diff --git a/.gitignore b/.gitignore index 7c050ad..d29777d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ slides_sources/build .idea .DS_Store +.cache* #ignore compiled files, sublime workspace and project files *.pyc *junk* @@ -8,8 +9,15 @@ slides_sources/build #ignore sublime workspace files *.sublime* +# emacs +*.*~ + +.project +.pydevproject # ignore .gitignore, so we can each have our own. .gitignore -*.pyc build/ + +.ipynb_checkpoints +.coverage diff --git a/Examples/advancedOO/constrained_int/constrained_int.py b/Examples/advancedOO/constrained_int/constrained_int.py new file mode 100644 index 0000000..d8eca17 --- /dev/null +++ b/Examples/advancedOO/constrained_int/constrained_int.py @@ -0,0 +1,9 @@ +class ConstrainedInt(int): + """keeps value between 0 and 255""" + def __new__(cls, value): + value = value % 256 + self = int.__new__(cls, value) + return self + + def __add__(self, other): + return int(self) + int(other) diff --git a/Examples/advancedOO/constrained_int/readme.rst b/Examples/advancedOO/constrained_int/readme.rst new file mode 100644 index 0000000..b6f50ab --- /dev/null +++ b/Examples/advancedOO/constrained_int/readme.rst @@ -0,0 +1,59 @@ +__new__ example +--------------- + +A little excercise for __new__ and making a new class act like a regular type. + +Here is a replacement for int which can only take new values between 0 and 255: + +:: + + class ConstrainedInt(int): + def __new__(cls, value): + value = value % 256 + self = int.__new__(cls, value) + return self + +A reminder of Magic methods: +----------------------------- + +Magic Methods + +They all start with and end with '__', and do things like support operators and comparisons, and provide handlers for the object lifecycle. + +__cmp__(self, other) + +__eq__(self, other) + +__add__(self, other) + +Also, __call__, __str__, __repr__, __sizeof__, +__setattr__, __getattr__, __len__, __iter__, +__contains__, __lshift__, __rshift__, __xor__, +__div__, __enter__, __exit__, + +and my personal favorite __rxor__(self,other)...... + +The list is really long, it's mostly important to get a flavor of how +they are used in Python so you can find and implement the right one when +you need it. + +See ``http://www.rafekettler.com/magicmethods.html`` for more + +Exercise +--------- + +Our ConstrainedInt handles initialization for us, but doesn't handle +modification of the value + +Develop ConstrainedInt until it passes all tests in +``test_constrained_int.py`` + +:: + + class ConstrainedInt(int): + """keeps value between 0 and 255""" + def __new__(cls, value): + value = value % 256 + self = int.__new__(cls, value) + return self + diff --git a/Examples/advancedOO/constrained_int/test_constrained_int.py b/Examples/advancedOO/constrained_int/test_constrained_int.py new file mode 100644 index 0000000..02c014a --- /dev/null +++ b/Examples/advancedOO/constrained_int/test_constrained_int.py @@ -0,0 +1,19 @@ +import unittest + +from constrained_int import ConstrainedInt + + +class TestConstrainedInt(unittest.TestCase): + + def test_constrainedint(self): + x = ConstrainedInt(10) + self.assertEqual(x, 10) + + def test_constrainedint_mods_correctly(self): + x = ConstrainedInt(256) + self.assertEqual(x, 0) + + def test_constrainedint_handles_addition(self): + x = ConstrainedInt(10) + x += 246 + self.assertEqual(x, 0) diff --git a/Examples/advancedOO/diamond.py b/Examples/advancedOO/diamond.py new file mode 100644 index 0000000..bdeae19 --- /dev/null +++ b/Examples/advancedOO/diamond.py @@ -0,0 +1,40 @@ +class A(object): + def do_your_stuff(self): + print("doing A's stuff") + + +class B(A): + def do_your_stuff(self): + A.do_your_stuff(self) + print("doing B's stuff") + + +class C(A): + def do_your_stuff(self): + A.do_your_stuff(self) + print("doing C's stuff") + + +class D(B, C): + def do_your_stuff(self): + B.do_your_stuff(self) + C.do_your_stuff(self) + print("doing D's stuff") + + +if __name__ == '__main__': + a = A() + print("\ncalling A's method") + a.do_your_stuff() + + print("\ncalling B's method") + b = B() + b.do_your_stuff() + + print("\ncalling C's method") + c = C() + c.do_your_stuff() + + print("\ncalling D's method") + d = D() + d.do_your_stuff() diff --git a/Examples/advancedOO/even_int.py b/Examples/advancedOO/even_int.py new file mode 100644 index 0000000..a02a3f3 --- /dev/null +++ b/Examples/advancedOO/even_int.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python + +""" +Using new to create an even integer + +rounds the input to the nearest even integer. + +will even convert a string to an int... + +""" + + +class EvenInt(int): + """ + An integer that is always even + """ + pass diff --git a/Examples/advancedOO/even_int_solution.py b/Examples/advancedOO/even_int_solution.py new file mode 100644 index 0000000..ba7ab67 --- /dev/null +++ b/Examples/advancedOO/even_int_solution.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python + +""" +Using new to create an even integer + +rounds the input to the nearest even integer. + +will even convert a string to an int... + +""" + + +class EvenInt(int): + """ + An integer that is always even + """ + def __new__(cls, number): + num = round(float(number) / 2) * 2 + + return int.__new__(cls, num) + diff --git a/Examples/advancedOO/invalid_mro.py b/Examples/advancedOO/invalid_mro.py new file mode 100644 index 0000000..30c0857 --- /dev/null +++ b/Examples/advancedOO/invalid_mro.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python3 + + +class X(): + pass + + +class Y(): + pass + + +class A(X, Y): + pass + + +class B(Y, X): + pass + + +class C(A, B): + pass diff --git a/Examples/advancedOO/kwargs.py b/Examples/advancedOO/kwargs.py new file mode 100644 index 0000000..e1881b7 --- /dev/null +++ b/Examples/advancedOO/kwargs.py @@ -0,0 +1,5 @@ +def foo(bar, **kwargs): + print(bar) + +foo("lion") +foo(bar="lion") diff --git a/Examples/advancedOO/logging_dict.py b/Examples/advancedOO/logging_dict.py new file mode 100644 index 0000000..0b9052d --- /dev/null +++ b/Examples/advancedOO/logging_dict.py @@ -0,0 +1,40 @@ +import collections +import logging + +logger = logging.getLogger() +logger.setLevel(logging.INFO) + + +class Base(): + def __str__(self): + return "\n".join(['%s : %s' % (key, self[key]) for key in self.keys()]) + + +class LoggingDict(Base, dict): + def __setitem__(self, key, value): + logging.info('Setting %s to %s' % (key, value)) + super(LoggingDict, self).__setitem__(key, value) + + def __getitem__(self, key): + value = super(LoggingDict, self).__getitem__(key) + logging.info('Getting %s (%s)' % (key, value)) + return value + + +class UpperCaseDict(Base, dict): + def __setitem__(self, key, value): + super(UpperCaseDict, self).__setitem__(key.upper(), value.upper()) + + +class LoggingOrderedDict(UpperCaseDict, LoggingDict, collections.OrderedDict): + pass + +d = logging_dict = LoggingDict() +d = upper_case_dict = UpperCaseDict() +d = logging_ordered_dict = LoggingOrderedDict() + +d['a_key'] = 'my_value' +d['b_key'] = 'my_value' +d['c_key'] = 'my_value' + +print(d) diff --git a/Examples/advancedOO/mixins.py b/Examples/advancedOO/mixins.py new file mode 100644 index 0000000..13db5f1 --- /dev/null +++ b/Examples/advancedOO/mixins.py @@ -0,0 +1,50 @@ +import time + + +class Base(): + def log(self): + pass + + +class LoggingMixin(Base): + def log(self): + print("time is: %s" % time.time()) + # print("this is:", self) + super().log() + + +class Vehicle(Base): + def log(self): + print("this is:", self.__repr__()) + super().log() + + +class TwoWheeledVehicle(Vehicle): + pass + + +class LightVehicle(Vehicle): + pass + + +class HeavyVehicle(Vehicle): + pass + + +class Bike(LoggingMixin, TwoWheeledVehicle, LightVehicle): + pass + + +class MotorCycle(TwoWheeledVehicle, HeavyVehicle): + pass + + +class Tank(HeavyVehicle): + pass + + +bike = Bike() +tank = Tank() + +bike.log() +tank.log() diff --git a/Examples/advancedOO/mro.py b/Examples/advancedOO/mro.py new file mode 100644 index 0000000..760d70a --- /dev/null +++ b/Examples/advancedOO/mro.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python + + +class A(object): + def my_method(self): + print("called A") + + +class B(A): + def my_method(self): + print("called B") + super(B, self).my_method() + + +class C(A): + def my_method(self): + print("called C") + super(C, self).my_method() + + +class B1(B): + def my_method(self): + print("called B1") + super(B1, self).my_method() + + +class C1(C): + def my_method(self): + print("called C1") + super(C1, self).my_method() + + +class D(B1, C1): + def my_method(self): + print("called D") + super(D, self).my_method() + +print(D.mro()) +d = D() +print(d.my_method()) diff --git a/Examples/advancedOO/new_example.py b/Examples/advancedOO/new_example.py new file mode 100644 index 0000000..1e1da1f --- /dev/null +++ b/Examples/advancedOO/new_example.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python + +""" +example of __new__ +""" + + +# the general case: +class Test(): + def __new__(cls, arg): + print("in __new__:") + print(arg) + obj = super().__new__(cls) + # you _could_ do something with arg here: + obj.arg = arg + # but it's usally better to save that for the __init__ + return obj + + def __init__(self, arg): + print("in __init__") + print(arg) + self.that = arg + + +# subclassing a string + +class CapitalString(str): + """ + A string class that is always capitalized... + """ + def __new__(cls, in_string): + print("in CapitalString.__new__") + print(cls) + # return str.__new__(cls, in_string.title()) + return super().__new__(cls, in_string.title()) + +if __name__ == "__main__": + print(CapitalString("this is a string")) diff --git a/Examples/advancedOO/shapes.py b/Examples/advancedOO/shapes.py new file mode 100644 index 0000000..cbc3545 --- /dev/null +++ b/Examples/advancedOO/shapes.py @@ -0,0 +1,25 @@ +class Shape: + def __str__(self): + return "Shape:{}".format(self.__class__.__name__) + + +class Rectangle(Shape): + def __init__(self, width, height): + # validate inputs: + assert(width > 0) + assert(height > 0) + + self.width = width + self.height = height + + def area(self): + """returns the area of this Rectangle""" + return self.width * self.height + + +class Square(Rectangle): + def __init__(self, length): + self.width = length + self.height = length + +print(Square(10).area()) diff --git a/Examples/advancedOO/super_test.ipynb b/Examples/advancedOO/super_test.ipynb new file mode 100644 index 0000000..c9c23dd --- /dev/null +++ b/Examples/advancedOO/super_test.ipynb @@ -0,0 +1,607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Some example code demonstrating some super() behaviour\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class A():\n", + " def __init__(self):\n", + " print(\"in A __init__\")\n", + " print(\"self's class is:\", self.__class__)\n", + " s = super().__init__()\n", + "\n", + "class B():\n", + " def __init__(self):\n", + " print(\"in B.__init__\")\n", + " s = super().__init__()\n", + "\n", + "class C():\n", + " def __init__(self):\n", + " print(\"in C.__init__\")\n", + " s = super().__init__()\n", + "\n", + "class D(C, B, A):\n", + " def __init__(self):\n", + " print(\"self's class is:\", self.__class__)\n", + " super().__init__()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "D's mro: (, , , , )\n", + "self's class is: \n", + "in C.__init__\n", + "in B.__init__\n", + "in A __init__\n", + "self's class is: \n" + ] + } + ], + "source": [ + "print(\"D's mro:\", D.__mro__)\n", + "d = D()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## super's parameters\n", + "\n", + "To do its thing, super() needs to know two things:\n", + "\n", + "``super(type, obj)``\n", + "\n", + "It needs to know that type (class) that you want the super-classes of, AND it needs to know the actual object instance at the time it is called.\n", + "\n", + "python3 fills these in for you at run time, but in python2, you needed to specify them:\n", + "\n", + "```\n", + "class A(object):\n", + " def __init__(self):\n", + " super(A, self).__init__()\n", + "```\n", + "\n", + "But why do you need BOTH `A` and `self`? -- isn't `self` an instance of `A`?\n", + "\n", + "Not neccesarily -- if A's method is being called from a subclass, then `self` will be an instance of the subclass. `super()` requires that the object be an instance of the class (or a subclass).\n", + "\n", + "This distiction will come up later....\n", + "\n", + "Again, py3 takes care of this for you, though you CAN still spell it out." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ", >\n" + ] + } + ], + "source": [ + "s_c = super(C, d)\n", + "print(s_c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works because `d` is a `D` object, which is a subclass of `C`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "in C.__init__\n" + ] + } + ], + "source": [ + "c = C()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "super(type, obj): obj must be an instance or subtype of type", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: super(type, obj): obj must be an instance or subtype of type" + ] + } + ], + "source": [ + "super(D, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this fails: `C` is NOT a subclass of `D`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ", >\n" + ] + } + ], + "source": [ + "s_a = super(A, d)\n", + "print(s_a)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ", >\n" + ] + } + ], + "source": [ + "s_b = super(B, d)\n", + "print(s_b)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(, , , , )\n" + ] + } + ], + "source": [ + "print(D.__mro__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Use super() everywhere." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# without super()\n", + "\n", + "class A():\n", + " def this(self):\n", + " print(\"in A.this\")\n", + "\n", + "class B():\n", + " def this(self):\n", + " print(\"in B.this\")\n", + "\n", + "class C(A,B):\n", + " def this(self):\n", + " print(\"in C.this\")\n", + " A.this(self)\n", + " B.this(self)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running without super()\n", + "in C.this\n", + "in A.this\n", + "in B.this\n" + ] + } + ], + "source": [ + "print(\"Running without super()\")\n", + "c = C()\n", + "c.this()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C's `this` explicitly called both A and B's methods -- so they all get called." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using super in just C:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class A():\n", + " def this(self):\n", + " print(\"in A.this\")\n", + "\n", + "class B(A):\n", + " def this(self):\n", + " print(\"in B.this\")\n", + "\n", + "class C(B):\n", + " def this(self):\n", + " print(\"in C.this\")\n", + " super().this()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(, , , )\n", + "in C.this\n", + "in B.this\n" + ] + } + ], + "source": [ + "print(C.__mro__)\n", + "c = C()\n", + "c.this()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** `A.this` did NOT get called!\n", + "\n", + "Even though it is in in the MRO.\n", + "\n", + "Python stopped when it found the method in B." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using super everywhere:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class Base():\n", + " def this(self):\n", + " pass # just so there is a base that has the method\n", + "\n", + "class A(Base):\n", + " def this(self):\n", + " print(\"in A.this\")\n", + " super().this()\n", + "\n", + "class B(Base):\n", + " def this(self):\n", + " print(\"in B.this\")\n", + " super().this()\n", + "class C(A,B):\n", + " def this(self):\n", + " print(\"in C.this\")\n", + " super().this()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "in C.this\n", + "in A.this\n", + "in B.this\n", + "(, )\n" + ] + } + ], + "source": [ + "c = C()\n", + "c.this()\n", + "print(Base.__mro__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now both A and B's methods get called -- probably what you want.\n", + "\n", + "But if you don't want both called -- better to just be Explicit, rather than use super():" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Base():\n", + " def this(self):\n", + " pass # just so there is a base that has the method\n", + "\n", + "class A(Base):\n", + " def this(self):\n", + " print(\"in A.this\")\n", + " super().this()\n", + "\n", + "class B(Base):\n", + " def this(self):\n", + " print(\"in B.this\")\n", + " super().this()\n", + "\n", + "class C(A,B):\n", + " def this(self):\n", + " print(\"in C.this\")\n", + " A.this(self)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "in C.this\n", + "in A.this\n", + "in B.this\n" + ] + } + ], + "source": [ + "c = C()\n", + "c.this()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Whoa** -- A and B's method DID get called! -- why?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(__main__.A, __main__.Base, object)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.__mro__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "B is not there.\n", + "\n", + "But:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class Base():\n", + " def this(self):\n", + " pass # just so there is a base that has the method\n", + "\n", + "class A(Base):\n", + " def this(self):\n", + " print(\"in A.this\")\n", + " print(\"self's class:\", self.__class__)\n", + " super().this()\n", + "\n", + "class B(Base):\n", + " def this(self):\n", + " print(\"in B.this\")\n", + " super().this()\n", + "\n", + "class C(A,B):\n", + " def this(self):\n", + " print(\"in C.this\")\n", + " A.this(self)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "in C.this\n", + "in A.this\n", + "self's class: \n", + "in B.this\n", + "(, , , , )\n" + ] + } + ], + "source": [ + "c = C()\n", + "c.this()\n", + "print(C.__mro__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember, `super()` is dynamic -- what it calls is determined at run time.\n", + "\n", + "That's how it knows to call ``B``'s method too. Which is why we say that using `super()` is *part* of the interface of the class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/advancedOO/test_even_int.py b/Examples/advancedOO/test_even_int.py new file mode 100644 index 0000000..783da6d --- /dev/null +++ b/Examples/advancedOO/test_even_int.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python + +""" +tests for an even integer class +""" + +from even_int import EvenInt +# from even_int_solution import EvenInt + + +# And some tests -- try with py.test or nosetests... +def test_subclass(): + assert issubclass(EvenInt, int) + + +def test_instance(): + i = EvenInt(3) + assert isinstance(i, int) + + +def test_isinstance(): + i = EvenInt(2) + assert isinstance(i, EvenInt) + + +def test_even(): + assert EvenInt(4) == 4 + + +def test_odd1(): + assert EvenInt(3) == 4 + + +def test_odd2(): + assert EvenInt(2.99) == 2 + + +def test_negative(): + assert EvenInt(-2) == -2 + + +def test_negative_odd(): + assert EvenInt(-1) == 0 + + +def test_negative_odd2(): + assert EvenInt(-1.1) == -2 + + +def test_string_odd(): + assert EvenInt("3") == 4 + + +def test_string_even(): + assert EvenInt("12") == 12 + + +def test_string_float(): + assert EvenInt("4.45") == 4 diff --git a/Examples/advancedOO/unpickle.py b/Examples/advancedOO/unpickle.py new file mode 100644 index 0000000..59c59c0 --- /dev/null +++ b/Examples/advancedOO/unpickle.py @@ -0,0 +1,12 @@ +import pickle + + +class Foo: + def __init__(self): + print("init called") + +foo = Foo() + +foo_pickle = pickle.dumps(foo) + +new_foo = pickle.loads(foo_pickle) diff --git a/Examples/capitalize/cap_script.py b/Examples/capitalize/cap_script.py new file mode 100755 index 0000000..2e5089c --- /dev/null +++ b/Examples/capitalize/cap_script.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +""" +A really simple script just to demonstrate disutils +""" + +import sys, os +import capital_mod + + +if __name__ == "__main__": + try: + infilename = sys.argv[1] + except IndexError: + print("you need to pass in a file to process") + + root, ext = os.path.splitext(infilename) + outfilename = root + "_cap" + ext + + # do the real work: + print("Capitalizing: %s and storing it in %s"%(infilename, outfilename)) + capital_mod.capitalize(infilename, outfilename) + + print("I'm done") + diff --git a/Examples/capitalize/capital_mod.py b/Examples/capitalize/capital_mod.py new file mode 100644 index 0000000..dfe054f --- /dev/null +++ b/Examples/capitalize/capital_mod.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python + +""" +A really simple module, just to demonstrate packaging +""" + +def capitalize_line(instr): + """ + capitalizes the input string + + :param instr: the string to capitalize it should be a single line. + :type instr: string + + :returns: a capitalized version of instr + """ + + return " ".join( word.capitalize() for word in instr.split() ) + + +def capitalize(infilename, outfilename): + """ + reads the contents of infilename, and writes it to outfilename, but with + every word capitalized + + note: very primitive -- it will mess some files up! + + this is called by the capitalize script + + :param infilename: The file name you want to process + :type infilename: string + + :param outfilename: the name of the new file that will be created + :type outfilename: string + + :returns: None + + :raises: IOError if infilename doesn't exist. + """ + infile = open(infilename, 'U') + outfile = open(outfilename, 'w') + + for line in infile: + outfile.write(capitalize_line(line)) + outfile.write("\n") + + return None + + +class Something(object): + """ + this is the class docstring + """ + def __init__(self, input): + """ + the __init__ docstring + + :param input: someinput + :type input: some_type + """ + pass diff --git a/Examples/capitalize/sample_text_file.txt b/Examples/capitalize/sample_text_file.txt new file mode 100644 index 0000000..a64b50f --- /dev/null +++ b/Examples/capitalize/sample_text_file.txt @@ -0,0 +1,7 @@ +This is a really simple Text file. +It is here so that I can test the capitalize script. + +And that's only there to try out distutils. + +So there. + \ No newline at end of file diff --git a/Examples/capitalize/sample_text_file_cap.txt b/Examples/capitalize/sample_text_file_cap.txt new file mode 100644 index 0000000..1a192bc --- /dev/null +++ b/Examples/capitalize/sample_text_file_cap.txt @@ -0,0 +1,7 @@ +This Is A Really Simple Text File. +It Is Here So That I Can Test The Capitalize Script. + +And That's Only There To Try Out Distutils. + +So There. + diff --git a/Examples/capitalize/test_capital_mod.py b/Examples/capitalize/test_capital_mod.py new file mode 100644 index 0000000..7cd6cc6 --- /dev/null +++ b/Examples/capitalize/test_capital_mod.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python + +""" +test code for capitalize module + +can be run with py.test or nosetests +""" + +import capital_mod + +#print(capitalize.__file__) + +def test_capitalize_line(): + line = "this is a Line to capitalize" + expected = "This Is A Line To Capitalize" + + assert capital_mod.capitalize_line(line) == expected + +def test_capitalize(): + """ test an actual string """ + capital_mod.capitalize("sample_text_file.txt", "sample_text_file_cap.txt") + contents = open("sample_text_file_cap.txt", 'U').read() + expected = """This Is A Really Simple Text File. +It Is Here So That I Can Test The Capitalize Script. + +And That's Only There To Try Out Distutils. + +So There.""" + assert contents.strip() == expected + + + diff --git a/Examples/datetime_example.py b/Examples/datetime_example.py new file mode 100644 index 0000000..3717497 --- /dev/null +++ b/Examples/datetime_example.py @@ -0,0 +1,41 @@ +import datetime + +today = datetime.date.today() +print(today) +print('ctime :', today.ctime()) +tt = today.timetuple() +print('tuple : tm_year =', tt.tm_year) +print(' tm_mon =', tt.tm_mon) +print(' tm_mday =', tt.tm_mday) +print(' tm_hour =', tt.tm_hour) +print(' tm_min =', tt.tm_min) +print(' tm_sec =', tt.tm_sec) +print(' tm_wday =', tt.tm_wday) +print(' tm_yday =', tt.tm_yday) +print(' tm_isdst =', tt.tm_isdst) +print('ordinal:', today.toordinal()) +print('Year :', today.year) +print('Mon :', today.month) +print('Day :', today.day) + +d1 = datetime.date(2008, 3, 29) +print('d1:', d1.ctime()) + +d2 = d1.replace(year=2009) +print('d2:', d2.ctime()) + +today = datetime.date.today() +print('Today :', today) + +one_day = datetime.timedelta(days=1) +print('One day :', one_day) + +yesterday = today - one_day +print('Yesterday:', yesterday) + +tomorrow = today + one_day +print('Tomorrow :', tomorrow) + +print() +print('tomorrow - yesterday:', tomorrow - yesterday) +print('yesterday - tomorrow:', yesterday - tomorrow) diff --git a/Examples/debugging/long_loop.py b/Examples/debugging/long_loop.py new file mode 100644 index 0000000..3da4c5e --- /dev/null +++ b/Examples/debugging/long_loop.py @@ -0,0 +1,13 @@ + +def long_loop(): + for i in range(int(1e04)): + i+1 + if i == 777: + raise Exception("terrible bug") + result = 1 + 1 + return result + +print(long_loop()) + +s = "next statement" +print(s) diff --git a/Examples/debugging/wikidef/api.py b/Examples/debugging/wikidef/api.py new file mode 100644 index 0000000..e9401b5 --- /dev/null +++ b/Examples/debugging/wikidef/api.py @@ -0,0 +1,30 @@ +import requests + + +class ParseError(Exception): + pass + + +class Wikipedia(object): + """Wikipedia API interface""" + + api_endpoint = "http://en.wikipedia.org/w/api.php?" + + @classmethod + def article(cls, title): + """Return contents of article + + arguments: + + title -- title of article + """ + data = {'action': 'parse', 'format': 'json', 'prop':'text', 'page': title} + response = requests.get(cls.api_endpoint, params=data) + json_response = response.json() + + try: + contents = json_response['parse']['text']['*'] + except KeyError: + raise ParseError(json_response) + + return contents diff --git a/Examples/debugging/wikidef/define.py b/Examples/debugging/wikidef/define.py new file mode 100755 index 0000000..5677bbb --- /dev/null +++ b/Examples/debugging/wikidef/define.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python3 + +""" +Script to contact Wikipedia and get articles on a specified topic. +python define.py interesting_topic +""" + +import sys + +from definitions import Definitions +from html2text import html2text + +title = len(sys.argv) == 2 and sys.argv[1] or "" + +definition = Definitions.article(title) +txt = html2text(definition) +print(txt.encode('utf-8')) + diff --git a/Examples/week-01-testing/wikidef/definitions.py b/Examples/debugging/wikidef/definitions.py similarity index 100% rename from Examples/week-01-testing/wikidef/definitions.py rename to Examples/debugging/wikidef/definitions.py diff --git a/Examples/debugging/wikidef/html2text.py b/Examples/debugging/wikidef/html2text.py new file mode 100644 index 0000000..1540a7b --- /dev/null +++ b/Examples/debugging/wikidef/html2text.py @@ -0,0 +1,767 @@ +#!/usr/bin/env python3 +"""html2text: Turn HTML into equivalent Markdown-structured text.""" +__version__ = "3.1" +__author__ = "Aaron Swartz (me@aaronsw.com)" +__copyright__ = "(C) 2004-2008 Aaron Swartz. GNU GPL 3." +__contributors__ = ["Martin 'Joey' Schulze", "Ricardo Reyes", "Kevin Jay North"] + +# TODO: +# Support decoded entities with unifiable. + +try: + True +except NameError: + setattr(__builtins__, 'True', 1) + setattr(__builtins__, 'False', 0) + +def has_key(x, y): + if hasattr(x, 'has_key'): return x.has_key(y) + else: return y in x + +try: + import htmlentitydefs + import urlparse + import HTMLParser +except ImportError: #Python3 + import html.entities as htmlentitydefs + import urllib.parse as urlparse + import html.parser as HTMLParser +try: #Python3 + import urllib.request as urllib +except: + import urllib +import optparse, re, sys, codecs, types + +try: from textwrap import wrap +except: pass + +# Use Unicode characters instead of their ascii psuedo-replacements +UNICODE_SNOB = 0 + +# Put the links after each paragraph instead of at the end. +LINKS_EACH_PARAGRAPH = 0 + +# Wrap long lines at position. 0 for no wrapping. (Requires Python 2.3.) +BODY_WIDTH = 78 + +# Don't show internal links (href="#local-anchor") -- corresponding link targets +# won't be visible in the plain text file anyway. +SKIP_INTERNAL_LINKS = True + +# Use inline, rather than reference, formatting for images and links +INLINE_LINKS = True + +# Number of pixels Google indents nested lists +GOOGLE_LIST_INDENT = 36 + +IGNORE_ANCHORS = False +IGNORE_IMAGES = False + +### Entity Nonsense ### + +def name2cp(k): + if k == 'apos': return ord("'") + if hasattr(htmlentitydefs, "name2codepoint"): # requires Python 2.3 + return htmlentitydefs.name2codepoint[k] + else: + k = htmlentitydefs.entitydefs[k] + if k.startswith("&#") and k.endswith(";"): return int(k[2:-1]) # not in latin-1 + return ord(codecs.latin_1_decode(k)[0]) + +unifiable = {'rsquo':"'", 'lsquo':"'", 'rdquo':'"', 'ldquo':'"', +'copy':'(C)', 'mdash':'--', 'nbsp':' ', 'rarr':'->', 'larr':'<-', 'middot':'*', +'ndash':'-', 'oelig':'oe', 'aelig':'ae', +'agrave':'a', 'aacute':'a', 'acirc':'a', 'atilde':'a', 'auml':'a', 'aring':'a', +'egrave':'e', 'eacute':'e', 'ecirc':'e', 'euml':'e', +'igrave':'i', 'iacute':'i', 'icirc':'i', 'iuml':'i', +'ograve':'o', 'oacute':'o', 'ocirc':'o', 'otilde':'o', 'ouml':'o', +'ugrave':'u', 'uacute':'u', 'ucirc':'u', 'uuml':'u', +'lrm':'', 'rlm':''} + +unifiable_n = {} + +for k in unifiable.keys(): + unifiable_n[name2cp(k)] = unifiable[k] + +def charref(name): + if name[0] in ['x','X']: + c = int(name[1:], 16) + else: + c = int(name) + + if not UNICODE_SNOB and c in unifiable_n.keys(): + return unifiable_n[c] + else: + try: + return unichr(c) + except NameError: #Python3 + return chr(c) + +def entityref(c): + if not UNICODE_SNOB and c in unifiable.keys(): + return unifiable[c] + else: + try: name2cp(c) + except KeyError: return "&" + c + ';' + else: + try: + return unichr(name2cp(c)) + except NameError: #Python3 + return chr(name2cp(c)) + +def replaceEntities(s): + s = s.group(1) + if s[0] == "#": + return charref(s[1:]) + else: return entityref(s) + +r_unescape = re.compile(r"&(#?[xX]?(?:[0-9a-fA-F]+|\w{1,8}));") +def unescape(s): + return r_unescape.sub(replaceEntities, s) + +### End Entity Nonsense ### + +def onlywhite(line): + """Return true if the line does only consist of whitespace characters.""" + for c in line: + if c is not ' ' and c is not ' ': + return c is ' ' + return line + +def optwrap(text): + """Wrap all paragraphs in the provided text.""" + if not BODY_WIDTH: + return text + + assert wrap, "Requires Python 2.3." + result = '' + newlines = 0 + for para in text.split("\n"): + if len(para) > 0: + if para[0] != ' ' and para[0] != '-' and para[0] != '*': + for line in wrap(para, BODY_WIDTH): + result += line + "\n" + result += "\n" + newlines = 2 + else: + if not onlywhite(para): + result += para + "\n" + newlines = 1 + else: + if newlines < 2: + result += "\n" + newlines += 1 + return result + +def hn(tag): + if tag[0] == 'h' and len(tag) == 2: + try: + n = int(tag[1]) + if n in range(1, 10): return n + except ValueError: return 0 + +def dumb_property_dict(style): + """returns a hash of css attributes""" + return dict([(x.strip(), y.strip()) for x, y in [z.split(':', 1) for z in style.split(';') if ':' in z]]); + +def dumb_css_parser(data): + """returns a hash of css selectors, each of which contains a hash of css attributes""" + # remove @import sentences + importIndex = data.find('@import') + while importIndex != -1: + data = data[0:importIndex] + data[data.find(';', importIndex) + 1:] + importIndex = data.find('@import') + + # parse the css. reverted from dictionary compehension in order to support older pythons + elements = [x.split('{') for x in data.split('}') if '{' in x.strip()] + elements = dict([(a.strip(), dumb_property_dict(b)) for a, b in elements]) + + return elements + +def element_style(attrs, style_def, parent_style): + """returns a hash of the 'final' style attributes of the element""" + style = parent_style.copy() + if 'class' in attrs: + for css_class in attrs['class'].split(): + css_style = style_def['.' + css_class] + style.update(css_style) + if 'style' in attrs: + immediate_style = dumb_property_dict(attrs['style']) + style.update(immediate_style) + return style + +def google_list_style(style): + """finds out whether this is an ordered or unordered list""" + if 'list-style-type' in style: + list_style = style['list-style-type'] + if list_style in ['disc', 'circle', 'square', 'none']: + return 'ul' + return 'ol' + +def google_nest_count(style): + """calculate the nesting count of google doc lists""" + nest_count = 0 + if 'margin-left' in style: + nest_count = int(style['margin-left'][:-2]) / GOOGLE_LIST_INDENT + return nest_count + +def google_has_height(style): + """check if the style of the element has the 'height' attribute explicitly defined""" + if 'height' in style: + return True + return False + +def google_text_emphasis(style): + """return a list of all emphasis modifiers of the element""" + emphasis = [] + if 'text-decoration' in style: + emphasis.append(style['text-decoration']) + if 'font-style' in style: + emphasis.append(style['font-style']) + if 'font-weight' in style: + emphasis.append(style['font-weight']) + return emphasis + +def google_fixed_width_font(style): + """check if the css of the current element defines a fixed width font""" + font_family = '' + if 'font-family' in style: + font_family = style['font-family'] + if 'Courier New' == font_family or 'Consolas' == font_family: + return True + return False + +def list_numbering_start(attrs): + """extract numbering from list element attributes""" + if 'start' in attrs: + return int(attrs['start']) - 1 + else: + return 0 + +class _html2text(HTMLParser.HTMLParser): + def __init__(self, out=None, baseurl=''): + HTMLParser.HTMLParser.__init__(self) + + if out is None: self.out = self.outtextf + else: self.out = out + self.outtextlist = [] # empty list to store output characters before they are "joined" + try: + self.outtext = unicode() + except NameError: # Python3 + self.outtext = str() + self.quiet = 0 + self.p_p = 0 # number of newline character to print before next output + self.outcount = 0 + self.start = 1 + self.space = 0 + self.a = [] + self.astack = [] + self.acount = 0 + self.list = [] + self.blockquote = 0 + self.pre = 0 + self.startpre = 0 + self.code = False + self.br_toggle = '' + self.lastWasNL = 0 + self.lastWasList = False + self.style = 0 + self.style_def = {} + self.tag_stack = [] + self.emphasis = 0 + self.drop_white_space = 0 + self.inheader = False + self.abbr_title = None # current abbreviation definition + self.abbr_data = None # last inner HTML (for abbr being defined) + self.abbr_list = {} # stack of abbreviations to write later + self.baseurl = baseurl + + if options.google_doc: + del unifiable_n[name2cp('nbsp')] + unifiable['nbsp'] = ' _place_holder;' + + def feed(self, data): + data = data.replace("", "") + HTMLParser.HTMLParser.feed(self, data) + + def outtextf(self, s): + self.outtextlist.append(s) + if s: self.lastWasNL = s[-1] == '\n' + + def close(self): + HTMLParser.HTMLParser.close(self) + + self.pbr() + self.o('', 0, 'end') + + self.outtext = self.outtext.join(self.outtextlist) + + if options.google_doc: + self.outtext = self.outtext.replace(' _place_holder;', ' '); + + return self.outtext + + def handle_charref(self, c): + self.o(charref(c), 1) + + def handle_entityref(self, c): + self.o(entityref(c), 1) + + def handle_starttag(self, tag, attrs): + self.handle_tag(tag, attrs, 1) + + def handle_endtag(self, tag): + self.handle_tag(tag, None, 0) + + def previousIndex(self, attrs): + """ returns the index of certain set of attributes (of a link) in the + self.a list + + If the set of attributes is not found, returns None + """ + if not has_key(attrs, 'href'): return None + + i = -1 + for a in self.a: + i += 1 + match = 0 + + if has_key(a, 'href') and a['href'] == attrs['href']: + if has_key(a, 'title') or has_key(attrs, 'title'): + if (has_key(a, 'title') and has_key(attrs, 'title') and + a['title'] == attrs['title']): + match = True + else: + match = True + + if match: return i + + def drop_last(self, nLetters): + if not self.quiet: + self.outtext = self.outtext[:-nLetters] + + def handle_emphasis(self, start, tag_style, parent_style): + """handles various text emphases""" + tag_emphasis = google_text_emphasis(tag_style) + parent_emphasis = google_text_emphasis(parent_style) + + # handle Google's text emphasis + strikethrough = 'line-through' in tag_emphasis and options.hide_strikethrough + bold = 'bold' in tag_emphasis and not 'bold' in parent_emphasis + italic = 'italic' in tag_emphasis and not 'italic' in parent_emphasis + fixed = google_fixed_width_font(tag_style) and not \ + google_fixed_width_font(parent_style) and not self.pre + + if start: + # crossed-out text must be handled before other attributes + # in order not to output qualifiers unnecessarily + if bold or italic or fixed: + self.emphasis += 1 + if strikethrough: + self.quiet += 1 + if italic: + self.o("_") + self.drop_white_space += 1 + if bold: + self.o("**") + self.drop_white_space += 1 + if fixed: + self.o('`') + self.drop_white_space += 1 + self.code = True + else: + if bold or italic or fixed: + # there must not be whitespace before closing emphasis mark + self.emphasis -= 1 + self.space = 0 + self.outtext = self.outtext.rstrip() + if fixed: + if self.drop_white_space: + # empty emphasis, drop it + self.drop_last(1) + self.drop_white_space -= 1 + else: + self.o('`') + self.code = False + if bold: + if self.drop_white_space: + # empty emphasis, drop it + self.drop_last(2) + self.drop_white_space -= 1 + else: + self.o("**") + if italic: + if self.drop_white_space: + # empty emphasis, drop it + self.drop_last(1) + self.drop_white_space -= 1 + else: + self.o("_") + # space is only allowed after *all* emphasis marks + if (bold or italic) and not self.emphasis: + self.o(" ") + if strikethrough: + self.quiet -= 1 + + def handle_tag(self, tag, attrs, start): + #attrs = fixattrs(attrs) + if attrs is None: + attrs = {} + else: + attrs = dict(attrs) + + if options.google_doc: + # the attrs parameter is empty for a closing tag. in addition, we + # need the attributes of the parent nodes in order to get a + # complete style description for the current element. we assume + # that google docs export well formed html. + parent_style = {} + if start: + if self.tag_stack: + parent_style = self.tag_stack[-1][2] + tag_style = element_style(attrs, self.style_def, parent_style) + self.tag_stack.append((tag, attrs, tag_style)) + else: + dummy, attrs, tag_style = self.tag_stack.pop() + if self.tag_stack: + parent_style = self.tag_stack[-1][2] + + if hn(tag): + self.p() + if start: + self.inheader = True + self.o(hn(tag)*"#" + ' ') + else: + self.inheader = False + return # prevent redundant emphasis marks on headers + + if tag in ['p', 'div']: + if options.google_doc: + if start and google_has_height(tag_style): + self.p() + else: + self.soft_br() + else: + self.p() + + if tag == "br" and start: self.o(" \n") + + if tag == "hr" and start: + self.p() + self.o("* * *") + self.p() + + if tag in ["head", "style", 'script']: + if start: self.quiet += 1 + else: self.quiet -= 1 + + if tag == "style": + if start: self.style += 1 + else: self.style -= 1 + + if tag in ["body"]: + self.quiet = 0 # sites like 9rules.com never close + + if tag == "blockquote": + if start: + self.p(); self.o('> ', 0, 1); self.start = 1 + self.blockquote += 1 + else: + self.blockquote -= 1 + self.p() + + if tag in ['em', 'i', 'u']: self.o("_") + if tag in ['strong', 'b']: self.o("**") + if tag in ['del', 'strike']: + if start: + self.o("<"+tag+">") + else: + self.o("") + + if options.google_doc: + if not self.inheader: + # handle some font attributes, but leave headers clean + self.handle_emphasis(start, tag_style, parent_style) + + if tag == "code" and not self.pre: self.o('`') #TODO: `` `this` `` + if tag == "abbr": + if start: + self.abbr_title = None + self.abbr_data = '' + if has_key(attrs, 'title'): + self.abbr_title = attrs['title'] + else: + if self.abbr_title != None: + self.abbr_list[self.abbr_data] = self.abbr_title + self.abbr_title = None + self.abbr_data = '' + + if tag == "a" and not IGNORE_ANCHORS: + if start: + if has_key(attrs, 'href') and not (SKIP_INTERNAL_LINKS and attrs['href'].startswith('#')): + self.astack.append(attrs) + self.o("[") + else: + self.astack.append(None) + else: + if self.astack: + a = self.astack.pop() + if a: + if INLINE_LINKS: + self.o("](" + a['href'] + ")") + else: + i = self.previousIndex(a) + if i is not None: + a = self.a[i] + else: + self.acount += 1 + a['count'] = self.acount + a['outcount'] = self.outcount + self.a.append(a) + self.o("][" + str(a['count']) + "]") + + if tag == "img" and start and not IGNORE_IMAGES: + if has_key(attrs, 'src'): + attrs['href'] = attrs['src'] + alt = attrs.get('alt', '') + if INLINE_LINKS: + self.o("![") + self.o(alt) + self.o("]("+ attrs['href'] +")") + else: + i = self.previousIndex(attrs) + if i is not None: + attrs = self.a[i] + else: + self.acount += 1 + attrs['count'] = self.acount + attrs['outcount'] = self.outcount + self.a.append(attrs) + self.o("![") + self.o(alt) + self.o("]["+ str(attrs['count']) +"]") + + if tag == 'dl' and start: self.p() + if tag == 'dt' and not start: self.pbr() + if tag == 'dd' and start: self.o(' ') + if tag == 'dd' and not start: self.pbr() + + if tag in ["ol", "ul"]: + # Google Docs create sub lists as top level lists + if (not self.list) and (not self.lastWasList): + self.p() + if start: + if options.google_doc: + list_style = google_list_style(tag_style) + else: + list_style = tag + numbering_start = list_numbering_start(attrs) + self.list.append({'name':list_style, 'num':numbering_start}) + else: + if self.list: self.list.pop() + self.lastWasList = True + else: + self.lastWasList = False + + if tag == 'li': + self.pbr() + if start: + if self.list: li = self.list[-1] + else: li = {'name':'ul', 'num':0} + if options.google_doc: + nest_count = google_nest_count(tag_style) + else: + nest_count = len(self.list) + self.o(" " * nest_count) #TODO: line up
  1. s > 9 correctly. + if li['name'] == "ul": self.o(options.ul_item_mark + " ") + elif li['name'] == "ol": + li['num'] += 1 + self.o(str(li['num'])+". ") + self.start = 1 + + if tag in ["table", "tr"] and start: self.p() + if tag == 'td': self.pbr() + + if tag == "pre": + if start: + self.startpre = 1 + self.pre = 1 + else: + self.pre = 0 + self.p() + + def pbr(self): + if self.p_p == 0: self.p_p = 1 + + def p(self): self.p_p = 2 + + def soft_br(self): + self.pbr() + self.br_toggle = ' ' + + def o(self, data, puredata=0, force=0): + if self.abbr_data is not None: self.abbr_data += data + + if not self.quiet: + if options.google_doc: + # prevent white space immediately after 'begin emphasis' marks ('**' and '_') + lstripped_data = data.lstrip() + if self.drop_white_space and not (self.pre or self.code): + data = lstripped_data + if lstripped_data != '': + self.drop_white_space = 0 + + if puredata and not self.pre: + data = re.sub('\s+', ' ', data) + if data and data[0] == ' ': + self.space = 1 + data = data[1:] + if not data and not force: return + + if self.startpre: + #self.out(" :") #TODO: not output when already one there + self.startpre = 0 + + bq = (">" * self.blockquote) + if not (force and data and data[0] == ">") and self.blockquote: bq += " " + + if self.pre: + bq += " " + data = data.replace("\n", "\n"+bq) + + if self.start: + self.space = 0 + self.p_p = 0 + self.start = 0 + + if force == 'end': + # It's the end. + self.p_p = 0 + self.out("\n") + self.space = 0 + + if self.p_p: + self.out((self.br_toggle+'\n'+bq)*self.p_p) + self.space = 0 + self.br_toggle = '' + + if self.space: + if not self.lastWasNL: self.out(' ') + self.space = 0 + + if self.a and ((self.p_p == 2 and LINKS_EACH_PARAGRAPH) or force == "end"): + if force == "end": self.out("\n") + + newa = [] + for link in self.a: + if self.outcount > link['outcount']: + self.out(" ["+ str(link['count']) +"]: " + urlparse.urljoin(self.baseurl, link['href'])) + if has_key(link, 'title'): self.out(" ("+link['title']+")") + self.out("\n") + else: + newa.append(link) + + if self.a != newa: self.out("\n") # Don't need an extra line when nothing was done. + + self.a = newa + + if self.abbr_list and force == "end": + for abbr, definition in self.abbr_list.items(): + self.out(" *[" + abbr + "]: " + definition + "\n") + + self.p_p = 0 + self.out(data) + self.outcount += 1 + + def handle_data(self, data): + if r'\/script>' in data: self.quiet -= 1 + + if self.style: + self.style_def.update(dumb_css_parser(data)) + + self.o(data, 1) + + def unknown_decl(self, data): pass + +def wrapwrite(text): + text = text.encode('utf-8') + try: #Python3 + sys.stdout.buffer.write(text) + except AttributeError: + sys.stdout.write(text) + +def html2text_file(html, out=wrapwrite, baseurl=''): + h = _html2text(out, baseurl) + h.feed(html) + h.feed("") + return h.close() + +def html2text(html, baseurl=''): + return optwrap(html2text_file(html, None, baseurl)) + +class Storage: pass +options = Storage() +options.google_doc = False +options.ul_item_mark = '*' + +if __name__ == "__main__": + baseurl = '' + + p = optparse.OptionParser('%prog [(filename|url) [encoding]]', + version='%prog ' + __version__) + p.add_option("-g", "--google-doc", action="store_true", dest="google_doc", + default=False, help="convert an html-exported Google Document") + p.add_option("-d", "--dash-unordered-list", action="store_true", dest="ul_style_dash", + default=False, help="use a dash rather than a star for unordered list items") + p.add_option("-b", "--body-width", dest="body_width", action="store", type="int", + default=78, help="number of characters per output line, 0 for no wrap") + p.add_option("-i", "--google-list-indent", dest="list_indent", action="store", type="int", + default=GOOGLE_LIST_INDENT, help="number of pixels Google indents nested lists") + p.add_option("-s", "--hide-strikethrough", action="store_true", dest="hide_strikethrough", + default=False, help="hide strike-through text. only relevent when -g is specified as well") + (options, args) = p.parse_args() + + # handle options + if options.ul_style_dash: + options.ul_item_mark = '-' + else: + options.ul_item_mark = '*' + + BODY_WIDTH = options.body_width + GOOGLE_LIST_INDENT = options.list_indent + + # process input + if len(args) > 0: + file_ = args[0] + encoding = None + if len(args) == 2: + encoding = args[1] + if len(args) > 2: + p.error('Too many arguments') + + if file_.startswith('http://') or file_.startswith('https://'): + baseurl = file_ + j = urllib.urlopen(baseurl) + text = j.read() + if encoding is None: + try: + from feedparser import _getCharacterEncoding as enc + except ImportError: + enc = lambda x, y: ('utf-8', 1) + encoding = enc(j.headers, text)[0] + if encoding == 'us-ascii': + encoding = 'utf-8' + data = text.decode(encoding) + + else: + data = open(file_, 'rb').read() + if encoding is None: + try: + from chardet import detect + except ImportError: + detect = lambda x: {'encoding': 'utf-8'} + encoding = detect(data)['encoding'] + data = data.decode(encoding) + else: + data = sys.stdin.read() + wrapwrite(html2text(data, baseurl)) diff --git a/Examples/debugging/wikidef/test_wikidef.py b/Examples/debugging/wikidef/test_wikidef.py new file mode 100644 index 0000000..0601444 --- /dev/null +++ b/Examples/debugging/wikidef/test_wikidef.py @@ -0,0 +1,21 @@ +import unittest + +from api import Wikipedia, ParseError +from definitions import Definitions + +class WikiDefTest(unittest.TestCase): + + def setUp(self): + pass + + def tearDown(self): + pass + + def test_article_success(self): + article = Definitions.article("Robot") + self.assertIn("mechanical", article) + + def test_missing_article_failure(self): + missing_article_title = "!!!!!-NonExistentArticle" + self.assertRaises(ParseError, Definitions.article, missing_article_title) + diff --git a/Examples/debugging/wikidef/test_wikidef_with_mock.py b/Examples/debugging/wikidef/test_wikidef_with_mock.py new file mode 100644 index 0000000..1423b54 --- /dev/null +++ b/Examples/debugging/wikidef/test_wikidef_with_mock.py @@ -0,0 +1,40 @@ +import unittest + +from mock import patch + +from api import Wikipedia, ParseError +from definitions import Definitions + +class WikiDefTest(unittest.TestCase): + + def setUp(self): + pass + + def tearDown(self): + pass + + def test_article_success(self): + article = Definitions.article("Robot") + self.assertIn("mechanical", article) + + def test_missing_article_failure(self): + missing_article_title = "!!!!!-NonExistentArticle" + self.assertRaises(ParseError, Definitions.article, missing_article_title) + + # patch with a decorator + @patch('definitions.Wikipedia.article') + def test_article_success_decorator_mocked(self, mock_method): + article = Definitions.article("Robot") + mock_method.assert_called_once_with("Robot") + + @patch.object(Wikipedia, 'article') + def test_article_success_decorator_mocked(self, mock_method): + article = Definitions.article("Robot") + mock_method.assert_called_once_with("Robot") + + # patch with a context manager + def test_article_success_context_manager_mocked(self): + with patch.object(Wikipedia, 'article') as mock_method: + article = Definitions.article("Robot") + mock_method.assert_called_once_with("Robot") + diff --git a/Examples/documentation/Capitalize/CHANGES.txt b/Examples/documentation/Capitalize/CHANGES.txt new file mode 100644 index 0000000..41ae95a --- /dev/null +++ b/Examples/documentation/Capitalize/CHANGES.txt @@ -0,0 +1,2 @@ +v 0.0.1, 2014:03:23 -- Initial release. + diff --git a/Examples/documentation/Capitalize/LICENSE.txt b/Examples/documentation/Capitalize/LICENSE.txt new file mode 100644 index 0000000..258cd06 --- /dev/null +++ b/Examples/documentation/Capitalize/LICENSE.txt @@ -0,0 +1,4 @@ +The license for the capitalize package + +Public Domain: do with it what you will. + diff --git a/Examples/documentation/Capitalize/MANIFEST.txt b/Examples/documentation/Capitalize/MANIFEST.txt new file mode 100644 index 0000000..86604e4 --- /dev/null +++ b/Examples/documentation/Capitalize/MANIFEST.txt @@ -0,0 +1 @@ +include *.txt \ No newline at end of file diff --git a/Examples/documentation/Capitalize/README.txt b/Examples/documentation/Capitalize/README.txt new file mode 100644 index 0000000..2e92101 --- /dev/null +++ b/Examples/documentation/Capitalize/README.txt @@ -0,0 +1,11 @@ +=================== +Capitalize Package +=================== + +A basic pacakge for Capitalizing text in files + +Purpose +======== + +Nothing useful, just something to demonstrate packaging with. + diff --git a/Examples/documentation/Capitalize/bin/cap_script.py b/Examples/documentation/Capitalize/bin/cap_script.py new file mode 100755 index 0000000..08f999e --- /dev/null +++ b/Examples/documentation/Capitalize/bin/cap_script.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +""" +A really simple script just to demonstrate disutils +""" + +import sys, os +from capitalize import capital_mod + + +if __name__ == "__main__": + try: + infilename = sys.argv[1] + except IndexError: + print "you need to pass in a file to process" + + root, ext = os.path.splitext(infilename) + outfilename = root + "_cap" + ext + + # do the real work: + print "Capitalizing: %s and storing it in %s"%(infilename, outfilename) + capital_mod.capitalize(infilename, outfilename) + + print "I'm done" + \ No newline at end of file diff --git a/Examples/documentation/Capitalize/capitalize/__init__.py b/Examples/documentation/Capitalize/capitalize/__init__.py new file mode 100644 index 0000000..de8010b --- /dev/null +++ b/Examples/documentation/Capitalize/capitalize/__init__.py @@ -0,0 +1,9 @@ +# init for Capitalize package + +""" +A little package to make it easy to Capitalize things. + +Really useless -- but here to demostrate packaging and documentation +""" + +__version__ = "0.1.1" diff --git a/Examples/documentation/Capitalize/capitalize/capital_mod.py b/Examples/documentation/Capitalize/capitalize/capital_mod.py new file mode 100644 index 0000000..b6c23a9 --- /dev/null +++ b/Examples/documentation/Capitalize/capitalize/capital_mod.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python + +""" +A really simple module, just to demonstrate packaging and documentation +""" + + +def capitalize_line(instr): + """ + capitalizes the input string + + :param instr: the string to capitalize: it should be a single line. + :type instr: string + + :returns: a capitalized version of instr + """ + + return " ".join(word.capitalize() for word in instr.split()) + + +def capitalize(infilename, outfilename): + """ + reads the contents of infilename, and writes it to outfilename, but with + every word capitalized + + note: very primitive -- it will mess some files up! + + This is called by the capitalize script + + :param infilename: The file name you want to process + :type infilename: string + + :param outfilename: the name of the new file that will be created + :type outfilename: string + + :returns: None + + :raises: IOError if infilename doesn't exist. + """ + infile = open(infilename, 'U') + outfile = open(outfilename, 'w') + + for line in infile: + outfile.write(capitalize_line(line)) + outfile.write("\n") + + return None + + +class Something(object): + """ + this is the class docstring + + this is only here to demostrate how classes get documented. + """ + def __init__(self, input): + """ + the __init__ docstring + + :param input: someinput + :type input: some_type + """ + pass diff --git a/Examples/documentation/Capitalize/capitalize/test/__init__.py b/Examples/documentation/Capitalize/capitalize/test/__init__.py new file mode 100644 index 0000000..02d4ce5 --- /dev/null +++ b/Examples/documentation/Capitalize/capitalize/test/__init__.py @@ -0,0 +1,10 @@ +""" +Test package for capitalize module +""" + +import os + + +def runall(): + import pytest + pytest.main(os.path.split(__file__)[0]) diff --git a/Examples/documentation/Capitalize/capitalize/test/sample_text_file.txt b/Examples/documentation/Capitalize/capitalize/test/sample_text_file.txt new file mode 100644 index 0000000..8619a8b --- /dev/null +++ b/Examples/documentation/Capitalize/capitalize/test/sample_text_file.txt @@ -0,0 +1,6 @@ +This is a really simple Text file. +It is here so that I can test the capitalize script. + +And that's only there to try out packaging and documentation. + +So there. diff --git a/Examples/documentation/Capitalize/capitalize/test/test_capital_mod.py b/Examples/documentation/Capitalize/capitalize/test/test_capital_mod.py new file mode 100644 index 0000000..19da70a --- /dev/null +++ b/Examples/documentation/Capitalize/capitalize/test/test_capital_mod.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python + +""" +test code for capitalize module + +can be run with py.test or nosetests +""" + +import tempfile +import pytest + +from capitalize import capital_mod + + +# a sample pyteset fixture: +# this one creates a temp file, writes somethign to it, and returns the +# name of that file to the whatever uses the fixture. +@pytest.fixture +def test_textfile(): + f = tempfile.NamedTemporaryFile(mode='w', + encoding='utf-8', + newline=None, + delete=False) + f.write("""This is a really simple Text file. +It is here so that I can test the capitalize script. + +And that's only there to try out packaging and documentation. + +So there. +""" + ) + + return f.name + + +def test_capitalize_line(): + line = "this is a Line to capitalize" + expected = "This Is A Line To Capitalize" + + assert capital_mod.capitalize_line(line) == expected + + +def test_capitalize(test_textfile): + """ test an actual file """ + # capitalize the file + capital_mod.capitalize(test_textfile, "sample_text_file_cap.txt") + + # now check it + with open("sample_text_file_cap.txt", 'U') as infile: + contents = infile.read() + + expected = """This Is A Really Simple Text File. +It Is Here So That I Can Test The Capitalize Script. + +And That's Only There To Try Out Packaging And Documentation. + +So There.""" + + assert contents.strip() == expected diff --git a/Examples/documentation/Capitalize/doc/Makefile b/Examples/documentation/Capitalize/doc/Makefile new file mode 100644 index 0000000..38245c3 --- /dev/null +++ b/Examples/documentation/Capitalize/doc/Makefile @@ -0,0 +1,230 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +PAPER = +BUILDDIR = build + +# User-friendly check for sphinx-build +ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) + $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don\'t have Sphinx installed, grab it from http://sphinx-doc.org/) +endif + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source +# the i18n builder cannot share the environment and doctrees with the others +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source + +.PHONY: help +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + @echo " dirhtml to make HTML files named index.html in directories" + @echo " singlehtml to make a single large HTML file" + @echo " pickle to make pickle files" + @echo " json to make JSON files" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " qthelp to make HTML files and a qthelp project" + @echo " applehelp to make an Apple Help Book" + @echo " devhelp to make HTML files and a Devhelp project" + @echo " epub to make an epub" + @echo " epub3 to make an epub3" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " latexpdf to make LaTeX files and run them through pdflatex" + @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" + @echo " text to make text files" + @echo " man to make manual pages" + @echo " texinfo to make Texinfo files" + @echo " info to make Texinfo files and run them through makeinfo" + @echo " gettext to make PO message catalogs" + @echo " changes to make an overview of all changed/added/deprecated items" + @echo " xml to make Docutils-native XML files" + @echo " pseudoxml to make pseudoxml-XML files for display purposes" + @echo " linkcheck to check all external links for integrity" + @echo " doctest to run all doctests embedded in the documentation (if enabled)" + @echo " coverage to run coverage check of the documentation (if enabled)" + @echo " dummy to check syntax errors of document sources" + +.PHONY: clean +clean: + rm -rf $(BUILDDIR)/* + +.PHONY: html +html: + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." + +.PHONY: dirhtml +dirhtml: + $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." + +.PHONY: singlehtml +singlehtml: + $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml + @echo + @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." + +.PHONY: pickle +pickle: + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle + @echo + @echo "Build finished; now you can process the pickle files." + +.PHONY: json +json: + $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json + @echo + @echo "Build finished; now you can process the JSON files." + +.PHONY: htmlhelp +htmlhelp: + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in $(BUILDDIR)/htmlhelp." + +.PHONY: qthelp +qthelp: + $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp + @echo + @echo "Build finished; now you can run "qcollectiongenerator" with the" \ + ".qhcp project file in $(BUILDDIR)/qthelp, like this:" + @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/capitalize.qhcp" + @echo "To view the help file:" + @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/capitalize.qhc" + +.PHONY: applehelp +applehelp: + $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp + @echo + @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." + @echo "N.B. You won't be able to view it unless you put it in" \ + "~/Library/Documentation/Help or install it in your application" \ + "bundle." + +.PHONY: devhelp +devhelp: + $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp + @echo + @echo "Build finished." + @echo "To view the help file:" + @echo "# mkdir -p $$HOME/.local/share/devhelp/capitalize" + @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/capitalize" + @echo "# devhelp" + +.PHONY: epub +epub: + $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub + @echo + @echo "Build finished. The epub file is in $(BUILDDIR)/epub." + +.PHONY: epub3 +epub3: + $(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3 + @echo + @echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3." + +.PHONY: latex +latex: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo + @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." + @echo "Run \`make' in that directory to run these through (pdf)latex" \ + "(use \`make latexpdf' here to do that automatically)." + +.PHONY: latexpdf +latexpdf: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through pdflatex..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: latexpdfja +latexpdfja: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through platex and dvipdfmx..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: text +text: + $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text + @echo + @echo "Build finished. The text files are in $(BUILDDIR)/text." + +.PHONY: man +man: + $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man + @echo + @echo "Build finished. The manual pages are in $(BUILDDIR)/man." + +.PHONY: texinfo +texinfo: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo + @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." + @echo "Run \`make' in that directory to run these through makeinfo" \ + "(use \`make info' here to do that automatically)." + +.PHONY: info +info: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo "Running Texinfo files through makeinfo..." + make -C $(BUILDDIR)/texinfo info + @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." + +.PHONY: gettext +gettext: + $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale + @echo + @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." + +.PHONY: changes +changes: + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes + @echo + @echo "The overview file is in $(BUILDDIR)/changes." + +.PHONY: linkcheck +linkcheck: + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in $(BUILDDIR)/linkcheck/output.txt." + +.PHONY: doctest +doctest: + $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest + @echo "Testing of doctests in the sources finished, look at the " \ + "results in $(BUILDDIR)/doctest/output.txt." + +.PHONY: coverage +coverage: + $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage + @echo "Testing of coverage in the sources finished, look at the " \ + "results in $(BUILDDIR)/coverage/python.txt." + +.PHONY: xml +xml: + $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml + @echo + @echo "Build finished. The XML files are in $(BUILDDIR)/xml." + +.PHONY: pseudoxml +pseudoxml: + $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml + @echo + @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." + +.PHONY: dummy +dummy: + $(SPHINXBUILD) -b dummy $(ALLSPHINXOPTS) $(BUILDDIR)/dummy + @echo + @echo "Build finished. Dummy builder generates no files." diff --git a/Examples/documentation/Capitalize/doc/make.bat b/Examples/documentation/Capitalize/doc/make.bat new file mode 100644 index 0000000..977466e --- /dev/null +++ b/Examples/documentation/Capitalize/doc/make.bat @@ -0,0 +1,281 @@ +@ECHO OFF + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set BUILDDIR=build +set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source +set I18NSPHINXOPTS=%SPHINXOPTS% source +if NOT "%PAPER%" == "" ( + set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% + set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% +) + +if "%1" == "" goto help + +if "%1" == "help" ( + :help + echo.Please use `make ^` where ^ is one of + echo. html to make standalone HTML files + echo. dirhtml to make HTML files named index.html in directories + echo. singlehtml to make a single large HTML file + echo. pickle to make pickle files + echo. json to make JSON files + echo. htmlhelp to make HTML files and a HTML help project + echo. qthelp to make HTML files and a qthelp project + echo. devhelp to make HTML files and a Devhelp project + echo. epub to make an epub + echo. epub3 to make an epub3 + echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter + echo. text to make text files + echo. man to make manual pages + echo. texinfo to make Texinfo files + echo. gettext to make PO message catalogs + echo. changes to make an overview over all changed/added/deprecated items + echo. xml to make Docutils-native XML files + echo. pseudoxml to make pseudoxml-XML files for display purposes + echo. linkcheck to check all external links for integrity + echo. doctest to run all doctests embedded in the documentation if enabled + echo. coverage to run coverage check of the documentation if enabled + echo. dummy to check syntax errors of document sources + goto end +) + +if "%1" == "clean" ( + for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i + del /q /s %BUILDDIR%\* + goto end +) + + +REM Check if sphinx-build is available and fallback to Python version if any +%SPHINXBUILD% 1>NUL 2>NUL +if errorlevel 9009 goto sphinx_python +goto sphinx_ok + +:sphinx_python + +set SPHINXBUILD=python -m sphinx.__init__ +%SPHINXBUILD% 2> nul +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +:sphinx_ok + + +if "%1" == "html" ( + %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/html. + goto end +) + +if "%1" == "dirhtml" ( + %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. + goto end +) + +if "%1" == "singlehtml" ( + %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. + goto end +) + +if "%1" == "pickle" ( + %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the pickle files. + goto end +) + +if "%1" == "json" ( + %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the JSON files. + goto end +) + +if "%1" == "htmlhelp" ( + %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run HTML Help Workshop with the ^ +.hhp project file in %BUILDDIR%/htmlhelp. + goto end +) + +if "%1" == "qthelp" ( + %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run "qcollectiongenerator" with the ^ +.qhcp project file in %BUILDDIR%/qthelp, like this: + echo.^> qcollectiongenerator %BUILDDIR%\qthelp\capitalize.qhcp + echo.To view the help file: + echo.^> assistant -collectionFile %BUILDDIR%\qthelp\capitalize.ghc + goto end +) + +if "%1" == "devhelp" ( + %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. + goto end +) + +if "%1" == "epub" ( + %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The epub file is in %BUILDDIR%/epub. + goto end +) + +if "%1" == "epub3" ( + %SPHINXBUILD% -b epub3 %ALLSPHINXOPTS% %BUILDDIR%/epub3 + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The epub3 file is in %BUILDDIR%/epub3. + goto end +) + +if "%1" == "latex" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdf" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdfja" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf-ja + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "text" ( + %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The text files are in %BUILDDIR%/text. + goto end +) + +if "%1" == "man" ( + %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The manual pages are in %BUILDDIR%/man. + goto end +) + +if "%1" == "texinfo" ( + %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. + goto end +) + +if "%1" == "gettext" ( + %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The message catalogs are in %BUILDDIR%/locale. + goto end +) + +if "%1" == "changes" ( + %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes + if errorlevel 1 exit /b 1 + echo. + echo.The overview file is in %BUILDDIR%/changes. + goto end +) + +if "%1" == "linkcheck" ( + %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck + if errorlevel 1 exit /b 1 + echo. + echo.Link check complete; look for any errors in the above output ^ +or in %BUILDDIR%/linkcheck/output.txt. + goto end +) + +if "%1" == "doctest" ( + %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest + if errorlevel 1 exit /b 1 + echo. + echo.Testing of doctests in the sources finished, look at the ^ +results in %BUILDDIR%/doctest/output.txt. + goto end +) + +if "%1" == "coverage" ( + %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage + if errorlevel 1 exit /b 1 + echo. + echo.Testing of coverage in the sources finished, look at the ^ +results in %BUILDDIR%/coverage/python.txt. + goto end +) + +if "%1" == "xml" ( + %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The XML files are in %BUILDDIR%/xml. + goto end +) + +if "%1" == "pseudoxml" ( + %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. + goto end +) + +if "%1" == "dummy" ( + %SPHINXBUILD% -b dummy %ALLSPHINXOPTS% %BUILDDIR%/dummy + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. Dummy builder generates no files. + goto end +) + +:end diff --git a/Examples/documentation/Capitalize/doc/source/conf.py b/Examples/documentation/Capitalize/doc/source/conf.py new file mode 100644 index 0000000..3c76e48 --- /dev/null +++ b/Examples/documentation/Capitalize/doc/source/conf.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# capitalize documentation build configuration file, created by +# sphinx-quickstart on Tue Apr 19 19:51:59 2016. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys +import os + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +#sys.path.insert(0, os.path.abspath('.')) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +#needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.coverage', + 'sphinx.ext.viewcode', +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = 'capitalize' +copyright = '2016, Chris Barker' +author = 'Chris Barker' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = '0.1.0' +# The full version, including alpha/beta/rc tags. +release = '0.1.0' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = [] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +#modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +#keep_warnings = False + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. + +html_theme = 'haiku' +# html_theme = 'alabaster' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +#html_theme_options = {} + +# Add any paths that contain custom themes here, relative to this directory. +#html_theme_path = [] + +# The name for this set of Sphinx documents. +# " v documentation" by default. +#html_title = 'capitalize v0.1.0' + +# A shorter title for the navigation bar. Default is the same as html_title. +#html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +#html_logo = None + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +#html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Add any extra paths that contain custom files (such as robots.txt or +# .htaccess) here, relative to this directory. These files are copied +# directly to the root of the documentation. +#html_extra_path = [] + +# If not None, a 'Last updated on:' timestamp is inserted at every page +# bottom, using the given strftime format. +# The empty string is equivalent to '%b %d, %Y'. +#html_last_updated_fmt = None + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +#html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +#html_sidebars = {} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +#html_additional_pages = {} + +# If false, no module index is generated. +#html_domain_indices = True + +# If false, no index is generated. +#html_use_index = True + +# If true, the index is split into individual pages for each letter. +#html_split_index = False + +# If true, links to the reST sources are added to the pages. +#html_show_sourcelink = True + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +#html_show_sphinx = True + +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +#html_show_copyright = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +#html_use_opensearch = '' + +# This is the file name suffix for HTML files (e.g. ".xhtml"). +#html_file_suffix = None + +# Language to be used for generating the HTML full-text search index. +# Sphinx supports the following languages: +# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' +# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr', 'zh' +#html_search_language = 'en' + +# A dictionary with options for the search language support, empty by default. +# 'ja' uses this config value. +# 'zh' user can custom change `jieba` dictionary path. +#html_search_options = {'type': 'default'} + +# The name of a javascript file (relative to the configuration directory) that +# implements a search results scorer. If empty, the default will be used. +#html_search_scorer = 'scorer.js' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'capitalizedoc' + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { +# The paper size ('letterpaper' or 'a4paper'). +#'papersize': 'letterpaper', + +# The font size ('10pt', '11pt' or '12pt'). +#'pointsize': '10pt', + +# Additional stuff for the LaTeX preamble. +#'preamble': '', + +# Latex figure (float) alignment +#'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'capitalize.tex', 'capitalize Documentation', + 'Chris Barker', 'manual'), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +#latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +#latex_use_parts = False + +# If true, show page references after internal links. +#latex_show_pagerefs = False + +# If true, show URL addresses after external links. +#latex_show_urls = False + +# Documents to append as an appendix to all manuals. +#latex_appendices = [] + +# If false, no module index is generated. +#latex_domain_indices = True + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'capitalize', 'capitalize Documentation', + [author], 1) +] + +# If true, show URL addresses after external links. +#man_show_urls = False + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'capitalize', 'capitalize Documentation', + author, 'capitalize', 'One line description of project.', + 'Miscellaneous'), +] + +# Documents to append as an appendix to all manuals. +#texinfo_appendices = [] + +# If false, no module index is generated. +#texinfo_domain_indices = True + +# How to display URL addresses: 'footnote', 'no', or 'inline'. +#texinfo_show_urls = 'footnote' + +# If true, do not generate a @detailmenu in the "Top" node's menu. +#texinfo_no_detailmenu = False diff --git a/Examples/documentation/Capitalize/doc/source/index.rst b/Examples/documentation/Capitalize/doc/source/index.rst new file mode 100644 index 0000000..c4a966f --- /dev/null +++ b/Examples/documentation/Capitalize/doc/source/index.rst @@ -0,0 +1,27 @@ +.. capitalize documentation master file, created by + sphinx-quickstart on Tue Apr 19 19:51:59 2016. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to capitalize's documentation! +====================================== + +Capitalize is a small packge that helps you capitilze text files. + +Contents: + +.. toctree:: + :maxdepth: 2 + + tutorial.rst + reference.rst + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + diff --git a/Examples/documentation/Capitalize/doc/source/reference.rst b/Examples/documentation/Capitalize/doc/source/reference.rst new file mode 100644 index 0000000..d1683e9 --- /dev/null +++ b/Examples/documentation/Capitalize/doc/source/reference.rst @@ -0,0 +1,17 @@ +============= +Lib Reference +============= + +the reference docs + +The capitalize packge +===================== + +.. automodule:: capitalize + +capital_mod module +------------------- + +.. automodule:: capitalize.capital_mod + :members: + diff --git a/Examples/documentation/Capitalize/doc/source/tutorial.rst b/Examples/documentation/Capitalize/doc/source/tutorial.rst new file mode 100644 index 0000000..8fd8eaa --- /dev/null +++ b/Examples/documentation/Capitalize/doc/source/tutorial.rst @@ -0,0 +1,17 @@ +======== +Tutorial +======== + +A short tutorial for capitalize + +Easy Start +========== + +just do: + +.. code-block:: bash + + $ capitalize.py a_text_file + + + diff --git a/Examples/documentation/Capitalize/setup.cfg b/Examples/documentation/Capitalize/setup.cfg new file mode 100644 index 0000000..9af7e6f --- /dev/null +++ b/Examples/documentation/Capitalize/setup.cfg @@ -0,0 +1,2 @@ +[aliases] +test=pytest \ No newline at end of file diff --git a/Examples/documentation/Capitalize/setup.py b/Examples/documentation/Capitalize/setup.py new file mode 100755 index 0000000..4a6957e --- /dev/null +++ b/Examples/documentation/Capitalize/setup.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python + +""" +This is about as simple a setup.py as you can have + +It installs the capitalize module and script + +""" + +from setuptools import setup + +import capitalize # to get __version__ + +setup( + name='Capitalize', + version=capitalize.__version__, + author='Chris Barker', + author_email='PythonCHB@gmail.com', + packages=['capitalize', + 'capitalize/test'], + scripts=['bin/cap_script.py', ], + setup_requires=['pytest-runner'], + tests_require=['pytest'], + license='LICENSE.txt', + description='Not very useful capitalizing module and script', + long_description=open('README.txt').read(), +) diff --git a/Examples/extensions/c-api/add.c b/Examples/extensions/c-api/add.c new file mode 100644 index 0000000..7104782 --- /dev/null +++ b/Examples/extensions/c-api/add.c @@ -0,0 +1,29 @@ +#include + +static PyObject * +add(PyObject *self, PyObject *args) +{ + int x; + int y; + int sts; + + if (!PyArg_ParseTuple(args, "ii", &x, &y)) + return NULL; + sts = x+y; + return Py_BuildValue("i", sts); +} + +// Module's method table and initialization function +// see: https://docs.python.org/2/extending/extending.html#the-module-s-method-table-and-initialization-function +static PyMethodDef AddMethods[] = { + {"add", add, METH_VARARGS, "add two numbers"}, + {NULL, NULL, 0, NULL} // sentinel +}; + + +PyMODINIT_FUNC +initadd(void) { + // Module's initialization function + // Will be called again if you use Python's reload() + (void) Py_InitModule("add", AddMethods); +} diff --git a/Examples/extensions/c-api/divide/divide.c b/Examples/extensions/c-api/divide/divide.c new file mode 100644 index 0000000..6ebad27 --- /dev/null +++ b/Examples/extensions/c-api/divide/divide.c @@ -0,0 +1,29 @@ +#include + +static PyObject * +divide(PyObject *self, PyObject *args) +{ + double x; + double y; + double sts; + + if (!PyArg_ParseTuple(args, "dd", &x, &y)) + return NULL; + sts = x/y; + return Py_BuildValue("d", sts); +} + +// Module's method table and initialization function +// See: http://docs.python.org/extending/extending.html#the-module-s-method-table-and-initialization-function +static PyMethodDef DivideMethods[] = { + {"divide", divide, METH_VARARGS, "divide two numbers"}, + {NULL, NULL, 0, NULL} +}; + +PyMODINIT_FUNC // does the right thing on Windows, Linux, etc. +initdivide(void) { + // Module's initialization function + // Will be called again if you use Python's reload() + + Py_InitModule("divide", DivideMethods); +} diff --git a/Examples/extensions/c-api/divide/setup.py b/Examples/extensions/c-api/divide/setup.py new file mode 100755 index 0000000..d5810ab --- /dev/null +++ b/Examples/extensions/c-api/divide/setup.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python + +from setuptools import setup, Extension + +setup( + name='Cdiv', + version='1.0', + description='sample method that does exceptions', + ext_modules=[Extension('divide', sources=['divide.c'])], +) diff --git a/Examples/extensions/c-api/divide/test_divide.py b/Examples/extensions/c-api/divide/test_divide.py new file mode 100644 index 0000000..15e3698 --- /dev/null +++ b/Examples/extensions/c-api/divide/test_divide.py @@ -0,0 +1,17 @@ +""" +test file for the divide function +""" + +import pytest + +from divide import divide + + +def test_divide(): + assert divide(3, 4) == 0.75 + + +def test_divide_zero(): + # if it acts like python, it should raise an exception + with pytest.raises(ZeroDivisionError): + divide(3, 0) diff --git a/Examples/extensions/c-api/setup.py b/Examples/extensions/c-api/setup.py new file mode 100755 index 0000000..b571728 --- /dev/null +++ b/Examples/extensions/c-api/setup.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python + +from setuptools import setup, Extension + +setup( + name='Cadd', + version='1.0', + description='simple c extension for an example', + ext_modules=[Extension('add', sources=['add.c'])], +) diff --git a/Examples/extensions/c-api/test_add.py b/Examples/extensions/c-api/test_add.py new file mode 100644 index 0000000..259ca28 --- /dev/null +++ b/Examples/extensions/c-api/test_add.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python + +""" +simple test for the add C extension +""" + +import pytest + +import add + +def test_basic(): + assert add.add(3,4) == 7 + +def test_negative(): + assert add.add(-12, 5) == -7 + +def test_float(): + with pytest.raises(TypeError): + add.add(3, 4.0) + + diff --git a/Examples/extensions/ctypes/Makefile b/Examples/extensions/ctypes/Makefile new file mode 100644 index 0000000..6ac01dd --- /dev/null +++ b/Examples/extensions/ctypes/Makefile @@ -0,0 +1 @@ +all: add; gcc -shared -o add.so add.c diff --git a/Examples/extensions/ctypes/add.c b/Examples/extensions/ctypes/add.c new file mode 100644 index 0000000..38d99ad --- /dev/null +++ b/Examples/extensions/ctypes/add.c @@ -0,0 +1,10 @@ +#include + +int add(int x, int y) { + return x+y; +} +int main(void) { + int w = 3; + int q = 2; + printf("%d + %d = %d\n\n", w, q, add(w,q)); +} diff --git a/Examples/extensions/ctypes/add.dll b/Examples/extensions/ctypes/add.dll new file mode 100644 index 0000000..29de56d Binary files /dev/null and b/Examples/extensions/ctypes/add.dll differ diff --git a/Examples/extensions/ctypes/pointers.py b/Examples/extensions/ctypes/pointers.py new file mode 100755 index 0000000..4991d41 --- /dev/null +++ b/Examples/extensions/ctypes/pointers.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python + +from ctypes import * + +libc = CDLL("libc.dylib") + +i = c_int() +f = c_float() +s = create_string_buffer('\000' * 32) +print i.value, f.value, repr(s.value) +libc.sscanf("1 3.14 Hello", "%d %f %s",byref(i), byref(f), s) +print i.value, f.value, repr(s.value) diff --git a/Examples/extensions/ctypes/qsort_test.py b/Examples/extensions/ctypes/qsort_test.py new file mode 100755 index 0000000..279915d --- /dev/null +++ b/Examples/extensions/ctypes/qsort_test.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python + +from ctypes import * + +libc = CDLL("libc.dylib") + +TenInts = c_int * 10 +data = TenInts(*range(10,0,-1)) + +# http://www.gnu.org/software/libc/manual/html_node/Array-Sort-Function.html +# void qsort (void *array, size_t count, size_t size, comparison_fn_t compare) + +# http://docs.python.org/2/library/ctypes.html#ctypes.CFUNCTYPE +CMPFUNC = CFUNCTYPE(c_int, POINTER(c_int), POINTER(c_int)) + +def py_cmp_func(a, b): + # print "py_cmp_func", a[0], b[0] + return a[0] - b[0] + +for i in data: + print i + +libc.qsort(data, len(data), sizeof(c_int), CMPFUNC(py_cmp_func)) + +for i in data: + print i diff --git a/Examples/extensions/ctypes/test_add.py b/Examples/extensions/ctypes/test_add.py new file mode 100644 index 0000000..58aa9cf --- /dev/null +++ b/Examples/extensions/ctypes/test_add.