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December 22, 2016 16:45
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Stackoverflow data dump analysis\n", | |
"\n", | |
"The goal of this case study is to demonstrate how Postgres, a relational database and Python be used for efficient data analysis. We will be getting our hands dirty with some Postgres syntax (just enough to make our SQL queries efficient and quick) and a couple of Python libraries - `psycopg2` and `sqlalchemy`." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The data that we would be using is the Stackoverflow data dump, which is downloadable from archive.org: [URL](https://archive.org/details/stackexchange)\n", | |
"\n", | |
"Data dump that we have presently is from September-2016 and we need only the following files from the collection : \n", | |
"- stackoverflow.com-Badges.7z\n", | |
"- stackoverflow.com-Comments.7z\n", | |
"- stackoverflow.com-PostHistory.7z\n", | |
"- stackoverflow.com-PostLinks.7z\n", | |
"- stackoverflow.com-Posts.7z\n", | |
"- stackoverflow.com-Tags.7z\n", | |
"- stackoverflow.com-Users.7z\n", | |
"- stackoverflow.com-Votes.7z\n", | |
"\n", | |
"You can download these files via torrent OR by downloading them directly." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Before we begin" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The overall structure of our data querying mechanism would be as follows. \n", | |
"\n", | |
"1. We identify the columns that will provide us with the data we need for analysis from the Postgres database.\n", | |
"2. Next, we translate this into a Postgres query.\n", | |
"3. And, we pass this through Pandas's `read_sql` function to obtain the data.\n", | |
"\n", | |
"So, let's begin with installing Postgres." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Installing Postgres" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"If Postgres is not already present on your Operating System, you can download an executable from the website [here](https://www.postgresql.org/download/). \n", | |
"\n", | |
"Once you through with installing Postgres on your computer, you'd have created a password for the default user _postgres_.\n", | |
"\n", | |
"We will use that account for authentication, so let's keep that handy." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Installing dependencies\n", | |
"\n", | |
"We will be using two Python packages `psycopg2` and `sqlalchemy` to connect and run our queries against Postgres.\n", | |
"Although, `pyscopg2` alone would suffice, it is basically meant to be used to send SQL statements to the database. \n", | |
"\n", | |
"`sqlalchemy` on the other hand, is a Pythonic way of high-performance database access across a variety of databases. And because of this it would be a good idea to make it part of our conversation. We will limit our use of SQLAlchemy constructs to the bare necessities but you could dig deeper beginning with the basic tutorial [here](http://docs.sqlalchemy.org/en/latest/orm/tutorial.html).\n", | |
"\n", | |
"\n", | |
"For now, let's run `pip install psycopg2 sqlalchemy` from the command line to install these dependencies.\n", | |
"\n", | |
"Now let's import the packages we need:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"c:\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", | |
" warnings.warn(self.msg_depr % (key, alt_key))\n" | |
] | |
} | |
], | |
"source": [ | |
"import psycopg2\n", | |
"import pandas as pd\n", | |
"import sqlalchemy as sa\n", | |
"import time\n", | |
"import seaborn as sns\n", | |
"import re\n", | |
"\n", | |
"sns.set_context(\"talk\")\n", | |
"from matplotlib import pyplot as plt\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Please make sure that you are on the same package versions as shown below:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Pandas version: 0.18.1\n", | |
"Pyscopg2 version: 2.6.2 (dt dec pq3 ext lo64)\n", | |
"Sqlalchemy version: 1.0.9\n" | |
] | |
} | |
], | |
"source": [ | |
"print \"Pandas version: \", pd.__version__\n", | |
"print \"Pyscopg2 version: \", psycopg2.__version__\n", | |
"print \"Sqlalchemy version: \", sa.__version__" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Creating the database\n", | |
"\n", | |
"First, use `psql -U postgres` to login into the Postgres database\n", | |
"\n", | |
"\n", | |
"<img src=\"postgres_login.png\" style=\"float:left;\" />" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"\n", | |
"Once you are successfully logged in, you can use the `CREATE DATABASE` statement to create a new database. The 'CREATE DATABASE' statement tells Postgres to create a database with the name that follows that statement. For example, the following statement creates a new database called 'stackoverflow'.\n", | |
"\n", | |
"\n", | |
"<img src=\"postgres_create_db.png\" style=\"float:left;\" />" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Importing data \n", | |
"\n", | |
"We are going to use a python script to import these stackoverflow xml files into Postgres tables under the _stackoverflow_ database that you have already created above. \n", | |
"\n", | |
"Please follow the below steps to import the data:\n", | |
"\n", | |
"1. The stackoverflow dumps are in .7z format and you'll need 7-zip to extract the xml files from them. You can install 7-zip from [here](http://www.7-zip.org/download.html). \n", | |
"\n", | |
"2. The script and the accompanying files can be downloaded from this [github repository](https://github.com/fx86/stackexchange-dump-to-postgres). Download it and unzip it into a folder.\n", | |
"\n", | |
"3. Place all the extracted xml files within the folder that you have downloaded in step 2.\n", | |
