3Tp0hR3cmLixPHcv3/fsIr/+6FD+Pv789OPP1KieHFKly7NDzOm4+HhadgHQEREJDUY5z6WJJKo92kDtm3bTnDwRS5cuMDPP8+jfoNGwLPfaPNvZmZmuH37Dd7ex7gYHMju33ZSrVo1Q32ePHkIDwulQYP6rFu7huCgAJo1bUrmzJmYNfMnvL2PERR4gb17dtO4USOjsW1sbJgxYxoBF85x6qQ33bp1TTZ/xowZmTFjGmf9zhAUeIHly5bi6OjwSs7Xm6KkX0RE3nqu5ctidfmPx9bF3rjCufFfYp6UwJzZM/E4cphJE8fj4eFBw4aNjFb//83f/xxNm7XA0dGRjRvW89uunXzXvx/XIyIMbb7+xo0bN/5kw/q1LFgwj5UrfiEqKorY2FhDm44dO3Pnzh02bFjH6lUrCbkcwlfde7yqjy8iIvJGmZubU6NWDSbM/QH3RTNxXzyTGUt+pnr1aixbtpz79+8n63P37l3g+d5o80+dO3eiW7eujB41hho1a3HgwEEWL1qQLPEeMngQCxYupGq1jzlw8CBp0qTFx+cM7dt/SfWPa7BixQp++GE6H35Y2tBn2LChVHJ1pVOnLnzW5nNcXSpSsmQJo3GnT5tKqZIl+bJDJxo0bISZmRnLli3F0vLtuWneLFfuPMkfNEyl7OzsuHDenyLvf0BUVJSpwxERkVekUf16fFvMipjAPx7fwNyCKxW+pNt3w197LLlyvccJ7+O0bNVazy6KiEiqkylTJkbPmMShe6EcvnKeh0mJADjlzMfyVt/i5Xua3h26EhYenqxvlY8+YvnypUZvtClcuDAHD+yjbr36nD59OtlGfie8j7N4yRJ+/PEnwzjbft3K6dOnGTxkKHny5OHYUU+GDR/BggULnxr70qWLCQwIZNToMaRLlw4/Xx+++daNLVu2Gj7bCe9jLF+xghEjRuLo6MCRw4do2Kgx3t4nAMicORPex4/xzbdu/Prrtuc6Z6bOQ7XSLyIib719Bw/xwLH8E+vTFHVh/c79r2XuSpVcqVWzJnnz5sXZuSyzZ83k8uXLeHkdfS3ziYiImIqVlRVjfpzMzIseHAz1NyT8AH/tYbsp9DSDp44lQ4YMyfo/6402/2ZnZ0euXO9x/Li3Uflxb28K/au9z2njR+bMzc35+uve7Nn9G76+PgRcOEfVKlXIbZ8bAAeH/KRJk4YT/0/mAW7fvk1QUNDf8RYqTHx8PCdPnjKU3br1qE3hQsnjTamU9IuIyFvv3r17HA2OwMqhZLI6y/SZueNYkd37DryWuS0trRg4cAAH9u9lwfx53LwZSbPmLbWBkIiIpDr1Gn7KzpuB3LqffLX68u0/SUxKxD5jVpZcPE7bju2TD/Ccb7T5t3/XmZklL4u5b/wGnq+6daVrl87Mmj2bli1bUbNWHQ4cPIi1lbVhzmd5UhszMzPeptvllfSLiEiqMHLyD3haFsS8RhfSFSiJTd73sarcmqtlWtOp77Cn/jHxMg4ePMjHn9SgYKEilCpdhk6duxAWFvZa5hIRETGljz6txfGrQY+tuxsbg2fIeVqWrMT1qDsULlvSKGnOkCGD0Rtt/vK0N9pERUVx9eo1ypcvZ1TuXNaZwGe8AadChfLs2vUbGzZs5OxZf0JCQijg6Giov3jxEnFxcZQpW8ZQljFjRgoUKGA4vhBwASsrK8qU+dBQljlzJgoUKEBAQMBT509J3p7dB0RERJ4iKSmJEROnkyFDBmpUq4JN2vQc3rSKkJAQU4cmIiKSKsRbP33N2P3Aeha3+Jrlrb7FP/QipUuX5t69e1St8hFffNGOqtU+NrzRZsSI77GwtGSc+9invtFm9pw59Ovbh5CQEPz8/GjVsiVOTsXo1bv3U2O5eCmET+vVxdm5LLdv36Fb1y5kz57d8OVCTEwMv6xazbChQ7h16xY3bvzJwAH9SUz8+5GFixcvsXPnLiZNnMCAAYOIio5i8KBBXL12jV27fvuPZ890lPSLiEiqcvfuXTZs+dXUYYiIiKQ6z7pnLvxuJG1+mUKncjWp+/6H1Nqwjj9v3uSMzxkGDhoMPHqjzZgxo9mwYR2JiYnsP3CAoUOfvNHuggULSW9nx/Dhw8iWNSsBAQF82aETFy9eemos06fPIF/evKxc8ehtAstXrGTnrl1kSP/3XgOjR4/B1jYdixctJCoqirlzfyZ9+vRG47j16cuoUd+zZMkirK2t8fI6Srt2X7xVj/Fp934RERERERF5pslL5zLJb89ztXUrUYNhHXoRHx//mqNK+Uydh+qZfhEREREREXmm4D/8yJcp+zPbZUiTjpiwCCX8KYSSfhEREREREXmmVYuW8XmBcpg/Y+f79u+7sHzuwjcUlTyLkn4RERERERF5pshbt1g04Qf6lqpF+jQ2yerTWlrTvXg19i9bT3DwRRNEKI+jjfxERERERETkufxx8hQ3+4+kQ69u2BTORlDMTRKTknBIlxmzyGhWuP/IOf9zpg5T/kFJv4iIiIiIiDy30NBQRg0Yio2NDfnz58fc3Ix1V8K4e/euqUOTx3hrkv6vv+5NjU8+xsnJibi4OD4oVtzUIYmIiIiIiLyz7t+/z7lzWtVP6d6aZ/qtrazY+us2lixdZupQRERERERERN4Kb81K/+QpUwFo2bKFiSMREREREREReTu8NUn/i7C2tsba2tpwbGtra8JoRERERERERN6sVJ309+7Vk759+5g6DBERERERERGTMGnS37eP2zOT8jp1P8XHx+eFxv/xp5nM/Xme4djW1pZTJ71faCwRERERERGRt41Jk/5FixazefOWp7YJvXLlhcePi4sjLi7uhfuLiIiIiIiIvM1MmvRH3rpF5K1bpgxBREREREREJNV6a57pt8+dm0yZM2GfOzcWFhY4ORUD4OLFS8TExJg4OhEREREREZGU561J+vt9149W/3hd3+7fdgHQrHkLPD29TBWWiIiIiIiISIr11iT9bm59cHPTTvwiIiIiIiIiz8vc1AGIiIiIiIiIyOuhpF9EREREREQklVLSLyIiIiIiqca6tWsYOXLEc7V1calIeFgoGTJkeGKbvn3c2P3bzlcVnsgbp6RfRERERERStGnTphIeFsr48e7J6tzdxxIeFsq0aVMB6NylKxMnTn5lc8+eM5eWrVq/svFE3jQl/SIiIiIikuKFhYXRqGFD0qZNayhLkyYNjRs15MqVK4ay27dvEx0d/crmjYmJ4dat269sPJE3TUm/iIiIiIikeGfO+BIWFk7dunUMZfXq1iU8/Cq+vn6Gsn/f3m9tbc3QIYPxPn6Ui8GBHD78O5+1bmU0dsmSJdixfRtBgRfYsnkjBQsWMNT9+/Z+CwsLRo8aif9ZX3x9fRgyeBDTp09l4YL5hjbVqlVj08b1hjZLliwif/78hvo8efIQHhZK3bp1WLt2NUGBF9i9exdly5Z5NSdL5B+U9IuIiIiIyFth9Zo1tG7V0nDcunVLVq1e/dQ+P8yYTqNGDRk6bARVq33MwIGDiI6JMWozYEB/Ro4aTZ26n5KQ8JCpU578eEDPnj1o0rQJbn360bhRE+zSp6dO7dpGbdKls2Huz/Oo92l9WrVqTVJiEgvmz8PMzMyo3cAB/ZkzZy41a9UmODiYWTN/wsLC4nlPh8hzsTR1ACIiIiIiIs9j/br1DBo4gDx58pCUlISzczm6d++Jq4vLY9sXKOBIw4YNaNX6Mw4dOgzA5cuXk7WbMGEiXl5eAPw0cybLly0lTZo0xMbGJmvbscOX/PTjTHbufLT6P2TIUD75uLpRm+3bdxgd9+nbD98zpylSpAjnz583lM+ZM5e9e/cBMHnyVA4e2IejgwOBQUHPe0pEnklJv4iIiIiIpChmZma4uFamQOEPCAm+wF/r45G3brF37z5atmiOmZkZe/ftJfLWrSeO4+TkREJCAp6eXk+d7+xZf8PPEdcjAMiWNSth4eFG7dKnT0+OHDk49ccfhrLExER8fM5gbv73TdT58+en/3f9KFPmQ7JkyWKos7fPbZT0n/U/9/e8EY/mzZotm5J+eaWU9IuIiIiISIpRsGAh+g6dytGQDFyMsCB/1UQqVs7E5eBHifmq1asZO2Y0AIOHDH3qWA8ePHiuORMSEgw/J5EEgJn5k5+ETkpKMjr+9237SxYvJDz8Kt/1H8C1a9cxNzfnwP69WFtZ/2ve+GRjmpsbjyXysvRMv4iIiIiIpAhWVlb0Gz6DyTvs2OfzkIvX4jjol4DvFQsKFilBmjRp2L//AFZW1lhZWXPgwMGnjufvfw5zc3NcXCq+kvju3btHREQEH35Y2lBmbm5O8eJOhuPMmTNRpEgRps/4gcOHjxAYGEimjBlfyfwiL0Ir/SIiIiIikiLUqdeQ3/zsiP/HyjtAYhJcu2tBg0YtWLdmOVWrPXqGPjEx8anjXblyhbVr1zF1ymSGDRuB39mz5MljT7Zs2di69dcXinHhosX07tWTSxcvERgUSMcOHciYMaNhpf727TtERkby+edtiIiIwN4+N4MHDXqhuUReBa30i4iIiIhIilCshDP+Vx6fyN+7n0SxkmUBiIqKIioq6rnGHDhoML9u2467+1h+P7ifSZMmks4m3QvHOHPmLDZt2syMGdPYsnkz0dHRHDh40LDpX1JSEt179KRkiRLs27ub778fwegxY194PpGXZZYrd56kZzdLHezs7Lhw3p8i73/w3P+TEBERERGRN+PLTj24lLY1QVfjk9XlzW5FCcstzJ355NfpmYKZmRm/H9zPlq2/MmlSyopNUgZT56Fa6RcRERERkRRh47oV1C+d/DV5AI3KxLJu1aIXGrdlyxb4n/U1HPft48bu33Yatenbx43Tf5wkPCyUOrVrP3Ese3t72rT5jAIFHClatCjjx7uTN29eNm7c9NzxHPXyoHPnTv/5c4i8CD3TLyIiIiIiKcKdO3fYu2kW3Rv0ZK2XJX/efUiW9Ba0qwK5bG7w69bNvPfee9y8eRM/Pz/mzV/A4cNH/vM8s+fMZeGiv79AKFSoEH379qFjx86cOHmSO3fuPLFvUlIirVq2YPiwoZiZmXH+/HlatW5DYGDgc89ft1597sfE/Oe4RV6Ekn4REREREUkxdmzbiM8fx2jTvjuZSuQiKSGaGmUrcysygTFj3fH398fS0opq1ariPnYMVapW/89zxMTEEPOPpNvBIT8AO3ftembf8PCrNGrc9LF1lpaWRq//e5LIyMjnjFTk5en2fhERERERSVHCwsKY5D6UIX07kT9XRhISEqj3aQO2bdtOcPBFLly4wM8/z6N+g0YAdO3ahb17dhMYcB7v40dxdx9LunRP3qzvn7f39+3jxtIliwEIDwslPCwUePSsvtu33+DtfYyLwYHs/m0n1apVM4yRJ08ewsNCadCgPuvWriE4KIBmTZsybdpUFi6Yz1fdunHqpDe+vj64jx2DpeXf663/vr3/v8Yv8l8o6RcRERERkRQpU6ZMVK9ejcWLl3D//v1k9Xfv3gUevbpv2PDhVP+4Bt9860blSq4MHTrkueaYPWcu37r1AaBU6TKUKl0GgM6dO9GtW1dGjxpDjZq1OHDgIIsXLcDR0cGo/5DBg1iwcCFVq33MgYMHAXB1dSG/Q35atGjFt9+60bJlC1q2bPHEGF4mfpFn0e39IiIiIiKSIlT6qDIN2jQHu7QkmkEuu0yYm5tz/dq1p/abP3+B4efQ0FAmTprM+HHuDB787MQ5JiaGu3cefXlw48YNQ/lX3boxc9ZsNm/ZAsBY93G4urrSpXNnBg8Zamg3b/4Cduww3hTwzp07DBkylMTERAKDgtizdy8fVa7MypW/vPL4RZ5FSb+IiIiIiJjcwFHD+DOnDT9e8iIh8SEAxXPmo3a5SrTq1gEfP1/O+59/bF9XVxe+7t2LwoWLkD69HRYWltjYpMXGxuaxdwg8i52dHblyvcfx495G5ce9vSlW7AOjMp/TPsn6n79wgcTERMNxxPUIin5Q9Inzver4Rf5Jt/eLiIiIiIhJdezelbMZ4tkafMqQ8ANcvv0niUmJnLoTRq8Rg7C1tU3W197enmVLl3Lu/AW6dO1Knbr1GPL/lXgrK6uXiispKcno2MwseVnM/eS78CfEG2/ml0QS5maPT71eZ/wioKRfRERERERMyMLCAqdKzhy9GpSs7m5sDJ4h52lewpUtYb40+6ylUX2GDBkoVaoklpYWjBw5ipMnTxEcfJGc7+V8qZiioqK4evUa5cuXMyp3LutMYMDzv5rvebyO+EX+SUm/iIiIiIiYTOWPKnH0zpUn1rsfWI+5mTnDa7aibuMGODo6UKhQITp17MDWLZsICQnBysqKjh07kC9fPpo1a0q7dp+/dFyz58yhZ4/uNGzYgIIFCzB40ECcnIoxf8GCZ3f+D15X/CJ/UdIvIiIiIiImkytvHq7G3HliffjdSNr8MoXjVwIpW9SJfXv3sGrVSipXrszAQYPx8zvLiO9H0rNHD/bv20PTJk0YN278S8e1YMFC5s79meHDh7F3z26qV6/Glx06cfHipZce+59eV/wifzHLlTtP0rObpQ52dnZcOO9Pkfc/ICoqytThiIiIiIi88+o3rM/DT5w4GR78zLZuRasxsEPPNxCVyKtj6jxUK/0iIiIiImIyvx/4nUpZ8j+zXbZ06YkMufoGIhJJXZT0i4iIiIiIydy9e5e4q5HktMv01HYtC5Zj5fzFbyQmkZSiZcsW+J/1fakxlPSLiIiIiIhJTRk5jq4OFcj1hMS/deHy+O44wJUrT97wT95d06ZNJTwslF49exiV16ldm/Cw0DcSQ4ECjgQFXqBJ48ZG5WZmZqz6ZeUbieFJlPSLiIiIiIhJRUdH07/b19RJeo++JWrwiUNxKuUrSpsiFej//secWrmNtStWmzpMScHu339Ajx7dyZgxo0nmDw6+iLv7OEaPGUWOHDkM5V9160r+/PleaExLS8tXEpuSfhERERERMbmYmBjGDx/N8M7fELhoG3+uOsCmMT/Rt2N3ft93wNThSQp3+PAhbty4Qe9eT9/o0dm5LBvWryMoMADv40cZPWokNjY2AHTs8CV79+w2tP3rToEv27c3lK1csZxBAwc8duwFCxfh5+fHpEkTAChUsCD9+vVj2LARAPTs2QNv72NcDA5k9287qVatmqFvnjx5CA8LpUGD+qxbu4bgoACaNW2abI7MmTOx7detLF60kDRp0jzXuVHSLyIiIiIiKcaDBw84duwYvx869Mpfjyep18OHiYwbP5EOHTqQK9d7j21TtGhRVq5YzvYdO6hRsyZfde9B+fLlcB87BgAPTy/ef78IWTJnBqCiS0Vu3rxJRZcKAFhYWODsXBZPr6NPjKOPW18qlC9PmzafMW36VLZs3cqevXuBR18qjB41hho1a3HgwEEWL1qAo6ODUf8hgwexYOFCqlb7mAMHDxrV5cr1Hhs3rCcwKJBOnbsQGxv7XOdGSb+IiIg81l+rDk5OxZ7Y5lVsMCQiIvIq7Ny5E7+zfvTr2/ex9d27d2Pjpk3Mn7+Aixcv4e19gmHDRtC8eTPSpEnDuXPnuHXrFhVdKgLg6lKRuXN/xqXio+PSpUuRJk0ajh079sQYwsLDGTFiJBPGj+O9nDkZPnyEoW7evPls3rKFoKBgxrqPw8/vLF06dzbqP2/+Anbs2EloaCjXr183lBco4MjmTRs5dOgw33zjxsOHD5/7vCjpFxEReUu0a/c5F877Y2FhYShLly4dIZeC2bhhvVHb8uXLEx4WSoECjq81pi1btlL5o6qvfNyjXh507tzplY8rIiJvP+eyHzJ7/PcsmzaaORNHki1rFkPd2LHjaNGiOYULF07Wr2SJErRs0YKAC+cM/1auXI6FhQV58+YFwMvrKK4uLmTIkIEiRYqwdNmj+kKFCuHi4sKZM77ExMQ8Nb7Va9ZwPSKCBQsWce/ePWxtbQE4cfKUUbvj3t4UKlzIqMzntE+y8dKmTcumjRvYuXMXw/7xJcLzejU7A4iIiMhr5+HhgZ2dHaVKleTk//9wqFChPDdu3KBUqZLYpE3L/QcPAHB1deHq1WsEB198rTE9ePCAB/+fU0RE5HUbN/Q7ytvdJ/bUSpLiY8lqlQaHHtO5Z/EosT569CgHDh5k0MABrFmz1qivubk5y5evYMHCRcnGDQsLA8DT05O2bdtSoUJ5zp715+7du3h5HcXFpSKuLi54eno+V5wPExJIeJhgVJaUlGR0bGaWvCzmfvIvFOLi4jh06DCffPIJs+fM4erVa88Vw1+00i8iIvKWCAoK5urVa7i6uBjKXF1c2LXrN0JCQnAu52wod3GpiIeHB02bNmHH9m1cOO/PH6dOMPOnH8maNauhXcaMGfnpxx844/MHQYEBHD78O61atjSaN1++fKxdu5qgwAvs3r2LsmXLGOr+fXt/3z5u7P5tJ82aNeWolwfn/P2YPWumYZUDwNbWlp9+/IHAgPOcOulNly6dWbd2DSNHPlq9WLd2DXnz5mXUyO8JDws1et1SvXp12b9vDxeDAznq5UG3bl2NYj3q5UHv3r2YOmUyF877c/yYF23btnnRUy4iIilI80b1KWd+jQfHtpIU/+h59qT4WB7eDCUj92nZpAEA7u7jqVmzBs7OZY36nznjS5H3i3Dp0qVk/+Lj44G/n+v/9NN6hgTf08uLjz6q/P/n+b3+c9zR0dEAOP/j9+ejY2cCAwKf2T8xMZHeX3/DmTNnWLNmNTlz5vxP8yvpFxEReYt4enri6upqOHZ1dcXD0xNPr6OGcisrK5zLlsXDwwNrK2smTppEjZq16dipM3nz5WX6tKmG/v2/60eRIoVp+/kXVK1WnUGDBhN5K9JozoED+jNnzlxq1qpNcHAws2b+ZPSIwb/lz5+fOrVr80X7DnzRvgMVK1ag1z92U/5+xHDKlXPmyw4daf1ZWyqUL0+JEsUN9Z27dCU8PJyJkyZTqnQZSpV+9EdSiRIlmDtnNpu3bOWTGjWZMnUa/b/rR8uWLYzm79atK6d9fKhVuy5Llixl/Dh3ChUs+AJnW0REUpKWdaoR6/v7Y+sS792kea1Hj5udO3eODRs30qFDB6M2M2fNwrlsWdzHjsHJqRiOjg7UqlmTMaNHGdr89Vx/0yZN8Pgr6ff0pE7t2qRNm5Zjx46/cPxdunSmYcMGFCxYgMGDBuLkVIz5CxY8V9/ExER69urN2bNnWbtmFdmzZ3/ueXV7v4iISApmZmZGhfLlyZfbnhuRNzl67BjDhw3FwsKCtGnTUry4E15eR7GwsKBTx0d/3JQpUwYbGxuOeHhy+fJlw1iXL19m2LAR7Nj+K+nSpSMmJgZ7e3t8ff3w8Xn0DOGVK1eSxTBnzlz27t0HwOTJUzl4YB+ODg4EBgU9NmZzc3O+detjWNlYv34DlStXYsKER6v8LVo0p2ev3hw+fAQAtz59OXXS29D/9u3bPHz4kKioKG7cuGEo79a1C4cPH2H69BnAo3ciFylcmO5fdTO6hXPfvn0sWbIUgJ9mzqJLl864uLo8MV4REXk7pCP+qfW2/6ifOHEyDerXN6r39z9H02YtGDigPxs3rMfMzIxLISFs2bLVqJ2nlxd1atfm6NFHG/adPevP3Xv3uBxymaioqBeOf+GixQwfPoxsWbMSEBDAlx06/ac3VDx8+JAePXoxZ/Ys1q5ZRbPmLbl58+Yz+ynpFxERSaGa1KtPy5p1ifW5SOzVP7HOnYfMlT/E1taW0qVLkTFjRoKDg7l58yaenl78MGM6NjY2uLpU5MqVK1y+fJniTk707dsHJ6diZMqUCXPzRzf52dvbExAQwJKlS5k/72dKlCjOwYO/s3PXLry9TxjFcdb/nOHniIgIALJmy/bEJDo0NNSQ8ANcj4gg2/8fKcifPx/W1tb8ceoPQ/29e/cIeo6EvHDhQuza9ZtR2fHj3nTu3Alzc3MSExMB8D/rb9Qm4sYNw/wiIvL2emhm+dgE9vJydwASavUylIWFhVGgYPLN/E6fPs1nbdo+dZ6uXb9KVla8eMn/FGuFiq7JymbOnMWECRMf2/7KlSvkts+brHzNmrVGX2w/fPiQLl27/adYlPSLiIikQK2bNOPTfE4ET1ttVB5+5A9KVCrP4H7f8Yefr+FdwTdu3CA0NJTy5crhWsmVI0c8sLGx4ZdfVnDw4O/06v0NkTdvYm9vzy+/rMDa2gqA/fsPUK58RWrU+ISPKldm9apVLFmyhFGjxxjmTEj4e+Xkrw2HzM3Nnhh7QkLyjYv++rLBzOxRvyT+vZnRk8f7Z5vkmyAl7xf/r/n5x/wiIvL2OnslgjIZspJwN/nqtmWGrPiG3XhML9FvQBERkRTG2tqaJh99zOWNBx5bH+Z1mqKOBalStYrRLsKenl5UrVaVMh+W4YiHB4UKFSJr1qy4jxvHsWPHCAwKImu25CvekZGRrFmzlt5ff8OI779/rRvfXboUQlxcHKVLlzaU2dnZ4eho/GrBuPj4ZPsGXLgQQPny5Y3KnJ3LEhx80bDKLyIiqdfk2QtJrNYR87TpjMrN09iQWK0jk2YvNFFkKZtW+kVERFKYhnXrcvvg6SfWXz1xFoePK1A0dw48Pf/eRdjTy4vx49yxsUmLh4cHDx7EEhsbS8cOHVi6bDlF338ft2+/MRrru3598fE5w/kLF7C2tqZmjU8IeI6dhF9UdHQ0a9euY9jQIdy+fZs//7xJv359SExM5J+L+FdCr1CxQgU2b95CXGwskbduMXfuz2zf/ivffvsNW7ZsoWzZsnTo8CWDBg95bfGKiEjKcfPmTToOGsOY73pjb52Axd0IHmbMQXicFUMHj+XPP/80dYgpkpJ+ERGRFKZYoSLc3eX7xPprJ85imcaae7fvGP2B4+XlRfr06bl48RLh4VcBcHPry8CB/enYsQO+vr6MGj2GJYv/fj9xXHw8gwYNIG/evNx/8IBjR4/RvUfPZHO+St+PHMWE8eNYumQx9+7dY9bsOeTOlZvYBw8MbSZNnsyECePxOHKItGnTkts+L2d8fen2VXe+69eXb7/5moiICCZNmpLsPcwiIpJ6Xb16jU59hpAxY0ayZ8vGjT//5M6dO6YOK0Uzy5U7T9Kzm6UOdnZ2XDjvT5H3P3ipXRdFREReJ7eveuBw6jpRV5/8bKKZhTnpO9Wm99BBbzCy18PGxoaTJ44zatRoflm1+tkdRERE3iKmzkP1TL+IiEgKs2HHNrJVL/PUNu9VKs3mvb89tU1KVdzJicaNGpE/f35KFC/OzJ9+BEi2M7+IiIi8PN3eLyIiksKEhIRwO4MVtjmyEB0Rmaze0iYNtq7F2NtzhgmiezW++qorBQsWJC4unjNnfGjStDmRt26ZOiwREZFUR0m/iIhICtR/7Ehmu0/EztOfCK8zJCU+ehovW8nCZK5bAbcx3yd7fd3bwtfPjzp1PzV1GCIiIu8EJf0iIiIpUHR0NF+69abWx5/Q9KsGWCZCkoU5e497sr5Pb2JjY00dooiIiLwFlPSLiIikUImJiezcs5ude3abOhQRERF5S2kjPxEREREREZFUSkm/iIiIiIiISCqlpF9EREREREQklVLSLyIiIiIiIpJKKekXERERERERSaWU9IuIiIiIiIikUkr6RURERERERFIpJf0iIiIiIiIiqZSSfhEREREREZFUSkm/iIiIiIiISCqlpF9EREREREQklVLSLyIiIiIiIpJKKekXERERERERSaWU9IuIiIiIiIikUkr6RURERERERFIpJf0iIiIiIiIiqZSSfhEREREREZFUSkm/iIiIiIiISCqlpF9EREREREQklVLSLyIiIiIiIpJKKekXERERERERSaWU9IuIiIiIiIikUkr6RURERERERFIpJf0iIiIiIiIiqZSSfhEREREREZFUSkm/iIiIiIiISCqlpF9EREREREQklVLSLyIiIiIiIpJKWZo6AFOwtbU1dQgiIiIiIiLyDjB1/vlOJf1/nexTJ71NHImIiIiIiIi8S2xtbYmKinrj85rlyp0n6Y3PakI5c+YkOjra1GGYlK2tLadOevNhGed3/ly8C3S93x261u8WXe93h671u0XX+92ha/1usbW15fr16yaZ+51a6QdMdqJToujoaJN80ySmoev97tC1frfoer87dK3fLbre7w5d63eDKa+xNvITERERERERSaWU9IuIiIiIiIikUkr630FxcXFMmTKVuLg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Runs763613556576.036.013.055.065.0
TerrainParks211422.01.01.04.02.0
LongestRun_mi12121.02.01.02.01.2
SkiableTerrain_ac1610640307778001610.0640.030.0777.0800.0
