{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Popular Data Science Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our goal in this project is to use [Data Science Stack Exchange](https://datascience.stackexchange.com) to determine what content should a data science education company create, based on interest by subject." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stack Exchange" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### What kind of questions are welcome on this site?\n", "\n", "On DSSE's help center's [section on questions](https://datascience.stackexchange.com/help/asking) , we can read that we should:\n", "\n", "* Avoid subjective questions.\n", "* Ask practical questions about Data Science — there are adequate sites for theoretical questions.\n", "* Ask specific questions.\n", "* Make questions relevant to others.\n", "\n", "All of these characteristics, if employed, should be helpful attributes to our goal.\n", "\n", "In the help center we also learned that in addition to the sites mentioned in the _Learn_ section, there are other two sites that are relevant:\n", "\n", "* [Open Data](https://opendata.stackexchange.com/help/on-topic) (Dataset requests)\n", "* [Computational Science](https://scicomp.stackexchange.com/help/on-topic) (Software packages and algorithms in applied mathematics)\n", "\n", "#### What, other than questions, does DSSE's [home](https://datascience.stackexchange.com) subdivide into?\n", "\n", "On the [home page](https://datascience.stackexchange.com/) we can see that we have four sections:\n", "\n", "* [Questions](https://datascience.stackexchange.com/questions) — a list of all questions asked;\n", "* [Tags](https://datascience.stackexchange.com/tags) — a list of tags (keywords or labels that categorize questions);\n", "\n", " ![tags_ds](https://dq-content.s3.amazonaws.com/469/tags_ds.png)\n", "* [Users](https://datascience.stackexchange.com/users) — a list of users;\n", "* [Unanswered](https://datascience.stackexchange.com/unanswered) — a list of unanswered questions;\n", "\n", "The tagging system used by Stack Exchange looks just like what we need to solve this problem as it allow us to quantify how many questions are asked about each subject.\n", "\n", "Something else we can learn from exploring the help center, is that Stack Exchange's sites are heavily moderated by the community; this gives us some confidence in using the tagging system to derive conclusions.\n", "\n", "#### What information is available in each post?\n", "\n", "Looking, just as an example, at [this](https://datascience.stackexchange.com/questions/19141/linear-model-to-generate-probability-of-each-possible-output?rq=1) question, some of the information we see is:\n", "\n", "* For both questions and answers:\n", " * The posts's score;\n", " * The posts's title;\n", " * The posts's author;\n", " * The posts's body;\n", "* For questions only:\n", " * How many users have it on their \"\n", " * The last time the question as active;\n", " * How many times the question was viewed;\n", " * Related questions;\n", " * The question's tags;\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stack Exchange Data Explorer\n", "\n", "Perusing the table names, a few stand out as relevant for our goal:\n", "\n", "* Posts\n", "* PostTags\n", "* Tags\n", "* TagSynonyms\n", "\n", "Running a few exploratory queries, leads us to focus our efforts on `Posts` table. For examples, the `Tags` table looked very promising as it tells us how many times each tag was used, but there's no way to tell just from this if the interest in these tags is recent or a thing from the past.\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IdTagNameCountExcerptPostIdWikiPostId
2machine-learning691949094908
46python390755235522
81neural-network292388858884
194deep-learning278689568955
77classification189949114910
324keras173692519250
128scikit-learn130358965895
321tensorflow122491839182
47nlp1162147146
24r11144948
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting the Data\n", "\n", "To get the relevant data we run the following query.\n", "\n", "```\n", "SELECT Id, CreationDate,\n", " Score, ViewCount, Tags,\n", " AnswerCount, FavoriteCount\n", " FROM posts\n", " WHERE PostTypeId = 1 AND YEAR(CreationDate) = 2019;\n", "```\n", "\n", "Here's what the first few rows look like:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IdPostTypeIdCreationDateScoreViewCountTagsAnswerCountFavoriteCount
4441912019-01-23 09:21:13121<machine-learning><data-mining>0
4442012019-01-23 09:34:01025<machine-learning><regression><linear-regression><regularization>0
4442312019-01-23 09:58:4121651<python><time-series><forecast><forecasting>0
4442712019-01-23 10:57:09055<machine-learning><scikit-learn><pca>1
4442812019-01-23 11:02:15019<dataset><bigdata><data><speech-to-text>0
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring the Data\n", "\n", "We can read in the data while immediately making sure `CreationDate` will be stored as a datetime object:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# We import everything that we'll use\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "questions = pd.read_csv(\"2019_questions.csv\", parse_dates=[\"CreationDate\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running [`questions.info()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) should gives a lot of useful information." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 8839 entries, 0 to 8838\n", "Data columns (total 7 columns):\n", "Id 8839 non-null int64\n", "CreationDate 8839 non-null datetime64[ns]\n", "Score 8839 non-null int64\n", "ViewCount 8839 non-null int64\n", "Tags 8839 non-null object\n", "AnswerCount 8839 non-null int64\n", "FavoriteCount 1407 non-null float64\n", "dtypes: datetime64[ns](1), float64(1), int64(4), object(1)\n", "memory usage: 483.5+ KB\n" ] } ], "source": [ "questions.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that only `FavoriteCount` has missing values. A missing value on this column probably means that the question was is not present in any users' favorite list, so we can replace the missing values with zero.\n", "\n", "The types seem adequate for every column, however, after we fill in the missing values on `FavoriteCount`, there is no reason to store the values as floats.\n", "\n", "Since the `object` dtype is a catch-all type, let's see what types the objects in `questions[\"Tags\"]` are." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([], dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "questions[\"Tags\"].apply(lambda value: type(value)).unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that every value in this column is a string. On Stack Exchange, each question can only have a maximum of five tags ([source](https://meta.stackexchange.com/a/18879)), so one way to deal with this column is to create five columns in `questions` called `Tag1`, `Tag2`, `Tag3`, `Tag4`, and `Tag5` and populate the columns with the tags in each row.\n", "\n", "However, since doesn't help is relating tags from one question to another, we'll just keep them as a list." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cleaning the Data\n", "\n", "We'll begin by fixing `FavoriteCount`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Id int64\n", "CreationDate datetime64[ns]\n", "Score int64\n", "ViewCount int64\n", "Tags object\n", "AnswerCount int64\n", "FavoriteCount int64\n", "dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "questions.fillna(value={\"FavoriteCount\": 0}, inplace=True)\n", "questions[\"FavoriteCount\"] = questions[\"FavoriteCount\"].astype(int)\n", "questions.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now modify `Tags` to make it easier to work with." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IdCreationDateScoreViewCountTagsAnswerCountFavoriteCount
511563822019-07-25 15:00:20034[machine-learning, python, pandas, natural-lan...00
2178583122019-08-28 09:44:00141[neural-network, pytorch]01
2536581512019-08-25 01:01:29037[dataset, audio-recognition]20
\n", "
" ], "text/plain": [ " Id CreationDate Score ViewCount \\\n", "511 56382 2019-07-25 15:00:20 0 34 \n", "2178 58312 2019-08-28 09:44:00 1 41 \n", "2536 58151 2019-08-25 01:01:29 0 37 \n", "\n", " Tags AnswerCount \\\n", "511 [machine-learning, python, pandas, natural-lan... 0 \n", "2178 [neural-network, pytorch] 0 \n", "2536 [dataset, audio-recognition] 2 \n", "\n", " FavoriteCount \n", "511 0 \n", "2178 1 \n", "2536 0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "questions[\"Tags\"] = questions[\"Tags\"].str.replace(\"^<|>$\", \"\").str.split(\"><\")\n", "questions.sample(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Most Used and Most Viewed\n", "\n", "We'll begin by counting how many times each tag was used" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "tag_count = dict()\n", "\n", "for tags in questions[\"Tags\"]:\n", " for tag in tags:\n", " if tag in tag_count:\n", " tag_count[tag] += 1\n", " else:\n", " tag_count[tag] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For improved aesthetics, let's transform `tag_count` in a dataframe." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Count
machine-learning2693
data-mining217
regression347
linear-regression175
regularization50
python1814
time-series466
forecast34
forecasting85
scikit-learn540
\n", "
" ], "text/plain": [ " Count\n", "machine-learning 2693\n", "data-mining 217\n", "regression 347\n", "linear-regression 175\n", "regularization 50\n", "python 1814\n", "time-series 466\n", "forecast 34\n", "forecasting 85\n", "scikit-learn 540" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tag_count = pd.DataFrame.from_dict(tag_count, orient=\"index\")\n", "tag_count.rename(columns={0: \"Count\"}, inplace=True)\n", "tag_count.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now sort this dataframe by `Count` and visualize the top 20 results." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Count
machine-learning-model224
statistics234
clustering257
predictive-modeling265
r268
dataset340
regression347
pandas354
lstm402
time-series466
cnn489
nlp493
scikit-learn540
tensorflow584
classification685
keras935
neural-network1055
deep-learning1220
python1814
machine-learning2693
\n", "