"\n", | |
"4. Open `load_into_pg.py` script in a code editor and change the default values of username and password in lines 137 and 142. This will save you from having to pass a username and password, each time when you run the script on the command line.\n", | |
"\n", | |
"5. After completing the above steps, the following commands should be executed to import the data - \n", | |
"\n", | |
"```python load_into_pg.py Badges\n", | |
"python load_into_pg.py Posts\n", | |
"python load_into_pg.py Tags (not present in earliest dumps)\n", | |
"python load_into_pg.py Users\n", | |
"python load_into_pg.py Votes\n", | |
"python load_into_pg.py PostHistory\n", | |
"python load_into_pg.py Comments\n", | |
"python load_into_pg.py PostLinks\n", | |
"```\n", | |
"\n", | |
"Follow is the type of output you should expect to see -\n", | |
"\n", | |
"<img src=\"postgres_importing_data.png\" style=\"float:left;\" />\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### About the data-import mechanism\n", | |
"\n", | |
"The `load_into_pg.py` script implicitly executes a sql file before and after importing each XML file. \n", | |
"\n", | |
"The SQL file that is run before importing the data, ends with file name `_pre.sql` and it creates the table required for storing data from that XML file.\n", | |
"\n", | |
"The SQL file which is executed after importing the data ends with a file name `_post.sql` and it creates indexes on the created table to make our database queries faster.\n", | |
"\n", | |
"If you'd like to alter any of these sql files, you can find them in the `sql/` directory within the downloaded github folder." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Connecting to the database\n", | |
"\n", | |
"SQLAlchemy provides a `create_engine` method which takes a connection string and returns a connection object. \n", | |
" We will then use this connection object, to pass the query to Postgres with Pandas's `read_sql`.\n", | |
"\n", | |
"The `connection_string` is just a python string which tells `create_engine` method - which Postgres database to connect to, what IP address is it running at, and what user credentials to connect with." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"postgresql://postgres:fibinse@localhost:5432/stackoverflow\n" | |
] | |
} | |
], | |
"source": [ | |
"parameters = { \n", | |
" 'username': 'postgres', \n", | |
" 'password': 'fibinse',\n", | |
" 'server': 'localhost',\n", | |
" 'database': 'stackoverflow'\n", | |
" }\n", | |
"connection_string = 'postgresql://{username}:{password}@{server}:5432/{database}'.format(**parameters)\n", | |
"\n", | |
"# Now, let's see what our connection string actually looks like:\n", | |
"print connection_string" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"collapsed": true | |
}, | |
"source": [ | |
"What we have done here is that we have created a dictionary called `parameters` which contains all user-input we need to form a complete connection string. We then embed these values into a connection string format and store it into a variable called `connection_string`." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"#Let's use the connection string above to create a connection engine\n", | |
"engine = sa.create_engine(connection_string, encoding=\"utf-8\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Now, let's see what tables have been created in the `stackoverflow` database. The tables within the database can be listed by running a Postgres query through Pandas's `read_sql` function with the following basic format:\n", | |
"\n", | |
"`pd.read_sql(postgres-query-here, SQLAlchemy-engine)`" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Tables listed in your stackoverflow database are: \n", | |
"\n", | |
"[u'accepted_answer_id', u'badges', u'comments', u'posthistory', u'postlinks', u'posts', u'qn_ans_timing', u'tags', u'users', u'votes']\n" | |
] | |
} | |
], | |
"source": [ | |
"table_list = pd.read_sql('''SELECT table_name FROM information_schema.tables \n", | |
"WHERE table_type = 'BASE TABLE' \n", | |
"AND table_schema = 'public'\n", | |
"ORDER BY table_type, table_name;''',con=engine)['table_name'].tolist()\n", | |
"\n", | |
"print \"Tables listed in your stackoverflow database are: \\n\\n\", table_list" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Before going on further, a quick readup on Postgres SQL's syntax is advised - [URL](!https://www.postgresql.org/docs/9.0/static/sql.html)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"#Counts new user registration by year\n", | |
"user_count_by_yr = '''select extract(year from creationdate) as year, count(id) as user_count from users group by 1;'''\n", | |
"\n", | |
"def convert_ts(temp):\n", | |
" '''changes year column of DataFrame'''\n", | |
" return temp.year.apply(lambda v: pd.to_datetime('01-01-{:d}'.format(int(v))))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x1b2d4748>" | |
] | |
}, | |
"execution_count": 13, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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QEWmm9uwvd4OEEyZy84uobT/GQesluqXTJVPrJaKF1+PhsjMMPr+fz5duZ+53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\nAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1b2d4240>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"temp = pd.read_sql(user_count_by_yr, engine)\n", | |
"\n", | |
"temp['year'] = convert_ts(temp)\n", | |
"temp.set_index('year').plot(figsize=(8,6), title='Number of users per year')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Where does Python rank " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 313, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"temp = pd.read_sql(\"select * from tags\", engine)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 314, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Python ranks : 7 \n" | |