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python + +import sys + +lib_ext = ('.so' if ('darwin' in sys.platform or + 'linux' in sys.platform) + else ".dll") + +import ctypes +import ctypes.util + +# # now load our own shared library +# add = ctypes.cdll.LoadLibrary("add"+lib_ext) + +# print "This should be 7:", +# print add.add(3,4) + +# Using an already-loaded shared library: +## loading libc and libm (math) +if 'darwin' in sys.platform: + # OS-X likes full paths... + libc_name = ctypes.util.find_library("libc") + libm_name = ctypes.util.find_library("libm") + print libc_name + libc = ctypes.CDLL(libc_name) + libm = ctypes.CDLL(libm_name) +elif 'linux' in sys.platform: + # linux uses the shared lib search path +# libc = ctypes.CDLL("libc.so") + libc = ctypes.util.find_library("libc") + libc = ctypes.CDLL("/lib/x86_64-linux-gnu/libc.so.6") + libm = ctypes.CDLL("/lib/x86_64-linux-gnu/libm.so.6") +elif 'win' in sys.platform: + # Windows can find already loaded dlls by name + libc = ctypes.cdll.msvcrt # lib c and libm are in msvcrt + libm = ctypes.cdll.msvcrt + +libc.printf("printed via libc printf()\n") + +print "passing different types to printf:" +#libc.printf("An int %d, a double %f\n", 1234, 3.14) +libc.printf("An int %d, a double %f\n", 1234, ctypes.c_double(3.14)) +#libc.printf("An int %d, a double %f\n", 1234) + + +## Calling libm +## prototype for pow(): +## double pow ( double x, double y ) + +print "This should be 81.0:", +print libm.pow(ctypes.c_double(3), ctypes.c_double(4)) + +## need to set the return type! +libm.pow.restype = ctypes.c_double +print "This should be 81.0:", +print libm.pow(ctypes.c_double(3), ctypes.c_double(4)) + +## if you are going to call the same function a lot, +## you can specify the arument types: + +libm.pow.restype = ctypes.c_double +libm.pow.argtypes = [ctypes.c_double, ctypes.c_double] +print "This should be 81.0:", +print libm.pow(3, 4.0) + +## much easier! + + + + + + + + diff --git a/Examples/extensions/cython/AGC_example/README.rst b/Examples/extensions/cython/AGC_example/README.rst new file mode 100644 index 0000000..a33e243 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/README.rst @@ -0,0 +1,26 @@ +Automatic Gain Control Example +============================== + +Automatic Gain Control is a signal processing procedure: + +http://seismicreflections.globeclaritas.com/2013/04/agc-equaliser.html + +This is an example of using fortran, C, and Cython to produce a fast AGC +filter for Python/numpy. + +Thanks for the folks at IRIS (http://www.iris.edu) for this example and +the Fortran code. + +Using these examples: +===================== + +The c and cython versions should be usable with any pyton set up to +compile extensions. They use setuptools to call the compiler. + +The Fortran / f2c version requires a compatible Fortran compiler. g77 +should work fine, but getting a compatible build on anything but Linux +is a trick, and out of scope for this README (sorry). Google for it -- +you should find what you need. + + + diff --git a/Examples/extensions/cython/AGC_example/agc_c.c b/Examples/extensions/cython/AGC_example/agc_c.c new file mode 100644 index 0000000..7e8a6ae --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_c.c @@ -0,0 +1,32 @@ +/* C version of AGC code */ + +#include +#include + +void AGC(int nAGC, int npts, float *amp, float *ampAGC); + +void AGC(int nAGC, int npts, float *amp, float *ampAGC) { + + float fmax; + float absamp[npts]; + int i, j, nAGC2; + + + for (i = 0; i < npts; i++){ + ampAGC[i] = 0.0; + absamp[i] = fabs(amp[i]); + } + nAGC2 = nAGC / 2; + + for (i = nAGC2; i < npts-nAGC2; i++){ + fmax = 0.0; + for ( j=(i-nAGC2); j < i+nAGC2+1; j++ ){ + if ( absamp[j] > fmax ) { + fmax = absamp[j]; + } + } + ampAGC[i] = amp[i] / fmax; + } + return; + } + diff --git a/Examples/extensions/cython/AGC_example/agc_c_cy.pyx b/Examples/extensions/cython/AGC_example/agc_c_cy.pyx new file mode 100644 index 0000000..dc1f720 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_c_cy.pyx @@ -0,0 +1,22 @@ +""" +Cython file to call the C routine +""" + +import numpy as np +cimport numpy as cnp + +cdef extern: + void AGC(int nAGC, int npts, float *amp, float *ampAGC) + + +def agc (int nAGC, float[:] amp): + + # create the output array + cdef cnp.ndarray[float, ndim=1] ampAGC = np.zeros_like(amp) + + npts = amp.shape[0] + + # call the C function + AGC(nAGC, npts, &[0], &AGC[0]) + + return ampAGC diff --git a/Examples/extensions/cython/AGC_example/agc_cython.pyx b/Examples/extensions/cython/AGC_example/agc_cython.pyx new file mode 100644 index 0000000..2495e8e --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_cython.pyx @@ -0,0 +1,80 @@ +#!/usr/bin/env python + +""" +cython version version of the Automatic Gain Control function + +Timings: + first version (pure python): + +In [23]: timeit agc_cython.agc(10, np.arange(1000, dtype=np.float32)) +100 loops, best of 3: 10.7 ms per loop + +typing inputs: + +100 loops, best of 3: 17.3 ms per loop + +Typing counters and memoryviews of arrays +1000 loops, best of 3: 1.68 ms per loop + +typing loop counters +1000 loops, best of 3: 613 us per loop + +forgot to type fmax: +10000 loops, best of 3: 119 us per loop + +added boundscheck=False +10000 loops, best of 3: 117 us per loop + +use memoryviews of cython arrays +10000 loops, best of 3: 39.4 us per loop + +use cython arrays: +100 loops, best of 3: 6.12 ms per loop + +c array for temp array: +100 loops, best of 3: 6.34 ms per loop + +""" + +import cython +import numpy as np +cimport numpy as cnp + +#from libc.stdlib cimport malloc, free + +@cython.boundscheck(False) +@cython.cdivision(True) +def agc( int nAGC, cnp.ndarray[float, ndim=1, mode='c'] amp): + """ + run an automatic gain control filter onver the input array + + :param nAGC: width of window, number of elements. + :type nAGC: integer + + :param amp: input amplitude data + :type amp: 1-d numpy array of float32 + + :returns ampAGC: a numpy array of the filtered data. + + """ + + cdef float fmax + cdef unsigned int i, j + + cdef unsigned int npts = amp.shape[0] + + cdef unsigned int nAGC2 = nAGC / 2 + + cdef cnp.ndarray[float, ndim=1, mode='c'] ampAGC = np.zeros_like(amp) + cdef cnp.ndarray[float, ndim=1, mode='c'] absamp = np.abs(amp) + + + for i in range(nAGC2, npts - nAGC2): + fmax=0.0 + for j in range(i-nAGC2,i+nAGC2+1): + if absamp[j] > fmax: + fmax = absamp[j] + ampAGC[i] = amp[i]/fmax + + return ampAGC + diff --git a/Examples/extensions/cython/AGC_example/agc_program.f b/Examples/extensions/cython/AGC_example/agc_program.f new file mode 100644 index 0000000..2a01c65 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_program.f @@ -0,0 +1,95 @@ +c +c Program to read in a time series, AGC it, dump out a new time series. +c +c +c +c +c23456789012345678901234567890123456789012345678901234567890123456789012 + + parameter(npts0=1000000) + character*180 infile,outfile + real amp(npts0),ampAGC(npts0),ampAGC2(npts0),absamp(npts0) + + print *,'Enter input timeseries' + read (*,'(a)') infile + + print *,'Enter output filename ' + read (*,'(a)') outfile + + print *,'Enter npts length of AGC window' + read *,nAGC + + nAGC2=nAGC/2 + nrepeat=20 + +c--------- read in file & zero out the ampAGC array + + open(4,file=infile) + do i=1,npts0 + read(4,*,end=201) amp(i) + ampAGC(i)=0. + ampAGC2(i)=0. + absamp(i)=abs(amp(i)) + enddo +201 close(4) + npts=i-1 + +c-------- less code, simpler, waaay slower + + call system_clock(count_rate=icr) + call system_clock (i1) + do irepeat=1,nrepeat !----- loop over it many times, just for timing purposes + + do i=nAGC2+1,npts-nAGC2 + fmax=0. + do j=i-nAGC2,i+nAGC2 + if (absamp(j).gt.fmax) fmax=absamp(j) + enddo + ampAGC(i)=amp(i)/fmax + enddo + + enddo !----- do irepeat=1,nrepeat + call system_clock (i2) + print *, 'Slow time= ', real(i2-i1)/icr + +c--------- Automatic Gain Control +c Only 'rescan' the window for a max if the previous window's +c max came from the very first point in it's window. + + call system_clock(count_rate=icr) + call system_clock (i1) + do irepeat=1,nrepeat + + fmax=0. + do i=1,nAGC + if (absamp(i).gt.fmax) fmax=absamp(i) + enddo + ampAGC2(nAGC2+1)=amp(nAGC2+1)/fmax + do i=nAGC2+2,npts-nAGC2 + j0=i-nAGC2-1 + j1=i-nAGC2 + j2=i+nAGC2 + fmax=max(fmax,absamp(j2)) + if (absamp(j0).eq.fmax) then + fmax=0. + do j=j1,j2 + if (absamp(j).gt.fmax) fmax=absamp(j) + enddo + endif + ampAGC2(i)=amp(i)/fmax + enddo + + enddo !----- do irepeat=1,nrepeat + call system_clock (i2) + print *, 'Fast time= ', real(i2-i1)/icr + +c--------- write output + + open(4,file=outfile) + do i=1,npts + write(4,*) i,amp(i),ampAGC(i),ampAGC2(i) + enddo + close(4) + + end + diff --git a/Examples/extensions/cython/AGC_example/agc_python.py b/Examples/extensions/cython/AGC_example/agc_python.py new file mode 100644 index 0000000..1d4539a --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_python.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python + +""" +Pure python (and not very numpy smart) version of the +Automatic Gain Control function +""" + +import numpy as np + + +def agc(nAGC, amp): + """ + run an automatic gain control filter onver the input array + + :param nAGC: width of window, number of elements. + :type nAGC: integer + + :param amp: input amplitude data + :type amp: 1-d numpy array of float32 + + :returns ampAGC: a numpy array of the filtered data. + + """ + + # make sure input array is as expected: + amp = np.asarray(amp, dtype=np.float32) + + if len(amp.shape) != 1: + raise ValueError("amp must be a rank-1 array") + + npts = amp.shape[0] + + nAGC2 = nAGC / 2 + ampAGC = np.zeros_like(amp) + absamp = np.zeros_like(amp) + + absamp = np.abs(amp) + + for i in xrange(nAGC2, npts - nAGC2): + fmax = 0.0 + for j in range(i - nAGC2, i + nAGC2 + 1): + if absamp[j] > fmax: + fmax = absamp[j] + ampAGC[i] = amp[i] / fmax + + return ampAGC diff --git a/Examples/extensions/cython/AGC_example/agc_subroutine.f b/Examples/extensions/cython/AGC_example/agc_subroutine.f new file mode 100644 index 0000000..365f2e2 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_subroutine.f @@ -0,0 +1,31 @@ +c +c Subrooutine to compute an automatic gain control filter. +c +c + subroutine AGC(nAGC,npts,amp,ampAGC) + +CF2PY INTENT(OUT) :: ampAGC +CF2PY INTENT(HIDE) :: npts + + real fmax,amp(npts),absamp(npts),ampAGC(npts) + integer i,j,npts,nAGC,nAGC2 + + do i=1,npts + ampAGC(i)=0. + absamp(i)=abs(amp(i)) + enddo + + nAGC2=nAGC/2 + + do i=nAGC2+1,npts-nAGC2 + fmax=0. + do j=i-nAGC2,i+nAGC2 + if (absamp(j).gt.fmax) fmax=absamp(j) + enddo + ampAGC(i)=amp(i)/fmax + enddo + + return + + end + diff --git a/Examples/extensions/cython/AGC_example/agc_subroutine.pyf b/Examples/extensions/cython/AGC_example/agc_subroutine.pyf new file mode 100644 index 0000000..53dfa82 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/agc_subroutine.pyf @@ -0,0 +1,16 @@ +! -*- f90 -*- +! Note: the context of this file is case sensitive. + +python module agc_subroutine ! in + interface ! in :agc_subroutine + subroutine agc(nagc,npts,amp,ampagc) ! in :agc_subroutine:agc_subroutine.f + integer :: nagc + integer, optional,intent(hide),check(len(amp)>=npts),depend(amp) :: npts=len(amp) + real dimension(npts) :: amp + real dimension(npts),intent(out),depend(npts) :: ampagc + end subroutine agc + end interface +end python module agc_subroutine + +! This file was auto-generated with f2py (version:2). +! See http://cens.ioc.ee/projects/f2py2e/ diff --git a/Examples/extensions/cython/AGC_example/plot_agc.py b/Examples/extensions/cython/AGC_example/plot_agc.py new file mode 100644 index 0000000..76738f8 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/plot_agc.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python + +""" +test code for the "agc_subroutine" extension, generated from fortran by f2py +""" +import numpy as np +import matplotlib.pyplot as plt + +import agc_subroutine +import agc_python + +print "the doctring for agc:" +print agc_subroutine.agc.__doc__ + +# to call it: +# create a noisy array: + +t = np.linspace(0,20,100).astype(np.float32) + +signal = np.sin(t) + +# add some noise +signal += (np.random.random(signal.shape)-0.5) * 0.3 + +# create an array for the result: +#filtered = np.zeros_like(signal) + +# run it through the AGC filter: +filtered = agc_subroutine.agc(10, signal) + +# try the python version +filtered2 = agc_python.agc(10, signal) + +if np.allclose(filtered2, filtered2): + print "the same" +else: + print "not the same" + +## plot the results + +fig = plt.figure(figsize=(10,5)) +ax = fig.add_subplot(1,1,1) +ax.plot(t, signal, t, filtered, t, filtered2) + +plt.show() + + + + diff --git a/Examples/extensions/cython/AGC_example/setup.py b/Examples/extensions/cython/AGC_example/setup.py new file mode 100755 index 0000000..deb3be5 --- /dev/null +++ b/Examples/extensions/cython/AGC_example/setup.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python + +from setuptools import setup, Extension +from Cython.Build import cythonize + +import numpy + +cy = Extension("agc_cython", + ["agc_cython.pyx"], + include_dirs=[numpy.get_include()], + ) +c_wrap = Extension("agc_c_cy", + ["agc_c_cy.pyx", "agc_c.c"], + include_dirs=[numpy.get_include()] + ) + +setup(ext_modules=cythonize([cy, c_wrap])) diff --git a/Examples/extensions/cython/AGC_example/test_agc.py b/Examples/extensions/cython/AGC_example/test_agc.py new file mode 100644 index 0000000..3a75dea --- /dev/null +++ b/Examples/extensions/cython/AGC_example/test_agc.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python + +""" +test code for various AGC modules + +designed to be run with py.test or nose + +some timing code to cut&paste into iPython: + +import numpy as np +import agc_cython +import agc_python +import agc_subroutine +import agc_c_cy + +timeit agc_cython.agc(10, np.arange(1000, dtype=np.float32)) + +timeit agc_python.agc(10, np.arange(1000, dtype=np.float32)) + +timeit agc_subroutine.agc(10, np.arange(1000, dtype=np.float32)) + +timeit agc_c_cy.agc(10, np.arange(1000, dtype=np.float32)) + +""" + +import numpy as np + +import agc_cython +import agc_python +import agc_c_cy + +# The Fortran / f2py version +# import agc_subroutine + + +def test_cython(): + # just make sure it runs. + signal = np.arange(20, dtype=np.float32) + + result = agc_cython.agc(4, signal) + + +def test_c_wrap(): + # just make sure it runs. + signal = np.arange(20, dtype=np.float32) + + result = agc_c_cy.agc(4, signal) + + +# def test_subroutine(): +# # the Fortran / f2py version +# # just make sure it runs. +# signal = np.arange(20, dtype=np.float32) + +# result = agc_subroutine.agc(4, signal) + + +def test_cy_py_same(): + signal = np.arange(20, dtype=np.float32) + + cy_result = agc_cython.agc(4, signal) + py_result = agc_python.agc(4, signal) + c_cy_result = agc_c_cy.agc(4, signal) +# sub_result = agc_subroutine.agc(4, signal) + + print "cy:", cy_result + print "py:", py_result + print "c_cy", c_cy_result +# print "subroutine", sub_result + + assert np.array_equal(cy_result, py_result) + assert np.array_equal(cy_result, c_cy_result) +# assert np.array_equal(cy_result, sub_result) diff --git a/Examples/extensions/cython/NotesAboutCythonExample.txt b/Examples/extensions/cython/NotesAboutCythonExample.txt new file mode 100644 index 0000000..159c474 --- /dev/null +++ b/Examples/extensions/cython/NotesAboutCythonExample.txt @@ -0,0 +1,41 @@ +# Notes about Cython examples: + +## Installing Cython: + +$ pip install Cython + +Cython files are named with the "pyx" extension (Pyrex legacy) + +Note: Python syntax highlighting works pretty well in most editors. + + +## For simple functions: + +def is callable from Python + +cdef is only callable from Cython (or C) + + +## generating C code: + +$ cython cy_add.pyx + +generating "annotated" C code (HTML): + +$ cython -a cy_add.pyx + +Look at the html -- the yellow highlights are places where calls into the Python runtime are used. + +## To build extension: + +python cy_setup.py build_ext --inplace + +(or develop or install....) + +running nose or py.test should run all the tests. Or you can run each one on it's own. + + + + + + diff --git a/Examples/extensions/cython/add.c b/Examples/extensions/cython/add.c new file mode 100644 index 0000000..725a3c6 --- /dev/null +++ b/Examples/extensions/cython/add.c @@ -0,0 +1,6 @@ +#include +#include "add.h" + +int add(int x, int y) { + return x+y; +} diff --git a/Examples/extensions/cython/add.h b/Examples/extensions/cython/add.h new file mode 100644 index 0000000..50cf633 --- /dev/null +++ b/Examples/extensions/cython/add.h @@ -0,0 +1,4 @@ + +int add(int x, int y); + + diff --git a/Examples/extensions/cython/cy_add1.pyx b/Examples/extensions/cython/cy_add1.pyx new file mode 100644 index 0000000..3cc8a2c --- /dev/null +++ b/Examples/extensions/cython/cy_add1.pyx @@ -0,0 +1,22 @@ +""" +Cython implementation of the add.c example + +This one pure python + +""" + +def add(x, y): + + result = x + y + + return result + + + + + + + + + + diff --git a/Examples/extensions/cython/cy_add2.pyx b/Examples/extensions/cython/cy_add2.pyx new file mode 100644 index 0000000..0e794f5 --- /dev/null +++ b/Examples/extensions/cython/cy_add2.pyx @@ -0,0 +1,23 @@ +""" +Cython implementation of the add.c example + +This one with type defs + +""" + +def add(int x, int y): + + cdef int result=0 + result = x + y + + return result + + + + + + + + + + diff --git a/Examples/extensions/cython/cy_add_c.pyx b/Examples/extensions/cython/cy_add_c.pyx new file mode 100644 index 0000000..414b9d9 --- /dev/null +++ b/Examples/extensions/cython/cy_add_c.pyx @@ -0,0 +1,28 @@ +# distutils: sources = add.c + +""" +Cython implementation of the add.c example + +This one calls an actual C function to do the work + +creates a cython function to call the C function. + +""" + + +# telling cython what the function we want to call looks like. +cdef extern from "add.h": + # pull in C add function, renaming to c_add for Cython + int c_add "add" (int x, int y) + + +def add(x, y): + return c_add(x, y) + + + + + + + + diff --git a/Examples/extensions/cython/integrate/cy_integrate1.pyx b/Examples/extensions/cython/integrate/cy_integrate1.pyx new file mode 100644 index 0000000..af930fc --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate1.pyx @@ -0,0 +1,30 @@ +#!/usr/bin/env python + +# a cython version with no changes -- pure python! + +import cython + +cdef double f(double x): + """simple objective function to integrate""" + + return x**2.0 - x + +@cython.cdivision(True) +def double integrate_f(double a, double b, int N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple rectangle rule approach. + """ + + cdef double s = 0.0 + cdef double dx = 0.0 + cdef int i = 0 + + dx = (b - a) / N + + for i in range(N): + s += f(a + i * dx) + return s * dx diff --git a/Examples/extensions/cython/integrate/cy_integrate2.pyx b/Examples/extensions/cython/integrate/cy_integrate2.pyx new file mode 100644 index 0000000..37ccead --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate2.pyx @@ -0,0 +1,28 @@ +#!/usr/bin/env python + +# a cython version with some static typing + + +# The objective function is only called from within Cython, +# so we can make it a cdef function: + +cdef f(x): + """simple objective function to integrate""" + return x**2-x + +def integrate_f(a, b, N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + + s = 0 + dx = (b-a)/N + for i in range(N): + s += f(a+i*dx) + return s * dx + + diff --git a/Examples/extensions/cython/integrate/cy_integrate3.pyx b/Examples/extensions/cython/integrate/cy_integrate3.pyx new file mode 100644 index 0000000..3249725 --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate3.pyx @@ -0,0 +1,30 @@ +#!/usr/bin/env python + +# a cython version with some static typing + + +# The objective function is only called from within Cython, +# so we can make it a cdef function: + +# but a non-typed cdef isn't any faster -- so add the type defs: + +cdef double f(double x): + """simple objective function to integrate""" + return x**2 - x + +def integrate_f(a, b, N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + + s = 0 + dx = (b-a)/N + for i in range(N): + s += f(a+i*dx) + return s * dx + + diff --git a/Examples/extensions/cython/integrate/cy_integrate4.pyx b/Examples/extensions/cython/integrate/cy_integrate4.pyx new file mode 100644 index 0000000..71d55e0 --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate4.pyx @@ -0,0 +1,35 @@ +#!/usr/bin/env python + +# a cython version with some static typing + + +# The objective function is only called from within Cython, +# so we can make it a cdef function: + +# but a non-typed cdef isn't any faster -- so add the type defs: + +cdef double f(double x): + """simple objective function to integrate""" + return x**2 - x + +# But this function isn't typed, so it still needs +# to convert py_objects to/from the f() +# so add some more type definitions: + +def integrate_f(a, b, N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + cdef double s, dx + + s = 0 + dx = (b-a)/N + for i in range(N): + s += f(a+i*dx) + return s * dx + + diff --git a/Examples/extensions/cython/integrate/cy_integrate5.pyx b/Examples/extensions/cython/integrate/cy_integrate5.pyx new file mode 100644 index 0000000..10da193 --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate5.pyx @@ -0,0 +1,38 @@ +#!/usr/bin/env python + +# a cython version with some static typing + + +# The objective function is only called from within Cython, +# so we can make it a cdef function: + +# but a non-typed cdef isn't any faster -- so add the type defs: + +cdef double f(double x): + """simple objective function to integrate""" + return x**2 - x + +# But this function isn't typed, so it still needs +# to convert py_objects to/from the f() +# so add some more type definitions: + +# typeeverythign passed in to the funciton, +# should be pure C speed from there: + +def integrate_f(double a, double b, int N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + cdef double s, dx + + s = 0 + dx = (b-a) / N + for i in range(N): + s += f(a+i*dx) + return s * dx + + diff --git a/Examples/extensions/cython/integrate/cy_integrate6.pyx b/Examples/extensions/cython/integrate/cy_integrate6.pyx new file mode 100644 index 0000000..f15395d --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate6.pyx @@ -0,0 +1,46 @@ +#!/usr/bin/env python + +# a cython version with some static typing + + +# The objective function is only called from within Cython, +# so we can make it a cdef function: + +# but a non-typed cdef isn't any faster -- so add the type defs: + +cdef double f(double x): + """simple objective function to integrate""" + return x**2 - x + +# But this function isn't typed, so it still needs +# to convert py_objects to/from the f() +# so add some more type definitions: + +# type everythign passed in to the funciton, +# should be pure C speed from there: + +# not much help -- what did we miss? +# check out the annotated version: +# cython -a cy_integrate5.pyx +# still a lot of yellow in that loop! +# +# oops! didn't type def the counter, i! + +def integrate_f(double a, double b, int N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + cdef double s, dx + cdef int i + + s = 0 + dx = (b-a) / N + for i in range(N): + s += f(a + i*dx) + return s * dx + + diff --git a/Examples/extensions/cython/integrate/cy_integrate7.pyx b/Examples/extensions/cython/integrate/cy_integrate7.pyx new file mode 100644 index 0000000..7f38f3e --- /dev/null +++ b/Examples/extensions/cython/integrate/cy_integrate7.pyx @@ -0,0 +1,56 @@ +#!/usr/bin/env python + +import cython + +# a cython version with some static typing + + +# The objective function is only called from within Cython, +# so we can make it a cdef function: + +# but a non-typed cdef isn't any faster -- so add the type defs: + +cdef double f(double x): + """simple objective function to integrate""" + return x**2 - x + +# But this function isn't typed, so it still needs +# to convert py_objects to/from the f() +# so add some more type definitions: + +# type everythign passed in to the funciton, +# should be pure C speed from there: + +# not much help -- what did we miss? +# check out the annotated version: +# cython -a cy_integrate5.pyx +# still a lot of yellow in that loop! +# +# oops! didn't type def the counter, i! + +# much better! -- but still a bit of yellow in one line: +# dx = (b-a) / N +# C has different rules for roudning division for negative numbers +# than python. Cython acts like python unless you tell it not to +# with the "cdvision" decorator: (you need to import cython for this) + + +@cython.cdivision(True) +def integrate_f(double a, double b, int N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + cdef double s, dx + cdef int i + + s = 0 + dx = (b-a) / N + for i in range(N): + s += f(a + i*dx) + return s * dx + + diff --git a/Examples/extensions/cython/integrate/integrate.py b/Examples/extensions/cython/integrate/integrate.py new file mode 100644 index 0000000..6d3b21c --- /dev/null +++ b/Examples/extensions/cython/integrate/integrate.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python + +cdef f(double x): + """simple objective function to integrate""" + + return x**2.0 - x + +def integrate_f(a, b, N): + """ + integrates the function: + + f(x) = x**2 - x + + from a to b, using N steps, using the simple recatngle rule approach. + """ + + s = 0 + dx = (b-a)/N + for i in range(N): + s += f(a+i*dx) + return s * dx diff --git a/Examples/extensions/cython/integrate/setup.py b/Examples/extensions/cython/integrate/setup.py new file mode 100755 index 0000000..dfaee3d --- /dev/null +++ b/Examples/extensions/cython/integrate/setup.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python + +from setuptools import setup +from Cython.Build import cythonize + +setup(name="cython_example", + ext_modules=cythonize(['cy_integrate1.pyx', +# 'cy_integrate2.pyx', +# 'cy_integrate3.pyx', +# 'cy_integrate4.pyx', +# 'cy_integrate5.pyx', +# 'cy_integrate6.pyx', +# 'cy_integrate7.pyx', + ]) + ) diff --git a/Examples/extensions/cython/integrate/test_integrate.py b/Examples/extensions/cython/integrate/test_integrate.py new file mode 100755 index 0000000..219b088 --- /dev/null +++ b/Examples/extensions/cython/integrate/test_integrate.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python + +""" +Simple test script to get gross timings + +Not unit tests +""" + +from timer_context import Timer + + +def run_test(msg): + print msg + with Timer(): + result = integrate_f(0.0, 10.0, 1000000) + print "result:", result + print + +from integrate import integrate_f +run_test("Pure Python version:") + +from cy_integrate1 import integrate_f +run_test("First Cython version:") + +from cy_integrate2 import integrate_f +run_test("Second Cython version:") + +from cy_integrate3 import integrate_f +run_test("Third Cython version:") + +from cy_integrate4 import integrate_f +run_test("Fourth Cython version:") + +from cy_integrate5 import integrate_f +run_test("Fifth Cython version:") + +from cy_integrate6 import integrate_f +run_test("Sixth Cython version:") + +from cy_integrate7 import integrate_f +run_test("Seventh Cython version:") + diff --git a/Examples/extensions/cython/integrate/timer_context.py b/Examples/extensions/cython/integrate/timer_context.py new file mode 100644 index 0000000..d8ef93e --- /dev/null +++ b/Examples/extensions/cython/integrate/timer_context.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +""" +timer_context + +A context manager that does simple code timing + +Adapted from: + +http://preshing.com/20110924/timing-your-code-using-pythons-with-statement/ + +NOTE: this is only good for crude timing -- use the timeit module to do it better. +""" + +import time + +class Timer(object): + def __enter__(self): + self.start = time.clock() + return self + + def __exit__(self, *args): + self.end = time.clock() + self.interval = self.end - self.start + print "this code took %f seconds"%self.interval diff --git a/Examples/extensions/cython/setup.py b/Examples/extensions/cython/setup.py new file mode 100755 index 0000000..11c797a --- /dev/null +++ b/Examples/extensions/cython/setup.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python + +from setuptools import setup +from Cython.Build import cythonize + +setup(name="cython_example", + ext_modules=cythonize(['cy_add_c.pyx', + 'cy_add1.pyx', + 'cy_add2.pyx', + ]) + + ) diff --git a/Examples/extensions/cython/test_cy_add1.py b/Examples/extensions/cython/test_cy_add1.py new file mode 100755 index 0000000..27069e0 --- /dev/null +++ b/Examples/extensions/cython/test_cy_add1.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python + +""" +simple test code for cy_add1 + +can be run by itself, or with nose or pytest +""" + +import cy_add1 + + +def test_1(): + assert cy_add1.add(3, 4) == 7 + +def test_2(): + assert cy_add1.add(5, 6) == 11 + +def test_error(): + """ + you should get an TypeError if you try to add something other than numbers + """ + try: + print cy_add1.add('this', 5) + assert False # shouldn't get here! + except TypeError: + # should have gotten an exception + pass + +if __name__ == "__main__": + test_1() + test_2() + test_error() + + print "if you didn't get an assertion, it worked" + + + diff --git a/Examples/extensions/cython/test_cy_add2.py b/Examples/extensions/cython/test_cy_add2.py new file mode 100644 index 0000000..e7e8b79 --- /dev/null +++ b/Examples/extensions/cython/test_cy_add2.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python + +""" +simple test code for cy_add1 + +can be run by itself, or with nose or pytest +""" + +import cy_add2 + + +def test_1(): + assert cy_add2.add(3, 4) == 7 + +def test_2(): + assert cy_add2.add(5, 6) == 11 + +def test_error(): + """ + you should get an TypeError if you try to add something other than numbers + """ + try: + print cy_add2.add('this', 5) + assert False # shouldn't get here! + except TypeError: + # should have gotten an exception + pass + +if __name__ == "__main__": + test_1() + test_2() + test_error() + + print "if you didn't get an assertion, it worked" + + + diff --git a/Examples/extensions/cython/test_cy_add_c.py b/Examples/extensions/cython/test_cy_add_c.py new file mode 100644 index 0000000..6c9d425 --- /dev/null +++ b/Examples/extensions/cython/test_cy_add_c.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python + +""" +simple test code for cy_add1 + +can be run by itself, or with nose or pytest +""" + +import cy_add_c + + +def test_1(): + assert cy_add_c.add(3, 4) == 7 + +def test_2(): + assert cy_add_c.add(5, 6) == 11 + +def test_error(): + """ + you should get an TypeError if you try to add something other than numbers + """ + try: + print cy_add_c.add('this', 5) + assert False # shouldn't get here! + except TypeError: + # should have gotten an exception + pass + +if __name__ == "__main__": + test_1() + test_2() + test_error() + + print "if you didn't get an assertion, it worked" + + + diff --git a/Examples/extensions/distutils/add.py b/Examples/extensions/distutils/add.py new file mode 100644 index 0000000..1404096 --- /dev/null +++ b/Examples/extensions/distutils/add.py @@ -0,0 +1,2 @@ +def add(x, y): + return x+y diff --git a/Examples/extensions/distutils/setup.py b/Examples/extensions/distutils/setup.py new file mode 100644 index 0000000..1c68d60 --- /dev/null +++ b/Examples/extensions/distutils/setup.py @@ -0,0 +1,9 @@ +from setuptools import setup + +setup( + name='add', + version='1.0', + description='Add description', + ext_modules=[], + py_modules=['add'] +) diff --git a/Examples/extensions/pure-c/Makefile b/Examples/extensions/pure-c/Makefile new file mode 100644 index 0000000..0da750b --- /dev/null +++ b/Examples/extensions/pure-c/Makefile @@ -0,0 +1 @@ +all: add; gcc -o add add.c diff --git a/Examples/extensions/pure-c/add.c b/Examples/extensions/pure-c/add.c new file mode 100644 index 0000000..38d99ad --- /dev/null +++ b/Examples/extensions/pure-c/add.c @@ -0,0 +1,10 @@ +#include + +int add(int x, int y) { + return x+y; +} +int main(void) { + int w = 3; + int q = 2; + printf("%d + %d = %d\n\n", w, q, add(w,q)); +} diff --git a/Examples/extensions/swig/README.rst b/Examples/extensions/swig/README.rst new file mode 100644 index 0000000..a3fd7fa --- /dev/null +++ b/Examples/extensions/swig/README.rst @@ -0,0 +1,16 @@ +Simple SWIG Example +=================== + +This dir has a very, very simple example of using SWIG to generate a +wrapper around a simple C function. + +The logic, such as it is, is in the add.i interface file. + +To build, siply call: + +python ./setup.py build_ext --in_place + +distutils understands SWIG, so it will run SWIG for you. + +You, of course, need to have SWIG isntalled on your system. + diff --git a/Examples/extensions/swig/add.c b/Examples/extensions/swig/add.c new file mode 100644 index 0000000..38d99ad --- /dev/null +++ b/Examples/extensions/swig/add.c @@ -0,0 +1,10 @@ +#include + +int add(int x, int y) { + return x+y; +} +int main(void) { + int w = 3; + int q = 2; + printf("%d + %d = %d\n\n", w, q, add(w,q)); +} diff --git a/Examples/extensions/swig/add.i b/Examples/extensions/swig/add.i new file mode 100644 index 0000000..7d5d868 --- /dev/null +++ b/Examples/extensions/swig/add.i @@ -0,0 +1,5 @@ +%module add +%{ +%} + +extern int add(int x, int y); diff --git a/Examples/extensions/swig/add.py b/Examples/extensions/swig/add.py new file mode 100644 index 0000000..e2f4dd2 --- /dev/null +++ b/Examples/extensions/swig/add.py @@ -0,0 +1,99 @@ +# This file was automatically generated by SWIG (http://www.swig.org). +# Version 3.0.5 +# +# Do not make changes to this file unless you know what you are doing--modify +# the SWIG interface file instead. + + + + + +from sys import version_info +if version_info >= (2, 6, 0): + def swig_import_helper(): + from os.path import dirname + import imp + fp = None + try: + fp, pathname, description = imp.find_module('_add', [dirname(__file__)]) + except ImportError: + import _add + return _add + if fp is not None: + try: + _mod = imp.load_module('_add', fp, pathname, description) + finally: + fp.close() + return _mod + _add = swig_import_helper() + del swig_import_helper +else: + import _add +del version_info +try: + _swig_property = property +except NameError: + pass # Python < 2.2 doesn't have 'property'. + + +def _swig_setattr_nondynamic(self, class_type, name, value, static=1): + if (name == "thisown"): + return self.this.own(value) + if (name == "this"): + if type(value).__name__ == 'SwigPyObject': + self.__dict__[name] = value + return + method = class_type.__swig_setmethods__.get(name, None) + if method: + return method(self, value) + if (not static): + if _newclass: + object.__setattr__(self, name, value) + else: + self.__dict__[name] = value + else: + raise AttributeError("You cannot add attributes to %s" % self) + + +def _swig_setattr(self, class_type, name, value): + return _swig_setattr_nondynamic(self, class_type, name, value, 0) + + +def _swig_getattr_nondynamic(self, class_type, name, static=1): + if (name == "thisown"): + return self.this.own() + method = class_type.__swig_getmethods__.get(name, None) + if method: + return method(self) + if (not static): + return object.__getattr__(self, name) + else: + raise AttributeError(name) + +def _swig_getattr(self, class_type, name): + return _swig_getattr_nondynamic(self, class_type, name, 0) + + +def _swig_repr(self): + try: + strthis = "proxy of " + self.this.__repr__() + except: + strthis = "" + return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) + +try: + _object = object + _newclass = 1 +except AttributeError: + class _object: + pass + _newclass = 0 + + + +def add(x, y): + return _add.add(x, y) +add = _add.add +# This file is compatible with both classic and new-style classes. + + diff --git a/Examples/extensions/swig/ruby/add.c b/Examples/extensions/swig/ruby/add.c new file mode 100644 index 0000000..38d99ad --- /dev/null +++ b/Examples/extensions/swig/ruby/add.c @@ -0,0 +1,10 @@ +#include + +int add(int x, int y) { + return x+y; +} +int main(void) { + int w = 3; + int q = 2; + printf("%d + %d = %d\n\n", w, q, add(w,q)); +} diff --git a/Examples/extensions/swig/ruby/add.i b/Examples/extensions/swig/ruby/add.i new file mode 100644 index 0000000..c0c7e12 --- /dev/null +++ b/Examples/extensions/swig/ruby/add.i @@ -0,0 +1,6 @@ +%module add +%{ +extern int add(int x, int y); +%} + +extern int add(int x, int y); diff --git a/Examples/extensions/swig/ruby/extconf.rb b/Examples/extensions/swig/ruby/extconf.rb new file mode 100644 index 0000000..f528034 --- /dev/null +++ b/Examples/extensions/swig/ruby/extconf.rb @@ -0,0 +1,3 @@ +require 'mkmf' +create_makefile('add') + diff --git a/Examples/extensions/swig/setup.py b/Examples/extensions/swig/setup.py new file mode 100644 index 0000000..9c5809d --- /dev/null +++ b/Examples/extensions/swig/setup.py @@ -0,0 +1,11 @@ +#!/usr/bin/env python + +from setuptools import setup, Extension + +setup( + name='add', + py_modules=['add'], + ext_modules=[ + Extension('_add', sources=['add.c', 'add.i']) + ] +) diff --git a/Examples/extensions/swig/test_add.py b/Examples/extensions/swig/test_add.py new file mode 100644 index 0000000..915d006 --- /dev/null +++ b/Examples/extensions/swig/test_add.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python + +""" +simple test file for SWIG-ified add function +""" + +import pytest + +from add import add + + +def test_add1(): + assert add(3, 4) == 7 + + +def test_add_float(): + # only integers! + with pytest.raises(TypeError): + assert add(3.1, 4.1) == 7.2 + + +def test_add_string(): + # only integers! + with pytest.raises(TypeError): + assert add('2', 4) == 6 diff --git a/Examples/logging/example.py b/Examples/logging/example.py new file mode 100755 index 0000000..d8f0bb9 --- /dev/null +++ b/Examples/logging/example.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 + +""" +Example code for using logging in a complex system +""" +import sys +import logging +import random +import time +import worker + +# The logging configuration: +# In real life, you might be pulling this from a config file, or ... + +# each level of logging gives more information +# uncomment the level you want +# log_level = logging.CRITICAL +# log_level = logging.ERROR +# log_level = logging.WARNING +# log_level = logging.INFO +log_level = logging.DEBUG + +# pretty simple format -- time stamp and the message +# this can get fancy: +# https://docs.python.org/3/library/logging.html#logrecord-attributes +format = '%(asctime)s %(levelname)s - %(module)8s - line:%(lineno)d - %(message)s' + +# configure the logger +# basicConfig configures the "root logger" -- usually the one you want. +# this sets it up to write to a file +logging.basicConfig(filename='example.log', + filemode='a', # use 'a' if you want to preserve the old log file + format=format, + level=log_level) + +# and this will make it write to stdout as well +# you would turn this off in production... +if True: # turn this off with False + std_handler = logging.StreamHandler(sys.stdout) + # give this a different formatter: + formatter = logging.Formatter('%(levelname)9s - %(module)s - %(message)s') + std_handler.setFormatter(formatter) + std_handler.setLevel(logging.DEBUG) + logging.getLogger().addHandler(std_handler) + +# Run the "application": +while True: # keep it running "forever" + # do something random: + logging.debug("calling a random worker") + random.choice(worker.workers)() + # wait a random length of time : + time.sleep(random.random()) diff --git a/Examples/logging/worker.py b/Examples/logging/worker.py new file mode 100644 index 0000000..14e8804 --- /dev/null +++ b/Examples/logging/worker.py @@ -0,0 +1,51 @@ +""" +A module with lots of random stuff that may happen + +This is designed to simulate a complex system -- +maybe event driven, etc. + +""" + +import logging + +import random + + +def work1(): + logging.info("work1 doing a job") + + +def work2(): + """ + This one randomly logs a warning + """ + logging.info("work2 doing a job") + if random.randint(1, 5) == 1: + logging.warning("something weird happened in work2!") + + +def work3(): + """ + This one randomly logs an error + """ + logging.info("work3 doing a job") + if random.randint(1, 5) == 1: + logging.error("Error in work3: bad input") + + +called = [0] + + +def work4(): + """ + Here something critical can go wrong and crash the system + """ + logging.info("work4 doing something") + called[0] += 1 + logging.debug("work4 called {:d} times".format(called[0])) + if called[0] >= 4: # don't want it to crash right away :-) + if random.randint(1, 5) == 1: + logging.critical("Something went terribly wrong! Aborting!!!") + raise ValueError("got really, really bad input") + +workers = [work1, work2, work3, work4] diff --git a/Examples/matplotlib/lena.png b/Examples/matplotlib/lena.png new file mode 100644 index 0000000..b333140 Binary files /dev/null and b/Examples/matplotlib/lena.png differ diff --git a/Examples/matplotlib/matplotlib-instructor.ipynb b/Examples/matplotlib/matplotlib-instructor.ipynb new file mode 100644 index 0000000..9988403 --- /dev/null +++ b/Examples/matplotlib/matplotlib-instructor.ipynb @@ -0,0 +1,1304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting with matplotlib\n", + "\n", + "__Software Carpentry Bootcamp \n", + "eResearch NZ 2013__ \n", + "\n", + "Prepared by: Ariel Rokem\n", + "Thanks to: Justin Kitzes, Paul Ivanov\n", + "\n", + "(updated for py3 for UWPCE: Christopher Barker: 2016)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Getting Started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 What is matplotlib?\n", + "\n", + "Matplotlib is the most popular and mature library for plotting data using Python. It has all of the functionality you would expect, including the ability to control the formatting of plots and figures at a very fine level.\n", + "\n", + "The official matplotlib documentation is at http://matplotlib.org/ \n", + "The matplotlib gallery is at http://matplotlib.org/gallery.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1.1 Architecture\n", + "Matplotlib is designed be be used in a wide variety of ways: for interactive plotting, generating images for publication or web sites, embedding in GUI programs, etc. It can generate PNG images, PDF, SVG, and native images for a number of GUI toolkits.\n", + "\n", + "To support this, it has a varierty of \"back ends\" -- each for a different purpose, or for use with a different GUI toolkit. The system is designed to abstract the all the plotting commands and interaction, so that the code you wirte to gernerate plot is almost identical regradless of how you use it.\n", + "\n", + "One use-case is embedding it for inline display in an iPython noteboo, such as this one. That is what this tutorial uses, but the plotting commands and principles are the same regardless of your use case.\n", + "\n", + "#### 1.1.2 APIs\n", + "\n", + "Matplotlib has essentially two APIs: the core object oreiented API that mpl itself is written in, and a MATLAB-like \"state machine\" API (pylab). While the pylab API makes it a bit easier to quickly plot things up at the command line, it creates (or encourages, anyway) a very hard to maintain and re-use codeing style. And in most cases, the object oriented API requires only a few extra keystrokes, but keeps things much cleaner if (when) you want to move your plotting ocde to be a part of a larger application.\n", + "\n", + "This tutoroal woill use the OO API as much as possible, but many examples use the other API, so it can be helful to understand the realtionship between them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Importing matplotlib\n", + "\n", + "Matplotlib is often used through 'pyplot', which provides a high-level interface for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The inline flag means that images will be shown here in the notebooks, rather\n", + "# than in pop-up windows.\n", + "\n", + "%matplotlib inline\n", + "\n", + "# If you are using 'regular' Python, however, you'll want the following. You'll\n", + "# need to also separately import numpy and any other packages that you might need.\n", + "\n", + "# We, of course, need numpy:\n", + "import numpy as np\n", + "\n", + "# The more-or-less accepted way to import MPL\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Creating Figures\n", + "\n", + "There are two major challenges with creating figures. First is understanding the\n", + "syntax to actually make the basic plot appear. Second is formatting the basic plot to look\n", + "exactly how you would like it to look. In general, the formatting will probably take you\n", + "longer...\n", + "\n", + "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making\n", + "plots - using the Matlab-like clone, and using the object-oriented approach. The latter\n", + "provides better control over plot features, while only requiring slightly more typing. It's\n", + "easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 A first plot\n", + "\n", + "In simple matplotlib plotting, there are two concepts to distinguish:\n", + "\n", + "- __Figure__ - the entire figure, like what you might see in a journal, including all\n", + "subplots, axes, lines, labels, etc. The whole enchilada. IT is more or less like a piece of paper.\n", + " \n", + "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in articles. Each subplot will contain one Axes object, which is the container where all of the useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", + "\n", + "Also -- in MAtplotlib, the axes object does most of teh work -- it is the interface to all plotting.\n", + "\n", + "For example, here's how to make one figure with two subplots, the second of which contains two lines." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mc86/McKZjAxYuVK3Fc4hkI1LFwvr448/3p6PW+rbuv06qasaeFScqiCpm74B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\nzzewCCE6oDawWDXrbse3P8D/AduklH7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make some data to plot\n", + "x = np.linspace(0, 2*np.pi)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# First, create an empty figure with 2 subplots\n", + "# - The arguments (1, 2) indicate 1 row and 2 cols\n", + "# - The function plt.subplots returns an object for the figure and for\n", + "# each axes\n", + "# - There are multiple ways to accomplish this same goal, but this is\n", + "# probably the simplest and most extensible -- notice that each\n", + "# subplot is associated with one of the axes objects.\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "# The axes objects have a huge nuber of methods -- the core plotting routines,\n", + "# and the methods to manipluate the plots.\n", + "\n", + "# Next, put one line on the first axis and both lines on the second axis\n", + "ax1.plot(x, y1)\n", + "\n", + "ax2.plot(x, y1, label='sin') # The labels are what appear in the legend\n", + "ax2.plot(x, y2, label='cos')\n", + "# - On the second axes, add a legend to distinguish the two lines\n", + "ax2.legend()\n", + "\n", + "# Finally, save the figure as a png file\n", + "# if you want to save it for use elsewhere.\n", + "# note that in inline mode, the notebook displays the plot.\n", + "fig.savefig('myfig.png') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1 - Simple formatting\n", + "\n", + "There are lots of formatting options to play with. Modify the code above to make some changes to the\n", + "formatting of these plots.\n", + "\n", + "First, make some changes to the axes. HINT: These adjustments are methods to the ax1 and ax2 objects,\n", + "and (conveniently) they all start with the text 'set_'. Try typing 'ax1.set_' and hitting tab to\n", + "see some options.\n", + "\n", + "* Change the x axis on ax1 to run from 0 to 4. (HINT: set_xlim)\n", + "* Add labels to the x axis on both subplots (HINT: set_xlabel, set_ylabel)\n", + "\n", + "Second, make some changes to the lines that you plotted using ax1.plot(...). These changes can be made\n", + "by looking at the various arguments that you can give to the plot method. You can do this easily by\n", + "typing ax1.plot? in the cell below and running it - this will give you pop-up help for the plot method.\n", + "\n", + "* Make the sine line on ax1 red and dashed.\n", + "* Put a circular black marker on top of the cos line on ax2. Make it really big.\n", + "\n", + "_Bonus_: Eliminate the box around the legend on the second subplot." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#ax1.plot?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Other types of plots\n", + "\n", + "In the example above, we used the plot method to make line plots. There \n", + "are also methods to make scatter plots, barplots, histograms, loglog\n", + "plots, semilog plots, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# to see the methods, try typing:\n", + "# ax1." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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W5N3dMtG0tkofKitNMigpkYm8qUnyEu29d3CbgV1N5EcyAJztBmplvN9+wFlnSfuKDNIx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\nMrBqlXw2/QWAOxz1gAOc+zz3XGHKoKVFnt3U5DYTHXigqI4wMmhulnO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make some data to plot\n", + "x = np.arange(0, 100)\n", + "y = np.random.rand(100) # 100 random numbers\n", + "\n", + "# Make a figure with 6 subplots and axes\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)\n", + "\n", + "# Add data to each axis. Optional arguments to each method will customize each plot.\n", + "ax1.plot(x, y)\n", + "ax2.hist(y)\n", + "ax3.scatter(x, y)\n", + "ax4.boxplot(y)\n", + "ax5.loglog(x, y)\n", + "ax6.semilogx(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Plotting images\n", + "\n", + "Matplotlib also makes it easy to plot images. For this, you can use the plot method imshow\n", + "(syntax borrowed from Matlab)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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BQuC6EjMUbXtNgdeIxG+b7E7PYfPeU/hlHIZhhgrCwoLWJ+XW3OOljNB7tNrC\niuLhWEdsRkUsYvCh1fS4dxvffNaejUfTyDrajb0/dae4YzyBZJjQezFNVuWTeLiY8H0VzPdcy8f1\nbmDSmLmc3tKIw98mE17UlMqRxXQ6e4BBQwIca6ioPBhG/opI8ouq0FNPcbRPc3IKmnHC7Q8NFbnH\ndnP0nViufjWdYW8tJq6oEP9YL71idtBv6AqeyX2X5Hr5RDbOo2PpPsaf/474rAI8b5XToHEeaflH\nyN2SiDcxkpIXrmL1m0MZf2A31+d/ii0bsissgcQrTrAy+3qcdmn0GFdC0cCzlPgbsfZge7q8so2o\np8sIy63gWzGB5bkjaLJ0H3s/HcCassfIjBxIxqYEzm1uSUSKQe+xqzn7ZGMiA0UkDD5BQYWHIx80\nZmifj5n65Hb2u5B0UrF9dyv2r2nK+wPu5okhX3A8ZiLd4hbRadcC8osicB+IZE9lO3pm7+DGVl8w\nMG8dRw61QrYq46dpXej/yVqSU89xcEUsaxa3YeT59RgzYxne6Ad6Jm1lT6tUfiy/Ct8XLnt0D2J8\npTT6IpszG6IoahVG3oQoIl6zMYc6NI07QSmRLE8czam7GzN280a+jitm94QBpOcl0HBHLv1OboI8\ng3pTmjPKm44TEcEP60eR060NbR5PR54XJE2IpX/ZD5C5mq8OjidvWQzDPzxE0uu52JkKf0uT0ozo\nfznuLjmhCxnSioVAGNVJUBdQCIIRrNbVdj5Ry8URlGCUCtoQXdtGSQttaYSUuLUSojVL8BDZan3B\nfqgFKIIRuCkFxi/ItfoDWmkI6eiyFglrQLlBHVtrFbwfrYP3AzUTTk07hATlBgubHDf4GSEunMcI\nfl4ITcANYJoeXKXwCCtowNH6whfVU1swGal1cIXzrzZcC5KrxDTMoJRSyzpaszpQoN3QpOlqDFPX\nfBY0MpiWRLkhJ4sg+KxEsE/ROthEpUJWRx0s1tKVVJWWcTYjiyYx0eApwpVRmB4vDZratOuWytal\nx/FGhgeNQFoiMLAMEwOXQECy+3AmW9Nz8VsxCBV8bjHhURiGJuDXuE6wb2vnQC4Fdj7bC/msw41t\nP2PC8W84ekcT4k5X8XH0NPaldeDRNm/zUvKz3BF4jyNLGuOfn0e+14enHezf357p3kcY0vwbyswI\n9McWPVfMJb5sFZ+sv56nlr/Bk6teIndTCju7tGf+YyOo+kCS+Mh0XtiymNSsfKZnPkxOQiK3nn6b\nvelTWTE+ajq7AAAgAElEQVR/MlcVzyG14iQ7F3dlwytdkOHfcfXaHxlg/IRK9WBXNKDDvv2cz2jA\nKwPfYebxO7l66HKuvmoZUdml/Onk47y//X62PtqN3MpUVuy7hoUJo5g++W4emPsuujE4GSYffnkX\nr529hWMZbRi7MpuFfxxB35INxB4ooHBvfUqXDKDJM9noj1rg3aU5aw0lM6UHKY2380WrqUwYt5CU\nTofo8PU3bPu8K/mDnmP9p334umoSfT/ZwhjfEuZ2msicVpNoEJmM/Yif/WUdKH6xHsboWzjeK40m\n5UcZY37HdZsXcbhRGksDV+ObU4ZcEeCvqR8SW1xMaRcfux7ryHe9r+PNV+4kNeVOhuxdRfScHMzt\ne8lqV8wNE7+msCSOLlds4/HvnuG404ZDp/ry4hv30qIfOFIRE1XC3LLreXrfq4hSxZLxw2lYdY5h\n1jwGrNhG65wzeB4OULEtgricAsIjK3mjxSOU5UTxu5yZTD00k4cXvcfnV9/IoU4FTPTMR4fD41++\nxavnHuPsY2mcMDzEFe6hfcW+fznuLtlui1lvPKLhgoZuWRbCK9Gui1vlx1AACiVU0M6mTVwHTFMi\nULj+SkryqsjLqiAzN0BJWTmRUT4sjyQszMQNuL9IYoJSgZoVgWVZGIbA55M4TvBY0zBC1ZUX68za\ndWucFbX1bDtUNerxeLBDlZ61veJATZl69WdNIZC2n4BWVLk2mAaVfgfT8hFwHKyQTdBrOyAE0heO\nxsAjDFzh/GaRUW37YNAV+Rv+axdsLfHbGr8CSwiU69QkgyMiIggLt4LVnOESLBCmwNAm0tBBCUxo\nQCF+Gfhqgh5yefHWBLW3KHAcB60UtuNHhENKr1Q8zeKRvigMEYPQpVScymLt5wcJ5AUwLSO4WtN+\nYmOjMIViZ/p5dh3Np6gKsCQ+IyyYjA7dvxOwsQwDv99f0/+f5K27JKX/A7esZVuTLlhlDuo7MGae\no+nPxRwb1552U/bxQNsZNJidx8gN84lZVsTk+l+xY21/+s9cxe3WDDa8egWP9hmNbjyYEX9eQ8er\ntpOxMo2vHhqLp2AhASeOyI9TcQ/lUjFf06x9Im9904/HZ/6EvtpD+3r7MIRDAIt38u/lioFzaP/6\nWa7f9DEdi/aw889D+X3mNB7t8Abx479CDEsm4u6mWMrmjEqmm7uLXu/v4fmWz+McUjzy5yfYlFCf\nnRseYMGNU7j8htWUl3hZvmYYPebt4PstY8nrFM2JiKbE5pZhlbmsSulH8/DTPL7wHU5PEzinwrHj\nm5IadoTtV/fgSu9qGl99gjv2fERBRgPeHPkIe3Z0ozIrnJsW30XXzOMkdY3lvZsfZEfP7tjvmSy8\n+Rq+6jqRchlBs72nWXjHRNbdNJD7ek+nQ+s9eE8FeH3ls8Qn5LOzWTfufvIN1uYOBddLYvMsei3a\nyMr8q7E9Jt3jt+M/FsG2WZ1o+OFO9nYfxz3vTWfSvG8YfXYxWRMa8m3zMfyh919Z8+JgCr7bRsRu\nSX670dzabzaiLWw2+9A26zB2ocX59vEcmJLKcdmCV5a9wKDXHub8lg6cu24knf+xgwWnJvFGykOM\nmreMuIISvjg2hZcPPc3kxZ/y+IPvENbQz8Kx1/BR5u1snDsI68lKvhzTid6DzhGbpJHtXER3kG3/\nN5b+h1Cb+ETAwfb7UZX+UBStcL0Cy/ptt4IhZTCi/cX5bNsOari1yvJro5oAPKYXyw3tnwIYIrQu\n+MVEIGXQL35x0c4Fgg86SYyaZX7t6LCa7Kvlk4DhQUUmUikUts8kvEEc9evVw9YKr+0gpaCktIz8\nszlQUISvsgqPcAgYYAgvhmFe1Laavghdu5rU/yOo0ARQnRO4yEtPaEXiXrCOSuNCXri6P38pA9m2\n/Su7ZfXzAHCFiWFInKoqco7k0CImDm1VghWHEmF4EyNIvTyJ3XOPYpjBPg3zhOMPOOxNz2dndiFF\nhOHzmIRpCAhdk4StfiaXUmapjZuafUSsfzRLvutEj5VLef7BJdzy0BfMmPIgW4d0ZW39/tQfVEjD\n5UXMyZvEs09dxrgO05jU+WcaHigk8PZhnLTpfHzNQ/RavI2vD0wmX8TyZ+Menv7wfd59/AY8RiTz\n1CR+ODEMf70Sjm64jpl/u4+X9z1Pw3NHqJe+n/db3sP4FxZzruoIAaMV1zZvQsbSKD6fdhvPPfMi\n43IW80Rhf7auncR1LY/xiOdVTj3hIY5SPM0rcbZ4+SbqOorH+rj7x6fZ6vPQbEY6Wz7tzbniJGLv\nLiPFc4qbus3ieV6ky7qDzDl4Pd83HsawFovpv3s703vcTUm9Kr7x30hOj468ceYhcj8s5bk5T5Db\npjELi69lR3hPVDtFv6i16BVeMi5PoqTzTiZFLOTT5TcT2OBh8uivuPXYbBrNPMSmlJ4kTfJS0Dqe\n10c+QsZrzei8awVmvwB0FFS97eOLBRM5NrgDKsWEtg4VppfI8HK+PTOR/KeqmD/xRmKGK/586xPY\nSR7G7F3E/kAaI+QKzrVtQHbfhrT1HuCaHxcxN3kM1uD+NKgowBPuZWTJd8x7eAo3pH3FWy0eZYi5\nkkPfd+C571+k61dbiO+bw9H3biLlr8uxFr7O4itHURIVzdyPJ9Dl0AES9+zESHbJvCyFuX+6kfvM\nWexdVsx39TqwuXc/Krv48Hztp/VdimerpnFlvXWMarGAwmZRQME/HXeXjNBNbaC0g3IdvGESKfy4\nUoPfj1GlcBAoS+JRBoYw8WsXZYEhNBKFUjZK20ipIWRzFErjURKPK1CeWoU8EKzMxEUS/G4YBoGq\ncjw+DzIkyQgpMSWEKo+CH4Mal8vF0X6Q1FzXxe/3Iw2Dcq9Elgfw6aDcUqUcylwv3tgEVJgP1+PF\nEQYVBAiL9BEeBkaYgeMzUIZEYuL1emmYFIto2wI74GBVOeTvzcA9k0eZXYpPOURISQAb5RV4icQl\n6Ck3PGFBUtXuxUSng4lXx1WEmR6U3wnKKsoE7aK1i9Y2RjC9iel6sBUoG6RlBFcn2g3tGyPACUbd\n2gnKRaYhEcJGOcF9cIRWoCSuK5HSDElmwQSuEC4mmvLiEiqKK/BExkNkFTIsDIinYetSmrT1knvc\nJsyj0JbmQEYZm0+V4zgSSyoc7cdvmXg8ErAJqlhBh0tleXkwOPiNifx/Em3OH2Hr/J/RcxuRmdOF\nuTExDJmwnNRVG2i+eQ8lIxOgoaaXuZu+P23lsfRijlzZk61FLfGdr8/s5Mdws45SMcDlyw86smBd\nF3IG9aTq9xak1WeRZxKywkcT/yleb/cwzj31+Tr+XiaOmUDEFz+z8WB7rgzYPFI2lz/PeIuJzy4i\ncs0P7DjVkvTGU8nt0YB4bx6vnX6SLvet5fzyYyz9qCNxab/jAe9fED3g2BWppHiPcbPvI/pVbSBJ\nefj52cvJrSqjYEc0Aztv59omC8gRsaRGHGXmortoFXmExMA5uny0l2U7R3HrbV/QL28r711xO/Ex\nfkZ3nE7L4gwKT0bQ//BWsr+L4efGPYjsUcGjJ96gxBuDTjWY3vBBdpUPINJ2qbwunLd2PcrQ/B8Q\nrVxyB7Wg4e4y+s9aw+I7RrB8Wgz5y/KI75CHT/vg8yr8J87x2fabGDpsBe0rDrG1qjdfx0xm5/4+\n3NF4NmemtCWxdQHlnjg2t+zHmEmLiBpYgPvpTtwG58i8rDELT1xG5gzJQ81nsOH2AWxsMYDeZdtJ\ndY+SXz+Wvz1yDxELA+xo3RkZsEndlc6t2TPJIok7X3+NuYPuIOOWESRdtodeMp9G1t9Z9ecIvuk0\ngCVRI1m9dzD2Ng/Zvsa4TQyaj4Oo7l6qbC+JOpsbRnxKbqMm7PykO+13HCawy2VvUgr/KwldKYWQ\nAo/HIlgQqtGVVagKP06ZQwDAY+DxRADBaDwYLIpf7bVSe3nvusEtrATmr2yL1X/o1T+b8kLkXx1V\nGwiqi0KrnRLSNGp02WoZ5pfWOKEhrLgCYYOSHvyWF9kgnnKfl4DXg9+pwlUBPJZFmMdLbFwYAe3H\n8ILhkQQIri4qKytxHIcojxdpGVSGQ/yg9li2ojKriJwDGZRn5RBlGkhX4DcCNSuOYLKTX/nRhdJo\nx0FgELAD6OqKzv8EXKWCe4CJC8VV1dJKNWkGXAcpnJpnEezLYEHsRcljaaC1iakMwm3NyW3ptIhM\nxfRYOFIgLS9RDVNoOcghL/8wyrU4fOo8Gw4X4NohWU4IhAw6gKrlrIqKippnYJrmRVtIXCrMnTGE\nfUea063haVJbl/N94Qg+9dxA47O5NIw6xtaKgewtb8VwOZfPm03CN7WKowM6s2lXHxK9WSQU5ZJ8\nQxhR8eWcubo1lZURxEWcpOxgJhzaRnJiNofiehExsBRlllF23iHFOs3HZ24l6Ug6jaLOUy/OIv8A\n6O/OUjqgM9c6S1mc34Wfmg0gpdsJztGQeAqorN+WymLBkY0VzD46kBhPLlPbfs3CE8NpkJhHs8Yn\nOR6XyvmJDUi/02Xg1Ts5ZpbQ/uhmJm2ay4GE1vSI2s7+BYlkRxTTLW8nDYrzWewZyl+/vIWbr1pI\n0uVFFCRUEsCk5HQcs/J/x4PH32fvj91JT2qH12dTr8MZGn28hBY3h/F91FByTtbHcy5AbKCIhObn\nKDoYR0kvH+faNCEspoIGe/K4v3A6f5vbBH9eMyI6CcINQPhxdTaHnW48vud5IjIViUVnGBS1mK0D\nBmMkKdYlXkvxTxZFC+M4ZrXmlnqfc3eXD4lJbktMjxL2JXRk/8rutF+wmi0t+rO3XjdSBmfSr/Bn\nIgoqEQPg/JUNKNvnsOxAP7Y3a8vYTnPpcfZrEhnI7u1p9Gu7jd2xSRhlYdyQtpGjVzajzxmX5K45\nqEOK6DNFWO0qaNAsG/mjy6aOE6hIacw1e76nRfppSm6OwTztMKbtfGIT88jJTaJBcfG/HHeXTnJx\nFa4AyxPUaJU/gPYr3Eo/FaUVaNOL6fGhhAKhgglLQKpQpaAQQbeF0ii/i7YVytAoQ+Ai0KFKxmo5\nROlgdCmEUUNoQdJTNWXsIlR9qlQoOlehLxlK8oVkCiklATeA9jtY2kB4PNhIVFQMjtL4lcSIjUWH\nhaFtF6EVXiFxtU246SHgVlJWEnTlGI6JDNiER0fjVO/PYlk4OGjlYBomhh3ARGI1b0BaaiMKMk6S\ns2UP4aUB/AK8wkDiYojgXid2SLeuKYzSCqVdlBY4jkIQ2nY25N03kEilg9G5DEbpUgTf08oBKYMb\ndYX2tDG4sJ2B67oox7lob5ugrVEjQn2rqx1CBPtYILArbLR2KTx5luToBITXg6M10uMhJrUx7XqU\nsH5JOpv25qF0NNqowrAsLKN6FRXcykHr4G6YrnLxWh4wNLbtx1UuhvnbzqD/CXz5zVgih8ADgz+n\nS/keflw1kuL3Y4lJMjnSqTM/iYEUHrfIb+5hd1pHDl3ThtKj0ThRXlKGlXHfoWlsvmI+SbqExJ7R\n9I0/Tfz2A2R8cp4deYpHp7zLhzKMFbFDOBmVSLO1G2h12RH+tvt+RhROY0rkFxyp35qXmjyB3H+Q\n06ca02B4LAObpHPauw5hOzTYnE+boyd4Yf0D7D7lIz75MPUSCtkc6M5g70aKPorBo8+xq19zFg+9\njqG9tnKV50u6dHIo9McT+KkM64diWvc/xNp1aVx7YhY7dtZjV4tkIkfFk5q6glnzJjG020baNt/P\n+sj+LDk9mi6HD7Bla1+WqZPM9N3B4UAbBpxax/6oNLZ+24QeHaNo2voE4e1LSY4/S7NvM5mY8C3t\nDh5k5MqvCUs8T2XjKKzb4JEvprPgiteoWHcGoyJAeAub7j12sOO9OMirJGpeIUtLJ+LWEzyU+Bdm\npRucym2F/+di3BPh+EVD8qKi2Cl7w05o/4ggNsxg98Z4slf5mByezrzEa9md2Znrds2lMt2kPDuK\nUb7vWamGsaNnV1KW7SHd14pP0ybxh1Orqbd4D0flWPoc242uOkrRES+VByLZFOjB/TfPou+O7eQc\naYSbYBDes4RkncmWmJ4sE0Op7+YxpOA7AsdcFhTcSeTsU/R6YD3lkbGUltWjQX4+TPvn4+6SEboh\nJFqCNg0c149TVYW/3E9JQRX+cjCjICIyHG0oxIWiSaQAqUVwC1khQ24VgavAAZRh4JoC1IVyeNcN\n7gXj6mD2TiuFa+igo0ZUbzt7wZZ4IbqVoW1Xars8glqxci0i4xqSV1lJTNMUKhwbXVBIeVU52gSr\nsgLLtVFoKv2AgLi42NB2wSFPvBb4POG4ysVfUo5rCIQUmD4vLhpDSjymB4RAmQYRKCrdSqLaJhPX\nPJGdqzYQllFGhNZIpVBehV8oRGglUX1PWmvcmnsUwcQzAke5QbIN9WdQjgo6VgxCWjpuaJMvN5RM\nNoPSihA15f9CBDch0zLooAltiRk8j4TqWFmE1k5KgjAlhoK89HPUTyjBGxZBcKlmYcVF06xDS159\nZQmVshmGU45hebBdF1MGx47j2rjKgyA4cRnSAhFMviIN0G7QNXWJMKhTBvGdz9K+wQ5KN0UTuzuf\nZ8qfI2JMBctXjqCR7yyv9X4RMUrT5ZOnuG3cu2R82Jwrk9Zz3aNzOHWuGYdu6sT3Xw5h5Yrh9A38\nTGqjSj4c8gYtln1GZdNIjm9K4/Cq9owJT+feB3dw5YdrELeWsNieRNS2M/jagHyoE/GvzeXJIQuI\naFvImH7f0q/4J7Yd6UaPF/dyZ84/SM8qoF73cHpOSWDMFRsYUr6WPZ3actvJuby+oD8L59ajb+Yn\nTHzhKN277+O2l9/meI/+dOx2ngxfLPmqORMeu5e1l7/MLncoKwaPpGv3o3R+6wNaLbUx3vbyRfhk\nNqf0o5l9jMPjG/LM+Id4/p73yG6URPfxWxnkXU3aOwX8tfff2fAsvNrzSWJvyePbruN484rHmLds\nAk1PnqLXUyupsLuTM74lDV7K4ciYJnR5yKDt6FcQA6IJGx/Js3kv8+hdH1I0LZOGTaqI9CjsAZKe\nVx2j3quvcefemVxtPcDVo46yY/Rk3o+7m4d/nIb9J2hxfQbNTp/A/TKbQ3vP8VZcF37sMYm/DH+M\nNV9ew+xF19HN/xMfHH+ZEeN+4K997mf8HXNZvXEo+890IG2cw/pbznJ3yYu8vucvpDyXQ/1by5g6\n8ymssZn84cP34HObrxLHk9M6mfc/eJCc7ESmfvUuE8PmcXnhT/x8siHLAy14XT7IhgWSFRNv4PqV\na0lys5nfYzjw6T8dd5fUtmh5TJDg+B38fj+VVRUU5jkEKiSJsWEEAhX4wn2/KpipfQ4pBdoysA0Q\n2sUJVOERkijjwr4oSqlgCX6tTbJUMOTEUL8m9H+23W31caZponxQbJRgJkZQXHqWQEE5UnnwILED\nLpYHDEdjOwGUUkRHR1NyvoDwqEikT+I4NmZYGH5/VciyaWB5vXjCvFQ5NmERYSh9oWDIdV2qtI3P\n60VUOnjCvHQbejnnWxdweu02oorKCdMmrkdg1pKggri471zXDRK81mjlolR1EvifE2C1zz4QCGCg\naiQnx3FwbAdD/vofbNR+Tr+EEXotyonk7MF0GtUT4KmHoj4VgXMc25tBaaWPStPCZ7oYWFimRe3/\nHaG1Dl7fcQgPD8dxnGD7jKD85PVeur1cXlQP88Zzt3LrvX9h5PQd7MntjLkIhnRcyc1hnzIh4SuK\nrAgi5pex6124/p2HuN6VpLZVZDa4jDseeBdjeRnfxw3j7LFm7PmyBQ0GDWD0WyuY2fB5Zt97I8ff\nbwbZp9nXx+S99nfA3zT6s5PcMHs+d7y7i33ruxK/RTHh5wqufGEXc28eS2n9DK6cs56S92MZ3Hgl\nW3y9uUPewjUtd+Hb3Zgn5r3FVz9uYYZ4gNeffpxlV47k+sC3vCVnEXgpgukf/Y4pI59j7PjmBG5v\nSsGB9rT+4CTyd+Pg5PtwZ0+m1D/AMPk9by99kXkFtzDxkc+4bc5nvDrneRYd78WUDfchOM7PI2+h\n3XXneHnbszw+Zxod2cfB3HaQAO89cAfe/Aqmv/8oOllgZdssf38Y2c84rD4zjGV7bufH9uMIi9rH\nK7tmMyY+hzfX3cLyleE8U3o/O/7eg7c23kvSs3mY3aswGsHefd3+D3PvGR5VufZ9/67VpqcXIKET\nepHem7ABRaUp2LD3uu26LYi9oqhb7B0LIKACIgKC9N5bSGghhfQ2dbX3w5oE9L7dx/vu5z0On/Vp\nkmNmzWTNlfM61//8F6Y/8xlb7xvMgdfa8PLHb9Pp0zwOTuyBvgGOGfAOz/L48uvQ23oY+20hU08c\nZU8fi+HPvcrt7WbxRc+HmZk4g872ldAXNt/VH3bv5eSFYzieDSvvgek3QM9Pv6fd7DyyphSTF+oA\nwxT0N7IZ99FKnsmdTLt/rCbwYE9eqrufDw/dxVvNb2HOZffyry2zuNb3GQu1OwneqPF81f1czEma\nn8rjlNGEA30y/uO6+9sKetgfxasIlJiMFZQQERfhSoPSympkWSXdNHDh/oNLoWVZGFjI2FgCbMNC\n0i3MqIkRtdBk1RlwWgqRcxKGVM2NaZroihvTtnHLAkPXURUZ3W7w+o4LnYTTJYMjTLUFYMnIJih2\nFNtt0vK89qz+aTtZ2a3Rq4IYwsayQSgQMwxkTcUWNpZtocdpgaFwGEmSCIfCKLgxcKiQpjBwB3xo\nHo2IEUa2wOXRMEzHNKxB9KPrOppsYRo2iuolbIElayS1TCLzslHsXb6BytxCUt0BdI+LWNQZPkq2\n4QwqUTBNCwMZXXUYMcKyHBVr3MsGbIQkI8kSiiywTAthatgxO+526WwQiuJCAAoCw4wiY4NuIiQJ\nSXbucGKmDfEwjD87RTbAQQILyRZEymPUHasmPTUV2y5F7DrJu7N/Jyy8uEU1qpGIptTg1bwOlCNJ\nCFMmHA4jhMDlcsX1CDaaZKOoji4gVP3/r33u/5fjyik/UDCtGRdUL+fxF16m7L5UrjG+4953Z/N7\nj2HkX9ySm9u+R7hngFsTv+fON15j7Vdp7FZGEEvqQ/hDP7ff+B6XPvQj/pwQ710whPyiFJ6omseB\nBzvw6epb6DpuD10vj5BVUcjFB1azb05Xjs1oyarMyfQvy+dicym+IUEeUZ5hgz2ap0/PpCIzhUWt\nL6ZZ21Jqxify3IiHuL/mW9auG8O3S4ZSd2AV20caXPbGPJ795DmuH/IlyU2q2PdlS+7dfj1DRIy3\n2nxAyZxirPdtekW68mTkUdbdNoznds5gZNPVTDywiMD6Gh768k1G7JvH4VYDuKrbAiomJtG6qpIt\nNY/BuBiLL72aE/9ezuTf3iV79Enem3YzA+7bDMfhwtBiDuY24eldd9GxTxVfPjqdX1qP5CNfdzaM\nGcpFPX/m0dxnKbnKz8BTB3iq/nnaSXlccsk6DoybwPJmnZi+61ueT3mS7rl7YDvcvP9Vom9vJVwY\npL15mGQOM3ftBYRzE3k8/BQPrfk3Dxx4j8qRTZHPK+cfrpV8qt3BMeMFvhp+DXU366Tv/ZnRc4Ks\n4EXYeBSlJsSHkansadGWf1x5gF7dmnH9+U9z67XvsXD9NMRHGgNLf2P95y15x36YnbsFtS93Z3XC\nTex/tA9WskC9p54tWj/UN4KMDf1IdXWAW8o/4s3md5MsD2HAa6+xcMdktHYK45Zt4oP/sO7+toLu\nk1SEbmIEY9jhKPUVtZwsjlJWa5KeESAq2UTsCD7b9YcO3bZtzPjgTTd0/seENH4oqtooEvpPR0OX\np2ka4IiaolGnEDRKx0W00ctEjwkO7c7H70+gvr4ej9dLOBZB83jiYpg/wjMNDoqapjV2tHptCNkl\nY6sSvoAbQ3cMyDweD6ZlISwL0zQa+dtCCDRNQ9gxxJ9oi0KRqQ9IdJ92IVU788j9bQtqKIpkm7hk\n0GQbWVIQmJiWY0VsnWN8de4hxWmgkhDYQqAoKkLI6LpxdviL486oWAKPR3OKc3xY/Vdq1nN/37DJ\nNihUTdvC7fZQf7qYtKxkKkpP8uJjawhWpeBy2SDVIywDTXXonw2bQSwWa+SgK4pCJBLBdCKVcPk1\n6uuCVNSF/uN3/1eHECIb5742Ewcx+tC27beEEMnAd0BL4AQw1bbt/3VKdc23r5M0WaZ76hGCtYn8\n6+SzbIn2Z+7kqQzVN1KxNJVZSQ8z9IK15HboyYcLHub0wVz6DdrHtKQlVG1P4vP7pnPb8c8JjXZR\nV9KOQ7/0ZsYjXiqTS+l3469clPs7a06PpDolg7SMEiqeCfJKwRPMX3w5Gysz6Rmu4vxhv/HM6ue4\nL+MtdjbtQWZiERl6MRnWcV6acD9WC5uus46zMcUk6UmT0TVb6BLdx8wX7iK48Rinu3fi+IiLqe/t\nYsd3Y6m5ZS0XXLyadT37sb28D+HThTw+7xpSvx1K5jUFqIe3MHd1F/YUDSVWVUjdoynEXtvGO10n\n8V31VHxmLQNe3ETbVvlY/96E/ksRv8vDyKtsydjvP+TlKz6Ef5/gjo8P03SYm+0De3HkkwDSCJsu\nnQ4TunIa1fvacaA4lcVXTeDK877ELIVJbb9nvRjG9up+jH5pKV0TC2g1pgD7ApPMsmLMiEJ2p1Je\nfP8N0juHUY+GuPbCn0gcLXNC6cJzoSfZvGI0ydtf476r3+VnxrF3e18udy/iVv8HzCidxeDAGnye\nFaT6jpHWt4yHFz7M7En38Gu4P5VWBhXVMZK66ox6eB5D3AdYO3oUrrQw2WmlZD1TxzE6k09zxhze\nS8Hio6w40x/RqglaMMzPF0zgrn3vMDB/I7qusc93Hrfs/ojTN3Unck0agyZvQuyoo2LBf167f5/b\nopyIiNYgxcKEwyGqQzqVdRFMTFTFQkXgEh5ky1FFGraFKWwkG9Q4GoskiNomMbMhteis016DovQP\n4iI9DJKEISnIsur4kcdl/o3dp0O7doqQ6YiMvC4fdeEKoopJVnY7co8W4ZZVTEsnHKzBFjZ62EDV\nvKHwnKsAACAASURBVCT7PNTrUTAcgU+i24PQVMCmvrYGf0IANBUjHhtnxGwkl0w0GELSVITq5KZ6\n3V6iRswp8nHbAEOoyEJGkkAV8axQ04NXkVA0m/T+rYilwZ6vtqIFowQUk7DHQnaruKQApnAoilgm\nqurC0AHLi6FLWC4TgwCWUJBVAwWQLRXD1lElQTQUw3ZsJDHMIC63guYSaJqCHnWYMA2QiyRJKJJM\nfLZ81ohNmE7MnOTYFQjJwrJUXLKOYtocXFbA59/spCCSgKSZqJaBZqvIUgg4GxJiGAbhWJRASoLz\nWI9hYuNxCQJJSdTqOjWxGty+/xpDN4D7bdveLYTwAzuEECuA64GVtm2/IoR4BHgMePR/O8GIwb9z\nUjuP5cf7E67USU6opOPA/bQ2jjNc/Y06LYHt+T3wflfMPTe8xhe/30j/vuu4ZOgmunUuIeCrZ9/u\nzhh7QvxUeQlqs0SuabWK8q1R5lV04YLUjzhc34kt5iBcZWc4tEGlel4zzn/kV5IO1LLF15QlgyYy\nNWc3l8YWsntvNpTlsy+pM2lWJv6h1Zz6ugkVUjPKv2lGYocqrujzMRlJB5nYL5fYEYkTOTls7nEB\nhwv60LT0DHfd9hOfu3rR+8SXnErpSPFoi2QjxOrQjbTqEOCVpEdRD+dSkdeJ5JwSXDeXk33Vb3z9\nZms2bmoOFW3p4DtN19IfSTBrGV20j4y+YULdTdyZEdLqqmlxwTEm/PIBRbtbcCyrMyE1CXtNAesL\nbTKf07k8eQ1hVzs2hgexvHocU/RvmDNvJLKvOUpXnep9Bt9/1Rl3ektWjhlDP2sbZw43YYvVn7ok\nDwXhAEISuEoMrEGJdPUdZ9D+fRRXN2Vok3UET9XijgqyE08ilQrG7FnJkPvXsq5wKH2VLShJHakY\n04eh09ZTmN6bYxe3JWo0oc/+IzRbVsI7re7BSIjQaeEuotFyjl2WQdKQLNJFhLlf3sKkKT8wYOsB\nEjPKye5eiFWSTuVchaVjpqGkx2ixooCyHSkUe1WEdYpIJy+LS8dwT84H4KljXt40YPNfLty/raBH\n9TBSNIYdE9RV2RQX1hGLmY7DIjQGWDQckiRhxT1F/tyVS8KhsZ0NoQBJkrHiVMSGLleW5D90t5wj\nDmro+KS4x3jDe2LbRCIRFFmhaYssCgoLEUJy4u9MA1sSyB4Xzr5jOd2rJhMzdNwej/NaRcHj8ZCY\nmEgoEsG2TSwBAV8SoVAIxVbRNA9YFrLdwC5xuvpoNNr4GQ3TwYqj0Shut5tYLIzlsgjWx9CiXlQt\ngaxOQ/HM6Id1ppKNX83HVxkkyZSJuWLAOfx5w8A045td/HIqioqsKiiKYzFsW6qjOhXOz6FwjHA4\ngiRbjtFYGEdZKoNlGfFzKI1mZlIccvkfZVWc7dRl2UYWHoIhifk/baI04sHGgX8sy0IoEi6Xhi0c\nZW4sFsM0Tfx+P6ZpNuLmmqbh9XuIRk0qSuvxyBbTJvX7r9ambdslQEn8cb0Q4hBOfugEYHj8aZ8D\na/iLgn5g5hAWfHUZG07kMK751zyivsTCYZO4+rG5aMnFNB1h0y+wnopXocXt2ylt7ub8rtvQM5rz\n0YnxdJN3k9NuHSdOBzlVmkTTgUWcH1rHyQVNKGtu0nJvPls6D6fEn0jq/jyqNkYZPiUX6S6LqXO/\n4ZT8EBvPv5q+7StIKztD69uWoGZXEvWkIfXPpnZsOicfb0Nkh5taTwKjU1aSvXsnxekmm7sM5c77\n1vF26q2UF7Sm4sMMWu49zQNzZxGNPMrJaR3J9JYwLnsZWvs6MtrnUD9Bp2hlC7q2SKZLlzN0TNxM\nr77F/HvreZwvnaFcP8bRM4Wk2FX0rVhN52SLxO5Rtqf2I5KcjHApbO4widROxXQbWMHeym5sKuhE\npMSgb8d83k2fQqtObbl+4UKOFLdio9qD6mWprC0bRP7vbqI9O9E7eAxvqIItXVtCcleQUrn7x5l8\nfex69tg9SA8dZWbt3SQmZ3DR8RWsWTWAotpkJu/4keYuQfbLpziRn0pli1YYKX6yE4vZZXTn4gcX\n4fughCHGGraLwZS1Gs744R+ytMNEfCUhhpX+xrjEFVghlceDz9Pz8Z3M/3UTuXMDuCSBOynCgOr1\nfP30SC43vqF59hmady2gg1zKNqk3Jb0kyjpno+dBbp+2HCxrxo59mfTrv5Pa2Bo2r+xDsy2VZFfn\nEUxLg/K/Xrt/n9tiNIgVNQjXRKmsjFFdbeH2uTGjelzE88fnO3FrAoTtpBLZDkvE8S9xaDBCnH3h\nua9vkJ8L/qgAbejo/2hQZcbDM84qDw3bxqWqBIMRYjGDaMzGMi00l4bX5yVmO2wR23SYM6ZuoGqq\nM7CLxRDCTTAURFNVx/MEkBUZPRpFkgSqoqDHdAIBH7KqImsqqDLhmMNbb/h8su3EzRm6Sb0RJhyJ\nospZpKT7cHsULDuGokZokiLja92C9KRpLJjxJqqpoQkFWYlb5pom2ALTspFQGtL10I0Yum4jyU4X\njS2QJLnR70XXdWqq68ECIyaQZQVZ8aAqGnbcD6Ax8k6WGx0aG2YgjWNXm0Y/HJ8mETM11m48QkEF\n1NgyKjFHmas65zFMEwubmG4Qi8ZISExEkhXqIrG4MZcLn89HYnISBSfP4JE9DO2fQ5e2/v/jdSqE\naAWch9MWZdq2fSb+N5UIIf5yQrVN6k3p6WR6ZZ+g76Q6lB8lJo79Af+hCvYeUYlVJ9N2rJ/6XhEW\nP+3mZv0Jiu/vw9JjV7N2yRg+91xB7Yq9PNpkKyOzFnCy10hmSo9TeLA1P428iEBXNxdWvk32N8fZ\nu6sJvXqUkzjnXewEqL0zgPK+hftzg5p7AhSVJvCpMZwJ15zmpvbLyCgJUn4mjVGvrCX6QD1pCQaf\n3XEDSwbdjvtMiKfNK/ktZTidlIPs3dCLU8fbE83xEjolcc+2F3lMe4eRZetoue8YBUYSo37+hunl\n67irTy/enhGmbIHKlnd68MqcOfxccgmvNXuI9GuLWbC7NScLfMgfdWBVUTo5X59iVtF9LPluMKH1\nNt6eEj1XHObe/rN5vukjTFmwhcoSlbETi5jS+xfSjxynV9URghvD+KU8rAGJvPny3Sz49GZ6bPuY\nlxc9wkl5JG/c9gItst+BWli0pTlFYw1GaEsY+PUBbhjyMTMv6oH/syhnVvv5raYdv2n3cOay4WSV\n1dLi6VNYUcGGk8PYa3enyYWnuUN+h6kfv0TqDfXURcbizgvTvjCX8crNTPnpJ65e8hnJ08pZ98wQ\nhvMrs0L3M0J+mIiSTq/1OxhUt42qVJsU+z18b0DBrGZEi9ysWDeGp3rOpP5tF68mPkD3T3dR0L0l\n8pU1jJu3iFdfXMB3TSfxUGg2H//wIKN2fMVdbV5gw/+NBd0bNYnWm4RrLEoqaogpAnfcdAu7gVVx\ndphmWRYoshMIgUAxJGRTwhIydrzLbDC6gngSkazEGS7xvFLTRkKA5Fiz2liY5lns1+kaHQuAhvcU\nQiDJEkLVKDlTS13YRFZdCJeEpGoYSJiGiaa5nc7eNDENh50iayqyApJsE4uFse34kC4cw6P6CdfV\n4E9OxIhESEhLJhaM4GsaoCYcxKcGkG1QhIQtCYxIFLehUFdTi+l2oSUnkt48BWHEsKwYsVgUWZYJ\n10fxmCpBRSXQshndRo9g/9LfSAC8XmdOYMc3Btu2EJaJaktYQsIWBkKoCDRMU0cIAyFcIExUzckN\njUUkqmqiBMNBZEVF02Rnoz3nep0r3jrXbVGyBcKKX18bFEmAKbPrQCG/7yuhOiYjafW4hIUi5LgQ\nzMa2BTETbKEiazKS4iYWizW+n6qqeL1eYlEdrCCD+ralf58WKNp/h6E3HHG4ZQFwb7xT//Pg4S8H\nNNNOzmPyUwv5ae4k7rv932SlFrLvy24kPh4haUh33qm5m6++Hg/f70Hb2JNJ9lgG/HMDQ5SNlM7q\nxCz/dXzZNY1pzxv08OzhVG4pZpLKkz1fosvQPUxo9j3/WvQqd3X7HgbBvlB3rvrweybc8i2jklcx\n2fM92YeKeeaBp5k/rx/P2x/wxO134JkSJaVdc54OPcVtb87m4vU/kvJtIYfkbnw85xp4uY7U7m78\n34UQzWyeGP0cg/tsYf6WCUTOCzKCR/lx6KN80fIRTv7ekutLPuLF1Q+iv6PCs6uoctVTIwIU9BRs\nfaYHa3v3ZsKKpdz7/Xs8n7+I6kkB1hQN5YprF2FNkJh2+Zf05yt+WxOgTXEuvy2ZQ9fq/Tzw3ls8\npz1B55RVXPrWAygjYzywYiw7vz6fjeZUzq84zu3vz2Y+U7lTvMHbrz3IxEsW8N6Q25ie+yVruw+H\nx+Dft3/C6vluqIcP7n2Q5MJKPp8yjfvVd0jrdIbX1sxBeeAYd82/hi9mzKN3+Ah3f/5vjEyZCa0W\n8ukdt/DV/KvZybW0SwrDlel0WXOYsVPXQhPoOXsrr/oepSo9kfMi27jLfpdmW6pQYpfwZpMnKH8w\nmXGtfyIwM8r2vr05sxVuFa+z/86eDH5gA7dF3+aNoke4pOpXbnv6Lf6xbBX3HXqbo7e25WDnRXSZ\nmYd3fwRGw6ou4zA+iAI3/fWa/bvMuYoev8WuOROk4FgVRVU6lsuLJHSsmElSQKFVywDJ6S48KS40\nrwcD2ynoukCOWti1dVSXhymt1MktiFEXDOF2O+ZSqiqhCAtFVjAMA7fbjSQERszxShGSiaY5xVuV\n/ujJopxT0M+VzttARVUttuJCUlRk1dUoSGoYfIp4IlJ9LIrmdePyerDQnYFmnPZnGiaGaZCYkkzE\n0FHdGt5AAEmRMVSJ5CYZlNZWkpic7MBGlkkwGMI0DWKJmaSkpZCU4CMarUdgYQgV4ucWcWjEsmOo\n3gAulwdVl5j7wtsYu0/i9/tRFNnpuiXTSSzSZbxuiaQEicRUL4mJfrw+F7oRce5ybBdgEAqFqaky\nOZpbwp7TFSR4FLq2a0aLzEQCPgWXh0YvGUVR0HUDSVIaC7qzaTo+Mw0dvKqqFFbpfPXDRo5XyKC6\nkYgScLtwado5cJlEXSiG2+3GE4exTNPEFJCUlIQsy/h8PuqrK+jQNoPundNJ8lqoQtD52Vf/KyBd\nCKEAS4CfbdueHf/dIWCEbdtnhBBNgN9s2+70v7zWvu2aNPbu7Y5xWmVYhszcKR+w5fKePPToTVyc\nspXh4hDVhxMgLYyY5uFfa15i2uqPyZpSwLwrr2XJiosYfsFCKpq3pvSb5tTWJOEfVENGm2KW519C\n4Lla0jPKeSj0Gi2bneDVsXcw/Y6nePH7T3gh+iw5rlx2N+3GQdGRvOqW7DvRnYFPP4s1sQUts5KZ\n8sy3lKVGefLiLwmP8RDu4GLkojUMenozD+bMZr/UmapZfuZsupsFc0eSlnmYGTM/pRgfiw/fxCWV\ny7nsxHwO7m3NlJIPqOnbiUGXLeP8nr/Tw9jFqWXNeP2jR0m41eJUuBWpFZUkZNQyMmk1z+16nILF\nLfnu80ls6dqH/XM70v3H7cweeC/pN4foX7CbvOXtyfhpB5PEF9w1ZREiI0DG00cx7/FTsS0FtTxG\n+RQfk957jCt+yGffjU24PPVnLkzfzpkzGRS9mcILA6ZSMacHgY51jLJWMVlZgb7Hx93bZuOeVM9d\ntXPI+WgpCz/vxGtpb9DiozQWLpiKvLECa6KC8oCLneEeDPJuY8L13zH1tnmMH7+M3T805csn+3LX\n0n10tvJ4+JHXWbplMN0T53Oj/RCdwrWcKmzHjzc8xoCWubj1CJ+7r2P7ht68df9NdPk6l5YFhfiS\nQmieGAWtm/Hg4osZ+VoRJ35pSejDAqYkf073nCxu2XkZ/W5Zy9Jf4bjRkkC2n/LFn/7fZ85lVldR\nWhWhIGxgKBou244PyuI0Qojf7itgKQgZdN1CAyRhEhQxDNnANnRky0LoJrKmoMQDLkzbRJEV3G63\nYxBlGI0uig7Oa2IaFi631gi7OIlJEgZmo2oUQLdtgqEQlqyCLSHbDgzicjkZnA5OrhIxAdPZSLya\njGVEkDQnJCMWcyT6mkvDq3gxYzoeRUEyQApFsN0KMVmiPlxLgqYRqQ8iC5Wq8lrwB8ho3wGPX8G2\nLKLRCIYBum4ScGmYtkUMR3nrGImpWDGDiB3GdLm48Par+HrG21jlQZIVFzG3gq1ICENBtm2EZaII\nE0U49goN18KyDEzbCRGxJQGyhOLSiOkyFeE6zpRWk+Bzo2outIZQEqJYtoGiyJjmWfMsy7JAMh0x\nmJDBUtAjJqtXnqAy7EZKVrGjkGQl41bD6IaOLSlYkkrMMBs3gFgsFp8rgDfZQ0TIJHgSUC2DDq0z\n6NApE1+SSjQWIRj+PxIWfQIcbCjm8eNH4DrgZeBa4Ie/evGx47dzcctTZF5mc2ZgKnckzCZxZZAT\ne/tw8tpqXP12oq/O4IVlM6FU4Nm8jIVFrRi0wc3E7B/IGZPLiMx1lO7OoL5FAvOOTWPJ5vGYmsJd\nHd+gz9Q9zEm6lXfMOxme8Bs3tfyE0R33kp73L3o228uRrLbIpslFub/w9tDbKHq9PUNHx0gau4eT\nTbqS723HiKMrePz3l3lq23gKAkNRi0yyRQG4QCqAT7beSuqWSp468AQHjqrMemsonWZ52Z97HtXl\nGaxThuJLPs09Ox6h+ZvJpPUs56fPJrE2ciG+frVkX5fHti907CbZhArTwJ1ByrByfp84iBWDx3Be\nzg66K7uYXz4F0yuTNqCGE7fYvJp5L7vG9WZJ21H8/NtNVG8ZgOelWkIdE7EWylgnqujebzvt0nMp\n3xlmxU3juGrU5+Qf6MSMmjG4J+lsm9+HXeXJPBv7N/sODWCHOoL0IUHa5ORx4b9epN2FMeo7pHCw\neU/adi9n1CMnWL2zLw9smkXO+N1YSQol9ybi8e9n6sWL0IoOkf3eYbqtzuXkKRf7zObsaQE/PDuN\nPR16cGn/ZVwX+AAzks6Mhz5CugFyriohkFrJiaI2BCu9bB7Ui2F1W2lnHWNJ7GJ+8E+gbdd8njz4\nDIW3DSVl/ixSmpXDNTaJc1vw1LoHaBX8hsThbRgyqS0XHa6lST48uPivF+3fVtBrq2SqKw10XTgC\nEONsRLsjijExLfMs/znuqmiZliNNb3RZtDEkiMmgysJJLVIFQv+j855lObfxzrkFkmSj/il1CBzY\nRpLEH27nqyqrsGxQFA1Z0hyP8HOUmA3n12MmXp8Hn8+NboSQFAnN5XJ8Y+LDQnAGh9FotNGZUUgg\nuyRcLk9jAayvCFOj19Oqa3s8CV4MK0ooGEXTtEZWjqZpRKIRZEVxCq8S73wVFUmVsWUnaNmfnsj4\nm6cw/8U52FGBZEtIpoWKjcuOC67inf6fD2c+0eAmGfeBNw0qIxIFQZnUoE6Cu46o6kVRGzZhA9My\ngT8KtFyWhipskA0s2c+OnafZUVqJ6XKBbiBhYmhgmE7WqS0EejSGaYPf6wUcyqSzkdrYloTqkXC5\nTRJ9gjY5GXj9KkKKEY3V4XUn/ldrUwgxGLgK2CeE2IWzs/8Lp5DPE0LcAJwEpv7VOYLtvLTveYzu\naYWsUEdyMKsjSrFN0nTBatGTZrFc2vQpx/v+AVpthyaX7KT+RAq1B1twbJdgwG0b+eHQZG5d9j7r\n7SGcqcwkWOSnLjeRm274hJW9x5HepJRTG1uwcv0YXEVhcq/tRs+cnagndCqSUtnfrCuVKWlEZDfq\neWHWHhtIwrwgKQNssroazFk+iL4HV6KUtSHafjCRfh6sHiaxdSGW3jiGzJRS6u0ADPLTt18+Fb62\nrA1eRPVqnX6FR0kXhylBUHhzT1r2zee8ZfuYv/Qq1lV2o5O9lStvWEK34loW/SwY0KGYGqsDeds6\n8bb3HrYP68v4z5bQvnIb69f05JeK4Xz040Cmla6jW7vlHMrsRp3UlFNqGmcOR8lOd3OyNoepoQWc\n6NAC0UNicMYOzn+omC7/LmLS2JXsGN6T75J7EWnrZ+Tc9Ry86hqyWpTRLPIz+XJr6lQ/PzcdQ78r\nF3Eqsyfp0RCtYtWkpZWQMHQhe5KG4f82l1at8qBGpvRAB9ZfPZZn9ERG1q+i+/Z9aBUmnZOLub/7\nCjLx8emGO+nl2szVWR+S5C3kR3sgY5vvZ+O0frTreRLZH6OwvBllv6fz1uP/5Pai9/gm83LWtBxB\n8eB0bMXk+blPUN2rO57WKtmDiwkmpHOq+SDW1w7jNV4hUGHQrU01ZU0yObqo1X9cu39bQT9RblNe\nbaApKgq2oxg9Z4hmW3ajZty0TIQV57xIEqbpJMoLRUOSbUwjiKooKHHMFeK8bc4ZyMV9W4h7lDjc\nxLMF7NzIMkl2vEg87gDBUARZdsfDL+JRcIqMW1ExLBPDtIgaOsgmml/FEjFilsASDhwj21BbVUVC\nQiJul4YkydSHQ43whMfjQagqhm3iERZGUCfvZC1pOc3o3KEFlgymbWEZOm6XD0M3sIVEJBgEQNE0\nLMvE43ZjYYNpUx+td/5mWUIPR5AViexeneg2bTL7f95MSnUIKxZF9vuJ6Ra6IrAl1zl0lHhshhDx\nwXMcdrIsLN3AEDaG5qEyYlJdGyaoGiiKhj/gQtVsLFt3Crp91mbY2QAVFJeJpYSpqK3h9+35RKMC\nxTTwyQKP5kJVQLdVhCwTCYaJRaN4PF7cskpNXS2qx+UMq4WNorlREKQGNDq0SyA1TUOSBXXBCJrH\njSfJ+1+tTdu2N/Dn3ejsMfr/zTmu6f0b/vpiCvfIRIM+KlqlYNYKlEuh7F2JvLoUAqOraK4vpWNu\nG1o+AtXbYEc4QKGh0HKB4LnyJ/DEwhyNtcVXUc+F4aVIsoG/Msh7RyfS5sIihtprqN2exKHfWlJ0\njcXmrB4cXdCO6I56IqNqOJnTnONKa7LvOsHiS3Joe6yWG9M20y5hH+99MYxd/gkU9OlHzyEnSelU\nzdpTA4gUnuSz6ZczqfoXjta0YVNNG4ZlfcsYbR17rMsZFfmZYZnb6VS9mX1V2czouoD9i3+g/osE\nimLN6Zi5l/71a6k3U2neIUr/jxfQtZfKIbkZGzZmUrxxCBwpoOmh09R6vSgVJYTya/hq6TQevnYd\nZ2wImjJWrURL+yi92nxPqRhD4UmT7KzD1Pboh52ThCdVoe/jBtm7ThB2uQmMCpKZVUYkL8gTLZ6n\n6roUEvxB+tZupV11K+b+fCWbYkPpcu8RFq6/kCv0H2nvKUZtZlBplzCt87e0v+RXkgsjZBslZI05\nwvqeU/gxK53JrT8l4BGU9UylSmtJi+MBTq5pxvklqxiWspw2pYc4aiZyXPLzousZTitvUimSST1Q\nRs6OI+SqHdEjGqUFfvIDrdAHSgwYsp5u2w6w5OBE+ny8ltYPVVASzmDNqQEUdWiB3MXNiZ2Tab6z\nhLZpR9if3I3vu14C82f+5br72wp6eU29I3dXZBRJwhYCw/ijl3VjtJodLzFCIGwJWzi8Z8OwMGwb\nj8tNbX0VIctAkf1I2BD3UD/XKxu7wSu8gfp4doB37mA0FovicfswTYtwKIqsyEiyTCwWa8SCjVgM\n3bKQZBmvz4dhO5CCoirIqoSlC9S4WMnn9WHoOtg20jmDQk1z8i8j4RAZ6VmcqazldHUpvQYNwXIL\n9EgoDqEI3JqLcDSKZdnUhsN4vV4iEac713WdYDDoOA02RLa5HIxfQqAgEQtHGTvtAsb8YwRfzHiN\nupPFGKEwPtmNacXNx/70HTlQSfxzmjbCNpElgVuWkKI2FWW1HIpUkyA1QVZDuFwKsiLi3jsiHiV4\n9vpLsoVh2himm19/30dxvYkku5EkgazKzrWTIGYa8cGygaYqeNwuwqGQw7bBET1JQiDbNqkBmQ6t\n0shMcoFlYCJQNTdJyX6E9PclFnVcc4JTOyrZ0G8cZecP5k51Fke3tUVNsJkeXEoPew8bT/dhrqsj\nRs9rWGJP4pvy8chZQS41P2Dbkzm07pvHkxc8y7UdZjNz9SxST4X49cFhzGs3kdpu5XhalDJg4gFa\nxfKRbi1i9b06K9t/wlOHZtLvl9lc8eN7pEzvxhdXT2d81hJS0vO4oPVW+g4so6C6LRO143ye8gp1\nE3O4ImMmgV1Rnlh5P1nWIvaXXM3G2l50P/8w9uJDfH1DJvd230TXObt48rrZfN1iGnvzxzH42820\nfOsAoX2neCP5KqxLmnLp4B9o3bSOB3c+T84jP/BIyQw+f+8FVqmTkBPq8aYfxbP2R1wja9g9tgcZ\nS3bxj4JaNjd5gjoTls3O4YKOX1OZ4OeUK5ubumzlpYrRZLZbwqfGxdQFu5Aj5TIvYTLHzTa8edlj\nXDH8c8rdKRytymFA9hai/1S5JzqbxFWnCP2cyJJjk/l3+F4SqopQDhr4Z+xl3zVNWDt8OBmpFexO\n7Mr1679k0b/G8+WsnvRJ2EmvAQW0eauA/rPWMafjg+QMKiNt9Blm77yHlZ+Oo9nuXGZHHmHf9K4c\nHTSAPr7tRPVCdo2HZT+OIhCp4cH1OxlXuhTzQZOxWSvR7qxkTK+1fJp0E6X1qVxZ+i2XJ37L0R1g\nft2K5bGBLDkxkJKsdkjTs/ny5RspyGzBmN5LadqrEP8NOsz463X39wmLJAlNkbEt+xybWwe3bize\nf2Yw22eLvI0zgBSIxs5a0zSHPmg7/HHH9vV/wVHP+T9viGST4t26Y+7kyMmLC8tByEQiUQKBwB9y\nRk3TbPychmEgqQr+BF9jmIXX623E5t1uN6FQyPEqN01M28nzdLsdlWg0FiM/7xSu9Gb0GtyDqCeI\nJjSHxaObWEIQjepYqoplg9/vJxgMIitK4xBSiT+2LAtZObv5GIaBGRW4PBrloVMkpSYw/en7+eCh\nF6gvKEMoJj7NE4dIztIKG0YuAoGux9B1hyIoyTKaaeASLsIm1EcVTpeH8XvAH3ChuVTk+KDZ+lPW\nhKpYuBQPe/cVsT+vlqCajiIsZNmhMZqWiW1aCFnCME1cbnfcMte53j6/36GI4jgtJvsCdOmQyOWP\nWgAAIABJREFUQdM0FfQokksjHAuTkpIYt0D++wr6U3e8zxXiG+Yc/YCiQ6v4dtBknrjvNVZUjGFg\nm02wG7o/c4wBKV7OvLmbJvZp/nH/Eua8MZhhX75Di0c68c1Nk7luzNd8uLEfGdoWnnQtpu+WTVy5\nYz61akcypE/4teAClldfjOhZj7J9B69lv89XXW/n972D+P2oF/F6e+TdmSS/U83j1yzm/dc7Mej6\nxxAdx+LNqGNRYReefHQap8in4y0m09/7lpFjP+ba2bdjLF/Bzps7IswhjOpxhIvfdvPIvVO4YvZC\n+jXfRFJaPT1KN7Loi4F0HGizdIcg1Ksry07ezhvPvUabG4vZOuMOtNej/FwBhCxSBtUy4Ps8+lgR\nUi61WXhVM1rWyvQTsOZ3k41rJGYoP/GEeBGxagtLX01gWdYLyP6+zPhuJeHEH1lRF2bX1r7s/6Q7\n9vkSSPBp9U2IlYI+rq10vvYA87Mm8K8Nr3PZO5dxoOkkNna8EzbUEJWPstwezSTxDOV2H96I3kdS\nRT0fnbmB/gPWMcq/iouf2cw+urEy3JHvnr6czp8dJLKwDKs+zM6MXqyo+QdNoyeYtbcP907fTsGC\nLjyZ+yxdp++HdolMngdPTKnmyBulFN0awtO8Ba9Mu5H3N99J96V7efGfM2i3/Sjp4TNIKyXskVC3\n2OLeRXMY/9rvPHTbAh48MYvAplp+nTGG61/+lB+2Xcqtgfd4a9vDjPgP6+7vExaZOjLOoKuhlAjJ\njS1MhCLhGIWrgAy2jGU6Ln3oIAsZLBkJDWyTGCZoMhY2qiSjibNDzgYGix3HzkEgqZLzsyEwbSdq\nzYwXxrBh0zoni2BpCYQVTFlHclmNwqMG3BvlrADIpbqQhUUoGMTr82HbdqMAqAHz9fl8mKZJKGpg\nCRXbiBCuqSVsKxh+N+3P60xEWKAYuGU/eiyEBbhdPurqIng9PsK1tUiyjEtz4fL7HDfEuAiqIeTa\ntm3ckkJUj6LGPeRNYWPKMm7Tjx4UmFqUyffdyAf3vojLMqgjgmzIqLqCMBWwJGxJxhIykq3gsFOc\nEAtJstB8CZiRIG7VRV0kSn6pid9lkeDVcFky7kQvSBaqpCNFQHP7iQmBpEBRRYjVW08RlQIoWgy3\nbaDKCkKSHb90ScEwJXTTsWMwbYlgKIxbtTGNKLasoBsWAY+frq2a0rKpH5QgwbCBLNkkJPlQXQIn\nbunvSy+65/O3OL/5augFeYk5vPTTU0Q6ubjq27m4f48wcOg6rnzuE3rO3Ix2ZjXFWWl8V3wVlRdl\n83TbxVy15B6aXlrIzIrxvB6dzBzXLZQwgiuL3+XY8535pGgKhuXi65obuSD0M7P63odxn8H1N87l\ntjMfMvEmN1UDPAzKX84tF/WidepxwgMl6ocOY5JVzjUjJjLz5cdof5OBN3EY4eMhZHZQqbXgjrQN\n1O5Mgdqx8E4xE/mOGc2+hL1u2B8lZWktBQnNsHZp7H6lCw9GpzNnywxmRW7h+toddL/8MAnj6inI\na855z+1GeGyKzdNQu4Deod08uH4tl94yl8um/gCJw1nXM4vAuF95NvcBKiaAeRFsa96PU1ujdL7I\nT7+bVvLZbSXMTr2Tdysf4qo1X3CmbSq7JnbjgctugGhbbv3yWyaOXc7e/F58uvpWLjp/IR/1m07L\nxN14cw6wbHo+c567gdSYwau5T3P/tOe56NAv3C7P4eOeY7j8gov5KWE8TWdXUdI3jYKyVhRubQVb\ngHwgXM0j6kx6f/MDtxtTKVk7lZfNRZQtbcWt09/htNWEN5c9wJMHHmHdeghfkgxNevD2jm70jOzi\nxucX8kmv62iyrBiPFKbvG+uYz2Q6px/E0zHEwvHjSTwGvs1R/EcM7MMx6o4dYsTAX3mh7wwe3nQX\nSQer6XJp3X9cd39bQdfitLQG+AFw7G0tE9NycPP/LU5MxCGIc/1BGp7nBEGbmKZNQ3ZFI51PljFN\no/F5zoBP/CGsIRaLoQuD7C5ZbDh+gLCZhOpJAFHnDD3jUWoNAqYG5kVD2pEkO/RBv99PJBJphEEa\nQh+cc0SwRZRQ0CaCQUrbFLJymuJKdyHFdGy9DksHoaoIWaGmthZZ0qivr0dzaY53TCSC6nUjq0p8\nWKs0KlIbrodlOZsQOCHYaEpjTJ2maWidmtF70lAOLFiNFvFTb2skn6O0/U+HLMnI8eJrY1IfCnG0\nWMflVdFlP27FxpeajGlHkI0YPkmAYhGMuFmz7SAnwzaG8OIxDFxuGVVTGxWglmWhG6KR2mgYjkhL\nkSwsJCKRMJLiolOn1rTrmIis2EQiYGPiD7gbqapCsoC/r6CnFZTxTW57fvn1QsJ9B5B+fzGBtAru\njr5Pq5an2FiQyZOnL6PL5Ot499W7ySsvpeW16+hdDYkfVPNA/qtwdYTyAjeTmi6is/YiGSJEU28h\nt3x2Bd9VjWLoK7+S4F3CxrKOzMlsw6wbVvD8RU/x0+aLMVtbJA8q59eu4xmWtoZvP51OV/sI7hYq\n23t0pOqn8zlh5HBr8afsy+xDzZ1eTuUMQ6pI4oWnnuGerW/R9c69XFQ/nzGrVpAdi3GsRzZvmDfh\n//wQG38byTb3aJSOsO+ibO57KcQF1joKPJ2p3Rzm0fXXktwtjbuPjqfP214CmXBedAeXpy8mx1XC\nzCN38bznUQ5M74XeUaLGSOEfuVsJpoCnNTT1FWNdncjhnztT+IHMd61f4Z457zGrZhj31xxj7LF9\n+Pef5pqTR0klQBdToU3yMZT2FmuPduf7qV3YMmQgibdfxfUbP2bCG/ORu5o8l/IED+59na37B1Js\ntOOygnm80O4NCu528dKJGRiz9nP9Bcs5f/gaFiZM5t7CN+n/z3VcOfUjvv6hH1uO98QancFFqWto\n+8Upbvu+gtVl3Qh3b4MZUTh9PJO6F+sp/+A4NE+lfGpLtiV5kFurfD31Zl555THKmmeyrXVvctd1\n55GkZ8i/pR3Jx0LMtJ5hqWccpTvSeHHtMzwbuJvHU2cwcsBvSKfLWX56LM8X3AZM+st19/f5oTc4\n7sUpbZIkYUXD2IaNMASyJSHZDcKgs7CJgYVsC2TTRjJtTElgEreLjcMnQggs22wcqiqK809uai6E\ncH4tNXiFi7ha1DCwdYOUNgEkn0qszkfUo2Ea9QQUgWmbyJqEbdmOG6MkIccLaIMK0qu5cWsuQjV1\nyLKMJikIWUaPmSiKY5VrhhXCUZN6YdNpcC+0DB+SZCBME9sW6Dogq6gIIjVBTNsmkO7HlgQWNpFo\nPaqiIBsxJMvEkmWCwWDjBokQVNRW43K5nIGzqoJpEwmbuN1K3LtcQnapXHDTRPat3YJV6wMDIraN\nR1YdL3ZMZGxs2WGryJIHyzSQFRmBwG0LgjETUwZLVTlVFcYsjCLadGDYpFEkpieiCIUjJ3LJCPiJ\n5Z5ix8q97DpeStjtQ1UFIhxns8TXQUPIiCkMxxnSjGDZJh6XByksCNk2hmHRoVUyXdoF0JQ6gkGI\n6CaJaUkoLgFCd9Kl9Hhc3t90JPqqOdh2JJV1LRhctQNv69aUkUbeKZPSWFs8NSG6uEpYetc9PLW4\nmuE/vUUsJ4m8Tk0wzhes7T+Cq5M/YMO+CbS95Ax9eszDRYQN1ihuWrqYNZM8nLfzKOl7j9Ck2WFa\nDa5j/zuwvVrGXZDHmSWd2HG8L0ZqJXff+iO/txzFT5UT2JwykPQutYw+sY3Rq8ogBWpH+zgl55D3\nUxYJ6jH23NKDpu0LCG09TY5nH66JGr8ljaJ1kzz2B/qQPfoEZ6QWbCsbCFkuGKOzvm46mceaUri9\nPf3TVnC5/0NEXgbZj43mOu9ikpvFaN3sOF2LjlG2KIV90waybHMLogUSWqLFmTIb7Ug9qecJ7jh/\nDm0zDtPzjEx2egXR/hITq3ey5OPX+XnkNDaWdqdPdBdt+5RzQ+daPv79nyydF2H7r62I0g4pLHN9\n4SKqvmjOr+9ewkcnb+eS1V8S2Lufym7prP3HYLqu+po+9YX0PrONAaW7aXNLFrenX0jd0T5MWLuf\n3nmbGVoVpaKqF6f7pVFnN6GLdhqrIpNQj9YcD5dyen07bshbzMGxI9hcmEnipiAD9W0opRFKx/Qj\nPz+P847kMcR/kAQRZWexj0t2LoR2BWxMHEvssnQGZa7j1wVdMbIFuc3bo2XqaB1jHOzeA3VLEuOi\nK6hql4C3m03bWCm92f7XXFn+5gg6oNGPo7HjbmBTWI797J+FT2fHmQ3yccfm9WwMnYklJMQ5HIVI\nJIKQJHTLAAlkRcLCwrRx8HIDsGyqhUGvof0oPXkcKWajaRLCsDBMHXfAjyRJ6JaOaZjEg3KQJMcl\nsYFJo+s6Ho/H2UAMA7fHhSRkiouLsS0JW5YQyR769e5FtV5HIDGVcH0d1VVVyC4fXn8CdeEYtdU1\nZKSnY5hm/M5CRihS4x2FrTvwh4hHrkWj0UZ6ZEpKCqFQCNOykCwLIUlYhtWYuqTrOrKhgCLoOmIg\nexfuIBoVSML1P7+oOB2/caO0HIgrhoUlC2JYhC2bGsnHRVeNY9yUfuhKmOKyEhTZT3rHNkQUC3+H\nZuxbtYsq240Rk/DIgAqaS8U0rUYqp6qq2LaNqiqEQiHcbjeKqlBTGyIqbJq3yGRArxzSAwq6oSCA\n5GQfngAgWVjG//wT/o6jqaeYtKE+OuaEaJufS56dTdjysqkomaauJK5M3MngzKN800zlVftC3J1L\nqfTmYPaUyLjsDBdV/UAzrYwJpQs5fmknNiX2oH/+Zmoz08huHaBFQT6n8ltSqXgY4C9jaNZRPj0+\nih1qJ0Z0Pkp+ZR9Kv8lklHc5RYOz6V/9BQeSL6DWH6B7p3xuarIc36oQy3eMZ9PAXhyZm0N0dQj3\nlDLKpTQmDPqegvUVHDjYlHW9/0H1gGbMKHiGj4f9k/PuOUjABX027CGtqpx92Z1Jv7ea4rzOxNa4\naJkYRvGmsHjJILyLOxH5eSvGin2orc9Q5m7Ct0WTeC/1Mi4s+YikY24IqiRHK/jcdxWpfW2mim+x\n9wny8tqT7K+n+OoM5r1zGZPSfyQzpwnZncoJZUp4L0vmZM1YtPJ2/w9z7x0lZZmu715frNzVuekE\nNDnnnEFEQYwgigFM6JizYkLMjtkxoCImzIJKEFEEBUFyzjk1dA6V68vnj+pu3bPHOeucvX8/512L\ntaqKqm6oetdT73c/93PdeA9s4ZdD7Un6O3Bmr2O0Dpr0yXiD3E1h9mf2pK5zJv7vw8jeOL3e3EDW\n54cYtXEx/V2HcDLAWxfnknZfUD8pQPHCeqq+SCeU4aP92O2s+7I/aaPSkcYEKFRNXIkj+NaHWXKo\nG5cZh7nAWI69zc+xNc0ozt0A/xCZdJ+LTxfEGVy+hHtbfEOVWsyluQ+zpMfD/Eg7dmR3wN0+k68D\nE1m9vD9bSrrzTmwaE/fNp7X7EF9MnIRjWpTvhyNKa9r3OsGAqq0Unyxnxr/Zd3/dYFFjoITTIIGI\nIoahp4ISGqWYhhCK1DmrwUrXeFMEwzaxTCPVCLV//yKwRBupgb5omUaDE0RPJepIMjip03wKiZty\nYziOgO1X8BVmED20H8FJxeK5FQm/z0PCSGnhjZmVmuOgKCqKIqfCKhQZUUgNEMViMVwuNTWOrieJ\nxwwsyyYajdFjYB/sHDe2y8Hn8ZGI1KEn9VQqkK5Tfuo0vvQMsrOzsEwLTdfwulVo8Lk38sRN08Sy\nHAw7gWGYBAIpbokoSsRjMVxuN4ae+vIRJQFFVlKwMtvBEgTEuIDgERhy4Vg2/bgZ03LAbGhDpzAu\nf3jfHUQpdYVgWiY6JknHJomNjoM36GL6Q7fSaUARp6uOIhou8ovboHls/KqCSxExTZ2zJ1/GL788\nS4YYQLQSoDhYTkrKajydm6aJIMvoesoKKUsKmqajCQIur0q7tkXk5/mRSBDVHNxuD26vhChqqauy\nvzBV8Y/rZF1znGMiR0a0YP+ANricJBErwGD1AFN7/kb/1tX8EhqA/lsVOasW8/IlN3LdBfMY5fmJ\nzFO1aLkuzvpxJXPHjmONbKB9Y+D7qYrefTaz6sUBzLpgEFVrMymr78bwXfsZOP8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9AAAg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\nkgKWb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ACBcxOrf6hdn+Pz2LaDIJz9+uxzcimLlmWjSiKq4CGzezopOQFExcYRVTyilyYJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read an image file for first subplot, generate random array for second\n", + "img1 = plt.imread('lena.png')\n", + "img2 = np.random.rand(128, 128)\n", + "\n", + "# Make figure\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "ax1.imshow(img1)\n", + "ax2.imshow(img2)\n", + "\n", + "ax1.set_axis_off() # Hide \"spines\" on first axis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. The matplotlib gallery\n", + "\n", + "It can be very intimidating to try to craft exactly the figure that you want, especially if\n", + "you are used to being able to adjust things visually using a program like Excel.\n", + "\n", + "If you get stuck and don't know where to start, or just want to learn more about what\n", + "matplotlib can do, a great option is to have a look at the matplotlib gallery, which can be\n", + "found at http://matplotlib.org/gallery.html. A good way to get started is to find a figure\n", + "here that sort of looks like what you want, copy the code, and modify it for your own needs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2 - Exploring the matplotlib gallery\n", + "\n", + "Have a look at the matplotlib gallery, find a cool looking figure, copy the code\n", + "into the box below, and modify it. Note that some of the examples might require packages that\n", + "are not installed on your machine (in particular those that make maps) -\n", + "if this is the case, pick another example for the purposes of this exercise.\n", + "\n", + "In IPython, you can use the \"load magic\". Type %loadpy and then the URL of the py file containing the\n", + "code, and it will automatically copy it into a cell below. Run the cell with the code to see the\n", + "figure." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+9FHWvvMO1y9f3jK3Z/z4j2oS9/zHjnqaLq2s0RwFP2WU8C4lvEc0vWjHpSQw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\n9gzj228Pmy3CQxUUFJGVlUedOuY2CGnVwGoHIn6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YeuONrPjzn0NqR7gQl55O+/PO4/D334falBPpPAwOb1KhnGdJsHz6WcDhOp/z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\nhQr+kvBv0pNPrqBOnYo8/vjttkMBTGln3jpTUhT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ctm2bLCoq0rVo9yQvL09OnTpVjhgx4oHrGmWdvkJj588//2T69OkNxmvkypUr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\nFhtj9otIfxF5LeOYNcAREYkBpgMDHR1XOWbp0qV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88847j/UaAXNzc55++mmOHDmiM/24ubmxYMEC0tLS9C3ePTEwMGD58uUMHz4c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\nxhVLX/fHlZGRwdChQ5k1axaNGzc2LEdJVrZsWT7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gdCTDiAiffPIJn3/+OVu3bqVFixZG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\nbGxssLW1pXz58lSsWJEqVapQvXp17OzscHR0xM7OThd2zexMuXqnL7A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D7zvH3MCVwHfQzdn4+0IfTWUkwv5AgJxgkIJQ/dusScbQzVFS6R4PaR1UwTeVsAjF4TAF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\nFBYtghdegJo13U7zA6ezT9NpXicSpyXSrn47t+M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IVE1DR0VIAyF14loFMXs5fTkwp3DEBvFE+/BE+/DG+nCkwjhSR2kMH0UXKgOW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+hd1TSs/gIAA9t/iEyZkyvAkSN1SClobAzj9aoFz4dRXqZSJDo6AHDPyI1uV+wZyWJ+Sv/rOWCv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\nAhpWf5HKu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6+W+voEdrtebkVFGk3TiTdLjmedFWTx4iHsdnPCTBw9qgdlT6exeb3UXHgh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\nETYzK+g/fDsg88fErWwAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# %load http://matplotlib.org/mpl_examples/pylab_examples/contour_demo.py\n", + "#!/usr/bin/env python\n", + "\"\"\"\n", + "Illustrate simple contour plotting, contours on an image with\n", + "a colorbar for the contours, and labelled contours.\n", + "\n", + "See also contour_image.py.\n", + "\"\"\"\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.cm as cm\n", + "import matplotlib.mlab as mlab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "matplotlib.rcParams['xtick.direction'] = 'out'\n", + "matplotlib.rcParams['ytick.direction'] = 'out'\n", + "\n", + "delta = 0.025\n", + "x = np.arange(-3.0, 3.0, delta)\n", + "y = np.arange(-2.0, 2.0, delta)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n", + "Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n", + "# difference of Gaussians\n", + "Z = 10.0 * (Z2 - Z1)\n", + "\n", + "\n", + "\n", + "# Create a simple contour plot with labels using default colors. The\n", + "# inline argument to clabel will control whether the labels are draw\n", + "# over the line segments of the contour, removing the lines beneath\n", + "# the label\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "plt.clabel(CS, inline=1, fontsize=10)\n", + "plt.title('Simplest default with labels')\n", + "\n", + "\n", + "# contour labels can be placed manually by providing list of positions\n", + "# (in data coordinate). See ginput_manual_clabel.py for interactive\n", + "# placement.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]\n", + "plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)\n", + "plt.title('labels at selected locations')\n", + "\n", + "\n", + "# You can force all the contours to be the same color.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours dashed')\n", + "\n", + "# You can set negative contours to be solid instead of dashed:\n", + "matplotlib.rcParams['contour.negative_linestyle'] = 'solid'\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours solid')\n", + "\n", + "\n", + "# And you can manually specify the colors of the contour\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " linewidths=np.arange(.5, 4, .5),\n", + " colors=('r', 'green', 'blue', (1,1,0), '#afeeee', '0.5')\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Crazy lines')\n", + "\n", + "\n", + "# Or you can use a colormap to specify the colors; the default\n", + "# colormap will be used for the contour lines\n", + "plt.figure()\n", + "im = plt.imshow(Z, interpolation='bilinear', origin='lower',\n", + " cmap=cm.gray, extent=(-3,3,-2,2))\n", + "levels = np.arange(-1.2, 1.6, 0.2)\n", + "CS = plt.contour(Z, levels,\n", + " origin='lower',\n", + " linewidths=2,\n", + " extent=(-3,3,-2,2))\n", + "\n", + "#Thicken the zero contour.