diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..88afca6 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +import-c-go-il/callback/callback +pandas-pyweb-il/56*.csv +pandas-pyweb-il/screen.m* +python-logging/data +python-logging/kibana-latest/ +python-logging/logstash*.jar diff --git a/README.md b/README.md new file mode 100644 index 0000000..61e07e3 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +Various talks given by [Miki](mailto:miki@353solutions.com) diff --git a/import-c-go-il/callback/callback.c b/import-c-go-il/callback/callback.c new file mode 100644 index 0000000..ba61724 --- /dev/null +++ b/import-c-go-il/callback/callback.c @@ -0,0 +1,8 @@ +#include +#include "_cgo_export.h" + +void +c_func() { + printf("Calling Go\n"); + go_func(); +} diff --git a/import-c-go-il/callback/callback.go b/import-c-go-il/callback/callback.go new file mode 100644 index 0000000..7708c1e --- /dev/null +++ b/import-c-go-il/callback/callback.go @@ -0,0 +1,16 @@ +package main + +import "fmt" + +// #include "callback.h" +import "C" + +//export go_func +func go_func() { + fmt.Println("Hello from Go") +} + +func main() { + C.c_func(); +} + diff --git a/import-c-go-il/callback/callback.h b/import-c-go-il/callback/callback.h new file mode 100644 index 0000000..1806400 --- /dev/null +++ b/import-c-go-il/callback/callback.h @@ -0,0 +1,6 @@ +#ifndef CALLBACK_H +#define CALLBACK_H + +void c_func(); + +#endif diff --git a/import-c-go-il/err.go b/import-c-go-il/err.go new file mode 100644 index 0000000..64f035d --- /dev/null +++ b/import-c-go-il/err.go @@ -0,0 +1,17 @@ +package main + +// #include +// #cgo LDFLAGS: -lm +import "C" + +import ( + "fmt" +) + +func main() { + i, err := C.sqrt(-1) + if err != nil { + fmt.Printf("error: %s\n", err) // Go will use strerror for message + } + fmt.Printf("i = %v\n", i) +} diff --git a/import-c-go-il/import-c.slide b/import-c-go-il/import-c.slide new file mode 100644 index 0000000..ee54288 --- /dev/null +++ b/import-c-go-il/import-c.slide @@ -0,0 +1,125 @@ +import "C" +Calling C From Go +19 Mar 2015 +Tags: go c + +Miki Tebeka +CEO, 353solutions +miki@353solution +http://353solutions.com +@tebeka + +* Why C? + +After all we have Go, which is much better :) + +However, there is a *huge* volume of software written in C. +We'd like to re-use it and not re-write it. + +* import "C" (A.K.A cgo) + +Provides an easy bridge between Go and C + +The Downside +- Hard to find docs (see reference slide) +- Pay attention to memory leaks +- Compilation time grows +- For C++ you need `extern`"C"` and C like wrapper +- Users need C compilers and libraries installed + +Note that are other options (such as [[http://www.swig.org/][swig]]) + + + +* Minimal Example + +.play sqrt.go + + +* Strings + +.play str.go + +* Ahhhhhh! + +.image picard1.jpg 400 600 + +* Strings + +.play str2.go + +* But ... + +.image picard2.jpg 400 600 + +* Strings + +.play str3.go + +* Structs + +.play struct.go + +* Calling Go from C + +`callback.c` + +.code -numbers callback/callback.c + +`callback.h` + +.code -numbers callback/callback.h + +* Calling Go from C + +`callback.go` + +.code -numbers callback/callback.go + +* Go Style Errors + +.play err.go + +* Case Study - snowball + +- [[https://bitbucket.org/tebeka/snowball]] +- Port of [[http://snowball.tartarus.org/][snowball]] stemmer to Go +- Had to flatten directories and rewrite #include +- Wasn't that painful + +.play stem.go + +* snowball - imports and definition + +.code -numbers snowball.go /START_IMPORT/,/END_IMPORT/ + +* snowball - New and free + +.code -numbers snowball.go /START_NEW/,/END_NEW/ + + +* snowball - Stem + +snowball uses `sb_symbol*` for string + +.code -numbers snowball.go /START_STEM/,/END_STEM/ + +* snowball - List + +The list of languages is a `static`char*` array with a `NULL` sentinal + +.code -numbers snowball.go /START_LIST/,/END_LIST/ + + +* References + +- [[http://golang.org/cmd/cgo/][cgo docs]] +- [[http://golang.org/doc/articles/c_go_cgo.html][C? Go? Cgo!]] +- [[http://golang.org/misc/cgo/][cgo examples]] +- [[https://github.com/golang/go/wiki/cgo][cgo wiki]] +- [[http://stackoverflow.com/search?q=%5Bgo%5D+cgo][StackOverflow]] + +* Now Go Hack! + +.image real-programmers.jpg 400 600 + diff --git a/import-c-go-il/picard1.jpg b/import-c-go-il/picard1.jpg new file mode 100644 index 0000000..a27de20 Binary files /dev/null and b/import-c-go-il/picard1.jpg differ diff --git a/import-c-go-il/picard2.jpg b/import-c-go-il/picard2.jpg new file mode 100644 index 0000000..c4fecf9 Binary files /dev/null and b/import-c-go-il/picard2.jpg differ diff --git a/import-c-go-il/real-programmers.jpg b/import-c-go-il/real-programmers.jpg new file mode 100644 index 0000000..3be2218 Binary files /dev/null and b/import-c-go-il/real-programmers.jpg differ diff --git a/import-c-go-il/snowball.go b/import-c-go-il/snowball.go new file mode 100644 index 0000000..ff6aed1 --- /dev/null +++ b/import-c-go-il/snowball.go @@ -0,0 +1,98 @@ +// snowball stemmer +// +// Example: +// stemmer = snowball.New("english") +// fmt.Println(stemmer.stem("running")) // Will print "run" +package snowball + +// START_IMPORT OMIT +import ( + "fmt" + "runtime" + "unsafe" +) +/* +#include +#include "libstemmer.h" +*/ +import "C" + + +// Stemmer structure +type Stemmer struct { + lang string + stmr *C.struct_sb_stemmer +} + +// END_IMPORT OMIT + +const ( + Version = "0.1.2" +) + +// START_NEW OMIT +// New creates a new stemmer for lang +func New(lang string) (*Stemmer, error) { + stmr := &Stemmer{ + lang, + C.sb_stemmer_new(C.CString(lang), nil), + } + + if stmr.stmr == nil { + return nil, fmt.Errorf("can't create stemmer for lang %s", lang) + } + + runtime.SetFinalizer(stmr, free) // Free C memory when GCed + + return stmr, nil +} + +// free C resources +func free(stmr *Stemmer) { + if stmr.stmr != nil { + C.sb_stemmer_delete(stmr.stmr) + stmr.stmr = nil + } +} +// END_NEW OMIT + +// Lang return the stemmer language +func (stmr *Stemmer) Lang() string { + return stmr.lang +} + +// START_STEM OMIT +// Stem returns them stem of word (e.g. running -> run) +func (stmr *Stemmer) Stem(word string) string { + ptr := unsafe.Pointer(C.CString(word)) + defer C.free(ptr) + + w := (*C.sb_symbol)(ptr) + res := unsafe.Pointer(C.sb_stemmer_stem(stmr.stmr, w, C.int(len(word)))) + size := C.sb_stemmer_length(stmr.stmr) + + buf := C.GoBytes(res, size) + return string(buf) +} +// END_STEM OMIT + +// START_LIST OMIT +// List returns the list of languages supported by snowball +func List() []string { + names := []string{} + + // We don't need to free since sb_stemmer_list return pointer to static variable + cp := uintptr(unsafe.Pointer(C.sb_stemmer_list())) + size := unsafe.Sizeof(uintptr(0)) + + for { + name := C.GoString(*(**C.char)(unsafe.Pointer(cp))) + if len(name) == 0 { + break + } + names = append(names, name) + cp += size + } + return names +} +// END_LIST OMIT diff --git a/import-c-go-il/sqrt.go b/import-c-go-il/sqrt.go new file mode 100644 index 0000000..512a86d --- /dev/null +++ b/import-c-go-il/sqrt.go @@ -0,0 +1,14 @@ +package main + +import "fmt" + +// #include +// #cgo LDFLAGS: -lm +import "C" + + +func main() { + v := 16.0 + s := C.sqrt(C.double(v)) + fmt.Printf("sqrt(%f) = %f\n", v, s) +} diff --git a/import-c-go-il/stem.go b/import-c-go-il/stem.go new file mode 100644 index 0000000..b8d4dee --- /dev/null +++ b/import-c-go-il/stem.go @@ -0,0 +1,15 @@ +package main + +import ( + "fmt" + "bitbucket.org/tebeka/snowball" +) + +func main() { + stemmer, _ := snowball.New("english") // Error ignored for brevity + + word := "running" + stem := stemmer.Stem(word) + fmt.Printf("%s -> %s\n", word, stem) +} + diff --git a/import-c-go-il/str.go b/import-c-go-il/str.go new file mode 100644 index 0000000..5116fb5 --- /dev/null +++ b/import-c-go-il/str.go @@ -0,0 +1,15 @@ +package main + +// #include +import "C" + +import ( + "fmt" +) + +func main() { + str := "Gophers Rock!" + dup := C.strdup(C.CString(str)) + fmt.Printf("dup: %s\n", dup) +} + diff --git a/import-c-go-il/str2.go b/import-c-go-il/str2.go new file mode 100644 index 0000000..0613b50 --- /dev/null +++ b/import-c-go-il/str2.go @@ -0,0 +1,17 @@ +package main + +// #include +// #include +import "C" + +import ( + "fmt" +) + +func main() { + str := "Gophers Rock!" + dup := C.strdup(C.CString(str)) + + gostr := C.GoString(dup) + fmt.Println(gostr) +} diff --git a/import-c-go-il/str3.go b/import-c-go-il/str3.go new file mode 100644 index 0000000..babd0a1 --- /dev/null +++ b/import-c-go-il/str3.go @@ -0,0 +1,23 @@ +package main + +// #include +// #include +import "C" +import "fmt" +import "unsafe" + +func free(cstr *C.char) { + C.free(unsafe.Pointer(cstr)) +} + +func main() { + str := "Gophers Rock!" + cstr:= C.CString(str) + defer free(cstr) + + dup := C.strdup(C.CString(str)) + defer free(dup) + + gostr := C.GoString(dup) + fmt.Println(gostr) +} diff --git a/import-c-go-il/struct.go b/import-c-go-il/struct.go new file mode 100644 index 0000000..4cf374b --- /dev/null +++ b/import-c-go-il/struct.go @@ -0,0 +1,23 @@ +package main + +// meanwhile at stdlib.h: +// typedef struct +// { +// int quot; /* Quotient. */ +// int rem; /* Remainder. */ +// } div_t; + +// #include +import "C" +import "fmt" + +func main() { + var dt C.div_t // Look Ma! C types + + dt = C.div(16, 6) + fmt.Printf("quot: %d, rem: %d\n", dt.quot, dt.rem) + + dt.quot = 17 // Mutate the struct + fmt.Printf("%v\n", dt) // %v works as well +} + diff --git a/numpy-testing/numpy-testing.ipynb b/numpy-testing/numpy-testing.ipynb new file mode 100644 index 0000000..f4ad241 --- /dev/null +++ b/numpy-testing/numpy-testing.ipynb @@ -0,0 +1,648 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# numpy Testing\n", + "### Miki Tebeka .:. [353solutions](http://353solutions.com) .:. Highly effective Python, Scientific Python and Go workshops\n", + "\n", + "We'll explore certain caveats while testing [numpy](http://docs.scipy.org/doc/numpy/reference/) code.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Naive Approach" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_mul\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.21\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "def test_mul():\n", + " arr = np.array([0.0, 1.0, 1.1])\n", + " v, expected = 1.1, np.array([0.0, 1.1, 1.21])\n", + " assert arr * v == expected, 'bad multiplication'\n", + " \n", + "test_mul()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, True], dtype=bool)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([1,2,3]) == np.array([1,7, 3])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = np.array([1,2,3]) == np.array([1,7, 3])\n", + "np.all(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "bad multiplication", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_mul\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.21\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: bad multiplication" + ] + } + ], + "source": [ + "def test_mul():\n", + " arr = np.array([0.0, 1.0, 1.1])\n", + " v, expected = 1.1, np.array([0.0, 1.1, 1.21])\n", + " assert np.all(arr * v == expected), 'bad multiplication'\n", + " \n", + "test_mul()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(1.1 + 1.1 + 1.1, 3.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def test_mul():\n", + " arr = np.array([0.0, 1.0, 1.1])\n", + " v, expected = 1.1, np.array([0.0, 1.1, 1.21])\n", + " assert np.allclose(arr * v, expected), 'bad multiplication'\n", + " \n", + "test_mul()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in true_divide\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0marr2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest_div\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_div\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0marr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0marr2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_div\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "source": [ + "def test_div():\n", + " arr1, arr2 = np.array([1.0, np.inf, 2.0]), np.array([2.0, np.inf, 2.0])\n", + " expected = np.array([0.5, np.nan, 1.0])\n", + " assert np.allclose(arr1/arr2, expected)\n", + "\n", + "test_div()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "False\n" + ] + } + ], + "source": [ + "n1 = np.inf/np.inf\n", + "n2 = np.inf/np.inf\n", + "\n", + "print(n1 is n2)\n", + "print(n1 == n2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.isnan(np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in true_divide\n" + ] + } + ], + "source": [ + "def test_div():\n", + " arr1, arr2 = np.array([1.0, np.inf, 2.0]), np.array([2.0, np.inf, 2.0])\n", + " expected = np.array([0.5, np.nan, 1.0])\n", + " expected[np.isnan(expected)] = 0.0\n", + " result = arr1/arr2\n", + " result[np.isnan(result)] = 0.0\n", + " assert np.allclose(result, expected)\n", + "\n", + "test_div()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in true_divide\n" + ] + } + ], + "source": [ + "def test_div():\n", + " arr1, arr2 = np.array([1.0, np.inf, 2.0]), np.array([2.0, np.inf, 2.0])\n", + " expected = np.array([0.5, np.nan, 1.0])\n", + " assert np.allclose(arr1/arr2, expected, equal_nan=True)\n", + "\n", + "test_div()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1. , 0. , 3.3])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nan_to_num(np.array([1.0, np.nan, 3.3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# State of Things" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import load_digits\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "digits = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(digits.images[73], interpolation='none', cmap=plt.cm.gray)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.target[73]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.90214797136038183" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3)\n", + "clf = RandomForestClassifier()\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.92283214001591085" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3)\n", + "clf = RandomForestClassifier()\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7410197383643774" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7003536363511258" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from random import seed" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.23796462709189137" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seed(3)\n", + "random()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.23796462709189137" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seed(3)\n", + "random()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.89260143198090691" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_state = 21\n", + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3,\n", + " random_state=random_state)\n", + "clf = RandomForestClassifier(random_state=random_state, n_jobs=4)\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.89260143198090691" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_state = 21\n", + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3,\n", + " random_state=random_state)\n", + "clf = RandomForestClassifier(random_state=random_state, n_jobs=4)\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TL;DR\n", + "\n", + "* Use `np.allclose`\n", + "* Mind the nan\n", + "* Set your state" + ] + } + ], + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/numpy-testing/solution.ipynb b/numpy-testing/solution.ipynb new file mode 100644 index 0000000..40abd15 --- /dev/null +++ b/numpy-testing/solution.ipynb @@ -0,0 +1,646 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# numpy Testing\n", + "### Miki Tebeka .:. [353solutions](http://353solutions.com) .:. Highly effective Python, Scientific Python and Go workshops\n", + "\n", + "We'll explore certain caveats while testing [numpy](http://docs.scipy.org/doc/numpy/reference/) code.\n", + "\n", + "#### TL;DR\n", + "Use [np.allclose](http://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html) when comparing numpy arrays. Beware of `nan`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Naive Approach" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_mul\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.21\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "def test_mul():\n", + " arr = np.array([0.0, 1.0, 1.1])\n", + " v, expected = 1.1, np.array([0.0, 1.1, 1.21])\n", + " assert arr * v == expected, 'bad multiplication'\n", + " \n", + "test_mul()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is due to the fact that when we compare two numpy arrays with `==` we'll get an array of boolean values comparing each element." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, True], dtype=bool)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([1,2,3]) == np.array([1, 1, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the truth value of an array (as the error says) is ambiguous." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\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[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "bool(np.array([1, 2, 3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to use [np.all](http://docs.scipy.org/doc/numpy/reference/generated/numpy.all.html) to check that all elements are equal." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([True, True, True])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using np.all" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "bad multiplication", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_mul\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.21\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad multiplication'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_mul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: bad multiplication" + ] + } + ], + "source": [ + "def test_mul():\n", + " arr = np.array([0.0, 1.0, 1.1])\n", + " v, expected = 1.1, np.array([0.0, 1.1, 1.21])\n", + " assert np.all(arr * v == expected), 'bad multiplication'\n", + " \n", + "test_mul()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is due to the fact that floating points are not exact." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2100000000000002" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1.1 * 1.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is *not* a bug in Python but how floating points are implemented. You'll get the same result in C, Java, Go ...\n", + "To overcome this we're going to use [np.allclose](http://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html).\n", + "\n", + "BTW: If you're really intersted in floating points, read [this article](http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using np.allclose" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def test_mul():\n", + " arr = np.array([0.0, 1.0, 1.1])\n", + " v, expected = 1.1, np.array([0.0, 1.1, 1.21])\n", + " assert np.allclose(arr * v, expected), 'bad multiplication'\n", + " \n", + "test_mul()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Oh nan, Let Me Count the Ways ..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in true_divide\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "bad nan", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0marr2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad nan'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtest_div\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtest_div\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0marr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0marr2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bad nan'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtest_div\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: bad nan" + ] + } + ], + "source": [ + "def test_div():\n", + " arr1, arr2 = np.array([1.0, np.inf, 2.0]), np.array([2.0, np.inf, 2.0])\n", + " expected = np.array([0.5, np.nan, 1.0])\n", + " assert np.allclose(arr1 / arr2, expected), 'bad nan'\n", + " \n", + "test_div()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is due to the fact the `nan` does not equal itself." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.nan == np.nan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check is a number is `nan` we need to use [np.isnan](http://docs.scipy.org/doc/numpy/reference/generated/numpy.isnan.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.isnan(np.inf/np.inf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have two options to solve this:\n", + "\n", + "1. Convert all `nan` to numbers\n", + "2. Use `equal_nan` argument to `np.allclose`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 1: Convert `nan` to Numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in true_divide\n" + ] + } + ], + "source": [ + "def test_div():\n", + " arr1, arr2 = np.array([1.0, np.inf, 2.0]), np.array([2.0, np.inf, 2.0])\n", + " expected = np.array([0.5, np.nan, 1.0])\n", + " result = arr1 / arr2\n", + " \n", + " result[np.isnan(result)] = 0.0\n", + " expected[np.isnan(expected)] = 0.0\n", + " # or\n", + " # expected = np.nan_to_num(expected)\n", + " assert np.allclose(result, expected), 'bad nan'\n", + " \n", + "test_div()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Option 2: Use `equal_nan` in `np.allclose`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in true_divide\n" + ] + } + ], + "source": [ + "def test_div():\n", + " arr1, arr2 = np.array([1.0, np.inf, 2.0]), np.array([2.0, np.inf, 2.0])\n", + " expected = np.array([0.5, np.nan, 1.0])\n", + " assert np.allclose(arr1 / arr2, expected, equal_nan=True), 'bad nan'\n", + " \n", + "test_div()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## State of Things" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import load_digits\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "digits = load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(digits.images[73], interpolation='none', cmap=plt.cm.gray)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.target[73]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.91487669053301512" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3)\n", + "clf = RandomForestClassifier()\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.91010342084327767" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3)\n", + "clf = RandomForestClassifier()\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.89260143198090691" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_state = 21\n", + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3, random_state=random_state)\n", + "clf = RandomForestClassifier(random_state=random_state)\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.89260143198090691" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_state = 21\n", + "X_test, X_train, y_test, y_train = \\\n", + " train_test_split(digits.data, digits.target, test_size=0.3, random_state=random_state)\n", + "clf = RandomForestClassifier(random_state=random_state)\n", + "clf.fit(X_train, y_train)\n", + "clf.score(X_test, y_test)" + ] + }, + { + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/pandas-adc/Data-Solution.ipynb b/pandas-adc/Data-Solution.ipynb new file mode 100644 index 0000000..fd36e94 --- /dev/null +++ b/pandas-adc/Data-Solution.ipynb @@ -0,0 +1,1592 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas\n", + "------\n", + "\n", + "[pandas](http://pandas.pydata.org/) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#This Workshop\n", + "We'll show you the basics on how to work with data. First part will (by ![Miki](http://intranet/badge/tebekamiki.jpg \"Miki\")) will focus on cleaning data and second part (by ![Eric](http://intranet/badge/klusmaneric.jpg \"Eric\")) will focus on analytics and visualization and on using pandas for to do QA of Tableau reports.\n", + "\n", + "We highly recommend [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (pandas main developer); it is an excellent book.\n", + "\n", + "# IPython Notebook\n", + "This is an [IPython notebook](http://ipython.org/notebook.html): you can do many cool things with it - including running code!\n", + "\n", + "Note that this notebook server was started with `--pylab=inline` for some import shortcuts and inline graphs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Data\n", + "We'll use an [sqlite3](http://docs.python.org/2/library/sqlite3.html) database. Pandas has a [read_frame](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.sql.read_frame.html) function that get data from an SQL connection to a [DataFrame](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html).\n", + "\n", + "Download [the database](http://dm-miki.s3.amazonaws.com/talks/edw.sqlite.bz2) and uncompress it. Then use `sqlite3` and `read_frame` to load the table `FACT_OMNITURE_PAGE_VIEWS` to a DataFrame called `df`. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import sqlite3\n", + "from pandas.io.sql import read_frame\n", + "db = sqlite3.connect('edw.sqlite')\n", + "df = read_frame('SELECT * FROM FACT_OMNITURE_PAGE_VIEWS', db)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First Look at the Data\n", + "\n", + "## `df` value" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n",
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 10000 entries, 0 to 9999\n",
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+        "DAY_KEY                     10000  non-null values\n",
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+        "CONTENT_KEY                 10000  non-null values\n",
+        "SEARCH_PHRASE_KEY           10000  non-null values\n",
+        "USER_AGENT_KEY              10000  non-null values\n",
+        "PAGE_TYPE_KEY               10000  non-null values\n",
+        "SUB_PAGE_TYPE_KEY           10000  non-null values\n",
+        "QCD_PRIMARY_KEY             10000  non-null values\n",
+        "VISIT_KEY                   10000  non-null values\n",
+        "EVENT_DTM                   10000  non-null values\n",
+        "IP_ADDRESS                  10000  non-null values\n",
+        "IP_ADDRESS_NBR              10000  non-null values\n",
+        "GOOGLE_RANK                 1032  non-null values\n",
+        "VISITORIZED                 10000  non-null values\n",
+        "VISITOR_ID                  10000  non-null values\n",
+        "VERSION_ID                  10000  non-null values\n",
+        "REFERRER                    8162  non-null values\n",
+        "FULL_URI                    10000  non-null values\n",
+        "UTM_SOURCE                  335  non-null values\n",
+        "UTM_MEDIUM                  391  non-null values\n",
+        "UTM_CAMPAIGN                379  non-null values\n",
+        "LTV_TAG                     4405  non-null values\n",
+        "EDW_CREATED_DTM             10000  non-null values\n",
+        "SOURCE_FILE_TRACKING_ID     10000  non-null values\n",
+        "ODS_OMD_WORK_INSTANCE_ID    10000  non-null values\n",
+        "EDW_OMD_WORK_INSTANCE_ID    10000  non-null values\n",
+        "dtypes: int64(19), object(13)\n",
+        "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 2, + "text": [ + "\n", + "Int64Index: 10000 entries, 0 to 9999\n", + "Data columns (total 32 columns):\n", + "DAY_KEY 10000 non-null values\n", + "CONTENT_METADATA_KEY 10000 non-null values\n", + "CATEGORY_KEY 10000 non-null values\n", + "SITE_KEY 10000 non-null values\n", + "SEARCH_ENGINE_KEY 10000 non-null values\n", + "GEOGRAPHY_KEY 10000 non-null values\n", + "REFERRER_HOST_KEY 10000 non-null values\n", + "STUDIO_KEY 10000 non-null values\n", + "CONTENT_KEY 10000 non-null values\n", + "SEARCH_PHRASE_KEY 10000 non-null values\n", + "USER_AGENT_KEY 10000 non-null values\n", + "PAGE_TYPE_KEY 10000 non-null values\n", + "SUB_PAGE_TYPE_KEY 10000 non-null values\n", + "QCD_PRIMARY_KEY 10000 non-null values\n", + "VISIT_KEY 10000 non-null values\n", + "EVENT_DTM 10000 non-null values\n", + "IP_ADDRESS 10000 non-null values\n", + "IP_ADDRESS_NBR 10000 non-null values\n", + "GOOGLE_RANK 1032 non-null values\n", + "VISITORIZED 10000 non-null values\n", + "VISITOR_ID 10000 non-null values\n", + "VERSION_ID 10000 non-null values\n", + "REFERRER 8162 non-null values\n", + "FULL_URI 10000 non-null values\n", + "UTM_SOURCE 335 non-null values\n", + "UTM_MEDIUM 391 non-null values\n", + "UTM_CAMPAIGN 379 non-null values\n", + "LTV_TAG 4405 non-null values\n", + "EDW_CREATED_DTM 10000 non-null values\n", + "SOURCE_FILE_TRACKING_ID 10000 non-null values\n", + "ODS_OMD_WORK_INSTANCE_ID 10000 non-null values\n", + "EDW_OMD_WORK_INSTANCE_ID 10000 non-null values\n", + "dtypes: int64(19), object(13)" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Columns" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "Index([u'DAY_KEY', u'CONTENT_METADATA_KEY', u'CATEGORY_KEY', u'SITE_KEY', u'SEARCH_ENGINE_KEY', u'GEOGRAPHY_KEY', u'REFERRER_HOST_KEY', u'STUDIO_KEY', u'CONTENT_KEY', u'SEARCH_PHRASE_KEY', u'USER_AGENT_KEY', u'PAGE_TYPE_KEY', u'SUB_PAGE_TYPE_KEY', u'QCD_PRIMARY_KEY', u'VISIT_KEY', u'EVENT_DTM', u'IP_ADDRESS', u'IP_ADDRESS_NBR', u'GOOGLE_RANK', u'VISITORIZED', u'VISITOR_ID', u'VERSION_ID', u'REFERRER', u'FULL_URI', u'UTM_SOURCE', u'UTM_MEDIUM', u'UTM_CAMPAIGN', u'LTV_TAG', u'EDW_CREATED_DTM', u'SOURCE_FILE_TRACKING_ID', u'ODS_OMD_WORK_INSTANCE_ID', u'EDW_OMD_WORK_INSTANCE_ID'], dtype=object)" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Some Values from DAY_KEY, SITE_KEY and IP_ADDRESS" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df[['DAY_KEY', 'SITE_KEY', 'IP_ADDRESS']].head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DAY_KEYSITE_KEYIP_ADDRESS
0 20131101 180001 66.87.65.133
1 20131101 180001 161.31.62.126
2 20131101 1280001 24.171.168.246
3 20131101 730001 130.85.58.235
4 20131101 180001 80.2.127.232
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + " DAY_KEY SITE_KEY IP_ADDRESS\n", + "0 20131101 180001 66.87.65.133\n", + "1 20131101 180001 161.31.62.126\n", + "2 20131101 1280001 24.171.168.246\n", + "3 20131101 730001 130.85.58.235\n", + "4 20131101 180001 80.2.127.232" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Types" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.dtypes" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "DAY_KEY int64\n", + "CONTENT_METADATA_KEY int64\n", + "CATEGORY_KEY int64\n", + "SITE_KEY int64\n", + "SEARCH_ENGINE_KEY int64\n", + "GEOGRAPHY_KEY int64\n", + "REFERRER_HOST_KEY int64\n", + "STUDIO_KEY int64\n", + "CONTENT_KEY int64\n", + "SEARCH_PHRASE_KEY int64\n", + "USER_AGENT_KEY int64\n", + "PAGE_TYPE_KEY int64\n", + "SUB_PAGE_TYPE_KEY int64\n", + "QCD_PRIMARY_KEY int64\n", + "VISIT_KEY int64\n", + "EVENT_DTM object\n", + "IP_ADDRESS object\n", + "IP_ADDRESS_NBR int64\n", + "GOOGLE_RANK object\n", + "VISITORIZED object\n", + "VISITOR_ID object\n", + "VERSION_ID object\n", + "REFERRER object\n", + "FULL_URI object\n", + "UTM_SOURCE object\n", + "UTM_MEDIUM object\n", + "UTM_CAMPAIGN object\n", + "LTV_TAG object\n", + "EDW_CREATED_DTM object\n", + "SOURCE_FILE_TRACKING_ID int64\n", + "ODS_OMD_WORK_INSTANCE_ID int64\n", + "EDW_OMD_WORK_INSTANCE_ID int64\n", + "dtype: object" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cleaning Data\n", + "\n", + "You should know you data. However, everything that is an `object` is probably a string and need to be checked.\n", + "\n", + "For example `EVENT_DTM` (event date time) should be a [datetime](http://docs.python.org/2/library/datetime.html) object. Check whether that's currently the case or not." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df['EVENT_DTM'][0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 6, + "text": [ + "u'2013-11-01 05:51:12.507'" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fix `EVENT_DTM` column to be of `pandas.Timestamp` object. You'll find [pandas.to_datetime](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.tseries.tools.to_datetime.html#pandas.tseries.tools.to_datetime) helpful (you can ignore the milliseconds for simplicity)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "df['EVENT_DTM'] = df['EVENT_DTM'].map(pd.to_datetime)\n", + "df['EVENT_DTM'][0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "Timestamp('2013-11-01 05:51:12.507000', tz=None)" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cleanup `GOOGLE_RANK` to be a number or `nan`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df['GOOGLE_RANK'].head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "0 None\n", + "1 2\n", + "2 5\n", + "3 None\n", + "4 None\n", + "Name: GOOGLE_RANK, dtype: object" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def to_int(v):\n", + " try:\n", + " return int(v)\n", + " except (ValueError, TypeError):\n", + " return np.nan\n", + "df['GOOGLE_RANK'] = df['GOOGLE_RANK'].map(to_int)\n", + "df['GOOGLE_RANK'].head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "0 NaN\n", + "1 2\n", + "2 5\n", + "3 NaN\n", + "4 NaN\n", + "Name: GOOGLE_RANK, dtype: float64" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Joining Tables\n", + "\n", + "In the current DataFrame, we have only `SITE_KEY`, however we'd like to have the site name as well. Load another DataFrame from the `DIM_SITE` table and join it with the previous data frame on `SITE_KEY`.\n", + "\n", + "See [pandas.merge](http://pandas.pydata.org/pandas-docs/dev/merging.html#database-style-dataframe-joining-merging) function." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sites = read_frame('SELECT * FROM DIM_SITE', db)\n", + "sites" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n",
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 187 entries, 0 to 186\n",
+        "Data columns (total 17 columns):\n",
+        "SITE_KEY                       187  non-null values\n",
+        "SITE_ID                        184  non-null values\n",
+        "PROPERTY_NAME                  187  non-null values\n",
+        "PROPERTY_CODE                  184  non-null values\n",
+        "BUSINESS_OWNER_NAME            169  non-null values\n",
+        "ACQUISITION_DATE               99  non-null values\n",
+        "GENERAL_LEDGER_DIVISION_NBR    187  non-null values\n",
+        "OMNITURE_SITE_NAME             186  non-null values\n",
+        "SOURCE_SYSTEM_ID               186  non-null values\n",
+        "SITE_STATUS_CODE               187  non-null values\n",
+        "PROPERTY_TYPE                  187  non-null values\n",
+        "EDW_CREATED_DTM                187  non-null values\n",
+        "EDW_MODIFIED_DTM               19  non-null values\n",
+        "SOURCE_FILE_TRACKING_ID        187  non-null values\n",
+        "ODS_OMD_WORK_INSTANCE_ID       187  non-null values\n",
+        "EDW_OMD_WORK_INSTANCE_ID       187  non-null values\n",
+        "GOOGLE_ANALYTICS_ID            187  non-null values\n",
+        "dtypes: int64(6), object(11)\n",
+        "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "\n", + "Int64Index: 187 entries, 0 to 186\n", + "Data columns (total 17 columns):\n", + "SITE_KEY 187 non-null values\n", + "SITE_ID 184 non-null values\n", + "PROPERTY_NAME 187 non-null values\n", + "PROPERTY_CODE 184 non-null values\n", + "BUSINESS_OWNER_NAME 169 non-null values\n", + "ACQUISITION_DATE 99 non-null values\n", + "GENERAL_LEDGER_DIVISION_NBR 187 non-null values\n", + "OMNITURE_SITE_NAME 186 non-null values\n", + "SOURCE_SYSTEM_ID 186 non-null values\n", + "SITE_STATUS_CODE 187 non-null values\n", + "PROPERTY_TYPE 187 non-null values\n", + "EDW_CREATED_DTM 187 non-null values\n", + "EDW_MODIFIED_DTM 19 non-null values\n", + "SOURCE_FILE_TRACKING_ID 187 non-null values\n", + "ODS_OMD_WORK_INSTANCE_ID 187 non-null values\n", + "EDW_OMD_WORK_INSTANCE_ID 187 non-null values\n", + "GOOGLE_ANALYTICS_ID 187 non-null values\n", + "dtypes: int64(6), object(11)" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "joined = pd.merge(df, sites, how='outer', on='SITE_KEY')\n", + "joined" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n",
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 10121 entries, 0 to 10120\n",
+        "Data columns (total 48 columns):\n",
+        "DAY_KEY                        10000  non-null values\n",
+        "CONTENT_METADATA_KEY           10000  non-null values\n",
+        "CATEGORY_KEY                   10000  non-null values\n",
+        "SITE_KEY                       10121  non-null values\n",
+        "SEARCH_ENGINE_KEY              10000  non-null values\n",
+        "GEOGRAPHY_KEY                  10000  non-null values\n",
+        "REFERRER_HOST_KEY              10000  non-null values\n",
+        "STUDIO_KEY                     10000  non-null values\n",
+        "CONTENT_KEY                    10000  non-null values\n",
+        "SEARCH_PHRASE_KEY              10000  non-null values\n",
+        "USER_AGENT_KEY                 10000  non-null values\n",
+        "PAGE_TYPE_KEY                  10000  non-null values\n",
+        "SUB_PAGE_TYPE_KEY              10000  non-null values\n",
+        "QCD_PRIMARY_KEY                10000  non-null values\n",
+        "VISIT_KEY                      10000  non-null values\n",
+        "EVENT_DTM                      10000  non-null values\n",
+        "IP_ADDRESS                     10000  non-null values\n",
+        "IP_ADDRESS_NBR                 10000  non-null values\n",
+        "GOOGLE_RANK                    512  non-null values\n",
+        "VISITORIZED                    10000  non-null values\n",
+        "VISITOR_ID                     10000  non-null values\n",
+        "VERSION_ID                     10000  non-null values\n",
+        "REFERRER                       8162  non-null values\n",
+        "FULL_URI                       10000  non-null values\n",
+        "UTM_SOURCE                     335  non-null values\n",
+        "UTM_MEDIUM                     391  non-null values\n",
+        "UTM_CAMPAIGN                   379  non-null values\n",
+        "LTV_TAG                        4405  non-null values\n",
+        "EDW_CREATED_DTM_x              10000  non-null values\n",
+        "SOURCE_FILE_TRACKING_ID_x      10000  non-null values\n",
+        "ODS_OMD_WORK_INSTANCE_ID_x     10000  non-null values\n",
+        "EDW_OMD_WORK_INSTANCE_ID_x     10000  non-null values\n",
+        "SITE_ID                        10106  non-null values\n",
+        "PROPERTY_NAME                  10121  non-null values\n",
+        "PROPERTY_CODE                  10106  non-null values\n",
+        "BUSINESS_OWNER_NAME            10066  non-null values\n",
+        "ACQUISITION_DATE               9068  non-null values\n",
+        "GENERAL_LEDGER_DIVISION_NBR    10121  non-null values\n",
+        "OMNITURE_SITE_NAME             10108  non-null values\n",
+        "SOURCE_SYSTEM_ID               10108  non-null values\n",
+        "SITE_STATUS_CODE               10121  non-null values\n",
+        "PROPERTY_TYPE                  10121  non-null values\n",
+        "EDW_CREATED_DTM_y              10121  non-null values\n",
+        "EDW_MODIFIED_DTM               1964  non-null values\n",
+        "SOURCE_FILE_TRACKING_ID_y      10121  non-null values\n",
+        "ODS_OMD_WORK_INSTANCE_ID_y     10121  non-null values\n",
+        "EDW_OMD_WORK_INSTANCE_ID_y     10121  non-null values\n",
+        "GOOGLE_ANALYTICS_ID            10121  non-null values\n",
+        "dtypes: datetime64[ns](1), float64(20), int64(5), object(22)\n",
