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aparrish/environment.yml

Last active Jul 23, 2019
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Flat, randomness, curves, asemic writing
name: rwet
channels:
- conda-forge
dependencies:
- python
- numpy
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Flat, randomness, curves, asemic writing\n",
"\n",
"By [Allison Parrish](https://www.decontextualize.com/)\n",
"\n",
"This is a tutorial on how to use [flat](https://xxyxyz.org/flat), the random functions from [Numpy](http://www.numpy.org/) and [bezmerizing](https://github.com/aparrish/bezmerizing/) (a small library I made with simple functions for working with Bézier curves) to make various kinds of asemic writing.\n",
"\n",
"## Installation and preliminaries\n",
"\n",
"There are a number of Python libraries for drawing vector graphics, but [flat](https://xxyxyz.org/flat) is my favorite one to work with. It's small, powerful and elegant. It's also written in pure Python (i.e., does not need compiled code) and has no external dependencies, which makes it especially easy to install:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: flat in /Users/allison/anaconda/lib/python3.6/site-packages\n",
"\u001b[33mYou are using pip version 9.0.3, however version 18.1 is available.\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"!pip install flat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, I'm only using a subset of flat's functionality. The cell below imports the three functions from the library that I'm going to use."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from flat import document, shape, rgba"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The flat library can export images in a handful of formats, including PNG. We can leverage this to \"embed\" the results of running flat code right into a Jupyter Notebook as we're working. The cell below creates a function `show()` that takes a flat `page` object, renders it as a PNG, and displays it in the notebook. I use this extensively below!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image, display\n",
"def show(page):\n",
" display(Image(page.image(kind='rgba').png()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Bezmerizing](https://github.com/aparrish/bezmerizing/) is a small library I made with a handful of useful functions for working with Bézier curves, including a function for making smooth curved paths based on a list of points, and a function that can style such paths by adjusting their thickness along the path. Install it directly from GitHub like so:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting https://github.com/aparrish/bezmerizing/archive/master.zip\n",
" Downloading https://github.com/aparrish/bezmerizing/archive/master.zip\n",
"\u001b[K \\ 225kB 759kB/s\n",
" Requirement already satisfied (use --upgrade to upgrade): bezmerizing==0.0.2 from https://github.com/aparrish/bezmerizing/archive/master.zip in /Users/allison/anaconda/lib/python3.6/site-packages\n",
"Requirement already satisfied: flat>=0.0.3 in /Users/allison/anaconda/lib/python3.6/site-packages (from bezmerizing==0.0.2)\n",
"Requirement already satisfied: scipy>=1.1.0 in /Users/allison/anaconda/lib/python3.6/site-packages (from bezmerizing==0.0.2)\n",
"\u001b[33mYou are using pip version 9.0.3, however version 18.1 is available.\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"!pip install https://github.com/aparrish/bezmerizing/archive/master.zip"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from bezmerizing import smooth_point_path, fancy_curve"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assuming you're running this notebook with Anaconda, you probably already have Numpy installed. [Numpy](http://www.numpy.org/) is a widely-used Python library for math and statistics. I'm going to show you how to use a handful of functions from Numpy for generating random numbers. Use the cells below to import them into your notebook:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from numpy.random import uniform, normal, choice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making shapes with flat\n",
"\n",
"Drawing with flat consists of a few steps:\n",
"\n",
"1. Create a document\n",
"2. Add a page to the document\n",
"3. Create shapes\n",
"4. Place shapes on the page\n",
"5. Display the page (or the document).\n",
"\n",
"A minimal example skips steps 3 and 4:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm') # create a document 100mm x 100mm (can replace 'mm' with 'pt')\n",
"page = d.addpage() # add a page\n",
"show(page) # show the page"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... but doesn't display anything. We've just made a blank page.\n",
"\n",
"To add a shape, create a `shape` object with the `shape()` function. The object returned from this function has methods that let you set the fill, stroke, and stroke width of the shape. Colors are specified with the `rgba()` function, which takes four parameters: the red, green, and blue components of the color (in the range 0—255), along with an alpha value (which controls how \"transparent\" the color will be—0 is completely transparent and 255 is completely opaque)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<flat.shape.shape at 0x10f5a4528>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"figure = shape()\n",
"figure.fill(rgba(255, 128, 255, 255))\n",
"figure.stroke(rgba(128, 0, 255, 255))\n",
"figure.width(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `shape` object acts as a kind of container for a list of paths to draw with the fill and stroke you specified when you created the object. Add a rectangle to the shape with the `.rectangle()` method:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"rect = figure.rectangle(5, 5, 90, 90)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This creates a rectangle at position (5, 5) on the page with a width of 90 and a height of 90. ([See the documentation for a full list of shape methods and an explanation of what their parameters mean.](https://xxyxyz.org/flat))\n",
"\n",
"> Note for first-time programmers: Any word to the *left* of an equal sign (`=`) is called a *variable*. A \"variable\" takes the result of running some code and stores it for later under the name that you give it—sort of naming a file or putting a label on a folder. I used the word `rect` above to store the result of creating the `rectangle` shape, but you can use whatever word you want, as long as you use the same word when referring back to that value later. (There's nothing magical about calling it `rect`—I could have called is `lawrence` or `afkjsdhf`. Using `rect` just makes it easier to remember what I was using that variable for.\n",
"\n",
"To make this rectangle appear on the page, pass the value returned from the method to the `.place()` method of the page:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAEbCAYAAADqLSAhAAAEBklEQVR4nO3UoW1cQRSG0cdDjEzNXMY2YBqQGlyFO3AVwS5mq3AVL1mFhCbSflfyPeBIQ0bzk/mO8zwPgHsbHwDsMD4A2GF8ALDD+ABgh/EBwA7jA4AdxgcAO4wPAHYYHwDsMD4A2GF8ALDDP1/4cXx8vB7XK7Db03G53DU2t0fefp+A3Z6PlxexAe5ObIBEHpvr9/P8fAW+uveH4djcRpxvwFf38/kUG+D+xAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMQGSIgNkBAbICE2QEJsgITYAAmxARJiAyTEBkiIDZAQGyAhNkBCbICE2AAJsQESYgMkxAZIiA2QEBsgITZAQmyAhNgACbEBEmIDJMZj8/7wZwTwtf3970diA+wkNkBCbIDE3WPzdFwut0eA3b4dj493jQ3A/xgfAOwwPgDYYXwAsMP4AGCH8QHADuMDgB3GBwA7jA8AdhgfAOwwPgDY4ReezA2FmSwHBAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"page.place(rect)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nice! You'll notice that flat's coordinate system is set up such that the *upper left-hand corner* of the page is (0, 0). The X-axis increases as you move to the right of the page, while the Y-axes increases as you move *downwards*. (This is the opposite of how it works in a regular Cartesian coordinate system, but it's very common in computer graphics for some reason.)\n",
"\n",
"Because it's so common to set the fill, stroke and stroke width at once, flat supports a \"chained\" syntax so that you can set them all one one line. It looks like this:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"figure = shape().fill(rgba(255, 128, 255, 255)).stroke(rgba(128, 0, 255, 255)).width(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With all of this in mind, I put in the cell below all of the code necessary to draw the rectangle, from the creation of the document to displaying the page. Most of the rest of the examples in this tutorial will look like this, with all of the code to display a particular shape in the same cell."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().fill(rgba(255, 128, 255, 255)).stroke(rgba(128, 0, 255, 255)).width(4)\n",
"rect = figure.rectangle(5, 5, 90, 90)\n",
"page.place(rect)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To add another shape to the drawing, add a second call to a `shape` method. Another easy one to try out is `.circle()`, which draws a circle of a particular radius centered on the given (x, y) coordinate. Note that you have to call `page.place()` with all of the components of the shape you want to appear on in the drawing."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().fill(rgba(255, 128, 255, 255)).stroke(rgba(128, 0, 255, 255)).width(4)\n",
"rect = figure.rectangle(5, 5, 90, 90)\n",
"circ = figure.circle(33, 33, 25)\n",
"page.place(rect)\n",
"page.place(circ)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want the circle to be a different color (or have a different stroke color or stroke width) from the rectangle, you need to create a second `shape` object:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"# shape for rectangle\n",
"figure = shape().fill(rgba(255, 128, 255, 255)).stroke(rgba(128, 0, 255, 255)).width(4)\n",
"# shape for circle\n",
"green_figure = shape().fill(rgba(128, 240, 128, 255)).nostroke()\n",
"# add rectangle to figure\n",
"rect = figure.rectangle(5, 5, 90, 90)\n",
