├── .gitignore ├── Notebooks ├── Lecture 1.ipynb ├── Lecture 10.ipynb ├── Lecture 11.ipynb ├── Lecture 12.ipynb ├── Lecture 2a.ipynb ├── Lecture 2b.ipynb ├── Lecture 2c.ipynb ├── Lecture 2d.ipynb ├── Lecture 3.ipynb ├── Lecture 4.ipynb ├── Lecture 5.ipynb ├── Lecture 6.ipynb ├── Lecture 7.ipynb └── Lecture 8.ipynb ├── README.md └── snippets.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | 91 | # Stuff for teaching 92 | files/ 93 | images/ 94 | *.DS_Store 95 | -------------------------------------------------------------------------------- /Notebooks/Lecture 2b.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "# Introduction to Python\n", 12 | "*Space-Time Analytics — Lecture 2b*\n", 13 | "\n", 14 | "* **Prof. Carson J. Q. Farmer** \n", 15 | " [@carsonfarmer](https://twitter.com/carsonfarmer) \n", 16 | " [carsonfarmer.com](https://carsonfarmer.com) \n", 17 | " [carson.farmer@colorado.edu](mailto:carson.farmer@colorado.edu) \n", 18 | " [github.com/carsonfarmer](https://github.com/carsonfarmer) \n", 19 | " Guggenheim Building Room 207 \n", 20 | " Wednesdays 2:00-3:00 PM and 4:15-5:15 PM " 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": { 26 | "slideshow": { 27 | "slide_type": "slide" 28 | } 29 | }, 30 | "source": [ 31 | "## Introduction\n", 32 | "\n", 33 | "To use the powerful range of tools, functions, commands, and spatial libraries available in `Python`, you first need to learn a little bit about the syntax and meaning of `Python` commands. Once you have learned this, operations become simple to perform..." 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": { 39 | "slideshow": { 40 | "slide_type": "slide" 41 | } 42 | }, 43 | "source": [ 44 | "## Invoking an Operation\n", 45 | "\n", 46 | "Complex computations are built up from simpler computations. This may seem obvious, but it is a powerful idea. An **algorithm** is just a description of a computation in terms of other computations that you already know how to perform. To help distinguish between the computation as a whole and the simpler parts, it is helpful to introduce a new word: an **operator** performs a computation." 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": { 52 | "slideshow": { 53 | "slide_type": "subslide" 54 | } 55 | }, 56 | "source": [ 57 | "It's helpful to think of the computation carried out by an operator as involving four parts:\n", 58 | "\n", 59 | "1. The name of the operator\n", 60 | "2. The input arguments\n", 61 | "3. The output value\n", 62 | "4. Side effects" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": { 68 | "slideshow": { 69 | "slide_type": "slide" 70 | } 71 | }, 72 | "source": [ 73 | "## Inputs and Outputs\n", 74 | "\n", 75 | "A typical operation takes one or more **input arguments** and uses the information in these to produce an **output value**.\n", 76 | "\n", 77 | "Along the way, the computer might take some action: display a graph, store a file, make a sound, etc. These actions are called **side effects**." 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": { 83 | "slideshow": { 84 | "slide_type": "subslide" 85 | } 86 | }, 87 | "source": [ 88 | "Since `Python` is a general-purpose programming language, we usually need to `import` special packages for doing specific things (like working with spatial data). You can think of this as adding words to the language. For *Scientific `Python`*, the most important library that we need is `numpy` (*Numerical Python*), which can be loaded like this:" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 34, 94 | "metadata": { 95 | "collapsed": false, 96 | "slideshow": { 97 | "slide_type": "-" 98 | } 99 | }, 100 | "outputs": [], 101 | "source": [ 102 | "import numpy as np # Importing numpy 'as' np is just to reduce typing..." 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": { 108 | "slideshow": { 109 | "slide_type": "slide" 110 | } 111 | }, 112 | "source": [ 113 | "## Syntax\n", 114 | "\n", 115 | "To tell the computer to perform a computation — call this **invoking an operation** or giving a **command** — you need to provide the name and the input arguments in a specific format. The computer then returns the output value.\n", 116 | "\n", 117 | "For example, the command `np.sqrt(25)` invokes the square root operator (named `sqrt` from the `numpy` library) on the argument `25`. The output from the computation will, of course, be `5`.\n", 118 | "\n", 119 | "The syntax of invoking an operation consists of the operator's name, followed by round parentheses. The input arguments go inside the parentheses." 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": { 125 | "slideshow": { 126 | "slide_type": "subslide" 127 | } 128 | }, 129 | "source": [ 130 | "* The software program that you use to invoke operators is called an **interpreter**\\*\n", 131 | "* You enter your commands as a 'dialog' between you and the interpreter\n", 132 | "* Commands can be entered as part of a 'script', directly at a 'command prompt' or within a `Notebook` like we're doing now:" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 37, 138 | "metadata": { 139 | "collapsed": false 140 | }, 141 | "outputs": [ 142 | { 143 | "data": { 144 | "text/plain": [ 145 | "5.0" 146 | ] 147 | }, 148 | "execution_count": 37, 149 | "metadata": {}, 150 | "output_type": "execute_result" 151 | } 152 | ], 153 | "source": [ 154 | "np.median(np.arange(1, 10))" 155 | ] 156 | }, 157 | { 158 | "cell_type": "markdown", 159 | "metadata": {}, 160 | "source": [ 161 | "
\n", 162 | "The interpreter is the program you are running in the background when you start a `Python` `Notebook`\n", 163 | "
" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": { 169 | "slideshow": { 170 | "slide_type": "slide" 171 | } 172 | }, 173 | "source": [ 174 | "* In the previous situation, the 'prompt' is `In [3]:`, and the 'command' is `np.sqrt(25)`.\n", 175 | "* When you press '⌘/Ctrl + Return', the interpreter reads your command and performs the computation.\n", 176 | "* For commands such as the one above, the interpreter will print the output value from the computation." 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": { 182 | "slideshow": { 183 | "slide_type": "slide" 184 | } 185 | }, 186 | "source": [ 187 | "## Arguments \n", 188 | "\n", 189 | "Often, operations involve more than one argument. The various arguments are separated by commas. For example, here is an operation named `arange` from the `numpy` library that produces 'a range' of numbers (increasing values between 3 and 10):" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 59, 195 | "metadata": { 196 | "collapsed": false 197 | }, 198 | "outputs": [ 199 | { 200 | "data": { 201 | "text/plain": [ 202 | "4.0" 203 | ] 204 | }, 205 | "execution_count": 59, 206 | "metadata": {}, 207 | "output_type": "execute_result" 208 | } 209 | ], 210 | "source": [ 211 | "np.sqrt(x)" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 58, 217 | "metadata": { 218 | "collapsed": false 219 | }, 220 | "outputs": [ 221 | { 222 | "data": { 223 | "text/plain": [ 224 | "0.17629237645606133" 225 | ] 226 | }, 227 | "execution_count": 58, 228 | "metadata": {}, 229 | "output_type": "execute_result" 230 | } 231 | ], 232 | "source": [ 233 | "np.mean(np.random.normal(size=)" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": { 239 | "slideshow": { 240 | "slide_type": "slide" 241 | } 242 | }, 243 | "source": [ 244 | "## Order of Arguments\n", 245 | "\n", 246 | "In the previous example, the first argument tells where to start the range and the second tells where to end it. The order of the arguments is important. For instance, *here* is the range produced when 10 is the first argument, 3 is the second, and the third is -1 (decreasing values between 10 and 3):" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 44, 252 | "metadata": { 253 | "collapsed": false 254 | }, 255 | "outputs": [ 256 | { 257 | "data": { 258 | "text/plain": [ 259 | "array([10, 9, 8, 7, 6, 5, 4])" 260 | ] 261 | }, 262 | "execution_count": 44, 263 | "metadata": {}, 264 | "output_type": "execute_result" 265 | } 266 | ], 267 | "source": [ 268 | "np.arange(10, 3, -1)" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": { 274 | "slideshow": { 275 | "slide_type": "slide" 276 | } 277 | }, 278 | "source": [ 279 | "## Named Arguments\n", 280 | "\n", 281 | "For some operators, particularly those that have many input arguments, some of the arguments can be referred to by name rather than position. This is particularly useful when the named argument has a sensible default value. For example, the `arange` operator from the `numpy` library can be instructed what type of output values to produce (integers, floats, etc). This is accomplished using an argument named `dtype`:" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": 49, 287 | "metadata": { 288 | "collapsed": false 289 | }, 290 | "outputs": [ 291 | { 292 | "data": { 293 | "text/plain": [ 294 | "3.3333333333333335" 295 | ] 296 | }, 297 | "execution_count": 49, 298 | "metadata": {}, 299 | "output_type": "execute_result" 300 | } 301 | ], 302 | "source": [ 303 | "10/3" 304 | ] 305 | }, 306 | { 307 | "cell_type": "markdown", 308 | "metadata": { 309 | "slideshow": { 310 | "slide_type": "slide" 311 | } 312 | }, 313 | "source": [ 314 | "## No Arguments\n", 315 | "\n", 316 | "Note that all the values in the range now have decimal places. Depending on the circumstances, all four parts of an operation need not be present. For example, the `ctime` operation from the `time` library returns the current time and date; no input arguments are required:" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": 50, 322 | "metadata": { 323 | "collapsed": false 324 | }, 325 | "outputs": [ 326 | { 327 | "data": { 328 | "text/plain": [ 329 | "'Mon Aug 29 13:24:45 2016'" 330 | ] 331 | }, 332 | "execution_count": 50, 333 | "metadata": {}, 334 | "output_type": "execute_result" 335 | } 336 | ], 337 | "source": [ 338 | "import time\n", 339 | "time.ctime()" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "
\n", 347 | "In the previous example, we first imported the `time` library, which provides a series of commands that help us work with dates and times. Next, even though there are no arguments, the parentheses are still used when calling the `ctime` command. Think of the pair of parentheses as meaning, '*do this*'.\n", 348 | "
" 349 | ] 350 | }, 351 | { 352 | "cell_type": "markdown", 353 | "metadata": { 354 | "slideshow": { 355 | "slide_type": "slide" 356 | } 357 | }, 358 | "source": [ 359 | "## Naming and Storing Values\n", 360 | "\n", 361 | "Often the value returned by an operation will be used later on. Values can be stored for later use with the **assignment operator**. This has a different syntax that reminds the user that a value is being stored. Here's an example of a simple assignment:" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 51, 367 | "metadata": { 368 | "collapsed": false 369 | }, 370 | "outputs": [], 371 | "source": [ 372 | "x = 16" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": { 378 | "slideshow": { 379 | "slide_type": "fragment" 380 | } 381 | }, 382 | "source": [ 383 | "The command has stored the value `16` under the name `x`. The syntax is always the same: an equal sign (`=`) with a name on the left side and a value on the right." 