+        "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 11, + "text": [ + "\n", + "Int64Index: 10121 entries, 0 to 10120\n", + "Data columns (total 48 columns):\n", + "DAY_KEY 10000 non-null values\n", + "CONTENT_METADATA_KEY 10000 non-null values\n", + "CATEGORY_KEY 10000 non-null values\n", + "SITE_KEY 10121 non-null values\n", + "SEARCH_ENGINE_KEY 10000 non-null values\n", + "GEOGRAPHY_KEY 10000 non-null values\n", + "REFERRER_HOST_KEY 10000 non-null values\n", + "STUDIO_KEY 10000 non-null values\n", + "CONTENT_KEY 10000 non-null values\n", + "SEARCH_PHRASE_KEY 10000 non-null values\n", + "USER_AGENT_KEY 10000 non-null values\n", + "PAGE_TYPE_KEY 10000 non-null values\n", + "SUB_PAGE_TYPE_KEY 10000 non-null values\n", + "QCD_PRIMARY_KEY 10000 non-null values\n", + "VISIT_KEY 10000 non-null values\n", + "EVENT_DTM 10000 non-null values\n", + "IP_ADDRESS 10000 non-null values\n", + "IP_ADDRESS_NBR 10000 non-null values\n", + "GOOGLE_RANK 512 non-null values\n", + "VISITORIZED 10000 non-null values\n", + "VISITOR_ID 10000 non-null values\n", + "VERSION_ID 10000 non-null values\n", + "REFERRER 8162 non-null values\n", + "FULL_URI 10000 non-null values\n", + "UTM_SOURCE 335 non-null values\n", + "UTM_MEDIUM 391 non-null values\n", + "UTM_CAMPAIGN 379 non-null values\n", + "LTV_TAG 4405 non-null values\n", + "EDW_CREATED_DTM_x 10000 non-null values\n", + "SOURCE_FILE_TRACKING_ID_x 10000 non-null values\n", + "ODS_OMD_WORK_INSTANCE_ID_x 10000 non-null values\n", + "EDW_OMD_WORK_INSTANCE_ID_x 10000 non-null values\n", + "SITE_ID 10106 non-null values\n", + "PROPERTY_NAME 10121 non-null values\n", + "PROPERTY_CODE 10106 non-null values\n", + "BUSINESS_OWNER_NAME 10066 non-null values\n", + "ACQUISITION_DATE 9068 non-null values\n", + "GENERAL_LEDGER_DIVISION_NBR 10121 non-null values\n", + "OMNITURE_SITE_NAME 10108 non-null values\n", + "SOURCE_SYSTEM_ID 10108 non-null values\n", + "SITE_STATUS_CODE 10121 non-null values\n", + "PROPERTY_TYPE 10121 non-null values\n", + "EDW_CREATED_DTM_y 10121 non-null values\n", + "EDW_MODIFIED_DTM 1964 non-null values\n", + "SOURCE_FILE_TRACKING_ID_y 10121 non-null values\n", + "ODS_OMD_WORK_INSTANCE_ID_y 10121 non-null values\n", + "EDW_OMD_WORK_INSTANCE_ID_y 10121 non-null values\n", + "GOOGLE_ANALYTICS_ID 10121 non-null values\n", + "dtypes: datetime64[ns](1), float64(20), int64(5), object(22)" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initial Insights\n", + "\n", + "## Most Popular Site\n", + "\n", + "Find the most popular site by using [pandas.value_counts](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html#pandas.Series.value_counts) on `PROPERTY_NAME`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sites = pd.value_counts(joined['PROPERTY_NAME'])\n", + "sites.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 12, + "text": [ + "eHow 3720\n", + "ehowenespanol 1698\n", + "Cracked 1104\n", + "Livestrong 839\n", + "i-am-bored 634\n", + "dtype: int64" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Busiest Hour\n", + "Find the busiest hour." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "hours = pd.value_counts(df['EVENT_DTM'].map(lambda v: v.hour))\n", + "xs = range(0, 25) # We want all hours\n", + "ys = [hours.get(x, 0) for x in xs]\n", + "bar(xs, ys)\n", + "grid()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "svg": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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+ "# Saving\n", + "\n", + "Save `joined` to disk to a file called `joined.p`" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "joined.to_pickle('joined.p')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/pandas-adc/Data.ipynb b/pandas-adc/Data.ipynb new file mode 100644 index 0000000..1c8e908 --- /dev/null +++ b/pandas-adc/Data.ipynb @@ -0,0 +1,286 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas\n", + "------\n", + "\n", + "[pandas](http://pandas.pydata.org/) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#This Workshop\n", + "We'll show you the basics on how to work with data. First part will (by ![Miki](http://intranet/badge/tebekamiki.jpg \"Miki\")) will focus on cleaning data and second part (by ![Eric](http://intranet/badge/klusmaneric.jpg \"Eric\")) will focus on analytics and visualization and on using pandas for to do QA of Tableau reports.\n", + "\n", + "We highly recommend [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (pandas main developer); it is an excellent book.\n", + "\n", + "# IPython Notebook\n", + "This is an [IPython notebook](http://ipython.org/notebook.html): you can do many cool things with it - including running code!\n", + "\n", + "Note that this notebook server was started with `--pylab=inline` for some import shortcuts and inline graphs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Data\n", + "We'll use an [sqlite3](http://docs.python.org/2/library/sqlite3.html) database. Pandas has a [read_frame](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.sql.read_frame.html) function that get data from an SQL connection to a [DataFrame](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html).\n", + "\n", + "Download [the database](http://dm-miki.s3.amazonaws.com/talks/edw.sqlite.bz2) and uncompress it. Then use `sqlite3` and `read_frame` to load the table `FACT_OMNITURE_PAGE_VIEWS` to a DataFrame called `df`. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First Look at the Data\n", + "\n", + "## `df` value" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Columns" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Some Values from DAY_KEY, SITE_KEY and IP_ADDRESS" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Types" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cleaning Data\n", + "\n", + "You should know you data. However, everything that is an `object` is probably a string and need to be checked.\n", + "\n", + "For example `EVENT_DTM` (event date time) should be a [datetime](http://docs.python.org/2/library/datetime.html) object. Check whether that's currently the case or not." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fix `EVENT_DTM` column to be of `pandas.Timestamp` object. You'll find [pandas.to_datetime](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.tseries.tools.to_datetime.html#pandas.tseries.tools.to_datetime) helpful (you can ignore the milliseconds for simplicity)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cleanup `GOOGLE_RANK` to be a number or `nan`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Joining Tables\n", + "\n", + "In the current DataFrame, we have only `SITE_KEY`, however we'd like to have the site name as well. Load another DataFrame from the `DIM_SITE` table and join it with the previous data frame on `SITE_KEY`.\n", + "\n", + "See [pandas.merge](http://pandas.pydata.org/pandas-docs/dev/merging.html#database-style-dataframe-joining-merging) function." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initial Insights\n", + "\n", + "## Most Popular Site\n", + "\n", + "Find the most popular site by using [pandas.value_counts](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html#pandas.Series.value_counts) on `PROPERTY_NAME`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Busiest Hour\n", + "Find the busiest hour." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Saving\n", + "\n", + "Save `joined` to disk to a file called `joined.p`" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/pandas-adc/Data2-Solution.ipynb b/pandas-adc/Data2-Solution.ipynb new file mode 100644 index 0000000..52b19b0 --- /dev/null +++ b/pandas-adc/Data2-Solution.ipynb @@ -0,0 +1,1283 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Second Data Set\n", + "We'll use a [csv](http://en.wikipedia.org/wiki/Comma-separated_values) file in this second part of the workshop. Pandas has a [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) function that get data from an SQL connection to a [DataFrame](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).\n", + "\n", + "This data is generated by the following SQL:\n", + "\n", + "`\n", + "SELECT\n", + " Date(Convert_tz(From_unixtime(o.order_time), 'UTC', 'US/Pacific')) AS date\n", + " ,Ifnull(p.product_name, 'GiftCard') AS product\n", + " ,Ifnull(c.country_name, '-') AS country\n", + "\t,oi.order_item_amount AS price\n", + " ,oi.order_item_cost AS cost\n", + " ,SUM(oi.order_item_quantity) AS qty\n", + " ,SUM(oi.order_item_quantity * oi.order_item_amount) AS rev\n", + " ,SUM((oi.order_item_amount - oi.order_item_cost) * oi.order_item_quantity) AS gm\n", + "FROM\n", + " orders AS o\n", + " JOIN orders_items AS oi USING(order_id)\n", + " -- ON o.order_id = oi.order_id\n", + " LEFT JOIN products AS p USING(product_id)\n", + " -- ON oi.product_id = p.product_id\n", + " LEFT JOIN countries c\n", + " ON o.order_shipping_country = c.country_code\n", + "WHERE\n", + " o.order_time >= Unix_timestamp(Convert_tz('2013-01-01','US/Pacific', 'UTC'))\n", + " AND o.order_time < Unix_timestamp(\n", + " Convert_tz(\n", + " Date(Convert_tz(now(), 'UTC', 'US/Pacific')),\n", + " 'US/Pacific', 'UTC')\n", + " )\n", + " AND o.order_type IN (0, 12) -- 0 TYPE_STANDARD, 12 TYPE_SPLIT_PARENT\n", + " AND o.order_status NOT IN (0, 11, 12) -- 0 PENDING,11 CANCELLED,12 RETURNED\n", + "GROUP BY 1,2,3,4,5`\n", + "\n", + "\n", + "\n", + "The data file is `product_country.sql.csv.bz2`, in the directory you cloned `miki/talks/pandas` into, or download it from [Amazon S3](http://dm-miki.s3.amazonaws.com/talks/product_country.sql.csv.bz2). \n", + "\n", + "Use [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) to load the data file to a DataFrame called `df2`. Set the index columns to `date`, `product`, `country`, `price`, and `cost`, and specify that `date` is to be parsed into proper datetime date values. Don't forget to specify that the data file is bzip2-compressed." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here\n", + "rcParams['figure.figsize'] = 20, 10 # makes the graphs larger\n", + "\n", + "import pandas as pd\n", + "df2 = pd.read_csv('product_country.sql.csv.bz2',\n", + " index_col=['date', 'product', 'country', 'price', 'cost'],\n", + " parse_dates='date',\n", + " compression='bz2')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First Look at the Data\n", + "\n", + "## `df2` as a Value\n", + "Since we're in IPython, we can just evaluate `df2` to see what it is." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here\n", + "df2" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n",
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "MultiIndex: 221847 entries, (2013-01-01 00:00:00, Art Print, Australia, 18.0, 4.25) to (2014-02-04 00:00:00, Wall Clock, United States, 30.0, 14.25)\n",
+        "Data columns (total 3 columns):\n",
+        "qty    221847  non-null values\n",
+        "rev    221847  non-null values\n",
+        "gm     221847  non-null values\n",
+        "dtypes: float64(2), int64(1)\n",
+        "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 2, + "text": [ + "\n", + "MultiIndex: 221847 entries, (2013-01-01 00:00:00, Art Print, Australia, 18.0, 4.25) to (2014-02-04 00:00:00, Wall Clock, United States, 30.0, 14.25)\n", + "Data columns (total 3 columns):\n", + "qty 221847 non-null values\n", + "rev 221847 non-null values\n", + "gm 221847 non-null values\n", + "dtypes: float64(2), int64(1)" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `df2` as Data\n", + "Use the [head](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.head.html) method of the `df2` DataFrame to see some actual data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here\n", + "df2.head(20)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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qtyrevgm
dateproductcountrypricecost
2013-01-01Art PrintAustralia18.004.25 3 54.00 41.25
18.724.25 3 56.16 43.41
21.004.75 1 21.00 16.25
21.844.75 1 21.84 17.09
22.994.75 1 22.99 18.24
25.004.75 1 25.00 20.25
30.009.60 1 30.00 20.40
Brazil26.004.75 1 26.00 21.25
Canada16.004.25 1 16.00 11.75
17.084.25 1 17.08 12.83
18.004.75 1 18.00 13.25
19.764.25 1 19.76 15.51
24.004.75 1 24.00 19.25
25.009.60 1 25.00 15.40
28.004.25 1 28.00 23.75
35.0016.20 2 70.00 37.60
37.0016.20 1 37.00 20.80
Denmark22.004.75 1 22.00 17.25
France22.004.75 1 22.00 17.25
40.009.60 1 40.00 30.40
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + " qty rev gm\n", + "date product country price cost \n", + "2013-01-01 Art Print Australia 18.00 4.25 3 54.00 41.25\n", + " 18.72 4.25 3 56.16 43.41\n", + " 21.00 4.75 1 21.00 16.25\n", + " 21.84 4.75 1 21.84 17.09\n", + " 22.99 4.75 1 22.99 18.24\n", + " 25.00 4.75 1 25.00 20.25\n", + " 30.00 9.60 1 30.00 20.40\n", + " Brazil 26.00 4.75 1 26.00 21.25\n", + " Canada 16.00 4.25 1 16.00 11.75\n", + " 17.08 4.25 1 17.08 12.83\n", + " 18.00 4.75 1 18.00 13.25\n", + " 19.76 4.25 1 19.76 15.51\n", + " 24.00 4.75 1 24.00 19.25\n", + " 25.00 9.60 1 25.00 15.40\n", + " 28.00 4.25 1 28.00 23.75\n", + " 35.00 16.20 2 70.00 37.60\n", + " 37.00 16.20 1 37.00 20.80\n", + " Denmark 22.00 4.75 1 22.00 17.25\n", + " France 22.00 4.75 1 22.00 17.25\n", + " 40.00 9.60 1 40.00 30.40" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot the Revenue Series by Day\n", + "\n", + "## First, summarize the data\n", + "\n", + "Take the `sum()` of revenue per day by using the [groupby](http://pandas.pydata.org/pandas-docs/dev/groupby.html) method on the `df2` Data Frame, to make a new Data Frame named `df3`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here\n", + "df3 = df2.groupby(level='date').sum()\n", + "print(df3)\n", + "df3.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "DatetimeIndex: 400 entries, 2013-01-01 00:00:00 to 2014-02-04 00:00:00\n", + "Data columns (total 3 columns):\n", + "qty 400 non-null values\n", + "rev 400 non-null values\n", + "gm 400 non-null values\n", + "dtypes: float64(2), int64(1)\n" + ] + }, + { + "html": [ + "
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qtyrevgm
date
2013-01-01 1374 44752.34 25554.79
2013-01-02 1696 53025.65 30038.49
2013-01-03 1660 50173.92 29041.58
2013-01-04 1631 49603.00 28173.89
2013-01-05 1437 43324.95 24926.86
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + " qty rev gm\n", + "date \n", + "2013-01-01 1374 44752.34 25554.79\n", + "2013-01-02 1696 53025.65 30038.49\n", + "2013-01-03 1660 50173.92 29041.58\n", + "2013-01-04 1631 49603.00 28173.89\n", + "2013-01-05 1437 43324.95 24926.86" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now, plot the data\n", + "\n", + "Find the total revenue per day by calling the [plot](http://pandas.pydata.org/pandas-docs/stable/visualization.html) method on the `df3.rev` Series." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here\n", + "df3.rev.plot()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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fkyZNkr7+demqq6Rf/lIqLEz2qoD4MTMJAAAAAABDzExCb8JmEqLi2dT40M0O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tz9+cwz9E9LeTn1Lfv0sXZObtGkCT3PZDJRNencVnXvbr3UbfXq8IrETpNJMrYvvkjP/jfeoOMb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\nIiIiTTCZRHZxbqp7GDf9MLbaYjz1w9hqi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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Plot Gross Margin (gm) by Product by Date\n", + "##First summarize the data\n", + "`sum()` the data to the level of `date` and `product` using the [groupby](http://pandas.pydata.org/pandas-docs/dev/groupby.html) method on the `df2` Data Frame." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here\n", + "product_by_date = df2.groupby(level=['date', 'product']).sum()\n", + "product_by_date.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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qtyrevgm
dateproduct
2013-01-01Art Print 240 6694.74 4695.89
Framed Art Print 95 4775.50 2684.55
GiftCard 8 500.00 500.00
Hoody 26 1044.00 346.55
Laptop & iPad Skin 43 1185.00 773.75
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 6, + "text": [ + " qty rev gm\n", + "date product \n", + "2013-01-01 Art Print 240 6694.74 4695.89\n", + " Framed Art Print 95 4775.50 2684.55\n", + " GiftCard 8 500.00 500.00\n", + " Hoody 26 1044.00 346.55\n", + " Laptop & iPad Skin 43 1185.00 773.75" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Put the `product` names into the column headings\n", + "Use the [unstack](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.unstack.html) method of the `product_by_date` Data Frame to move the product names from the row labels into the column headings." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "product_by_date = product_by_date.unstack()\n", + "product_by_date.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n",
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "DatetimeIndex: 5 entries, 2013-01-01 00:00:00 to 2013-01-05 00:00:00\n",
+        "Data columns (total 57 columns):\n",
+        "(qty, Art Print)             5  non-null values\n",
+        "(qty, Framed Art Print)      5  non-null values\n",
+        "(qty, GiftCard)              5  non-null values\n",
+        "(qty, Hoody)                 5  non-null values\n",
+        "(qty, Kids T-Shirt)          0  non-null values\n",
+        "(qty, Laptop & iPad Skin)    5  non-null values\n",
+        "(qty, Mug)                   0  non-null values\n",
+        "(qty, Onesie)                0  non-null values\n",
+        "(qty, Stationery Cards)      5  non-null values\n",
+        "(qty, Stretched Canvas)      5  non-null values\n",
+        "(qty, T-shirt)               5  non-null values\n",
+        "(qty, Throw Pillow)          5  non-null values\n",
+        "(qty, Tote Bag)              0  non-null values\n",
+        "(qty, Tote Bag (old))        5  non-null values\n",
+        "(qty, Unisex Tank Top)       0  non-null values\n",
+        "(qty, Wall Clock)            0  non-null values\n",
+        "(qty, iPad Case)             0  non-null values\n",
+        "(qty, iPhone & iPod Case)    5  non-null values\n",
+        "(qty, iPhone & iPod Skin)    5  non-null values\n",
+        "(rev, Art Print)             5  non-null values\n",
+        "(rev, Framed Art Print)      5  non-null values\n",
+        "(rev, GiftCard)              5  non-null values\n",
+        "(rev, Hoody)                 5  non-null values\n",
+        "(rev, Kids T-Shirt)          0  non-null values\n",
+        "(rev, Laptop & iPad Skin)    5  non-null values\n",
+        "(rev, Mug)                   0  non-null values\n",
+        "(rev, Onesie)                0  non-null values\n",
+        "(rev, Stationery Cards)      5  non-null values\n",
+        "(rev, Stretched Canvas)      5  non-null values\n",
+        "(rev, T-shirt)               5  non-null values\n",
+        "(rev, Throw Pillow)          5  non-null values\n",
+        "(rev, Tote Bag)              0  non-null values\n",
+        "(rev, Tote Bag (old))        5  non-null values\n",
+        "(rev, Unisex Tank Top)       0  non-null values\n",
+        "(rev, Wall Clock)            0  non-null values\n",
+        "(rev, iPad Case)             0  non-null values\n",
+        "(rev, iPhone & iPod Case)    5  non-null values\n",
+        "(rev, iPhone & iPod Skin)    5  non-null values\n",
+        "(gm, Art Print)              5  non-null values\n",
+        "(gm, Framed Art Print)       5  non-null values\n",
+        "(gm, GiftCard)               5  non-null values\n",
+        "(gm, Hoody)                  5  non-null values\n",
+        "(gm, Kids T-Shirt)           0  non-null values\n",
+        "(gm, Laptop & iPad Skin)     5  non-null values\n",
+        "(gm, Mug)                    0  non-null values\n",
+        "(gm, Onesie)                 0  non-null values\n",
+        "(gm, Stationery Cards)       5  non-null values\n",
+        "(gm, Stretched Canvas)       5  non-null values\n",
+        "(gm, T-shirt)                5  non-null values\n",
+        "(gm, Throw Pillow)           5  non-null values\n",
+        "(gm, Tote Bag)               0  non-null values\n",
+        "(gm, Tote Bag (old))         5  non-null values\n",
+        "(gm, Unisex Tank Top)        0  non-null values\n",
+        "(gm, Wall Clock)             0  non-null values\n",