" ], "text/plain": [ " Count\n", "machine-learning-model 224\n", "statistics 234\n", "clustering 257\n", "predictive-modeling 265\n", "r 268\n", "dataset 340\n", "regression 347\n", "pandas 354\n", "lstm 402\n", "time-series 466\n", "cnn 489\n", "nlp 493\n", "scikit-learn 540\n", "tensorflow 584\n", "classification 685\n", "keras 935\n", "neural-network 1055\n", "deep-learning 1220\n", "python 1814\n", "machine-learning 2693" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "most_used = tag_count.sort_values(by=\"Count\").tail(20)\n", "most_used" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The threshold of `20` is somewhat arbitrary and we can experiment with others, however, popularity of the tags rapidly declines, so looking at these tags should be enough to help us with our goal. Let's visualize these data." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "most_used.plot(kind=\"barh\", figsize=(16,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some tags are very, very broad and are unlikely to be useful; e.g.: `python`, `dataset`, `r`. Before we investigate the tags a little deeper, let's repeat the same process for views.\n", "\n", "We'll use Python's builtin [`enumerate()`](https://docs.python.org/3/library/functions.html#enumerate) function. Its utility is well understood by seeing it action." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 I\n", "1 t\n", "2 e\n", "3 r\n", "4 a\n", "5 t\n", "6 e\n", "7 \n", "8 t\n", "9 h\n", "10 i\n", "11 s\n", "12 !\n" ] } ], "source": [ "some_iterable = \"Iterate this!\"\n", "\n", "for i,c in enumerate(some_iterable):\n", " print(i,c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the elements of `some_iterable`, `enumerate` gives us the index of each of them." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tag_view_count = dict()\n", "\n", "for idx, tags in enumerate(questions[\"Tags\"]):\n", " for tag in tags:\n", " if tag in tag_view_count:\n", " tag_view_count[tag] += questions[\"ViewCount\"].iloc[idx]\n", " else:\n", " tag_view_count[tag] = 1\n", " \n", "tag_view_count = pd.DataFrame.from_dict(tag_view_count, orient=\"index\")\n", "tag_view_count.rename(columns={0: \"ViewCount\"}, inplace=True)\n", "\n", "most_viewed = tag_view_count.sort_values(by=\"ViewCount\").tail(20)\n", "\n", "most_viewed.plot(kind=\"barh\", figsize=(16,8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see them side by side." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([],\n", " dtype=object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=1, ncols=2)\n", "fig.set_size_inches((24, 10))\n", "most_used.plot(kind=\"barh\", ax=axes[0], subplots=True)\n", "most_viewed.plot(kind=\"barh\", ax=axes[1], subplots=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "in_used = pd.merge(most_used, most_viewed, how=\"left\", left_index=True, right_index=True)\n", "in_viewed = pd.merge(most_used, most_viewed, how=\"right\", left_index=True, right_index=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Relations Between Tags" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way of trying to gauge how pairs of tags are related to each other, is to count how many times each pair appears together. Let's do this.\n", "\n", "We'll begin by creating a list of all tags." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "all_tags = list(tag_count.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now create a dataframe where each row will represent a tag, and each column as well. Something like this:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
tag1tag2tag3
tag1
tag2
tag3
" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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machine-learningdata-miningregressionlinear-regression
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linear-regressionNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " machine-learning data-mining regression linear-regression\n", "machine-learning NaN NaN NaN NaN\n", "data-mining NaN NaN NaN NaN\n", "regression NaN NaN NaN NaN\n", "linear-regression NaN NaN NaN NaN" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "associations = pd.DataFrame(index=all_tags, columns=all_tags)\n", "associations.iloc[0:4,0:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now fill this dataframe with zeroes and then, for each lists of tags in `questions[\"Tags\"]`, we will increment the intervening tags by one. The end result will be a dataframe that for each pair of tags, it tells us how many times they were used together." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "associations.fillna(0, inplace=True)\n", "\n", "for tags in questions[\"Tags\"]:\n", " associations.loc[tags, tags] += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataframe is quite large. Let's focus our attention on the most used tags. We'll add some colors to make it easier to talk about the dataframe. (At the time of this writing, GitHub's renderer does not display the colors, we suggest you use this solution notebook together with [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
machine-learning-model statistics clustering predictive-modeling r dataset regression pandas lstm time-series cnn nlp scikit-learn tensorflow classification keras neural-network deep-learning python machine-learning
machine-learning-model22433217128457441892117101937139
statistics32343161617163122136019311123589
clustering3325701652532009240120824561
predictive-modeling211602651372841331611262711133235123
r7161613268610232224111010952463
dataset121757634061476111199281320325399
regression81622810634761124623793431422159119
pandas4354214635471913373331124462
lstm513133711740287241924320133691036171
time-series72220312262419874668012925513344105131
cnn41062116124848970572011611816062124
nlp439141123190749312113523247271113
scikit-learn18624121937372120125401547342416235188
tensorflow9006199343957111558420256108136167106
classification21191227102834320252035472068558655998259
keras173011101331313351116233425658935235247280195
neural-network101181392042169331182424108652351055305137366
deep-learning1912232532211103441607216136592473051220160429
python37354535245359244611056271235167982801371601814499
machine-learning1398961123639911962711311241131881062591953664294992693
" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "relations_most_used = associations.loc[most_used.index, most_used.index]\n", "\n", "def style_cells(x):\n", " helper_df = pd.DataFrame('', index=x.index, columns=x.columns)\n", " helper_df.loc[\"time-series\", \"r\"] = \"background-color: yellow\"\n", " helper_df.loc[\"r\", \"time-series\"] = \"background-color: yellow\"\n", " for k in range(helper_df.shape[0]):\n", " helper_df.iloc[k,k] = \"color: blue\"\n", " \n", " return helper_df\n", "\n", "relations_most_used.style.apply(style_cells, axis=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cells highlighted in yellow tell us that `time-series` was used together with `r` 22 times. The values in blue tell us how many times each of the tags was used. We saw earlier that `machine-learning` was used 2693 times and we confirm it in this dataframe.\n", "\n", "It's hard for a human to understand what is going on in this dataframe. Let's create a heatmap. But before we do it, let's get rid of the values in blue, otherwise the colors will be too skewed." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "for i in range(relations_most_used.shape[0]):\n", " relations_most_used.iloc[i,i] = pd.np.NaN" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "sns.heatmap(relations_most_used, cmap=\"Greens\", annot=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most used tags also seem to have the strongest relationships, as given by the dark concentration in the bottom right corner. However, this could simply be because each of these tags is used a lot, and so end up being used together a lot without possibly even having any strong relation between them.\n", "\n", "A more intuitive manifestation of this phenomenon is the following. A lot of people buy bread, a lot of people buy toilet paper, so they end up being purchased together a lot, but purchasing one of them doesn't increase the chances of purchasing the other." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another shortcoming of this attempt is that it only looks at relations between pairs of tags and not between multiple groups of tags. For example, it could be the case that when used together, `dataset` and `scikit-learn` have a \"strong\" relation to `pandas`, but each by itself doesn't.\n", "\n", "So how do we attack both these problems? There is a powerful data mining technique that allows us to handle this: [association rules](https://en.wikipedia.org/wiki/Association_rule_learning). Association rules allow us to analytically spot relations like \"people who purchase milk, also purchase eggs\". Moreover, we can also measure how strong this relations are on several fronts: how common the relation is, how strong it is, and how independent the components of the relationship are (toilet paper and bread are probably more independent than eggs and milk — you'll learn more about [statistical independence](https://en.wikipedia.org/wiki/Independence_(probability_theory)) in the next step).\n", "\n", "\n", "We won't get into the details of it, as the technique is out of scope for this course, but it is a path worth investigating!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Enter Domain Knowledge\n", "\n", "[Keras](https://keras.io/), [scikit-learn](https://scikit-learn.org/), [TensorFlow](https://www.tensorflow.org/) are all Python libraries that allow their users to employ deep learning (a type of neural network).\n", "\n", "Most of the top tags are all intimately related with one central machine learning theme: deep learning. If we want to be very specific, we can suggest the creation of Python content that uses deep learning for classification problems (and other variations of this suggestion).\n", "\n", "At the glance of an eye, someone with sufficient domain knowledge can tell that the most popular topic at the moment, as shown by our analysis, is deep learning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Just a Fad?\n", "\n", "Let's read in the file into a dataframe called `all_q`. We'll parse the dates at read-time." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "all_q = pd.read_csv(\"all_questions.csv\", parse_dates=[\"CreationDate\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the same technique as before to clean the tags column." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "all_q[\"Tags\"] = all_q[\"Tags\"].str.replace(\"^<|>$\", \"\").str.split(\"><\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before deciding which questions should be classified as being deep learning questions, we should decide what tags are deep learning tags.\n", "\n", "The definition of what constitutes a deep learning tag we'll use is: a tag that belongs to the list `[\"lstm\", \"cnn\", \"scikit-learn\", \"tensorflow\", \"keras\", \"neural-network\", \"deep-learning\"]`.\n", "\n", "This list was obtained by looking at all the tags in `most_used` and seeing which ones had any relation to deep learning. You can use Google and read the tags descriptions to reach similar results.\n", "\n", "We'll now create a function that assigns `1` to deep learning questions and `0` otherwise; and we use it." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def class_deep_learning(tags):\n", " for tag in tags:\n", " if tag in [\"lstm\", \"cnn\", \"scikit-learn\", \"tensorflow\",\n", " \"keras\", \"neural-network\", \"deep-learning\"]:\n", " return 1\n", " return 0" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "all_q[\"DeepLearning\"] = all_q[\"Tags\"].apply(class_deep_learning)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IdCreationDateTagsDeepLearning