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": { 389 | "slideshow": { 390 | "slide_type": "subslide" 391 | } 392 | }, 393 | "source": [ 394 | "Such stored values are called **objects**. Making an assignment to an object defines the object. Once an object has been defined, it can be referred to and used in later computations. Notice that an assignment operation does not return a value or display a value. Its sole purpose is to have the side effects of defining the object and thereby storing a value under the object's name.\n", 395 | "\n", 396 | "To refer to the value stored in the object, just use the object's name itself. For instance:" 397 | ] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": 52, 402 | "metadata": { 403 | "collapsed": false 404 | }, 405 | "outputs": [ 406 | { 407 | "data": { 408 | "text/plain": [ 409 | "16" 410 | ] 411 | }, 412 | "execution_count": 52, 413 | "metadata": {}, 414 | "output_type": "execute_result" 415 | } 416 | ], 417 | "source": [ 418 | "x" 419 | ] 420 | }, 421 | { 422 | "cell_type": "markdown", 423 | "metadata": { 424 | "slideshow": { 425 | "slide_type": "slide" 426 | } 427 | }, 428 | "source": [ 429 | "# Computation on Variables\n", 430 | "\n", 431 | "Doing a computation on the value store in an object is much the same (and provides and extremely rich syntax for performing complex calculations):" 432 | ] 433 | }, 434 | { 435 | "cell_type": "code", 436 | "execution_count": 53, 437 | "metadata": { 438 | "collapsed": false 439 | }, 440 | "outputs": [ 441 | { 442 | "data": { 443 | "text/plain": [ 444 | "4.0" 445 | ] 446 | }, 447 | "execution_count": 53, 448 | "metadata": {}, 449 | "output_type": "execute_result" 450 | } 451 | ], 452 | "source": [ 453 | "np.sqrt(x)" 454 | ] 455 | }, 456 | { 457 | "cell_type": "markdown", 458 | "metadata": { 459 | "slideshow": { 460 | "slide_type": "subslide" 461 | } 462 | }, 463 | "source": [ 464 | "## Variable Names\n", 465 | "\n", 466 | "You can create as many objects as you like and give them names that remind you of their purpose.\n", 467 | "Some examples: `wilma`, `ages`, `temp`, `dog_houses`, `foo3`. There *are* some general rules for object names:\n", 468 | "\n", 469 | "
\n", 470 | "
\n", 471 | "For the sake of readability, keep object names short. But if you really must have an object named something like `ages_of_children_from_the_clinical_trial`, feel free (it's just more typing for you later!).\n", 472 | "
" 473 | ] 474 | }, 475 | { 476 | "cell_type": "markdown", 477 | "metadata": { 478 | "slideshow": { 479 | "slide_type": "subslide" 480 | } 481 | }, 482 | "source": [ 483 | "* Use only letters and numbers and 'underscores' (`_`)\n", 484 | "* Do **not** use spaces anywhere in the name (`Python` won't let you)\n", 485 | "* A number cannot be the first character in the name\n", 486 | "* Capital letters are treated as distinct from lower-case letters (*i.e.*, `Python` is *case-sensitive*)\n", 487 | " * the objects named `wilma`, `Wilma`, and `WILMA` are all different\n", 488 | "* If possible, use an 'underscore' between words (*i.e.*, `my_object`)" 489 | ] 490 | }, 491 | { 492 | "cell_type": "markdown", 493 | "metadata": { 494 | "slideshow": { 495 | "slide_type": "slide" 496 | } 497 | }, 498 | "source": [ 499 | "## Objects\n", 500 | "\n", 501 | "Objects can store all sorts of things, for example a range of numbers:" 502 | ] 503 | }, 504 | { 505 | "cell_type": "code", 506 | "execution_count": 10, 507 | "metadata": { 508 | "collapsed": false 509 | }, 510 | "outputs": [], 511 | "source": [ 512 | "x = np.arange(1, 7)" 513 | ] 514 | }, 515 | { 516 | "cell_type": "markdown", 517 | "metadata": { 518 | "slideshow": { 519 | "slide_type": "fragment" 520 | } 521 | }, 522 | "source": [ 523 | "When you assign a new value to an existing object, as just done to `x` above, the former values of that object is erased from the computer memory. The former value of `x` was `16`, but after the new assignment above, it is:" 524 | ] 525 | }, 526 | { 527 | "cell_type": "code", 528 | "execution_count": 11, 529 | "metadata": { 530 | "collapsed": false 531 | }, 532 | "outputs": [ 533 | { 534 | "data": { 535 | "text/plain": [ 536 | "array([1, 2, 3, 4, 5, 6])" 537 | ] 538 | }, 539 | "execution_count": 11, 540 | "metadata": {}, 541 | "output_type": "execute_result" 542 | } 543 | ], 544 | "source": [ 545 | "x" 546 | ] 547 | }, 548 | { 549 | "cell_type": "markdown", 550 | "metadata": { 551 | "slideshow": { 552 | "slide_type": "slide" 553 | } 554 | }, 555 | "source": [ 556 | "## Object Assignment\n", 557 | "\n", 558 | "The value of an object is changed only via the assignment operator. Using an object in a computation does not change the value. For example, suppose you invoke the square-root operator on `x`:" 559 | ] 560 | }, 561 | { 562 | "cell_type": "code", 563 | "execution_count": 12, 564 | "metadata": { 565 | "collapsed": false 566 | }, 567 | "outputs": [ 568 | { 569 | "data": { 570 | "text/plain": [ 571 | "array([ 1. , 1.41421356, 1.73205081, 2. , 2.23606798,\n", 572 | " 2.44948974])" 573 | ] 574 | }, 575 | "execution_count": 12, 576 | "metadata": {}, 577 | "output_type": "execute_result" 578 | } 579 | ], 580 | "source": [ 581 | "np.sqrt(x)" 582 | ] 583 | }, 584 | { 585 | "cell_type": "markdown", 586 | "metadata": {}, 587 | "source": [ 588 | "The square roots have been returned as a value, but this doesn't change the value of `x`:" 589 | ] 590 | }, 591 | { 592 | "cell_type": "code", 593 | "execution_count": 13, 594 | "metadata": { 595 | "collapsed": false 596 | }, 597 | "outputs": [ 598 | { 599 | "data": { 600 | "text/plain": [ 601 | "array([1, 2, 3, 4, 5, 6])" 602 | ] 603 | }, 604 | "execution_count": 13, 605 | "metadata": {}, 606 | "output_type": "execute_result" 607 | } 608 | ], 609 | "source": [ 610 | "x" 611 | ] 612 | }, 613 | { 614 | "cell_type": "markdown", 615 | "metadata": { 616 | "slideshow": { 617 | "slide_type": "subslide" 618 | } 619 | }, 620 | "source": [ 621 | "If you want to change the value of `x`, you need to use the assignment operator:" 622 | ] 623 | }, 624 | { 625 | "cell_type": "code", 626 | "execution_count": 14, 627 | "metadata": { 628 | "collapsed": false 629 | }, 630 | "outputs": [], 631 | "source": [ 632 | "x = np.sqrt(x)" 633 | ] 634 | }, 635 | { 636 | "cell_type": "markdown", 637 | "metadata": { 638 | "slideshow": { 639 | "slide_type": "fragment" 640 | } 641 | }, 642 | "source": [ 643 | "
