Part_1a__Fundamentals_of_programming.ipynb

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   "source": [
    "# Fundamentals of Programming\n",
    "\n",
    "Evan Bianco\n",
    "[agilegeoscience](http://agilegeoscience.com), [@EvanBianco](http://twitter.com/EvanBianco)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Variables and assignment\n",
    "\n",
    "- Native data types\n",
    "\n",
    "- Operators and expressions\n",
    "\n",
    "- Data collections and data structures\n",
    "\n",
    "- Procedures and control: loops and making decisions\n",
    "\n",
    "- Getting data, manipulating data\n",
    "\n",
    "- Defining functions and calling functions \n",
    "\n",
    "- Writing and running programs \n",
    "\n",
    "- Objects and classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "# Variables and assignment"
   ]
  },
  {
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   "metadata": {
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   "outputs": [],
   "source": [
    "x = 7\n",
    "y = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "x.__repr__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Checking the `type` of a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "del y"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "%whos "
   ]
  },
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   },
   "source": [
    "# Native data `types`"
   ]
  },
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   "outputs": [],
   "source": [
    "z = 1.4 + 2.3"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "collapsed": true,
    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [],
   "source": [
    "c = 2 + 1.5j  # same as writing: complex(2, 1.5) \n",
    "c"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "5 / 3"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "5 // 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Why are there 2 kinds of numbers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Strings `str`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "slideshow": {
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   "outputs": [],
   "source": [
    "s1 = '#Nordegg:'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "s1.strip('#:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "s1.startswith('Nor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `str` indexing (how to count, part 1)\n",
    "\n",
    "----\n",
    "- **Exercise**: return the `e` character in `s`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Try `help(s)`, `s1?`, `s1??`, `s1.<tab>`, `s1.upper()` , `s1.strip()`, `s1.startswith()`, `s1.pop()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Can we add strings together? "
   ]
  },
  {
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   "source": [
    "s2, s3 = 'Limestone', 'Shale'\n",
    "\n",
    "s2 + s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(s2 + s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(s2 * 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(s2 + 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "lithology = s + s2 + ' has minor ' + s3 + ' fragments'\n",
    "lithology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "'{0} {1} has minor {2} fragments'.format(s, s2, s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## String methods and string formatting\n",
    "----\n",
    "- **Exercise**: Use a combination of string methods on `s` text formatting to produce the following output:\n",
    "\n",
    "    `> The Nordegg limestone has minor shale fragments` \n",
    "    \n",
    "    (ensure sentence case and remove `'#'`, `':'`, `'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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    }
   },
   "source": [
    "# Operators and expressions"
   ]
  },
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   "source": [
    "* mathematical operations\n",
    "\n",
    "* comparison operations\n",
    "\n",
    "* bitwise operations\n",
    "\n",
    "* augmented assignment, copies, and pointers\n",
    "\n",
    "* boolean expressions\n",
    "\n",
    "* conversion functions"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "### mathematical operations"
   ]
  },
  {
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     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### comparison operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### augmented assignment, copies and pointers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": [
    "y += y / 125.0\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### boolean expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": [
    "type(ord('\\t'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### conversion functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data collections and data structures\n",
    "\n",
    "`list, dict, tuples, sets`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `list`\n",
    "\n",
    "Lists in Python are one-dimensional, ordered containers whose elements may be any Python objects. Lists are *mutable* and have methods for adding and removing elements to and from themselves. The literal syntax for lists is surround commas seperated values with square brackets (`[]`). The square brackets are a syntactic hint that lists are indexable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# [1,1] + [3,3] + [4, 4]\n",