+        "(gm, iPad Case)              0  non-null values\n",
+        "(gm, iPhone & iPod Case)     5  non-null values\n",
+        "(gm, iPhone & iPod Skin)     5  non-null values\n",
+        "dtypes: float64(57)\n",
+        "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "\n", + "DatetimeIndex: 5 entries, 2013-01-01 00:00:00 to 2013-01-05 00:00:00\n", + "Data columns (total 57 columns):\n", + "(qty, Art Print) 5 non-null values\n", + "(qty, Framed Art Print) 5 non-null values\n", + "(qty, GiftCard) 5 non-null values\n", + "(qty, Hoody) 5 non-null values\n", + "(qty, Kids T-Shirt) 0 non-null values\n", + "(qty, Laptop & iPad Skin) 5 non-null values\n", + "(qty, Mug) 0 non-null values\n", + "(qty, Onesie) 0 non-null values\n", + "(qty, Stationery Cards) 5 non-null values\n", + "(qty, Stretched Canvas) 5 non-null values\n", + "(qty, T-shirt) 5 non-null values\n", + "(qty, Throw Pillow) 5 non-null values\n", + "(qty, Tote Bag) 0 non-null values\n", + "(qty, Tote Bag (old)) 5 non-null values\n", + "(qty, Unisex Tank Top) 0 non-null values\n", + "(qty, Wall Clock) 0 non-null values\n", + "(qty, iPad Case) 0 non-null values\n", + "(qty, iPhone & iPod Case) 5 non-null values\n", + "(qty, iPhone & iPod Skin) 5 non-null values\n", + "(rev, Art Print) 5 non-null values\n", + "(rev, Framed Art Print) 5 non-null values\n", + "(rev, GiftCard) 5 non-null values\n", + "(rev, Hoody) 5 non-null values\n", + "(rev, Kids T-Shirt) 0 non-null values\n", + "(rev, Laptop & iPad Skin) 5 non-null values\n", + "(rev, Mug) 0 non-null values\n", + "(rev, Onesie) 0 non-null values\n", + "(rev, Stationery Cards) 5 non-null values\n", + "(rev, Stretched Canvas) 5 non-null values\n", + "(rev, T-shirt) 5 non-null values\n", + "(rev, Throw Pillow) 5 non-null values\n", + "(rev, Tote Bag) 0 non-null values\n", + "(rev, Tote Bag (old)) 5 non-null values\n", + "(rev, Unisex Tank Top) 0 non-null values\n", + "(rev, Wall Clock) 0 non-null values\n", + "(rev, iPad Case) 0 non-null values\n", + "(rev, iPhone & iPod Case) 5 non-null values\n", + "(rev, iPhone & iPod Skin) 5 non-null values\n", + "(gm, Art Print) 5 non-null values\n", + "(gm, Framed Art Print) 5 non-null values\n", + "(gm, GiftCard) 5 non-null values\n", + "(gm, Hoody) 5 non-null values\n", + "(gm, Kids T-Shirt) 0 non-null values\n", + "(gm, Laptop & iPad Skin) 5 non-null values\n", + "(gm, Mug) 0 non-null values\n", + "(gm, Onesie) 0 non-null values\n", + "(gm, Stationery Cards) 5 non-null values\n", + "(gm, Stretched Canvas) 5 non-null values\n", + "(gm, T-shirt) 5 non-null values\n", + "(gm, Throw Pillow) 5 non-null values\n", + "(gm, Tote Bag) 0 non-null values\n", + "(gm, Tote Bag (old)) 5 non-null values\n", + "(gm, Unisex Tank Top) 0 non-null values\n", + "(gm, Wall Clock) 0 non-null values\n", + "(gm, iPad Case) 0 non-null values\n", + "(gm, iPhone & iPod Case) 5 non-null values\n", + "(gm, iPhone & iPod Skin) 5 non-null values\n", + "dtypes: float64(57)" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Keep just the Gross Margin\n", + "Set the `product_by_date` DataFrame to just the subset of columns that hold gross margin (`gm`)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "product_by_date = product_by_date['gm']\n", + "product_by_date.head() " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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productArt PrintFramed Art PrintGiftCardHoodyKids T-ShirtLaptop & iPad SkinMugOnesieStationery CardsStretched CanvasT-shirtThrow PillowTote BagTote Bag (old)Unisex Tank TopWall ClockiPad CaseiPhone & iPod CaseiPhone & iPod Skin
date
2013-01-01 4695.89 2684.55 500 346.55NaN 773.75NaNNaN 127.08 1186.38 1337.00 1491.54NaN 127.8NaNNaNNaN 11868.0 416.25
2013-01-02 6151.93 2588.52 75 316.75NaN 666.25NaNNaN 91.72 1008.32 1531.35 1915.55NaN 155.1NaNNaNNaN 15038.5 499.50
2013-01-03 6457.19 2484.98 97 385.35NaN 742.50NaNNaN 356.08 591.16 1404.60 1824.42NaN 220.8NaNNaNNaN 13794.5 683.00
2013-01-04 6761.30 2125.80 75 429.15NaN 531.75NaNNaN 251.68 779.87 1683.10 1710.44NaN 252.8NaNNaNNaN 12999.5 573.50
2013-01-05 5561.05 2215.95 50 438.20NaN 730.50NaNNaN 91.60 486.88 1366.00 1918.33NaN 128.6NaNNaNNaN 11338.5 601.25
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "product Art Print Framed Art Print GiftCard Hoody Kids T-Shirt \\\n", + "date \n", + "2013-01-01 4695.89 2684.55 500 346.55 NaN \n", + "2013-01-02 6151.93 2588.52 75 316.75 NaN \n", + "2013-01-03 6457.19 2484.98 97 385.35 NaN \n", + "2013-01-04 6761.30 2125.80 75 429.15 NaN \n", + "2013-01-05 5561.05 2215.95 50 438.20 NaN \n", + "\n", + "product Laptop & iPad Skin Mug Onesie Stationery Cards \\\n", + "date \n", + "2013-01-01 773.75 NaN NaN 127.08 \n", + "2013-01-02 666.25 NaN NaN 91.72 \n", + "2013-01-03 742.50 NaN NaN 356.08 \n", + "2013-01-04 531.75 NaN NaN 251.68 \n", + "2013-01-05 730.50 NaN NaN 91.60 \n", + "\n", + "product Stretched Canvas T-shirt Throw Pillow Tote Bag Tote Bag (old) \\\n", + "date \n", + "2013-01-01 1186.38 1337.00 1491.54 NaN 127.8 \n", + "2013-01-02 1008.32 1531.35 1915.55 NaN 155.1 \n", + "2013-01-03 591.16 1404.60 1824.42 NaN 220.8 \n", + "2013-01-04 779.87 1683.10 1710.44 NaN 252.8 \n", + "2013-01-05 486.88 1366.00 1918.33 NaN 128.6 \n", + "\n", + "product Unisex Tank Top Wall Clock iPad Case iPhone & iPod Case \\\n", + "date \n", + "2013-01-01 NaN NaN NaN 11868.0 \n", + "2013-01-02 NaN NaN NaN 15038.5 \n", + "2013-01-03 NaN NaN NaN 13794.5 \n", + "2013-01-04 NaN NaN NaN 12999.5 \n", + "2013-01-05 NaN NaN NaN 11338.5 \n", + "\n", + "product iPhone & iPod Skin \n", + "date \n", + "2013-01-01 416.25 \n", + "2013-01-02 499.50 \n", + "2013-01-03 683.00 \n", + "2013-01-04 573.50 \n", + "2013-01-05 601.25 " + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Find the top 3 products\n", + "There are a lot of products. Use the `sum()` method of the `product_by_date` DataFrame to find total gross margin by product." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "product_by_date.sum().order(ascending=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "product\n", + "iPhone & iPod Case 4109925.00\n", + "Art Print 3621201.30\n", + "Throw Pillow 1642305.62\n", + "Framed Art Print 1368167.02\n", + "T-shirt 1150088.78\n", + "Stretched Canvas 455008.65\n", + "Mug 281916.80\n", + "Tote Bag 281639.20\n", + "Laptop & iPad Skin 227714.00\n", + "iPhone & iPod Skin 218467.25\n", + "Hoody 173869.68\n", + "GiftCard 135163.30\n", + "Stationery Cards 123254.33\n", + "Tote Bag (old) 98802.60\n", + "Unisex Tank Top 96848.68\n", + "iPad Case 87429.00\n", + "Kids T-Shirt 17716.68\n", + "Wall Clock 17569.00\n", + "Onesie 16625.30\n", + "dtype: float64" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Keep just the top 3 products\n", + "Update the `product_by_date` DataFrame by setting it to just a subset of the columns, specified as a list of column names." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "product_by_date = product_by_date[['iPhone & iPod Case', 'Art Print', 'Throw Pillow']]\n", + "product_by_date.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
productiPhone & iPod CaseArt PrintThrow Pillow
date
2013-01-01 11868.0 4695.89 1491.54
2013-01-02 15038.5 6151.93 1915.55
2013-01-03 13794.5 6457.19 1824.42
2013-01-04 12999.5 6761.30 1710.44
2013-01-05 11338.5 5561.05 1918.33
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "product iPhone & iPod Case Art Print Throw Pillow\n", + "date \n", + "2013-01-01 11868.0 4695.89 1491.54\n", + "2013-01-02 15038.5 6151.93 1915.55\n", + "2013-01-03 13794.5 6457.19 1824.42\n", + "2013-01-04 12999.5 6761.30 1710.44\n", + "2013-01-05 11338.5 5561.05 1918.33" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Plot the gross margin for the top 3 products\n", + "Use the [plot](http://pandas.pydata.org/pandas-docs/dev/visualization.html) method of the `product_by_date` DataFrame" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "product_by_date.plot()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 11, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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aaj6KRqB3NELkZg/ZmkWedpCrWeRpD9naQ7bmkKU9ZGtWMBgMDcL2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\nXgZ181Pbm3VmGC0hbNhA10O+fy+/bNWqLqxYAZjNnWhtBXp6u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0vHb5/FGSiqdFVIi0zz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\nMHUqK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nUms3GkgtrSpZwjW7WyfooeHMyI082bTV+3VZUU4Pzx0088rT91yrm2x8dzbXGH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\nJsbTTfJpLl68iJdeegnly5dH2bJlM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+ "text": [ + "" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#If there's time\n", + "##Pricing policies\n", + "What is Society6's apparent pricing policy? Is gross margin invariably a fixed share of revenue, or do, say, different products have different relative gross margins? \n", + "\n", + "To take a first look, let's recreate the full `product_by_date` DataFrame from above" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "product_by_date = df2.groupby(level=['date', 'product']).sum()\n", + "product_by_date.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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qtyrevgm
dateproduct
2013-01-01Art Print 240 6694.74 4695.89
Framed Art Print 95 4775.50 2684.55
GiftCard 8 500.00 500.00
Hoody 26 1044.00 346.55
Laptop & iPad Skin 43 1185.00 773.75
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 12, + "text": [ + " qty rev gm\n", + "date product \n", + "2013-01-01 Art Print 240 6694.74 4695.89\n", + " Framed Art Print 95 4775.50 2684.55\n", + " GiftCard 8 500.00 500.00\n", + " Hoody 26 1044.00 346.55\n", + " Laptop & iPad Skin 43 1185.00 773.75" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, using [matplotlib.pyplot](http://matplotlib.org/api/pyplot_api.html)'s [scatter](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter) method, let's create a scatterplot of revenue vs. gross margin for each combination of day and product." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(product_by_date.rev, product_by_date.gm)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 13, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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p/TjclClTcvbZZ6epqSmjRo3KyJEjkyTnnHNOxo0bl9ra2lRVVb1u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\nb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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/pandas-adc/Data2.ipynb b/pandas-adc/Data2.ipynb new file mode 100644 index 0000000..6fc2b03 --- /dev/null +++ b/pandas-adc/Data2.ipynb @@ -0,0 +1,307 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Second Data Set\n", + "We'll use a [csv](http://en.wikipedia.org/wiki/Comma-separated_values) file in this second part of the workshop. Pandas has a [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) function that get data from an SQL connection to a [DataFrame](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).\n", + "\n", + "This data is generated by the following SQL:\n", + "\n", + "`\n", + "SELECT\n", + " Date(Convert_tz(From_unixtime(o.order_time), 'UTC', 'US/Pacific')) AS date\n", + " ,Ifnull(p.product_name, 'GiftCard') AS product\n", + " ,Ifnull(c.country_name, '-') AS country\n", + "\t,oi.order_item_amount AS price\n", + " ,oi.order_item_cost AS cost\n", + " ,SUM(oi.order_item_quantity) AS qty\n", + " ,SUM(oi.order_item_quantity * oi.order_item_amount) AS rev\n", + " ,SUM((oi.order_item_amount - oi.order_item_cost) * oi.order_item_quantity) AS gm\n", + "FROM\n", + " orders AS o\n", + " JOIN orders_items AS oi USING(order_id)\n", + " -- ON o.order_id = oi.order_id\n", + " LEFT JOIN products AS p USING(product_id)\n", + " -- ON oi.product_id = p.product_id\n", + " LEFT JOIN countries c\n", + " ON o.order_shipping_country = c.country_code\n", + "WHERE\n", + " o.order_time >= Unix_timestamp(Convert_tz('2013-01-01','US/Pacific', 'UTC'))\n", + " AND o.order_time < Unix_timestamp(\n", + " Convert_tz(\n", + " Date(Convert_tz(now(), 'UTC', 'US/Pacific')),\n", + " 'US/Pacific', 'UTC')\n", + " )\n", + " AND o.order_type IN (0, 12) -- 0 TYPE_STANDARD, 12 TYPE_SPLIT_PARENT\n", + " AND o.order_status NOT IN (0, 11, 12) -- 0 PENDING,11 CANCELLED,12 RETURNED\n", + "GROUP BY 1,2,3,4,5`\n", + "\n", + "\n", + "\n", + "The data file is `product_country.sql.csv.bz2`, in the directory you cloned `miki/talks/pandas` into, or download it from [Amazon S3](http://dm-miki.s3.amazonaws.com/talks/product_country.sql.csv.bz2). \n", + "\n", + "Use [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) to load the data file to a DataFrame called `df2`. Set the index columns to `date`, `product`, `country`, `price`, and `cost`, and specify that `date` is to be parsed into proper datetime date values. Don't forget to specify that the data file is bzip2-compressed." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First Look at the Data\n", + "\n", + "## `df2` as a Value\n", + "Since we're in IPython, we can just evaluate `df2` to see what it is." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `df2` as Data\n", + "Use the [head](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.head.html) method of the `df2` DataFrame to see some actual data." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# You code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot the Revenue Series by Day\n", + "\n", + "## First, summarize the data\n", + "\n", + "Take the `sum()` of revenue per day by using the [groupby](http://pandas.pydata.org/pandas-docs/dev/groupby.html) method on the `df2` Data Frame, to make a new Data Frame named `df3`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now, plot the data\n", + "\n", + "Find the total revenue per day by calling the [plot](http://pandas.pydata.org/pandas-docs/stable/visualization.html) method on the `df3.rev` Series." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Plot Gross Margin (gm) by Product by Date\n", + "##First summarize the data\n", + "`sum()` the data to the level of `date` and `product` using the [groupby](http://pandas.pydata.org/pandas-docs/dev/groupby.html) method on the `df2` Data Frame." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Put the `product` names into the column headings\n", + "Use the [unstack](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.unstack.html) method of the `product_by_date` Data Frame to move the product names from the row labels into the column headings." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Keep just the Gross Margin\n", + "Set the `product_by_date` DataFrame to just the subset of columns that hold gross margin (`gm`)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here " + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Find the top 3 products\n", + "There are a lot of products. Use the `sum()` method of the `product_by_date` DataFrame to find total gross margin by product." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Keep just the top 3 products\n", + "Update the `product_by_date` DataFrame by setting it to just a subset of the columns, specified as a list of column names." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Plot the gross margin for the top 3 products\n", + "Use the [plot](http://pandas.pydata.org/pandas-docs/dev/visualization.html) method of the `product_by_date` DataFrame" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#If there's time\n", + "##Pricing policies\n", + "What is Society6's apparent pricing policy? Is gross margin invariably a fixed share of revenue, or do, say, different products have different relative gross margins? \n", + "\n", + "To take a first look, let's recreate the full `product_by_date` DataFrame from above" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, using [matplotlib.pyplot](http://matplotlib.org/api/pyplot_api.html)'s [scatter](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter) method, let's create a scatterplot of revenue vs. gross margin for each combination of day and product." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Your code goes here" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/pandas-adc/README.md b/pandas-adc/README.md new file mode 100644 index 0000000..b9e8169 --- /dev/null +++ b/pandas-adc/README.md @@ -0,0 +1,27 @@ +[Pandas](http://pandas.pydata.org/) Workshop + +# Installing + +To avoid the pain of installing matplotlib on OSX, I highly recommend using +[Anaconda](http://continuum.io/downloads). However if your'e like me ... ;) + + ./bootstrap.sh + +Note: I tested that on my setup which has [homebrew](http://brew.sh/) Python and +[XQuartz](http://xquartz.macosforge.org/landing/). + +# Running + + ./run-notebook.sh + +# Notebooks + +* [Data](http://nbviewer.ipython.org/url/dm-miki.s3.amazonaws.com/talks/Data.ipynb) +* [Data2](http://nbviewer.ipython.org/url/dm-miki.s3.amazonaws.com/talks/Data2.ipynb) +* [Data Solutions](http://nbviewer.ipython.org/url/dm-miki.s3.amazonaws.com/talks/Data-Solution.ipynb) +* [Data2 Solutions](http://nbviewer.ipython.org/url/dm-miki.s3.amazonaws.com/talks/Data2-Solution.ipynb) + +# Download + +* Here's [the tarball](http://dm-miki.s3.amazonaws.com/talks/pandas.tar.bz2) +* Or you can view the [git repo](http://gitlab.prod.dm.local/miki/talks/tree/master/pandas) diff --git a/pandas-adc/bootstrap.sh b/pandas-adc/bootstrap.sh new file mode 100755 index 0000000..37dbe80 --- /dev/null +++ b/pandas-adc/bootstrap.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +bzip2 -d -c edw.sqlite.bz2 > edw.sqlite + +virtualenv venv +. ./venv/bin/activate +pip install -r requirements.txt diff --git a/pandas-adc/edw.sqlite.bz2 b/pandas-adc/edw.sqlite.bz2 new file mode 100644 index 0000000..5165fcc Binary files /dev/null and b/pandas-adc/edw.sqlite.bz2 differ diff --git a/pandas-adc/gendb.py b/pandas-adc/gendb.py new file mode 100755 index 0000000..1bd4664 --- /dev/null +++ b/pandas-adc/gendb.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python + +from zipfile import ZipFile +import sqlite3 +from os.path import isfile +from os import remove +import pandas as pd +import numpy as np + +def sql_vals(values): + return tuple(int(val) if type(val) == np.int64 else val for val in values) + + +def insert_from(db, table, fo): + df = pd.read_csv(fo, sep='\1') + names = ', '.join(df.columns) + qmarks = ', '.join(['?'] * len(df.columns)) + + sql = 'INSERT INTO {} ({}) VALUES ({})'.format( + table, names, qmarks) + + for row in df.itertuples(index=False): + values = sql_vals(row) + db.execute(sql, values) + + +def main(): + zf = ZipFile('data.zip') + dbfile = 'edw.sqlite' + if isfile(dbfile): + remove(dbfile) + + db = sqlite3.connect(dbfile) + + with open('schema.sql') as fo: + db.executescript(fo.read()) + + # Populate facts + for name in zf.namelist(): + if '2013' in name: + insert_from(db, 'FACT_OMNITURE_PAGE_VIEWS', zf.open(name)) + + # Populate DIM_SITE + insert_from(db, 'DIM_SITE', zf.open('dim_site.csv')) + + db.commit() + + +if __name__ == '__main__': + main() diff --git a/pandas-adc/product_country.sql.csv.bz2 b/pandas-adc/product_country.sql.csv.bz2 new file mode 100644 index 0000000..c319511 Binary files /dev/null and b/pandas-adc/product_country.sql.csv.bz2 differ diff --git a/pandas-adc/requirements.txt b/pandas-adc/requirements.txt new file mode 100644 index 0000000..71abddb --- /dev/null +++ b/pandas-adc/requirements.txt @@ -0,0 +1,7 @@ +ipython +jinja2 +matplotlib +pandas +pygments +pyzmq +tornado diff --git a/pandas-adc/run-notebook.sh b/pandas-adc/run-notebook.sh new file mode 100755 index 0000000..8e316ca --- /dev/null +++ b/pandas-adc/run-notebook.