\n", + "zc = CS.collections[6]\n", + "plt.setp(zc, linewidth=4)\n", + "\n", + "plt.clabel(CS, levels[1::2], # label every second level\n", + " inline=1,\n", + " fmt='%1.1f',\n", + " fontsize=14)\n", + "\n", + "# make a colorbar for the contour lines\n", + "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n", + "\n", + "plt.title('Lines with colorbar')\n", + "#plt.hot() # Now change the colormap for the contour lines and colorbar\n", + "plt.flag()\n", + "\n", + "# We can still add a colorbar for the image, too.\n", + "CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)\n", + "\n", + "# This makes the original colorbar look a bit out of place,\n", + "# so let's improve its position.\n", + "\n", + "l,b,w,h = plt.gca().get_position().bounds\n", + "ll,bb,ww,hh = CB.ax.get_position().bounds\n", + "CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])\n", + "\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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X4MfcO0qpXsAHWusj9ilSSr0AfKG1ftM63gpM01ofcw20yb1j8BcFJbLByuKv\n4PKz4IpzIDoi0K2StAzXXLOCvXsrefPNGaSlBW4V7Lp16zjnnHO4/vrr+c1vftMlQzizs7MZMmQI\nBw4cIDTUvztatZSKCjeJiQupqrrqqCk82ir3jrJKY7wPXGU1ZgJQ0hzBNxj8SUIM3He95NvPzoNZ\nN8Izb0BRaWDbFRLiYOHC6Zx1Vhrjxr3PRx/tPfaLWolRo0axevVqVqxYwZQpU9i0aVPA2hIofvnL\nX3LLLbe0O8EH2X9hxIjYluXa94cVrZRaiLhk4oFc4AEgCNBa6wXWOfOBWUAlMFdrvbaZdRtL39Aq\n7MuVXbY++RrOnARXz4F+PQLbpuXLD3D11cuZNq0bf/7zyQHbEN3n8/Hiiy/ywAMPcN555/Hggw92\n6KRrzeXxxx9n4cKFfPvtt+1ye8Zf//p7tNY88cRJRz3vaJY+Wut2XaSJBkPrUVii9fx/aT3lKq2v\ne1Drz1dr7fEErj3l5bX6lltW6pSUhfqVV7Zrr9cXsLYUFRXpO++8U8fGxuqbbrpJ79ixI2BtaU3c\nbre+5557dL9+/XR2dnagm9MoLpdHp6b+S//4Y+Exz7V0s1FNNfn0DQYLVy18/DUs/EhcPpfOgvNO\nDdxeu99+m89tt61Ca81TT53M5MmBi3LOyclh/vz5LFiwgIkTJ3Ldddcxa9YsnM72lUOoDm1ttt4c\nduzYwbx58wgLC+O1114jKal9bkr/179u4f3397B48bG3hjObqBgMx8mGHfDmx7B0FZw8HC6eCRNH\ntP2uXD6f5vXXM/jtb9cyYkQcjz02lhEj4tq2EQ2oqqpi4cKFvPLKK2RkZHDxxRdz2WWXMXHixIDt\nSbtr1y527NhBSkoKQ4cOPdiOV199lddffx2lFH/4wx8YMeKIGBMAVq9ezapVq7j11lvb7b66eXnV\nDB/+Dh9/PJPRo48dVWRE32A4QSqq4H8rYNEnUFQGP5kh1n+vlLZth8vl5YUXtvK73/3IpElJ3Hvv\nSMaODWx65szMTP71r3/x73//m4KCAmbPns2sWbM4/fTTjzvcsaKigtLSUuLj4wk5LBaxtLSUhx9+\nmLVr1+LxeBgwYAD3338/vXr1Ys2aNdx///2UlZURERHB3XffzfTp09mwYQNPP/00M2fOpKCggC1b\ntnDzzTczaFD73ZqyKbxeH+eeu5SRI+P43e/GNes1RvQNBj+wbTe8+zl8sAz6pEoHMHMSRLZhhGVV\nlYeXX94lq5elAAAgAElEQVTGH/6wkaFDY/jlL4dx+undA74D186dO1m8eDEff/wxy5cvJz09nSlT\npjBhwgROOukk+vXr12QbV6xYwc9//nMyMjJ45513mDNnDj6f76DVfeDAAa699lomTpzI3LlzCQkJ\nITo6GofDwZVXXsnw4cO56667ePLJJ8nOzubZZ5/liSeeoKSkhCeeeAKAq666ikmTJnHDDTccl+un\nPXD33d+xenU+n346C6ezeSMRs12iweAHBvaGu+fB5y9LpM+KNXDadXDHH+DL78DdBpsvhYU5uO22\noWRkXMQll/Thjju+ZcSId/n737dTXX30BmRnV/LOO7spLa0F8NtOTAB9+/bl5ptv5oMPPiA/P59n\nn32W7t27s2jRImbMmEFcXBzPPfdco68dMGAAn332Gdddd12j+X+CgoIYOHAg06dPJzU1lfj4eBwO\nB2VlZcTExByMKjr99NMB2LJlCy6Xi9ra2oN1JCQksG3bNr+/79bmd79bz4cf7uWtt05ttuAfi663\n8sJgaCFBTjh9gpSScpn8feltuPc5sfzPmQajB0FrGpPBwXbmzRvA3Ln9+fTT/Tz99CYefXQdO3Zc\nhN3euDh8/30BTz+9mSuvXM6qVecybFhsq7QtKCiIyZMnM3ny5IOP5eXlNbklYXKyTFCHhoaSn58P\nHCrMTqeTgoICLrjgAsaMGcPZZ5/NrbfeSlVV1SGx9HFxcTgcDvLy8khKSmLr1q0Hn0tJSeGHH37w\n6/tsTXw+zT33fM/77+/h889nk5BwjOW3x4ERfYOhBcREwk9nSdmXC/9bLvn8XbUwe4qUQX1arwNQ\nSnHmmamceWYqJSWuJgUf4PTTuzNuXAKzZy9h4MDG9+dtLddHcyJi4uPjD4p+Q4KDg7npppuYN28e\nkZGRPP/889xxxx089dRTOJ1Oiosl16PdbickJISqqipSU1MPyZIZGxuL13vi+WrakrKyWubN+4qc\nnCq+/vps4uP9J/hg3DsGg99IS4ZfXAwfPAfP3iOP3fYEnHsr/OVN2NXKmYtjYo6+kCs83MmHH+6l\nX79InE7bEUneiopc3H77ambP/oS//nVLmyeBi4+Pp6CgAKDhOh2CgoKYMGEC06dPZ+zYsZx//vms\nX78eEAt+zRrZTcrr9ZKRkUH//v3p06cPQUFBB106O3bsYNKkSQDt2p+/Zk0B48a9T2JiCJ99Nsvv\ngg/G0jcY/I5SMLivlNuvhPXb4KOv4Jr7IC4aZk2WEUDPNo4AAvjyyxxmzpRNz0VYRQQzM8t48cVt\neDya228fxoIF25g8OflgeGhNjafVk8ElJydTVVUFcEjOn4aTugBlZWVUVMjuURMnTuT1119n+fLl\nrFmzBp/PR3p6OrW1tVxxxRVceeWV2Gw2evfufTB1dHsU/ZoaD488so6XX97O00+fzGWX9Wu1axnR\nNxhaEaVg1CAp98yDtVsk4dvP7oHkeJg5WeYBWrsD8Pk0+fk1bNlSypNPyhJ+m01Zog9vvrmL0FA7\nN9wwkL59o1i8eB9vvJHJiBFxfPttPq+9lsF//rOLOXN68uijY0lO9m9eGq/XS01NDTt27GDDhg2E\nhoZSVFSE0+lkxIgRPPPMMyxevJiamhqCgoJYsGABAKNHj+bhhx/m7rvvpk+fPtx7772AjA4uuOAC\n0tLSCA4OplevXu1yIZnWmvfe28OvfvUto0bFs27deaSktG76BxOyafALPg3ehgW59dUV6xyN3NfW\nfai/hfqMfXXZ+5SSW5sSX6RSYLeO7YcXWnfy1J94vLBms0wCL/2mvgM4YyL0bqUUN6tX53HXXd+z\nbNlZ+Hwam63+wzr//KVce+0AZs5Mw+m0cfbZS5g7tz8XXdSHs85awowZKdx553CuuGIZs2alcsUV\n6QDs21dJcbGL4cNPfMGY1ppzzz2XzZs3ExISQlpaGr/61a/weDzExcUxYcIENmzYQGFhIUlJSSQl\nJZGQENg1Cv5gxYoD3HffWgoLa/jzn0/mjDP8t8XI0UI2jaVvOCY+DW4Nbl/9rcd6zGPd9wGOwwS4\nTpht1nPKZgk39WLeUOTh0I5AEohYHUWDjqMW2fjc20hHY1dyLYcCpwKHTW6dCpzW/fbQMTjsstL3\n5OFw73Xw/SZYshKuuhfio8X6P3MS9E1r+bV++KGQp57aSFZWBSNHijg3FPwdO0qx22306xeF02mj\nrKwWn0/Tt28kH320l6goJ/Pm9Qege/cwCgtdACxdms1//rOL1atl8vWll6YwfnzicbdPKcWHH354\n1HOGDx9+3PW2R7TWLF26n8cfX09WVgX33TeKq65Kx+Fou+lVI/oGQMS1VoPLB7UNi2WpNxRNhw3C\nbIcKa50VHuj34DmsuH1Q6avvsLxa2h1ks4p1P9h6P4F4Dw47TBgh5d7r4Iet8MlKmHc/RITBmRNl\nBHCiUUD9+0cxdWoyxcW1/OMfO+jTJ5JTTulGWloY3bqFUVnpISEhGLdbtm384oscoqKCiIoKYvny\nXPr3jyI+PoSaGg99+0aSm1sNwAMP/MDttw9lwYIp/P73P/L99wWcdFJCu/SZB5rycjdvvJHJ/Pmb\nAbjzzuFcfnk/v8XeHw9G9LsY2rLQa3zg8orIuyxBdDYQw1A7RDtFFAMlhseLqrPoj3JO3ail1uoI\nXD4o98qxT9d3AME2CLFBsF3ef1tht8O4oVJ+fS38uB0+XSVRQDYbnHaydAAjB8hxc4iIcHL99YO4\n/npJQbBnTwU7dpSxZk0Bl1/ej5Ej4zhwoPrgFnwvvbSd00/vTlpaGLt2lR+03vPyag6OFr75Jo+y\nMjcXXdQHgMmTk3nssfXcdNNg/38oHRSfT7Ns2QFeey2Dd9/NYvr0FJ59dgIzZqQEtGM0ot+J0Zbl\nXlNXLJG3KUvQbBDpgARL6G0dQNhbik1BsJL3fjheXd8JunxQUSufW93nFWKDELvctkVHaLPVTwL/\n6mrYsgs+WwUP/kUWhZ12siwQGzdUFow1l549I+jZM4LTTqufPJgxI4XLLvuSbt1CGTMmgauuSick\nxMHnn+ccdO1s3lxCcbGLqVOTeeKJH5k9u9739N13BQet1sPnC7oSHo+Pr77K5Z13snjrrd0kJYXw\ns5/147HHxrb6BG1zMRO5nQi3D6p9UO2V4vKJONUJVZ1o2bvm//GEaDgyqvHWd6AAodaIKDQAn+vu\nbFi6Gj5bLfH/U8dIJzB1DISfYGBNTY2HdeuKGD8+EZtN4Xb7uPHGlZxxRncuvLA3Z575CZdf3pef\n/3wg48a9xwsvTGbcOJlQPeWU/zF3bn/mzh3Q4XLbtJS9eyv49NP9fPJJNp9+mk2fPpH85Cc9ufji\nPgweHJi83CbhWidEW0JU7YUq61ZrS4QCJERdhbq5g4YdbI3PcovZIMz6Dtpq0jivCD63OoB122Ds\nEJgxHk49CRJbmIX522/zmTdvBeHhDk4/vTuPPTaO8nI3N964kttuG8L48Ynk5lZz0knvs2HD+URH\nBx1Rxz33fEdFhYcRI2IZPjyOIUNiGj2vI+B2+9i8uZhVq/JZuTKPFSsOUF7u4dRTUzjzzFRmzUol\nNTVwexzXYUS/E+CzRL7KK6XaWz+hGmoXoWkvkSldkUM6YasjVsj3UleC2uD7Ka+Er36QTmDFD5IC\nuq4D6N/rxK9fVeXBZuPgAq0FC7aydOl+rr12AB9/nI1S8NRTJzfq2lm5MpfVq/P58cdiNm4sZsuW\nEqKjgxg8OJqBA6NJT48iPT2KPn0i6d07goiIwMfT+3yavXsr2b69lM2bS9i4sZgffyxi06YSevQI\nZ8KEJCZMSGTKlGQGD45pd+4sI/odEG1ZklVeqPSKayHIVi8goW08wWg4PurcQnWddJVXQk7D7BBu\nldbupGvd8P1m+OJbyQKqNUw/CWacBCcNO755gMPJzq7k2Wc38803ecyd25+LL+5DRISzWa4dn0+z\nZ08FW7eWsm1bKRkZZWRmlrNzZzlZWRWEhTno2TOc1NRwuncPJSUljKSkEBITQ4iPDyEuLpjoaCdR\nUUFERDgIDrY3y53k9fqorPRQVuamtLSWoiIXBQU15OXVkJNTRXZ2FXv2VJKVVUFWVgVxccEMGBDF\n4MExDBsWy4gRsYwcGU9kZOA7pWPR6qKvlJoFPI1E7v1Na/3kYc9PA94DdloP/Vdr/Wgz6+4Soq+t\niJJKS+SrvBIiGd7AUjSumhbgdUNRBtRWQGQKRPkhAP44qW0wUqu0cn+FN+gEWjNUW2vI2ANffCcd\nQMZeCRGdNhZOGdtyN5C/0FqTl1fD3r2VZGdXkpNTTU5OFXl5NeTn11BYWENxcS2lpbWUlbmpqHDj\ndvsIDrYTHGzH6bRhs4no+Xwar1fjdvtwuby43T7Cwx1WOKqTuLhgEhJCSEoKISUljNTUMHr0CKdX\nrwj69IkkLKzjxrm0qugrpWzAduA0YD/wHfBTrfXWBudMA36ptZ5zAvV3WtH3aKj01Au9ol4AwhzG\nkvcb2gcF26B8P4REi/AnDoFwK/NjXS6CmhLIWQveWrAHyfMJg8DmkDpc5XJ+ULg81pImWesiKr3y\nG2jYyUdYI7nW9BgUlcKKtbDse1i5Dnp0E/GfOgaG92/7bSFbgtfro6bGS22tD7fbh9erD46gHA4b\nQUFSmjsi6Ay09orc8cAOrXWWdbF/Az8Bth52Xtf4tI9CncumwhL6Wp813HdAQpC4bwx+pE7MK/Oh\nukiEPjJFhL0oQ0Rd+zj406ytlPtx6SLqylb/XOleyN8E7ioIT4a0k6Vj8HmhZBcU7pC64weC89jh\nM6pB6Gics/63UemB/FqJvKpzBUU4/P/biIuWnb9+MkM2f1m/TTqAB/8qE8OTRkkHMGkUJLZO2n2/\nYbfbCA+3ER74+dMOgT9EPxXY2+B4H9IRHM5EpdQ6IBu4U2u92Q/XbvfUWfMVlkXnsIkllxQkf+ou\nYngECA0oqC4ERzCEWOFzyi4FRNjrRpLeWgiOFNFvSHUxlO2FxKEQ0wv2fgP5m6HbKCjcDhU5kDRM\nxL8oA5KH13c4zUSpejdeIrJmoO53U1AtaS0iHPXuPn+OApyO+gVhv7waDhTUTwb/7mVISYQpo6UD\nGDukZXMBhsDTVk6rNUBPrXWVUmo28C4woLETlVLTgelt1C6/U+ebL/eKRV9nsUU4ROgDsOq666Ks\nD9vnFcvdbqmV9kkn4POCzc7BzkF7oXQPlOdAZHdx7QSFi6g7QiHMSvJls1sjBKRDiegG0T3A55bX\nemrA0bI86HYFUU4pdZFBFV4osEYBdSOAiFaYC+iWABedIcXjlVXBX/8Az7wBmXtlV7CJI6UM6NX8\nlcGGtkUp9WCDwy+11l+Cf0Q/G+jZ4DjNeuwgWuuKBvcXK6X+opSK01oXHV6Z1bAvGzT8AT+0sVXR\nVpRGndBrrJWuQf63yrokWouQusqgtlx88rWVENtHxLkxqgohZ41Y744QmbitE393JYTGW4JPfecQ\n2R1C40TQ87dAcSYkj5Br253SUcgLwB4sIwBlgxDL/xEUIXW6ylss+g1Rqn79RWKQJLmrsH5ruS5x\nEUU45Dfn77BQhx3GDJZy6+VQWgHfboBv1sOiJXI8fhicPEKSx/XubkavgUJrWbX94bK6Y/1gY+f5\nQ/S/A9KVUr2AHOCnwGUNT1BKJWutc63745EJ5CMEvyPh0/LHK/fIMNxppTRIC5E/ofnhnyA+j/jf\nqwrltqZYJlgBgqMgKFJcMKGx4DyKEzcsHnqdAh6X1FGSBXGSToDqIojte+RrnGFSABIHw4F14K6W\nf5PN2cAN5BJXkc8tjzka7FhV17G0Ig4bxNggxim/wyqv/Bb3VMsMRKTVAYS2wu8wOkJy/5wxUY5z\n8mH1BikL3gKvV8JBxw0VV1C/NDMSaE20hh1Z8Mk38MnXsk3n2acc/TUt/nVqrb1KqVuAJdSHbG5R\nSv1CntYLgIuUUjcCbqAauLSl1w0EHi3WVbkVbRFq3DYtx+OCigNSKvNEoIOjxJUSGifiHBLTrMnR\nI3CESAmJhpLdsG+VWOIhsWLpg4i6I0TU0euudwG5ZQcnlE3aU1NSPyKoLpZ2OcNkFFAn8h6XnGNv\nu9WmNmW5eRyQHGS5gTxwwCW/10i7dADhrTR/lJII550qRWvYewC+2wRrNsHf34GKKhkljLZGC0P7\nmTmBluL2wLqtEn77+bdyfOZEeOxWGDFAvufbr2z69WZx1jFw+0Tky60FUuHWnyjCYeLmTwitxdIu\n2ysRMTXFEkUT0U1KWELrWMrVxVCeLcKcOKS+E9n1OaSMlY5h52eWKydI/PuJQ8Ut5KmGrOWQPFJe\nk7MG+pwqor9pEaTPlI5h9zJ5L/ED6l1HR7x/X33n0crU1v12rbmlOhdQRBu6HHMLZbOYtVskZfTu\nbBjQW7KEjhwII/pD9yQzMj4Wew+IS23lOlj1o+zHPO0kOG28bMt5+OdnVuQeJ24flB32Z4myrCXj\nnz8BtJYJz+KdYnGjZOIzqgdEJLe6O6TZeGrE8vfUiGjXxfGDROkUbhfR7jFZ3Ed1jxdlyGjB6xaX\nUlOjEq1h47/FPRWeXN/R2Vvf9HVb8wBlnsAaL5XVsCkD1m+XMNENO2SyeGi6jAKG9JXSlTsCrWH3\nfli7WVZUf7cRaj0yZ1IXRXWsMFoj+s2gzioq88gfpKHQd9UfX4txV4sgFu0QX31sP4jpLW6bzvKh\nah9U5Io7KCy+Piy0KbxuqMqvd2lVFcjnEdldSnhSq48E6tyUZR7JFRRml996oEavuYWwMQM2Z8KW\nnbB5J9S4ZEQwoJfkDErvAf16QExk27evNdEacgqs950JGzKkI4wIlZTaY4fASUNlB7Xj+csY0W+C\nOou+zCN5Ulrb/9llqCqAvE3iwonuKe6O8GTzoTaGzyOdRkUOlGVLdFJENxkFRaVJyGgr4tX1LqCq\ndtAB1FFUClt3yyTl9ixJIbFzH4QEQ99UiRLq2V0SyvXoJu6OE00p3Rb4fHCgELL2SyrszL3ynrZl\nyTqJwX3qRzsjBkBSC9NiGNFvwCFC7xORj3KYhVItRmsRrgPrJGQxcYiIfcPIFsOxcVfL3EPZPukE\ngiLEFRbdq9VHSHUdQN0IILxBB9Ae3Jpay2rhzH2QlQ1ZObAnB/bmQnYuBAVB90RISYCkeEiOg4RY\n2XM4PgZioyT6KCLMfxFFWkO1C4rLpBSWQEEx5BbJIrecfMjOE2s+Klw6q96pEtWU3ktGMgmtkHK/\ny4u+p4HQuxoIvbHo/UTFAdi/RiY8k0dCXL82m6zs1GifRDSV7pGQU7SIf0xvyw3Udh1AICaBjwet\nZXSQUyBim1soHUR+sTxeV0orxHUUHgrhYRAWAqHBEBwkUUVOB9ht9Z2CT4uV7vFIlExNrYRFVlVD\nRTWUV1khtFHSqcTHiL89KQ6S46UDSk2G1CS5TlvRJUW/sR+tmYz1M64yyP5OJmm7jTZi35poba03\n2C3F6xbxj+sn4aet2AF4GvyXarwd32jyeCWUtLIKqmrEUnfVSipqj1eKz1f/3uw2cDikUwhyQkgQ\nhIWK3z0yvH2GoHYZ0a9bMFXqrvdPRrej4WmnweeFvA3it08aBklD208ETlehutiKhtolx7H9JGdQ\ncOvOdB4+DxZldQCtsRDMcOJ0atHXVnraUo9EJIRafshIE0ffOlQVQNYK8TX3mCi3Bv9RWyvO6eai\ntXwnRRnSAQRHQ3x/iOnT6qGgtVYHUOqW1CN1HUBIB0rL3FnpdKJfl4Cq1LI4nEos+ihH625E0aXR\nGvI2ioWferKsSO3Ipl15OeTmQkEBlJRARQVUV4PbXT+2dzggOBjCwiAqCmJiID4ekpLkcX9TVQV/\n+xusXg1//CN063Z8r/d5ZQK4cDtU5or7J2FQq7t/tJa5sjKP/CftDf6PZqV6YOg0ol9rCX2pW3KM\nRDnlx2Xy0Lcy3lpZkequht7TW92F4De8Xti2Ddatg40bYetWyMiA3bvlueRkSEgQMY+MhJAQsbJt\nVrpljwdcLhHj0lLpHAoLIT9fzu/RA/r0gb59YcAAGDQIhg6VOo8Hn0+u6XLBjz/ClVfCuHEwf760\n7URwV0mO/8JtkhwucbB01K3shtNa9gcuc8s8QLC9fgRgRt5tR4cX/UKXprSBDzHaASHGh9g2uMoh\nc4lsEJJ6ctPpBdoDXi989x0sWQLLlsn9pCQYPRqGD4fBgyE9HXr3FjE90R+QzycjhD17pAPJzITt\n26VT2bRJRgbPPAMXX9x0HVrDgQOQklJfp80mHdQ770hncs01J9a+w69Ttg8KtogbKK6/hNO2cvw/\n1M+xlbnFBRtuh2inRACZ/27r0uFFf1+1JroDRwt0WKoKYeenkl44cUigW9M4bjd8+im89RZ88IG4\nRGbOhBkzYMIEcce0JVrD3r0yakhKavq84mK44w4ZPTz/PHTvLq/94x/lueuuE+E/zs1YjoqrTDZ/\nKcqQxV9JQ+v3CGhlvLre/19rhU1HO80EcGvR4UW/vbexU1KZL4LfY5L4htsbu3bBiy/CP/8p4njp\npXDeedCrV6Bbdnw89BBMmgRnnAHLl8Pbb8OUKUeOEhqKf3m5uJdOFI9L/P75myTjaLeRbbpi+vAJ\n4GjjpvU7RvQNx0d1EWR8DD2nSBqF9sT69fDII/Dll3D11WIRDxoU6FYdP17vkbuPP/KIjFxuvlnm\nG+qEvqHgz58Pn38ucwzPPAPDhp14G3xesfrzNkjCuG6jJf9PG4n/IQEZbhH9aKfx//sDI/qG5lNb\nAds/hNTxjW80EigyM+E3v4EVK+DOO+H66+nwO2Fra7nnjTeK5R4UBOeeK5b/4ecpBb/+tfj8n34a\nFi+GL76A//73yM7juNvhg+JdcOAH2RYyZYzM4bQhusEam8oGiymN///EOJromxU1hnq8bti5VBZc\ntRfBd7ngiSfguefEB/73v3d8sa9DKRHsU0+Fxx+HvDxx7UD9SKDudvt2WLQIli6ViWiPBzZsEFdP\nXYSP2w3OE4jNVzZZ2RvbB4oyYc8KyRbafZzk+2kDlKrf8atuBXBBLRxoGLzRjmMIOhLG0jfUs3uZ\n/Pt6Tm0f5tXmzfDTn4rIPf+8hEh2Zv78Z1i1Ct5888jnrrkGYmPlHIA1a+DBB+XckhJ46SX46iuZ\nvH7kkZa1w+eFgq2Qu17ceyljT2znMj/g8on1X+oBR138v1PuG5rGuHcMx6YoUzJkDpzTJpt6HJM3\n3oD/+z+x8ufNax+dUFvy9NMQHQ1z50o00EknyRqDCGsF9Ny5MHAgXHUVPPywfD633AK33w433SST\n2i3FWws5P4jfP3mERPsEKLfS4SvvTfjn0THuHcPRcVdD9mrod2bgBV9rsWBfe0181i2ZqOzI/Pzn\nUFQk9/fuFV9/RISkaVi5Unz7L74Iv/qVLA677DIZCV1yibiA6kS/Lv7/RLAHQdrJsqp33zci/j0m\nyW5nbYxqsBdwXfhnYS3kaLH+jfun+fil21ZKzVJKbVVKbVdK3d3EOc8qpXYopdYppUb547oGP7H/\nO1m000Yx202itViq//sffPNN1xV8EIHvaUVODRxYL/ILFkiqht/8Rvz8e/bAnDn1rq+FC2HsWLn/\n7rtw991wyinw7bcn3paQaOg3U0I7d30uG8x73S17fy3AriDWCb3DoFeorM7fWwO7qqDYLZ2CoWla\nLPpKKRswH5gJDAUuU0oNOuyc2UA/rXV/4BfACy29rsFPVBXIZh3d2kE/fN998PXXYqkmt7012W6J\nj5c5jU8+kXQSt94qcfzLlsnEb91n9cEH4hKaOVM+x3vvhalTJaz1gQdkxHCiKCWT+4MvkDj/re/K\njl8BJtgGScGQHgaJQeICyqiEfTXiBjKe4SPxh3tnPLBDa50FoJT6N/ATYGuDc34CvAqgtV6tlIpW\nSiVrrQP/q+nq7F8jgh9ot84rr8ik5KpVJ55vpjMzfryEZzbMwunxSC6gWGuX7Pnz4YorICdHrPyr\nr5ZRAMCf/iSunpbiCIbe02RTl12fieun26iA76NwuPun1AN5tXI/2gExTrP4qw5/fAypQEMTYp/1\n2NHOyW7kHENbU1UoG3PEDwhsOzZsgLvugg8/PP5kZV2NhmmXQ0Nlsdq2bfL52e1w/vlyXFYmk70g\nVv8pp/jX7I3pBYPOE2s/4xOZF2on2BXEOaFvGPQIkVW/u6sgq1oigXxd3PpvdxO5SqnpwPQAN6Nr\nkL9JcuoEMoma2y3W6R/+0DFX1gaS66+XpG1XXy0Tvc89J3MBn34qI4PERInj37JFBN/fIyhnGKTP\nlAifbe9D39MCPy90GCF2KYlBsvirxA0HXBL7H+Ps3IkblVIPNjj8Umv9JfhH9LOBhmv106zHDj+n\nxzHOAcBq2Jd1x0qpB/zQRsPheGtl79XuJwW2Hc89J0nSrr46sO3oqNx/v0zqOqy/cmGhxOvPny/H\n330n6ZonTRLR92cCNxC3Tvexsogrc0m7zdVkU/Upnt1W6ofsGnk82kr+1tli/7XWDzb2uD9E/zsg\nXSnVC8gBfgpcdtg57wM3A28qpSYAJcafH2BKsiCiW8AW3QCSP+aJJ2RCsrOaW22Bo8HfODISJk6E\nf/wDRo2STnXyZLjgAnm+tT7n2D6yz0Lmp+CpEV9/O8Vpg4QgiHdCtU+s/8xKif2PcXb+bL5+WZyl\nlJoFPIPMEfxNa/2EUuoXgNZaL7DOmQ/MAiqBuVrrtc2s2yzOag0yP5VojLh+gWvDE09I/vnXXgtc\nGzoj69dLyoq4OInXv+QSSc/gbyu/MVxlkqwvcags5uog1MX+l7glDUSMZf131MlfsyLXcCg+L2xY\nCEMvluyKgcDrlZTI770nm5wY/E9pqYRwtjW1FbDjI8nh1F73YTgKNV4osTJ/Btsh1ooKsh2jv6zx\nQpVX8gcFepvIo4l+B+3HDC2iqkCG4oESfJDc8fHxRvBbk0AIPkBQBKTPhtwfoXhnYNrQAkLs0C0Y\n0t61dlQAABglSURBVMPF4i/2QEbV0Rd9lbgh1yWpovfWQKWn7dp7vLS76B1DG1CVL5tmBJJ33z36\ndoKGjk1wpKT1yPgYgiIhPDHQLTpubMra4MUpk79N5fh3+WQhWFyQWPn5tWLxhzvaxqN2vBhLvytS\nVQhhbbyN4OEsXSorRw2dl9A46DFZUjd4agLdmhbRlLumLhGcwyaCDyKqdnWk4Nf6pDMINEb0uyKu\nUsmXHijKyiArC0aMCFwbDG1DTC+J7Mla0SlzIvgQMQ+1lNTjk8VgPg4V/CqvuH/yayVNRHUAxd+I\nflfEVS5D7kCxZYskETuRDT8MJ47PJ+mXKyra9ropY2VytwP695tDtRfCrPWNtVpcQaENlLXK2hEs\nwiEJ4hKsHEF1tHVfaES/q+HzysKsQE7i7t4t6YANbYvNBrm5svtYm17XLvstZ38rv71OhMcnWT7r\n3D8VHnHthDZY5F7mkefr3D9eLZE+IKOE/FpZJ5DrapsMoUb0uxpelyTNCuTsUl4eJCUF7vpdmdtu\ngxdeaHvzMjwRotIkoqcTEWSTsqtKVvi6taR9rgvv9PjE8g+316/4rfLKymCflqRwGkkT7dHSabQ2\nRvS7Gj4P2ALsVqmq6jz73HY0pkyB6mpJctfWpIyWbRg9rra/diuhFHQPgfgg2cUrJVisdbeV0LRW\ni5VfF/lTZ+EH2SQXkELcPXYlxWP1xT5rsVieS6KD/IkR/a6GzxvwNLjtMo6tq6CUpFv+6KO2v3ZQ\nhOy5W7it7a/dykRZK3htSkS/0is/8yAlLpw60S92yx4ANgUuryR8s1uvcSrpBEBcPVVeGQXsq/Hv\nxK8RfUPbExIi1qYhMJxyiiRlCwQJg6FgW6eM5Kkj3MrgqZRVEIu92C1CHuuUx1y6fgLY4xPXULBN\nXDwuHyQFQXKwJITzp7VvRL+rYbODDnCwcHx8/f6vhrZn5MjAuHdAUi8rJavCuwB2BSkhcuv2QWqI\nuHu8Wiz9ugnfGp/0g2F22fs3usG8gKZe9P3RVxrR72rYnOLXDySpqbBvX2Db0JXp3Rv275f8R22N\nUhDdC8pasHVjB8OhZEvHpOD6zdvtSqz6utW8ZR5r1y9kBBDbYNqtzC3zBf7CiH5Xwx4kYXOBHF6n\np8um3obA4HBAVBQUFwfm+pHdoTwnMNduJzhtYtXvrRa3T7RTQjorPYcKfJVXFnqFW+Ge/pgKM6Lf\n1bDZweaQ0M1A0aOHRPDk5weuDV2doCDZbzcQhCdCdSFoP4eldDDigiSpW0qITASDWP91eDUUueut\nfn/ZaUb0uyJBEVBbGbjrKyXb+a1aFbg2dHVcLggODsy17UFgD5ZVuoZDduxy2iRsc1cV5NRI9E+c\nJfqHW/lu34l1BEb0uyJBEbLZRSA55RT44ovAtqGr4vFIKoaoqMC1IThK0oEYDsGuoGeoRO3EOGUe\noKk8/rkuyKyCglqJ/mkuRvS7IsHRknQtkMycCYsXB7YNXZV9+2RFdCBzHzlCOnzmzdYkzC4Tu0cj\nNURKrU/Ef1+1zAkcy/o3ot8VCY2D6gCHTI4dK9k2t2wJbDu6Ihs3wtAAb2Vod4LPHdg2dHCUleOn\ne4jMDYTZIbcWdlYd/XVG9LsiYfGBj5O22eDSS+GNNwLbjq7I6tUwblyAG6E69QKttsauZGK4T6h0\nAkejRaKvlIpVSi1RSm1TSn2ilGp0fzal1G6l1Hql1A9KqW9bck2DHwiOlrBNd4BXxc6dC//4h/iY\nDW3HZ5/B9OmBbYP2BT4dSCekzvo/Gi391O8BlmqtBwKfA79u4jwfMF1rPVprPb6F1zS0FKUgLBEq\n/397dx4kdX3mcfz9zDDDMcMNDsh946IgusEDVBBRYc1CXJPSxKyucVPZjUdF3eiqVcHEP3STVJmK\nZZUaXbMbjUnhEVFjGMVxMYpBFJUbRW6Y4RxgYJim+9k/nlZHnAu6e76/7t/zqqKkm67+PRPCp3/9\nPZ5vddg6TjvNNgo9/3zYOuKkuhpWrrSJ9JBSCRvice0u09CfDfw2/fvfAnOaeZ1k4Voum7r2h4M7\nQlcBP/oR/Pzn/lW/vTz9tDVcC7Vc8zOJQ1DSJWwNMZVpEJ+kqtUAqroDaK5JugKVIrJERP41w2u6\nbOh6MuyPQCuEOXNsQreyMnQlhU8VfvMbuPba0JXYPpESb68dQiuLgkBEKoGKxk9hIX53Ey9v7nZt\nsqpuF5G+WPivUtVAbf4cAJ17QzJh6/U7BlyvXVwM99wDd94JF11kE7wuNxYutH4706aFrSOZsOWa\npR76IbQa+qo6o7k/E5FqEalQ1WoR6QfUNPMe29P/3SkizwGTgCZDX0SmAlNbL91lRAS6D4J9G6Hi\ntLC1XHEF/PKXtpLnu98NW0shu/deuOOO8GcZHN4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4PP3007jdbl566aWgq/G2JD169GDu3LlMnTqVMWPGsHTpUrtNOoKwMCezZ0/g\nwQdXs2tX+Ul/nhF9Q5sgOhJ+/0t44DmoddttzbHJzIxulj9vc+H1evnHP/7BP//5z3Yl+HU4HA5u\nvfVW/vOf/zBlyhSWLVtmt0lH0KNHHH/8Yz/uuefHk/4sI/qGNsO0iZCUAH95BnyBGdF+Qpx1Vhpf\nfpmLxxMcRu7evZuQkBAGDx5stym2csEFF/Dyyy9z+eWXc+DAAbvNOYJbbx3AV1/lkpNTcVKf0yyi\nr5T6j1IqXyl1VK+qUuoppdQ2pdRqpVTr6UI3tBqcTnjsVtibD3c+Ebw1/u7d4+jXL4HXX8+22xQA\nXC4XYWGtaOqwFmTKlClcdtll/PWvf7XblCOIjQ1l2rRuvPXW9pP6nOaq6b8KHDVYTik1GeihtT4F\nuAl4vpmuazAcRmQ4vPwAuN1w/X2QG3wVNgAeeeQ07rvvJwoK7E37C9CzZ08OHjxIbm6u3aYEBX/+\n85958803KSkpsduUI5gypQtffHFy31OziL7WeglwrOTQFwP/tY5dBsQrpVKb49oGQ0MiwuGJO+Gs\nkTDtDvhskcyeFUyMHp3C1Vf34JprFtnu5gkJCWH69Ok89NBDttoRLHTs2JFTTz2VH374wW5TjuC0\n05JZvbqQkxm7FCiffgawx28713rPYGgRHA74zaXw7J/h5Q/gxgcgZ7/dVh3OQw8NA+CGGxbj89lb\nKs2cOZMvvviCd99911Y7goW0tDSKiorsNuMIkpMjcLl8uFze4x98FIKuq14pNQGYYLMZhjbCoF7w\n7j/h9c/gl3fClPHw2yuCY47c0FAH779/NhdcMJcrr1zIa6+Nsy2tcnJyMh9++CETJ05EKcXll19u\nix3Bwt69e0lKSrLbjEZxOhVut8ZvHFqjKKUe8NtcqLVeCM04R65SKgv4VGt9RKJspdTzwAKt9TvW\n9mZgvNY6vwmfa9IwGJqFghKZYOWLJXDV+fCrCyE+xm6rJC3Dr3+9mD17KnnnnTPp3Nm+tAerV6/m\nwgsvZPr06dxzzz3tMoQzNzeXfv36kZeXdyi1c7BQUeGmY8fZVFVde8zRuYGaI1dZS2N8AlxrGTMK\nKGmK4BsMzUlyAtw7XfLt5x6ASTfDv9+EolJ77YqICGH27Amcf35nTjvtE+bM2XP8k1qIIUOGsGzZ\nMhYvXszYsWPZsGGDbbbYxW233cYtt9wSdIIPMv/CoEEdTiodQ7PU9JVSsxGXTBKQD9wPhCGZ3l60\njnkamAQ7YQXPAAAgAElEQVRUAtdrrX9q4mebmr6hRdibDy+9D199B+eNgeumQI/M45/Xknz7bR7X\nXfct48d34oknRto2P67P5+OFF17g/vvv55JLLuGBBx4gPb2VTFRwEjz88MPMnj2b5cuXExUVfNNV\n3n33SrTWPPro8GMed6yavu2pk4+3YFIrG1qYwhKtn35L67HXan3jA1p/s0xrj8c+e8rLa/Utt3yv\n09Jm61df3aq9XvvSLxcVFek77rhDd+jQQf/ud7/T27Zts82WlsTtduuZM2fqHj16BG0aaZfLozMy\n3tJr1xYe91hMamWD4fi4auHL72D2HHH5TJsEl5xl39SLy5cfZMaMpWit+de/RnL66fZFOe/fv5+n\nn36aF198kdGjR3PjjTcyadIkQkODK4fQibBp0yamT59OVFQUr7/+OikpKXab1CjPPbeJTz7ZzRdf\nHD9/uKnpGww/k7Vbtf7zU1qPvErrGY9ovfgne2r/Xq9Pv/baVt2ly9v6wgvn6jVrjl/La0kqKyv1\nSy+9pMeMGaNTUlL073//e71kyRLt9XpttetE2LNnj77pppt0cnKyfvLJJ4P6HvLzq3RKypv6p58K\nmnQ8pqZvMJwYFVXw+WJ49ysoKoOLz5Taf1bjmXpbDJfLy/PPb+aRR9YyZkwKf/7zYIYNszc98/bt\n23nrrbd4++23KSgoYPLkyUyaNIlzzjknaMMdvV4v8+fP5+WXX2b+/PlMnz6dO++8M2jtBfB6fVx0\n0XwGD07kkUdOa9I5x6rpG9E3GJrIll3w0Tfw6SLoliEFwMQxEBvACMuqKg8vv7yFxx9fT//+Cdx2\n2wDOOSfd9jlfd+zYwRdffMGXX37Jt99+S8+ePRk7diyjRo1i+PDh9OjRwzYbS0tLWbRoEV9++SUf\nffQR6enp3HDDDVx99dXExwfXRDaNcdddK1i27CDz5k0iNLRpAZdG9A2GZqTWDd/+CJ8uhB/Wwtih\nMGUCnD4UQgMU1u5yeXnzze088YSEVN56a3+uvLI7kZH2x9XX1tayYsUKlixZwrJly1ixYgUVFRUM\nGTKEQYMGMWDAAHr37k337t1JS0vD6XQ227XLysrYsmUL69evZ+XKlSxdupStW7cycuRIJk6cyJQp\nU+jdu3ezXa+leeSRNbzxxnYWLTqf5OTjjMbyw4i+wdBClJRL5++nC2HXPqn5XzgehvaBQFRstdbM\nm7ePJ5/cwPLlB7nuup789rd9gm4qxgMHDrBq1SrWrl3Lxo0b2bp1Kzt27Dg0jWFGRgapqakkJyeT\nmJhIfHw80dHRREZGEhoaisPhwOfz4Xa7qampoaKigtLSUgoLC8nPzyc3N5ecnByqq6vp1asX/fr1\nY9iwYYwaNYphw4YRHm5P6OuJ4vNpZs5cySef7OabbyaTnv7zwkeN6BsMAWBvPnz+rbh/XLUweaws\nfboFpgDYsaOMF1/cwiuvbGPgwA7ccEMvfvGLrKCo/R+NmpoacnNz2bdvH3l5eRQUFFBcXExpaSmV\nlZW4XC5qa2vx+Xw4HA5CQ0OJiIggJiaG+Ph4EhMTSU1NJSMjg8zMTFJTU213dZ0sZWW13HDDEvbv\nr+KTT84hKanpNfw6jOgbDAFEa9i8U9I9fLEEwsPg/HFSAHQLQJpBl8vLxx/n8Oqr21i27CBXXNGN\na67pyejRKTgcrVsQ2zo//ljAlVcu5Oyz03niiREnnIvJiL7BYBNaw5otMGeJjPxNjIdJp0sB0CUA\nEUB791by3/9m88Yb26mq8nDVVd256qoeDBjQoeUvbmgyNTUe/va31bz88laefHIkV17Z46Q+z4i+\nwRAE+Hzw0yap/c/9HlKTYOLp0g/Q0gWA1po1a4p4883tvP32TmJjQ5k6tSuXX96VAQNOLpeL4cTR\nWvPxx7u5/fblDBmSxKxZo0hLO/n0D0b0DS2OT4PXf0FefXWLdYxG1rW1DvWvUJ+xry57n1Ly6lCS\nHVApcFrbzoYLgfGdNwceL/y4UTqB5/9QXwCcOxq6tnCKG59Ps2zZQd59dyfvv7+LiAgnl1ySxcUX\nd2HkyI44nWbq7ECweHEe9977E4WFNTzxxEjOPbf5fH9G9A0nhU+DW4PbV//qsd7zWOs+IKSBANcJ\ns8NaV3XCTb2Y+4s8HF4QyLBCq6DwKzjqXr2NFDROJXaEKAhVEOKQ11AFodZ6sBUMHi+s3CC1//nL\nICleav/njYHunVv22lprVq4s4OOPd/Pxx7vJz6/mggsyufDCTM45J534eDN3bnOitWb+/H08/PAa\ncnIquPfeIVx7bU9CQpq3oDWibzguWkOtBpcPav0Xq6buL5p1QuovrHW1cLvvwdNg8S+o3FbhEKIg\nzGEt1nq4Q963+x68Xli1Gb76Hub9ADFRcN5oaQEEIgpo585yPvtsD59/vofvvstn6NAkJk7M4Lzz\nMjj11CTTCjhBysvdvPnmdp5+eiMAd9wxkKuu6tHkwVY/FyP6hkNoq4Ze4wOXV0TeZQliqL8Y+oli\nMIhhc1HXaqn1SUHgsgq2Wp/sqysAwh0Q4YBwp9y/Lbb6YO1WmLdUWgEOB5w9UgqAwb1kuyWpqvKw\naNF+5s7dx9y5ueTlVTNhQifOPDONCRPS6NcvwUQDHQOfT7NoUR6vv57NRx/lMGFCGrfc0pczz0xr\n8T4UI/qtFK+GSo8IbuwJRG5pq+ZeU7dYIu9QlqD5LWEOeb8949X1haDLKhRr/J5XhAMinPIa6IJQ\na9i0E75eCvOXyqCws0fCOaPgtP4QFoBkl7m5lSxYsJ8FC/azcGEepaW1jB2byrhxqZx+eipDhyYR\nHt58o2tbIx6PjyVL8vnwwxzee28XKSkRXH11D66+ukezdNA2FSP6rRC3D/bW1ItLpAOSjuNedfug\n2gfVXllcPjm/TqjqRMvZzsX95+DfMqrx1hegIN9JpFNeA/1cd+WK///rZbAzF8adKoXAuFMhOkAT\nPu3dW8nixXksXpzPDz8cYOvWMgYN6sDIkR0ZPrwjw4cn07NnXJtvDezZU8G8efv46qtc5s3LpVu3\nWC6+uAtTp3ajb1978nIb0Q9itIYSD1R6IcoJHUJE5Evc4nJICRcxz62R9SinnFNXy6z0QLFHRF5r\nS4RsEqL2Ql3fgX8BW+OT1lKkQ76jSGfgOo0PFME3VgGwegsM6wdnjoCzhkPHxJa/fh0VFW5Wrixg\n+fKDrFhRwIoVBRQVuRg0qANDhiQxaFAHBg5MpF+/hFbbQex2+9i4sZilSw/y/fcHWLw4j/JyD2ed\nlcZ552UwaVIGGRn2zXFchxH9IKLuVurEoMIDxW6IDxWhj3FCYhjku0Swk63/Rr5LfM5pEYeLfpVX\nCoeoAIqM4Ui01Rqo9sp3UuWTiKQoZ/0SFoDvp7wSlqySQmDxKkkBXVcAnJIV+N9HUZGL1asLWbOm\niLVri1m/vphNm0qIjw+jb994eveOp2fPOHr2jKNbt1i6do0hJsb+iVl8Ps2ePZVs3VrKxo0lrF9f\nzNq1RWzYUEJmZjSjRqUwalRHxo5NpW/f4OvbMKIfBLi8cLBWaocdQsVHH+6QGny4Q8S90gPlXogL\nkVDIYg9kWU31Ki/sq4Ge0YeLviE4qXMLVXnrFx8i/tHW0tKFdK0bVm6EBcth4QqxacJwOHM4DB8Q\nmH6AxvD5NLt3V7B5cylbtpSSnV3G9u3l7NhRTk5OBVFRIXTpEk1GRjTp6ZGkpUWRkhJBx44RJCVF\nkJgYTnx8KHFxYcTEhBAe7mxSx6jX66Oy0kNZmZvS0lqKilwUFNRw4EAN+/dXkZtbxe7dleTkVJCT\nU0FiYji9esXRt28CAwZ0YNCgDgwenERsrP2F0vFocdFXSk0CnkQi9/6jtX6swf7xwMfADuutD7TW\nDzXxs1u16NcJdGGtuARSw+GASwQhI0Le92px3Xi11PZ9QGIobK+EXjH1rYMdVZAVZV80ieHkqPXV\nFwCVXnkv2q8QaOZQ7cPQGrJ3w4IVUgBk74FRg2D8MDhjWGDdQMdCa82BAzXs2VNJbm4l+/dXs39/\nFQcO1HDwYA2FhTUUF9dSWlpLWZmbigo3breP8HAn4eFOQkMdOBwiej6fxuvVuN0+XC4vbreP6OgQ\n4uLCiIsLJTExnOTkCFJSIkhLiyIjI4rMzGiysmLo1i2WqKjgTVR3PFpU9JVSDmArcDawD1gB/FJr\nvdnvmPHAbVrrKSfw+a1C9N0+KHLLQKL4EPHp1gm+V0NBrXSkxofKn39fDaRH1LsDOoXLsWUeqfGn\nRcCuKnH1xIXIOQW10iIIM6HSrZ66cRGVXvm+q7wyDiLaKS6+SGfLRlMVlcLin2DRSvh+NWR2EvEf\ndyoMPAWaMcV9i+P1+qip8VJb68Pt9uH16kMtqJAQB2FhsjS1RdAWOJboN0dRNgLYprXOsS72NnAx\nsLnBcW32afu0CL5PS+dpnksEPdwSZwdSyw9R8mcPs0L+XD75o2sr0ibC+qPV/S4TLZePyytuoUgr\ntNK4d1o/SkG4kt9IYqh8p9U++b4P1srvoc4VFBPS/AV9YrzM/HXxmeD2SFK4RSvhgeekY3jMECkA\nxgyBjkGem83pdBAd7SDa/v7TVkFziH4GsMdvey9SEDRktFJqNZAL3KG13tgM1w4ayjxwivWj81lu\nmuQw6YytSz/g0hCJlH5hDun4Sw6FMgWlHhF9bY0aBYi1Ov+K3dIHEGd9W0bw2x5K1Xf4dqR+jEaF\nFwqqJa1FTIgUAlHN3AoIDZFY/9P6w23XQV5BfWfwIy9DWkeZHWzMEIkMsqsvwNA8BMpp9SPQRWtd\npZSaDHwE9GrsQKXUBGBCgOxqFnxILbzGK8Id5RTRr6utgbxWWyNf62p4JR5QYZAQAgdqIada/ux1\nETvKirFPa0VNbVvRPvB5/BavvNcwpZvDCcoBjhBrCQ26ktSpIC5UlrrIoAqvuPhcvvoWQEwL9AV0\nSobLz5XF45VRwd+tgn+/Cdv3yKxgowfL0iur5UcGG04MpdQDfpsLtdYLoXl8+qOAB7TWk6ztmYBu\n2Jnb4JydwDCtdVETPj/offoNffZun4h+iEMidUCicYqsTtpO4XK8on7AlccaWBVlYuuPRGvw1ICr\nDGrLobYCaivBXQWeatnncYnQHxLykHpx9/csap+1eKVQ8Lnl1RECIeHgDIeQCAiNhJBICI2CsGgI\ni5HFGW57AeGxCoAKa3xHuEMKgNiQlg8LLa2A5evghzWwdK1sjxgAIwfByIGSITTIys92Q92o7c8W\nwV03tGxHrhPYgnTk7geWA1dqrTf5HZOqtc631kcA/6e17trEzw960ddaXDBuKzrH51cIxPk1hT1a\nIneqfdLESo8Qn77BD58HqougqlBea4qhpkT2hcdBWCyEx4oYh0Zb4hwhYuwMtUT+Z6J94HWD1yWF\nh6dGFneVtVRaBU2FfNnhsWJLeBxEJMgSHi/XDzA+Kyy0wgvlHineYq0CINLR8gK8/yAsW1e/eL0S\nDnpaf3EF9ehsWgItidawLQe++kEm6XHVwgVnwK3XBCZk89/Uh2w+qpS6Canxv6iU+j1wM+AGqoFb\ntdbLmvjZQSP6Hi21q7qRsv7UeGG/CzIjpZM2pwo6hsufsMqKvQ91SKsgGDJSBg0eF1TkyVJ5QEQ+\nPA6ikiEyESI6iKiGBii3wPHwuKS1UVMqLY+aEllcZdIiiEy0liSISpLCKUAccgNZ4z08WvqFYq2+\ngJb+zWkNe/JgxQb4cYOMEaioglP7wtC+8tq/h+kTOFncHli9WcJvv1ku2+eNlhnZBvWy0pabwVkn\njtsnNahyrwh7tPUnim/kh5vnqk/aFaYk7LIOpzJRN4AVplIEZXugdI+IfHQKxHSSJSpZXC2tDe2T\ngqCm2GqlFMqrwwlRHeUeozsG9P5q6367HvlN1rmAYlo4HNSf/EKZLOanTZIyelcu9OoqWUIH94ZB\np0B6ivlfHI89eeJS+361uNU6p8L44XD2COjb/cjnZ0T/Z+L2STSO/58lzqotHevPUhd259NyjsFC\naxHB4h1QsgtQEJ8JcZkQk9o6Rb4paC2tgsqDUHXQasmUSEsgOrW+oAuAW8ht9QOUeQ6vvMSEBLYP\nqbIaNmTDmq0SJrpum3QW9+8prYB+3WVpzwWB1rBrH/y0UVpLK9ZDrUf6TOqiqI4XRmtEvwnU1YrK\nPPIH8Rf69vrjO2nc1VCUDUXbxFffoQckdBXRa68P1euWAqDOpVVVIM8jNl2W6JQT65f4GdS5Kcus\nRH1RTvmtB7oAqCO/ENZnw8btsGkHbNwBNS5pEfTKkpxBPTOhRyYkxAbevpZEa9hfYN33dliXLQVh\nTCQM6SP9IsP7ywxqP+cvY0T/KNTV6Ms80gkbSP9nm6aqAA5sEBdOfBdI6iU1W/NQj8TngYp8qNgP\nZbnSMojpJK2guM7ST9CCeHW9C6gqCAqAOopKYfMu6aTcmiMpJHbshYhw6J4hUUJd0iWhXGYncXcE\nKqX0ieDzQV4h5OyTVNjb98g9bcmRcRJ9u9W3dgb1gpSTTIthRN+Pw4TeVz/oKcoI/cmhtQhX3mpw\nlUPHfiL2IeHHP9dQj7saynOhbK8UAmEx4gqLz2rxFlJdAVDXAoj2KwCCIYmk1jJaePteyMmFnP2w\nez/syYfcfAgLg/SOkJYMKUmQmgjJHWTO4aQE6BAH8TEyBWVzRRRpDdUuKC6TpbAECoohv0gGue0/\nCLkHpDYfFy2FVdcMiWrqmSUtmeQWSLnf7kXf4yf0Lj+hNzX6ZqIiD/b9KDHzqYMhsUeLuyjaBdon\n/QClu6EkB9Ai/gldLTdQ4AoAOzqBfw5aS+tgf4GIbX6hFBAHi+X9uqW0QlxH0ZEQHQVRERAZDuFh\nElUUGgJOR32h4NNSS/d4JEqmplbCIquqoaIayqtkPE5CnBQqSQnib09JhNQkKYAyUiEjRa4TKNql\n6Df2o21KZ6zhZ+Aqg9wV0knbaagR+5ZEa4kMKtkli9ct4p/YQ8JDW7AA8Pj9l2q8rb/S5PFKKGll\nFVTVSE3dVSupqD1eWXy++ntzOiAkRAqFsFCICIOoSPG7x0YHZwhquxF9n5YIhVJ3vX8yPoiap20G\nnxcOrBO/fcoASOnfdiNwgpXqYisaaqdsd+gBiT1l4FgL0rAfLM4qAAIxEMzQdNq06GsrPW2pRyIS\nIi0/ZKzNHVFtlqoCyFksvubM0fJqsA+t5TspypYCIDwekk6BhG4tHgpaaxUApVZK8boCoC5brME+\n2pzo1408LLVqHKFKavRxIS07EUW7Rms4sF5q+BkjoUMjI0JaE+XlkJ8PBQVQUgIVFVBdDW53fds+\nJATCwyEqCuLiICEBkpIgJUXeDzZ8XukALtwKlfni/knu0+LuH20NSCzzyH/S6fd/NGlG7KHNiH6t\nJfSlbklvEBcqPy4zqUgL462FnG8lsqTrhBZ3ITQbXi9s2QKrV8P69bB5M2Rnw65dsi81FZKTRcxj\nYyEiQkJAHNakBR4PuFxQVQWlpVI4FBbCwYNyfGYmdOsG3btDr17Qpw/07y+faTfuKijcBoVbJC9R\nx75SULewG05rmR+4zC39AOHO+haAaXkHjlYv+oUuTamfDzE+RJKZteaKZqvBVQ7b50JsmtTwHUHc\ndvd6YcUKmDsXFi2S9ZQUGDoUBg6Evn2hZ0/o2lWE/kR/QD6ftBB275YCZPt22LpVCpUNG6RlMGQI\nnHYajBwJo0dDok3zEWottf+CTeIGSjxFwmlbOP4f6vvYytzigo12SvqSmFbaAdyaaPWiv7daE9+K\nowVaLVWFsGMepA4SoQhG3G6YNw/eew8+/RQ6dYKJE+HMM2HUKHHHBBKtYc8eWLUKli+HZcvkNSsL\nxo8Xu846CzrYMB2VqwwObhT/f1ymdMBHBaZV4tX1/v9aX33+KtMB3DK0etEPdhvbJJUHRfAzx4hv\nONjYuRNeeAFee01cLNOmwSWXiLgGGx6PuJgWLoSvv4bvvoPBg+Gii2DKFHELBdQel/j9D26QLKad\nBgd0xHTDDuB446ZtdozoG34e1UWQ/SV0GStpFIKJNWvgb38TAb3uOrjxxsCL5slSXS32f/opfPyx\ndBL/8pdw1VVwyimBs8PnlVr/gXUyJ0GnoZL/J0Dif1hAhltEPz7U+P+bAyP6hqZTWwFbP4OMEdLx\nFyxs3w733AOLF8Mdd8D06bSJmbB9PnEBvf22LN26wfXXw5VXSmEQCLQPindC3iqZLSztVOnDCSDa\nb4xNpd9gSuP/PzGM6BuahtcN2z6XQT4pA+y2RnC54NFHYdYs+NOf4I9/bBti3xgeD3z1FbzyCixY\nIC6rW26RiKBAoH1QtF3EPyIB0k+TfD8Bpm4EcIlb1g8FbwRxDEGwYUTf0DR2LZJqVZdxwVG92rhR\n3B5du8Izz0iIZHth3z546SXptxg4EGbOhAkTAvO9+LxQsBny14h7L22YbTOXuXxS+y/1yIx08SES\nqh0SBD/PYMaIvuH4FG2XDJm9p9gy1+sRvPkm/M//SC3/hhuCoxCyA5cL3ngDHntM4v8ffBDOOScw\n1/bWwv5V4vdPHSTRPjblVmo48t6Efx4bI/qGY+Ouhs0fQo/zAhbCd1S0hgcegNdfh08+gQFB4may\nG68X3n0X/vIXafE89piMAwgENaWw9wf5nWSOkdnObOSw8E8ttX/j/jmcY4l+sxTbSqlJSqnNSqmt\nSqm7jnLMU0qpbUqp1UqpIc1xXUMzsW+FDNoJBsG/9Vb4/HP44Qcj+P44neLq2rBBfP1TpsBvfiOj\ng1uaiHjoMVFCO3d+A3uXSv+PTTgVdAiFrlGQFSmj8/fUwM4qKHZLoWA4Oict+kopB/A0MBHoD1yp\nlOrT4JjJQA+t9SnATcDzJ3tdQzNRVSCTdXQKgnL43nslhn3+fEmRYDiS0FCJXNq0SaJ7+veHV1+V\nArMlUUqiufr+QuL8N38kM37ZTLgDUsKhZxR0DBMXUHYl7K0RN5BxEhzJSbt3lFKjgPu11pOt7ZmA\n1lo/5nfM88ACrfU71vYmYILW+ri/GuPeaWGyv5LOuo597bXj1Vfh73+HpUuDI3dNa2HNGvj1ryE9\nHf7zHxmRHAhKcmDPd5LQrdOQoJpHwavF919i1frjQyAhtH0N/mpp904GsMdve6/13rGOyW3kGEOg\nqSqUiTmSetlrx7p1cOed8NlnRvB/LoMHS5qHYcPg1FOllRQIErKgzyVS28/+Svz9QYJTQWIodI+C\nzAgZ9burCnKqpR/A187rkEE384VSagIwwWYz2gcHN0hOHTuTqLnd8KtfweOPt76RtcFCaKhE9Ywf\nD9dcI/0it9/e8mEtoVHQc6JE+Gz5BLqfbX+/UAMinLJ0DJPBXyVuyHNJ7H9CaNtO3KiUesBvc6HW\neiE0j+jnAv5j9Ttb7zU8JvM4xwBgGbawblspdX8z2GhoiLdW5l5NH26vHbNmiUviuuvstaMtcPbZ\nMrp3yhRJKf388zInQEuiHJA+TAZxbZ8btLmaHKo+xbPbSv2QWyPvx1vJ39pa7L/W+oHG3m8O984K\noKdSKkspFQb8EvikwTGfANfCoT6Akqb48w0tSEkOxHSybdANIDnqH30Unnyy7Va3Ak1mpqSq2LNH\non1qawNz3Q7dJOR3zw8ysCuICXVAchj0iIJO4TIAbHsl7K1uH52/Jy36WmsvcAswF9gAvK213qSU\nukkpNd06Zg6wUymVDbwA/O5kr2s4SUp2yZR6dvLcc5IGua/NnchtjZgYGePgdsPVV0uMfyCISoZe\nF0D+Wpk/OchRSubRTo+AntEQHQIHayG7Cg66JBtoW8QMzmqP+Lywbjb0nyrZFe3A65XkYh9/LJOc\nGJoflwsuuEAydz77bOBaU7UVsG2O5G8K1nkYjkGNF0qszJ/hTugQIgngHK2oMdrig7MMrYyqApny\n0C7BB/j2W5ngxAh+yxEeDh98IM/6+QAOjQmLgZ6TpcZfvCNw120mIpzi9ukZDQkhUOyR2n+eSwqE\n1k7QRe8YAkDVQZk0w04++gimTrXXhvZAXJy0pkaPhjFjJMQzEITHio8/+0sIi4XojoG5bjPiUNYE\nL6Hi6ilxy8jfUCWRP7GtNO+/qem3R6oKISrA0wg2ZP588ecbWp6ePSUk9tprJX1zoIhMhMzTJXWD\npyZw120BwvxG/iaFSern7ErYXwPV3tbV+WtEvz3iKpV86XZRVgY5OTBokH02tDeuu04Gvj37bGCv\nm5AlkT05i1uXMh4FpaSGnxkp0T+hDgn93FkNRbWtI++PEf32iKtcmtx2sWkT9O4tg4oMgUEpCY19\n+GGoqAjstdOGSeduK/TvH4sQv9DP1DCo9kntf1+N5AAK1jLOiH57w+eVgVl2duLu2gXdg2gqxvbC\nwIEwbpzMzBVIHE6Zbzl3ufz22hhKSbhnRgT0iJYkcPku2FEFhbUy+1cwYUS/veF1QUi4vYOhDhyA\nlBT7rt+emTFDInkCXQ2N7ghxnSWipw0TosTn3y0S0iIOH/hVGSQDv4zotzd8HnDY7Fapqmq789wG\nO2PHQnW1JLkLNGlDZbSuxxX4aweYhgO/opyQXwvbq6CgFjw2Dvwyot/e8HntT4OrtUm7YBdKSW6e\nOXMCf+2wGEnjXbgl8Ne2EaeCRKv2nxEhuX+2V9mX9sGIviHwRERIbdNgD2ecAUuW2HPt5L5QsCU4\n/BwBRimIdIrbp2Hah0DW/o3otzccTtA2DytMSoKiInttaM8MHmyPewckP49SMiq8HVM35WO3KOjs\nV/vfE4DavxmR295whIpf304yMmDvXnttaM907Qr79kn+I2eA51JQCuKzoGxPqxyl2xJEOmVJsSZ8\nP1gLeVpG/caHyFiA5sTU9NsbzjAJm7Ozed2zJ2zdat/12zshIZKeobjYnuvHpkP5fnuuHcT41/7r\nfP87WsD3b0S/veFwgiNEQjftIjNTIngOHrTPhvZOWFjgcu03JLojVBeCbqO5i5uBlvT9G9Fvj4TF\nQP1njq4AABbJSURBVG2lfddXCkaMkEnQDfbgckkWTjtwhoEzXEbpGo6Jf+0/s5kif4zot0fCYsBV\nZq8NZ5wBCxbYa0N7xeORVAxxcfbZEB4n6UAMTSaikdr/icT9G9Fvj4THS9I1O5k4Eb74wl4b2it7\n98qIaDtzH4VEtPrMm3ZRV/vvasX91/rV/psy6teIfnskMhGqbQ6ZHDZMsm1u2mSvHe2R9euhf397\nbXCGgs9trw2tnLq4/4ajfndUHfs8I/rtkagk++OkHQ6YNg3efddeO9ojy5bBaafZbIRqlwO0Wgr/\nUb/px8mleFJx+kqpDsA7QBawC7hCa32E30AptQsoBXyAW2s94mSuazhJwuMlbNNdDaGR9tnxl79A\nVJR912+vfP01/PWv9tqgffanA2mD1NX+j8XJPvWZwHytdW/gG+DuoxznAyZorYcawQ8ClIKojlCZ\nb68dCQkSOmgIHPn5sHGjdKTbic8tLh5DwDlZ0b8YeM1afw245CjHqWa4lqE5iU2Dijy7rTAEmrff\nloRrdoVr1uGuglDTyrODkxXiFK11PoDWOg84WpJ0DcxTSq1QSt14ktc0NAex6VBmUiG0K7SGl1+G\nX//abktknEioSa9tB8f16Sul5gGp/m8hIn5vI4cfrWfmdK31fqVUR0T8N2mtbUrzZwAgMgm8bonX\nD7cxXtsQOL75RvLtnHmmvXZ43RKuGWZE3w6OK/pa63OPtk8pla+UStVa5yulOgEHjvIZ+63Xg0qp\nD4ERQKOir5SaAEw4vumGk0IpiM+EkhxIHWi3NY3j8UieGEPz8NBDMHOm/XMZVBdBZAfTkdvCKKUe\n8NtcqLVeCKD0SYRNKaUeA4q01o8ppe4COmitZzY4JgpwaK0rlFLRwFzgr1rruU28hj4ZGw3HoHQP\n5K+BXhfabcmRVFTARx9Jx+Ntt9ltTetn3jz4/e9hwwb7J6Q/sF5amJlj7LWjDaOUQmvdaOl+skXt\nY8C5SqktwNnAo9YF05RSn1nHpAJLlFKrgKXAp00VfEMLE5sONaX2D4ffvBlWr67f1hpiYiTv/p13\nwqxZ9tnWFnC74U9/gkcftV/wQQIIolOPf5yhRTgp0ddaF2mtz9Fa99Zan6e1LrHe36+1vtBa36m1\nHmKFaw7UWj/aHIYbmgGHEzp0g+Lt9tpRWwvXXCORJVDvfigrg1tugfh4M5DnZPj3vyE9HS691G5L\nJD6/Ik8qHAZbME619k7iKVC4zV5RHTQIXngBtvsVPu+8I1P6DRoEU6fa74durWzeLDX8Z58NjmdY\nkS+BA3YOCmznmF6y9k5UsuTXL98HcRn22ODxwJgxsgC8/z58/z307SuCHxlpJlM/EVwuuPpq+Nvf\noEcPu60RSnbJ5OgG2zA1/faOUtCxLxRsts+GkBDYsUNSAyxdKktWFlxxhaT/NYJ/YvzpT9ClC/z2\nt3ZbImifiH5CN7stadcY0TdAhx7iZ7Uzx36XLvDhhyL0mZkwfTokJ8u+OsH3NUga7jZZGo/Kf/4j\nOXb+3/8LngKzbC+Ex0JEvN2WtGtOKmQzEJiQzQCRuwK0FzqPsteOyy6T+XPXrTv6Mf/8p8zvuny5\nrA8M0nEGdjF3Llx7LSxaBL17221NPdvniWsnOYhsaqO0ZMimoa2Q0h+KsiXzpp28/z786leHdyx7\nPPXrM2fCSy/JJCy/+AVcdx3/v707DbKyvBI4/j+9At1AszRNy9KgzSIk4jKCOyhjFOJCOaRKv0zN\nGCspp5yZKsXSGUyFMfNBJ1UxJmZSjqWTxKWsRE0MMMkImp6ASiIggoCAAaGh6WZv6H25Zz6c2/Zt\n6P3e2+977z2/Koq+S/V7GqrP+9zzPM95qKwc+jjDatMm+/d7441wJfzmc1B/DMZcHHQkGc+TvjO5\nI+wX8tinQUcCjz1mJYlXXoHa2s5duU89ZUcsbtwIN95oyzynTvVdux02b7Zmaj/7GdxwQ9DRdHV8\nF4yb4Z01Q8CTvutUchmc3BP8aL9DQwNUVdnXv/oV/OhH8OqrnbX+Tz+FMWM86QNs2ABLl1pDtaVL\ng46mq7YmOLUPigM+rcsBnvRdrLxCGFturRnC4FvfsmWbqja6f/ZZKC+3144dg5UrYf58KC62Yxdf\neAHefjvYmIPw5ptW6nr1VRvph82xnVA0zRushYQPkVxXJfNg91tQPCc83TdF4FTMmb6ffQaPPgpX\nX203hocftteHDYMPP7RJ4Ce6awKbZlTh6afhueds8vaKK4KO6EKtjbYceFYIb0YZypO+6yp3OEz4\niq3muXhx0NF0+sY3LNH/8pdQUGAHq69aBQ8+aI8ffth27+7dC888E3S0yVdbCw88AIcO2Zm3kwLa\nWNeXmk9srih/ZNCRuChP+u5CE+baaP9cVXh6pNx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XW7dulcXFxfoW7YHcvn1bTpw4UQ4aNOih6xplnb5CQ+f3339nypQpj43XyOXL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\nYk9P72iP5OTkxMCBAy1uDt3T05MNGzYwePBgfaWtRzh8+DB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88847j/UaAXNzc55++mmOHDmiM/24ubmxYMEC0tLS9C3ePTEwMGD58uUMHz4c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\nxhVLX/fHlZGRwdChQ5k1axaNGzc2LEdJVrZsWT7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gdCTDiAiffPIJn3/+OVu3bqVFixZG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\nbGxssLW1pXz58lSsWJEqVapQvXp17OzscHR0xM7OThd2zexMuXqnL7A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D7zvH3MCVwHfQzdn4+0IfTWUkwv5AgJxgkIJQ/dusScbQzVFS6R4PaR1UwTeVsAjF4TAF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\nFBYtghdegJo13U7zA6ezT9NpXicSpyXSrn47t+M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IVE1DR0VIAyF14loFMXs5fTkwp3DEBvFE+/BE+/DG+nCkwjhSR2kMH0UXKgOW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+hd1TSs/gIAA9t/iEyZkyvAkSN1SClobAzj9aoFz4dRXqZSJDo6AHDPyI1uV+wZyWJ+Sv/rOWCv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\nAhpWf5HKu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6+W+voEdrtebkVFGk3TiTdLjmedFWTx4iHsdnPCTBw9qgdlT6exeb3UXHgh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\nETYzK+g/fDsg88fErWwAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#!/usr/bin/env python\n", + "\"\"\"\n", + "Illustrate simple contour plotting, contours on an image with\n", + "a colorbar for the contours, and labelled contours.\n", + "\n", + "See also contour_image.py.\n", + "\"\"\"\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.cm as cm\n", + "import matplotlib.mlab as mlab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "matplotlib.rcParams['xtick.direction'] = 'out'\n", + "matplotlib.rcParams['ytick.direction'] = 'out'\n", + "\n", + "delta = 0.025\n", + "x = np.arange(-3.0, 3.0, delta)\n", + "y = np.arange(-2.0, 2.0, delta)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n", + "Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n", + "# difference of Gaussians\n", + "Z = 10.0 * (Z2 - Z1)\n", + "\n", + "\n", + "\n", + "# Create a simple contour plot with labels using default colors. The\n", + "# inline argument to clabel will control whether the labels are draw\n", + "# over the line segments of the contour, removing the lines beneath\n", + "# the label\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "plt.clabel(CS, inline=1, fontsize=10)\n", + "plt.title('Simplest default with labels')\n", + "\n", + "\n", + "# contour labels can be placed manually by providing list of positions\n", + "# (in data coordinate). See ginput_manual_clabel.py for interactive\n", + "# placement.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]\n", + "plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)\n", + "plt.title('labels at selected locations')\n", + "\n", + "\n", + "# You can force all the contours to be the same color.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours dashed')\n", + "\n", + "# You can set negative contours to be solid instead of dashed:\n", + "matplotlib.rcParams['contour.negative_linestyle'] = 'solid'\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours solid')\n", + "\n", + "\n", + "# And you can manually specify the colors of the contour\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " linewidths=np.arange(.5, 4, .5),\n", + " colors=('r', 'green', 'blue', (1,1,0), '#afeeee', '0.5')\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Crazy lines')\n", + "\n", + "\n", + "# Or you can use a colormap to specify the colors; the default\n", + "# colormap will be used for the contour lines\n", + "plt.figure()\n", + "im = plt.imshow(Z, interpolation='bilinear', origin='lower',\n", + " cmap=cm.gray, extent=(-3,3,-2,2))\n", + "levels = np.arange(-1.2, 1.6, 0.2)\n", + "CS = plt.contour(Z, levels,\n", + " origin='lower',\n", + " linewidths=2,\n", + " extent=(-3,3,-2,2))\n", + "\n", + "#Thicken the zero contour.\n", + "zc = CS.collections[6]\n", + "plt.setp(zc, linewidth=4)\n", + "\n", + "plt.clabel(CS, levels[1::2], # label every second level\n", + " inline=1,\n", + " fmt='%1.1f',\n", + " fontsize=14)\n", + "\n", + "# make a colorbar for the contour lines\n", + "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n", + "\n", + "plt.title('Lines with colorbar')\n", + "#plt.hot() # Now change the colormap for the contour lines and colorbar\n", + "plt.flag()\n", + "\n", + "# We can still add a colorbar for the image, too.\n", + "CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)\n", + "\n", + "# This makes the original colorbar look a bit out of place,\n", + "# so let's improve its position.\n", + "\n", + "l,b,w,h = plt.gca().get_position().bounds\n", + "ll,bb,ww,hh = CB.ax.get_position().bounds\n", + "CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Formatting figures\n", + "\n", + "The formatting of figures often takes longer than actually setting them up and adding data.\n", + "There are many different approaches to formatting figures in matplotlib (many goals can be\n", + "accomplished in different ways, using different commands), and you will come across many\n", + "of these as you learn more. The tips below give a few simple ways to get started." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Common formatting tricks\n", + "\n", + "There are hundreds of formatting options available in matplotlib, many of which you will end\n", + "up using occasionally. There are a few options, however, that you will use very frequently.\n", + "A short list of these might include:\n", + "\n", + "- Changing axis limits\n", + "- Changing line colors\n", + "- Changing lines to dashed (for black and white figures)\n", + "- Adding markers to lines\n", + "- Make tick labels point outward instead of inward\n", + "- Get rid of the box surrounding the plot\n", + "- Adding subplot letters, like (a) and (b)\n", + "\n", + "Here's how to accomplish all of these things." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make some data to plot\n", + "x = np.linspace(0, 2*np.pi)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# First, create an empty figure with 1 subplot\n", + "fig, ax1 = plt.subplots(1, 1)\n", + "\n", + "# Add title and labels\n", + "ax1.set_title('My Plot')\n", + "ax1.set_xlabel('x')\n", + "ax1.set_ylabel('y')\n", + "\n", + "# Change axis limits\n", + "ax1.set_xlim([0,2])\n", + "ax1.set_ylim([-1, 2])\n", + "\n", + "# Add the lines, changing their color, style, and marker\n", + "ax1.plot(x, y1, 'k--o', label='sin') # Black line, dashed, with 'o' markers\n", + "ax1.plot(x, y2, 'r-^', label='cos') # Red line, solid, with triangle-up markers\n", + "\n", + "# Adjust tick marks and get rid of 'box'\n", + "ax1.tick_params(direction='out', top=False, right=False) # Turn ticks out\n", + "ax1.spines['top'].set_visible(False) # Get rid of top axis line\n", + "ax1.spines['right'].set_visible(False) # Get rid of bottom axis line\n", + "\n", + "# Add subplot letter\n", + "ax1.annotate('(a)', (0.01, 0.96), size=12, xycoords='figure fraction')\n", + "\n", + "# Add legend\n", + "ax1.legend()\n", + "\n", + "# Finally, save the figure as a png file\n", + "fig.savefig('myfig-formatted.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Advanced formatting with rcParams\n", + "\n", + "A fast way to control many aspects of figure formatting is to temporarily modify a dictionary\n", + "called rcParams. This dictionary allows you to set, in one place, many of the options that\n", + "you will need to change before submitting your figures for publication, including figure\n", + "fonts, font sizes, figure size, figure dpi, etc., as well as many options regarding how\n", + "elements are spaced in your figures (ie, the distance between different elements of subfigures).\n", + "\n", + "WARNING: The rcParams dictionary is GLOBAL to matplotlib's plot library - therefore, if you\n", + "make a change to it, all future plots that you make will also have those changes (until you\n", + "close your Python session). If you do modify rcParams, it's good hygiene to set it back to the\n", + "defaults after you've made your plot, as described below.\n", + "\n", + "A description of the rcParams options can be found at\n", + "http://matplotlib.org/users/customizing.html\n", + "\n", + "Run the code below to see all of the different options you can set here. Once you've looked\n", + "this over, hit the Toggle button on the left to hide the output again." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RcParams({'agg.path.chunksize': 0,\n", + " 'animation.avconv_args': [],\n", + " 'animation.avconv_path': 'avconv',\n", + " 'animation.bitrate': -1,\n", + " 'animation.codec': 'mpeg4',\n", + " 'animation.convert_args': [],\n", + " 'animation.convert_path': 'convert',\n", + " 'animation.ffmpeg_args': [],\n", + " 'animation.ffmpeg_path': 'ffmpeg',\n", + " 'animation.frame_format': 'png',\n", + " 'animation.html': 'none',\n", + " 'animation.mencoder_args': [],\n", + " 'animation.mencoder_path': 'mencoder',\n", + " 'animation.writer': 'ffmpeg',\n", + " 'axes.axisbelow': False,\n", + " 'axes.edgecolor': 'k',\n", + " 'axes.facecolor': 'w',\n", + " 'axes.formatter.limits': [-7, 7],\n", + " 'axes.formatter.use_locale': False,\n", + " 'axes.formatter.use_mathtext': False,\n", + " 'axes.formatter.useoffset': True,\n", + " 'axes.grid': False,\n", + " 'axes.grid.axis': 'both',\n", + " 'axes.grid.which': 'major',\n", + " 'axes.hold': True,\n", + " 'axes.labelcolor': 'k',\n", + " 'axes.labelpad': 5.0,\n", + " 'axes.labelsize': 'medium',\n", + " 'axes.labelweight': 'normal',\n", + " 'axes.linewidth': 1.0,\n", + " 'axes.prop_cycle': cycler('color', ['b', 'g', 'r', 'c', 'm', 'y', 'k']),\n", + " 'axes.spines.bottom': True,\n", + " 'axes.spines.left': True,\n", + " 'axes.spines.right': True,\n", + " 'axes.spines.top': True,\n", + " 'axes.titlesize': 'large',\n", + " 'axes.titleweight': 'normal',\n", + " 'axes.unicode_minus': True,\n", + " 'axes.xmargin': 0.0,\n", + " 'axes.ymargin': 0.0,\n", + " 'axes3d.grid': True,\n", + " 'backend': 'module://ipykernel.pylab.backend_inline',\n", + " 'backend.qt4': 'PyQt4',\n", + " 'backend.qt5': 'PyQt5',\n", + " 'backend_fallback': True,\n", + " 'boxplot.bootstrap': None,\n", + " 'boxplot.boxprops.color': 'b',\n", + " 'boxplot.boxprops.linestyle': '-',\n", + " 'boxplot.boxprops.linewidth': 1.0,\n", + " 'boxplot.capprops.color': 'k',\n", + " 'boxplot.capprops.linestyle': '-',\n", + " 'boxplot.capprops.linewidth': 1.0,\n", + " 'boxplot.flierprops.color': 'b',\n", + " 'boxplot.flierprops.linestyle': 'none',\n", + " 'boxplot.flierprops.linewidth': 1.0,\n", + " 'boxplot.flierprops.marker': '+',\n", + " 'boxplot.flierprops.markeredgecolor': 'k',\n", + " 'boxplot.flierprops.markerfacecolor': 'b',\n", + " 'boxplot.flierprops.markersize': 6.0,\n", + " 'boxplot.meanline': False,\n", + " 'boxplot.meanprops.color': 'r',\n", + " 'boxplot.meanprops.linestyle': '-',\n", + " 'boxplot.meanprops.linewidth': 1.0,\n", + " 'boxplot.medianprops.color': 'r',\n", + " 'boxplot.medianprops.linestyle': '-',\n", + " 'boxplot.medianprops.linewidth': 1.0,\n", + " 'boxplot.notch': False,\n", + " 'boxplot.patchartist': False,\n", + " 'boxplot.showbox': True,\n", + " 'boxplot.showcaps': True,\n", + " 'boxplot.showfliers': True,\n", + " 'boxplot.showmeans': False,\n", + " 'boxplot.vertical': True,\n", + " 'boxplot.whiskerprops.color': 'b',\n", + " 'boxplot.whiskerprops.linestyle': '--',\n", + " 'boxplot.whiskerprops.linewidth': 1.0,\n", + " 'boxplot.whiskers': 1.5,\n", + " 'contour.corner_mask': True,\n", + " 'contour.negative_linestyle': 'solid',\n", + " 'datapath': '/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/mpl-data',\n", + " 'docstring.hardcopy': False,\n", + " 'errorbar.capsize': 3.0,\n", + " 'examples.directory': '',\n", + " 'figure.autolayout': False,\n", + " 'figure.dpi': 80.0,\n", + " 'figure.edgecolor': (1, 1, 1, 0),\n", + " 'figure.facecolor': (1, 1, 1, 0),\n", + " 'figure.figsize': [6.0, 4.0],\n", + " 'figure.frameon': True,\n", + " 'figure.max_open_warning': 20,\n", + " 'figure.subplot.bottom': 0.125,\n", + " 'figure.subplot.hspace': 0.2,\n", + " 'figure.subplot.left': 0.125,\n", + " 'figure.subplot.right': 0.9,\n", + " 'figure.subplot.top': 0.9,\n", + " 'figure.subplot.wspace': 0.2,\n", + " 'figure.titlesize': 'medium',\n", + " 'figure.titleweight': 'normal',\n", + " 'font.cursive': ['Apple Chancery',\n", + " 'Textile',\n", + " 'Zapf Chancery',\n", + " 'Sand',\n", + " 'Script MT',\n", + " 'Felipa',\n", + " 'cursive'],\n", + " 'font.family': ['sans-serif'],\n", + " 'font.fantasy': ['Comic Sans MS',\n", + " 'Chicago',\n", + " 'Charcoal',\n", + " 'ImpactWestern',\n", + " 'Humor Sans',\n", + " 'fantasy'],\n", + " 'font.monospace': ['Bitstream Vera Sans Mono',\n", + " 'DejaVu Sans Mono',\n", + " 'Andale Mono',\n", + " 'Nimbus Mono L',\n", + " 'Courier New',\n", + " 'Courier',\n", + " 'Fixed',\n", + " 'Terminal',\n", + " 'monospace'],\n", + " 'font.sans-serif': ['Bitstream Vera Sans',\n", + " 'DejaVu Sans',\n", + " 'Lucida Grande',\n", + " 'Verdana',\n", + " 'Geneva',\n", + " 'Lucid',\n", + " 'Arial',\n", + " 'Helvetica',\n", + " 'Avant Garde',\n", + " 'sans-serif'],\n", + " 'font.serif': ['Bitstream Vera Serif',\n", + " 'DejaVu Serif',\n", + " 'New Century Schoolbook',\n", + " 'Century Schoolbook L',\n", + " 'Utopia',\n", + " 'ITC Bookman',\n", + " 'Bookman',\n", + " 'Nimbus Roman No9 L',\n", + " 'Times New Roman',\n", + " 'Times',\n", + " 'Palatino',\n", + " 'Charter',\n", + " 'serif'],\n", + " 'font.size': 10.0,\n", + " 'font.stretch': 'normal',\n", + " 'font.style': 'normal',\n", + " 'font.variant': 'normal',\n", + " 'font.weight': 'normal',\n", + " 'grid.alpha': 1.0,\n", + " 'grid.color': 'k',\n", + " 'grid.linestyle': ':',\n", + " 'grid.linewidth': 0.5,\n", + " 'image.aspect': 'equal',\n", + " 'image.cmap': 'flag',\n", + " 'image.composite_image': True,\n", + " 'image.interpolation': 'bilinear',\n", + " 'image.lut': 256,\n", + " 'image.origin': 'upper',\n", + " 'image.resample': False,\n", + " 'interactive': True,\n", + " 'keymap.all_axes': ['a'],\n", + " 'keymap.back': ['left', 'c', 'backspace'],\n", + " 'keymap.forward': ['right', 'v'],\n", + " 'keymap.fullscreen': ['f', 'ctrl+f'],\n", + " 'keymap.grid': ['g'],\n", + " 'keymap.home': ['h', 'r', 'home'],\n", + " 'keymap.pan': ['p'],\n", + " 'keymap.quit': ['ctrl+w', 'cmd+w'],\n", + " 'keymap.save': ['s', 'ctrl+s'],\n", + " 'keymap.xscale': ['k', 'L'],\n", + " 'keymap.yscale': ['l'],\n", + " 'keymap.zoom': ['o'],\n", + " 'legend.borderaxespad': 0.5,\n", + " 'legend.borderpad': 0.4,\n", + " 'legend.columnspacing': 2.0,\n", + " 'legend.edgecolor': 'inherit',\n", + " 'legend.facecolor': 'inherit',\n", + " 'legend.fancybox': False,\n", + " 'legend.fontsize': 'large',\n", + " 'legend.framealpha': None,\n", + " 'legend.frameon': True,\n", + " 'legend.handleheight': 0.7,\n", + " 'legend.handlelength': 2.0,\n", + " 'legend.handletextpad': 0.8,\n", + " 'legend.isaxes': True,\n", + " 'legend.labelspacing': 0.5,\n", + " 'legend.loc': 'upper right',\n", + " 'legend.markerscale': 1.0,\n", + " 'legend.numpoints': 2,\n", + " 'legend.scatterpoints': 3,\n", + " 'legend.shadow': False,\n", + " 'lines.antialiased': True,\n", + " 'lines.color': 'b',\n", + " 'lines.dash_capstyle': 'butt',\n", + " 'lines.dash_joinstyle': 'round',\n", + " 'lines.linestyle': '-',\n", + " 'lines.linewidth': 1.0,\n", + " 'lines.marker': 'None',\n", + " 'lines.markeredgewidth': 0.5,\n", + " 'lines.markersize': 6.0,\n", + " 'lines.solid_capstyle': 'projecting',\n", + " 'lines.solid_joinstyle': 'round',\n", + " 'markers.fillstyle': 'full',\n", + " 'mathtext.bf': 'serif:bold',\n", + " 'mathtext.cal': 'cursive',\n", + " 'mathtext.default': 'it',\n", + " 'mathtext.fallback_to_cm': True,\n", + " 'mathtext.fontset': 'cm',\n", + " 'mathtext.it': 'serif:italic',\n", + " 'mathtext.rm': 'serif',\n", + " 'mathtext.sf': 'sans\\\\-serif',\n", + " 'mathtext.tt': 'monospace',\n", + " 'nbagg.transparent': True,\n", + " 'patch.antialiased': True,\n", + " 'patch.edgecolor': 'k',\n", + " 'patch.facecolor': 'b',\n", + " 'patch.linewidth': 1.0,\n", + " 'path.effects': [],\n", + " 'path.simplify': True,\n", + " 'path.simplify_threshold': 0.1111111111111111,\n", + " 'path.sketch': None,\n", + " 'path.snap': True,\n", + " 'pdf.compression': 6,\n", + " 'pdf.fonttype': 3,\n", + " 'pdf.inheritcolor': False,\n", + " 'pdf.use14corefonts': False,\n", + " 'pgf.debug': False,\n", + " 'pgf.preamble': [],\n", + " 'pgf.rcfonts': True,\n", + " 'pgf.texsystem': 'xelatex',\n", + " 'plugins.directory': '.matplotlib_plugins',\n", + " 'polaraxes.grid': True,\n", + " 'ps.distiller.res': 6000,\n", + " 'ps.fonttype': 3,\n", + " 'ps.papersize': 'letter',\n", + " 'ps.useafm': False,\n", + " 'ps.usedistiller': False,\n", + " 'savefig.bbox': None,\n", + " 'savefig.directory': '~',\n", + " 'savefig.dpi': 72.0,\n", + " 'savefig.edgecolor': 'w',\n", + " 'savefig.facecolor': 'w',\n", + " 'savefig.format': 'png',\n", + " 'savefig.frameon': True,\n", + " 'savefig.jpeg_quality': 95,\n", + " 'savefig.orientation': 'portrait',\n", + " 'savefig.pad_inches': 0.1,\n", + " 'savefig.transparent': False,\n", + " 'svg.fonttype': 'path',\n", + " 'svg.image_inline': True,\n", + " 'svg.image_noscale': False,\n", + " 'text.antialiased': True,\n", + " 'text.color': 'k',\n", + " 'text.dvipnghack': None,\n", + " 'text.hinting': 'auto',\n", + " 'text.hinting_factor': 8,\n", + " 'text.latex.preamble': [],\n", + " 'text.latex.preview': False,\n", + " 'text.latex.unicode': False,\n", + " 'text.usetex': False,\n", + " 'timezone': 'UTC',\n", + " 'tk.window_focus': False,\n", + " 'toolbar': 'toolbar2',\n", + " 'verbose.fileo': 'sys.stdout',\n", + " 'verbose.level': 'silent',\n", + " 'webagg.open_in_browser': True,\n", + " 'webagg.port': 8988,\n", + " 'webagg.port_retries': 50,\n", + " 'xtick.color': 'k',\n", + " 'xtick.direction': 'out',\n", + " 'xtick.labelsize': 'medium',\n", + " 'xtick.major.pad': 4.0,\n", + " 'xtick.major.size': 4.0,\n", + " 'xtick.major.width': 0.5,\n", + " 'xtick.minor.pad': 4.0,\n", + " 'xtick.minor.size': 2.0,\n", + " 'xtick.minor.visible': False,\n", + " 'xtick.minor.width': 0.5,\n", + " 'ytick.color': 'k',\n", + " 'ytick.direction': 'out',\n", + " 'ytick.labelsize': 'medium',\n", + " 'ytick.major.pad': 4.0,\n", + " 'ytick.major.size': 4.0,\n", + " 'ytick.major.width': 0.5,\n", + " 'ytick.minor.pad': 4.0,\n", + " 'ytick.minor.size': 2.0,\n", + " 'ytick.minor.visible': False,\n", + " 'ytick.minor.width': 0.5})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View rcParams\n", + "matplotlib.rcParams" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, save the default rcParams dictionary so we can 'reset' everything after we change it.\n", + "In a 'regular' Python interpreter, or inside a Python module, you can just use\n", + "plt.rcdefaults() to reset the defaults, instead of saving them here then resetting them\n", + "manually later. However, this work in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Save default rcParams so we can reset them later\n", + "# WARNING: Do not run this cell after changing rcParams, as it will overwrite the\n", + "# defaults that we are trying to preserve.\n", + "rcdef = plt.rcParams.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's make a simple plot, using mostly default formatting." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UMq6lcjKhNDLl10ir0uCcRxhj1wL4O6IE9Qjn/DPG2FXRP/MHAdwJ4FHG2EcA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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make sure rcParams is at default settings, since we're messing with it\n", + "plt.rcParams.update(rcdef)\n", + "\n", + "# Make a simple figure with default formatting\n", + "fig, axall = plt.subplots(1, 2) # axall is the tuple containing both axis objects\n", + "\n", + "for ax in axall:\n", + " ax.plot(np.random.rand(100), 'k-o', label='Random')\n", + " ax.set_ylim([0, 1.2])\n", + " ax.set_ylabel('Value')\n", + " ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many obvious formatting problems here. The legend is too big, the axis labels are\n", + "too small, the legend shouldn't have a box (arguably), and the y-axis label on the second\n", + "subplot is hidden behind the first subplot. Also, although you can't see it here, the figure\n", + "resolution is too low to print without appearing fuzzy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code below changes a whole bunch of values in rcParams to get a figure to look juuuuust\n", + "right. Then it makes the figures, saves it, and puts rcParams back to its default." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Choose a bunch of new parameter values\n", + "# In practice, you'll try modifying these, running the code and saving the figure,\n", + "# looking at the figure, then making more modifications until you're happy.\n", + "newparams = {'axes.labelsize': 14, 'axes.linewidth': 1, 'savefig.dpi': 300, \n", + " 'lines.linewidth': 1.5, 'figure.figsize': (8, 3),\n", + " 'figure.subplot.wspace': 0.4,\n", + " 'ytick.labelsize': 12, 'xtick.labelsize': 12,\n", + " 'ytick.major.pad': 5, 'xtick.major.pad': 5,\n", + " 'legend.fontsize': 12, 'legend.frameon': False, \n", + " 'legend.handlelength': 1.5}\n", + "\n", + "# Update the global rcParams dictionary with the new parameter choices\n", + "# Before doing this, we reset rcParams to its default again, just in case\n", + "plt.rcParams.update(rcdef)\n", + "plt.rcParams.update(newparams)\n", + "\n", + "# Make the new figure with new formatting\n", + "fig, axall = plt.subplots(1, 2)\n", + "\n", + "for ax in axall:\n", + " ax.plot(np.random.rand(100), 'k-o', label='Random')\n", + " ax.set_ylim([0, 1.2])\n", + " ax.set_ylabel('Value')\n", + " ax.legend()\n", + " \n", + "fig.savefig('myfig-advanced.png')\n", + "\n", + "# Put rcParams back to default\n", + "plt.rcParams.update(rcdef)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll want to check that your formatting looks good by looking at the file that's being saved\n", + "to your hard disk, since that's exactly how it will look to the publisher or when inserted\n", + "into your manuscript." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/matplotlib/matplotlib-learner.ipynb b/Examples/matplotlib/matplotlib-learner.ipynb new file mode 100644 index 0000000..cedb32d --- /dev/null +++ b/Examples/matplotlib/matplotlib-learner.ipynb @@ -0,0 +1,833 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting with matplotlib\n", + "\n", + "__Software Carpentry Bootcamp \n", + "eResearch NZ 2013__ \n", + "\n", + "Prepared by: Ariel Rokem\n", + "Thanks to: Justin Kitzes, Paul Ivanov\n", + "(updated for py3 for UWPCE: Christopher Barker: 2016)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Getting Started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 What is matplotlib?\n", + "\n", + "Matplotlib is the most popular and mature library for plotting data using Python. It has all of the functionality you would expect, including the ability to control the formatting of plots and figures at a very fine level.\n", + "\n", + "The official matplotlib documentation is at http://matplotlib.org/ \n", + "The matplotlib gallery is at http://matplotlib.org/gallery.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1.1 Architecture\n", + "Matplotlib is designed be be used in a wide variety of ways: for interactive plotting, generating images for publication or web sites, embedding in GUI programs, etc. It can generate PNG images, PDF, SVG, and native images for a number of GUI toolkits.\n", + "\n", + "To support this, it has a varierty of \"back ends\" -- each for a different purpose, or for use with a different GUI toolkit. The system is designed to abstract the all the plotting commands and interaction, so that the code you wirte to gernerate plot is almost identical regradless of how you use it.\n", + "\n", + "One use-case is embedding it for inline display in an iPython noteboo, such as this one. That is what this tutorial uses, but the plotting commands and principles are the same regardless of your use case.\n", + "\n", + "#### 1.1.2 APIs\n", + "\n", + "Matplotlib has essentially two APIs: the core object oreiented API that mpl itself is written in, and a MATLAB-like \"state machine\" API (pylab). While the pylab API makes it a bit easier to quickly plot things up at the command line, it creates (or encourages, anyway) a very hard to maintain and re-use codeing style. And in most cases, the object oriented API requires only a few extra keystrokes, but keeps things much cleaner if (when) you want to move your plotting ocde to be a part of a larger application.\n", + "\n", + "This tutoroal woill use the OO API as much as possible, but many examples use the other API, so it can be helful to understand the realtionship between them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Importing matplotlib\n", + "\n", + "Matplotlib is often used through 'pyplot', which provides a high-level interface for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The inline flag means that images will be shown here in the notebooks, rather\n", + "# than in pop-up windows.\n", + "\n", + "%matplotlib inline\n", + "\n", + "# If you are using 'regular' Python, however, you'll want the following. You'll\n", + "# need to also separately import numpy and any other packages that you might need.\n", + "\n", + "# We, of course, need numpy:\n", + "import numpy as np\n", + "\n", + "# The more-or-less accepted way to import MPL\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Creating Figures\n", + "\n", + "There are two major challenges with creating figures. First is understanding the\n", + "syntax to actually make the basic plot appear. Second is formatting the basic plot to look\n", + "exactly how you would like it to look. In general, the formatting will probably take you\n", + "longer...\n", + "\n", + "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making\n", + "plots - using the Matlab-like clone, and using the object-oriented approach. The latter\n", + "provides better control over plot features, while only requiring slightly more typing. It's\n", + "easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 A first plot\n", + "\n", + "In simple matplotlib plotting, there are two concepts to distinguish:\n", + "\n", + "- __Figure__ - the entire figure, like what you might see in a journal, including all\n", + "subplots, axes, lines, labels, etc. The whole enchilada. IT is more or less like a piece of paper.\n", + " \n", + "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in articles. Each subplot will contain one Axes object, which is the container where all of the useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", + "\n", + "Also -- in Matplotlib, the axes object does most of teh work -- it is the interface to all plotting.\n", + "\n", + "For example, here's how to make one figure with two subplots, the second of which contains two lines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Make some data to plot\n", + "x = np.linspace(0, 2*np.pi)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# First, create an empty figure with 2 subplots\n", + "# - The arguments (1, 2) indicate 1 row and 2 cols\n", + "# - The function plt.subplots returns an object for the figure and for\n", + "# each axes\n", + "# - There are multiple ways to accomplish this same goal, but this is\n", + "# probably the simplest and most extensible -- notice that each\n", + "# subplot is associated with one of the axes objects.\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "# The axes objects have a huge nuber of methods -- the core plotting routines,\n", + "# and the methods to manipluate the plots.\n", + "\n", + "# Next, put one line on the first axis and both lines on the second axis\n", + "line = ax1.plot(x, y1, '--r')\n", + "print(line)\n", + "ax1.set_xlim(0,4)\n", + "ax1.set_ylim(-1.2, 1.2)\n", + "ax1.set_xlabel('a label in the x axis')\n", + "ax1.set_ylabel('$\\gamma$')\n", + "\n", + "ax2.plot(x, y1, label='sin') # The labels are what appear in the legend\n", + "ax2.plot(x, y2, label='cos')\n", + "# - On the second axes, add a legend to distinguish the two lines\n", + "ax2.legend()\n", + "\n", + "# Finally, save the figure as a png file\n", + "# if you want to save it for use elsewhere.\n", + "# note that in inline mode, the notebook displays the plot.\n", + "fig.savefig('myfig.png') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1 - Simple formatting\n", + "\n", + "There are lots of formatting options to play with. Modify the code above to make some changes to the\n", + "formatting of these plots.\n", + "\n", + "First, make some changes to the axes. HINT: These adjustments are methods to the ax1 and ax2 objects,\n", + "and (conveniently) they all start with the text 'set_'. Try typing 'ax1.set_' and hitting tab to\n", + "see some options.\n", + "\n", + "* Change the x axis on ax1 to run from 0 to 4. (HINT: set_xlim)\n", + "* Add labels to the x axis on both subplots (HINT: set_xlabel, set_ylabel)\n", + "\n", + "Second, make some changes to the lines that you plotted using ax1.plot(...). These changes can be made\n", + "by looking at the various arguments that you can give to the plot method. You can do this easily by\n", + "typing ax1.plot? in the cell below and running it - this will give you pop-up help for the plot method.\n", + "\n", + "* Make the sine line on ax1 red and dashed.\n", + "* Put a circular black marker on top of the cos line on ax2. Make it really big.\n", + "\n", + "_Bonus_: Eliminate the box around the legend on the second subplot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ax1.plot?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Other types of plots\n", + "\n", + "In the example above, we used the plot method to make line plots. There \n", + "are also methods to make scatter plots, barplots, histograms, loglog\n", + "plots, semilog plots, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# to see the methods, try typing:\n", + "ax1.\n", + "# and then a after the dot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Make some data to plot\n", + "x = np.arange(0, 100)\n", + "y = np.random.rand(100) # 100 random numbers\n", + "\n", + "# Make a figure with 6 subplots and axes\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)\n", + "\n", + "# Add data to each axis. Optional arguments to each method will customize each plot.\n", + "ax1.plot(x, y)\n", + "ax2.hist(y)\n", + "ax3.scatter(x, y)\n", + "ax4.boxplot(y)\n", + "ax5.loglog(x, y)\n", + "ax6.semilogx(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Plotting images\n", + "\n", + "Matplotlib also makes it easy to plot images. For this, you can use the plot method imshow\n", + "(syntax borrowed from Matlab)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Read an image file for first subplot, generate random array for second\n", + "img1 = plt.imread('lena.png')\n", + "img2 = np.random.rand(128, 128)\n", + "\n", + "# Make figure\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "ax1.imshow(img1)\n", + "ax2.imshow(img2)\n", + "\n", + "ax1.set_axis_off() # Hide \"spines\" on first axis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. The matplotlib gallery\n", + "\n", + "It can be very intimidating to try to craft exactly the figure that you want, especially if\n", + "you are used to being able to adjust things visually using a program like Excel.\n", + "\n", + "If you get stuck and don't know where to start, or just want to learn more about what\n", + "matplotlib can do, a great option is to have a look at the matplotlib gallery, which can be\n", + "found at http://matplotlib.org/gallery.html. A good way to get started is to find a figure\n", + "here that sort of looks like what you want, copy the code, and modify it for your own needs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2 - Exploring the matplotlib gallery\n", + "\n", + "Have a look at the matplotlib gallery, find a cool looking figure, copy the code\n", + "into the box below, and modify it. Note that some of the examples might require packages that\n", + "are not installed on your machine (in particular those that make maps) -\n", + "if this is the case, pick another example for the purposes of this exercise.\n", + "\n", + "In IPython, you can use the \"load magic\". Type %loadpy and then the URL of the py file containing the\n", + "code, and it will automatically copy it into a cell below. Run the cell with the code to see the\n", + "figure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# %load http://matplotlib.org/mpl_examples/pylab_examples/contour_demo.py\n", + "#!/usr/bin/env python\n", + "\"\"\"\n", + "Illustrate simple contour plotting, contours on an image with\n", + "a colorbar for the contours, and labelled contours.\n", + "\n", + "See also contour_image.py.\n", + "\"\"\"\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.cm as cm\n", + "import matplotlib.mlab as mlab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "matplotlib.rcParams['xtick.direction'] = 'out'\n", + "matplotlib.rcParams['ytick.direction'] = 'out'\n", + "\n", + "delta = 0.025\n", + "x = np.arange(-3.0, 3.0, delta)\n", + "y = np.arange(-2.0, 2.0, delta)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n", + "Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n", + "# difference of Gaussians\n", + "Z = 10.0 * (Z2 - Z1)\n", + "\n", + "\n", + "\n", + "# Create a simple contour plot with labels using default colors. The\n", + "# inline argument to clabel will control whether the labels are draw\n", + "# over the line segments of the contour, removing the lines beneath\n", + "# the label\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "plt.clabel(CS, inline=1, fontsize=10)\n", + "plt.title('Simplest default with labels')\n", + "\n", + "\n", + "# contour labels can be placed manually by providing list of positions\n", + "# (in data coordinate). See ginput_manual_clabel.py for interactive\n", + "# placement.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]\n", + "plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)\n", + "plt.title('labels at selected locations')\n", + "\n", + "\n", + "# You can force all the contours to be the same color.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours dashed')\n", + "\n", + "# You can set negative contours to be solid instead of dashed:\n", + "matplotlib.rcParams['contour.negative_linestyle'] = 'solid'\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours solid')\n", + "\n", + "\n", + "# And you can manually specify the colors of the contour\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " linewidths=np.arange(.5, 4, .5),\n", + " colors=('r', 'green', 'blue', (1,1,0), '#afeeee', '0.5')\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Crazy lines')\n", + "\n", + "\n", + "# Or you can use a colormap to specify the colors; the default\n", + "# colormap will be used for the contour lines\n", + "plt.figure()\n", + "im = plt.imshow(Z, interpolation='bilinear', origin='lower',\n", + " cmap=cm.gray, extent=(-3,3,-2,2))\n", + "levels = np.arange(-1.2, 1.6, 0.2)\n", + "CS = plt.contour(Z, levels,\n", + " origin='lower',\n", + " linewidths=2,\n", + " extent=(-3,3,-2,2))\n", + "\n", + "#Thicken the zero contour.\n", + "zc = CS.collections[6]\n", + "plt.setp(zc, linewidth=4)\n", + "\n", + "plt.clabel(CS, levels[1::2], # label every second level\n", + " inline=1,\n", + " fmt='%1.1f',\n", + " fontsize=14)\n", + "\n", + "# make a colorbar for the contour lines\n", + "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n", + "\n", + "plt.title('Lines with colorbar')\n", + "#plt.hot() # Now change the colormap for the contour lines and colorbar\n", + "plt.flag()\n", + "\n", + "# We can still add a colorbar for the image, too.\n", + "CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)\n", + "\n", + "# This makes the original colorbar look a bit out of place,\n", + "# so let's improve its position.\n", + "\n", + "l,b,w,h = plt.gca().get_position().bounds\n", + "ll,bb,ww,hh = CB.ax.get_position().bounds\n", + "CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])\n", + "\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#!/usr/bin/env python\n", + "\"\"\"\n", + "Illustrate simple contour plotting, contours on an image with\n", + "a colorbar for the contours, and labelled contours.\n", + "\n", + "See also contour_image.py.\n", + "\"\"\"\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.cm as cm\n", + "import matplotlib.mlab as mlab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "matplotlib.rcParams['xtick.direction'] = 'out'\n", + "matplotlib.rcParams['ytick.direction'] = 'out'\n", + "\n", + "delta = 0.025\n", + "x = np.arange(-3.0, 3.0, delta)\n", + "y = np.arange(-2.0, 2.0, delta)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n", + "Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n", + "# difference of Gaussians\n", + "Z = 10.0 * (Z2 - Z1)\n", + "\n", + "\n", + "\n", + "# Create a simple contour plot with labels using default colors. The\n", + "# inline argument to clabel will control whether the labels are draw\n", + "# over the line segments of the contour, removing the lines beneath\n", + "# the label\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "plt.clabel(CS, inline=1, fontsize=10)\n", + "plt.title('Simplest default with labels')\n", + "\n", + "\n", + "# contour labels can be placed manually by providing list of positions\n", + "# (in data coordinate). See ginput_manual_clabel.py for interactive\n", + "# placement.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z)\n", + "manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]\n", + "plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)\n", + "plt.title('labels at selected locations')\n", + "\n", + "\n", + "# You can force all the contours to be the same color.\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours dashed')\n", + "\n", + "# You can set negative contours to be solid instead of dashed:\n", + "matplotlib.rcParams['contour.negative_linestyle'] = 'solid'\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " colors='k', # negative contours will be dashed by default\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Single color - negative contours solid')\n", + "\n", + "\n", + "# And you can manually specify the colors of the contour\n", + "plt.figure()\n", + "CS = plt.contour(X, Y, Z, 6,\n", + " linewidths=np.arange(.5, 4, .5),\n", + " colors=('r', 'green', 'blue', (1,1,0), '#afeeee', '0.5')\n", + " )\n", + "plt.clabel(CS, fontsize=9, inline=1)\n", + "plt.title('Crazy lines')\n", + "\n", + "\n", + "# Or you can use a colormap to specify the colors; the default\n", + "# colormap will be used for the contour lines\n", + "plt.figure()\n", + "im = plt.imshow(Z, interpolation='bilinear', origin='lower',\n", + " cmap=cm.gray, extent=(-3,3,-2,2))\n", + "levels = np.arange(-1.2, 1.6, 0.2)\n", + "CS = plt.contour(Z, levels,\n", + " origin='lower',\n", + " linewidths=2,\n", + " extent=(-3,3,-2,2))\n", + "\n", + "#Thicken the zero contour.\n", + "zc = CS.collections[6]\n", + "plt.setp(zc, linewidth=4)\n", + "\n", + "plt.clabel(CS, levels[1::2], # label every second level\n", + " inline=1,\n", + " fmt='%1.1f',\n", + " fontsize=14)\n", + "\n", + "# make a colorbar for the contour lines\n", + "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n", + "\n", + "plt.title('Lines with colorbar')\n", + "#plt.hot() # Now change the colormap for the contour lines and colorbar\n", + "plt.flag()\n", + "\n", + "# We can still add a colorbar for the image, too.\n", + "CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)\n", + "\n", + "# This makes the original colorbar look a bit out of place,\n", + "# so let's improve its position.\n", + "\n", + "l,b,w,h = plt.gca().get_position().bounds\n", + "ll,bb,ww,hh = CB.ax.get_position().bounds\n", + "CB.ax.set_position([ll, b+0.1*h, ww, h*0.8])\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Formatting figures\n", + "\n", + "The formatting of figures often takes longer than actually setting them up and adding data.\n", + "There are many different approaches to formatting figures in matplotlib (many goals can be\n", + "accomplished in different ways, using different commands), and you will come across many\n", + "of these as you learn more. The tips below give a few simple ways to get started." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Common formatting tricks\n", + "\n", + "There are hundreds of formatting options available in matplotlib, many of which you will end\n", + "up using occasionally. There are a few options, however, that you will use very frequently.\n", + "A short list of these might include:\n", + "\n", + "- Changing axis limits\n", + "- Changing line colors\n", + "- Changing lines to dashed (for black and white figures)\n", + "- Adding markers to lines\n", + "- Make tick labels point outward instead of inward\n", + "- Get rid of the box surrounding the plot\n", + "- Adding subplot letters, like (a) and (b)\n", + "\n", + "Here's how to accomplish all of these things." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Make some data to plot\n", + "x = np.linspace(0, 2*np.pi)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# First, create an empty figure with 1 subplot\n", + "fig, ax1 = plt.subplots(1, 1)\n", + "\n", + "# Add title and labels\n", + "ax1.set_title('My Plot')\n", + "ax1.set_xlabel('x')\n", + "ax1.set_ylabel('y')\n", + "\n", + "# Change axis limits\n", + "ax1.set_xlim([0,2])\n", + "ax1.set_ylim([-1, 2])\n", + "\n", + "# Add the lines, changing their color, style, and marker\n", + "ax1.plot(x, y1, 'k--o', label='sin') # Black line, dashed, with 'o' markers\n", + "ax1.plot(x, y2, 'r-^', label='cos') # Red line, solid, with triangle-up markers\n", + "\n", + "# Adjust tick marks and get rid of 'box'\n", + "ax1.tick_params(direction='out', top=False, right=False) # Turn ticks out\n", + "ax1.spines['top'].set_visible(False) # Get rid of top axis line\n", + "ax1.spines['right'].set_visible(False) # Get rid of bottom axis line\n", + "\n", + "# Add subplot letter\n", + "ax1.annotate('(a)', (0.01, 0.96), size=12, xycoords='figure fraction')\n", + "\n", + "# Add legend\n", + "ax1.legend()\n", + "\n", + "# Finally, save the figure as a png file\n", + "fig.savefig('myfig-formatted.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Advanced formatting with rcParams\n", + "\n", + "A fast way to control many aspects of figure formatting is to temporarily modify a dictionary\n", + "called rcParams. This dictionary allows you to set, in one place, many of the options that\n", + "you will need to change before submitting your figures for publication, including figure\n", + "fonts, font sizes, figure size, figure dpi, etc., as well as many options regarding how\n", + "elements are spaced in your figures (ie, the distance between different elements of subfigures).\n", + "\n", + "WARNING: The rcParams dictionary is GLOBAL to matplotlib's plot library - therefore, if you\n", + "make a change to it, all future plots that you make will also have those changes (until you\n", + "close your Python session). If you do modify rcParams, it's good hygiene to set it back to the\n", + "defaults after you've made your plot, as described below.\n", + "\n", + "A description of the rcParams options can be found at\n", + "http://matplotlib.org/users/customizing.html\n", + "\n", + "Run the code below to see all of the different options you can set here. Once you've looked\n", + "this over, hit the Toggle button on the left to hide the output again." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# View rcParams\n", + "matplotlib.rcParams" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, save the default rcParams dictionary so we can 'reset' everything after we change it.\n", + "In a 'regular' Python interpreter, or inside a Python module, you can just use\n", + "plt.rcdefaults() to reset the defaults, instead of saving them here then resetting them\n", + "manually later. However, this work in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Save default rcParams so we can reset them later\n", + "# WARNING: Do not run this cell after changing rcParams, as it will overwrite the\n", + "# defaults that we are trying to preserve.\n", + "rcdef = plt.rcParams.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's make a simple plot, using mostly default formatting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Make sure rcParams is at default settings, since we're messing with it\n", + "plt.rcParams.update(rcdef)\n", + "\n", + "# Make a simple figure with default formatting\n", + "fig, axall = plt.subplots(1, 2) # axall is the tuple containing both axis objects\n", + "\n", + "for ax in axall:\n", + " ax.plot(np.random.rand(100), 'k-o', label='Random')\n", + " ax.set_ylim([0, 1.2])\n", + " ax.set_ylabel('Value')\n", + " ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many obvious formatting problems here. The legend is too big, the axis labels are\n", + "too small, the legend shouldn't have a box (arguably), and the y-axis label on the second\n", + "subplot is hidden behind the first subplot. Also, although you can't see it here, the figure\n", + "resolution is too low to print without appearing fuzzy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code below changes a whole bunch of values in rcParams to get a figure to look juuuuust\n", + "right. Then it makes the figures, saves it, and puts rcParams back to its default." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Choose a bunch of new parameter values\n", + "# In practice, you'll try modifying these, running the code and saving the figure,\n", + "# looking at the figure, then making more modifications until you're happy.\n", + "newparams = {'axes.labelsize': 14, 'axes.linewidth': 1, 'savefig.dpi': 300, \n", + " 'lines.linewidth': 1.5, 'figure.figsize': (8, 3),\n", + " 'figure.subplot.wspace': 0.4,\n", + " 'ytick.labelsize': 12, 'xtick.labelsize': 12,\n", + " 'ytick.major.pad': 5, 'xtick.major.pad': 5,\n", + " 'legend.fontsize': 12, 'legend.frameon': False, \n", + " 'legend.handlelength': 1.5}\n", + "\n", + "# Update the global rcParams dictionary with the new parameter choices\n", + "# Before doing this, we reset rcParams to its default again, just in case\n", + "plt.rcParams.update(rcdef)\n", + "plt.rcParams.update(newparams)\n", + "\n", + "# Make the new figure with new formatting\n", + "fig, axall = plt.subplots(1, 2)\n", + "\n", + "for ax in axall:\n", + " ax.plot(np.random.rand(100), 'k-o', label='Random')\n", + " ax.set_ylim([0, 1.2])\n", + " ax.set_ylabel('Value')\n", + " ax.legend()\n", + " \n", + "fig.savefig('myfig-advanced.png')\n", + "\n", + "# Put rcParams back to default\n", + "plt.rcParams.update(rcdef)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll want to check that your formatting looks good by looking at the file that's being saved\n", + "to your hard disk, since that's exactly how it will look to the publisher or when inserted\n", + "into your manuscript." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/metaclasses/cool_meta.py b/Examples/metaclasses/cool_meta.py new file mode 100644 index 0000000..eb33fb9 --- /dev/null +++ b/Examples/metaclasses/cool_meta.py @@ -0,0 +1,17 @@ +class CoolMeta(type): + def __new__(meta, name, bases, dct): + print('Creating class', name) + return super(CoolMeta, meta).__new__(meta, name, bases, dct) + def __init__(cls, name, bases, dct): + print('Initializing class', name) + super(CoolMeta, cls).__init__(name, bases, dct) + def __call__(cls, *args, **kw): + print('calling CoolMeta to instantiate ', cls) + return type.__call__(cls, *args, **kw) + +class CoolClass(metaclass=CoolMeta): + def __init__(self): + print('And now my CoolClass object exists') + +print('everything loaded, instantiate a coolclass object now') +foo = CoolClass() diff --git a/Examples/metaclasses/mangler.py b/Examples/metaclasses/mangler.py new file mode 100755 index 0000000..fcf538f --- /dev/null +++ b/Examples/metaclasses/mangler.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python3 + + +class NameMangler(type): + + def __new__(cls, clsname, bases, _dict): + uppercase_attr = {} + for name, val in _dict.items(): + if not name.startswith('__'): + uppercase_attr[name.upper()] = val + uppercase_attr[name] = val + else: + uppercase_attr[name] = val + + return super().__new__(cls, clsname, bases, uppercase_attr) + + +class Foo(metaclass=NameMangler): + x = 1 + + +if __name__ == "__main__": + f = Foo() + print(f.x) + print(f.X) diff --git a/Examples/metaclasses/my_solution.py b/Examples/metaclasses/my_solution.py new file mode 100644 index 0000000..2605088 --- /dev/null +++ b/Examples/metaclasses/my_solution.py @@ -0,0 +1,55 @@ +# original class made as simple as possible +class TestClass(object): + + engine = None + + def __init__(self, config): + self.config = config + self.data = None + + def initialize(self): + print('initialize data') + self.data = 'initialized' + + def _get_engine(self): + if not TestClass.engine: + self.initialize() + TestClass.engine = 'on' + return TestClass.engine + + def get_privilege(self): + engine = self._get_engine() + return self.data, TestClass.engine + + +class MyClass(object): + + class __MyClass(object): + def __init__(self, db_config): + self.db_config = db_config + self.data = None + self.engine = None + + def initialize(self): + print('initialize data') + self.data = 'initialized' + + def _get_engine(self): + if not self.engine: + self.initialize() + self.engine = 'on' + return self.engine + + def get_privilege(self): + engine = self._get_engine() + return self.data, self.engine + + instance = None + + def __init__(self, db_config): + if not MyClass.instance: + MyClass.instance = MyClass.__MyClass(db_config) + + def __getattr__(self, name): + return getattr(self.instance, name) + diff --git a/Examples/metaclasses/singleton.py b/Examples/metaclasses/singleton.py new file mode 100644 index 0000000..890082b --- /dev/null +++ b/Examples/metaclasses/singleton.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python3 + +""" +example of using __metaclass__ to impliment the singleton pattern +""" + + +class Singleton(type): + instance = None + + def __call__(cls, *args, **kwargs): + if cls.instance is None: + cls.instance = super().__call__(*args, **kwargs) + return cls.instance + + +class MyClass(metaclass=Singleton): + pass + +object1 = MyClass() +object2 = MyClass() + +print(id(object1)) +print(id(object2)) diff --git a/Examples/metaclasses/start_class.py b/Examples/metaclasses/start_class.py new file mode 100644 index 0000000..dc8af24 --- /dev/null +++ b/Examples/metaclasses/start_class.py @@ -0,0 +1,55 @@ +import sqlalchemy + + +class AccessTable(object): + + engine = None + + def __init__(self, db_config): + self.db_config = db_config + + def initialize(self, engine): + """ + Set up schema if it does not already exist + """ + self.metadata = sqlalchemy.MetaData() + sqlalchemy.Table('access', self.metadata, + sqlalchemy.Column('name', sqlalchemy.String(256), primary_key=True), + sqlalchemy.Column('group', sqlalchemy.String(256), nullable=False), + sqlalchemy.Column('privilege', sqlalchemy.String(64), nullable=False), + sqlalchemy.Index('name', 'group', unique=True)) + self.metadata.create_all(engine) + + def _get_engine(self): + """ + Get a connection to the sqlalchemy db engine + + If the initial connection fails, it will try again next time + + The engine connection is kept as a class variable since sqlalchemy engines are intended to be + started once in the app's lifetime, not started up and shut down repeatedly. So this is just + a plain ol singleton + """ + if not AccessTable.engine: + AccessTable.engine = sqlalchemy.create_engine(self.db_config['engine']) + + self.initialize(AccessTable.engine) + return AccessTable.engine + + def get_privilege(self, name, group): + """ + Get the privilege for the given name and group + """ + # TODO consider making self.engine a @property instead + engine = self._get_engine() + + # TODO consider making self.metadata a @property + # Also, I don't like how self.metadata is not initialized until _get_engine is called ... should be more + # automatic than that + access = self.metadata.tables['access'] + select = sqlalchemy.select([access.c.privilege]).where(sqlalchemy.sql.and_(access.c.name == name, + access.c.group == group)) + + # TODO handle result including case where it doesn't find any records + _result = engine.execute(select) + return 'readwrite' diff --git a/Examples/nosql/address_book_model.py b/Examples/nosql/address_book_model.py new file mode 100644 index 0000000..942c3ed --- /dev/null +++ b/Examples/nosql/address_book_model.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python + +""" +sample data for NOSQL examples + +This version has a non-trival data model + +""" + +class Person(object): + """ + class to represent an individual person + """ + def __init__(self, + last_name, + first_name='', + middle_name='', + cell_phone='', + email='', + ): + """ + initialize a Person object: + """ + self.first_name = first_name.strip() + self.last_name = last_name.strip() + self.middle_name = middle_name.strip() + self.cell_phone = cell_phone.strip() + self.email = email.strip() + + @property + def name(self): + return " ".join([self.first_name, self.middle_name, self.last_name]) + + def __str__(self): + msg = '{first_name} {middle_name} {last_name}'.format(**self.__dict__) + return msg + def __repr__(self): + """ + not a good ___repr__, but want to have something here + """ + return self.