    "list(str(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fib = [1, 1, 2, 3, 5, 8] + [13]\n",
    "fib.append(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "del(fib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fib.extend([21.0, 34.0, 55.0])\n",
    "fib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fib += [89.0, 144.0]\n",
    "fib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fibm = np.array(fib[:-1])\n",
    "fibp = np.array(fib[1:])\n",
    "plt.plot(fibp/fibm)\n",
    "plt.title('Golden ratio')\n",
    "fibp/fibm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Indexing, slicing, striding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "In addition to accessing a single element in a `list` or `string`, we can also *slice* or *stride* into data structures to access multiple elements at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "name = 'Cambrian (C)'\n",
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "name = 'Cambrian (C)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Without using the Python interpreter, what is the expected output of the following commands?:\n",
    "\n",
    "- a) `name[:7]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- b) `name[:-4]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- c) `name[3:7]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- d) `name[::2]`"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "age_names = ['Cambrian (C)', 'Ordivician (O)',  'Silurian (S)',  'Devonian (D)', \n",
    "           'Mississipian (M)', 'Pennsylvanian (IP)', 'Permian (P)',\n",
    "           'Triassic (Tr)', 'Jurassic (J)',  'Cretaceous (C)', \n",
    "           'Tertiary (T)', 'Quaternary (Q)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Indexing practice\n",
    "\n",
    "----\n",
    "**Exercise**:\n",
    "\n",
    "- return the string: \n",
    "\n",
    "    ` > Triassic (Tr)` \n",
    "\n",
    "\n",
    "- return just the word: \n",
    "\n",
    "    ` > Triassic`\n",
    "\n",
    "\n",
    "- return the abbreviation:\n",
    "\n",
    "    ` > (Tr)` enclosed in parenthesis\n",
    "\n",
    "\n",
    "- return just the abbreviation: \n",
    "\n",
    "    ` > Tr` \n",
    "\n",
    "\n",
    "(bonus points if you can do (d) all in one line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Nested `list`\n",
    "\n",
    "lists can contain anything*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "age_list = [['Cambrian (C)', [544,495]], ['Ordivician (O)', [495, 492] ], \n",
    "            ['Silurian (S)', [442, 416]], ['Devonian (D)',[416, 354]], \n",
    "            ['Mississipian (M)', [354, 324]], ['Pennsylvanian (IP)', [324, 295]], \n",
    "            ['Permian (P)', [304, 248]], ['Triassic (Tr)', [248, 205]], \n",
    "            ['Jurassic (J)', [205, 144]], ['Cretaceous (C)', [160, 65]], \n",
    "            ['Tertiary (T)', [65, 1.8]], ['Quaternary (Q)']\n",
    "            ]\n",
    "# But at some point it just gets impractical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
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   "source": [
    "### `dicts`\n",
    "\n",
    "Dictionaries are probably *the most important* data structure in Python... with the possible exception of ndarrays. A dictionary, or `dict`, is a mutable, unordered collection of unique key / value pairs. \n",
    "\n",
    "For example, we could store a 'row' of our mini 'database' like this:\n",
    "\n",
    "    {\"Name\": \"Cambrian\", \"Abbreviation\": \"C\", \"Start\": 544, \"End\": 495}\n",
    "    \n",
    "The structure we choose depends on features we want. To preserve order, we might make a list of dicts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "ages = {\n",
    "    'Cambrian': {\"abbreviation\": \"C\", \"start\": 544, \"end\": 495},\n",
    "    'Ordivician': {\"abbreviation\": \"O\", \"start\": 495, \"end\": 492},  \n",
    "    'Silurian': {\"abbreviation\": \"S\", \"start\": 442, \"end\": 416}, \n",
    "    'Devonian': {\"abbreviation\": \"D\", \"start\": 416, \"end\": 354},\n",
    "    'Mississipian': {\"abbreviation\": \"M\", \"start\": 354, \"end\": 324},\n",
    "    'Pennsylvanian': {\"abbreviation\": \"IP\", \"start\": 324, \"end\": 295}, \n",
    "    'Permian': {\"abbreviation\": \"P\", \"start\": 304, \"end\": 248},\n",
    "    'Triassic': {\"abbreviation\": \"Tr\", \"start\": 248, \"end\": 205},\n",
    "    'Jurassic': {\"abbreviation\": \"J\", \"start\": 205, \"end\": 144}, \n",
    "    'Cretaceous': {\"abbreviation\": \"C\", \"start\": 160, \"end\": 65}, \n",
    "    'Tertiary': {\"abbreviation\": \"T\", \"start\": 65, \"end\": 1.8}\n",
    "}"
   ]
  },
  {
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   "source": [
    "----\n",
    "**Exercise**: what is the expected output of:\n",
    "\n",
    "* a) `ages['Triassic']`\n",
    "\n",
    "* b) `ages['Jurassic']['Start']`\n",
    "\n",
    "* c) What command would you type to return the age of the end of the Permian, 248?\n",
    "\n",
    "* d) The start of the Cretaceous is wrong: it should be 144. Change it to the correct value.\n",
    "\n",
    "* e) We've lost the dates for the Quaternary Period [1.8 mya to present (0)]. Index into that entry, and append it."