] | |
} | |
], | |
"source": [ | |
"temp['rank'] = temp['count'].rank(ascending=0).astype(int)\n", | |
"print \"Python ranks : {:.0f} \".format(temp.loc[temp['tagname'] == 'python','rank'].values[0])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"What are the other languages that rank higher ? " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 315, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"rank\n", | |
"1 javascript\n", | |
"2 java\n", | |
"3 c#\n", | |
"4 php\n", | |
"5 android\n", | |
"6 jquery\n", | |
"7 python\n", | |
"Name: tagname, dtype: object" | |
] | |
}, | |
"execution_count": 315, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"temp[temp['rank']<=7].sort_values('rank', ascending=True).set_index('rank')['tagname']" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### How did the top 20 most popular programming language fare across the years ? " | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"According to IEEE Spectrum, the top 10 programming languages are:\n", | |
"C, Java, Python, C++, R, C#, PHP, Javascript, Ruby and Go\n", | |
"\n", | |
"[src - http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages]\n", | |
"\n", | |
"Let's see how their year-on growth in the number of posts has been -" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"['c', 'java', 'python', 'c++', 'r', 'c#', 'php', 'javascript', 'ruby', 'go']" | |
] | |
}, | |
"execution_count": 22, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# counts programming language posts by year\n", | |
"pl_count_by_yr = '''select cast(extract(year from creationdate) as integer) as Year, count(id) as Count, \n", | |
" '{0}' as Language from posts \n", | |
" where \"tags\" like '%%<{0}>%%' group by year'''\n", | |
"\n", | |
"top_20_languages = map(str.strip, 'C, Java, Python, C++, R, C#, PHP, Javascript, Ruby , Go'.split(','))\n", | |
"\n", | |
"#let's convert them all into their lower cases as it is in the data\n", | |
"top_20_languages = map(str.lower, top_20_languages)\n", | |
"\n", | |
"top_20_languages" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": true | |
}, | |
"outputs": [], | |
"source": [ | |
"temp = pd.concat([\n", | |
" pd.read_sql(pl_count_by_yr.format(tag), engine)\n", | |
" for tag in top_20_languages\n", | |
" ])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 27, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"temp['year'] = convert_ts(temp)\n", | |
"temp['growth'] = temp.groupby('language')['count'].transform(pd.Series.pct_change)\n", | |
"temp = temp.pivot(index='year', columns='language', values='growth')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x1b6598d0>" | |
] | |
}, | |
"execution_count": 28, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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gThRFX1rv7RTwPHBAGIZbRVG0pHQ58ORg7t/W1kmhUJ65C/FUHIC1qzpZvXpQ\nWXjUicUCWlubylpfvcL6Vo61rSzrW1nW9/Ul77ib4hCG42NBwLhxDaxb102h+Pr1DaZOZU1HBtj0\nguW2ti4A1qzp5Prrv8fdd99BY2MTYTiTTCZLe3s3q1d3cNBBh3DVVV/nyCOP4/bb7+SUU05l9eoO\n1q5dy8UXX8Rjj/2OqVOnstNObwRg9ep2ttsu5NOfvoAf/eiHXHbZZUybNp2zzjqHvffel5UrV5FK\ntfRnlrq6Jurqmli2bBlBEBCPN/T/LpfL09WVZvXqDgqFIuPHb9n/u8bG8axcuYrVqzvo6OghlysM\nOgeV/nbLbajD8R3ABWEY/g34MXAgcDSwP9AKXBSG4anALsAxwPzB3LxQKJLf1LyOAUr2DcFnu3Nk\nenLE6+JluW8tK2d99c+sb+VY28qyvpVlfV9DrA6mb735n48HJCY2w+qOTc4JLQIM8N+gUChSLBb5\nxS8e5Oc/v5/vfOcmJkyYAMBRRy3o//fcY4+9Wbjw8zz88EOsXLmCd7xjX/L5Ilde+TUKhSJ33nkv\niUSCFSuWc889PyGfh2XLljNjxjZ87WtX09PTw6233synP30e9933MJMmTWLFipfYcccQgKeffoon\nnvg/9t13v7520f93VCxu+Pqll14iDHcG4MUXX2Ty5Cnk80V6s/nI+fsb0hZNURT9jd7tly6gdzum\nK4AToyj6I3AqkASWALcA50ZR9NjQmrv56tebB5rucKsmSZI0cF1dXSQSdSQSCTKZDDfc8F2WL19G\nPp8DIB6Pc9BBc7j00ouZNWs2iURvP19nZyfJZJIgCFi7to0rrvgqAPl8jief/Asf//hZLF36IqlU\niqamZlpaWgiCgDlz5vH971/HmjWr6ejo4KqrvsaaNWsAKG6ip/f73/8ua9asYdWqVVx//XXMm3co\nAHV1dXR1dVWqRIM25C2aoij6CfCTjby/ht5e0REhFo+RbKwj05Ul3Z6hcUJDtZskSZJqQBAEzJt3\nGI8//gfe+97DSKVS7Lbb29hvvwNYvHhx/3Vz5x7KrbfezNy5h/a/d8opH+C///sC5s07kJaWFg45\nZD7Tp2/F4sXPccABB/HMM3/ngx88ha6uLrbddlu+8IVFAJxwwsn09PRw0knHkc/nOfDAgznxxFNY\ntWrlP201+erXO+64Ex/4wEl0dnZw6KELOP74kwDYbbfdKRa/ybx5B3LnnfdSV1fd3YKCTaXpKiqu\nXt1R1i7VRGPlAAAgAElEQVTjl59bQ/uKTupbkkx78+Sy3bfWxOMBEyc2U+76qpf1rRxrW1nWt7Ks\nb2VVqr7Lli3l6KP/jQceeJTkABYyVduRRx7ORz/6cfbe+51lu2dfbcu+RH7Unx2/vtLJSZmODEUn\nhUuSpNeRzWZ59tlnaGhoqIkAWmvKcWJSzSjNCy0WIdOV3WCeqCRJ0vpuu+2HXHfdNf3D2bWhdvah\nHVMhNFEfJ14XI58t0NOeNoRKkqTXdPTRx3H00cdVuxmDcsstd1S7CQM2pobjgyDoH5JPt7tCXpIk\nqVrGVAgFqG/p7f1Mt6c3ucWBJEmSKmPMhdBU3xB8PlsgnxmuA8IkSZK0vjEXQpNNSUrbafU4JC9J\nklQVYy6EBrGg/wjPdHu6yq2RJEkam8ZcCAVcnCRJkobsyCMP5ze/ebTazahZYzKEluaFZrqyFPKF\nKrdGkiRp7BlT+4SWlFbIA6Q7MjSMT1WxNZIkiUKOWG7dZn88FoNidw+xdCfFTfQvFRLjIDawCLR8\n+TKOP/5ojjvuBG6++UZSqRTHHXcC733v+wD4/e8f46qrvs7SpS/y1re+jfPP/2+am5tZuPBCksl6\nnnrqCV544Xne/OZdOO+885k6depmf8fRZkyG0HhdnEQqQa4nR7rdECpJUlUVcrT+/TLi2bYh3aYI\njBvAdfm6Vtp2/MiAg2hPTzfPPfcMt99+D4sXP8fZZ5/B1ltvC8ATT/yZr33tKoIgxumn/wc//vEt\n/Scs3XvvT7jkksvZeedduPTSRVxwwSe5+urrNvPbjT5jcjgeINXi4iRJkrRpQRDw4Q+fQ319PWE4\nk3nzDuWBB+4FAo444kjGjRtPS0sLb3/7nixdurT/c7Nnz2XXXd9KXV0dp5/+YZ588i8sX76sel9k\nhBmTPaHQuzipY2UXPR0ZisUiQVA7Z61KkjSqxBK07fiRIQ3Hx2MwvrWJtW2dbGq5x2CG4wGSySRb\nbLFl/+stt5zMc889B0BLS0v/+3V1dXR1dfa/3mqrrft/bmlpIZVq4OWXX2bq1GkDfvZoNnZDaN/i\npGK+SLY7R7KxrsotkiRpDIslKCQnbvbHg3hA0NBMoTtFIV/eExEzmQwdHR00NzcDsGLFMqZMmco/\n/vHC635u1aqV/T+vXdtGOt3D5MmTy9q2WjZmh+PrGhLE4r29nw7JS5Kk11IsFrnqqivIZrM8+eRf\nuPfee5g791B6Z6G+tnvv/Sl/+9tfSafTfP3rl/G2t72dSZMMoSVjtic0CALqW+rpbuuhpz1Dy5Rq\nt0iSJI1UjY0NvOc9h5FKpTjrrHPZddfdgNefyveWt+zGxRcv5IUXFrP77ntwwQVfGJ7G1ogxG0Kh\nd6um7rYe0h1uWi9JkjYuCAL+4z9O44Mf/MgG799yyx0bvD7jjA1/v/XW23DRRV+uePtq1ZgdjodX\n5oXmenLks/kqt0aSJI1ExWKRYnmnmQpDaP/PPR7hKUmSNsIddCpjTA/Hx+Ixkk11ZDqzpNvTNE1s\nqHaTJEnSCDJ16jQeeeR3g/7cpz51QQVaM7qM6Z5Q6N0vFHBeqCRJ0jAa8yE01Tckn+7IUCw44UOS\nJGk4jPkQWt93fCdFSHfaGypJkjQcxnwITdQniCfjAKRdnCRJkjQsxnwIhVd6Qz05SZIkaXgYQnll\nXmhPR4aiG4FJkqQhWrnyJQqFQrWbMaIZQnllhXwhWyCXdtN6SZK0+dasWc2xx76HTKZ3mt/ChRdy\n5ZWXVblVI48hFEg21hHEejeidUhekiQNRU9PD+m0eWJTxvRm9SVBLKC+OUnPujTp9gzNk5qq3SRJ\nksaWfIZYx5LN/ngsFgBNxNo6N7nlYqF5K4gnX/eakuXLl3H88Udz3HEncPPNN5JKpTj22BNoaWnh\nu9/9Nj/4wW39137mMx9n55134YYbvkuxWOTwww/h61+/BoBly5Zx9tln8NRTTzBt2nQ+9an/Yqed\n3gjAD3/4A370o5vo6Ohg5sydOeusc9lmm215/PE/8NWvXsLb374n99xzN6lUive+92iOPfaEzSvS\nCGMI7VMKoT1uWi9J0vDKZ5h4w+7E258f8q1aB/K4lm1ZfdwfBhxEe3q6ee65Z7j99ntYvPg5zj77\nDD796f9i1aqV/O1vf2Wnnd5IV1cnv/3trznrrI9x4IGzOeqoBdx1173U16cAePzx33PZZVex3Xbb\n84UvXMA3vnE5X/nKFdxxx23cdNP3ueSSy9lmm2353veu49xzP8z3v38LAM8++3cOPngOd999P48+\n+gif/ewnmDNnHltuOWlzSzRiOBzfp7RCPtuVpZBzIrEkSeoVBAEf/vA51NfXE4YzmTt3Pg899HP2\n3Xc/HnzwfgAeeuhBdt55lw3C4fprnd/1rgPYYYcdicVi7LffgSxduhSA++67h6OOOobtt9+BRCLB\niSeeQjab5Y9//F8A4vE4xx57Qt/nDqChoYEXX3xx+L58BdkT2qe0OAl6T09qaE1VsTWSJI0h8SSr\nj/vDkIbj47GA1tYm2to6yZdxOB4gmUyyxRZb9r+eNGkKzz//PO9971F89auXcNppZ/DAA/cyZ87c\n17xHS0tL/891dXXk870LodesWc20adP7fxcEAZMnT+Gll1YwY8ZWNDe3EI/H+3+fSCQoFkdHZ5kh\ntE88EaOuIUG2O0dPe9oQKknScIonKYzffrM/HsQDmNhMgQ4K+fJut5jJZOjo6KC5uRmAFSuWMXny\nFPbYYy+6urr4wx8e489//j8+97kvDvreU6ZMZfnyZf2vi8UiK1YsZ+LELcrW/pHK4fj1lHpD084L\nlSRJfYrFIldddQXZbJYnn/wL9957D/PmHUoikWDWrIO44opL2XPPd/SH1Lq6OgA6Ozs2ee958w7j\nlltu4tlnnyGXy3HdddcQBAG77/72in6nkcCe0PXUNyfpeKmTdHvvpvVBEFS7SZIkaQRobGzgPe85\njFQqxVlnnctb3rIbAHPmzOPHP/4RJ574n/3XbrHFluy11z68731HsGjRpa973zlz5tHW1sYnP3kO\nbW1rmDnzzXz1q1f2L2j6Z6MnmwQj+ISg4urVHeTL3KX+erLdWV780woApv/LZJJNA58vUkvi8YCJ\nE5sZ7vqOFda3cqxtZVnfyrK+lVWp+i5fvoyjjlrA/fc/stFguGrVSo4//mjuvPPe/h7Q0aavtmVP\nv/aErieRShBLxCjkCvS0Z0ZtCJUkSQNXLBZ5dZ9dsVjkueee4aabbuCQQ+aN2gBaSc4JXU8QBP1b\nNTkvVJIkARudnte7bdPpPPPM3zcYitfA2RP6KvXNSbrX9Hh8pyRJYurUaTzyyO82+ru7775/mFsz\nutgT+iqpvhXyuXSeXCZf5dZIkiSNTobQV0k2J/sXntkbKkmSVBmG0FeJxQKSjb2Ti50XKkmSVBmG\n0I0oDcn3tBtCJUmSKsEQuhGlFfKZzgyFTZw/K0mSpMEzhG5E6fhOipBxSF6SJKnsDKEbkUjGSdTH\nARcnSZIkVYIh9DXUN/cOyffYEypJklR2blb/Gupb6ul8uZt0e4ZisbjR0xIkSVJ5FAvFIe3PXYgF\n9HRmyHbnyG9iPUciGSeIDex/15cvX8aJJx7DfvvN4pe/fJizz/44c+bM3ex26hWG0NdQWpxUyBXI\n9eSoa/BMWEmSKqFYKPLin5aTSw/PITGJ+jgzdp064CDa2dnJ9OkzuOuu+8jnPcimXByOfw3Jxrr+\nP063apIkaewKgoDZs+eSSCSor6+vdnNGDXtCX0MQBNQ3J+lZlybdkaFlclO1myRJ0qgUxAJm7Dp1\nSMPx8VjA+NZG1rZ1lXU4vmTixC02u23auLKF0DAMpwD/B5wURdFPwzBsBa4FDgTagM9FUXRtuZ43\nHOpb+kKoK+QlSaqoIBZQl9r8WBKPB6SaknSlM8Ty5d/j27Uh5VfO4fhvAxPXe/0toB2YBBwJLArD\ncM8yPq/iSicnZbtz5HOFKrdGkiRVQ7HowTWVUJYQGobhafQGzn/0vW4CFgDnR1GUjaLoMeBG4IRy\nPG+4lLZpAvcLlSRprLIXtDKGHELDMHwj8FHgdKD0r7QTkImi6Pn1Lo2AmUN93nCKJWLUNfauik+7\nX6gkSWPO1KnTeOSR35FKpardlFFnSHNCwzCMA9cDH4qiqC0Mw9KvmoDuV13eBTQO5v6xQU4aroSG\nliTZrizpjgzxePXbUw6luo6E+o5G1rdyrG1lWd/Ksr6VZX0rp1I1HerCpPOBx6Mouu9V73cBr/5P\nhkagYzA3b22t/or0wrQc61Z0kunIMKG1adCr6UaykVDf0cz6Vo61rSzrW1nWt7Ksb+0Yagg9Cpga\nhuHRfa/HAzcBi4BkGIZbRVG0pO93IfDkYG7e1tZJYRPbLFRavm/CQiFfZPmSNRvME61VsVhAa2vT\niKjvaGR9K8faVpb1rSzrW1nWt3JKtS23IYXQKIretP7rMAyfAz4YRdE9YRjuClwUhuGpwC7AMcD8\nwdy/UCiSr8A2C4MR1MWI1cUoZAt0rU2TGEUnJ42E+o5m1rdyrG1lWd/Ksr6VZX1rR7lPTCryyuKk\nU4EksAS4BTi3b5V8TQmCgFRf76cr5CVJksqjrCcmRVG0/Xo/rwGOfp3La0Z9Sz1da3o8vlOSJKlM\nPDt+AOpbentC85k8uXSuyq2RJEmqfYbQAUg2JfsnGbhfqCRJ0tAZQgcgFguob+rtDXVIXpIkbcy1\n136Tz3zmE9VuRs0whA5QaUjexUmSJOm1eMTnwJV1YdJoVt9SD8s6yHRmKeQLxOLmd0mSyiWfydPx\nYvtmfz4eD2BNhrVtXZvcoql5RgvxZHxA912+fBknnngM++03i0ceeYjW1laOOupYjjjiSABuvfWH\nPPTQz/na164GYN26tZx33kd5/PE/sO2223HeeZ9l++134CMfOZ23v31Pjj/+pL7r1vHud8/jllvu\nZOLELTb7e9cyQ+gApdbbpD7dkaFhvGfISpJUDvlMnhv3uY72F9YNy/NathnHsb8+acBBtLOzk+nT\nZ3D33fdz+un/8U+/X7/3849//F/++7+/xBe+cDHf+951fOITH+Wmm25j9uy53Hrrzf0h9Be/eIBd\nd33rmA2g4HD8gMWTcRL1vX+sLk6SJGlsmT17LolEglTq9Tuh3v72d/Cudx1APB7nhBNOpqurgyee\n+DOzZh3E888/zwsvPA/AAw/cy+zZc4ej6SOWPaGDUN9STy7dRdrFSZIklU08GefYX5805OH48a2N\nZR+Oh96ezoH2WE6dOrX/51gsxhZbbMnLL6/iLW/ZjX32eScPPng/hx22gKeffoovfenSAbdhNDKE\nDkKqJUnnqi7S7WmKxaKTjyVJKpN4Ms747Vo3//PxgIkTm2F1siLHdpb+Nz8Wi5PLZfvfX7du7QbX\nrV79cv/P+XyelStXMnXqNADmzJnHt799NS0tLeyzz740NjaWvZ21xOH4QahvqQegkC+S7XHTekmS\nxoJi8ZVQu/XW2/Db3/6GTCbDiy8u4d5779ng2v/5n9/ym9/8ilwux7e/fTVTpkxl5sydAdhnn3ey\ncuVL3H33HcyePW9Yv8NIZAgdhLqGBEG897+EHJKXJGlsWH/k89///UTy+RyHH34I55//SebPP2yD\na/faax9uvPF65s8/iKeffpKLLrqk//OJRIIDDjiQl15awV577TOs32Ekcjh+EIIgoL45Sc/aNOn2\nNC2Tm6rdJEmSVEFTp07jkUd+t97rqVx22Tc2uOaEE04G4OSTTx3Q/WbNmk0iYQSzJ3SQUn1D8p6c\nJEmSBqqtrY0nnvgLd9xxG4cdtqDazRkRDKGDVDo5KdeTI5/NV7k1kiSpFvzpT49z1lkfZPbsucyc\n+aZqN2dEsC94kOpftWl944SGKrZGkiTVgv33n8X++8+qdjNGFHtCBykWj5FsrANcnCRJkrS5DKGb\noTQk39OernJLJEmSapMhdDOU9gvNdGQoFsq/Ia4kSdJoZwjdDKV5ocUiZLqym7hakiRJr2YI3QyJ\n+jjxut7SOSQvSZI0eIbQzRAEQf+QvIuTJEmSBs8QuplKi5PS7ekNzpSVJEnSphlCN1Oqb15oPlsg\nn3HTekmSpMEwhG6mZFOSIOj92SM8JUmSBscQupmCWECy+ZUheUmSJA2cIXQIXJwkSZK0eQyhQ5Dq\nW5yU6cpSyBeq3BpJkqTaYQgdgtKm9QDpDntDJUmSBsoQOgTxujiJVAJwSF6SJGkwDKFDVBqS9+Qk\nSZKkgTOEDlH/4qSOjJvWS5IkDZAhdIhK80KL+SLZ7lyVWyNJklQbDKFDVNeQIBbv3bXe/UIlSZIG\nxhA6REEQ9A/Je3KSJEnSwBhCy6C+b3GS2zRJkiQNjCG0DErzQnM9OfKZfJVbI0mSNPIZQstg/U3r\ne+wNlSRJ2iRDaBnE4jGSTXWAi5MkSZIGwhBaJv37hbo4SZIkaZMMoWWS6huST3dmKBbctF6SJOn1\nGELLpLRCnmJvEJUkSdJrM4SWSaI+QTwZBxySlyRJ2hRDaBn17xfq4iRJkqTXZQgto9K80J6ODMWi\n80IlSZJeiyG0jEor5AvZArm0m9ZLkiS9FkNoGSUb6whiAeCQvCRJ0usxhJZREAv6T09ycZIkSdJr\nM4SWWWlxksd3SpIkvTZDaJmVekKzXVkKuUKVWyNJkjQyGULLrLQ4CSBtb6gkSdJGGULLLJ6IUdeQ\nAKDHxUmSJEkbZQitgFJvqD2hkiRJG5cY6g3CMDwK+C9ga2Ax8Jkoiu4Iw7AVuBY4EGgDPhdF0bVD\nfV4tqG9O0vFSJ+n23k3rgyCodpMkSZJGlCH1hIZhuBPwbeCkKIpagLOAm8MwnAh8C2gHJgFHAovC\nMNxziO2tCam+FfLFQpFMV7bKrZEkSRp5hhRCoyj6GzAliqL/CcMwAUwF1gFZYAFwfhRF2SiKHgNu\nBE