\n", 644 | "An assignment command like x = np.sqrt(x) can be confusing to people who are used to algebraic notation. In algebra, the equal sign describes a relationship between the left and right sides. So, $x = \\sqrt{x}$ tells us about how the quantity $x$ and the quantity $\\sqrt{x}$ are related. Students are usually trained to 'solve' such a relationship, going through a series of algebraic steps to find values for $x$ that are consistent with the mathematical statement (for $x = \\sqrt{x}$, the solutions are $x = 0$ and $x = 1$). In contrast, the assignment command x = np.sqrt(x) is a way of replacing the previous values stored in x with new values that are the square-root of the old ones.\n", 645 | "
" 646 | ] 647 | }, 648 | { 649 | "cell_type": "markdown", 650 | "metadata": { 651 | "slideshow": { 652 | "slide_type": "slide" 653 | } 654 | }, 655 | "source": [ 656 | "## Connecting Computations\n", 657 | "\n", 658 | "The brilliant thing about organizing operators in terms of unput arguments and output values is that the output of one operator can be used as an input to another. This lets complicated computations be built out of simpler ones.\n", 659 | "\n", 660 | "For example, suppose you have a list of `10000` voters in a precinct and you want to select a random sample of `20` of them for a survey. The `np.arange` operator can be used to generate a set of `10000` choices. The `np.random.choice` operator can then be used to select a subset of these values at random." 661 | ] 662 | }, 663 | { 664 | "cell_type": "markdown", 665 | "metadata": { 666 | "slideshow": { 667 | "slide_type": "subslide" 668 | } 669 | }, 670 | "source": [ 671 | "## Selecting Subsets\n", 672 | "\n", 673 | "One way to connect the computations is by using objects to store the intermediate outputs:" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": 15, 679 | "metadata": { 680 | "collapsed": false 681 | }, 682 | "outputs": [ 683 | { 684 | "data": { 685 | "text/plain": [ 686 | "array([ 507, 1714, 6574, 5847, 9101, 2708, 6878, 5201, 1361, 8036, 828,\n", 687 | " 9074, 2833, 1695, 5946, 9191, 3276, 8125, 3638, 7339])" 688 | ] 689 | }, 690 | "execution_count": 15, 691 | "metadata": {}, 692 | "output_type": "execute_result" 693 | } 694 | ], 695 | "source": [ 696 | "choices = np.arange(1, 10000)\n", 697 | "np.random.choice(choices, 20, replace=False) # Sample _without_ replacement" 698 | ] 699 | }, 700 | { 701 | "cell_type": "markdown", 702 | "metadata": { 703 | "slideshow": { 704 | "slide_type": "subslide" 705 | } 706 | }, 707 | "source": [ 708 | "## Passing Outputs\n", 709 | "\n", 710 | "You can also pass the output of an operator *directly* as an argument to another operator. Here's another way to accomplish exactly the same thing as the above (note that the values will differ because we are performing a *random* sample):" 711 | ] 712 | }, 713 | { 714 | "cell_type": "code", 715 | "execution_count": 16, 716 | "metadata": { 717 | "collapsed": false 718 | }, 719 | "outputs": [ 720 | { 721 | "data": { 722 | "text/plain": [ 723 | "array([2911, 2508, 4752, 7974, 98, 6490, 6520, 913, 5288, 2517, 8688,\n", 724 | " 1858, 2655, 2625, 4454, 5027, 7354, 1059, 9176, 978])" 725 | ] 726 | }, 727 | "execution_count": 16, 728 | "metadata": {}, 729 | "output_type": "execute_result" 730 | } 731 | ], 732 | "source": [ 733 | "np.random.choice(np.arange(1, 10000), 20, replace=False)" 734 | ] 735 | }, 736 | { 737 | "cell_type": "markdown", 738 | "metadata": { 739 | "slideshow": { 740 | "slide_type": "slide" 741 | } 742 | }, 743 | "source": [ 744 | "## Numbers and Arithmetic\n", 745 | "\n", 746 | "The `Python` language has a concise notation for arithmetic that looks very much like the traditional one:" 747 | ] 748 | }, 749 | { 750 | "cell_type": "code", 751 | "execution_count": 17, 752 | "metadata": { 753 | "collapsed": false, 754 | "format": "column" 755 | }, 756 | "outputs": [ 757 | { 758 | "data": { 759 | "text/plain": [ 760 | "9.0" 761 | ] 762 | }, 763 | "execution_count": 17, 764 | "metadata": {}, 765 | "output_type": "execute_result" 766 | } 767 | ], 768 | "source": [ 769 | "7. + 2." 770 | ] 771 | }, 772 | { 773 | "cell_type": "code", 774 | "execution_count": 18, 775 | "metadata": { 776 | "collapsed": false, 777 | "format": "column" 778 | }, 779 | "outputs": [ 780 | { 781 | "data": { 782 | "text/plain": [ 783 | "12.0" 784 | ] 785 | }, 786 | "execution_count": 18, 787 | "metadata": {}, 788 | "output_type": "execute_result" 789 | } 790 | ], 791 | "source": [ 792 | "3. * 4." 793 | ] 794 | }, 795 | { 796 | "cell_type": "code", 797 | "execution_count": 19, 798 | "metadata": { 799 | "collapsed": false, 800 | "format": "column" 801 | }, 802 | "outputs": [ 803 | { 804 | "data": { 805 | "text/plain": [ 806 | "2.5" 807 | ] 808 | }, 809 | "execution_count": 19, 810 | "metadata": {}, 811 | "output_type": "execute_result" 812 | } 813 | ], 814 | "source": [ 815 | "5. / 2." 