   ]
  },
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   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `tuples`\n",
    "\n",
    "*Tuples* are the immutable form of lists. They behave almost exactly the same as lists in every way except that you cannot change any of their values. There are no `append()` or `extend()` methods, and there are no *in-place* operators. \n",
    "\n",
    "They also differ from lists in their syntax. They are so central to how Python works, that *tuples* are defined by commas. Oftentimes, tuples will be seen surrounded by parentheses. These parentheses only serve to group actions or make the code more readable, not to actually define tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "a = (1,2,3,4)  # a length-4 tuple\n",
    "b = (42,)      # length-1 tuple defined by the comma\n",
    "c = (42)       # not a tuple, just the number 42\n",
    "d = ()         # length-0 tuple- no commas means no elements\n",
    "e = 42, 1      # a length-2 tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "You can concatenate tuples together in the same way as lists, but be careful about the order of opeartions. This is where parentheses come in handy,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "(1, 2) + (3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "(1,2)+(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Note that even though tuples are immutable, they may have mutable elements. Suppose that we have a list embedded in a tuple. This list may be modified in-place even though the list may not be removed or replaced wholesale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = 1.0, [2, 4], 16\n",
    "x[1].append(8)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `Set`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instances of the `set` type are equivalent to mathematical sets. Like their math counterparts, literal sets in Python are defined by comma seperated values between curly braces ({}). Sets are unordered containers of unique values. Duplicated elements are ignored. Beacuse they unordered, sets are not sequences and cannot be duplicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# a literal set formed with elements of various types\n",
    "{1.0, 10, \"one hundred\", (1, 0, 0, 0)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# a literal set OF special values\n",
    "{True, False, None, \"\", 0.0, 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# conversion from a list to a set\n",
    "set([2.0, 4, \"eight\", (16,), 4, 4, 2.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a good time to take a break\n",
    "\n",
    "- Variables and Assignment\n",
    "- Native data types\n",
    "- Operators and Expressions\n",
    "- Data collections and data structures\n",
    "- <font color='lightgrey'>Procedures and control: Loops and Making choices</font>\n",
    "- <font color='lightgrey'>Getting data, manipulating data</font>\n",
    "- <font color='lightgrey'>Defining functions and calling functions</font>\n",
    "- <font color='lightgrey'>Writing and running programs</font>\n",
    "- <font color='lightgrey'>Objects and classes</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "**Exercise** manipulating and viewing lists:\n",
    "\n",
    "* a) create a new list that has the integer 1 for sand and 2 for shale.\n",
    "* b) using `plt.plot()` create a plot of the porosity values\n",
    "* c) make a depth vs porosity plot so depth is vertically downwards\n",
    "* d) Use `plt.scatter()` to make a cross plot of `gamma` vs `porosity`\n",
    "* f) Explore other keyword arguments for `plt.plot()` and `plt.scatter` to pretty things up\n",
    "* bonus e) Use the keyword `c` in the call to `scatter` to distinguish between sand and shale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers = ['shale','shale','shale','sand','sand','sand','sand','shale','shale','shale']\n",
    "depth = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n",
    "porosity = [2, 3, 2, 14, 18, 17, 14, 2, 3, 3]\n",
    "gamma = [85, 90, 77, 23, 27, 31, 25, 110, 113, 108]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Procedures and control: loops and making decisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Loops\n",
    "\n",
    "*Doing stuff many times*\n",
    "\n",
    "the <code><font color=\"green\">while</font></code> loop\n",
    "\n",
    "the <code><font color=\"green\">for</font></code> loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "layers = ['shale','shale','shale','sand','sand','sand','sand','shale','shale','shale']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for loop syntax\n",
    "for val in gamma:\n",
    "    print(val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"#0A5394\">**\\*iteration, *iterable**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "[val*2 for val in gamma]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Turn these loops into a list comprehension:\n",
    "\n",
    "squares = []\n",
    "for n in range(20):\n",
    "    squares.append(n**2)\n",
    "    \n",
    "names = ['William Smith', 'Mary Anning', 'Steve Gould']\n",
    "short = []\n",
    "for name in names:\n",
    "    initial = name[0]\n",
    "    surname = name.split()[-1]\n",
    "    short.append(surname + initial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "\n",
    "## Making decisions\n",
    "\n",
    "The <code><font color=\"green\">if</font></code> statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layers = ['shale','shale','shale','sand','sand','sand','sand','shale','shale','shale']\n",