4YaoNrQSKVIJboLa37hUqSJP2zIQ/HR1HUFYbhG4C/AQFwOrADkImi6Pn1LwXePdTn1YIgCKhv\nSdK9psd5oZIkSRtRroVJLwAp4GDgK8C/At2vuqYLaCzT80a8VP/JSa6QlyRJerUh94QCRFFU2pX9\noTAMbwXeTm8oXV8j0DGY+8Zitbugp2F8ijX/WEcunaeYK5Coj1e7Sf1Kda3l+o5k1rdyrG1lWd/K\nsr6VZX0rp1I1HVIIDcNwHvDRKIpmr/d2Evg7MC8Mw62iKFpSuhx4cjD3b21tGkrzqqowvpFlT7xE\nsQiJYsDEic3VbtI/qeX61gLrWznWtrKsb2VZ38qyvrVjqD2h/wvsHobhcfQuPJrX93/vALYBLgrD\n8FRgF+AYYP5gbt7W1kmhUBxiE6sn2ZQk3ZFh1bK1kBo5W7LGYgGtrU01X9+RyvpWjrWtLOtbWda3\nsqxv5ZRqW25DCqFRFK0Iw/Bfga8CXwf+CiyIouivfeHzKmAJvVs1ndu3Sn7ACoUi+Xzt/iHVN/eG\n0O51mRH5PWq9viOd9a0ca1tZ1reyrG9lWd/aUY7V8b8C9tjI+2uAo4d6/1pW35KE5ZDpzFAoFJ2n\nIkmS1GfkjBGPQqXjOylCxq2aJEmS+hlCKyiRjPevinerJkmSpFcYQiusvm+/0B57QiVJkvoZQius\nNCSfbs9QLDpRWpIkCQyhFVff0tsTWsgVyPXkqtwaSZKkkcEQWmHJxjqCvlXxPe0OyUuSJIEhtOKC\nIOjvDU07L1SSJAkwhA6L0uIkV8hLkiT1MoQOg1Tf4qRsd458rlDl1kiSJFWfIXQYlHpCwd5QSZIk\nMIQOi1giRl1jHeC8UEmSJDCEDptX5oUaQiVJkgyhwyS13gr5YsFN6yVJ0thmCB0mpZOTioUima5s\nlVsjSZJUXYbQYZKojxOr6y2380IlSdJYZwgdJkEQkOqbF9rjCnlJkjTGGUKHUWlI3sVJkiRprDOE\nDqPS8Z35TJ5cOlfl1kiSJFWPIXQY1TclIej92d5QSZI0lhlCh1EQC3qDKNDj4iRJkjSGGUKHWWlI\n3uM7JUnSWGYIHWalxUmZziyFfKHKrZEkSaoOQ+gwK23TBO4XKkmSxi5D6DCLJ+Mk6uOAIVSSJI1d\nhtAqcL9QSZI01hlCqyC13uKkYrFY5dZIkiQNP0NoFZR6Qgv5ItkeN62XJEljjyG0CuoaEgTx3l3r\nHZKXJEljkSG0CoIgoL7Z/UIlSdLYZQitklTfkHyPPaGSJGkMMoRWSenkpFxPjnw2X+XWSJIkDS9D\naJXUu2m9JEkawwyhVRKLx0g21gEuTpIkSWOPIbSKSkPyPS5OkiRJY4whtIpK+4VmOjIUC25aL0mS\nxg5DaBWV5oUWi5Dpyla5NZIkScPHEFpFifo48brefwKH5CVJ0lhiCK2iIAj6h+RdnCRJksYSQ2iV\nlRYnpdvTFIvOC5UkSWODIbTKSicn5bMFcmk3rZckSWODIbTKko11BEHvz25aL0mSxgpDaJUFsYBk\n8ytD8pIkSWOBIXQESLk4SZIkjTGG0BGgtDgp05WlkC9UuTWSJEmVZwgdAUqb1oPzQiVJ0thgCB0B\n4nVxEqkE4JC8JEkaGwyhI0Sqb0jek5MkSdJYYAgdIfpPTurIuGm9JEka9QyhI0RpcVIxXyTbnaty\nayRJkirLEDpC1KUSxOK9u9a7X6gkSRrtDKEjRBAE/UPyPS5OkiRJo5whdAQpDcnbEypJkkY7Q+gI\nUtovNJfOk8/kq9waSZKkykkM9QZhGL4TuASYCawELo6i6JthGLYC1wIHAm3A56IounaozxvN1t+0\nvqcjQ9PEhiq2RpIkqXKG1BPaFzTvAC6NoqgVOApYGIbhQcA1QDswCTgSWBSG4Z5DbO+oFovHSDbV\nAQ7JS5Kk0W2ow/HbAndHUXQzQBRFjwMPAfsAC4DzoyjKRlH0GHAjcMIQnzfq9e8X6uIkSZI0ig1p\nOD6Koj8B7y+9DsNwAvAu4E9ANoqi59e/HHj3UJ43FqSak7QD6c4MxUKRIBZUu0mSJEllN+Q5oSVh\nGI4H7gQeo7c39COvuqQLaBzMPWNjMIA1tvb2hFKEXHeW1Lj6sj+jVNexWN/hYH0rx9pWlvWtLOtb\nWda3cipV07KE0DAMtwPuAv4GvA/YGUi96rJGoGMw921tbSpH82rOstRKMj05gjxMnNhcseeM1foO\nF+tbOda2sqxvZVnfyrK+taMcq+PfBtwDXB9F0cf63vsbkAzDcKsoipaULgWeHMy929o6KRTG3jnq\nyeY6Mj051ixvp35CZXpCW1ubxmx9K836Vo61rSzrW1nWt7Ksb+WUaltuQwqhYRhOoTeAXhJF0cWl\n96Mo6gjD8A7gojAMTwV2AY4B5g/m/oVCkXx+7P0hJZvrYVU33e1pcrkCQVCZbvCxWt/hYn0rx9pW\nlvWtLOtbWda3dgy1J/RkYEvgs2EYnt/3XhG4DPhP4GpgCb1bNZ3bt0pem1DaL7SQLZBL56lLlW3q\nriRJ0ogw1NXxFwEXvc4lRw/l/mNVsrGOIBZQLBRJt6cNoZIkadTx2M4RKIgF/b2hPe4XKkmSRiFD\n6AhV39IbQtMdhlBJkjT6GEJHqFJPaLYrSyFXqHJrJEmSyssQOkKVju8Ee0MlSdLoYwgdoeKJGHUN\nvQuSetrTVW6NJElSeRlCR7BSb6g9oZIkabQxhI5gpXmh6fYMxaIb70qSpNHDEDqCpfpWyBcLRTJd\n2Sq3RpIkqXwMoSNYIpUgluj9J0q7X6gkSRpFDKEjWBAEr+wX6uIkSZI0ihhCR7hUs5vWS5Kk0ccQ\nOsKVVsjn0nlymXyVWyNJklQehtARLtmchKD3Z4fkJUnSaGEIHeFisYBk0ytbNUmSJI0GhtAaUJoX\n2uO8UEmSNEoYQmtAaYV8pjNDoeCm9ZIkqfYZQmtAaXESRcjYGypJkkYBQ2gNSCTjJOrjgIuTJEnS\n6GAIrRGl3lDnhUqSpNHAEFoj6ptfWSFfLDovVJIk1TZDaI0oLU4q5ArkenJVbo0kSdLQGEJrRLKx\njiDWu2t9j/uFSpKkGmcIrRFBEPT3hnqOvCRJqnWG0BryyrxQV8hLkqTaZgitIam+FfLZ7hz5XKHK\nrZEkSdp8htAaUuoJBXtDJUlSbTOE1pBYIkZdYx3gvFBJklTbDKE1JrXefqGSJEm1yhBaY9ZfIV8s\nuGm9JEmqTYbQGlM6vrNYKJLpyla5NZIkSZvHEFpjEvVxYnW9/2wuTpIkSbXKEFpjgiDonxfa4+Ik\nSZJUowyhNag0JO/iJEmSVKsMoTWotDgpn8mTS+eq3BpJkqTBM4TWoPqmJAS9P9sbKkmSapEhtAYF\nsaA3iOK8UEmSVJsMoTWqf79QV8hLkqQaZAitUaXFSZnOLIV8ocqtkSRJGhxDaI0qbdMEniMvSZJq\njyG0RsWTcRKpBGAIlSRJtccQWsPqm0vzQg2hkiSpthhCa1hqvcVJxWKxyq2RJEkaOENoDSstTirk\ni2S73bRekiTVDkNoDatrSBDEe3etd16oJEmqJYbQGhYEwXrzQt0vVJIk1Q5DaI1L9Q3J97g4SZIk\n1RBDaI0rnZyU68mRz+ar3BpJkqSBMYTWuHo3rZckSTXIEFrjYvEYycY6wCF5SZJUOwyho0B9i4uT\nJElSbTGEjgKl/UIzHRmKBTetlyRJI58hdBQonZxULEKm0yF56f/bu/M4S867vvefp5az9HTPrhkt\nI02PsFXybkm2ZUuWZGkk2yIkJBAgJLlkgXBzX5hLcgkJWeAFhBvzAhwgFwLhxibECyTAZTEBY1uS\n0b4vXiQ/jGztmq1n7e2cWp7n/lF1Tp/unhnNjLrOnJ7+vv06rr1OnZ+6z3z7qaqnRERk9CmEngfC\nRkgYl/8pO7o5SURERFaBaKV2lCTJe4A/tNZeUk1vBD4B3AIcBX7GWvuJlXo/WWCMoTnRZO7wPN3p\nFC4610ckIiIicmor0hKaJMk/Bv4CiAdm/1dgGrgA+C7g56ugKjUYvDnJe10XKiIiIqPtdYfQJEn+\nDfAiW5YAACAASURBVPDDwM8OzFsHfDvwk9bazFr7CPAZ4Pte7/vJifWenFRkjryrTutFRERktK1E\nS+jHrbVXAY8OzLsCSK21LwzMs8CVK/B+cgKNsRhjynF1Wi8iIiKj7nVfE2qt3X+C2WPA/JJ5c9X8\n0xYE5mwPa+0Jy+tCO8e7pDMp4fZ1J121V1fVtx6qb31U23qpvvVSfeul+tanrpqu2I1JS8wBrSXz\nxoCZM9nJxo0nD1Ky3Oy2OfYd75LPZWzePP6a66u+9VJ966Pa1kv1rZfqWy/Vd/WoK4TuARpJkuyw\n1r5czUuAp89kJ0ePzuLU+frpi8u/VOaOd5k6cJwgOvHVFkFg2LhxnepbE9W3PqptvVTfeqm+9VJ9\n69Or7UqrJYRaa2eSJPlj4KNJkvwg8Fbge4FvPZP9OOcpCv0gna54bKFzgrnjXdobljZGL6b61kv1\nrY9qWy/Vt16qb71U39Wjzs7qfxBoAC8Dvwf8i+ouealJGIdErfLviu60bk4SERGR0bViLaHW2r8E\ntg1MHwG+Z6X2L6enNdFgppPTme6e60MREREROSk9tvM806z6C+3OpOq0XkREREaWQuh5pvfkJF94\nsvn8HB+NiIiIyIkphJ5n4lZEEJZ3yXd1Sl5ERERGlELoecYY0z8l39HNSSIiIjKiFELPQ71T8moJ\nFRERkVGlEHoeao6XITTvFhRpcY6PRkRERGS5kQ2h7tGP0dp3ByafPdeHsur0QihAZ0an5EVERGT0\n1PXYztdv5iVaMy/RPHgvnU3X0NlyPa6x8Vwf1aoQhAGNdTHpbEZ3usu6ze1zfUgiIiIii4xsSyiX\n3owPGhif0T78IBv3/BLjL/8+YWf/uT6yVaHfX6huThIREZERNLItocG3fDtHJq4jnnqI1qEHCIpZ\nmseeonnsKdKJhPmtN5CP7TzXhzmyWhMNpvdBdzbFOU8QmHN9SCIiIiJ9IxtCAXzUZv6Cm5jfch3N\no0/QnrqXMDtCY9rSmLZkYzuZ33oD2fgVYBSyBvWvC/WQzqa0qpZRERERkVEw0iG0L4jpbn4P3U3X\n0Dj+NdoH7ybq7ieee4H4xRfIm9uZ3/p+0g1vAxOe66MdCVEzImyEFGlBd1ohVEREREbL6gihPSYk\n3fB20vVvI555lvbU3cRzzxN19zPxyh9QHLiDzpbr6Wy6GoLGa+/vPNeaaDB7aL7qL3TiXB+OiIiI\nSN/qCqE9xpBNvJFs4o1Ecy/RnrqHxvQzhNlR1u37X7QP3kVn83vpbL4WH42d66M9Z5oTTWYPzdOZ\nSfHeY3TJgoiIiIyI1RlCB+RjlzJ92d8l7B6gNXUvzaNPERRzjB28k/ahe+lsehedLdfh4g3n+lCH\nrnddqMscebcgbq36/9wiIiJynjhvUknR3MbsJd/B/LbdtA7dT+vIoxiX0j50P63DD9Hd8A46W6+n\naG4714c6NI11MSYweOfpTncVQkVERGRknHepxMUbmLvwdua33kTr8EO0Dj9IUMzROvo4raOPk068\nqere6dJzfai1M8bQHG/QOd6lM50yfsG6c31IIiIiIsB5GEJ7fDTG/Labmd96Pa0jj9M6dC9hdozG\n9DM0pp8hG5tkfuuNZONvOK+7d2pOlCG0q8d3ioiIyAg5b0NoX9Cgs+W9dDa/m8axr9Ceuoeoe4B4\n7nniF58nb13I/NYbSNe/5bzs3ql3XWg2l1HkjjAa3YdkiYiIyNpx/ofQHhOSbnwn6Ya3E8/soT11\nD/HcC0SdfUy8/HsU8ReZ3/p+uhuvgiA+10e7YpoD/YOmMyntja1zeDQiIiIipZENoXvf9R648s2Y\n3bcR3PgBzPr1K7NjE5BNJGQTCdHsC2X3TjOWMDvC+N7PMnbgTjpb3kdn83vwYXtl3vMcCqOAuB2R\nzed0prsKoSIiIjISRjaEFnv3wt69cNcdEEWYa99LuPuDBB+4BbN584q8R75uJ9PrdhJ29pdh9NhX\nCIpZxg58kdbUPXQ3vZv5Le/DxysUgM+R5kSTbD6nO63rQkVERGQ0jOwFgps+9gsEN9wIUQR5jr/v\nXvKf+UnSW28k/YF/SPG7n8bv378i71W0tjOz429z9I3/nPnN78WbmMB1aR+6l017/iPrXvkjgu7U\nirzXudC7LrRbdVovIiIicq6ZEQ4l/vDhGfKjx3F3fwl35xdx990Dnc6ilczb30lw622Eu2/DXLJj\nRd7Y5LO0Dj9I6/BDBMV8eTAY0vVvZn7rDRTtS1bkfYYlm8945akysF/0tm2MrW+yefM4hw/PUBQj\n+99/1QpDo/rWRLWtl+pbL9W3XqpvfararnhXQiMbQg989HI/s/5K2ld+G/Ebb8e3t+Ln53D33Yv7\n4hdw93wJZmcXbWOufBPB7tsIbv0gwa7LX/9BFF1aRx+jNXUfYX68PztddzmdrTeSrbt8VXTv5L3n\npcf24nLH5smNbLpkQr+oNdIXYX1U23qpvvVSfeul+tZnzYVQfsr0D8xhODD+dswbbqdxxV8j3/p2\nfJriHnoAd8cXcF+6E44dW7S5ufxygt0fJNh9Gya58vU9N93lNI99mdbUPUTpwmn5vHVx1b3Tm8GM\n7JUNAOy3U8wf6bBuS5sLr9yqX9Qa6YuwPqptvVTfeqm+9VJ967PmQugn/9OP+Z2H7uJavkzTZIuW\nzcTb6Oz8IPEb/xrpjpvwpol/7BGKO76Au/OLcOjQ4p3tuJRw920Et96GecvbMMFZBkbviKct7am7\niedf7s8uGpuZ3/J+uhvfObLdOx175ThHXjpO1AzZ+a6L9YtaI30R1ke1rZfqWy/Vt16qb33WXAj9\nnSde9mle8Mreg8TP38Vlh+7iRh7mIrM4YOYmZnb79QRv/FbSnR+iGL8M/+UncV/8AsUdn4d9+xbv\nePuFhLfsJtj9QcxVV2PCs+ig3nuiueer7p329Ge7aJz5LdfR3fRufDhaXSF1jnfZ9/RBAHa+62K2\nXbRBv6g10RdhfVTbeqm+9VJ966X61mfNhdBP3/Arvn3ZBI33XkJ29STTzTbP7pum8+JjXLz/Tm7w\nD3GVsQRm8fHPTbwBLv8w6a7bSbdfi/+6LU/Zf/Hz+JdeXPwmm7cQ3HxLeVPTu6/FxGfeihl29lbd\nO30VQ3ksLmjS3fwe5je/Dx9PnHUNVpJznhcfeQU8bE+2cNkV2/SLWhN9EdZHta2X6lsv1bdeqm99\n1lwI/Wnz0/0DazQNFyctNr9jM81rdzD/1kt5atbw4ksvcsHeO3lf8RA3BY+xwSy+USmP15Nfdgvp\n5IfoXnob7tWjuC9+HnfHF/DP7ln8hhPrCW66meDW2wjedz2m2eRMBOlh2lP30Tz6OMbnAHgT0d14\nFfNbrsc1t5xlJVbOq189QDqTsuHica645lL9otZEX4T1UW3rpfrWS/Wtl+pbnzUXQv/ob3zcP//Q\nfo4dyJYta40ZdlzRYss7N9F810W8cMlWvjSVYV59mGvTB7g5eIQkWNzq6TFk264hm/wQ6eSHSGc3\nlN0+ffEL+Ke/uvgNxsYI3n9jeaf9DTdixtad9nGbfIbWoQdoHX6YwHX6752uf0vVvdPFZ16MFXL4\nhaMc3ztDc7zB22/+Fv2i1kRfhPVRbeul+tZL9a2X6lufNRdCnzlyt/fdJsVzTY7dsZ9X7n2Rlx85\nyMzh5aF0bCLgsiuabH3HBtzbtvHkxDoe3beXK+Ye5JbgEa4Llt/cVIxdRDr5QdKdHyKNriD/y/JO\ne//UEzBYk2aT4H3Xl90+3XgTZv2G0zp+U3RoHnmU9qH7CfLp/vx03RuYv+AG8rFdQ+/eafbQHAf3\nHAYD19yecPTYnH5Ra6AvwvqotvVSfeul+tZL9a3Pmguhn33+F/oHFhCxLriAdcEF8Nw6pu86xr67\nX+XlRw4wfzxftu34xoDJpMmWt0wwk2zi8RCmj36Fd/I4NwePcrFZ/PQjHzTILnk/6c4P0V3/brJH\nnqW44/P4Rx+BolhYMYow73lveaf9zbtP7/GhLqd57CnaU/cQpgs3VWXtHXS2vp904k1D694pTwte\nfnwvAFdet5PMOP2i1kBfhPVRbeul+tZL9a2X6lufNRdCH9r/B/5w5xVy3z3h8tiMsY5t+D3rmLmn\nw9TdB3n10Sm6M8tD6YYtIZNXNth65RiHJyd4wexnPP0yb/OPc5WxhMYtWj/fdEUZSLdcR/eZGdwd\nd+EevB/ygX0HAebqdxHeehvBzbditm8/9Qfyjsb0M7QP3kPUeaU/u2hsZX7r++lueAcE0ekX6Cy9\n/MRe8m7BjisvoLmlpV/UGuiLsD6qbb1U33qpvvVSfeuz5kIo4A8dmmY2P8qMO8BMcYAZt585dwiP\nO8HqhrbfhHtmnNn7HEfuPsK+Rw+TdYpla27eHjJ5ZZMLkxZHd0AnsGzOnuSt/sllNze5xnrSS3eT\nXngjnZda5F96+CSPD30Hwe7bCG/94KkfH+o90exztKfupjH7jYX3iSaY33I9nU3vgvDMboo6Ewef\nPczs1Bwbt4+z5Q2b9ItaA30R1ke1rZfqWy/Vt16qb33WZAg90Q+S8zmz7hAzbn8VTA/Q9cdPuAOT\nRbivTjD3QMjxe2Y48PhRinR5gN12ScTOpMElV8SwYz8FX2N79iSX8NLiA8KQb7ua7o7ddA5fQHrv\nHtzdf3nyx4fuvo3g8m856QcM51+hPXUvjeNfG+jeqUVn87V0trwXH42fVqHOxPF9Mxx+/ihRHHLZ\nuy/CnSjPy+uiL8L6qLb1Un3rpfrWS/Wtj0LoKWR+vt9S2gumBemy9VzXkz7ZZP6BmOn7uhx+chqX\nL9m/gQsvi5lMGux8Y4exCy2h/zLbimeIWXpz04Wkl+6mM7+TzmOHKO6696weHxp0D9E+dC/No09g\nfNly601Ed9M1ZfdOjU2vWYPTlc6mvPqVA+X7RgFRMyRqRiccBuFoP4p0VOmLsD6qbb1U33qpvvVS\nfeujEHomG3pPxx9luthfnsp3B5hzh4DF+yrmPLOPOjoPNZm5L+fYV+bwS1oGgwAumoyZTCImd+1l\n60VP0Syeou0PL37PoEF60XV0gyuZ/1pGdsdjMLX4Bih2XEp4y60Et34Q89bljw812TTtww/QPPww\ngSuvhfUEpBveWnbv1LrwjGtxotq8+tR+ss7ya2eXUkg9O/oirI9qWy/Vt16qb71U3/oohL5Ohc+Y\ndVPV9aVlOE39zOJ1jntmHnHMPACzD8DMM92luZUwgksuj5l8Y5fJyW9w8QUP0/DP9k+n96Qb3kA2\ndhWd52Lm77Dw6v7FO9q2nXD3rQS33Ia5+ppFjw81RYfm4YdpH36AIF84xnT8Cua33kA+tvN1de9k\n8DSDiMMHp0nnc/JuTt4t+sPTpZB6YvoirI9qWy/Vt16qb71U3/oohNYgdbP9ltIymB7EDZxyz494\nph8qmLnfMfOgZ37P8oAWNww7viVg8lsOsWvnV7l48wOEweJrRIt4nM76d5Ee2EDn7lcpnt27eCeb\nNhPcvJvw1tsw734PJm6U811G8+iTtA/dS5gutLxm7UuZ33oD2URyVt07neoX1XtPkRaLQqlC6pnR\nF2F9VNt6qb71Un3rpfrWRyF0GG/oHfP+aL+ldKbYz5w/Qq85NDvomX6wYPr+gpkHHN0Xlh9bo2W4\n7A0ZuyZfZtdlj3Hh1q9ilj7ffvxNpDMXkz56jPSJvcDAf9fe40N331o+PrTVKrt3Ov412lP3EHUW\nAmzevIDO1hvorn/bGXXv9Hp+URVSX5u+COuj2tZL9a2X6lsv1bc+CqHnSOGzgS6iypufMj8HQPqq\nY/oBx/QDBdMPOLJXlx9rex3svHyaycln2XXpw1ywde+iM+lZtJnZ/HLyZ3K6Dx3AZwPBq90muOGm\nhceHtseIZ79Be+oe4tlvLhxjvIHOluvobLzmtLp3qvMXVSFVX4R1Um3rpfrWS/Wtl+pbH4XQEdJ1\nM/1AOlMcYNYdpPAZ6Yue6ftdv7U0n1q+7boJx+TlB9m18yvs2vk0mzYd7odST8ic20n3xRbdhw9T\nHBto3Ww0CK67vrzT/qYPEEfTtKbuoTH9zEL3TmGbzub30tl8LT46+fPuz+Uv6loIqfoirI9qWy/V\nt16qb71U3/oohI4w7x1z7vCiYDrnDtP5hmfm/rKVdPrBguLo8m3Xb0yZ3PUSuy77Crt2PceGDQtd\nPKVuM/P7N9B9YpZ0bwyu+u8/8PjQ+IZ30s6+RvPYkwPdO8V0Nl1DZ8v1uMbGZe85yr+o50NIHeX6\nrnaqbb1U33qpvvVSfeujELrK5L7LrDvYv750OtvP8a/Plafu7y+YecThppdvt2nLLLt2Psvk5B52\n7Xqe8fHy7njnG3QOb6LzdE7nhQZuvrqbPggwV11D/OEPMHbNetrdpzGu7CPVE9Dd8HY6W99P0Vp4\nrOhq/kVdDSF1Ndd31Km29VJ966X61kv1rY9C6Crnvafrp/vXl06n+zjw5f1MP5gzfX/B7KMON798\nu60XHGHX5B4mJ59jcvJ5xsbKlbLp9cw9G9B5vkE2FdO7uSl491WMf9c1jO2YJ/ALjxZNJ5J+907n\n8y/qYEjNOsu7nyrS+kPq+Vzfc021rZfqWy/Vt16qb30UQs9DzhfMuUPMuAMcmH6FVx97hflHZ5l+\noGD2cYdf+tAn49m+7QC7dn2Tycnn2LnzBVqtLi5tMv9ig/nnGnRfaZY3N8UB6777WsZvuZSwtRC8\nsrGddLfdyMTOqzlyZPa8ru+JeOfJ05O3oq5ESG2OxWzeOs6x4/N4DxhO+KQsOXP6R6Zeqm+9VN96\nqb71UQhdI3LfYe/cq3zt1ec48uQrmC8fp/tozuxTDpY85MgYx8UXv8rk5PPs2vUcl176InGU093f\novN8g86LLfKZBu337mD8O95KfNHYwsaN9RRBG2ca+KB8ETTwQbM/3R8Pl85vwMD02fRVOqpWMqQu\nElA2Vhsg8P2hN+CNAwM+8Hjj8Hh84HDVuDMOZwq8cTgchSlwlPMKcgrjKMir6YKcDOfLdR0Fznuc\nL3De4XEU3pXjvXX8wmvZcj+4D1fuh4H9eb/sPQb32XuPYnB/A+v05hUU5b5O8h4FRX99jyc0EZGJ\nCIOQyEREQVxNR0RBVM2LiEw8sE5venB5uU0cxIQmXDI9sF4QEZqB9RZNL6wTmYH5QURkwnJ/A+87\nyn+Q6B/xeqm+9VJ966MQukYdnUu5/6UX+atXn8N9fS8b/2oG81TK/Nfcsqc5BUHBjh0v90Ppjh0v\nw5xh/oUmnRdbsO1Sxr/tTTTfdMGKHqP3AZ4IT4w3A6/BMBs28VGrGrbxcRvC1qJg2wu6BPGKBFvv\nPbnPSYsu3aJD13Wr8S6pGxx26BZpuaxap7e8W3QW1i26ZEVG7Bq0/TrG3DgTrGeCjWw0m9hktrIp\n2EIwAqHceUdORuYzMlJSn5L5lIyFYeqzxfP681MyskXzMp+RLlkv8711e/Oy1z4wWRR2e+E5MiFR\nMBCQq2C7EJjjEwbaMjBHA4E5JgrCZQG5nI4WArNZWK/cpty+EUVs2bSBuemcwIfEQYM4iPvHOcoB\nejVQSKqX6lsfhVBhNs2577nD/OU3DvDyvpfYuf8Q25+fofHVDtket2z9KMq49NKXmJx8jl27nufC\nC/aS7Y3J5jfjJy6CqIFpNTDNJqbZwLQiglaEGXgtmh5il0cuLXDdApeWrY+9YZ458qwgzwqyLCfL\nC9K8oJvldPOMbp4zn2fMZSlzRUanyJkvcrrekYdQBFAEhiJk0XR/PDQUQW9ZOd+dcHpgvWq8CMCF\nIRvDLbRNm5gGsWnQMI1qPD7BvOrFkvUG5kXE5bLq1aBBZOKh/bc4EwVF2TJatd6WLbZVq+7A0Btf\nvcpxjIeAal71N4jptRiDCQwE5RfhuvEmx2fn6BZdcnJyn5P7MnAXrpquhuV0Vk0X5C6jGFieu3xh\n2mXVNsUp9tVbr8D5s2wVX8XioEEURGU4NdGikDo4LOeXy6MTrLdoP8HAcnPy/Zz0faowvhooJNVL\n9a2PQqgs0skKHnzhCHfumeKebx6iMTfLm48c5pKXjrPumXn8iyd4xGicsnPnC0xOPseFF+4jCBzG\neILAg/fgTdkNVJUJKABXDo2jv46hSgkmKG/OCUNMGGHiiCCOMHGIacYErQam0SBoNQjGGgRjTYKx\nBmF7INgGw2lZ8c7juzm+U75cJy+n58uh6+T4ToHvZNXyolqelfN761Tr+06OP8mpeR8G0GjgWk18\ns4lvtaDVGw682u3yiVitNqbdrqbHCKqhGRsjaI/1x01/2zY+jsFXn6v3Gph2g/NP8XLO4/3prbe0\n5X0kVdffGgMMDoPF84wxC9fqLt0mOMH2g+sGBmNMedlEL2T3/ufLAF74MowX5BQ4cp/jqIKwzyn6\n4Tkl9zkZZejNSMlcVobrwYBcBelyulgIzD4nc0sDchmkF0/nZEuCdHEeheiAYCDUniTs1hGae/sx\nJw/Ng2dGFJLqpfrWRyFUTiorHA+/eJS79kzxpWenONbJGZvukkwdYecrx9j07Dzsy197R0NkTBmA\nF170A7Gprp0sp1lYHrAwrFrHTAAmNJgQgsBgojIgLKyzeNsgHNhH1foWDO7XnHg701uvdzzVvyvG\nO4xzUDhwDlMUGO8IKIiCgijIiUz5CsmJfEZITugzYpcSuoywSAmLlCAvg61PHT4rqvEC8uWt3ARB\nP5DSapUhtjdeBVxabWgvXlauO7Yw3mpX6w6E4t76cbzo9Kv3i4PuSUPtiV4nCLqnFZTX8q//EAJ1\nFaP71xsTOJrtiOn5uTLsUgbiciyrWpwXLudIfYfUpeV8l/WHmUvJXF4NM3KXLRpmLiXvLffL18tc\net6E5LC61CIOYhpBg3bcIjZNmmGbVtgbtmgNDqNy2ByY31yybHCb1dISXDeF0PoohMppyZ3nyZeP\nceeeKe56doqp2fIW+/GjXS55+ThvOniMsb/aR/NI6xwfqSwVhJ64BXHTE7c8UYNy2PTEsSNqeOLI\nEccFUeSIwqJ8BQuBNzQFETmRKcNu7DNCnxG5jNClhEWGKfJ+wO2H3nRhHlkVeoNgIby2BwNre6FV\ntgq0pl0F4oHW3aWtvb2wa6pwTLsN0eKgu9TS4BsA69e3OVr17OB92ULbC7n9cQ8sGfbXdUu2O9G6\nzi/f7iTbDK67dJs1o9dCHPT+CFwIwP3xpcteYznGV63J5Y13OTmFqYY+G7huOS+Hg2HXZ2RFFXb7\nwXjhdeIwvHz5CddzWRnYR0gUxMuCbBlaF4Lra4fdEwXdNo2gsWquBVYIrc+qDaFJklwF/AbwFuCv\ngP/DWvvQaWyqEPo6Oe/5yt7jZSDdM8Xe410AgugYceMFKGJwcTn0IRQxpojARXgXY1yI8QbjPJGB\nOCxoBI5G4IkDR9wfLrwaxtE2KW1SWr2XT2n6jCYZDZfR8GU4ilxO7DLCIicqCkKXE+RF2ZqYF5jc\nlcOiwBQOkzvIHaZw4Dzem/KmKG8WvZwzy+a9nvVO9HKuWo8Q7wNcNfQ+wPmAIg/Jsog8K4dZGpKl\nEd6f+5uWgrAgbvReeTmMq+lmThRX03FOFOWLx6OsHIY5YZgTRzlxkBGFGVGYE5uUKMiIghzjq/9O\nhYfClcP+tMcXDlz5UAUThBBFEJYvE4X4KK7GI4hiiCOIG8StBllRZbyyWW+hubp/qQgDzdgD8xhY\nHwOBWZg3sMz3/tENgoHlYPr7HNj/4HYsfj9PgA8iMEH5sxKEeFO+ICinCfFlk361LMAzOB2W+2Vh\nWTkvKPdhAjym3AflfI+phuXx98Z91UVDeTNhdbz0fpZ7nyWkcAvrVIUbbQOXSZxuyH09y71xCy3E\nPutfLtELqcvCrK9ad8mJmnBo+ijz2TydokMnr4bFwrCbL0x3q3mpW9pn33AEBAshNTpBwD1F620/\nAActWtHiENysloUmXLFjVQitz6oMoUmSNIFngX8PfBz4PuDngF3W2rnX2FwhdAV57/n6gRnu2jPF\nXc8e4sUjc7gRLK0xnjj0NCJHI3LE0fLxOPK0w5SxIGU8mGdd2GGd6TJGl7bpMmY6tOlWIbhLky5N\nl9LwvWFangrPM8K8bB0M8950dXq8Gjcr2JzlPTgXkmXxSV7RKZaVrzw/9fLeaxSCQxjmxHH2mq8o\nyojjM1t3+eUcp3qxaBo8q6Rh55wrqxXhgwYuaOJMEx80caac7o37apmrerkoTBNvWjjTKrcLFsZ9\n0KyGrYH9VEMTl/swDVxQ9rKxKrqAO80QHASGZjMmTXMW/dt7ih/I3iKPp/BF+XIFhc/7073rfsvp\n8trjYuDGu/41wj5ffGPewHXC3rvqfQb/3w98Ay6fGhxbOl2OLmyzdL89gQkGen0I+z03DPYWEfd6\ncAgiBntr6PfsEMTEJqYRNZiYaJOlruz5IYyIo5gwivr1X/pHhZye1RpCPwz8hrV2cmDel4Gfsdb+\n/mtsrhBak95fi4cOTZMX5bV5ZSOVL18Oit64B+fK8aI61VgMrOOrdYrqFGdvP4UbWLda33Hi91q0\nbzfwvtV856onIZ3kvcrjW7x+773cwHsVjoF9l3dcmaAgMA4TFBhT9IdB6AjIaZoOLdOhxRwtOrSY\np0WHJt1y6MuQ2/KdftBt+C4BrtfmVIaf/vhCu9SCctz4hXG8H4iRg6d2B+YP/O6agXHvPUUW4NKQ\nvBuQpyF5GpB3Q4o0LIfdgLwbUaQBRVrOL7phuW41LNeNyLJqfhqSp73piCxdxdehnWZgXT69fNny\nfZ1qG9+/3vm0XyxZPxhcdqp9uyXH4pbtk2X7qW5WNAVB73fDOILAVzcyuuq67ZPNW7z8RPPOZBuM\nwwQBhGcWgsvxZhVml4yb5pJQ3VhYf3DcNHBBa3WEYDkrhU8p+tc9Z+XFH6YgNw5nPK7qu7nfv3Ow\n9I+KkCA0ZXiOIsIwLINvGPcDcBw3aISN/s1qK9nyO0x1hdC6/xW5Enh6yTxbzZdzzBhDYCAIJ1tj\negAAEv1JREFUTe0/CGuJCWDDxnUcOTJLXpRtBHnhyZ0nd47cedLCkReOzDkK58hcQV4U5N6VN4R4\nR+F81eJRrpP3OnbvzfOuCte9jt97nbr7qpN43+8M3lfjvTu6e8NeS4XH0e8WoVovqLpRMvRaD6vQ\nQnnDWIDDZJ4g9QSpw3Q9JnUEKdB1BKmHrsd0HCb1hB2P6TpMx2O65TI6Ht9/geuA63hcB4req1tD\na0Xv0oqV37PUwZQ/j6YfWP0Jwm1RBWhXrleFWWM6BMHcwDbuhOMnm2cCX4aOsLw7MQgNBAEmCPrz\nTBhCEJbTVW8h5XTYv8SkfMXLbhLrXcrRb5TrXTkSlD2ReGOq6YGbz/rLKZcvujmtt1/orWzMwqUn\nC+9ZlTYwS46pt6C3LUu2HbwMpXcVzMB+qw/Qe89+S27/OBfew4TV5SADx2z6d30uXAqy7LKQfrHM\nwJ/l5b7LPyJauKBd9jt9CqFpENJg2Vq+er2OS38LoMDRoYPz0zjXxfkuhe9W4Tel8FkVfrPqwSNF\n9fI448thCD4wFGGAD8P+z1sYBgRBUAbfKgDHQUw8EHgbwcB4Nb9RTccjcq1v3dljHbD0tPscMHaC\ndZcJhtR9z1rTq6vqW48gMMRhQCMKiIIq5oxmt55npdcKnReuCta9kL14unDL5/Wnq1Ce9dbph3RP\nXriyS6jeXfR5Qd5JKToFPs0Ic1+ezqxuCKI/rG4Sotyu162Y763T62YM+tv0bypyYHrdUFXbmKoF\nvtzO9ZeV/zgNDqvtyzuSyqHz/ZZt41zZ5Wm1j4X3cRhfrteb19+2LHS5D1f9IdDb58D8/nh/WI27\nhWM1S4/ZDxyr6/9Hrbap3q+3fcFC7Xr1dWU5ekNfLIzX90NncN6AC1jd98yP1g1No8xULZBlll5o\nwR9s7S/Xq1r1KXstCYPyTFYYFgSBIwxdeWYrhDD0BKEniAxBBCYMCKKAIDKYKCSIAkwUYeJyGEQx\nJi6vSTdx79WAuIGJmxA3IW5A3CaIyy4LTdTbZ2/cVOMxQdgkjKtlp/vvr6eXaE9j3QLjur2/5qE4\nhvddMtel6zo4V4Vg1yX33aoVuLxWOTM5uSnITEEWOYrA4wIoTNkXNpHhI9/+62fxX/LU6g6hc0B7\nybwxYOZ0Nt64cd2KH5AsUH3rpfrK+aq8rKb3h4avWu09WV4+SCJNC4rMkeWuerCEo8gcebU8zx15\n5iiq8aJ65VlvvKDIPUVWUBTlPJf7an6BK8qXLwp8keELhy9yKAp8UXaVhqteVfdpFGV3aqa8xqia\n9tUfAA5TVOPVyzgWxnth3EN5HRAD83tBfElAz6uQXk27olqv+pvpNYdU2y78zQJ+7TQceB8svhLp\nnHBAWr1Wlgk8QdlgThCWobgfiMNeiA3LcByHVTiOMGFYBt4oGFjPLAm+C8uCOMaEDYJo4wnXi8KA\nOAoYiwNMuHx5EAUYExC4ei5LqTuEPgP80JJ5CfDp09n46NHZskVEVlQQGDZuXKf61kT1rY9qW6/X\nW98YiANot0Jorc5r385G7zr43jXrvXDulgR1h2d8vMX0zBxuWaPowHXdJ7hmfPH7VT1P9EJq1bLd\na/3ud1dWpThXuP46S7sd81VLv69awX1/u7Ini8HuxhYeXFEdYW+ZB9f7QH75AzSqkSVnEqpLhPrv\nX7bme1/+ceDxC8G/d