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +. ./venv/bin/activate && ipython notebook --pylab=inline diff --git a/pandas-adc/schema.sql b/pandas-adc/schema.sql new file mode 100644 index 0000000..f4e4171 --- /dev/null +++ b/pandas-adc/schema.sql @@ -0,0 +1,58 @@ +CREATE TABLE FACT_OMNITURE_PAGE_VIEWS +( + DAY_KEY INTEGER + , CONTENT_METADATA_KEY BIGINT + , CATEGORY_KEY INTEGER + , SITE_KEY INTEGER + , SEARCH_ENGINE_KEY INTEGER + , GEOGRAPHY_KEY BIGINT + , REFERRER_HOST_KEY INTEGER + , STUDIO_KEY BIGINT + , CONTENT_KEY BIGINT + , SEARCH_PHRASE_KEY BIGINT + , USER_AGENT_KEY BIGINT + , PAGE_TYPE_KEY INTEGER + , SUB_PAGE_TYPE_KEY INTEGER + , QCD_PRIMARY_KEY BIGINT + , VISIT_KEY BIGINT + , EVENT_DTM TIMESTAMP + , IP_ADDRESS CHARACTER VARYING(15) + , IP_ADDRESS_NBR BIGINT + , GOOGLE_RANK CHARACTER VARYING(10) + , VISITORIZED BOOLEAN + , VISITOR_ID CHARACTER VARYING(20) + , VERSION_ID CHARACTER VARYING(10) + , REFERRER NATIONAL CHARACTER VARYING(512) + , FULL_URI NATIONAL CHARACTER VARYING(512) + , UTM_SOURCE NATIONAL CHARACTER VARYING(25) + , UTM_MEDIUM NATIONAL CHARACTER VARYING(20) + , UTM_CAMPAIGN NATIONAL CHARACTER VARYING(20) + , LTV_TAG CHARACTER VARYING(8000) + , EDW_CREATED_DTM TIMESTAMP + , SOURCE_FILE_TRACKING_ID INTEGER + , ODS_OMD_WORK_INSTANCE_ID INTEGER + , EDW_OMD_WORK_INSTANCE_ID INTEGER +) +; + +CREATE TABLE DIM_SITE +( + SITE_KEY INTEGER PRIMARY KEY + , SITE_ID CHARACTER VARYING(6) + , PROPERTY_NAME CHARACTER VARYING(128) + , PROPERTY_CODE CHARACTER VARYING(6) + , BUSINESS_OWNER_NAME CHARACTER VARYING(50) + , ACQUISITION_DATE TIMESTAMP + , GENERAL_LEDGER_DIVISION_NBR INTEGER + , OMNITURE_SITE_NAME CHARACTER VARYING(128) + , SOURCE_SYSTEM_ID CHARACTER VARYING(50) + , SITE_STATUS_CODE CHARACTER VARYING(50) + , PROPERTY_TYPE CHARACTER VARYING(50) + , EDW_CREATED_DTM TIMESTAMP + , EDW_MODIFIED_DTM TIMESTAMP + , SOURCE_FILE_TRACKING_ID INTEGER + , ODS_OMD_WORK_INSTANCE_ID INTEGER + , EDW_OMD_WORK_INSTANCE_ID INTEGER + , GOOGLE_ANALYTICS_ID BIGINT +) +; diff --git a/pandas-pyweb-il/CME_CL1.csv b/pandas-pyweb-il/CME_CL1.csv new file mode 100644 index 0000000..d7faa38 --- /dev/null +++ b/pandas-pyweb-il/CME_CL1.csv @@ -0,0 +1,8093 @@ +Date,Open,High,Low,Last,Change,Settle,Volume,Open Interest +2015-07-02,56.87,57.95,56.5,,0.03,56.93,297046.0,326973.0 +2015-07-01,58.98,58.98,56.68,,2.51,56.96,373405.0,319172.0 +2015-06-30,58.27,59.69,57.94,59.4,1.14,59.47,282364.0,331764.0 +2015-06-29,58.84,59.27,58.04,,1.3,58.33,286035.0,332326.0 +2015-06-26,59.66,59.96,58.76,,0.07,59.63,231868.0,329016.0 +2015-06-25,60.22,60.46,59.43,,0.57,59.7,222341.0,330042.0 +2015-06-24,61.14,61.57,59.8,,0.74,60.27,312960.0,338243.0 +2015-06-23,60.21,61.49,59.55,,0.63,61.01,336224.0,332929.0 +2015-06-22,59.75,60.63,59.27,,0.41,60.38,255306.0,326906.0 +2015-06-19,60.5,60.56,58.88,,0.84,59.61,116656.0,46092.0 +2015-06-18,59.78,60.89,59.25,,0.53,60.45,181875.0,66622.0 +2015-06-17,60.03,61.38,58.85,,0.05,59.92,395462.0,92954.0 +2015-06-16,59.6,60.37,59.42,,0.45,59.97,233621.0,110232.0 +2015-06-15,59.9,59.98,58.73,,0.44,59.52,275115.0,136961.0 +2015-06-12,60.55,60.63,59.73,,0.81,59.96,246486.0,149983.0 +2015-06-11,61.14,61.53,60.21,,0.66,60.77,352345.0,190185.0 +2015-06-10,60.58,61.82,60.45,,1.29,61.43,459615.0,228108.0 +2015-06-09,58.31,60.68,58.23,,2.0,60.14,405385.0,271926.0 +2015-06-08,58.96,59.13,57.86,,0.99,58.14,339787.0,320038.0 +2015-06-05,57.99,59.23,56.83,,1.13,59.13,461012.0,355370.0 +2015-06-04,59.57,59.94,57.83,,1.64,58.0,363353.0,355247.0 +2015-06-03,61.02,61.43,59.34,,1.62,59.64,371298.0,370176.0 +2015-06-02,60.18,61.58,60.09,,1.06,61.26,344508.0,372076.0 +2015-06-01,60.29,60.64,59.33,,0.1,60.2,292085.0,389811.0 +2015-05-29,57.97,60.7,57.72,,2.62,60.3,451935.0,399553.0 +2015-05-28,57.63,58.07,56.51,,0.17,57.68,340602.0,397862.0 +2015-05-27,58.32,58.95,57.36,,0.52,57.51,329388.0,405472.0 +2015-05-26,60.05,60.25,57.71,,1.69,58.03,345262.0,418738.0 +2015-05-22,60.66,60.8,59.35,,1.0,59.72,248666.0,432754.0 +2015-05-21,58.81,60.94,58.69,,1.74,60.72,346607.0,439497.0 +2015-05-20,58.32,59.04,58.1,,0.99,58.98,302952.0,446845.0 +2015-05-19,60.32,60.42,57.93,,2.25,57.99,351784.0,441680.0 +2015-05-18,59.85,60.88,59.06,,0.26,59.43,144389.0,73396.0 +2015-05-15,59.67,59.97,58.42,,0.19,59.69,222089.0,95274.0 +2015-05-14,60.1,60.84,59.36,,0.62,59.88,358495.0,165577.0 +2015-05-13,61.23,61.85,60.02,,0.25,60.5,426770.0,207603.0 +2015-05-12,59.27,61.36,59.12,,1.5,60.75,397244.0,239092.0 +2015-05-11,59.43,59.85,58.75,,0.14,59.25,332725.0,277755.0 +2015-05-08,59.02,59.9,58.14,,0.45,59.39,434575.0,317625.0 +2015-05-07,60.69,61.31,58.49,,1.99,58.94,437126.0,361434.0 +2015-05-06,60.72,62.58,60.54,,0.53,60.93,423480.0,380717.0 +2015-05-05,58.95,61.1,58.63,,1.47,60.4,389803.0,400321.0 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Demo\n", + "### Miki Tebeka, [353Solutions](http://353solutions.com) .:. [PyWeb IL, July 2015](http://www.meetup.com/PyWeb-IL/events/223077234/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Few Points Before We Dive In\n", + "\n", + "* Home at http://pandas.pydata.org\n", + "* Easiest way to install is via [Anaconda](https://store.continuum.io/cshop/anaconda/)\n", + "* Mostly used for *OSE* part of [*OSEMIC*](https://peadarcoyle.wordpress.com/2015/03/22/an-extension-of-the-data-science-process-osemic/)\n", + "\n", + "## What You Get\n", + "* Reading from various sources (CSV, SQL, HDF5 ...)\n", + "* Labeled arrays (Series, DataFrame, Panel)\n", + " * Fancy indexing\n", + "* Time series handling\n", + "* Grouping\n", + "* Merging (Join)\n", + "* Statistics\n", + "* Plotting\n", + "* Excellent [documentation](http://pandas.pydata.org/pandas-docs/stable/)\n", + "\n", + "## And There's a Great Book\n", + "![pfda](http://akamaicovers.oreilly.com/images/0636920023784/cat.gif)\n", + "\n", + "Written by Wes McKinney, Pandas author." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## My Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The watermark extension is already loaded. To reload it, use:\n", + " %reload_ext watermark\n", + "07/07/2015 \n", + "\n", + "CPython 2.7.10\n", + "IPython 3.2.0\n", + "\n", + "numpy 1.9.2\n", + "pandas 0.16.2\n", + "matplotlib 1.4.3\n", + "\n", + "compiler : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n", + "system : Linux\n", + "release : 4.0.7-2-ARCH\n", + "machine : x86_64\n", + "processor : \n", + "CPU cores : 4\n", + "interpreter: 64bit\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -e -v -m -p numpy,pandas,matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data\n", + "We're going to use two data files\n", + "* [CME_CL1.csv](https://dl.dropboxusercontent.com/u/706094/pywebil7/CME_CL1.csv) - Crude oil data from [quandl](https://www.quandl.com/collections/markets/crude-oil)\n", + "* [weather.db](https://dl.dropboxusercontent.com/u/706094/pywebil7/weather.db) - Chicago weather data from [NOAA](http://www.ncdc.noaa.gov/cdo-web/) in sqlite3 format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initial Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Data from CSV" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8092\n" + ] + }, + { + "data": { + "text/html": [ + "
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DateOpenHighLowLastChangeSettleVolumeOpen Interest
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" + ], + "text/plain": [ + " Date Open High Low Last Change Settle Volume \\\n", + "Date \n", + "2015-07-02 2015-07-02 56.87 57.95 56.50 NaN 0.03 56.93 297046 \n", + "2015-07-01 2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 \n", + "2015-06-30 2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 \n", + "2015-06-29 2015-06-29 58.84 59.27 58.04 NaN 1.30 58.33 286035 \n", + "2015-06-26 2015-06-26 59.66 59.96 58.76 NaN 0.07 59.63 231868 \n", + "\n", + " Open Interest \n", + "Date \n", + "2015-07-02 326973 \n", + "2015-07-01 319172 \n", + "2015-06-30 331764 \n", + "2015-06-29 332326 \n", + "2015-06-26 329016 " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index = pd.to_datetime(df['Date'])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
Date
2015-07-0256.8757.9556.50NaN0.0356.93297046326973
2015-07-0158.9858.9856.68NaN2.5156.96373405319172
2015-06-3058.2759.6957.9459.41.1459.47282364331764
2015-06-2958.8459.2758.04NaN1.3058.33286035332326
2015-06-2659.6659.9658.76NaN0.0759.63231868329016
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-07-02 56.87 57.95 56.50 NaN 0.03 56.93 297046 326973\n", + "2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 319172\n", + "2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 331764\n", + "2015-06-29 58.84 59.27 58.04 NaN 1.30 58.33 286035 332326\n", + "2015-06-26 59.66 59.96 58.76 NaN 0.07 59.63 231868 329016" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('CME_CL1.csv', parse_dates=['Date'], index_col='Date')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boolean Indexing\n", + "Just like numpy arrays, you can pass an array/series of boolean values and you'll get only the rows there the value is True." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42.2785972068\n" + ] + }, + { + "data": { + "text/plain": [ + "Date\n", + "2015-07-02 True\n", + "2015-07-01 True\n", + "2015-06-30 True\n", + "2015-06-29 True\n", + "2015-06-26 True\n", + "2015-06-25 True\n", + "2015-06-24 True\n", + "2015-06-23 True\n", + "2015-06-22 True\n", + "2015-06-19 True\n", + "2015-06-18 True\n", + "2015-06-17 True\n", + "2015-06-16 True\n", + "2015-06-15 True\n", + "2015-06-12 True\n", + "2015-06-11 True\n", + "2015-06-10 True\n", + "2015-06-09 True\n", + "2015-06-08 True\n", + "2015-06-05 True\n", + "2015-06-04 True\n", + "2015-06-03 True\n", + "2015-06-02 True\n", + "2015-06-01 True\n", + "2015-05-29 True\n", + "2015-05-28 True\n", + "2015-05-27 True\n", + "2015-05-26 True\n", + "2015-05-22 True\n", + "2015-05-21 True\n", + " ... \n", + "1983-05-11 False\n", + "1983-05-10 False\n", + "1983-05-09 False\n", + "1983-05-06 False\n", + "1983-05-05 False\n", + "1983-05-04 False\n", + "1983-05-03 False\n", + "1983-05-02 False\n", + "1983-04-29 False\n", + "1983-04-28 False\n", + "1983-04-27 False\n", + "1983-04-26 False\n", + "1983-04-25 False\n", + "1983-04-22 False\n", + "1983-04-21 False\n", + "1983-04-20 False\n", + "1983-04-19 False\n", + "1983-04-18 False\n", + "1983-04-15 False\n", + "1983-04-14 False\n", + "1983-04-13 False\n", + "1983-04-12 False\n", + "1983-04-11 False\n", + "1983-04-08 False\n", + "1983-04-07 False\n", + "1983-04-06 False\n", + "1983-04-05 False\n", + "1983-04-04 False\n", + "1983-03-31 False\n", + "1983-03-30 False\n", + "Name: High, dtype: bool" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(df['High'].mean())\n", + "df['High'] > df['High'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2719\n" + ] + }, + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
Date
2015-07-0256.8757.9556.50NaN0.0356.93297046326973
2015-07-0158.9858.9856.68NaN2.5156.96373405319172
2015-06-3058.2759.6957.9459.41.1459.47282364331764
2015-06-2958.8459.2758.04NaN1.3058.33286035332326
2015-06-2659.6659.9658.76NaN0.0759.63231868329016
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-07-02 56.87 57.95 56.50 NaN 0.03 56.93 297046 326973\n", + "2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 319172\n", + "2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 331764\n", + "2015-06-29 58.84 59.27 58.04 NaN 1.30 58.33 286035 332326\n", + "2015-06-26 59.66 59.96 58.76 NaN 0.07 59.63 231868 329016" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "highs = df[df['High'] > df['High'].mean()]\n", + "print(len(highs))\n", + "highs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
Date
2015-06-3058.2759.6957.9459.41.1459.47282364331764
2015-06-2360.2161.4959.55NaN0.6361.01336224332929
2015-06-1659.6060.3759.42NaN0.4559.97233621110232
2015-06-0958.3160.6858.23NaN2.0060.14405385271926
2015-06-0260.1861.5860.09NaN1.0661.26344508372076
\n", + "
" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 331764\n", + "2015-06-23 60.21 61.49 59.55 NaN 0.63 61.01 336224 332929\n", + "2015-06-16 59.60 60.37 59.42 NaN 0.45 59.97 233621 110232\n", + "2015-06-09 58.31 60.68 58.23 NaN 2.00 60.14 405385 271926\n", + "2015-06-02 60.18 61.58 60.09 NaN 1.06 61.26 344508 372076" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from calendar import TUESDAY\n", + "df[df.index.dayofweek == TUESDAY].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " June 2015\n", + "Mo Tu We Th Fr Sa Su\n", + " 1 2 3 4 5 6 7\n", + " 8 9 10 11 12 13 14\n", + "15 16 17 18 19 20 21\n", + "22 23 24 25 26 27 28\n", + "29 30\n", + "\n" + ] + } + ], + "source": [ + "# Quick check\n", + "from calendar import month\n", + "print(month(2015, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## By Location\n", + "\n", + "There are 3 ways to access rows\n", + "* `.loc` - by label\n", + "* `.iloc` - by position\n", + "* `.ix` - Tries to do the right thing" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
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2015-07-0158.9858.9856.68NaN2.5156.96373405319172
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 319172" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc['2015-07-01']" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
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2015-06-1859.7860.8959.25NaN0.5360.4518187566622
2015-06-1760.0361.3858.85NaN0.0559.9239546292954
2015-06-1659.6060.3759.42NaN0.4559.97233621110232
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-06-18 59.78 60.89 59.25 NaN 0.53 60.45 181875 66622\n", + "2015-06-17 60.03 61.38 58.85 NaN 0.05 59.92 395462 92954\n", + "2015-06-16 59.60 60.37 59.42 NaN 0.45 59.97 233621 110232" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[10:13]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenVolume
Date
2014-12-3153.87247510
2014-12-3053.72230228
2014-12-2955.05253089
2014-12-2655.86138280
2014-12-2456.80174969
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" + ], + "text/plain": [ + " Open Volume\n", + "Date \n", + "2014-12-31 53.87 247510\n", + "2014-12-30 53.72 230228\n", + "2014-12-29 55.05 253089\n", + "2014-12-26 55.86 138280\n", + "2014-12-24 56.80 174969" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.ix['2014', ['Open', 'Volume']].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Changing Data\n", + "## Add \"Diff\" Column" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "2015-07-02 1.45\n", + "2015-07-01 2.30\n", + "2015-06-30 1.75\n", + "2015-06-29 1.23\n", + "2015-06-26 1.20\n", + "Name: Diff, dtype: float64" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Diff'] = df['High'] - df['Low']\n", + "df['Diff'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df[df['Diff'] == 0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Signal\n", + "We'll have buy/hold/sell signal. Looking at `High` column:\n", + "\n", + "* If tomorrows price lower in \\$1 or more - sell\n", + "* If tomorrow price higher in \\$1 or more - buy\n", + "* Else hold\n", + "\n", + "We'll be using pandas [categoraical](http://pandas.pydata.org/pandas-docs/version/0.16.2/categorical.html) data which is way more efficient than text." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "2015-07-02 hold\n", + "2015-07-01 sell\n", + "2015-06-30 hold\n", + "2015-06-29 hold\n", + "2015-06-26 hold\n", + "Name: Signal, dtype: category\n", + "Categories (3, object): [buy, hold, sell]" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def signal(diff):\n", + " \"\"\"Signal function. If price went up next day - we buy, if went down - we sell, otherwise hold\"\"\"\n", + " if diff < -1:\n", + " return 'sell'\n", + " elif diff > 1:\n", + " return 'buy'\n", + " else:\n", + " return 'hold'\n", + " \n", + "price = df['High']\n", + "shifted = price.shift()\n", + "diff = shifted - price\n", + "sig = diff.apply(signal)\n", + "df['Signal'] = sig.astype('category') # Pandas categorical data is much more efficient than string data\n", + "df['Signal'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Joining Data\n", + "\n", + "Let's add weather data from weather.db (sqlite3)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexAWNDSNOWSNWDSTATIONSTATION_NAMETMAXTMINTOBSTSUN...WT08WT09WT10WT11WT14WT15WT16WT17WT18WT22
DATE
1983-03-3018677-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US780-9999246...-9999-9999-9999-9999-9999-99991-9999-9999-9999
1983-03-3118678-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US7828-999942...1-9999-9999-9999-9999-99991-9999-9999-9999
1983-04-0118679-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US9428-9999114...1-9999-9999-9999-9999-99991-9999-9999-9999
1983-04-0218680-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US4422-99990...-9999-9999-9999-9999-9999-99991-9999-9999-9999
1983-04-0318681-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US4428-99990...-9999-9999-9999-9999-9999-99991-9999-9999-9999
\n", + "

5 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " index AWND SNOW SNWD STATION \\\n", + "DATE \n", + "1983-03-30 18677 -9999 0 NaN GHCND:USW00094846 \n", + "1983-03-31 18678 -9999 0 NaN GHCND:USW00094846 \n", + "1983-04-01 18679 -9999 0 NaN GHCND:USW00094846 \n", + "1983-04-02 18680 -9999 0 NaN GHCND:USW00094846 \n", + "1983-04-03 18681 -9999 0 NaN GHCND:USW00094846 \n", + "\n", + " STATION_NAME TMAX TMIN TOBS TSUN \\\n", + "DATE \n", + "1983-03-30 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 78 0 -9999 246 \n", + "1983-03-31 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 78 28 -9999 42 \n", + "1983-04-01 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 94 28 -9999 114 \n", + "1983-04-02 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 44 22 -9999 0 \n", + "1983-04-03 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 44 28 -9999 0 \n", + "\n", + " ... WT08 WT09 WT10 WT11 WT14 WT15 WT16 WT17 WT18 WT22 \n", + "DATE ... \n", + "1983-03-30 ... -9999 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-03-31 ... 1 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-04-01 ... 1 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-04-02 ... -9999 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-04-03 ... -9999 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sqlite3\n", + "conn = sqlite3.connect('weather.db')\n", + "weather = pd.read_sql('SELECT * FROM weather', conn, index_col='DATE', parse_dates=['DATE'])\n", + "weather.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date\n", + "2015-07-02 57.95\n", + "2015-07-01 58.98\n", + "2015-06-30 59.69\n", + "2015-06-29 59.27\n", + "2015-06-26 59.96\n", + "Name: High, dtype: float64\n", + "Date\n", + "2015-07-02 57.95\n", + "2015-07-01 58.98\n", + "2015-06-30 59.69\n", + "2015-06-29 59.27\n", + "2015-06-26 59.96\n", + "Name: High, dtype: float64\n" + ] + } + ], + "source": [ + "wdf = df.join(weather)\n", + "print(wdf['High'].head())\n", + "print(wdf['High'].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "1998-04-30 -9999\n", + "1998-04-29 -9999\n", + "1998-04-28 -9999\n", + "1998-04-27 -9999\n", + "1998-04-24 -9999\n", + "Name: SNOW, dtype: float64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wdf.query('SNOW < -100')['SNOW'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Series([], Name: SNOW, dtype: float64)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "wdf.ix[wdf['SNOW'] == -9999, 'SNOW'] = np.nan\n", + "wdf.query('SNOW < -100')['SNOW'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pylab\n", + "pylab.rcParams['figure.figsize'] = (12, 8) # Larger figures\n", + "import matplotlib\n", + "matplotlib.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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lfq+Y93ZDvvic/h5Ol/45MvlZCwG8vFu/DzZiUExERER5IyZODe8EjABL1TLP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r30Misevem4pBY9DnPog8tAd5aDfaC+8id22GzFNIL1/0FR+iTZ6GsGq7a+AH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\nn38eZ2dnLC0tMTMz4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['Rolling Mean'] = pd.rolling_mean(df['High'], window=7)\n", + "df.ix['2014'][['High', 'Rolling Mean']].