    "# the if statement:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"#0A5394\">**\\*conditionals**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Getting data..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ... from text files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can explicitly read from and write to files directly in your code. Python makes working with files pretty simple.\n",
    "\n",
    "The first step to working with a text file is to obtain a 'file object' using `open`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Open the file with read only permit\n",
    "fname = 'data/L30_tops.txt'\n",
    "\n",
    "with open(fname, 'r') as f:  # use 'w' to write and 'a' to append to a file. Add a 'b' for binary files.\n",
    "    header = f.readline()    # is string containing the first line in the file.\n",
    "    body = f.readlines()     # The variable \"lines\" is a list containing the remaining lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Every line you get this way ends in a newline character, `\\n`, so you'll often want to `strip()` it before doing anything with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fname = 'data/L30_tops.txt'\n",
    "with open(fname) as f:\n",
    "    for line in f:\n",
    "        if not line.startswith('#'):\n",
    "            print(line.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "----\n",
    "**Exercise** create a tops dictionary:\n",
    "\n",
    "* a) Modify the code snippet about to create a `dict` call tops, containing the formation name as the `key` and the `depth` as the value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Defining and calling functions\n",
    "\n",
    "the <code><font color=\"green\">def</font></code> statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def myfunc(args):\n",
    "    \"\"\"\n",
    "    Documentation string\n",
    "    \"\"\"\n",
    "    # statement\n",
    "    # statement\n",
    "    return # optional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"#0A5394\">**\\*scope**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "**Exercise**: write a function called `vshale` that that converts the list of `gamma` into a Vshale measurement. Use 20 API for 100% sand and use 150 API for 100% shale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def vshale(gamma, sand_end, shale_end):\n",
    "    # your code here\n",
    "    return # your output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "----\n",
    "\n",
    "**Exercise**: write a function called `process_tops` that takes a\n",
    "filename as input and return a dictionary of the tops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "topsfile = 'data/L30_tops.txt'\n",
    "\n",
    "def process_tops(file):\n",
    "    \"\"\"\n",
    "    Takes a file as input and returns a dictionary of tops\n",
    "    f : a filename path\n",
    "    \"\"\"\n",
    "    # Enter code here\n",
    "    return my_tops\n",
    "\n",
    "# process_tops(topsfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9SuaK/xOgaeLTveyluctEUV8S2J6pz0Fm8wPb8AAfQarnuIrDGjh7nSE3Bz2v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\nLv8P+krFKBw+sX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a3ffb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "% matplotlib inline\n",
    "\n",
    "topo = np.load('data/topography.npy')\n",
    "plt.imshow(topo)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing and running programs\n",
    "\n",
    "Put the previous function in a text file and give it the name, `load_tops.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your first module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import my_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "topsfile = 'data/L30_tops.txt'\n",
    "tops = load_tops.my_tops(topsfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ... from delimited files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "csv.\n",
    "with open('data/periods.csv', 'rt') as f:\n",
    "    reader = csv.DictReader(f, delimiter=',')\n",
    "    for row in reader:\n",
    "        print (row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "You can write out a delimited data using `csv.writer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "my_tops = {'GOC' : 1200.0 , 'OWC' : 1300.0, 'Top Reservoir' : 1100.0}\n",
    "\n",
    "with open('comma_delimited_stock_prices.txt', 'wb') as f:\n",
    "    writer = csv.writer(f, delimiter=',')\n",
    "    for name, depth in my_tops.items():\n",
    "        writer.writerow([name, depth])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ... from the web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use View Source in your browser to figure out where the age range is on the page, and what it looks like.\n",
    "\n",
    "Try to find the same string here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = \"http://en.wikipedia.org/wiki/Cretaceous\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "r = requests.get(url)\n",
    "r.text[:2000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using a [regular expression](https://docs.python.org/2/library/re.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "s = re.search(r'<i>(.+?million years ago)</i>', r.text)\n",
    "text = s.group(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "----\n",
    "**Exercise**: Make a function to get the start and end ages of *any* geologic period, taking the name of the period as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def get_age(period):\n",