816 | ] 817 | }, 818 | { 819 | "cell_type": "code", 820 | "execution_count": 20, 821 | "metadata": { 822 | "collapsed": false, 823 | "format": "column" 824 | }, 825 | "outputs": [ 826 | { 827 | "data": { 828 | "text/plain": [ 829 | "-5.0" 830 | ] 831 | }, 832 | "execution_count": 20, 833 | "metadata": {}, 834 | "output_type": "execute_result" 835 | } 836 | ], 837 | "source": [ 838 | "3. - 8." 839 | ] 840 | }, 841 | { 842 | "cell_type": "code", 843 | "execution_count": 21, 844 | "metadata": { 845 | "collapsed": false, 846 | "format": "column" 847 | }, 848 | "outputs": [ 849 | { 850 | "data": { 851 | "text/plain": [ 852 | "-3.0" 853 | ] 854 | }, 855 | "execution_count": 21, 856 | "metadata": {}, 857 | "output_type": "execute_result" 858 | } 859 | ], 860 | "source": [ 861 | "-3." 862 | ] 863 | }, 864 | { 865 | "cell_type": "code", 866 | "execution_count": 22, 867 | "metadata": { 868 | "collapsed": false, 869 | "format": "column" 870 | }, 871 | "outputs": [ 872 | { 873 | "data": { 874 | "text/plain": [ 875 | "25.0" 876 | ] 877 | }, 878 | "execution_count": 22, 879 | "metadata": {}, 880 | "output_type": "execute_result" 881 | } 882 | ], 883 | "source": [ 884 | "5.**2. # S ame as 5^2" 885 | ] 886 | }, 887 | { 888 | "cell_type": "markdown", 889 | "metadata": { 890 | "slideshow": { 891 | "slide_type": "slide" 892 | } 893 | }, 894 | "source": [ 895 | "## Complicating Things\n", 896 | "\n", 897 | "Arithmetic operators, like any other operators, can be connected to form more complicated computations. For instance:" 898 | ] 899 | }, 900 | { 901 | "cell_type": "code", 902 | "execution_count": 23, 903 | "metadata": { 904 | "collapsed": false 905 | }, 906 | "outputs": [ 907 | { 908 | "data": { 909 | "text/plain": [ 910 | "10.0" 911 | ] 912 | }, 913 | "execution_count": 23, 914 | "metadata": {}, 915 | "output_type": "execute_result" 916 | } 917 | ], 918 | "source": [ 919 | "8. + 4. / 2." 920 | ] 921 | }, 922 | { 923 | "cell_type": "markdown", 924 | "metadata": {}, 925 | "source": [ 926 | "
\n", 927 | "The a human reader, the command `8+4/2` might seem ambiguous. Is it intended to be `(8+4)/2` or `8+(4/2)`?\n", 928 | "The computer uses unambiguous rules to interpret the expression, but it's a good idea for you to use parentheses so that you can make sure that what you *intend* is what the computer carries out:\n", 929 | "
" 930 | ] 931 | }, 932 | { 933 | "cell_type": "code", 934 | "execution_count": 24, 935 | "metadata": { 936 | "collapsed": false 937 | }, 938 | "outputs": [ 939 | { 940 | "data": { 941 | "text/plain": [ 942 | "6.0" 943 | ] 944 | }, 945 | "execution_count": 24, 946 | "metadata": {}, 947 | "output_type": "execute_result" 948 | } 949 | ], 950 | "source": [ 951 | "(8. + 4.) / 2." 952 | ] 953 | }, 954 | { 955 | "cell_type": "markdown", 956 | "metadata": { 957 | "slideshow": { 958 | "slide_type": "slide" 959 | } 960 | }, 961 | "source": [ 962 | "## Mathematical Notation\n", 963 | "\n", 964 | "Traditional mathematical notations uses superscripts and radicals to indicate exponentials and roots — *e.g.* $3^2$ or $\\sqrt{3}$ or $\\sqrt[3]{8}$. This special typography doesn't work well with an ordinary keyboard, so `Python` and most other computer languages uses a different notation:" 965 | ] 966 | }, 967 | { 968 | "cell_type": "code", 969 | "execution_count": 25, 970 | "metadata": { 971 | "collapsed": false, 972 | "format": "column" 973 | }, 974 | "outputs": [ 975 | { 976 | "data": { 977 | "text/plain": [ 978 | "9.0" 979 | ] 980 | }, 981 | "execution_count": 25, 982 | "metadata": {}, 983 | "output_type": "execute_result" 984 | } 985 | ], 986 | "source": [ 987 | "3.**2." 988 | ] 989 | }, 990 | { 991 | "cell_type": "code", 992 | "execution_count": 26, 993 | "metadata": { 994 | "collapsed": false, 995 | "format": "column" 996 | }, 997 | "outputs": [ 998 | { 999 | "data": { 1000 | "text/plain": [ 1001 | "1.7320508075688772" 1002 | ] 1003 | }, 1004 | "execution_count": 26, 1005 | "metadata": {}, 1006 | "output_type": "execute_result" 1007 | } 1008 | ], 1009 | "source": [ 1010 | "np.sqrt(3.) # or 3.**0.5" 1011 | ] 1012 | }, 1013 | { 1014 | "cell_type": "code", 1015 | "execution_count": 27, 1016 | "metadata": { 1017 | "collapsed": false, 1018 | "format": "column" 1019 | }, 1020 | "outputs": [ 1021 | { 1022 | "data": { 1023 | "text/plain": [ 1024 | "2.0" 1025 | ] 1026 | }, 1027 | "execution_count": 27, 1028 | "metadata": {}, 1029 | "output_type": "execute_result" 1030 | } 1031 | ], 1032 | "source": [ 1033 | "8.**(1./3.)" 1034 | ] 1035 | }, 1036 | { 1037 | "cell_type": "markdown", 1038 | "metadata": { 1039 | "slideshow": { 1040 | "slide_type": "subslide" 1041 | } 1042 | }, 1043 | "source": [ 1044 | "There is a large set of mathematical functions: exponentials, logs, trigonometric and inverse trigonometric functions, etc. Some examples:\n", 1045 | "\n", 1046 | "\n", 1047 | "\n", 1048 | "\n", 1049 | "\n", 1050 | "\n", 1051 | "\n", 1052 | "\n", 1053 | "\n", 1054 | "\n", 1055 | "\n", 1056 | " \n", 1057 | " \n", 1058 | "\n", 1059 | "\n", 1060 | " \n", 1061 | " \n", 1062 | "\n", 1063 | "\n", 1064 | " \n", 1065 | " \n", 1066 | "\n", 1067 | "\n", 1068 | " \n", 1069 | " \n", 1070 | "\n", 1071 | "\n", 1072 | " \n", 1073 | " \n", 1074 | "\n", 1075 | "\n", 1076 | " \n", 1077 | " \n", 1078 | "\n", 1079 | "\n", 1080 | " \n", 1081 | " \n", 1082 | "\n", 1083 | "
Traditional/MathPython
$e^2$np.exp(2)
$\\log_{e}(100)$np.log(100)
$\\log_{10}(100)$np.log10(100)
$\\log_{2}(100)$np.log2(100)
$\\cos(\\frac{\\pi}{2})$np.cos(np.pi/2)
$\\sin(\\frac{\\pi}{2})$np.sin(np.pi/2)
$\\tan(\\frac{\\pi}{2})$np.tan(np.pi/2)