__str__() + + +class Address(object): + """ + class that represents an address + """ + def __init__(self, + line_1='', + line_2='', + city='', + state='', + zip_code='', + ): + """ + initialize an address + """ + + self.line_1=line_1.strip() + self.line_2=line_2.strip() + self.city=city.strip() + self.state=state.strip() + self.zip_code=str(zip_code).strip() + + def __str__(self): + msg = "{line_1}\n{line_2}\n{city} {state} {zip_code}\n".format(**self.__dict__) + return msg + +class Household(object): + """ + Class that represents a Household. + + A household has one or more people, and a Location + """ + + def __init__(self, + name = '', + people=(), + address=None, + phone='' + ): + self.name = name.strip() + self.people = list(people) + self.address = address + self.phone = phone.strip() + + def __str__(self): + msg = [self.name+":"] + msg += [" "+ person.name for person in self.people] + msg += [str(self.address)] + return "\n".join(msg) + def __repr__(self): + return self.__str__() + +class Business(Household): + """ + Class that represents a Business + + A business has one or more people, + and address and a phone number + """ + # Same as household now, but you never know. + pass + + +class AddressBook(object): + """ + And address book -- has people, households, businesses. + + All fully cross-referenced + """ + + def __init__(self, + people=(), + businesses=(), + households=(), + ): + self.people = list(people) + self.businesses = list(businesses) + self.households = list(households) + + def __str__(self): + msg = ["An Address Book:"] + msg += ["People:"] + msg += [" "+person.name for person in self.people] + msg += ["Households:"] + msg += [" "+house.name for house in self.households] + msg += ["Businesses:"] + msg += [" "+bus.name for bus in self.businesses] + + return "\n".join(msg) + + @property + def locations(self): + return self.households+self.businesses + + def find_people(self, name=''): + """ + find all the people with name in their name somewhere + """ + return [person for person in self.people if name.lower() in person.name.lower()] + + def find_zip_codes(self, zip_code): + """ + find all the locations with this zip_code + """ + return [location for location in self.locations if location.address.zip_code == str(zip_code).strip()] + + def find_state(self, state): + """ + find all the locations in this state + """ + return [location for location in self.locations if location.address.state == state] + + +def create_sample(): + """ + Create a sample Address Book + """ + + chris = Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + + emma = Person(last_name = 'Barker', + first_name='Emma', + middle_name='L', + cell_phone='(345) 555-9012', + email = 'emma@something.com', + ) + + donna = Person(last_name = 'Barker', + first_name='Donna', + middle_name='L', + cell_phone='(111) 555-1111', + email = 'dbarker@something.com', + ) + + barker_address = Address(line_1='123 Some St', + line_2='Apt 1234', + city='Seattle', + state='WA', + zip_code='98000',) + + the_barkers = Household(name="The Barkers", + people=(chris, donna, emma), + address = barker_address) + + + joseph = Person(last_name = 'Sheedy', + first_name='Joseph', + cell_phone='(234) 555-8910', + email = 'js@some_thing.com', + ) + + cris = Person(last_name = 'Ewing', + first_name='Cris', + cell_phone='(345) 555-6789', + email = 'cris@a_fake_domain.com', + ) + + fulvio = Person(last_name = 'Casali', + first_name= 'Fulvio', + cell_phone='(345) 555-1234', + email = 'fulvio@a_fake_domain.com', + ) + + fred = Person(first_name="Fred", + last_name="Jones", + email='FredJones@some_company.com', + cell_phone="403-561-8911", + ) + + python_cert_address = Address('UW Professional and Continuing Education', + line_2='4333 Brooklyn Ave. NE', + city='Seattle', + state='WA', + zip_code='98105', + ) + + python_cert = Business(name = 'Python Certification Program', + people=(chris, joseph, cris, fulvio), + address = Address('UW Professional and Continuing Education', + line_2='4333 Brooklyn Ave. NE', + city='Seattle', + state='WA', + zip_code='98105', + ) + ) + + + address_book = AddressBook() + + address_book.people.append(chris) + address_book.people.append(donna) + address_book.people.append(emma) + address_book.people.append(cris) + address_book.people.append(joseph) + address_book.people.append(fulvio) + + address_book.households.append(the_barkers) + address_book.businesses.append(python_cert) + + return address_book + +if __name__ == "__main__": + address_book = create_sample() + + print("Here is the address book") + print(address_book) + + diff --git a/Examples/nosql/address_book_mongo.py b/Examples/nosql/address_book_mongo.py new file mode 100644 index 0000000..c9db526 --- /dev/null +++ b/Examples/nosql/address_book_mongo.py @@ -0,0 +1,366 @@ +#!/usr/bin/env python + +""" +sample data for NOSQL examples + +This version uses mongoDB to store the data. + +NOTE: you need to start up the DB first: + +$ mongod --dbpath=mongo_data/ + +""" + +from bson.objectid import ObjectId + + +class PersistObject(object): + """ + mix-in class for object you want to be able to put in a mongoDB + + defines the basic to_dict and from_dict methods + """ + def to_dict(self): + """ + returns a dictionary of all the data in the object + + pretty simple in this case, but might be more to it + for a more complicated class + """ + return self.__dict__ + @classmethod + def from_dict(cls, dct): + """ + Returns a new object initialized from the values in dct + + just calls the usual __init__ in this case. but could be more + to it in a more complex case. + """ + return cls(**dct) + + +class Person(PersistObject): + """ + class to represent an individual person + """ + def __init__(self, + last_name, + first_name='', + middle_name='', + cell_phone='', + email='', + _id=None, + ): + """ + initialize a Person object: + """ + self.first_name = first_name.strip() + self.last_name = last_name.strip() + self.middle_name = middle_name.strip() + self.cell_phone = cell_phone.strip() + self.email = email.strip() + self._id = ObjectId() if _id is None else _id + + + @property + def name(self): + return " ".join([self.first_name, self.middle_name, self.last_name]) + + def __str__(self): + msg = '{first_name} {middle_name} {last_name}'.format(**self.__dict__) + return msg + def __repr__(self): + """ + not a good ___repr__, but want to have something here + """ + return self.__str__() + + + +class Address(PersistObject): + """ + class that represents an address + """ + def __init__(self, + line_1='', + line_2='', + city='', + state='', + zip_code='', + **kwargs + ): + """ + initialize an address + """ + + self.line_1=line_1.strip() + self.line_2=line_2.strip() + self.city=city.strip() + self.state=state.strip().upper() + self.zip_code=str(zip_code).strip() + + def __str__(self): + msg = "{line_1}\n{line_2}\n{city} {state} {zip_code}\n".format(**self.__dict__) + return msg + +class Household(PersistObject): + """ + Class that represents a Household. + + A household has one or more people, and a Location + """ + + def __init__(self, + name = '', + people=(), + address=None, + phone='', + **kwargs + ): + self.name = name.strip() + ##if it's already a ObjectID, then do'nt need to extract it. + try: + self.people = [p._id for p in people] + except AttributeError: + self.people = people + self.address = address + self.phone = phone.strip() + + def to_dict(self): + """ + convert to dict -- stores ids of people + """ + dct = self.__dict__ + dct['address'] = self.address.to_dict() + return dct + + @classmethod + def from_dict(cls, dct): + """ + creates a household object from a dict representation + unpacks the people _ids and address. + """ + dct['address'] = Address(**dct['address']) + return cls(**dct) + + + def __str__(self): + msg = [self.name+":"] + msg += [str(self.address)] + return "\n".join(msg) + def __repr__(self): + return self.__str__() + +class Business(Household): + """ + Class that represents a Business + + A business has one or more people, + and address and a phone number + """ + # Same as household now, but you never know. + pass + + +class AddressBook(object): + """ + And address book -- has people, households, businesses. + + All cross-referenced + """ + + def __init__(self, + people=(), + businesses=(), + households=(), + fresh = True, + ): + # create the DB + from pymongo import MongoClient + + client = MongoClient('localhost', 27017) + db = client.address_book + if fresh: + ## clean out old one + db.people.drop() + db.businesses.drop() + db.households.drop() + + # Use the DB to hold the data + self.people = db.people + self.businesses = db.businesses + self.households = db.households + + def add_person(self, person): + self.people.insert(person.to_dict()) + + def add_household(self, household): + print("adding a household") + self.households.insert(household.to_dict()) + + def add_business(self, business): + self.businesses.insert(business.to_dict()) + + def __str__(self): + msg = ["An Address Book:"] + msg += ["People:"] + msg += [" "+person.name for person in self.find_people()] + msg += ["Households:"] + msg += [" "+house.name for house in self.find_households()] + msg += ["Businesses:"] + msg += [" "+bus.name for bus in self.find_businesses()] + + return "\n".join(msg) + + def find_people(self, name=''): + """ + find all the people with name in their name somewhere + """ + ## fixme -- can this query be combined? + ## like this: db.inventory.find( { $or: [ { qty: { $lt: 20 } }, { sale: true } ] } ) + + cursor = self.people.find({"first_name": {'$regex' : '.*' + name + '.*', + '$options':'i'}}) + results = [Person.from_dict(p) for p in cursor] + + cursor = self.people.find({"last_name": {'$regex' : '.*' + name + '.*', + '$options':'i'}}) + + return results + [Person.from_dict(p) for p in cursor] + + def find_households(self): + cursor = self.households.find() + return [Household.from_dict(p) for p in cursor] + + + def find_businesses(self): + cursor = self.businesses.find() + return [Business.from_dict(p) for p in cursor] + + def find_zip_codes(self, zip_code): + """ + find all the locations with this zip_code + """ + zip_code = str(zip_code).strip() + cursor = self.households.find({"addresses.zip_code":zip_code}) + results = [Household.from_dict(dct) for dct in cursor] + + cursor = self.businesses.find({"address.zip_code":zip_code}) + results += [Business.from_dict(dct) for dct in cursor] + + return results + + def find_state(self, state): + """ + find all the locations in this state + """ + state = state.strip().upper() + cursor = self.households.find({"address.state":state}) + results = [Household.from_dict(dct) for dct in cursor] + + cursor = self.businesses.find({"address.state":state}) + results += [Business.from_dict(dct) for dct in cursor] + + return results + + +def create_sample(): + """ + Create a sample Address Book + """ + + chris = Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + + emma = Person(last_name = 'Barker', + first_name='Emma', + middle_name='L', + cell_phone='(345) 555-9012', + email = 'emma@something.com', + ) + + donna = Person(last_name = 'Barker', + first_name='Donna', + middle_name='L', + cell_phone='(111) 555-1111', + email = 'dbarker@something.com', + ) + + barker_address = Address(line_1='123 Some St', + line_2='Apt 1234', + city='Seattle', + state='WA', + zip_code='98000',) + + the_barkers = Household(name="The Barkers", + people=(chris, donna, emma), + address = barker_address) + + + joseph = Person(last_name = 'Sheedy', + first_name='Joseph', + cell_phone='(234) 555-8910', + email = 'js@some_thing.com', + ) + + cris = Person(last_name = 'Ewing', + first_name='Cris', + cell_phone='(345) 555-6789', + email = 'cris@a_fake_domain.com', + ) + + fulvio = Person(last_name = 'Casali', + first_name= 'Fulvio', + cell_phone='(345) 555-1234', + email = 'fulvio@a_fake_domain.com', + ) + + fred = Person(first_name="Fred", + last_name="Jones", + email='FredJones@some_company.com', + cell_phone="403-561-8911", + ) + + python_cert_address = Address('UW Professional and Continuing Education', + line_2='4333 Brooklyn Ave. NE', + city='Seattle', + state='WA', + zip_code='98105', + ) + + python_cert = Business(name = 'Python Certification Program', + people=(chris, joseph, cris, fulvio), + address = Address('UW Professional and Continuing Education', + line_2='4333 Brooklyn Ave. NE', + city='Seattle', + state='WA', + zip_code='98105', + ) + ) + + + address_book = AddressBook() + + address_book.add_person(chris) + address_book.add_person(donna) + address_book.add_person(emma) + address_book.add_person(cris) + address_book.add_person(joseph) + address_book.add_person(fulvio) + + address_book.add_household(the_barkers) + address_book.add_business(python_cert) + + return address_book + +if __name__ == "__main__": + address_book = create_sample() + + print("Here is the address book") + print(address_book) + + diff --git a/Examples/nosql/address_book_zodb.py b/Examples/nosql/address_book_zodb.py new file mode 100644 index 0000000..c5b7d4e --- /dev/null +++ b/Examples/nosql/address_book_zodb.py @@ -0,0 +1,285 @@ +#!/usr/bin/env python + +""" +sample data for NOSQL examples + +This version uses ZODB for the data management. + +""" + +import persistent # from ZODB +from persistent.list import PersistentList + +class Person(persistent.Persistent): + """ + class to represent an individual person + """ + def __init__(self, + last_name, + first_name='', + middle_name='', + cell_phone='', + email='', + ): + """ + initialize a Person object: + """ + self.first_name = first_name.strip() + self.last_name = last_name.strip() + self.middle_name = middle_name.strip() + self.cell_phone = cell_phone.strip() + self.email = email.strip() + + @property + def name(self): + return " ".join([self.first_name, self.middle_name, self.last_name]) + + def __str__(self): + msg = '{first_name} {middle_name} {last_name}'.format(**self.__dict__) + return msg + def __repr__(self): + """ + not a good ___repr__, but want to have something here + """ + return self.__str__() + + +class Address(persistent.Persistent): + """ + class that represents an address + """ + def __init__(self, + line_1='', + line_2='', + city='', + state='', + zip_code='', + ): + """ + initialize an address + """ + + self.line_1=line_1.strip() + self.line_2=line_2.strip() + self.city=city.strip() + self.state=state.strip() + self.zip_code=str(zip_code).strip() + + def __str__(self): + msg = "{line_1}\n{line_2}\n{city} {state} {zip_code}\n".format(**self.__dict__) + return msg + +class Household(persistent.Persistent): + """ + Class that represents a Household. + + A household has one or more people, and a Location + """ + + def __init__(self, + name = '', + people=(), + address=None, + phone='' + ): + self.name = name.strip() + self.people = list(people) + self.address = address + self.phone = phone.strip() + + def __str__(self): + msg = [self.name+":"] + msg += [" "+ person.name for person in self.people] + msg += [str(self.address)] + return "\n".join(msg) + def __repr__(self): + return self.__str__() + +class Business(Household): + """ + Class that represents a Business + + A business has one or more people, + and address and a phone number + """ + # Same as household now, but you never know. + pass + + +class AddressBook(persistent.Persistent): + """ + And address book -- has people, households, businesses. + + All fully cross-referenced + """ + + def __init__(self, + people=(), + businesses=(), + households=(), + ): + self.people = PersistentList(people) + self.businesses = PersistentList(businesses) + self.households = PersistentList(households) + + def __str__(self): + msg = ["An Address Book:"] + msg += ["People:"] + msg += [" "+person.name for person in self.people] + msg += ["Households:"] + msg += [" "+house.name for house in self.households] + msg += ["Businesses:"] + msg += [" "+bus.name for bus in self.businesses] + + return "\n".join(msg) + + @property + def locations(self): + return self.households+self.businesses + + def find_people(self, name=''): + """ + find all the people with name in their name somewhere + """ + return [person for person in self.people if name.lower() in person.name.lower()] + + def find_zip_codes(self, zip_code): + """ + find all the locations with this zip_code + """ + return [location for location in self.locations if location.address.zip_code == str(zip_code).strip()] + + def find_state(self, state): + """ + find all the locations in this state + """ + return [location for location in self.locations if location.address.state == state] + + +def create_sample(): + """ + Create a sample Address Book + + Uses the ZODB single file storage + + There are other storage options: + * In-memory + * Client/Server Model + """ + + import ZODB + + db = ZODB.DB('address_book_zodb.fs') + connection = db.open() + root = connection.root + import transaction + + ## now create some data. + chris = Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + + emma = Person(last_name = 'Barker', + first_name='Emma', + middle_name='L', + cell_phone='(345) 555-9012', + email = 'emma@something.com', + ) + + donna = Person(last_name = 'Barker', + first_name='Donna', + middle_name='L', + cell_phone='(111) 555-1111', + email = 'dbarker@something.com', + ) + + barker_address = Address(line_1='123 Some St', + line_2='Apt 1234', + city='Seattle', + state='WA', + zip_code='98000',) + + the_barkers = Household(name="The Barkers", + people=(chris, donna, emma), + address = barker_address) + + + joseph = Person(last_name = 'Sheedy', + first_name='Joseph', + cell_phone='(234) 555-8910', + email = 'js@some_thing.com', + ) + + cris = Person(last_name = 'Ewing', + first_name='Cris', + cell_phone='(345) 555-6789', + email = 'cris@a_fake_domain.com', + ) + + fulvio = Person(last_name = 'Casali', + first_name= 'Fulvio', + cell_phone='(345) 555-1234', + email = 'fulvio@a_fake_domain.com', + ) + + fred = Person(first_name="Fred", + last_name="Jones", + email='FredJones@some_company.com', + cell_phone="403-561-8911", + ) + + python_cert_address = Address('UW Professional and Continuing Education', + line_2='4333 Brooklyn Ave. NE', + city='Seattle', + state='WA', + zip_code='98105', + ) + + python_cert = Business(name = 'Python Certification Program', + people=(chris, joseph, cris, fulvio), + address = Address('UW Professional and Continuing Education', + line_2='4333 Brooklyn Ave. NE', + city='Seattle', + state='WA', + zip_code='98105', + ) + ) + + + root.address_book = AddressBook() + + root.address_book.people.append(chris) + root.address_book.people.append(donna) + root.address_book.people.append(emma) + root.address_book.people.append(cris) + root.address_book.people.append(joseph) + root.address_book.people.append(fulvio) + + transaction.commit() + + root.address_book.households.append(the_barkers) + root.address_book.businesses.append(python_cert) + + transaction.commit() + + ## close the db + db.close() + + ## now open it again + + db = ZODB.DB('address_book_zodb.fs') + connection = db.open() + root = connection.root + + return root.address_book + +if __name__ == "__main__": + address_book = create_sample() + + print("Here is the address book") + print(address_book) + + diff --git a/Examples/nosql/mongo_test.py b/Examples/nosql/mongo_test.py new file mode 100644 index 0000000..21f40d6 --- /dev/null +++ b/Examples/nosql/mongo_test.py @@ -0,0 +1,30 @@ +#!usr/bin/env python + +""" +simple test file for mongoDB +remember to start database: +$ mongod --dbpath=mongo_data/ + +""" + +from pymongo import MongoClient + +client = MongoClient('localhost', 27017) + +db = client.test_database + +collection = db.test_collection + +chris = {'last_name':'Barker', + 'first_name':'Chris', + 'middle_name':'H', + 'cell_phone':'(123) 555-7890', + 'email':'PythonCHB@gmail.com', + } + +collection.insert(chris) + +print("all the collections") +print(db.collection_names()) + +print(collection.find_one()) diff --git a/Examples/nosql/test_address_book_model.py b/Examples/nosql/test_address_book_model.py new file mode 100755 index 0000000..ebb7395 --- /dev/null +++ b/Examples/nosql/test_address_book_model.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python + +""" +test code for address book model code +if testing mongo, remember to start database first +$ mongod --dbpath=mongo_data/ +""" + +# import address_book_model as model +# import address_book_zodb as model +import address_book_mongo as model + + +a_book = model.create_sample() + + +def test_name_search(): + """find a single person by first name""" + + people = a_book.find_people('chris') + + assert len(people) == 1 + assert people[0].first_name == 'Chris' + assert people[0].last_name == 'Barker' + + +def test_name_search2(): + people = a_book.find_people('barKer') + first_names = [p.first_name for p in people] + + assert 'Chris' in first_names + assert 'Emma' in first_names + assert 'Donna' in first_names + + +def test_zip_search(): + locations = a_book.find_zip_codes(98105) + + assert len(locations) == 1 + assert locations[0].name == 'Python Certification Program' + + +def test_state_search(): + locations = a_book.find_state('WA') + names = [l.name for l in locations] + + assert "The Barkers" in names + assert "Python Certification Program" in names diff --git a/Examples/nosql/test_address_book_mongo.py b/Examples/nosql/test_address_book_mongo.py new file mode 100644 index 0000000..6a09c00 --- /dev/null +++ b/Examples/nosql/test_address_book_mongo.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python + +""" +test code for address book model code +remember to start mongo database first +$ mongod --dbpath=mongo_data/ +""" + +import address_book_mongo as model + + +def test_person_to_dict(): + chris = model.Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + + dct = chris.to_dict() + + assert dct['last_name'] == "Barker" + assert dct['email'] == 'PythonCHB@gmail.com' + +def test_person_to_from_dict(): + chris = model.Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + + dct = chris.to_dict() + chris2 = model.Person.from_dict(dct) + + print(chris2) + + assert chris2.last_name == 'Barker' + assert chris2.first_name == 'Chris' + assert chris2.middle_name == 'H' + assert chris2.cell_phone == '(123) 555-7890' + assert chris2.email == 'PythonCHB@gmail.com' + + +def test_household_to_dict(): + chris = model.Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + home = model.Household(name="The Barkers", + people=(chris,), + address=model.Address(line_1='123 Some St', + line_2='Apt 1234', + city='Seattle', + state='WA', + zip_code='98000',), + phone='123-456-8762', + ) + + home_dct = home.to_dict() + + assert home_dct['name'] == "The Barkers" + assert home_dct['address']['city'] == 'Seattle' + +def test_household_to_dict_to_object(): + chris = model.Person(last_name = 'Barker', + first_name='Chris', + middle_name='H', + cell_phone='(123) 555-7890', + email = 'PythonCHB@gmail.com', + ) + fred = model.Person(last_name = 'Jones', + first_name='Fred', + middle_name='B', + cell_phone='(123) 555-7890', + email = 'FredJones@gmail.com', + ) + home = model.Household(name="The Barkers", + people=(chris,), + address=model.Address(line_1='123 Some St', + line_2='Apt 1234', + city='Seattle', + state='WA', + zip_code='98000',), + phone='123-456-8762', + ) + + home2 = model.Household.from_dict(home.to_dict()) + + assert home2.name == home.name + assert home2.phone == home.phone + assert home2.address.line_1 == home.address.line_1 + assert home2.address.line_2 == home.address.line_2 + assert home2.people == home.people diff --git a/Examples/numpy/README.rst b/Examples/numpy/README.rst new file mode 100644 index 0000000..3f5a582 --- /dev/null +++ b/Examples/numpy/README.rst @@ -0,0 +1,51 @@ +===================== +Assorted numpy Demos +===================== + +This is an assortment of code that demonstrates various aspects of numpy. + +Jupyter notebooks +================= + +The *.ipynb files are jupyter notebook (formerly known as ipython notebook) files. + +To run them, you need the jupyter notebook installed:: + + $ python -m pip install -U jupyter + + +( -U means "upgrade" which should ensure that all the dependecies get upgraded, too) + +or, if you are running anaconda: + + $ conda install jupyter + +Once jupyter is installed, you can run it: + +To use these notebooks, cd to this directory, and start up the notebook:: + + $ cd Examples/numpy + $ jupyter notebook + +This should bring up your browser with a page with links to each of the enclosed notebooks. + +NOTE: If you have problems like "kernel stopped, trying to restart" you may have a version incompatiblily issue (I did April 24, 2016). There error message ends with somethign like: + +File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipywidgets/widgets/widget.py", line 154, in Widget in which to find _model_name. If empty, look in the global registry.""").tag(sync=True) + +AttributeError: 'Unicode' object has no attribute 'tag' + + +In that case try:: + + python -m pip install -U traitlets + +That will update traitlets, which is the source of the problem. + +Other files +============ + +``start.py`` has a couple utilites used by the other demos + +``sudoku-chb.py`` is a suduko game. The GUI is written with wxPython. But the interesting part (in this context) is the game board, which is represented by a numpy array, with each sub part of the board as views on that same array. + diff --git a/Examples/numpy/broadcasting.ipynb b/Examples/numpy/broadcasting.ipynb new file mode 100644 index 0000000..7404674 --- /dev/null +++ b/Examples/numpy/broadcasting.ipynb @@ -0,0 +1,565 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Intro to numpy broadcasting\n", + "-----------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = np.ones((3,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1. 1. 1. 1.]\n", + " [ 1. 1. 1. 1.]\n", + " [ 1. 1. 1. 1.]]\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 3. 3. 3. 3.]\n", + " [ 3. 3. 3. 3.]\n", + " [ 3. 3. 3. 3.]]\n" + ] + } + ], + "source": [ + "# the multiplication is \"broadcast\" over the whole array:\n", + "print(a * 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is core of \"vectorization\" -- doing operations over a whole array at the speed of C -- key to numpy performance.\n", + "\n", + "Regular python lists and comprehensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "l = range(10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000 loops, best of 3: 871 µs per loop\n" + ] + } + ], + "source": [ + "# using regular python list comprehensions\n", + "%timeit [i*3 for i in l]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the numpy way:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = np.arange(10000) # create an array" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The slowest run took 24.07 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "100000 loops, best of 3: 6.85 µs per loop\n" + ] + } + ], + "source": [ + "timeit a * 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### in-place operations\n", + "\n", + "One of the primary reasons \"augemented assignemnt\" was added to Python \n", + "is support in-place operations:\n", + " `+=`, `*=`, etc..." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.arange(10)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3 4 5 6 7 8 9 10 11 12]\n" + ] + } + ], + "source": [ + "a+=3\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A was changed in place -- no new memory allocation, loop at the speed of C.\n", + "\n", + "So \"augmented assigment\" operators are not just syntacitc sugar!\n", + "\n", + "How do you broadcast to multiple dimensions?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 3.33333333 6.66666667 10. ]]\n" + ] + } + ], + "source": [ + "x = np.linspace(0,10,4)\n", + "y = np.linspace(100,200,3)\n", + "\n", + "x.shape = (1, -1)\n", + "y.shape = (-1, 1)\n", + "\n", + "print(x)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 100.]\n", + " [ 150.]\n", + " [ 200.]]\n" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4,)\n", + "(1, 4)\n", + "[[ 0. 3.33333333 13.33333333 30. ]]\n" + ] + } + ], + "source": [ + "a = np.arange(4)\n", + "print (a.shape)\n", + "print (x.shape)\n", + "print (a * x)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4,)\n", + "(3, 1)\n", + "[[ 0. 100. 200. 300.]\n", + " [ 0. 150. 300. 450.]\n", + " [ 0. 200. 400. 600.]]\n" + ] + } + ], + "source": [ + "print(a.shape)\n", + "print(y.shape)\n", + "print(a * y)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 333.33333333 666.66666667 1000. ]\n", + " [ 0. 500. 1000. 1500. ]\n", + " [ 0. 666.66666667 1333.33333333 2000. ]]\n" + ] + } + ], + "source": [ + "print(np.sqrt(x**2 * y**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n", + "[[ 0 10 20 30]\n", + " [ 40 50 60 70]\n", + " [ 80 90 100 110]]\n" + ] + } + ], + "source": [ + "x = np.arange(12).reshape((3,4))\n", + "y = np.arange(12).reshape((3,4)) * 10\n", + "print(x)\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 11, 22, 33],\n", + " [ 44, 55, 66, 77],\n", + " [ 88, 99, 110, 121]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x + y" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "z = np.array([100, 200, 300, 400])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[100 200 300 400]\n" + ] + } + ], + "source": [ + "print(z)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 4)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 200, 600, 1200],\n", + " [ 400, 1000, 1800, 2800],\n", + " [ 800, 1800, 3000, 4400]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z * x" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[100],\n", + " [200],\n", + " [300]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z = np.array([100, 200, 300])\n", + "z.shape = (3,1)\n", + "z" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 100, 200, 300],\n", + " [ 800, 1000, 1200, 1400],\n", + " [2400, 2700, 3000, 3300]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z * x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/constructors.ipynb b/Examples/numpy/constructors.ipynb new file mode 100644 index 0000000..da76c45 --- /dev/null +++ b/Examples/numpy/constructors.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assorted numpy Constructors\n", + "-----------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Always need to import it... \n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The ones you will most commonly use\n", + "\n", + "Making arrays from scratch\n", + "\n", + "```\n", + "zeros()\n", + "ones()\n", + "linspace()\n", + "```\n", + "Or from existing data stracutures:\n", + "```\n", + "array()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1.],\n", + " [ 1., 1.],\n", + " [ 1., 1.]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# from scratch:\n", + "a = np.ones((3,2)) # default dtype is float64\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float64\n" + ] + } + ], + "source": [ + "print(a.dtype)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 0]\n", + " [0 0 0]]\n" + ] + } + ], + "source": [ + "print(np.zeros((2,3), dtype=np.int32))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.00000000e+00 -2.52435490e-29 3.57649012e-29]\n", + " [ 8.58993459e+09 9.40395929e-38 1.40129846e-45]\n", + " [ 3.60225631e-39 1.40129846e-45 3.60225631e-39]]\n" + ] + } + ], + "source": [ + "print (np.empty((3,3), dtype=np.float32)) # not initilized!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18]\n" + ] + } + ], + "source": [ + "# for a range:\n", + "# integers (much like the regular range...)\n", + "print(np.arange(2, 20, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]\n" + ] + } + ], + "source": [ + "# for floats\n", + "# from zero to 1 (inclusive)\n", + "# you want 11 total elements\n", + "print(np.linspace(0, 1, 11))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]\n" + ] + } + ], + "source": [ + "# you can do arange with floats, too\n", + "# but it can get wierd with floating point error!\n", + "print(np.arange(0,1.1, 0.1, dtype=np.float))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 10. 100. 1000.]\n" + ] + } + ], + "source": [ + "# if you are a math/scicnce geek, and want a log axis...\n", + "print(np.logspace(0, 3, 4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From existing data:\n", + "```\n", + "array()\n", + "asarray()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1, 2) (3, 4, 5) (5, 6)]\n" + ] + } + ], + "source": [ + "\n", + "# maybe the most common constructor \n", + "a = np.array([(1, 2),\n", + " (3, 4, 5),\n", + " (5, 6)]) # auto-determined dtype\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((3,), dtype('O'))" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape, a.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# maybe an array?\n", + "a = np.arange(5)\n", + "b = np.asarray(a)\n", + "print(a is b)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "# or not:\n", + "a = range(5)\n", + "b = np.asarray(a)\n", + "print(a is b)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4]\n" + ] + } + ], + "source": [ + "print(np.ascontiguousarray(a))# forces contiguous datablock" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 97 98 99 100 101 102 103]\n" + ] + } + ], + "source": [ + "# from binary data:\n", + "s = b'abcdefg'\n", + "a = np.frombuffer(s, dtype=np.uint8)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[18 4]\n" + ] + } + ], + "source": [ + "# \"fromstring\" is named from py2 when a string was a string of bytes\n", + "# you really want to use a bytes object here, not a string.\n", + "print(np.fromstring(b'\\x12\\x04', dtype=np.uint8))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# from (and to) binary file:\n", + "a = np.arange(5, dtype=np.float32) # jsut a binary dump!\n", + "a.tofile('junk.dat')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1065353216 1073741824 1077936128 1082130432]\n" + ] + } + ], + "source": [ + "# to read raw binary data you need the dtype.\n", + "# if you get it wrong, you get junk:\n", + "print(np.fromfile('junk.dat', dtype=np.int32))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 1. 2. 3. 4.]\n" + ] + } + ], + "source": [ + "# this works better\n", + "print(np.fromfile('junk.dat', dtype=np.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/dtypes.ipynb b/Examples/numpy/dtypes.ipynb new file mode 100644 index 0000000..fb71c07 --- /dev/null +++ b/Examples/numpy/dtypes.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "numpy custom data types\n", + "------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# data types\n", + "import numpy as np\n", + "from start import print_info" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# a custom dtype:\n", + "dt = np.dtype([('first_name', np.str_, 16),\n", + " ('last_name', np.str_, 24),\n", + " ('grades', np.float64, (2,)),\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create an array of this type:\n", + "a = np.empty((3,), dtype=dt)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[('Fred', 'Jones', [92.3, 86.2]) ('Bob', 'Smith', [85.1, 88.4])\n", + " ('George', 'Roberts', [76.3, 91.5])]\n", + "arr.shape: (3,)\n", + "arr.dtype: [('first_name', ' 8\n", + "mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And you can assign to the sub-array, too -- very handy:\n", + "\n", + "Set all the values greater than 8 to 8:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 8, 8, 0, 8, 8, 8])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[a > 8] = 8\n", + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### array indexing\n", + "\n", + "But what if you know the indexes of the elements you want?\n", + "\n", + "you can pass those in:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([8, 0, 8, 0])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[ [1, 3, 2, 3] ] # note that you can repeat the same index..." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# and it can work on higher rank arrays, also:\n", + "row_indices = [0, 2, 3]\n", + "A[row_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 22, 34])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col_indices = [1, 2, -1] # remember, index -1 means the last element\n", + "A[row_indices, col_indices]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions and methods for extracting data from arrays\n", + "\n", + "(these pre-date fancy indexing, and thus are less used. but sometimes the best way to do it)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### where\n", + "The index mask can be converted to position index using the where function" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1, 2, 4, 5, 6]),)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices = np.where(mask)\n", + "indices" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.85078687, 0.40800382, 0.32703315, 0.64197836, 0.30596153])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.random.rand(8)\n", + "x[indices] # this indexing is equivalent to the fancy indexing x[mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But `where()` does more:\n", + "\n", + "`where(condition, [x, y])`\n", + "\n", + "Return elements, either from `x` or `y`, depending on `condition`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.75489954, 0.85078687, 0.40800382, 0.10192573, 0.32703315,\n", + " 0.64197836, 0.30596153, 0.51362076])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 0, 0, 0, 0, 0, 0])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where( x < 0.7, 0, [1,2,3,4,5,6,7,8])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4]\n", + " [ 5 6 7 8 9]\n", + " [10 11 12 13 14]\n", + " [15 16 17 18 19]]\n", + "[[0 0 0 0 0]\n", + " [0 0 0 0 0]\n", + " [0 1 1 1 1]\n", + " [1 1 1 1 1]]\n" + ] + } + ], + "source": [ + "A = np.arange(20).reshape((4,5))\n", + "print(A)\n", + "mask1 = (A > 10).astype(np.uint8)\n", + "print(mask1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 1 1 1 1]\n", + " [1 1 1 1 1]\n", + " [1 1 0 0 0]\n", + " [0 0 0 0 0]]\n" + ] + } + ], + "source": [ + "mask2 = (A < 12).astype(np.uint8)\n", + "print(mask2)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 1 1 1 1]\n", + " [1 1 1 1 1]\n", + " [1 2 1 1 1]\n", + " [1 1 1 1 1]]\n" + ] + } + ], + "source": [ + "print(mask1 + mask2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(np.where(A > 10, 1, 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### diag\n", + "With the diag function we can also extract the diagonal and subdiagonals of an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [ 5, 6, 7, 8, 9],\n", + " [10, 11, 12, 13, 14],\n", + " [15, 16, 17, 18, 19]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 6, 12, 18])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.diag(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5, 11, 17])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can get the diagonals that are not the main ones\n", + "np.diag(A, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 7, 13, 19])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.diag(A, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### take\n", + "The take function is similar to fancy indexing described above\n", + "\n", + "`np.take(a, indices, axis=None, out=None, mode='raise')`\n", + "\n", + "Take elements from an array along an axis.\n", + "\n", + "This function does the same thing as \"fancy\" indexing (indexing arrays\n", + "using arrays); however, it can be easier to use if you need elements\n", + "along a given axis." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [ 5, 6, 7, 8, 9],\n", + " [10, 11, 12, 13, 14],\n", + " [15, 16, 17, 18, 19]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's say we want the 0,2,3 rows.\n", + "Easy with \"fancy indexing\"" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [15, 16, 17, 18, 19]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[ [0,2,3] ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what if we want the 0,2,3 columns?\n", + "\n", + "`take` makes that easy" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 2, 3],\n", + " [ 5, 7, 8],\n", + " [10, 12, 13],\n", + " [15, 17, 18]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.take([0,2,3], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [15, 16, 17, 18, 19]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.take([0,2,3], axis=0) # zero is the default axis" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [15, 16, 17, 18, 19]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[ [0,2,3] ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`take` is also a function (not method), and so works on lists and other objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-3, -1, 0])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.take([-3, -2, -1, 0, 1, 2], row_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choose\n", + "Constructs an array by picking elements from several arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5, -2, 7, 5])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "which = [1, 0, 2, 1]\n", + "# we are choosing from three different arrays:\n", + "# 0, 1, 2\n", + "# so you need to pass in three arrays\n", + "choices = [[-2,-2,-2,-2],\n", + " [5, 5, 5, 5],\n", + " [7, 7, 7, 7]]\n", + "\n", + "np.choose(which, choices)\n", + "# what shape will the result be?\n", + "# hint: all arrays must be the same size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This one is pretty tricky -- but handy when you need it." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/filter_example.py b/Examples/numpy/filter_example.py new file mode 100644 index 0000000..37456af --- /dev/null +++ b/Examples/numpy/filter_example.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python + +""" +Example of using stride trick to implement a moving filter +""" + +import numpy as np + + +def convert_2d_window(input, width): + """ + returns a 2-d array, converted with stride_tricks from the input + array, to provide a moving window + + The resulting 2-d array is size: (length, width_of_window), so that + you can do vector math over the window + + :param input: The input array -- should be 1-d + :width input: the width of the window in number of elements (integer) + + """ + # assure that the input array is as we expect + input = np.asarray(input).flatten() + + len = input.shape[0] + itemsize = input.itemsize + + # compute the new shape and strides + # final length is the original length minus the window width + # -- so we don't drop off the end of the data array + # the second dimension is the width of the window + shape = (len - width + 1, width) + + # The new strides are the item size along the main axis, and also the + # item size along the cross axis -- so that you gt the next item as the + # start of the next row. + strides = (itemsize, itemsize) + + # use as_strided to set up the new shape and strides + return np.lib.stride_tricks.as_strided(input, shape, strides) + + +def moving_average(input, width): + """ + compute the simple moving average of the input series + """ + + expanded = convert_2d_window(input, width) + avg = expanded.mean(axis=1) + + return avg + + +def scaled_by_max(input, width): + """ + compute the time series, scaled by the max of a moving window + (there is a name for this!) + """ + expanded = convert_2d_window(input, width) + max = expanded.max(axis=1) + filtered = input[:len(expanded)] / max + + return filtered + + +if __name__ == "__main__": + + # run some examples: + a = np.arange(25) + np.random.shuffle(a) + + result = moving_average(a, 3) + print(result) + + result2 = scaled_by_max(a, 4) + print(result2) diff --git a/Examples/numpy/memory_structure.ipynb b/Examples/numpy/memory_structure.ipynb new file mode 100644 index 0000000..23c6f57 --- /dev/null +++ b/Examples/numpy/memory_structure.ipynb @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Intro to numpy memory use and structure\n", + "-----------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys\n", + "# Always use this import line!\n", + "import numpy as np\n", + "# handy utility for printing numpy array description.\n", + "from start import print_info" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "size of empty list: 64\n" + ] + } + ], + "source": [ + "empty_list = sys.getsizeof([])\n", + "print(\"size of empty list:\", empty_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "size of empty ndarray: 96\n" + ] + } + ], + "source": [ + "empty_array = sys.getsizeof(np.array([]))\n", + "print(\"size of empty ndarray:\", empty_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "size of python float: 24\n" + ] + } + ], + "source": [ + "py_float = sys.getsizeof(1.0)\n", + "print(\"size of python float:\", py_float)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "size of numpy float64: 8\n" + ] + } + ], + "source": [ + "numpy_float = np.array(1.0).itemsize\n", + "print(\"size of numpy float64:\", numpy_float)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A 1000 element list of floats:\n", + "32064 bytes\n" + ] + } + ], + "source": [ + "print(\"A 1000 element list of floats:\")\n", + "print(empty_list + 1000*8 + 1000*py_float, 'bytes')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A 1000 element list of floats:\n", + "8096 bytes\n" + ] + } + ], + "source": [ + "print(\"A 1000 element list of floats:\")\n", + "print(empty_array + 1000*numpy_float, 'bytes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Demo of the underlying structure:\n", + "----------------------------------- " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", + "arr.shape: (12,)\n", + "arr.dtype: int64\n", + "arr.itemsize 8\n", + "arr.ndim: 1\n", + "arr.strides (8,)\n", + "arr.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : True\n", + " OWNDATA : True\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# create an array:\n", + "a = np.arange(12)\n", + "print_info(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n", + "arr.shape: (3, 4)\n", + "arr.dtype: int64\n", + "arr.itemsize 8\n", + "arr.ndim: 2\n", + "arr.strides (32, 8)\n", + "arr.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : True\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# reshape it\n", + "a.shape = (3,4)\n", + "print_info(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[[ 0 4 8]\n", + " [ 1 5 9]\n", + " [ 2 6 10]\n", + " [ 3 7 11]]\n", + "arr.shape: (4, 3)\n", + "arr.dtype: int64\n", + "arr.itemsize 8\n", + "arr.ndim: 2\n", + "arr.strides (8, 32)\n", + "arr.flags:\n", + " C_CONTIGUOUS : False\n", + " F_CONTIGUOUS : True\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# transpose it\n", + "b = a.transpose()\n", + "print_info(b) " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[[[ 0 1 2]\n", + " [ 3 4 5]]\n", + "\n", + " [[ 6 7 8]\n", + " [ 9 10 11]]]\n", + "arr.shape: (2, 2, 3)\n", + "arr.dtype: int64\n", + "arr.itemsize 8\n", + "arr.ndim: 3\n", + "arr.strides (48, 24, 8)\n", + "arr.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : True\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# reshape again\n", + "a.shape = (2,2,3)\n", + "print_info(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : True\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.flags" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[[ 3 4 5]\n", + " [ 9 10 11]]\n", + "arr.shape: (2, 3)\n", + "arr.dtype: int64\n", + "arr.itemsize 8\n", + "arr.ndim: 2\n", + "arr.strides (48, 8)\n", + "arr.flags:\n", + " C_CONTIGUOUS : False\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# take a slice:\n", + "s = a[:,1,:]\n", + "print_info(s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/object.ipynb b/Examples/numpy/object.ipynb new file mode 100644 index 0000000..5708b75 --- /dev/null +++ b/Examples/numpy/object.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "numpy data types and the object datatype\n", + "------------------------------------------" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# usually defaults to float(64 bit)\n", + "a = np.ones(3)\n", + "print(a.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# smart enough to match input:\n", + "a = np.array((3,5,7,8))\n", + "print(a.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# smart enough to match input:\n", + "a = np.array((3.0,5,7,8))\n", + "print(a.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# or you can specify:\n", + "a = np.array( (1,2,3), dtype=np.uint8 )\n", + "print(a.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#careful: integers overflow:\n", + "a = np.array( (1, 10, 100,), dtype=np.uint8)\n", + "\n", + "print(a)\n", + "\n", + "a *= 3\n", + "\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Object arrays:\n", + "# create an empty array:\n", + "\n", + "a = np.empty((2,3), dtype=np.object)\n", + "\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# put stuff into it:\n", + "\n", + "a[0,0] = \"a string\"\n", + "a[0,1] = 4.5\n", + "a[0,2] = (1,2,3)\n", + "a[1,:] = [6, {'a':3}, 3.4]\n", + "\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# a row:\n", + "print(a[1,:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# a column:\n", + "print(a[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# an item (a dict in this case)\n", + "print(a[1,1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(a[1,1]['a'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# can we do regular numpy math?\n", + "a += 3\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/reshaping.ipynb b/Examples/numpy/reshaping.ipynb new file mode 100644 index 0000000..45cea07 --- /dev/null +++ b/Examples/numpy/reshaping.ipynb @@ -0,0 +1,469 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from start import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reshaping" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.arange(12)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(12,)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n" + ] + } + ], + "source": [ + "b = np.reshape(a, (3,4) )\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b is a" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1]\n", + " [ 2 3]\n", + " [ 4 5]\n", + " [ 6 7]\n", + " [ 8 9]\n", + " [10 11]]\n" + ] + } + ], + "source": [ + "# unknown dimension\n", + "# -1 will be replaced by whatever is needed to make it fit.\n", + "a.shape = ( -1, 2)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "10\n", + "11\n", + "\n" + ] + } + ], + "source": [ + "# flatten\n", + "for i in a.flat: # iterator\n", + " print(i) \n", + "print(a.flat)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n" + ] + } + ], + "source": [ + "# ravel creates a new array with a view onto the same data \n", + "# oddly enough, means that same thing as unravel....\n", + "b = np.ravel(a)\n", + "b is a\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(6, 2)\n", + "[[ 0 2 4 6 8 10]\n", + " [ 1 3 5 7 9 11]]\n", + "(2, 6)\n" + ] + } + ], + "source": [ + "print(a.shape)\n", + "print(a.T) # Transpose -- swap dimensions\n", + "print(a.T.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 2, 4, 6, 8, 10, 1, 3, 5, 7, 9, 11])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.T.copy().ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1],\n", + " [ 2, 3],\n", + " [ 4, 5],\n", + " [ 6, 7],\n", + " [ 8, 9],\n", + " [10, 11]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking dimensions:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1],\n", + " [ 2, 3],\n", + " [ 4, 5],\n", + " [ 6, 7],\n", + " [ 8, 9],\n", + " [10, 11]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.atleast_2d(a)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(6, 2)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 3)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.atleast_2d(np.ones((3,))).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 1, 1, 6, 1)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reducing the number of dimensions:\n", + "a.shape = (2,1,1,6,1)\n", + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 6)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = np.squeeze(a)\n", + "b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4 5]\n", + " [ 6 7 8 9 10 11]]\n", + "[[ 6 7 8 9 10 11]\n", + " [ 0 1 2 3 4 5]]\n" + ] + } + ], + "source": [ + "# re-arranging:\n", + "print(b)\n", + "print(np.flipud(b)) # flip up-down" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 5 4 3 2 1 0]\n", + " [11 10 9 8 7 6]]\n" + ] + } + ], + "source": [ + "print(np.fliplr(b)) # flip left-right" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/slice.ipynb b/Examples/numpy/slice.ipynb new file mode 100644 index 0000000..53df7c8 --- /dev/null +++ b/Examples/numpy/slice.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "numpy slicing demo\n", + "--------------------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from start import print_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Slicing is much like regular python sequences\n", + "\n", + "Very much the same in 1-d" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.arange(10)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "[3 4 5]\n" + ] + } + ], + "source": [ + "#indexing\n", + "print(a[3])\n", + "\n", + "# slicing\n", + "print(a[3:6])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look familar?\n", + "\n", + "Now for 2-d" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4]\n", + " [ 5 6 7 8 9]\n", + " [10 11 12 13 14]\n", + " [15 16 17 18 19]]\n" + ] + } + ], + "source": [ + "# create a 2d array:\n", + "a = np.arange(20).reshape((4,5)) # note the nifty reshape!\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#index in 2-d:\n", + "a[1, 2] # row 1, column 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 7, 8],\n", + " [12, 13]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# slices get you a rectangular block\n", + "\n", + "a[1:3, 2:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 7, 12, 17])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[:,2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indexing reduces rank..\n", + "\n", + "(rank is numpy-spek for the number of dimensions)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 5)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10 11 12 13 14]\n", + "(5,)\n" + ] + } + ], + "source": [ + "r = a[2] # pulls out a row\n", + "print(r)\n", + "print(r.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 7 12 17]\n", + "(4,)\n" + ] + } + ], + "source": [ + "# now 1-d\n", + "# pulling our a column\n", + "c = a[:,2] # all rows, column 2\n", + "print(c)\n", + "print(c.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that that is a 1-d array, not a \"column\" nor a \"row\"\n", + "\n", + "If you to preserve the rank, you can use a size-1 slice:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2]\n", + " [ 7]\n", + " [12]\n", + " [17]]\n", + "(4, 1)\n" + ] + } + ], + "source": [ + "c = a[:, 2:3] # all rows, columns 2 to 3 (i.e only column 2)\n", + "print (c)\n", + "print (c.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So still 2-d, but a single column: a \"column vector\" in linear algebra." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Slice is a view\n", + "\n", + "It's a new array, but a view on the data held by that array\n", + "\n", + "Kinda-sorta like a shallow copy with lists, etc.\n", + "\n", + "Here are some tests to see what's going on" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arr :\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", + "arr.shape: (12,)\n", + "arr.dtype: int32\n", + "arr.itemsize 4\n", + "arr.ndim: 1\n", + "arr.strides (4,)\n", + "arr.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : True\n", + " OWNDATA : True\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# create an array:\n", + "a = np.arange(12, dtype=np.int32)\n", + "print_info(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3 4 5]\n", + "b :\n", + "[3 4 5]\n", + "b.shape: (3,)\n", + "b.dtype: int32\n", + "b.itemsize 4\n", + "b.ndim: 1\n", + "b.strides (4,)\n", + "b.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : True\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# A slice is a view: (look at the OWNDATA flag)\n", + "b = a[3:6]\n", + "print(b)\n", + "print_info(b, 'b')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", + "[3 4 5]\n" + ] + } + ], + "source": [ + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3 35 5]\n" + ] + } + ], + "source": [ + "b[1] = 35\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 35, 5, 6, 7, 8, 9, 10, 11], dtype=int32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[44 35 5]\n", + "[ 0 1 2 44 35 5 6 7 8 9 10 11]\n" + ] + } + ], + "source": [ + "# note \"OWNDATA\" flag -- b does not \"own\" its data.\n", + "\n", + "b[0] = 44\n", + "\n", + "print(b)\n", + "print(a)\n", + "\n", + "# a has changed!" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a :\n", + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]]\n", + "a.shape: (3, 4)\n", + "a.dtype: int64\n", + "a.itemsize 8\n", + "a.ndim: 2\n", + "a.strides (32, 8)\n", + "a.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# what about slicing higher-dim arrays:\n", + "a = np.arange(12).reshape(3,4)\n", + "print_info(a,'a')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b :\n", + "[1 5 9]\n", + "b.shape: (3,)\n", + "b.dtype: int64\n", + "b.itemsize 8\n", + "b.ndim: 1\n", + "b.strides (32,)\n", + "b.flags:\n", + " C_CONTIGUOUS : False\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# \"column\" slice:\n", + "b = a[:,1]\n", + "print_info(b,'b')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[1:3,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 4)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b :\n", + "[4 5 6 7]\n", + "b.shape: (4,)\n", + "b.dtype: int64\n", + "b.itemsize 8\n", + "b.ndim: 1\n", + "b.strides (8,)\n", + "b.flags:\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : True\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " UPDATEIFCOPY : False\n" + ] + } + ], + "source": [ + "# \"row\" slice:\n", + "b = a[1,:]\n", + "print_info(b,'b')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = np.arange(12)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.shape = (3,4)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "b = a.reshape((4,3)).copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2],\n", + " [ 3, 4, 5],\n", + " [ 6, 7, 8],\n", + " [ 9, 10, 11]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a[2,2] = 0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 0, 11]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2],\n", + " [ 3, 4, 5],\n", + " [ 6, 7, 8],\n", + " [ 9, 10, 11]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/start.py b/Examples/numpy/start.py new file mode 100755 index 0000000..6299514 --- /dev/null +++ b/Examples/numpy/start.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python + +""" +script to start up demo for numpy talk + +just the one unility at this point.. +""" + + +def print_info(a, name="arr"): + print(name, ":") + print(a) + print("%s.shape:" % name, a.shape) + print("%s.dtype:" % name, a.dtype) + print("%s.itemsize" % name, a.itemsize) + print("%s.ndim:" % name, a.ndim) + print("%s.strides" % name, a.strides) + print("%s.flags:\n" % name, a.flags) diff --git a/Examples/numpy/stride_tricks.ipynb b/Examples/numpy/stride_tricks.ipynb new file mode 100644 index 0000000..f02e9da --- /dev/null +++ b/Examples/numpy/stride_tricks.ipynb @@ -0,0 +1,458 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Advanced Experiments with striding\n", + "==============================\n", + "\n", + "A few examples of manipulating strides in numpy arrays.\n", + "\n", + "Numpy arrays are wrappers around a single contiguous block of data. The \"strides\" are what describes how the single block of data is interpreted as an n dimensional array.\n", + "\n", + "For the most part, this is all an implimentation detail, happening under the hood at the C level. But numpy lets you manipulate the strides, and this allows some really powerful tricks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# A basic 2d array:\n", + "a = np.zeros((3,4))\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#flatten it:\n", + "a.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# a basic 1d array\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# the strides:\n", + "a.strides" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This means that you need to skip 8 bytes to get from one element to the next (a double is 8 bytes per value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.itemsize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# reshape the array\n", + "a.shape = (2,6)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# now look at the strides\n", + "a.strides" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Same bytes in the data block, but the strides define how it is to be interpreted.\n", + "\n", + "You need to skip 48 bytes to get from the beginning of one row to the beginning of the next row (8 bytes per value, times 6 values per row)\n", + "\n", + "And still 8 bytes from one element to the next in one row." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# now a different data type\n", + "a = np.arange(12, dtype=np.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.strides" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "only one byte per element" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# reshape again\n", + "a.shape = (3,4)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.strides" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So 4 bytes to get from one row to the next, and one from one element to the next." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# make it 3-d\n", + "a.shape = (2,3,2)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.strides" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "so six bytes to get form one \"slab\" to the next, 2 to get from one row to next in that slab, and still 1 to get from item to item." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fancy stuff\n", + "================\n", + "\n", + "The ``stride_tricks`` module provides utilities to manipulate the strides of numpy arrays to do tricky things." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = np.arange(10, dtype=np.uint8)\n", + "a.shape = (2,5)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a2 = np.lib.stride_tricks.as_strided(a, (8, 3), (1,1) )\n", + "a2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "huh?\n", + "\n", + "``as_strided(a, (8, 3), (1,1) )``\n", + "\n", + "means: take the data in array, ``a``, and make a new array of shape (8,3) with strides (1,1).\n", + "\n", + "So the array is 8 x 3 == 24 elements in size, but the underlying data has only 10 elements in it. This works because the strides are set to (1,1), so to find the first element of the nth row, you go n*1 bytes from the beginning:\n", + "\n", + " * the zeroth row starts at byte 0\n", + " * the first row starts ar byte 1\n", + " * etc..\n", + "\n", + "Then to get the elements in each row you go one byte more:\n", + "\n", + " * the zeroth element in the zeroth row is at byte 0.\n", + " * the first element in the zeroth row is at byte 1\n", + " * the second element in the zeroth row is at byte 2\n", + " \n", + " * the zeroth element in the first row is at byte 1\n", + " * the first element in the first row is at byte 2\n", + " * the second element in the first row is at byte 3\n", + "\n", + "So that's how we get a bigger array than the number of elements -- elements are re-used in multiple parts of the array.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a2.strides" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.shape, a.size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a2.shape, a2.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "a and a2 share the same data, so if we change a, a2 changes also:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a[0,2] = 6\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how the 6 is reused in the first three rows...\n", + "\n", + "Why do this weird stuff?\n", + "----------------------------\n", + "\n", + "It lets you do tricks where you want to re-use the same values without actually needing to copy them. For instance, a moving average." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a.mean(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "``filter_example.py`` has a couple simple filters, using stride tricks to impliment them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import filter_example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create some data\n", + "a = np.arange(25)\n", + "np.random.shuffle(a)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filter_example.moving_average(a, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filter_example.scaled_by_max(a,4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Examples/numpy/striding_examples.txt b/Examples/numpy/striding_examples.txt new file mode 100644 index 0000000..36b3d84 --- /dev/null +++ b/Examples/numpy/striding_examples.txt @@ -0,0 +1,134 @@ + +In [3]: a = np.zeros((3,4)) + +In [4]: a +Out[4]: +array([[ 0., 0., 0., 0.], + [ 0., 0., 0., 0.], + [ 0., 0., 0., 0.]]) + +In [5]: a.flatten() +Out[5]: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) + +In [6]: a = np.arange(10) + +In [7]: a +Out[7]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + +In [8]: a.strides +Out[8]: (4,) + +In [9]: a.shape((2,5)) +--------------------------------------------------------------------------- +TypeError Traceback (most recent call last) + in () +----> 1 a.shape((2,5)) + +TypeError: 'tuple' object is not callable + +In [10]: a.shape = (2,5) + +In [11]: a +Out[11]: +array([[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]]) + +In [12]: a.stride +--------------------------------------------------------------------------- +AttributeError Traceback (most recent call last) + in () +----> 1 a.stride + +AttributeError: 'numpy.ndarray' object has no attribute 'stride' + +In [13]: a.strides +Out[13]: (20, 4) + +In [14]: a = np.arange(10, dtype=np.uint8) + +In [15]: a +Out[15]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8) + +In [16]: a.strides +Out[16]: (1,) + +In [17]: a.shape = 2,5 + +In [18]: a +Out[18]: +array([[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]], dtype=uint8) + +In [19]: a.strides +Out[19]: (5, 1) + +In [20]: a.shape +Out[20]: (2, 5) + +In [21]: np.lib.stride_tricks.as_strided(a, (8, 3), (1,1) ) +Out[21]: +array([[0, 1, 2], + [1, 2, 3], + [2, 3, 4], + [3, 4, 5], + [4, 5, 6], + [5, 6, 7], + [6, 7, 8], + [7, 8, 9]], dtype=uint8) + +In [22]: a.sum(axis=0) +Out[22]: array([ 5, 7, 9, 11, 13], dtype=uint32) + +In [23]: a +Out[23]: +array([[0, 1, 2, 3, 4], + [5, 6, 7, 8, 9]], dtype=uint8) + +In [24]: b = np.lib.stride_tricks.as_strided(a, (8, 3), (1,1) ) + +In [25]: b +Out[25]: +array([[0, 1, 2], + [1, 2, 3], + [2, 3, 4], + [3, 4, 5], + [4, 5, 6], + [5, 6, 7], + [6, 7, 8], + [7, 8, 9]], dtype=uint8) + +In [26]: b.sum(axis=1) +Out[26]: array([ 3, 6, 9, 12, 15, 18, 21, 24], dtype=uint32) + +In [27]: a.shape = (-1) + +In [28]: a +Out[28]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8) + +In [29]: a[3] = 9 + +In [30]: a +Out[30]: array([0, 1, 2, 9, 4, 5, 6, 7, 8, 9], dtype=uint8) + +In [31]: b +Out[31]: +array([[0, 1, 2], + [1, 2, 9], + [2, 9, 4], + [9, 4, 5], + [4, 5, 6], + [5, 6, 7], + [6, 7, 8], + [7, 8, 9]], dtype=uint8) + +In [32]: b.shape +Out[32]: (8, 3) + +In [33]: b.size +Out[33]: 24 + +In [34]: a.size +Out[34]: 10 + +In [35]: b.itemsize +Out[35]: 1 \ No newline at end of file diff --git a/Examples/numpy/sudoku-chb.py b/Examples/numpy/sudoku-chb.py new file mode 100644 index 0000000..66d792f --- /dev/null +++ b/Examples/numpy/sudoku-chb.py @@ -0,0 +1,1033 @@ +#!/usr/bin/env python + +""" +Yet another Sudoku Puzzle program. + +This one does not help you solve puzzles. + +However, it does display them, and will highlight the appropriate row, column, +or box if there is an error in the puzzle at any time, as you add numbers, etc. + +What's unique about this one is the use of numpy arrays. Numpy arrays have two +properties that make them handy for this problem: + +1) n-d arrays (in this case 2-d) +2) slices as views on the data + +The whole puzzle is a 9x9 array + each box is a 3x3 sub-array + each row is a length 9 sub-array + each column is a length 9 sub-array + +These all share the same memory + + -Chris Barker: PythonCHB@gmail.com + +""" + +licence="""This code is released under the creative commons Public Domain dedication: + +I have dedicated this work to the public domain by waiving all of my rights to +the work worldwide under copyright law, including all related and neighboring +rights, to the extent allowed by law. + +You can copy, modify, distribute and perform the work, even for commercial +purposes, all without asking permission + +http://creativecommons.org/publicdomain/zero/1.0/ + +(that being said,it would be nice to give me credit where credit is due...) +""" + +import random +import wx +import numpy as np + +class PuzzleGrid(object): + def __init__(self): + self.Grid = np.zeros((9,9), np.uint8) + Boxes = [] + for i in range(3): + for j in range(3): + Boxes.append(self.Grid[3*i:3*i+3, 3*j:3*j+3]) + + self.Boxes = Boxes + + def SetValue(self, (r,c), v): + self.Grid[r,c] = v + + def GetValue(self, (r, c)): + return self.Grid[r,c] + + def Solved(self): + """ + returns True if the puzzle is solved, False otherwise + """ + raise NotImplementedError + + def CheckRows(self): + """ + returns a values for each row: + 0 -- repeated values + 1 -- valid, but not solved + 2 -- solved + + """ + results = np.zeros((9,), np.uint8 ) + for i in range(9): + results[i] = self.CheckValid(self.Grid[i,:]) + return results + + def CheckColumns(self): + """ + returns a values for each row: + 0 -- repeated values + 1 -- valid, but not solved + 2 -- solved + + """ + results = np.zeros((9,), np.uint8 ) + for i in range(9): + results[i] = self.CheckValid(self.Grid[:,i]) + return results + + def CheckBoxes(self): + """ + returns a values for each row: + 0 -- repeated values + 1 -- valid, but not solved + 2 -- solved + + """ + results = np.zeros((9,), np.uint8 ) + for i in range(9): + results[i] = self.CheckValid(self.Boxes[i]) + return results + + + def CheckValid(self, A): + """ + CheckValid(A) -- checks if A has any digits repeated. + + returns 0 is there are any digits repeated (invalid) + returns 1 if none are repeated, but there are zeros (valid, but not solved) + returns 2 if all digits, 1-9 are there (solved) + + A should be a numpy array that has 9 elements + """ + + there = [] + for i in A.flat: + if i>0 and i in there: + return 0 + else: + there.append(i) + if 0 in there: + return 1 + return 2 + + def LoadFromString(self, str): + """ + loads a puzzle from a single "flat" ascii string of all the entries + """ + self.Grid.flat[:] = np.fromstring(str, dtype=np.uint8)-48 + + def __str__(self): + msg = [] + for i in range(9): + if not i%3: + msg.append("|-----------------------|\n") + msg.append("| %i %i %i | %i %i %i | %i %i %i |\n"%tuple(self.Grid[i])) + msg.append("|-----------------------|\n") + return "".join(msg) + + +class Grid(object): + def __init__(self, w, h): + size = min(w,h) + self.d = d = (size - 20) / 9 + self.x0 = (w - (self.d * 9)) / 2 + self.y0 = (h - (self.d * 9)) / 2 + self.font_size = max( int(11 * d/16.0), 10) + ##figure out the text offset + dc = wx.ScreenDC() + dc.SetFont(wx.FontFromPixelSize((self.font_size, self.font_size), + wx.FONTFAMILY_SWISS, + wx.FONTSTYLE_NORMAL, + wx.FONTWEIGHT_BOLD, + ) + ) + w,h = dc.GetTextExtent("5") + self.text_off_x = ( d - w )/2+2 # I don't know why I need to add the 2! + self.text_off_y = ( d - h )/2+2 + +class GridWindow(wx.Window): + def __init__(self, parent, *args, **kwargs): + wx.Window.__init__(self, parent, *args, **kwargs) + self.SetBackgroundColour("White") + + self.Puzzle = PuzzleGrid() + + self.InvalidRows = [] + self.InvalidColumns = [] + self.InvalidBoxes = [] + + self.Selected = (0,0) + + self.Bind(wx.EVT_PAINT, self.OnPaint) + self.Bind(wx.EVT_SIZE, self.OnSize) + self.Bind(wx.EVT_LEFT_DOWN, self.OnLeftDown) + self.Bind(wx.EVT_KEY_DOWN, self.OnKeyDown) + + self.OnSize() + def NewPuzzle(self, str): + """ + sets up a new puzzle from the given ascii string + """ + self.Puzzle.LoadFromString(str) + self.DrawNow() + + def InitBuffer(self): + w, h = self.GetClientSize() + self.buffer = wx.EmptyBitmap(w, h) + self.DrawNow() + + def OnSize(self, event=None): + size = self.GetClientSize() + if size[0] > 0 and size[1] > 1: + self.Grid = Grid(*size) + self.InitBuffer() + + def DrawNow(self): + dc = wx.BufferedDC(wx.ClientDC(self), self.buffer) + self.Draw(dc) + + def Draw(self, dc): + # Make grid info local: + d = self.Grid.d + x0 = self.Grid.x0 + y0 = self.Grid.y0 + font_size = self.Grid.font_size + text_off_x = self.Grid.text_off_x + text_off_y = self.Grid.text_off_y + + # draw the background: + dc.SetBackground(wx.Brush(self.GetBackgroundColour())) + dc.Clear() + dc.SetBrush(wx.Brush(wx.Colour(128,128,255))) + dc.SetPen(wx.TRANSPARENT_PEN) + dc.DrawRectangle(x0, y0, d*9, d*9 ) + + #draw The invalid rows + for i in self.InvalidRows: + dc.SetBrush(wx.Brush("Purple", wx.SOLID)) + dc.SetPen(wx.TRANSPARENT_PEN) + dc.DrawRectangle(x0, y0 + i*d, 9*d, d ) + + #draw The invalid columns + for i in self.InvalidColumns: + dc.SetBrush(wx.Brush("Purple", wx.SOLID)) + dc.SetPen(wx.TRANSPARENT_PEN) + dc.DrawRectangle(x0 + i*d, y0, d, 9*d ) + + #draw The invalid boxes + for i in self.InvalidBoxes: + dc.SetBrush(wx.Brush("Purple", wx.SOLID)) + dc.SetPen(wx.TRANSPARENT_PEN) + r = i//3 + c = i%3 + dc.DrawRectangle(x0 + c*3*d, y0 + r*3*d, 3*d, 3*d ) + + # draw the selected cell: + dc.SetBrush(wx.Brush("Red", wx.SOLID)) + dc.DrawRectangle(x0 + d*self.Selected[1], y0 + d*self.Selected[0], d, d) + + # draw the white lines: + dc.SetPen(wx.Pen("White", 2, wx.SOLID) ) + for i in range(10): + dc.DrawLine(x0, y0 + d*i, x0 + d*9, y0 + d*i) + dc.DrawLine(x0 + d*i, y0, x0 + d*i, y0 + d*9) + + # draw the numbers: + dc.SetFont(wx.FontFromPixelSize((font_size,font_size), + wx.FONTFAMILY_SWISS, + wx.FONTSTYLE_NORMAL, + wx.FONTWEIGHT_BOLD)) + for i in range(9): + for j in range(9): + val = self.Puzzle.Grid[i,j] + if val > 0: + dc.DrawText('%i'%val, x0 + d*j + text_off_x, y0 + d*i + text_off_y) + + # Draw the Big Grid + dc.SetPen(wx.Pen("Black", 3, wx.SOLID)) + dc.SetBrush(wx.TRANSPARENT_BRUSH) + + d*=3 + for i in range(4): + dc.DrawLine(x0, y0 + d*i, x0 + d*3, y0 + d*i) + dc.DrawLine(x0 + d*i, y0, x0 + d*i, y0 + d*3) + + + def OnPaint(self, event): + dc = wx.BufferedPaintDC(self, self.buffer) + + def CheckValid(self): + self.InvalidRows = np.nonzero(self.Puzzle.CheckRows() == 0)[0] + self.InvalidColumns = np.nonzero(self.Puzzle.CheckColumns() == 0)[0] + self.InvalidBoxes = np.nonzero(self.Puzzle.CheckBoxes() == 0)[0] + + def OnLeftDown(self, e): + """called when the left mouse button is pressed""" + x, y = e.GetPositionTuple() + i = (y - self.Grid.y0) / self.Grid.d + j = (x - self.Grid.x0) / self.Grid.d + if i >= 0 and i < 9 and j >= 0 and j < 9: + self.Selected = (i,j) + self.DrawNow() + + def OnKeyDown(self, e): + keycode = e.GetKeyCode() + i, j = self.Selected + if (keycode == wx.WXK_TAB or + keycode == wx.WXK_RIGHT or + keycode == wx.WXK_RETURN or + keycode == wx.WXK_SPACE): + j += 1 + elif keycode == wx.WXK_UP: + i -= 1 + elif keycode == wx.WXK_DOWN: + i += 1 + elif keycode == wx.WXK_LEFT: + j -= 1 + elif (keycode == wx.WXK_DELETE or + keycode == wx.WXK_BACK): + self.Puzzle.Grid[self.Selected] = 0 + self.CheckValid() + elif keycode >= ord("0") and keycode <= ord("9"): + self.Puzzle.Grid[self.Selected] = (keycode - ord("0")) + self.CheckValid() + j += 1 + if j > 8: + j = 0 + i += 1 + if j < 0: + j = 8 + i -= 1 + if i > 8: + i = 0 + if i < 0: + i = 8 + + self.Selected = (i,j) + self.DrawNow() + + def SetValue(self, value): + self.Puzzle.Grid[self.Selected] = value + +def test_PuzzleGrid(): + P = PuzzleGrid() + print P + print "These should all be true:" + # all valid, butnot solved + print P.CheckValid(np.array( (1,2,0,4,0,6,0,8,9), dtype=np.uint8 ) ) == 1 + print P.CheckValid(np.array( (0,0,0,0,0,0,0,0,0), dtype=np.uint8 ) ) == 1 + print P.CheckValid(np.array( (1,2,0,4,5,6,7,8,9), dtype=np.uint8) ) == 1 + + # solved + print P.CheckValid(np.array( (1,2,3,4,5,6,7,8,9), dtype=np.uint8) ) == 2 + print P.CheckValid(np.array( (1,2,9,4,5,6,7,8,3), dtype=np.uint8) ) == 2 + print P.CheckValid(np.array( (9,8,7,6,5,4,3,2,1), dtype=np.uint8) ) == 2 + + # all not valid + print P.CheckValid(np.array( (1,1,0,4,0,6,0,8,9), dtype=np.uint8 ) ) == 0 + print P.CheckValid(np.array( (1,0,0,4,0,6,0,9,9), dtype=np.uint8 ) ) == 0 + print P.CheckValid(np.array( (1,1,1,4,4,6,0,1,1), dtype=np.uint8 ) ) == 0 + + +class MainFrame(wx.Frame): + def __init__(self, parent): + wx.Frame.__init__(self, parent, title="Sudoku Machine", size=(500, 500)) + self.grid = GridWindow(self) + wx.CallAfter(self.OnNewGame)# so that the grid is fully initialized + ## Build the menu + MenuBar = wx.MenuBar() + + FileMenu = wx.Menu() + item = FileMenu.Append(wx.ID_ANY, text = "&New Game") + self.Bind(wx.EVT_MENU, self.OnNewGame, item) + item = FileMenu.Append(wx.ID_EXIT, text = "&Exit") + self.Bind(wx.EVT_MENU, self.OnQuit, item) + MenuBar.Append(FileMenu, "&File") + + HelpMenu = wx.Menu() + item = HelpMenu.Append(wx.ID_HELP, "&Help", + "Help for this simple test") + self.Bind(wx.EVT_MENU, self.OnHelp, item) + item = HelpMenu.Append(wx.ID_ABOUT, "&About", + "More information About this program") + self.Bind(wx.EVT_MENU, self.OnAbout, item) + MenuBar.Append(HelpMenu, "&Help") + + self.SetMenuBar(MenuBar) + + def OnNewGame(self,evt=None): + """ + Sets up a new randomly chosen game + """ + i = random.randint(0, len(Puzzles)-1) + self.grid.NewPuzzle(Puzzles[i]) + + def OnQuit(self,evt=None): + self.Destroy() + + def OnHelp(self, event): + dlg = wx.MessageDialog(self, "This would be help\n" + "If there was any\n", + "Test Help", wx.OK | wx.ICON_INFORMATION) + dlg.ShowModal() + dlg.Destroy() + + def OnAbout(self, event): + dlg = AboutDialog(None, + #icon1=wx.Bitmap("Images/GNOME64.png"), + #icon2=wx.Bitmap("Images/NOAA.png"), + short_name='Suduko', + long_name='A Sudoko Puzzle demo program', + version = "1.0", + description="This is a simple Sudoku puzzle program, written in Python, designed to demonstrate some features of the wxPython and numpy packages", + urls = ["http://www.seapig.org/SudukoGameBoard", + "mailto:PythonCHB@gmail.com"], + licence=licence, + developers = ["Chris Barker"]) + dlg.ShowModal() + dlg.Destroy() + + +## these are a bunch of puzzles I found here: +## http://mapleta.maths.uwa.edu.au/~gordon/sudokumin.php +## +## there are a LOT there, this is just a few of them +## +## they are licenced under a Creative Commons Attribution 2.5 License. +## http://creativecommons.org/licenses/by/2.5/ + +Puzzles = """000000010400000000020000000000050407008000300001090000300400200050100000000806000 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+000000054700030000000000000000400016380070000020000000000500800105000000006000300 +000000054900700000000000060006052000800000300000000700020300100040070000005000000 +000000056003800000400000000000062000000000410000000300000450100060100000720000000 +000000056080010000002000030000203000300600000010000900600700000000080400000000100 +000000057000040000000000003000307060800500400100000000000080100070000200030600000 +000000057000080010070020000301000000000600400500000600040000200000103000000500000 +000000059000130000000000000340000020050009000000800600800000307000054000000000100 +000000059700600000000300000059001000020040000000000130807000300000050000400000000 +000000061000027000000000000704000200000100040300000000510000700000048000090600000""".split('\n') + + + +import wx +from wx.lib.hyperlink import HyperLinkCtrl + +class AboutDialog(wx.Dialog): + """ + Simple about Dialog class -- I like it better than the one that wxPython comes with + + -Chris Barker + + """ + def __init__(self, parent, icon1=None, + icon2=None, + short_name=None, + long_name=None, + version=None, + description=None, + urls=None, + licence=None, + developers = []): + wx.Dialog.__init__(self, parent) + + self.icon1 = icon1 + self.icon2 = icon2 + self.short_name = short_name + self.long_name = long_name + self.version = version + self.version = version + self.description = description + self.urls = urls + self.licence = licence + self.developers = developers + + self.Build() + + def Build(self): + + # Build the header + Header = wx.BoxSizer(wx.HORIZONTAL) + if self.icon1: + Header.Add(wx.StaticBitmap(self, bitmap=self.icon1), 0) + else: + Header.Add((64,64)) + Header.Add((20,1),1) + if self.short_name: + Label = wx.StaticText(self, label=self.short_name) + of = Label.GetFont() + Font = wx.Font(int(of.GetPointSize() * 2), of.GetFamily(), wx.NORMAL, wx.FONTWEIGHT_BOLD) + Label.SetFont(Font) + Header.Add(Label, 0, wx.ALL|wx.ALIGN_CENTER_VERTICAL, 5) + else: + Header.Add((1,1), 1) + Header.Add((20,1),1) + if self.icon2: + Header.Add(wx.StaticBitmap(self, bitmap=self.icon2), 0) + else: + Header.Add((64,64)) + width = Header.MinSize[0] + + # Now the rest; + MainSizer = wx.BoxSizer(wx.VERTICAL) + + MainSizer.Add(Header, 0, wx.EXPAND|wx.ALIGN_CENTER|wx.ALL, 5) + + if self.long_name: + Label = wx.StaticText(self, label=self.long_name) + of = Label.GetFont() + Font = wx.Font(int(of.GetPointSize() * 1.5), of.GetFamily(), wx.NORMAL, wx.NORMAL) + Label.SetFont(Font) + MainSizer.Add(Label, 0, wx.TOP|wx.RIGHT|wx.LEFT|wx.ALIGN_CENTER, 5) + width = max(width, Label.Size[0]) + + if self.version: + Label = wx.StaticText(self, label="version: "+self.version) + #of = Label.GetFont() + #Font = wx.Font(int(of.GetPointSize() * 1.5), of.GetFamily(), wx.NORMAL, wx.NORMAL) + #Label.SetFont(Font) + MainSizer.Add(Label, 0, wx.BOTTOM|wx.ALIGN_CENTER, 5) + + if self.description: + Label = wx.StaticText(self, label=self.description) + #of = Label.GetFont() + #Font = wx.Font(int(of.GetPointSize() * 1.5), of.GetFamily(), wx.NORMAL, wx.NORMAL) + #Label.SetFont(Font) + + Label.Wrap(max(250, 0.9*width)) + MainSizer.Add(Label, 0, wx.ALL|wx.ALIGN_CENTER, 5) + + + if self.licence: + Label = wx.StaticText(self, label="License:") + of = Label.GetFont() + Font = wx.Font(of.GetPointSize(), of.GetFamily(), wx.NORMAL, wx.BOLD) + Label.SetFont(Font) + MainSizer.Add(Label, 0, wx.ALL|wx.ALIGN_LEFT, 5) + Label = wx.StaticText(self, label=self.licence) + Label.Wrap(max(250, 0.9*width)) + MainSizer.Add(Label, 0, wx.ALL|wx.ALIGN_CENTER, 2) + + if self.developers: + Label = wx.StaticText(self, label="Developed by:") + of = Label.GetFont() + Font = wx.Font(of.GetPointSize(), of.GetFamily(), wx.NORMAL, wx.BOLD) + Label.SetFont(Font) + MainSizer.Add(Label, 0, wx.ALL|wx.ALIGN_LEFT, 5) + + for developer in self.developers: + Label = wx.StaticText(self, label=" "+developer) + MainSizer.Add(Label, 0, wx.ALL|wx.ALIGN_LEFT, 0) + + if self.urls: + Label = wx.StaticText(self, label="For more information:") + of = Label.GetFont() + Font = wx.Font(of.GetPointSize(), of.GetFamily(), wx.NORMAL, wx.BOLD) + Label.SetFont(Font) + MainSizer.Add(Label, 0, wx.ALL|wx.ALIGN_LEFT, 5) + for url in self.urls: + Link = HyperLinkCtrl(self, + label=url, + URL=url) + MainSizer.Add(Link, 0, wx.ALL|wx.ALIGN_CENTER, 2) + + MainSizer.Add((1,5),1) + MainSizer.Add(wx.Button(self, id=wx.ID_OK, label="Dismiss"), 0, wx.ALL|wx.ALIGN_RIGHT,5) + SpaceSizer = wx.BoxSizer(wx.VERTICAL) + SpaceSizer.Add(MainSizer, 0, wx.ALL, 10) + self.SetSizerAndFit(SpaceSizer) + + +if __name__ == '__main__': + import sys + if len(sys.argv) > 1 and sys.argv[1].lower() == 'test': + test_PuzzleGrid() + else: + app = wx.App(0) + frame = MainFrame(None) + frame.Show(True) + app.MainLoop() + diff --git a/Examples/packaging/Capitalize/bin/cap_script b/Examples/packaging/Capitalize/bin/cap_script index 08f999e..4566590 100755 --- a/Examples/packaging/Capitalize/bin/cap_script +++ b/Examples/packaging/Capitalize/bin/cap_script @@ -4,7 +4,8 @@ A really simple script just to demonstrate disutils """ -import sys, os +import sys +import os from capitalize import capital_mod @@ -12,14 +13,13 @@ if __name__ == "__main__": try: infilename = sys.argv[1] except IndexError: - print "you need to pass in a file to process" + print("you need to pass in a file to process") root, ext = os.path.splitext(infilename) outfilename = root + "_cap" + ext - + # do the real work: - print "Capitalizing: %s and storing it in %s"%(infilename, outfilename) + print("Capitalizing: %s and storing it in %s" % (infilename, outfilename)) capital_mod.capitalize(infilename, outfilename) - - print "I'm done" - \ No newline at end of file + + print("I'm done") diff --git a/Examples/pandas/IntroductionToPandas.html b/Examples/pandas/IntroductionToPandas.html new file mode 100644 index 0000000..3a2d685 --- /dev/null +++ b/Examples/pandas/IntroductionToPandas.html @@ -0,0 +1,3741 @@ + + + + + + An Introduction to Pandas | synesthesiam + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    +