    "    url = \"http://en.wikipedia.org/wiki/\" + period\n",
    "    r = requests.get(url)\n",
    "    start, end = re.search(r'<i>([\\.0-9]+)–([\\.0-9]+)&#160;million years ago</i>', r.text).groups()\n",
    "    return float(start), float(end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "period = \"Cretaceous\"\n",
    "get_age(period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def duration(period):\n",
    "    t0, t1 = get_age(period)\n",
    "    duration = t0 - t1\n",
    "    response = \"According to Wikipedia, the {0} period was {1:.2f} Ma long. \".format(period, duration)\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "duration('Cretaceous')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using built-in functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Importing modules\n",
    "\n",
    "the <code><font color=\"green\">import</font></code> statement\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Python standard library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Built-in functions](https://docs.python.org/3/library/functions.html)\n",
    "\n",
    "[Built-in Types](https://docs.python.org/3/library/stdtypes.html)\n",
    "\n",
    "[docs.python.org](https://docs.python.org/3/library/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## External python languges\n",
    "\n",
    "The Python Package Index, [PyPI](https://pypi.python.org/pypi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [SciPy](http://www.scipy.org/) -  a collection of often-used libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using external libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "**Exercise**: \n",
    "\n",
    "Using numpy's load text function `np.loadtxt(...)`, load the data file into a variable called `data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = np.loadtxt('data/my_first_well.las')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "**Exercise**: \n",
    "\n",
    "a) write a function called `make_curves` that takes the data as input and return a dictionary of the curves. Keys are the curve name, values are the columns \n",
    "b) write a function that converts the sonic log (us/ft) to velocity (m/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Acoustic impedance\n",
    "vp = data['DT']\n",
    "rhob = well['RHOB']\n",
    "acimp = vp * rhob "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "**Exercise**: compute a reflection coefficient series by completing the following for loop\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rc_series = []  \n",
    "for layer in range(len(acimp)-1):\n",
    "    z2 = acimp[layer + 1]\n",
    "    z1 = acimp[layer]\n",
    "    coeff = rc(z2, z1)\n",
    "    rc_series.append(coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rc(ip2, ip1):\n",
    "    \"\"\"\n",
    "    returns the normal incidence reflection coefficient\n",
    "    between two layers with impedances Z2, Z1\n",
    "    ip2 : Impedance of the bottom layer \n",
    "    ip1 : Impedance of the upper layer\n",
    "    \"\"\"\n",
    "    return (ip2 - ip1) / (ip2 + ip1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "**Exercise**: make two tracks of a well log; plot the impedance in one track, and the R.C series in the other "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.subplot(121)\n",
    "plt.plot(ip, c='g', lw=4, alpha=0.5)\n",
    "plt.subplot(122)\n",
    "plt.plot(rc, c='g', lw=4, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Getting started with [bruges](https://github.com/agile-geoscience/notebooks/blob/master/Bruges_getting_started.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Writing and running programs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Objects and classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class Layers(object):\n",
    "    \n",
    "    def __init__(self, layers, label=None):\n",
    "        # Just make sure we end up with an array\n",
    "        self.layers = np.array(layers)\n",
    "        self.label = label or \"My log\"\n",
    "        self.length = self.layers.size  # But storing len in an attribute is unexpected...\n",
    "        \n",
    "    def __len__(self):  # ...better to do this.\n",
    "        return len(self.layers)\n",
    "        \n",
    "    def rcs(self):\n",
    "        uppers = self.layers[:-1]\n",
    "        lowers = self.layers[1:]\n",
    "        return (lowers-uppers) / (uppers+lowers)\n",
    "    \n",
    "    def plot(self, lw=0.5, color='#6699ff'):\n",
    "        fig = plt.figure(figsize=(2,6))\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.barh(range(len(self.layers)), self.layers, color=color, lw=lw, align='edge', height=1.0, alpha=1.0, zorder=10)\n",
    "        ax.grid(zorder=2)\n",
    "        ax.set_ylabel('Layers')\n",
    "        ax.set_title(self.label)\n",
    "        ax.set_xlim([-0.5,1.0])\n",
    "        ax.set_xlabel('Measurement (units)')\n",
    "        ax.invert_yaxis()  \n",
    "        #ax.set_xticks(ax.get_xticks()[::2])    # take out every second tick\n",
    "        ax.spines['right'].set_visible(False)  # hide the spine on the right\n",
    "        ax.yaxis.set_ticks_position('left')    # Only show ticks on the left and bottom spines\n",
    "        \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "velocities = [0.23, 0.34, 0.45, 0.25, 0.23, 0.35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "l = Layers(velocities, label='Well # 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l.plot()"
   ]
  }
 ],
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