"# add circle to green figure\n",
"circ = green_figure.circle(33, 33, 25)\n",
"# place them both\n",
"page.place(rect)\n",
"page.place(circ)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that `.nostroke()` causes the shape to not have a stroke at all. Similarly, `.nofill()` will make the shape have no fill color.\n",
"\n",
"> Exercise ideas: Modify the cell above to change the coordinates and colors of the rectangle and circle. Add a new element to the drawing using a new shape that we haven't discussed yet (say, `ellipse` or `line`) using a stroke and fill different from the other shapes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Randomness with numpy\n",
"\n",
"For the purposes of this tutorial, let's think about asemic writing as a kind of random process. Our method of generating asemic writing with a computer will be to simulate the random movement of a hand holding a pen. The movement of the pen is constrained in certain ways—i.e., at any point in the process, some pen movements are more likely to happen than other pen movements. To model this process, we need a way to generate random numbers.\n",
"\n",
"Fortunately, the Numpy library has a wide variety of random number generators with different useful properties. Let's talk about a few of them.\n",
"\n",
"### Uniform distribution\n",
"\n",
"The `uniform()` function returns a number between 0 and 1, drawn from a \"uniform distribution.\" The word \"uniform\" here means that every value in the domain has *the same chance of being generated*—no number or range of numbers is more likely than any other. Run the cell below multiple times to see what happens."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.1796433349521177"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniform()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using a `for` loop, you can generate a series of numbers to see what the distribution looks like. (You can change the number `8` to something larger to generate more numbers and better understand the distribution.)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5865737770988988\n",
"0.6704614529624002\n",
"0.6805290223564674\n",
"0.04872647713198186\n",
"0.8890701389219122\n",
"0.9152091711861574\n",
"0.5967815351632565\n",
"0.4398748310310159\n"
]
}
],
"source": [
"for i in range(8):\n",
" print(uniform())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you give two parameters to `uniform()`, the function will evaluate to a number chosen from that range (with the first parameter being the low end of the range, and the second parameter being the high end.)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-4.572127807726907\n",
"4.03738943395126\n",
"-2.2792192002939213\n",
"4.481387132959206\n",
"-2.3324570555720836\n",
"-4.195981683399873\n",
"1.416467349002838\n",
"2.2984595961077217\n"
]
}
],
"source": [
"for i in range(8):\n",
" print(uniform(-5, 5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you need an integer instead of a floating-point number (i.e., a number with a decimal point), you can wrap the call to `uniform()` in a call to `int()`. The cell below simulates rolling a [twenty-sided die](https://en.wikipedia.org/wiki/Dice#Standard_variations):"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"19\n",
"13\n",
"10\n",
"15\n",
"8\n",
"11\n",
"17\n"
]
}
],
"source": [
"for i in range(8):\n",
" print(int(uniform(20)+1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Exercise: Why do we need to add one to the result of the `uniform()` function in the example above?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Normal distribution\n",
"\n",
"A random number generator with a normal distribution (also called the Gaussian distribution or the \"bell curve\") produces values mostly clustered around a particular number. When generating a random number with a normal distribution, you specify the \"center\" of the distribution (i.e., the number that the values will cluster around) and the value for the standard deviation of the distribution, which controls the \"spread\" of numbers returned, above and below the center. (~70% of numbers will be within this range; ~95% will be within double the range.) Generate a random number with a normal distribution using the `normal()` function:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.13075582812882774"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normal(0, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first parameter is the center, and the second is the standard deviation. (These are often called *mu* and *sigma*, respectively.) Run it a number of times and you should see that the numbers generated mostly cluster around the center:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1.659739639447195\n",
"-1.6170661045937031\n",
"1.439469381556217\n",
"4.847874452515992\n",
"0.8174262080017588\n",
"-2.772870851057964\n",
"3.046463610435266\n",
"1.6859141660185353\n"
]
}
],
"source": [
"for i in range(8):\n",
" print(normal(0, 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normal distributions are very useful in computer-generated art because they allow us to introduce and control variation, keeping values mostly in the same range.\n",
"\n",
"### Truncated normal distribution\n",
"\n",
"You may have noticed that sometimes the random number generator for the normal distribution produces values well outside the standard deviation. That's not a bug—it's just how normal distributions work. (In any data set, there are sometimes extreme outliers.)\n",
"\n",
"Nevertheless, in computer-generated art there's often a need to generate random numbers from a normal distribution that are *constrainted* to a particular range. (For example, you might want to generate a random color with a normal distribution, but never want your color values to fall outside of the range 0–255.) For this, you can use a *truncated normal distribution*. The following cell defines a function `t_normal()` to produce such values."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"from scipy.stats import truncnorm\n",
"def t_normal(a, b, mu, sigma):\n",
" tn = truncnorm((a-mu)/sigma, (b-mu)/sigma, loc=mu, scale=sigma)\n",
" return tn.rvs(1)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first parameter is the low end of the range, the second parameter is the high end, the third parameter is the center and the fourth parameter is the standard deviation."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20.826393825533792"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_normal(0, 255, 10, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Choosing from a list with probabilities\n",
"\n",
"The `choice()` function implements a kind of *weighted* random choice, where you can set the weights randomly. The code in the cell below picks one of `50`, `100` or `150` (the values between the first set of brackets). It will pick these with 15% probability, 80% probability, and 5% probability respectively (as specified by the values between the second set of brackets)."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"choice([50, 100, 150], p=[0.15, 0.8, 0.05])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling this function multiple times should result in the specified distribution:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100\n",
"100\n",
"100\n",
"100\n",
"100\n",
"50\n",
"150\n",
"100\n"
]
}
],
"source": [
"for i in range(8):\n",
" print(choice([50, 100, 150], p=[0.15, 0.8, 0.05]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Without the `p` parameter, the `choice()` function defaults to a uniform distribution (i.e., no result is more likely than any other):"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50\n",
"100\n",
"100\n",
"150\n",
"150\n",
"150\n",
"100\n",
"150\n"
]
}
],
"source": [
"for i in range(8):\n",
" print(choice([50, 100, 150]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `sample()` function (from Python's standard `random` module) takes a list of values and returns the specified number of values, randomly selected (using a uniform distribution) from the list."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from random import sample"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[5, 15]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample([5, 10, 15, 20], 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing distributions with flat\n",
"\n",
"The code in the cell below places circles on a page with the (x, y) coordinates of their centers determined by different random distributions. The small red circles are uniformly distributed; the medium-sized green circles are normally distributed and the large blue circles are in a truncated normal distribution (centered on the center of the page, with the same spread.)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"uniform_fig = shape().nostroke().stroke(rgba(255, 0, 0, 255)).width(2).nofill()\n",
"normal_fig = shape().nostroke().stroke(rgba(64, 220, 64, 255)).width(2).nofill()\n",
"t_normal_fig = shape().nostroke().stroke(rgba(0, 128, 255, 255)).width(2).nofill()\n",
"mu = 50 # center of normal distributions\n",
"sigma = 20 # standard deviation of normal distributions\n",
"sample_n = 200 # number of \"samples\" (i.e. circles to draw for each distribution)\n",
"for i in range(sample_n):\n",
" c = uniform_fig.circle(uniform(100), uniform(100), 1)\n",
" page.place(c)\n",
"for i in range(sample_n):\n",
" c = normal_fig.circle(normal(mu, sigma), normal(mu, sigma), 1.5)\n",
" page.place(c)\n",
"for i in range(sample_n):\n",
" c = t_normal_fig.circle(t_normal(20, 80, mu, sigma), t_normal(20, 80, mu, sigma), 2)\n",
" page.place(c)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Exercise: Copy the visualization above, but rewrite it to visualize three normal distributions with different standard deviations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random compositions\n",
"\n",