    + synesthesiam
    + Michael Hansen +     +

    + + +
    +
    + +
    +
    + + +
    + +
    + +

    An Introduction to Pandas

    + +
    +

    +

    + + 4 Comments + + + +

    +

    Source

    + +
    + + +
    + +
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    + +
    +
    +
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    +
    +

    When dealing with numeric matrices and vectors in Python, NumPy makes life a lot easier. +For more complex data, however, it leaves a lot to be desired. +If you're used to working with data frames in R, doing data analysis directly with NumPy feels like a step back.

    +

    Fortunately, some nice folks have written the Python Data Analysis Library (a.k.a. pandas). +Pandas provides an R-like DataFrame, produces high quality plots with matplotlib, and integrates nicely with other libraries that expect NumPy arrays.

    +

    In this tutorial, we'll go through the basics of pandas using a year's worth of weather data from Weather Underground. +Pandas has a lot of functionality, so we'll only be able to cover a small fraction of what you can do. +Check out the (very readable) pandas docs if you want to learn more.

    +

    +
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    +
    +

    Getting Started +

    +
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    +
    +
    +
    +
    +

    Installing pandas should be an easy process if you use pip:

    + +
    sudo pip install pandas
+
+
    +

    For more complex scenarios, please see the installation instructions.

    +

    OK, let's get started by importing the pandas library.

    + +
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    +
    In [64]:
    +
    +
    +
    import pandas
+
    + +
    +
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    + +
    +
    +
    +
    +
    +
    +

    Next, let's read in our data. +Because it's in a CSV file, we can use pandas' read_csv function to pull it directly into a DataFrame.

    + +
    +
    +
    +
    +
    +
    In [65]:
    +
    +
    +
    data = pandas.read_csv("data/weather_year.csv")
+
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    +

    We can get a summary of the DataFrame by printing the object.

    + +
    +
    +
    +
    +
    +
    In [66]:
    +
    +
    +
    data
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[66]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+Int64Index: 366 entries, 0 to 365
+Data columns (total 23 columns):
+EDT                           366  non-null values
+Max TemperatureF              366  non-null values
+Mean TemperatureF             366  non-null values
+Min TemperatureF              366  non-null values
+Max Dew PointF                366  non-null values
+MeanDew PointF                366  non-null values
+Min DewpointF                 366  non-null values
+Max Humidity                  366  non-null values
+ Mean Humidity                366  non-null values
+ Min Humidity                 366  non-null values
+ Max Sea Level PressureIn     366  non-null values
+ Mean Sea Level PressureIn    366  non-null values
+ Min Sea Level PressureIn     366  non-null values
+ Max VisibilityMiles          366  non-null values
+ Mean VisibilityMiles         366  non-null values
+ Min VisibilityMiles          366  non-null values
+ Max Wind SpeedMPH            366  non-null values
+ Mean Wind SpeedMPH           366  non-null values
+ Max Gust SpeedMPH            365  non-null values
+PrecipitationIn               366  non-null values
+ CloudCover                   366  non-null values
+ Events                       162  non-null values
+ WindDirDegrees               366  non-null values
+dtypes: float64(4), int64(16), object(3)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    This gives us a lot of information. First, we can see that there are 366 rows (entries) -- a year and a day's worth of weather. Each column is printed along with however many "non-null" values are present. +We'll talk more about null (or missing) values in pandas later, but for now we can note that only the "Max Gust SpeedMPH" and "Events" columns have fewer than 366 non-null values. +Lastly, the data types (dtypes) of the columns are printed at the very bottom. We can see that there are 4 float64, 16 int64, and 3 object columns.

    + +
    +
    +
    +
    +
    +
    In [67]:
    +
    +
    +
    len(data)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[67]:
    + + +
    +
    366
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Using len on a DataFrame will give you the number of rows. You can get the column names using the columns property.

    + +
    +
    +
    +
    +
    +
    In [68]:
    +
    +
    +
    data.columns
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[68]:
    + + +
    +
    Index([EDT, Max TemperatureF, Mean TemperatureF, Min TemperatureF, Max Dew PointF, MeanDew PointF, Min DewpointF, Max Humidity,  Mean Humidity,  Min Humidity,  Max Sea Level PressureIn,  Mean Sea Level PressureIn,  Min Sea Level PressureIn,  Max VisibilityMiles,  Mean VisibilityMiles,  Min VisibilityMiles,  Max Wind SpeedMPH,  Mean Wind SpeedMPH,  Max Gust SpeedMPH, PrecipitationIn,  CloudCover,  Events,  WindDirDegrees], dtype=object)
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Columns can be accessed in two ways. The first is using the DataFrame like a dictionary with string keys:

    + +
    +
    +
    +
    +
    +
    In [69]:
    +
    +
    +
    data["EDT"]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[69]:
    + + +
    +
    0     2012-3-10
+1     2012-3-11
+2     2012-3-12
+3     2012-3-13
+4     2012-3-14
+5     2012-3-15
+6     2012-3-16
+7     2012-3-17
+8     2012-3-18
+9     2012-3-19
+10    2012-3-20
+11    2012-3-21
+12    2012-3-22
+13    2012-3-23
+14    2012-3-24
+...
+351    2013-2-24
+352    2013-2-25
+353    2013-2-26
+354    2013-2-27
+355    2013-2-28
+356     2013-3-1
+357     2013-3-2
+358     2013-3-3
+359     2013-3-4
+360     2013-3-5
+361     2013-3-6
+362     2013-3-7
+363     2013-3-8
+364     2013-3-9
+365    2013-3-10
+Name: EDT, Length: 366, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    You can get multiple columns out at the same time by passing in a list of strings.

    + +
    +
    +
    +
    +
    +
    In [70]:
    +
    +
    +
    data[["EDT", "Mean TemperatureF"]]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[70]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+Int64Index: 366 entries, 0 to 365
+Data columns (total 2 columns):
+EDT                  366  non-null values
+Mean TemperatureF    366  non-null values
+dtypes: int64(1), object(1)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    The second way to access columns is using the dot syntax. This only works if your column name could also be a Python variable name (i.e., no spaces), and if it doesn't collide with another DataFrame property or function name (e.g., count, sum).

    + +
    +
    +
    +
    +
    +
    In [71]:
    +
    +
    +
    data.EDT
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[71]:
    + + +
    +
    0     2012-3-10
+1     2012-3-11
+2     2012-3-12
+3     2012-3-13
+4     2012-3-14
+5     2012-3-15
+6     2012-3-16
+7     2012-3-17
+8     2012-3-18
+9     2012-3-19
+10    2012-3-20
+11    2012-3-21
+12    2012-3-22
+13    2012-3-23
+14    2012-3-24
+...
+351    2013-2-24
+352    2013-2-25
+353    2013-2-26
+354    2013-2-27
+355    2013-2-28
+356     2013-3-1
+357     2013-3-2
+358     2013-3-3
+359     2013-3-4
+360     2013-3-5
+361     2013-3-6
+362     2013-3-7
+363     2013-3-8
+364     2013-3-9
+365    2013-3-10
+Name: EDT, Length: 366, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    We'll be mostly using the dot syntax here because you can auto-complete the names in IPython. The first pandas function we'll learn about is head(). This gives us the first 5 items in a column (or the first 5 rows in the DataFrame).

    + +
    +
    +
    +
    +
    +
    In [72]:
    +
    +
    +
    data.EDT.head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[72]:
    + + +
    +
    0    2012-3-10
+1    2012-3-11
+2    2012-3-12
+3    2012-3-13
+4    2012-3-14
+Name: EDT, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Passing in a number n gives us the first n items in the column. There is also a corresponding tail() method that gives the last n items or rows.

    + +
    +
    +
    +
    +
    +
    In [73]:
    +
    +
    +
    data.EDT.head(10)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[73]:
    + + +
    +
    0    2012-3-10
+1    2012-3-11
+2    2012-3-12
+3    2012-3-13
+4    2012-3-14
+5    2012-3-15
+6    2012-3-16
+7    2012-3-17
+8    2012-3-18
+9    2012-3-19
+Name: EDT, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    This also works with the dictionary syntax.

    + +
    +
    +
    +
    +
    +
    In [74]:
    +
    +
    +
    data["Mean TemperatureF"].head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[74]:
    + + +
    +
    0    40
+1    49
+2    62
+3    63
+4    62
+Name: Mean TemperatureF, dtype: int64
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Fun with Columns +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    The column names in data are a little unweildy, so we're going to rename them. This is as easy as assigning a new list of column names to the columns property of the DataFrame.

    + +
    +
    +
    +
    +
    +
    In [75]:
    +
    +
    +
    data.columns = ["date", "max_temp", "mean_temp", "min_temp", "max_dew",
+                "mean_dew", "min_dew", "max_humidity", "mean_humidity",
+                "min_humidity", "max_pressure", "mean_pressure",
+                "min_pressure", "max_visibilty", "mean_visibility",
+                "min_visibility", "max_wind", "mean_wind", "min_wind",
+                "precipitation", "cloud_cover", "events", "wind_dir"]
+
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    +

    These should be in the same order as the original columns. Let's take another look at our DataFrame summary.

    + +
    +
    +
    +
    +
    +
    In [76]:
    +
    +
    +
    data
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[76]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+Int64Index: 366 entries, 0 to 365
+Data columns (total 23 columns):
+date               366  non-null values
+max_temp           366  non-null values
+mean_temp          366  non-null values
+min_temp           366  non-null values
+max_dew            366  non-null values
+mean_dew           366  non-null values
+min_dew            366  non-null values
+max_humidity       366  non-null values
+mean_humidity      366  non-null values
+min_humidity       366  non-null values
+max_pressure       366  non-null values
+mean_pressure      366  non-null values
+min_pressure       366  non-null values
+max_visibilty      366  non-null values
+mean_visibility    366  non-null values
+min_visibility     366  non-null values
+max_wind           366  non-null values
+mean_wind          366  non-null values
+min_wind           365  non-null values
+precipitation      366  non-null values
+cloud_cover        366  non-null values
+events             162  non-null values
+wind_dir           366  non-null values
+dtypes: float64(4), int64(16), object(3)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Now our columns can all be accessed using the dot syntax!

    + +
    +
    +
    +
    +
    +
    In [77]:
    +
    +
    +
    data.mean_temp.head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[77]:
    + + +
    +
    0    40
+1    49
+2    62
+3    63
+4    62
+Name: mean_temp, dtype: int64
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    There are lots useful methods on columns, such as std() to get the standard deviation. Most of pandas' methods will happily ignore missing values like NaN.

    + +
    +
    +
    +
    +
    +
    In [78]:
    +
    +
    +
    data.mean_temp.std()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[78]:
    + + +
    +
    18.436505996251075
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Some methods, like plot() and hist() produce plots using matplotlib. We'll go over plotting in more detail later.

    + +
    +
    +
    +
    +
    +
    In [79]:
    +
    +
    +
    data.mean_temp.hist()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[79]:
    + + +
    +
    <matplotlib.axes.AxesSubplot at 0x4e54a50>
    +
    + +
    + +
    +
    + + +
    + +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    By the way, many of the column-specific methods also work on the entire DataFrame. Instead of a single number, you'll get a result for each column.

    + +
    +
    +
    +
    +
    +
    In [80]:
    +
    +
    +
    data.std()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[80]:
    + + +
    +
    max_temp           20.361247
+mean_temp          18.436506
+min_temp           17.301141
+max_dew            16.397178
+mean_dew           16.829996
+min_dew            17.479449
+max_humidity        9.108438
+mean_humidity       9.945591
+min_humidity       15.360261
+max_pressure        0.172189
+mean_pressure       0.174112
+min_pressure        0.182476
+max_visibilty       0.073821
+mean_visibility     1.875406
+min_visibility      3.792219
+max_wind            5.564329
+mean_wind           3.200940
+min_wind            8.131092
+cloud_cover         2.707261
+wind_dir           94.045080
+dtype: float64
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Bulk Operations with apply() +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Methods like sum() and std() work on entire columns. We can run our own functions across all values in a column (or row) using apply().

    +

    To give you an idea of how this works, let's consider the "date" column in our DataFrame (formally "EDT").

    + +
    +
    +
    +
    +
    +
    In [81]:
    +
    +
    +
    data.date.head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[81]:
    + + +
    +
    0    2012-3-10
+1    2012-3-11
+2    2012-3-12
+3    2012-3-13
+4    2012-3-14
+Name: date, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    We can use the values property of the column to get a list of values for the column. Inspecting the first value reveals that these are strings with a particular format.

    + +
    +
    +
    +
    +
    +
    In [82]:
    +
    +
    +
    first_date = data.date.values[0]
+first_date
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[82]:
    + + +
    +
    '2012-3-10'
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    The strptime function from the datetime module will make quick work of this date string.

    + +
    +
    +
    +
    +
    +
    In [83]:
    +
    +
    +
    from datetime import datetime
+datetime.strptime(first_date, "%Y-%m-%d")
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[83]:
    + + +
    +
    datetime.datetime(2012, 3, 10, 0, 0)
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Using the apply() method, which takes an anonymous function, we can apply strptime to each value in the column. We'll overwrite the string date values with their Python datetime equivalents.

    + +
    +
    +
    +
    +
    +
    In [84]:
    +
    +
    +
    data.date = data.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d"))
+data.date.head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[84]:
    + + +
    +
    0   2012-03-10 00:00:00
+1   2012-03-11 00:00:00
+2   2012-03-12 00:00:00
+3   2012-03-13 00:00:00
+4   2012-03-14 00:00:00
+Name: date, dtype: datetime64[ns]
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Let's go one step futher. Each row in our DateFrame represents the weather from a single day. Each row in a DataFrame is associated with an index, which is a label that uniquely identifies a row.

    +

    Our row indices up to now have been auto-generated by pandas, and are simply integers from 0 to 365. If we use dates instead of integers for our index, we will get some extra benefits from pandas when plotting later on. Overwriting the index is as easy as assigning to the index property of the DataFrame.

    + +
    +
    +
    +
    +
    +
    In [85]:
    +
    +
    +
    data.index = data.date
+data
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[85]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 366 entries, 2012-03-10 00:00:00 to 2013-03-10 00:00:00
+Data columns (total 23 columns):
+date               366  non-null values
+max_temp           366  non-null values
+mean_temp          366  non-null values
+min_temp           366  non-null values
+max_dew            366  non-null values
+mean_dew           366  non-null values
+min_dew            366  non-null values
+max_humidity       366  non-null values
+mean_humidity      366  non-null values
+min_humidity       366  non-null values
+max_pressure       366  non-null values
+mean_pressure      366  non-null values
+min_pressure       366  non-null values
+max_visibilty      366  non-null values
+mean_visibility    366  non-null values
+min_visibility     366  non-null values
+max_wind           366  non-null values
+mean_wind          366  non-null values
+min_wind           365  non-null values
+precipitation      366  non-null values
+cloud_cover        366  non-null values
+events             162  non-null values
+wind_dir           366  non-null values
+dtypes: datetime64[ns](1), float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Now we can quickly look up a row by its date with the ix[] property.

    + +
    +
    +
    +
    +
    +
    In [86]:
    +
    +
    +
    data.ix[datetime(2012, 8, 19)]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[86]:
    + + +
    +
    date               2012-08-19 00:00:00
+max_temp                            82
+mean_temp                           67
+min_temp                            51
+max_dew                             56
+mean_dew                            50
+min_dew                             42
+max_humidity                        96
+mean_humidity                       62
+min_humidity                        28
+max_pressure                     29.95
+mean_pressure                    29.92
+min_pressure                     29.89
+max_visibilty                       10
+mean_visibility                     10
+min_visibility                      10
+max_wind                            14
+mean_wind                            3
+min_wind                            21
+precipitation                     0.00
+cloud_cover                          1
+events                             NaN
+wind_dir                             1
+Name: 2012-08-19 00:00:00, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    With all of the dates in the index now, we no longer need the "date" column. Let's drop it.