15231446752019-01-28 06:20:18[model-selection]0
440556392019-07-14 11:45:43[machine-learning, dataset, machine-learning-m...0
11720515232019-05-07 04:57:35[neural-network, gradient-descent, batch-norma...1
6262272322018-01-30 09:53:38[python, convergence]0
19292649302019-12-16 14:38:17[neural-network, deep-learning, keras, convolu...1
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" ], "text/plain": [ " Id CreationDate \\\n", "15231 44675 2019-01-28 06:20:18 \n", "440 55639 2019-07-14 11:45:43 \n", "11720 51523 2019-05-07 04:57:35 \n", "6262 27232 2018-01-30 09:53:38 \n", "19292 64930 2019-12-16 14:38:17 \n", "\n", " Tags DeepLearning \n", "15231 [model-selection] 0 \n", "440 [machine-learning, dataset, machine-learning-m... 0 \n", "11720 [neural-network, gradient-descent, batch-norma... 1 \n", "6262 [python, convergence] 0 \n", "19292 [neural-network, deep-learning, keras, convolu... 1 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_q.sample(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data-science-techonology landscape isn't something as dynamic to merit daily, weekly, or even monthly tracking. Let's track it quarterly.\n", "\n", "Since we don't have all the data for the first quarter of 2020, we'll get rid of those dates:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "all_q = all_q[all_q[\"CreationDate\"].dt.year < 2020]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a column that identifies the quarter in which a question was asked." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "def fetch_quarter(datetime):\n", " year = str(datetime.year)[-2:]\n", " quarter = str(((datetime.month-1) // 3) + 1)\n", " return \"{y}Q{q}\".format(y=year, q=quarter)\n", "\n", "all_q[\"Quarter\"] = all_q[\"CreationDate\"].apply(fetch_quarter)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IdCreationDateTagsDeepLearningQuarter
0454162019-02-12 00:36:29[python, keras, tensorflow, cnn, probability]119Q1
1454182019-02-12 00:50:39[neural-network]119Q1
2454222019-02-12 04:40:51[python, ibm-watson, chatbot]019Q1
3454262019-02-12 04:51:49[keras]119Q1
4454272019-02-12 05:08:24[r, predictive-modeling, machine-learning-mode...019Q1
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" ], "text/plain": [ " Id CreationDate \\\n", "0 45416 2019-02-12 00:36:29 \n", "1 45418 2019-02-12 00:50:39 \n", "2 45422 2019-02-12 04:40:51 \n", "3 45426 2019-02-12 04:51:49 \n", "4 45427 2019-02-12 05:08:24 \n", "\n", " Tags DeepLearning Quarter \n", "0 [python, keras, tensorflow, cnn, probability] 1 19Q1 \n", "1 [neural-network] 1 19Q1 \n", "2 [python, ibm-watson, chatbot] 0 19Q1 \n", "3 [keras] 1 19Q1 \n", "4 [r, predictive-modeling, machine-learning-mode... 0 19Q1 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_q.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the final stretch of this screen, we'll group by quarter and:\n", "\n", "* Count the number of deep learning questions.\n", "* Count the total number of questions.\n", "* Compute the ratio between the two numbers above." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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QuarterDeepLearningQuestionsTotalQuestionsDeepLearningRate
1718Q368515120.453042
716Q11105160.213178
615Q4663820.172775
2219Q480920360.397348
916Q31615850.275214
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" ], "text/plain": [ " Quarter DeepLearningQuestions TotalQuestions DeepLearningRate\n", "17 18Q3 685 1512 0.453042\n", "7 16Q1 110 516 0.213178\n", "6 15Q4 66 382 0.172775\n", "22 19Q4 809 2036 0.397348\n", "9 16Q3 161 585 0.275214" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quarterly = all_q.groupby('Quarter').agg({\"DeepLearning\": ['sum', 'size']})\n", "quarterly.columns = ['DeepLearningQuestions', 'TotalQuestions']\n", "quarterly[\"DeepLearningRate\"] = quarterly[\"DeepLearningQuestions\"]\\\n", " /quarterly[\"TotalQuestions\"]\n", "# The following is done to help with visualizations later.\n", "quarterly.reset_index(inplace=True)\n", "quarterly.sample(5)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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EYrZUUxkYhtEdGAe4ApNN0xxzlv1uAeYAbU3TXGkYRhSwCdhycpelpmk+eGG5z87RP2zO+IOVnZ2Nl5cXe/bsISIiwu44RWzdupW4uDhSU1PtjiIiIiIiIhVR9VbFi1kXD2g52p48cmm8655lxGyd8s8iVZ4KS3FG5138yzAMV+AD4HqgGXCHYRjNStjPD3gUWHbGSztM04w9+SizUtaZ+Pr6Fj5cXFzw8vIq/HzmzHNPbP7tt9/SsGHDS7r+r7/+ylVXXYWvry+BgYH06tWryGTOjlKrVi1+//33ws8bN26sUlZERERERC5O6npIXgg1OlgjZDGsUtbFE2rfYHc6uRjNRpS83TsSnOzuXRERO5y3mAXigO2mae40TTMHmA3cXMJ+rwBvAtllmK9CSE9PL3zUrVuXr7/+uvDz/v0dO6H54sWLueGGG7j99tvZv38/O3bsoGHDhnTo0IGkpCSHXltERERERKRMFOTDsvvBIwiu+sqatqBfAXT7E/IzIP5ZuxPKxTiyDDCgWpj17B0JEb3h0G8Q7zyLJImI2KU0xWxtYM9pnyed3FbIMIzLgTqmaS4s4fh6hmGsMQzjF8MwOl581IorKyuLhx9+mLCwMCIiInjyySfJzc3l8OHD9OrVi507dxaOsD18+DBLliyhXbt2BAQEEB4ezvDhw8nLyyvx3E8++SSDBw/mX//6F76+vgQHB/PWW2/RvHlzRo+2bveZMGEC1157beEx2dnZGIZRWNxmZWUxbNgw6tSpQ61atXjkkUc4ceIEAPv376d79+4EBgYSHBzM1VdfDcCtt95KSkoK3bp1w9fXl/fee4/Nmzfj5vb37Bi7d+/mhhtuoHr16jRu3Jhp06YVvjZixAj69+/PHXfcgZ+fHy1atCA+Pr7w9VdeeYWwsDD8/f2Jjo7mt99+K6PvhoiIiIiIOJ2t4+HwMmg9DjyD/95evRU0fhS2fwyHltqXTy7cgZ9h51Rr1GzvvVbR3jMBOs6FRg/Dprdg09t2pxQRsVVpitmSJqctvOfAMAwX4F3g8RL22wfUNU3zcuAxYJZhGP7FLmAYgw3DWGkYxsqzFZAV2Ysvvsi6detYv349q1atYvHixbz55psEBwfz5ZdfUr9+/cIRtsHBwbi7uzN+/HiOHDnCb7/9xtdff83kyZOLnffo0aOsXLmSW2+9tdhrffv25fvvvy9VvuHDh5OUlMT69evZsmULW7duZcwYaxrhN954gyZNmnDo0CH27dvHSy+9BMCcOXMICQnh+++/Jz09nUcffbTYeW+99VaaNGnCvn37mDVrFsOHD2fJkiWFr3/55Zfcd999pKamcs011zBs2DAA1q5dy6effkp8fDzHjh1j0aJFTjefroiIiIiIlJH0BFg3EsJvgMjbi7/eYhR4hcPyB6Cg8v1/sVLKz4blD4JvfbjsuaKvGYZVwNftC2uehJ3T7ckoIuIESlPMJgGnz8wdAew97XM/4DJgsWEYCUB7YIFhGG1M0zxhmuZhANM0VwE7gMZnXsA0zYmmabYxTbPN6SMuK4uZM2fy8ssvU6NGDUJDQ3nuueeYMWPGWfePi4ujbdu2uLq60qBBAwYNGsQvv/xSbL/Dhw8DEBYWVuy1sLAwDh48eN5seXl5fPLJJ4wbN47AwEACAgIYMWIEs2fPBsDd3Z29e/eye/duPDw86NSpU6ne87Zt21i7di2vvfYanp6etGnThnvuuafI+7766qvp2rUrrq6u3HXXXYUjZt3c3MjKymLjxo3k5+dTv3596tWrV6rrioiIiIhIBWKasOJBwIC2H1ml3Znc/aDNe5C6DraMK/eIchE2jIG0rdb31M27+OsurnDFdAi9BpbdB8mLyj+jiIgTKE0xuwJoZBhGPcMwPIDbgQWnXjRN85hpmjVM04wyTTMKWAr0ME1zpWEYNU8uHoZhGPWBRsDOMn8XTsw0Tfbv309kZGThtsjISJKTk896zMaNG7n++usJDQ3F39+fUaNGcejQoWL7BQdbt/js27ev2Gv79u2jZs2a5823d+9ecnNzad68OYGBgQQGBtKzZ09SUlIAGDlyJOHh4XTp0oWGDRvy73//+7znPHXemjVr4uXlVbjtzPddq1atwo+9vb1JT08HoHnz5owZM4aRI0cSEhJC//79OXDgQKmuKyIiIiIiFUjCTNj3HbR8HXzqnn2/iF4Q/k9Y/yJk7C6/fHLhjm2Gja9DZD8I63b2/Vw9odOXEBQLv98KB/8ov4wiIk7ivMWsaZp5wBDgO2AT8LlpmhsMwxhlGEaP8xzeCVhnGMZaYC7woGmaRy41dEViGAa1atUiMTGxcNvu3bupXbt24etnuv/++2nVqhU7duzg+PHjvPDCC5glrFgZFBRE69atmTNnTrHXPv/8c6666ioAfHx8yMzMLHxt//79hR+HhYXh5ubGjh07SE1NJTU1lWPHjhWOxg0ICGDcuHEkJiYyb948Xn311cLpCErKfkp4eDgHDx4kKyurxPd9Pvfccw9//PEHO3fuJDs7m+eee+78B4mIiIiISMWRfRBWD4MaV0Cjf517X8OANuPBLIBVxadREydhmrDiAXD1hlalGNTj7ged/wfeEbD4n5D6l+Mziog4kVLNG2Ca5jfAN2dse+Es+3Y+7eN5wLxLyHdOU+5t66hTl6k77riDl19+mZYtW5Kfn8/o0aO58847AQgNDSUlJYX09HR8fX0BSEtLIyAgAF9fXzZs2MCkSZPOeiv/m2++yU033USjRo24++67yc7OZsyYMaxatYoJEyYAEBsby5o1a9iwYQP16tVj1KhRhce7u7tz3333MXToUMaOHUtwcDBJSUls3ryZrl27smDBAmJiYoiKiiIgIABXV1dcXV0Ls+/cuZMrr7yyWK6GDRvSokULnnvuOV577TU2bNjAtGnTmD9//nm/Xhs3buTgwYO0b98eLy8vvLy8Cq8pIiIiIiKVxOrhkHsc2k22bm0/H98oiHkR4kdA0lcQcbPDI8oF2jkVUn6FuIngFVq6Y6rVhC7fww//gJ+vg25/gE/k+Y8TEakESjOVgVyiUaNG0axZM5o3b05sbCwdOnTgqaeeAqBly5b06NGDyMhIAgMDOXLkCO+++y6TJ0/G19eXhx9+mNtuu+2s57766qtZtGgRs2bNIjQ0lBo1ajB37lx+/vlnmjRpAkBMTAxPPfUUHTt2pGnTpnTu3LnIOcaOHUt4eDht2rQhICCA7t27s337dgA2bdpE586d8fPzo1OnTjzxxBO0b98esKY5GDlyJIGBgYwfP77IOQ3D4PPPP2fjxo3UqlWL2267jbfeeouOHTue9+uVlZXF448/To0aNQgLCyM9Pb1ImSwiIiIiIhXc3v9Z0xg0HwkBzUp/XNPHIKA5rHwEctMdl08uXPZBWPME1OwADQZe2LG+UdDlO8jLtMrZ7OJT+YmIVEZGSbfI28nHx8fMyMgotn3Tpk1ER0fbkKhiWblyJd26dWPevHl06dLF7jgVln7eREREREQcJDcNFjW3bmPvvtqaa/RCHFwCP1wJ0U/A5W85JqNcuD/uht2z4fr4CyvbT5fyO/zcFQJi4JqfwN23bDNKlTZw6opyv6Ydd3pXlfdZAZU4H6hGzFYybdq0Yd68ecTHx1NQUGB3HBERERERkaLWPgeZSRA3+cJLWTg5InMQbH4Xjq4t+3xy4fb/HyTMgOinLr6UBQi5Ejp8DkdXw2+9IT+n7DKKiDghFbOVUJcuXRg+fDguLvr2ioiIiIiIEzn4J2x9HxoPgZpXXPx5Yt8AjyBY/qC1IJjYJz/b+j74NrSmprhUETdB3CTY/wMsvUffXxGp1NTciYiIiIiIiOPl58DyQeAdAS1HX9q5PKvD5e/A4aWwfVLZ5JOL89doSN8OcR+Bm1fZnLPBAIgdA4mzYdUwcLIpGEVEykqFKmZ1a76UB2ebd1lEREREpFLYOAaObYS4j635ZS9VvbsgpDPEj4CsA5d+PrlwxzbCpjcg6k6odW3Znjv6KWuxt63vw4bXyvbcIiJOosIUsz4+PiQnJ5OTk6PiTBzGNE0OHz5MtWrV7I4iIiIiIlJ5HNsIG16FyH4Qfn3ZnNMwoO1HkJ8Bax4vm3NK6ZkFsPwBcPODVu+U/fkNw1rcLepOWPecRkaLSKXkZneA0oqIiODQoUMkJiaSl5dndxypxKpVq0ZERITdMUREREREKgezAJYNAnd/aD22bM8d0BSajYC/XoH695b9qE05ux2fwMHfod0UqBbimGsYLtD+EzhxGFY8CJ7BUKe3Y64lImKDClPMuri4EBISQkiIg/7CFxERERERkbK37SM49CdcMQOq1Sz78zd7BhJmwYqH4IZ14Kq73xwu6wCseRJCOkH9AY69los7dJwD/3ctLOkHXb6F0M6OvaaISDmpMFMZiIiIiIiISAWTsduaAzbsOojq75hruHlB2w8hbRtsGOOYa0hRax63ppBoO8GacsDR3Hyg80LwrQ+/3gxH4x1/TRGRcqBiVkRERERERMqeacKKfwGm4wu8sG4QeTtsfB2Ob3XcdQT2/QAJM60pJAKiy++6nsHQ5TtwD4Cfu0PajvK7toiIg6iYFRERERERkbKXOBv2fgMtRoNvlOOv1+pdcPWyymAtGO0YeVnWXK9+jaD5s+V/fZ86VjlbkAs/d4Os/eWfQUSkDKmYFRERERERkbKVfQhWPQrBcdB4SPlc06sWxL4OB36yRnRK2dvwKqTvtEZA2zWXb0A0dP7GKmUXXw85x+zJISJSBlTMioiIiIiISNla8zjkpEK7yeDiWn7XbTDYKoNXPwY5R8vvulVB6gbY+CbUuxtqXW1vlhrtoOMXkPoX/NoT8rPtzSMicpFUzIqIiIiIiEjZ2fsd7JoOzZ+BwJjyvbaLK8R9DDmHrUXHpGyYBbB8MHgEwOXv2J3GEn4dXDENUhbDH/2hIN/uRCIiF0zFrIiIiIiIiJSN3HRY8QD4N4XmI+3JEBQLjYfC9olw8A97MlQ2OybDoT/g8rehWg270/wtqh+0Ggt7voCVD2luYRGpcFTMioiIiIiISNlY9wJkJELcJHD1tC9Hi5fBO8JaqKog174clUHWfljzNIR0hnr32J2muKZDodkzVhG/7gW704iIXBAVsyIiIiIiInLpDi2HreOg0UMQcqW9Wdz9oPV7kLoetoyzN0tFt3o45GdC3AQwDLvTlKzlaGgw0FqcbMv7dqcRESk1FbMiIiIiIiJyafJzYPkgqBYGsa/bncYS0RNq3wTrXrRG8cqF2/stJM6G5s+CfxO705ydYUDbCdb3fNVQSJhtdyIRkVJRMSsiIiIiIiKXZtNb1ujUth+Bu7/daSyGAW1Ojp5c+ai9WSqivExY8RD4NYZmFWAhNRc3+McsCOkIS++GfT/YnUhE5LxUzIqIiIiIiMjFO7YZ/hoFdW+DiJvsTlOUTyTEvATJC2DPfLvTVCx/jYKMXRD3sb3zBV8INy/o9BX4R8NvveDwCrsTiYick4pZERERERERuThmASy/H9x8oLWTzuXadBgExsCqRyA33e40FUPqetj0DtQfAKGd7U5zYTwCocu34BkCi6+H41vsTiQiclYqZkVEREREROTibJ8IB3+HVv8Gr1C705TMxd2afzQzCda/aHca52cWwLLBVsF5+Vt2p7k4XmHQ5TswXOGnbpCZbHciEZESqZgVERERERGRC5eZBGueglrXQr177E5zbjX/AQ0Hw5ZxcDTe7jTObftEOLwULn8HPIPtTnPx/BtB5/9BzlH4+To4ccTuRCIixaiYFRERERERkQtjmtbCUGaeNQepYdid6Pxavg4e1WH5A1CQb3ca55S1D+JHQOjVUO8uu9NcuuqtoNN8SNsGv9xkLWgmIuJEVMyKiIiIiIjIhdkzF5K/hhavgG99u9OUjmd1a8qFw8thx0S70zinVcMgPxvaflQxyvbSqHU1/GMmHPoTfu8LBbl2JxIRKaRiVkRERERERErvxBFYOQSqt4EmQ+1Oc2Gi+lujQeOfgaz9dqdxLsnfwO7PoflI8G9sd5qyVfcWaPsh7F0Ey+63RnyLiDgBFbMiIiIiIiJSemuegBOHod1kcHGzO82FMQyroMvPgtWP2Z3GeeRlwMqHwBMPtyYAACAASURBVD8amj1ldxrHaPQgxLwMu6ZB/NN2pxERAVTMioiIiIiISGnt/xF2fgrRT0FQS7vTXBz/JtBsBCR+Bvt+sDuNc1j/MmQkQtwEcPW0O43jXPY8NHoYNr0Fm962O42IiIpZERERERERKYW8TGvhLL/GEPOC3WkuTfNnwK+RtYBZfrbdaex1dC1s/jc0GAghnexO41iGAa3HQd2+sOZJ2Dnd7kQiUsWpmBUREREREZHzW/8ipO+EuIngWs3uNJfGtZo1pUH6dtjwut1p7FOQD8sHg0d1iH3T7jTlw8UVrpgOodfAsvsgeZHdiUSkClMxKyIiIiIiIud2ZJU1qrLhYAi9yu40ZaPWtRDZDzaOgeNb7E5jj+0T4PByaPUueFa3O035cfWETl9CUCz8fisc/MPuRCJSRamYFRERERERkbMryIWlA6FaaOUbVdnq3+DqBSv+BaZpd5rylbkX4p+xCuqofnanKX/uftD5f+AdAYv/Cal/2Z1IRKogFbMiIiIiIiJydpvegdS10OZD8AiwO03Z8gqF2DFw4GdI+I/dacrXqqFg5kLbj6y5V6uiajWhy/fg5gU/X2ctgCYiUo5UzIqIiIiIiEjJjm+D9S9BnT5Qp6fdaRyj4WAIbg+rH4cTR+xOUz6SF8KeuXDZ8+DX0O409vKNgi7fWYvb/XwdZB+yO5GIVCEqZkVERERERKQ4swCW32/d6t9mvN1pHMdwgbgJkHME4kfYncbxctNhxcMQ0AyaPmF3GucQGANXfW2NmF18g/U1EhEpBypmRUREREREpLgdUyDlF2j1NnjVsjuNYwW1hCbDYMckOLjE7jSOtf4lyNwNbT8GVw+70ziPkCuhw+dwdDX81hvyc+xOJCJVgIpZERERERERKSpzL6x5EkK7QP377E5TPmJeAu86sPxBa8GzyujIGtgy1pq+IeRKu9M4n4ibIG4S7P8Blt5jjRoXEXEgFbMiIiIiIiJS1MohUHAC4iZWnYWh3H2hzftw7C/Y/K7dacpeQT4sHwyewdaCZ1KyBgOsr0/ibFg1DEzT7kQiUompmBUREREREZG/7fkCkr6EmJer3sJQETdD7R7W7f7pCXanKVvbPoQjK6HVWPAIsjuNc4t+Cpo+Blvfhw2v2Z1GRCoxFbMiIiIiIiJiyUm1FoYKutwqpqqiNu9bC4KtfKTyjJbMTIK1z0KtbhB5u91pnJ9hwOVvQdSdsO452D7R7kQiUkmpmBURERERERHLmifhxEFoNxlc3OxOYw+futZo4b0LIWm+3WnKxspHwcyDuI+qztQUl8pwgfafQNj1sOJf1khyEZEypmJWRERERERE4MDPsGMyNH0cqreyO429mjwKgS2sUbO5aXanuTRJX1lTU1z2IvjWtztNxeLiDh3nQPU4WNIPDiy2O5GIVDIqZkVERERERKq6vCxYNhh8G0DMi3ansZ+LO8R9DFl7YV0F/nrkplkLuQVcBtGP252mYnLzgc4LrVL715vhaLzdiUSkElExKyIiIiIiUtX99TKkb4d2k8DN2+40zqFGe2g4GLaOgyNr7E5zcda9YM0vGzfRKpvl4ngGQ5fvwD0Afu4OaTvsTiQilYSKWRERERERkarsyBrY9DY0GAihXexO41xiXwfPGrD8ASjItzvNhTmyCra+Bw0fhJpX2J2m4vOpY5WzBbnwczfI2m93IhGpBFTMioiIiIiIVFUFebBsEHjWtFahl6I8guDyf8ORFbD9Y7vTlF5BnjU1hWeIVS5L2QiIhs7fWKXs4ush55jdiUSkglMxKyIiIiIiUlVtfheOroY2460SUoqL6geh18DaZyBrn91pSmfreOv72noceATanaZyqdEOOn4BqX/Brz0hP9vuRCJSgamYFRERERERqYrStsP6FyCiJ9TpbXca52UY0PZDq4Bb/Zjdac4vYw+sew7Croe6t9qdpnIKvw6umAYpi+GP/hVvmgsRcRoqZkVERERERKoa07TmTXXxsEbLGobdiZybf2No/iwkzoZ939ud5txWPQJmAbT9QN9XR4rqB63Gwp4vYOVD1p8pEZELpGJWRERERESkqtn5KRz4yZpX1ru23WkqhmYjwK8xrHgI8rLsTlOyPV9C0lcQ8xL41rM7TeXXdCg0ewa2T4R1L9idRkQqIBWzIiIiIiIiVUnWflj9OIR0ggaD7E5Tcbh6QtuPIH0HbHjN7jTF5R6HlY9AYAtoOtzuNFVHy9HQYCBseBX+uBvmR8EsF+t510y704mIk1MxKyIiIiIiUpWsfATysyBuEhj6L+EFqXU1RN0Jm96AY5vtTlPU2uchay/EfQwu7nanqToMA9pOgKDWkDADMhMB03pePljlrIick/4VFhERERERqSr2zIc9cyHmRWveVLlwl78Nrj6w4kHnmVf08ArY+j40+hfUaG93mqrHxQ1OpBTfnp8Jax6HzGTn+VkREafiZncAERERERERKQc5x2Dlw9at7tFP2J2m4vIKhcvfsBZP2zUD6t9tb56CPGtkplctaOmEUyxUFZlJJW/PPgDzI8DNB/wanXw0PvloZP2CxDO4fLOKiNNQMSsiIiIiIlIVxD8N2fuh01e61f1SNRgEO6daoyFr/9PeYm3Le3A0Hq6cAx4B9uWo6rzrnpzG4AyeIdDiJTi+FdK2Wd+rPV+Amf/3Ph7V/y5s/RufVt42AnffcnsLIlL+VMyKiIiIiIhUdim/wvaPoenjENzG7jQVn+FizSv6bSur8G432Z4cGYmw7nkI/yfU6WNPBrG0HG2NXM7P/Hubqze0+jfU619034JcSE+AtK0nH9us4jZlsTVP7em8woqPsPVrBL4NrAXpRKRCUzErIiIiIiJSmeVnw7L7wacetHjZ7jSVR1ALaDocNr0N9e6FkCvL9/qmCSuGWB+3/cBahErsc6p8XTsSMndbI2hbji5eyoI1Yt2/kfXgn0Vfy8uE9B1/j7A9Vd4mfwXZp81ja7iAd+RpZe1pxa13JLi4OuytikjZUTErIiIiIiJSmf31ilXsXP2DNc+llJ2YlyDxc2shsO6rwdWj/K695wvYu9BajMwnsvyuK2dXr3/JReyFcPOGwBjrcaac1JNl7ckRtqdG2+6aDrnH/97PxQN865c8NYJXuEp8ESeiYlZERERERKSyOroWNr4J9e+FWtfanabycfOBNuPh1x6w5V1o9nT5XDfnGKx6BAJbQpOh5XNNsZ9HIAS3tR6nM01rNO3pI2xPlbf7voOCE3/vq0XIRJyKilkREREREZHKqCAflg0Cz+rWqEpxjIibIKInrH8Z6vYF33qOv+bakZC1HzrOBxf9t77KMwzwCrUeZ06pYRZAZpJV1p4+PcKRNRe/CNmumaWbskFEzkt/g4uIiIiIiFRGW8bBkZXQYbZGwjla6/dgUTSsHAJXLXTsreKHlsG2D6HxEKgR57jrSOVguIBPXetx5qj5glxI31V0hG3aeRYhw4CDS8DMtbZnJlqLnoHKWZGLoGJWRERERESksknfCeueg9o3WaM4xbF86kDMKFjzuDUKsW4fx1ynINcqwbzCoOWrjrmGVB0u7tbIWP/GxV/Ly4S07adNj3Dy+eCfQEHRffMzYe0zKmZFLoKKWRERERERkcrENGH5g2C4QdsPtdBPeWnyqLUI06pHIawbuPuV/TW2jIPUddBxHrj7l/35RU5x84agFtbjdLNcSt4/cw/8cRdE3Qm1rtEUGyKldJY/USIiIiIiIlIh7ZoO+3+A2DHgHWF3mqrDxQ3iPoasfbDu+bI/f3oCrHsRaveAiF5lf36R0vCuW/J2N19IXgiLu8P82rBqGBxeYf2iSETOSsWsiIiIiIhIZZF1AFYPh5odoNGDdqepemq0s77uW9+HI6vL7rymCSsftkY/t3lfo6DFPi1Hg6t30W2u3tB2AvTeDx2/gJodYdtH8F0cLGwK60dB2g578oo4uVIVs4ZhdDcMY4thGNsNwxhxjv1uMQzDNAyjzWnbnjl53BbDMK4ri9AiIiIiIiJSgtXDIC8D4iZZi/5I+Wv5GnjWhOUPQEH++fcvjT1zYe830OIVaxEnEbvU6w9xE8E7EjCs57iJ1nZXT6jTCzrOhd4HoN1k8AqH9S/B1w3huytgy3jIPmj3uxBxGuf9l9owDFfgA+B6oBlwh2EYzUrYzw94FFh22rZmwO1Ac6A78OHJ84mIiIiIiEhZSl4IibOh+XMQEG13mqrLIxBavQtHVlqjBi9VTiqsfBSCWkHjRy79fCKXql5/6JkA/Qqs55IW/fIIhAYD4dqf4eZEiH3DWiRs1SPwZRgs/ickfGb9IkmkCivNr1DjgO2mae40TTMHmA3cXMJ+rwBvAtmnbbsZmG2a5gnTNHcB20+eT0RERERERMpK7nFY8S8IuAyaPW13Gom8HWp1hbXPQubeSzvX2mfhRAq0m6gFlaRi8qkDzZ6CG9bCDesg+glIXQ9/9IMvQuGPu2Hvd1CQZ3dSkXJXmmK2NrDntM+TTm4rZBjG5UAd0zQXXuixJ48fbBjGSsMwVubl6Q+iiIiIiIjIBYl/FjKTrVuHXT3sTiOGAW0/hIIca87fi3XwT9g2wRopW7112eUTsUtgjLUw4c0JcM1iiLwDkhecXDQsQouGSZVTmmK2pFnFC/+EGIbhArwLPH6hxxZuMM2Jpmm2MU2zjZubfgMoIiIiIiJSageXwLYPocmj1uJT4hz8GkLzkbD7c9j77YUfX5ALyweDd21rblmRysRwgdCroN2kk4uGzbMWLdSiYeJIu2bC/CiY5WI975ppd6JSFbNJQJ3TPo8ATr8Xww+4DFhsGEYC0B5YcHIBsPMdKyIiIiIiIhcr/wQsG2QtCNXiVbvTyJmaPQX+TWDlw5CXdWHHbv43HPsL2owHdz/H5BNxBq7VoE5vq5ztvd9avNArHNa/aC0a9v0/YOsHWjRMLs2umdYvuzITAdN6Xj7Y9nK2NMXsCqCRYRj1DMPwwFrMa8GpF03TPGaaZg3TNKNM04wClgI9TNNceXK/2w3D8DQMox7QCFhe5u9CRERERESkKtrwGhzfDG0ngLuv3WnkTK6e0PYjSN8JG0aX/rj0nbD+ZYjoCRElLfEiUkl5BEHDQScXDdttLRqWlw4rh8CX4bD4xpOLhmXanVQqkvxsiH/KWoCuyPZMWDvSnkwnnXfeANM08wzDGAJ8B7gCn5imucEwjFHAStM0F5zj2A2GYXwObATygIdN08wvo+wiIiIiIiJVV+pfsPF1iLoTwrvbnUbOJrQLRN0Fm96EqP4QEH3u/U0TVjwEhiu0fq98Moo4o1OLhjV7Co6ug4SZkDgL/lgEbr4Q0Qvq3QmhV2thvKosL9OaYz0rCTKTIHPPyefTHifOMdo6c3f5ZS1BqX5yTdP8BvjmjG0vnGXfzmd8Phq4gF8NioiIiIiIyDkV5FtTGLgHQKt37U4j59Pqbdi7EFY8aC14ZJS0HMtJif+Ffd9B63FWMSUiENTCesS+Dim/WiXt7jmQMAOqhVqLiEX1txbJO9efL6lY8jLOXrae2p5zhD2H4e6PYP8xcDFgcFdvht7akCN5Idz2ZgoJ+6sTFZDG54/kEuQDX62E5+da+7q5uzO27u9ceeWVAOzevZtBgwaxZ88eDMPgm2++ISoqymFv0TCdbKU7Hx8fMyMjw+4YIiIiIiIizmvLe7BqKPxjJkT1szuNlMb2SdZ8hu0/hfr3lrxPzlFYGA3eEdBtGbi4lmtEkQolPxv2fgO7/gN7F0FBjjWnc2R/qNcffOsX2X3g1BXlHnHKvW3L/ZoV5n3mHi+5aD3989zU4sd51rT+jjztsS/Nl31pnrRqdzVp+f60bteR+fPnM3XqVKpXr86IESMY89RtHN38BW/cnkd6Nvh4guHmzTq/5+j72DQ2b94MQOfOnRk5ciRdu3YlPT0dFxcXvL29L/ErBECJvzHQWG8REREREZGKJCMR1j4LYddbo8SkYmgwEHZOhTVPQPiNUK1G8X3in7Fuue38jUpZkfM5tWhYnd7WLzV2z7VG0q5/wXrUuMKa6qVu35L/vIljmCbkHjt72ZqVBBl7IC+t+LHVQq2y1a8hhHa2PvY6vYStbX3fzxB28gHgB0RHR5OcnMxXX33F4sWLAbhn+Fg6d/iVN7w98WU3eNeFlqPJ2F8fw5gOwMaNG8nLy6Nr164A+Po6fu52FbMiIiIiIiIVwa6ZViGbuRswIKy7btmtSAwXiJsA/2sF8U9D+ylFXz+4BLZ/DE0fg+qt7MkoUlF5BEHD+61Hxm5I/MwaSbvyYevugrDrIOt+q/gz9EuPIjKTrUUk87PA1Qv8m1oF6NkU5FijlfOzrWPys2Hph1bZemqe17wz74Q3wCvMKlf9m0LotSeL1jp/l65e4eDqcclvJyEhgTVr1tCuXTsOHDhAWJhV2YaFhZFyNBt67gPgyy+/5JnrnyElJYVFixYBsHXrVgIDA+nduze7du3i2muvZcyYMbi6Ou5nRsWsiIiIiIiIs9s107oNvnBFaRPWPgOewdYtu1IxBMZYxeumN63pDEI6Wtvzc2D5A1ZJEfOyrRFFKjyfutDsaetxatGwhJlwZLVVynqFWcWjZw3Ocnd51ZGZDKnrwMy3Ps/PgtS11ohXN5+TxWtW0SLWLCh6DgPY/6P191dgCwi7odhUA3iFgYu7w99Oeno6ffr0YezYsfj7+59z3169etGrVy9+/fVXnn/+eX788Ufy8vL47bffWLNmDXXr1uW2225j6tSpDBw40GGZVcyKiIiIiIg4u7UjTytlT8rPtLarmK1YYl6A3f+1FgLrvsYaIbb5HTi2ATotAHfH3zorUmUUWTTse2tEZ9Y+a1Snq6c1StM7wlpIsaIzCyA3zSpPC3LBzLOeT//4zG0nDllTD5x5nvSd1seGYU0d4FrN+hpVC7VG1Z7a5uplfR172v8LpdzcXPr06UP//v3p3bs3AKGhoezbt4+wsDD27dtHSEhIseM6derEjh07OHToEBEREVx++eXUr2/NT9yzZ0+WLl2qYlZERERERKRKy9x9YdvFebn5QJvx8MtN8EWoNTINE6q3gYib7E4nUjkZLtYdBp7BEBAD2QcgKxkyEiB9l/XnsnAO05MLPV3oLf6XwjStEam5xyAntehzqbcdB0zY/8K5vw4u7mC4Wc9nlrKnC+sKLh5UhFHFpmkycOBAoqOjeeyxxwq39+jRg2nTpjFixAimTZvGzTffDMD27dtp0KABhmGwevVqcnJyCA4OJigoiKNHj3Lw4EFq1qzJTz/9RJs2bRyaXcWsiIiIiIiIMzMLrJFJ+VnFX/OuW/555NLlHLNuqT59xfFjG6wpKzQCWsSxDBfr1nqvMGvkaNY+ayTt8S3WwyPIKmqz9v59235+lnXLP5ylnDVLHpG6c6r1571IgZpa8raC3PPndg+0Rq56BFgf+9Yrvi01Egx3q3h1cTv58clnw6XoOff/X8n/trh6gYvnhX5lbbNkyRJmzJhBTEwMsbGxALz22muMGDGCvn37MmXKFOrWrcucOXMAmDdvHtOnT8fd3R0vLy/++9//YhgGrq6uvP3221xzzTWYpknr1q25//77HZrdMM/VjtvAx8fHzMg4c5JgERERERGRKmrdC/DXK9Z/qs3T/uPu6g1xE1XkVUTzoyAzsfh270jomVDeaUSqhIFTV5x7h/wsa5RsVhLkppe8j+EKntWhIM/6+7jg1BQB+SXuPqXeqL8/cfOxitNTBap7AHicUaoWee2MbW4+pVrw8bzv83RnzjF76j0Gtrig0cFT7m1b+mtWXSV+8zRiVkRERERExFkl/tcqZesPgNBrrDllM3dbI2VbjlYpW1FpagoR5+PqBX4NrUfywpL3MfOhIMf6RZmr59+jUV3czxilenJbjx0ni1Z/a7uzOVW+lteUDWXoggroMuKIAtoJfypERERERESEI6tg6b1QswO0/cgqAVTEVg7edc8yYlZTU4g4BVevs9/iX7Nj6c/jW7/sMjmKd+0KUcRWVi7n30VERERERETKVdY++OVm8AyBjl9YpaxUHi1H/73A0Cmu3tZ2EbGff1Prlv7TGa7WdpEypBGzIiIiIiIiziQ/G37tCTlHodsSqBZidyIpa6dGPmtqChHnVIFv8ZeKRSNmRUREREREnIVpwrL74fBy+McMCIq1O5E4Sr3+1kJf/QqsZ5WyYrP77ruPkJAQLrvsssJta9eu5YorriAmJoabbrqJ48ePA5Cbm8s999xDTEwM0dHRvP766wBs2bKF2NjYwoe/vz9jx4615f1cMu/aUOsaqH2j9axSVhxAxayIiIiIiIiz2PQWJPwHYkZBnd52pxGRKuTee+/l22+/LbJt0KBBjBkzhvXr19OrVy/eeustAObMmcOJEydYv349q1at4uOPPyYhIYEmTZoQHx9PfHw8q1atwtvbm169etnxdkQqBBWzIiIiIiIiziB5IcSPgLp94bLn7E4jIlVMp06dqF69epFtW7ZsoVOnTgB07dqVefPmAWAYBhkZGeTl5ZGVlYWHhwf+/v5Fjv2///s/GjRoQGRkZPm8AZEKSMWsiIiIiIiI3VI3wJI7IOhyaP8pGIbdiUREuOyyy1iwYAFgjZLds2cPALfccgs+Pj6EhYVRt25dnnjiiWKl7uzZs7njjjvKPbNIRaJiVkRERERExE4nDsOvPcDNF676Cty87U4kIgLAJ598wgcffEDr1q1JS0vDw8MDgOXLl+Pq6srevXvZtWsX77zzDjt37iw8LicnhwULFnDrrbfaFV2kQnCzO4CIiIiIiEiVVZALv90Cmclw7WLwjrA7kYhIoaZNm/L9998DsHXrVhYtWgTArFmz6N69O+7u7oSEhNChQwdWrlxJ/fr1Afjf//5Hq1atCA0NtS27SEWgEbMiIiIiIiJ2WfkopCyGdpOgRnu704iIFJGSkgJAQUEBr776Kg8++CAAdevW5aeffsI0TTIyMli6dClNmzYtPO6zzz7TNAYipaBiVkRERERExA5bP4TtEyD6Kah3l91pRKSKu+OOO7jiiivYsmULERERTJkyhc8++4zGjRvTtGlTwsPDGTBgAAAPP/ww6enpXHbZZbRt25YBAwbQokULADIzM/nhhx/o3bu3nW9HpELQVAYiIiIiIiLlbf9PsOpRCP8ntHzN7jQiInz22Wclbh86dGixbb6+vsyZM6fE/b29vTl8+HCZZhOprFTMioiIiIiIlKe07fD7LeDfBDrMAhdXuxOJSBU1cOqKcr/mlHvblvs1RZyVpjIQEREREREpLznH4JcegAGdFoC7v92JRERExCYaMSsiIiIiIlIeCvLhj36Qtg2u/h78GtidSERERGykYlZERERERKQ8rH0G9n4DbT+E0C52pxERERGbaSoDERERERERR9s5HTa9BY3+ZT1ERESkylMxKyIiIiIi4kgH/4Tl91ujZFuPszuNiIiIOAkVsyIiIiIiIo6SsQd+6wXedeDKOeDibnciERERcRKaY1ZERERERMQR8jLg15shLxOu+Qk8g+1OJCIiIk5ExayIiIiIiEhZM01YOgCOxsNVX0NAM7sTiYiIiJNRMSsiIiIiIlLW/noFds+B2Deh9j/tTiMiIiJOSHPMioiIiIiIlKXd82D9ixB1F0Q/YXcaERERcVIqZkVERERERMrK0Xj4824Ibg/tJoJh2J1IROT/2bv3MC3rOn/g75sZAYFQUVEEBVRMBQTxbIWn8LClleu2sbaraZmmHcyy2uxgxpqHzU62aavltqa/sjJrDTVNLE0BFRUP5AEU8oCo4GHkNNy/Px7E4SQDDHPP4fW6ruea5/k+9z3zHv9x5s13vh+gjVLMAgAAtITXn0smHJV065OM/k1S173qRABAG+aMWQAAgPXVuCD589HJgjnJmL8kG29ddSIAoI1TzAIAAKyPskwmnZLMuSN5x/9L+oyqOhEA0A44ygAAAGB9PHJR8sRPkmFfTQZ+sOo0AEA7oZgFAABYV0//IZny+WTbf0yGf63qNABAO6KYBQAAWBfzHklu/1CyyfBkvyuSwq9XAEDz+ckBAABgbS14MZlwZNKlW3LAb5P6nlUnAgDaGcO/AAAA1saSxcnt/5w0PJkc8qek58CqEwEA7ZBiFgAAYG3c89nk2T8m+1yebPmOqtMAAO2UowwAAACa67EfJ3/7fvL205MdPlJ1GgCgHVPMAgAANMdzE5JJn0j6HZ7sfn7VaQCAdk4xCwAAsCavTk/+8o/J23ZI3nFV0sWpcADA+lHMAgAAvJVFryQTjkqWNCajf5d03bTqRABAB+CfeQEAAFanXJLc8eHk5YeTA/+Q9B5SdSIAoINQzAIAAKzOfWclf78u2eN7Sb8xVacBADoQRxkAAACsyoyfJw+dm+zwsWSn06pOAwB0MIpZAACAFb0wKbnrxKTv6GTPHyRFUXUiAKCDUcwCAAA01fD35Lb3Jd23Tt55TVLXtepEAEAHpJgFAAB4w+LXk9venyx6JTnguqT7llUnAgA6KMO/AAAAkqQsa8cXvHh3Mvo3yabDq04EAHRgilkAAIAkeehbyZNXJSPGJQPeV3UaAKCDc5QBAADArN8m9/17MnBssuuXqk4DAHQCilkAAKBzm/tAcsexSZ+9kn0uS4qi6kQAQCegmAUAADqv+c8nE45KNupdO1e2fuOqEwEAnYQzZgEAgM6pcWHyl2OS+c8mh0xIevSvOhEA0IkoZgEAgM6nLJPJpyWzb0v2vzLZYu+qEwEAnYyjDAAAgM7nbz9IHv9xbdDXoH+pOg0A0AkpZgEAgM7l2T8m95yeDHhfMuKbVacBADopxSwAANB5vPxo8ud/Snrvkuz3s6TwKxEAUA0/hQAAAJ3DwrnJbUcmXeqTA65LNnpb1YkAgE7M8C8AAKDjW9KY3D42eeXx5OA/Jr0GV50IAOjkFLMAAEDHN+XM5Jnxyd6XJlsdWbWo8wAAIABJREFUUHUaAABHGQAAAB3c45cnj3w72emTyY4fqzoNAECSZhazRVEcXhTFtKIoHiuK4oureP/koigeKIpiSlEUfymKYtel64OKonh96fqUoih+1NLfAAAAwGo9f3sy6eRk63cno75ddRoAgGXWWMwWRVGX5OIkRyTZNcnYN4rXJn5eluXwsixHJjk/SdOfeB4vy3Lk0sfJLRUcAAB4C9OvTK4dlPy8S+3j9CurTtT6Xnsq+fPRSc9ByTt/URv6BQDQRjRnx+zeSR4ry/KJsiwXJrk6yfuaXlCW5ctNXvZMUrZcRAAAYK1MvzKZeFLS8GSSsvZx4kmdq5xd9Goy4aikcUEy+rqk62ZVJwIAWE5z/sm4f5KZTV7PSrLPihcVRXFqks8m6Zrk4CZvDS6K4t4kLyc5qyzLP6/i3pOSnJQkXbt2bXZ4AAAgSeP8ZOFLbz7uOT1pbFjhmobk3s8lfd+VdNsyqd+4mqytoVyS3HlcMu+B5ID/SzbZuepEAAAraU4xW6xibaUdsWVZXpzk4qIo/iXJWUmOS/JMku3KsnyhKIo9klxbFMXQFXbYpizLS5NcmiQ9e/a02xYAgM6nccHSYvXF5UvW1T6aXNc4v3lfY/6zyW8H1p7Xvy3p3vfNR7e+SfetVrHWN+m2eVK0o7nBD5ydzPx1svt/JtscXnUaAIBVak4xOyvJtk1eD0jy9Ftcf3WS/0qSsiwXJFmw9PndRVE8nmSnJJPXKS0AAKyv6Vcm9305aXgq6bFdMmJcMvjYlvncy8rVNRSpq3o0vv7Wn3uj3rU/x3/j0Xvn5V933Szp2qf28a/H1UrYFXXbMhl5bjJ/djL/uaUfZyevPpHMuTNZ8Hxtt+mKii61e5uWtcseW628Vt+zZf57rosnf5FM/Uay/UeSnU+vLgcAwBo0p5idlGRIURSDk/w9yYeS/EvTC4qiGFKW5aNLX74nyaNL17dM8mJZlo1FUWyfZEiSJ1oqPAAArJU3zl5948/83zh7NXmznG1cuHaFatNr1lSu1r+tVpx2W1qg9n77yuXqRiu87tYn2WiTtRtctfuFy3+fSVLXIxl10VuX0OWSZMGLtdJ2wew3i9v5s5u8fi55YWLt+eJXVv156nqsfvdt9yY7c7st3Y27vkO5mpbtSdJrSLLXfyXFqv74DwCgbVjjT0BlWS4uiuK0JDckqUtyeVmWDxZF8Y0kk8uyvC7JaUVRvDvJoiQvpXaMQZKMTvKNoigWJ2lMcnJZli9uiG8EAADW6L5/X/XZq3d+JJnyhaXlasOq733DG+XqG4+3DVm+RF1Vudp1s6TrputfQDbXG+Xr2u4MLrok3beoPTJ0zV9n8eu1Xbbzm5S2Kxa6DTOTF++uPS8Xr+qL1srZNR2n8EahW99r+cJ1xbI9SV6flTx1TcvthAYA2ACa9ZNhWZbXJ7l+hbWvNnn+6dXc96skv1qfgAAAsF7mz06euSl55oY3d1SuqFyU9DtsFWVqk+MBWrtcXV+Dj93wxWT9xkn9dknP7dZ8bbkkWTh3hd23qyh0X7q39nHR3FV/nrruy5e1syesomx/vVZKK2YBgDasnfxUCQAAzbRkUTLnr7Ui9pkbars1k6TbFrU/sV/VjtgeA5N9L2vdnJ1N0aW2o7hbnyQ7r/n6xgXJgjnLn4W7YqH7+rPJ4tdWff/qSngAgDZCMQsAQPv36vQ3i9hnb66dfVrUJVvsl+x2TtLv8KTPqGTGVas+e3XEuOqys2p13ZIe/WuPt3LtoNpZwSvq0YxdvAAAFVLMAgDQ/ix+LXnu1jfL2Ff+VlvvOTAZNLZWxG51cNJ1k+XvW9ezV2m7RoxTtgMA7ZJiFgCAtq8sk3lTk6fH14rY5/+cLFmY1G2c9D0wGfKJZJvDk7fttPxgqFVpjbNXaT3KdgCgnVLMAgDQNi14IXn2j8kz45Nnbkxef7q2vsmwZKdP1oZ19X1XbRgUnZuyHQBohxSzAAC0DUsWJy9MXFrE3pC8MClJmXTdLNl6TK2I7Xdo0mNA1UkBAGC9KWYBAKjOazOXnhM7vja0a9HcpOiS9Nk7Gf61WhnbZ6+kS13VSQEAoEUpZgEAaD2LX09m3/ZmGfvyw7X1jfsn2x5dOyd2q0OSbn2qzQkAABuYYhYAgA2nLGvl6zM31B6zJySN85Mu3ZK+o5MdTkz6HZ5ssuuah3YBAEAHopgFAKBlLZy7dGjX0jK2YWZtvffOyY4fXzq064Ckvke1OQEAoEKKWQAA1s+SxuTFyW8WsS/cmZRLko16J1u/Oxl2Vq2M7Tmw6qQAANBmKGYBAFh7DU+/WcQ+e1Oy8MUkRdJnj2TXf68VsVvsk3TZqOqkAADQJilmAQComX5lct+Xk4ankh7bJSPGJYOPrb3XuCB5/i+1gV3P3JDMfaC23n3rpP97a+fEbj0m6b5FdfkBAKAdUcwCAFArZSeelDQ21F43PJlM/GithF34QvLcrbX3umyUbPnOZOS3amXsprsZ2gUAAOtAMQsAQG2n7Bul7Bsa5yczfpb02jHZ/iPJNocnfQ9MNupVSUQAAOhIFLMAANSOL1ilIjnq0VaNAgAAnUGXqgMAANAG9Nh2NevbtW4OAADoJBSzAAAkfQ9Yea2uR20AGAAA0OIUswAAnd1rTyazfp1sstvSHbJF0mNgsvelyeBjq04HAAAdkjNmAQA6s7JMJp5ce37gdUnPgdXmAQCATkIxCwDQmc34efLM+GSP7yplAQCgFTnKAACgs5r/fHLPp5PN90mGnFp1GgAA6FQUswAAndU9pyeLXk72uSzpUld1GgAA6FQUswAAndHTf0hmXJns+qVk06FVpwEAgE5HMQsA0NkserU28Kv3LsnQf686DQAAdEqGfwEAdDb3n5U0zEzG/CWp61Z1GgAA6JTsmAUA6Ezm3JlM+14y5BPJlvtXnQYAADotxSwAQGfRuDC566NJj/7JyP+oOg0AAHRqjjIAAOgsHjovmfdgcsDvko16V50GAAA6NTtmAQA6g3kPJw9+Mxn4oaT/e6tOAwAAnZ5iFgCgoyuX1I4wqO+V7PHdqtMAAABxlAEAQMf36I+SOXck+/406d636jQAAEDsmAUA6Nhem5lM+WKy9Zhk8L9VnQYAAFhKMQsA0FGVZTLpE0nZmOx9SVIUVScCAACWcpQBAEBH9dQvkqd/n+z+n0mvwVWnAQAAmrBjFgCgI1rwQjL5k0mfvZK3f7rqNAAAwArsmAUA6IjuOSNZ+FJy8E1Jl7qq0wAAACuwYxYAoKN55qZk+hXJrmcmm42oOg0AALAKilkAgI5k8WvJxI8nb9spGfaVqtMAAACr4SgDAICO5P6vJa9NT949IanrXnUaAABgNeyYBQDoKF6YlEy7KNnx40nf0VWnAQAA3oJiFgCgI1iyKLnro0n3rZOR51WdBgAAWANHGQAAdAQPX5jMvT8ZfW3SdZOq0wAAAGtgxywAQHv38rTkgbOTbY9JBryv6jQAAEAzKGYBANqzckky8aSkbuNkz+9XnQYAAGgmRxkAALRnj/93Mvu2ZJ//Tjbeuuo0AABAM9kxCwDQXjU8ndz7+WSrg5PtT6g6DQAAsBYUswAA7VFZJpNPTZYsTPa+JCmKqhMBAABrwVEGAADt0cxfJ7OuTUael7xtx6rTAAAAa8mOWQCA9mbhS8nk05LNdk92/mzVaQAAgHVgxywAQHtz7+eTBc8nB/5f0sWPcwAA0B7ZMQsA0J48e0vy+GXJzmckfUZVnQYAAFhHilkAgPZi8evJxJOSXjskw79WdRoAAGA9+Ns3AID2YurZyauPJ4fcktT3qDoNAACwHuyYBQBoD168J3n4wmSHE5OtDqo6DQAAsJ4UswAAbd2SxcldH026bZnsfkHVaQAAgBbgKAMAgLbukYuSl+5N3nlN0nWzqtMAAAAtwI5ZAIC27JXHkge+mgx4f7Lt0VWnAQAAWohiFgCgrSrLZOJJSZeuyZ4/SIqi6kQAAEALcZQBAEBb9cRPkuf+lOz1o6RH/6rTAAAALciOWQCAtuj1Z5N7zkj6jk52/FjVaQBgJSeccEL69u2bYcOGLVv7+te/nv79+2fkyJEZOXJkrr/++iTJCy+8kIMOOii9evXKaaedtuz6hoaGvOc978nOO++coUOH5otf/GKrfx8AVVHMAgC0RZM/mTS+nux9aVL4kQ2Atuf444/P+PHjV1o//fTTM2XKlEyZMiX/8A//kCTp3r17zjnnnFx44YUrXf+5z30ujzzySO69997cfvvt+cMf/rDBswO0BX7KBwBoa2Zem8y8Jhn+1aT326tOAwCrNHr06PTp06dZ1/bs2TPvfOc707179+XWe/TokYMOOihJ0rVr14waNSqzZs1q8awAbZFiFgCgLVk4L5l8arLpbskun686DQCstR/84AfZbbfdcsIJJ+Sll15q9n1z587N7373uxxyyCEbMB1A26GYBQBoS6Z8IZn/bLLPfyddNqo6DQCslVNOOSWPP/54pkyZkn79+uWMM85o1n2LFy/O2LFj86lPfSrbb7/9Bk4J0DYoZgEA2orZtyWPXZK8/TPJ5ntVnQYA1tpWW22Vurq6dOnSJR/72McyceLEZt130kknZciQIfnMZz6zgRMCtB3NKmaLoji8KIppRVE8VhTFSiMSi6I4uSiKB4qimFIUxV+Koti1yXtfWnrftKIoDmvJ8AAAHUbj/OSujyU9Bye7faPqNACwTp555pllz3/zm99k2LBha7znrLPOyrx58/Kd73xnQ0YDaHPq13RBURR1SS5OMibJrCSTiqK4rizLh5pc9vOyLH+09Pqjknw7yeFLC9oPJRmaZJskfyyKYqeyLBtb+PsAAGjfpn4zeeVvyUE3JvU9q04DAGs0duzY3HrrrZkzZ04GDBiQs88+O7feemumTJmSoigyaNCgXHLJJcuuHzRoUF5++eUsXLgw1157bW688cb07t0748aNy84775xRo0YlSU477bR89KMfrerbAmg1ayxmk+yd5LGyLJ9IkqIork7yviTLitmyLF9ucn3PJOXS5+9LcnVZlguSTC+K4rGln++vLZAdAKBjeOn+5KHzksHHJf3GVJ0GAJrlqquuWmntxBNPXO31M2bMWOV6WZarXAfo6JpTzPZPMrPJ61lJ9lnxoqIoTk3y2SRdkxzc5N47V7i3/yruPSnJSUnStWvX5uQGAOgYljQmd52YdN0sGfWfVacBgLd04k8ntfrXvOx4564DHVNzzpgtVrG20j9nlWV5cVmWOyT5QpKz1vLeS8uy3LMsyz3r65vTFQMAdBB/+17y4uRkz+8n3TavOg0AANBKmlPMzkqybZPXA5I8/RbXX53k/et4LwBA5/Hq9OS+s5Jt3pts98Gq0wAAAK2oOcXspCRDiqIYXBRF19SGeV3X9IKiKIY0efmeJI8ufX5dkg8VRdGtKIrBSYYkmbj+sQEA2rmyTCZ+PCnqkr1+mBSr+kMjAACgo1rjuQFlWS4uiuK0JDckqUtyeVmWDxZF8Y0kk8uyvC7JaUVRvDvJoiQvJTlu6b0PFkXxi9QGhS1OcmpZlo0b6HsBAGg/pv8sefamZM+Lk57brvl6AACgQ2nWga5lWV6f5PoV1r7a5Pmn3+LecUnGrWtAAIAOZ/7s5J7Tky32T4acXHUaAACgAs05ygAAgJZ096eTxa8m+/w4Kfw4BgAAnZHfBAAAWtPff588eXUy9MvJJrtWnQYAAKiIYhYAoLUseiWZdEqyydBk1y9WnQaADeSEE05I3759M2zYsJXeu/DCC1MURebMmbPc+qRJk1JXV5drrrlm2dqZZ56ZoUOHZpdddsmnPvWplGW5wbMD0HoUswAArWXKl5KGvyf7/HdS17XqNABsIMcff3zGjx+/0vrMmTNz0003ZbvttltuvbGxMV/4whdy2GGHLVu74447cvvtt+f+++/P1KlTM2nSpEyYMGGDZweg9ShmAQBaw/O3J4/+MNnpk8kW+1adBoANaPTo0enTp89K66effnrOP//8FEWx3Pr3v//9/OM//mP69u27bK0oisyfPz8LFy7MggULsmjRomy11VYbPDsArUcxCwCwoTUuSO76WNJj22TEuKrTAFCB6667Lv3798+IESOWW//73/+e3/zmNzn55JOXW99vv/1y0EEHpV+/funXr18OO+yw7LLLLq0ZGYANrL7qAAAAHd6D5yYvP5wceH2yUa+q0wDQyhoaGjJu3LjceOONK733mc98Juedd17q6uqWW3/sscfy8MMPZ9asWUmSMWPG5Lbbbsvo0aNbJTMAG55iFgBgQ5r7YPLQfySDjk22OaLqNABU4PHHH8/06dOX7ZadNWtWRo0alYkTJ2by5Mn50Ic+lCSZM2dOrr/++tTX1+fRRx/Nvvvum169av+gd8QRR+TOO+9UzAJ0II4yAADYUJY0JnedmGzUOxl1UdVpAKjI8OHDM3v27MyYMSMzZszIgAEDcs8992TrrbfO9OnTl60fc8wx+eEPf5j3v//92W677TJhwoQsXrw4ixYtyoQJExxlANDBKGYBADaUR3+YvHBXMuq7Sfctq04DQCsZO3Zs9ttvv0ybNi0DBgzIZZddttaf45hjjskOO+yQ4cOHZ8SIERkxYkSOPPLIDZAWgKo4ygAAYEN47cnkvi8l/Q5PBv1L1WkAaEVXXXXVW74/Y8aMVa7/9Kc/Xfa8rq4ul1xySQumAqCtUcwCALS0skwmnlJ7vvePkqKoNg8AG9yJP53U6l/zsuP3avWvCUDLUcwCALS0J69KnvlDssd3k54Dq04DAAC0Qc6YBQBoSfPnJHd/Otl8n2TIqVWnAQAA2ijFLABAS7rn9GTh3GSf/0661FWdBgAAaKMUswAALeXp8cmM/02GfinZdFjVaQAAgDZMMQsA0BIWvZpMOjnpvXMy9MtVpwEAANo4w78AAFrC/Wclrz2ZjPlLUtet6jQAAEAbZ8csAMD6mnNXMu17yZBPJFu+o+o0AABAO6CYBQBYH40Lk7s+mvTon4w8t+o0AABAO+EoAwCA9fHw+cm8qckBv0s26l11GgAAoJ2wYxYAYF3NeziZek6y3T8n/d9bdRoAAKAdUcwCAKyLckky8WNJfc9kj+9WnQYAAGhnHGUAALAuHrskef72ZN+fJhtvVXUaAACgnbFjFgBgbTXMSu79QrL1u5PB/1Z1GgAAoB1SzAIArI2yTCaekpSNyd6XJEVRdSIAAKAdcpQBAMDaeOqXydO/T3b/z6TX9lWnAQAA2ik7ZgEAmmvBi8ndn0z67Jm8/VNVpwEAANoxxSwAwJpMvzK5dlDyq82T+bOTAUcnXfzhEQAAsO4UswAAb2X6lcnEk5KGJ99ce/CbtXUAAIB1pJgFAHgr9305aWxYfq2xobYOAACwjhSzAABvpeGptVsHAABoBsUsAMBb2bjfqtd7bNe6OQAAgA5FMQsAsDplmXTdfOX1uh7JiHGtnwcAAOgwFLMAAKsz69pk3gPJwA8nPQYmKWof9740GXxs1ekAAIB2rL7qAAAAbdLihuTuzySbDk/2+0nSxY9NAABAy/EbBgDAqjz4H7UBX/vfppQFAABanKMMAABW9PLfkocvSAb9a9L3XVWnAQAAOiDFLABAU2WZTP5kUtc92f38qtMAAAAdlL/LAwBoatZvkmdvTEZ9J9l466rTAAAAHZQdswAAb1j8WnL36bWBXzudWnUaAACgA7NjFgDgDQZ+AQAArcSOWQCA5M2BX4P/zcAvAGCZE044IX379s2wYcOWrX3+85/PzjvvnN122y0f+MAHMnfu3CTJwoUL85GPfCTDhw/PiBEjcuutt670+Y466qjlPhfQeSlmAQCWDfzaOBlp4BcA8Kbjjz8+48ePX25tzJgxmTp1au6///7stNNOOffcc5MkP/7xj5MkDzzwQG666aacccYZWbJkybL7fv3rX6dXr16tFx5o0xSzAAAzf10b+LXbOcnGW1WdBgBoQ0aPHp0+ffost3booYemvr527NG+++6bWbNmJUkeeuihHHLIIUmSvn37ZtNNN83kyZOTJK+++mq+/e1v56yzzmrF9EBbppgFADq3xa8l95yebLpbMuQTVacBANqZyy+/PEcccUSSZMSIEfntb3+bxYsXZ/r06bn77rszc+bMJMlXvvKVnHHGGenRo0eVcYE2xFQLAKBzmzouaZiZ7P9zA78AgLUybty41NfX59hjj01SO4/24Ycfzp577pmBAwdm//33T319faZMmZLHHnssF110UWbMmFFtaKDN8NsHANB5vTwteeTCZPBxSd93Vp0GAGhHrrjiivz+97/PzTffnKIokiT19fW56KKLll2z//77Z8iQIZkwYULuvvvuDBo0KIsXL87s2bNz4IEHrnI4GNB5KGYBgM5p2cCvHsnI86pOAwC0I+PHj895552XCRMmLHc0QUNDQ8qyTM+ePXPTTTelvr4+u+66a3bdddeccsopSZIZM2bkve99r1IWUMwCAJ3UzF8lz96U7PE9A78AgNUaO3Zsbr311syZMycDBgzI2WefnXPPPTcLFizImDFjktQGgP3oRz/K7Nmzc9hhh6VLly7p379/fvazn1WcHmjLFLMAQOezbODXiGTIKVWnAQDasKuuumqltRNPPHGV1w4aNCjTpk17y883aNCgTJ06tUWyAe2bYhYA6HymfjNpmJW842oDvwCAlZz400mt/jUvO36vVv+aQLW6VB0AAKBVzXskeeQ/k+2PT7Z8R9VpAACATkoxCwB0HmWZ3G3gFwAAUD1/uwcAdB4zf5U8+8dkj+8n3ftWnQYAAOjE7JgFADqHRa/WBn5tNjIZcnLVaQAAgE7OjlkAoHN48I2BX//PwC8AAKBydswCAB3fvIeTh/8z2f4jyZb7V50GAABAMQsAdHBlmUz+ZFLfKxn5rarTAAAAJHGUAQDQ0c28Jnnu5mTPHxj4BQAAtBl2zAIAHdeiV5O7T0822z3Z0cAvAACg7VDMAgAd19Rzktf/nux5cdKlruo0AJ3aCSeckL59+2bYsGHL1j7/+c9n5513zm677ZYPfOADmTt3bpLkpptuyh577JHhw4dnjz32yC233LLsni9/+cvZdttt06tXr1b/HgCgJSlmAYCOad7DySPfTrY/Idlyv6rTAHR6xx9/fMaPH7/c2pgxYzJ16tTcf//92WmnnXLuuecmSbbYYov87ne/ywMPPJArrrgi//qv/7rsniOPPDITJ05s1ewAsCEoZgGAjqcsk8mnGfgF0IaMHj06ffr0WW7t0EMPTX19bfTJvvvum1mzZiVJdt9992yzzTZJkqFDh2b+/PlZsGDBsuv69evXiskBYMNQzAIAHc9Tv0yeuyUZMS7pvmXVaQBohssvvzxHHHHESuu/+tWvsvvuu6dbt24VpAKADae+6gAAAC1q0SvJPW8M/Pp41WkAaIZx48alvr4+xx577HLrDz74YL7whS/kxhtvrCgZAGw4zdoxWxTF4UVRTCuK