Snow Making_ac113603010480113.060.030.0104.080.0
daysOpenLastYear15045150122115150.045.0150.0122.0115.0
yearsOpen604436814960.044.036.081.049.0
averageSnowfall66935069260250669.0350.069.0260.0250.0
AdultWeekend855334897885.053.034.089.078.0
projectedDaysOpen15090152122104150.090.0152.0122.0104.0
NightSkiing_ac550550.0NaN3030.0NaN8080.0
resorts_per_state
state_total_skiable_area_ac228022802280157715772280.02280.02280.01577.01577.0
state_total_days_open345345345237237345.0345.0345.0237.0237.0
state_total_terrain_parks444664.04.04.06.06.0
state_total_nightskiing_ac5805805808080580.0580.0580.080.080.0
resorts_per_100kcapita0.4100910.4100910.4100910.02747740.02747740.0274770.027477
resorts_per_100ksq_mile
\n", "
" - ], - "text/plain": [ - " 0 1 \\\n", - "Name Alyeska Resort Eaglecrest Ski Area \n", - "Region Alaska Alaska \n", - "state Alaska Alaska \n", - "summit_elev 3939 2600 \n", - "vertical_drop 2500 1540 \n", - "base_elev 250 1200 \n", - "trams 1 0 \n", - "fastSixes 0 0 \n", - "fastQuads 2 0 \n", - "quad 2 0 \n", - "triple 0 0 \n", - "double 0 4 \n", - "surface 2 0 \n", - "total_chairs 7 4 \n", - "Runs 76 36 \n", - "TerrainParks 2 1 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 1610 640 \n", - "Snow Making_ac 113 60 \n", - "daysOpenLastYear 150 45 \n", - "yearsOpen 60 44 \n", - "averageSnowfall 669 350 \n", - "AdultWeekend 85 53 \n", - "projectedDaysOpen 150 90 \n", - "NightSkiing_ac 550 NaN \n", - "resorts_per_state 3 3 \n", - "state_total_skiable_area_ac 2280 2280 \n", - "state_total_days_open 345 345 \n", - "state_total_terrain_parks 4 4 \n", - "state_total_nightskiing_ac 580 580 \n", - "resorts_per_100kcapita 0.410091 0.410091 \n", - "resorts_per_100ksq_mile 0.450867 0.450867 \n", - "\n", - " 2 3 \\\n", - "Name Hilltop Ski Area Arizona Snowbowl \n", - "Region Alaska Arizona \n", - "state Alaska Arizona \n", - "summit_elev 2090 11500 \n", - "vertical_drop 294 2300 \n", - "base_elev 1796 9200 \n", - "trams 0 0 \n", - "fastSixes 0 1 \n", - "fastQuads 0 0 \n", - "quad 0 2 \n", - "triple 1 2 \n", - "double 0 1 \n", - "surface 2 2 \n", - "total_chairs 3 8 \n", - "Runs 13 55 \n", - "TerrainParks 1 4 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 30 777 \n", - "Snow Making_ac 30 104 \n", - "daysOpenLastYear 150 122 \n", - "yearsOpen 36 81 \n", - "averageSnowfall 69 260 \n", - "AdultWeekend 34 89 \n", - "projectedDaysOpen 152 122 \n", - "NightSkiing_ac 30 NaN \n", - "resorts_per_state 3 2 \n", - "state_total_skiable_area_ac 2280 1577 \n", - "state_total_days_open 345 237 \n", - "state_total_terrain_parks 4 6 \n", - "state_total_nightskiing_ac 580 80 \n", - "resorts_per_100kcapita 0.410091 0.0274774 \n", - "resorts_per_100ksq_mile 0.450867 1.75454 \n", - "\n", - " 4 \n", - "Name Sunrise Park Resort \n", - "Region Arizona \n", - "state Arizona \n", - "summit_elev 11100 \n", - "vertical_drop 1800 \n", - "base_elev 9200 \n", - "trams 0 \n", - "fastSixes 0 \n", - "fastQuads 1 \n", - "quad 2 \n", - "triple 3 \n", - "double 1 \n", - "surface 0 \n", - "total_chairs 7 \n", - "Runs 65 \n", - "TerrainParks 2 \n", - "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800 \n", - "Snow Making_ac 80 \n", - "daysOpenLastYear 115 \n", - "yearsOpen 49 \n", - "averageSnowfall 250 \n", - "AdultWeekend 78 \n", - "projectedDaysOpen 104 \n", - "NightSkiing_ac 80 \n", - "resorts_per_state 2 \n", - "state_total_skiable_area_ac 1577 \n", - "state_total_days_open 237 \n", - "state_total_terrain_parks 6 \n", - "state_total_nightskiing_ac 80 \n", - "resorts_per_100kcapita 0.0274774 \n", - "resorts_per_100ksq_mile 1.75454 " ] }, - "execution_count": 48, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "# DataFrame's merge method provides SQL-like joins\n", - "# here 'state' is a column (not an index)\n", - "ski_data = ski_data.merge(state_summary, how='left', on='state')\n", - "ski_data.head().T" - ] + "execution_count": 103 }, { "cell_type": "markdown", @@ -3264,9 +4013,12 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:25:06.499887Z", + "start_time": "2026-06-12T00:25:06.471443Z" + } + }, "source": [ "ski_data['resort_skiable_area_ac_state_ratio'] = ski_data.SkiableTerrain_ac / ski_data.state_total_skiable_area_ac\n", "ski_data['resort_days_open_state_ratio'] = ski_data.daysOpenLastYear / ski_data.state_total_days_open\n", @@ -3275,7 +4027,9 @@ "\n", "ski_data.drop(columns=['state_total_skiable_area_ac', 'state_total_days_open', \n", " 'state_total_terrain_parks', 'state_total_nightskiing_ac'], inplace=True)" - ] + ], + "outputs": [], + "execution_count": 104 }, { "cell_type": "markdown", @@ -3293,16 +4047,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-12T00:28:16.552498Z", + "start_time": "2026-06-12T00:28:16.386508Z" + } + }, "source": [ "#Code task 12#\n", "#Show a seaborn heatmap of correlations in ski_data\n", "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n", "plt.subplots(figsize=(12,10))\n", - "sns.___(ski_data.___);" - ] + "#sns.corr(ski_data.sns.heatmap);\n", + "sns.heatmap(ski_data.corr());" + ], + "outputs": [ + { + "ename": "ValueError", + "evalue": "could not convert string to float: 'Alyeska Resort'", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mValueError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[105]\u001B[39m\u001B[32m, line 6\u001B[39m\n\u001B[32m 4\u001B[39m plt.subplots(figsize=(\u001B[32m12\u001B[39m,\u001B[32m10\u001B[39m))\n\u001B[32m 5\u001B[39m \u001B[38;5;66;03m#sns.corr(ski_data.sns.heatmap);\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m sns.heatmap(\u001B[43mski_data\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcorr\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m);\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/core/frame.py:11076\u001B[39m, in \u001B[36mDataFrame.corr\u001B[39m\u001B[34m(self, method, min_periods, numeric_only)\u001B[39m\n\u001B[32m 11074\u001B[39m cols = data.columns\n\u001B[32m 11075\u001B[39m idx = cols.copy()\n\u001B[32m> \u001B[39m\u001B[32m11076\u001B[39m mat = \u001B[43mdata\u001B[49m\u001B[43m.\u001B[49m\u001B[43mto_numpy\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mfloat\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_value\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mnan\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m 11078\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m method == \u001B[33m\"\u001B[39m\u001B[33mpearson\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m 11079\u001B[39m correl = libalgos.nancorr(mat, minp=min_periods)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/core/frame.py:2002\u001B[39m, in \u001B[36mDataFrame.to_numpy\u001B[39m\u001B[34m(self, dtype, copy, na_value)\u001B[39m\n\u001B[32m 2000\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m dtype \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 2001\u001B[39m dtype = np.dtype(dtype)\n\u001B[32m-> \u001B[39m\u001B[32m2002\u001B[39m result = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_mgr\u001B[49m\u001B[43m.\u001B[49m\u001B[43mas_array\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_value\u001B[49m\u001B[43m=\u001B[49m\u001B[43mna_value\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 2003\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m result.dtype \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m dtype:\n\u001B[32m 2004\u001B[39m result = np.asarray(result, dtype=dtype)\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/core/internals/managers.py:1713\u001B[39m, in \u001B[36mBlockManager.as_array\u001B[39m\u001B[34m(self, dtype, copy, na_value)\u001B[39m\n\u001B[32m 1711\u001B[39m arr.flags.writeable = \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[32m 1712\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1713\u001B[39m arr = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_interleave\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_value\u001B[49m\u001B[43m=\u001B[49m\u001B[43mna_value\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1714\u001B[39m \u001B[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001B[39;00m\n\u001B[32m 1715\u001B[39m \u001B[38;5;66;03m# to further copy if copy=True or setting na_value\u001B[39;00m\n\u001B[32m 1717\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m na_value \u001B[38;5;129;01mis\u001B[39;00m lib.no_default:\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/core/internals/managers.py:1772\u001B[39m, in \u001B[36mBlockManager._interleave\u001B[39m\u001B[34m(self, dtype, na_value)\u001B[39m\n\u001B[32m 1770\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 1771\u001B[39m arr = blk.get_values(dtype)\n\u001B[32m-> \u001B[39m\u001B[32m1772\u001B[39m \u001B[43mresult\u001B[49m\u001B[43m[\u001B[49m\u001B[43mrl\u001B[49m\u001B[43m.\u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m]\u001B[49m = arr\n\u001B[32m 1773\u001B[39m itemmask[rl.indexer] = \u001B[32m1\u001B[39m\n\u001B[32m 1775\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m itemmask.all():\n", + "\u001B[31mValueError\u001B[39m: could not convert string to float: 'Alyeska Resort'" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 105 }, { "cell_type": "markdown", @@ -3333,9 +4119,7 @@ }, { "cell_type": "code", - "execution_count": 51, "metadata": {}, - "outputs": [], "source": [ "# define useful function to create scatterplots of ticket prices against desired columns\n", "def scatterplots(columns, ncol=None, figsize=(15, 8)):\n", @@ -3351,41 +4135,30 @@ " nsubplots = nrow * ncol \n", " for empty in range(i+1, nsubplots):\n", " axes.flatten()[empty].set_visible(False)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 13#\n", "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n", "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n", "features = [___ for ___ in ski_data.columns if ___ not in [___, ___, ___, ___]]" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 53, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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RegionAlaskaAlaskaAlaskaArizonaArizona
stateAlaskaAlaskaAlaskaArizonaArizona
summit_elev3939260020901150011100
vertical_drop2500154029423001800
base_elev2501200179692009200
trams10000
fastSixes00010
fastQuads20001
quad20022
triple00123
double04011
surface20220
total_chairs74387
Runs7636135565
TerrainParks21142
LongestRun_mi12121.2
SkiableTerrain_ac161064030777800
Snow Making_ac113603010480
daysOpenLastYear15045150122115
yearsOpen6044368149
averageSnowfall66935069260250
AdultWeekend8553348978
projectedDaysOpen15090152122104
NightSkiing_ac550NaN30NaN80
resorts_per_state33322
resorts_per_100kcapita0.4100910.4100910.4100910.02747740.0274774
resorts_per_100ksq_mile0.4508670.4508670.4508671.754541.75454
resort_skiable_area_ac_state_ratio0.706140.2807020.01315790.4927080.507292
resort_days_open_state_ratio0.4347830.1304350.4347830.5147680.485232
resort_terrain_park_state_ratio0.50.250.250.6666670.333333
resort_night_skiing_state_ratio0.948276NaN0.0517241NaN1
total_chairs_runs_ratio0.09210530.1111110.2307690.1454550.107692
total_chairs_skiable_ratio0.004347830.006250.10.0102960.00875
fastQuads_runs_ratio0.02631580000.0153846
fastQuads_skiable_ratio0.001242240000.00125