    + +
    +
    +
    +
    +
    +
    In [87]:
    +
    +
    +
    data = data.drop(["date"], axis=1)
+data.columns
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[87]:
    + + +
    +
    Index([max_temp, mean_temp, min_temp, max_dew, mean_dew, min_dew, max_humidity, mean_humidity, min_humidity, max_pressure, mean_pressure, min_pressure, max_visibilty, mean_visibility, min_visibility, max_wind, mean_wind, min_wind, precipitation, cloud_cover, events, wind_dir], dtype=object)
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Note that we need to pass in axis=1 in order to drop a column. For more details, check out the documentation for drop.

    + +
    +
    +
    +
    +
    +
    +
    +
    +

    Handing Missing Values +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Pandas considers values like NaN and None to represent missing data. The pandas.isnull function can be used to tell whether or not a value is missing.

    +

    Let's use apply() across all of the columns in our DataFrame to figure out which values are missing.

    + +
    +
    +
    +
    +
    +
    In [88]:
    +
    +
    +
    empty = data.apply(lambda col: pandas.isnull(col))
+empty
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[88]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 366 entries, 2012-03-10 00:00:00 to 2013-03-10 00:00:00
+Freq: D
+Data columns (total 22 columns):
+max_temp           366  non-null values
+mean_temp          366  non-null values
+min_temp           366  non-null values
+max_dew            366  non-null values
+mean_dew           366  non-null values
+min_dew            366  non-null values
+max_humidity       366  non-null values
+mean_humidity      366  non-null values
+min_humidity       366  non-null values
+max_pressure       366  non-null values
+mean_pressure      366  non-null values
+min_pressure       366  non-null values
+max_visibilty      366  non-null values
+mean_visibility    366  non-null values
+min_visibility     366  non-null values
+max_wind           366  non-null values
+mean_wind          366  non-null values
+min_wind           366  non-null values
+precipitation      366  non-null values
+cloud_cover        366  non-null values
+events             366  non-null values
+wind_dir           366  non-null values
+dtypes: bool(22)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    We got back a dataframe (empty) with boolean values for all 22 columns and 366 rows. Inspecting the first 10 values of the "events", column we can see that there are some missing values because a True was returned from pandas.isnull.

    + +
    +
    +
    +
    +
    +
    In [89]:
    +
    +
    +
    empty.events.head(10)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[89]:
    + + +
    +
    date
+2012-03-10     True
+2012-03-11    False
+2012-03-12    False
+2012-03-13     True
+2012-03-14     True
+2012-03-15    False
+2012-03-16     True
+2012-03-17    False
+2012-03-18    False
+2012-03-19     True
+Freq: D, Name: events, dtype: bool
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Looking at the corresponding rows in the original DataFrame reveals that pandas has filled in NaN for empty values in the "events" column.

    + +
    +
    +
    +
    +
    +
    In [90]:
    +
    +
    +
    data.events.head(10)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[90]:
    + + +
    +
    date
+2012-03-10                  NaN
+2012-03-11                 Rain
+2012-03-12                 Rain
+2012-03-13                  NaN
+2012-03-14                  NaN
+2012-03-15    Rain-Thunderstorm
+2012-03-16                  NaN
+2012-03-17     Fog-Thunderstorm
+2012-03-18                 Rain
+2012-03-19                  NaN
+Freq: D, Name: events, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    This isn't exactly what we want. One option is to drop all rows in the DataFrame with missing "events" values.

    + +
    +
    +
    +
    +
    +
    In [91]:
    +
    +
    +
    data.dropna(subset=["events"])
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[91]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 162 entries, 2012-03-11 00:00:00 to 2013-03-06 00:00:00
+Data columns (total 22 columns):
+max_temp           162  non-null values
+mean_temp          162  non-null values
+min_temp           162  non-null values
+max_dew            162  non-null values
+mean_dew           162  non-null values
+min_dew            162  non-null values
+max_humidity       162  non-null values
+mean_humidity      162  non-null values
+min_humidity       162  non-null values
+max_pressure       162  non-null values
+mean_pressure      162  non-null values
+min_pressure       162  non-null values
+max_visibilty      162  non-null values
+mean_visibility    162  non-null values
+min_visibility     162  non-null values
+max_wind           162  non-null values
+mean_wind          162  non-null values
+min_wind           162  non-null values
+precipitation      162  non-null values
+cloud_cover        162  non-null values
+events             162  non-null values
+wind_dir           162  non-null values
+dtypes: float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    The DataFrame we get back has only 162 rows, so we can infer that there were 366 - 162 = 204 missing values in the "events" column. Note that this didn't affect data; we're just looking at a copy.

    +

    Instead of dropping the rows with missing values, let's fill them with empty strings (you'll see why in a moment). This is easily done with the fillna() function. We'll go ahead and overwrite the "events" column with empty string missing values instead of NaN.

    + +
    +
    +
    +
    +
    +
    In [92]:
    +
    +
    +
    data.events = data.events.fillna("")
+data.events.head(10)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[92]:
    + + +
    +
    date
+2012-03-10                     
+2012-03-11                 Rain
+2012-03-12                 Rain
+2012-03-13                     
+2012-03-14                     
+2012-03-15    Rain-Thunderstorm
+2012-03-16                     
+2012-03-17     Fog-Thunderstorm
+2012-03-18                 Rain
+2012-03-19                     
+Freq: D, Name: events, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Accessing Individual Rows +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Sometimes you need to access individual rows in your DataFrame. The irow() function lets you grab the ith row from a DataFrame (starting from 0).

    + +
    +
    +
    +
    +
    +
    In [93]:
    +
    +
    +
    data.irow(0)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[93]:
    + + +
    +
    max_temp              56
+mean_temp             40
+min_temp              24
+max_dew               24
+mean_dew              20
+min_dew               16
+max_humidity          74
+mean_humidity         50
+min_humidity          26
+max_pressure       30.53
+mean_pressure      30.45
+min_pressure       30.34
+max_visibilty         10
+mean_visibility       10
+min_visibility        10
+max_wind              13
+mean_wind              6
+min_wind              17
+precipitation       0.00
+cloud_cover            0
+events                  
+wind_dir             138
+Name: 2012-03-10 00:00:00, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Of course, another option is to use the index.

    + +
    +
    +
    +
    +
    +
    In [94]:
    +
    +
    +
    data.ix[datetime(2013, 1, 1)]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[94]:
    + + +
    +
    max_temp              32
+mean_temp             26
+min_temp              20
+max_dew               31
+mean_dew              25
+min_dew               16
+max_humidity          92
+mean_humidity         83
+min_humidity          74
+max_pressure        30.2
+mean_pressure      30.11
+min_pressure       30.04
+max_visibilty          9
+mean_visibility        5
+min_visibility         2
+max_wind              14
+mean_wind              5
+min_wind              15
+precipitation          T
+cloud_cover            8
+events                  
+wind_dir             353
+Name: 2013-01-01 00:00:00, dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    You can iterate over each row in the DataFrame with iterrows(). Note that this function returns both the index and the row. Also, you must access columns in the row you get back from iterrows() with the dictionary syntax.

    + +
    +
    +
    +
    +
    +
    In [95]:
    +
    +
    +
    num_rain = 0
+for idx, row in data.iterrows():
+    if "Rain" in row["events"]:
+        num_rain += 1
+
+"Days with rain: {0}".format(num_rain)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[95]:
    + + +
    +
    'Days with rain: 121'
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Filtering +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Most of your time using pandas will likely be devoted to selecting rows of interest from a DataFrame. In addition to strings, the dictionary syntax accepts things like this:

    + +
    +
    +
    +
    +
    +
    In [96]:
    +
    +
    +
    freezing_days = data[data.max_temp <= 32]
+freezing_days
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[96]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 21 entries, 2012-11-24 00:00:00 to 2013-03-06 00:00:00
+Data columns (total 22 columns):
+max_temp           21  non-null values
+mean_temp          21  non-null values
+min_temp           21  non-null values
+max_dew            21  non-null values
+mean_dew           21  non-null values
+min_dew            21  non-null values
+max_humidity       21  non-null values
+mean_humidity      21  non-null values
+min_humidity       21  non-null values
+max_pressure       21  non-null values
+mean_pressure      21  non-null values
+min_pressure       21  non-null values
+max_visibilty      21  non-null values
+mean_visibility    21  non-null values
+min_visibility     21  non-null values
+max_wind           21  non-null values
+mean_wind          21  non-null values
+min_wind           21  non-null values
+precipitation      21  non-null values
+cloud_cover        21  non-null values
+events             21  non-null values
+wind_dir           21  non-null values
+dtypes: float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    We get back another DataFrame with fewer rows (21 in this case). This DataFrame can be filtered down even more.

    + +
    +
    +
    +
    +
    +
    In [97]:
    +
    +
    +
    freezing_days[freezing_days.min_temp >= 20]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[97]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 7 entries, 2012-11-24 00:00:00 to 2013-03-06 00:00:00
+Data columns (total 22 columns):
+max_temp           7  non-null values
+mean_temp          7  non-null values
+min_temp           7  non-null values
+max_dew            7  non-null values
+mean_dew           7  non-null values
+min_dew            7  non-null values
+max_humidity       7  non-null values
+mean_humidity      7  non-null values
+min_humidity       7  non-null values
+max_pressure       7  non-null values
+mean_pressure      7  non-null values
+min_pressure       7  non-null values
+max_visibilty      7  non-null values
+mean_visibility    7  non-null values
+min_visibility     7  non-null values
+max_wind           7  non-null values
+mean_wind          7  non-null values
+min_wind           7  non-null values
+precipitation      7  non-null values
+cloud_cover        7  non-null values
+events             7  non-null values
+wind_dir           7  non-null values
+dtypes: float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Or, using boolean operations, we could apply both filters to the original DataFrame at the same time.

    + +
    +
    +
    +
    +
    +
    In [98]:
    +
    +
    +
    data[(data.max_temp <= 32) & (data.min_temp >= 20)]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[98]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 7 entries, 2012-11-24 00:00:00 to 2013-03-06 00:00:00
+Data columns (total 22 columns):
+max_temp           7  non-null values
+mean_temp          7  non-null values
+min_temp           7  non-null values
+max_dew            7  non-null values
+mean_dew           7  non-null values
+min_dew            7  non-null values
+max_humidity       7  non-null values
+mean_humidity      7  non-null values
+min_humidity       7  non-null values
+max_pressure       7  non-null values
+mean_pressure      7  non-null values
+min_pressure       7  non-null values
+max_visibilty      7  non-null values
+mean_visibility    7  non-null values
+min_visibility     7  non-null values
+max_wind           7  non-null values
+mean_wind          7  non-null values
+min_wind           7  non-null values
+precipitation      7  non-null values
+cloud_cover        7  non-null values
+events             7  non-null values
+wind_dir           7  non-null values
+dtypes: float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    It's important to understand what's really going on underneath with filtering. Let's look at what kind of object we actually get back when creating a filter.

    + +
    +
    +
    +
    +
    +
    In [99]:
    +
    +
    +
    temp_max = data.max_temp <= 32
+type(temp_max)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[99]:
    + + +
    +
    pandas.core.series.TimeSeries
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    This is a pandas Series object, which is the one-dimensional equivalent of a DataFrame. Because our DataFrame uses datetime objects for the index, we have a specialized TimeSeries object.

    +

    What's inside the filter?

    + +
    +
    +
    +
    +
    +
    In [100]:
    +
    +
    +
    temp_max
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[100]:
    + + +
    +
    date
+2012-03-10    False
+2012-03-11    False
+2012-03-12    False
+2012-03-13    False
+2012-03-14    False
+2012-03-15    False
+2012-03-16    False
+2012-03-17    False
+2012-03-18    False
+2012-03-19    False
+2012-03-20    False
+2012-03-21    False
+2012-03-22    False
+2012-03-23    False
+2012-03-24    False
+...
+2013-02-24    False
+2013-02-25    False
+2013-02-26    False
+2013-02-27    False
+2013-02-28    False
+2013-03-01    False
+2013-03-02     True
+2013-03-03    False
+2013-03-04    False
+2013-03-05    False
+2013-03-06     True
+2013-03-07    False
+2013-03-08    False
+2013-03-09    False
+2013-03-10    False
+Freq: D, Name: max_temp, Length: 366, dtype: bool
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Our filter is nothing more than a Series with a boolean value for every item in the index. When we "run the filter" as so:

    + +
    +
    +
    +
    +
    +
    In [101]:
    +
    +
    +
    data[temp_max]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[101]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 21 entries, 2012-11-24 00:00:00 to 2013-03-06 00:00:00
+Data columns (total 22 columns):
+max_temp           21  non-null values
+mean_temp          21  non-null values
+min_temp           21  non-null values
+max_dew            21  non-null values
+mean_dew           21  non-null values
+min_dew            21  non-null values
+max_humidity       21  non-null values
+mean_humidity      21  non-null values
+min_humidity       21  non-null values
+max_pressure       21  non-null values
+mean_pressure      21  non-null values
+min_pressure       21  non-null values
+max_visibilty      21  non-null values
+mean_visibility    21  non-null values
+min_visibility     21  non-null values
+max_wind           21  non-null values
+mean_wind          21  non-null values
+min_wind           21  non-null values
+precipitation      21  non-null values
+cloud_cover        21  non-null values
+events             21  non-null values
+wind_dir           21  non-null values
+dtypes: float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    pandas lines up the rows of the DataFrame and the filter using the index, and then keeps the rows with a True filter value. That's it.

    +

    Let's create another filter.

    + +
    +
    +
    +
    +
    +
    In [102]:
    +
    +
    +
    temp_min = data.min_temp >= 20
+temp_min
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[102]:
    + + +
    +
    date
+2012-03-10    True
+2012-03-11    True
+2012-03-12    True
+2012-03-13    True
+2012-03-14    True
+2012-03-15    True
+2012-03-16    True
+2012-03-17    True
+2012-03-18    True
+2012-03-19    True
+2012-03-20    True
+2012-03-21    True
+2012-03-22    True
+2012-03-23    True
+2012-03-24    True
+...
+2013-02-24     True
+2013-02-25     True
+2013-02-26     True
+2013-02-27     True
+2013-02-28     True
+2013-03-01     True
+2013-03-02     True
+2013-03-03    False
+2013-03-04    False
+2013-03-05     True
+2013-03-06     True
+2013-03-07     True
+2013-03-08     True
+2013-03-09     True
+2013-03-10     True
+Freq: D, Name: min_temp, Length: 366, dtype: bool
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Now we can see what the boolean operations are doing. Something like & (not and)...

    + +
    +
    +
    +
    +
    +
    In [103]:
    +
    +
    +
    temp_min & temp_max
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[103]:
    + + +
    +
    date
+2012-03-10    False
+2012-03-11    False
+2012-03-12    False
+2012-03-13    False
+2012-03-14    False
+2012-03-15    False
+2012-03-16    False
+2012-03-17    False
+2012-03-18    False
+2012-03-19    False
+2012-03-20    False
+2012-03-21    False
+2012-03-22    False
+2012-03-23    False
+2012-03-24    False
+...
+2013-02-24    False
+2013-02-25    False
+2013-02-26    False
+2013-02-27    False
+2013-02-28    False
+2013-03-01    False
+2013-03-02     True
+2013-03-03    False
+2013-03-04    False
+2013-03-05    False
+2013-03-06     True
+2013-03-07    False
+2013-03-08    False
+2013-03-09    False
+2013-03-10    False
+Freq: D, Length: 366, dtype: bool
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    ...is just lining up the two filters using the index, performing a boolean AND operation, and returning the result as another Series.

    +

    We can do other boolean operations too, like OR:

    + +
    +
    +
    +
    +
    +
    In [104]:
    +
    +
    +
    temp_min | temp_max
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[104]:
    + + +
    +
    date
+2012-03-10    True
+2012-03-11    True
+2012-03-12    True
+2012-03-13    True
+2012-03-14    True
+2012-03-15    True
+2012-03-16    True
+2012-03-17    True
+2012-03-18    True
+2012-03-19    True
+2012-03-20    True
+2012-03-21    True
+2012-03-22    True
+2012-03-23    True
+2012-03-24    True
+...
+2013-02-24     True
+2013-02-25     True
+2013-02-26     True
+2013-02-27     True
+2013-02-28     True
+2013-03-01     True
+2013-03-02     True
+2013-03-03    False
+2013-03-04    False
+2013-03-05     True
+2013-03-06     True
+2013-03-07     True
+2013-03-08     True
+2013-03-09     True
+2013-03-10     True
+Freq: D, Length: 366, dtype: bool
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Because the result is just another Series, we have all of the regular pandas functions at our disposal. The any() function returns True if any value in the Series is True.

    + +
    +
    +
    +
    +
    +
    In [105]:
    +
    +
    +
    temp_both = temp_min & temp_max
+temp_both.any()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[105]:
    + + +
    +
    True
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Sometimes filters aren't so intuitive. This (sadly) doesn't work:

    + +
    +
    +
    +
    +
    +
    In [106]:
    +
    +
    +
    try:
+    data["Rain" in data.events]
+except:
+    pass # "KeyError: no item named False"
+
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    +

    We can wrap it up in an apply() call fairly easily, though:

    + +
    +
    +
    +
    +
    +
    In [107]:
    +
    +
    +
    data[data.events.apply(lambda e: "Rain" in e)]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[107]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 121 entries, 2012-03-11 00:00:00 to 2013-03-05 00:00:00
+Data columns (total 22 columns):
+max_temp           121  non-null values
+mean_temp          121  non-null values
+min_temp           121  non-null values
+max_dew            121  non-null values
+mean_dew           121  non-null values
+min_dew            121  non-null values
+max_humidity       121  non-null values
+mean_humidity      121  non-null values
+min_humidity       121  non-null values
+max_pressure       121  non-null values
+mean_pressure      121  non-null values
+min_pressure       121  non-null values
+max_visibilty      121  non-null values
+mean_visibility    121  non-null values
+min_visibility     121  non-null values
+max_wind           121  non-null values
+mean_wind          121  non-null values
+min_wind           121  non-null values
+precipitation      121  non-null values
+cloud_cover        121  non-null values
+events             121  non-null values
+wind_dir           121  non-null values
+dtypes: float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Grouping +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Besides apply(), another great DataFrame function is groupby(). +It will group a DataFrame by one or more columns, and let you iterate through each group.

    +

    As an example, let's group our DataFrame by the "cloud_cover" column (a value ranging from 0 to 8).

    + +
    +
    +
    +
    +
    +
    In [108]:
    +
    +
    +
    cover_temps = {}
+for cover, cover_data in data.groupby("cloud_cover"):
+    cover_temps[cover] = cover_data.mean_temp.mean()  # The mean mean temp!
+cover_temps
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[108]:
    + + +
    +
    {0: 59.730769230769234,
+ 1: 61.415094339622641,
+ 2: 59.727272727272727,
+ 3: 58.0625,
+ 4: 51.5,
+ 5: 50.827586206896555,
+ 6: 57.727272727272727,
+ 7: 46.5,
+ 8: 40.909090909090907}
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    When you iterate through the result of groupby(), you will get a tuple. +The first item is the column value, and the second item is a filtered DataFrame (where the column equals the first tuple value).

    +

    You can group by more than one column as well. +In this case, the first tuple item returned by groupby() will itself be a tuple with the value of each column.

    + +
    +
    +
    +
    +
    +
    In [109]:
    +
    +
    +
    for (cover, events), group_data in data.groupby(["cloud_cover", "events"]):
+    print "Cover: {0}, Events: {1}, Count: {2}".format(cover, events, len(group_data))
+
    + +
    +
    +
    + +
    +
    + + +
    +
    +
    +
    Cover: 0, Events: , Count: 99
+Cover: 0, Events: Fog, Count: 2
+Cover: 0, Events: Rain, Count: 2
+Cover: 0, Events: Thunderstorm, Count: 1
+Cover: 1, Events: , Count: 35
+Cover: 1, Events: Fog, Count: 5
+Cover: 1, Events: Fog-Rain, Count: 1
+Cover: 1, Events: Rain, Count: 4
+Cover: 1, Events: Rain-Thunderstorm, Count: 2
+Cover: 1, Events: Thunderstorm, Count: 6
+Cover: 2, Events: , Count: 20
+Cover: 2, Events: Fog, Count: 1
+Cover: 2, Events: Rain, Count: 5
+Cover: 2, Events: Rain-Thunderstorm, Count: 4
+Cover: 2, Events: Snow, Count: 1
+Cover: 2, Events: Thunderstorm, Count: 2
+Cover: 3, Events: , Count: 12
+Cover: 3, Events: Fog, Count: 2
+Cover: 3, Events: Fog-Rain-Thunderstorm, Count: 3
+Cover: 3, Events: Fog-Thunderstorm, Count: 1
+Cover: 3, Events: Rain, Count: 9
+Cover: 3, Events: Rain-Thunderstorm, Count: 4
+Cover: 3, Events: Snow, Count: 1
+Cover: 4, Events: , Count: 16
+Cover: 4, Events: Fog, Count: 3
+Cover: 4, Events: Fog-Rain, Count: 2
+Cover: 4, Events: Fog-Rain-Thunderstorm, Count: 2
+Cover: 4, Events: Rain, Count: 10
+Cover: 4, Events: Rain-Thunderstorm, Count: 6
+Cover: 4, Events: Snow, Count: 1
+Cover: 5, Events: , Count: 9
+Cover: 5, Events: Fog-Rain, Count: 1
+Cover: 5, Events: Fog-Rain-Snow, Count: 1
+Cover: 5, Events: Rain, Count: 13
+Cover: 5, Events: Rain-Thunderstorm, Count: 3
+Cover: 5, Events: Snow, Count: 2
+Cover: 6, Events: , Count: 3
+Cover: 6, Events: Fog-Rain, Count: 2
+Cover: 6, Events: Fog-Rain-Snow, Count: 1
+Cover: 6, Events: Fog-Rain-Thunderstorm, Count: 2
+Cover: 6, Events: Rain, Count: 9
+Cover: 6, Events: Rain-Thunderstorm, Count: 4
+Cover: 6, Events: Snow, Count: 1
+Cover: 7, Events: , Count: 5
+Cover: 7, Events: Fog-Rain, Count: 1
+Cover: 7, Events: Fog-Rain-Thunderstorm, Count: 1
+Cover: 7, Events: Fog-Snow, Count: 3
+Cover: 7, Events: Rain, Count: 6
+Cover: 7, Events: Rain-Thunderstorm, Count: 3
+Cover: 7, Events: Snow, Count: 1
+Cover: 8, Events: , Count: 5
+Cover: 8, Events: Fog-Rain, Count: 4
+Cover: 8, Events: Fog-Rain-Snow, Count: 1
+Cover: 8, Events: Fog-Rain-Snow-Thunderstorm, Count: 1
+Cover: 8, Events: Fog-Snow, Count: 2
+Cover: 8, Events: Rain, Count: 11
+Cover: 8, Events: Rain-Snow, Count: 3
+Cover: 8, Events: Snow, Count: 6
+
    +
    +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Creating New Columns +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Weather events in our DataFrame are stored in strings like "Rain-Thunderstorm" to represent that it rained and there was a thunderstorm that day. Let's split them out into boolean "rain", "thunderstorm", etc. columns.

    +

    First, let's discover the different kinds of weather events we have with unique().

    + +
    +
    +
    +
    +
    +
    In [110]:
    +
    +
    +
    data.events.unique()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[110]:
    + + +
    +
    array([, Rain, Rain-Thunderstorm, Fog-Thunderstorm, Fog-Rain, Thunderstorm,
+       Fog-Rain-Thunderstorm, Fog, Fog-Rain-Snow,
+       Fog-Rain-Snow-Thunderstorm, Fog-Snow, Snow, Rain-Snow], dtype=object)
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Looks like we have "Rain", "Thunderstorm", "Fog", and "Snow" events. Creating a new column for each of these event kinds is a piece of cake with the dictionary syntax.

    + +
    +
    +
    +
    +
    +
    In [111]:
    +
    +
    +
    for event_kind in ["Rain", "Thunderstorm", "Fog", "Snow"]:
+    col_name = event_kind.lower()  # Turn "Rain" into "rain", etc.
+    data[col_name] = data.events.apply(lambda e: event_kind in e)
+data
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[111]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 366 entries, 2012-03-10 00:00:00 to 2013-03-10 00:00:00
+Freq: D
+Data columns (total 26 columns):
+max_temp           366  non-null values
+mean_temp          366  non-null values
+min_temp           366  non-null values
+max_dew            366  non-null values
+mean_dew           366  non-null values
+min_dew            366  non-null values
+max_humidity       366  non-null values
+mean_humidity      366  non-null values
+min_humidity       366  non-null values
+max_pressure       366  non-null values
+mean_pressure      366  non-null values
+min_pressure       366  non-null values
+max_visibilty      366  non-null values
+mean_visibility    366  non-null values
+min_visibility     366  non-null values
+max_wind           366  non-null values
+mean_wind          366  non-null values
+min_wind           365  non-null values
+precipitation      366  non-null values
+cloud_cover        366  non-null values
+events             366  non-null values
+wind_dir           366  non-null values
+rain               366  non-null values
+thunderstorm       366  non-null values
+fog                366  non-null values
+snow               366  non-null values
+dtypes: bool(4), float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Our new columns show up at the bottom. We can access them now with the dot syntax.

    + +
    +
    +
    +
    +
    +
    In [112]:
    +
    +
    +
    data.rain
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[112]:
    + + +
    +
    date
+2012-03-10    False
+2012-03-11     True
+2012-03-12     True
+2012-03-13    False
+2012-03-14    False
+2012-03-15     True
+2012-03-16    False
+2012-03-17    False
+2012-03-18     True
+2012-03-19    False
+2012-03-20    False
+2012-03-21    False
+2012-03-22     True
+2012-03-23     True
+2012-03-24     True
+...
+2013-02-24    False
+2013-02-25    False
+2013-02-26     True
+2013-02-27    False
+2013-02-28     True
+2013-03-01    False
+2013-03-02    False
+2013-03-03    False
+2013-03-04     True
+2013-03-05     True
+2013-03-06    False
+2013-03-07    False
+2013-03-08    False
+2013-03-09    False
+2013-03-10    False
+Freq: D, Name: rain, Length: 366, dtype: bool
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    We can also do cool things like find out how many True values there are (i.e., how many days had rain)...

    + +
    +
    +
    +
    +
    +
    In [113]:
    +
    +
    +
    data.rain.sum()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[113]:
    + + +
    +
    121
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    ...and get all the days that had both rain and snow!

    + +
    +
    +
    +
    +
    +
    In [114]:
    +
    +
    +
    data[data.rain & data.snow]
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[114]:
    + +
    +
    &ltclass 'pandas.core.frame.DataFrame'&gt
+DatetimeIndex: 7 entries, 2012-11-12 00:00:00 to 2013-03-05 00:00:00
+Data columns (total 26 columns):
+max_temp           7  non-null values
+mean_temp          7  non-null values
+min_temp           7  non-null values
+max_dew            7  non-null values
+mean_dew           7  non-null values
+min_dew            7  non-null values
+max_humidity       7  non-null values
+mean_humidity      7  non-null values
+min_humidity       7  non-null values
+max_pressure       7  non-null values
+mean_pressure      7  non-null values
+min_pressure       7  non-null values
+max_visibilty      7  non-null values
+mean_visibility    7  non-null values
+min_visibility     7  non-null values
+max_wind           7  non-null values
+mean_wind          7  non-null values
+min_wind           7  non-null values
+precipitation      7  non-null values
+cloud_cover        7  non-null values
+events             7  non-null values
+wind_dir           7  non-null values
+rain               7  non-null values
+thunderstorm       7  non-null values
+fog                7  non-null values
+snow               7  non-null values
+dtypes: bool(4), float64(4), int64(16), object(2)
+
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Plotting +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    We've already seen how the hist() function makes generating histograms a snap. Let's look at the plot() function now.

    + +
    +
    +
    +
    +
    +
    In [115]:
    +
    +
    +
    data.max_temp.plot()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[115]:
    + + +
    +
    <matplotlib.axes.AxesSubplot at 0x4bb1b10>
    +
    + +
    + +
    +
    + + +
    + +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    That one line of code did a lot for us. First, it created a nice looking line plot using the maximum temperature column from our DataFrame. Second, because we used datetime objects in our index, pandas labeled the x-axis appropriately.

    +

    Pandas is smart too. If we're only looking at a couple of days, the x-axis looks different:

    + +
    +
    +
    +
    +
    +
    In [116]:
    +
    +
    +
    data.max_temp.tail().plot()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[116]:
    + + +
    +
    <matplotlib.axes.AxesSubplot at 0x5563bd0>
    +
    + +
    + +
    +
    + + +
    + +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Prefer a bar plot? Pandas has got your covered.

    + +
    +
    +
    +
    +
    +
    In [117]:
    +
    +
    +
    data.max_temp.tail().plot(kind="bar", rot=10)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[117]:
    + + +
    +
    <matplotlib.axes.AxesSubplot at 0x5305e10>
    +
    + +
    + +
    +
    + + +
    + +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    The plot() function returns a matplotlib AxesSubPlot object. You can pass this object into subsequent calls to plot() in order to compose plots.

    +

    Although plot() takes a variety of parameters to customize your plot, users familiar with matplotlib will feel right at home with the AxesSubPlot object.

    + +
    +
    +
    +
    +
    +
    In [118]:
    +
    +
    +
    ax = data.max_temp.plot(title="Min and Max Temperatures")
+data.min_temp.plot(style="red", ax=ax)
+ax.set_ylabel("Temperature (F)")
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[118]:
    + + +
    +
    <matplotlib.text.Text at 0x5a6f8d0>
    +
    + +
    + +
    +
    + + +
    + +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Getting Data Out +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    Writing data out in pandas is as easy as getting data in. To save our DataFrame out to a new csv file, we can just do this:

    + +
    +
    +
    +
    +
    +
    In [119]:
    +
    +
    +
    data.to_csv("data/weather-mod.csv")
+
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    +

    Want to make that tab separated instead? No problem.

    + +
    +
    +
    +
    +
    +
    In [120]:
    +
    +
    +
    data.to_csv("data/weather-mod.tsv", sep="\t")
+
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    +

    There's also support for reading and writing Excel files, if you need it.

    + +
    +
    +
    +
    +
    +
    +
    +
    +

    Miscellanea +

    +
    +
    +
    +
    +
    +
    +
    +
    +

    We've only covered a small fraction of the pandas library here. +Before I wrap up, however, there are a few miscellaneous tips I'd like to go over.

    +

    First, it can be confusing to know when an operation will modify a DataFrame and when it will return a copy to you. +Pandas behavior here is entirely dictated by NumPy, and some situations are unintuitive.

    +

    For example, what do you think will happen here?

    + +
    +
    +
    +
    +
    +
    In [121]:
    +
    +
    +
    for idx, row in data.iterrows():
+    row["max_temp"] = 0
+data.max_temp.head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[121]:
    + + +
    +
    date
+2012-03-10    56
+2012-03-11    67
+2012-03-12    71
+2012-03-13    76
+2012-03-14    80
+Freq: D, Name: max_temp, dtype: int64
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Contrary to what you might expect, modifying row did not modify data! +This is because row is a copy, and does not point back to the original DataFrame.

    +

    Here's the right way to do it:

    + +
    +
    +
    +
    +
    +
    In [122]:
    +
    +
    +
    for idx, row in data.iterrows():
+    data.ix[idx, "max_temp"] = 0
+any(data.max_temp != 0)  # Any rows with max_temp not equal to zero?
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[122]:
    + + +
    +
    False
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    Just to make you even more confused, this also doesn't work:

    + +
    +
    +
    +
    +
    +
    In [123]:
    +
    +
    +
    for idx, row in data.iterrows():
+    data.ix[idx]["max_temp"] = 100
+data.max_temp.head()
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[123]:
    + + +
    +
    date
+2012-03-10    0
+2012-03-11    0
+2012-03-12    0
+2012-03-13    0
+2012-03-14    0
+Freq: D, Name: max_temp, dtype: int64
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    When using apply(), the default behavior is to go over columns.

    + +
    +
    +
    +
    +
    +
    In [124]:
    +
    +
    +
    data.apply(lambda c: c.name)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[124]:
    + + +
    +
    max_temp                  max_temp
+mean_temp                mean_temp
+min_temp                  min_temp
+max_dew                    max_dew
+mean_dew                  mean_dew
+min_dew                    min_dew
+max_humidity          max_humidity
+mean_humidity        mean_humidity
+min_humidity          min_humidity
+max_pressure          max_pressure
+mean_pressure        mean_pressure
+min_pressure          min_pressure
+max_visibilty        max_visibilty
+mean_visibility    mean_visibility
+min_visibility      min_visibility
+max_wind                  max_wind
+mean_wind                mean_wind
+min_wind                  min_wind
+precipitation        precipitation
+cloud_cover            cloud_cover
+events                      events
+wind_dir                  wind_dir
+rain                          rain
+thunderstorm          thunderstorm
+fog                            fog
+snow                          snow
+dtype: object
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    You can make apply() go over rows by passing axis=1

    + +
    +
    +
    +
    +
    +
    In [125]:
    +
    +
    +
    data.apply(lambda r: r["max_pressure"] - r["min_pressure"], axis=1)
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[125]:
    + + +
    +
    date
+2012-03-10    0.19
+2012-03-11    0.24
+2012-03-12    0.25
+2012-03-13    0.15
+2012-03-14    0.11
+2012-03-15    0.11
+2012-03-16    0.07
+2012-03-17    0.11
+2012-03-18    0.12
+2012-03-19    0.11
+2012-03-20    0.10
+2012-03-21    0.12
+2012-03-22    0.10
+2012-03-23    0.27
+2012-03-24    0.09
+...
+2013-02-24    0.15
+2013-02-25    0.33
+2013-02-26    0.40
+2013-02-27    0.23
+2013-02-28    0.28
+2013-03-01    0.11
+2013-03-02    0.11
+2013-03-03    0.08
+2013-03-04    0.20
+2013-03-05    0.26
+2013-03-06    0.53
+2013-03-07    0.13
+2013-03-08    0.13
+2013-03-09    0.36
+2013-03-10    0.05
+Freq: D, Length: 366, dtype: float64
    +
    + +
    + +
    +
    + +
    +
    +
    +
    +
    +
    +

    When you call drop(), though, it's flipped. To drop a column, you need to pass axis=1

    + +
    +
    +
    +
    +
    +
    In [126]:
    +
    +
    +
    data.drop(["events"], axis=1).columns
+
    + +
    +
    +
    + +
    +
    + + +
    +
    Out[126]:
    + + +
    +
    Index([max_temp, mean_temp, min_temp, max_dew, mean_dew, min_dew, max_humidity, mean_humidity, min_humidity, max_pressure, mean_pressure, min_pressure, max_visibilty, mean_visibility, min_visibility, max_wind, mean_wind, min_wind, precipitation, cloud_cover, wind_dir, rain, thunderstorm, fog, snow], dtype=object)
    +
    + +
    + +
    +
    + +
    +
    +
    + +
    + +
    +