4rGiKL64ivc/WxTFQ0VR3F8Uxc1FUQxs8l5jURRTlj6ua8nwAAArmXpO8vrTyV4/NPALoB244oor8vvf/z5XXnlliqJYtj5r1qx84AMfyP/8z/9khx12qDAhAGwYa9wxWxRFXZKLk4xJMivJpKIorivL8qEml92bZM+yLBuKojglyflJ/nnpe6+XZTmyhXMDAKxs3kPJIxfVBn5tsW/VaQBYg/Hjx+e8887LhAkT0qNHj2Xrc+fOzXve856ce+65ecc73lFhQgDYcJqzY3bvJI+VZflEWZYLk1yd5H1NLyjL8k9lWTYsfXlnkgEtGxMAYA3KMpn8SQO/ANqosWPHZr/99su0adMyYMCAXHbZZTnttNPyyiuvZMyYMRk5cmROPvnkJMkPfvCDPPbYYznnnHMycuTIjBw5MrNnz06SnHnmmRkwYEAaGhoyYMCAfP3rX6/wuwKAddecM2b7J5nZ5PWsJPu8xfUnJvlDk9fdi6KYnGRxkm+VZXntijcURXFSkpOSpGvXrs2IBACwgqd+URv4tdcPDfwCaIOuuuqqldZOPPHEVV571lln5ayzzlrle+eff37OP//8Fs0GAFVoTjFbrGKtXOWFRfHhJHsmOaDJ8nZlWT5dFMX2SW4piuKBsiwfX+6TleWlSS5Nkp49e67ycwMArNaiV5J7PptsNirZ4aSq0wCw1Ik/nVTJ173s+L0q+boAsDaac5TBrCTbNnk9IMnTK15UFMW7k3w5yVFlWS54Y70sy6eXfnwiya1Jdl+PvAAAK5v6DQO/AACAdqU5xeykJEOKohhcFEXXJB9Kcl3TC4qi2D3JJamVsrObrG9WFEW3pc+3SPKOJE2HhgEArJ95DyWPfCfZ4cRki7c6bQkAAKDtWONRBmVZLi6K4rQkNySpS3J5WZYPFkXxjSSTy7K8LskFSXol+WVRFEnyVFmWRyXZJcklRVEsSa0E/lZZlopZAKBllGUy+bRko7clI86tOg0AAECzNeeM2ZRleX2S61dY+2qT5+9ezX13JBm+PgEBAFbryf+XPPenZK//MvALAABoV5pzlAEAQNuz6JXk3s8mffZIdvhY1WkAAADWSrN2zAIAtDkPnJ28/mzyrmsN/AIAANodO2YBgPZn7oPJtDcGfu1ddRoAAIC1ppgFANqXZQO/ehv4BQAAtFuOMgAA2pcnr05m35rs9aOk+xZVpwEAAFgndswCAO3HopeTe89I+uyZ7PDRqtMAAACsMztmAYD2w8AvAACgg7BjFgBoH+ZOTaZ9t7ZT1sAvAACgnVPMAgBt37KBX5skI/6j6jQAAADrzVEGAEDb9+RVyewJyd6XGPgFAAB0CHbMAgBt26KXk3vOSPrslWx/YtVpAAAAWoQdswBA23b/15P5zyUHXGfgFwAA0GHYMQsAtF1zpyZ/+16y48eSzfeqOg0AAECLUcwCAG1TWSaTTzXwCwCJ+BDeAAAgAElEQVQA6JAcZQAAtE0zfp7Mvi3Z+9Kk2+ZVpwEAAGhRdswCAG3PwnnJvZ9LNt872cHALwAAoOOxYxYAaHse+PrSgV+/Swr/jgwAAHQ8ftMBANqWuQ8kf/t+suNJyeZ7Vp0GAABgg1DMAgBtR1kmk05Num6ajBhXdRoAAIANxlEGAEDbMePK5Pk/J3v/2MAvAACgQ7NjFgBoG5Yb+HVC1WkAAAA2KDtmAYC24YGvJ/NnJwf83sAvAACgw/NbDwBQvZfuXzrw6+MGfgEAAJ2CYhYAqFZZJpMN/AIAADoXRxkAANWa8b/J839J9vnvpFufqtMAAAC0CjtmAYDqLJyX3Pv5ZPN9ku0/UnUaAACAVmPHLABQnQe+Vhv4deD/GfgFAAB0Kn4DAgCq8dJ9tYFfQ05O+uxRdRoAAIBWpZgFAFrfsoFffZLdvll1GgAAgFbnKAMAoPVN/1ny/O3JPpcZ+AUAAHRKdswCAK1r4dxkyhsDv46vOg0AAEAl7JgFAFrX/V9L5j+fHPgHA78AAIBOy29DAEDreem+5NEfJENOSfqMqjoNAABAZRSzAEDrKJe8OfBrhIFfAABA5+YoAwCgdTQd+NV1s6rTAAAAVMqOWQBgw1s4N5lyZrL5vgZ+AQAAxI5ZAKA13P/VZMEcA78AAACW8psRALBhvTQlefTiZEcDvwAAAN6gmAUANpxySTLp1KTr5smIc6pOAwAA0GY4ygAA2HCm/08y545kn8sN/AIAAGjCjlkAYMNYODe598xki/2S7Y+rOg0AAECbYscsALBh3P+VZOELyZ43GPgFAACwAr8lAQAt78V7k0d/mAz5RNJn96rTAAAAtDmKWQCgZZVLkslLB37tZuAXAADAqjjKAABoWU9ckcz5a7LvT5Kum1adBgAAoE2yYxYAaDkLX0qmfCHZYv9k8L9VnQYAAKDNUswCQEubfmVy7aDk511qH6dfWXWi1nPf0oFfe11s4BcAAMBbcJQBALSk6VcmE09KGhtqrxuerL1OksHHVperNbx4T/LYfyVDTk02G1l1GgAAgDZNMQsALem+L79Zyr6hsSGZdHIy74HaQKxufZKuSx9Nn9dvXE3mllAuSSadmnTbItntG1WnAQAAaPMUswDQkhqeWvX64leTR76dLFm0+nvrur91cdutz6rfr+uRFMWG+X6a64krkhfuTPb9qYFfAAAAzaCYBYCW1HXT2gCsFfUYmLxverL4tWThi7XHgheWfnyxyVqT5688mryw9LolC1b/Nbt0fevidnXFbn2v9St0p19Z2yHc8FSSInnbkGTwv6775wMAAOhEFLMA0FIe+3GtlC3qkrLxzfW6HsmIcbUSdKNetUfP7Zr/ecsyaXz9rUvcpiXvq9OThXfXnq94rEJTRf3yxW3XPkm3zVdT6DYpdjfqncz4+fJn6aZMXpuZzLiq45+lCwAA0AIUswDQEqb/bzLx40m/I5KB/5zc/7XaTtIe29VK2fUpK4siqe9Re/QYsHb3Ns6vlcULXkwWvvAWxe6Lyet/r52Du+CF2tELq81TVyuLs2T59SXzaztoFbMAAABrpJgFgPX11DXJncclWx2UvOtXtSFe2x9Xdaqauu7Jxv1qj7XRuLBW6K6uxH3wm6u+b3Vn7AIAALAcxSwArI9Zv0tuH5tssV9ywHW1UrYjqOuabLxV7bEq03+WNDy58nqPtTiiAQAAoBPrUnUAAGi3nrkp+csxyWa7Jwden9T3rDpR6xkxrnZ2blNvnKULAADAGilmAWBdPDchue19Se9dkoPG1wZidSaDj032vjTpMTBJUfu496XOlwUAAGgmRxkAwNqac2cy4b1Jz0HJwTcm3fpUnagag49VxAIAAKwjO2YBYG28eE/yp8OT7lslB/8x6d636kQAAAC0Q4pZAGiuuVOTPx2adN00OeSWpMc2VScCAACgnVLMAkBzvDwtueXdSZduycE3Jz23qzoRAAAA7ZhiFgDW5NUnkpsPSVLWStm37VB1IgAAANo5w78A4K28NrNWyja+nhzyp2STnatOBAAAQAegmAWA1Xn9meSWQ5KFL9bOlN1st6oTAQAA0EEoZgFgVeY/XztT9vWnk4NuTPrsUXUiAAAAOpBmnTFbFMXhRVFMK4risaIovriK9z9bFMVDRVHcXxTFzUVRDGzy3nFFUTy69HFcS4YHgA1i4UvJnw6tnS17wO+TLfevOhEAAAAdzBqL2aIo6pJcnOSIJLsmGVsUxa4rXHZvkj3LstwtyTVJzl96b58kX0uyT5K9k3ytKIrNWi4+ALSwRS8nfzo8mfdQ8q5rk60OrDoRAAAAHVBzdszuneSxsiyfKMtyYZKrk7yv6QVlWf6pLMuGpS/vTDJg6fPDktxUluWLZVm+lOSmJIe3THQAaGGLX0tufW/y4j3JO3+ZbHNY1YkAAADooJpTzPZPMrPJ61lL11bnxCR/WMd7AaAajfOT296fzLk92f/KZMBRVScCAACgA2tOMVusYq1c5YVF8eEkeya5YG3uLYripKIoJhdFMXnx4sXNiAQALahxYfLnY5Jnb072+Uky8INVJwKgE7vooosydOjQDBs2LGPHjs38+fNzyy23ZNSoURk2bFiOO+64vPF707x583LkkUdmxIgRGTp0aH7yk59UnB4AaK7mFLOzkmzb5PWAJE+veFFRFO9O8uUkR5VluWBt7i3L8tKyLPcsy3LP+vr65mYHgPW3ZHFyx9jk6f9L9v5Rsv2/VZ0IgE7s73//e773ve9l8uTJmTp1ahobG/Pzn/88xx13XK6++upMnTo1AwcOzBVXXJEkufjii7Prrrvmvvvuy6233pozzjgjCxcurPi7AACaoznF7KQkQ4qiGFwURdckH0pyXdMLiqLYPcklqZWys5u8dUOSQ4ui2Gzp0K9Dl64BQPWWNCZ/PS6Z+etk1HeSHU+qOhEAZPHixXn99dezePHiNDQ0pGfPnunWrVt22mmnJMmYMWPyq1/9KklSFEVeeeWVlGWZV199NX369InNLgDQPqyxmC3LcnGS01IrVB9O8ouyLB8siuIbRVG8cQDfBUl6JfllURRTiqK4bum9LyY5J7Vyd1KSbyxdA4BqlUuSiSclT/48GXFusvOnq04EAOnfv38+97nPZbvttku/fv2yySab5IMf/GAWLVqUyZMnJ0muueaazJxZG+Vx2mmn5eGHH84222yT4cOH57vf/W66dGnO/hsAoGrN+qfUsiyvT3L9CmtfbfL83W9x7+VJLl/XgADQ4soymfyp5InLk2FfSYZ+sepEAJAkeemll/Lb3/4206dPz6abbpp/+qd/ypVXXpmrr746p59+ehYsWJBDDz102a7YG264ISNHjswtt9ySxx9/PGPGjMm73vWu9O7du+LvBABYE/+UCkDnUpbJlDOTRy9OdvlcMvzsqhMBwDJ//OMfM3jw4Gy55ZbZaKONcvTRR+eOO+7Ifvvtlz//+c+ZOHFiRo8enSFDhiRJfvKTn+Too49OURTZcccdM3jw4DzyyCMVfxcAQHMoZgHoXB74evLwhcmQU5OR5ydFUXUiAFhmu+22y5133pmGhoaUZZmbb745u+yyS2bPro3yWLBgQc4777ycfPLJy66/+eabkyTPPfdcpk2blu23376y/ABA8ylmAeg8HvxWMvUbyQ4nJnt+TykLQJuzzz775JhjjsmoUaMyfPjwLFmyJCeddFIuuOCC7LLLLtltt91y5JFH5uCDD06SfOUrX8kdd9yR4cOH55BDDsl5552XLbbYouLvAgBoDuM6AegcHvlOct+XkoH/kux1SVL4t0kA2qazzz47Z5+9/FE7F1xwQS644IKVrt1mm21y4403tlY0AKAF+a0UgI7v0UuSe05Ptj062e+KpEtd1YkAAADo5OyYBaBje+J/kkmnJNv8Q7L/VUkX/+sDoHon/nRSq3/Ny47fq9W/JgCwenbMAtBxPfmL5K6PJFsdnLzrV0ld16oTAQAAQBLFLAAd1azfJnccm2zxjuSA3yZ13atOBAAAAMsoZgHoeJ6+IfnLB5M+o5IDf5/U96w6EQAAACxHMQtAx/Lcrcmf359ssmty0Phko95VJwIAAICVKGYB6DievyOZ8N6k1/bJQTcmXTerOhEAAACskmIWgI7hxbuTW49IuvdLDv5j0n3LqhMBAADAailmAWj/Xro/ueXQpGuf5JBbko37VZ0IAAAA3pJiFoD2bd4jyS3vTuo2Tg65Oem5bdWJAAAAYI0UswC0X688ntxySFJ0qZWyvbavOhEAAAA0S33VAQBgnbz2VHLzwcmSBckhtya93151IgAAAGg2xSwA7U/D07VSdtG82pmymw6rOhEAAACsFcUsAO3L/Nm14wvmP5ccfFPSZ1TViQAAAGCtKWYBaD8WvJjcMiZ57cnkwD8kW+xbdSIAAABYJ4pZANqHhfOSPx2WvPxIcsDvkq0OqDoRAAAArDPFLABt36JXkwnvSV6akrzr10m/Q6tOBAAAAOtFMQtA27b49eS2o5I5f03ecXUy4MiqEwEAAMB661J1AABYrcYFyZ+PTp67Ndn3imS7f6o6EQAAALQIO2YBaJuWLEpu/1DyzPhk70uTwR+uOhEAAAC0GDtmAWh7ljQmf/23ZNa1yR7fS3b8WNWJAAAAoEUpZgFoW8olycSPJk9enYw8L3n7J6tOBAAAAC1OMQtA21GWyeTTkid+mgz7WrLrmVUnAgAAgA1CMQtA21CWyT1nJI/+V7LLmcnwr1WdCAAAADYYxSwAbcP9X0mmXZTs9Mlk5LeSoqg6EQAAAGwwilkAqjd1XPLguGSHjyZ7fEcpCwAAQIenmAWgWo9clNx/VjLow8leP0oK/2sCAACg4/PbLwDVefS/kns+m2x7TLLvT5IudVUnAgAAgFZRX3UAADqR6Vcm9305aXgq6donWfhC0v/IZP8rky7+lwQAAEDn4bdgAFrH9CuTiScljQ211wtfSNIl2fbopK5rpdEAAACgtTnKAIDWcd+X3yxll1mS3P/1KtIAAABApRSzALSOhqfWbh0AAAA6MMUsABvey39b/RmyPbZr3SwAAADQBihmAdhwyjJ5/CfJ+FFJ0TXp0m359+t6JCPGVZMNAAAAKqSYBWDDWDg3uX1sctcJSZ+9kiMfSfa5LOkxMElR+7j3pcngY6tOCgAAAK1uNX9XCgDr4fnbkzuOTRpmJSP+I9nlzKRLXa2EVcQCAACAYhaAFrRkcfLgfyRTz056DkrG3J5ssU/VqQAAAKDNUcwC0DJee6q2S/b5vySDPpzsdXGyUe+qUwEAAECbpJgFYP09dU1y18eSsjHZ72fJ4A9XnQgAAADaNMO/AFh3i19L7vpo8pd/SnrvlBxxr1IWgA1i2rRpGTly5LJH7969853vfCe//OUvM3To0HTp0iWTJ09e6b6nnnoqvXr1yoUXXlhBagCA1bNjFoB18+K9yR1jk5f/luz6pWS3s5MuG1WdCoAO6u1vf3umTJmSJGlsbEz//v3zgQ98IA0NDfn1/2fvzuOrKO/+/7+uBAIERERBwUgRN8IaQMQFEWuRRaxF0IrwUxBb660t1SLtXduqdatai1b9tmKleFsKLVYsKq4gYEEExIBoFRFQFkVR9rAkZH5/nIAsAYIkZ8g5r+fjcR4555qZM++LZJjMJ9e55umnueaaa0rd7oYbbqB79+7JjCpJklQmFmYlSQcmKoYPHoT8X0C1o+Dbr8Ix3447lSQpjUycOJETTjiBb33rW/tc75lnnqFJkybUrFkzSckkSZLKzqkMJEllt2klTL4A5twIDbtDj3kWZSVJSTdmzBj69u27z3U2btzIPffcwy233JKkVJIkSQfGwqwkqWxWvAgvtILPJ0P7P8HZ46DakXGnkqS9WrNmDX369KFp06bk5ubyxhtvMHfuXM444wxatmzJhRdeyLp16wAoLCzkyiuvpGXLluTm5nL33XfHnF57s3XrVsaPH88ll1yyz/VuueUWbrjhBmrVqpWkZJIkSQfGqQwkSfu2bUti2oIPHoA6LeHbk6BO87hTSdJ+DR48mG7duvHUU0+xdetWCgoK6NKlC7///e8555xzGDFiBPfddx+33347Y8eOZcuWLbzzzjsUFBTQrFkz+vbtS+PGjePuhnbzwgsv0LZtW44++uh9rvfmm2/y1FNPMXToUNasWUNGRgbVq1fn+uuvT1JSSZKkfbMwK0nau7XvJ27wtTofTr4e2twHmdXjTiVJ+7Vu3TqmTp3KyJEjAcjKyiIrK4sPPviATp06AdClSxe6du3K7bffTgiBjRs3UlRUxKZNm8jKyqJ27dox9kB7M3r06P1OYwDw+uuv73h+6623UqtWLYuykiTpkOJUBpKkPUURLPwLvNgOCpZCp/Fw6kMWZSVVGosWLaJevXoMHDiQNm3acPXVV7Nx40ZatGjB+PHjARg7dixLly4FoE+fPtSsWZMGDRrQqFEjhgwZQt26dePsgkpRUFDAK6+8wsUXX7yjbdy4ceTk5PDGG29wwQUX0LVr1xgTSpIklZ2FWUnSrrauhv9cCjN/APXOhO7zIOfCuFNJ0gEpKipizpw5XHvttbz99tvUrFmT3/3ud4wYMYJHHnmEdu3asX79erKysgCYOXMmmZmZrFixgsWLF3P//fezaNGimHuh3WVnZ/Pll19y+OGH72jr1asXy5YtY8uWLaxcuZKXXnppj+1uvfVWhgwZksyokiRJ++VUBpKkr33+OkzvB5s+hbx7IfdnEPwbnqTKJycnh5ycHDp06AAkRsT+7ne/4/bbb+fll18GYMGCBTz//PMA/P3vf6dbt25UrVqV+vXrc9ZZZzF79myaNGkSWx/S1aCRs5K+z8cHtE/6PiVJkrzaliRBcRHM+w1M7AwZ1eD8N6DZTRZlJVVaxxxzDMcddxwffPABABMnTqRZs2Z8/vnnABQXF3PHHXfwox/9CIBGjRoxadIkoihi48aNzJgxg6ZNm8aWX5IkSanPK25JSncblsCrnWD+7XD8FdB9Dhx5atypJOmgPfTQQ/Tr149WrVqRn5/PL3/5S0aPHs3JJ59M06ZNadiwIQMHDgTguuuuY8OGDbRo0YL27dszcOBAWrVqFXMPJEmSlMqcykCS0tmSMTDrmsTzM0dD48vizSNJ5SgvL4/Zs2fv0jZ48GAGDx68x7q1atVi7NixyYomSZIkWZiVpLRUuAHe+jEsGglHnQFnjoJax8edSpIkSZKktGFhVpLSzZezYfrlsOEjaPFraPEbyPB0IKlyiuNGUeDNoiRJknTwvBKXpHQRFcN/74d5N0P1o+G816B+p7hTSZIkSZKUlizMSlI62PQpvHEFfPYqHHcxnPYYVKsbdypJkiRJktKWhVlJSnXLn4MZA6FoI5w2HE64GkKIO5UkSZIkSWktI+4AkqQKsm0zzP4JTLkQsnOg2xw48QcWZSUpBa1Zs4Y+ffrQtGlTcnNzeeONN3Ys+/3vf08IgVWrVgFw3333kZeXR15eHi1atCAzM5OvvvoqruiSJElpyxGzkpSK1r4H0y6DNe/AKT+FvN9BZrW4U0mSKsjgwYPp1q0bTz31FFu3bqWgoACApUuX8sorr9CoUaMd6950003cdNNNADz77LMMGzaMunWd3kaSJCnZHDErSakkiuDDP8OL7WDTZ3DO89BumEVZSUph69atY+rUqQwaNAiArKws6tSpA8ANN9zAvffeS9jLpyVGjx5N3759k5ZVkiRJX7MwK0mpYsuX8PrFMOtaqNcJesyDY3vEnUqSVMEWLVpEvXr1GDhwIG3atOHqq69m48aNjB8/nmOPPZbWrVuXul1BQQEvvvgivXv3TnJiSZIkgVMZSFJqWPkaTP//YMvn0OZ+aPpTCP7tTZLSQVFREXPmzOGhhx6iQ4cODB48mFtvvZWpU6fy8ssv73W7Z599lrPOOstpDCRJkmJSpqv2EEK3EMIHIYSFIYRflLK8UwhhTgihKITQZ7dl20II+SWP8eUVXJIEFBfC3Jth4nlQtRac/ybk3mhRVpLSSE5ODjk5OXTo0AGAPn36MGfOHBYvXkzr1q1p3Lgxy5Yto23btnz22Wc7thszZozTGEiSJMVovyNmQwiZwCNAF2AZMCuEMD6Kovd2Wu0TYAAwpJS32BRFUV45ZJUk7WzDIph2OXz5JpwwCNo9CFVqxp1KkpRkxxxzDMcddxwffPABp5xyChMnTqRt27ZMnDhxxzqNGzdm9uzZHHXUUQCsXbuWKVOm8Le//S2u2JIkSWmvLFMZnAYsjKJoEUAIYQxwEbCjMBtF0ZKSZcUVkFGStLvFoxJzyYZM6PhPaHRJ3IkkSTF66KGH6NevH1u3bqVJkyb89a9/3ef648aN4/zzz6dmTf+gJ0mSFJeyFGaPBZbu9HoZ0OEA9lE9hDAbKAJ+F0XRM7uvEEL4IfBDSNxFVpK0F4XrYNb1sORJqNcRzhwFNRvFnUqSFLO8vDxmz5691+VLlizZ5fWAAQMYMGBAxYaSJEnSPpWlMBtKaYsOYB+NoihaEUJoAkwKIbwTRdFHu7xZFA0HhgPUrFnzQN5bktLHqpkwvS9sXAItb4XmN0OG93CUpHQxaOSspO/z8QHtk75PSZKkdFGWu8MsA47b6XUOsKKsO4iiaEXJ10XAZKDNAeSTJBVvg3fvhlfOguIi+M5UaHmLRVlJkiRJkiqxshRmZwEnhRCODyFkAZcB48vy5iGEI0II1UqeHwWcxU5z00qS9qNgObzWBeb+Eo67GHrMhXpnxZ1KkiRJkiQdpP0Ot4qiqCiEcD3wEpAJjIii6N0Qwm+B2VEUjQ8htAfGAUcAF4YQbouiqDmQCzxaclOwDBJzzFqYlaSyWPZvmHEVFG+BDiOgyQAIpc0uI0mSJEmSKpsyfQ42iqIJwITd2n6z0/NZJKY42H276UDLg8woSalv8SiYezMUfALZOXDYybByIhzRFs4aDbVPjjuhJEmSJEkqR05QKElxWzwKZv4QthUkXhcsTTwa9oCzx0FmVrz5JEmSJElSuSvLHLOSpIo09+avi7I7W/OuRVlJkiRJklKUhVlJilvBJwfWLqnCNG7cmJYtW5KXl8epp54KQH5+PqeffvqOtpkzZwIwefJkDj/8cPLy8sjLy+O3