"The following code uses random numbers to set the stroke color, fill color, and position of a rectangle on the page. (Run it multiple times to see the results.)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"stroke_c = rgba(int(t_normal(0, 100, 50, 25)),\n",
" int(t_normal(0, 100, 50, 25)),\n",
" int(uniform(255)),\n",
" 255)\n",
"fill_c = rgba(int(uniform(255)),\n",
" int(uniform(255)),\n",
" int(uniform(255)),\n",
" choice([40, 255], p=[0.9, 0.1]))\n",
"figure = shape().stroke(stroke_c).fill(fill_c).width(uniform(5))\n",
"rect = figure.rectangle(\n",
" normal(25, 10), normal(25, 10),\n",
" normal(50, 10), normal(20, 10))\n",
"page.place(rect)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Play around with the parameters a bit until you understand how it works. Anywhere you see a call to `uniform()`, `normal()`, `t_normal()` or `choice()`, you can replace it with a call to another one of those same functions. (Note that colors values *always* have to be integers and always need to be between 0–255.)\n",
"\n",
"The cell below uses a `for` loop to draw multiple rectangles:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"for i in range(int(uniform(8, 24))):\n",
" stroke_c = rgba(int(t_normal(0, 100, 50, 25)),\n",
" int(t_normal(0, 100, 50, 25)),\n",
" int(uniform(255)),\n",
" 255)\n",
" fill_c = rgba(int(uniform(255)),\n",
" int(uniform(255)),\n",
" int(uniform(255)),\n",
" choice([40, 255], p=[0.9, 0.1]))\n",
" figure = shape().stroke(stroke_c).fill(fill_c).width(uniform(5))\n",
" rect = figure.rectangle(normal(25, 10), normal(25, 10),\n",
" normal(50, 10), normal(20, 10))\n",
" page.place(rect)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's another example that uses circles instead of rectangles:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"for i in range(12):\n",
" fill_c = rgba(0, 0, int(uniform(255)), choice([40, 80], p=[0.5, 0.5]))\n",
" figure = shape().nostroke().fill(fill_c).width(np.random.uniform(5))\n",
" rect = figure.circle(normal(50, 10), normal(50, 10), t_normal(0, 100, 25, 15))\n",
" page.place(rect)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Exercise: Write code to generate random compositions by copying one of the cells above and modifying the parameters. Also try adding in new shapes or combining different kinds of shapes.\n",
"\n",
"> Exercise: Can you figure out how to make the \"page\" bigger? How do you need to adjust the parameters in the random number-generating functions to compensate?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Polylines\n",
"\n",
"Okay, so, random numbers are good! But how does this help us model pen strokes? Good question. We're getting there! The next step in our asemic journey is to create a *series of lines* linking a given list of points (specified as (x, y) coordinates). The name for this shape—a series of connected lines—is a *polyline*.\n",
"\n",
"The first step in making a polyline is to generate some points. The following cell creates a Python list with randomly-generated points:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"pts = []\n",
"for i in range(10):\n",
" pts.append([uniform(100), uniform(100)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the points look like. This is a data structure called a *list of lists* (i.e., a list that has other lists as its elements), which for various reasons is a convenient way to store polylines and polygons."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[[2.6763965211166294, 25.659138354375344],\n",
" [82.26073644670053, 77.59081072583858],\n",
" [24.773719659622714, 15.953688831433638],\n",
" [96.68216877150023, 38.25114219800586],\n",
" [96.44669482295144, 54.73196837693114],\n",
" [45.52819621144284, 59.51208423857736],\n",
" [21.10649106954446, 30.4825009079498],\n",
" [60.31442268689315, 46.523018939048335],\n",
" [68.2755663052924, 94.79875467212021],\n",
" [52.80732784194058, 69.11788986805466]]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And here's some code to draw the points on a page, using a circle to mark each point."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(1)\n",
"for item in pts: # loop through the pts list, assigning each point to variable item\n",
" c = figure.circle(item[0], item[1], 2)\n",
" page.place(c)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The flat library has a built-in shape type, `.polyline()`, which draws lines connecting points. But the function wants the points as a list of points with the `x` and `y` coordinates directly following one another:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(1)\n",
"lines = figure.polyline([20, 10, 40, 75, 90, 85, 15, 15])\n",
"page.place(lines)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get around this requirement of the library, the function below takes a list of lists and flattens it into a list (with the `x` and `y` components of the coordinates appropriately interleaved)."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"def flatten(t):\n",
" from itertools import chain\n",
" return list(chain(*t))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code in the cell below draws a polyline from our randomly-generated points and draws circles where each point lies:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(1)\n",
"lines = figure.polyline(flatten(pts))\n",
"page.place(lines)\n",
"for item in pts:\n",
" c = figure.circle(item[0], item[1], 2)\n",
" page.place(c)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Combining all of this in the same cell, and getting rid of the circles on the points, gives us some code that generates designs that *almost* look like alien glyphs:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pts = []\n",
"for i in range(10):\n",
" pts.append([uniform(100), uniform(100)])\n",
"\n",
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(1)\n",
"lines = figure.polyline(flatten(pts))\n",
"page.place(lines)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Polylines on a grid\n",
"\n",
"Let's pursue this aesthetic a little bit further. Introducing further constraints to the randomness will result in designs that give the impression of conforming to an underlying system (and, let's hypothesize, thereby give an impression of being more like \"writing\"). The code below also uses random polylines, but constrains the polylines to begin and end on certain points in the grid."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(4).join('round')\n",
"pts = []\n",
"for i in range(12):\n",
" x = 5 + choice([0, 0.5, 1]) * 90\n",
" y = 5 + choice([0, 0.5, 1]) * 90\n",
" pts.append([x, y])\n",
"lines = figure.polyline(flatten(pts))\n",
"page.place(lines)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The key bit of code here is:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50.0"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5 + choice([0, 0.5, 1]) * 90"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... which is a Python expression that returns one of `0`, `0.5`, and `1` (at random), multiplies the result by 90 and adds five. Using this expression for both the `x` and `y` coordinate of each point on the polyline leads to a result wherein each polyline randomly connects one of nine points on a grid.\n",
"\n",
"The cell below defines a function, `make_char()`, that generates a series of points using a more general version of this expression, allowing you to specify an x-offset and y-offset (to set the upper left-hand corner of the grid) and a size (the multiplier). It adds four to twelve segments to the polyline (randomly determined)."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"def make_char(xoffset, yoffset, size):\n",
" pts = []\n",
" for i in range(int(t_normal(4, 12, 8, 2))):\n",
" x = xoffset + (choice([0, 0.5, 1]) * size)\n",
" y = yoffset + (choice([0, 0.5, 1]) * size)\n",
" pts.append([x, y])\n",
" return pts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results of the `make_char()` function:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[[10.0, 10.0], [0.0, 5.0], [5.0, 5.0], [5.0, 10.0]]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"make_char(0, 0, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cell draws these \"characters\" on a grid, so we can see many variations at once:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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J+yQB1CZbkc4/3nD2bmQbzAlWMLKaKgGp1gFF4uiqqT9V2lxAVYf6IIXbk4DKBBSuW6ui8G+BVL79/EQHpA4w2iUD1CZ7EQ6bT/F1t/62M9rNBlYwoprKDampAApjodXUzgKqOjQEKWwTBSoHoJaqKFwnkEq/f/seJV+6DZgaUJvGH+m0CahjwXiYwtJn8rCCEdVUTkhNCVAkprVqCv8WUPHvk0FIYbtRoLIBCtcvVVG4TiCVbr8OfefUdxswCaA2jTPCcevvoMDy1J+j/6RhBYHVVC5ITRFQGNdSNYXrBFS8+8QLUtg2CFQ9gFqponC9QCp+f/bCydJew+q7xvXmdEj1o+BA53t/qOsLKmw7SVhBYDWVA1JTBRSJb6mawnUCKr794Q0pbO8FKhegcNtKFYXrBVLjYw6CE+lHb/FZbwNG+RUQgNebJEJAhe0nBysIqKZSQ2rqgMIYV6opXC+g4tkfQZDCPr2gGgCUtYrCbQKp8FhHwYnsi6XvoCD1Gyw8gwh61VEoqLDPZGAFAdVUSkjNAVAk1pVqCtcLqMrvi2BIYT8rqPoAhdutVRRuE0j5xzgaTti/9yGJVLDycWTUu/jGgAr7TQJW4FlNpYLUnACF8VqrKdwmoCq7L0ZBCvuaoBoClLOKwu0CqeHYouBE9oPXU3yxsBpyZH/jwnex0isDdCnp+0WlLQKS4ILVg7h3sqf/XtVUCkipZTNjP20M3E+pdbbSM5S2zJxjazWF25KCChaP127GfVyhL3p5nNLR3L6gP6Mhhf0pqLSsgMK2zioKt98e0kLqeuZzSetMWHzoPS7BvvpziIAT2nikca050zOOf4bV69Sp2mDveAPOXGoxGqPgF87i0sFKJ/V23CdlgO+D1RTJzesjxrn9iH1RQkdkzq+zmsLtFFQ/Vjpy5Di3VPqc0huAGVS4nIEx/Vrp9yo4zqMghTa6c6UDg63NUBWlP6z9Q2cjMqZXVHD+2LR5ZFz6w+MoOGH/x2WIabfeMQcc2h4PGNPo+UrPC9ALlY5NcHLeifuEDPR5sJpSy1exzQMixzIfAe30/MB9FaNzybh6ttbsMzFDTzWF26NABeuA0n98SWlXxuNJL2fA8v5lBxXEV1K26TZO0xuMdn3fRWlAnU1tRMZ0C6WnFzx3qF6kdLXjfPa+G+WIa1sYASfsawLqRZFxalAOVoe+zt1G6S+VfkZOjIuU7sJ5crQgGKimyPYoSKEtfUvqy5YD+80Q+QnMY2x9n/tbOJ6+aG1fKL+91RS20aB6IwSCClYBtfLpveBxpJcOUPqTsJ7+4PlQAagg7jsp8118J8D6rb81UEH/E30rgILI231MedTfKz9F6RskDj177aNgHeJRkIrwzQTUjsXGDnRUYBW+c3urqZSQIjaLwooLUGT83moK2wSBqnZAkW3soBoLKRNQZP0KqMD9uygKqJ/iRb4pSA3AaXNs032PVBxSnIDaNP5IpwVWYflyVlM5IEVsZ4cVN6DQh8FqCtt5gaoVQJE2rKAaAykXoMh2CqpLwP52CRNQ+rvrJE/3Fcrb1piHr7vgRNqyQIobUJt8iAxAYOWXJ2c1lRNSZAwXrN4UA6saAEV8GaymsF0vqFoDFGnLBqpQSA0BirSjoFqqomyAwvXVQ6oHTie4zkcOSNUAqE1+JApGYDWcI2s1VQJSZKxdYP1BjShY1QQo9MermsK2VlC1CijShwVUIZDyBRRpfzo5Ts/DvFgBhe2rhdQYOJG+RSFVC6A2+ZI4MIGVOzfWaqokpMiYUbCqDVDEL69qCtuaoHpUy4AifYuDyhdSoYDCPt13UZ1e6gIUtq8OUjFwIjaKQaomQG3yJ1OQAit7XlaqKQ5IkbGDYaWWO9QIKPTNu5rC9hRUnZoFFLFRFFQ+kFLLk0cAij7R9wxYns9oBVDYpxpIpYATsVUEUmp5bE2A2uRT5oAFVsv5WKmmOCFF/PKCVc2AIj56V1PYfjcj5scw+q6XP48BFLHVgUpf3J6Q2e+hmXmDAYX91p7og8Utvu+Q/XSlbmDpww6plHAiNrNDqkZAbfKr0E4TWK3nYqmaqgFSxLc+WN2JAOoKpZtx++uIIeS7Kf2Dzc8ZsY5+M0Wk38kARWwWAVUfpAxAPSvAJq2ibq30OstxafvBLxukcsCJ2M4KqVoBtcm3wjtx9rACo5qqCVLERxestD5SK6CI/z6/m6KA0k8+7gkjfvCbyF8TUA9PaPsFuUHlgtRYQGHfrop6NQFU95i59Qe/2K84pHLCiYyRDVIWQO1UKnde/rEMOnNYETCdWiOkiJ+7wOrralZuA9YmGH6nnwmoPXD9qDdTRPqaDVBkjKygskEqElBdFaWvDxdTQJE2rjdTFINUCTiRsbJAqnZAbfKRdXA7rN6udGfuxGSOm1ZTn64YUvQ7KJuqhRW455uyAopsLwaqEoAiY2UDlQmpGEBhf/OJPtdDErY3U2SHVEk4kTGTQ6oFQG3yk9sBTNbsYAXLb32uDlIGoK5Quhkk/J1VAf9XqqkhQJG+2UFVElBkzCygopBKAKgDfABF2pugygYpB5y+kBNOZOykkGoFUJt85XbASJyG1VlzgBWsV1PVQcoGKGN7E7ACUk35Aor0zQYqDkCRsZODikDq7TGAQlvv9gUU6UNB9dbUkOKEE/EhGaRaAtQmf7kdcCRxFrCC5WqqCkgNAcpoWzWsYL2a0vqaL6BI/+Sg4gQU8SEpqGB51tUYQN3dOI4GAUX6mq9QioZUDXAiviSBVGuA2uQztwMDCbXB6lHcfiWMj1ZTD42wszms3j5MoU+g3SF92tH/NhXk+HLijzegjNx2oNJzq90i0p8XGDnyyW8OUR8eEhnTN4mtUYBCO6MARfqfQG1ExnQvxzF9OfP+Gg0pWHwAbQpQm/zmdsAzuR2srgOPNwm0JFh/8evoJxth9QeptWg0eBPm9zBYVCyj3yQBC1DpCdoencCf91SwX0ydEhnTa9DOiyPtdLdjR1d3ank82rg40pcXVrBfTOk7HKNnhlbLI4itJgC1yW9uBwKTnH2mV4aY9AMJd0xg5x5KL0+gU5QerPSACD0SFp9EWadaJ7nZW2lrbj/QFz0z6tGR+U2p+8buJ7VsBwlmLIbFh629EtjZMfYirJatlO4Pi99lce+jTreKjEkv+0MFt+KD/OZ2QCQSiUQil9gdEIlEIpHIJXYHRCKRSCRyid0BkUgkEolcYndAJBKJRCKX2B0QiUQikcgldgdEIpFIJHKJ3QGRSCQSiVxid0AkEolEIpfYHRCJRCKRyCV2B0QikUgkcondAZFIJBKJXGJ3QCQSiUQil9gdEIlEIpHIpeEGABuUDkyg3bmDJTHtWcs0ElMULOZfSjHlwvaJjr0DatnfsJjtdUduPxLHtAXmOMW+2j6BP7eCiU3ro49fpe24/SD+7J9gXx/sM/3IkCN6DpNUE3b9SGnvyMTcFYMbPTeQWg4iPlWz06cktbwe83tMpJ2rEh5/f8GdF4zp72ExxfoFSvuMtLEVngd3444H/Tk34X76XIQfdObZ93HnJWF+9QcAPSv0p6CCD1tqeXbC/a3VW8AMOXNHw9i/4IXDV181+kdBQS3XoJ3bRtjYQPy5WkCV/ADeneT3/0baegex9dPAY69T1/807txgTD8lPv0CFlPTB8FKLXtg/+u440F/PkxiGrOPfkz6v3vE+BpOF5gXP+68JMqtBtR3SFyjp49P6NOTiD+fDdjP/+GA1AG943k4dJTST9DYeeA5q6NadlD6KPb7ptK+CZITDSm08wmSoA8IqNJJLWeQ3D4t0tZ2uH82XZCV9gvsfy9Yr+JvyZ0b9Ola9On9Sr8cA6uKIXXUiL53Ijn5EATc7iNw6vJIFT3bNbcsgKoFUtuQa/EjPNrvpHSy0vdIHFeQ/+/f29/TqSBQ5QAU2k0FqX2MHS+gSrN/difHSTSk0OZoUKnlvdjvZdy5IT51F2T9vei+Sn8VCqupQGosoAbgpHUhd04S5JQC6tKaIIX+PQ39+Rw4bkH2wOlI3P6lZJBCg16gygUotJ0EUmjrTQKqtILlKioJpNBuMKigwioK/VqDFFkXBKspQGoMoCxwulHp68Yx9yvdjjsnkfk0AbUNxlUTpJzV1BCcSLu0kEKjvaDKCSi0nxJS+8DqJzEB1fh8dlWUfijgn1JCCu0HgQoqrKLQrxVIkW1esGodUqGAcsDpDUonKf3MOIcv4M5HZC5XAIXrq4IU+rRUTfnCifRPDyk0bAVVbkDhGMkghfa6auoTeOETUI3PZVdFXaz0utSQwjG8QAWVVlHomxNSpE0vrFqGVAigeuC0QenBBFCXYo50u2arKBegcFuNkKLV1FW+cCL980AKjZug2jE3oHDc1JDqqimthwqoRueRVlH3yAUpHGsQVFBpFYW+DUKKtHXB6tAWIeULqD444XYKqNfC+iPwzVZRfYDC7TVCaieyP73hRPrngxQOQEHVKRugcMykkEKbXTWl/91fQDUqh2tVFP6dDVJo3wkqqLiKQv+8IUX6mLBaE3c86N8gpHwANQQnbGMCagO2a7aKGgIUtqkGUmC/rad1ZqCdvJDCQR5mOJn1IMkEKVpN7SOgCs7fUhWF67JCCsewggoqrqLQv2BIkb4drOg5F/w7qwwx9UJqCFA+cMJ2JqA2w3bNVlE+gMJ27JBywElXTt3vGZ1P+jnsZa+k6HdQnbx/RzVyzOSQQrtr1RT+LaDyz91SFYXrskMKxzFB9QSouIpCn0dDitg4zDjvRv0oOGFMTkj1AcoXTtjWBqgN0HAV5QsobMsGqR44HYnbg343Rexm/U7KfEhC/wI5+Ae/I8bNBamlagrXCaiG87ZSReH6IpDCsSioOlVZRaG/KSC1B4k1+HdWGWKyQsoFqBA4YfsVQOH6ZquoEEBh++KQGoKT0Xbwd1OWPtme7rM+xQcj30wROHYWSKHtpWoK1wmo+nO2UkXh+mKQwvE0qH5ITqTDuXPT42tKSF2Hf4/6UXDCmFYgZQNUKJzQjgtQG6DRKioUUNinGKRC4ET6BFdTWSDlAhTZnhVUmSG1Uk3hegGVPV/WKgq3FYUUjnk5OaGCX6FU0M/kkCLrWWBlQsoCqLuHwgntWAGF25qsosYACvtlh9QYOBn9g6qp5JAaAhRplw1UOSGF9leqKVwvoFrNlbWKwm2lK6nuiT59EfxszaDKCSmyvSisKKQMQOnz9W9C4YQ2+wC1ARqsosYCCvtmg1QsnIid0Hf6pYOUL6BI+yygKgApazWF2wRU67lwVlG4vTSk1p7og8iX0hbwNTukSLsisCKQOglWfzcTBCe05wQUbm+uiooBFPZPDqlUcDJseldTySAVCijSLzmockMKx7BWU7hNQLWxv4rC7SUfnFj5XVTNoCoJKdI+K6xgeaqO0XBCW0OA2gCNVVGxgEIbySCVA07Etnc1lQRSarkZLL8q/uVKTwzQW0lf/an7SYH9TXW2ckLKWU3h9ipBpZajlV4UmV8fvYjsh9c62twI5SBl/V0UrD71d0aC2H9XnxOR/nb+3CbCxqjXIsEqrPS/+kPZbpExmT/q1AqCE9rpBRS2KVZFqeXwBMfMa4y86NvRV45Q1z8KUmp5FmSAkzGGVzUFiSD1CMvBV4NuF5HAffHg6ft1vLOawu1VgUott6lgn9iU+3dSvW+XAPvj6bE6OdJnaus4GDHTqlqeg/2/FnEOvJn4cVbCmP4aAuGENnwAtQEKVlFqOaeCc8hU1B0ptZyKdq6AxHAiY3hVU5AIUros1Pf59UtDz4uQmeg3Bfb/itE/ZmbeE9HG23ra9FZT2KYaUOkDF1anHtH6ROR+M/V3xPanPfZ3bkgNvl0CFqD6OvHp45HH8KsjfTb3kX4xpzesYPG9z02ok0b6oH8IezbxIeqRfVifbXjU/gYPQGG7ot9FqeXxCc4ZOsFqtBLEtCtkgpMxjs98U/lfixTg8HuMZHt/R6WW+yrdYPTPCils11tNYZv9oBJQoT+6ovpLcsL/Wukipbskst/7XRRpV+K1SN7v6IME31Gp5dmQBlK2Bwu8YAXLgHrmyPEpoDRcHphgX8TMzOsLqA3Q2HdRI3P5Mcfx0Xshr03gUU3VCil9fzZkhl8KqPOVvlUQUoPVFLarClToU3JYwcATfUbbEpAKekdfLKgyQOplIbCqFVBod+zMvF6AwrbNPdEXmItJwMmIqbeaqhVS+uTzneHXBJS+pRX9dJ8vpLDtYDWF7aoDFfqVDFbgWUVh29xvQR/1pvMYUGWAlJ4+/ukIHf33F2H9Q9gSrGoGFNoeMzNvCKA2wESrqCnCicTWW01VCyn8e2iG3xVA4frSkPKqprBtlaBC36JgBQFVFLbPDanRbzofC6ockMK/O1Bp/aHSMwxYUVUHKLQfOjOvN6Cw/eSqqCnDyYjTWU1VDSlc55rh1woo3FYUUtjeq5rCttWCCv0bBSsIqKKwfc5JD6PnixoDqlyQwnUUVE9V2hZhZV7Agp8GzA0oHCNkZt5QQG2ACVVRc4ETiddZTVUPKVxvguoIF6CwPQekvKspbF81qNBHb1hBYBWFfXJCKsl8UaGgygkpXG+C6iTHxcz7acASgMJxfGfmDQIU9plEFTU3OBmxW6upJiCF22wz/K4ACtsWhxT28a6msH31oEI/B2EFgVUU9skCKUg8624IqHJDCrfR76g24v+fCeuVlfU7K8c4RQCFY/nMzDsGUBug8SpqznAiObBWU81ACrf/qbEDt3S044JUUDWFfZoAFfrqgtX9IbCKQnu5IJV81l1fUJWAFG7/AjkPvmxs84JVSUDheEMz8wYDCvs1W0UJnFbysVJNNQMpsP8OyvrUHxeksF9QNYV9mgEV+mvCqtNXA+0khxQkrqIM24OgKgEpWH+Kj1ZST7W064PV5iUBhf70zcw7FlAboMEqSuDkzMtKNdUEpGD1IYkHQP9Tf5yQCq6msF9ToEKfNazeYZxkIU8D5oBU8irKsN8LqtyQAuMxczC+o3LYssGq089KAAr9cM3MOwpQ2LepKkrg5JWjpWqqekhZANU93ed8PJ0TUtg3uJrCfi2Cqvsuqrvg6X99nwZMCinIWEUZ4zhBlRNSJqDI+kFQYbttEWzmBXLUuwFHxGSbmTcGUBugkSpK4BSUq6VqqmpIuQBF2rseT+eG1KhqCvs2AyownuiDwEfXM0AqaxVljGUFVS5IuQBF2vtUVOa7+KiC3g04MiZzZt7RgML+1VdRAqfReaPV1JerhNQQoEifFVBxQwr7j6qmsG8ToALHE32+sEoJKShURRljroAqB6SGAEX6OEEFlockYMTTgJEx0Zl5YwG1ASquogRO0fmj1ZRX7ko51kHqDB9AkX4mqIq9u6+n/+hqCvtXDSrw+F3UEKwSQ6pYFWWMa4Kqm7YhFaRe4gMo0m8FVDZAGX2KwIpA6s9iAIW2qqyiBE5Jc/m0kByWcsp8C/ogoEhf2++o2CCFNkZXU9i/WlBB2Dv6XLD6SApIAUMVZYxvm48q5VvQg97FZ4DqaX2AMvplhRWszsw7FlAboLIqSuCUJadmNVUFpK4aAyjS3wRVCkhpvW6k3k9sjJqC2wDVu7kPHPRpdxLXPwXkw/wQ0ukFkf6wVFGGDyaoRn0wIfZofoLfxQerP/j1fszcAavP6PWRMX3TiOvskefV9VBJFYXXHNsx/eERce3NHU9tguVq6s69bQs5dBY680EYObMkOWj0gbxThC8Pdhx8Y/XICF80qP5d6TDugwb9uX/i3JwS6c89YVGZFa+iDD80qP4fxvRHkbY+CZEAh3VQjfodFCzD6p0J8nNBwmNGV1LsVVTi8+Be3PHUJlhUU11+rD9sX2tbyKFdlO4zFlDEzm2V7h5pYwsNFliQPFYnaHuR/ljfrsF04OjvOI5LkJeXKj1WaSvumBLmRp9URyhtHWlnAySYhFIth0PkLSeE1a0S+HJL3OcpzqkqbqOpZS9YvP3jrAQxRed4ilLLvkr3H2zH7ahIJBKJRC6xOyASiUQikUvsDohEIpFI5BK7AyKRSCQSucTugEgkEolELrE7IBKJRCKRS+wOiEQikUjkErsDIpFIJBK5xO6ASCQSiUQusTsgEolEIpFL7A6IRCKRSOQSuwMikUgkErnE7oBIJBKJRC6xOyASiUQikUvDDQC2VtohgWqakmKrRDHpKQr2SKBtEsWUwpdbJMpN1JQWomLnQpJp5FPZmaLUsn2ic2pyOfaJacjAfZVugDQTf+lJ43ontyqUlH2U/jtRTCl1i4iY9qjAf5vuHXPwKl2mdDkwz8ejljuQmI7iPoYTxnU8xnRypJ3T0c4fcMeUMDe/o/QdpTfByNm30c7zEp5Peir7mJjuqvQNvb9j7CTMsZ7QVM/qfLPedgNGjiUJ+jksgBWinxpJHj2jbsLE3MnwKTSmTqkv6KOrKbUclsGfFPk4ICKmHYgdPflccVDBAk7mrLPP4D6GE8a3FleknQ8QW5OAuFreQGL6hdIbYQSsYAGElNeb0ZOsquX5xM5pAHygMq5Z46ePV8vmSv+Ghp4U6ISeAfevse+PlA7lPvCIb+9Bv14ZYaNL8LdH9NW35t6F/b+vdFBkPAcaJ4K+aGwX0P9xSr/Cvn8SOPa9lN5Lxtb7+mUQOeW7AamioCJw+qUFvJO4jamWJ5CYro20tbeRo+ZBpZZzSDzdcRAMK7wOvhX7X690n5H+dOdnDKSeY+wnNlDB8geb3tmYfYw9Gg19DTynA68ZUOifvqjfpPTjsRe+sZBKDSgSj/6PrlyvCwHVWEDlghOxb1ZS2UE1ACetp3Aet4ljpXFdlsAevaj/pHVQkXhOgsU05381FlYpQJUYUt8gsRQHFaze+YmGVFA1VTugiJ9R1dQYSOUAFNrtIHWV0n6+oBoDqNxwIuN0kNK3Om6VE1QWON1oAZQ+sadYRaWC1G3x4j0JUFFIkXWjYRULqsSQOkPpBC5QwXIVFQ8pNOpVTbUCKPQ1qpoKhVQuQJFYNkEK/x4EVSigSsGJjLcGKfw7OagccHoDxnWTcSJNtYpKAim0e7Zht1lQ2SBFto2CVQyoUkMK/y4OKlivon5IrlFJIDVYTbUEKOLz6GoqBFI5AYX2lyCF65ygCgFUaTiRcZcgheuSgKoHThtgcXvnJtTHYNpV1AsTQ0pXUz83ctckqKAHUqRNMKxgJKggA6RwXVFQwXoVdarSlyAVpNC4s5qCBgGFfo+upsATUpAZUCSOJUjh+hVQgSeggAlOZPwVSOH60aCCHjjhdgqoZyt9HqZdRT0EEkIK7XfV1NuUzoNGQQUekCJtg2AFI0AFmSCF64uACparqF0gA6Ss1RQ0Ciji/6hqCjwgBQUAheNYIYXbKKiorIDihhPxwwop3BYEqiE4YRsKqGcqPQqmXUXdHfJAqqum9EV1f2gUVBAAKdLHG1YQCCrICCnclh1UQKoo/DstpNDoUjUFjQMKYxpVTcEApDA/74bMgCIxWCGF2/czAHW6pU0VcCL+OCGF2wdB5QMnbGcCSn8gm3QVhX8nhxTa7aqpt2AumwPVGEiRvl6wCgFVbkjh9mygAqOKwnVZIEWrqae0DigSV3A11QcpA1DfywkoHG8IUo8zILX2HVVtcCI+90IK21hB5QsnbLsEKFw3+SoK1+WCFK2m9oMGQQURkCI2BmEFnqCCApDCNllABUYVhevSQwoNd9UUvag1CyiMKbiaAgekYBVQBxby3wopWP4O6kJYv/WnP9FcXhuciN+DkMJ2FFQaRn/vAyfsawPULKooXJcFUmh7rZoieW0GVCkgRWz1wsoHVKUghe2SggosVRSuzwaprQxINQ0oEldQNWWDFAegcFzXgxMrD0nA6m9kflkTnIjvXpDCtrbXQjnhhH1WAIXrZ1FF4fqckFqqpnBdM6BKCSli0wmrIVCVhBS2TQYqsFRRuD7L7T76HVQnr7dQ1C4IrKZMSHEBivi+BCkTULB6W6/TZyHgFUoFY/K53ed6Q8QNffuwB1CzqaJwfTZIof2laorkuHpQ5YAUse2C1R1coCoNKWwfDSpwVFG4LfnTfRRQPyYHftA7/WoWBFRTFFKcgEJfzB/zUkD9Ldi/czocAl+hVDimvqf7XN85aRAPPUxhBRRum00VhdtyQ2qlmsL11YMqJ6TIGDZYna90pQkqDkhhnyhQgaOKwm3pIAWWp/hgxDv9ahcEVFPkxP8+J6CI35sgZQCKauU7Jwh4hRJDTLYf8/o8Su586g/6ATWrKgq3ZYUUjrFSTZF8VwuqEpAiY5mwom872QQqLkhhv1Gggp4qCrengZQNUOQgG/WG9JoFntWUBQIsgEJfzLeg98LJ6FslqGD53X3eT+th3xVQ9QEK+8yqisLtJSBlraZwW7WgKgkpMqYJK5uKQwr7BoMKeqoo3B4PKRegyPbZVlO1AIr4HAQno391oILVqTo6fVnp8Ur3G9CjLH1dgJpdFYXbs0MKx7FWUyT3FFRP9di3Q7pXAp+LQ4qM3cHKfH8kG6SwvzeoYKCKwjZJIPX75OBZeYoPClZTarkjLKoca8CJxxqspowDhw1Q6Mvhhj/6BAt6Wg8qAxW4IRWjFUDhWLOrorBNt/3vMvvirKZwOwVVCl2dwGc2SBEfNKwuNGLbMsKeLio+qvS0CBteoIKBKgrbJIHUKWjkzIHA9X++BpmqKVgA6ltDviQcb7CagsW7yayfDksLFlOt0wP5ejyAdg20Uw2oYBlS38CTa4yGqom5VlHfIm1OLeCPs5oi++HFEfuZ6u0J/GWHFPHljmRfsX+IGgIVeFRR2C4ppJwHMWSupgxAfVhp+0I7orea0utx+/O4Dxrik/6C9f0xsKoFVBDwO6kBO/dDOx91bJ9dFWUA6spCPvVWU7WpJkihP9VACv1xggo8qihsVwZS2C5LNcUFKBy7t5qqEVLEtyhY1QCqEpCCGVZRHIAiY/dWUzVJIOXl0wqowLOKwv5FIZW8muIEFPHBWU3VDCni42hYcYOqEKRmVUVxAgrHb6aaEkh5+2WCyquKwr7lIIVtk1VTNQAK/XBWUy1Aivg6ClacoMoNKZhZFcUNKOJHE9WUQCrINwoqryoK+xWHVJJqqhZAEX+s1VRLkCI+B8OKC1QFIDWbKqoWQKEvTVRTAqlg/06gH5AA+tmCfcpCCttHVVO1AQp9slZTLUKK+B4EKw5Q5YQUzKiKqglQxKfqqymBVLB/5kueB3/wywWp0dVUjYAivq1UUy1DisTgDavSoMoMqVlUUTUCCv2qvpoSSAX7130X9UXw/8FveUhhn+BqqmZAoX8r1dQUIEXi84JVSVDlghTMpIqqFVDE36qrKYFUkG9LT/SB/w9+2SAVVE3VDiji51I1NSVIkRgHYVUKVBkhNfkqqnZAoc9VV1MCqSDfbLPuDoKKDVLYz6uaagVQ6OtSNTVFSJFYe2FVAlQ5IAUzqKJaABTxu9pqSiDl7VfffFFDb6ZghdRgNdUSoIjPa9XUlCFF4nXCKjeoMkFq6lUUVdWAQt+rraYEUt5+Db3pvO/NFHyQwr7OaqpFQKHftJo6f+qQInG7YHVoLlBlgNQ/wvSrqGYARfyvspoSSHn55PuOPiuoaoCUtZpqFVDE/66a6jR5SJHYbbD6G1h8Gk4KqgyQ6jT1KqoZQGEMVVZTAikvn0LeLmF7hRIvpLD/UjXVOqAwpq6amh2kSA5MWFElAVVGSE25imoKUCSO6qopgdSgP97v6CN9TFBVASlaTb2ydUCRuGg1NTtIkTy4YPWZWFBlgtSUq6gmAYWxVFdNCaQG/fGuoox+5iuUeCGFNh5tONQ0oDAmOhNu7zTzc1APrLaNsJkKUkcQf6ZaRTULKBJPVdXUVCGF165nR+oCCKyiDB9MUEVB6sFKr1Y6JiIptJr6WOuAInH9BGN6IbcvtQhh9Q1y8P12hK1t8Nh7RQKfqvoUmiDP25GYfsrtT6KYdDX1WX0B4/YF/fkdPP5WZiRn8qfb36Onj0c7z4HVD5NjdWqEH/Rdf3v1ti2U4H2Unj4VQGFMt1b6E6Wbc/tSm9TyW0rPV9qxAl+2UHqW0kHcviSO671Kb+T2I3FM7D7UKrU8LOZDH7FzJMI3Vvqcip3t4hCl4wfbcSdfJBKJRCKX2B0QiUQikcgldgdEIpFIJHKJ3QGRSCQSiVxid0AkEolEIpfYHRCJRCKRyCV2B0QikUgkcondAZFIJBKJXGJ3QCQSiUQil9gdEIlEIpHIJXYHRCKRSCRyid0BkUgkEolcYndAJBKJRCKX2B0QiUQikcgldgdEIpFIJHKpf+Nivo/v4cRUUfMmqeWuSveMtHGa0qV6IkXuxKE/Jytdm0CfVNo5wo9dEk5k1mlsLL/G/t9UujP3Pkq0n3dSulzpa0r35/ZnqlLLqxKdT5/gjsWI6zClfbj9qElq+QO8TvwtDEzkOGTo8caF6/QxsFLLvkrfQeCNBhXx4xDuJKM/r0wIhaMj/HhoBkil0GHc+yhy/2o4nWzE9L8jbX4WL6S7c8dXm2AxHXmK4+5q7lhITAcr3aB0jYBqCU5U+/X2GTAIFoPXh8JKLVvBgpgbY0BFfKhlSmd9EdszUl/EmB4Q4ceBxj76F1hUwQcH6vXY/7IA//XYFxnjf0LpPtz7J3K/ngzrdxE6fV5ps0jb16KtPbnjrE0EUvtHnlO34o6FxLSD0scwrtmCygGnTvv39vUwDg7DQbCCBKCCyiCVaOd9MAOktN4IgbdF1fJE7HueR9s9YHF75qfY5yaldykdwJ3TiDy64KR1ViygcAyBlDs3HaRG3/quUXMGVQ+c7qb0pSSQwoFcoAqCVSyoBFJOGzZIBYPKB1IzhFMyQOFYAil3biYJKYxtVqDqgxNpkw5SaNAElU7034fCKgZUAimnDRekgkDVB6kZwekKpZcr/Sw1oHBMgZQ7N5OFFMY3eVD5wIm0TQspNGqC6p1K9w6F1VhQCaScNjpIXWXsnx+HgMoGqZnB6UilY3IBCscWSLlzM2lIYYyTBFUInEif9JBCwyugwvVBsBoDKoGU0waF1NEkTy8PARWF1NzghNuzAgrHEEi5czN5SGGckwHVGDiRvnkghcatoMJt3rAKBZVAymljDVJGnrSO9AUVgdTGOcEJ22QHFI4jkHLnZhaQwlibBlUMnIiNfJDCAZygwu1esAoBlUDKacOEFK2mHuQDKlhUTj8i/WYBJ2xXBFA4lkDKnZvZQArjbQ5UKeBEbOWFFA7SCypsMwgrX1AJpJw2liBl5Goj/m0FFaze1us0eThh22KAwvEEUu7czApSGHMToEoJJ2IzP6RwoEFQYbteWPmASiDltGGD1FI1hesoqN6r9BpYvq3XtR/8nVSNCoETti8KKBxTIOXOzewghXFXC6occCK2y0AKB/MCFbZ1wmoIVAIpp40VSBn52kjWnWg54Dbd1oOAH/PWpFA4YZ/igMJxBVLu3MwSUhh7VaDKCScyRjlI4YDeoML2Lljt4QKVQMppwwUpWk39Hthv630K1m/9NQWpMXDCfiyAwrEFUu7czBZSGD87qErAiYxVFlI4aBCosI8NVq9Q+qgJKoGU04YVUkbOOnUPRDwLjO+oWoHUWDhhXzZA4fgCKXduZg0pzAELqErCiYxZHlI4cDCosJ8Jq5+R/28ClUDKacP2nVT3QATdF/ptzAeQNkcaoHpSzZCKgRP2ZwUU+iCQcudm9pDCPBQDFQecyNg8kMLBR4EK+5qwskkgtWyD/pjX9iPctdxZ+lJQdaoKUrFwQhvsgEI/BFLu3Aik1nORFVSccCI+8EEKHTBB9X68gPrqeFhMOSGQGrZB39238iNcsDzpZ/Q3QfUm7rygX9FwQjsPI/3/RunWgcdip+iLJ4HUgSN96LT7phMs3p8tgAnYFl+qghQsJhON2Uexur3Sv2NOboQEoFLLSx3X1OfB4oNcSXVj80AKEwKOhMTqCO4DOGGOUkCK3gq1/giX5s9h40jS5lLuvKBPRxCfguGENrZNfOw9JDKmaxP6cm6EH3R//2kF+3pP4g87pGDxITnHtStGH4+M6YoKYrDp4F6/C+xsvdxoOHXdCNH+1fyOIEF+UkBKP7p/KSwmH7T+CBcGqilscyFur+Z2n1rOgBFwIv13VHofxvXfCY69+0bG87KRPth8OWvE+EdaLhJPY97HGlBfIf7UAKkHJrhmpdDSvoqMKeVM4im1Xa/fhXa4Xk5Dh36pdMJIO9egjdtyH8QJcxMNqYCxeg92aOTpvsL7548wJ7+GyKnjE/iib93+D/qjP5RsE9D3SMcF4reYYzIBVQWk0Lf/A4s7E1rFQa6WvZW+hjnRs13vlMDmsUo/R5uvAijDgCifCyY8GlQCqeixhr6bEkgt56N5QNUKJ/SNAuqztUEKfWQBVQ5AEdtNgar0Do8ClUAqyXjOakogtZSLpgFVM5zQPxNQ+o0zVT04QXwtCqqcgCJjNAMqjh0+GlQCqSTjOaspgdRaHpoFVO1wQh9XAIXrq4QU+lYEVCUARcZqAlRcO3wUqARSyca0VlMCqXYB1QKc0E8roHBbtZBC/7KCqiSgyJjVg4pzhweDSiCVbExrNTV3SLUIqFbghL46AYXbq4YU+pgFVByAImNXDSruHR4EKoFU0nFXqqk5Q6o1QLUEJ/S3F1DYpnpIoZ9JQcUJKOJDtaDidyAAVAKppOPa5puaJaRaAlRrcEKfBwGF7ZqAFPqaBFQ1AIr4UiWo2B3A5HiBSiCVfOylamqOkGoFUC3CCf32AhS2bQZS6G8UqGoCFPGpOlCxO0CSMwgqgVTysZeqqblBqgVAtQon9N0bUNi+KUihz6NAVSOgiG9VgYo9IUZyekElkMoyPr3wzQZStQOqZThhTEGAwj7NQQr9DgJVzYAiPlYDKvZkWJLjBJVAKsv4tJp65xwgVTmgHtwynDCmYEBhvyYhhb57gaoFQBFfqwAVeyIcybGCSiCVzQfzgjhZSFUMqBtahxPGNApQ2LdZSKH/vaBqCVDEZ3ZQsSehJzkroBJIZfOBVlOThVTFgGoeThjTaEBh/6YhhTFYQdUioIjvrKBiT8BAckxQdSewQCq9H5OGVIWAmgycMKYoQKGN5iGFcSyBqmVAkZjYQMUevEdyKKiqgpRa9lN6SKS6mLghRaupa7hzmzi2mgD1mCnBCWPa04jlcZHngp7X7KwI/ZnSrsw56UBF89IkoEhMLKBiD9wzOSaoduf2Cf1KOYnYERXEs+YPty+J43puDYBCX74+FTiRmP4i4XmQSpdXkJf/MxVAkZg6UL1GabMiY3IHHZAcvfye0jO4fSE+/S+lyxLoDUpbVxDP7ZTOVbonty8ZYrsbtw/kmNH/OZ7bl4QxHaKhoPTJyPMgJaQezZ0XzM2JSmdOAVAkpruWAtSm8bgDFolEIpHIJXYHRCKRSCRyid0BkUgkEolcYndAJBKJRCKX2B0QiUQikcgldgdEIpFIJHKJ3QGRSCQSiVxid0AkEolEIpfYHRCJRCKRyCV2B0QikUgkcondAZFIJBKJXGJ3QCQSiUQil9gdEIlEIpHIJXYHRCKRSCRyid0BkUgkEolcYnegVen5iZTeobRvBb5soXSO0qMi7eyh9EaloyNsbK10ktJu3HlBf7ZV2jKBnbvAYtZX9lmhMaa7RtrQy/Nw7qXTIm3pifCuU/qTCBub6f5Kf17D3GqiesTuQKtCQFUxi62GUwpf1HIBmTTu2MC+Gk5PUfoG9n8Fd17Qrxcp/YfSgyNsHEDy8hrmeDSg3q/0Qz3Z4Ij+erm/0k/oJIGRPr3JmHAwCFYIqHNI/0sEVKJO7A60KrW8mZxUBzH78sTYi41a7qD0KxLTz31AZYGT1ueVHl7BPtpJ6bvo01EjbWhAfYfEdjZjPB2g9B//rXTngL4dnD5qAEXrPyP9uqXSry12B2FlAZSASrR8jHA70KrU8kpyQv0dsy8pIHU+2jhP6VVDoOqBk67qNufeP+jji9CvjwCEH+sWQGm9mimWUYAagFOnyxL5uHcIrAxA6cruaKW7kw8WAiqRQGp04pYhdRMwVlOxkIJFFfVLpV8obcALmxVULcAJ/YyqogxAXar0x1yQGgMoTzg9MyWkyNiDsLIBimwTUInWjyduB1oVgVR3MrJVUwkgdT72P5esM0H1iBbgRPwfXUVZALWN0rM5IBUKKAec9AX/3QYw9AeTh+SAFPHFBasXuQBF+gqoRItjgduBVkUg9QqlG4CxmoqBFBhVlLFNL6+1XGSqhRP6PbqKsgEK1xeHVAigeuCkn5g7ygQU9skKKeKbC1YbbYAi/QRUIoHU6MStQ0o/xvtnwFhNRULqfDCqKFxvu62n9eZa4UR8H1VFuQCF24pCyhdQA3DaUekgG6CwbxFIkfE2OEDlfMBCQCVid6BVGZDaDRirqbGQAksV5YCTrpyugYGHKWoQjKyi+gCF24tBygdQQ3DCNk5A4fZikAL3U3yDsBJQzVvsDrQqCin8m62aioDU+djv3B44bbqthxfFwaf+uAUjqqghQGGbIpAaApQPnLBdL6CwTanbfdaHJCDgaUAB1XzF7kCrskCKrZoaAylYr6I2ImAHH4ioHVQwooryARS2yw6pPkD5wgnbDgIK22WHlAtQRhsvWAmo5il2B1qVCSlcx1JNjYTU2y0XhcEHImoGFQRWUb6AwrZZIeUCVAicsL0XoLBt7qf7BgFltB+ElYBqfmJ3oFU5IMVSTYVACha39c4IhZNhozpQQWAVFQIobJ8NUjZAhcIJ7XgDCttng1QooIy+vbASUM1L7A60KhukcH3xasoHUuB+Wm/Uo+S1gQoCqqhQQGGfLJByACoITmgnCFDYJwukYgBl2HHCSkA1H7E70Kp6IFW8muqDVA+ctPaJHLcKUEFAFTUGUNgvOaQsgHp6KJzQTjCgsF9ySKUClGHTBat3CKimL3YHWpULUritaDVlg5QDTvq23o/x/+cmGpsdVOBZRY0FFPZNCikDUFr/EwontDMKUNg3KaRyAMqw74JVJwHVBMXuQKsagFTRaopCqgdO+rbeHcHxdonI8dlABZ5VVAygsH8ySFkAFQwntDMaUNg/GaRyA8oYqw9WAqqJid2BVtUHKdxerJoyIOV8lBwcb5dI5AMLqMCjiooFFNpIAikE1C9i4IR2ogCFNpJAqiSgjHH7YCWgmoiGDgL95eTVuNN/GKkvK916tKMAO6MfH4aI1/Lggf0FpROjEjcMqWLVlFqeapygK0/rQc87+kibP8c2x4/0g4JKa/TssbhcMnBM3WTEbWtjXrx+NvL47frHQuojhj9BcEIbKQC1G+kfC6mD8LgqBihjfCusIm0ep3S90pVKu5aOyfBFX/sex+kDa/wDyTnccpLH6LiInUTtbBWxw9+V6CDuhRS2KVJNqeV1JDfWp/VgoIqCZbi8JMKXa4idwyPs7JD42EshfSF+dOS++hu09RYIhBP2j73Fp+F0nWHjjxMcg/ot+cUBZfhwPxpXpK2nGDl6CTDACq99n0QfnsyZX7b96pGkU8iOegYmLVRXwEhIwSqgHhO50y9JdBD7QKpINQUEUo7tvVUULANKa1SlCsuA+mRkTBRStmNqL1h/Y8bxlu36A9aNuP1jSrcaeexSbZdgX+lbY2P7xjwkYYOT1qvH+lOTYFFN/T+MSVc/OyfYT58ycqUr6mKwgmVA/afS7bjzzLJvPZP1AkyUnl78CSOS/Z4xkEoNKLRZDFLYLns15QGp88FRRdUIKLTXQeoGx3bnd1GQ4Duo2jQWUFOHE8ZoAmqnRHY1qM605C47rARQJBcBSRsNqjGQygEotFsaUtmrqT5IQf98UVUCCm06IQU9T/QJoJaOu0nDCePMAihi3wWqbLASQBn5CEzeKFCFQioXoNB2UUhh26zV1ACkzgdLFVUzoNBuH6SsVZQAaj5wwlizAoqMQ0Gln1w9XekfcsBKAGXJyYgkBoMqBFI5AYX2OSCVtZpyQQocVVTtgELbVkiBo4qaO6DmBCeMtwigyHgmqH5L6dCUsBJAOfIyMplBoPKFVG5A4RjFIYXts1VTPZA6H4wqqgVAoX0XpFaqqDkDam5wwpiLAoqMuwIqXB8NKwFUT24idpg3qHwgVQJQOA4XpLJVUzZIgX3W3SYAhWOsQAosVdRcATVHOGHcLIAi41tBhdtGwUoANZDzyB3mBaohSJUCFI7FAinsk6WackDqfCBVVEuAwnFskFqqouYIqLnCCWNnBRTxwwkq3O4NKwGUR74T7LBBUPVBqiSgcDxOSGWppkxIgVFFtQYoHGsJUmBUUXMD1JzhhPFXASjiTy+osE0vrARQnrlOtMN6QeWCVGlA4ZhskMJ+yaspC6TOx7/PbRFQOJ4JKVpFzQZQc4cT5qAqQBG/BkGF7Wyw0v2+KoDyyHPCHeYElQ1SHIDCcbkhlbyaguXXItEq6uIWAYVjrkEKlquoZ84BUAKntdxUCSjinxeosK0Jq053546jZqXeYVZQmZDiAhSOzQop7Ju0mjIgdb7lJGgKUDguhVRXRX1/BoA6VOC0lpuqAUX8DAGVee3rKiuWdwO2oBw7bAVUFFKcgEJfaoBU0mrKgNQvYfUkaApQODZ9d993jXimCiibZgcnzE0TgCL++nxHZX4H9RjI9KPgKSnXDlsCFYHUEzgBhb6xQwr7J6umDEg1Dygc3/UW9LkAapZwwtw0BSjid9/j6c6HJCDxj4Knppw7jILKdhIWBxT6VQukklVTPZBqElDogw1ScwDUbOGEuWkSUMR/25spvJ7iE1g5cpp5h70A7Ceinuht35HaMtKnS4gfT43QDWjj9RGxdHMLfSAyJhukmgUU+mFC6r0TAdQxAidnbpoGFInD9VJar6f4csKqxXOoxA5zgWqsbhvpz78l9ieV9oqI6fxEgKKTXLIBCn3Znvjy4xZPLkdcAid3bo6FxVOpzQKKxGKCKvgxcwNW+o7LgQn8umpxuPHnyNvnQjuMVi9XR2r0FPToy4EZABMTj35a7X0wYpZWEtOusYBCOxuU/lnpIvYDc7FcABO5xUfi6vbTewVO1vw8sHVAkVg0qE5Ren8ooAw7GlZ/msCfLfQ5xZ2XYL+5HRCJRCKRyCV2B0QikUgkcondAZFIJBKJXGJ3QCQSiUQil9gdEIlEIpHIJXYHRCKRSCRyid0BkUgkEolcYndAJBKJRCKX2B0QiUQikcgldgdEIpFIJHKJ3QGRSCQSiVxid0AkEolEIpfYHRCJRCKRyCV2B0QikUgkcondAZFIJBKJXCozCMBdlc5SOieBTkrk02bcyZ+q1LJHLXMl6fm1lP5Y6eBIO7fU8/FwxzNlqeVBSm9IcI14aQJffkPpxVOZ2wpj0rNd60lo78vtS5DfhZJzkcfEgb7SM1TuFuHLUcSWgCr9vj4AFhM5nlYDqNTyu93+jrCxp9JXlN4qoMq6r5JNQprAl/9BW+dx5yVhfk8iOfpQK7AqlZz3YGIuw0SNVZfg0VPIq+X3iZ1HcO+AKYkASv+hZ9TdsgKfTo6BFAGU/uOzSjtzxzRVGaCJuU6cmNCXq7jzkjC/z7EAvXpYlUpOB6njIu1ckwBSJ5Id9DmQairVPjYBVcWU7zGQsgDq5tzxTFlq2Yecm89l9mXKkHojLKa1/34LsCqVnFohJdVUmv1bJaDQt1GQEkCx7a+NY/ZXRj+mCKkz8O+dW4BVqeTUDCmppuL2SbWAQv+CISWAYt1fVVRTc4AUWV81rEolp0ZIvZvYk2pqXC6rBhT6GAQpARS/aqim5gQpsr1KWJVKTo2QepvS06SaGp3H6gGFfnpDSgBVh2qopuYIKdKuKliVSk6tkNpGqqlROWwCUOirF6QEUHWJu5qaM6RI+ypgVSo5VUIK/5ZqKix/zQAK/R2ElACqPnFXUwKppX6ssCqVnJohJdWUf+6aAhT63AspAVS94qymBFLW/iywKpWcaiGF66SaGs5bc4BCv52QEkDVLc5qSiDVa6corEolp3ZISTXVn7MmAYW+WyElgGpDXNWUQMrLXhFYlUpO1ZDC9VJN2fPVLKDQ/xVICaDaEVc1JZAKspsVVqWS0wKkpJpazVXTgMIYliAlgGpPHNWUQGqU/SywKpWc6iGF26SaWs9F84DCOE4mJ4wAqkFxVFMCqahxksKqVHJagZRUUxunAyiMhUJKANWoSldTAqkk4yWBVankNAEp3D7rampKgMJ4KKQEUI2qdDUlkEo6bhSsSjlZI6S0PmXRvxoXNVsbrZ8pXRLjS22aGqAwJgqpSQAKFjOs/nPPsemjT5O8/DZ3TJ5xF6umPK8BpbV5ZEwskCLj22D1doB+DpVyriZI/SYsZvfdmEj349jhGfbR5ACFcf0O2VdTAdSXEh6/Wmdyx+UZe7FqKnF+UylqVmi17KF0r5jrZ4K83tsS1/a9fQo5Vg2k0M4G3FkuHU4S+DJj24vJtnfABG4JThVQGNv9MK6PcvuSIBYNqI8aJ3jfcUx1iNJplgvEX7a0v6nvmcd5A45zXkCOc6vZa40DTloPH+xbyMGqIOU51sp3U2o5Bha3+fQfZ7V80JA4JwsojG8SkDIA9Qv89zqPfpvpC4HSZ8iF4VqlZyptyx3XiDwUqabUcg6OcRJ3zC2rB06P9LZRyNEWIbX0pJ8Aqk1NAVIGoL6pdNgQpKYGJyO27NWUQCo6f9FwWrNVyOHmIIXjPY0k9+cCqPbUOqQsgNoXFt8tWCE1ZTiRGLNXUwKp0XlLBqc1m4UcbxVS2xiJFkA1ppYhZQMUrl+B1BzgZOQmazUlkArOV3I4rdkuFECrkDrGSHjUI6A1aE6AwnibhJQLULhtDVJzgxPJQdZqSiDlnadscFobo1AgzUEKlr+D6tT0WyjmBiiMuTlI9QEKt+9BjslZwcnIQ7ZqSiA1mJ/scFobq1BATUEKVh+SaP4tFHMEFMbdFKQ8AKUrpycbF4ZZwYnkIls1JZBy5qUYnNbGLBRYM5CyAEpfFJp+p99cAYWxNwOpPkCB/bbexjnCychZlmpKILWSj+JwWhu7UIBNQMoGKLKtyWpqzoDC+JuAlAtQDjj9Gv/9Abff3MpVTQmk1vLABqc1HwoFWj2k+gCF25urpuYOKMxB9ZCyAcoBp+623gYwnu6bs3JUU3OHVA1wWvOlUMBVQ2oIUKRdM9WUAGotD1VDygKo2/fAaVvs4/yd1ByVo5qaK6RqgtOaT4UCrxZSvoDCtk1UUwKopVxUCykLoP6wD06kn0BqNZdJq6m5QapGOK35VigBVUIqBFCkT9XVlABqJR9VQgpWXxb7wyE4kb4CqdWcJK2m5gKpmuG05mOhRFQHqTGAwn7VVlMCKGtOqoOUBVBecCL9BVL2vCSrpqYOqRbgtOZroYRUBamxgCL9q6umBFDOvFQFKQTUKDgRGwIpe16SVVNThVRLcFrzuVBiqoGUWnY3do5+nPdXgaL9H8C+ExdPgl1v+BUaky22WN2jgtzUBqlPGDkK/p2TQKo3N0mqqSlCChZzUjUDpzW/CyVnc6UtILLqQBtbAIz3Wy23U/rPRBfh7yodxL4TAbaHxfTSKQGTQgdWkJvN8Jip4r2LarkQc3NmKJyIjbVzgTue2gTr1dTZkXaSXLNqEixmJW8GTmt+czvAtLO6C1esqjmAE8a0pdLWCSQXUJFIFC12B0QikUgkcondAZFIJBKJXGJ3QCQSiUQil9gdEIlEIpHIJXYHRCKRSCRyid0BkUgkEolcYndAJBKJRCKX2B0QiUQikcgldgdEIpFIJHKJ3QGRSCQSiVxid0AkEolEIpfYHRCJRCKRyCV2B0QikUgkcondAZFIJBKJXCozCMDBSm/RUzhE2NDLYUqHcicN/dFTWrwZFrPhXhup52qDFcSkp9h4rNItuH1Bf7ZSek4lOh0qmcgRc/Mgpbtz+0H8+Q2lm0Xa0NeJo7hjmapwOp8TlZ5fwfnU6eaDfhdIzHGwPtHW1friHthfL0crfQxt6Gnfd6lgh/8+uCf7G6P9K4jpRPTle9y+oD+2qdbZFRnTSXhy7hBh463oy5e49xH6sxXJzx8pbTfCxi2JjVGTQSaOaRelU5T+kNuXhDEdw33ujLnu5U7Kwy1OXQQeoMKFwolq9PTxCWPbM+GOeh1UMHOsWl5AfDq4An+2UTqjEqWC1LVoZ8+R/Z9HfPkq9z5Cnw4zjudvK/0hBMBKLS8n/XeuIKb7oi8/qMGfRDHtbOynCys4r2416HfGhJiA+rDSD2EAVLiYcNLTtL8QD5gqIIW+6lscX0efPq60I7dPkfE8keT8Um5/alINkDIApbU7d16Ib0cZvmld5wMrWFRRN9QEKfTrQ+jPKdy+JIzpVkpfwLi+4AMJbuVKhAmoXyndTOk3XaAagNOO2OaamiCFPk0GVAakboIKqqlaxA0pC6DO4M6JxccjlX4SCitYrqJqgtTkqimMqylQ5UiA7Rbfc8n2FVANwYn0rQ5S6NckQGVASqqp5dywQcoCKA2Caqoow1cNqh+jn/+l9Ok+WMF6FXVTbZBC/yZXTWFczYAqdeA2QOn70zcz2lFQUVnhRPpVCSn0rXlQEUi9h1w4pJrayAcpAqhfEx+qq6IMnymo3qh0rNKnbLCC9SrqUnJNqAlSk6ymMLYmQJUyYBNQ/w5GFYXt9KIrpxuN9icPXdhrhhT61zSoCKTOoxcPbr9qEAekDECdiR8aqq2iDN9NUG2u9FADVr8k/z+4RkhhLJOspjC26kGVKlATUI8Fo4oicLI9rbd2629gnKohhT42CyoDUvQ2zOyrqdKQMgD1B0oXQwNVlBGDDVRggZWWrqy6D661QWqy1RTGVzWoUgRoAuoWSlfi/5/rgNPabT3oeZjCMlb1kEI/mwQVhRT+LdXUem6KQcoCqHtAQ1WUEcsKqHD9bsZ1g2oPbr8tcUy2msL4qgVVbGA2QHU/GNNV1MNccDLseIGqFUihrxRU/9QCqCyQkmpqPTdFIGUCCtc1V0UZMa2AinwAei+sVlY/hcDfWRWIYdLVFMZYJahiArIBSi9XGuudcDLsDYKqJUihv02ByoQUrpNqamMZSDkA1WwVZcRGQfUejGftww8u5nXD63dWBWOYdDWFMVYHqrGBuAD1ylA4GXZ7QdUapNDnDa2AygEpqaY25oeUDVC4vukqyoiRgkrrMmN7d+7/LjieBmT2f/LVFMZZFajGBGADlO2BCG84Gfb7fvDbHKTQ7yZAZYMUrp99NZUTUj2AmkQVZcR/Asnl54C8Doyc9/r1PXoxbwOywwpmUE1hnNWAKtRxE1C/Y4HTxtgTygWqViGFvlcPqh5Izb6aygUpF6Bw22SqKBKT+XYJ+jDFyiPotcEKZlJNYaxVgCrEYRNQXyT//y75/3OTOGZ/M0WzkMKYqgaVC1K4bdbVVA5IDQBqilUU/bCjYzcfpnD+TqomWMFMqimMlR1Uvo7a3iTRwUnf1nsU/r3ydonIBJmg+q+WIYUxVQuqAUjNuprKAKlXuwCF7aZcRV2Kfx9pgOpHLkgRG+ywghlVUxgvK6h8HLQBiv7OSS9rv4vKkCDbK5SahRTGVCWo+iCF22dbTWWAVB+gpl5FHUzWU1B1Grzwc8MKZlRNYbxsoBpy7D4WQGldAouZdt8Cy7f6/pasN3VQRIJMUFUBKVhMEuiKd0j/ROLRr5DarIJ4Okh90LF9ttVUQkjR82gFUNimt4pSyxa1nAMBcTs/4FhA5V2dOGCldUykv7dWOlXpQMf25qopteyudL7S92AxcWboNesDRo4vjrj+dRp+PdhAUO8DO6TG6LjIBP8mrFdxtVQe5iP3Y6XfYXbrCuKhb0G/j6PNLKspur8i7XQPGj3Vsb23ioIFoP4aFhXZnbjz4hnz4IcbWIBqI8a19Ygx9lL6GtlPl0T6fDaxdYijTVPVFKy/rq4mxc3MC4uHFfSF+KkYoKkLyWBPcrTptFeCJOvH3W/PvbOJP/cciNlX9+COBeN5Mtmf14MFVD4XnCmKnliRdvQn9Nv1bHdWUbAOKP3Hj2z7p0aB5wcbWLwq6TcCbWs4vVbp5ziGvoWqP1zvHenz+42L6QqooLFqCo+f11tA8deB16uu32UJrn3Dt3YjAtZLtu+iROUFq/NJuUA1u2oqFaQGxnBWURZAHcqdE8+YsnyoccDpbeDxydzT/jmWi7kNVE1VU+jz9krPUfpvEtsnlX4LYJgJsLhNp//z2CL+RgRK39GX7Ik+EZ8IpN4Mi3vWVlDluvDUrEKQslZRrQIKfU/6gSY3nMg4HaRe0QcqaKyaMnwfBasmIIWLVFETEyxP1bHFAKhmVU3lhhQ4qqjGAZXsw0wpOJHxOkidpLTPAKiaq6YM/4Ng1QqkpIqaoGD1LehOUKW8ALWgApBaqaJaBhT6H/1BpjScyLhrkMK/naCChqspI2YvWFUPKVykipqgTEjhuj5QzaaaygkpsFRREwBU1IcYLjiR8Zcghev6QNV0NWXE3gurFiAlVdREZYMUrreCKvZC1JIyQ2qpimodUBjDqA8w3HAifqxACtdbQQUTqaaMWF2w+l61kMJFqqiJygUp3OYC1SyqqVyQAqOKmgiggj+81AIn4o8VUrjNBarJVFNGvDZYbcTjNP/4gc5KFTVh9UEKt6+AaswFqUVlhNRaFTUFQGFM3h9caoMT8csJKdy+AiqYYDVlxNzBisbt/ej66HEDHNSLVFET1hCksI0NVJOvpnJACparqD0nAiivDy21won41wspbGMD1SSrKSPui424s8IqxDGpoiYuH0hhOxNUD/O5MLWsTJDqTvZXTwFQGFPvB5ba4UT8HIQUtjNB1VUak6ymMObuwYknQcSPgr3H83RKL1JFTVy+kMK2Jqg+0ndxal2pIQXrVZS+WHcv7mwdUM4qqhU4EX+9IIVtTVB1U46cwh1HptwsPd0HkW+wGBzP0ympomagEEhhewqqTpOspjJAyrxl0jSgMKaVKqo1OBG/vSGF7U1QTbaaMiFF1meBlY9DepEqagYKhRT2sYFqctVUSkjBooqaGqCWqqhW4UTiCYIU9rGB6hTuWDLkpvd3Uqlh5eOQVFEz0RhIYT8bqCZVTSWG1CVTAhTG1FVR/9IynEg8wZDCfjZQTaqaGoIUaZcEVkPOSBU1I42FFPY1QfVd7ngS5yYJpNRykHEBmwKgbmm5MDcJJxLTKEhhXxNUl3HHkzg3QW+ciIXVkPF74MEmVdQMFAMp7K9B9Q/kQBw9G3NtSgipn8OEAIUx/dlU4ERiolN1fGmEbjRAtQN3TAlz85aRufm65cPMZ2Bgkksfh+6s9FDuxIjySy276IsLRMwSjKD6AR6AR3HHlDA3H08Eqe53NL/NHVPC3LwAY/p863AiMT3LckGN0T7cMSXMzYsS5kU/Gdx7O5Q9YNE0pZabc/uQIaYNCWxspbQjdywZcrMFtw859jd+aIvRXZRuyx1L4rzoZZ8EudEF0C6D43EHLBKJRCKRS+wOiEQikUjkErsDIpFIJBK5xO6ASCQSiUQusTsgEolEIpFL7A6IRCKRSOQSuwMikUgkErn0/wHC463JjMiS0QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 150 # change the size of the page\n",
"d = document(size, size, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(2).join('round')\n",
"grid_n = 8 # change the number of elements in the grid on one side\n",
"grid_size = size / grid_n\n",
"for i in range(grid_n):\n",
" for j in range(grid_n):\n",
" pts = make_char(3 + i * grid_size, 3 + j * grid_size, grid_size * 0.67)\n",
" lines = figure.polyline(flatten(pts))\n",
" page.place(lines)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're getting somewhere. When your sci-fi author friend texts you and says \"Hey, I need something that looks like a writing system for space aliens for the cover of my next book,\" you've got something all ready to go.\n",
"\n",
"> Exercise: Can you make these polylines fall on a 4x4 grid instead of a 3x3 grid? Or a 5x5 grid?\n",
"\n",
"> Exercise: Add some randomness to each point's placement, so not all points fall directly on the grid.\n",
"\n",
"> Exercise: Change the aspect ratio of the \"characters\"—make them wider than they are tall, or taller than they are wide."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpolating curves\n",
"\n",
"Due to certain properties of human physiology, handwriting hardly ever consists of exactly straight lines. To simulate the curved lines of conventional handwriting, we need to find a way to change our polylines into *curves*. One way of doing this is a [Catmull-Rom spline](https://en.wikipedia.org/wiki/Centripetal_Catmull%E2%80%93Rom_spline), which produces a sequence of [Bézier curves](https://pomax.github.io/bezierinfo/) that pass through a given set of points. My Bezmerizing library has a function `smooth_point_path()` that uses Catmull-Rom to produce a path (i.e., a sequence of drawing instructions) that can draw such a curve given a set of points.\n",
"\n",
"The following cell generates some points with a normal distribution:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"pts = []\n",
"for i in range(10):\n",
" pts.append([normal(50, 25), normal(50, 25)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then this cell calls `smooth_point_path()` on the points, returning a list of curves:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"curve_path = smooth_point_path(pts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For our purpose, the exact nature of Bézier curves and the flat library's path commands are not consequential. Suffice it to say that you can pass the result of `smooth_point_path()` to the `.path()` method of a flat `shape` object. The cell below draws both the points and the curve passing through those points:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d = document(100, 100, 'mm')\n",
"page = d.addpage()\n",
"line_figure = shape().stroke(rgba(0, 0, 255, 128)).width(1)\n",
"curve_figure = shape().stroke(rgba(255, 0, 0, 255)).width(4)\n",
"lines = line_figure.polyline(flatten(pts))\n",
"curve = curve_figure.path(curve_path)\n",
"page.place(lines)\n",
"page.place(curve)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can give any list of points to `smooth_point_path()`. The following example copies the grid of alien letterforms above, but uses `smooth_point_path()` to convert the points to curves before drawing. The results are much more \"organic\":"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 150\n",
"d = document(size, size, 'mm')\n",
"page = d.addpage()\n",
"figure = shape().stroke(rgba(0, 0, 0, 255)).width(2).join('round')\n",
"grid_n = 8\n",
"grid_size = size / grid_n\n",
"for i in range(grid_n):\n",
" for j in range(grid_n):\n",
" pts = make_char(3 + i * grid_size, 3 + j * grid_size, grid_size * 0.67)\n",
" instrs = smooth_point_path(pts)\n",
" curve = figure.path(instrs)\n",
" page.place(curve)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scribbling in a line\n",
"\n",
"Writing in certain alphabets proceeds in a linear direction across the page with connected letters; English cursive does this in particular. To simulate this, I wrote a function that generates a polyline with random points evenly spaced along a line horizontally, with the y-coordinate of each point set according to a random number selected from a normal distribution centered on zero."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"def make_scribble(xoffset, yoffset, width, height, steps, stddev=0):\n",
" pts = []\n",
" for i in range(steps):\n",
" x = xoffset + ((width / steps) * i) + normal(0, stddev)\n",
" y = yoffset + normal(0, height)\n",
" pts.append([x, y])\n",
" return pts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what it looks like. (The green line is the original polyline; the black line is the smoothed curve.)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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9X77rx6prjsoyX2Hbby5tx3gyN9dGx/W8diSTOQsn87PPNast5cITJevaGK/h/xpv96IxZvN3H4qNdr3Jeb+c6p0/p5zZbX4r429jMS4mo3OC/v10ldtsXwl6Z0UxfzjqOooB6yybxMSdVSwxJ97CW2mPFdGSNgHNHeK2f49hsIUG4+5l/KZVhQPiIy2CXKLyvlsD/wlRbAsaC0pCPBo/hiSNpl3Ki7YrCcM0CaPqI+aSCTgMU7jALqHfJR5Xh1c/NJFdjzaSDfgej3KJ25JL3516p9jgeGQE3wmLzOL6+0r+vXdxVXaFtHSVVHg4ZpRRdkmymSZ5Tm2KdqroPVriJkqYtVi0nkkkY7i2YmW0xVNV6MPDca7G6DNkDC8OHi/fVzqfMuq7kzVa2z6Em8n2Rl5xjYpMy+tcedrMJCJD9LEFy/bAHdzOatlmsXOLm9tERbFGYY+6jOdeorq6x2uyTOjCBzA38S/L0QTNaUAU3PRzzdNX2ZcdomPS8N0hsNAZFkV7G23ascfRWfX1xBVhDWjU8SqvaygqWTyrs/T39fgGu6neVXPKLYQRsNhav4jRWYls7TtkyGp0PFW1mDO8ztJR2E71+fPwMn7DkNJa5QY9bzjve3BP2uNFtKRNQIUdZVenJn7el3gPFqb/XWlwisdPWRirYjKgAXxOOYsMTKOyGwf5DTIUzOOkZSwYbTAPF6nDbOBrdUeGtOfmlemNJ1XPPDg46/P9uGjaorgkv02wbbm9/xX1+W/+2gR9dQoO4rMuxgtYmdtidPZ9bWzZZfEdF8ja3xcVboAP4W/FN6oKaemjBeXP+KKMsi2wLhfk6By8WWV5xur4VOp/YFAGvf0UBG2JwmklGtFuV1GUMOPlS7AW74k6MrUvGhSTg9d76oeROAZjuAWdoroa5SUC0/M9xHr+xpZ5SPX9X4FyttB/q4w+BbS9WBqjNfdqSm8GWIBs+5eqa9OCz7sSP2kONFI71RwBC5WxAt/tCzID/fLbk9daxNkoMzYYbOWz9XDBeE1YBXQ9KS/X+bCl/j6S4yIyuN8hp9yGii49Opo3ul5nf62psptXsd1uwZt8xjL1Hly4jeNyez135cSfdyTv83F+2uNFtKRNgIhYEgNQ4iwcdkp9ID5BKx3R3IEmOEYRt92Rg+AnbtLDuWFGC9WjBct3k26kVpMwWpT3KeMZq3Apr6Xs8VFBGqKjAbMXyHNtNgaKE+sjuZZugUtljtlJbrRdMsosiL/zHU6XEWS3nN9bvpp3FXCuBocn1G6Dpe06QFLJ7+kNKo7IWuEzr1Qsh4NxE1nMHUOY8QdjyrXEMLbFQxpH0WcbcDo+pvJXJUTLqniC9a2FSWWVnw//lVRehcWmEe8wnzQUp2Wrr2Fsq73b2vixCs98Spv9EtiZ87wVN40Z4Si44jN8RN40s8mIzeZmOIBS8+UhtkyD+xjmN1lDmszGY6BYF5szeQa+oexiar8xCtZXqL6eckkeovlafP1biy3zVDajmVdmR5a4s2Ht1ZxhTD/mZWsbU711MjYqsGPsR1nfMLzRqHp6cKX5Sm2+hP5el2LwU/lMNK8jZFuXEbGcbMCDwai8onhHFdHXHi8q3lbreqcU7gNv4cTqrMkYx9ttbgIBC/Oezbs1rilarqMOXUximhk2y9vjNvtEaTOfl5VhYat/pOQ0RBx64cWsswJpGY0fciIuKBotsV1xWxnzizKr/1VwX8Ey21OCvkf15UklMINns1O4RUxEDSfLp0FyqFOFcwKeoAV5gXgJUTKmxclYlQtoAiptXhcFPVIt9sQ1ob8s7kTVcmYZIyPvpA1wHLeglZQDqSX+G1OuOyX5WlyLn+s+WwA7hAUqVgpvAC1/0jFjGWf/ofyVMjpdtDoeSA18hy3xCmkaiudzPq8hCz8reOa0TfSZnRRv145xouPC6/F8iBsyvgH0D+W4tyzIvylC8SCOxDdVV1l9UqDOpSiI1XKWyB0b77EdllKdecHzNKcekya1qDs7zuaqEmmUCuY2CwLIDNl2FJlDHMejxHC1xM1pj59Ex0ULHKT17xx8klidJjTakeCRYiyLlSv2XUsy+TOD1jVy8e+Kw+Wd1yI7uSyZjKvkFdm+3iuS/bW5+qsTJlet7VYMiVYzvOiwG1fKa/XZIYk/7xHew5KxXWw0LakTcIvSC5oq2hbLPJuTMIgGyGvjGS4q7+FjbCNrdPuipP1Lo2gz1eWefM7VlK9sMfuINEb2KXE2LZvKzuIYvKu8LxNIY1uVPb3oM9rzV7agt8WJBct0JQXfs0yH/OBQvA6TKn9ElDwP3bCrtBZD2VKBUcGp3DQuES2xAQE1SU/hEyID2EbFW0Eks34hi/w+2JgbwQfk5N8J/dWgoFkw8z9jmHpipyJl3hCDsAjZG6PA/t0zfzG0MSbvt79keACZs7+p/BdK5qiF165ayDqV5wGlPnyQ5TfF3dUczxW9w2Kcl19okc4fc2dy+T5W/RlrvK4yLSsTPGBpRFfi/JqodUC/5Zw/HnfrORVL1zAbrM1gLrqf6e+2ZEOi6L4N3iBhHkumzr891HkTGZORom/NvLKtcLI2uS75+Y2yyg1VaD+ro/D8Ny2a2SIdhekl6BsohnQV9lyRTXlOA9bi3Dbhq1XxgJgV9qVl6prOlbW2mADKMbkGxaFYjyqYifB+/P1l+Cnjs2HSgvfHs1ll12TPPKdnrZ9RthX+zT1tV32eeJBYmEpgDwlyMzOFVqxOiqP0FGcn/syPePdOLlhmo2hJ9eHk8LgFv0de9zncqMY+uUAnnaworiPxKIbw/oIbcRTg7iNUN8fNuTonPSrSeOAOMiwj4qUsWOzWl6T9uFyS4u3kYKNYHd/HSXYZA21CsA/Zsggdu0mFunm+ZT0Zn3vF+HSKjsBgQZu2obz6hhimDdEZK2vjXlIMYUFJG8vjUk1K4B+NbLP+2gD+iWn6mwOdPfypDDvBzaBC7Q2iQF2zuPHXhmB7sZ4l3ER+UDvUyPCwg9yVV5TNRm59K7OnTGL7IOOzTtL6DNMRTOM1V/NwU/5BtJZ19mzjSZmKj4nXCjY1rI8wpm7hz3OPJ7P6UWCWV4j9fQ0ZyAM5Ai2eUJnaVV6DsAvrvA7fZ4ztHoGptyPEiowubeEmU19Lpv2x8HcrTCbjNEB1NWjNgIVsMAHg2Cj4G47gzI1stfKOnzGYMzaKGvznonW2wFbBVbxgQDdrZ4Vw2Bzvl6CvhivWzyEMwuJpj6OExmIPnMh+O1vaskRt0si4vKl4V2QxCzy7L/t6GnbSGp4XEFTz1rTkh9XnhoPZ290KC9L3VVbZ0aznfD0rKxI5e/9ZPF4dQR1m1WXPHJVt+8cWPUiafOCGRJ/Xgfd03i2aR8qg9B7chvdk3pvxnpddbOq5zvESGrpxEZmq7xVwDntjH7zKRuygDjq4rOfNw7vMkOkZg+NuHYZtFDFdHOgvaxNCtsu2vpuXDNr3kgzlWmjPYunvcbDKx3rtqNzhHGZ/V5mCwZxsg1EshcuyVahazHbhNnpn9mIG88O4Qwv7JP7uKUqY9uGlJd51gAynlxMT1CDvlFDP/lL3jqpXx2NR3m+zv6Kw37GhyQvU1ZaYokByv3GBq5UmIc/V1TY+Sqvm8ltvR/MvSiuRd1vHnLIbyzJq5+zcU1ykflYbV5jMMJbupdhTUZj8sqJaiyHdhYzVDfpNj8Y+v9H0b0y2xuIALZYtfdZ9vzkmBo1KbA4dvv/j6quWKlNWcELYMc6ZLH9ctoqeLML3IZJvRRpFWCyjh6QRrg/wdic3mG3iGbYy67xRR03DovlMafx3CficvLIHUASLhJuixr0wFu5Sju1Rkq5jGX/YFmiCR188UpLGTblZPqrnjqzK2Iiu9WCrcBMEDIRFjHpYWs3EsnjX1d0Bt8kepVV8bCBel2kcf6/2zIvrBDMYeEj7w1MZn7XmPjGL+4RpIFuHz7rLBnFEZDqQVUcbjRSzMytoy9mItttMQtuGeUfL6yn0yLzJRrTGMrzfaJwNU6L0pPbgY3n/C/8Kjd2Gnf8bF/jZeZytbVx7crDcpo2rU/gMGF93bmhHHkU5RUltm1PWXBdjKhwcbymmR89oYeWidn0wPD0lp1xLbMnN8f7sIyv05tJqUtn60gjk5Q6yjRc3851HSiopyFCIienH4fiJNBO9Mz7vJ43WzrLtySzfAwvxmd9patZiebVdSQ8KTsivg/ttg/MG8bpW6tc1srNssw2314a/g5insgKpwQLc2XHWjfzNhdxwzWZiMNncnP4WY7ab1K8/1H12KZm+/fUuvXLK7qjFcoNs7zPshQ9DELRGZ2LnOLsnjJP9yv7NYPxHffUQl4d/8D6RW+MueI3S5TOsZ/XG0pTx/h1s/MIyTMcwcuiPlbnYzgqZptcpQOu94Rx/l5j6u2BbOUrXKl0Aige6y/jdIRrLW2UHrCTD+bS2mApddjlPJktgmj/LkPKDYEjZIJsAMtYvSsiaFxvp746ceXak3Ab/znmXdriO83pPMSwlHR+4+byGCaIrNlo4aztKR2qtcEnJujoFrWGHZIzjY/rpZERHzL/3bVVyjNU9rw17395nnuRzIcE8sCzL+kBcHPNdDds9Stzxixj1KZlrbCgzSrGFFsuOMk3h4H8hOnmfUG51MTCL4OWY53SnMGhxekrGMmvA+x0UPMLG53zeT5rtVZOzYVK9W/O+C3dVczxURE8qD+3F+yvei2doG7bjwvFgvlZE3KdtRltne56gvTQjv+jMk2VKdPJh2jpuLF8CFMPSA78G9Z3C8qMbueyp0h7clFN2oLQVo+vtOOq+W5vD3OxPFsg3ioSpm6fwu36lgx7BtEg2EYfU262gM7dvkyq65g8omE7I4qe8IfoLuq9n/WYot/fo/P/vDe7b9pikBb999pGFru3IQEQeZwWP4DLKt4JpbXbQO3zE+tpzO/4+JG1cPqfsBjhLxwX1MXxO5MJ0qsrm0nGQ3nFQtkoWq5AdiSLvNioOiurakTVFWphty/6NZRQbICnwEwUEO02BymrF0J6ohW8skjH2vjRjc3o3k8Gx9AB4Gz+G1BUFjasxH+uI5kVekETYVm0hE97idvxwnW1cybQlsCNgM8JeKlsLwrkxOjD1t5XzfqGuNqxnpjSRGUdQZEWeDGvAXg1oN2P8poWj2+7hs+XF5K2YfVwEM/J/V5tmWTZcpPZYteeC8YbAHKvXhHk8qgw6V5GB60bx3peNHDvm1Wpj0bwgH1DftqFQ1zrePT2RZ67BUREd2xc0uG7E++xOodlCNuTFbIK1+nr87j/cYywuzGqiIcsFnevvONnW1WTHKeK+8XTwpNtQf/dh/36k38eHplgOT3BlNDOCRKMVU6C8PMTyyZqnEnBsfRqZn6esUc8bzfucfC1mWkjnoXfyPiFbZS/pJEr+ljXBOeEv1+ft8g1zOWjOUOf1L54Ajddr4sCjOBFleRLATCptcD+BXzI+W10L8IDsaLK2eGuRWyU638/5rh/+wo3qcklxWUZjdWefNaVp4nWcjApPqj8q4KR8VGfxyHfhRiR7bEYG8kCUa/fQgm/wiZitT3KllDLbbB4uRrPwnBbAPE0U2dHRQfItyd3zGoFFYXEtZmJwlByPUv1jQTt2QE7ZXaX23areEwZ74PXgZrlxTtkTJc3Ml7OJdsKFWqhaNN5jiSP5Pdn1FNB8FHjfFsSrGYzHTDE0C7Cm5zl+rpDkfjkaEVUUNuLs+ONw9k93xWOqFdNsI8Vcrx/m6I7oNu1YQZsNlj5OmoQOMfGUTJo1z5YR7KnPyYz2V313lqStHdeE6IggNz7IAGn8VmBtZY5J/WYXCTNZ7uqs47IQEPOUcurJqXMhMke1uDlrPejEHvk95EG9u/z8FLmi4H13lE2vHZXuL9uO/FhX23KLvUN0lzSohR2LjOGGHDkHNPqINafuS8KYuYhzuRNFxieV1mKk3N0TP6JS+56gI56ZqIJtJexg8SRYMMY4x4O9FKTxFO4dZ1BgOzVG8F4JjysDeY4Qw+sf0ugsHu1xHIv3Kt5SAYcKWJTyyIwgK4RHo99vYzwbojBvkffdWM7gU5IdI1yjrqBgVjL8SVOh6R+4Ce+3eLfNNm6VFGQL+BHc7jMWMRzCjTYybFw7ZlB0x3kc/Cdo8McOClgUnZ5ibaKz0xYKVldyg7CNSRlcL4o8I8JnC0jyGxkZy9Z93h3na9NuhVNj6+pC+cC0N8tmZ2Il6/HPoMko6CmRRc+6MLuSesv82/hGe+j3qyTSN7bBXsQJFnnCDGzA79eWJ9IF8Rm2xTBarJHdi8f10GbbAv+V7cAxGXEhuvGv7yUtXplT/lhptfrXq4c5sR8LbZvlYcWl/3ypmXOlLYvXbJJ+X1zf6HY8F1/jSD27omBqMJ0mKFOB4yLDA4cbyeb4N8e4MXaduUw2PGLvDvKmeZ8L27ccsTeyJdZVW5yKv+MNZUpvKca0qPZKC//bYoJvj/nuOjHIvcnInsLZGRncTkMJt3FKr28EreEqOfW14OY2nYtxbTGGK+c3Wyr9xmHZ5/9sx/3FQLWIl6BLju3IOPPDrM/35ogcL9p61X22OfsoGnux60FM3WYr879wNJUXsBDHc6s8Q9+tWlZ9q3PLjQxlE4tjojlpGeK7w2L3XEKh4wvcyvtUzrworktRm74GPnMtbc6bVSkPkuk2LIXG3fUMa8Z3lygw6tocSWviUoXryNGAk2mZohhk+WP2aB2xbhN5P/LXH2gvKdAfiCiZIEF9EC5L7P2O5pg6N34tx074MNhiDk7seU/wXrdx3raJ9m+TPsySFJoR8UbRmXVOB1uk1h/CIFo8fDavDPY2UsTfWM4dwyiLRZMr1itBC7FJXLdzgXibEtYglY318sj53e6KK7FfhqbEJvgRmBHiyNTb1myFF4IGJfYYAqZcteOzh+W9Uy/hncrtNOKeh5dBT3tuzD9LIlyQkl4HboRTpYEwI7XEAhpiPS7LkRq4Yg0GzEbGMkZvXzjjLLfXj0NbrV2knk3EHDzBfm+ZFWZ9DR0drZl9jEdm59Lg5lunwcIy3LqjKMTZEZkX5sYTBfjbLeeZG2tB2jr/XDynXEcxCUUytfPJM0Ko/8SMgxVR4xK2h21aPcmGNCDGDEyijHKYjSGj2A0Hsd1eCUz/JJjbss2zEWXUs4VsCtaPyZnUgyxKpJJfFYvw/oZMWWe1xQZF69yBc/8WlVsw77u1ORIiCXmPMt/zGJUfmpOI1nJXTYF5DsYaSpeocy+tM+tmB4akSDYxeEXVu/iewTVujD4reRxX95tWGCf7viXzPexwLUf3rqqvLO0I58FJYW24stznl/H+Kygr+Tdkfk1C7xc5QJDuvrJMGai1LVlNURscFdzpE3uPnHeqYXtP15EQ0DXru65caezZbbnDtOK4iU4VsuJm4Uyyysfl94uYa2PGT8M7+vsVzquVVK6gIbsxtWSUZ5OhmsW5XjDEQgXv1pKM10xFv4/ZO0nP02GvTSw6Mlnfz2zOV6OvGkRPkz+wV7aBZ9Z3LXCnziYHBnXe4thH3GyrwsdOaEdZyiZyV1xdoJMvk2S1P27D1XiHNdqHJfMowYwujbFaPdswj7LEO7LfyYgmS2bnB3LJBQevJlFLMjImmQ6v19LgQQ76P2VLfSVoekTqzT25zW1NqThSOT6QaP+0Imtix3cFPGVK0HeVNFSLFY5nw43u78FOqODZLBnQF9XvvbOPImG/PouTdaw0dXXaCyxNtnGS2qLOIJMb+F+CXciYrDpWwGNho9wyp+7eshUZUzjHkzHdsNR4pprvQzr65NsmiUl/nN+tUx+vJbG+sSVpNBdUi+I7WMc058HMl8s8N+fC86oM3DM0ADAZbmVu+Uso8V9ZSWFhLrCmTd0t26ZE14bcLCJvHUXCplAxORh2F+5vYxiNyR8bbwPAMXmwAmgunK8piq3PzHnN7qtd3vFln+BSHatZLFpnS46naKM9NYe28TrO7JwRnO0x9lGUBmW5Cvp2uDRDY+tDFITP20nYWKX8MAUwuxsLmzCiXuucwNg7QIzhavmBQCmkvR1yZCUaFI7PeiDM1b2TrDfrGS3Ievyg9ahO46A5vAXbO7IB7SrYuruHmP+Wdf0SHYXnpc0BFL/aMnFPwzycdT9K8Pqp1HrAXeImOSIchv9U0C/tSO2IXGEL5sZugsuQeKNhvtH44AVWMpowTLC24/ytcWWhMcjx35lv+JOFQalWX1Xct2kTkNOIB8sw9HS8rr+PJXsRZXc+qchvVtAZ/3YFOhGUdT+SCn0HMjY34RrVd0VJWiwOazTosxfITmTAfhTDNTLUP48G0UpafAoOXpiXgS14b3FImRt8jXLEmjfHdxUsWsdKC3Unt7cbyCxFxtQljQwr7IN5KJfN4gSz44mC8Xlif7s42bdPRVOxaKt/kuv6LvGqZtt41Z8HajHIy3VFOeqzoKmrU7ViGzKc/8zZtGtwqLQ57XJUyX8ijffka44QmfTOkIFhjHcP7EjSPPMs8vTF6rlanCsGZsmccvPpiGwpfFelOTKI7ztZzN/OotVwF0od+9j2vDEX54ghblQOMTEJZoNwIr7NayNj8s/LiP68Lg4XU19TOOs5zObkdm3Gnxb4fqBsUvaVLVDpfG1DyepGxuGr59TTVjZFR4s5rsh2ia3+cHDv3y6nzmODRucW/d0KHQITVJGdiDbQIZxz7yqZYruMz/sFYaDsoxn1tY3+yMssLz5Lg/q8NVeFn7O1o3Xf9Q5eOe0rF4iKPvM4ilnRsUpJTXsjxvLtElbXrM8rp/H9N42TujmMXcle31YvwKpf7Gi2f77htsbZImR6TBM3jOLIE/pdwThGGb9bgizJrxIue5fWUsJW3Hm5D5g29yxpmeoZ7DbYVkew7bI9+TJ+e5yesyD+VsZzzlVYjV/0HrE2NRS2BnMffKVa/dSgvk2bgJxG7ItlYOHNZ8nE8VU2Z5RYrKBNCWw5OYib0pUql5tmvg2H4K/BUr0jJfydQ/btvLD8efUujonhd7kc8ZlSQy8fuQ9yK9tUqv6WmFjy3SzXzT0c9IdhFDe/Q4I3R9lRaVVHd9ZhqQWma2DORgNjdhR9zp84XaIJuXMFtLXCjmzraAEoHCLemKedufD/u961P+v7lSn3R54DZ8b+vgPu1ubRqd4NGSeQ3YxsEhbOeM7Oat8lso1ZsSc+DvFH8oIBcny8ETawoTk09yDVL0m78DX77zxcQkb1nlA2N4fMANHXubQHXCPmiW22O3Nx/YbS/nS2vH1Y1MNNi7F5mp1fHySvEc/vIg3o5fgt5/PV5IWxa4bXWg3n4Ovs63Xywzxk/G4TufkPyg9SWfe+27Hl74vvt5yyLTi/flECkBg7PPb/1GAjE0tLwXp3wydisHK0MTBzX9uAR+NN/T2YlEbHVBXbicDyNb8h7UhdCAH0pFD2ueormJ4ltq6BZGM/FFOZiNYDS3Lrel905Edjtll5H+f0SPVx7zLftYZz81yuNS8gxjZN6/WDyulnmpGq5RGEZc2+TMLU3RmfbaV+WKF+PJJtf1AiTevI/IHr02bhKDw29hDsMMqYh8vIrp6pdru8THqO1vHf/TJSX7JIuU7ce96UgbzpYEy4e5XttXxkPIxlOQMiD7t4O1BbHyeIqSuacsb2F5hzw2MS52xdiz22p2AygmxiWZrVpkLqBMQ05mR5HtkxkjEXLThESsWx6UPe+1NN5OE5dS2LA/j5VdFRA+uaRwN0AQ2c4onqLC1eZBuyaE6du2tx3CuSUihFXxY0AePLeLen5T3yBbe/w8k+RAZdlcXvQIiHUqvfxm4Gje6DtqQuaqd/VUDX0gpGeAS+Llm2Cxc0k0bnyY7rAWMV7+ME3U9Sb57tRShzqibaJpE9BYxa23RG6jctM8ptKol6zWy7EBzPze30eImWC9U4STvtWcrcolfmvRMuIOP0k6QwS4LRKzLAkyfaR9LQZIdZ74SNpNlrRA6jstu8q+JTP0l2cBbml8q8d5H+GR7GbaOlK0TsyK/Bcy3TJmqkFtTlsjdizsGXA9O7Z2x9rbm1TBdDW1CjykX1Wo2ZzsUDI8JWg5ukGYwdhxhK1uO5fAa2RJ2tOLZnBm1m+5zvBik1zO2RZxbf9erAPFe80BuDqo3t0nopH+uRlY4i2JYVziGjrn3EfK/U+OBwuoZxDjwsOrrFltmS61mkUS0rSKeNBdk01mqNn4QcI3mOibXV392K28Al8G57KZP6uHphBAvhbB1pt6gPvqp1x44H146M0dnrp4RcZbER3WH54h4I7zdc5coKqojIc/EOaWNGkj3NmF9ZZebHv3RicLr2x85Gh1KBfMF14Hjeu5K6aH+JDbsBM7Cwo+U9izPhMGF+eZb7mP3/FMf/JmJg89MP2TPPjBdI00LqBMQ05hh53NgdRQMuGcJeHWAamWWzLc3FnZqL3U4Zi8V97KQouFhBbyBbwHAcO/GsfPU17NjkQE0GhdfG3/BaiL1SMqM2LKKlHSP8LYTXO0y/q0jlqkF5Kuk6TgttVQJowfRWJoHvK2PlcgPu7RiMOB8ro+wZinC7U/b5PVbBX2VP0gb3FvntCJzAMn8NxnomU07UIv5ZTrlVdU6+ffbkZdv/qvg/MdolmFnr+2HsTeX9Iv+6iePANDbL4CFkxoQx1uJifnd0tmSJfjhY6mqUH5elUX1lRvqncSm0duteOHUGLAdxFLirrHxXJZ87iOxepKHoVfdZB47MKANxdgDHzpwxtlksHG87h4FcmCMtWGFbLZs7JmyMyI4sHVNuG/XhUvHjkL32z5Cmo2hahJw6+8r+bg18HPNdG47AmcEo1ax93lQ+OlQedE5jexFpB3/93ZuUQt5DXGvsy7IiPWfUtYhsyM7ScXmjNB8woWM/aepmFCzTiquqGeovnR2PrEB9ZoM4ScLBd9rE8zZ/jtPzgsdXo9LBlEHLGlxRazGhPmwA16VnQgb5XTLKbSsj4bPwrv7eDncF9/xC7t3766jI7vnFEJTtKi9mZQjek52ZhQnsmXOE3gMn6aj3ZBlx9w2/mZf4SuPnQc3M3+NjdS3wjF5K03FWYU0uIg+5j+UhfCQZ7xM5s6IQA/mu5Vfz3qM8g/+mQuoExDToglwEZ6hzhoi5GFDGb7YOZ6TZXjQtyR5FVu51xozs0EmBaSro7mqMj7jenfMND7WpWaj/iRo4NRxk07Gi6ivoPZPxWwtM947cKWulVbCEnJW30QCsy0Fclltog/th27qAe+uXVb4jzg0JQEu6v8LkwF1h+aey7FJwPxmKyOC7oMeOjQcyULVc+LTQsgfWDQadE3LK9VMi0yPwZcZnreWRMyI7kVxW3/bjW/SImGriN0Q2LaPi+gkbcuI/re+H1H1mDsMNkLQb1Vdmd2Z9tWXh3FRkgcaFdsoLvNegZ3ZRchF7eF1EZ5mxRh4Ye+fQ102aj1O0yPeMGWsTgs1UQYZD68I+/P0NJaJ5z49LgrfW2AL1nCUt76L5UiZM+9hNEXizg7W1Zsta23XMN6YNY3BSiAS7Pmf3dK4eVlnFge30jjbeHuE7bh15XuICvBvyssWmuiha32CuL++WP4eL0LUKzoaFZchj7jLKcOZzs40Miwumkgll18DGLPeszA7OETPaJpsZ5bMmhlxM+ycxXovQMh/73DwGzdZJgiyuIssVGcEPzijXV+v+q/hVfxt7cZrKxHrEwcJeHBHW+TLsbWJ+34/r3IfSbn1D2u4ns7Mj/1qCq5ppR0/SXMo9qdhb46cGtaJ/vcKmEtqLtuOcvCPemyqUWZlibi3Htnk8Wgyza4InbX7MuQm818w/skwTqRNQoFG34xJl3iDlun4uKq+jR3XcVJ/9dEX8J0iXdZwmt8LrQzyJsUXqGy7OeLn8yJX6vh1pm6YFcp1gb2ObZVlGmhowwJccfD+zng3TbuuCdHbkohMF3BtXVvm18VzwIisn+nB7LijTVX8nLKHPViXLMUWLnBmNti/yWws3NyO4NC/G+3AFzMpJAqdF6yhpeKZnfDa/bInWKuoRNQb1gfQMYwqWbUOZxjbSHvUxc7ADt+lIHVwydlFifWWsywn4SRsQovbMK7MkR/0H+j4vDEMDn/lACItfF/4A+2JysH3LS29AlvR+2Y50zG8XSoQfh42iaOoLbv3vSFhpU3gR5TrwVvByid3QYcc1dryyYbZ2EKbp+BNn9UxpfaZmMjhYnqxZFIMn3g7MjFLNHmhPbovfcrNoobINCmrH622lErkzJPx8gGzVJqqvoP1FkbrODPniKo7rk1PPjjpWHFE8FxF6sVRkWBybADmjvnNV3z6KcjwP7marH5yt3eBomaYYNFU0Js6g50tpkZYgO2Gz+kPS0i/bIByRjvRHGfIvyhFxE1egnURfXlyiut8sydXnGjmMNEgQReSldAH/O0tHn/cGZilKjJpnzhCu3yOQT0WJGDZ8iw/CuI49xYBp2M127fQoNxVb5s8SaLpkh0PQd1/yXjDelCAtpE5AQcI65J81FhkEVv7zcCZfZ/CnkG8XZi/4XFx3lxTWrj44XF59bSipRzYAsdI3r3skof4FL4YEgq9X9G5RQsiy3y+V9jfrhFu1YNuZbun2P4aLUeTZ0KvM+u/RhvDnKPgXN6T3sZd+X9qTzTzgrN235TTfCNcFD6Bct/GWZDRmk/2wRbNF+KyvPJn6Zbvb5v3OMhpZiESLAV3k3WFHPZGLaJ1dA3/5jDRGBbwKqtZf65Nte1LP3S32+y24LEe2YX0SGh/XSoJcut4GhgLJ9+HIr29M+Y2VTuI0LfbZR70WFyjaKPLO8nPqGCfBZFh83BOYPum8ujw9sfmsYKk6TMt7WE6Czrak3o4TTQVvTNiq9bZaHJcTgxFyrLYXFpPajixe4aoRaTsbbExu419xgb5jXd2xEBn+2dx0i3piFqlrDR0jnFLaDq5EPcfriGjF4naF6mOb07vgMxRyGbZrfr7Vt2KMFfsIy1Is/VyCjo7eKLwsEASrso/FG/l+T2hubIGTuEIMD8JS3vEazMnfxvC+ZCBeZr8MKTzOVL6G9w7lxzoqQt+aiHLBPYiuXO+KaOLCNRAFbKOyynYkCxkFtcwTSEM/RcLEAtgqfNYnBFXMGk+y/ftBecaq2k8Vt1vaBCT2IuYO+6BU8+eFv+dVRwzXBGmRUW452R6sjikF61qGW1ZkLBabcRwma14WuOgoEm1Jd7o5DdZmZB6mBim4VGbjXgqAt62khXLr30/RlS1282pYmxLmTLTWs0oaesKM50xVfymexqF4PkTBzdPysdYfg9dXt/C7lYMhc1HPtgraaAPKtLV897qcYjgH78pmLOZcuqr91Vk+Y2bvcmEMna24Pc1SjKCEPE94na2NfONowxNT+AzrX0sLfp5busZTT8rEJtkvWW9fAZPczYFgSOnFUcymuZpflB0luO77Htx2vpZkXVDDALMnM5X9ZfW2BkYvmfPpiltiG8kmQULfLNIyKQnrQfqud4E6h0pzbPcYlWvwkSTskM4k7xtIwVFk1aLN5/kG1tUSq3BOvtkwzU9GPeMCs1H0SFPjbEv28NPx2rtQpq/m7tVi2H4XOnZVpN+TIi87ztLDQ2LWJ5OaHyXovkjelidytduPlFydrwkO5c5UcM67ybz9JC3k+yXrblt5sM2mAuzo3Y5oF8NpMd8tLVvDcdo/f4/tY4lXZoQULXVhQrAS7/+W9kBu8vdLm4AEO+q4sOEpngbaUaaP1ObP5ZRrhwtlCFowYy83rNdCtNSNCzxrA2kZ7F6h8ESe04EOnOh2/DN/8WzqHPLbyu1x4Xi3yAJtOC8l01+0aE7AJyGNxMRymCPY0cKyLP82e/hSbgIHRJtSXrm++Ci4/mthR3tsLo0c8tWqDRxzPSizmPHxbLaVJDhcz+21hLq6Kn1lFkCvSLp+Mea7xUPakLzEro143hGyB9g76nNuo0uIuepSOHAcLNbT0WIGP6j7rCUpto2iQ2mGE3YwtRTb+mPZR+RJ9GQFJgdN7aFF6mhFpvc3PFnP6HHc7iGma4koXQPMxmpl/v0ZfuU3x8pQflV8W2hsijnqodXGoqnbB39qRLvOQ/xCpnEmfiadUZTtso6GY+urIZsUBUY9o8F1dKeY+G157yU2OjIsjvXSMoFRpgIZLsgmfLDtfw053/pxjDwVbHfKSl+RwFjeRUzLrRxZt1HMigycj4gptwVXrVrdkXt+2d6kzREwneRjsEjjees2GbfTtKb3ycn1uBreCHZ19elhduR9c3k5G5v0/dImIMGOWk9ZladIIrC/Lgs2IHlSFLbnsnVXYY0EufgfycXb970KPKs1F78okFtLJZmsWhyGlNt0B22K+2bn6Mkr14fbXBQTqOhZe0z9N0jStjuK1VKW0STMVNKM5r5Q5udZyo8UE3CQE/RFbZzBloNb/CEhVkciHkOBls9kwzE8MqLFoxx/6+kZiScSLEHHAvKqOUTBD3O/W0+Mz+BkNFZ1Y2OMjjwihqA/N4jIeDXWTi38Zmm0gQWynMnNezN91peMRBTvqSy7EF6TpIlYCbtnfb4we/drMixR7KdeRevoQSboaz1TtgYchR+ExIijwjPaER9xmzUX2N80PtvHR0DPoCvyjZkXL6ERyU1DXQ9xXYnmRaTB2LVR/XSBtBIltQwF61iJIkIUKbhojKHwPLN1+1VHpG3qo7jXfb8KntHRcE1OEkrzY7VQEufiEdzL9TfyaF0jqfFagub+HJkmLP1GBus35SKMcRCBiXsITOzM+vEypwJ2imGJX/fDFzmfWwjUL2SWkGOjx53vmuCiX5/qZgzvU5uGEa3o/dImIMGO6iCJx4wmB5D1OJ0cZrRg7Z5XdlFOpC81wfLOQyVFWOjyLaWOKxgNlZvxJuRWP26qCZhSm/ZgO8zmcpMX3yOr3EiylFHW24q8MmAeBXtzm9lNv83LMF3kd+Z19p2eafdC8doCmDnmd5qIkefJUPw1MLyXJNhG91MWrcWVZHFs6lsaiP7ZMXearL925bITGTNnZ59vjX1CtNLrEnzvdaVtGx95aJntVHCfLfoMXq9hU5abzLljkbqH81eRLVBZ8Z7w+xHCadlxhHAxt8TITbUcDdAtYm56YE+sQbn1A2nejClaLKPM1sYjKiL1eNVbNCGgrjEy5my0LV3ds41BuKPySOE5dc2DNclsvKz3a1DuJwqD08JGV9Qmqq58Dc6Rm7SJPBmMNiwK1GWcG6P1TvPl0Lkd2fPaECTDnAVMU9agBLENaKMWXFGiJ0/Re9oRaSEPovFadx5Te+bZls1J0Do6imPDxPQryOIfznsD3oviAIU2WFGCe8uc34zScfQWGUEPr+e9S73bfHNB6gQk3FlPSXo9nNPqOUrQ62igLhNT7iR1Xt/8TLawM/cpUseVDHZGKSSRbNzNGdwQXwoB/bYv0ObdcQkn+qHyVCs73Hzd77tS0muV7x5cRl/fyEWyVncBTYzKREav2jgpDd4YXI7zzpgbMebOQi9pkaZxiVwyeBPEpgKpel/146iPPKKyjkkxkJvuW/p8bILv3R/DWOf/RVnqcSbnXqTtPKXE747Txn03mZujeB+DNwNTUpbrNCzntrnkvhY9V5/14v01BZv5ymOSNP+flDr+Go7u9zFCv8tPAhoZlk8sV0LnHFkgoba1+CLXYHkyATs1Puorx+W9OkbpUziOUBFaOis55Fma3+X+Zn6yMT9K63QILqj7vCPZpCg0R54NkQQD8xS7pm5OFw3WmDS0Vthzn9GzbylSbiH2y20Uyq5uSvqq9t4dORNXl3b+RvyN96N4QUf3T8RrDG2eKtDk+fVpJ7jqPEfBseygmE32bmkTkOjL2KJl7oPPy3bbvJ1ioxvDXL1tKO+WHxCMcs5Jsh1okc/4/BEB27bMQ+BAPBz7fXvK77ahDkk2r0wZdA3FAEqBHxfWGME8DB6Cuf1GqTL2x8Pc5u2LRGK9hGfsoM36ZUzFRpRqomOwqkcnjqWlhmymaSR6Zudi4+b9VAg4tluC791V0bYnY5b+fgCfYjM9o2j4Blj+HEtD0pfM4Fek9iFumrvrd2WlQ5D034KMzKfarGXXRKnzOgW2hFCSURaDZELQq6ThUVIRuc6WDMLZ5P1pknX3hmttMurZgyPDUkRMbsBv+8u2ap/CMZQK/G4sesPsln7BBhGzjV3xuBwPgGML/GYwBYOvMEbeVo3Kf9aA91yRs2Q29pRGZp2iZc0Lqk8yXodpA78HloVirlmYkq/1dxt5ZcWV7yXh4uUo9Yqub3nPl62Jaw5InYCEO6ov5YzZ4sCfLKwihx2HmPfD5VzccjoQa2FCCFS0Y9rv0xwAO37YTJz8tNjvLZJGdBSybwq0rcyFcHyR78eG7NV36e+xlNPHxh9VNoKGfloMTiZL/E+8GAyYr0qq/gppOVhHPFtnG0yTto8IK7B2gs8yNmOa4j2ZSPA/MimLxNsqxPz2QbXZGYpLbBmvp1T47AfEpB5Flq0v769IRVc9u2Bk65zfmwgzU88epN9ZpNdmtzgn2FfzYX2+7/PauCvSLpnggEfYRhsUjwwd28Zmj2bRfy1IRx8swzX5NyVgLJJ9HpEOp+ru37HPPhznodvc6RxSpL3tiP9NZMf2skS8sX2AKD3ED3IE6a5ch93xncI7pP4uebSmTUAVOutRLVrj1UmbFSgDDMGXIbFY/Tn7+hjObWA2F83YSKp/RACyjPgxaASygqxheaynnEtRrq6yzuMTp69n4X7SZm95pI6Ljhy4IU4JmdQTc9NGdITwJcdTbQZT3WQB/HJo2UABvs7JjiRLFuDncPTSK+Hnva4jy6W5ZX2lOWMflgzkBYtQbWU7hTabp7JQCiZ4KMLtU1xkr8ctcoKPnr1V2XUMIyM6rm4xv6yS58+J4IZ0p+wMe+GsCtt6pDSzOQloy/ztNmrfvbk+zOT4MK1mq6Zx725QGzWzIHRN9t5RUucXie9h1msxoRxyyj+ngIJbcbUZyvv5hoUqqPp7pU1AFTpqCRyNSRhM9qa4QfBtwep7T7TjfR7vj7hUD9Nil4ir8NwCWATWq9guh9bnS0JL3i/gw3Ck0CxdImFHRpYH7KIovxSX969D1NNEjcBhNdeEjTrybikZpblK77uIPLWejkLEh8/aSj8yUGkLEjVyNk2JvLCOIYMRBVL8vhwJLlzXa+NbR4ajlWoT2pPhjgxATU/bWc9+BRV4KWE+Lsrd9BvTIpUMeDang9dGSuh5uI4dym+nNtzAZogpOacBz7TrNPWzGSSP07FhngeVo3mg2H6ZU+4fiuV1IR43Q2LckB8TqDkgdQKq8lK9ebcokUkcZkUiqf413Tfznhf/DZJcrPHsHxWw03KTlCfU54LCETiS7feTNnVg67RpLED3MLmU3hnll8IzmI5Vi6vFG/icwzVuzP4nCpi2SJL1V0BHDTrhB9mMtYk0WmTbl5OBYOsoQFrCz7tYx2AT8GXI1/ZCBb9tATPJb6AnkNrcYsBE8VAs/EOTxhWa06D23gSf41mYl+jwsn/XF9cFV/1DGvHs7cgcXfR7pFvHnA1YANaVZLP2rbmAY2xyjgqJ0pk2ASl20II4G7Mk/e2E0fg9MmhkUNVso0qm1FbduTn+oiBbiymZ2uL8t52j25fvJq0RSJDuIQrG9lzw6JnC/u4tmhO1r4BFWbEkmxvCkrd+Uo72oorv/CxeIh0rR+kfMBiHhkBwD1ThWYfI9qVWc8g+SMzVvIxnW/Se89FBGtpBaY6zOQVkeI9SKITz5OrcpazfbISnZZQPbJM2/Y7mARMkFIfpO8zEI7x3bJ72qakTkOrLL02ZfrG6DfrbwNwcljZdzRG8blbApyPxAO+HyA5+Gtqrqll7G0nzIjJy/SSKOyMNRnvF2GhUkLUCz9qbm8dz2Cw5T6wG0jFenjGHRu6sGIkb5BkI/L0Kz1pPbsK1sruxD5qdx5Ejq7/aoCveVp6oUxSDJjbJatZv9qdIENkmNSj5o2PuA6LciDNwd3DeWal4wtvU6EybgJQ7qSvxEuqtxO9srlqItAHL1LRB2Mgs8k0UUXgSigT3SxtazFtxAv4CixzbTxb+wFdVfWarpgk8VuSdRyl8/M3BzugsbmXHV4dpNw0YN79ogWvlG+CcAF5LYX6y++ZMMZozw5jyIppGnIMfFE8qw/HC4eD1CAXdaO53Rde06YmlMW0C0gaiyMZbIPLgSFyin5vADezSwKlbw5n7X7+0aSqjf7/DZzJcPViGtTHZfucmwA4S7LjwQ8zQ33fjy+B+2+B8R0Wft4OSLlj9/3PBYM6AhLqe+KfCFhyovosNUig7HYuQvK084VJxz3Y0T5jGHmspNGRt2rQUpDFtAhxzDqSOXAEXAgozPyRtesqkebLy4hzJZTqKuvl42jRV+X0XkJH3l9yQFsYimILfsKTeuyqh4slI7YzLxNhsnva7Oyrsu14YJTfv3XRsOzDve7NLNPutIcklXHXMHTCNPVriPt5Tm6v2PnUCHI5qgteTyhV2A+XUKNN7s8teW4V3/kzvvDtOVZC9FsqTVhWtCpe49tgP+6X9zo4G9l8/3C6n+oXyU5jwWkHhMrri5bTpdDRPYNsob19zROoEOBzVBCxnjB2mTcLPuFDMzQVp09QE73wvjpWL9hd4oTIX7QY9by4JRf9HBK8lFcX7UEVpzpLAMT9GKhdUM41j5UgfFuI1bRoK0pY2AQ5HNcHrL0roWKuYRvZBWdmn52TwOlZJY+0er3e+KG2aHM0X2AiTFf8G2CDr81VwTmCOffw45jikToDDUU3AYrHsGTb6Pf4YARp1nNAxi6HbLm2aHM0XaIUzlEZliSjBbN3nO+CucJQ7Om0aHY5KkToBDkc1YV5Ciq5s96Z/DHflcP1X77y7jhs6p02To/lCnqIXiRH+MOvz4/ES2R4r0CyDtDkcxZA6AQ5HNWHu6lg2MDcraKFOJcFnCu890HJqYWGMTJsWR/OGMb9YHbPwmly+66Kz40p8hr01Z9ZMm0aHo1KkToDDUU0oVkf3wNwsjKlp09Ok774J7yIB2hyO34EavCK38EHYqe6zx/Az1hdzs2ja9DkclSJ1AhyOaoML9006pGmNf6RNS5O+d0sPqucoD7wuw9mcI6fj/8LfrfAh/+6tKMYe3NQxxyF1AhyOaoNXF1hmc6Bj2rQ4HM0RvHbH8mRm3sM0CgM1aIN++Jl/t4zSeDgccxpSJ8DhcDgc6YJXfzvBxCT8hlV496Mw8E51ssk7HE2B1AlwOBwOR7qAHd4CX+MkMjQX4ApsgovxYHWyyTscTYHUCXA4HA5H+oClWe1HhuYzfIeD8aQie8NTazjmTKROgMPhcDjSB69DdTT1Ir7GS/gRo9wN3DHnInUCHA6Hw5E+eA0Qc3Mkpit0wjaYxb+7pk2Xw9EQpE6Aw+FwONJHuN7FIiEu1Ip4PW2aHI6GInUCHA6Hw9E8wOtAaW+eILrgmLTpcTgaitQJcDgcDkfzQPCaOhbb4VYP3ueYk5E6AQ6Hw+FoXkALZ2wcczZSJ8DhcDgcDocjSaROgMPhcDgcDkeSSJ0Ah8PhcDgcjiSROgEOh8PhcDgcSSJ1AhwOh8PhcDiSROoEOBwOh8PhcCSJ1AlwOBwOh8PhSBKpE+BwOBwOh8ORJFInwOFwOBwOhyNJpE6Aw+FwOBwOR5JInQCHw+FwOByOJJE6AQ6Hw+FwOBxJInUCHA6Hw+FwOJJE6gQ4HA6Hw+FwJInUCXA4HA6Hw+FIEqkT4HA4HA6Hw5EkUifA4XA4HA6HI0mkToDD4XA4HA5HkkidAIfD4XA4HI4kkToBDofD4XA4HEkidQIcDofD4XA4kkTqBDgcDofD4XAkidQJcDgcDofD4UgSqRPgcDgcDofDkSRSJ8DhcDgcDocjSaROgMPhcDgcDkeSSJ0Ah8PhcDgcjiSROgEOh8PhcDgcSSJ1AhwOh8PhcDiSROoEOBwOh8PhcCSJ/wfEAwqNeBtHYwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 200\n",
"d = document(200, 50, 'mm')\n",
"page = d.addpage()\n",
"line_figure = shape().stroke(rgba(0, 255, 0, 255)).width(1)\n",
"curve_figure = shape().stroke(rgba(0, 0, 0, 255)).width(2)\n",
"pts = make_scribble(xoffset=10,\n",
" yoffset=25,\n",
" width=180,\n",
" height=5,\n",
" steps=100,\n",
" stddev=0)\n",
"lines = line_figure.polyline(flatten(pts))\n",
"curve = curve_figure.path(smooth_point_path(pts))\n",
"page.place(curve) # comment to hide curves\n",
"page.place(lines) # comment to hide non-smooth lines\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function also has a `stddev` parameter that allows you to adjust the standard deviation of a normally-distributed random number that adjusts the spacing of each point along the x-axis. This allows for the possibility of letters looping back on themselves:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"size = 200\n",
"d = document(200, 50, 'mm')\n",
"page = d.addpage()\n",
"line_figure = shape().stroke(rgba(0, 255, 0, 255)).width(1)\n",
"pts = make_scribble(xoffset=10,\n",
" yoffset=25,\n",
" width=180,\n",
" height=5,\n",
" steps=100,\n",
" stddev=3)\n",
"curve = curve_figure.path(smooth_point_path(pts))\n",
"page.place(curve)\n",
"show(page)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following example displays multiple \"lines\" of these scribbles, moving down the page, using a `for` loop:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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