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"resort_night_skiing_state_ratio 0.948276 NaN \n", - "total_chairs_runs_ratio 0.0921053 0.111111 \n", - "total_chairs_skiable_ratio 0.00434783 0.00625 \n", - "fastQuads_runs_ratio 0.0263158 0 \n", - "fastQuads_skiable_ratio 0.00124224 0 \n", - "\n", - " 2 3 \\\n", - "Name Hilltop Ski Area Arizona Snowbowl \n", - "Region Alaska Arizona \n", - "state Alaska Arizona \n", - "summit_elev 2090 11500 \n", - "vertical_drop 294 2300 \n", - "base_elev 1796 9200 \n", - "trams 0 0 \n", - "fastSixes 0 1 \n", - "fastQuads 0 0 \n", - "quad 0 2 \n", - "triple 1 2 \n", - "double 0 1 \n", - "surface 2 2 \n", - "total_chairs 3 8 \n", - "Runs 13 55 \n", - "TerrainParks 1 4 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 30 777 \n", - "Snow Making_ac 30 104 \n", - "daysOpenLastYear 150 122 \n", - "yearsOpen 36 81 \n", - "averageSnowfall 69 260 \n", - "AdultWeekend 34 89 \n", - "projectedDaysOpen 152 122 \n", - "NightSkiing_ac 30 NaN \n", - "resorts_per_state 3 2 \n", - "resorts_per_100kcapita 0.410091 0.0274774 \n", - "resorts_per_100ksq_mile 0.450867 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.0131579 0.492708 \n", - "resort_days_open_state_ratio 0.434783 0.514768 \n", - "resort_terrain_park_state_ratio 0.25 0.666667 \n", - "resort_night_skiing_state_ratio 0.0517241 NaN \n", - "total_chairs_runs_ratio 0.230769 0.145455 \n", - "total_chairs_skiable_ratio 0.1 0.010296 \n", - "fastQuads_runs_ratio 0 0 \n", - "fastQuads_skiable_ratio 0 0 \n", - "\n", - " 4 \n", - "Name Sunrise Park Resort \n", - "Region Arizona \n", - "state Arizona \n", - "summit_elev 11100 \n", - "vertical_drop 1800 \n", - "base_elev 9200 \n", - "trams 0 \n", - "fastSixes 0 \n", - "fastQuads 1 \n", - "quad 2 \n", - "triple 3 \n", - "double 1 \n", - "surface 0 \n", - "total_chairs 7 \n", - "Runs 65 \n", - "TerrainParks 2 \n", - "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800 \n", - "Snow Making_ac 80 \n", - "daysOpenLastYear 115 \n", - "yearsOpen 49 \n", - "averageSnowfall 250 \n", - "AdultWeekend 78 \n", - "projectedDaysOpen 104 \n", - "NightSkiing_ac 80 \n", - "resorts_per_state 2 \n", - "resorts_per_100kcapita 0.0274774 \n", - "resorts_per_100ksq_mile 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.507292 \n", - "resort_days_open_state_ratio 0.485232 \n", - "resort_terrain_park_state_ratio 0.333333 \n", - "resort_night_skiing_state_ratio 1 \n", - "total_chairs_runs_ratio 0.107692 \n", - "total_chairs_skiable_ratio 0.00875 \n", - "fastQuads_runs_ratio 0.0153846 \n", - "fastQuads_skiable_ratio 0.00125 " - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "ski_data.head().T" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# Save the data \n", "\n", "datapath = '../data'\n", "save_file(ski_data, 'ski_data_step3_features.csv', datapath)" - ] + ], + "outputs": [], + "execution_count": null } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -3946,7 +4264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.13.9" }, "toc": { "base_numbering": 1, diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb index 94ff2aeba..a5a751a3e 100644 --- a/Notebooks/04_preprocessing_and_training.ipynb +++ b/Notebooks/04_preprocessing_and_training.ipynb @@ -97,9 +97,12 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-03T23:59:52.596998Z", + "start_time": "2026-06-03T23:59:44.786504Z" + } + }, "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -122,7 +125,9 @@ "import datetime\n", "\n", "from library.sb_utils import save_file" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "markdown", @@ -133,457 +138,36 @@ }, { "cell_type": "code", - "execution_count": 2, "metadata": { - "scrolled": true + "scrolled": true, + "ExecuteTime": { + "end_time": "2026-06-03T23:59:52.884951Z", + "start_time": "2026-06-03T23:59:52.608195Z" + } }, + "source": [ + "ski_data = pd.read_csv('../data/ski_data_step3_features.csv')\n", + "ski_data.head().T" + ], "outputs": [ { - 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01234
NameAlyeska ResortEaglecrest Ski AreaHilltop Ski AreaArizona SnowbowlSunrise Park Resort
RegionAlaskaAlaskaAlaskaArizonaArizona
stateAlaskaAlaskaAlaskaArizonaArizona
summit_elev3939260020901150011100
vertical_drop2500154029423001800
base_elev2501200179692009200
trams10000
fastSixes00010
fastQuads20001
quad20022
triple00123
double04011
surface20220
total_chairs74387
Runs7636135565
TerrainParks21142
LongestRun_mi12121.2
SkiableTerrain_ac161064030777800
Snow Making_ac113603010480
daysOpenLastYear15045150122115
yearsOpen6044368149
averageSnowfall66935069260250
AdultWeekend8553348978
projectedDaysOpen15090152122104
NightSkiing_ac550NaN30NaN80
resorts_per_state33322
resorts_per_100kcapita0.4100910.4100910.4100910.02747740.0274774
resorts_per_100ksq_mile0.4508670.4508670.4508671.754541.75454
resort_skiable_area_ac_state_ratio0.706140.2807020.01315790.4927080.507292
resort_days_open_state_ratio0.4347830.1304350.4347830.5147680.485232
resort_terrain_park_state_ratio0.50.250.250.6666670.333333
resort_night_skiing_state_ratio0.948276NaN0.0517241NaN1
total_chairs_runs_ratio0.09210530.1111110.2307690.1454550.107692
total_chairs_skiable_ratio0.004347830.006250.10.0102960.00875
fastQuads_runs_ratio0.02631580000.0153846
fastQuads_skiable_ratio0.001242240000.00125
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\n", - "resort_night_skiing_state_ratio 0.948276 NaN \n", - "total_chairs_runs_ratio 0.0921053 0.111111 \n", - "total_chairs_skiable_ratio 0.00434783 0.00625 \n", - "fastQuads_runs_ratio 0.0263158 0 \n", - "fastQuads_skiable_ratio 0.00124224 0 \n", - "\n", - " 2 3 \\\n", - "Name Hilltop Ski Area Arizona Snowbowl \n", - "Region Alaska Arizona \n", - "state Alaska Arizona \n", - "summit_elev 2090 11500 \n", - "vertical_drop 294 2300 \n", - "base_elev 1796 9200 \n", - "trams 0 0 \n", - "fastSixes 0 1 \n", - "fastQuads 0 0 \n", - "quad 0 2 \n", - "triple 1 2 \n", - "double 0 1 \n", - "surface 2 2 \n", - "total_chairs 3 8 \n", - "Runs 13 55 \n", - "TerrainParks 1 4 \n", - "LongestRun_mi 1 2 \n", - "SkiableTerrain_ac 30 777 \n", - "Snow Making_ac 30 104 \n", - "daysOpenLastYear 150 122 \n", - "yearsOpen 36 81 \n", - "averageSnowfall 69 260 \n", - "AdultWeekend 34 89 \n", - "projectedDaysOpen 152 122 \n", - "NightSkiing_ac 30 NaN \n", - "resorts_per_state 3 2 \n", - "resorts_per_100kcapita 0.410091 0.0274774 \n", - "resorts_per_100ksq_mile 0.450867 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.0131579 0.492708 \n", - "resort_days_open_state_ratio 0.434783 0.514768 \n", - "resort_terrain_park_state_ratio 0.25 0.666667 \n", - "resort_night_skiing_state_ratio 0.0517241 NaN \n", - "total_chairs_runs_ratio 0.230769 0.145455 \n", - "total_chairs_skiable_ratio 0.1 0.010296 \n", - "fastQuads_runs_ratio 0 0 \n", - "fastQuads_skiable_ratio 0 0 \n", - "\n", - " 4 \n", - "Name Sunrise Park Resort \n", - "Region Arizona \n", - "state Arizona \n", - "summit_elev 11100 \n", - "vertical_drop 1800 \n", - "base_elev 9200 \n", - "trams 0 \n", - "fastSixes 0 \n", - "fastQuads 1 \n", - "quad 2 \n", - "triple 3 \n", - "double 1 \n", - "surface 0 \n", - "total_chairs 7 \n", - "Runs 65 \n", - "TerrainParks 2 \n", - "LongestRun_mi 1.2 \n", - "SkiableTerrain_ac 800 \n", - "Snow Making_ac 80 \n", - "daysOpenLastYear 115 \n", - "yearsOpen 49 \n", - "averageSnowfall 250 \n", - "AdultWeekend 78 \n", - "projectedDaysOpen 104 \n", - "NightSkiing_ac 80 \n", - "resorts_per_state 2 \n", - "resorts_per_100kcapita 0.0274774 \n", - "resorts_per_100ksq_mile 1.75454 \n", - "resort_skiable_area_ac_state_ratio 0.507292 \n", - "resort_days_open_state_ratio 0.485232 \n", - "resort_terrain_park_state_ratio 0.333333 \n", - "resort_night_skiing_state_ratio 1 \n", - "total_chairs_runs_ratio 0.107692 \n", - "total_chairs_skiable_ratio 0.00875 \n", - "fastQuads_runs_ratio 0.0153846 \n", - "fastQuads_skiable_ratio 0.00125 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '../data/ski_data_step3_features.csv'", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mFileNotFoundError\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m ski_data = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m../data/ski_data_step3_features.csv\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 2\u001B[39m ski_data.head().T\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1026\u001B[39m, in \u001B[36mread_csv\u001B[39m\u001B[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001B[39m\n\u001B[32m 1013\u001B[39m kwds_defaults = _refine_defaults_read(\n\u001B[32m 1014\u001B[39m dialect,\n\u001B[32m 1015\u001B[39m delimiter,\n\u001B[32m (...)\u001B[39m\u001B[32m 1022\u001B[39m dtype_backend=dtype_backend,\n\u001B[32m 1023\u001B[39m )\n\u001B[32m 1024\u001B[39m kwds.update(kwds_defaults)\n\u001B[32m-> \u001B[39m\u001B[32m1026\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:620\u001B[39m, in \u001B[36m_read\u001B[39m\u001B[34m(filepath_or_buffer, kwds)\u001B[39m\n\u001B[32m 617\u001B[39m _validate_names(kwds.get(\u001B[33m\"\u001B[39m\u001B[33mnames\u001B[39m\u001B[33m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[32m 619\u001B[39m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m620\u001B[39m parser = \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 622\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[32m 623\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m parser\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1620\u001B[39m, in \u001B[36mTextFileReader.__init__\u001B[39m\u001B[34m(self, f, engine, **kwds)\u001B[39m\n\u001B[32m 1617\u001B[39m \u001B[38;5;28mself\u001B[39m.options[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m] = kwds[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m 1619\u001B[39m \u001B[38;5;28mself\u001B[39m.handles: IOHandles | \u001B[38;5;28;01mNone\u001B[39;00m = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1620\u001B[39m \u001B[38;5;28mself\u001B[39m._engine = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1880\u001B[39m, in \u001B[36mTextFileReader._make_engine\u001B[39m\u001B[34m(self, f, engine)\u001B[39m\n\u001B[32m 1878\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[32m 1879\u001B[39m mode += \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m-> \u001B[39m\u001B[32m1880\u001B[39m \u001B[38;5;28mself\u001B[39m.handles = \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1881\u001B[39m \u001B[43m \u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1882\u001B[39m \u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1883\u001B[39m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mencoding\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1884\u001B[39m \u001B[43m \u001B[49m\u001B[43mcompression\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mcompression\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1885\u001B[39m \u001B[43m \u001B[49m\u001B[43mmemory_map\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mmemory_map\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1886\u001B[39m \u001B[43m \u001B[49m\u001B[43mis_text\u001B[49m\u001B[43m=\u001B[49m\u001B[43mis_text\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1887\u001B[39m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mencoding_errors\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mstrict\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1888\u001B[39m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mstorage_options\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1889\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1890\u001B[39m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m.handles \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 1891\u001B[39m f = \u001B[38;5;28mself\u001B[39m.handles.handle\n", + "\u001B[36mFile \u001B[39m\u001B[32m~/anaconda3/lib/python3.13/site-packages/pandas/io/common.py:873\u001B[39m, in \u001B[36mget_handle\u001B[39m\u001B[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[39m\n\u001B[32m 868\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(handle, \u001B[38;5;28mstr\u001B[39m):\n\u001B[32m 869\u001B[39m \u001B[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001B[39;00m\n\u001B[32m 870\u001B[39m \u001B[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001B[39;00m\n\u001B[32m 871\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ioargs.encoding \u001B[38;5;129;01mand\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m ioargs.mode:\n\u001B[32m 872\u001B[39m \u001B[38;5;66;03m# Encoding\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m873\u001B[39m handle = \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[32m 874\u001B[39m \u001B[43m \u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 875\u001B[39m \u001B[43m \u001B[49m\u001B[43mioargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 876\u001B[39m \u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m=\u001B[49m\u001B[43mioargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 877\u001B[39m \u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m=\u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 878\u001B[39m \u001B[43m \u001B[49m\u001B[43mnewline\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m 879\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 880\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 881\u001B[39m \u001B[38;5;66;03m# Binary mode\u001B[39;00m\n\u001B[32m 882\u001B[39m handle = \u001B[38;5;28mopen\u001B[39m(handle, ioargs.mode)\n", + "\u001B[31mFileNotFoundError\u001B[39m: [Errno 2] No such file or directory: '../data/ski_data_step3_features.csv'" + ] } ], - "source": [ - "ski_data = pd.read_csv('../data/ski_data_step3_features.csv')\n", - "ski_data.head().T" - ] + "execution_count": 2 }, { "cell_type": "markdown", @@ -601,288 +185,48 @@ }, { "cell_type": "code", - "execution_count": 3, "metadata": {}, - "outputs": [], "source": [ "big_mountain = ski_data[ski_data.Name == 'Big Mountain Resort']" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 4, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameBig Mountain Resort