v/1tnNElSZIkVTJOZSBJcatWD7Z8vmd7dqPkZ5HEa6+9xlFHHbXj9dChQ7nlllvo3r07EyZMYOjQoUyePBmAs88+m+eeey6mpJIkSZIqM0fMSlKc1r4PheuBsGt7Zja0vjOWSJJ2FUJg3bp1AKxdu5aGDRvGnEiSJElSKrAwK0lx2bwKpvSErMOg7TDI/hYQEl9PGw7H94s7oZR2Qgicf/75tGvXjuHDhwPwwAMPcNNNN3HccccxZMgQ7r777h3rv/HGG7Ru3Zru3bvz7rvvxhVbkiRJUiXkVAaSFIdtW+D1i6FgGXxnMhx1OjQdHHcqKe1NmzaNhg0b8vnnn9OlSxeaNm3KU089xbBhw+jduzf//Oc/GTRoEK+++ipt27bl448/platWkyYMIHvfe97fPjhh3F3QZIkSVIl4YhZSUq2KIKZ18AXr8PpIxNFWUmHhO3TFNSvX59evXoxc+ZMnnjiCS6++GIALrnkkh03/6pduza1atUCoEePHhQWFrJq1ap4gkuSJEmqdCzMSlKy/fdeWPwEtLwVGl8WdxpJJTZu3Mj69et3PH/55Zdp0aIFDRs2ZMqUKQBMmjSJk046CYDPPvuMKIoAmDlzJsXFxRx55JHxhJckSZJU6TiVgSQl09KnIf8X8K2+0OI3caeRtJOVK1fSq1cvAIqKirj88svp1q0btWrVYvDgwRQVFVG9evUdc88+9dRT/OlPf6JKlSrUqFGDMWPGEELY1y4kSZIkaQcLs5KULF+9BdP7w5Gnw+kjwAKOdEhp0qQJc+fO3aO9Y8eOvPXWW3u0X3/99Vx//fXJiCZJkiQpBVmYlaRkKFgOU74L1epBp2cgs3rciaS0NWjkrKTv8/EB7ZO+T0mSJEmHNguzklTRijYmirKF6+H8aVDj6LgTSZIkSZKkmFmYlaSKFBUnpi9Ykw+dnoU6LeNOJEmSJEmSDgEWZiWpIs39JSx7Bto9CMf2iDuNJEmSJEk6RGTEHUCSUtZHf4X37oGTroWTfxx3GkmSJEmSdAixMCtJFWHlFJh1DRzTJTFaNoS4E0mSJEmSpEOIhVlJKm/rF8LrF0OtE6DjPyGjatyJJGkXjRs3pmXLluTl5XHqqacCcNNNN9G0aVNatWpFr169WLNmDQCjRo0iLy9vxyMjI4P8/Pw440uSJEkpwcKsJJWnrathSs/ECNlznoOsOnEnkqRSvfbaa+Tn5zN79mwAunTpwvz585k3bx4nn3wyd999NwD9+vUjPz+f/Px8nnzySRo3bkxeXl6c0SVJkqSUYGFWkspLcSG8fglsWARnj4PDTog7kVRuShthOXbsWJo3b05GRsaO4h5AYWEhV155JS1btiQ3N3dHgU+HtvPPP58qVRL3hT399NNZtmzZHuuMHj2avn37JjuaJEmSlJIszEpSeYgimH09rJwIpz0G9c+OO5GS4ECKlQDz5s3jjDPOoHnz5rRs2ZLNmzfHEfsb232EZYsWLXj66afp1KnTLuuNHTuWLVu28M477/DWW2/x6KOPsmTJkhgSa29CCJx//vm0a9eO4cOH77F8xIgRdO/efY/2f/zjHxZmJUmSpHJSJe4AkpQSPngAFg6HZv8LTa6MO42S6LXXXuOoo47a8Xp7sfKaa67ZZb2ioiL69+/Pk08+SevWrfnyyy+pWrVyzz+cm5tbansIgY0bN1JUVMSmTZvIysqidu3aSU6nfZk2bRoNGzbk888/p0uXLjRt2nRHgf3OO++kSpUq9OvXb5dt3nzzTbKzs2nRokUckSVJkqSU44hZSTpYy56FOT+D43pD6zviTqOY5XFvbOAAACAASURBVObmcsopp+zR/vLLL9OqVStat24NwJFHHklmZmay431j+xthubM+ffpQs2ZNGjRoQKNGjRgyZAh169ZNUlKVRcOGDQGoX78+vXr1YubMmQA88cQTPPfcc4waNYoQwi7bjBkzxtGykiRJUjmyMCtJB2P1XJjeF+q2hTP+D4L/raaTAylWLliwgBACXbt2pW3bttx7771JSlk+pk2bxpw5c3jhhRd45JFHmDp16l7XnTlzJpmZmaxYsYLFixdz//33s2jRoiSm1b5s3LiR9evX73j+8ssv06JFC1588UXuuecexo8fT3Z29i7bFBcXM3bsWC677LI4IkuSJEkpyakMJOmb2vQZTLkQqtaBTuOhSvb+t1FK2dfHwXdXVFTEf/7zH2bNmkV2djbnnXce7dq147zzzkty6m+mtBGWe+vr3//+d7p160bVqlWpX78+Z511FrNnz6ZJkybJjKy9WLlyJb169QISP5eXX3453bp148QTT2TLli106dIFSNwA7M9//jMAU6dOJScnx++hJEmSVI4szErSN1G0CaZeBFu+hC7/geyGcSdSDA6kWJmTk8M555yzYz7aHj16MGfOnEpRmN24cSPFxcUcdthhO0ZY/uY3v9nr+o0aNWLSpEn079+fgoICZsyYwU9/+tMkJta+NGnShLlz5+7RvnDhwr1u07lzZ2bMmFGRsSRJkqS0Y2FWkg5UVAwzBsCXs+Dsp6Fum7gTKQYHWqzs2rUr9957LwUFBWRlZTFlyhRuuOGGJCb+5vY2wnLcuHH8+Mc/5osvvuCCCy4gLy+Pl156ieuuu46BAwfSokULoihi4MCBtGrVKuZepJ9BI2clfZ+PD2if9H1KkiRJlZWFWUk6UO/cCp/8E/LuheO+F3caxeRAi5VHHHEEN954I+3btyeEQI8ePbjgggti7kXZ7G2EZa9evXb8G+ysVq1ajB07NhnRJEmSJKnSsjArSQdi8SiYfzs0uQpyh8SdRjE60GIlQP/+/enfv39FR/vGHGEpSZIkScljYVaSyuqLafDmVVC/M7T/E4QQdyIliQVLSZIkSVJ5y4g7gCRVChsWw9ReUPNbcPa/IDMr7kSSJEmSJKkSszArSfuzdS1M6QlREZzzHFSrG3ciSZIkSZJUyTmVgSTtS3ERTLsM1i2Ac1+C2ifHnUiSJEmSJKUAC7OStC9zboRPX4TTHoNjvh13GkmSJEmSlCKcykCS9mbBI7DgIWj6Mzjx6rjTSJIkSZKkFGJhVpJKs+IleGswHHsh5N0TdxpJkiRJkpRiLMxK0u7WvAvTLoXDW8CZf4eMzLgTSZIkSZKkFGNhVpJ2tvkLmNITMrPhnGehaq24E0mSJEmSpBTkzb8kabttm2Hq92DzZ3DeFKh5XNyJJEmSJElSirIwK0kAUQRv/gBWTYeO/4SjTos7kSRJkiRJSmFOZSBJAO/eBUv+Bq3ugEaXxJ1GkiRJkiSlOAuzkvTJWJj3K2jcH5r/Mu40kiRJkiQpDViYlZTeVs2EN66AemdBh79ACHEnkiRJkiRJacDCrKT0tfETmPpdqN4Azh4HmdXiTiRJkiRJktKEN/+SlJ4K18OUC2HbJvj2RKheL+5EkiRJkiQpjViYlZR+irfB9H6wdj6cMwHqNI87kSRJkiRJSjMWZiWln/yfw/Jn4dSHoWHXuNNIkiRJkqQ05ByzktLLwsfg/fvh5B/DydfFnUaSJEmSJKUpC7OS0sdnk2DW/0CDbtD2D3GnkSRJkiRJaczCrKT0sO4DeL031D4FzhoDGc7kIkmSJEmS4mNhVlLq2/IlTO4JGVXhnGch6/C4E0mSJEmSpDTnkDFJqW3b1sRI2YJP4LzXoNbxcSeSJEmSJEmyMCsphUURzLoWPp8CZ/wN6p0ZdyJJkiRJkiTAqQwkpbL//h4WjYAWv4bj+8WdRpIkSZIkaQcLs5JS09JnIP/n0OhSaHlr3GnSyubNmznttNNo3bo1zZs355ZbbgFg4sSJtG3blry8PDp27MjChQsB+MMf/kCzZs1o1aoV5513Hh9//HGc8SVJkiRJSgoLs5JSz1dvw/R+cGR7OH0kBP+rS6Zq1aoxadIk5s6dS35+Pi+++CIzZszg2muvZdSoUeTn53P55Zdzxx13ANCmTRtmz57NvHnz6NOnD0OHDo25B5IkSZIkVTyrFZJSS8EKmHIhVDsSOv0bqtSIO1HaCSFQq1YtAAoLCyksLCSEQAiBdevWAbB27VoaNmwIwLnnnkt2djYAp59+OsuWLYsnuCRJkiRJSeTNvySljqKNMPW7ULgWukyDGsfEnShtbdu2jXbt2rFw4UKuu+46OnTowF/+8hd69OhBjRo1qF27NjNmzNhju8cff5zu3bvHkFiSJEmSpORyxKyk1BAVwxtXwFdz4KzRcESruBOltczMTPLz81m2bBkzZ85k/vz5DBs2jAkTJrBs2TIGDhzIjTfeuMs2f/vb35g9ezY33XRTTKklSZIkSUoeR8xKSg1zfwVLn4a2f4Bje8adRiXq1KlD586deeGFF5g7dy4dOnQA4Pvf/z7dunXbsd6rr77KnXfeyZQpU6hWrVpccSVJkiRJShpHzEqq/BY9Ae/dDSf+EE75adxp0t4XX3zBmjVrANi0aROvvvoqubm5rF27lgULFgDwyiuvkJubC8Dbb7/NNddcw/jx46lfv35suSVJkiRJSiZHzEqq3D5/HWb+AI7+Npz6MIQQd6K09+mnn3LllVeybds2iouLufTSS+nZsyePPfYYvXv3JiMjgyOOOIIRI0YAcNNNN7FhwwYuueQSABo1asT48ePj7IIkSZIkSRXOwqykymv9R/B6L6h5PJz9FGRUjTuRgFatWvH222/v0d6rVy969eq1R/urr76ajFiSJEmSJB1SnMpAUuW0dQ1M6QlRBJ2fh6wj4k4kSZIkSZJUZo6YlVT5FBfCfy6BDR/Bua/AYSfGnSgtDRo5K+n7fHxA+6TvU5IkSZKkimBhVlLlEkUw+8fw2avQYQQcfU7ciSRJkiRJkg5YmaYyCCF0CyF8EEJYGEL4RSnLO4UQ5oQQikIIfXZbdmUI4cOSx5XlFVxSmvrgj7DwUWj2czhhYNxpJEmSJEmSvpH9FmZDCJnAI0B3oBnQN4TQbLfVPgEGAH/fbdu6wC1AB+A04JYQghNBSvpmlj8Pb98IOd+D1nfFnUaSJEmSJOkbK8tUBqcBC6MoWgQQQhgDXAS8t32FKIqWlCwr3m3brsArURR9VbL8FaAbMPqgk0tKfYtHwdyboeATqH4MbPkK6rSGM/8GwXsXSpIkSZKkyqsshdljgaU7vV5GYgRsWZS27bG7rxRC+CHwQ4CsrKwyvrWklLZ4FMz8IWwrSLze/CkQoMlVUKVmrNEkSZIkSZIOVlmGnIVS2qIyvn+Zto2iaHgURadGUXRqlSrej0wHYfEoeKYx/D0j8XXxqLgT6ZvYtgXyf/51UXaHCP77+1giSZIkSZIklaeyVEGXAcft9DoHWFHG918GdN5t28ll3FY6MLuPsCz4OPEa4Ph+8eXS14o2JUa+FqzY9eumT2HTipKvn8LWr/b+HgWfJC+vJEmSJElSBSlLYXYWcFII4XhgOXAZcHkZ3/8l4K6dbvh1PvC/B5xSKou5N+85wnJbAeQPhQZdIetwyKgaT7ZUV7ghUVDdV7F10wooXLvnthlVE/PH1mgIh50E9Tslnr8/rPQCbXajiu+PJEmSJElSBdtvYTaKoqIQwvUkiqyZwIgoit4NIfwWmB1F0fgQQntgHHAEcGEI4bYoippHUfRVCOF2EsVdgN9uvxGYVO72NpJy0wp4ul7ieZVakFUHqtaBrCP2fJ5V8rzqTs+3f61SK71uOBVFULR+18JqacXWTZ8m1ttdRjWo0SDxOLwZHHNeyeuGUL3B18+r1S3937Xm8buOgAbIzIbWd1ZcnyvY5s2b6dSpE1u2bKGoqIg+ffpw22238fDDD/PAAw/w0Ucf8cUXX3DUUUcB8P777zNw4EDmzJnDnXfeyZAhQ2LugSRJkiRJKi9lmtA1iqIJwITd2n6z0/NZJKYpKG3bEcCIg8go7d/mLyCzOmzbtOeyrCOh5a2wdTUUroGta75+XrAUtr6TaCtcyz6nTw4ZUPXw0gu3ZSn0VqlRfv1dPCoxQrjgk8QI0tZ3ln26hihK9HVvRdbtXzd/CkUb99w+s8bXRdUjWkODbpC9W7G1RoNEv0Np00yX0fb+fNN+HoKqVavGpEmTqFWrFoWFhXTs2JHu3btz1lln0bNnTzp37rzL+nXr1uWPf/wjzzzzTDyBJUmSJElShfFOW6r8Vr4G0/vBtq0QqkJU+PWyzGxo92DZinlRMRSu27Vwu/351jUlr1fvunzd+1+37XGjqt1kVPu6WFt1PyN09yj01oGMksN1X3PpNuxeerF1826F122b98xXpebXRdUjT92z0Lr9edXaB1dwPRDH96vUhdjdhRCoVasWAIWFhRQWFhJCoE2bNqWuX79+ferXr8/zzz+fzJiSJEmSJCkJLMyq8iougnduhXfvgtonQ+cXYM38bz7CMmR8XSyl8YHn2bZ1p2JuKcXdXQq7a2DLl7Dho6+XR0X7fv/t0zBsWrlr8RkSRdo3+pe+XdXaXxdVjzy99GJrjQZQ9bAD77MO2LZt22jXrh0LFy7kuuuuo0OHDnFHkiRJkiRJMbAwq8pp48cw7XJYNR2aXAWn/jEx4vOI1vGNsMzMgsz6UL3+gW8bRYni6r5G6G5vW/TXvb9P22G7FV0bJP5ddMjIzMwkPz+fNWvW0KtXL+bPn0+LFi3ijiVJkiRJkpLMwqwqn0/+BW9eDdE2OHM0NL4s7kQHL4REAbVKTcg+dt/rfjYpMX3B7rK/BU1/WjH5VO7q1KlD586defHFFy3MSpIkSZKUhtLoFvOq9Io2wcwfwX/6JKYu6JGfGkXZA9X6zsTcuTvLzE6065D2xRdfsGbNGgA2bdrEq6++StOmTWNOJUmSJEmS4mBhVpXDmnfhpfaw8FHIHQrfeR1qNYk7VTyO7wenDU+MkCUkvp42vFLfJGvp0qWce+655Obm0rx5cx588EEA8vPzOf3008nLy+PUU09l5syZu2w3a9YsMjMzeeqpp+KIfcA+/fRTzj33XFq1akX79u3p0qULPXv25I9//CM5OTksW7aMVq1acfXVVwPw2WefkZOTwx/+8AfuuOMOcnJyWLduXcy9kCRJkiRJ5cGpDHRoiyJYOBzm/DRxE6tzX4IG58edKn7H96vUhdjdValShfvvv5+2bduyfv162rVrR5cuXRg6dCi33HIL3bt3Z8KECQwdOpTJkycDiZto/fznP6dr167xhj8ArVq14u23396j/Sc/+Qk/+clP9mg/5phjWLZsWTKiSZIkSZKkJLMwq0PX1tXw5g9h6VNwzPlwxv9BjaPjTqUK0KBBAxo0aADAYYcdRm5uLsuXLyeEsGOE6Nq1a2nYsOGObR566CF69+7NrFmzYsm8L4NGJj/T4wPaJ32fkiRJkiTpm7Mwq0PTF9NhWl/YtALy7oXcn0Fw5o10sGTJEt5++206dOjAAw88QNeuXRkyZAjFxcVMnz4dgOXLlzNu3DgmTZp0SBZmJUmSJEmS9sdKlw4txdvg3bvg1U6QUQW6TINmN1mUTRMbNmygd+/ePPDAA9SuXZs//elPDBs2jKVLlzJs2DAGDRoEwE9/+lPuueceMjMzY04sSZIkSZL0zThiVoeOghXwxv8HKyfBty6D9n+GrMPjTqUkKSwspHfv3vTr14+LL74YgCeeeGLHjcAuueSSHTfFmj17NpdddhkAq1atYsKECVSpUoXvfe978YSXJEmSJEk6QBZmdWhY/jzMGABFBdDhcWgyEEKIO5WSJIoiBg0aRG5uLjfeeOOO9oYNGzJlyhQ6d+7MpEmTOOmkkwBYvHjxjnUGDBhAz549LcpKkiRJkqRKxcKs4rVtC+T/L3wwDOq0grP+AYc3jTuVkmzatGk8+eSTtGzZkry8PADuuusuHnvsMQYPHkxRURHVq1dn+PDhMSeVJEmSJEkqHxZmFZ91H8K0y2D1HDj5emhzH2RWjzuVYtCxY0eiKCp12VtvvbXPbUeOHFkBiSRJkiRJkiqWhVnFY/GTMOt/ICMLOj0DORfFnUhJMGjkrKTv8/EB7ZO+T0mSJEmSpP2xMKvkKlwPs66DJU9C/U5w5ijIzok7lSRJkiRJkpRUFmaVPF+9BdP6woaPoOWt0PxXkJEZdypJkiRJkiQp6SzMquJFEXzwAOT/HKofDee9lhgtK0mSJEmSJKUpC7OqWJu/gBkDYMWExDyyHR6HakfGnUqSJEmSJEmKlYVZVZzPJsEb/WHLV3Dqw3DS/0AIcaeSJEmSJEmSYmdhVuWvuAjeuQXevRtqnwKdX4AjWsedSpIkSZIkSTpkWJhV+dqwBKZfDqvegBMGQbsHoUrNuFNJkiRJkiRJh5SMuAMohXzyFLyQB2vmw5mjocNfKqQou3TpUs4991xyc3Np3rw5Dz74IABjx46lefPmZGRkMHv27F22mTdvHmeccQbNmzenZcuWbN68udxzSZIkSZIkSWXliFkdvKJNMOcGWPgoHHkanDUaajWpsN1VqVKF+++/n7Zt27J+/XratWtHly5daNGiBU8//TTXXHPNrvGKiujfvz9PPvkkrVu35ssvv6Rq1aoVlk+SJEmSJEnaHwuzOjhr5sO0y2Dtu5A7FFrfARkVW/Rs0KABDRo0AOCwww4jNzeX5cuX06VLl1LXf/nll2nVqhWtWyfmuT3yyCMrNJ8kSZIkSZK0P05loG8miuDDR+Gl9rDlCzj3JWhzT4UXZXe3ZMkS3n77bTp06LDXdRYsWEAIga5du9K2bVvuvffeJCaUJEmSJEmS9uSIWR24ravhzR/A0n/BMefDGf8HNY5OeowNGzbQu3dvHnjgAWrXrr3X9YqKivjPf/7DrFmzyM7O5rzzzqNdu3acd955SUwrSZIkSZIkfc0RszowX0yHCXmw7N+Qdy+c+0IsRdnCwkJ69+5Nv379uPjii/e5bk5ODueccw5HHXUU2dnZ9OjRgzlz5iQpqSRJkiRJkrQnC7Mqm+JtMP9OeLUTZFSBLtOg2U0Qkv8jFEURgwYNIjc3lxtvvHG/63ft2pV58+ZRUFBAUVERU6ZMoVmzZklIKkmSJEmSJJXOwmwKueqqq6hfvz4tWrTY0Zafn8/pp59OXl4ep556KjNnzgRg9erV9OrVi1atWnHaaacxf/78vb9xwQp4rQvM+xU0uhS6zYGjTqvo7uzVtGnTePLJJ5k0aRJ5eXnk5eUxYcIExo0bR05ODm+88QYXXHABXbt2BeCII47gxhtvpH379uTl5dG2bVsuuOCC2PIfiNK+p3PnzuWMM86gZcuWXHjhhaxbt27HsrvvvpsTTzyRU045hZdeeimOyJIkSZIkSSoD55hNIQMGDOD666/niiuu2NE2dOhQbrnlFrp3786ECRMYOnQokydP5q677iIvL49x48bx/vvvc9111zFx4sQ933T58zBjABQVQIcR0GQAhJC0PpWmY8eORFFU6rJevXqV2t6/f3/69+9fkbEqRGnf06uvvprf//73nHPOOYwYMYL77ruP22+/nffee48xY8bw7rvvsmLFCr7zne+wYMECMjMzY+yBJEmSJEmSSuOI2RTSqVMn6tatu0tbCGHHiMq1a9fSsGFDAN57770dN79q2rQpS5YsYeXKlV9vuG0LvHUDTOkJNY6Fbm/BCQNjL8qmm9K+px988AGdOnUCoEuXLvzrX/8C4N///jeXXXYZ1apV4/jjj+fEE0/cMUJakiRJkiRJhxZHzKa4Bx54gK5duzJkyBCKi4uZPn06AK1bt+bpp5+mY8eOzJw5k48//phly5Zx9NFHw7oPYdplsHoOnPxjaHMvZFZPevZBI2clfZ8Ajw9oH8t+y6pFixaMHz+eiy66iLFjx7J06VIAli9fzumnn75jvZycHJYvXx5XTEmSJEmSJO2DI2ZT3J/+9CeGDRvG0qVLGTZsGIMGDQLgF7/4BatXryYvL4+HHnqINm3aUKVKFVj8JLzYFjYugU7PwKl/jKUoq70bMWIEjzzyCO3atWP9+vVkZWUBlDq9Q3CEsyRJkiRJ0iHJEbMp7oknnuDBBx8E4JJLLuHqq68GoHbt2vz1r38FEgW9449vzPGf3wXv/hPqd4IzR0F2TkyptS9Nmzbl5ZdfBmDBggU8//zzQGKE7PbRswDLli3bMXWFJEmSJEmSDi2OmE1xDRs2ZMqUKQBMmjSJk046CYA1a9awdetWAP7y4K/pdPxqaq96ClreBt+eZFH2EPb5558DUFxczB133MGPfvQjAL773e8yZswYtmzZwuLFi/nwww857bTT4owqSZIkSZKkvXDEbArp27cvkydPZtWqVeTk5HDbbbfx2GOPMXjwYIqKiqhevTrDhw8H4L///S9XXHEFmdvW0uyoVTz+kwZw3nOJ0bI6ZJT2Pd2wYQOPPPIIABdffDEDBw4EoHnz5lx66aU0a9aMKlWq8Mgjj5CZmRlnfEmSJEmSJO2FhdkUMnr06FLb33rrrT3azmhzIh8+djKsmAA5F0GHx6HakRUdUQdob9/TwYMHl9p+8803c/PNN1dkJEmSJEmSJJWDtCjMXnXVVTz33HPUr1+f+fPnA3Drrbfy2GOPUa9ePQDuuusuevTowahRo7jvvvt2bDtv3jzmzJlDXl5eLNnLzeJRMPdmKPgEqtWD4i2wbTOc