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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ZKBRixYoViI6Oxs6dO3NdS0pKgoWFBe7fv1/s2fjk5GRcuXIFwcHBCAkJQWZmJrp27Ypu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VVlkvXYAD/lIhKJBC4uLkhKSmI/TFX1G+ejw4hsZxEGkyFXtcIiV2KxmPdyPRsbG0ydOhUnTpzA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\nPD09bfwpHJOzZ8+yYcMG5s2bh6urK5s3b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PBEGwj2rVq2VrjAtCtuhwCL11A8bf/L3Topyhswt298Ll1zgtiVAkKjm+0cJa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UbjBqaR4QND0A1rOW5tkhfYJKMqKlGfuh3zxDHMFzZgPPh1zLCOvu811JnnoGbUQc0MqJgC3jLw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\nWW3bhDZR4lqm1PUx2HLXrVuHhQsXQhAErF+/HosWL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pandas.tools.plotting import scatter_matrix\n", + "scatter_matrix(wdf.ix['2014', ['High', 'Volume', 'SNOW', 'TMAX']], diagonal='kde');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More To Read\n", + "\n", + "* [Pandas documentation](http://pandas.pydata.org/pandas-docs/stable/)\n", + "* [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)\n", + "* [PyVideo](http://pyvideo.org/search?models=videos.video&q=pandas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Want More?\n", + "\n", + "We have an upcoming _Introduction to Data Science with Python_ workshop - http://j.mp/353pywebil7\n", + "\n", + "Use **PYWEBIL7** discount code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/pandas-pyweb-il/README.md b/pandas-pyweb-il/README.md new file mode 100644 index 0000000..323cb57 --- /dev/null +++ b/pandas-pyweb-il/README.md @@ -0,0 +1,9 @@ +Pandas demo talk at [PyWeb IL](http://www.meetup.com/PyWeb-IL/events/223077234/) + +[Miki Tebeka](mailto:miki@353solutions.com) ∷ CEO, +[353Solutions](http://353solusions.com) + +# Data + +* Crude Oil Data from [qandl](https://www.quandl.com/collections/markets/crude-oil) +* Weather data from [NOAA](http://www.ncdc.noaa.gov/cdo-web/) diff --git a/pandas-pyweb-il/Talk.ipynb b/pandas-pyweb-il/Talk.ipynb new file mode 100644 index 0000000..9d850ba --- /dev/null +++ b/pandas-pyweb-il/Talk.ipynb @@ -0,0 +1,1784 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas Demo\n", + "### Miki Tebeka, [353Solutions](http://353solutions.com)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Few Points Before We Dive In\n", + "\n", + "* Home at http://pandas.pydata.org\n", + "* Easiest way to install is via [Anaconda](https://store.continuum.io/cshop/anaconda/)\n", + "* Mostly used for *OSE* part of [*OSEMIC*](https://peadarcoyle.wordpress.com/2015/03/22/an-extension-of-the-data-science-process-osemic/)\n", + "\n", + "## What You Get\n", + "* Reading from various sources (CSV, SQL, HDF5 ...)\n", + "* Labeled arrays (Series, DataFrame, Panel)\n", + " * Fancy indexing\n", + "* Time series handling\n", + "* Grouping\n", + "* Merging (Join)\n", + "* Statistics\n", + "* Plotting\n", + "* Excellent [documentation](http://pandas.pydata.org/pandas-docs/stable/)\n", + "\n", + "## And There's a Great Book\n", + "![pfda](http://akamaicovers.oreilly.com/images/0636920023784/cat.gif)\n", + "\n", + "Written by Wes McKinney, Pandas author." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## My Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The watermark extension is already loaded. To reload it, use:\n", + " %reload_ext watermark\n", + "05/07/2015 \n", + "\n", + "CPython 2.7.10\n", + "IPython 3.2.0\n", + "\n", + "numpy 1.9.2\n", + "pandas 0.16.2\n", + "matplotlib 1.4.3\n", + "\n", + "compiler : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n", + "system : Linux\n", + "release : 4.0.7-2-ARCH\n", + "machine : x86_64\n", + "processor : \n", + "CPU cores : 4\n", + "interpreter: 64bit\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -e -v -m -p numpy,pandas,matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8092\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-07-02 56.87 57.95 56.50 NaN 0.03 56.93 297046 326973\n", + "2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 319172\n", + "2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 331764\n", + "2015-06-29 58.84 59.27 58.04 NaN 1.30 58.33 286035 332326\n", + "2015-06-26 59.66 59.96 58.76 NaN 0.07 59.63 231868 329016" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('CME_CL1.csv', parse_dates=['Date'], index_col='Date')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "42.2785972068\n" + ] + } + ], + "source": [ + "print(df['High'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "2015-07-02 True\n", + "2015-07-01 True\n", + "2015-06-30 True\n", + "2015-06-29 True\n", + "2015-06-26 True\n", + "2015-06-25 True\n", + "2015-06-24 True\n", + "2015-06-23 True\n", + "2015-06-22 True\n", + "2015-06-19 True\n", + "2015-06-18 True\n", + "2015-06-17 True\n", + "2015-06-16 True\n", + "2015-06-15 True\n", + "2015-06-12 True\n", + "2015-06-11 True\n", + "2015-06-10 True\n", + "2015-06-09 True\n", + "2015-06-08 True\n", + "2015-06-05 True\n", + "2015-06-04 True\n", + "2015-06-03 True\n", + "2015-06-02 True\n", + "2015-06-01 True\n", + "2015-05-29 True\n", + "2015-05-28 True\n", + "2015-05-27 True\n", + "2015-05-26 True\n", + "2015-05-22 True\n", + "2015-05-21 True\n", + " ... \n", + "1983-05-11 False\n", + "1983-05-10 False\n", + "1983-05-09 False\n", + "1983-05-06 False\n", + "1983-05-05 False\n", + "1983-05-04 False\n", + "1983-05-03 False\n", + "1983-05-02 False\n", + "1983-04-29 False\n", + "1983-04-28 False\n", + "1983-04-27 False\n", + "1983-04-26 False\n", + "1983-04-25 False\n", + "1983-04-22 False\n", + "1983-04-21 False\n", + "1983-04-20 False\n", + "1983-04-19 False\n", + "1983-04-18 False\n", + "1983-04-15 False\n", + "1983-04-14 False\n", + "1983-04-13 False\n", + "1983-04-12 False\n", + "1983-04-11 False\n", + "1983-04-08 False\n", + "1983-04-07 False\n", + "1983-04-06 False\n", + "1983-04-05 False\n", + "1983-04-04 False\n", + "1983-03-31 False\n", + "1983-03-30 False\n", + "Name: High, dtype: bool" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['High'] > df['High'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2719\n" + ] + }, + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
Date
2015-07-0256.8757.9556.50NaN0.0356.93297046326973
2015-07-0158.9858.9856.68NaN2.5156.96373405319172
2015-06-3058.2759.6957.9459.41.1459.47282364331764
2015-06-2958.8459.2758.04NaN1.3058.33286035332326
2015-06-2659.6659.9658.76NaN0.0759.63231868329016
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-07-02 56.87 57.95 56.50 NaN 0.03 56.93 297046 326973\n", + "2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 319172\n", + "2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 331764\n", + "2015-06-29 58.84 59.27 58.04 NaN 1.30 58.33 286035 332326\n", + "2015-06-26 59.66 59.96 58.76 NaN 0.07 59.63 231868 329016" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "highs = df[df['High'] > df['High'].mean()]\n", + "print(len(highs))\n", + "highs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
Date
2015-06-3058.2759.6957.9459.41.1459.47282364331764
2015-06-2360.2161.4959.55NaN0.6361.01336224332929
2015-06-1659.6060.3759.42NaN0.4559.97233621110232
2015-06-0958.3160.6858.23NaN2.0060.14405385271926
2015-06-0260.1861.5860.09NaN1.0661.26344508372076
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-06-30 58.27 59.69 57.94 59.4 1.14 59.47 282364 331764\n", + "2015-06-23 60.21 61.49 59.55 NaN 0.63 61.01 336224 332929\n", + "2015-06-16 59.60 60.37 59.42 NaN 0.45 59.97 233621 110232\n", + "2015-06-09 58.31 60.68 58.23 NaN 2.00 60.14 405385 271926\n", + "2015-06-02 60.18 61.58 60.09 NaN 1.06 61.26 344508 372076" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from calendar import TUESDAY\n", + "df[df.index.dayofweek == TUESDAY].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " June 2015\n", + "Mo Tu We Th Fr Sa Su\n", + " 1 2 3 4 5 6 7\n", + " 8 9 10 11 12 13 14\n", + "15 16 17 18 19 20 21\n", + "22 23 24 25 26 27 28\n", + "29 30\n", + "\n" + ] + } + ], + "source": [ + "from calendar import month\n", + "print(month(2015, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowLastChangeSettleVolumeOpen Interest
Date
2015-07-0256.8757.9556.50NaN0.0356.93297046326973
2015-07-0158.9858.9856.68NaN2.5156.96373405319172
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" + ], + "text/plain": [ + " Open High Low Last Change Settle Volume Open Interest\n", + "Date \n", + "2015-07-02 56.87 57.95 56.50 NaN 0.03 56.93 297046 326973\n", + "2015-07-01 58.98 58.98 56.68 NaN 2.51 56.96 373405 319172" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# .loc - by label\n", + "# .iloc - by offset\n", + "# .ix - tries to do the right thing\n", + "df.loc['2015-07']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenVolume
Date
2014-12-3153.87247510
2014-12-3053.72230228
2014-12-2955.05253089
2014-12-2655.86138280
2014-12-2456.80174969
\n", + "
" + ], + "text/plain": [ + " Open Volume\n", + "Date \n", + "2014-12-31 53.87 247510\n", + "2014-12-30 53.72 230228\n", + "2014-12-29 55.05 253089\n", + "2014-12-26 55.86 138280\n", + "2014-12-24 56.80 174969" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.ix['2014', ['Open', 'Volume']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "2015-07-02 1.45\n", + "2015-07-01 2.30\n", + "2015-06-30 1.75\n", + "2015-06-29 1.23\n", + "2015-06-26 1.20\n", + "Name: Diff, dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Diff'] = df['High'] - df['Low']\n", + "df['Diff'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16\n" + ] + } + ], + "source": [ + "print(len(df[df['Diff'] == 0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "2015-07-02 hold\n", + "2015-07-01 sell\n", + "2015-06-30 hold\n", + "2015-06-29 hold\n", + "2015-06-26 hold\n", + "Name: Signal, dtype: category\n", + "Categories (3, object): [buy, hold, sell]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def signal(diff):\n", + " if diff < -1:\n", + " return 'sell'\n", + " elif diff > 1:\n", + " return 'buy'\n", + " else:\n", + " return 'hold'\n", + " \n", + "price = df['High']\n", + "shifted = price.shift()\n", + "diff = shifted - price\n", + "sig = diff.apply(signal)\n", + "df['Signal'] = sig.astype('category')\n", + "df['Signal'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexAWNDSNOWSNWDSTATIONSTATION_NAMETMAXTMINTOBSTSUN...WT08WT09WT10WT11WT14WT15WT16WT17WT18WT22
DATE
1983-03-3018677-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US780-9999246...-9999-9999-9999-9999-9999-99991-9999-9999-9999
1983-03-3118678-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US7828-999942...1-9999-9999-9999-9999-99991-9999-9999-9999
1983-04-0118679-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US9428-9999114...1-9999-9999-9999-9999-99991-9999-9999-9999
1983-04-0218680-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US4422-99990...-9999-9999-9999-9999-9999-99991-9999-9999-9999
1983-04-0318681-99990NaNGHCND:USW00094846CHICAGO OHARE INTERNATIONAL AIRPORT IL US4428-99990...-9999-9999-9999-9999-9999-99991-9999-9999-9999
\n", + "

5 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " index AWND SNOW SNWD STATION \\\n", + "DATE \n", + "1983-03-30 18677 -9999 0 NaN GHCND:USW00094846 \n", + "1983-03-31 18678 -9999 0 NaN GHCND:USW00094846 \n", + "1983-04-01 18679 -9999 0 NaN GHCND:USW00094846 \n", + "1983-04-02 18680 -9999 0 NaN GHCND:USW00094846 \n", + "1983-04-03 18681 -9999 0 NaN GHCND:USW00094846 \n", + "\n", + " STATION_NAME TMAX TMIN TOBS TSUN \\\n", + "DATE \n", + "1983-03-30 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 78 0 -9999 246 \n", + "1983-03-31 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 78 28 -9999 42 \n", + "1983-04-01 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 94 28 -9999 114 \n", + "1983-04-02 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 44 22 -9999 0 \n", + "1983-04-03 CHICAGO OHARE INTERNATIONAL AIRPORT IL US 44 28 -9999 0 \n", + "\n", + " ... WT08 WT09 WT10 WT11 WT14 WT15 WT16 WT17 WT18 WT22 \n", + "DATE ... \n", + "1983-03-30 ... -9999 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-03-31 ... 1 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-04-01 ... 1 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-04-02 ... -9999 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "1983-04-03 ... -9999 -9999 -9999 -9999 -9999 -9999 1 -9999 -9999 -9999 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sqlite3\n", + "conn = sqlite3.connect('weather.db') # NOAA\n", + "weather = pd.read_sql('SELECT * FROM weather', conn, index_col='DATE', parse_dates=['DATE'])\n", + "weather.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date\n", + "2015-07-02 57.95\n", + "2015-07-01 58.98\n", + "2015-06-30 59.69\n", + "2015-06-29 59.27\n", + "2015-06-26 59.96\n", + "Name: High, dtype: float64\n", + "Date\n", + "2015-07-02 57.95\n", + "2015-07-01 58.98\n", + "2015-06-30 59.69\n", + "2015-06-29 59.27\n", + "2015-06-26 59.96\n", + "Name: High, dtype: float64\n" + ] + } + ], + "source": [ + "wdf = df.join(weather)\n", + "print(wdf['High'].head())\n", + "print(df['High'].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "1998-04-30 -9999\n", + "1998-04-29 -9999\n", + "1998-04-28 -9999\n", + "1998-04-27 -9999\n", + "1998-04-24 -9999\n", + "Name: SNOW, dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wdf.query('SNOW < -100')['SNOW'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Series([], Name: SNOW, dtype: float64)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "wdf.ix[wdf['SNOW'] == -9999, 'SNOW'] = np.nan\n", + "wdf.query('SNOW < -100')['SNOW'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pylab\n", + "pylab.rcParams['figure.figsize'] = (12, 8)\n", + "import matplotlib\n", + "matplotlib.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ptn84+SIf2xreYVCcdwyKiYiIqLgMDwGBJN0XRJmGLw5XeFuWblAsuzshk/38ilSZ/lYR\nERFRKZLDQ9B+fB9w6njik8yZ4nJKFTtNddSqWrhx5Ej72krI3/xPoYeRsSRV7ERERET5pd302dQn\nKWU60c4cFJd6+UR/T6FHkDFmiomIiKi0lG35hKmOuoTLJ0pVmf5WERERUalJuw61XMsnHOWTKZZv\n7S/0EDLGoJiIiIiKg8+nP045DWLhxxKfV7aZYuuCYnXNCmhP/jbHAeXg8MHCvXeWyvS3ioiIiEpO\nMFPscgPNrYnPK9OWbBGfS8t+op2UEhjoA17vsGBQ2ZMlVu/NoJiIiIiKg98IilU1cjJdNFGmyzyb\nO07kkik2vlzIk8dyHFCOSqyDBoNiIiIiKg7BTPFgP1BTl/g8c1CsBuwdUz5pFgXFwS8X3pHcxpMh\n9bqlkD5v+IAvv++fKwbFREREVBz8Rk2xz6eXUCRirin2ehOfV2LkG6+adnIoPQh+ufDn/95o/35P\nePvmq/L+/rlgUExERETFwW/KFDuTLKVgLq0osWxkUq+/YtqxICjuOgU50JfTkNIlg5ntvc/l5f3s\nwKCYiIiIikMwUxzwQzhdic8zl0+MlFFQfPrZ+uOU03K7jqm1nbZmRW7XSlcOEwOLBYNiIiIiKg4B\nX3g7SVAcETCXU6Y4mG11uXPrquEvQJ21hfXL8vC7UG/7at6y3EEMiomIiKg4+E2LdyQrnwDCPX3z\nPJnMVooRlglhTU1xHmn3/atl15Jv7wcOH8xfltvAoJiIiIiKgz+9TDGAUAmFLIOJdvLEUajXLQVO\nnYBy650ABHJKFZvvY768+Zp116qqtu5aGWBQTEREREVBZpIpDk62K4NaVpw4oj/2dgEOR84xcXSm\nWPb1QPvdlhwumGf+/Ge6AQbFREREVCzMwZwjRVAsHPpjia2aFpf5MygWhGYBPzDrnPDl9z4Hue1n\nuV83CTHvksj9Kz4PzJyb3cUKkekGg2IiIiIqFuYV0FIFxcHgMZdFLoqF+TP4/UZpSI41xS4XlLsf\n0fdT3UsrjJsYsSsmTQXcVdldqwD9lQEGxURERFQszKvTORzJzw22ZSuLTLEpKA74AOQ20U76A3r5\niXEPZT56B6tRX06qaoCR4aQvkaoK7bGtsU/4fMDZc4CpMywcYGoMiomIiKg4mIPiVGUEwZpiWQ6Z\nYlMA3DLWku4TwukKZ4hf2Jnb+NJhqu0WK1YB1amDYvR0Qv5Cz2arG9dDvf9b+nG/D6ipBY4d1icg\n5gmDYiIiIioOAVNQbF6gI57gUs/lkCkOlkrMnAsxdgKw/2VoG9dnf7mAX+/ekSrbbqWAP/R+yoKP\n6r2WDx6A7OtO/BrjZyelBPbtBfY+p9c///Z/IGrrAG+KoNpiDIqJiIioOBw7FN5OGRSXU6bY4s/g\n9+lBaT5qiYO8IxBXXgflgV/q+0Y9sXbLlxK/Jjix0vT55bPb9Y3mVjtGmRSDYiIiIioKoYAISD9T\nrJV+plgGJxju25vTddTv/hNkb3coUyxcKXo9W0h2noAYPRbC5dYPBB+T0NavMjaMz++uCpfNmFY3\nlHmaTMmgmIiIiEpPGU20kw9viHtcvfXqlK9V714L7c9/1Hf2vwzte/+id7Bw5TFLDADDQ0BtXXg/\nVZ9ps+CXgpo6SJ/eeUK+8Jfw8778dKNgUExERETFYcIU004FlU8YxCWXRx7o6Ur9ojdehdzz1/D+\nu2/qtdnGioDis9cD9Q0WjjIBTYuoYRaextC27O9L/trDB4NnAsFOGeYvO3lazINBMRERERUH85/J\n3Sn+/F5OfYqTkIP9qU965XnII++G94MT7QAg4IOYv0i/lp1ZdVVNXMM80Jv0pXL3M8ZGeHzK1TeF\nT8hT32IGxURERFQc1ADQOh5AZKYxLkcZrWgX5B3RH08/O3RI/t/jab1U+/63wzumoFh8eBnE8i8Z\nbd5s/AKhqYAS2e1C+bdt+kai+vCmFgCAfPzX+n5fj/66ux+BOGNWOMju7rR8uPEkLfh44IEHsGfP\nHjQ0NGDDBr3eZWBgAJs2bcKpU6cwZswYrFmzBnV1eg3J1q1bsX37diiKgpUrV2Lu3CyX9yMiIqLK\no6oQl34E8rWXUp8bDMDKqHxCPvdn4Lp/gJi/GPLN1/SDpv6/SZ04Et4Odp8AIBRFz6orip5VV2xq\n06aqgCMy1yoUBZg0LXH5g98HjBoNdJ+KfN0oPViG2w0MByBPHYcwfVGwS9JM8aJFi7Bu3bqIY9u2\nbcOcOXOwefNmzJ49G9u26d8CDh06hJ07d2Ljxo1Yt24dHn74YWhl/icNIiIisoY8eADoOglx8WVw\n3PKt1C8ox0xxcKlkc2Y1i1hKPvVHoKo68qBQ7C010RKUT7jccSfKyRNHgMH+5MFu8Gc8PGTRIJNL\nGhTPnDkzlAUO2r17NxYsWAAAWLhwIXbt2gUA2LVrFy6++GI4nU60trZi3LhxOHDggE3DJiIionIi\n//qUvuFIs41YMGAqg5ZsIfVGyYjfl/y8NIjooNjhSD/rnA01tnwCgB4Umz6PPPqe3mItWAMdPU4T\nsfDjxouKtCVbb28vmpqaAACNjY3o7dWLp7u7u9HS0hI6r6WlBV1dacyaJCIiIgpKdxW2YFayjMon\ngg035J9+HT7mHYE8eSzz5Y6jg00lcaZYHjsMefjduM+lLU75BAC9BMIUFGv//BX4dvwB0m+sXpik\ns4RyxechFi/J22TKnCbaiRSNtVM9T0RERJVLSgntR9/TuyIEE77prsKmlNMyz5HE0qvCO6eOQz62\nNeNrxHStSFI+od29FtptN2b8HpEXSS9TDADaqeN6R4rxkyFfei7y/KkzIvcVJdzH2GYZd3ZubGxE\nT08Pmpqa0N3djcZGPdXf3NyMzs7w7MDOzk40NzfHvL6jowMdHR2h/fb2dng8nmzGXlLcbndFfM58\n4L20Du+ldXgvrcN7aZ1iv5dSVdH7zONo+PKtGHG54AXgaWqCSGPhh36nCyqAKpcL1Xn4jHbeyx7j\n0eFwwuPxwD9pKgaNY3LX03BdtBh+IO7798Qc0dW2joPLdH6vw4H62looca7R666CTHD9dPVqGuob\nm6DUR15jsKYWLocCt3HtHgAORUGVEJDnXgjv734BAFDGToAyeiyqln4uYtzDVdUQbmt/xlu2bAlt\nt7W1oa2tDUAWQfH555+PHTt2YNmyZXjqqacwb9680PHNmzdjyZIl6OrqwrFjxzBjxoyY15vfPKi/\nP40efCXO4/FUxOfMB95L6/BeWof30jq8l9Yp1nsp978MefwwxAV6/9z+UychjclUA8PDaV1DNbKH\n3pER+PPwGfNxL+WV16K/vx8yqqTAv/NJAGnES9POAN55AwAwPHocRkznSyEw0NcHocSGftJo35bL\n55NqAAPDQxBRiXtNKAj09cHb3w954qh+zF0F7/AQoBoLfqgqxLcehAQwAkSMWwsEgOFhy37GHo8H\n7e3tcZ9LGhTfe++92LdvH/r6+nDDDTegvb0dy5Ytw6ZNm7B9+/ZQSzYAmDRpEubPn481a9bA4XDg\nmmuuYfkEERERxdC++08AAHH+JfoBrxeYMAXiAwsyv1iZ1BQrN6yFmDRN3zHiJ/Hxdsjfb0n4Ghk9\ncU4xVcU6oyYsKkkm2uU4AU9d83lgZDhBuzcJ2fECcMnlkAf26YcczvDEvFT1woqi96/Og6RB8erV\nq+MeX79+fdzjy5cvx/Lly3MfFREREZUluW9veCdYK+odBlQ/4MliOeJy6D4xcWpo0RIA4ZZsTaMA\nd1WopZl8+3WI6WeGz/NHBYtJg+L4NcXS7899ItuAkcWNExTLnU+G6r7lI/fqBwN+yF//HGLBR4EZ\nMxMv7gFAPvM40NcD+eFlsR01LJZx+QQRERFRtuRb+8M7weV7+/uAQCA2kEt+JeOhDDLFUiLUegII\nB4lCRASs8uCByKA4ENW6LSIojgrxnK64rd60VZ9KObxg5wtl088g6pN8cYnXOSQYEJsDb6M8RB47\nDOUf7kj+5sYqd+jtjvziYAMu80xERET5Y8oKyl/+CACg/fwHRlCcRa6uHLpPSBmZLa0zJpUpSmRp\ng7FKXUh0OzMRDutiSlgbmoC+3tyGuW8v5OF3oX79mrjPxy2bdVfpj4MDoUP+F3bq5085DUJxQCRZ\nZU+svDnm9XZhUExERET5Ywrc5Knj+qGz3wcE/JkFxbKMMsVARFAsxoyD8uDWmDZqoro28jXBzO/Z\nc/RHI1Os3PXD2OvXe4DBvtzG6PPqLdS6TkJKCXnsEGSc1erMlHXf1TeMumBx4UKob72ub3/mb1O+\npZi/GGidEPpdsRODYiIiIsofczIx+Of+aWfomeJ0V7MzK4tMsRZTVyscjshyiNbxQF195OsCfmDs\nRCg3GnO9jPNF85iYtxA1daEOH5kNLZyplj/6Xjg7vedZaOtXQVv9+eQXaJ2gf9nRVGDUaGDamRBG\nJjydhgxCCIgLLgUOvZPx2DPFoJiIiIjyx7Q4hzjLyHBqmp5JzCZTXA4T7SQQ+W3BYA4am5pjJ8T5\nvEBVFaAY5yUpQ0BVeMJeRqJLLozMvDx6SN9PtSS106m3XvP79ZpjlxNSUyHO/2D6Y3BX6RMxbcaJ\ndkRERJQ3covpT/vBzKemGssEp7nEM1Be5RNSxo2JQ5nfjyyHfO+t2Ky4dwRwV4eDZyVJrtNdrZ9v\nfltz5njaGfFfd/igPoZPtAOD/eFMcZqTIoUQelA7MqQH7U4XMDKS2c9aCMgj70FqGkSyz5gjZoqJ\niIgofyZMCW8H+89qWuJlglPJ0xLA9pIRtdbmw4DRlkyI2C8AXiNTHHxt0qA4TqbYa1ooJUEpg/aD\nO/WNppZwRh8AGkclfq+Y93ZDvvic/h5Ol/45MvlZCwG8vFu/DzZiUExERER5IyZODe8EjABL1TLP\nFAflaWEHWyXKFA/0hR+FEpsp9o3owW5ameI4QbGR9RXnfzBhbbb42Kf1umCHsdBGsFzC3BWjphbi\nymsTv3d/L+Rv/xs4fhgimGHOpFQmGPS/3gFpYw05g2IiIiLKG/MSxrLjBWND1f+0n0n2MBgclUOm\nOLpPcZDRgk1csyamZzEASK9XX9AiuAJeqppib3SmeARoHQ/xkU8mnrBYVw9x9hxjZTk1HFgPmDpZ\nzHo/lA8tTfYJTZ/JCIqbWtI7Hwj3Ov7LdmjfT9HXOAcMiomIiCh//KbA7M3X9GBraBBy19OQL+9K\n/zrBIC5QLpnieEGxHkAKp0u/T1HlE/KRe4FAINTFQQ4PJn6POJli7fabgBNHjdZv8b9cyJ8/CPnn\nPwKaBvnK85BGYC1/8Ui6nw4wLzgSzBSPnZD+67tPhbf3Ppf+6zLEoJiIiIjyJ3rBidPPhnzvHX37\n1Im0LyPmLwYAyDx0JbCd1OKXPhiZX6mqRuAam82V/Xp3CLHksxCTT0v8HlXVibtPKHFKM6Lf55Xn\ngf5ewFh4IxPKF28M7wSz39ELkSR778cfzfg9s8GgmIiIiPLH74Oy9u7QrpgwJbyww1nvS/syymVL\noKxaVx7lE1qCiWfBQFnT9LZr8QJXY1EL5YqrYvsYmwiXGzKq+0TIyDBw6B3IV16IOCxfe0m/9m33\nx36ZMV87VSeKCZPD21XV+mMGQXFWKx1mgUExERER5Y/PG176FwDqGvRFKMaMg1jw0cyu5XCUR/mE\nqsbNFIvxRjCpqXpbsug+xUBkB4lknK6YSYniggUQf7smVNKibb4NMmCq+X7vbX2joQlixsy4l1W+\nsQli+ReTvnWw1lmsXB0un3Clv1CLcs3f5yUwZlBMRERE+eP3RwZELqceFAYyXLwDMBaGKN2gWEoJ\n+db+hC3KxBSjHEJVIURkTXEoQHakec98XuDVF0O72uO/hjz6ntHxI1zPLH98f/j9W4yV8eo9EB++\nInz845/RHz93PcTU0+OuoBfN8dCjUC5aHH6rwYH0xg29O4Zy/dfSPj9bXLyDiIiI8sfvA1ymTPHx\no8Drr+jbmQbFjtjsZ6nQfvVjwFUF+eh/AjV1ydupaWps3a8xMU65xdyNIcmyyWfrpSkyEIBwOiH/\nR19ERTicEdeVf9kOXLNG33G4gPedDyEEZDDDe95FoUx/aEXCTIydiKolV8I/+9zMXvf+CzJ/rwwx\nKCYiIqL1xzg5AAAgAElEQVT88fsiM8Wjx4a3Hen/SV0/v3TLJ+Qf/je8oyWYaBekarGLd6ga4K6C\nGD8prfcTtXq9sfzLdogPfjj8hMsFTJwS/0WqP/RFJdjhAqoa/vllsbqcEAI1K76MQH9/xq8Lsmtl\nO5ZPEBERUf5okYt0iDnzws9luniH01mSE+3k0fciD6iB5D2atTjdJ7TsFjuR238XeaCpBcLUM1hc\n+pHwucNDENU1EaeLltZwXbCNSy7HIy6+TN9I1nouBwyKiYiIKH80LbxC2bQzgOrq8HPxljpOxuHU\nJ+mVGHnwzcgDAX/q8onoTHG2y2JHBbmoqY0anCnw7jwBRNcLOxzhMpcES0PbRbn6Zn2j61TyE7O9\nvi1XJSIiIopHSr29GKCvqGYOupQMg6wSnGgnpQSGh2KfcMQPyZSbv6l35VCUyBXtEnSsSGr8ZIg5\nH4h6A/0aoYl05i8Z3pHYIFpxFCxTDEBfCCRRv+UcMSgmIiKi/NE0QBgZzqPv6csUB2WaeXSUXvmE\nfPZJyP98MPaJBFlyMfs8iOra0EQ7ufc5aLuezqp8Qsw+N3YunvG+4szZ+r6pRls+ti0cAANQVt8O\n8bFPFzYorvMAA5nVI6eLE+2IiIgoL+TQoD7RLmEwlUVQXGoT7U4ei3tYpPpCIPTFO7T7v6XvfueH\nmZdPKI7YLxHBn4VbX0wj2KdYvW6pfty04IdoO0ffcDohgczLXazgrgICPlsuzUwxERER5YX83x/r\nG4kCwGzKJ0qtpjjeAhzpECLys2Yz0S5eZj14z1uMLiCBALR4mWyzAmaKhcOhL3ttA2aKiYiIKD+C\nE8UUBcr9v4jtIpBp5lEkWPq4iMm39mf3QkWJyNrKnU/EZp1TfadwKLFBcbDn8NgJUFbfDu3XP4d8\neXf4+XgBaHCiXQar0llGUUI9mi2/tC1XJSIiIopm6logqqogmpoBAModDxqHM8wUCwGgtIJiHHpH\nfzznQuD0s9N/nVAgt/40tCv37c38vR1OQFMhn98Zvmy1qfuE0wm8/Xrka+IFxcFjNXWZjyFXikPv\n0WzHpW25KhEREVEMPeiNCX4bRmV/vRKKibWHNgDG0s2iqjpiEltK0ffMm0UHBsUBqAFoD35H393w\nk8jn4ywZHeoNbCKNiW4Zf4mxgsNhW6aY5RNERESUH4nqf91uwFhxLSNKaZVPyOee0jdmzoX41NWQ\n/7Ep/RdH1+/6s5hs5lAis6zR7dbiLLMtWsfHHjt3PoQxMS/v4k0WtAiDYiIiIsqPBJ0ihOKAY/N/\nZnHB0gqKg0TbuXrpSJzMbNqOH878NUb5RIjLHft8GkRVNXDexZm/vxUcrCkmIiKiUmd1pwgBlEr9\nhDR/9ip9cpuy5Mr0LzA0kPsgjPKJoJjyhziZ4qKj2Fc+waCYiIiI8kJavhJZ4TLF8sQRqGtWQP37\nL0D7/S9Sv2DI1GnDZXR8OP1siMs/md4bWlEy4BuBfOqPiZ/PJXOdLw77JtqVwKcnIiKiciDqPNbm\ndUXhJtrJg28BA336zuF3U7/AHNQabdBitpOJt1DHqNHpvTboxNHkz1d4prgEPj0RERGVheoaiCuv\nte56hSyfGBkKb1elEdiayiciJqmlO2HNvFBH82ig61Q4KE9Xqm4RteEWa+JDSyP6IhcNhwPw27Ng\nC4NiIiIiyo+AP7M2ZKkIpXAT7bzDkeNIxVTLa84Oi9POSi+sN3efCL4+JnucxlLRyZ429SxWrPzy\nYqVRo4GDB2y5NINiIiIiyg+/3+JV0ArYfcIcCKfTrjdB+YQ4631wPPRohu9nbEe3VEt9kdCWcuP6\nuGco6++FfPfNDK+bP2JUC7QX/2rLtRkUExERUX6oAWsncykC8Psgpcz/QhI+U5/gdDLF5r7C0a3Q\nMiDmXQJ5+CAAQLlmTYavNn2BcMSpUQYgppwGYSwwUpQUB9DXY8+lbbkqERERURQZ8ENYmSkOBnZ7\nn7PumumKmOyVPFsthwYiV6BLd3Jd5FX0B4cDOGJM7Mt0JUBzVr0UJtXFId94FbApk12ad4SIiIhK\nj9/ammJh1NTK3u60KhgsZS6HSNEuTbv5KqCxOXwgm0xxMKB1V4ePJcj2JiLed364JVsptF+LR42/\nAIwVmCkmIiIi26nXLdUnSNkRjCVYKc8qMhBAz2cXQZozreZMsZZG39zervC2K4d7YK4jzjQonvuB\n8E6JZopRpy8HLm2oJWdQTERERPnR15NxIJcW1Z4WXSGvv2y8jykQNi9EomQYTjmzyRTrD+K0s8LH\nsvmCMX5y9q8tBsHfH3P3D4swKCYiIiJbSXNW1Y6g2OZMMYaNnsRGUKxetxTyT78OP988JuJ02d0J\n+XpH4utlU1fd0AQAEOddFD7mzPxeio8s1zdKNSgOJoiPHbb80gyKiYiIyF7mDGu8ldlyZXf5RLBz\nRFQ9q/jUlyDmXRLTFk675x+h3fOPiS+YRUAqPvYpKN/5Yc7XCX0pKdXyCSMq1u64xfIrMygmIiIi\ne5mDVhsyxWL6GZZeT0oZWbMaLJWImlAnWscD4yYCMqqmuDF5VwiRabkFAOGugmiJzEjH3Mt02tIF\nX2NHxj4PxKxzbLs2g2IiIiKyl2pjUDxjFlBVnfq8DGhf+Qzk738RPhAMijU1IliWqhZ3VT1x5vvC\n5wT8oT7G4sprgXMvgmWyyTgrpZ0pFpOn23btrO/I1q1b8fTTT0MIgSlTpmDVqlXwer3YtGkTTp06\nhTFjxmDNmjWoq6tLfTEiIiIqXxHlExbn4xSRqk1w5vw+yG0/Az7Rru8HewyrgYjPIqqrIYUAtKgB\nVJuCdJ8XqK6G43v/re9/aKl148zmC0YoU2zlyoLlIavfzBMnTuCJJ57AXXfdhQ0bNkDTNDzzzDPY\ntm0b5syZg82bN2P27NnYtm2b1eMlIiKiUmMun7CwTzEAI1ObRku0LMjBfn0juHqdqgKnjulve+Ei\noO0cvWRBRvUpNgJnqaqAqlleR63c9E19DNlct8TLJwAA77/QlstmFRTX1tbC4XDA6/VCVVV4vV40\nNzdj9+7dWLBgAQBg4cKF2LVrl6WDJSIiohIUCLdME+MmWX99G3rWAoB8+Xl9wx/OFGvrVwHQl1gW\nikMPeKMzxcHPGwjoAbPV2fFcBEtBSrR8AgCUT18NtI63/LpZ3ZH6+nr8zd/8DVatWgW32425c+di\nzpw56O3tRVOT3jKksbERvb29lg6WiIiIStCJo/ZdW4mt6bWK/OFG4MKFCSfa6e8vYt/fbwTFql9f\n2MPqoDiH5ftkMGtfqi3ZANt+5lndkWPHjuF3v/sdvv/976O2thYbN27En//854hzRIIZkB0dHejo\nCPfua29vh8fjyWYYJcXtdlfE58wH3kvr8F5ah/fSOryX1imWe+mDBu+0Gai/7XsQ5hXZLDDgdKGq\nuhouCz9nj2m7tvskRlQVfgC1VVUYAKBMOS10X0eqayCHB1Fjev/BnlPwA6ivrgYcDvQ7HJb+HPw1\ntRgEYq45UlWFkTjHzeRFi9D78AZ4GhsTxmp2y/X3Uh0eRP/JY6jzDkMZ3Zrx67ds2RLabmtrQ1tb\nG4Asg+K33noLZ511VugDXXDBBXj99dfR1NSEnp4eNDU1obu7G42NjTGvNb95UH9/fzbDKCkej6ci\nPmc+8F5ah/fSOryX1uG9tE6x3Eutrw8YPxkD/gDgt3Y8qqpCGxrEiJWfs84DGPXEA3t3Q3adAgAM\n9fcBE6cCK28O3VfN5wO8XgT6+yFPHAFGhqE997T+2p4evWMFhKU/BzmkLyYSfU3NmBCY7L2kqgIt\nrRgYGLBsPJnK9fcy9PmfeQLK4iUZv3d7e3vc57LK50+YMAFvvPEGfD4fpJR46aWXMGnSJJx33nnY\nsWMHAOCpp57CvHnzsrk8ERERlRO/D3BlsbRxOuzoPmEqd5DPPQX09UAZMw7yyHt6aYR5mWYRLp/Q\n/unL0O68NfycGrCnfCKRNBK/wuGA4zsP2z8WO/lG9Mf6Bksvm1WmeNq0abj00kuxdu1aCCEwffp0\nfOhDH8LIyAg2bdqE7du3h1qyERERUWVSv38HlCuvtTcotqP7hGliIN54FWgcBef8hfAffU//LG7z\nZ5GQp46bXhuI3BawZxW/SqbpP29h8WTBrK92xRVX4Iorrog4Vl9fj/Xr1+c8KCIiIsofdcM3ICZN\nh3LlNdZe+MW/Qr7/QnuDYsD6SVdqAGhqAXo69f2BPiijxwGH3tE7Ubiqwm/9+G+AzhORK+AFBfxG\nUGxxpjh6ZbtKY2SIparmMucwRhH1CCEiIqKCeO0lyJ2P23NtIfSSA5dNi0XY0YlA04BJU8P7QoGo\nrYPc8QdgoD/is4jlX9Q33tofe53ebmi332x5T2AxYQocDz1q6TVLiWgcBXH+ByGf/J2l12VQTERE\nRMDQILQnfmv9dRVFrwF1V6U+N1uWZ4o1iAsWhvcdDgjzUtKm8gkxcRowfnL8YQ0YrWktXoaaAHn4\nIHDgVUuvyaCYiIiIAADyv//dumv1dgMAhNsN+dg2yLffsOzaESyuKZZSAlKDuGABlDsf0g8qCkRV\nOKiPWEnO5dLLJKLLQ86cDQwPGefYWDpSqTxGCYVm3c+eQTERERFZb6BPf3TopQbKhQvteR+ru08Y\n3SKEEHp2Wwh9opw7QbbX5dZrpk0Le4gVq4C3XwcOvqnvNzRZOEACEO4AMjJs2SUZFBMREZH1ghk8\nNQBMPxNoHGXP+3hHIAct7FGsmZZldrr00oyoTHEElwvw+yH/usN0TA+U5TNGnbbFC5YQgFf36I8j\nQ5ZdkkExERERhch4SxlnQTNKMWSwV6+wKeTo2AP50+9bdz1NC7dQC06oS5Up9nkhn/hN6JCYcXbk\nOXZ99gomgn95sOj3FWBQTEREVFGk3xdaoQ0ApLknL6CXAljh9Q79MRCIzL4WO9U0VofRuVaIxJli\npyv2njlcEB/8cGhX9nbZMNB4CrNsc0GMm6Q/mvtC56hEfkOJiIjICvKJ30D7+t9CDhnL/Pb1RJ7g\n98e+KBf5XtUtS3LfXn2SXSDcU1kEx9zTCZEgUyzitVtzOSNLJva/bPVwK5649CP6hsqgmIiIiLJh\nTICTv3hE3ze6RIRYlSkO6jwBHD5Y1EGxHB6CtnE9cPIY4Euw0EiiTHE8TlfkKnaTp+c+SIogPI3A\nlNMZFBMREVG2jD+xB3v7Rv9p3+KgWO7+P+Nti/hP+/1GtjxYCuGOCoonToXIZLKcwwU0G6vOnTEL\nyudvsGacFMnptLR8wtpFo4mIiKi4GcGpfOZxyHPnQ3aG64sxcSpw+B1g7ITc30dR9LKJY4fD+zYQ\nS67UF3LIhWp0ypBawkyxcLkBTyPQ35v6ei4nUO8BADi+9p3cxkaJORysKSYiIqIsmRK22n3/Cvnn\nP4YPHD4I7QcWBXHVNeEOAYB95RNjxmWWxTWR772tbwTbx737FuD3xq6+Fxx7mp9BKI6iLhcpG04X\noFpXA8+fGBERUSXTVIgPLYXyze9Zdkl5/AgwNAj5lx3hg3a1JRNKxss8y8MHoV63FNq/3Gwc0Nt6\naQ98G/LpP8VmioP1wY4Uf2AfPxniE+3GsBhi2c7hYEs2IiIisoiqAjV1EJOmWXZJ7Rtfjj1oV5Ao\nBKBluKTdyaMRu9rtN4e25c4nAJ838nzzYh7JNDRBWbZC3z5zNsRFl2U2LsqMw9qaYgbFREREFSVq\nwtvJYzGdFWSGmdeERo02va2NQbHUMntNXUNoM+5nfWt/5L7Rdk1MmwHUeRIP5QOXhrc9jVBW3pzw\nXLKAw2lp9wlOtCMiIqok8bpARNfQ+n2xxzJ5i+VfBA69A9TVQ27/fdbXSe/NBGRvN+TIcPq1xU5T\n+BOdFQYgLlwUecAonxArV0NoCf5cX10DJdg7t1CKucOHDYTTCamqli1ZwkwxERFRJYmTVRWexsgD\nI8O5vYeiAI2jgNHjTO9rUfY5mlCA11+B9sNN6b/GXIca77OObo3cN8onhNMJkejLgoW1rZQmdp8g\nIiIiAJCamnmpgxqn1KCxWX+sqdMfR4ayG8+RdyGPvgd0ngSaWiA+tNT8bFbXTEUoRp6wpzP9F5mz\nvUODMU/LPX+JepMUuUghrF/0hFJzurh4BxEREQHaVz8Lue3nmb0oXhBRo5cdKPc8AkyYknWmWPvm\njdBu+ypk10mI1nGRHRhcKSapZUtELUaSDnNWN7jctVl0GUaqwGvClPTfm6zjcDAoJiIiIgA+L+Tb\n+1OfZxbwAzNmRR6r0oNAUVUN1NUDwzmUTwgB+P2A09TWrLYOomFU9tdM+n5GKPPuW2m/RO59LrwT\nJ1OsfHpl5AFfiixwqq4UZA+LJ9oxKCYiIiplGXZ1kP19UC5bEnnQXCdbXZt1+YT+BhJ4dU94MtuU\n0yHef2H210slWNmQQQcK+cRvwtvxMsXBMhIA4vpboXzu+uRDmDETaGhK+/3JIha3ZGP3CSIiolL2\n6p7Mzn9hJ2BqHQYgIqAT1TV6J4dsxxNcHc4Iih3rM5gAlwXZ35fbBYbjfAEwlX0o8y5JeQlx5bUQ\nV16b2zgoc1y8g4iIiEImTU/4lO+5p6F+52uxk/Gmn6H3EG4eA/GxT0OYJ5LV1MYPFDMVp9WZLXJo\nHYfGZqD7VOzxDBcaEUJE3kPKDyf7FBMREVFQkglsvscfBd58DXijAzhzth4cOxz6ymt3PgQIQASX\nMA6qqwcG+3Mf18z3536NNIjWCXpfi9q6VKfGciiQv/9F7PGSXaK5wgJzhxPwjlh2uVL9qRMREREA\ndJ2CPPhm/OeC9ZbBbOrIMKCqEE4XhMMRGxAD+qQ7b259isVFl+Uvc2qUaYi5H0j/NbPPg3LTP+tB\nlYmy9m5jg+FRSXA69UmdFuFPnYiIqJT1dkH71pq4T2ndRu9eI/iVW3+a+nruqphuC/L1jszGlEtJ\nQ6aMJZhDvZbTEfDrAbEj6ktBda3+aNeS1GStqmpgOM5EySzxp05ERFSmZLAMwujMILf/LvWLXO6I\nemDp90G75x8ze+N8BsXOLCpBAwG9jZo5Uz5mHDPEpaaqhhPtiIiIKp1849XIfW/sxDYZDG4zCRzq\n6uO3KQOgXrcUcv/Lqa9hRU1yuoIlEJlUa6gBPZg2ZYod3/73cCu6qjwG9ZQ9RWFQTEREVOm0u9dG\nHjh5JOYcZfRYfSODIFXUNwADpvONzhXBDhbyxNGI8+ULz0J2nYJ8+fnwweiyBDtl814Bvx4UG5li\nEWxRN3k6lC9/HaLOY+EAyS7C6YRk9wkiIiKK0N0Z057NMXEqtEPvQPvev8Dx0KPA5OlAbX3y69Q3\nAAOm3r/Bbm7BxTGiSgy0H9yp9znu6wkfdLmRN45g940MUsWBgP664CS9D1+hPzpdwHkXWzxAsk1V\nNTBiXfcJBsVERETlIE7GTI5EdZEYMw7KBxYkv47LHTmjPxgMa0Z0HK+rhDkgjrdvp2CmOJNuF2oA\ncJnKJxwMh0pSde6dUsxYPkFERFQO4tRWxgTFgUDqiWkxCyIYwXBwAZB0JqMFyzbywQho5WNb039N\nwK9nioMT7ZL0eqYiVl2jtxm0CINiIiKiMiADsZliNTgp7uw5+mMgkDor6nSF+xsD4QxxcPnmNNqV\niWWfT3mOZYLZ3jifP6FA1ES7bBb+KEYVtnYHnG7A70t9XpoYFBMREZWDV56P2JVGECsuXAQx2ag1\nfnVP6us4nYBqXhAhmCkOBsWpI6+4i4LYRGTdki080S6vLeTIOk5nZl+GUmBQTEREVAbkX3ZElEvI\nZx7XN6qr9XKBoFQtrJxOYKAf8p03jAtFlU+Uw8IWqpExD2bNXQyKS5LDyZZsREREFIe5Xdqw0XN3\n4jQgEID2/36l77edk/waRjcH7bvf0PejyieEUgZ/ow8u3mFkv0U+W8iRdWL+qpEbBsVERESlaspp\nEJd+NLxvmgQnH/1PfcPlhjx2CPKXP9L3UwWAwedDs/r1oFhbfZW+ayqfCPYuNhOfuz7d0ReE1DQj\nU+ywNMtYFDLpwFEOLP4ZsgcJERFRiZFSAg4HlH+8B8Lpgvr0Y3rW0xQgiEsuh9vTCJ/TCZhWvxMp\nAicR3V0iWEscPkF//1deACZPi3m9snhJxp8nr1QVcDr1+2Dhwg9FIc6XlLLmcLGmmIiIqKIF/IAQ\n+mITADB2AgBA+9aa8DmaBsXTAJHrQhrRcZaiAMcPQ/ve7UBvHvsRJ5NJn2HVH17wo9wyxZXG4Uj5\nxUb6/aFJp6kwKCYiIio1b3SkzpD5fXpXBU9jbu8VkykWgKof0370vdyubRHlpn8GxoxL7+TuznBp\niMaguKQ5HICmJQ16tVWfSruHNYNiIiKiEqLe9lVof/xVxDHxifbQtuw8Afnum5C93RDuKqAuxbLO\nSUivNxQAh99MCQfKh94GxoyDWPgxff9952f9XjmpqUu9fLVBu+9fwzvMFJc0IURaHSjkqy9CDvRB\npjgv65riwcFBPPjggzh06BAAYNWqVRg/fjw2bdqEU6dOYcyYMVizZg3q6sqkITYREVExOHwQ6DwB\nnHNh6JBy4UKoP9yo7/R2Q7vzVn178Sf0LguZajsH6NgD7cbPQMxfHPu8OTM3dgKUz98AdccfIGbO\nzfy9rKCItOtpxUWLIV8zFjUpt5riSptoB4RXYEy2KuG+vdDWrND/Lfz62YSnZZ0pfuSRR3DOOedg\n06ZN+O53v4uJEydi27ZtmDNnDjZv3ozZs2dj27Zt2V6eiIiIEhkZhmhoiv+cqSRAuN2Z1dsaHKtv\nD23LZ5/Ur3WF0X1CaoBvJHyy0bdYeXArxIeWZvxelhAitswjEVcVxNTT9W1miktfGnXFIYHk7duy\nCoqHhobw2muvYfHixcZ4HKitrcXu3buxYMECAMDChQuxa9eubC5PREREcWg7nwzv1HninzTQF952\nuVO3YEtXcNU3CWh3rQ0fNwIN4XCk7GxhG6Gk33lBU8MLkFRat4Zy5HBalvHPqnzixIkTaGhowAMP\nPICDBw9i+vTpuPrqq9Hb24umJv2ba2NjI3p7ey0ZJBEREUGfYBdUXRP5nLsK8HnDpQEwlkA2MsXi\n+q9BzMm+5leMnwI55fTYjKzfusUTsiaQfoCrqqEvCsqKVZCH3rZvXGQ/h3VLPWeVKVZVFW+//TYu\nv/xy3HXXXaiuro4plSjYt0UiIqIyJTtPhHeqqiOeUzb9TN9wm1qwOV2hAFCcNRsi6jUZaTsHGNUC\n+fzOyON+X/bXtIzQa63TIH/9c6C3S3/VGbOgLPqEnQPLswqMvZzWLfWcVaa4paUFzc3NmDFjBgDg\nwgsvxNatW9HU1ISenh40NTWhu7sbjY2xbWA6OjrQ0RH+ptve3g6PJ8GfgMqI2+2uiM+ZD7yX1uG9\ntA7vpXV4LxPr93kR/M9/deMoVEXcJw8GL1wIDA4gmLt1VdfA09SEXgCeUaMg0uzQAADRHYgbGhvR\n19MJbe9zEccVTS34z8sf8GEQQH1dXeziI1F6AIhD72Q85lL4vfRWuTEMFP04rbyXfS436qqq4Ihz\nPSkl4tUsbNmyJbTd1taGtrY2AFkGxU1NTRg9ejSOHDmCCRMm4KWXXsLkyZMxefJk7NixA8uWLcNT\nTz2FefPmxbzW/OZB/f392QyjpHg8nor4nPnAe2kd3kvr8F5ah/cyMbW7M7TtBeCLuk+a4oB8+k+h\n/QCAwSG9J2//0AiEmn0NbX9/P7Q4GTltZLjgPy85dhIAoP/k8bQCf01VMx5zKfxeal4vgOKPq6y8\nl5qiYLCvF6Ih9noyEIhsIWhob2+PORfIoSXbypUrcd999yEQCGDs2LFYtWoVNE3Dpk2bsH379lBL\nNiIiIrJI18nwdnNr7PNVVZEBgMMZnmjnsGJpgjh/ni+CmmJRUws0jwGGBpP2Kw4u8iCmn5mvoZHd\nDh+E9qufwHHTP8c+F1zAxlisRVx0WdJLZR0UT5s2DXfeeWfM8fXr12d7SSIiIjKRx49AGEs4x4jT\nkk2ePBZ5QFEghIDyte+El4ROV/OYyCAciF+yGiiGmmIYq5ulqC3t7QYAiCuvzcOAKG9e3h3/+P6X\n9IC4qRno6QJGj016Ga5oR0REVITkiaPQvvHlxCcEW6SZiJbI7LHSPFo/fsasjN9f+fpdsQdNk+jF\nRz+lbwwPZ3xtWwglclGReDQVaGnNbcJhUavAiXZJaN//tr4xdqI+MdWZPBfMoJiIiKjIyEAA2j/9\nXewTbjdw9hwAgPA0xDwtLl8W2nY89Ki+zHOWhBFQx9U8BsqnvgTUe4pnVTgljaBYDejnUUVRvroe\nyt3/AXHa2UnPy7p8goiIiGxy8mj8404XlC9/HSLRwh3GRDjlH75tz7jefcsYhzP8PkOD9rxXptIK\nijXrFjOhkhH6y8BZs5Oex69LRERExca0NLM018kGAsmXbTYWMRAp/uOfM6M+WUycmlVphi3SzRRn\nsew1FS9x+TK9ZjiRM9P/t8CgmIiIqOiYWqeZV+tSA8nrIidOhbLmdstGoay9G3hfnFXwUtRmFoQS\n23orhqaWd/lEBZYUiwUfC31Ji+GugvKFVWlfq4x/M4iIiEqUqR+wfOZxyEBAbyemqkkznUJRIGad\nY9kwxOlnQ/m7r0G59+f6gRlGVnikSCbXmXm9kK+8kPwcVQWUMi6fyL4NdemqbwAG+uI/19CU0c+b\nQTEREVGeyYNvQr1uaeLnn30yvP2f/wZ0ngAOvAoAECK/6UBRVR2qYRbTztAPnkhQ81xIxw9DbvtZ\n8nNUtTiz3JS9mlrA74MMxOmXrWVWQ86gmIiIKM+0byVf3Er+4X+jXqBBu2edjSNKU6mXHqhlXj5R\ngYQQ+s81XmtATdVb9aWJvxlERETFLtWiFPliBJTi0ysLPJDMSJ8X0u83aorLuHyiAmuKg+T/+1Xs\nQU3L6EsQ/4ZARERU7IqpFzBQcm3NtNtvAlpaoSz8OFBdU+jhkB0G+iAD/siVGzMMipkpJiIiKkLi\nA0kDIR0AACAASURBVJeGd1QNOP1siOu/VrgBAeEAo9Tqck8cBfbthRzsh6iPXfSEyoCU0G74VKhW\nX+75iz4BjzXFRERExUn6fWmdJz5kmoinqYAQEI2jbBpVmoL1maXa67e3S1+Fj8qLpxE4630Rh7S/\n7tA32H2CiIioSJ08lvqc6hpg7ERg5lwAgOw6mfFMelsoJR4Ud50C3NWFHgVZTMw5P6buXgS/wGWw\n1DmDYiIionzyelOfEwyAjfZr8t/vKYrOCeKcC/XHqhINLH3ewn+xsFWFzrRzugB/uCWblDK0EqRg\n+QQREVGRUk3/8e7ujH+Oz2v82dcU5BTBamxi4lR9oyr97FsxkccOl149dEYqcfUO6EGx6d8VXvwr\n8MKzGV+GQTEREVE+mTJaePv1mKdDq7IpCqCYguKeruJpJ+ZLry666Bw8UOaZ4gqlKPpfVwzaA9/O\n6jLl/HWJiIio+ARM7dXidEKQ772tbygKIjLF/b3AhCn2ji0Nyte+A5x2VqGHkb2+3kKPgKymKHqH\nlhwxKCYiIson8595vSOxz7eMgZh3CYQQEFXV4T+Iu6syqo+0izhjVqGHkBNpLJddniq0plhRIH/1\n45jD4vJlmV3GqvEQERFRatIfzhRrWx6OPcHnBVxuAIC46vpwq6lS7fiQJ+LDVwDQJ1klP5GhT9kR\nCb4sZlhuxN8MIiKiPJJP/ja8MzICuf+VyBN83lAbKdEwCsrq2/XjRZAlLmZK+zV6wKuq0H71k9Dx\nmCCZ97H8JEqQd5/K6DIMiomIiPLJ/Of7nk5o310X+bzfB7jdoV0R7JbATHFqUoP8v8cg//DL8DEt\nstZUTDk9z4OiQpHvvpXR+QyKiYiICmH85IhdqapQv/tP+kSweAsOOPif7HTInz+oPwYzxNGLOlxx\nVb6HlD+iQmuKu06GNsVnrwtvz1+U0WX4L4yIiCgP1OuWQvq8QGMzAEBMnh7xvPzRZmD/y5Anj8YP\nirsy+1NwxZNGhjgqU1zWGfdU9dRlSj67PbStXPY34X8/5k4vaWBQTEREZLNQ1nJoEOjt0rdbxkSe\n85cd4XNcblCOgi26Dh+MOCwKvAAKWU+sWBV1wMiYqwyKiYiIiotq/Al/eDB8rLo2/rn7X4Z88a/2\nj6nMaas+BdnXA+3OWwEAyvp7CzwisosY1RL/+NwLMroOg2IiIiK7BYzexEODQL1H33a6Ep4uFnws\n8kDzmPgnUnI9pmW06zyFGwfZK8FfVsT0MzK6DINiIiIiu5mDYocRDNcnDtLE6LGRB6LrYik9DvMX\njwqot63UiXYWLX9extXmRERERcIIiuXQAGC0WBOjRicO0yZNi9hVPnc9ZD+XJ86YOUbkF4vyFf1l\nwDuSVV0+g2IiIiK7+bz64/AQ4DKyl06XvtiEjA3WhCuytEKcO79SF/DNjbkbw6gWiMVLCjcWsk+8\nDHltfcaXYfkEERGRzeRBYxGB4cGIWmJl40+A2jrInq4CjazMmb5wCKcLyueuL+BgyDaTp8U5mHm5\nDINiIiIiu40M6Y+93UB1Tfi4ogBDg9BuvVrfnzg170Mra5VWMlGhf04Q1bUQly+LPJhFz2YGxURE\nRHYzFhGQJ45CjBodPi4i/zMsPhz1H3bKjZRA6wSIhR8v9EjyowLmEiYUveBNFkExa4qJiIjsFuw+\n8fJuyDHj9G0BPVNsIi5YADF2fH7HVs68I8CJIxCfvbbQIyG7uatzvgSDYiIiIpvJP/wyvHPyWHg7\nOih2OoEZs/I0qvIn335D37CoZRcVsabmyP04E1hTYVBMRERkt9p6IKalmogpnyCL1RkdCAKZLfdb\nsiq0phgw/soyY2b4QBalJPzXSEREZLeR4dhjQgBKBUcxNhALo1YCNOpKpTfO/aeyIhQFIlia1NIK\nTJic8TUYFBMREdlIdncCvhFg+pkAAGX9pvCTzBRbSiz8ROQBVdWPK7zPlUS5/X4oq2/P+HUsnyAi\nIrJTXw8wZly4Fduk6aGnRKUuy2uXqMy7fPZJfYNfPiqKqMpu0h1/S4iIiOzkHdHbRTn0yV7MWtop\n6kvGW/vjHiaKh/8yiYiILCZHhiCDdcQ+r94uypHij7NjJ9o/sHKXKPNeMZliRv+5YPkEERGRxbRv\n3woIAcft90PbfBsAQFnzL5CjxyZ8jfjApXkaXRlLNHGxYspUKnn1jtwxKCYiIrKQHB4Cjr4Xc1zM\nej/ErPfrOy2tQKuxSMf4ycDR96As/VweR1mmEmWEZ74/v+OgkpRTUKxpGtauXYvm5masXbsWAwMD\n2LRpE06dOoUxY8ZgzZo1qKurs2qsRDmTfh/gdHFyCxHZRrvps7EHZ58bsev4zsOhbeVLXwV6uuwe\nVmVI8P/toqoq7nEis5yKbH7/+99j0qRJoQBj27ZtmDNnDjZv3ozZs2dj27ZtlgySyCraqk9D/om/\nl0SUH1IzWoLNX5zwHHH62RDnXZSvIZW3iqkdToQJn1xk/dvT2dmJPXv2YPHixZBGc+zdu3djwYIF\nAICFCxdi165d1oySyErvvV3oERBROTvrfaFN+fxOAIDCeuH84F8BKQdZB8U//vGPsWLFCiim1jK9\nvb1oamoCADQ2NqK3N3pJS6LCkSeP6Y8+X4FHQkSFIocGIe1e8jfgD2+/+qK970WRuEIg5SCrmuLn\nn38eDQ0NmD59Ojo6OuKek6hms6OjI+I17e3t8Hg82QyjpLjd7or4nPmQ7b3sv+PvAQBOqaKePwsA\n/L20Eu+ldey8lz3XLYX7Y59C7ZdutOX6Ukr0vvkaAEB4GuH0jcAPFOx3o9J+L7WAD31xjltxD0rh\nXnqrqzGMwv2+pavQ93LLli2h7ba2NrS1tQHIMijev38/nn/+eezZswd+vx/Dw8O477770NjYiJ6e\nHjQ1NaG7uxuNjY0xrzW/eVB/f382wygpHo+nIj5nPmR7L9V+/f8qA0ND/FkY+HtpHd5L69h9L31H\nD0G16frSr2eJxUWXAYEAAjU1EBdfVrDfjUr7vZSDg3GPW3EPSuFeal4vgOKPqwp5Lz0eD9rb2+M+\nl1VQfNVVV+Gqq64CALz66qt49NFH8dWvfhU/+9nPsGPHDixbtgxPPfUU5s2bl/2oiazWeUJ/VG3+\n0ykRFTc76059I0BtHXD2HMj/2KS/3RftyUpTesQnv1DoIVCJsGSaZrBUYtmyZXj55Zdx880345VX\nXsGyZcusuDyRtVS10CMgokKyISiWB/ZBDg0aSzpXQ0w/M/x2U2dY/n6UgIyzeEV/Bc1vivf5KW05\nL94xa9YszJo1CwBQX1+P9evX5zwoIlu0tOrZ4uqaQo+EiArK+qBYu+vrEJf9DeQTv9EPVFeHn3Q4\nLH8/SiQ2KJSPPwpceW0BxkKlptIb+lEl8Y4UegREJUmePAbtF48UehjWeWEn5Knjll82FBADgGYK\nztgmrLCcrkKPgEoEg2KqHMPGBAxNK+w4iEqM3Pkk5GNbCz2MnMlB08Se/ng9CrKj/f4XsQfrinv2\nf9mqqdcX8GhoKvRICoNfwHLCoJgqR7CWmDXFRJnxDgMAtIc3QJbyl8rjR8LbFi77K7f+NGJfXHI5\nRFUVHA89qh/Q+P85+SKqquD4921MflBWGBRT5eF/oIgyM6IHxfKvTwH+0l38Rr79hmkn9wlJCb8g\nnHZW5H7FLz2cf8rKmyGu+ftCD4NKDP+lUuUYO1F/ZKaYKDPmevxSzsD5wp9D/vZ/crqU7OmC9nfL\nIEeGYp4TZ0b24kfjqJzeizIn5syDcuFCfWd0a0HHQqWDQTFVjnoPxOeuBw4egHrd0kKPhqhkyOf+\nHN4J+KH9+D7IUly+2BTcy4MHcrtW10n9Ov/7YwCAuPyT+uPfroFonRA6zfHQoxD1Dbm9F2VNWb8J\nyroNhR4GlQgGxVQ5AgHA5S70KIhK2/AQ5P/9Cdq2nxV6JBmRPi/k/pchln9RPzBhSm4XNCbqye5O\nAIC4+DLjjUo4k16GxJTTIWpqCz0MKhE59ykmKhmqCrjDk2ukqkKwfyhRUjK69ranS398+/X8DyYH\n2lc+AwCQNXUQ8xcBp8/M7Xr3/6u+sfc5AIDINcgmooJjppgqhxqAMGeKA/7CjYWoVAQil0W3o79v\nPinLv6h/ObarNtrBXBNRqWJQTJVDVQF3OCjW7rilgIMhKiz55muQLzyb+sSobhPykXv1Hryt420a\nmc1q6wFFsbTMQVzxef3xizdCtJ1j2XWJKL8YFFNFkFICJ44ALlNv0qPvQTJbTBVK+9FmaD+4M/WJ\n3adij42bWFJ/aZHBkg9AX35ZKDllitWbPxexL867CACgXHI5J9URlTD+nYcqg8/IdkX3WO14EZg7\nL//jISq0BH/m155+DKJhFITx70L7t7tjT3r3LaC6xs7RWUb6fdBuvTp8wF2tZ4qzCIrVB74N4WkC\nhgYjn3BX5zZIIioKzBRTZfB79cdxEyOPt4zJ/1iIikGCSabyJ/dDu/9fIYOL3HQeh/jAgsiTnK6Y\nWuOi1d8bsSucTqN8IvHiHdLnjdt/GHv+AvnnP4Z2lVXr9A0n80tE5YBBMVUGnxcYNRqoqYs8HvBD\nnjjCvsVUeVJNCDv0/9u77/Coqu3h4999JhUIKXQIoQQRCFIUUVRUmp0iKog/LyoqomK7dpGLXgXF\ngmJF0VcQC0UF4dooIihKUUHpvRgghBASCCFtzn7/OFNTh2SSSZj1eR6ezJw5c2bPZpJZZ5+1196D\n+euP1lWWKEdKQFQ0AOqaIa70Cb1/L/YXH6vMllZMoaAYKDZ9Qqfsd6VTmffegHnfja7KG+bcGehj\nGUUP0/V81C33uftHCFGjVdugWJsm9jsHYC76OtBNETWcOfcTzEn/sWoU2xwf+XDHpd/8fEjZH7jG\nCREoZZRUM597yJpUB+4A8nimtRhFnwHWCaXW6NU/w84tldzYCjhx3HXTNnW+dcNQRZZ7N8fejf5h\nLjrrmHvbg9YEOv3tHMzHRngf15E+YlzUD2VIaUdRTSgV6BbUaNUuKNa5uejsLMy7Bln3Z3+I3rQW\n7bnMaA2gc7Ixv50T6GYIQP/+Mxzab020c3555Z60fhbky7LPQpRBXXWD932bDVCOYNkxmvpBNV01\nrHD+L4CyFZ8+kX4Y86GbPZ6b5b7dJN5rV+MOqV4jxOmm2gXF5piRmA/cZN2pGwO1amO+Ng5z9JBT\nP9acjzB/XujnFvr42m8+j547IyCvLQrxHMUpPKJTkA/2GpIbKYQ/xcQBVv6sl7BwaNLcul2vIeqy\nQSjHvl5CQ6zfH8P6GtGrllVma8tNO5Zj9mIYkJKMzslGe54Uh5c8YU61aOO+k5CI6tzdj60Uwk9K\nyZUXZasWQbG5YCb6YLJ1J/Oo9bNzd4yxr2Gb/Dnq8sEA6B2bSjyGzs3BfucA7Pdej/21cWjTjl44\nF/3xW+jCFQeqwrYNVruOpFb9awtvhlH8bUAfz0Rv21jFDRKiGnCWDvMYDdU5J60rJ7Ucufd5uRAd\nW/zzQ0KtoNjjcm2R1e+qmH3sPUXmB+g5HxXdUSn0qmVW3vDcGa7vCF1Mup7zPemt691Pb3eWH1st\nhKguqsWUWT3/M/T8z6CNteym8eZMVIR7rXLj+lux//AV5sQnUN0vQV1+LSqhtfdBnMF0Xh5sWot5\n17Xu4/9vFuraf1X6+3AyPUdM0g5BvYZV9tqiGM4v7fiWqMJB8UeTA9AgIQJLH8uA5D3WnRNZEFMP\nXZCPed9Qa5tzOfTI2lbwC6j+w7zLsIWEWjn5yuN3Ki+31NHWSpeSXPJjtaPctz3/DmRnuZeu9mA8\nMgHz1afBObDhsZKfuqBvRVsqROWQnOIKqRYjxS47NgN4BcSF6dXLMJ97EF2Qj/5nN3rrBuz3D8Mc\nc5e1Q6NmGI+6C9KrvgPR387xukRW2aMZ2plbF98KnV5M4XtRtRxfgMY1NxZ9LLFdFTdGiMAzHx5u\n3WjRxgqKKbTCozMozs9zBcXGgGEYlw1y73MsA04ctwY0nHJOer2OPpiMdvxdr0zmgpnYX37K/bp7\nd3rvEFcf49m33Pc9g+JatSGzaFDMGR2sFTALHwu8VsYUQpw+qkVQrDwmLBiTis/DNd79EuPljzCe\neRPAyjP+7wOYrzwFJ90TKWzPv4tqm4Tx6seoEQ+hhlgzhs1R11qXygsKMEcOxPzuS+xvPe/zBD69\n5e9TL9tVvyF65U+n9hzhf86RLGdwPPZ1jPFTILEdquPZAWyYEFVLZx71/jsWEwfZjuoMzpFjw0A5\nl3A+mgapB0s8nvnMfd4bCgXF5itPYU58vNjn2u8cgN5XTMBZDnr+Z66UNQDz+YfcDzZNwLjvPyjP\nNJB8j9X4bKHWlcZCVx+VYUBoqFWNost53i8oq9YJcVqqHkFxUleMfz+HGno7ylEHs8g+IaGomHqo\nZi2sDR5/AAHUDSNcATOAqhuD0aMXSimrFBdgTnsD825HfvJX0+Gv1T5P4PM179Q+0hpJUZdcgWoc\nD1nF1MgUVctZeincGv1SCa1RDZui2nVyTpwXIjh4pAAAqMhaaGd1BmeJsXe/Ql091FXHWJeWkgDg\n/JsMmB6jtdaTrV8wu+PvbmF6/z5fW27tX5CPOevDU3oOOdlF65N7LMyhD+zFnDLRWqUPUMNHu79L\nso6jF33tvnpZJwrj1emoyJKvZgohaq7qERTXqYtq3xmj70DfnuBx6UrdeCfGe/MwLhvkDpgLH//6\nW60bf6/xfsBx6VwfSUWXsDqTzs3FnDIRveDzMpul9+8DbdXzVFcPtXLrHH9oRWDo5N3uEbDCaTmq\n9FWthDjteKSRGQ89670yXa06gDVCqmrXQXW/2NqvfzFpRyXJTMdcsQS9ZzvmgpnuhTNKWv3uVCu/\nHMtAL/7apxQ414p8J09CZKElqT1HtNetsn46VqVTF/b1/i45tN+dS511HFW3hImHQogar1oExafK\n9vYXGOPeAEBddFmRyVOFGb2vwZjsznsznp6EuuI6bE+8BID5xB2YLzxa/JP37UT/scKndpnPjLZu\nNGyCiq2Hat3Wp+eJymPO+9R9J6LQF6PCp5MdIU4bBR6VeM5IsgJBZ2AaGgbxLd2PO66wkZBY7KFc\naW/792KMm4waaf0N1fM/w5zwiHeuMbgrPHgGtOVdKrpQmoarTRf2wZjoGEnOz0ebprVv4d99z+c3\naQ616mC8OQtj4ode3yeqnzVQ41rNLrZ++dorRFWRiXYVUiODYgDqWxUdlOOSeFmUYxQErHqTxnW3\neO9QTG6bzs1Be6Zp1KqDPnoEffyY934b13rl6RnPvWvdiK0Pjb0LvosAKm6kWIggojOOum6r0DBr\nkt2hA9aGgnyMe8e4993pmPhcwpesSurqvhMWjnJceVNndiz2Coye9QH6n92YIwdif/M5a6Pp28I5\n5sql2N8eD0ePWMf66VvvHeo3QvXpj7p8MCqugbXtn92Ql2O1rVB9cu2RPkFGOqpHLytFz/lc53ts\n39m6cfggxuTPMV78wKf2CiFqphobFaiIWu4lO311Zgm1JR2XyuzvvmiNLAB62wbM0UPQ8z4BwJjw\nPmRnYT52G+a/b7ZW3jt+DH04BfP1ca5DGY9OcI80hEVYBeKPHUUEiOeXc+HRIkeqixBBw7mEccOm\nAOg1P6OXLLC2ZWd55d4a948r/GxvnlfoTO16rv5tqfd+jlKbetn3mJ86BgycqWyeE95KoX9eBOtW\nueu+O8pcmou+xvxsChzPRPUfhnIuOpLQ2qookZ1dNHUCXCPF6rJB1kTtRk2Lf2HHHBfj5ntQtWqX\neVVSiICTlMAKqRZ1iquKMWAYenNS0e2DbsZ8ezz8+SscToFGTdEedSuNV6cXySMzRxda9vT6W9Gr\nf0a17ejeGGHV69QLZsGoEtIzROXyCoq966cW+fIW4nTnSGFQHawRUHXdLegvp2N/9gE4me1etANQ\ncfVLH3jwHH2NrYcKj4BzLoA/fnXv8sgESDwT8/l/w/69sGe79zEKr6ZXEsfIrp76inXfkXahv5zm\nzpP2nPxWNwbz+6+spd2LWebZ6DsA3aaDO0WkpIlzzioThSfqCSFOS0F12qvadsQYeJPXNtvU+agu\n52E8OgHANWriHCEGXAGxc+JJsce+7FpsY1/z3ugoYq8P7XdP+hBVq8A9ElX4EmrhmfgiOLjyQ4OE\nueR/mN9/iV63EvLyrMWPho0EQLV21OlO3m3dP5V8ROeoaYs2VkAMKI8FMtQNt1llD0NCrYAYvCb6\nAb4HxYUnLBfkYX/rea/jeeUCJyRaAXj2CWjUrMjh1DkXYlx3C9rRLuWoxVxEdCzUb1Tyqn5CVDeS\nU1whQTVSXBrnCK9e+g364susEePC+9zxMOrWB9B//upaoMN45wsrN6+4Y4aEQlJX2LiW/D9+g3ad\nK+8