    Comments

    + + +
    + + + + + +
    + + +
    + + + + + + + +
    +
    + Contents © 2015 Michael Hansen - Powered by Nikola + +
    +
    + +
    +
    +
    +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/Examples/pandas/IntroductionToPandas_files/88x31.png b/Examples/pandas/IntroductionToPandas_files/88x31.png new file mode 100644 index 0000000..4de2222 Binary files /dev/null and b/Examples/pandas/IntroductionToPandas_files/88x31.png differ diff --git a/Examples/pandas/IntroductionToPandas_files/alfie.f51946af45e0b561c60f768335c9eb79.js b/Examples/pandas/IntroductionToPandas_files/alfie.f51946af45e0b561c60f768335c9eb79.js new file mode 100644 index 0000000..e23cb5f --- /dev/null +++ b/Examples/pandas/IntroductionToPandas_files/alfie.f51946af45e0b561c60f768335c9eb79.js @@ -0,0 +1 @@ +!function(self_name){var viglink,$;$={each:function(a,b){var c,d;for(c=0,d=a.length;d>c;c++)b(a[c],c)},extend:function(a){var b,c,d,e,f=arguments.length;for(a=a||{},c=1;f>c;c++)if(e=arguments[c],void 0!==e&&null!==e)for(d in e)b=e[d],a!==b&&void 0!==b&&(a[d]=b);return 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+} + +.dl-horizontal:after { + clear: both; +} + +.dl-horizontal dt { + float: left; + width: 160px; + overflow: hidden; + clear: left; + text-align: right; + text-overflow: ellipsis; + white-space: nowrap; +} + +.dl-horizontal dd { + margin-left: 180px; +} + +hr { + margin: 20px 0; + border: 0; + border-top: 1px solid #eeeeee; + border-bottom: 1px solid #ffffff; +} + +abbr[title], +abbr[data-original-title] { + cursor: help; + border-bottom: 1px dotted #999999; +} + +abbr.initialism { + font-size: 90%; + text-transform: uppercase; +} + +blockquote { + padding: 0 0 0 15px; + margin: 0 0 20px; + border-left: 5px solid #eeeeee; +} + +blockquote p { + margin-bottom: 0; + font-size: 17.5px; + font-weight: 300; + line-height: 1.25; +} + +blockquote small { + display: block; + line-height: 20px; + color: #999999; +} + +blockquote small:before { + content: '\2014 \00A0'; +} + +blockquote.pull-right { + float: right; + padding-right: 15px; + padding-left: 0; + border-right: 5px solid #eeeeee; + border-left: 0; +} + +blockquote.pull-right p, +blockquote.pull-right small { + text-align: right; +} + +blockquote.pull-right small:before { + content: ''; +} + +blockquote.pull-right small:after { + content: '\00A0 \2014'; +} + +q:before, +q:after, +blockquote:before, +blockquote:after { + content: ""; +} + +address { + display: block; + margin-bottom: 20px; + font-style: normal; + line-height: 20px; +} + +code, +pre { + padding: 0 3px 2px; + font-family: Monaco, Menlo, Consolas, "Courier New", monospace; + font-size: 12px; + color: #333333; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +code { + padding: 2px 4px; + color: #d14; + white-space: nowrap; + background-color: #f7f7f9; + border: 1px solid #e1e1e8; +} + +pre { + display: block; + padding: 9.5px; + margin: 0 0 10px; + font-size: 13px; + line-height: 20px; + word-break: break-all; + word-wrap: break-word; + white-space: pre; + white-space: pre-wrap; + background-color: #f5f5f5; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.15); + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +pre.prettyprint { + margin-bottom: 20px; +} + +pre code { + padding: 0; + color: inherit; + white-space: pre; + white-space: pre-wrap; + background-color: transparent; + border: 0; +} + +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} + +form { + margin: 0 0 20px; +} + +fieldset { + padding: 0; + margin: 0; + border: 0; +} + +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 20px; + font-size: 21px; + line-height: 40px; + color: #333333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} + +legend small { + font-size: 15px; + color: #999999; +} + +label, +input, +button, +select, +textarea { + font-size: 14px; + font-weight: normal; + line-height: 20px; +} + +input, +button, +select, +textarea { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; +} + +label { + display: block; + margin-bottom: 5px; +} + +select, +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + display: inline-block; + height: 20px; + padding: 4px 6px; + margin-bottom: 10px; + font-size: 14px; + line-height: 20px; + color: #555555; + vertical-align: middle; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +input, +textarea, +.uneditable-input { + width: 206px; +} + +textarea { + height: auto; +} + +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + background-color: #ffffff; + border: 1px solid #cccccc; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border linear 0.2s, box-shadow linear 0.2s; + -moz-transition: border linear 0.2s, box-shadow linear 0.2s; + -o-transition: border linear 0.2s, box-shadow linear 0.2s; + transition: border linear 0.2s, box-shadow linear 0.2s; +} + +textarea:focus, +input[type="text"]:focus, +input[type="password"]:focus, +input[type="datetime"]:focus, +input[type="datetime-local"]:focus, +input[type="date"]:focus, +input[type="month"]:focus, +input[type="time"]:focus, +input[type="week"]:focus, +input[type="number"]:focus, +input[type="email"]:focus, +input[type="url"]:focus, +input[type="search"]:focus, +input[type="tel"]:focus, +input[type="color"]:focus, +.uneditable-input:focus { + border-color: rgba(82, 168, 236, 0.8); + outline: 0; + outline: thin dotted \9; + /* IE6-9 */ + + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); +} + +input[type="radio"], +input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + *margin-top: 0; + line-height: normal; +} + +input[type="file"], +input[type="image"], +input[type="submit"], +input[type="reset"], +input[type="button"], +input[type="radio"], +input[type="checkbox"] { + width: auto; +} + +select, +input[type="file"] { + height: 30px; + /* In IE7, the height of the select element cannot be changed by height, only font-size */ + + *margin-top: 4px; + /* For IE7, add top margin to align select with labels */ + + line-height: 30px; +} + +select { + width: 220px; + background-color: #ffffff; + border: 1px solid #cccccc; +} + +select[multiple], +select[size] { + height: auto; +} + +select:focus, +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.uneditable-input, +.uneditable-textarea { + color: #999999; + cursor: not-allowed; + background-color: #fcfcfc; + border-color: #cccccc; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); +} + +.uneditable-input { + overflow: hidden; + white-space: nowrap; +} + +.uneditable-textarea { + width: auto; + height: auto; +} + +input:-moz-placeholder, +textarea:-moz-placeholder { + color: #999999; +} + +input:-ms-input-placeholder, +textarea:-ms-input-placeholder { + color: #999999; +} + +input::-webkit-input-placeholder, +textarea::-webkit-input-placeholder { + color: #999999; +} + +.radio, +.checkbox { + min-height: 20px; + padding-left: 20px; +} + +.radio input[type="radio"], +.checkbox input[type="checkbox"] { + float: left; + margin-left: -20px; +} + +.controls > .radio:first-child, +.controls > .checkbox:first-child { + padding-top: 5px; +} + +.radio.inline, +.checkbox.inline { + display: inline-block; + padding-top: 5px; + margin-bottom: 0; + vertical-align: middle; +} + +.radio.inline + .radio.inline, +.checkbox.inline + .checkbox.inline { + margin-left: 10px; +} + +.input-mini { + width: 60px; +} + +.input-small { + width: 90px; +} + +.input-medium { + width: 150px; +} + +.input-large { + width: 210px; +} + +.input-xlarge { + width: 270px; +} + +.input-xxlarge { + width: 530px; +} + +input[class*="span"], +select[class*="span"], +textarea[class*="span"], +.uneditable-input[class*="span"], +.row-fluid input[class*="span"], +.row-fluid select[class*="span"], +.row-fluid textarea[class*="span"], +.row-fluid .uneditable-input[class*="span"] { + float: none; + margin-left: 0; +} + +.input-append input[class*="span"], +.input-append .uneditable-input[class*="span"], +.input-prepend input[class*="span"], +.input-prepend .uneditable-input[class*="span"], +.row-fluid input[class*="span"], +.row-fluid select[class*="span"], +.row-fluid textarea[class*="span"], +.row-fluid .uneditable-input[class*="span"], +.row-fluid .input-prepend [class*="span"], +.row-fluid .input-append [class*="span"] { + display: inline-block; +} + +input, +textarea, +.uneditable-input { + margin-left: 0; +} + +.controls-row [class*="span"] + [class*="span"] { + margin-left: 20px; +} + +input.span12, +textarea.span12, +.uneditable-input.span12 { + width: 926px; +} + +input.span11, +textarea.span11, +.uneditable-input.span11 { + width: 846px; +} + +input.span10, +textarea.span10, +.uneditable-input.span10 { + width: 766px; +} + +input.span9, +textarea.span9, +.uneditable-input.span9 { + width: 686px; +} + +input.span8, +textarea.span8, +.uneditable-input.span8 { + width: 606px; +} + +input.span7, +textarea.span7, +.uneditable-input.span7 { + width: 526px; +} + +input.span6, +textarea.span6, +.uneditable-input.span6 { + width: 446px; +} + +input.span5, +textarea.span5, +.uneditable-input.span5 { + width: 366px; +} + +input.span4, +textarea.span4, +.uneditable-input.span4 { + width: 286px; +} + +input.span3, +textarea.span3, +.uneditable-input.span3 { + width: 206px; +} + +input.span2, +textarea.span2, +.uneditable-input.span2 { + width: 126px; +} + +input.span1, +textarea.span1, +.uneditable-input.span1 { + width: 46px; +} + +.controls-row { + *zoom: 1; +} + +.controls-row:before, +.controls-row:after { + display: table; + line-height: 0; + content: ""; +} + +.controls-row:after { + clear: both; +} + +.controls-row [class*="span"], +.row-fluid .controls-row [class*="span"] { + float: left; +} + +.controls-row .checkbox[class*="span"], +.controls-row .radio[class*="span"] { + padding-top: 5px; +} + +input[disabled], +select[disabled], +textarea[disabled], +input[readonly], +select[readonly], +textarea[readonly] { + cursor: not-allowed; + background-color: #eeeeee; +} + +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"][readonly], +input[type="checkbox"][readonly] { + background-color: transparent; +} + +.control-group.warning .control-label, +.control-group.warning .help-block, +.control-group.warning .help-inline { + color: #c09853; +} + +.control-group.warning .checkbox, +.control-group.warning .radio, +.control-group.warning input, +.control-group.warning select, +.control-group.warning textarea { + color: #c09853; +} + +.control-group.warning input, +.control-group.warning select, +.control-group.warning textarea { + border-color: #c09853; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.warning input:focus, +.control-group.warning select:focus, +.control-group.warning textarea:focus { + border-color: #a47e3c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e; +} + +.control-group.warning .input-prepend .add-on, +.control-group.warning .input-append .add-on { + color: #c09853; + background-color: #fcf8e3; + border-color: #c09853; +} + +.control-group.error .control-label, +.control-group.error .help-block, +.control-group.error .help-inline { + color: #b94a48; +} + +.control-group.error .checkbox, +.control-group.error .radio, +.control-group.error input, +.control-group.error select, +.control-group.error textarea { + color: #b94a48; +} + +.control-group.error input, +.control-group.error select, +.control-group.error textarea { + border-color: #b94a48; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.error input:focus, +.control-group.error select:focus, +.control-group.error textarea:focus { + border-color: #953b39; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392; +} + +.control-group.error .input-prepend .add-on, +.control-group.error .input-append .add-on { + color: #b94a48; + background-color: #f2dede; + border-color: #b94a48; +} + +.control-group.success .control-label, +.control-group.success .help-block, +.control-group.success .help-inline { + color: #468847; +} + +.control-group.success .checkbox, +.control-group.success .radio, +.control-group.success input, +.control-group.success select, +.control-group.success textarea { + color: #468847; +} + +.control-group.success input, +.control-group.success select, +.control-group.success textarea { + border-color: #468847; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.success input:focus, +.control-group.success select:focus, +.control-group.success textarea:focus { + border-color: #356635; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b; +} + +.control-group.success .input-prepend .add-on, +.control-group.success .input-append .add-on { + color: #468847; + background-color: #dff0d8; + border-color: #468847; +} + +.control-group.info .control-label, +.control-group.info .help-block, +.control-group.info .help-inline { + color: #3a87ad; +} + +.control-group.info .checkbox, +.control-group.info .radio, +.control-group.info input, +.control-group.info select, +.control-group.info textarea { + color: #3a87ad; +} + +.control-group.info input, +.control-group.info select, +.control-group.info textarea { + border-color: #3a87ad; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} + +.control-group.info input:focus, +.control-group.info select:focus, +.control-group.info textarea:focus { + border-color: #2d6987; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3; +} + +.control-group.info .input-prepend .add-on, +.control-group.info .input-append .add-on { + color: #3a87ad; + background-color: #d9edf7; + border-color: #3a87ad; +} + +input:focus:invalid, +textarea:focus:invalid, +select:focus:invalid { + color: #b94a48; + border-color: #ee5f5b; +} + +input:focus:invalid:focus, +textarea:focus:invalid:focus, +select:focus:invalid:focus { + border-color: #e9322d; + -webkit-box-shadow: 0 0 6px #f8b9b7; + -moz-box-shadow: 0 0 6px #f8b9b7; + box-shadow: 0 0 6px #f8b9b7; +} + +.form-actions { + padding: 19px 20px 20px; + margin-top: 20px; + margin-bottom: 20px; + background-color: #f5f5f5; + border-top: 1px solid #e5e5e5; + *zoom: 1; +} + +.form-actions:before, +.form-actions:after { + display: table; + line-height: 0; + content: ""; +} + +.form-actions:after { + clear: both; +} + +.help-block, +.help-inline { + color: #595959; +} + +.help-block { + display: block; + margin-bottom: 10px; +} + +.help-inline { + display: inline-block; + *display: inline; + padding-left: 5px; + vertical-align: middle; + *zoom: 1; +} + +.input-append, +.input-prepend { + display: inline-block; + margin-bottom: 10px; + font-size: 0; + white-space: nowrap; + vertical-align: middle; +} + +.input-append input, +.input-prepend input, +.input-append select, +.input-prepend select, +.input-append .uneditable-input, +.input-prepend .uneditable-input, +.input-append .dropdown-menu, +.input-prepend .dropdown-menu, +.input-append .popover, +.input-prepend .popover { + font-size: 14px; +} + +.input-append input, +.input-prepend input, +.input-append select, +.input-prepend select, +.input-append .uneditable-input, +.input-prepend .uneditable-input { + position: relative; + margin-bottom: 0; + *margin-left: 0; + vertical-align: top; + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.input-append input:focus, +.input-prepend input:focus, +.input-append select:focus, +.input-prepend select:focus, +.input-append .uneditable-input:focus, +.input-prepend .uneditable-input:focus { + z-index: 2; +} + +.input-append .add-on, +.input-prepend .add-on { + display: inline-block; + width: auto; + height: 20px; + min-width: 16px; + padding: 4px 5px; + font-size: 14px; + font-weight: normal; + line-height: 20px; + text-align: center; + text-shadow: 0 1px 0 #ffffff; + background-color: #eeeeee; + border: 1px solid #ccc; +} + +.input-append .add-on, +.input-prepend .add-on, +.input-append .btn, +.input-prepend .btn, +.input-append .btn-group > .dropdown-toggle, +.input-prepend .btn-group > .dropdown-toggle { + vertical-align: top; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-append .active, +.input-prepend .active { + background-color: #a9dba9; + border-color: #46a546; +} + +.input-prepend .add-on, +.input-prepend .btn { + margin-right: -1px; +} + +.input-prepend .add-on:first-child, +.input-prepend .btn:first-child { + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.input-append input, +.input-append select, +.input-append .uneditable-input { + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.input-append input + .btn-group .btn:last-child, +.input-append select + .btn-group .btn:last-child, +.input-append .uneditable-input + .btn-group .btn:last-child { + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.input-append .add-on, +.input-append .btn, +.input-append .btn-group { + margin-left: -1px; +} + +.input-append .add-on:last-child, +.input-append .btn:last-child, +.input-append .btn-group:last-child > .dropdown-toggle { + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.input-prepend.input-append input, +.input-prepend.input-append select, +.input-prepend.input-append .uneditable-input { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-prepend.input-append input + .btn-group .btn, +.input-prepend.input-append select + .btn-group .btn, +.input-prepend.input-append .uneditable-input + .btn-group .btn { + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.input-prepend.input-append .add-on:first-child, +.input-prepend.input-append .btn:first-child { + margin-right: -1px; + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.input-prepend.input-append .add-on:last-child, +.input-prepend.input-append .btn:last-child { + margin-left: -1px; + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.input-prepend.input-append .btn-group:first-child { + margin-left: 0; +} + +input.search-query { + padding-right: 14px; + padding-right: 4px \9; + padding-left: 14px; + padding-left: 4px \9; + /* IE7-8 doesn't have border-radius, so don't indent the padding */ + + margin-bottom: 0; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +/* Allow for input prepend/append in search forms */ + +.form-search .input-append .search-query, +.form-search .input-prepend .search-query { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.form-search .input-append .search-query { + -webkit-border-radius: 14px 0 0 14px; + -moz-border-radius: 14px 0 0 14px; + border-radius: 14px 0 0 14px; +} + +.form-search .input-append .btn { + -webkit-border-radius: 0 14px 14px 0; + -moz-border-radius: 0 14px 14px 0; + border-radius: 0 14px 14px 0; +} + +.form-search .input-prepend .search-query { + -webkit-border-radius: 0 14px 14px 0; + -moz-border-radius: 0 14px 14px 0; + border-radius: 0 14px 14px 0; +} + +.form-search .input-prepend .btn { + -webkit-border-radius: 14px 0 0 14px; + -moz-border-radius: 14px 0 0 14px; + border-radius: 14px 0 0 14px; +} + +.form-search input, +.form-inline input, +.form-horizontal input, +.form-search textarea, +.form-inline textarea, +.form-horizontal textarea, +.form-search select, +.form-inline select, +.form-horizontal select, +.form-search .help-inline, +.form-inline .help-inline, +.form-horizontal .help-inline, +.form-search .uneditable-input, +.form-inline .uneditable-input, +.form-horizontal .uneditable-input, +.form-search .input-prepend, +.form-inline .input-prepend, +.form-horizontal .input-prepend, +.form-search .input-append, +.form-inline .input-append, +.form-horizontal .input-append { + display: inline-block; + *display: inline; + margin-bottom: 0; + vertical-align: middle; + *zoom: 1; +} + +.form-search .hide, +.form-inline .hide, +.form-horizontal .hide { + display: none; +} + +.form-search label, +.form-inline label, +.form-search .btn-group, +.form-inline .btn-group { + display: inline-block; +} + +.form-search .input-append, +.form-inline .input-append, +.form-search .input-prepend, +.form-inline .input-prepend { + margin-bottom: 0; +} + +.form-search .radio, +.form-search .checkbox, +.form-inline .radio, +.form-inline .checkbox { + padding-left: 0; + margin-bottom: 0; + vertical-align: middle; +} + +.form-search .radio input[type="radio"], +.form-search .checkbox input[type="checkbox"], +.form-inline .radio input[type="radio"], +.form-inline .checkbox input[type="checkbox"] { + float: left; + margin-right: 3px; + margin-left: 0; +} + +.control-group { + margin-bottom: 10px; +} + +legend + .control-group { + margin-top: 20px; + -webkit-margin-top-collapse: separate; +} + +.form-horizontal .control-group { + margin-bottom: 20px; + *zoom: 1; +} + +.form-horizontal .control-group:before, +.form-horizontal .control-group:after { + display: table; + line-height: 0; + content: ""; +} + +.form-horizontal .control-group:after { + clear: both; +} + +.form-horizontal .control-label { + float: left; + width: 160px; + padding-top: 5px; + text-align: right; +} + +.form-horizontal .controls { + *display: inline-block; + *padding-left: 20px; + margin-left: 180px; + *margin-left: 0; +} + +.form-horizontal .controls:first-child { + *padding-left: 180px; +} + +.form-horizontal .help-block { + margin-bottom: 0; +} + +.form-horizontal input + .help-block, +.form-horizontal select + .help-block, +.form-horizontal textarea + .help-block, +.form-horizontal .uneditable-input + .help-block, +.form-horizontal .input-prepend + .help-block, +.form-horizontal .input-append + .help-block { + margin-top: 10px; +} + +.form-horizontal .form-actions { + padding-left: 180px; +} + +table { + max-width: 100%; + background-color: transparent; + border-collapse: collapse; + border-spacing: 0; +} + +.table { + width: 100%; + margin-bottom: 20px; +} + +.table th, +.table td { + padding: 8px; + line-height: 20px; + text-align: left; + vertical-align: top; + border-top: 1px solid #dddddd; +} + +.table th { + font-weight: bold; +} + +.table thead th { + vertical-align: bottom; +} + +.table caption + thead tr:first-child th, +.table caption + thead tr:first-child td, +.table colgroup + thead tr:first-child th, +.table colgroup + thead tr:first-child td, +.table thead:first-child tr:first-child th, +.table thead:first-child tr:first-child td { + border-top: 0; +} + +.table tbody + tbody { + border-top: 2px solid #dddddd; +} + +.table .table { + background-color: #ffffff; +} + +.table-condensed th, +.table-condensed td { + padding: 4px 5px; +} + +.table-bordered { + border: 1px solid #dddddd; + border-collapse: separate; + *border-collapse: collapse; + border-left: 0; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.table-bordered th, +.table-bordered td { + border-left: 1px solid #dddddd; +} + +.table-bordered caption + thead tr:first-child th, +.table-bordered caption + tbody tr:first-child th, +.table-bordered caption + tbody tr:first-child td, +.table-bordered colgroup + thead tr:first-child th, +.table-bordered colgroup + tbody tr:first-child th, +.table-bordered colgroup + tbody tr:first-child td, +.table-bordered thead:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child td { + border-top: 0; +} + +.table-bordered thead:first-child tr:first-child > th:first-child, +.table-bordered tbody:first-child tr:first-child > td:first-child, +.table-bordered tbody:first-child tr:first-child > th:first-child { + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-bordered thead:first-child tr:first-child > th:last-child, +.table-bordered tbody:first-child tr:first-child > td:last-child, +.table-bordered tbody:first-child tr:first-child > th:last-child { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -moz-border-radius-topright: 4px; +} + +.table-bordered thead:last-child tr:last-child > th:first-child, +.table-bordered tbody:last-child tr:last-child > td:first-child, +.table-bordered tbody:last-child tr:last-child > th:first-child, +.table-bordered tfoot:last-child tr:last-child > td:first-child, +.table-bordered tfoot:last-child tr:last-child > th:first-child { + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; +} + +.table-bordered thead:last-child tr:last-child > th:last-child, +.table-bordered tbody:last-child tr:last-child > td:last-child, +.table-bordered tbody:last-child tr:last-child > th:last-child, +.table-bordered tfoot:last-child tr:last-child > td:last-child, +.table-bordered tfoot:last-child tr:last-child > th:last-child { + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-bottomright: 4px; +} + +.table-bordered tfoot + tbody:last-child tr:last-child td:first-child { + -webkit-border-bottom-left-radius: 0; + border-bottom-left-radius: 0; + -moz-border-radius-bottomleft: 0; +} + +.table-bordered tfoot + tbody:last-child tr:last-child td:last-child { + -webkit-border-bottom-right-radius: 0; + border-bottom-right-radius: 0; + -moz-border-radius-bottomright: 0; +} + +.table-bordered caption + thead tr:first-child th:first-child, +.table-bordered caption + tbody tr:first-child td:first-child, +.table-bordered colgroup + thead tr:first-child th:first-child, +.table-bordered colgroup + tbody tr:first-child td:first-child { + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-bordered caption + thead tr:first-child th:last-child, +.table-bordered caption + tbody tr:first-child td:last-child, +.table-bordered colgroup + thead tr:first-child th:last-child, +.table-bordered colgroup + tbody tr:first-child td:last-child { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -moz-border-radius-topright: 4px; +} + +.table-striped tbody > tr:nth-child(odd) > td, +.table-striped tbody > tr:nth-child(odd) > th { + background-color: #f9f9f9; +} + +.table-hover tbody tr:hover > td, +.table-hover tbody tr:hover > th { + background-color: #f5f5f5; +} + +table td[class*="span"], +table th[class*="span"], +.row-fluid table td[class*="span"], +.row-fluid table th[class*="span"] { + display: table-cell; + float: none; + margin-left: 0; +} + +.table td.span1, +.table th.span1 { + float: none; + width: 44px; + margin-left: 0; +} + +.table td.span2, +.table th.span2 { + float: none; + width: 124px; + margin-left: 0; +} + +.table td.span3, +.table th.span3 { + float: none; + width: 204px; + margin-left: 0; +} + +.table td.span4, +.table th.span4 { + float: none; + width: 284px; + margin-left: 0; +} + +.table td.span5, +.table th.span5 { + float: none; + width: 364px; + margin-left: 0; +} + +.table td.span6, +.table th.span6 { + float: none; + width: 444px; + margin-left: 0; +} + +.table td.span7, +.table th.span7 { + float: none; + width: 524px; + margin-left: 0; +} + +.table td.span8, +.table th.span8 { + float: none; + width: 604px; + margin-left: 0; +} + +.table td.span9, +.table th.span9 { + float: none; + width: 684px; + margin-left: 0; +} + +.table td.span10, +.table th.span10 { + float: none; + width: 764px; + margin-left: 0; +} + +.table td.span11, +.table th.span11 { + float: none; + width: 844px; + margin-left: 0; +} + +.table td.span12, +.table th.span12 { + float: none; + width: 924px; + margin-left: 0; +} + +.table tbody tr.success > td { + background-color: #dff0d8; +} + +.table tbody tr.error > td { + background-color: #f2dede; +} + +.table tbody tr.warning > td { + background-color: #fcf8e3; +} + +.table tbody tr.info > td { + background-color: #d9edf7; +} + +.table-hover tbody tr.success:hover > td { + background-color: #d0e9c6; +} + +.table-hover tbody tr.error:hover > td { + background-color: #ebcccc; +} + +.table-hover tbody tr.warning:hover > td { + background-color: #faf2cc; +} + +.table-hover tbody tr.info:hover > td { + background-color: #c4e3f3; +} + +[class^="icon-"], +[class*=" icon-"] { + display: inline-block; + width: 14px; + height: 14px; + margin-top: 1px; + *margin-right: .3em; + line-height: 14px; + vertical-align: text-top; + background-image: url("../img/glyphicons-halflings.png"); + background-position: 14px 14px; + background-repeat: no-repeat; +} + +/* White icons with optional class, or on hover/focus/active states of certain elements */ + +.icon-white, +.nav-pills > .active > a > [class^="icon-"], +.nav-pills > .active > a > [class*=" icon-"], +.nav-list > .active > a > [class^="icon-"], +.nav-list > .active > a > [class*=" icon-"], +.navbar-inverse .nav > .active > a > [class^="icon-"], +.navbar-inverse .nav > .active > a > [class*=" icon-"], +.dropdown-menu > li > a:hover > [class^="icon-"], +.dropdown-menu > li > a:focus > [class^="icon-"], +.dropdown-menu > li > a:hover > [class*=" icon-"], +.dropdown-menu > li > a:focus > [class*=" icon-"], +.dropdown-menu > .active > a > [class^="icon-"], +.dropdown-menu > .active > a > [class*=" icon-"], +.dropdown-submenu:hover > a > [class^="icon-"], +.dropdown-submenu:focus > a > [class^="icon-"], +.dropdown-submenu:hover > a > [class*=" icon-"], +.dropdown-submenu:focus > a > [class*=" icon-"] { + background-image: url("../img/glyphicons-halflings-white.png"); +} + +.icon-glass { + background-position: 0 0; +} + +.icon-music { + background-position: -24px 0; +} + +.icon-search { + background-position: -48px 0; +} + +.icon-envelope { + background-position: -72px 0; +} + +.icon-heart { + background-position: -96px 0; +} + +.icon-star { + background-position: -120px 0; +} + +.icon-star-empty { + background-position: -144px 0; +} + +.icon-user { + background-position: -168px 0; +} + +.icon-film { + background-position: -192px 0; +} + +.icon-th-large { + background-position: -216px 0; +} + +.icon-th { + background-position: -240px 0; +} + +.icon-th-list { + background-position: -264px 0; +} + +.icon-ok { + background-position: -288px 0; +} + +.icon-remove { + background-position: -312px 0; +} + +.icon-zoom-in { + background-position: -336px 0; +} + +.icon-zoom-out { + background-position: -360px 0; +} + +.icon-off { + background-position: -384px 0; +} + +.icon-signal { + background-position: -408px 0; +} + +.icon-cog { + background-position: -432px 0; +} + +.icon-trash { + background-position: -456px 0; +} + +.icon-home { + background-position: 0 -24px; +} + +.icon-file { + background-position: -24px -24px; +} + +.icon-time { + background-position: -48px -24px; +} + +.icon-road { + background-position: -72px -24px; +} + +.icon-download-alt { + background-position: -96px -24px; +} + +.icon-download { + background-position: -120px -24px; +} + +.icon-upload { + background-position: -144px -24px; +} + +.icon-inbox { + background-position: -168px -24px; +} + +.icon-play-circle { + background-position: -192px -24px; +} + +.icon-repeat { + background-position: -216px -24px; +} + +.icon-refresh { + background-position: -240px -24px; +} + +.icon-list-alt { + background-position: -264px -24px; +} + +.icon-lock { + background-position: -287px -24px; +} + +.icon-flag { + background-position: -312px -24px; +} + +.icon-headphones { + background-position: -336px -24px; +} + +.icon-volume-off { + background-position: -360px -24px; +} + +.icon-volume-down { + background-position: -384px -24px; +} + +.icon-volume-up { + background-position: -408px -24px; +} + +.icon-qrcode { + background-position: -432px -24px; +} + +.icon-barcode { + background-position: -456px -24px; +} + +.icon-tag { + background-position: 0 -48px; +} + +.icon-tags { + background-position: -25px -48px; +} + +.icon-book { + background-position: -48px -48px; +} + +.icon-bookmark { + background-position: -72px -48px; +} + +.icon-print { + background-position: -96px -48px; +} + +.icon-camera { + background-position: -120px -48px; +} + +.icon-font { + background-position: -144px -48px; +} + +.icon-bold { + background-position: -167px -48px; +} + +.icon-italic { + background-position: -192px -48px; +} + +.icon-text-height { + background-position: -216px -48px; +} + +.icon-text-width { + background-position: -240px -48px; +} + +.icon-align-left { + background-position: -264px -48px; +} + +.icon-align-center { + background-position: -288px -48px; +} + +.icon-align-right { + background-position: -312px -48px; +} + +.icon-align-justify { + background-position: -336px -48px; +} + +.icon-list { + background-position: -360px -48px; +} + +.icon-indent-left { + background-position: -384px -48px; +} + +.icon-indent-right { + background-position: -408px -48px; +} + +.icon-facetime-video { + background-position: -432px -48px; +} + +.icon-picture { + background-position: -456px -48px; +} + +.icon-pencil { + background-position: 0 -72px; +} + +.icon-map-marker { + background-position: -24px -72px; +} + +.icon-adjust { + background-position: -48px -72px; +} + +.icon-tint { + background-position: -72px -72px; +} + +.icon-edit { + background-position: -96px -72px; +} + +.icon-share { + background-position: -120px -72px; +} + +.icon-check { + background-position: -144px -72px; +} + +.icon-move { + background-position: -168px -72px; +} + +.icon-step-backward { + background-position: -192px -72px; +} + +.icon-fast-backward { + background-position: -216px -72px; +} + +.icon-backward { + background-position: -240px -72px; +} + +.icon-play { + background-position: -264px -72px; +} + +.icon-pause { + background-position: -288px -72px; +} + +.icon-stop { + background-position: -312px -72px; +} + +.icon-forward { + background-position: -336px -72px; +} + +.icon-fast-forward { + background-position: -360px -72px; +} + +.icon-step-forward { + background-position: -384px -72px; +} + +.icon-eject { + background-position: -408px -72px; +} + +.icon-chevron-left { + background-position: -432px -72px; +} + +.icon-chevron-right { + background-position: -456px -72px; +} + +.icon-plus-sign { + background-position: 0 -96px; +} + +.icon-minus-sign { + background-position: -24px -96px; +} + +.icon-remove-sign { + background-position: -48px -96px; +} + +.icon-ok-sign { + background-position: -72px -96px; +} + +.icon-question-sign { + background-position: -96px -96px; +} + +.icon-info-sign { + background-position: -120px -96px; +} + +.icon-screenshot { + background-position: -144px -96px; +} + +.icon-remove-circle { + background-position: -168px -96px; +} + +.icon-ok-circle { + background-position: -192px -96px; +} + +.icon-ban-circle { + background-position: -216px -96px; +} + +.icon-arrow-left { + background-position: -240px -96px; +} + +.icon-arrow-right { + background-position: -264px -96px; +} + +.icon-arrow-up { + background-position: -289px -96px; +} + +.icon-arrow-down { + background-position: -312px -96px; +} + +.icon-share-alt { + background-position: -336px -96px; +} + +.icon-resize-full { + background-position: -360px -96px; +} + +.icon-resize-small { + background-position: -384px -96px; +} + +.icon-plus { + background-position: -408px -96px; +} + +.icon-minus { + background-position: -433px -96px; +} + +.icon-asterisk { + background-position: -456px -96px; +} + +.icon-exclamation-sign { + background-position: 0 -120px; +} + +.icon-gift { + background-position: -24px -120px; +} + +.icon-leaf { + background-position: -48px -120px; +} + +.icon-fire { + background-position: -72px -120px; +} + +.icon-eye-open { + background-position: -96px -120px; +} + +.icon-eye-close { + background-position: -120px -120px; +} + +.icon-warning-sign { + background-position: -144px -120px; +} + +.icon-plane { + background-position: -168px -120px; +} + +.icon-calendar { + background-position: -192px -120px; +} + +.icon-random { + width: 16px; + background-position: -216px -120px; +} + +.icon-comment { + background-position: -240px -120px; +} + +.icon-magnet { + background-position: -264px -120px; +} + +.icon-chevron-up { + background-position: -288px -120px; +} + +.icon-chevron-down { + background-position: -313px -119px; +} + +.icon-retweet { + background-position: -336px -120px; +} + +.icon-shopping-cart { + background-position: -360px -120px; +} + +.icon-folder-close { + width: 16px; + background-position: -384px -120px; +} + +.icon-folder-open { + width: 16px; + background-position: -408px -120px; +} + +.icon-resize-vertical { + background-position: -432px -119px; +} + +.icon-resize-horizontal { + background-position: -456px -118px; +} + +.icon-hdd { + background-position: 0 -144px; +} + +.icon-bullhorn { + background-position: -24px -144px; +} + +.icon-bell { + background-position: -48px -144px; +} + +.icon-certificate { + background-position: -72px -144px; +} + +.icon-thumbs-up { + background-position: -96px -144px; +} + +.icon-thumbs-down { + background-position: -120px -144px; +} + +.icon-hand-right { + background-position: -144px -144px; +} + +.icon-hand-left { + background-position: -168px -144px; +} + +.icon-hand-up { + background-position: -192px -144px; +} + +.icon-hand-down { + background-position: -216px -144px; +} + +.icon-circle-arrow-right { + background-position: -240px -144px; +} + +.icon-circle-arrow-left { + background-position: -264px -144px; +} + +.icon-circle-arrow-up { + background-position: -288px -144px; +} + +.icon-circle-arrow-down { + background-position: -312px -144px; +} + +.icon-globe { + background-position: -336px -144px; +} + +.icon-wrench { + background-position: -360px -144px; +} + +.icon-tasks { + background-position: -384px -144px; +} + +.icon-filter { + background-position: -408px -144px; +} + +.icon-briefcase { + background-position: -432px -144px; +} + +.icon-fullscreen { + background-position: -456px -144px; +} + +.dropup, +.dropdown { + position: relative; +} + +.dropdown-toggle { + *margin-bottom: -3px; +} + +.dropdown-toggle:active, +.open .dropdown-toggle { + outline: 0; +} + +.caret { + display: inline-block; + width: 0; + height: 0; + vertical-align: top; + border-top: 4px solid #000000; + border-right: 4px solid transparent; + border-left: 4px solid transparent; + content: ""; +} + +.dropdown .caret { + margin-top: 8px; + margin-left: 2px; +} + +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + list-style: none; + background-color: #ffffff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + *border-right-width: 2px; + *border-bottom-width: 2px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -webkit-background-clip: padding-box; + -moz-background-clip: padding; + background-clip: padding-box; +} + +.dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.dropdown-menu .divider { + *width: 100%; + height: 1px; + margin: 9px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.dropdown-menu > li > a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: normal; + line-height: 20px; + color: #333333; + white-space: nowrap; +} + +.dropdown-menu > li > a:hover, +.dropdown-menu > li > a:focus, +.dropdown-submenu:hover > a, +.dropdown-submenu:focus > a { + color: #ffffff; + text-decoration: none; + background-color: #0081c2; + background-image: -moz-linear-gradient(top, #0088cc, #0077b3); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); + background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); + background-image: -o-linear-gradient(top, #0088cc, #0077b3); + background-image: linear-gradient(to bottom, #0088cc, #0077b3); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); +} + +.dropdown-menu > .active > a, +.dropdown-menu > .active > a:hover, +.dropdown-menu > .active > a:focus { + color: #ffffff; + text-decoration: none; + background-color: #0081c2; + background-image: -moz-linear-gradient(top, #0088cc, #0077b3); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); + background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); + background-image: -o-linear-gradient(top, #0088cc, #0077b3); + background-image: linear-gradient(to bottom, #0088cc, #0077b3); + background-repeat: repeat-x; + outline: 0; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); +} + +.dropdown-menu > .disabled > a, +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + color: #999999; +} + +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + text-decoration: none; + cursor: default; + background-color: transparent; + background-image: none; + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.open { + *z-index: 1000; +} + +.open > .dropdown-menu { + display: block; +} + +.dropdown-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 990; +} + +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} + +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px solid #000000; + content: ""; +} + +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 1px; +} + +.dropdown-submenu { + position: relative; +} + +.dropdown-submenu > .dropdown-menu { + top: 0; + left: 100%; + margin-top: -6px; + margin-left: -1px; + -webkit-border-radius: 0 6px 6px 6px; + -moz-border-radius: 0 6px 6px 6px; + border-radius: 0 6px 6px 6px; +} + +.dropdown-submenu:hover > .dropdown-menu { + display: block; +} + +.dropup .dropdown-submenu > .dropdown-menu { + top: auto; + bottom: 0; + margin-top: 0; + margin-bottom: -2px; + -webkit-border-radius: 5px 5px 5px 0; + -moz-border-radius: 5px 5px 5px 0; + border-radius: 5px 5px 5px 0; +} + +.dropdown-submenu > a:after { + display: block; + float: right; + width: 0; + height: 0; + margin-top: 5px; + margin-right: -10px; + border-color: transparent; + border-left-color: #cccccc; + border-style: solid; + border-width: 5px 0 5px 5px; + content: " "; +} + +.dropdown-submenu:hover > a:after { + border-left-color: #ffffff; +} + +.dropdown-submenu.pull-left { + float: none; +} + +.dropdown-submenu.pull-left > .dropdown-menu { + left: -100%; + margin-left: 10px; + -webkit-border-radius: 6px 0 6px 6px; + -moz-border-radius: 6px 0 6px 6px; + border-radius: 6px 0 6px 6px; +} + +.dropdown .dropdown-menu .nav-header { + padding-right: 20px; + padding-left: 20px; +} + +.typeahead { + z-index: 1051; + margin-top: 2px; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #e3e3e3; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} + +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} + +.well-large { + padding: 24px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.well-small { + padding: 9px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + -moz-transition: opacity 0.15s linear; + -o-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} + +.fade.in { + opacity: 1; +} + +.collapse { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition: height 0.35s ease; + -moz-transition: height 0.35s ease; + -o-transition: height 0.35s ease; + transition: height 0.35s ease; +} + +.collapse.in { + height: auto; +} + +.close { + float: right; + font-size: 20px; + font-weight: bold; + line-height: 20px; + color: #000000; + text-shadow: 0 1px 0 #ffffff; + opacity: 0.2; + filter: alpha(opacity=20); +} + +.close:hover, +.close:focus { + color: #000000; + text-decoration: none; + cursor: pointer; + opacity: 0.4; + filter: alpha(opacity=40); +} + +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} + +.btn { + display: inline-block; + *display: inline; + padding: 4px 12px; + margin-bottom: 0; + *margin-left: .3em; + font-size: 14px; + line-height: 20px; + color: #333333; + text-align: center; + text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); + vertical-align: middle; + cursor: pointer; + background-color: #f5f5f5; + *background-color: #e6e6e6; + background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); + background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); + background-image: linear-gradient(to bottom, #ffffff, #e6e6e6); + background-repeat: repeat-x; + border: 1px solid #cccccc; + *border: 0; + border-color: #e6e6e6 #e6e6e6 #bfbfbf; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + border-bottom-color: #b3b3b3; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn:hover, +.btn:focus, +.btn:active, +.btn.active, +.btn.disabled, +.btn[disabled] { + color: #333333; + background-color: #e6e6e6; + *background-color: #d9d9d9; +} + +.btn:active, +.btn.active { + background-color: #cccccc \9; +} + +.btn:first-child { + *margin-left: 0; +} + +.btn:hover, +.btn:focus { + color: #333333; + text-decoration: none; + background-position: 0 -15px; + -webkit-transition: background-position 0.1s linear; + -moz-transition: background-position 0.1s linear; + -o-transition: background-position 0.1s linear; + transition: background-position 0.1s linear; +} + +.btn:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.btn.active, +.btn:active { + background-image: none; + outline: 0; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn.disabled, +.btn[disabled] { + cursor: default; + background-image: none; + opacity: 0.65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; +} + +.btn-large { + padding: 11px 19px; + font-size: 17.5px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.btn-large [class^="icon-"], +.btn-large [class*=" icon-"] { + margin-top: 4px; +} + +.btn-small { + padding: 2px 10px; + font-size: 11.9px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.btn-small [class^="icon-"], +.btn-small [class*=" icon-"] { + margin-top: 0; +} + +.btn-mini [class^="icon-"], +.btn-mini [class*=" icon-"] { + margin-top: -1px; +} + +.btn-mini { + padding: 0 6px; + font-size: 10.5px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.btn-block { + display: block; + width: 100%; + padding-right: 0; + padding-left: 0; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +.btn-block + .btn-block { + margin-top: 5px; +} + +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} + +.btn-primary.active, +.btn-warning.active, +.btn-danger.active, +.btn-success.active, +.btn-info.active, +.btn-inverse.active { + color: rgba(255, 255, 255, 0.75); +} + +.btn-primary { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #006dcc; + *background-color: #0044cc; + background-image: -moz-linear-gradient(top, #0088cc, #0044cc); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc)); + background-image: -webkit-linear-gradient(top, #0088cc, #0044cc); + background-image: -o-linear-gradient(top, #0088cc, #0044cc); + background-image: linear-gradient(to bottom, #0088cc, #0044cc); + background-repeat: repeat-x; + border-color: #0044cc #0044cc #002a80; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.btn-primary:hover, +.btn-primary:focus, +.btn-primary:active, +.btn-primary.active, +.btn-primary.disabled, +.btn-primary[disabled] { + color: #ffffff; + background-color: #0044cc; + *background-color: #003bb3; +} + +.btn-primary:active, +.btn-primary.active { + background-color: #003399 \9; +} + +.btn-warning { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #faa732; + *background-color: #f89406; + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(to bottom, #fbb450, #f89406); + background-repeat: repeat-x; + border-color: #f89406 #f89406 #ad6704; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.btn-warning:hover, +.btn-warning:focus, +.btn-warning:active, +.btn-warning.active, +.btn-warning.disabled, +.btn-warning[disabled] { + color: #ffffff; + background-color: #f89406; + *background-color: #df8505; +} + +.btn-warning:active, +.btn-warning.active { + background-color: #c67605 \9; +} + +.btn-danger { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #da4f49; + *background-color: #bd362f; + background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -o-linear-gradient(top, #ee5f5b, #bd362f); + background-image: linear-gradient(to bottom, #ee5f5b, #bd362f); + background-repeat: repeat-x; + border-color: #bd362f #bd362f #802420; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.btn-danger:hover, +.btn-danger:focus, +.btn-danger:active, +.btn-danger.active, +.btn-danger.disabled, +.btn-danger[disabled] { + color: #ffffff; + background-color: #bd362f; + *background-color: #a9302a; +} + +.btn-danger:active, +.btn-danger.active { + background-color: #942a25 \9; +} + +.btn-success { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #5bb75b; + *background-color: #51a351; + background-image: -moz-linear-gradient(top, #62c462, #51a351); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351)); + background-image: -webkit-linear-gradient(top, #62c462, #51a351); + background-image: -o-linear-gradient(top, #62c462, #51a351); + background-image: linear-gradient(to bottom, #62c462, #51a351); + background-repeat: repeat-x; + border-color: #51a351 #51a351 #387038; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.btn-success:hover, +.btn-success:focus, +.btn-success:active, +.btn-success.active, +.btn-success.disabled, +.btn-success[disabled] { + color: #ffffff; + background-color: #51a351; + *background-color: #499249; +} + +.btn-success:active, +.btn-success.active { + background-color: #408140 \9; +} + +.btn-info { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #49afcd; + *background-color: #2f96b4; + background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4)); + background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -o-linear-gradient(top, #5bc0de, #2f96b4); + background-image: linear-gradient(to bottom, #5bc0de, #2f96b4); + background-repeat: repeat-x; + border-color: #2f96b4 #2f96b4 #1f6377; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.btn-info:hover, +.btn-info:focus, +.btn-info:active, +.btn-info.active, +.btn-info.disabled, +.btn-info[disabled] { + color: #ffffff; + background-color: #2f96b4; + *background-color: #2a85a0; +} + +.btn-info:active, +.btn-info.active { + background-color: #24748c \9; +} + +.btn-inverse { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #363636; + *background-color: #222222; + background-image: -moz-linear-gradient(top, #444444, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#444444), to(#222222)); + background-image: -webkit-linear-gradient(top, #444444, #222222); + background-image: -o-linear-gradient(top, #444444, #222222); + background-image: linear-gradient(to bottom, #444444, #222222); + background-repeat: repeat-x; + border-color: #222222 #222222 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.btn-inverse:hover, +.btn-inverse:focus, +.btn-inverse:active, +.btn-inverse.active, +.btn-inverse.disabled, +.btn-inverse[disabled] { + color: #ffffff; + background-color: #222222; + *background-color: #151515; +} + +.btn-inverse:active, +.btn-inverse.active { + background-color: #080808 \9; +} + +button.btn, +input[type="submit"].btn { + *padding-top: 3px; + *padding-bottom: 3px; +} + +button.btn::-moz-focus-inner, +input[type="submit"].btn::-moz-focus-inner { + padding: 0; + border: 0; +} + +button.btn.btn-large, +input[type="submit"].btn.btn-large { + *padding-top: 7px; + *padding-bottom: 7px; +} + +button.btn.btn-small, +input[type="submit"].btn.btn-small { + *padding-top: 3px; + *padding-bottom: 3px; +} + +button.btn.btn-mini, +input[type="submit"].btn.btn-mini { + *padding-top: 1px; + *padding-bottom: 1px; +} + +.btn-link, +.btn-link:active, +.btn-link[disabled] { + background-color: transparent; + background-image: none; + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; +} + +.btn-link { + color: #0088cc; + cursor: pointer; + border-color: transparent; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-link:hover, +.btn-link:focus { + color: #005580; + text-decoration: underline; + background-color: transparent; +} + +.btn-link[disabled]:hover, +.btn-link[disabled]:focus { + color: #333333; + text-decoration: none; +} + +.btn-group { + position: relative; + display: inline-block; + *display: inline; + *margin-left: .3em; + font-size: 0; + white-space: nowrap; + vertical-align: middle; + *zoom: 1; +} + +.btn-group:first-child { + *margin-left: 0; +} + +.btn-group + .btn-group { + margin-left: 5px; +} + +.btn-toolbar { + margin-top: 10px; + margin-bottom: 10px; + font-size: 0; +} + +.btn-toolbar > .btn + .btn, +.btn-toolbar > .btn-group + .btn, +.btn-toolbar > .btn + .btn-group { + margin-left: 5px; +} + +.btn-group > .btn { + position: relative; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-group > .btn + .btn { + margin-left: -1px; +} + +.btn-group > .btn, +.btn-group > .dropdown-menu, +.btn-group > .popover { + font-size: 14px; +} + +.btn-group > .btn-mini { + font-size: 10.5px; +} + +.btn-group > .btn-small { + font-size: 11.9px; +} + +.btn-group > .btn-large { + font-size: 17.5px; +} + +.btn-group > .btn:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-topleft: 4px; +} + +.btn-group > .btn:last-child, +.btn-group > .dropdown-toggle { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-bottomright: 4px; +} + +.btn-group > .btn.large:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 6px; + border-bottom-left-radius: 6px; + -webkit-border-top-left-radius: 6px; + border-top-left-radius: 6px; + -moz-border-radius-bottomleft: 6px; + -moz-border-radius-topleft: 6px; +} + +.btn-group > .btn.large:last-child, +.btn-group > .large.dropdown-toggle { + -webkit-border-top-right-radius: 6px; + border-top-right-radius: 6px; + -webkit-border-bottom-right-radius: 6px; + border-bottom-right-radius: 6px; + -moz-border-radius-topright: 6px; + -moz-border-radius-bottomright: 6px; +} + +.btn-group > .btn:hover, +.btn-group > .btn:focus, +.btn-group > .btn:active, +.btn-group > .btn.active { + z-index: 2; +} + +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} + +.btn-group > .btn + .dropdown-toggle { + *padding-top: 5px; + padding-right: 8px; + *padding-bottom: 5px; + padding-left: 8px; + -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group > .btn-mini + .dropdown-toggle { + *padding-top: 2px; + padding-right: 5px; + *padding-bottom: 2px; + padding-left: 5px; +} + +.btn-group > .btn-small + .dropdown-toggle { + *padding-top: 5px; + *padding-bottom: 4px; +} + +.btn-group > .btn-large + .dropdown-toggle { + *padding-top: 7px; + padding-right: 12px; + *padding-bottom: 7px; + padding-left: 12px; +} + +.btn-group.open .dropdown-toggle { + background-image: none; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group.open .btn.dropdown-toggle { + background-color: #e6e6e6; +} + +.btn-group.open .btn-primary.dropdown-toggle { + background-color: #0044cc; +} + +.btn-group.open .btn-warning.dropdown-toggle { + background-color: #f89406; +} + +.btn-group.open .btn-danger.dropdown-toggle { + background-color: #bd362f; +} + +.btn-group.open .btn-success.dropdown-toggle { + background-color: #51a351; +} + +.btn-group.open .btn-info.dropdown-toggle { + background-color: #2f96b4; +} + +.btn-group.open .btn-inverse.dropdown-toggle { + background-color: #222222; +} + +.btn .caret { + margin-top: 8px; + margin-left: 0; +} + +.btn-large .caret { + margin-top: 6px; +} + +.btn-large .caret { + border-top-width: 5px; + border-right-width: 5px; + border-left-width: 5px; +} + +.btn-mini .caret, +.btn-small .caret { + margin-top: 8px; +} + +.dropup .btn-large .caret { + border-bottom-width: 5px; +} + +.btn-primary .caret, +.btn-warning .caret, +.btn-danger .caret, +.btn-info .caret, +.btn-success .caret, +.btn-inverse .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.btn-group-vertical { + display: inline-block; + *display: inline; + /* IE7 inline-block hack */ + + *zoom: 1; +} + +.btn-group-vertical > .btn { + display: block; + float: none; + max-width: 100%; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-group-vertical > .btn + .btn { + margin-top: -1px; + margin-left: 0; +} + +.btn-group-vertical > .btn:first-child { + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.btn-group-vertical > .btn:last-child { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.btn-group-vertical > .btn-large:first-child { + -webkit-border-radius: 6px 6px 0 0; + -moz-border-radius: 6px 6px 0 0; + border-radius: 6px 6px 0 0; +} + +.btn-group-vertical > .btn-large:last-child { + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; +} + +.alert { + padding: 8px 35px 8px 14px; + margin-bottom: 20px; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + background-color: #fcf8e3; + border: 1px solid #fbeed5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.alert, +.alert h4 { + color: #c09853; +} + +.alert h4 { + margin: 0; +} + +.alert .close { + position: relative; + top: -2px; + right: -21px; + line-height: 20px; +} + +.alert-success { + color: #468847; + background-color: #dff0d8; + border-color: #d6e9c6; +} + +.alert-success h4 { + color: #468847; +} + +.alert-danger, +.alert-error { + color: #b94a48; + background-color: #f2dede; + border-color: #eed3d7; +} + +.alert-danger h4, +.alert-error h4 { + color: #b94a48; +} + +.alert-info { + color: #3a87ad; + background-color: #d9edf7; + border-color: #bce8f1; +} + +.alert-info h4 { + color: #3a87ad; +} + +.alert-block { + padding-top: 14px; + padding-bottom: 14px; +} + +.alert-block > p, +.alert-block > ul { + margin-bottom: 0; +} + +.alert-block p + p { + margin-top: 5px; +} + +.nav { + margin-bottom: 20px; + margin-left: 0; + list-style: none; +} + +.nav > li > a { + display: block; +} + +.nav > li > a:hover, +.nav > li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} + +.nav > li > a > img { + max-width: none; +} + +.nav > .pull-right { + float: right; +} + +.nav-header { + display: block; + padding: 3px 15px; + font-size: 11px; + font-weight: bold; + line-height: 20px; + color: #999999; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + text-transform: uppercase; +} + +.nav li + .nav-header { + margin-top: 9px; +} + +.nav-list { + padding-right: 15px; + padding-left: 15px; + margin-bottom: 0; +} + +.nav-list > li > a, +.nav-list .nav-header { + margin-right: -15px; + margin-left: -15px; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); +} + +.nav-list > li > a { + padding: 3px 15px; +} + +.nav-list > .active > a, +.nav-list > .active > a:hover, +.nav-list > .active > a:focus { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); + background-color: #0088cc; +} + +.nav-list [class^="icon-"], +.nav-list [class*=" icon-"] { + margin-right: 2px; +} + +.nav-list .divider { + *width: 100%; + height: 1px; + margin: 9px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.nav-tabs, +.nav-pills { + *zoom: 1; +} + +.nav-tabs:before, +.nav-pills:before, +.nav-tabs:after, +.nav-pills:after { + display: table; + line-height: 0; + content: ""; +} + +.nav-tabs:after, +.nav-pills:after { + clear: both; +} + +.nav-tabs > li, +.nav-pills > li { + float: left; +} + +.nav-tabs > li > a, +.nav-pills > li > a { + padding-right: 12px; + padding-left: 12px; + margin-right: 2px; + line-height: 14px; +} + +.nav-tabs { + border-bottom: 1px solid #ddd; +} + +.nav-tabs > li { + margin-bottom: -1px; +} + +.nav-tabs > li > a { + padding-top: 8px; + padding-bottom: 8px; + line-height: 20px; + border: 1px solid transparent; + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.nav-tabs > li > a:hover, +.nav-tabs > li > a:focus { + border-color: #eeeeee #eeeeee #dddddd; +} + +.nav-tabs > .active > a, +.nav-tabs > .active > a:hover, +.nav-tabs > .active > a:focus { + color: #555555; + cursor: default; + background-color: #ffffff; + border: 1px solid #ddd; + border-bottom-color: transparent; +} + +.nav-pills > li > a { + padding-top: 8px; + padding-bottom: 8px; + margin-top: 2px; + margin-bottom: 2px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.nav-pills > .active > a, +.nav-pills > .active > a:hover, +.nav-pills > .active > a:focus { + color: #ffffff; + background-color: #0088cc; +} + +.nav-stacked > li { + float: none; +} + +.nav-stacked > li > a { + margin-right: 0; +} + +.nav-tabs.nav-stacked { + border-bottom: 0; +} + +.nav-tabs.nav-stacked > li > a { + border: 1px solid #ddd; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.nav-tabs.nav-stacked > li:first-child > a { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-topleft: 4px; +} + +.nav-tabs.nav-stacked > li:last-child > a { + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -moz-border-radius-bottomright: 4px; + -moz-border-radius-bottomleft: 4px; +} + +.nav-tabs.nav-stacked > li > a:hover, +.nav-tabs.nav-stacked > li > a:focus { + z-index: 2; + border-color: #ddd; +} + +.nav-pills.nav-stacked > li > a { + margin-bottom: 3px; +} + +.nav-pills.nav-stacked > li:last-child > a { + margin-bottom: 1px; +} + +.nav-tabs .dropdown-menu { + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; +} + +.nav-pills .dropdown-menu { + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.nav .dropdown-toggle .caret { + margin-top: 6px; + border-top-color: #0088cc; + border-bottom-color: #0088cc; +} + +.nav .dropdown-toggle:hover .caret, +.nav .dropdown-toggle:focus .caret { + border-top-color: #005580; + border-bottom-color: #005580; +} + +/* move down carets for tabs */ + +.nav-tabs .dropdown-toggle .caret { + margin-top: 8px; +} + +.nav .active .dropdown-toggle .caret { + border-top-color: #fff; + border-bottom-color: #fff; +} + +.nav-tabs .active .dropdown-toggle .caret { + border-top-color: #555555; + border-bottom-color: #555555; +} + +.nav > .dropdown.active > a:hover, +.nav > .dropdown.active > a:focus { + cursor: pointer; +} + +.nav-tabs .open .dropdown-toggle, +.nav-pills .open .dropdown-toggle, +.nav > li.dropdown.open.active > a:hover, +.nav > li.dropdown.open.active > a:focus { + color: #ffffff; + background-color: #999999; + border-color: #999999; +} + +.nav li.dropdown.open .caret, +.nav li.dropdown.open.active .caret, +.nav li.dropdown.open a:hover .caret, +.nav li.dropdown.open a:focus .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; + opacity: 1; + filter: alpha(opacity=100); +} + +.tabs-stacked .open > a:hover, +.tabs-stacked .open > a:focus { + border-color: #999999; +} + +.tabbable { + *zoom: 1; +} + +.tabbable:before, +.tabbable:after { + display: table; + line-height: 0; + content: ""; +} + +.tabbable:after { + clear: both; +} + +.tab-content { + overflow: auto; +} + +.tabs-below > .nav-tabs, +.tabs-right > .nav-tabs, +.tabs-left > .nav-tabs { + border-bottom: 0; +} + +.tab-content > .tab-pane, +.pill-content > .pill-pane { + display: none; +} + +.tab-content > .active, +.pill-content > .active { + display: block; +} + +.tabs-below > .nav-tabs { + border-top: 1px solid #ddd; +} + +.tabs-below > .nav-tabs > li { + margin-top: -1px; + margin-bottom: 0; +} + +.tabs-below > .nav-tabs > li > a { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.tabs-below > .nav-tabs > li > a:hover, +.tabs-below > .nav-tabs > li > a:focus { + border-top-color: #ddd; + border-bottom-color: transparent; +} + +.tabs-below > .nav-tabs > .active > a, +.tabs-below > .nav-tabs > .active > a:hover, +.tabs-below > .nav-tabs > .active > a:focus { + border-color: transparent #ddd #ddd #ddd; +} + +.tabs-left > .nav-tabs > li, +.tabs-right > .nav-tabs > li { + float: none; +} + +.tabs-left > .nav-tabs > li > a, +.tabs-right > .nav-tabs > li > a { + min-width: 74px; + margin-right: 0; + margin-bottom: 3px; +} + +.tabs-left > .nav-tabs { + float: left; + margin-right: 19px; + border-right: 1px solid #ddd; +} + +.tabs-left > .nav-tabs > li > a { + margin-right: -1px; + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.tabs-left > .nav-tabs > li > a:hover, +.tabs-left > .nav-tabs > li > a:focus { + border-color: #eeeeee #dddddd #eeeeee #eeeeee; +} + +.tabs-left > .nav-tabs .active > a, +.tabs-left > .nav-tabs .active > a:hover, +.tabs-left > .nav-tabs .active > a:focus { + border-color: #ddd transparent #ddd #ddd; + *border-right-color: #ffffff; +} + +.tabs-right > .nav-tabs { + float: right; + margin-left: 19px; + border-left: 1px solid #ddd; +} + +.tabs-right > .nav-tabs > li > a { + margin-left: -1px; + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.tabs-right > .nav-tabs > li > a:hover, +.tabs-right > .nav-tabs > li > a:focus { + border-color: #eeeeee #eeeeee #eeeeee #dddddd; +} + +.tabs-right > .nav-tabs .active > a, +.tabs-right > .nav-tabs .active > a:hover, +.tabs-right > .nav-tabs .active > a:focus { + border-color: #ddd #ddd #ddd transparent; + *border-left-color: #ffffff; +} + +.nav > .disabled > a { + color: #999999; +} + +.nav > .disabled > a:hover, +.nav > .disabled > a:focus { + text-decoration: none; + cursor: default; + background-color: transparent; +} + +.navbar { + *position: relative; + *z-index: 2; + margin-bottom: 20px; + overflow: visible; +} + +.navbar-inner { + min-height: 40px; + padding-right: 20px; + padding-left: 20px; + background-color: #fafafa; + background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2)); + background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2); + background-image: -o-linear-gradient(top, #ffffff, #f2f2f2); + background-image: linear-gradient(to bottom, #ffffff, #f2f2f2); + background-repeat: repeat-x; + border: 1px solid #d4d4d4; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0); + *zoom: 1; + -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); + -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); + box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); +} + +.navbar-inner:before, +.navbar-inner:after { + display: table; + line-height: 0; + content: ""; +} + +.navbar-inner:after { + clear: both; +} + +.navbar .container { + width: auto; +} + +.nav-collapse.collapse { + height: auto; + overflow: visible; +} + +.navbar .brand { + display: block; + float: left; + padding: 10px 20px 10px; + margin-left: -20px; + font-size: 20px; + font-weight: 200; + color: #777777; + text-shadow: 0 1px 0 #ffffff; +} + +.navbar .brand:hover, +.navbar .brand:focus { + text-decoration: none; +} + +.navbar-text { + margin-bottom: 0; + line-height: 40px; + color: #777777; +} + +.navbar-link { + color: #777777; +} + +.navbar-link:hover, +.navbar-link:focus { + color: #333333; +} + +.navbar .divider-vertical { + height: 40px; + margin: 0 9px; + border-right: 1px solid #ffffff; + border-left: 1px solid #f2f2f2; +} + +.navbar .btn, +.navbar .btn-group { + margin-top: 5px; +} + +.navbar .btn-group .btn, +.navbar .input-prepend .btn, +.navbar .input-append .btn, +.navbar .input-prepend .btn-group, +.navbar .input-append .btn-group { + margin-top: 0; +} + +.navbar-form { + margin-bottom: 0; + *zoom: 1; +} + +.navbar-form:before, +.navbar-form:after { + display: table; + line-height: 0; + content: ""; +} + +.navbar-form:after { + clear: both; +} + +.navbar-form input, +.navbar-form select, +.navbar-form .radio, +.navbar-form .checkbox { + margin-top: 5px; +} + +.navbar-form input, +.navbar-form select, +.navbar-form .btn { + display: inline-block; + margin-bottom: 0; +} + +.navbar-form input[type="image"], +.navbar-form input[type="checkbox"], +.navbar-form input[type="radio"] { + margin-top: 3px; +} + +.navbar-form .input-append, +.navbar-form .input-prepend { + margin-top: 5px; + white-space: nowrap; +} + +.navbar-form .input-append input, +.navbar-form .input-prepend input { + margin-top: 0; +} + +.navbar-search { + position: relative; + float: left; + margin-top: 5px; + margin-bottom: 0; +} + +.navbar-search .search-query { + padding: 4px 14px; + margin-bottom: 0; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 13px; + font-weight: normal; + line-height: 1; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +.navbar-static-top { + position: static; + margin-bottom: 0; +} + +.navbar-static-top .navbar-inner { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; + margin-bottom: 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-static-top .navbar-inner { + border-width: 0 0 1px; +} + +.navbar-fixed-bottom .navbar-inner { + border-width: 1px 0 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-fixed-bottom .navbar-inner { + padding-right: 0; + padding-left: 0; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.navbar-static-top .container, +.navbar-fixed-top .container, +.navbar-fixed-bottom .container { + width: 940px; +} + +.navbar-fixed-top { + top: 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-static-top .navbar-inner { + -webkit-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); + -moz-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); + box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); +} + +.navbar-fixed-bottom { + bottom: 0; +} + +.navbar-fixed-bottom .navbar-inner { + -webkit-box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1); + -moz-box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1); + box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1); +} + +.navbar .nav { + position: relative; + left: 0; + display: block; + float: left; + margin: 0 10px 0 0; +} + +.navbar .nav.pull-right { + float: right; + margin-right: 0; +} + +.navbar .nav > li { + float: left; +} + +.navbar .nav > li > a { + float: none; + padding: 10px 15px 10px; + color: #777777; + text-decoration: none; + text-shadow: 0 1px 0 #ffffff; +} + +.navbar .nav .dropdown-toggle .caret { + margin-top: 8px; +} + +.navbar .nav > li > a:focus, +.navbar .nav > li > a:hover { + color: #333333; + text-decoration: none; + background-color: transparent; +} + +.navbar .nav > .active > a, +.navbar .nav > .active > a:hover, +.navbar .nav > .active > a:focus { + color: #555555; + text-decoration: none; + background-color: #e5e5e5; + -webkit-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); + -moz-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); +} + +.navbar .btn-navbar { + display: none; + float: right; + padding: 7px 10px; + margin-right: 5px; + margin-left: 5px; + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #ededed; + *background-color: #e5e5e5; + background-image: -moz-linear-gradient(top, #f2f2f2, #e5e5e5); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5)); + background-image: -webkit-linear-gradient(top, #f2f2f2, #e5e5e5); + background-image: -o-linear-gradient(top, #f2f2f2, #e5e5e5); + background-image: linear-gradient(to bottom, #f2f2f2, #e5e5e5); + background-repeat: repeat-x; + border-color: #e5e5e5 #e5e5e5 #bfbfbf; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); +} + +.navbar .btn-navbar:hover, +.navbar .btn-navbar:focus, +.navbar .btn-navbar:active, +.navbar .btn-navbar.active, +.navbar .btn-navbar.disabled, +.navbar .btn-navbar[disabled] { + color: #ffffff; + background-color: #e5e5e5; + *background-color: #d9d9d9; +} + +.navbar .btn-navbar:active, +.navbar .btn-navbar.active { + background-color: #cccccc \9; +} + +.navbar .btn-navbar .icon-bar { + display: block; + width: 18px; + height: 2px; + background-color: #f5f5f5; + -webkit-border-radius: 1px; + -moz-border-radius: 1px; + border-radius: 1px; + -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); +} + +.btn-navbar .icon-bar + .icon-bar { + margin-top: 3px; +} + +.navbar .nav > li > .dropdown-menu:before { + position: absolute; + top: -7px; + left: 9px; + display: inline-block; + border-right: 7px solid transparent; + border-bottom: 7px solid #ccc; + border-left: 7px solid transparent; + border-bottom-color: rgba(0, 0, 0, 0.2); + content: ''; +} + +.navbar .nav > li > .dropdown-menu:after { + position: absolute; + top: -6px; + left: 10px; + display: inline-block; + border-right: 6px solid transparent; + border-bottom: 6px solid #ffffff; + border-left: 6px solid transparent; + content: ''; +} + +.navbar-fixed-bottom .nav > li > .dropdown-menu:before { + top: auto; + bottom: -7px; + border-top: 7px solid #ccc; + border-bottom: 0; + border-top-color: rgba(0, 0, 0, 0.2); +} + +.navbar-fixed-bottom .nav > li > .dropdown-menu:after { + top: auto; + bottom: -6px; + border-top: 6px solid #ffffff; + border-bottom: 0; +} + +.navbar .nav li.dropdown > a:hover .caret, +.navbar .nav li.dropdown > a:focus .caret { + border-top-color: #333333; + border-bottom-color: #333333; +} + +.navbar .nav li.dropdown.open > .dropdown-toggle, +.navbar .nav li.dropdown.active > .dropdown-toggle, +.navbar .nav li.dropdown.open.active > .dropdown-toggle { + color: #555555; + background-color: #e5e5e5; +} + +.navbar .nav li.dropdown > .dropdown-toggle .caret { + border-top-color: #777777; + border-bottom-color: #777777; +} + +.navbar .nav li.dropdown.open > .dropdown-toggle .caret, +.navbar .nav li.dropdown.active > .dropdown-toggle .caret, +.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret { + border-top-color: #555555; + border-bottom-color: #555555; +} + +.navbar .pull-right > li > .dropdown-menu, +.navbar .nav > li > .dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.navbar .pull-right > li > .dropdown-menu:before, +.navbar .nav > li > .dropdown-menu.pull-right:before { + right: 12px; + left: auto; +} + +.navbar .pull-right > li > .dropdown-menu:after, +.navbar .nav > li > .dropdown-menu.pull-right:after { + right: 13px; + left: auto; +} + +.navbar .pull-right > li > .dropdown-menu .dropdown-menu, +.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu { + right: 100%; + left: auto; + margin-right: -1px; + margin-left: 0; + -webkit-border-radius: 6px 0 6px 6px; + -moz-border-radius: 6px 0 6px 6px; + border-radius: 6px 0 6px 6px; +} + +.navbar-inverse .navbar-inner { + background-color: #1b1b1b; + background-image: -moz-linear-gradient(top, #222222, #111111); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111)); + background-image: -webkit-linear-gradient(top, #222222, #111111); + background-image: -o-linear-gradient(top, #222222, #111111); + background-image: linear-gradient(to bottom, #222222, #111111); + background-repeat: repeat-x; + border-color: #252525; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0); +} + +.navbar-inverse .brand, +.navbar-inverse .nav > li > a { + color: #999999; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); +} + +.navbar-inverse .brand:hover, +.navbar-inverse .nav > li > a:hover, +.navbar-inverse .brand:focus, +.navbar-inverse .nav > li > a:focus { + color: #ffffff; +} + +.navbar-inverse .brand { + color: #999999; +} + +.navbar-inverse .navbar-text { + color: #999999; +} + +.navbar-inverse .nav > li > a:focus, +.navbar-inverse .nav > li > a:hover { + color: #ffffff; + background-color: transparent; +} + +.navbar-inverse .nav .active > a, +.navbar-inverse .nav .active > a:hover, +.navbar-inverse .nav .active > a:focus { + color: #ffffff; + background-color: #111111; +} + +.navbar-inverse .navbar-link { + color: #999999; +} + +.navbar-inverse .navbar-link:hover, +.navbar-inverse .navbar-link:focus { + color: #ffffff; +} + +.navbar-inverse .divider-vertical { + border-right-color: #222222; + border-left-color: #111111; +} + +.navbar-inverse .nav li.dropdown.open > .dropdown-toggle, +.navbar-inverse .nav li.dropdown.active > .dropdown-toggle, +.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle { + color: #ffffff; + background-color: #111111; +} + +.navbar-inverse .nav li.dropdown > a:hover .caret, +.navbar-inverse .nav li.dropdown > a:focus .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.navbar-inverse .nav li.dropdown > .dropdown-toggle .caret { + border-top-color: #999999; + border-bottom-color: #999999; +} + +.navbar-inverse .nav li.dropdown.open > .dropdown-toggle .caret, +.navbar-inverse .nav li.dropdown.active > .dropdown-toggle .caret, +.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.navbar-inverse .navbar-search .search-query { + color: #ffffff; + background-color: #515151; + border-color: #111111; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -webkit-transition: none; + -moz-transition: none; + -o-transition: none; + transition: none; +} + +.navbar-inverse .navbar-search .search-query:-moz-placeholder { + color: #cccccc; +} + +.navbar-inverse .navbar-search .search-query:-ms-input-placeholder { + color: #cccccc; +} + +.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder { + color: #cccccc; +} + +.navbar-inverse .navbar-search .search-query:focus, +.navbar-inverse .navbar-search .search-query.focused { + padding: 5px 15px; + color: #333333; + text-shadow: 0 1px 0 #ffffff; + background-color: #ffffff; + border: 0; + outline: 0; + -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); +} + +.navbar-inverse .btn-navbar { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #0e0e0e; + *background-color: #040404; + background-image: -moz-linear-gradient(top, #151515, #040404); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404)); + background-image: -webkit-linear-gradient(top, #151515, #040404); + background-image: -o-linear-gradient(top, #151515, #040404); + background-image: linear-gradient(to bottom, #151515, #040404); + background-repeat: repeat-x; + border-color: #040404 #040404 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0); + filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); +} + +.navbar-inverse .btn-navbar:hover, +.navbar-inverse .btn-navbar:focus, +.navbar-inverse .btn-navbar:active, +.navbar-inverse .btn-navbar.active, +.navbar-inverse .btn-navbar.disabled, +.navbar-inverse .btn-navbar[disabled] { + color: #ffffff; + background-color: #040404; + *background-color: #000000; +} + +.navbar-inverse .btn-navbar:active, +.navbar-inverse .btn-navbar.active { + background-color: #000000 \9; +} + +.breadcrumb { + padding: 8px 15px; + margin: 0 0 20px; + list-style: none; + background-color: #f5f5f5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.breadcrumb > li { + display: inline-block; + *display: inline; + text-shadow: 0 1px 0 #ffffff; + *zoom: 1; +} + +.breadcrumb > li > .divider { + padding: 0 5px; + color: #ccc; +} + +.breadcrumb > .active { + color: #999999; +} + +.pagination { + margin: 20px 0; +} + +.pagination ul { + display: inline-block; + *display: inline; + margin-bottom: 0; + margin-left: 0; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + *zoom: 1; + -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.pagination ul > li { + display: inline; +} + +.pagination ul > li > a, +.pagination ul > li > span { + float: left; + padding: 4px 12px; + line-height: 20px; + text-decoration: none; + background-color: #ffffff; + border: 1px solid #dddddd; + border-left-width: 0; +} + +.pagination ul > li > a:hover, +.pagination ul > li > a:focus, +.pagination ul > .active > a, +.pagination ul > .active > span { + background-color: #f5f5f5; +} + +.pagination ul > .active > a, +.pagination ul > .active > span { + color: #999999; + cursor: default; +} + +.pagination ul > .disabled > span, +.pagination ul > .disabled > a, +.pagination ul > .disabled > a:hover, +.pagination ul > .disabled > a:focus { + color: #999999; + cursor: default; + background-color: transparent; +} + +.pagination ul > li:first-child > a, +.pagination ul > li:first-child > span { + border-left-width: 1px; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-topleft: 4px; +} + +.pagination ul > li:last-child > a, +.pagination ul > li:last-child > span { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-bottomright: 4px; +} + +.pagination-centered { + text-align: center; +} + +.pagination-right { + text-align: right; +} + +.pagination-large ul > li > a, +.pagination-large ul > li > span { + padding: 11px 19px; + font-size: 17.5px; +} + +.pagination-large ul > li:first-child > a, +.pagination-large ul > li:first-child > span { + -webkit-border-bottom-left-radius: 6px; + border-bottom-left-radius: 6px; + -webkit-border-top-left-radius: 6px; + border-top-left-radius: 6px; + -moz-border-radius-bottomleft: 6px; + -moz-border-radius-topleft: 6px; +} + +.pagination-large ul > li:last-child > a, +.pagination-large ul > li:last-child > span { + -webkit-border-top-right-radius: 6px; + border-top-right-radius: 6px; + -webkit-border-bottom-right-radius: 6px; + border-bottom-right-radius: 6px; + -moz-border-radius-topright: 6px; + -moz-border-radius-bottomright: 6px; +} + +.pagination-mini ul > li:first-child > a, +.pagination-small ul > li:first-child > a, +.pagination-mini ul > li:first-child > span, +.pagination-small ul > li:first-child > span { + -webkit-border-bottom-left-radius: 3px; + border-bottom-left-radius: 3px; + -webkit-border-top-left-radius: 3px; + border-top-left-radius: 3px; + -moz-border-radius-bottomleft: 3px; + -moz-border-radius-topleft: 3px; +} + +.pagination-mini ul > li:last-child > a, +.pagination-small ul > li:last-child > a, +.pagination-mini ul > li:last-child > span, +.pagination-small ul > li:last-child > span { + -webkit-border-top-right-radius: 3px; + border-top-right-radius: 3px; + -webkit-border-bottom-right-radius: 3px; + border-bottom-right-radius: 3px; + -moz-border-radius-topright: 3px; + -moz-border-radius-bottomright: 3px; +} + +.pagination-small ul > li > a, +.pagination-small ul > li > span { + padding: 2px 10px; + font-size: 11.9px; +} + +.pagination-mini ul > li > a, +.pagination-mini ul > li > span { + padding: 0 6px; + font-size: 10.5px; +} + +.pager { + margin: 20px 0; + text-align: center; + list-style: none; + *zoom: 1; +} + +.pager:before, +.pager:after { + display: table; + line-height: 0; + content: ""; +} + +.pager:after { + clear: both; +} + +.pager li { + display: inline; +} + +.pager li > a, +.pager li > span { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +.pager li > a:hover, +.pager li > a:focus { + text-decoration: none; + background-color: #f5f5f5; +} + +.pager .next > a, +.pager .next > span { + float: right; +} + +.pager .previous > a, +.pager .previous > span { + float: left; +} + +.pager .disabled > a, +.pager .disabled > a:hover, +.pager .disabled > a:focus, +.pager .disabled > span { + color: #999999; + cursor: default; + background-color: #fff; +} + +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000000; +} + +.modal-backdrop.fade { + opacity: 0; +} + +.modal-backdrop, +.modal-backdrop.fade.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.modal { + position: fixed; + top: 10%; + left: 50%; + z-index: 1050; + width: 560px; + margin-left: -280px; + background-color: #ffffff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.3); + *border: 1px solid #999; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + outline: none; + -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -webkit-background-clip: padding-box; + -moz-background-clip: padding-box; + background-clip: padding-box; +} + +.modal.fade { + top: -25%; + -webkit-transition: opacity 0.3s linear, top 0.3s ease-out; + -moz-transition: opacity 0.3s linear, top 0.3s ease-out; + -o-transition: opacity 0.3s linear, top 0.3s ease-out; + transition: opacity 0.3s linear, top 0.3s ease-out; +} + +.modal.fade.in { + top: 10%; +} + +.modal-header { + padding: 9px 15px; + border-bottom: 1px solid #eee; +} + +.modal-header .close { + margin-top: 2px; +} + +.modal-header h3 { + margin: 0; + line-height: 30px; +} + +.modal-body { + position: relative; + max-height: 400px; + padding: 15px; + overflow-y: auto; +} + +.modal-form { + margin-bottom: 0; +} + +.modal-footer { + padding: 14px 15px 15px; + margin-bottom: 0; + text-align: right; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 #ffffff; + -moz-box-shadow: inset 0 1px 0 #ffffff; + box-shadow: inset 0 1px 0 #ffffff; +} + +.modal-footer:before, +.modal-footer:after { + display: table; + line-height: 0; + content: ""; +} + +.modal-footer:after { + clear: both; +} + +.modal-footer .btn + .btn { + margin-bottom: 0; + margin-left: 5px; +} + +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} + +.modal-footer .btn-block + .btn-block { + margin-left: 0; +} + +.tooltip { + position: absolute; + z-index: 1030; + display: block; + font-size: 11px; + line-height: 1.4; + opacity: 0; + filter: alpha(opacity=0); + visibility: visible; +} + +.tooltip.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.tooltip.top { + padding: 5px 0; + margin-top: -3px; +} + +.tooltip.right { + padding: 0 5px; + margin-left: 3px; +} + +.tooltip.bottom { + padding: 5px 0; + margin-top: 3px; +} + +.tooltip.left { + padding: 0 5px; + margin-left: -3px; +} + +.tooltip-inner { + max-width: 200px; + padding: 8px; + color: #ffffff; + text-align: center; + text-decoration: none; + background-color: #000000; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} + +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top-color: #000000; + border-width: 5px 5px 0; +} + +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-right-color: #000000; + border-width: 5px 5px 5px 0; +} + +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-left-color: #000000; + border-width: 5px 0 5px 5px; +} + +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-bottom-color: #000000; + border-width: 0 5px 5px; +} + +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1010; + display: none; + max-width: 276px; + padding: 1px; + text-align: left; + white-space: normal; + background-color: #ffffff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -webkit-background-clip: padding-box; + -moz-background-clip: padding; + background-clip: padding-box; +} + +.popover.top { + margin-top: -10px; +} + +.popover.right { + margin-left: 10px; +} + +.popover.bottom { + margin-top: 10px; +} + +.popover.left { + margin-left: -10px; +} + +.popover-title { + padding: 8px 14px; + margin: 0; + font-size: 14px; + font-weight: normal; + line-height: 18px; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + -webkit-border-radius: 5px 5px 0 0; + -moz-border-radius: 5px 5px 0 0; + border-radius: 5px 5px 0 0; +} + +.popover-title:empty { + display: none; +} + +.popover-content { + padding: 9px 14px; +} + +.popover .arrow, +.popover .arrow:after { + position: absolute; + display: block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} + +.popover .arrow { + border-width: 11px; +} + +.popover .arrow:after { + border-width: 10px; + content: ""; +} + +.popover.top .arrow { + bottom: -11px; + left: 50%; + margin-left: -11px; + border-top-color: #999; + border-top-color: rgba(0, 0, 0, 0.25); + border-bottom-width: 0; +} + +.popover.top .arrow:after { + bottom: 1px; + margin-left: -10px; + border-top-color: #ffffff; + border-bottom-width: 0; +} + +.popover.right .arrow { + top: 50%; + left: -11px; + margin-top: -11px; + border-right-color: #999; + border-right-color: rgba(0, 0, 0, 0.25); + border-left-width: 0; +} + +.popover.right .arrow:after { + bottom: -10px; + left: 1px; + border-right-color: #ffffff; + border-left-width: 0; +} + +.popover.bottom .arrow { + top: -11px; + left: 50%; + margin-left: -11px; + border-bottom-color: #999; + border-bottom-color: rgba(0, 0, 0, 0.25); + border-top-width: 0; +} + +.popover.bottom .arrow:after { + top: 1px; + margin-left: -10px; + border-bottom-color: #ffffff; + border-top-width: 0; +} + +.popover.left .arrow { + top: 50%; + right: -11px; + margin-top: -11px; + border-left-color: #999; + border-left-color: rgba(0, 0, 0, 0.25); + border-right-width: 0; +} + +.popover.left .arrow:after { + right: 1px; + bottom: -10px; + border-left-color: #ffffff; + border-right-width: 0; +} + +.thumbnails { + margin-left: -20px; + list-style: none; + *zoom: 1; +} + +.thumbnails:before, +.thumbnails:after { + display: table; + line-height: 0; + content: ""; +} + +.thumbnails:after { + clear: both; +} + +.row-fluid .thumbnails { + margin-left: 0; +} + +.thumbnails > li { + float: left; + margin-bottom: 20px; + margin-left: 20px; +} + +.thumbnail { + display: block; + padding: 4px; + line-height: 20px; + border: 1px solid #ddd; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); + -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055); + -webkit-transition: all 0.2s ease-in-out; + -moz-transition: all 0.2s ease-in-out; + -o-transition: all 0.2s ease-in-out; + transition: all 0.2s ease-in-out; +} + +a.thumbnail:hover, +a.thumbnail:focus { + border-color: #0088cc; + -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); +} + +.thumbnail > img { + display: block; + max-width: 100%; + margin-right: auto; + margin-left: auto; +} + +.thumbnail .caption { + padding: 9px; + color: #555555; +} + +.media, +.media-body { + overflow: hidden; + *overflow: visible; + zoom: 1; +} + +.media, +.media .media { + margin-top: 15px; +} + +.media:first-child { + margin-top: 0; +} + +.media-object { + display: block; +} + +.media-heading { + margin: 0 0 5px; +} + +.media > .pull-left { + margin-right: 10px; +} + +.media > .pull-right { + margin-left: 10px; +} + +.media-list { + margin-left: 0; + list-style: none; +} + +.label, +.badge { + display: inline-block; + padding: 2px 4px; + font-size: 11.844px; + font-weight: bold; + line-height: 14px; + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + white-space: nowrap; + vertical-align: baseline; + background-color: #999999; +} + +.label { + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.badge { + padding-right: 9px; + padding-left: 9px; + -webkit-border-radius: 9px; + -moz-border-radius: 9px; + border-radius: 9px; +} + +.label:empty, +.badge:empty { + display: none; +} + +a.label:hover, +a.label:focus, +a.badge:hover, +a.badge:focus { + color: #ffffff; + text-decoration: none; + cursor: pointer; +} + +.label-important, +.badge-important { + background-color: #b94a48; +} + +.label-important[href], +.badge-important[href] { + background-color: #953b39; +} + +.label-warning, +.badge-warning { + background-color: #f89406; +} + +.label-warning[href], +.badge-warning[href] { + background-color: #c67605; +} + +.label-success, +.badge-success { + background-color: #468847; +} + +.label-success[href], +.badge-success[href] { + background-color: #356635; +} + +.label-info, +.badge-info { + background-color: #3a87ad; +} + +.label-info[href], +.badge-info[href] { + background-color: #2d6987; +} + +.label-inverse, +.badge-inverse { + background-color: #333333; +} + +.label-inverse[href], +.badge-inverse[href] { + background-color: #1a1a1a; +} + +.btn .label, +.btn .badge { + position: relative; + top: -1px; +} + +.btn-mini .label, +.btn-mini .badge { + top: 0; +} + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-moz-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-ms-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-o-keyframes progress-bar-stripes { + from { + background-position: 0 0; + } + to { + background-position: 40px 0; + } +} + +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +.progress { + height: 20px; + margin-bottom: 20px; + overflow: hidden; + background-color: #f7f7f7; + background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9)); + background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: linear-gradient(to bottom, #f5f5f5, #f9f9f9); + background-repeat: repeat-x; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0); + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} + +.progress .bar { + float: left; + width: 0; + height: 100%; + font-size: 12px; + color: #ffffff; + text-align: center; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #0e90d2; + background-image: -moz-linear-gradient(top, #149bdf, #0480be); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be)); + background-image: -webkit-linear-gradient(top, #149bdf, #0480be); + background-image: -o-linear-gradient(top, #149bdf, #0480be); + background-image: linear-gradient(to bottom, #149bdf, #0480be); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0); + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + -webkit-transition: width 0.6s ease; + -moz-transition: width 0.6s ease; + -o-transition: width 0.6s ease; + transition: width 0.6s ease; +} + +.progress .bar + .bar { + -webkit-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -moz-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15); +} + +.progress-striped .bar { + background-color: #149bdf; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + -webkit-background-size: 40px 40px; + -moz-background-size: 40px 40px; + -o-background-size: 40px 40px; + background-size: 40px 40px; +} + +.progress.active .bar { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -moz-animation: progress-bar-stripes 2s linear infinite; + -ms-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} + +.progress-danger .bar, +.progress .bar-danger { + background-color: #dd514c; + background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -o-linear-gradient(top, #ee5f5b, #c43c35); + background-image: linear-gradient(to bottom, #ee5f5b, #c43c35); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0); +} + +.progress-danger.progress-striped .bar, +.progress-striped .bar-danger { + background-color: #ee5f5b; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-success .bar, +.progress .bar-success { + background-color: #5eb95e; + background-image: -moz-linear-gradient(top, #62c462, #57a957); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957)); + background-image: -webkit-linear-gradient(top, #62c462, #57a957); + background-image: -o-linear-gradient(top, #62c462, #57a957); + background-image: linear-gradient(to bottom, #62c462, #57a957); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0); +} + +.progress-success.progress-striped .bar, +.progress-striped .bar-success { + background-color: #62c462; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-info .bar, +.progress .bar-info { + background-color: #4bb1cf; + background-image: -moz-linear-gradient(top, #5bc0de, #339bb9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9)); + background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9); + background-image: -o-linear-gradient(top, #5bc0de, #339bb9); + background-image: linear-gradient(to bottom, #5bc0de, #339bb9); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0); +} + +.progress-info.progress-striped .bar, +.progress-striped .bar-info { + background-color: #5bc0de; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-warning .bar, +.progress .bar-warning { + background-color: #faa732; + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(to bottom, #fbb450, #f89406); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0); +} + +.progress-warning.progress-striped .bar, +.progress-striped .bar-warning { + background-color: #fbb450; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.accordion { + margin-bottom: 20px; +} + +.accordion-group { + margin-bottom: 2px; + border: 1px solid #e5e5e5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.accordion-heading { + border-bottom: 0; +} + +.accordion-heading .accordion-toggle { + display: block; + padding: 8px 15px; +} + +.accordion-toggle { + cursor: pointer; +} + +.accordion-inner { + padding: 9px 15px; + border-top: 1px solid #e5e5e5; +} + +.carousel { + position: relative; + margin-bottom: 20px; + line-height: 1; +} + +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} + +.carousel-inner > .item { + position: relative; + display: none; + -webkit-transition: 0.6s ease-in-out left; + -moz-transition: 0.6s ease-in-out left; + -o-transition: 0.6s ease-in-out left; + transition: 0.6s ease-in-out left; +} + +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + display: block; + line-height: 1; +} + +.carousel-inner > .active, +.carousel-inner > .next, +.carousel-inner > .prev { + display: block; +} + +.carousel-inner > .active { + left: 0; +} + +.carousel-inner > .next, +.carousel-inner > .prev { + position: absolute; + top: 0; + width: 100%; +} + +.carousel-inner > .next { + left: 100%; +} + +.carousel-inner > .prev { + left: -100%; +} + +.carousel-inner > .next.left, +.carousel-inner > .prev.right { + left: 0; +} + +.carousel-inner > .active.left { + left: -100%; +} + +.carousel-inner > .active.right { + left: 100%; +} + +.carousel-control { + position: absolute; + top: 40%; + left: 15px; + width: 40px; + height: 40px; + margin-top: -20px; + font-size: 60px; + font-weight: 100; + line-height: 30px; + color: #ffffff; + text-align: center; + background: #222222; + border: 3px solid #ffffff; + -webkit-border-radius: 23px; + -moz-border-radius: 23px; + border-radius: 23px; + opacity: 0.5; + filter: alpha(opacity=50); +} + +.carousel-control.right { + right: 15px; + left: auto; +} + +.carousel-control:hover, +.carousel-control:focus { + color: #ffffff; + text-decoration: none; + opacity: 0.9; + filter: alpha(opacity=90); +} + +.carousel-indicators { + position: absolute; + top: 15px; + right: 15px; + z-index: 5; + margin: 0; + list-style: none; +} + +.carousel-indicators li { + display: block; + float: left; + width: 10px; + height: 10px; + margin-left: 5px; + text-indent: -999px; + background-color: #ccc; + background-color: rgba(255, 255, 255, 0.25); + border-radius: 5px; +} + +.carousel-indicators .active { + background-color: #fff; +} + +.carousel-caption { + position: absolute; + right: 0; + bottom: 0; + left: 0; + padding: 15px; + background: #333333; + background: rgba(0, 0, 0, 0.75); +} + +.carousel-caption h4, +.carousel-caption p { + line-height: 20px; + color: #ffffff; +} + +.carousel-caption h4 { + margin: 0 0 5px; +} + +.carousel-caption p { + margin-bottom: 0; +} + +.hero-unit { + padding: 60px; + margin-bottom: 30px; + font-size: 18px; + font-weight: 200; + line-height: 30px; + color: inherit; + background-color: #eeeeee; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.hero-unit h1 { + margin-bottom: 0; + font-size: 60px; + line-height: 1; + letter-spacing: -1px; + color: inherit; +} + +.hero-unit li { + line-height: 30px; +} + +.pull-right { + float: right; +} + +.pull-left { + float: left; +} + +.hide { + display: none; +} + +.show { + display: block; +} + +.invisible { + visibility: hidden; +} + +.affix { + position: fixed; +} + +/*! + * Bootstrap Responsive v2.3.2 + * + * Copyright 2013 Twitter, Inc + * Licensed under the Apache License v2.0 + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Designed and built with all the love in the world by @mdo and @fat. + */ + +.clearfix { + *zoom: 1; +} + +.clearfix:before, +.clearfix:after { + display: table; + line-height: 0; + content: ""; +} + +.clearfix:after { + clear: both; +} + +.hide-text { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} + +.input-block-level { + display: block; + width: 100%; + min-height: 30px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +@-ms-viewport { + width: device-width; +} + +.hidden { + display: none; + visibility: hidden; +} + +.visible-phone { + display: none !important; +} + +.visible-tablet { + display: none !important; +} + +.hidden-desktop { + display: none !important; +} + +.visible-desktop { + display: inherit !important; +} + +@media (min-width: 768px) and (max-width: 979px) { + .hidden-desktop { + display: inherit !important; + } + .visible-desktop { + display: none !important ; + } + .visible-tablet { + display: inherit !important; + } + .hidden-tablet { + display: none !important; + } +} + +@media (max-width: 767px) { + .hidden-desktop { + display: inherit !important; + } + .visible-desktop { + display: none !important; + } + .visible-phone { + display: inherit !important; + } + .hidden-phone { + display: none !important; + } +} + +.visible-print { + display: none !important; +} + +@media print { + .visible-print { + display: inherit !important; + } + .hidden-print { + display: none !important; + } +} + +@media (min-width: 1200px) { + .row { + margin-left: -30px; + *zoom: 1; + } + .row:before, + .row:after { + display: table; + line-height: 0; + content: ""; + } + .row:after { + clear: both; + } + [class*="span"] { + float: left; + min-height: 1px; + margin-left: 30px; + } + .container, + .navbar-static-top .container, + .navbar-fixed-top .container, + .navbar-fixed-bottom .container { + width: 1170px; + } + .span12 { + width: 1170px; + } + .span11 { + width: 1070px; + } + .span10 { + width: 970px; + } + .span9 { + width: 870px; + } + .span8 { + width: 770px; + } + .span7 { + width: 670px; + } + .span6 { + width: 570px; + } + .span5 { + width: 470px; + } + .span4 { + width: 370px; + } + .span3 { + width: 270px; + } + .span2 { + width: 170px; + } + .span1 { + width: 70px; + } + .offset12 { + margin-left: 1230px; + } + .offset11 { + margin-left: 1130px; + } + .offset10 { + margin-left: 1030px; + } + .offset9 { + margin-left: 930px; + } + .offset8 { + margin-left: 830px; + } + .offset7 { + margin-left: 730px; + } + .offset6 { + margin-left: 630px; + } + .offset5 { + margin-left: 530px; + } + .offset4 { + margin-left: 430px; + } + .offset3 { + margin-left: 330px; + } + .offset2 { + margin-left: 230px; + } + .offset1 { + margin-left: 130px; + } + .row-fluid { + width: 100%; + *zoom: 1; + } + .row-fluid:before, + .row-fluid:after { + display: table; + line-height: 0; + content: ""; + } + .row-fluid:after { + clear: both; + } + .row-fluid [class*="span"] { + display: block; + float: left; + width: 100%; + min-height: 30px; + margin-left: 2.564102564102564%; + *margin-left: 2.5109110747408616%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .row-fluid [class*="span"]:first-child { + margin-left: 0; + } + .row-fluid .controls-row [class*="span"] + [class*="span"] { + margin-left: 2.564102564102564%; + } + .row-fluid .span12 { + width: 100%; + *width: 99.94680851063829%; + } + .row-fluid .span11 { + width: 91.45299145299145%; + *width: 91.39979996362975%; + } + .row-fluid .span10 { + width: 82.90598290598291%; + *width: 82.8527914166212%; + } + .row-fluid .span9 { + width: 74.35897435897436%; + *width: 74.30578286961266%; + } + .row-fluid .span8 { + width: 65.81196581196582%; + *width: 65.75877432260411%; + } + .row-fluid .span7 { + width: 57.26495726495726%; + *width: 57.21176577559556%; + } + .row-fluid .span6 { + width: 48.717948717948715%; + *width: 48.664757228587014%; + } + .row-fluid .span5 { + width: 40.17094017094017%; + *width: 40.11774868157847%; + } + .row-fluid .span4 { + width: 31.623931623931625%; + *width: 31.570740134569924%; + } + .row-fluid .span3 { + width: 23.076923076923077%; + *width: 23.023731587561375%; + } + .row-fluid .span2 { + width: 14.52991452991453%; + *width: 14.476723040552828%; + } + .row-fluid .span1 { + width: 5.982905982905983%; + *width: 5.929714493544281%; + } + .row-fluid .offset12 { + margin-left: 105.12820512820512%; + *margin-left: 105.02182214948171%; + } + .row-fluid 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25.53464266230224%; + } + .row-fluid .offset2 { + margin-left: 19.65811965811966%; + *margin-left: 19.551736679396257%; + } + .row-fluid .offset2:first-child { + margin-left: 17.094017094017094%; + *margin-left: 16.98763411529369%; + } + .row-fluid .offset1 { + margin-left: 11.11111111111111%; + *margin-left: 11.004728132387708%; + } + .row-fluid .offset1:first-child { + margin-left: 8.547008547008547%; + *margin-left: 8.440625568285142%; + } + input, + textarea, + .uneditable-input { + margin-left: 0; + } + .controls-row [class*="span"] + [class*="span"] { + margin-left: 30px; + } + input.span12, + textarea.span12, + .uneditable-input.span12 { + width: 1156px; + } + input.span11, + textarea.span11, + .uneditable-input.span11 { + width: 1056px; + } + input.span10, + textarea.span10, + .uneditable-input.span10 { + width: 956px; + } + input.span9, + textarea.span9, + .uneditable-input.span9 { + width: 856px; + } + input.span8, + textarea.span8, + .uneditable-input.span8 { + width: 756px; + } + input.span7, + textarea.span7, + .uneditable-input.span7 { + width: 656px; + } + input.span6, + textarea.span6, + .uneditable-input.span6 { + width: 556px; + } + input.span5, + textarea.span5, + .uneditable-input.span5 { + width: 456px; + } + input.span4, + textarea.span4, + .uneditable-input.span4 { + width: 356px; + } + input.span3, + textarea.span3, + .uneditable-input.span3 { + width: 256px; + } + input.span2, + textarea.span2, + .uneditable-input.span2 { + width: 156px; + } + input.span1, + textarea.span1, + .uneditable-input.span1 { + width: 56px; + } + .thumbnails { + margin-left: -30px; + } + .thumbnails > li { + margin-left: 30px; + } + .row-fluid .thumbnails { + margin-left: 0; + } +} + +@media (min-width: 768px) and (max-width: 979px) { + .row { + margin-left: -20px; + *zoom: 1; + } + .row:before, + .row:after { + display: table; + line-height: 0; + content: ""; + } + .row:after { + clear: both; + } + [class*="span"] { + float: left; + min-height: 1px; + margin-left: 20px; + } + .container, + .navbar-static-top .container, + .navbar-fixed-top .container, + .navbar-fixed-bottom .container { + width: 724px; + } + .span12 { + width: 724px; + } + .span11 { + width: 662px; + } + .span10 { + width: 600px; + } + .span9 { + width: 538px; + } + .span8 { + width: 476px; + } + .span7 { + width: 414px; + } + .span6 { + width: 352px; + } + .span5 { + width: 290px; + } + .span4 { + width: 228px; + } + .span3 { + width: 166px; + } + .span2 { + width: 104px; + } + .span1 { + width: 42px; + } + .offset12 { + margin-left: 764px; + } + .offset11 { + margin-left: 702px; + } + .offset10 { + margin-left: 640px; + } + .offset9 { + margin-left: 578px; + } + .offset8 { + margin-left: 516px; + } + .offset7 { + margin-left: 454px; + } + .offset6 { + margin-left: 392px; + } + .offset5 { + margin-left: 330px; + } + .offset4 { + margin-left: 268px; + } + .offset3 { + margin-left: 206px; + } + .offset2 { + margin-left: 144px; + } + .offset1 { + margin-left: 82px; + } + .row-fluid { + width: 100%; + *zoom: 1; + } + .row-fluid:before, + .row-fluid:after { + display: table; + line-height: 0; + content: ""; + } + .row-fluid:after { + clear: both; + } + .row-fluid [class*="span"] { + display: block; + float: left; + width: 100%; + min-height: 30px; + margin-left: 2.7624309392265194%; + *margin-left: 2.709239449864817%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .row-fluid [class*="span"]:first-child { + margin-left: 0; + } + .row-fluid .controls-row [class*="span"] + [class*="span"] { + margin-left: 2.7624309392265194%; + } + .row-fluid .span12 { + width: 100%; + *width: 99.94680851063829%; + } + .row-fluid .span11 { + width: 91.43646408839778%; + *width: 91.38327259903608%; + } + .row-fluid .span10 { + width: 82.87292817679558%; + *width: 82.81973668743387%; + } + .row-fluid .span9 { + width: 74.30939226519337%; + *width: 74.25620077583166%; + } + .row-fluid .span8 { + width: 65.74585635359117%; + 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11.32596685082873%; + *margin-left: 11.219583872105325%; + } + .row-fluid .offset1:first-child { + margin-left: 8.56353591160221%; + *margin-left: 8.457152932878806%; + } + input, + textarea, + .uneditable-input { + margin-left: 0; + } + .controls-row [class*="span"] + [class*="span"] { + margin-left: 20px; + } + input.span12, + textarea.span12, + .uneditable-input.span12 { + width: 710px; + } + input.span11, + textarea.span11, + .uneditable-input.span11 { + width: 648px; + } + input.span10, + textarea.span10, + .uneditable-input.span10 { + width: 586px; + } + input.span9, + textarea.span9, + .uneditable-input.span9 { + width: 524px; + } + input.span8, + textarea.span8, + .uneditable-input.span8 { + width: 462px; + } + input.span7, + textarea.span7, + .uneditable-input.span7 { + width: 400px; + } + input.span6, + textarea.span6, + .uneditable-input.span6 { + width: 338px; + } + input.span5, + textarea.span5, + .uneditable-input.span5 { + width: 276px; + } + input.span4, + textarea.span4, + .uneditable-input.span4 { + width: 214px; + } + input.span3, + textarea.span3, + .uneditable-input.span3 { + width: 152px; + } + input.span2, + textarea.span2, + .uneditable-input.span2 { + width: 90px; + } + input.span1, + textarea.span1, + .uneditable-input.span1 { + width: 28px; + } +} + +@media (max-width: 767px) { + body { + padding-right: 20px; + padding-left: 20px; + } + .navbar-fixed-top, + .navbar-fixed-bottom, + .navbar-static-top { + margin-right: -20px; + margin-left: -20px; + } + .container-fluid { + padding: 0; + } + .dl-horizontal dt { + float: none; + width: auto; + clear: none; + text-align: left; + } + .dl-horizontal dd { + margin-left: 0; + } + .container { + width: auto; + } + .row-fluid { + width: 100%; + } + .row, + .thumbnails { + margin-left: 0; + } + .thumbnails > li { + float: none; + margin-left: 0; + } + [class*="span"], + .uneditable-input[class*="span"], + .row-fluid [class*="span"] { + display: block; + float: none; + width: 100%; + margin-left: 0; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .span12, + .row-fluid .span12 { + width: 100%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .row-fluid [class*="offset"]:first-child { + margin-left: 0; + } + .input-large, + .input-xlarge, + .input-xxlarge, + input[class*="span"], + select[class*="span"], + textarea[class*="span"], + .uneditable-input { + display: block; + width: 100%; + min-height: 30px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + } + .input-prepend input, + .input-append input, + .input-prepend input[class*="span"], + .input-append input[class*="span"] { + display: inline-block; + width: auto; + } + .controls-row [class*="span"] + [class*="span"] { + margin-left: 0; + } + .modal { + position: fixed; + top: 20px; + right: 20px; + left: 20px; + width: auto; + margin: 0; + } + .modal.fade { + top: -100px; + } + .modal.fade.in { + top: 20px; + } +} + +@media (max-width: 480px) { + .nav-collapse { + -webkit-transform: translate3d(0, 0, 0); + } + .page-header h1 small { + display: block; + line-height: 20px; + } + input[type="checkbox"], + input[type="radio"] { + border: 1px solid #ccc; + } + .form-horizontal .control-label { + float: none; + width: auto; + padding-top: 0; + text-align: left; + } + .form-horizontal .controls { + margin-left: 0; + } + .form-horizontal .control-list { + padding-top: 0; + } + .form-horizontal .form-actions { + padding-right: 10px; + padding-left: 10px; + } + .media .pull-left, + .media .pull-right { + display: block; + float: none; + margin-bottom: 10px; + } + .media-object { + margin-right: 0; + margin-left: 0; + } + .modal { + top: 10px; + right: 10px; + left: 10px; + } + .modal-header .close { + padding: 10px; + margin: -10px; + } + .carousel-caption { + position: static; + } +} + +@media (max-width: 979px) { + body { + padding-top: 0; + } + .navbar-fixed-top, + .navbar-fixed-bottom { + position: static; + } + .navbar-fixed-top { + margin-bottom: 20px; + } + .navbar-fixed-bottom { + margin-top: 20px; + } + .navbar-fixed-top .navbar-inner, + .navbar-fixed-bottom .navbar-inner { + padding: 5px; + } + .navbar .container { + width: auto; + padding: 0; + } + .navbar .brand { + padding-right: 10px; + padding-left: 10px; + margin: 0 0 0 -5px; + } + .nav-collapse { + clear: both; + } + .nav-collapse .nav { + float: none; + margin: 0 0 10px; + } + .nav-collapse .nav > li { + float: none; + } + .nav-collapse .nav > li > a { + margin-bottom: 2px; + } + .nav-collapse .nav > .divider-vertical { + display: none; + } + .nav-collapse .nav .nav-header { + color: #777777; + text-shadow: none; + } + .nav-collapse .nav > li > a, + .nav-collapse .dropdown-menu a { + padding: 9px 15px; + font-weight: bold; + color: #777777; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + } + .nav-collapse .btn { + padding: 4px 10px 4px; + font-weight: normal; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + } + .nav-collapse .dropdown-menu li + li a { + margin-bottom: 2px; + } + .nav-collapse .nav > li > a:hover, + .nav-collapse .nav > li > a:focus, + .nav-collapse .dropdown-menu a:hover, + .nav-collapse .dropdown-menu a:focus { + background-color: #f2f2f2; + } + .navbar-inverse .nav-collapse .nav > li > a, + .navbar-inverse .nav-collapse .dropdown-menu a { + color: #999999; + } + .navbar-inverse .nav-collapse .nav > li > a:hover, + .navbar-inverse .nav-collapse .nav > li > a:focus, + .navbar-inverse .nav-collapse .dropdown-menu a:hover, + .navbar-inverse .nav-collapse .dropdown-menu a:focus { + background-color: #111111; + } + .nav-collapse.in .btn-group { + padding: 0; + margin-top: 5px; + } + .nav-collapse .dropdown-menu { + position: static; + top: auto; + left: auto; + display: none; + float: none; + max-width: none; + padding: 0; + margin: 0 15px; + background-color: transparent; + border: none; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; + } + .nav-collapse .open > .dropdown-menu { + display: block; + } + .nav-collapse .dropdown-menu:before, + .nav-collapse .dropdown-menu:after { + display: none; + } + .nav-collapse .dropdown-menu .divider { + display: none; + } + .nav-collapse .nav > li > .dropdown-menu:before, + .nav-collapse .nav > li > .dropdown-menu:after { + display: none; + } + .nav-collapse .navbar-form, + .nav-collapse .navbar-search { + float: none; + padding: 10px 15px; + margin: 10px 0; + border-top: 1px solid #f2f2f2; + border-bottom: 1px solid #f2f2f2; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + } + .navbar-inverse .nav-collapse .navbar-form, + .navbar-inverse .nav-collapse .navbar-search { + border-top-color: #111111; + border-bottom-color: #111111; + } + .navbar .nav-collapse .nav.pull-right { + float: none; + margin-left: 0; + } + .nav-collapse, + .nav-collapse.collapse { + height: 0; + overflow: hidden; + } + .navbar .btn-navbar { + display: block; + } + .navbar-static .navbar-inner { + padding-right: 10px; + padding-left: 10px; + } +} + +@media (min-width: 980px) { + .nav-collapse.collapse { + height: auto !important; + overflow: visible !important; + } +} + +/* +:Author: David Goodger (goodger@python.org) +:Id: $Id: html4css1.css 7514 2012-09-14 14:27:12Z milde $ +:Copyright: This stylesheet has been placed in the public domain. + +Default cascading style sheet for the HTML output of Docutils. + +See http://docutils.sf.net/docs/howto/html-stylesheets.html for how to +customize this style sheet. +*/ + +/* used to remove borders from tables and images */ +.borderless, table.borderless td, table.borderless th { + border: 0 } + +table.borderless td, table.borderless th { + /* Override padding for "table.docutils td" with "! important". + The right padding separates the table cells. */ + padding: 0 0.5em 0 0 ! important } + +.first { + /* Override more specific margin styles with "! important". */ + margin-top: 0 ! important } + +.last, .with-subtitle { + margin-bottom: 0 ! important } + +.hidden { + display: none } + +a.toc-backref { + text-decoration: none ; + color: black } + +blockquote.epigraph { + margin: 2em 5em ; } + +dl.docutils dd { + margin-bottom: 0.5em } + +object[type="image/svg+xml"], object[type="application/x-shockwave-flash"] { + overflow: hidden; +} + +/* Uncomment (and remove this text!) to get bold-faced definition list terms +dl.docutils dt { + font-weight: bold } +*/ + +div.abstract { + margin: 2em 5em } + +div.abstract p.topic-title { + font-weight: bold ; + text-align: center } + +div.admonition, div.attention, div.caution, div.danger, div.error, +div.hint, div.important, div.note, div.tip, div.warning { + margin: 2em ; + border: medium outset ; + padding: 1em } + +div.admonition p.admonition-title, div.hint p.admonition-title, +div.important p.admonition-title, div.note p.admonition-title, +div.tip p.admonition-title { + font-weight: bold ; + font-family: sans-serif } + +div.attention p.admonition-title, div.caution p.admonition-title, +div.danger p.admonition-title, div.error p.admonition-title, +div.warning p.admonition-title, .code .error { + color: red ; + font-weight: bold ; + font-family: sans-serif } + +/* Uncomment (and remove this text!) to get reduced vertical space in + compound paragraphs. +div.compound .compound-first, div.compound .compound-middle { + margin-bottom: 0.5em } + +div.compound .compound-last, div.compound .compound-middle { + margin-top: 0.5em } +*/ + +div.dedication { + margin: 2em 5em ; + text-align: center ; + font-style: italic } + +div.dedication p.topic-title { + font-weight: bold ; + font-style: normal } + +div.figure { + margin-left: 2em ; + margin-right: 2em } + +div.footer, div.header { + clear: both; + font-size: smaller } + +div.line-block { + display: block ; + margin-top: 1em ; + margin-bottom: 1em } + +div.line-block div.line-block { + margin-top: 0 ; + margin-bottom: 0 ; + margin-left: 1.5em } + +div.sidebar { + margin: 0 0 0.5em 1em ; + border: medium outset ; + padding: 1em ; + background-color: #ffffee ; + width: 40% ; + float: right ; + clear: right } + +div.sidebar p.rubric { + font-family: sans-serif ; + font-size: medium } + +div.system-messages { + margin: 5em } + +div.system-messages h1 { + color: red } + +div.system-message { + border: medium outset ; + padding: 1em } + +div.system-message p.system-message-title { + color: red ; + font-weight: bold } + +div.topic { + margin: 2em } + +h1.section-subtitle, h2.section-subtitle, h3.section-subtitle, +h4.section-subtitle, h5.section-subtitle, h6.section-subtitle { + margin-top: 0.4em } + +h1.title { + text-align: center } + +h2.subtitle { + text-align: center } + +hr.docutils { + width: 75% } + +img.align-left, .figure.align-left, object.align-left { + clear: left ; + float: left ; + margin-right: 1em } + +img.align-right, .figure.align-right, object.align-right { + clear: right ; + float: right ; + margin-left: 1em } + +img.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left } + +.align-center { + clear: both ; + text-align: center } + +.align-right { + text-align: right } + +/* reset inner alignment in figures */ +div.align-right { + text-align: inherit } + +/* div.align-center * { */ +/* text-align: left } */ + +ol.simple, ul.simple { + margin-bottom: 1em } + +ol.arabic { + list-style: decimal } + +ol.loweralpha { + list-style: lower-alpha } + +ol.upperalpha { + list-style: upper-alpha } + +ol.lowerroman { + list-style: lower-roman } + +ol.upperroman { + list-style: upper-roman } + +p.attribution { + text-align: right ; + margin-left: 50% } + +p.caption { + font-style: italic } + +p.credits { + font-style: italic ; + font-size: smaller } + +p.label { + white-space: nowrap } + +p.rubric { + font-weight: bold ; + font-size: larger ; + color: maroon ; + text-align: center } + +p.sidebar-title { + font-family: sans-serif ; + font-weight: bold ; + font-size: larger } + +p.sidebar-subtitle { + font-family: sans-serif ; + font-weight: bold } + +p.topic-title { + font-weight: bold } + +pre.address { + margin-bottom: 0 ; + margin-top: 0 ; + font: inherit } + +pre.literal-block, pre.doctest-block, pre.math, pre.code { + margin-left: 2em ; + margin-right: 2em } + +pre.code .ln { color: grey; } /* line numbers */ +pre.code, code { background-color: #eeeeee } +pre.code .comment, code .comment { color: #5C6576 } +pre.code .keyword, code .keyword { color: #3B0D06; font-weight: bold } +pre.code .literal.string, code .literal.string { color: #0C5404 } +pre.code .name.builtin, code .name.builtin { color: #352B84 } +pre.code .deleted, code .deleted { background-color: #DEB0A1} +pre.code .inserted, code .inserted { background-color: #A3D289} + +span.classifier { + font-family: sans-serif ; + font-style: oblique } + +span.classifier-delimiter { + font-family: sans-serif ; + font-weight: bold } + +span.interpreted { + font-family: sans-serif } + +span.option { + white-space: nowrap } + +span.pre { + white-space: pre } + +span.problematic { + color: red } + +span.section-subtitle { + /* font-size relative to parent (h1..h6 element) */ + font-size: 80% } + +table.citation { + border-left: solid 1px gray; + margin-left: 1px } + +table.docinfo { + margin: 2em 4em } + +table.docutils { + margin-top: 0.5em ; + margin-bottom: 0.5em } + +table.footnote { + border-left: solid 1px black; + margin-left: 1px } + +table.docutils td, table.docutils th, +table.docinfo td, table.docinfo th { + padding-left: 0.5em ; + padding-right: 0.5em ; + vertical-align: top } + +table.docutils th.field-name, table.docinfo th.docinfo-name { + font-weight: bold ; + text-align: left ; + white-space: nowrap ; + padding-left: 0 } + +h1 tt.docutils, h2 tt.docutils, h3 tt.docutils, +h4 tt.docutils, h5 tt.docutils, h6 tt.docutils { + font-size: 100% } + +ul.auto-toc { + list-style-type: none } + +a.footnote-reference { + line-height: 0px; +} + +/* code.css file generated by Nikola */ +pre.code .hll { background-color: #ffffcc } +pre.code { background: #f8f8f8; } +pre.code .c { color: #408080; font-style: italic } /* Comment */ +pre.code .err { border: 1px solid #FF0000 } /* Error */ +pre.code .k { color: #008000; font-weight: bold } /* Keyword */ +pre.code .o { color: #666666 } /* Operator */ +pre.code .cm { color: #408080; font-style: italic } /* Comment.Multiline */ +pre.code .cp { color: #BC7A00 } /* Comment.Preproc */ +pre.code .c1 { color: #408080; font-style: italic } /* Comment.Single */ +pre.code .cs { color: #408080; font-style: italic } /* Comment.Special */ +pre.code .gd { color: #A00000 } /* Generic.Deleted */ +pre.code .ge { font-style: italic } /* Generic.Emph */ +pre.code .gr { color: #FF0000 } /* Generic.Error */ +pre.code .gh { color: #000080; font-weight: bold } /* Generic.Heading */ +pre.code .gi { color: #00A000 } /* Generic.Inserted */ +pre.code .go { color: #888888 } /* Generic.Output */ +pre.code .gp { color: #000080; font-weight: bold } /* Generic.Prompt */ +pre.code .gs { font-weight: bold } /* Generic.Strong */ +pre.code .gu { color: #800080; font-weight: bold } /* Generic.Subheading */ +pre.code .gt { color: #0044DD } /* Generic.Traceback */ +pre.code .kc { color: #008000; font-weight: bold } /* Keyword.Constant */ +pre.code .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */ +pre.code .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */ +pre.code .kp { color: #008000 } /* Keyword.Pseudo */ +pre.code .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */ +pre.code .kt { color: #B00040 } /* Keyword.Type */ +pre.code .m { color: #666666 } /* Literal.Number */ +pre.code .s { color: #BA2121 } /* Literal.String */ +pre.code .na { color: #7D9029 } /* Name.Attribute */ +pre.code .nb { color: #008000 } /* Name.Builtin */ +pre.code .nc { color: #0000FF; font-weight: bold } /* Name.Class */ +pre.code .no { color: #880000 } /* Name.Constant */ +pre.code .nd { color: #AA22FF } /* Name.Decorator */ +pre.code .ni { color: #999999; font-weight: bold } /* Name.Entity */ +pre.code .ne { color: #D2413A; font-weight: bold } /* Name.Exception */ +pre.code .nf { color: #0000FF } /* Name.Function */ +pre.code .nl { color: #A0A000 } /* Name.Label */ +pre.code .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */ +pre.code .nt { color: #008000; font-weight: bold } /* Name.Tag */ +pre.code .nv { color: #19177C } /* Name.Variable */ +pre.code .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */ +pre.code .w { color: #bbbbbb } /* Text.Whitespace */ +pre.code .mb { color: #666666 } /* Literal.Number.Bin */ +pre.code .mf { color: #666666 } /* Literal.Number.Float */ +pre.code .mh { color: #666666 } /* Literal.Number.Hex */ +pre.code .mi { color: #666666 } /* Literal.Number.Integer */ +pre.code .mo { color: #666666 } /* Literal.Number.Oct */ +pre.code .sb { color: #BA2121 } /* Literal.String.Backtick */ +pre.code .sc { color: #BA2121 } /* Literal.String.Char */ +pre.code .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */ +pre.code .s2 { color: #BA2121 } /* Literal.String.Double */ +pre.code .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */ +pre.code .sh { color: #BA2121 } /* Literal.String.Heredoc */ +pre.code .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */ +pre.code .sx { color: #008000 } /* Literal.String.Other */ +pre.code .sr { color: #BB6688 } /* Literal.String.Regex */ +pre.code .s1 { color: #BA2121 } /* Literal.String.Single */ +pre.code .ss { color: #19177C } /* Literal.String.Symbol */ +pre.code .bp { color: #008000 } /* Name.Builtin.Pseudo */ +pre.code .vc { color: #19177C } /* Name.Variable.Class */ +pre.code .vg { color: #19177C } /* Name.Variable.Global */ +pre.code .vi { color: #19177C } /* Name.Variable.Instance */ +pre.code .il { color: #666666 } /* Literal.Number.Integer.Long */ +table.codetable { width: 100%;} td.linenos {text-align: right; width: 4em;} + +/* + Colorbox Core Style: + The following CSS is consistent between example themes and should not be altered. +*/ +#colorbox, #cboxOverlay, #cboxWrapper{position:absolute; top:0; left:0; z-index:9999; overflow:hidden;} +#cboxWrapper {max-width:none;} +#cboxOverlay{position:fixed; width:100%; height:100%;} +#cboxMiddleLeft, #cboxBottomLeft{clear:left;} +#cboxContent{position:relative;} +#cboxLoadedContent{overflow:auto; -webkit-overflow-scrolling: touch;} +#cboxTitle{margin:0;} +#cboxLoadingOverlay, #cboxLoadingGraphic{position:absolute; top:0; left:0; width:100%; height:100%;} +#cboxPrevious, #cboxNext, #cboxClose, #cboxSlideshow{cursor:pointer;} +.cboxPhoto{float:left; margin:auto; border:0; display:block; max-width:none; -ms-interpolation-mode:bicubic;} +.cboxIframe{width:100%; height:100%; display:block; border:0; padding:0; margin:0;} +#colorbox, #cboxContent, #cboxLoadedContent{box-sizing:content-box; -moz-box-sizing:content-box; -webkit-box-sizing:content-box;} + +/* + User Style: + Change the following styles to modify the appearance of Colorbox. They are + ordered & tabbed in a way that represents the nesting of the generated HTML. +*/ +#cboxOverlay{background:#000;} +#colorbox{outline:0;} + #cboxContent{margin-top:20px;background:#000;} + .cboxIframe{background:#fff;} + #cboxError{padding:50px; border:1px solid #ccc;} + #cboxLoadedContent{border:5px solid #000; background:#fff;} + #cboxTitle{position:absolute; top:-20px; left:0; color:#ccc;} + #cboxCurrent{position:absolute; top:-20px; right:0px; color:#ccc;} + #cboxLoadingGraphic{background:url(images/loading.gif) no-repeat center center;} + + /* these elements are buttons, and may need to have additional styles reset to avoid unwanted base styles */ + #cboxPrevious, #cboxNext, #cboxSlideshow, #cboxClose {border:0; padding:0; margin:0; overflow:visible; width:auto; background:none; } + + /* avoid outlines on :active (mouseclick), but preserve outlines on :focus (tabbed navigating) */ + #cboxPrevious:active, #cboxNext:active, #cboxSlideshow:active, #cboxClose:active {outline:0;} + + #cboxSlideshow{position:absolute; top:-20px; right:90px; color:#fff;} + #cboxPrevious{position:absolute; top:50%; left:5px; margin-top:-32px; background:url(images/controls.png) no-repeat top left; width:28px; height:65px; text-indent:-9999px;} + #cboxPrevious:hover{background-position:bottom left;} + #cboxNext{position:absolute; top:50%; right:5px; margin-top:-32px; background:url(images/controls.png) no-repeat top right; width:28px; height:65px; text-indent:-9999px;} + #cboxNext:hover{background-position:bottom right;} + #cboxClose{position:absolute; top:5px; right:5px; display:block; background:url(images/controls.png) no-repeat top center; width:38px; height:19px; text-indent:-9999px;} + #cboxClose:hover{background-position:bottom center;} + +body { + padding-top: 60px; +} +@media (max-width: 979px) { + body { + padding-top: 0px; + } +} +#container { + width: 960px; + margin: 50 auto; +} + +#contentcolumn { + max-width: 760px; +} +#q { + width: 150px; +} + +img { + max-width: 90%; +} + +.postbox { + border-bottom: 2px solid darkgrey; + margin-bottom: 12px; +} + +.footerbox {padding: 15px; text-align: center; margin-bottom: 15px;} + +td.label { + /* Issue #290 */ + background-color: inherit; +} + +.footnote-reference { + /* Issue 290 */ + vertical-align: super; + font-size: xx-small; +} + +.caption { + /* Issue 292 */ + text-align: center; + padding-top: 1em; +} + +div.figure > img, +div.figure > a > img { + /* Issue 292 */ + display: block; + margin-left: auto; + margin-right: auto; +} + +div.sidebar, div.admonition, div.attention, div.caution, div.danger, div.error, div.hint, div.important, div.note, div.tip, div.warning { + /* Issue 277 */ + border: 1px solid #aaa; + border-radius: 5px; +} + +blockquote p, blockquote { + font-size: 17.5px; + font-weight: 300; + line-height: 1.25; +} + +ul.bricks > li { + display: inline; + background-color: lightblue; + padding: 8px; + border-radius: 5px; + line-height: 3; + white-space:nowrap; + margin: 3px; +} + +ul.breadcrumb > li:before { + content: " / "; +} + +pre, pre code { + white-space: pre; + word-wrap: normal; + overflow: auto; +} + +article.post-micro { + font-family: Georgia, 'Times New Roman', Times, serif; + font-size: 1.5em; +} + +/* fix anchors for headers */ +h1, h2, h3 { + margin-top: -40px; + padding-top: 60px; +} + +h4, h5, h6 { + margin-top: -50px; + padding-top: 60px; +} + +.image-block { + display: inline-block; +} + +.flowr_row { + width: 100%; +} + +.tags { + padding-left: 0; + margin-left: -5px; + list-style: none; + text-align: center; + +} + +.tags > li { + display: inline-block; + min-width: 10px; + padding: 3px 7px; + font-size: 12px; + font-weight: bold; + line-height: 1; + color: #fff; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + background-color: #999; + border-radius: 10px; +} + +.tags > li a { + color: #fff; +} + +.metadata p:before, +.postlist .listdate:before { + content: " — "; +} + +.metadata p:first-of-type:before { + content: ""; +} + +.metadata p { + display: inline; +} + +.posttranslations h3 { + display: inline; + font-size: 1em; + font-weight: bold; +} + +.posttranslations h3:last-child { + display: none; +} + +.entry-content { + margin-top: 1em; +} + +.navbar .brand { + padding: 0 20px; +} + +.navbar .brand #blog-title { + padding: 10px 0; + display: inline-block; +} + +#notebook-container { + padding-left: 0; +} + +ul.nav-list { + list-style-type: none; + padding-left: 0; +} + +.nav-list li a { + padding-left: 5px; +} + +#titlebox { + float: right; +} + +#titlelogo { + float: right; + margin-left: 15px; +} + +#blog-title a { + font-family: monospace; + color: #333; +} + +#blog-title a:hover { + text-decoration: none; + color: #005580; +} + +.table-list td { + padding-bottom: 20px; +} + +hr.light { + border-top: 1px solid #EEEEEE; +} + +.half-spacer { + margin-top: 15px; +} + +.spacer { + margin-top: 30px; +} + +.postbox { + border: none; +} + +.prompt { + display: none; +} + +.cell .output 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b}),define("core/models/RichMedia",["backbone"],function(a){"use strict";var b=a.Model.extend({idAttribute:"url",defaults:{mediaType:null,html:"",htmlWidth:null,htmlHeight:null,thumbnailUrl:"",thumbnailWidth:null,thumbnailHeight:null,url:"",resolvedUrl:"",title:"",description:"",providerName:""}});return b}),define("core/models/Media",["underscore","backbone","core/models/LegacyMedia","core/models/RichMedia"],function(a,b,c,d){"use strict";var e=b.Model.extend(a.extend({},d.prototype,c.prototype,{idAttribute:"id"}),{MEDIA_TYPES:{IMAGE:"1",IMAGE_UPLOAD:"2",YOUTUBE_VIDEO:"3",WEBPAGE:"4",TWITTER_STATUS:"5",FACEBOOK_PAGE:"6",FACEBOOK_POST:"7",FACEBOOK_PHOTO:"8",FACEBOOK_VIDEO:"9",SOUNDCLOUD_SOUND:"10",GOOGLE_MAP:"11",VIMEO_VIDEO:"12",VINE_VIDEO:"14"},WEBPAGE_TYPES:["4","6","7"]});return e}),define("core/models/Thread",["underscore","backbone","core/utils","core/api","core/config","core/advice","core/models/User"],function(a,b,c,d,e,f,g){"use strict";var h=b.Model.extend({defaults:{author:null,category:null,createdAt:null,forum:null,identifiers:[],ipAddress:null,isClosed:!1,isDeleted:!1,hasStreaming:!1,link:null,message:null,slug:null,title:null,userSubscription:!1,posts:0,likes:0,dislikes:0,userScore:0},initialize:function(a,b){var c=this;b=b||{},c.moderators=b.moderators,c.forum=b.forum,c.on("change:userScore",function(){var a=c.get("userScore");a>0&&0===c.get("likes")&&c.set("likes",a)})},_vote:function(a,b){var c=a-b;return 0===c?c:(this.set("likes",this.get("likes")+c),c)},vote:function(a){var b=this,c=b._vote(a,b.get("userScore"));0!==c&&(this.set("userScore",a),d.call("threads/vote.json",{data:{thread:this.id,vote:a},method:"POST",success:function(a){a.response.id&&b.trigger("vote:success",a)}}))},fetch:function(a){var b,c=this,f=c.attributes;a=a||{},b=f.identifier?"ident:"+f.identifier:"link:"+f.url,d.call("threads/details.json",{data:{thread:b,forum:f.forum},success:function(b){c.set(b.response),a.success&&a.success()},error:function(){return e.debug?void c.save({},{success:a.success}):void console.log("Couldn't find thread; not creating in production.")}})},_toggleState:function(a,b){b||(b={});var c=a?"open.json":"close.json";this.set("isClosed",!a),d.call("threads/"+c,{method:"POST",data:{thread:this.id},success:b.success,error:b.error})},open:function(a){this._toggleState(!0,a)},close:function(a){this._toggleState(!1,a)},sync:function(){var a=this,b=a.attributes;d.call("threads/create.json",{data:{title:b.title,forum:b.forum,identifier:b.identifier,url:b.url},method:"POST",success:function(b){a.set(b.response)}})},incrementPostCount:function(a){var b=this.get("posts")+a;this.set("posts",b>0?b:0)},isModerator:function(b){var c;if(this.moderators)return c=b instanceof g||a.isObject(b)?b.id:b,c=parseInt(c,10),a(this.moderators).contains(c)},subscribe:function(a,b){a=a!==!1;var c=this.get("userSubscription");if(c!==a){a?this.set("userSubscription",b||!0):this.set("userSubscription",!1);var e=a?"subscribe.json":"unsubscribe.json",f={thread:this.id};b?f.email=b:a||"string"!=typeof c||(f.email=c),d.call("threads/"+e,{data:f,method:"POST"})}},relatedIds:function(){var b=this.get("forum");return a.isObject(b)&&(b=b.id),{forum:this.get("forum"),thread:this.id}},twitterText:function(a){var b=140-(a.length+1),d=c.htmlDecode(this.get("clean_title"));return d=c.niceTruncate(d,b)},permalink:function(){return this.get("url")||this.get("link")||this.currentUrl},toJSON:function(){var a=b.Model.prototype.toJSON.call(this);return a.permalink=this.permalink(),a}});return f.withAdvice.call(h.prototype),h.withThreadVoteCollection=function(a){this.after("initialize",function(){this.votes=new a,this.on("vote:success",function(a){this.votes.get(a.response.id)||this.votes.add({id:a.response.id,score:a.response.vote,currentUser:!0})},this)})},h.withPostCollection=function(b){this.after("initialize",function(c){c=c||{},this.posts=new b(c.posts,{thread:this,cursor:c.postCursor,order:c.order,perPage:this.postsPerPage}),this.listenTo(this.posts,"add reset",function(b){b=b.models?b.models:[b],this.users&&a.each(b,function(a){this.users.get(a.author.id)||this.users.add(a.author)}),this.recalculatePostCount()}),this.listenTo(this.posts,"change:isDeleted change:isFlagged",function(a,b){b&&this.incrementPostCount(-1)})}),this.recalculatePostCount=function(){var a=this.get("posts");a>50||(a=this.posts.reduce(function(a,b){return b.isPublic()?a+1:a},0),this.set("posts",a))}},h}),define("core/models/ThreadVote",["backbone"],function(a){"use strict";var b=a.Model.extend({defaults:{score:0}});return b}),define("core/models/Notification",["underscore","backbone","moment","core/models/mixins","core/models/Post"],function(a,b,c,d,e){"use strict";var f=b.Model.extend({defaults:function(){return{sender:"",timestamp:c().valueOf(),type:0,formatted:"",theme:"",createdAt:null,post:null}},set:function(a,c){return a&&"string"!=typeof a&&a.post&&(this.post=new e(a.post),delete a.post),b.Model.prototype.set.call(this,a,c)},toJSON:function(){var a=b.Model.prototype.toJSON.call(this);return this.post&&(a.post=this.post.toJSON()),a.relativeCreatedAt=this.getRelativeCreatedAt(),a}});return d.withCreatedAt.call(f.prototype),f}),define("core/collections/MediaCollection",["backbone","core/models/Media"],function(a,b){"use strict";var c=a.Collection.extend({model:b});return c}),function(a,b){if("function"==typeof define&&define.amd)define("backbone.uniquemodel",["backbone"],function(a){a.UniqueModel=b(a)});else if("undefined"!=typeof exports){var c=require("backbone");c.UniqueModel=b(c)}else a.Backbone.UniqueModel=b(a.Backbone)}(this,function(a){"use strict";function b(a,c,d){c=c||_.uniqueId("UniqueModel_"),d=d||b.STORAGE_DEFAULT_ADAPTER;var e=b.addModel(a,c,d);return e.modelConstructor}function c(b,c,e){var f=this;this.instances={},this.Model=b,this.modelName=c,this.storage=null,"localStorage"===e?this.storage=new d(this.modelName,localStorage):"sessionStorage"===e&&(this.storage=new d(this.modelName,sessionStorage)),this.storage&&(this.storage.on("sync",this.storageSync,this),this.storage.on("destroy",this.storageDestroy,this));var g=function(a,b){return f.get(a,b)};_.extend(g,a.Events),g.prototype=this.Model.prototype,this.modelConstructor=g}function d(a,b){this.modelName=a,this.store=b,d.instances[a]=this,d.listener||(d.listener=window.addEventListener?window.addEventListener("storage",d.onStorage,!1):window.attachEvent("onstorage",d.onStorage))}var e={};return b.STORAGE_DEFAULT_ADAPTER="memory",b.STORAGE_KEY_DELIMETER=".",b.STORAGE_NAMESPACE="UniqueModel",b.getModelCache=function(a){var b=e[a];if(!b)throw"Unrecognized model: "+a;return b},b.addModel=function(a,b,d){if(e[b])return e[b];var f=new c(a,b,d);return e[b]=f,f},b.clear=function(){for(var a in e)e.hasOwnProperty(a)&&delete e[a]},_.extend(c.prototype,{newModel:function(a,b){var c=new this.Model(a,b);return this.storage&&(c.id&&this.storage.save(c.id,c.attributes),c.on("sync",this.instanceSync,this),c.on("destroy",this.instanceDestroy,this)),c},instanceSync:function(a){this.storage&&this.storage.save(a.id,a.attributes)},instanceDestroy:function(a){this.storage&&this.storage.remove(a.id)},storageSync:function(a,b){this.get(b,{fromStorage:!0})},storageDestroy:function(a){var b=this.instances[a];b&&(b.trigger("destroy",b),delete this.instances[a])},add:function(a,b,c){var d=this.newModel(b,c);return this.instances[a]=d,d},get:function(a,b){b=b||{};var c=this.Model,d=a&&a[c.prototype.idAttribute];if(!d)return this.newModel(a,b);var e=this.instances[d];if(this.storage&&!b.fromStorage&&!e){var f=this.storage.getFromStorage(this.storage.getStorageKey(d));f&&(e=this.add(d,f,b))}return e?(e.set(a),b.fromStorage||this.instanceSync(e)):(e=this.add(d,a,b),b.fromStorage&&this.modelConstructor.trigger("uniquemodel.add",e)),e}}),d.instances={},d.listener=null,d.onStorage=function(a){var c=a.key,e=new RegExp([b.STORAGE_NAMESPACE,"(\\w+)","(.+)"].join("\\"+b.STORAGE_KEY_DELIMETER)),f=c.match(e);if(f){var g=f[1],h=f[2],i=d.instances[g];i&&i.handleStorageEvent(c,h)}},_.extend(d.prototype,{handleStorageEvent:function(a,b){var c=this.getFromStorage(a);c?this.trigger("sync",b,c):this.trigger("destroy",b)},getFromStorage:function(a){try{return JSON.parse(this.store.getItem(a))}catch(b){return}},getStorageKey:function(a){var c=[b.STORAGE_NAMESPACE,this.modelName,a].join(b.STORAGE_KEY_DELIMETER);return c},save:function(a,b){if(!a)throw"Cannot save without id";var c=JSON.stringify(b);this.store.setItem(this.getStorageKey(a),c)},remove:function(a){if(!a)throw"Cannot remove without id";this.store.removeItem(this.getStorageKey(a))}},a.Events),_.extend(b,{ModelCache:c,StorageAdapter:d}),b}),define("common/models",["require","jquery","underscore","backbone","moment","modernizr","core/api","core/UniqueModel","core/models/User","core/models/Post","core/models/Media","core/models/LegacyMedia","core/models/RichMedia","core/models/Thread","core/models/Vote","core/models/ThreadVote","core/models/Notification","core/collections/MediaCollection","core/utils/guid","common/utils","core/utils","common/urls","shared/corefuncs","shared/urls","backbone.uniquemodel"],function(a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x){"use strict";var y=d.Model.extend({defaults:{settings:{}},toJSON:function(){return c.extend(d.Model.prototype.toJSON.apply(this,arguments),{homeUrl:x.apps.home+"home/forums/"+this.id+"/"})}}),z=n.extend({initialize:function(b,d){n.prototype.initialize.apply(this,arguments),d=d||{};var e=this,f=a("common/collections");e.users=new f.UserCollection(d.users,{thread:e}),e.forum&&(e.moderatorList=new f.ModeratorCollection(null,{forum:e.forum.get("id")})),e.posts=new f.SubpaginatedPostCollection(d.posts,{thread:e,cursor:d.postCursor,order:d.order,perPage:u.isMobileUserAgent()?20:50}),e.votes=new f.ThreadVoteCollection,e.posts.on("add reset",function(a){a=a.models?a.models:[a],c.each(a,function(a){e.users.get(a.author.id)||e.users.add(a.author)}),e.recalculatePostCount()}),e.listenTo(e.posts,"change:isDeleted change:isFlagged",function(a,b){b&&e.incrementPostCount(-1)}),e.queue=new f.QueuedPostCollection(null,{thread:e})},recalculatePostCount:function(){var a=this.get("posts");a>50||(a=this.posts.buffer.reduce(function(a,b){return b.isPublic()?a+1:a},0),this.set("posts",a))},toJSON:function(){return c.extend(n.prototype.toJSON.apply(this,arguments),{homeUrl:x.apps.home+"home/discussions/"+this.relatedIds().forum+"/"+this.get("slug")+"/"})}});n.withThreadVoteCollection.call(z.prototype,d.Collection);var A=z.extend({defaults:c.extend({postsInInterval:0,posts:0,topPost:null},z.prototype.defaults)}),B=j.extend({initialize:function(){j.prototype.initialize.apply(this,arguments);var b=a("common/collections");this.usersTyping=new b.TypingUserCollection},isAuthorSessionUser:function(a){return a.user.id&&this.author&&this.author.id?this.author.id===a.user.id:!1},canBeEdited:function(a,b){return!b.get("isClosed")&&!this.get("isDeleted")&&a.isLoggedIn()&&this.isAuthorSessionUser(a)&&this.get("raw_message")&&!this.get("isHighlighted")&&!this.get("isSponsored")},canBeRepliedTo:function(a,b){return!b.get("isClosed")&&a.get("canReply")&&!this.get("isDeleted")&&(this.get("isApproved")||b.isModerator(a.user))},canBeShared:function(){return!this.get("isDeleted")&&!this.get("isSponsored")},getParent:function(){var a=this.get("parent");if(a)return new h(B,{id:a+""})}},{fetchContext:function(a,d,e){e=e||{};var f=b.Deferred();return g.call("posts/getContext.json",{method:"GET",data:{post:a},success:function(a){var b=c.filter(a.response,function(a){return a.thread===d.get("id")});return b?(c.each(b,function(a){a=new h(B,a),e.requestedByPermalink&&(a.requestedByPermalink=!0),d.posts.add(a)}),void f.resolve(b)):void f.reject()}}),f.promise()}});j.withAuthor.call(B.prototype,h.wrap(i)),j.withMediaCollection.call(B.prototype,r),h.addType("Post",B);var C=d.Model.extend({defaults:{user:null,message:null,parentId:null,immedReply:!1,createdAt:void 0},getVisibleParent:function(a){for(var b,c=this;c.get("parentId");){if(b=a.posts.get(c.get("parentId")))return b;if(c=a.queue.get(c.get("parentId")),!c)return null}return null},toPost:function(a){var b=this,c=a.posts.get(b.get("parentId")),d=c?c.get("depth")+1:0,e=new h(B,{id:b.id,thread:a.id,message:b.get("message"),parent:b.get("parentId"),depth:d,createdAt:b.get("createdAt"),isRealtime:!0,media:b.get("media"),isImmediateReply:b.get("immedReply")});return e.author=b.get("user"),e}}),D=d.Model.extend({defaults:{user:null,post:null,thread:null,client_context:null,typing:!0},idAttribute:"client_context",set:function(){return this.lastModified=new Date,d.Model.prototype.set.apply(this,arguments)},sync:function(){var a=this.toJSON(),b=w.serialize(v.realertime+"/api/typing",a);try{t.CORS.request("GET",b).send()}catch(c){}}},{make:function(a){return a.client_context||(a.client_context=s.generate()),new h(D,a)}});h.addType("TypingUser",D),c.extend(i.prototype,{getFollowing:function(){var b=a("common/collections/profile");return this.following||(this.following=new b.FollowingCollection(null,{user:this}))}}),h.addType("User",i);var E=i.extend({defaults:c.extend({numPosts:0},i.prototype.defaults)}),F=f.sessionstorage?"sessionStorage":null,G=d.UniqueModel(i,"User",F),H=d.UniqueModel(E,"User",F),I=d.Model.extend({});return{UniqueModel:h,Forum:y,Thread:z,TopThread:A,Post:B,QueuedPost:C,TypingUser:D,User:i,TopUser:E,Vote:o,ThreadVote:p,Notification:q,Switch:I,LegacyMedia:l,RichMedia:m,Media:k,SyncedUser:G,SyncedTopUser:H}}),define("common/Session",["jquery","underscore","backbone","modernizr","core/api","core/bus","core/models/Session","core/strings","core/utils/cookies","common/collections/LoggedOutCache","common/models","common/urls","common/utils","shared/corefuncs"],function(a,b,c,d,e,f,g,h,i,j,k,l,m,n){"use strict";var o,p=c.Model.extend({storageKey:"disqus.bus",initialize:function(){d.localstorage&&a(window).on("storage",b.bind(this.onStorageEvent,this))},broadcast:function(a,b){if(d.localstorage){var c=JSON.stringify({name:a,data:b,time:(new Date).getTime()});try{window.localStorage.setItem(this.storageKey,c)}catch(e){return}}},onStorageEvent:function(a){var b=a.originalEvent.key,c=a.originalEvent.newValue;if(c&&b===this.storageKey)try{c=JSON.parse(c),this.trigger(c.name,c.data)}catch(d){return}}}),q=new p,r=g.extend({_defaults:{canReply:!0,canModerate:!1,mustVerifyEmail:!1,audienceSyncVerified:!1,sso:null},defaults:function(){var a=new j.Collection(j.LOGGED_OUT_NOTES,{session:this});return b.extend(this._defaults,{notificationCount:a.getUnread().length})},openAuthWindow:function(a,b,c){return m.windowOpen(a,"_blank",{width:b,height:c})},start:function(a){this.set(a),this.listenTo(q,"auth:success",this.fetch),this.listenTo(f.frame,{"!auth:success":function(a){a&&(a.sessionId&&e.headers({"X-Sessionid":a.sessionId}),a.message&&this.trigger("alert",a.message,{type:"info"}),a.logEvent&&f.trigger("uiAction:"+a.logEvent)),q.broadcast("auth:success"),this.fetch()}}),this.bindAudienceSyncHandlers()},stop:function(){this.stopListening(),this.off()},shouldFetchSession:function(){return this.get("remoteAuthS3")||i.read("disqusauth")},getUserInstance:function(a){return new k.UniqueModel(k.User,a)},toJSON:function(){var a=this.user.toJSON.apply(this.user,arguments);return a.thread.canReply=this.get("canReply"),a.thread.canModerate||(a.thread.canModerate=this.get("canModerate")),a},fetch:function(){var a=this.has("thread")?this.fetchThreadDetails(this.get("thread")):g.prototype.fetch.apply(this,arguments),b=this;return a.done(function(){b.set("notificationCount",0)}),a},fetchNotificationCount:function(){var b=this;return b.isLoggedIn()?e.call("timelines/getUnreadCount.json",{data:{type:"notifications"}}).done(function(a){b.set("notificationCount",a.response)}):a.Deferred().resolve()},fetchThreadDetails:function(a){var c=this;c._request&&(c._request.abort(),c._request=null);var d={thread:a.id,post:a.posts.pluck("id")};return d["_"+(new Date).getTime()]=1,c._request=e.call("embed/threadDetails.json",{data:d,success:function(d){var e=d.response,f={};e.user&&b.extend(f,e.user,{votes:e.votes}),c.set(e.session),f.id?(c.setUser(new k.UniqueModel(k.User,f)),a.users.add(c.user),e.thread&&(a.set("userScore",e.thread.userScore),a.set("userSubscription",e.thread.userSubscription))):c.setUser(c.getAnonUserInstance())},complete:function(){c._request=null}}),c._request},logout:function(){var a=this.get("sso");this.isSSO()&&a&&a.logout?f.frame.sendHostMessage("navigate",a.logout):window.location.replace(n.serialize(l.logout,{redirect:window.location.href}))},isSSO:function(){return this.user&&"sso"===this.user.get("user_type")},authenticate:function(a){var c=this.authServices[a];if(c){if(b.isFunction(c))return c.call(this);f.trigger("uiAction:openLogin",a);var d={forum:this.get("thread")&&this.get("thread").forum.id};"https:"===window.location.protocol&&(d.redirect_secure=1),c.csrf&&(d.ctkn=this.getCsrfToken()),b.extend(d,c.params),this.openAuthWindow(n.serialize(c.url,d),c.width,c.height)}},authServices:{disqus:{url:l.login,width:460,height:355},disqusDotcom:{url:l.dotcomLogin,width:478,height:590,params:{next:l.login}},twitter:{url:l.oauth.twitter,width:650,height:680,csrf:!0},facebook:{url:l.oauth.facebook,width:550,height:300,csrf:!0},google:{url:l.oauth.google,width:445,height:635,csrf:!0},sso:function(){var a=this.get("sso"),c=parseInt(a.width||"800",10),d=parseInt(a.height||"500",10),e=this.openAuthWindow(a.url,c,d);!function g(){m.isWindowClosed(e)?f.frame.sendHostMessage("reload"):b.delay(g,500)}()}},verifyEmail:function(){var a=n.serialize(l.verifyEmail,{f:this.get("thread").forum.id});this.openAuthWindow(a,460,355)},bindAudienceSyncHandlers:function(){this.listenTo(this,"change:id change:audienceSyncVerified",function(){this.get("audienceSyncVerified")&&f.frame.sendHostMessage("session.identify",this.user.id)}),this.listenTo(f.frame,{"!audiencesync:grant":function(){this.set("audienceSyncVerified",!0)}})},getAudienceSyncUrl:function(){var a={client_id:this.get("apiKey"),response_type:"audiencesync",forum_id:this.get("thread").forum.id};return"https:"===window.location.protocol&&(a.ssl=1),n.serialize(l.authorize,a)},audienceSync:function(){this.openAuthWindow(this.getAudienceSyncUrl(),460,355)},needsAudienceSyncAuth:function(a){return a.get("settings").audienceSyncEnabled&&this.isLoggedIn()&&!this.get("audienceSyncVerified")},getLoggedOutUserFlags:function(){return this._loggedOutUserFlags?this._loggedOutUserFlags:(this._loggedOutUserFlags=new j.Collection(j.LOGGED_OUT_FLAGS,{session:this}),this._loggedOutUserFlags)}});return{get:function(){return o=o||new r},setDefaults:function(a){if(o)throw new Error("Session defaults cannot be changed after a session instance is created!");return r._defaults=b.extend(r.prototype._defaults,a),r._defaults},forget:function(){o&&(o.stop(),o=null)}}}),define("common/analytics/google",["require","common/keys","common/defines"],function(a,b,c){"use strict";var d,e=function(a){d=a},f=function(a){d?d(a):window._gaq.push(a)},g=function(){var d="";if(!c.debug||d){l(b.googleAnalytics),m(".disqus.com");var 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e=[];this.on("add",c),this.fetch(a.extend({},b,{add:!0,remove:!1,cursor:this.cursor.next,limit:this.PER_PAGE,success:function(){d.trigger("add:many",e,d,b),d.off("add",c),b.success&&b.success.apply(this,arguments)}}))},parse:function(a){return this.cursor=a.cursor||{hasNext:!1},a.response},getLength:function(){return this.length}});return c}),define("common/collections",["underscore","backbone","moment","core/api","core/collections/UserCollection","core/collections/MediaCollection","core/collections/VoteCollection","core/collections/UpvotersUserCollection","core/collections/PaginatedCollection","common/models","common/cached-storage"],function(a,b,c,d,e,f,g,h,i,j,k){"use strict";var l=b.Collection.extend({model:j.ThreadVote}),m=i.extend({PER_PAGE:50,model:j.UniqueModel.wrap(j.Post),url:d.getURL("threads/listPostsThreaded"),initialize:function(a,b){i.prototype.initialize.apply(this,arguments),b=b||{},this.thread=b.thread,this.setOrder(b.order)},fetch:function(b){return 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DISQUS.testing&&(l.BaseCollection=g,l.BaseContentCollection=i),l}),define("discovery/custom-comments",["jquery","underscore","core/models/Media","shared/urls","shared/corefuncs","common/utils","common/urls","common/templates","exports"],function(a,b,c,d,e,f,g,h,i){"use strict";i.getProtocol=function(a){var b=(a||"").match(/^\s*(\w+:)?\/\//);return b?(b[1]||"").toLowerCase():null},i.getPageProtocol=function(){return window.location.protocol},i.forceWebProtocol=function(a,b){var c=i.getProtocol(a);if(null===c)return"";var e=i.getPageProtocol();return c||(c=e),"http:"===e&&(b=!0),"http:"===c&&b||(c="https:"),d.ensureHttpBasedProtocol(a,c)};var j=/<(\S+)[^<]+$/;i.extractTrackingTags=function(a){if(b.isArray(a))return b.map(a,function(a){return{tag:"img",url:a}});for(var c=f.bleachFindUrls(a),d=[],e=0;e0&&(k=c[e-1].endIndex);var l=a.substr(k,h-k),m=j.exec(l);if(m){var n=m[1].toLowerCase();if("img"===n||"iframe"===n){var o=i.forceWebProtocol(g.url);o&&d.push({tag:n,url:o})}}}return d},i.handlers={},i.adsNativeVideoTransformer=function(a){return{layout:"iframe",media_url:i.forceWebProtocol(a.embedUrl),target_url:i.forceWebProtocol(a.url,!0),brand_name:a.promotedBy,brand_image_url:i.forceWebProtocol(a.brandImageUrl),summary:a.summary,tracking_pixels_onload:i.extractTrackingTags(a.trackingTags),tracking_pixels_onview:i.extractTrackingTags(a.viewableTags)}};var k=function(a){return{title:a.title,summary:a.summary,target_url:i.forceWebProtocol(a.url),brand_name:a.promotedBy,thumbnail_url:i.forceWebProtocol(a.imageSrc),layout:"image_target",tracking_pixels_onload:i.extractTrackingTags(a.trackingTags),tracking_pixels_onview:i.extractTrackingTags(a.viewableTags)}};i.handlers.adsnativeVideoAds=function(b,c){return a.ajax({dataType:"jsonp",url:"https://api.adsnative.com/v1/ad.json",data:{url:c,zid:"PySlQoeeO71JbyG53QDzMCzp2uRP_ip0yZmgoltF"}}).always(function(a){var c=a.ad;return"OK"===a.status&&c&&"video"===c.type?void b.set(i.adsNativeVideoTransformer(c)):void b.clear()})},i.handlers.adsnative=function(b,c){return a.ajax({dataType:"jsonp",url:"https://api.adsnative.com/v1/ad.json",data:{url:c}}).always(function(a){return"OK"===a.status&&a.ad?void("video"===a.ad.type?b.set(i.adsNativeVideoTransformer(a.ad)):"story"===a.ad.type?b.set(k(a.ad)):b.clear()):void b.clear()})};var l=function(a){var b=(a.main_media&&a.main_media[0]||{}).url,c=(a.custom||{}).brand_name,d=i.forceWebProtocol(decodeURIComponent(a.click_url));return{title:a.title,summary:a.description,target_url:d,brand_name:c,thumbnail_url:i.forceWebProtocol(b),layout:"image_target",tracking_pixels_onload:i.extractTrackingTags(a.impression_trackers),tracking_pixels_onclick:i.extractTrackingTags(a.click_trackers)}},m=function(a){var b=(a.main_media&&a.main_media[0]||{}).url;return{media_url:i.forceWebProtocol(b),target_url:i.forceWebProtocol(a.click_url,!0),brand_name:(a.custom||{}).brand_name||a.title,brand_image_url:i.forceWebProtocol(a.icon_img_url),summary:a.description,layout:"iframe",tracking_pixels_onload:i.extractTrackingTags(a.impression_trackers),tracking_pixels_onclick:i.extractTrackingTags(a.click_trackers)}};i.handlers.appnexus=function(b,c){return b.get("placement_id")?a.get("https://mobile.adnxs.com/mob",{id:b.get("placement_id"),size:"1x1",st:"web",version:1,referrer:c}).always(function(a){if(!a)return void b.clear();var c=(a["native"]||a.ads||[])[0];if("ok"!==a.status||!c)return void b.clear();var d=!1;try{d=JSON.parse(c.custom.object).is_video}catch(e){}b.set(d?m(c):l(c))}):void b.clear()},i.handlers.appnexus_dr=function(b){return b.get("placement_id")?a.get("https://ib.adnxs.com/mob",{id:b.get("placement_id"),format:"json",psa:0}).always(function(a){if(!(a&&"ok"===a.status&&a.ads&&a.ads.length))return void b.unset("layout");var c=a.ads[0];return c.content&&c.width&&c.height?void b.set({content:c.content,width:c.width,height:c.height}):void b.unset("layout")}):void b.clear()}}),define("discovery/helpers",["underscore","jquery","remote/config","core/switches"],function(a,b,c,d){"use strict";var e=function(b,e,f){var g=c.discovery||{},h=d.isFeatureActive("discovery_override")&&e.get("discoveryOverride"),i=h||b.discoveryVariant,j=g.variantSpecific||{};if(i)i=a.extend(a.omit(g,"variantSpecific"),{name:i,thumbnailsEnabled:b.discoveryThumbnailsEnabled},j[i]);else{if(!b.sponsoredCommentsEnabled)return;i={organicEnabled:!1,promotedEnabled:!1}}return f.disable_promoted&&!b.discoveryLocked&&(i.promotedEnabled=!1,i.thumbnailsEnabled=!1),i},f=!1,g=!1,h=function(a){f=!!a.lineTruncationEnabled,g=!!a.consoleLoggingEnabled},i=function(a){return b(a).closest("body").length&&!a.offsetTop},j=function(){};window.console&&(j=function(){if(g){var b=a.toArray(arguments);b.unshift("[Discovery]"),window.console.log.apply?window.console.log.apply(window.console,b):window.console.log(b.join(" "))}});var k=function(b){return a.isUndefined(b)?g:void(g=!!b)},l=function(b){return a.isUndefined(b)?f:void(f=!!b)},m=function(c,d){function e(){return k.scrollHeight-k.offsetHeight>.2*l}function g(){i.lastChild&&!a.contains(["...","…"],i.lastChild.nodeValue)&&(m=i.appendChild(window.document.createTextNode(" "+o)),e()&&(i.removeChild(m),i.removeChild(i.lastChild),g()))}if(f){if(!c.closest("body").length)return void j("lineTruncate called on el not on DOM");if(c.text().length<1)return void j("lineTruncated called on empty el");var h=function(a){return 3!==a.nodeType};if(a.any(c.children(),h))return void j("lineTruncate called on non-flat el");var i=c[0],k=i;if("block"!==c.css("display"))for(;k.parentNode&&(k=k.parentNode,"block"!==b(k).css("display")););var l=parseFloat(c.css("font-size"),10);if(e()){d=d||{};var m,n=d.lines||1,o=d.ellipsis,p=c.text();if(p.length){var q=c.width()/l,r=parseInt(q*n,10),s=p.split(/\s/),t=0;c.empty();for(var u=0,v=s.length;v>u&&(t+=s[u].length+1,!(t>=r));u++)i.appendChild(document.createTextNode(" "+s[u]));if(e()){do m=i.removeChild(i.lastChild);while(e())}else{do m=i.appendChild(document.createTextNode(" "+s[u++]));while(!e()&&v>u);i.removeChild(m)}o&&(a.isString(o)||(o="…"),g())}}}},n=function(b){function c(a,b){return a+b}var d,e,f=a.keys(b),g=Math.floor(a.reduce(b,c,0)/2),h=f.length+1,i=g+1,j=new Array(h);for(d=0;h>d;d++)j[d]=new Array(i),j[d][0]={};for(e=1;i>e;e++)j[0][e]=!1;var k,l,m,n={};for(e=1;i>e;e++)for(d=1;h>d;d++)k=f[d-1],l=b[k],m=a.clone(j[d-1][e]),!m&&e>=l&&(m=a.clone(j[d-1][e-l]),m&&(m[k]=l,n=m)),j[d][e]=m;return[n,a.omit(b,a.keys(n))]},o=["product","zone","service","experiment","variant"],p=function(b){b=b||"";var c=a.object(o,b.split(":"));return{bin:b,experiment:c.experiment||"",variant:c.variant||""}};return{generateVariantConfig:e,config:h,looksAdblocked:i,log:j,allowLog:k,allowLineTruncate:l,lineTruncate:m,balancedPartition:n,binToEventParams:p}}),define("discovery/variants",[],function(){"use strict";return{"default":{maxPerColumn:2,inlineMeta:!1,contentPreviews:!0,promotedEnabled:!1,topPlacementEnabled:!1},promoted:{maxPerColumn:4,inlineMeta:!0,contentPreviews:!1,promotedEnabled:!0,topPlacementEnabled:!1,promotedSide:"right"},max:{maxPerColumn:4,inlineMeta:!0,contentPreviews:!1,promotedEnabled:!0,topPlacementEnabled:!0,promotedSide:"left"},thumbnails:{maxOrganicThumbnailLinks:0,maxPromotedThumbnailLinks:4,promotedSide:"left",numLinesHeadline:4}}}),define("discovery/views",["backbone","underscore","jquery","stance","core/analytics/jester","core/bus","core/strings","common/templates","common/utils","discovery/helpers"],function(a,b,c,d,e,f,g,h,i,j){"use strict";var k=g.get,l=a.View.extend({getTemplateContext:function(){return this.appContext||(this.appContext=this.model.toJSON()),{variant:this.appContext}},template:function(a,b){return b=b||this.templateName,h.render(b,a)},report:function(a){this.model.report(b.extend({object_type:"advertisement",area:"promoted_discovery"},a))}}),m=function(a){var e=a.prototype;return a.extend({events:{"click [data-redirect]":"handleClick"},templateName:"discoveryCollection",handleClick:function(a){this.swapHref(a.currentTarget)},swapHref:function(a){a.setAttribute("data-href",a.getAttribute("href")),a.setAttribute("href",a.getAttribute("data-redirect")),b.delay(function(){a.setAttribute("href",a.getAttribute("data-href"))},100)},initialize:function(a){e.initialize.call(this,a),this.elementsSelector="li.discovery-post",this.$elements=this.$el.find(this.elementsSelector),this.initContext=a.context;var b=this.collection;this.listenTo(b,{remove:this.remove,reset:this.render}),this.visibilityTrackers={},this.queueViewEvents()},queueViewEvents:function(){if(this.model.get("promotedEnabled")){var a=function(){return c(this.el).height()/2};b.each(this.visibilityTrackers,function(a,b){a&&(this.collection.get(b)||(this.stopListening(a),this.visibilityTrackers[b]=null))},this),b.each(this.$elements,function(c){var e=c.getAttribute("data-id");if(!this.visibilityTrackers[e]){var f=this.collection.get(e);if(f){var g=d({el:c,timer:null,topEdgeOffset:a,bottomEdgeOffset:a});this.visibilityTrackers[e]=g;var h=b.bind(function(){this.reportIABView(e),this.stopListening(g)},this);this.listenTo(g,{enter:function(){g.timer=b.delay(h,1e3)},exit:function(){clearTimeout(g.timer)}})}}},this)}},reportIABView:function(a){var c=this.collection.get(a),d=c.get("tracking_pixels_onview")||[];i.loadPixels(d,b.bind(this.pixelLoadErrorCallback,this,a),this.$el)},loadOnLoadPixels:function(){this.collection.each(function(a){var c=a.get("tracking_pixels_onload")||[],d=a.id;i.loadPixels(c,b.bind(this.pixelLoadErrorCallback,this,d),this.$el)},this)},pixelLoadErrorCallback:function(a,b){this.report({verb:"load",adverb:"failed",adjective:"tracking-pixel",object_type:"service",object_id:b,advertisement_id:a})},truncate:function(){var a=this.$el.find(".line-truncate");b.each(a,function(a){var b=c(a);j.lineTruncate(b,{lines:parseInt(b.attr("data-line-truncate"),10),ellipsis:!0})})},getTemplateContext:function(){var a=e.getTemplateContext.call(this);b.extend(a,this.initContext),a.collection=this.collection.toJSON();var c=this.collection.at(0);if(c){var d=c.has("id")?"organic-":"promoted-",f=c.idAttribute;b.each(a.collection,function(a){a.advertisement_id=a[f],a.domIdSuffix=a[f],a.domIdSuffix=d+a.domIdSuffix})}return a},render:function(){var a=this.getTemplateContext();return this.$el.html(this.template(a)),this.$elements=this.$el.find(this.elementsSelector),this.truncate(),this.loadOnLoadPixels(),this.queueViewEvents(),this},remove:function(a,d,e){if(0===arguments.length)return l.prototype.remove.call(this);var f=b.toArray(this.$elements),g=f.splice(e.index,1)[0];return c(g).remove(),this.$elements=c(f),this.queueViewEvents(),this}})}(l),n=function(a,b){this.modelIds=a||[],this.$elements=c(b||[])};b.extend(n.prototype,{height:function(){var a=this;a.heights=[];var d=c(a.$elements),e=d.first().offset().top,f=function(){var a=d.last();return a.offset().top+a.height()}(),g=f-e,h=0;return b.each(d,function(b){var d=c(b).height();a.heights.push(d),h+=d}),this.interstice=(g-h)/(d.length-1),g}});var o=function(){this.divideIntoColumns=function(){var a=this,b=a.subviews[0];a.left=new n,a.right=new n;var c=0;b.collection.each(function(d,e){var f=c++%2===0?"left":"right";a[f].modelIds.push(d.id),Array.prototype.push.call(a[f].$elements,b.$elements[e])})},this.removeOneFromColumn=function(a,c){var d,e=b.chain(a.modelIds).map(function(b,c){return[b,a.heights[c]]}).sortBy(function(a){return-1*a[1]}).find(function(a){return a[1]<=c}).value()[0],f=this.subviews[0].collection,g=f.models,h=f.get(e),i=g.indexOf(h),j=[],k=[],l=[k,j],m=g.length;for(d=0;m>d;d++)l[d%2].push(g[d]);var n=l[i%2];n.splice(b.indexOf(n,h),1),g=[];var o=(i+1)%2;for(d=0;m-1>d;d++)g.push(l[(d+o)%2].shift());f.reset(g)},this.balanceColumns=function(){var a=this.subviews[0],c=a.collection,d={};c.each(function(b,c){d[c]=a.$elements[c]});var e=j.balancedPartition(d);e=b.sortBy(e,"length");var f=e[1],g=e[0],h=c.models,i=new Array(h.length);b.each(f,function(a,b){i[2*b]=h[b]}),b.each(g,function(a,b){i[2*b+1]=h[b]}),c.reset(h)},this.shortenColumn=function(a,b){var c=this.subviews[0].collection;c.length%2!==0&&a===this.left?this.removeOneFromColumn(a,this.fudge*b):this.balanceColumns()}},p=function(){this.divideIntoColumns=function(){var a=this,b=a.subviews,c=b[0],d=b[1],e=c.collection.model.prototype.idAttribute;a.left=new n(c.collection.pluck(e),c.$elements);var f=d.collection.model.prototype.idAttribute;a.right=new n(d.collection.pluck(f),d.$elements)},this.shortenColumn=function(a,c){for(var d=a===this.left?this.subviews[0]:this.subviews[1],e=a===this.left?this.right:this.left,f=e,g=c/f.$elements.length,h=d.collection,i=b.chain(a.modelIds).map(function(b,c){return[b,a.heights[c]]}).sortBy(function(a){return a[1]}).value(),j=[],k=0,l=c,m=g;i.length;){var n=i.pop(),o=n[0],p=n[1],q=p+a.interstice;if(k+q>c&&(f=a),l=Math.abs(c-(k+q)),m=l/f.$elements.length,!(m>=g)){g=m;var r=a.modelIds.indexOf(o);a.modelIds.splice(r,1),Array.prototype.splice.call(a.$elements,r,1),k+=q,j.push(o)}}h.remove(j)}},q=function(a){this.fudge=a.fudge,this.subviews=a.views.slice(0,2),1===this.subviews.length?o.call(this):p.call(this)};b.extend(q.prototype,{ascendingByHeight:function(){var a=this.left,c=this.right,d=[[a,a.height()],[c,c.height()]];return b.sortBy(d,function(a){return a[1]})},evenColumns:function(a){var c=this.ascendingByHeight(),d=c[0][0],e=c[0][1],f=c[1][0],g=c[1][1];if(e!==g){var h=g-e,i=this.fudge*h,j=b.find(f.heights,function(a){return a+f.intersticee)){var f=d/e;b.each(a.$elements,function(a){var b=c(a),d=parseInt(b.css("margin-bottom"),10),e=d+f;b.css("margin-bottom",e+"px")});var g=a===this.left?this.right:this.left,h=a===this.right?"left":"right";g.$elements.css("clear",h)}},render:function(){return this.divideIntoColumns(),this.evenColumns(),this}});var r=function(a){var c=a.prototype;return a.extend({templateName:"discoveryMain",topEdgeOffset:0,bottomEdgeOffset:1/0,events:{"click [data-action=discovery-help]":function(a){a.preventDefault(),this.model.set("help",!0)},"click [data-action=discovery-help-close]":function(a){a.preventDefault(),this.model.set("help",!1)}},toggleHelp:function(a){var b=this;b.$el.find("#discovery-note").toggle(),a.trigger("resize")},rerenderHelp:function(){var a=this.$el.find("#discovery-note");a.length&&a.html(this.template(this.getTemplateContext(),"discoveryNote"))},initialize:function(a){c.initialize.call(this,a),this.listenTo(this.model,{"change:display":this.show,"change:help":this.toggleHelp}),this.listenTo(this.model.get("session"),"change",this.rerenderHelp),this.$el.css({position:"absolute",visibility:"hidden",display:"block",width:this.$el.width()-20+"px"})},createSections:function(){var a=this.model,c=a.get("sectionNames"),d=a.get("sectionIds");return b.map(a.collections,function(b,e){var f,g="";return b===a.threads?f="organic":b===a.ads&&(f="promoted",b.each(function(a){if(a)switch(a.get("brand")){case"Women & Co":g=k("Sponsored Stories");break;case"American Express OPEN":g=k("Sponsored Links")}})),{id:d[e],className:c[e],type:f,showThumbnailsInRows:"promoted"===f&&a.getThumbnailLinksMobile("Promoted"),promotedTitle:g}})},areThumbnailsEnabled:function(){return this.model.get("maxOrganicThumbnailLinks")||this.model.get("maxPromotedThumbnailLinks")},getTemplateContext:function(){var a=this.model,b=this.createSections();return{id:a.get("innerContainerId"),sections:b,styleVariant:a.get("styleVariant"),forum:a.get("sourceForum"),discoverySettingsUrl:a.get("promotedEnabled")&&a.get("discoverySettingsUrl"),session:a.get("session").toJSON(),thumbnailsEnabled:this.areThumbnailsEnabled()}},render:function(){this.$el.html(this.template(this.getTemplateContext()))},show:function(a){a.get("display")&&(this.$el.css({position:"static",visibility:"visible",width:"100%"}),a.trigger("resize"))},remove:function(a){var b;return a&&a.cloneContainer&&(b=this.el.cloneNode(!1),this.$el.attr("id",""),this.$el.after(b)),c.remove.call(this)}})}(l);return{BaseCollectionView:m,TwoColumn:q,MainView:r}}),define("discovery/views/BaseUnit",["backbone","underscore","jquery","stance","common/utils","core/analytics/jester","discovery/helpers"],function(a,b,c,d,e,f,g){"use strict";return a.View.extend({setupViewEvents:function(){var a=this,b=function(b,c){var d=!1,e=null,f=function(){d=!0,a.trigger("view:"+c.delayed,a)};a.listenTo(b,{enter:function(){a.trigger("view:"+c.enter,a),d||(e=window.setTimeout(f,1e3))},exit:function(){a.trigger("view:"+c.exit,a),window.clearTimeout(e)}})},c=function(){return a.$el.height()/2};b(d({el:a.el,topEdgeOffset:c,bottomEdgeOffset:c}),{enter:"50in",exit:"50out",delayed:"iab"}),b(d({el:a.el}),{enter:"enter",exit:"exit",delayed:"full"})},getProvider:function(){return this.model.get("ad_provider")||this.model.get("provider")},reportIABView:function(){this._fireViewPixels(),this.report({verb:"view",adverb:"iab-scroll"})},reportLoad:function(){this._fireLoadPixels(),this.report({verb:"load"})},reportNoOffer:function(){this.report({verb:"fail",object_type:"provider",object_id:this.getProvider(),adverb:"no_ads"})},reportTimeout:function(){this.report({verb:"fail",object_type:"provider",object_id:this.getProvider(),adverb:"timeout"})},reportRequest:function(){this.report({verb:"call",object_type:"provider",object_id:this.getProvider(),adjective:1})},fireClickPixels:function(){var a=this.model.get("tracking_pixels_onclick");e.loadPixels(a,b.bind(this._reportPixelErrors,this),this.$el)},report:function(a){f.client.emit(b.defaults(a,{object_type:"advertisement",object_id:"["+this.model.id+"]",advertisement_id:this.model.id,ad_product_name:this.productName,zone:"thread",area:"sponsored_comment"},g.binToEventParams(this.requestBin)))},_fireViewPixels:function(){var a=this.model.get("tracking_pixels_onview")||[],c=this.model.get("tracking_pixel_url");c&&a.push({tag:"img",url:c}),e.loadPixels(a,b.bind(this._reportPixelErrors,this),this.$el)},_fireLoadPixels:function(){var a=this.model.get("tracking_pixels_onload")||[];e.loadPixels(a,b.bind(this._reportPixelErrors,this),this.$el)},_reportPixelErrors:function(a){this.report({verb:"load",adverb:"failed",adjective:"tracking-pixel",object_type:"service",object_id:a})}})}),define("discovery/views/Iframe",["jquery","underscore","backbone","core/utils/urls"],function(a,b,c,d){"use strict";var e=c.View.extend({tagName:"iframe",initialize:function(c){var e=c.src||this.el.src;this._source=e,this._origin=d.getOrigin(e),this._onMessage=b.bind(this._onMessage,this),a(window).on("message",this._onMessage),this._ready=!1,this._buffer=[],this.once("message",this._onLoad)},_onLoad:function(){this._ready=!0,b.each(this._buffer,this.postMessage,this)},_onMessage:function(a){a=a.originalEvent,a&&a.origin===this._origin&&this.trigger("message",a.data,this)},render:function(){return this.$el.attr("src",this._source),this},postMessage:function(a){if(this._ready)try{this.el.contentWindow.postMessage(a,this._origin)}catch(b){}else this._buffer.push(a)},remove:function(){return a(window).off("message",this._onMessage),c.View.prototype.remove.apply(this,arguments)}});return e}),define("discovery/views/IframeUnit",["jquery","underscore","backbone","core/bus","core/strings","common/templates","discovery/views/BaseUnit","discovery/views/Iframe"],function(a,b,c,d,e,f,g,h){"use strict";var i=g.extend({READY_TIMEOUT:45e3,proxyEvents:{"view:iab":"disqus.inView","view:50in":"disqus.view:50in","view:50out":"disqus.view:50out"},initialize:function(a){this.setupViewEvents(),this.listenTo(this,"view:iab",this.reportIABView),this.listenTo(this,"all",this._proxyEvents),this.listenTo(d.frame,"embed.resized",b.debounce(b.bind(this.render,this),100)),this.requestBin=a.requestBin},isEmbedHidden:function(){return!a("body").width()},_proxyEvents:function(a){this.proxyEvents[a]&&this.frame&&this.frame.postMessage(this.proxyEvents[a])},remove:function(){return this._removeFrame(),g.prototype.remove.apply(this,arguments)},_removeFrame:function(){this.frame&&(this.stopListening(this.frame),this.frame.remove())},render:function(){if(this.isEmbedHidden())return this;this.stopListening(d.frame,"embed.resized"),this._removeFrame();var a=this.serializeData();return this.$el.hide().html(f.render("iframeAd",a)),this.frame=new h({el:this.$('[data-role="iframe-ad"]')[0],src:a.iframeUrl}),this.listenTo(this.frame,"message",this.handleMessage),this.frame.render(),this.reportRequest(),this._providerTimeout=b.delay(b.bind(this.timeoutUnit,this),this.READY_TIMEOUT),this.listenTo(this,"iframe:ready",function(){window.clearTimeout(this._providerTimeout)}),this},timeoutUnit:function(){this.reportTimeout(),this.remove()},_postMessage:function(a){this.frame.postMessage(JSON.stringify(a))}});return i}),define("discovery/views/VideoIframeUnit",["jquery","underscore","backbone","core/bus","core/analytics/jester","core/strings","common/templates","discovery/views/IframeUnit"],function(a,b,c,d,e,f,g,h){"use strict";var i="ready",j="noOffer",k={ready:i,noOffer:j,"viroolWidget.playerReady":i,"viroolWidget.noOffers":j},l=h.extend({productName:"video",getIframeUrl:function(){return this.model.get("media_url")},serializeData:function(){var c,d=this.getIframeUrl(),e=this.model.get("width")||640;return e=Math.min(e,a("body").width()),c=e/1.6,{iframeUrl:b.template(d)({width:e,height:c}),height:c,width:"100%",title:this.model.get("title"),summary:this.model.get("summary"),targetUrl:this.model.get("target_url"),brandName:this.model.get("brand_name"),brandImageUrl:this.model.get("brand_image_url")}},handleMessage:function(a){switch(k[a]){case i:this.trigger("iframe:ready",a),this.$el.show(),this.frame&&this.stopListening(this.frame),this.reportLoad();break;case j:this.reportNoOffer(),this.remove()}}});return l}),define("discovery/views/IABDisplayUnit",["jquery","underscore","remote/config","discovery/views/IframeUnit"],function(a,b,c,d){"use strict";var e=d.extend({className:"iab-ad_centered",initialize:function(){this.listenTo(this,"iframe:ready",this.showAd),d.prototype.initialize.apply(this,arguments)},getIframeUrl:function(){return c.discovery.iabDisplayUrl||""},serializeData:function(){var b=this.getIframeUrl(),c=this.model.get("width")||640,d=this.model.get("height")||480;if(!(c>a("body").width()))return{iframeUrl:b,height:d,width:c}},handleMessage:function(a){switch(a.eventName){case"iframeLoaded":this.initAd();break;case"ready":this.trigger("iframe:ready",a)}},showAd:function(a){a.width&&a.height&&(this.frame.$el.width(a.width),this.frame.$el.height(a.height)),this.$el.show(),this.reportLoad()},initAd:function(){this._postMessage({method:"initAd",content:this.model.get("content")})}});return e}),define("discovery/views/VastUnit",["jquery","underscore","stance","remote/config","discovery/views/IframeUnit"],function(a,b,c,d,e){"use strict";var f=e.extend({events:{"mouseenter [data-role=iframe-wrapper]":"turnSoundOn","mouseleave [data-role=iframe-wrapper]":"turnSoundOff"},productName:"video",supportedSizes:[{width:320,height:180},{width:480,height:352},{width:640,height:360}],initialize:function(){this.listenTo(this,{"view:50in":this.startAd,"view:50out":this.pauseAd}),this.listenTo(this,"iframe:ready",this.showPlayer),e.prototype.initialize.apply(this,arguments)},getIframeUrl:function(){return d.discovery.videoPlayerUrl||""},handleMessage:function(a){switch(a.eventName){case"iframeLoaded":this.initVideoPlayer();break;case"ready":this.trigger("iframe:ready",a);break;case"play":this.reportPlay();break;case"error":this.reportError();break;case"ended":a.isVast||this.showReplayOverlay(!0),this.reportVideoComplete()}},serializeData:function(){var c,d=this.getIframeUrl(),e=this.model.get("width")||640;return e=Math.min(e,a("body").width()),c=e/1.6,{iframeUrl:b.template(d)({width:e,height:c}),height:c,width:"100%"}},initVideoPlayer:function(){var a=this.$el.parent().width(),c=b.find(this.supportedSizes,function(b){return b.width>a});c=c||b.last(this.supportedSizes),this._postMessage({method:"initPlayer",url:this.model.get("media_url"),width:c.width,height:c.height})},showPlayer:function(a){a.width&&a.height&&(this.frame.$el.width(a.width),this.frame.$el.height(a.height)),this.$el.show(),this.reportLoad()},showReplayOverlay:function(a){this.$("[data-role=iframe-wrapper]").toggleClass("generic-ad__hidden",a)},reportPlay:function(){this.hasStarted=!0,this.report({verb:"start-playing"})},reportError:function(){this.report({verb:"error",adjective:this.player.src()})},reportVideoComplete:function(){this.hasStarted&&this.report({verb:"finish-playing"})},startAd:function(){this._postMessage({method:"playVideo"})},pauseAd:function(){this._postMessage({method:"pauseVideo"})},turnSoundOn:function(){this._postMessage({method:"turnSoundOn"})},turnSoundOff:function(){this._postMessage({method:"turnSoundOff"})}});return f}),define("discovery/views/ImageUnit",["underscore","discovery/views/BaseUnit","common/templates"],function(a,b,c){"use strict";var d=b.extend({productName:"native_dr",className:"style-variant-default",events:{"click a":"fireClickPixels"},initialize:function(b){this.render=a.compose(this.render,a.bind(this.trigger,this,"render")),this.setupViewEvents(),this.listenTo(this,"view:iab",this.reportIABView),this.listenTo(this,"render",this.reportLoad),this.requestBin=b.requestBin},serializeData:function(){var a=this.model;return{title:a.get("title"),summary:a.get("summary"),targetUrl:a.get("target_url"),brandName:a.get("brand_name"),thumbnailUrl:a.get("thumbnail_url")}},render:function(){return this.$el.html(c.render("imageAd",this.serializeData())),this}});return d}),define("discovery/views/Placement",["underscore","backbone","common/templates","discovery/views/IframeUnit","discovery/views/VideoIframeUnit","discovery/views/IABDisplayUnit","discovery/views/VastUnit","discovery/views/ImageUnit","core/logger"],function(a,b,c,d,e,f,g,h,i){"use strict";var j=b.View.extend({template:"adPlacement",itemViewContainer:".post-list",LAYOUT_TO_CLASS:{image_target:h,iframe:e,iab_display:f,video_vast:g},initialize:function(a){this.visible=a.visible,this.requestBin=a.requestBin,this.initSubViews(),this.listenTo(this.model,"change",function(){this.initSubViews(),this.render()})},initSubViews:function(){if(this.removeSubViews(),this.model){var a=this.model.get("layout"),b=this.LAYOUT_TO_CLASS[a];if(!b)return void i.debug("Specified Placement layout "+a+" was not found.");var c=new b({model:this.model,requestBin:this.requestBin,excludeAnchor:!0});this.adView=c}},removeSubViews:function(){this.adView&&this.adView.remove()},setVisible:function(a){a!==this.visible&&(this.visible=a,this.render())},remove:function(){return this.removeSubViews(),b.View.prototype.remove.apply(this,arguments)},render:function(){if(this.visible&&this.adView){var a=this.$el.html(c.render(this.template)).find(this.itemViewContainer);a.append(this.adView.render().$el),this.adView.delegateEvents(),this.$el.show()}else this.$el.hide();return this}});return j}),define("templates/discovery",["handlebars"],function(a){return a.template({1:function(a,b,c,d){var e,f='\n"},2:function(a,b,c,d){var e,f="";return e=b["if"].call(a,null!=(e=null!=a?a.user:a)?e.isEditable:e,{name:"if",hash:{},fn:this.program(3,d),inverse:this.noop,data:d}),null!=e&&(f+=e),f+"\n"},3:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression;return'\n" +},5:function(a,b,c,d){var e,f="";return e=b["if"].call(a,null!=(e=null!=a?a.user:a)?e.isPrivate:e,{name:"if",hash:{},fn:this.program(6,d),inverse:this.program(8,d),data:d}),null!=e&&(f+=e),f},6:function(a,b,c,d){var e=this.escapeExpression;return'\n"},8:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression,h='\n'},9:function(){return"following"},11:function(a,b,c,d,e){var f,g="";return f=b.each.call(a,null!=a?a.collection:a,{name:"each",hash:{},fn:this.program(12,d,e),inverse:this.noop,data:d}),null!=f&&(g+=f),g},12:function(a,b,c,d,e){var f,g=this.lambda,h=this.escapeExpression,i='\n"},13:function(){return" hasthumbnail"},15:function(a){var b=this.lambda,c=this.escapeExpression;return'
    \n
    \n'},17:function(a,b,c,d){var e,f="";return e=b["if"].call(a,b.gt.call(a,null!=a?a.posts:a,0,{name:"gt",hash:{},data:d}),{name:"if",hash:{},fn:this.program(18,d),inverse:this.program(20,d),data:d}),null!=e&&(f+=e),e=b["if"].call(a,null!=a?a.brand:a,{name:"if",hash:{},fn:this.program(23,d),inverse:this.noop,data:d}),null!=e&&(f+=e),f},18:function(a,b,c,d){var e,f='\n';return e=this.invokePartial(c.discoveryPostCount,"","discoveryPostCount",a,void 0,b,c,d),null!=e&&(f+=e),f+"\n"},20:function(a,b,c,d){var e,f="";return e=b["if"].call(a,null!=a?a.createdAgo:a,{name:"if",hash:{},fn:this.program(21,d),inverse:this.noop,data:d}),null!=e&&(f+=e),f},21:function(a){var b=this.lambda,c=this.escapeExpression;return''+c(b(null!=a?a.createdAgo:a,a))+"\n"},23:function(a){var b=this.lambda,c=this.escapeExpression;return'\n'+c(b(null!=a?a.brand:a,a))+"\n\n"},25:function(a,b,c,d){var e,f='
      \n';return e=b["if"].call(a,b.gt.call(a,null!=a?a.posts:a,0,{name:"gt",hash:{},data:d}),{name:"if",hash:{},fn:this.program(26,d),inverse:this.noop,data:d}),null!=e&&(f+=e),e=b["if"].call(a,null!=a?a.createdAgo:a,{name:"if",hash:{},fn:this.program(28,d),inverse:this.noop,data:d}),null!=e&&(f+=e),f+"
    \n"},26:function(a,b,c,d){var e,f='
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    3. \n"},28:function(a){var b=this.lambda,c=this.escapeExpression;return'
  3. '+c(b(null!=a?a.createdAgo:a,a))+"
    4. \n"},30:function(a,b,c,d){var e,f="";return e=this.invokePartial(c.discoveryContentPreview,"","discoveryContentPreview",a,void 0,b,c,d),null!=e&&(f+=e),f},32:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression,h='href="'+g(f(null!=a?a.redirectUrl:a,a))+'" ';return e=b["if"].call(a,null!=a?a.brand:a,{name:"if",hash:{},fn:this.program(33,d),inverse:this.noop,data:d}),null!=e&&(h+=e),h+"\n"},33:function(){return'target="_blank" rel="nofollow norewrite"'},35:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression,h="\n'+g(b.gettext.call(a,\n