3zeLZzRGHj1P0/hCPCkcykuy3Fpjs8KyIpymBf4zEHm8LnDZx6qoS98Ocw8\nvgA3MHR5+UeFy8txl0NRDV0O3p35HwfeGYp0aQNn74diNNv7r/VvWfF91h1C7wSaSZL8EPBfKO+O\n3wb8xels7JzXjUk1Un3rpfrWR7Wtl+p75gLKa+yj0MBJ8ne/C6E4UH1rMOwumnwVxvPckacFRe7I\nuuUjpousIOvm5FlOnubkaUae5xRpRp4VFGlOkeVla3+aUWQ5rpvh0hyXZuWT+fIclxaQFtUDTFwV\nmhdCdDnuF1790OxxGfiiHLqCclgF6SJnJFrWaw2h1to0SZLbKQPof6DsrumvW2vn63xfERERkTqZ\n6qaxRhzSiEen5d97z8LVJL66dH7xPOehKAqK1JF3C7IqEOdZSlGF5yIrw3OR5bi8nqcu1n5TtLX2\nq8D1db+PiIiIyFpnjCEEwt6dXCcVlbePn4YwrKfVVB2giYiIiMjQKYSKiIiIyNAphIqIiIjI0CmE\nioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSK\niIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqI\niIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiI\niMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiI\nyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI\n0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQ\nKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQKYSKiIiIyNAphIqIiIjI0CmEioiIiMjQRSu1oyRJfgXo\nWmv/5cC8W4FfAiaBx4EfsNbuWan3FBEREZHV6XW3hCZJsjlJkv8GfGTJ/G3AHwD/CtgE3AH84et9\nPxERERFZ/VbidPy9QEoZOAd9B/CEtfbPrLU58LPAxUmSvGsF3lNEREREVrHXPB2fJEkIjJ9gkbPW\nTgO3WGv3JUnyW0uWXwk83Zuw1rokSb5RzX/0dRyziIiIiKxyp9MS+gHgCHB4yespAGvtvpNstw6Y\nWzJvDhg7mwMVERERkfPHa7aEWmvv4OxO288B7SXzxoCZ091BEJizeFt5Lb26qr71UH3ro9rWS/Wt\nl+pbL9W3PnXVdMXujj+BZ4Dv6k0kSRIAb2DgFP1rMBs3rqvjuKSi+tZL9a2Palsv1bdeqm+9VN/V\no85+Qv8QuCZJkr+ZJEkM/ATwkrX2yRrfU0RERERWgdpCqLV2P/DtwE8BU8AtlHfMi4iIiMgaZ7z3\n5/oYRERERGSN0WM7RURERGToFEJFREREZOgUQkVERERk6BRCRURERGToFEJFREREZOgUQkVERERk\n6Op8YlJfkiTvB34RuBI4CPyCtfY3kyTZCHyCsg/Ro8DPWGs/MbDdR4HvB0LgvwP/l7XWV8t+BPgR\nYCNwB/BD1toDw/g8o+Zs61tta4DfB+6w1v7ngfnfC/wssA24C/gB1Xfl6juw/MeAq62131vzxxhJ\nNf3s/hPgxyh/di3wo9bae4fxeUZNTfX9Gcrv5XHgUeCHrbWn+yS880rN3w27gc8DE9bauXo/yeip\n6Wf3T6vtcsAA3lq7fhifZ9TUVN8bgF8GrgC+Cfwza+1dpzqO2ltCqw/0x8AvWWs3At8N/IfqF+z/\nBaaBCygf8fnzSZK8p9ruI8DtwFuBNwHvB360WvbdlE9g+jvVtk8Df1L3ZxlFZ1vfatudwJ8Cf3PJ\nPt8O/DrwPcBWYD/wW/V/mtFTR32rZeuSJPkY8HPAmuyst6af3Q8A/zfwndU+fw34bJIkm+r/RKOl\npvp+P+VDR66x1m4A7gU+OYSPM3Lq+m4Y2PfH6/0Eo6vG2r4TuN5au95aO7GGA2gd3w0XVfv899ba\nCeCjwB8kSdI81bEM43T8TuBPrbX/A8Ba+wTwJeA6yicq/aS1NrPWPgJ8Bvi+aru/D/yytfZA1QL3\nUeAfVsu+A/hNa+3D1tqC8qlMb06S5C1D+Dyj5qzqWz1K9THgKeD+Jfv8u8AfWWsftdZ2gX8FfDhJ\nkguG8HlGTR31hfKPpssof+HXqjpquwP4eWvtV6p9/negAPTdsAL1tdZ+HHi3tXZfkiQTlGei1uQZ\nEur7boCyEeB3aj360bbita3+/boA+NqQPsMoq+Nn9/uAz1tr/6ja5+9Stqa6Ux1I7SHUWvuUtfYf\n9KarFokbqsnMWvvC4OqUTcNUw6eXLEuq8RBYenrCA29cqeNeLV5HfXPgzdbaf1OND1pUe2vtYeAw\nC/VfM2qqL8Dfs9Z+F+VpkDWpjtpaaz9lrf3FgX1eT3naeM2dLq7rZ9daO58kyT+gPFX3vwH/ro7j\nH3V11TdJkr8HbAB+g/KU8ZpTU22vAmaA/5UkyYEkSe5JkuS99XyC0VZTfa8GXk2S5P9LkmQqSZL7\ngNham53qWIZ6Y1KSJBsoW4AeoUzd80tWmQPGqvF1LA6ac0CQJEmj2scPJkny9iqZ/wTQBlr1Hf3o\nO5P6Wmv9Ka7xXFr7RduuVStYX6y1+2o6zFVpJWs7sM83U1639BPVH1JrVg31/QzQpLz04fPV6b01\na6XqmyTJZcBPA/+omrUmL9UZtII/uy3K1rsfBi4BPg38eZIk22o47FVjBeu7GfgBykugtgOfogz8\nG071/kMLoUmS7ALuA6aA76T8i2RpaByr5kP5wdtLluXW2tRa+0ngVymvP/gm5ed4mvIv8zXpLOp7\nKktrfybbnpdWuL4yoI7aJknyQcrrFf+TtfYXVuhQV6U66ludqsuttR8DjgMfWJmjXX1Wqr7VzR7/\nDfi31tr9LLSCrsnWUFjZn11r7Z9Ya/+6tfbr1c/vbwAvATev8GGvGiv83dAF/sxae4e1trDW/nq1\n3fWn2mgoITRJkquBB4E/t9b+reo6wz1AI0mSHYOrsnDa7BkWn/69sppHkiQXAv/DWrvLWnsp8EuU\nd2M9Ue8nGU1nWd9TWVT7JEm2Apuq+WtODfWVSh21TZLkHwH/E/in1tqPrvQxryYrXd8kSX4qSZKf\nXTK7wRptAFjh+u4ArgV+PUmSw8CTlAH0pSRJrlv5ox9tNfzsfmeSJN+1ZHYL6KzUMa8mNXz3Wsqz\nI4NCXuOPqNq7aEqSZDvw58AvDrZIWGtnkiT5Y+CjSZL8IOVd8N9LeUc8lE25P5YkyV2U1x78OGU3\nTQC3Aj+eJMlNQAb8P8Dnqr8e15SzqO+3nsZufwf4UpIknwAep7wp7M+stUdW/AOMuJrqK9RT2+ru\nzl8DbrPW3lfPka8ONf3sPgh8KkmS36X8R+ffAsc4+Q02562Vrq+19iXKS6F6+98JPAdcYq1deor0\nvFbTz+54td1XKcPWP6cMoZ9f6eMfdTXV95PA/UmS3A58DvgIZSg9ZRdNw+gn9B9TdvPzE0mS/GQ1\nzwO/AvwT4L8AL1N2CfAvrLWPVuv8Z8p+/h6m/Ev7k5QtnlhrP5WU3Qg9Q9ma+6dA/yLbNeZM6/vI\nCfax6Loja+1TSdnX4m9RXttxDwvXKK01K15f6aujtv8SiCmv9YKqL0Dgb1tr19o/NnV8N3wuSZJ/\nTXkp1AbK8Plha21az0cYacP4bvCszdPxdfzs/nZ1FvVzlNcvPg7cvtYCfqWO+j6ZJMnfAH4e+F3g\nr4Bvs6/Rx63xXv8+ioiIiMhw6bGdIiIiIjJ0CqEiIiIiMnQKoSIiIiIydAqhIiIiIjJ0CqEiIiIi\nMnQKoSIiIiIydAqhIiIiIjJ0CqEiIiIiMnQKoSIiIiIydAqhIiIiIjJ0w3h2vIjIeSFJkl8D3mSt\nvWVg3keAHwBuAH4Z+FtADnwB+GfW2oPVetcCPwe8m7IB4DHgn1prv5YkyU2Uz1v+DPD9wMettT86\ntA8mInIOqCVUROT0fRq4IUmS7QPz/k41/78ClwA3A7cA64DPAiRJMg78GXAf8Bbgesrv348N7Gc7\nsAO4Cvi1Wj+FiMgIMN77c30MIiKrRpIk3wT+o7X2V5Mk2Qk8C+wG7gK2W2unqvXGgUOUofQbwN+3\n1n5sYD/fD/yUtfbSqiX0TuAaa+2Tw/1EIiLnhk7Hi4icmc8A3wP8ajW8D5gADPDNJEnMwLohkFhr\n70+S5BNJkvyfwDuBBLgaOLJk38/VffAiIqNCIVRE5Mx8CvjxJEkuAb4b+E3K79J54B2UYXTQwSRJ\nLgIeBb4CfA74JPAm4N8tWXe+xuMWERkpCqEiImfAWvv1JEmeBP53yus7fw+4AGgBbWvt0wBJkmwC\nfhv418CHgDlr7Yd7+0mS5HaWB1YRkTVDIVRE5Mx9BvhZ4C+stUeAI0mSfBb4VHW3/DHKm47eAOwB\n3gpclCTJh4CvA7cDHwE65+LgRURGge6OFxE5c79D2fL56YF53wc8AfwJ5XWiGfBBa20K/E/g45Sn\n8p+gvKP+B4ENSZLsGuJxi4iMDN0dLyJyhpIkuZrybvbt1truuT4eEZHVSKfjRUROU5IkFwA3AT8C\n/LYCqIjI2dPpeBGR0zdOeVrdAD91bg9FRGR10+l4ERERERk6tYSKiIiIyNAphIqIiIjI0CmEioiI\niMjQKYSKiIiIyNAphIqIiIjI0P3/KQcpOvhXrpIAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1b502400>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6', 'purple']\n", | |
"temp[1:].plot(color=colors, figsize=(11,9))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Number of R questions seems to have nose-dived. But 2008-10 were the first years of StackOverflow's starting up and so let's see the patterns after things stabilised." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 29, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x1b8a5128>" | |
] | |
}, | |
"execution_count": 29, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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uAB3NHTRWNsc5IhEREUkE2dk5zJw5myefnMukSWO49967uPbaP5OdrelFe4Nq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\nxmOAFIkE5q8fwPzpjyA 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