+jCc9D/gTaJiNWjkrKTv8/EB7ZO+T0mSJEmSJCWkRWF2wIABXH/99VxxxRW7tN9www0MGTJkl7Z+/frRr18/AN555x0uuuiiQ64oe8BFvILlsOYjiK7ctb12U5h+Ikyfvd+3sIgnSZIkSZIklZ+0uPlXp06dqFu37gFvN3r0aPr27VsBiZJs3XsQbduzfePHyc8iSZIkSZIkKT0Ks3vz8MMP06pVK6666ipWr169x/J//OMflbMwG22DzSthzTvw2auwbUvp623blNxckiRJkiRJkoA0Lsxee+21fPTRR+Tn59OgQQN+9rOf7bL8zTffJDs7mxYtWsSU8ABt2wQbl8CXM+HTl+DLWVCwDLLqQEbV0rfJrJHUiJIkSZIkSZIS0mKO2dIcffTRO57/4Ac/oGfPnrssHzNmzCE+WjaCrath8+eJ0bGF6xPNVbKh5regen3IOhJCRskcs/N2nc4gZCbmmJUkSZIkSZKUdGlbmP30009p0KABAOPGjdtlZGxxcTFjx45l6tSpccUr3Zav4NMXYfWGREG2uBBCgKy6cHizRDG2Sq09t8s+NvF13fuJkbWZNRJF2e3tkiRJkiRJkpIqLQqzffv2ZfLkyaxatYqcnBxuu+02Jk+eTH5+PiEEGjduzKOPPrpj/alTp5KTk0OTJk1iTA1EEaydD8ufhxXPw6rpEBXD5jug+tGJR7Wj9j5Vwc6yj7UQK0mSJEmSJB0i0qIwO3r06D3aBg0atNf1O3fuzIwZMyoy0t4VbYKVr8GK5xIF2YJPEu1HtIHmN0PDC+C5AIR48kmSJEmSJEk6aClVmB00clbS9/n4gPYH/yYblyZGxC5/DlZOSkw3UKUmHPMdaPFraNgDshvutEHy+ylJkiRJkiSp/KRUYbbSKN4GX84omaLgOVjzTqK9VhM44Wo4tifUPwcyq8WbU5IkSZIkSVKFsDCbLFu+gk9fSoyM/fRF2PIlhCpQryO0uQ8a9oTapyRu5iVJkiRJkiQppVmYrShRBGvf+3qu2FXTIdqWuFlXwwsSjwbnQ1aduJNKkiRJkiRJSjILs+WpaBN8PjkxV+yK52Hjx4n2I/Kg2f/CsRdA3faQkRlrTEmSJEmSJEnxsjB7sAqWJUbELn8OVk5M3LgrMztx467mN5fcuOvYuFNKkiRJkiRJOoRYmD1gEWxdA5tXwubP4Zkhieaax8MJgxJzxR59DmRWjzemJEmSJEmSpEOWhdmyKC6ELV98XYwtLoQAZNWFvHvh2J5Qu6k37pIkSZIkSZJUJhZmC5bDuvdLpiCokSiwZh8LResTRdjNK2HrVxABGVWhen2ofjRUq5d43ax93D2QJEmSJEmSVMmkd2G2YDmsmQfRtsTrbZtgTT6sfReKtybaqtaGWicmirFZdUgMlZUkSZIkSZKkby69C7Pr3v+6KLtdFAFFUKdlohjrXLGSJEmSJEmSyll6F2a3bSq9PSqGmt9KbhZJkiRJkiRJaSMj7gCxyqxxYO2SJEmSJEmSVA7SuzBbuymEzF3bQmaiXZIkSZIkSZIqSHpPZZB9bOLruvcT0xpk1kgUZbe3S5IkSZIkSVIFSO/CLCSKsBZiJUmSJEmSJCVRek9lIEmSJEmSJEkxsDArSZIkSZIkSUlmYVaSJEmSJEmSkqxMhdkQQrcQwgchhIUhhF+UsrxaCOEfJcvfDCE0LmlvHELYFELIL3n8uXzjS5IkSZIkSVLls9+bf4UQMoFHgC7AMmBWCGF8FEXv7bTaIGB1FEUnhhAuA+4Bvl+y7KMoivLKObckSZIkSZIkVVplGTF7GrAwiqJFURRtBcYAF+22zkXAEyXPnwLOCyGE8ospSZIkSZIkSamjLIXZY4GlO71eVtJW6jpRFBUBa4EjS5YdH0J4O4QwJYRwdmk7CCH8MIQwO4Qwu6io6IA6IEmSJEmSJEmVzX6nMgBKG/kalXGdT4FGURR9GUJoBzwTQmgeRdG6XVaMouHAcICaNWvu/t6SJEmSJEmSlFLKMmJ2GXDcTq9zgBV7WyeEUAU4HPgqiqItURR9CRBF0VvAR8DJBxtakiRJkiRJkiqzshRmZwEnhRCODyFkAZcB43dbZzxwZcnzPsCkKIqiEEK9kpuHEUJoApwELCqf6JIkSZIkSZJUOe13KoMoiopCCNcDLwGZwIgottTyMwAAFKtJREFUit4NIfwWmB1F0XjgceDJEMJC4CsSxVuATsBvQwhFwDbgR1EUfVURHZEkSZIkSZKkyqIsc8wSRdEEYMJubb/Z6flm4JJStvsX8K+DzChJkiRJkiRJKaUsUxlIkiRJkiRJksqRhVlJkiRJkiRJSjILs5IkSZIkSZKUZBZmJUmSJEmSJCnJLMxKkiRJkiRJUpJZmJUkSZIkSZKkJLMwK0mSJEmSJElJZmFWkiRJkiRJkpLMwqwkSZIkSZIkJZmFWUmSJEmSJElKMguzkiRJkiRJkpRkFmYlSZIkSZIkKckszEqSJEmSJElSkoUoiuLOsIsQQjGwKYZdVwGKYthvstnP1GI/U0+69NV+ppZ06SekT1/tZ2pJl35C+vTVfqYW+5l60qWv9jO1pEs/IZ6+FkRRVG/3xkOuMBuXEMLsKIpOjTtHRbOfqcV+pp506av9TC3p0k9In77az9SSLv2E9Omr/Uwt9jP1pEtf7WdqSZd+wqHVV6cykCRJkiRJkqQkszArSZIkSZIkSUlmYfZrw+MOkCT2M7XYz9STLn21n6klXfoJ6dNX+5la0qWfkD59tZ+pxX6mnnTpq/1MLenSTziE+uocs5IkSZIkSZKUZI6YlSRJkiRJkqQkszArSZIkSZIkSUlmYVaSJEmSJEmSkszC7F6EEELcGZIhXfopSZIkSZVFulynpUs/JWlvLMyWIoRQA8iKO0dFCyHUAWrHnSMZQghNSr6m9Ik/1fu3XQjhyBBC3bhzVLQQQsMQwrFx56hoIYTTQghpcT7yGE0tHqOpx2M0tXiMqrLyejS1eC2aejyPppa4z6OewHcTQhgGjAIeDCH0ijtPRQkhPAw8DjwUQvhe3HkqUgjhf4EFIYRWURRFceepKCGEm4EuIYSU/iUuhPAIMBL4awjhhzHHqTAhhD8DjwAjQwjXxJ2nooQQfgRMB84PIVSLO09F8hhNLR6jqcdjNLV4jKaWEMLgEMKJcedIBq9HU4vXoqnH82hqORTOoxZmdxJC+AvQELgTmAV0LflLSEr95afkADsaGAr8F6i/2/KU6i+wElgFPBFCOANSr48hhAeA24ELgbNT9YQYQhhO4uf1OhIniZ4hhJPiTVX+QgiPAvWAAcBfgKN2W55KP7/LSx7/A/RIsb7t4DGaWjxGU4/HaGrxGE0tIYT/BwwDbto+8jBVeT26Y3kq9ddr0RTieXTH8lT6GY79PFol2Ts8VIUQ2gDVoij6fsnrtUB/4PAoir6MNVw5CiG0AzZEUfSjktfHAt8pGYa/Ioqi/0uVv+SFEEJJXyYCN5L4ONDIEEJP4Hjg5TjzlZcQwuHAHKAlcAFwaUn761EUbQ0hZERRVBxnxvIQQjgZ2ARcF0VRYQhhPbAFyI43WfkqObF/HEXRNSWvOwCdQgj1gE+iKPpDKhyjOx2fU4H7gS+AHwNrQggNgRdT5f9ej1GP0crIY9RjtLLyGE2tYzSE0ABYAJwM3AD8KoRwRxRFi0qWh1T4foLXo6l2Peq1aGqdQ8HzqOfRimNhtkQURW+HEO4NIVQHiqIoWhhCWAp8C1gUc7xyE0XRWyGEdwBCCF2BtsA9QBFwVwhhQxRFT8eZsbzs9J9FDeCqKIq+U/LXj7eAz4CTU+GXuSiK1oYQnomiaB3wbgjhV8AlJI7vl1LlRBhF0YKQ+NhIlRBCcRRFq0MInwPNgLkxxys3URR9GEK4ByCEcCaJ/g0BvgJeDiFsjKLo0TgzloedjrvDgR7AxcBqEh/d2wykxP9D4DGKx2il5DHqMVpZeYym1jEaRdGnIYR/RFH0KXBdCOExEsXZ30VRtKCy/x6/M69HU+t61GvR1DqHgudRPI9WmLQvzIYQOpKYcHwOsDCKos0hhKoli2sAtUrWuxd4Moqid+JJenB26udcEgcUwAfAhdv/ChBCaE0KTDJf0tdawHxgTRRF74cQppT8ktOIxDD1jBBCiyiK5seZ9WCEEL5L4mf0Y+Dd7e1RFN0RQrgF6B5C2Az8GrgniqJX4kl6cEr6mU3iF9L/RlG0KSQ+HrONxM9rdsl6jwOPRVE0I7awB2Gnfm7/fq4DFgPfj6Jobck6Q0mcOCqtkn5WB5YC70VR9EkI4UXgSOAs4EsSH/c6N4TwahRFW+NLe3A8Rj1GKyOPUY9Rj9FDW7ocoyW/zx9G4tpl9fb2KIp+UPKzekMI4W7gLuCRKIreiCfpwfN6NLWuR70WTa1zKHgexfNohZ9H07owG0IYCRxD4gSxGKgRQvhhFEWrSlZ5N7FaGA4UV+KT4Ej27Oc1URQt2W3Vjuz0i09ltJe+DgTWljzejKIoN4RwPXAlcFNcWQ9GCOEJEvMybQE2ALkhhB5RFH0GEEXRbSEx0fy/gImV9URYSj+bhRC6b+8n8DawLSTmwCmuxCfB0vrZo2SUyM5z3PQB/hNHxvKwl36eT+JjIx8Dr0dR1DKEcCmQF0XRhPjSHhyPUY/Ryshj1GPUY/TQli7H6P6u0aIoGhQSc5TOB56v5EXZkXg9urNKfT3qtWhqnUPB86jn0SSdR6MoSssH8Adg/E6vGwMPAq8BdUvafkXiIxUP77ReiDt7OfbziJK2Y4F/Ao/HnbcC+voQ8CJwIvAzoHrcOcuhn3cBz+7WNgxYAtTbqe1F4KmdXmfEnb0c+1m/5PV1QDHw/3Zap7Ido/v9fgI5wFjgL3HnLed+PkBipMQRwA9S4fgs6/e0pM1jtBI8PEY9Rnd67TF6CD48RlPrGKUM1y4l7S8C/9jpdaU6PsvQV69HK9ljL/30WvTr15XxGPU86nk0KY8M0lAIIZPER2OGlrwOUeKvdbeQ+GjFpSWrbiFxwr++ZL2MqOS7Vxnsp5/z+LqfnUlMtD6oZL1K93Oxj77+msSB9p0oiu6PEh8Nytppu8p4N8FqwN0A2/sSRdH/396dx8pV1mEc/z4tFYoWWUQErQEUiqAsSlVKg8YEFCRhqyCLTXFXNGDlD0WCguLCIhoLBUShYEEUFEQii6hEIGKoFrGiErSGAFoiUBYplPbnH+87dCi9S3svd+Z93+eT3PTOcu493849Z868c+acz5Dekfxsvv5lpI91zciXSzzo+mCds/N9HgXmRsQn8/2KWkazIR9PYBfgzxHx4Xy/4pZR1tx5HGmjbTbpSb77o3sl8zLqZdTLaH/zMupl1MtoHxrGa5fOSbEmA5fFqpNkFbd8+vVoXa9H/Vq0yudQ8POon0fHSIn/mSMWEZ1jgeydL0f+91HgHtIByImI0yPiSChzZTJE599Z1Tk//0EW2QnDekx367r78q7pSltpQjqWz4EAkc50OT5f/2vSR2eIiCci4hoo9zFl8M4t8/XzI+IYqL7z2og4Gars/CUwuWuZXT7A9CXxMupltKbOGpfRl9LGMjpYZ03L6HA6a1hGB+qsZhnN2/MbMvBrl93y5fsiYh6U+3gO0Vrb69HBHtMqXo8O4/Gs6bVoK8+h4OdRP4+OkaYGZiXt2XVxAelMiC/Pt3XerboeWCxpx67pVNIf3lp0/kvSG7qnLakTht16HekxnQJlPgFKep+krfLF64CXStol/22uyNf/Hlgi6R3d05b0mK5F538kvbN7Wnf2n2F23k76u92rN3M5OiR9XtLe+eJ1wMsqXUaH21n6364761tGz5N0cr54LfUuo8PtLP1v150VLaOSdpf0lnzxN8CUQV677NA9bUmPJ6xVa+mvR9fmMS329egwO2t4LfpqSZPyxSuBjWt8DoW1ai39+cWdffQ8qgLXC+tE0keA84ADI+JnkjYlHSPjz8BZkQ88LukGYCpwFzAjIpb0aJbXSSud0E6r0sH+N41VHwWZRDqG0TPAFcBtEfG0pF8AbwIeIP2fPNCreV4X7nQnBXYCSLqUtJfErIh4NLeeQTpT64+ppNWd7qTATgBJZwPvAn4FHEfaO/gs4Engp1TS6k53Umbn+cC2pHXPr4E5wFXA3cCZtWzPQzut7qyu8xzgtaQ9fx+LiC9K+jbp06o/opJ1EbTT6s7+62xpYPY9pINvB3BaRFwgaTNgLmmgYGvSmRLvJx1kfXlEFHdGyFY6oY1WSXOAnSNir3x5k4h4RNL6wBdIB5J/I+nA3P8GjiUdWP3JHs3yOnGnOymwE0DSTsA3ImL/fHkb0tmElwKnANsAO1N4qzvdSYGdAJLmks6OfQJwKenkHPMlbUxaH20B7Erhre50J2V2ngi8PSL2l/Q2YJ+I+LKkiaSdLyYAkyl8ex7aaXVndZ0nANOAw0nrpF8Cv4iI2Up787+eNKC1mILXRdBOqzv7s3O9XvzSsaRVx7+4HTid9E7VDyQ9DCwDPkE6CPCbgP9FxG97NrMj0EontNMq6bWklciNkjYA3g/sK2k5sCgiTsr325P0DtBdvZvbdedOd/ZubkfFUmD9vCH+ceAA0vrnL8AxkQ4gPx1YWnirO91ZHElTSTshdI7PeDbwMUm/i4h780b7SuBtwOOltrrTnT2c3ZF6FPhh/n5f4GhJuwH3R8TMPBC9B/BEqdvzXVppdWddnU8CF0XE48Djki4BjpO0MiKOB5A0jfLXRdBOqzv7sTMiqv8CxpMGCm4AXg7sTnpRsmSA+6vX8+xOt+b53hP4CukYTHcC0/PXTcCn3VnWlzvr6szzvilwLnAE8D1g87x+ugGYV0urO93Z63keQeu4/O96pBNYnA/s1bmullZ3urPX87yOnbOAhcA80l6FO5C26+8ATq+ls6VWd1bX+cnctyswhTQYPZ10WLKptXS21OrO/uysdo9ZSReSNlw+EOmgvo9JugZ4JbAPcC/pwL+HRcTl3dNGfmRK0EontNOaO18SEUdGxK2SgrRhfn5E/CPf50rSIRyex539x511dcIL1kUPS1pA+mjp3aSPqa2Q9CHgc1rtrKUltbrTnZ1pS+qEF6yPVkqaEOkMuw9KWgKcRvoI6rOrT1tSqzvd2VFg54SIOCoiLpK0ENgEuDsi/prvcyyw3+rTltQJ7bS6s+rOcyS9Evga6ZOpt0fELZJmkY5T/zwldUI7re7s/85xvfzlLxZJJwE7AhPzLssdzwB/A94bEW8GZpI+BlSkVjqhndauzvUlzQeIiNuAMzqDW9m7gUlr+BFFcKc7S7XauqjT+l3gW6Tjcu4taVfSic4iCjpraTd3urNnMztCq62PLgGIiOWSJuTvTySdHXtW7+Zy5NzpzhJ1dW6gdAJCImIh6UXzwZK2znedTTq8SrFaaXVn1Z2XAUTEl4AjgZkR8fV8161In74pViut7iyjs8qTf0l6HfndK+BiYFmsOk7TJ4AL8jvQRWulE9ppXUPn0xFxRNftmwFnA09FxNG9mcuRc+dzt7uzMGtofSYiDs+3HQJsT/q4zNKIODZfr16/C7u23OnOEjthja1PRcRR+bbxpL32v0raw2lez2Z0hNzpzhINsT3/NeAg4J/AfyJiVq/mczS00urO6ju7txfGkQ7XcB7pEzgH92xGR0Erre4so7PKgVlI//mRPga0GekBeKYzUCDpVaSVZvHxrXRCO61DdM4Eto6IU7rv28PZXWfudGcPZ3dE1tC6IiIOy7dtCyzu9JXc6k539nB2R2SI9dFmpEGv//V0JkeBO91ZoiHWRfsBf41Vh0CqbV1UZas7q+/sXhftA7wiIi7Nl4t8E7ejlVZ39n9ntQOz3SRtAZwFLCeNol8T6SN8VWmlE9ppzZ3fBFaQ3uW5vGtlUuwT/urc6c5SdbWuJLVeGxHn5dv66gl/JNzpzlJ1tT5L+ujaz2tsdac7S9TVCelQR89tz9fUCe20urPazhXAxsAVEXFxvq3WbfqqW93Zn53VD8x2VoySNgIeAq7qvLNVk1Y6oZ3WNXReHRGHdt/W2zkcHe50Z6kaXhe5s2CtdEI7re6sizvr00qrO+sy2DZ9bVppdWf/qn5gtkPSucBGsWpX5r4bJR8NrXRCO63urIs769NKqzvr0kontNPqzrq4sz6ttLqzLq10Qjut7uw/LQ3MTo6I+/L3ffuAjFQrndBOqzvr4s76tNLqzrq00gnttLqzLu6sTyut7qxLK53QTqs7+08zA7Mdnd2aez0fL7ZWOqGdVnfWxZ31aaXVnXVppRPaaXVnXdxZn1Za3VmXVjqhnVZ39o/mBmbNzMzMzMzMzMzMem1cr2fAzMzMzMzMzMzMrDUemDUzMzMzMzMzMzMbYx6YNTMzMzMzMzMzMxtjHpg1MzMzs6JIeo2kqyXdI+kfkuZIWn+UfvYsSVuNxs8yMzMzMxuMB2bNzMzMrBiSBPwEuCoitgO2AyYCp43Czx4PzALWamBW0noj/d1mZmZm1h4PzJqZmZlZSd4FLIuICwEiYgXwGWCmpE9JmtO5o6SfS3pn/n6upDskLZJ0ctd9Fks6SdItwOHA7sB8SQslTZT0Fkk3S1og6XpJW+bpfiPpq5JuBo4dq3gzMzMzq4ff3TczMzOzkuwELOi+IiIek7SYwbdtvxARD+e9Ym+StHNE/CnftiwipgNI+jBwfETcIWkC8B3ggIh4SNJhwKnAB/N0G0fEO0YvzczMzMxa4oFZMzMzMyuJgBjg+sEcKumjpO3fLYEdgc7A7OUDTDMFeCNwYzqCAuOBB7tuH2g6MzMzM7MheWDWzMzMzEqyCDik+wpJGwFbAP8Ftu+6aYN8+zbA8cDUiHhE0kWd27InB/hdAhZFxB4D3D7QdGZmZmZmQ/IxZs3MzMysJDcBG0qaCc+dsOtMYA7wT2BXSeMkTQbemqfZiDSIulTSFsC+g/z8x4FJ+fu/AZtL2iP/rgmSdhrtIDMzMzNrkwdmzczMzKwYERHAQcAMSfeQ9pJdGRGnAreSBmfvAs4A/pCnuRP4I2lv2+/n+w3kIuBcSQtJhy6YAXxD0p3AQmDai5BlZmZmZg1S2rY1MzMzMyuPpGnAZcDBEbFgqPubmZmZmfULD8yamZmZmZmZmZmZjTEfysDMzMzMzMzMzMxsjHlg1szMzMzMzMzMzGyMeWDWzMzMzMzMzMzMbIx5YNbMzMzMzMzMzMxsjHlg1szMzMzMzMzMzGyMeWDWzMzMzMzMzMzMbIz9H585FSztLIf4AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax1 = quarterly.plot(x=\"Quarter\", y=\"DeepLearningRate\",\n", " kind=\"line\", linestyle=\"-\", marker=\"o\", color=\"orange\",\n", " figsize=(24,12)\n", " )\n", "\n", "ax2 = quarterly.plot(x=\"Quarter\", y=\"TotalQuestions\",\n", " kind=\"bar\", ax=ax1, secondary_y=True, alpha=0.7, rot=45)\n", "\n", "for idx, t in enumerate(quarterly[\"TotalQuestions\"]):\n", " ax2.text(idx, t, str(t), ha=\"center\", va=\"bottom\")\n", "xlims = ax1.get_xlim()\n", "\n", "ax1.get_legend().remove()\n", "\n", "handles1, labels1 = ax1.get_legend_handles_labels()\n", "handles2, labels2 = ax2.get_legend_handles_labels()\n", "ax1.legend(handles=handles1 + handles2,\n", " labels=labels1 + labels2,\n", " loc=\"upper left\", prop={\"size\": 12})\n", "\n", "\n", "for ax in (ax1, ax2):\n", " for where in (\"top\", \"right\"):\n", " ax.spines[where].set_visible(False)\n", " ax.tick_params(right=False, labelright=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems that deep learning questions was a high-growth trend since the start of DSSE and it looks like it is plateauing. There is no evidence to suggest that interest in deep learning is decreasing and so we maintain our previous idea of proposing that we create deep learning content." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 4 }