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    \n    \n"},37:function(a,b,c,d){var e,f="";return e=b["if"].call(a,b.eq.call(a,null!=a?a.posts:a,1,{name:"eq",hash:{},data:d}),{name:"if",hash:{},fn:this.program(38,d),inverse:this.program(40,d),data:d}),null!=e&&(f+=e),f},38:function(a,b,c,d){var e=this.escapeExpression;return e(b.gettext.call(a,"1 comment",{name:"gettext",hash:{},data:d}))+"\n"},40:function(a,b,c,d){var e=this.escapeExpression;return e(b.gettext.call(a,"%(numPosts)s comments",{name:"gettext",hash:{numPosts:null!=a?a.posts:a},data:d}))+"\n"},42:function(a,b,c,d,e){var f,g=this.lambda,h=this.escapeExpression,i='
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    \n',f=this.invokePartial(c.discoveryNote,"","discoveryNote",a,void 0,b,c,d),null!=f&&(i+=f),i+='
    \n\n
    \n\n
    \n\n",f=b.each.call(a,null!=a?a.sections:a,{name:"each",hash:{},fn:this.program(47,d,e),inverse:this.noop,data:d}),null!=f&&(i+=f),i+"\n
    \n"},43:function(){return" discovery-thumbnails"},45:function(a){var b=this.lambda,c=this.escapeExpression;return"style-variant-"+c(b(null!=a?a.styleVariant:a,a))},47:function(a,b,c,d,e){var f,g=this.lambda,h=this.escapeExpression,i='
    \n
    \n\n',f=b["if"].call(a,b.eq.call(a,null!=a?a.type:a,"organic",{name:"eq",hash:{},data:d}),{name:"if",hash:{},fn:this.program(50,d,e),inverse:this.noop,data:d}),null!=f&&(i+=f),i+="\n",f=b["if"].call(a,b.eq.call(a,null!=a?a.type:a,"promoted",{name:"eq",hash:{},data:d}),{name:"if",hash:{},fn:this.program(52,d,e),inverse:this.noop,data:d}),null!=f&&(i+=f),i+'\n
    \n
      \n
    \n
    \n'},48:function(){return" thumbnails-rows"},50:function(a,b,c,d,e){var f=this.escapeExpression;return"

    "+f(b.gettext.call(a,"Also on %(forumName)s",{name:"gettext",hash:{forumName:b.getPartial.call(a,"forumName",null!=e[2]?e[2].forum:e[2],{name:"getPartial",hash:{},data:d})},data:d}))+"

    \n"},52:function(a,b,c,d){var e,f="

    ";return e=b["if"].call(a,null!=a?a.promotedTitle:a,{name:"if",hash:{},fn:this.program(53,d),inverse:this.program(55,d),data:d}),null!=e&&(f+=e),f+"

    \n"},53:function(a){var b=this.lambda,c=this.escapeExpression;return"\n"+c(b(null!=a?a.promotedTitle:a,a))+"\n"},55:function(a,b,c,d){var e=this.escapeExpression;return e(b.gettext.call(a,"Around The Web",{name:"gettext",hash:{},data:d}))+"\n"},57:function(a,b,c,d){var e=this.escapeExpression;return''+e(b.gettext.call(a,"Learn more",{name:"gettext",hash:{},data:d}))+"\n"},59:function(a,b,c,d){var e=this.escapeExpression;return'\n'+e(b.gettext.call(a,"give us feedback",{name:"gettext",hash:{},data:d}))+""},61:function(a){var b=this.lambda,c=this.escapeExpression;return""+c(b(null!=a?a.name:a,a))+"\n"},63:function(a,b,c,d){var e,f=this.escapeExpression,g='
    \n\n'+f(b.gettext.call(a,"Disqus helps you find new and interesting content, discussions and products. Some sponsors and ecommerce sites may pay us for these recommendations and links. %(learnMore)s or %(feedback)s.",{name:"gettext",hash:{feedback:b.getPartial.call(a,"feedback",{name:"getPartial",hash:{},data:d}),learnMore:b.getPartial.call(a,"learnMore",{name:"getPartial",hash:{},data:d})},data:d}))+"\n";return e=b.if_all.call(a,null!=(e=null!=(e=null!=a?a.session:a)?e.thread:e)?e.canModerate:e,null!=a?a.discoverySettingsUrl:a,{name:"if_all",hash:{},fn:this.program(64,d),inverse:this.noop,data:d}),null!=e&&(g+=e),g+"
    \n"},64:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression;return'
    \n'+g(b.gettext.call(a,"Change %(Discovery)s settings for %(forumName)s",{name:"gettext",hash:{forumName:null!=(e=null!=a?a.forum:a)?e.name:e,Discovery:"Discovery"},data:d}))+"\n"},66:function(){return'
      \n'},68:function(a,b,c,d){var e,f=this.escapeExpression,g=this.lambda,h='\n"},69:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression,h='
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      \n\n\n'+g(b.gettext.call(a,"Sponsored By %(name)s",{name:"gettext",hash:{name:b.tag.call(a,"strong",{name:"tag",hash:{text:null!=a?a.brandName:a},data:d})},data:d}))+'\n\n

      \n'+g(f(null!=a?a.summary:a,a))+'\n

      \n      \n
      \n
      \n\n'+g(b.gettext.call(a,"Learn More",{name:"gettext",hash:{},data:d}))+' \n\n
      \n
      \n'},70:function(){return"has-brand-logo"},72:function(a){var b=this.lambda,c=this.escapeExpression;return'\n'},74:function(a,b,c,d){var e,f=this.escapeExpression,g='
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      \n';return e=b["if"].call(a,null!=a?a.title:a,{name:"if",hash:{},fn:this.program(75,d),inverse:this.noop,data:d}),null!=e&&(g+=e),g+='\n
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      \n
      \n\n
      \n
      \n',e=b["if"].call(a,null!=a?a.description:a,{name:"if",hash:{},fn:this.program(77,d),inverse:this.noop,data:d}),null!=e&&(g+=e),g+"
      \n
      \n
      \n"},75:function(a){var b=this.lambda,c=this.escapeExpression;return'

      '+c(b(null!=a?a.title:a,a))+"

      \n"},77:function(a){var b=this.lambda,c=this.escapeExpression;return'

      \n'+c(b(null!=a?a.description:a,a))+"\n

      \n"},79:function(a,b,c,d){var e,f=this.lambda,g=this.escapeExpression,h='
      \n
      \n\n
      \n
      \n
      \n';return e=b["if"].call(a,null!=a?a.brandName:a,{name:"if",hash:{},fn:this.program(80,d),inverse:this.noop,data:d}),null!=e&&(h+=e),h+''+g(b.gettext.call(a,"Sponsored",{name:"gettext",hash:{},data:d}))+'\n
      \n
      \n\n
      \n

      \n'+g(f(null!=a?a.title:a,a))+'\n

      \n
      \n'+g(f(null!=a?a.summary:a,a))+'\n
      \n
      \n'+g(b.gettext.call(a,"Learn More",{name:"gettext",hash:{},data:d}))+"\n
      \n
      \n
      \n"},80:function(a){var b=this.lambda,c=this.escapeExpression;return'\n'+c(b(null!=a?a.brandName:a,a))+'\n\n'},compiler:[6,">= 2.0.0-beta.1"],main:function(a,b,c,d,e){var f,g="";return 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r.DiscoveryApp=a.Model.extend({defaults:{name:"promoted",inlineMeta:!0,contentPreviews:!1,organicEnabled:!0,promotedEnabled:!0,topPlacementEnabled:!1,redirectUrl:i.shouldSample(j.discovery.NEW_REDIRECT_PERCENTAGE)?"http://disq.us/url":"http://redirect.disqus.com/url",sourceThread:null,sourceForum:null,sourceThreadUrl:null,discoverySettingsUrl:null,help:!1,display:!1,columnEveningEnabled:!0,numColumns:2,minPerColumn:1,maxPerColumn:null,toleranceCoefficient:1.2,maxOrganicTextLinks:null,maxPromotedTextLinks:null,maxOrganicThumbnailLinks:null,maxPromotedThumbnailLinks:null,maxPromotedThumbnailLinksMobile:null,minWidthForColumnLayout:440,containerId:"discovery",innerContainerName:"discovery-main",sectionNames:null,collectionTagName:"ul",collectionClassName:"discovery-posts",promotedSide:"right",organicThumbnailClass:"discovery-thumbnails",promotedThumbnailClass:"doublethumbnails",styleVariant:"default",sponsoredCommentStyleVariant:"default",injectScriptUrl:void 0,hasSpaceForSponsoredComment:!1,sponsoredCommentsEnabled:!1,sponsoredCommentId:null,consoleLoggingEnabled:h.debug,lineTruncationEnabled:!0,session:null,numLinesHeadline:2,requestBinOverride:null,thumbnailMinHeight:200,thumbnailMinWidth:200,thumbnailAspectRatioMin:.5,thumbnailAspectRatioMax:1.8,shouldEvenThumbnails:!1,thumbnailTimeout:2e3,minThumbnailsNeeded:null,js:null,css:null,seenByUserThresholdTime:2e3,trackAdVisibility:null},get:function(b){if(b in this.constructor.loggedinOverrides){var c=this.get("session");if(c&&c.isLoggedIn())return this.constructor.loggedinOverrides[b]}return a.Model.prototype.get.apply(this,arguments)},initialize:function(a){var c=this;c.collections=[],c.ads=new k.AdvertisementCollection,c.augmentCollection(c.ads,"Promoted"),c.threads=new k.RelatedThreadCollection,c.augmentCollection(c.threads,"Organic"),c.userSuggestions=a.userSuggestions,c.configure(a),c.once("change:display",function(){c.onComplete()}),b.bindAll(c,"getContentPreviews","validateData","prepareData","showData","reportIfVisible")},trackVisibility:function(){this.debouncedReportIfVisible=b.debounce(this.reportIfVisible,this.get("seenByUserThresholdTime")),this.visibilityTracker=d(this.mainView),this.listenTo(this.visibilityTracker,"enter",this.debouncedReportIfVisible)},configure:function(a){a=a||{};var c=this,d=n[c.get("name")]||{};if(c.get("thumbnailsEnabled")&&b.extend(d,n.thumbnails),c.set(b.defaults(a,d)),c.has("maxPerColumn")){var e=c.get("promotedEnabled")?1:2;c.set("maxOrganicTextLinks",e*c.get("maxPerColumn")),c.set("maxPromotedTextLinks",c.get("maxPerColumn"))}else c.has("maxOrganicTextLinks")&&c.set("maxPerColumn",c.get("maxOrganicTextLinks"));c.set("innerContainerId",c.get("innerContainerName")+"-"+c.cid),c.get("sectionNames")||c.set("sectionNames",["col-organic","col-promoted"]),c.set("sectionIds",b.map(c.get("sectionNames"),function(a){return a+"-"+c.cid}))},numSections:function(){return this.collections.length||(this.get("promotedEnabled")?2:1)},commonClickMetadata:function(){var a=this.get("sourceForum"),b={redirectUrl:this.get("redirectUrl"),sourceThreadId:this.get("sourceThread").id,forumId:a.pk,forum:a.id,majorVersion:this.majorVersion(),requestBin:this.get("requestBin"),service:this.get("service")},c=this.get("session");return c&&c.isLoggedIn()&&(b.userId=c.user.id),b},augmentModels:function(a){a.invoke("set",this.commonClickMetadata())},getViewportWidth:function(){return c(document).width()},getThumbnailLinksMobile:function(a){return this.getViewportWidth()<=480&&this.get("max"+a+"ThumbnailLinksMobile")},augmentCollection:function(a,b){var c=this;a.meta={max:function(){return c.getThumbnailLinksMobile(b)||c.get("max"+b+"ThumbnailLinks")||c.get("max"+b+"TextLinks")},min:function(){return c.get("minThumbnailsNeeded")||c.get("numColumns")/c.numSections()*c.get("minPerColumn")},name:b}},run:function(){var a=b.bind(this.configureContainerId,this),c=b.bind(this.onComplete,this);this.getData().then(a).then(this.validateData).then(this.prepareData).then(this.showData).otherwise(c)},getDataOrganic:function(){var a=this,b={timeout:s,data:{thread:this.get("sourceThread").id,limit:2*a.threads.meta.max()},sourceThread:this.get("sourceThread"),reset:!0};b.humanFriendlyTimestamp=!0;var c=e(a.threads.fetch(b));return a.get("contentPreviews")&&(c=c.then(function(){return a.getContentPreviews().otherwise(function(a){m.log("There was a problem getting snippets: 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c=a.get("provider")||a.get("ad_provider");if(c&&b.has(l.handlers,c))return l.handlers[c](a,this.get("sourceThreadUrl"))}},configureContainerId:function(){this.get("topPlacementEnabled")&&this.set("containerId",t)},getData:function(){var a=e();if(this.get("organicEnabled")){var c=this.getDataOrganic();a=a.always(function(){return c})}if(this.get("promotedEnabled")||this.get("sponsoredCommentsEnabled")){var d=this.getDataPromoted();a=a.always(function(){return d})}return a.always(b.bind(this.sequenceDataCollections,this))},sequenceDataCollections:function(){this.collections=[this.threads],this.get("promotedEnabled")&&this.collections.push(this.ads),this.collections=b.compact(this.collections);var a=this.get("promotedEnabled")&&1===this.collections.length,c=this.get("promotedEnabled")&&"left"===this.get("promotedSide");(c||a)&&(this.get("sectionNames")&&this.get("sectionNames").reverse(),this.get("sectionIds")&&this.get("sectionIds").reverse(),this.collections.reverse())},getContentPreviews:function(){var a=this.threads.map(function(a){return parseInt(a.get("id"),10)});if(a.length0;)a=c[--f],b=a.meta.min(),a.lengthe;e++)a=c[e],b=a.meta.max(),a.length>b&&a.reset(a.slice(0,b));this.get("shouldEvenThumbnails")&&this.threads.length%2!==0&&(m.log("Number of related threads ("+this.threads.length+") is odd.","Removing one to make even."),this.threads.pop())},isThumbnailOk:function(a){var b=this,c=a.width,d=a.height,e=c/d;return c>=b.get("thumbnailMinWidth")&&d>=b.get("thumbnailMinHeight")&&e>=b.get("thumbnailAspectRatioMin")&&e<=b.get("thumbnailAspectRatioMax")},areThumbnailsGood:function(a,d){var f=this;return a.map(function(a){var g,h=new Image,i=e.defer();return c(h).on("load",function(){f.isThumbnailOk(this)?(d.push(a),i.resolve(a.id)):(m.log("Image is not good. 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e=this.get("sectionIds"),f=this.get("collectionTagName"),g=this.get("collectionClassName");this.views=b.map(this.collections,function(a,b){var d=!!this.get("max"+a.meta.name+"ThumbnailLinks"),h=new o.BaseCollectionView({model:this,collection:a,el:c("#"+e[b]+" "+f+"."+g),context:{thumbnailsEnabled:d}});return a.meta.view=h,h},this),this.get("maxPromotedThumbnailLinks")?d.$el.find("#"+this.get("innerContainerId")).addClass("doublethumbnails"):2===this.views.length&&d.$el.find("#"+this.get("innerContainerId")).addClass("doublesection"),b.invoke(this.views,"render")},canShowSponsoredComment:function(){return this.get("hasSpaceForSponsoredComment")&&this.get("sponsoredCommentsEnabled")},renderTopPlacement:function(){var a=document.getElementById(t),b=this.ads.getTopPlacementAd();if(a&&b){var c=new 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k=h.Popup=function(a){j.call(this,a),this.windowName=a.windowName};DISQUS.extend(k.prototype,j.prototype),k.prototype.load=function(){this.window=a.open("",this.windowName||"_blank"),this.window.location=this.url()},k.prototype.isKilled=function(){return j.prototype.isKilled()||this.window.closed};var l=h.Iframe=function(a){j.call(this,a),this.styles=a.styles||{},this.tabIndex=a.tabIndex||0,this.title=a.title||"Disqus",this.container=a.container,this.elem=null};DISQUS.extend(l.prototype,j.prototype),l.prototype.load=function(){var a=this.elem=c.createElement("iframe");a.setAttribute("id","dsq-"+this.uid),a.setAttribute("data-disqus-uid",this.uid),a.setAttribute("allowTransparency","true"),a.setAttribute("frameBorder","0"),a.setAttribute("scrolling","no"),this.role&&a.setAttribute("role",this.role),a.setAttribute("tabindex",this.tabIndex),a.setAttribute("title",this.title),this.setInlineStyle(this.styles)},l.prototype.getOffset=function(a){return h.getOffset(this.elem,a)},l.prototype.setInlineStyle=function(a,b){var c={};DISQUS.isString(a)?c[a]=b:c=a;var d=this.elem.style;return"setProperty"in d?void DISQUS.each(c,function(a,b){d.setProperty(b,""+a,"important")}):this._setInlineStyleCompat(c)},l.prototype._setInlineStyleCompat=function(a){this._stylesCache=this._stylesCache||{},DISQUS.extend(this._stylesCache,a);var b=[];DISQUS.each(this._stylesCache,function(a,c){b.push(c+":"+a+" !important")}),this.elem.style.cssText=b.join(";")},l.prototype.removeInlineStyle=function(a){var b=this.elem.style;return"removeProperty"in b?void b.removeProperty(a):this._removeInlineStyleCompat(a)},l.prototype._removeInlineStyleCompat=function(a){this._stylesCache&&(delete this._stylesCache[a],this._setInlineStyleCompat({}))},l.prototype.hide=function(){this.setInlineStyle("display","none")},l.prototype.show=function(){this.removeInlineStyle("display")},l.prototype.destroy=function(){return 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a=this.elem,d=DISQUS.isString(this.container)?b(this.container):this.container;d.appendChild(a),this.window=a.contentWindow;try{this.window.document.open()}catch(e){a.src='javascript:var d=document.open();d.domain="'+c.domain+'";void(0);'}return this.document=this.window.document,this.document.write(this.contents),this.document.close(),this.updateHeight(),this},n.prototype.updateHeight=function(){var a,b=this.document.body;b&&(a=b.offsetHeight+"px",this.setInlineStyle({height:a,"min-height":a,"max-height":a}))},n.prototype.show=function(){this.setInlineStyle("display","block")},n.prototype.click=function(a){var b=this,c=b.document.body;h.addEvent(c,"click",function(c){a.call(b,c)})},n.prototype.setBodyClass=function(a){this.document.body.className=a},h.on=DISQUS.Events.on,h.off=DISQUS.Events.off,h.trigger=DISQUS.Events.trigger,h}),DISQUS.define("JSON",function(a){"use strict";var b,c=new DISQUS.Sandbox({container:"disqus_thread",styles:{display:"none"}});try{b=c.load().window.JSON}catch(d){}return b||(b=a.JSON),{stringify:b.stringify,parse:b.parse}}),DISQUS.define("next.host.utils",function(a,b){"use strict";function c(a){a=a.toLowerCase(),a=a.replace(/\s/,"");var b={aliceblue:"#F0F8FF",antiquewhite:"#FAEBD7",aqua:"#00FFFF",aquamarine:"#7FFFD4",azure:"#F0FFFF",beige:"#F5F5DC",bisque:"#FFE4C4",black:"#000000",blanchedalmond:"#FFEBCD",blue:"#0000FF",blueviolet:"#8A2BE2",brown:"#A52A2A",burlywood:"#DEB887",cadetblue:"#5F9EA0",chartreuse:"#7FFF00",chocolate:"#D2691E",coral:"#FF7F50",cornflowerblue:"#6495ED",cornsilk:"#FFF8DC",crimson:"#DC143C",cyan:"#00FFFF",darkblue:"#00008B",darkcyan:"#008B8B",darkgoldenrod:"#B8860B",darkgray:"#A9A9A9",darkgreen:"#006400",darkgrey:"#A9A9A9",darkkhaki:"#BDB76B",darkmagenta:"#8B008B",darkolivegreen:"#556B2F",darkorange:"#FF8C00",darkorchid:"#9932CC",darkred:"#8B0000",darksalmon:"#E9967A",darkseagreen:"#8FBC8F",darkslateblue:"#483D8B",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",darkturquoise:"#00CED1",darkviolet:"#9400D3",deeppink:"#FF1493",deepskyblue:"#00BFFF",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1E90FF",firebrick:"#B22222",floralwhite:"#FFFAF0",forestgreen:"#228B22",fuchsia:"#FF00FF",gainsboro:"#DCDCDC",ghostwhite:"#F8F8FF",gold:"#FFD700",goldenrod:"#DAA520",gray:"#808080",green:"#008000",greenyellow:"#ADFF2F",grey:"#808080",honeydew:"#F0FFF0",hotpink:"#FF69B4",indianred:"#CD5C5C",indigo:"#4B0082",ivory:"#FFFFF0",khaki:"#F0E68C",lavender:"#E6E6FA",lavenderblush:"#FFF0F5",lawngreen:"#7CFC00",lemonchiffon:"#FFFACD",lightblue:"#ADD8E6",lightcoral:"#F08080",lightcyan:"#E0FFFF",lightgoldenrodyellow:"#FAFAD2",lightgray:"#D3D3D3",lightgreen:"#90EE90",lightgrey:"#D3D3D3",lightpink:"#FFB6C1",lightsalmon:"#FFA07A",lightseagreen:"#20B2AA",lightskyblue:"#87CEFA",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#B0C4DE",lightyellow:"#FFFFE0",lime:"#00FF00",limegreen:"#32CD32",linen:"#FAF0E6",magenta:"#FF00FF",maroon:"#800000",mediumaquamarine:"#66CDAA",mediumblue:"#0000CD",mediumorchid:"#BA55D3",mediumpurple:"#9370DB",mediumseagreen:"#3CB371",mediumslateblue:"#7B68EE",mediumspringgreen:"#00FA9A",mediumturquoise:"#48D1CC",mediumvioletred:"#C71585",midnightblue:"#191970",mintcream:"#F5FFFA",mistyrose:"#FFE4E1",moccasin:"#FFE4B5",navajowhite:"#FFDEAD",navy:"#000080",oldlace:"#FDF5E6",olive:"#808000",olivedrab:"#6B8E23",orange:"#FFA500",orangered:"#FF4500",orchid:"#DA70D6",palegoldenrod:"#EEE8AA",palegreen:"#98FB98",paleturquoise:"#AFEEEE",palevioletred:"#DB7093",papayawhip:"#FFEFD5",peachpuff:"#FFDAB9",peru:"#CD853F",pink:"#FFC0CB",plum:"#DDA0DD",powderblue:"#B0E0E6",purple:"#800080",red:"#FF0000",rosybrown:"#BC8F8F",royalblue:"#4169E1",saddlebrown:"#8B4513",salmon:"#FA8072",sandybrown:"#F4A460",seagreen:"#2E8B57",seashell:"#FFF5EE",sienna:"#A0522D",silver:"#C0C0C0",skyblue:"#87CEEB",slateblue:"#6A5ACD",slategray:"#708090",slategrey:"#708090",snow:"#FFFAFA",springgreen:"#00FF7F",steelblue:"#4682B4",tan:"#D2B48C",teal:"#008080",thistle:"#D8BFD8",tomato:"#FF6347",turquoise:"#40E0D0",violet:"#EE82EE",wheat:"#F5DEB3",white:"#FFFFFF",whitesmoke:"#F5F5F5",yellow:"#FFFF00",yellowgreen:"#9ACD32"};return b[a]||""}function d(a){if(!a||"embed.js"!==a.substring(a.length-8))return null;for(var b,c=[/(https?:)?\/\/(www\.)?disqus\.com\/forums\/([\w_\-]+)/i,/(https?:)?\/\/(www\.)?([\w_\-]+)\.disqus\.com/i,/(https?:)?\/\/(www\.)?dev\.disqus\.org\/forums\/([\w_\-]+)/i,/(https?:)?\/\/(www\.)?([\w_\-]+)\.dev\.disqus\.org/i],d=c.length,e=0;d>e;e++)if(b=a.match(c[e]),b&&b.length&&4===b.length)return b[3];return null}function e(a,b){var c,e,f,g=a.getElementsByTagName("script"),h=g.length;b=b||d;for(var i=h-1;i>=0;i--)if(c=g[i],e=c.getAttribute?c.getAttribute("src"):c.src,f=b(e),null!==f)return f.toLowerCase();return null}function f(a,b){for(var c=0,d=new Array(a.length),e=0;e<=a.length;e++){d[e]=new Array(b.length);for(var f=0;f<=b.length;f++)d[e][f]=0}for(var g=0;gc&&(c=d[g+1][h+1]));return c}function g(){for(var a=t.getElementsByTagName("h1"),c=t.title,d=c.length,e=c,g=.6,h=0;hg&&(g=h,e=i))}(a[h]);return e}function h(a,b,c){if(c=c||b,a===t)return"";var 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b,c=l(a,"span","font-family","fontFamily"),d=c.split(","),e={courier:1,times:1,"times new roman":1,georgia:1,palatino:1,serif:1},f=0;f2?b[b.length-2]:"";return c.match(/^[0-9a-f]{32}$/i)&&c},x={isIE:function(){return!!t.documentMode},isSafari:function(){var a=navigator.userAgent.toLowerCase();return a.indexOf("safari")>-1&&-1===a.indexOf("chrome")}},y={getItem:function(c){try{return a.localStorage.getItem(c)}catch(d){return b}},setItem:function(b,c){try{return a.localStorage.setItem(b,c)}catch(d){return}}};return{MAX_Z_INDEX:2147483647,getShortnameFromUrl:d,getForum:e,guessThreadTitle:g,getContrastYIQ:i,ensureHexColor:j,getElementStyle:l,getAnchorColor:m,normalizeFontValue:n,isSerif:o,getBrowserSupport:p,logStat:q,getComputedStyle:r,pageVisibility:u,getScrollbarWidth:v,getLoaderVersionFromUrl:w,browser:x,storage:y}}),DISQUS.define("next.host.app",function(a){"use strict";var b=DISQUS.isOwn,c=DISQUS.extend,d=DISQUS.use("next.host"),e=d.urls,f=document,g=f.documentElement,h=f.location.protocol,i={getRegistry:function(){var a=this._registry;return a?a:this._registry={}},register:function(a){var b=this.getRegistry();b[a.uid]=a},unregister:function(a){var b=this.getRegistry();delete b[a.uid]},listByKey:function(){return this.getRegistry()},list:function(){var a=this.getRegistry(),c=[];for(var d in a)b(a,d)&&c.push(a[d]);return c},get:function(a){var c=this.getRegistry();return b(c,a)?c[a]:null}},j=function(a){var b=this.constructor;this.uid=DISQUS.getUid(),b.register&&b.register(this),this.settings=a||{};var c=[],d=this;do c.unshift(d),d=d.constructor.__super__;while(d);for(var e=0,f=c.length;f>e;e++)d=c[e],d.events&&this.on(d.events,this),d.onceEvents&&this.once(d.onceEvents,this)};c(j.prototype,DISQUS.Events),j.prototype.destroy=function(){var a=this.constructor;this.off(),this.stopListening(),a.unregister&&a.unregister(this)},j.extend=function(a,d){var e,f=this;e=a&&b(a,"constructor")?a.constructor:function(){return f.apply(this,arguments)},c(e,f,d);var g=function(){this.constructor=e};return g.prototype=f.prototype,e.prototype=new g,a&&c(e.prototype,a),e.__super__=f.prototype,e};var k=j.extend({name:null,loaderVersion:null,frame:null,origin:e.ensureHttpBasedProtocol("http://disqus.com",h),state:null,getUrl:function(a,b){return a=this.loaderVersion?DISQUS.extend({version:this.loaderVersion},a):DISQUS.extend({disqus_version:"f7574a91"},a),e.ensureHttpBasedProtocol(e.get(this.name,a,b),h)},getFrame:function(){var a,b=this.settings,c={target:this.getUrl(),origin:this.origin,uid:this.uid};return b.windowName?c.windowName=b.windowName:c.container=this.settings.container||f.body,this.getFrameSettings&&(c=this.getFrameSettings(c)),new(a=c.windowName?DISQUS.Popup:DISQUS.Channel)(c)},setState:function(a){var b=this.constructor;return a in b.states?(this.state=b.states[a],void this.trigger("state:"+a)):!1},init:function(){var a,b=this;b.frame=a=this.getFrame(),b.listenTo(a,"all",function(c,d){b.trigger("frame:"+c,d,a)}),b.trigger("change:frame",a),b.frame.load(function(){b.setState("LOADED")}),b.setState("INIT")},destroy:function(){var a=this.frame;a&&(this.stopListening(a),a.destroy()),this.setState("KILLED"),this.frame=null,j.prototype.destroy.call(this)},events:{"frame:ready":function(){this.setState("READY")}}},{states:{INIT:0,LOADED:1,READY:2,RUNNING:3,KILLED:4}});c(k,i);var l=k.extend({getUrl:function(){var b=this.settings,c={f:b.forum,t_i:b.identifier,t_u:b.url||a.location.href,t_s:b.slug,t_e:b.title,t_d:b.documentTitle,t_t:b.title||b.documentTitle,t_c:b.category,s_o:b.sortOrder,l:b.language};return b.unsupported&&(c.n_s=b.unsupported),k.prototype.getUrl.call(this,c)},getFrameInitParams:function(b,c){var d=this.settings,e={permalink:d.permalink,anchorColor:d.anchorColor,referrer:a.location.href,hostReferrer:f.referrer,colorScheme:d.colorScheme,typeface:d.typeface,remoteAuthS3:d.remoteAuthS3,apiKey:d.apiKey,sso:d.sso,parentWindowHash:a.location.hash,forceAutoStyles:d.forceAutoStyles,layout:d.layout,timestamp:this.timestamp};return c&&c.elem&&a.navigator.userAgent.match(/(iPad|iPhone|iPod)/)&&(e.width=c.elem.offsetWidth),e.initialPosition=this.getViewportAndScrollStatus(),e},listenToScrollEvent:function(a){var b=this,c=b.getScrollContainer();if(c===g)return b.listenTo(DISQUS,"window.scroll",a),function(){b.stopListening(DISQUS,"window.scroll",a)};var d=DISQUS.throttle(function(){a.call(b)},250,50);return DISQUS.addEvent(c,"scroll",d),function(){DISQUS.removeEvent(c,"scroll",d)}},getScrollContainer:function(){if(this.scrollContainer)return this.scrollContainer;if(!this.settings.enableScrollContainer)return g;var a=this.settings.container;do{var b=d.utils.getComputedStyle(a,"overflow-y","overflowY");if(("scroll"===b||"auto"===b)&&a.clientHeight=c&&(h=d>=e,i=h&&g>=c);var j=this.frame;j.sendMessage("window.scroll.always",a),h&&j.sendMessage("window.scroll",a),i!==this.wasInViewport&&(j.sendMessage(i?"window.inViewport":"window.scrollOffViewport"),this.wasInViewport=i)}},getBestNextFrameHeight:function(a){var b=this.getViewportAndScrollStatus();if(!b||this.settings.enableScrollContainer||!this.getScrollContainer())return a;var c=b.frameOffset;if(a>=c.height)return a;var d=this.getDocumentHeight(),e=d-(c.height+c.top),f=b.pageOffset+b.height-(c.top+e);return f>a?f+1:a},events:{"state:INIT":function(){this.settings.unsupported||(this.settings.windowName||(this.listenToScrollEvent(this.communicateViewportAndScrollStatus),this.listenTo(DISQUS,"window.resize",this.communicateViewportAndScrollStatus)),this.timestamp=+new Date)},"state:LOADED":function(){var a=this.frame,b=a.elem;this.settings.unsupported?(a.setInlineStyle("height","500px"),b.setAttribute("scrolling","yes"),b.setAttribute("horizontalscrolling","no"),b.setAttribute("verticalscrolling","yes"),a.show()):this.settings.windowName||(this.rendered=!1,a.setInlineStyle("height","0"),b.setAttribute("scrolling","no"),b.setAttribute("horizontalscrolling","no"),b.setAttribute("verticalscrolling","no"))},"frame:ready":function(a,b){var c=this.getFrameInitParams(a,b);b.sendMessage("init",c)},"frame:resize":function(a,b){var c=a.height;b.elem&&this.rendered&&(c=this.getBestNextFrameHeight(c),b.setInlineStyle("height",c+"px"),b.sendMessage("embed.resized")),this.communicateViewportAndScrollStatus()},"frame:rendered":function(a,b){this.rendered=!0,this.wasInViewport=!1,b.trigger("resize",a),b.sendMessage("embed.rendered")},"frame:fail":function(a,b){b.elem&&b.setInlineStyle("height",a&&a.height||"75px")},"frame:scrollTo":function(b,c){if(c.elem&&c.getOffset){var 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      Disqus seems to be taking longer than usual. Reload?

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