RegionMontana
stateMontana
summit_elev6817
vertical_drop2353
base_elev4464
trams0
fastSixes0
fastQuads3
quad2
triple6
double0
surface3
total_chairs14
Runs105
TerrainParks4
LongestRun_mi3.3
SkiableTerrain_ac3000
Snow Making_ac600
daysOpenLastYear123
yearsOpen72
averageSnowfall333
AdultWeekend81
projectedDaysOpen123
NightSkiing_ac600
resorts_per_state12
resorts_per_100kcapita1.12278
resorts_per_100ksq_mile8.16104
resort_skiable_area_ac_state_ratio0.140121
resort_days_open_state_ratio0.129338
resort_terrain_park_state_ratio0.148148
resort_night_skiing_state_ratio0.84507
total_chairs_runs_ratio0.133333
total_chairs_skiable_ratio0.00466667
fastQuads_runs_ratio0.0285714
fastQuads_skiable_ratio0.001
\n", - "
" - ], - "text/plain": [ - " 124\n", - "Name Big Mountain Resort\n", - "Region Montana\n", - "state Montana\n", - "summit_elev 6817\n", - "vertical_drop 2353\n", - "base_elev 4464\n", - "trams 0\n", - "fastSixes 0\n", - "fastQuads 3\n", - "quad 2\n", - "triple 6\n", - "double 0\n", - "surface 3\n", - "total_chairs 14\n", - "Runs 105\n", - "TerrainParks 4\n", - "LongestRun_mi 3.3\n", - "SkiableTerrain_ac 3000\n", - "Snow Making_ac 600\n", - "daysOpenLastYear 123\n", - "yearsOpen 72\n", - "averageSnowfall 333\n", - "AdultWeekend 81\n", - "projectedDaysOpen 123\n", - "NightSkiing_ac 600\n", - "resorts_per_state 12\n", - "resorts_per_100kcapita 1.12278\n", - "resorts_per_100ksq_mile 8.16104\n", - "resort_skiable_area_ac_state_ratio 0.140121\n", - "resort_days_open_state_ratio 0.129338\n", - "resort_terrain_park_state_ratio 0.148148\n", - "resort_night_skiing_state_ratio 0.84507\n", - "total_chairs_runs_ratio 0.133333\n", - "total_chairs_skiable_ratio 0.00466667\n", - "fastQuads_runs_ratio 0.0285714\n", - "fastQuads_skiable_ratio 0.001" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "big_mountain.T" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(277, 36)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "ski_data.shape" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 6, "metadata": {}, - "outputs": [], "source": [ "ski_data = ski_data[ski_data.Name != 'Big Mountain Resort']" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 7, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(276, 36)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "ski_data.shape" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -907,113 +251,80 @@ }, { "cell_type": "code", - "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(193.2, 82.8)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "len(ski_data) * .7, len(ski_data) * .3" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 9, "metadata": {}, - "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(ski_data.drop(columns='AdultWeekend'), \n", " ski_data.AdultWeekend, test_size=0.3, \n", " random_state=47)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((193, 35), (83, 35))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "X_train.shape, X_test.shape" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((193,), (83,))" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "y_train.shape, y_test.shape" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 1#\n", "#Save the 'Name', 'state', and 'Region' columns from the train/test data into names_train and names_test\n", "#Then drop those columns from `X_train` and `X_test`. Use 'inplace=True'\n", "names_list = ['Name', 'state', 'Region']\n", - "names_train = X_train[___]\n", - "names_test = X_test[___]\n", - "X_train.___(columns=names_list, inplace=___)\n", - "X_test.___(columns=names_list, inplace=___)\n", + "names_train = X_train[names_list]\n", + "names_test = X_test[names_list]\n", + "X_train.drop(columns=names_list, inplace=True)\n", + "X_test.drop(columns=names_list, inplace=True)\n", "X_train.shape, X_test.shape" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 2#\n", "#Check the `dtypes` attribute of `X_train` to verify all features are numeric\n", - "X_train.___" - ] + "X_train.dtypes" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 3#\n", "#Repeat this check for the test split in `X_test`\n", - "X_test.___" - ] + "X_test.dtypes" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1038,15 +349,15 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 4#\n", "#Calculate the mean of `y_train`\n", - "train_mean = y_train.___\n", + "train_mean = y_train.mean()\n", "train_mean" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1057,18 +368,18 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 5#\n", "#Fit the dummy regressor on the training data\n", "#Hint, call its `.fit()` method with `X_train` and `y_train` as arguments\n", "#Then print the object's `constant_` attribute and verify it's the same as the mean above\n", "dumb_reg = DummyRegressor(strategy='mean')\n", - "dumb_reg.___(___, ___)\n", - "dumb_reg.___" - ] + "dumb_reg.fit(X_train, y_train)\n", + "dumb_reg.constant_" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1124,9 +435,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 6#\n", "#Calculate the R^2 as defined above\n", @@ -1140,11 +449,13 @@ " ypred -- the predicted values\n", " \"\"\"\n", " ybar = np.sum(y) / len(y) #yes, we could use np.mean(y)\n", - " sum_sq_tot = np.___((y - ybar)**2) #total sum of squares error\n", - " sum_sq_res = np.___((y - ypred)**2) #residual sum of squares error\n", - " R2 = 1.0 - ___ / ___\n", + " sum_sq_tot = np.sum((y - ybar)**2) #total sum of squares error\n", + " sum_sq_res = np.sum((y - ypred)**2) #residual sum of squares error\n", + " R2 = 1.0 - sum_sq_tot / sum_sq_res\n", " return R2" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1155,24 +466,13 @@ }, { "cell_type": "code", - "execution_count": 18, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "y_tr_pred_ = train_mean * np.ones(len(y_train))\n", "y_tr_pred_[:5]" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1183,24 +483,13 @@ }, { "cell_type": "code", - "execution_count": 19, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "y_tr_pred = dumb_reg.predict(X_train)\n", "y_tr_pred[:5]" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1211,23 +500,12 @@ }, { "cell_type": "code", - "execution_count": 20, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "r_squared(y_train, y_tr_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1245,24 +523,13 @@ }, { "cell_type": "code", - "execution_count": 21, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.0031235200417913944" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "y_te_pred = train_mean * np.ones(len(y_test))\n", "r_squared(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1296,9 +563,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 7#\n", "#Calculate the MAE as defined above\n", @@ -1311,50 +576,30 @@ " y -- the observed values\n", " ypred -- the predicted values\n", " \"\"\"\n", - " abs_error = np.abs(___ - ___)\n", - " mae = np.mean(___)\n", + " abs_error = np.abs(y - ypred)\n", + " mae = np.mean(abs_error)\n", " return mae" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 23, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "17.923463717146785" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mae(y_train, y_tr_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 24, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "19.136142081278486" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mae(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1381,11 +626,9 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [], "source": [ "#Code task 8#\n", "#Calculate the MSE as defined above\n", @@ -1398,50 +641,30 @@ " y -- the observed values\n", " ypred -- the predicted values\n", " \"\"\"\n", - " sq_error = (___ - ___)**2\n", - " mse = np.mean(___)\n", + " sq_error = (y - ypred)**2\n", + " mse = np.mean(sq_error)\n", " return mse" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 26, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "614.1334096969057" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mse(y_train, y_tr_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 27, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "581.4365441953481" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mse(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1452,23 +675,12 @@ }, { "cell_type": "code", - "execution_count": 28, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([24.78171523, 24.11299534])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "np.sqrt([mse(y_train, y_tr_pred), mse(y_test, y_te_pred)])" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1493,23 +705,12 @@ }, { "cell_type": "code", - "execution_count": 29, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, -0.0031235200417913944)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1520,23 +721,12 @@ }, { "cell_type": "code", - "execution_count": 30, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(17.92346371714677, 19.136142081278486)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1547,23 +737,12 @@ }, { "cell_type": "code", - "execution_count": 31, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(614.1334096969046, 581.4365441953483)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1588,99 +767,47 @@ }, { "cell_type": "code", - "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, -3.041041349306602e+30)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# train set - sklearn\n", "# correct order, incorrect order\n", "r2_score(y_train, y_tr_pred), r2_score(y_tr_pred, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 33, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.0031235200417913944, 0.0)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# test set - sklearn\n", "# correct order, incorrect order\n", "r2_score(y_test, y_te_pred), r2_score(y_te_pred, y_test)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 34, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.0, -3.041041349306602e+30)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# train set - using our homebrew function\n", "# correct order, incorrect order\n", "r_squared(y_train, y_tr_pred), r_squared(y_tr_pred, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 35, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/guy/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in double_scalars\n", - " from ipykernel import kernelapp as app\n" - ] - }, - { - "data": { - "text/plain": [ - "(-0.0031235200417913944, -inf)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# test set - using our homebrew function\n", "# correct order, incorrect order\n", "r_squared(y_test, y_te_pred), r_squared(y_te_pred, y_test)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1737,57 +864,14 @@ }, { "cell_type": "code", - "execution_count": 36, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "summit_elev 2215.000000\n", - "vertical_drop 750.000000\n", - "base_elev 1300.000000\n", - "trams 0.000000\n", - "fastSixes 0.000000\n", - "fastQuads 0.000000\n", - "quad 1.000000\n", - "triple 1.000000\n", - "double 1.000000\n", - "surface 2.000000\n", - "total_chairs 7.000000\n", - "Runs 28.000000\n", - "TerrainParks 2.000000\n", - "LongestRun_mi 1.000000\n", - "SkiableTerrain_ac 170.000000\n", - "Snow Making_ac 96.500000\n", - "daysOpenLastYear 109.000000\n", - "yearsOpen 57.000000\n", - "averageSnowfall 120.000000\n", - "projectedDaysOpen 115.000000\n", - "NightSkiing_ac 70.000000\n", - "resorts_per_state 15.000000\n", - "resorts_per_100kcapita 0.248243\n", - "resorts_per_100ksq_mile 22.902162\n", - "resort_skiable_area_ac_state_ratio 0.051458\n", - "resort_days_open_state_ratio 0.071225\n", - "resort_terrain_park_state_ratio 0.069444\n", - "resort_night_skiing_state_ratio 0.077081\n", - "total_chairs_runs_ratio 0.200000\n", - "total_chairs_skiable_ratio 0.040323\n", - "fastQuads_runs_ratio 0.000000\n", - "fastQuads_skiable_ratio 0.000000\n", - "dtype: float64" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# These are the values we'll use to fill in any missing values\n", "X_defaults_median = X_train.median()\n", "X_defaults_median" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1798,16 +882,16 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 9#\n", "#Call `X_train` and `X_test`'s `fillna()` method, passing `X_defaults_median` as the values to use\n", "#Assign the results to `X_tr` and `X_te`, respectively\n", - "X_tr = X_train.