$\\cos^{-1}(-1)$np.acos(-1)
\n", 1084 | "\n", 1085 | "[pint]: https://pint.readthedocs.org/en/latest/\n", 1086 | "[quantities]: http://pythonhosted.org/quantities/\n", 1087 | "[units]: https://pypi.python.org/pypi/units/\n", 1088 | "[sympy.physics.units]: http://docs.sympy.org/latest/modules/physics/units.html\n", 1089 | "[etc]: http://conference.scipy.org/scipy2013/presentation_detail.php?id=174" 1090 | ] 1091 | }, 1092 | { 1093 | "cell_type": "markdown", 1094 | "metadata": { 1095 | "slideshow": { 1096 | "slide_type": "subslide" 1097 | } 1098 | }, 1099 | "source": [ 1100 | "## Scientific Notation\n", 1101 | "\n", 1102 | "Numbers can be written in **scientific notation**. For example, the 'universal gravitational constant' that describes the gravitational attraction between masses is $6.67428 \\times 10^{11}$ (with units meters-cubed per kilogram per second squared).\n", 1103 | "\n", 1104 | "In the computer notation, this would be written as `6.67428e-11`.\n", 1105 | "\n", 1106 | "
\n", 1107 | "
\n", 1108 | "The `Python` language does not directly support the recording of units. Though we will learn about some *add-on* libraries that help with this later in the semester.\n", 1109 | "
" 1110 | ] 1111 | }, 1112 | { 1113 | "cell_type": "markdown", 1114 | "metadata": { 1115 | "slideshow": { 1116 | "slide_type": "slide" 1117 | } 1118 | }, 1119 | "source": [ 1120 | "## Floating Points\n", 1121 | "\n", 1122 | "Computer arithmetic is accurate and reliable, but it often involves very slight rounding of numbers. Ordinarily, this is not noticeable. However, it can become apparent in some calculations that produce results that are (near) zero. For example, mathematically, $sin(\\pi) = 0$, however, the computer does not duplicate the mathematical relationship exactly:" 1123 | ] 1124 | }, 1125 | { 1126 | "cell_type": "code", 1127 | "execution_count": 28, 1128 | "metadata": { 1129 | "collapsed": false 1130 | }, 1131 | "outputs": [ 1132 | { 1133 | "data": { 1134 | "text/plain": [ 1135 | "1.2246467991473532e-16" 1136 | ] 1137 | }, 1138 | "execution_count": 28, 1139 | "metadata": {}, 1140 | "output_type": "execute_result" 1141 | } 1142 | ], 1143 | "source": [ 1144 | "np.sin(np.pi)" 1145 | ] 1146 | }, 1147 | { 1148 | "cell_type": "markdown", 1149 | "metadata": { 1150 | "slideshow": { 1151 | "slide_type": "fragment" 1152 | } 1153 | }, 1154 | "source": [ 1155 | "Whether a number like this is properly interpreted as 'close to zero' depends on the context and, for quantities that have units, on the units themselves." 1156 | ] 1157 | }, 1158 | { 1159 | "cell_type": "markdown", 1160 | "metadata": { 1161 | "slideshow": { 1162 | "slide_type": "slide" 1163 | } 1164 | }, 1165 | "source": [ 1166 | "## Special 'Numbers'\n", 1167 | "\n", 1168 | "There are several 'special' numbers in the `Python` world; two of which are `inf`, which stands for $\\infty$ (infinity), and `nan`, which stands for 'not a number' (nan results when a numerical operation isn't define), for instance:" 1169 | ] 1170 | }, 1171 | { 1172 | "cell_type": "code", 1173 | "execution_count": 29, 1174 | "metadata": { 1175 | "collapsed": false 1176 | }, 1177 | "outputs": [ 1178 | { 1179 | "name": "stderr", 1180 | "output_type": "stream", 1181 | "text": [ 1182 | "/Users/carsonfarmer/miniconda3/envs/space-time/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in double_scalars\n", 1183 | " if __name__ == '__main__':\n" 1184 | ] 1185 | }, 1186 | { 1187 | "data": { 1188 | "text/plain": [ 1189 | "inf" 1190 | ] 1191 | }, 1192 | "execution_count": 29, 1193 | "metadata": {}, 1194 | "output_type": "execute_result" 1195 | } 1196 | ], 1197 | "source": [ 1198 | "np.float64(1.) / 0." 1199 | ] 1200 | }, 1201 | { 1202 | "cell_type": "code", 1203 | "execution_count": 30, 1204 | "metadata": { 1205 | "collapsed": false 1206 | }, 1207 | "outputs": [ 1208 | { 1209 | "name": "stderr", 1210 | "output_type": "stream", 1211 | "text": [ 1212 | "/Users/carsonfarmer/miniconda3/envs/space-time/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in double_scalars\n", 1213 | " if __name__ == '__main__':\n" 1214 | ] 1215 | }, 1216 | { 1217 | "data": { 1218 | "text/plain": [ 1219 | "nan" 1220 | ] 1221 | }, 1222 | "execution_count": 30, 1223 | "metadata": {}, 1224 | "output_type": "execute_result" 1225 | } 1226 | ], 1227 | "source": [ 1228 | "np.float64(0.) / 0." 1229 | ] 1230 | }, 1231 | { 1232 | "cell_type": "markdown", 1233 | "metadata": { 1234 | "slideshow": { 1235 | "slide_type": "subslide" 1236 | } 1237 | }, 1238 | "source": [ 1239 | "
\n", 1240 | "Mathematically oriented readers will wonder why Python should have any trouble with a computation like $\\sqrt{-9}$; the result is the imaginary number $3\\jmath$ (imaginary numbers may be represented by a $\\jmath$ or a $\\imath$, depending on the field). Python works with complex numbers, but you have to explicitly tell the system that this is what you want to do. To calculate $\\sqrt{-9}$ for example, simply use np.sqrt(-9+0j).\n", 1241 | "
" 1242 | ] 1243 | }, 1244 | { 1245 | "cell_type": "markdown", 1246 | "metadata": { 1247 | "slideshow": { 1248 | "slide_type": "slide" 1249 | } 1250 | }, 1251 | "source": [ 1252 | "## Types of Objects\n", 1253 | "\n", 1254 | "Most of the examples used so far have dealt with numbers. But computers work with other kinds of information as well: text, photographs, sounds, data-sets\\*, and so on.\n", 1255 | "\n", 1256 | "
\n", 1257 | "
\n", 1258 | "\\* In `Python`, data frames are not 'built in' as part of the basic language, but the excellent [`pandas`](http://pandas.pydata.org/) library provides data frames and a whole slew of other functionality for researchers doing data analysis with `Python`. We will be learning more about `pandas` coming up.\n", 1259 | "
" 1260 | ] 1261 | }, 1262 | { 1263 | "cell_type": "markdown", 1264 | "metadata": { 1265 | "slideshow": { 1266 | "slide_type": "subslide" 1267 | } 1268 | }, 1269 | "source": [ 1270 | "## Types...\n", 1271 | "\n", 1272 | "The word **type** is used to refer to the *kind* of information. Modern computer languages support a great variety of types. It's important to know about the types of data because operators expect their input arguments to be of specific types. When you use the wrong type of input, the computer might not be able to process your command." 1273 | ] 1274 | }, 1275 | { 1276 | "cell_type": "markdown", 1277 | "metadata": { 1278 | "slideshow": { 1279 | "slide_type": "slide" 1280 | } 1281 | }, 1282 | "source": [ 1283 | "## A Note on Strings\n", 1284 | "\n", 1285 | "Whenever you refer to an object name, make sure that you don't use quotes. For example, in the following, we are first assigning the string `\"python\"` to the `name` object, and then returning (and printing automatically) the `name` object." 1286 | ] 1287 | }, 1288 | { 1289 | "cell_type": "code", 1290 | "execution_count": 31, 1291 | "metadata": { 1292 | "collapsed": false 1293 | }, 1294 | "outputs": [ 1295 | { 1296 | "data": { 1297 | "text/plain": [ 1298 | "'python'" 1299 | ] 1300 | }, 1301 | "execution_count": 31, 1302 | "metadata": {}, 1303 | "output_type": "execute_result" 1304 | } 1305 | ], 1306 | "source": [ 1307 | "name = \"python\"\n", 1308 | "name" 1309 | ] 1310 | }, 1311 | { 1312 | "cell_type": "markdown", 1313 | "metadata": {}, 1314 | "source": [ 1315 | "Inputting the *string* **doesn't** refer to an object: it will merely 'print' the text itself:" 1316 | ] 1317 | }, 1318 | { 1319 | "cell_type": "code", 1320 | "execution_count": 32, 1321 | "metadata": { 1322 | "collapsed": false 1323 | }, 1324 | "outputs": [ 1325 | { 1326 | "data": { 1327 | "text/plain": [ 1328 | "'name'" 1329 | ] 1330 | }, 1331 | "execution_count": 32, 1332 | "metadata": {}, 1333 | "output_type": "execute_result" 1334 | } 1335 | ], 1336 | "source": [ 1337 | "\"name\"" 1338 | ] 1339 | }, 1340 | { 1341 | "cell_type": "markdown", 1342 | "metadata": { 1343 | "slideshow": { 1344 | "slide_type": "slide" 1345 | } 1346 | }, 1347 | "source": [ 1348 | "## Names vs Strings\n", 1349 | "\n", 1350 | "Similarly, if you omit the quotation marks from around the text, `Python` will treat it as an object name and will look for *that* named object.\n", 1351 | "\n", 1352 | "For instance, here we 'lookup' the value contained in a `python` object and insert that value into `name`:" 1353 | ] 1354 | }, 1355 | { 1356 | "cell_type": "code", 1357 | "execution_count": 33, 1358 | "metadata": { 1359 | "collapsed": false 1360 | }, 1361 | "outputs": [ 1362 | { 1363 | "ename": "NameError", 1364 | "evalue": "name 'python' is not defined", 1365 | "output_type": "error", 1366 | "traceback": [ 1367 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 1368 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 1369 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 1370 | "\u001b[0;31mNameError\u001b[0m: name 'python' is not defined" 1371 | ] 1372 | } 1373 | ], 1374 | "source": [ 1375 | "name = python" 1376 | ] 1377 | }, 1378 | { 1379 | "cell_type": "markdown", 1380 | "metadata": { 1381 | "slideshow": { 1382 | "slide_type": "subslide" 1383 | } 1384 | }, 1385 | "source": [ 1386 | "## Oops!\n", 1387 | "\n", 1388 | "As it happens, there was no object named `python` because it had not been defined by any previous assignment command. So, the computer generated an error.\n", 1389 | "\n", 1390 | "And on that note... its time to move on!" 1391 | ] 1392 | } 1393 | ], 1394 | "metadata": { 1395 | "anaconda-cloud": {}, 1396 | "celltoolbar": "Slideshow", 1397 | "kernelspec": { 1398 | "display_name": "Python [default]", 1399 | "language": "python", 1400 | "name": "python3" 1401 | }, 1402 | "language_info": { 1403 | "codemirror_mode": { 1404 | "name": "ipython", 1405 | "version": 3 1406 | }, 1407 | "file_extension": ".py", 1408 | "mimetype": "text/x-python", 1409 | "name": "python", 1410 | "nbconvert_exporter": "python", 1411 | "pygments_lexer": "ipython3", 1412 | "version": "3.5.2" 1413 | }, 1414 | "latex_envs": { 1415 | "bibliofile": "biblio.bib", 1416 | "cite_by": "apalike", 1417 | "current_citInitial": 1, 1418 | "eqLabelWithNumbers": true, 1419 | "eqNumInitial": 0 1420 | }, 1421 | "nav_menu": {}, 1422 | "toc": { 1423 | "navigate_menu": false, 1424 | "number_sections": false, 1425 | "sideBar": true, 1426 | "threshold": 6, 1427 | "toc_cell": false, 1428 | "toc_section_display": "block", 1429 | "toc_window_display": false 1430 | } 1431 | }, 1432 | "nbformat": 4, 1433 | "nbformat_minor": 0 1434 | } 1435 | -------------------------------------------------------------------------------- /Notebooks/Lecture 6.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "# Visualizing Time\n", 12 | "\n", 13 | "*Space-Time Analytics — Lecture 6*\n", 14 | "\n", 15 | "* **Prof. Carson J. Q. Farmer** \n", 16 | " [@carsonfarmer](https://twitter.com/carsonfarmer) \n", 17 | " [carsonfarmer.com](https://carsonfarmer.com) \n", 18 | " [carson.farmer@colorado.edu](mailto:carson.farmer@colorado.edu) \n", 19 | " [github.com/carsonfarmer](https://github.com/carsonfarmer) \n", 20 | " Guggenheim Building Room 207 \n", 21 | " Wednesdays 2:00-3:00 PM and 4:15-5:15 PM " 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": { 27 | "slideshow": { 28 | "slide_type": "slide" 29 | } 30 | }, 31 | "source": [ 32 | "## Today\n", 33 | "\n", 34 | "* This week's student presentations \n", 35 | " [Space-Time Regression/Modeling](http://link.springer.com/article/10.1007%2Fs10651-014-0305-4) \n", 36 | "* Guest speaker! \n", 37 | " Yi Qiang (Postdoc) \n", 38 | " \"New Representations of Time in Visual Analytics and GIS\"\n", 39 | "* Lab 1 \n", 40 | " Fixing Git/GitHub stuff (almost)" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": { 46 | "slideshow": { 47 | "slide_type": "slide" 48 | } 49 | }, 50 | "source": [ 51 | "## Student Presentation\n", 52 | "\n", 53 | "[**Space-Time Regression/Modeling**](http://link.springer.com/article/10.1007%2Fs10651-014-0305-4) " 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 2, 59 | "metadata": { 60 | "collapsed": false, 61 | "hideCode": true, 62 | "hidePrompt": true, 63 | "slideshow": { 64 | "slide_type": "-" 65 | } 66 | }, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "image/png": 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eioBD2+RzyUjy3RoQJl5d/ynK7WdGME1iTTI0zUNOyHA2Xz4xzEec9ZDw6e3q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\nDqUlxhXVNGaRygqC0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i+LWoUs5TcprYx+NsqULjFpbLYeyZG2+DuBGV5vPufrPfakSwhd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71 | "text/plain": [ 72 | "" 73 | ] 74 | }, 75 | "metadata": { 76 | "image/png": { 77 | "width": 450 78 | } 79 | }, 80 | "output_type": "display_data" 81 | } 82 | ], 83 | "source": [ 84 | "display(Image(\"./images/presentation4.png\", width=450, embed=True))" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": { 90 | "slideshow": { 91 | "slide_type": "slide" 92 | } 93 | }, 94 | "source": [ 95 | "![That's all folks](http://media3.giphy.com/media/jYAGkoghdmD9S/giphy.gif)" 96 | ] 97 | } 98 | ], 99 | "metadata": { 100 | "celltoolbar": "Slideshow", 101 | "kernelspec": { 102 | "display_name": "Python [conda env:space-time]", 103 | "language": "python", 104 | "name": "conda-env-space-time-py" 105 | }, 106 | "language_info": { 107 | "codemirror_mode": { 108 | "name": "ipython", 109 | "version": 3 110 | }, 111 | "file_extension": ".py", 112 | "mimetype": "text/x-python", 113 | "name": "python", 114 | "nbconvert_exporter": "python", 115 | "pygments_lexer": "ipython3", 116 | "version": "3.5.2" 117 | }, 118 | "toc": { 119 | "navigate_menu": false, 120 | "number_sections": false, 121 | "sideBar": true, 122 | "threshold": 6, 123 | "toc_cell": false, 124 | "toc_section_display": "block", 125 | "toc_window_display": false 126 | } 127 | }, 128 | "nbformat": 4, 129 | "nbformat_minor": 0 130 | } 131 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # space-time-analytics 2 | Lecture notes and various items for a course on space-time analytics 3 | -------------------------------------------------------------------------------- /snippets.md: -------------------------------------------------------------------------------- 1 | ## Using Weights to Specify Dependence 2 | 12 = [(Yi1 - Y-bar1)*(Yi2 - Y-bar2)] / [(Yi1 - Y-bar1)2 * (Yi2 - Y-bar2)2]1/2 3 | display(YouTubeVideo("MQACCcfTpXc")) 4 | 5 | 6 | * Measured using a **semivariogram** \* 7 | * Look at pairs of observations separated by a distance $h$ 8 | * Gives us a lot of data — every observation has $n-1$ possible pairs! 9 | * Illustrates the spatial structure of a variable 10 | 11 | 12 |
13 | \* We usually just call it a variogram (there is a technical difference between the two but for clarity we'll use the term 'variogram' generally) 14 |
15 | 16 | 17 | ## Calculating Semivariance 18 | 19 | * For each pair we know the distance between points 20 | * We measure the similarity of the point pairs (semivariance) 21 | * We plot the semivariance for a range of distances 22 | 23 | 24 |
25 | \* In practice there is an important distinction between a *theoretical* and an *empirical* variogram — an empirical variogram is used to *fit* a theoretical variogram 26 |
27 | 28 | 29 | * $N(h)$ denotes the *set* of pairs of observations separated by distance $h$ 30 | * $|N(h)|$ is the number of pairs in the set, and 31 | * $h$ is usually an approximate distance implemented using a certain tolerance 32 | 33 | $$ 34 | \hat\lambda(h) = {1 \over {|N(h)|}} \sum_{(i,j)\in N(h)} (x_i - x_j)^2 35 | $$ 36 | 37 | 38 |
39 | The $\in$ under the summation denotes all $i$,$j$ pairs within the set $N(h)$ 40 |
41 | 42 | 43 | ## Meuse Soil Data 44 | 45 | * The Maas river bank soil pollution data (Limburg, The Netherlands) 46 | * Sampled along the Dutch bank of the river Maas (Meuse) north of Maastricht 47 | * Data are those used in Burrough and McDonnell (1998, pp. 309–311) 48 | * The [version we use here](https://github.com/filipkral/meuse) is a subset of the data provided with the `gstat` and `sp` `R` packages 49 | 50 | 51 | 52 | ## Variogram Parts 53 | 54 | * Range 55 | * Distance at which point pairs stop being similar 56 | * In our `meause` example 57 | * Nearby samples have similar levels of zinc 58 | * Means samples separated by less than ... will be similar 59 | * Beyond 'range' samples are "independent" 60 | * Sill 61 | * Background level of variance. Sort of a baseline for study region 62 | * Nugget 63 | * Small scale discontinuity... error? 64 | 65 | 66 | 67 | ## Loading/Plotting Data 68 | 69 | # Grab the data from online 70 | meuse = pd.read_csv("https://raw.githubusercontent.com/filipkral/meuse/master/meuse.txt") 71 | # Quick plot using simple pandas plotting... 72 | ax = meuse.plot.scatter('x', 'y', c='zinc', s=100).set_axis_off() 73 | # You can see the top of the data frame with: 74 | # meuse.head() 75 | --------------------------------------------------------------------------------