___(___)\n", - "X_te = X_test.___(___)" - ] + "X_tr = X_train.fillna(X_defaults_median)\n", + "X_te = X_test.fillna(X_defaults_median)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1825,19 +909,19 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 10#\n", "#Call the StandardScaler`s fit method on `X_tr` to fit the scaler\n", "#then use it's `transform()` method to apply the scaling to both the train and test split\n", "#data (`X_tr` and `X_te`), naming the results `X_tr_scaled` and `X_te_scaled`, respectively\n", "scaler = StandardScaler()\n", - "scaler.___(X_tr)\n", - "X_tr_scaled = scaler.___(X_tr)\n", - "X_te_scaled = scaler.___(X_te)" - ] + "scaler.fit(X_tr)\n", + "X_tr_scaled = scaler.transform(X_tr)\n", + "X_te_scaled = scaler.transform(X_te)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1848,12 +932,12 @@ }, { "cell_type": "code", - "execution_count": 39, "metadata": {}, - "outputs": [], "source": [ "lm = LinearRegression().fit(X_tr_scaled, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1864,16 +948,16 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 11#\n", "#Call the `predict()` method of the model (`lm`) on both the (scaled) train and test data\n", "#Assign the predictions to `y_tr_pred` and `y_te_pred`, respectively\n", - "y_tr_pred = lm.___(X_tr_scaled)\n", - "y_te_pred = lm.___(X_te_scaled)" - ] + "y_tr_pred = lm.predict(X_tr_scaled)\n", + "y_te_pred = lm.predict(X_te_scaled)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1884,25 +968,14 @@ }, { "cell_type": "code", - "execution_count": 41, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8177988515690604, 0.7209725843435142)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# r^2 - train, test\n", "median_r2 = r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)\n", "median_r2" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1913,17 +986,17 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 12#\n", "#Now calculate the mean absolute error scores using `sklearn`'s `mean_absolute_error` function\n", "# as we did above for R^2\n", "# MAE - train, test\n", - "median_mae = ___(y_train, y_tr_pred), ___(y_test, y_te_pred)\n", + "median_mae = mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)\n", "median_mae" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1934,16 +1007,16 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 13#\n", "#And also do the same using `sklearn`'s `mean_squared_error`\n", "# MSE - train, test\n", - "median_mse = ___(___, ___), ___(___, ___)\n", + "median_mse = mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_train, y_tr_pred)\n", "median_mse" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1968,16 +1041,16 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 14#\n", "#As we did for the median above, calculate mean values for imputing missing values\n", "# These are the values we'll use to fill in any missing values\n", - "X_defaults_mean = X_train.___()\n", + "X_defaults_mean = X_train.mean()\n", "X_defaults_mean" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1995,13 +1068,13 @@ }, { "cell_type": "code", - "execution_count": 45, "metadata": {}, - "outputs": [], "source": [ "X_tr = X_train.fillna(X_defaults_mean)\n", "X_te = X_test.fillna(X_defaults_mean)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2012,15 +1085,15 @@ }, { "cell_type": "code", - "execution_count": 46, "metadata": {}, - "outputs": [], "source": [ "scaler = StandardScaler()\n", "scaler.fit(X_tr)\n", "X_tr_scaled = scaler.transform(X_tr)\n", "X_te_scaled = scaler.transform(X_te)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2031,12 +1104,12 @@ }, { "cell_type": "code", - "execution_count": 47, "metadata": {}, - "outputs": [], "source": [ "lm = LinearRegression().fit(X_tr_scaled, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2047,13 +1120,13 @@ }, { "cell_type": "code", - "execution_count": 48, "metadata": {}, - "outputs": [], "source": [ "y_tr_pred = lm.predict(X_tr_scaled)\n", "y_te_pred = lm.predict(X_te_scaled)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2064,63 +1137,30 @@ }, { "cell_type": "code", - "execution_count": 49, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8170154093990025, 0.716381471695996)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 50, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(8.536884040670973, 9.416375625789271)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 51, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(112.37695054778276, 164.3926930952436)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2168,56 +1208,34 @@ }, { "cell_type": "code", - "execution_count": 52, "metadata": {}, - "outputs": [], "source": [ "pipe = make_pipeline(\n", " SimpleImputer(strategy='median'), \n", " StandardScaler(), \n", " LinearRegression()\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 53, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "sklearn.pipeline.Pipeline" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "type(pipe)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 54, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(True, True)" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "hasattr(pipe, 'fit'), hasattr(pipe, 'predict')" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2235,14 +1253,14 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 15#\n", "#Call the pipe's `fit()` method with `X_train` and `y_train` as arguments\n", - "pipe.___(___, ___)" - ] + "pipe.fit(X_train, y_train)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2253,13 +1271,13 @@ }, { "cell_type": "code", - "execution_count": 56, "metadata": {}, - "outputs": [], "source": [ "y_tr_pred = pipe.predict(X_train)\n", "y_te_pred = pipe.predict(X_test)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2270,23 +1288,12 @@ }, { "cell_type": "code", - "execution_count": 57, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8177988515690604, 0.7209725843435142)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2297,92 +1304,48 @@ }, { "cell_type": "code", - "execution_count": 58, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8177988515690604, 0.7209725843435142)" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "median_r2" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 59, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(8.547850301825427, 9.40702011858132)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "Compare with your earlier result:" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 60, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(8.547850301825427, 9.40702011858132)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "median_mae" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 61, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(111.89581253658478, 161.73156451192284)" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2393,23 +1356,12 @@ }, { "cell_type": "code", - "execution_count": 62, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(111.89581253658478, 161.73156451192284)" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "median_mse" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2449,9 +1401,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 16#\n", "#Add `SelectKBest` as a step in the pipeline between `StandardScaler()` and `LinearRegression()`\n", @@ -2459,10 +1409,12 @@ "pipe = make_pipeline(\n", " SimpleImputer(strategy='median'), \n", " StandardScaler(),\n", - " ___(___),\n", + " SelectKBest(f_regression),\n", " LinearRegression()\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2473,27 +1425,12 @@ }, { "cell_type": "code", - "execution_count": 64, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", - " ('standardscaler', StandardScaler()),\n", - " ('selectkbest',\n", - " SelectKBest(score_func=)),\n", - " ('linearregression', LinearRegression())])" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "pipe.fit(X_train, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2504,53 +1441,31 @@ }, { "cell_type": "code", - "execution_count": 65, "metadata": {}, - "outputs": [], "source": [ "y_tr_pred = pipe.predict(X_train)\n", "y_te_pred = pipe.predict(X_test)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 66, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.7674914326052744, 0.6259877354190833)" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 67, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.501495079727484, 11.201830190332057)" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2568,19 +1483,19 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 17#\n", "#Modify the `SelectKBest` step to use a value of 15 for k\n", "pipe15 = make_pipeline(\n", " SimpleImputer(strategy='median'), \n", " StandardScaler(),\n", - " ___(___, k=___),\n", + " SelectKBest(f_regression, k=15),\n", " LinearRegression()\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2591,28 +1506,12 @@ }, { "cell_type": "code", - "execution_count": 69, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n", - " ('standardscaler', StandardScaler()),\n", - " ('selectkbest',\n", - " SelectKBest(k=15,\n", - " score_func=)),\n", - " ('linearregression', LinearRegression())])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "pipe15.fit(X_train, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2623,53 +1522,31 @@ }, { "cell_type": "code", - "execution_count": 70, "metadata": {}, - "outputs": [], "source": [ "y_tr_pred = pipe15.predict(X_train)\n", "y_te_pred = pipe15.predict(X_test)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 71, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.7924096060483825, 0.6376199973170795)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 72, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.211767769307116, 10.488246867294356)" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2689,33 +1566,22 @@ }, { "cell_type": "code", - "execution_count": 73, "metadata": {}, - "outputs": [], "source": [ "cv_results = cross_validate(pipe15, X_train, y_train, cv=5)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 74, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "cv_scores = cv_results['test_score']\n", "cv_scores" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2726,23 +1592,12 @@ }, { "cell_type": "code", - "execution_count": 75, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.6327128053007867, 0.09502487849877672)" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "np.mean(cv_scores), np.std(cv_scores)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2753,23 +1608,12 @@ }, { "cell_type": "code", - "execution_count": 76, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.44, 0.82])" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "np.round((np.mean(cv_scores) - 2 * np.std(cv_scores), np.mean(cv_scores) + 2 * np.std(cv_scores)), 2)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2797,15 +1641,15 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 18#\n", "#Call `pipe`'s `get_params()` method to get a dict of available parameters and print their names\n", "#using dict's `keys()` method\n", - "pipe.___.keys()" - ] + "pipe.get_params().keys()" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2816,13 +1660,13 @@ }, { "cell_type": "code", - "execution_count": 78, "metadata": {}, - "outputs": [], "source": [ "k = [k+1 for k in range(len(X_train.columns))]\n", "grid_params = {'selectkbest__k': k}" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2834,83 +1678,60 @@ }, { "cell_type": "code", - "execution_count": 79, "metadata": {}, - "outputs": [], "source": [ "lr_grid_cv = GridSearchCV(pipe, param_grid=grid_params, cv=5, n_jobs=-1)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 80, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5,\n", - " estimator=Pipeline(steps=[('simpleimputer',\n", - " SimpleImputer(strategy='median')),\n", - " ('standardscaler', StandardScaler()),\n", - " ('selectkbest',\n", - " SelectKBest(score_func=)),\n", - " ('linearregression',\n", - " LinearRegression())]),\n", - " n_jobs=-1,\n", - " param_grid={'selectkbest__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", - " 12, 13, 14, 15, 16, 17, 18, 19, 20,\n", - " 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", - " 30, ...]})" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "lr_grid_cv.fit(X_train, y_train)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 81, "metadata": {}, - "outputs": [], "source": [ "score_mean = lr_grid_cv.cv_results_['mean_test_score']\n", "score_std = lr_grid_cv.cv_results_['std_test_score']\n", "cv_k = [k for k in lr_grid_cv.cv_results_['param_selectkbest__k']]" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 19#\n", "#Print the `best_params_` attribute of `lr_grid_cv`\n", - "lr_grid_cv.___" - ] + "lr_grid_cv.best_params" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 20#\n", "#Assign the value of k from the above dict of `best_params_` and assign it to `best_k`\n", - "___ = lr_grid_cv.___['selectkbest__k']\n", + "best_k = lr_grid_cv.best_params['selectkbest__k']\n", "plt.subplots(figsize=(10, 5))\n", "plt.errorbar(cv_k, score_mean, yerr=score_std)\n", "plt.axvline(x=best_k, c='r', ls='--', alpha=.5)\n", "plt.xlabel('k')\n", "plt.ylabel('CV score (r-squared)')\n", "plt.title('Pipeline mean CV score (error bars +/- 1sd)');" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2928,12 +1749,12 @@ }, { "cell_type": "code", - "execution_count": 84, "metadata": {}, - "outputs": [], "source": [ "selected = lr_grid_cv.best_estimator_.named_steps.selectkbest.get_support()" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2944,9 +1765,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 21#\n", "#Get the linear model coefficients from the `coef_` attribute and store in `coefs`,\n", @@ -2955,8 +1774,10 @@ "#sorting the values in descending order\n", "coefs = lr_grid_cv.best_estimator_.named_steps.linearregression.coef_\n", "features = X_train.columns[selected]\n", - "pd.Series(___, index=___).___(ascending=___)" - ] + "pd.Series(coefs, index=features).sort_values(ascending=False)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -2990,9 +1811,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 22#\n", "#Define a pipeline comprising the steps:\n", @@ -3000,11 +1819,13 @@ "#StandardScaler(),\n", "#and then RandomForestRegressor() with a random state of 47\n", "RF_pipe = make_pipeline(\n", - " ___(strategy=___),\n", - " ___,\n", - " ___(random_state=___)\n", + " SimpleImputer(strategy='median'),\n", + " StandardScaler,\n", + " RandomForestRegressor(random_state=47)\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3015,57 +1836,35 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 23#\n", "#Call `cross_validate` to estimate the pipeline's performance.\n", "#Pass it the random forest pipe object, `X_train` and `y_train`,\n", "#and get it to use 5-fold cross-validation\n", - "rf_default_cv_results = cross_validate(___, ___, ___, cv=___)" - ] + "rf_default_cv_results = cross_validate(RF_pipe, X_train, y_train, cv=5)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 88, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "rf_cv_scores = rf_default_cv_results['test_score']\n", "rf_cv_scores" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 89, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.6975826707112506, 0.07090742940774528)" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "np.mean(rf_cv_scores), np.std(rf_cv_scores)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3083,41 +1882,7 @@ }, { "cell_type": "code", - "execution_count": 90, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'randomforestregressor__n_estimators': [10,\n", - " 12,\n", - " 16,\n", - " 20,\n", - " 26,\n", - " 33,\n", - " 42,\n", - " 54,\n", - " 69,\n", - " 88,\n", - " 112,\n", - " 143,\n", - " 183,\n", - " 233,\n", - " 297,\n", - " 379,\n", - " 483,\n", - " 615,\n", - " 784,\n", - " 1000],\n", - " 'standardscaler': [StandardScaler(), None],\n", - " 'simpleimputer__strategy': ['mean', 'median']}" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "n_est = [int(n) for n in np.logspace(start=1, stop=3, num=20)]\n", "grid_params = {\n", @@ -3126,42 +1891,44 @@ " 'simpleimputer__strategy': ['mean', 'median']\n", "}\n", "grid_params" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 24#\n", "#Call `GridSearchCV` with the random forest pipeline, passing in the above `grid_params`\n", "#dict for parameters to evaluate, 5-fold cross-validation, and all available CPU cores (if desired)\n", - "rf_grid_cv = GridSearchCV(___, param_grid=___, cv=___, n_jobs=-1)" - ] + "rf_grid_cv = GridSearchCV(RF_pipe, param_grid=grid_params, cv=5, n_jobs=-1)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 25#\n", "#Now call the `GridSearchCV`'s `fit()` method with `X_train` and `y_train` as arguments\n", "#to actually start the grid search. This may take a minute or two.\n", - "rf_grid_cv.___(___, ___)" - ] + "rf_grid_cv.fit(X_train, y_train)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 26#\n", "#Print the best params (`best_params_` attribute) from the grid search\n", - "rf_grid_cv.___" - ] + "rf_grid_cv.best_params()" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3172,45 +1939,23 @@ }, { "cell_type": "code", - "execution_count": 94, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "rf_best_cv_results = cross_validate(rf_grid_cv.best_estimator_, X_train, y_train, cv=5)\n", "rf_best_scores = rf_best_cv_results['test_score']\n", "rf_best_scores" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 95, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.7097384501425082, 0.06451341966873386)" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "np.mean(rf_best_scores), np.std(rf_best_scores)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3221,9 +1966,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 27#\n", "#Plot a barplot of the random forest's feature importances,\n", @@ -3232,13 +1975,15 @@ "#create a pandas Series object of the feature importances, with the index given by the\n", "#training data column names, sorting the values in descending order\n", "plt.subplots(figsize=(10, 5))\n", - "imps = rf_grid_cv.best_estimator_.named_steps.randomforestregressor.___\n", - "rf_feat_imps = pd.Series(___, index=X_train.columns).sort_values(ascending=False)\n", + "imps = rf_grid_cv.best_estimator_.named_steps.randomforestregressor.feature_importances_\n", + "rf_feat_imps = pd.Series(imps, index=X_train.columns).sort_values(ascending=False)\n", "rf_feat_imps.plot(kind='bar')\n", "plt.xlabel('features')\n", "plt.ylabel('importance')\n", "plt.title('Best random forest regressor feature importances');" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3283,56 +2028,34 @@ }, { "cell_type": "code", - "execution_count": 97, "metadata": {}, - "outputs": [], "source": [ "# 'neg_mean_absolute_error' uses the (negative of) the mean absolute error\n", "lr_neg_mae = cross_validate(lr_grid_cv.best_estimator_, X_train, y_train, \n", " scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 98, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10.499032338015297, 1.6220608976799646)" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "lr_mae_mean = np.mean(-1 * lr_neg_mae['test_score'])\n", "lr_mae_std = np.std(-1 * lr_neg_mae['test_score'])\n", "lr_mae_mean, lr_mae_std" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 99, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11.793465668669327" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_test, lr_grid_cv.best_estimator_.predict(X_test))" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3343,55 +2066,33 @@ }, { "cell_type": "code", - "execution_count": 100, "metadata": {}, - "outputs": [], "source": [ "rf_neg_mae = cross_validate(rf_grid_cv.best_estimator_, X_train, y_train, \n", " scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 101, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.644639167595688, 1.3528565172191818)" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "rf_mae_mean = np.mean(-1 * rf_neg_mae['test_score'])\n", "rf_mae_std = np.std(-1 * rf_neg_mae['test_score'])\n", "rf_mae_mean, rf_mae_std" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 102, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9.537730050637332" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "mean_absolute_error(y_test, rf_grid_cv.best_estimator_.predict(X_test))" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3423,9 +2124,7 @@ }, { "cell_type": "code", - "execution_count": 103, "metadata": {}, - "outputs": [], "source": [ "fractions = [.2, .25, .3, .35, .4, .45, .5, .6, .75, .8, 1.0]\n", "train_size, train_scores, test_scores = learning_curve(pipe, X_train, y_train, train_sizes=fractions)\n", @@ -3433,33 +2132,22 @@ "train_scores_std = np.std(train_scores, axis=1)\n", "test_scores_mean = np.mean(test_scores, axis=1)\n", "test_scores_std = np.std(test_scores, axis=1)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 104, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], "source": [ "plt.subplots(figsize=(10, 5))\n", "plt.errorbar(train_size, test_scores_mean, yerr=test_scores_std)\n", "plt.xlabel('Training set size')\n", "plt.ylabel('CV scores')\n", "plt.title('Cross-validation score as training set size increases');" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -3477,9 +2165,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#Code task 28#\n", "#This may not be \"production grade ML deployment\" practice, but adding some basic\n", @@ -3492,25 +2178,27 @@ "#and the current datetime (`datetime.datetime.now()`) to the `build_datetime` attribute\n", "#Let's call this model version '1.0'\n", "best_model = rf_grid_cv.best_estimator_\n", - "best_model.version = ___\n", - "best_model.pandas_version = ___\n", - "best_model.numpy_version = ___\n", - "best_model.sklearn_version = ___\n", + "best_model.version = 1.0\n", + "best_model.pandas_version = pd.__version__\n", + "best_model.numpy_version = np.__version__\n", + "best_model.sklearn_version = sklearn_version\n", "best_model.X_columns = [col for col in X_train.columns]\n", - "best_model.build_datetime = ___" - ] + "best_model.build_datetime = datetime.datetime.now()" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "# save the model\n", "\n", "modelpath = '../models'\n", "save_file(best_model, 'ski_resort_pricing_model.pkl', modelpath)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", diff --git a/anaconda_projects/.DS_Store b/anaconda_projects/.DS_Store new file mode 100644 index 000000000..3ce66bf72 Binary files /dev/null and b/anaconda_projects/.DS_Store differ diff --git a/anaconda_projects/db/project_filebrowser.db b/anaconda_projects/db/project_filebrowser.db new file mode 100644 index 000000000..ffdfcb381 Binary files /dev/null and b/anaconda_projects/db/project_filebrowser.db differ diff --git a/data/ski_data_cleaned.csv b/data/ski_data_cleaned.csv new file mode 100644 index 000000000..4259e45d8 --- /dev/null +++ b/data/ski_data_cleaned.csv @@ -0,0 +1,278 @@ +Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac +Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0 +Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0, +Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0 +Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0, +Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0 +Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0, +Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0, +Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0, +Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,, +Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0, +Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0 +Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0, +Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0, +Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0 +Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0, +Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0, +Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0, +Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0, +Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0, +Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0, +Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0, +Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0 +Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0, +Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0, +Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0, +Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0, +Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0, +Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0 +Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0, +Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0, +Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0, +Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0 +Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0 +Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0 +Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0 +Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0 +Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0 +Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,, +Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,, +Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0, +Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0, +Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0 +Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0 +Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0 +Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,, +Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0, +Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0 +Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0 +Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0 +Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0 +Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0 +Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0 +Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0 +Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0 +Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0, +Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0 +Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0 +Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0, +New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0 +Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0 +Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0, +Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0 +Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0 +Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0 +Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0 +Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,, +Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0 +Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,, +Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0 +Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0 +Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0 +Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0, +Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0 +Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0 +Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0 +Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0, +Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0 +Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0, +Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0 +Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0 +Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0, +Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0 +Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0, +Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0 +Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0, +Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0 +Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0 +Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0 +Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0 +Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0 +Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0 +Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0 +Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0 +Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0 +Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0 +Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0 +The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0 +Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0 +Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0 +Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0 +Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0 +Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0 +Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0 +Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0 +Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0 +Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0 +Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0, +Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0 +Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0 +Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0 +Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0 +Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0 +Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0 +Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0 +Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,, +Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0, +Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0, +Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0 +Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0, +Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,, +Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0 +Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0, +Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0, +Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0, +Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0 +Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0, +Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0, +Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0, +Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0, +Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0, +Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0, +Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0 +Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0, +Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0 +Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0, +Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0 +King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0 +Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0, +Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0 +Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0, +Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0, +Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0 +Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0, +Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0 +Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0 +Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0, +Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0, +Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,, +Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0, +Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0, +Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0, +Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0, +Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0, +Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0, +Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,, +Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0 +Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0 +Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0 +Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0 +Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0 +Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0 +Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0 +Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0 +Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0, +Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0 +Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0, +Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0 +Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0 +Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0 +McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0, +Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0 +Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0 +Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0 +Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0, +Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,, +Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0 +Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0 +Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0 +Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0 +Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0 +Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0 +West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0 +Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0, +Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0 +Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0 +Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0 +Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0 +Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0 +Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0 +Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0 +Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0 +Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0 +Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0 +Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0 +Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0 +Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0 +Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0 +Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0, +Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0, +Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0 +Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0 +Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0, +Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0 +Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0, +Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0 +Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0 +Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0 +Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0 +Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0 +Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0 +Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0 +Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0, +Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0 +Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0 +Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0 +Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0 +Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0 +Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0 +Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0 +Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0 +Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0, +Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0, +Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0, +Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0, +Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0, +Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0, +Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0 +Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,, +Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0, +Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0 +Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0, +Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0 +Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0, +Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0, +Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0 +Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0 +Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0, +Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0, +Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0, +Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,, +Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0, +Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,, +Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0, +Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0, +Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0, +Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0 +49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0 +Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0, +Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,, +Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0, +Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0 +The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0 +White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0 +Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0, +Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0 +Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0 +Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0 +Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0 +Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0 +Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0, +Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0 +Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0 +Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0 +Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0 +Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0 +Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0 +Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0 +Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0 +Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0 +Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0, +Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0 +Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0, +Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0, +Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0, +Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0 +Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,, +White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,, diff --git a/data/state_summary.csv b/data/state_summary.csv new file mode 100644 index 000000000..53979a710 --- /dev/null +++ b/data/state_summary.csv @@ -0,0 +1,36 @@ +state,resorts_per_state,state_total_skiable_area_ac,state_total_days_open,state_total_terrain_parks,state_total_nightskiing_ac,state_population,state_area_sq_miles +Alaska,3,2280.0,345.0,4.0,580.0,731545,665384 +Arizona,2,1577.0,237.0,6.0,80.0,7278717,113990 +California,21,25948.0,2738.0,81.0,587.0,39512223,163695 +Colorado,22,43682.0,3258.0,74.0,428.0,5758736,104094 +Connecticut,5,358.0,353.0,10.0,256.0,3565278,5543 +Idaho,12,16396.0,1136.0,27.0,415.0,1787065,83569 +Illinois,4,191.0,221.0,6.0,191.0,12671821,57914 +Indiana,2,165.0,157.0,4.0,165.0,6732219,36420 +Iowa,3,140.0,100.0,5.0,140.0,3155070,56273 +Maine,9,3216.0,865.0,17.0,388.0,1344212,35380 +Maryland,1,172.0,121.0,3.0,118.0,6045680,12406 +Massachusetts,11,1166.0,671.0,18.0,583.0,6892503,10554 +Michigan,28,4406.0,2389.0,63.0,1946.0,9986857,96714 +Minnesota,14,1560.0,1490.0,29.0,1020.0,5639632,86936 +Missouri,2,60.0,69.0,2.0,47.0,6137428,69707 +Montana,12,21410.0,951.0,27.0,710.0,1068778,147040 +Nevada,4,2110.0,415.0,9.0,0.0,3080156,110572 +New Hampshire,16,3427.0,1847.0,43.0,376.0,1359711,9349 +New Jersey,2,190.0,170.0,4.0,181.0,8882190,8723 +New Mexico,9,5223.0,966.0,18.0,50.0,2096829,121590 +New York,33,5514.0,2384.0,72.0,2836.0,19453561,54555 +North Carolina,6,370.0,506.0,9.0,335.0,10488084,53819 +Ohio,5,421.0,489.0,12.0,421.0,11689100,44826 +Oregon,10,11774.0,1180.0,22.0,1127.0,4217737,98379 +Pennsylvania,19,1888.0,1404.0,47.0,1528.0,12801989,46054 +Rhode Island,1,30.0,100.0,1.0,30.0,1059361,1545 +South Dakota,2,950.0,183.0,3.0,0.0,884659,77116 +Tennessee,1,0.0,83.0,1.0,0.0,6829174,42144 +Utah,13,30508.0,1544.0,26.0,642.0,3205958,84897 +Vermont,15,7239.0,1777.0,50.0,50.0,623989,9616 +Virginia,4,269.0,366.0,4.0,135.0,8535519,42775 +Washington,10,15330.0,1022.0,21.0,1997.0,7614893,71298 +West Virginia,4,542.0,342.0,9.0,187.0,1792147,24230 +Wisconsin,15,1750.0,1519.0,40.0,1065.0,5822434,65496 +Wyoming,8,6523.0,716.0,14.0,110.0,578759,97813 diff --git a/raw_data/state_summary.csv b/raw_data/state_summary.csv new file mode 100644 index 000000000..8c6d29316 --- /dev/null +++ b/raw_data/state_summary.csv @@ -0,0 +1,36 @@ +state,summit_elev,vertical_drop,base_elev,trams,fastEight,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekday,AdultWeekend,projectedDaysOpen,NightSkiing_ac +Alaska,2876.3333333333335,1444.6666666666667,1082.0,0.3333333333333333,0.0,0.0,0.6666666666666666,0.6666666666666666,0.3333333333333333,1.3333333333333333,1.3333333333333333,4.666666666666667,41.666666666666664,1.3333333333333333,1.3333333333333333,760.0,67.66666666666667,115.0,46.666666666666664,362.6666666666667,47.333333333333336,57.333333333333336,130.66666666666666,290.0 +Arizona,11300.0,2050.0,9200.0,0.0,0.0,0.5,0.5,2.0,2.5,1.0,1.0,7.5,60.0,3.0,1.6,788.5,92.0,118.5,65.0,255.0,81.5,83.5,113.0,80.0 +California,8524.47619047619,1643.047619047619,6903.428571428572,0.38095238095238093,0.0,0.2857142857142857,2.0476190476190474,0.6666666666666666,2.4285714285714284,2.9047619047619047,2.857142857142857,11.571428571428571,53.95238095238095,4.2631578947368425,1.842857142857143,1235.6190476190477,475.88235294117646,152.11111111111111,66.19047619047619,310.9047619047619,78.21428571428571,81.41666666666667,146.45,146.75 +Colorado,11535.772727272728,2499.8636363636365,9066.454545454546,0.6818181818181818,0.0,0.8181818181818182,4.0,0.8181818181818182,1.6818181818181819,2.3636363636363638,4.5,14.863636363636363,121.23809523809524,3.7,2.7181818181818183,3121.909090909091,324.57894736842104,148.0909090909091,60.22727272727273,303.40909090909093,90.71428571428571,90.71428571428571,144.76190476190476,107.0 +Connecticut,930.0,510.0,420.0,0.0,0.0,0.0,0.0,0.0,2.2,1.0,2.8,6.0,17.2,2.5,0.7,71.6,67.8,88.25,58.8,73.4,47.8,56.8,116.0,51.2 +Idaho,7415.166666666667,1820.8333333333333,5595.833333333333,0.16666666666666666,0.0,0.08333333333333333,1.4166666666666667,0.25,1.3333333333333333,1.75,1.5833333333333333,6.583333333333333,57.75,2.7,1.7750000000000001,1366.3333333333333,171.28571428571428,113.6,61.833333333333336,292.72727272727275,56.55555555555556,55.9,131.125,83.0 +Illinois,707.5,254.25,453.25,0.0,,0.0,0.0,1.25,1.0,0.5,4.5,7.25,12.75,1.5,0.30000000000000004,47.75,47.75,73.66666666666667,52.25,34.25,35.0,43.333333333333336,84.5,47.75 +Indiana,850.0,350.0,500.0,0.0,0.0,0.0,0.5,1.5,3.0,0.5,4.0,9.5,19.0,2.0,0.7,82.5,82.5,78.5,40.0,21.0,45.0,48.5,85.0,82.5 +Iowa,1178.0,350.0,861.3333333333334,0.0,0.0,0.0,0.0,0.6666666666666666,1.0,1.0,1.3333333333333333,4.0,14.333333333333334,1.6666666666666667,0.6,46.666666666666664,46.666666666666664,100.0,42.0,38.333333333333336,35.666666666666664,41.666666666666664,100.0,46.666666666666664 +Maine,1945.0,1132.4444444444443,719.2222222222222,0.1111111111111111,0.0,0.0,0.6666666666666666,1.0,0.8888888888888888,1.4444444444444444,1.7777777777777777,5.888888888888889,55.666666666666664,2.8333333333333335,1.3444444444444443,402.0,248.85714285714286,108.125,54.333333333333336,115.85714285714286,51.5,61.0,112.0,77.6 +Maryland,3115.0,700.0,2415.0,0.0,,0.0,0.0,2.0,5.0,0.0,5.0,12.0,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,59.0,79.0,120.0,118.0 +Massachusetts,1473.5454545454545,632.0,824.7272727272727,0.0,0.0,0.09090909090909091,0.2727272727272727,0.6363636363636364,1.0909090909090908,1.0909090909090908,3.8181818181818183,7.0,23.90909090909091,1.6363636363636365,1.05,106.0,88.9090909090909,111.83333333333333,56.54545454545455,84.77777777777777,40.9,57.2,117.42857142857143,72.875 +Michigan,1195.8275862068965,394.2068965517241,794.7241379310345,0.0,0.0,0.034482758620689655,0.10344827586206896,1.0689655172413792,1.5172413793103448,1.6896551724137931,3.310344827586207,7.724137931034483,27.071428571428573,2.75,0.6464285714285714,154.68965517241378,128.22222222222223,103.8695652173913,124.51724137931035,133.53846153846155,45.458333333333336,52.57692307692308,111.36363636363636,84.6086956521739 +Minnesota,1251.5,361.7142857142857,886.2142857142857,0.07142857142857142,0.0,0.07142857142857142,0.14285714285714285,1.5,1.0,2.4285714285714284,2.7857142857142856,8.0,25.428571428571427,2.6363636363636362,0.823076923076923,111.42857142857143,99.85714285714286,106.42857142857143,49.642857142857146,64.0,44.59571428571429,49.667142857142856,111.57142857142857,78.46153846153847 +Missouri,1833.0,305.0,1558.0,0.0,0.0,0.0,0.0,1.0,2.0,0.5,2.5,6.0,15.5,2.0,0.2,30.0,30.0,69.0,35.0,23.0,43.0,48.0,85.0,23.5 +Montana,8164.583333333333,2161.0833333333335,6060.5,0.08333333333333333,0.14285714285714285,0.16666666666666666,0.8333333333333334,0.5,2.25,2.4166666666666665,2.5833333333333335,8.916666666666666,84.58333333333333,3.0,2.3583333333333334,1784.1666666666667,255.85714285714286,105.66666666666667,62.083333333333336,272.75,51.90909090909091,51.90909090909091,118.55555555555556,236.66666666666666 +Nevada,9132.25,1300.0,7442.5,0.0,0.0,0.5,0.25,1.5,0.75,1.0,1.0,5.0,32.25,3.0,1.575,527.5,218.5,103.75,46.75,208.75,78.5,81.0,113.0, +New Hampshire,2525.5,1370.4375,1142.5625,0.125,0.0,0.0625,1.1875,0.6875,1.8125,1.0625,2.5625,7.5,50.4375,3.0714285714285716,1.575,214.1875,179.875,123.13333333333334,61.125,132.375,65.57142857142857,76.5,128.6,62.666666666666664 +New Jersey,1103.0,657.0,445.0,0.5,,0.0,1.0,1.0,0.5,1.5,3.0,7.5,29.0,2.0,1.15,95.0,95.0,85.0,56.0,55.0,79.99,79.99,100.0,90.5 +New Mexico,10803.888888888889,1683.111111111111,9136.555555555555,0.2222222222222222,0.0,0.0,0.3333333333333333,1.0,2.0,1.5555555555555556,1.8888888888888888,7.0,61.333333333333336,2.25,2.3444444444444446,580.3333333333334,232.42857142857142,107.33333333333333,61.44444444444444,203.0,65.66666666666667,65.66666666666667,116.125,50.0 +New York,2145.848484848485,889.2727272727273,1217.8484848484848,0.09090909090909091,0.0,0.09090909090909091,0.3939393939393939,0.7878787878787878,1.0606060606060606,1.878787878787879,2.4545454545454546,6.757575757575758,34.90909090909091,2.88,1.18125,167.0909090909091,144.25,108.36363636363636,56.75757575757576,131.26666666666668,50.03225806451613,58.945454545454545,112.125,113.44 +North Carolina,4726.0,675.8333333333334,4045.0,0.0,0.0,0.16666666666666666,0.0,1.3333333333333333,0.3333333333333333,1.6666666666666667,2.0,5.5,16.6,1.5,0.9333333333333332,61.666666666666664,61.666666666666664,101.2,53.5,49.5,41.833333333333336,64.16666666666667,97.6,55.833333333333336 +Ohio,1235.4,267.0,971.2,0.0,0.0,0.0,0.0,0.8,3.8,2.0,3.4,10.0,13.2,2.4,0.3,84.2,84.2,97.8,56.0,61.6,42.2,45.4,95.0,84.2 +Oregon,6742.4,1694.0,5144.4,0.0,0.0,0.1,1.9,0.3,1.1,1.6,1.5,6.5,42.3,3.6666666666666665,1.72,1177.4,36.333333333333336,118.0,63.1,334.0,58.857142857142854,59.857142857142854,138.9,187.83333333333334 +Pennsylvania,1821.1052631578948,699.6842105263158,1122.6315789473683,0.0,0.0,0.15789473684210525,0.2631578947368421,1.2105263157894737,1.263157894736842,2.3157894736842106,2.6842105263157894,7.894736842105263,21.57894736842105,2.611111111111111,0.9388888888888888,104.88888888888889,101.27777777777777,93.6,51.10526315789474,54.44444444444444,52.705882352941174,63.6875,95.88888888888889,89.88235294117646 +Rhode Island,315.0,245.0,70.0,0.0,,0.0,0.0,0.0,0.0,2.0,2.0,4.0,13.0,1.0,0.2,30.0,30.0,100.0,54.0,40.0,,,100.0,30.0 +South Dakota,6975.0,1020.0,5970.0,0.0,0.0,0.0,1.5,0.0,1.0,0.5,1.5,4.5,46.5,1.5,1.4,475.0,137.5,91.5,58.0,175.0,51.5,51.5,100.5, +Tennessee,3300.0,600.0,2700.0,0.0,,0.0,0.0,2.0,0.0,1.0,1.0,4.0,10.0,1.0,1.0,,,83.0,44.0,35.0,36.0,65.0,94.0, +Utah,9709.076923076924,2261.3076923076924,7479.307692307692,0.6923076923076923,0.0,0.5384615384615384,3.3076923076923075,1.0769230769230769,2.230769230769231,1.6153846153846154,1.6923076923076923,11.153846153846153,105.92307692307692,2.6,1.8923076923076925,2346.769230769231,326.3333333333333,140.36363636363637,56.92307692307692,406.15384615384613,89.08333333333333,93.0,137.0,160.5 +Vermont,3500.0666666666666,1991.9333333333334,1503.3333333333333,0.4666666666666667,0.0,0.4666666666666667,1.6,1.8666666666666667,1.4666666666666666,2.1333333333333333,2.6666666666666665,10.666666666666666,79.86666666666666,4.166666666666667,2.7333333333333334,482.6,278.6,136.69230769230768,67.73333333333333,225.46666666666667,83.5,87.9,135.76923076923077,50.0 +Virginia,2847.5,828.25,2003.0,0.0,,0.5,0.0,1.25,0.5,0.75,2.25,5.25,14.5,2.0,0.8,67.25,81.0,91.5,51.0,37.25,51.0,68.0,103.25,45.0 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