NiOIVs1KVS606XkvcitOf3rbBVTbMmPw5qtbpPwKo582AnJOu6oPq2n+5TxA9g9LCq4SWxXmM\nvTvc2zxW7zQuu5bSqN7X+B4UF5ZfAH+tLvnYva5GfzvHase//1vycXZtsX6WEBSr0DBsL0wtXxuF\nEDVOUI0Ul8UY/TQA5mfvQVQ06saRqP+72/W4UgoVGopx3iXYps63RplLCIhdz3FM3Mh+dWzlNVyU\nSJ2RBA2bFHsZ2Hj5I4zHXwxAq0RV0nm5rolVOtM9SuxrVZkar0Fj7/ueAWC7Tu7bJ7M5JY6RWTV8\ntGuTs1pDYZ7bjeenYEz8f9Ak3vfqE22909705nXuYw8fjfHWbO/9PU92ape80IZyTLjWp/rehRCn\nJRkp9qA6d0f16e9KoVBndkR5ligqj6iYijdMlJ/NQPXpX+xDKiwc2nRAnXcJetWyKm6YqAitNWRn\neV2uL4n55J0QWx/b05PQK9155PrTd6HnZZXZzOqh8ChoqPu+CvH4Cijm6lhplGEUOdlUoWEY782z\nKj94snm8Tr0GqJBQdEioV3pTSXRBATgXT2rbEXJzwFkZ4+ohqPN7oUILv0fHYEVc/dIrFJnW+Lk6\n75Iy2yGEOP3JSHEhqttF7jt+mFyhrhzsNdosQJumexWtymaaZZZeUzeNKlqZQlRb9kdvw3zhUcwH\n/6/U/fSRw+gNf8KxDPclfs8FfArnt56unDV5nbmGZglVVxoXzb0tD2UYqBbetY2VR+UfV/6uj0Ex\n+/e4n3thH4zBwyHLWppa9R9WNCDGIze6WcvS2+qoLiFVJcTpQ3KKK0L+EhSi2rTHmOSYZBdd8VFe\nFVELdckVEBqKzi1n/txpRi+ej/nAsCp6Me1dOqo4Slk5lyUFC6J6yTgCu7eVuZv5xO2Yk59x3XdO\nqvJ0uv5Omh+/hfnLImtE/eA/AFbKAhTNs3cGqdFxldYe1fFsjOeneG8LCSlxJVEvxzK979e1AlkS\nWqNstqL7Oxgvfohx5yOlH7vj2RivfVL6PkKIoCFBcTFUVF0rX7ikGcmnejylUBGR5Z9UcrqpyqoP\npln2bFzHw5JCcXozn7nPddsY+zoAetHcQDWn0uh/dqN/Xoie/iakHgTAeOZNVGw9a4fCI6thVnqB\ncc+Tldou1aipd7qFjyPF5hvPQiv36qAqvpV1w/HeSny9eg1QJZVac+6jFKpOyTnHQtQ8Uqe4IiQo\nriphERIUO1WwuLg9ZT86Pc3H1zJ9GCl2PH6yfCkdOiPd9/YIv9I+5MGqS67w3lAnCuo6rgKdhuXZ\ndEqy+05+HjRpjnIsUGS8/hnqqiHeT0hsB3ENvFb9rBK+pk9gzfcAR6k1sMpdlrDUsxBClJdMtKsi\nKjzcWnJUUJEzWXP5Dxyf8Ta0aIPt6Uk+PKHsnGL3UHE52/TordZRrroB49p/le8gonyyjhetrgBo\nj7q26qZR6GXfux9UBiomznX7tJN2CBo2gTp10ft2ek+sq1008DXuebLkPOPKFBIC2zdhf+6hojXe\nHbTjBFqd0QHDY5TZGPt6YNoshDitnYbfCNWTCpeRYhez/EGxnvG2dSOs9FJ47if4klPs3LdiE69k\nhbwAKGFRHL3QnRbhtajDNTe60gTULfedehmymiAtFdWpO+zfh/5octGFLwpRIaFWJZaqFhJijWTv\n21nyPo6RZK+VQrFSMVST+MpsnRA1lEy0qwgJiqtKWAScppN6Tl35gmKvWqLbN6E9Fw0oiU85xdbj\nettGny7HA5hzPkLn56E9L+EeTXONbPlCm3Z05lGf9w92+sRx79QAgLxcdKGTTX04xcoPr9/IVYfW\nyRh4E6pNBwBU3Rj08UKTuHxgfvgaOnnPKT+vqujMdGukOLeapxfk+5A6kZ9X8hLMQgjhZxIUV5FA\npk/YJzyC+cNX6Py8gLx+EeXMKTZf8p4IZD7/b99eq8xlLx2Pr12JOcW3xTz0wrlw4B84sA8A49m3\nrAc2/Onb85P3YN51LeYjt5S9swDA/Ggy5th73BuiotE/L8S89wbXJn0sAzKt6grqzI4YV1wHgPH0\nJIz/vu19wMjasOEPzJ8XYq75xed26JVL0T99W/43Utky0lH13SvLqauHlLJzANndlSf0rq3F77Np\n3ek5mi+EqJYkKK4iKjyA1Sd2b0N/MQ22rA/M6xdW3ol2Kf8UPVRuGScapulejrYkNo9fg0IVR3Ru\nrlepNr3lb7SjpJVe/gPmC49aDzRpbm3LOlb6azmb9ez97mMePWIFc4D5/ZeYn07BfucA9B+/+nSs\noFE4ODqeiV7zMwD6YDL60AHMh4djfmOtbqZXLHHtqlq0QTn+j1waNrF+btuIfv8ltGPk0j7hEfSm\ntaU2Rf+1ptTHA0Vnn7BqMse5g2KqegKdrxq56yKbLzxq1ZQuRFaaE0JUJQmKq0p4eJHLvFVOV5OJ\nKeVtRzE1TfW3X5T+UtpEGaWPFCvPoNmRW6lzc7DfOQBz9A3o76zX0Pn5mK8+7ZpYp5dbk7eMh5+3\nSjv1vubUys05SkGZj92G+fBwdG4O+svprlHIIqkCwa7QxCp1w23uh/5zD+bTo6w7zuDKUXGhKkXP\nRgAAIABJREFUJCo61roREWH9dKYb7N6G+dq40tuSdeppF5VNHzvqrv/tLL8GqJZnBKZBZVANGmO8\nPcd131lTWv+1mqwXHrdSkTKPonr0DlALhaiBJKW4QspVfSItLY23336bzMxMlFL06dOHq666iqys\nLF577TXS0tJo0KABDz30ELVrV3xVuNOBCgl1XWqvSl6LE1SD2dr64D/ozX9Zt08cR9WOQhcUeC83\nW4bwK68n72gaeuVP6G9nw7U3l7yzT9UnPDhTLTwCUj3vE/Q5FxY/qat5K1S7TtZ+G/6E1APoa4a6\nV9QqhnNE0nj1Y8y7BrmbOnoIRMcCCiIi0Xt8yJkOJoVPpk6WnDOrul/sFTSXuN+Am9C/O1InMo64\nTlTKVFCAOetDjKG3+7Z/FdCb/3bfCbcCfWPsa+4yZtWQCgu3+tzjCov51vOYgPHXavTXn6J6XR24\nBgohgkq5RopDQkK45ZZbmDRpEuPHj+eHH34gOTmZefPm0alTJyZPnkzHjh2ZN2+ev9tbY+WtWIz+\n7ssqfU1tml65j+ZHk6v09Ytjvv4MOGr6mg/+n5UucPdgzBnvoAsFnTo/H33QI2WiWQvUrfcTecu9\nGLf/G+ORCdZ+paVjZB2H0LIrVagrr7duOEfzC9WvNcfe7c57PKMDxj1PWf8efMa1jzH8XutG4RXD\nPN9T2iHMe66DsDCUYWA88RLqmqHudgy9A9sr01B9+8O6lSUex/z03XJNEisP52RAnX44sCvAeZ7U\nNWnuGqn34kh/Uf0GomLqFX28ENWhi/tk9chhrxXWPCdd6uNW0KY9JofpxV9jfvXxqbyDSqNNu7VY\nh4Oy2aBdJ/ChDwLNGPeGdSM61vtvgKNqiF76TQBaJUQNJWt3VEi5guKYmBhatmwJQEREBM2aNSM9\nPZ3ff/+dSy65BIBLL72UNWuqZ95dQPgy09rP9A9z0V985N6QneV+LDfHu3JCVUk/7HVXfznd+rn8\ne8zRQ70fW7kU8z/3ogvyraBk/15UbH33Dm2TrP2+/wqdUzT3UO/bBTs2+VS+zRg8HOO5d2HnFitt\nYvpb0Lk7hrMWcuNm6L/XoK6/FdtjL6K6nm/9qxvrOoY68yxISHRN9CrMXPkTev7n1p08a9KjSmyH\nMfD/MB54xpoMdm5Pa3u3iwCs3GKPtBv72xPQ+3aif/oO8+FbMGe8jV67EvPXJVQGfTgF85FbMGdO\nxXz8dvTsD8p1HPt9Q7E/NqJijfEIio0nJmLc9mCRXZSz/3xNGYh2//+Z38y28nEdo6zOYFnv3o75\nb8fViGMZXnnnztSaQNOfvmdVakhshxpp5bnbHn4eVbfiS9VXOpvzKpGyTmId9EHrao3q0SsAjRJC\nBKMK5xSnpqayZ88ezjjjDDIzM4mJsf4IR0dHk5lZ/fLugolzspC642HU4FugYVPXY+aYUZj3DS3p\nqZXTnqNHSt/BozqG1hq91hopNe++DvOlx60H2nd27eNMUdBfTccc/3DR4zmDfl/TJzxTODLT4egR\nVIs2GOOnQMp+WLsSdUGf0o+xb2fxbQH0h5PQv/1Y7GOq49moFm3cG2pHuW6aEx93b1+3EvO5hxwH\nNK3Jfu9MQH80GfPXJWVPPDxVR61Rfb1kgfVz+Q/lO07OSatk3e++V3lwMhfPt0bFPdInVK06qI5n\ney8bDODZh76I8zjJ2r0N88XHIDcHzr4AnXUc++MjMCdY/5/2B4ZZn4v4lhgT3j/l91FZtGm689uH\n3uk6saoxbI6c/sx0zIeHuzY7T+jVsLsC0SohaibJKa6QCgXFOTk5vPrqq9x6661ERkZ6PVZaTmUw\nqv24b6W+/EHv2op95CDYYuUYGuddgup1FaQewP7W89jvHOAazfS1Lm+521KQ755BftAjp9oj6HNx\njALrnJOYIwfC+t/dj6XsR13Yp+jnypHPqxISrUDaM6Xg5AmrwkCbdr411uad16z6DbB+epxMFNvu\nYujDKeicbGuk90SW12PGhPcxJn5Y6vOVUhgvfmCt1rZvl3Ucz/5wBn8eQaD+aHKZEw9PWeHJobVO\nfY6A+fNC9+33XvK5QgeA3rgWPesD+Kf0BSicVO+rMV7/zOfjK8OG8d68Iv8fqk4U7N7qSvUBIPuE\nVW0kItKaJPbcu1YbA7zcsP7gVddt1ap6TqorVURkqQ8rqVMshKgi5V7muaCggFdffZWLL76Y7t2t\ndemjo6PJyMggJiaGo0ePEh0dXeR5GzduZOPGja77Q4YMISrKt0CjJrO17UB2dKzf3qvOz8O+bzch\niWcWeezYh5O8RtWioqIgKooMgL9Wu7YbTZoTcXAfYa0r74v0xBvPkb/mF2Jm/ECe1uR370mte5/C\nvmsbWc8+4NovrM815C3/gTp16pC/eR2uZIiQUNeqVkbqQaKioggLC3P1Y/4Nt3LipacwUg8Q+uMC\ncmZ+QMxMa2W5PHs++W07Ujvat0vIplmAK1xTBnU6n4vN8To5199KyDk9CCnmM+31fnv2I//nRZhP\njaT2oxM4AYQumkto955kAVGTP8XWqGmpx3CJisI+5hWOP+i4dD/vE9dD4V26E/GiY7QyL5fMW64E\nQH87m6jhd/t2fPDqy+Lk2Qw8E1OMujGn/BnO+Niq4axi4tAZ6dTOO4ktqlkZzwLzRBbHXreqQETa\nbOQ1bkb+vl2EnHUOdTzaoKd9i7bbMZxLGNf1cbKcBx0VhfN0qvbYSRRs+JPcuZ8Uv/OWv4mKikKH\nJJAJRKb8Q+hZ55TZl5UlS5sUAHVenEpIDf1bmvfvZ8meVLTiR+jFl1G7hr6n6iJQn8vTUU3oy7yI\nCLKh2rcz0H05e/Zs1+2kpCSSkqx0zHIFxVprpkyZQrNmzbj6avfM4G7duvHTTz8xaNAgli1bxrnn\nnlvkuZ4v7nT8+PEi+51u6tgUOjfHb+/V/vKTsG0jxvtfo5SyKjrExKH37UKnHgTAGPMqxNUv8pqq\n19WowcMxJ40l+83nObl1A+q6W7xLk3nQBflW9YzytHPLesjP41h6utWu8Eiy8vIhvhXG5M/Bbkf/\n9iMFF/aF35Zy7P1X0Eu/hS7nYwz+l1Vj1bChN61FN27G8ePHiYqKcr+nhDYYDz6LfeLj2B3VGo7t\n3Ipq2BTz918hLMznPtc57tQD2/vzrGDQ+dzLB5Pveb8kw++DnxcBcOLlpwDIi21A7jirLnF2raiy\nj+HZplD38rvmvl0QFobx9Ovk12tAQZbHCHTHc1DhEeg/VpA56RmMO61L/nrHJohvhf7jV1SX81C1\nvWvWevVl4ddO2Y8+6siPDguDBk0wTxz32l/v24X+7guMux7zfm5BgZV6Eer43CS0Ro0eix5zF8cf\nvxM18lGvy/w6Owv9xTSM4aPd79djqeYTrzztSnWwtzij+DZX8HfLuPcpSOxATlRd7F/O8H7sqVfQ\nq5ejF8/HeOi/rtdXPXqTvfpnjJZtS+1Lf9Mb/oQOnVGGDdMWghrxECfrNapwHwSKznafeqmel6Ed\nVxfsQ+4Miu+HylSVn8vTXU3oS9PxPVbd2xnIvoyKimLIkOIXNSpXULx161Z+/vlnEhISeOwx68vw\npptuYtCgQbz22mssXbrUVZJNOIRFQM5J9MFkVJP4ih/PMRveHDkQ29T5mJPGej1svPtViWXO1BWD\nURGRrkvDeuE8qNfQqrNbiN69HXPCwxj/fg7lkc/rM8fEOvPBYZCXhzr/Unc7HJfi1WWOsmTRcVZA\nDKgWiV6LLajzLin5Nep6j96an7+P7YFnXAs7cOsDxTypGI70CeOhZ33b30f6k3es47576tVHVFi4\nFVBeehX647esPizm82N7YBw6PQ39xwr06mXoEQ+CUpgTn0D96x70jHfQ4DqJ8oU51hpxVn0HYAy9\nA11QgDl6CNpuR9lsmCt/goPJVp6wR1Bsv3OA+yBx9SEqGmPMqyjDhurbH/3dl+hl34MjKNY7t2C+\n95IVRDuCYvsjt0DhJbBzc1G3P4TqXspnoQJUl/Pdt5s2R29ai+o/DBXfEhISMVq1haF3eD8prj76\nm9nojmfDeRdXSrsK0+lpmJOfwRg3GX3iBHrVMgzHxMway7OCTLtO4AiKVXh4CU8QQgj/K1dQ3K5d\nO2bNmlXsY2PHji12e7BzBqjmf+4pOjmoPHLdeYzmqmXerzX4lmIDYmPcZIht4BotNJ59C/PBmwDQ\nn78PxQXFjlxOc8kCbKcQFGut4fBB9wZntYWL+pX8JI/ya8qxPK9PoguVnfJYGUvd7sNS0E6OhTuI\nqFgOo/H6Z5gP3oS66gb0xrWwdweq52XlHm23jX0dAPuvS0pdFVHF1Uf1vgb94/8wR13rfuCf3a6b\n+rcfy54sCF4ruqkzOlg/Q0KspXlT9qMNA/3hJPf+O7dA6zOL1hJOT0MNuMl1FcIYfAtm/UZeq5eZ\nL3qPMgOugNgYNxn952/oBTMhJg7VoAnKqPw1h9SQ261qIu3OQpXyeVADbkJ/MxvztXHoz4ufROl3\nu7YAYL461l3ft7quWlcOqnYUtm4XYq+mi44IUb3JfK6KkBXtaiBtt0N6GsbY16F2lGuijbr5HgCM\nK4sPKFV8K6/L56p2HWxT51t1cs/ugTlzatGav47gRIVHoE9klV4T2NPmdZhjRhVtw5lnlfgUNcjK\nnTXem3dKi3kUN5pkrloGDZtwKqt5KaUwnp8Crdr6/Jxij1O7DmroHahLrsR44iWMd7/0SgsoL9vj\nE10BckmMYSOLbNM/fee+c6DoUtnF0Z4TzGwewXzTBMxnRmM+510OzXzxMVi3CvMuRzDe8WyMZxy5\nxJ2906hUTD1YuxK9dT32Md6VBfSxo5ieE8fiW2EMsE7c2L+3yoI/pZSVblLGCZIyDNTA/wOgYMOf\n3qPklcR87yXrhueExTbtK/11K1WjZhAWjvHqx9ChC3UeeR7j8sGBbpUQIshIUFwD6SXzITwSldAa\nda512VT1G4hxyRUY7399ysdTzVvBoQPoJQswRw70/mI/YeX86NXLMR+8CT3jbd8OWsxqY8aTL5f6\nFOPqIdbl/fKOBCZ1dVcE+OBVSD3oHv31kWrU1C+VU4y+A6yR25CQco8Ql5e6yeNkpEFj9+2Yeugf\nvnItRFGqHZutY101BDp1K/q4R/k8J88qE7YHnkE1S8B4e07RFdUaNrH2f2WM9X8EGHc/aW17+Ba0\n48qH8cbMou/NH6lHfmY4Fl/Jnfcp4Ki4YppWekkVUNcMrZLR88qkmrfC9vYcVN0YqVwkRIXI6h0V\nUbP/kp4m7C8/ic7OKntHBz3nI1f6hLruFmujI/gr1xdK7ShrFM7zNXJzsL/5nFWf1mN0V+/1delh\n6xdT9R2IGvmY9cXdumiljMIq8oWo6jdCNS5U1SAIcxKNXldhmzof463ZrmBT3foAxs1WjrD575tL\nLSOm1/+BXrEYdf2tGNfe7PV/ojxHJB2fC+Pdr6B+I6uEXos21hUM5/7FnJSoxvFewbrx37dRZ/dA\n9R/m2mabOt+rFJdt6nz/pB1VEjXgJgo2OlJO0g7BkVSrLrUvJyCnwFy9HADjNXdlDMMxUi2EEKJi\nJCiuDrZtLLLSG+Cu71tY3RjUnY8AuC/vHjpQ/tePLFp71hw9BP62ViQ0rhlqBVmvfwb7dqGPpJZ5\nSL13p9W+nv0wzr2o0r+4jUmfoIbeWfQBH5Z4Pl2p8AhwnCSoFq1Rnbu7Hyzm8+ZkvmFNNFS9+xc9\n5v/djfGStaiCcddjGC9MtUbDr7Zm8qrLBqESWpfZNuPZt1BXDUFdfLlrQqW6/FrUDbdZVUlqGJXU\n1XXbHHuPK7faq7a0H+ipr1ivV6cu1KneJZeEEIEgV1oqQoLiANPOy9Aeq66ZU1/B/vYEzPtvxPzs\nPe/9T2bDsQxUdJz39lNYEKEIZ9msmDiMlz/yvuSekIhyLpDhyEc2n7gD+50DrAoBJXFOCPOoIFGZ\nVFRdlON9GA846p127h7UQTGACg2zRl3jWwFgvPMFJLaDDPfqguaS/3lfqbCFoC690tWfXsczDFRs\nPYx3vkBFRaPqN7KOe1E/axGM7r5VYFChYRjX3ozxr3vd28IjMC671lWVpEYplIe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r30Misevem4pBY9DnPog8tAd5aDfaC+8id22GzFNIL1/0FR+iTZ6GsGq7a+AH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\nn38eZ2dnLC0tMTMz4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['Rolling Mean'] = pd.rolling_mean(df['High'], window=7)\n", + "df.ix['2014', ['High', 'Rolling Mean']].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZKBRixYoViI6Oxs6dO3NdS0pKgoWFBe7fv1/s2fjk5GRcuXIFwcHBCAkJQWZmJrp27Ypu\n3bqha9euqFWrVrHa6dy5M3x9fdGsWbNi3V+csTxw4AAuXbqEHTt2FKvNwvj4+OD+/fv51oAX5u3b\ntzh48CB8fX1Rt25djBs3Dv379y/w6yk+Ph5OTk7w9PTMdxa7vAQHB2Pq1KnYvXt3gb9wlVZJkmyZ\nqMn++PEjHj16BEtLSwBfFklUqVIFt27dEv0Wa2FhgZs3b0ozTCIiIrlx5MgRDBo0qFgJNgAMHDgQ\nZ8+eLdGBJCkpKbh+/XqRJQg5GjVqhOHDh2PZsmWixy5evIgePXrgwYMHOHv2LKZOnVpgCYdAIMDk\nyZNx5coVvHnzJte1sLAwdOrUqUTlLtra2rCyssLKlStx9epVHD58GG3atMGpU6fQtWvXPIl8ft69\ne4fExEQ0adKk2P0WR9++fREaGooPHz6Uua0//vijVElv9erV4ebmhr/++gtubm7Yu3cvOnbsiPXr\n1+PVq1e57g0KCoKVlRWGDRuG/v37lznmsujWrRvWrFmDUaNGITw8XGpxyESSnZSUBE1NTWzevBle\nXl7YunUr0tPTkZKSIqq70tLSQkpKipQjJSIikn3Z2dk4fPhwgbsw5Kdu3bpo3bo1zp8/X+zn5Owq\nUpItcqdOnYqwsDCcOHECY8eOxdy5c7F48WLs2LGjwG0Gv6ahoYHu3bvn2Us6NDQUXbp0KXYc3xII\nBDAwMMDo0aPh4+ODgIAA7Ny5E9nZ2YU+78GDB2jRooXYj0OvXr06TE1NSzQe+UlKSsLDhw9hbm5e\n6jaUlZXRr18/HD58GHv37kV8fDzMzc0xY8YM3L17F+vWrcOsWbOwZcsWuLm5ycRuLr169cK6desw\nZswY3Lp1SyoxyMQWfllZWXj27BnGjh0LQ0ND/P777wgMDMx1T0EDFhUVhaioKNHn9vb23A9bTqmq\nqnLsZBjHR3FwLOWDUCjE27dvkZiYiBcvXiA1NRXVq1eHnp4e9PT0Cl08GBYWBi0tLXTs2LFECY+j\noyP27NmDatWqoXLlyqLFejl/ch7L+fvMmTOws7Mr0deThoYGFixYgBkzZuDHH3/Evn37Srz4cMSI\nEVi5cqXoIBehUIgrV67Ay8tLbF/bnTp1QvXq1XH79m107969wPuePn2K77//vkT9Fvd70M7ODkeO\nHIGzs3Ox2/5WQEAAevXqVezSl6KYmZnBzMwMr1+/xp49e+Dq6oqGDRsiJCQk3zp5aRo0aBCqVq2K\ncePGYfLkyRg7diyqV69epjbzG7uAgADRx8bGxjA2NgYgI0l2zZo1UaNGDRgaGgIAOnbsiGPHjkFb\nWxvJycnQ1tbG27dvc50ilePrF5ODtYbyiXWiso3jozg4lrLvzZs3GDZsGP7991/o6OhAR0cHtWvX\nxn///YeEhAQkJiaiWrVq0NXVha6uLurVq5fr4z179mDIkCF4//59ifrt0aMHwsLCcODAAaSnp4v+\nZGRk5Pk7IyMDWlpaWL9+fYm/nqytrWFpaYkqVarg06dP+PTpU4me36FDB8TGxiIiIgJNmjRBdHQ0\nhEIh6tSpI9avbQcHB/j4+KB9+/YF3hMeHg5TU9MS9Vvc78FevXph7dq12L17d7Fq3vNz9OhRjBw5\nUuzf86qqqnBxccG4ceNEv8jJ4v8rHTt2hL+/PzZv3ozWrVvD1tYW48ePh76+fqna+3bsNDQ0YG9v\nn++9MpFka2tro1atWkhISICuri7u3buH+vXro379+ggKCsKgQYMQHBwMU1NTaYdKREQkUcnJyXB0\ndET37t3h7e0tSmC+/uGenZ2NV69eISEhAS9evEBCQgISEhLw4MEDJCQkIDk5GfPnzy9x31WrVi3R\naX9CobDUpQFF7RJRGGVlZQwaNAhHjx6Fh4cHwsLC0LVrV7GXKQwaNAgrVqzAmzdv8pzMmCMyMhJj\nxowRa7851NXVsWnTJjg5OcHU1BT169cv0fPfvHmDiIgI+Pr6SiQ+oOBKA1nSokUL/Prrr0hISICP\njw/69OkDCwsLTJw4Ea1atSrweREREVi3bh02bNhQ4LaRhZGZ3UViY2Oxbds2fP78GXXq1MGkSZOQ\nnZ3NLfwqEM6uyTaOj+LgWMqu1NRUODo6on379pg3b16uBIbjlltkZCRcXFzw119/Yfz48bC2tsbg\nwYPF3s+UKVPQpk0buLi45LmWnp4OY2NjREVFFbn399dKOpZbt27FmTNncPjw4SJPvfzawYMHcfHi\nRbHsUKJI3r17Bz8/P+zYsQNNmjSBm5sbLCwscn2/HT9+HL/88gtatWoFDQ0NbNmyBQKBQD638BMn\nJtnyiT9AZBvHR3FwLGVTzja1RkZGWLp0aZ4ZQo5bbjn7IS9cuBDjx49HcHBwiQ9OKY6wsDDMnz8/\n38NN7t69i1mzZuHChQslarOkY5mdnQ1HR0f88MMPmDFjRrGf5+zsDFtbWwwcOLBE8VUUnz59wvHj\nx7Ft2zYIhUJMmDABAwcOxIYNG3DkyBH4+vrCwMAAVlZWcHNzE60/KG6SLRPlIkRERBXd9u3boaOj\ngyVLlsjFW/DSJhAIMHToUCxYsAC6uroSSbCBLwsgP378iLt376JNmza5rkVGRuZZFyYJSkpKWL9+\nPfr16wdVVVXUrVsXWlpa0NLSgra2tujjrw+4effuHW7evIktW7ZIPD55paqqCjs7O9ja2iI4OBhb\nt27Fzz//DCMjI5w6dUr0NbVp0yY4ODjA1NS00PKSbzHJJiIikgEnT57EihUrCj0lj3IbPHgwli5d\nmm8ph7goKSnBwcEBfn5++SbZJiYmEuv7a/Xq1cO2bdsQGBiIyMhIpKSkICUlBcnJyUhJScG7d++g\noqIiSrwFAgE6duyIatWqlUt88kwgEMDCwgIWFhaIi4tD3bp1c+1407JlS0ybNg1Tp04t0bsWTLKJ\niIik7PHjx3j37h3atWsn7VDkSr169WBnZyfx0wXt7e3Ro0cPzJs3L9fasMjIyHItxcjZPi8/QqEQ\nHz58yJV4GxgYlFtsiqJhw4b5Pj5u3DhcvnwZa9aswaRJk4rVFmuySWaw3lC2cXwUB8dS9qxZswap\nqamF7gjCcZOuiRMnonnz5nB3dwfw5YyPFi1a4Pbt2yXem5tjKZ+eP3+OPn36IDQ0VLTbjMwfq05E\nRFRRCYVCnDx5EjY2NtIOhQrx008/YefOnXjx4gUAICYmBjo6OjzYqQLR19fHwIEDsXPnzmLdzySb\niIhIih49eoSPHz+ibdu20g6FCtGgQQOMGDECy5YtA1B+ix5JtsycORN79uxBSkpKkfcyySYiIiqB\nyMhIxMXFia29kydPYsCAAdxRRA5MnToVV65cwe3bt3H//v1yW/RIssPAwAA9e/bErl27iryXSTYR\nEVExCYVCTJ8+HQMGDEBERIRY2stJskn2VatWDZ6enpg3bx6T7Aps6tSp2LVrV5F19UyyiYiIiunG\njRvIzMzEqlWrMHLkSISEhJSpvQcPHiAzMxOtW7cWU4QkaXZ2dvj8+TP++uuvEu2ZTIqjSZMmMDc3\nx++//17ofUyyiYiIvpKamorJkyfj3bt3ea7t3r0bI0eORJ8+fbBjxw5MnToVx44dQ3p6OkqzWRdL\nReSPkpISFi5cCCMjI4kdgEOyb9q0aUUugOQ+2URERF/59ddfcf78eejo6GDevHmix1++fInLly+L\nFr6ZmZnh4MGDGD9+PGbNmoVPnz5BXV09z58qVaqIPlZTU0OdOnWgr68PXV1dnDx5Elu3bpXWS6VS\nMjU1xfnz56UdBklR8+bN0bFjx0LvYZJNRET0/2JjY+Hn54djx45h2LBhcHJyQtOmTQEABw4cgLW1\nNbS0tET3GxkZISwsDMCXfZPT09Px8eNHpKWl5fqT89iHDx+QlJSEhw8f4uLFi2jRogXreuUU332g\n3377rdDrTLKJiIj+3+LFizFhwgQYGxtj2rRpmDdvHvbv34/s7Gzs3bu30B0FKlWqhKpVq+Y6EZCI\nFJeycuFpNGuyiYhIIeTMGpfWlStXEBkZCVdXVwDA6NGjkZCQgHPnzuHixYuoW7cuF7oRUbFxJpuI\niORednY2xo4di7dv3yIgIACampolen5WVhbmzZuHn3/+GWpqagAAFRUVLFy4EF5eXtDX18eoUaMk\nEToRKSjOZBMRkdzbvXs3UlNT0bZtW4wePbrYM9pCoRAJCQnYsGEDtLS0YGVlleu6ubk5TExM8PDh\nQ1hbW0sidCJSUJzJJiIiuRYdHY01a9bg+PHjMDAwwPTp0+Hq6gofHx+oqqqK7svOzkZMTAyioqIQ\nGRkp+qOkpAQTExMsW7Ys38VsS5YswaNHj0Qz3ERExSEQlmZjTxmXkJAg7RCoFDQ0NIo8PYmkh+Oj\nOBRpLDMzMzFo0CDY2dlh9OjRosfGjx+PypUrw8LCQpRMP3z4EDVr1oSJiQmMjY1hYmICExMT1KlT\nRy52ilCkcavoOJby69ux09XVLfBezmQTEZHc2rhxI7S0tHLVS6uoqGDr1q2YPXs2/vrrL5iYmKB/\n//4wNjaGtra2FKMlooqESTYREcmluLg4+Pr64ty5c3lmotXU1LBu3TopRUZExIWPREQkp+7cuYOO\nHTuiXr160g6FiCgPJtlERCSXHj58CCMjI2mHQUSULybZREQklx49eoQWLVpIOwwionwxySYiIrn0\n+PFjNG/eXNphEBHli0k2ERHJnffv3+Ply5do1KiRtEMhIsoXk2wiIpI7jx8/RtOmTVGpUiVph0JE\nlC8m2UREJHdYj01Eso5JNhERyZ3Hjx8zySYimSYzh9FMnjwZ6urqUFJSQqVKlbBs2TIEBATg0qVL\n0NTUBAA4OTmhTZs2Uo6UiIik7eHDh7C0tJR2GEREBZKZJBsA5s+fj2rVqok+FwgEsLa2hrW1tRSj\nIiIiWcOZbCKSdTJVLiIUCov1GBERVVwvX75EVlYW6tSpI+1QiIgKJDMz2QKBAIsWLYKSkhJ69uyJ\nnj17AgDOnDmDkJAQNG7cGCNHjkTVqlWLbOvVq1eoVauWpEMmIiIpePjwIVq0aAGBQCDtUIiICiQQ\nyshU8du3b1G9enW8e/cOixYtwtixY6Grqyuqx/b398fbt2/h5uaW63lRUVGIiooSfW5vbw87Ozvs\n2rWrXOOnslNVVcWnT5+kHQYVgOOjOOR9LH/77Tf8/fffWLt2rbRDKVfyPm70PxxL+fXt2GloaCAg\nIED0ubGxMYyNjQHI0Ex29erVAQCampro0KEDoqOjYWRkJLpuaWmJFStW5Hne1y8mx40bN3D8+HEu\nipEzGhoaSE1NlXYYVACOj+KQ97G8e/cuWrduLdevoTTkfdzofziW8uvbsdPQ0IC9vX2+98pETXZG\nRgbS0tIAAOnp6bh37x4aNGiA5ORk0T03btxAgwYNitXeihUrMHv2bHz48EEi8RIRkfQ8fvw41yQM\nEZEskomZ7JSUFKxatQoAkJ2djS5duqB169bYtGkTYmNjIRAIULt2bbi6uharPXNzc5iZmWHVqlWY\nP3++BCMnIqLylJ2djcePH6NZs2bSDoWIqFAyU5MtTgkJCXjz5g0sLS3x+++/c29tOcG3z2Qbx0dx\nyPNYxsXFwdbWFjdv3pR2KOVOnseNcuNYyq9vx05XV7fAe2WiXEQSatSogV9++QUeHh7IzMyUdjhU\nRpMnT0Z8fLy0wxCLrKwshXktRDmys7PLpR8ep05E8kJhk2wAGDJkCGrVqoXt27dLOxQqg6ysLJw+\nfRpnzpwRe9tCobBc9mIXCoW4f/8+FixYAFNTU5ibm+Pu3bsS75eovAwaNAjnzp2TeD9MsolIXih0\nki0QCLB8+XJs2bIFsbGx0g6HSunFixfIzMzE+fPnxdZmVlYWAgIC0KFDBxw5ckRs7X7rn3/+wYYN\nG2BhYYHx48dDTU0N/v7+WLNmDaZPny5a8EskzxITE3Hv3j0sWLBA4tuSMckmInmh0Ek2ADRs2BBT\npkyBl5cXT4+UU7GxsTAxMcHt27fFUsMWFBSEPn36YP/+/XBycsL+/fvFEOX/vHnzBrt378agQYNg\nZWWFxMRErF69GlevXoWXlxeaNm2KgQMHolmzZqIFv0TyLCwsDL169ULjxo2xe/duifUjFAoRGRmJ\n5s2bS6xFC8WPAAAgAElEQVQPIiJxkYndRSTNxcUFgYGBOHToUIF7GZLsiouLQ8uWLVGrVi0EBwfD\n2tq6VO1ERkZiyZIleP78OX766Sf07dsXnz9/xu7duxETE4PGjRuXOsa0tDScO3cOx44dw/Xr19G9\ne3dMnjwZFhYWUFFRyXN/zrssPXv2RO/evdGxY8dS900kbSEhIejSpQt++OEH2NrawtbWVnT2gTj5\n+PhAU1OTM9lEJBcUfiYbAJSVlbFq1SosWbIEr169knY4VEJxcXFo2LAhevXqVaqSkfj4eEyfPh3O\nzs7o27cvLl26hH79+kEgEEBFRQWDBw/GoUOHStxuVlYWQkJCMGPGDLRr1w7+/v6wsrLCzZs3sXnz\nZvTq1SvfBDtHjRo1sHz5cri7u3NPd5JbQqEQYWFhMDc3R7NmzdC/f3+sX79e7P3cv38fGzZswObN\nm6GsXCHmh4hIzlWIJBsAWrVqBVtbW+6bLYdiY2NhYGCAnj174tKlS8jKyirW81JSUrBkyRL07t0b\nenp6CA0NxahRo/Ikvvb29jh06FCx281hY2ODZcuWwcTEBEFBQfDz84OdnR2qVatW7DZyZrFZNkLy\n6unTp1BWVkajRo0AAD/++COOHDmCmJgYsfXx/v17uLm5YfHixWjYsKHY2iUikqQKk2QDwKxZsxAe\nHo7Lly9LOxQqgZyZbH19fdSpUwe3b98u9P6MjAzs2LEDXbt2RXJyMi5evAhPT88Ck18jIyPUrl0b\nV65cKXZMz549w4sXL/DHH3/AxcUFOjo6JXpNX/Pw8MChQ4fw8ePHUrdBJC2hoaEwNzeHQCAAANSq\nVQsTJ07E0qVLxdbHnDlzYGZmhoEDB4qtTSIiSatQSXaVKlWwfPlyzJ49mwmNnBAKhaIkG0CRJSNC\noRBOTk4ICQlBQEAAVq1ahbp16xbZj729Pfz9/YsdV3BwcK7Eoix0dXVhamqKEydOlLktovIWGhqK\nLl265HrMxcUFt27dwtOnT8vc/r59+xAREYFFixaVuS0iovJUoZJsAOjWrRtMTU359rycePPmDZSU\nlESLqIpKssPCwvDq1Svs3r27RIujBg4ciEuXLiElJaVY9wcHB8PCwqLY7Rdl+PDhYt/lhEjSMjMz\nce3atTxJtpqaGpydneHj41Pqtj99+oS5c+fit99+w44dO1ClSpWyhktEVK4qXJINAPPnz8exY8d4\nGIgciI2NzVWD2aZNG7x58wZxcXH53r9x40ZMmTIFSkol+9KuUaMGunbtipMnTxZ5b2ZmJq5evYqu\nXbuWqI/CdO/eHQkJCXjw4IHY2iSStIiICDRs2BA1a9bMc83Z2RknTpzA27dvS9zuixcvYGdnh9jY\nWPz5559o1qyZOMIlIipXFTLJrlmzJubMmYNffvlF2qFQEb4uFQEAJSUl9OjRI9/Z7PDwcMTFxWHQ\noEGl6qu4JSPh4eEwMDDIN7EoLWVlZTg6OsLPz09sbRJJWmhoaIG/bOro6KBnz544cOBAidoMCwuD\nlZUVevTogd9//x3a2triCJWIqNxVyCQb+HIE8IMHD8RyuAlJzrdJNvClZOTkyZN5dgPZtGkT3Nzc\nCt02rzAWFhb4999/8ezZs0LvCwoKQrdu3UrVR2EcHR1x7NixPKdA8hAlklUhISEwNzcv8LqLiwt8\nfX3x+fPnItvKzs7Gpk2bMHXqVKxfvx7Tpk0r8TtSRESypML+D6aiogJjY2OWjMi42NhY0dZgObp3\n7w5VVVW4ubkhIyMDAPDw4UNERETAwcGh1H0pKyujX79+OHXqVKH3hYSESCTJ1tPTw/fffy/qPzs7\nGzt37oSxsTHOnDkj9v7Kg1AoxNWrV0u8PWJF9+zZMwwbNgzZ2dnSDqVAqampiIqKgqmpaYH3fPfd\nd9DX18eff/5ZaFspKSkYN24czp49i9OnTxeauBMRyYsKm2QDQNu2bXHnzh1ph0GFyG8mW01NDXv3\n7oVQKISzszNSU1Px22+/wcXFBerq6mXqb8CAAYUm2W/evEFMTAzatWtXpn4KkrMAMj4+Ho6Ojjh+\n/DhWrVoFLy+vIhMVWZORkQEPDw/Y2tri9OnT0g5Hrty5cwehoaG4cOGCtEMp0LVr19C2bdsiv+fG\njRtX6ALIyMhI9O/fH/r6+jhy5Ah0dXXFHSoRkVRU+CQ7IiJC2mFQIeLi4vLMZANfEu2tW7fCwMAA\ngwYNQlBQEEaOHFnm/szMzJCYmFhgyUhoaCg6duwIVVXVMveVnx49euCff/5Bnz590KlTJxw7dgxW\nVlbYv38/Zs+eLTfJamJiIuzs7JCSkoK1a9fC19dX2iHJlcePH6Nly5bYvHmztEPJV1JSEpYuXVqs\nfav79u2LhISEfN819Pf3h6OjIzw9PbFo0SKJfV8REUlDhU+y79y5w5pXGfXx40e8e/euwH2uK1Wq\nhOXLl2PgwIGYNWsWNDQ0ytxnpUqV0L9//wJns4OCgsS6dd+3VFRUsG7dOhw4cADTp08XHR9tYmKC\nffv2Yc6cOcXaAUWaIiMjYWVlhe7du2Pbtm0YOnQo/v33X0RGRko7NLnx9OlTTJkyBUlJSbh586a0\nw8nlxYsXsLW1hY2NDYYPH17k/crKynB1dcWYMWPg6emJP//8E69evYKnpyc2b96MI0eO8JAZIlJI\nFTrJrl+/Pj5//oyEhARph0L5iIuLQ/369Qtd/CQQCDBt2jSMGTNGbP0OGDAg30RWKBQWudBLHLp1\n64ZWrVrledzExAT79+/H3Llzcfz4cYnGUBbe3t6YOXMm3N3doaSkBGVlZYwaNQq7du2SdmhyI2cm\n29XVFVu3bpV2OCLx8fGwtbWFvb093N3di/28cePG4cCBA2jSpAn27t0LMzMzvHv3DqdPn+b2fESk\nsJSlHYA0CQQCUcmInp6etMOhb3y7R3Z56dChA16+fImYmBg0btxY9Pjjx4+hqqoKAwODco8ph7Gx\nMfz8/ODk5AShUFjq7QolJTw8HK9fv86zAHX48OHo3Lkz5syZI9atDxVRWloa/vvvPzRq1Aj6+vpY\nu3YtoqOjYWhoKNW4/v33X9jb22PMmDFwdXUt0XMFAgGaN2+O5s2bY8KECcjKykKlSpUkFCkRkWyo\n0DPZwJfDTbj4UTblt+ixPBRUMnL27FmxHaVeFkZGRjhw4AAWLlyIo0ePSjWWb/n4+GDMmDF5Eqga\nNWqgX79+3Ae8GP7++280bNgQKioqUFdXx6hRo7B9+3apxvTs2TMMHToUEyZMKHGCnR8m2ERUEVT4\nJPv7779nki2j8tu+r7xYW1vnKhk5ePAgfv/9d7GWpZRFixYtcODAASxevBiHDx+WdjgAvtTqBgcH\nY9iwYfleHzt2LHbv3l2sPZMrssePH+cqoRg9ejROnz6NpKQkqcQTHR0NOzs7TJs2DaNHj5ZKDERE\n8qjCJ9mtW7fG/fv3ZeYH/6dPn6QdgsyQ1kw28KVk5NWrV/j777+xadMmrF+/HocPH0aLFi2kEk9+\nmjdvDn9/fyxbtgwBAQFF3v/582fEx8dL7Gt99+7dGDJkCDQ1NfO9bmJigvr168vtnt/l5cmTJ2je\nvLno85o1a2LQoEHYuHGjVGJxcHCAh4cHRowYUe79ExHJswqfZGtra6NOnTp48uSJtEPB06dPYWlp\nKe0wZIY0k+yckpExY8bg2LFjCAwMRJMmTaQSS2GaNm0Kf39/rFixosgj4T08PNCzZ08YGhqiQ4cO\nsLW1RVxcnFjiSEtLg5+fX5Ez/WPGjMHy5cvx+++/i61vRfPkyZM8iwFnzZqF06dP4+rVq+UWx4MH\nD+Dg4ICffvqpTIc8ERFVVBU+yQZk51CaGzdu4NmzZ0hMTJR2KFKXmZmJFy9eoH79+lKLYdiwYWjW\nrBmOHDlS4DaCssDQ0BD+/v5YtWoVDhw4kO89jx49wqVLl3D9+nU8efIEhw8fhqGhYal2/Pj8+TPO\nnj2L169fix47duwY2rRpk2uhaH6sra3h5eWFiIgIDBw4EF26dMHcuXNx+fLlPMfJV1T5Jdk1atTA\n8uXLMXPmTLx//17iMdy/fx9OTk5YsGABhg4dKvH+iIgUEZNsfKnLloVDaXJi4FHvX7YKq127NipX\nriy1GFq1aoWdO3dCW1tbajEUl6GhIQICArB69WqcO3cuz/WVK1di0qRJ0NTUhKqqKho0aICJEyfi\n6NGjSE9PL3Y/z58/x9ChQ7Fq1Sp06dIF06ZNQ3h4OHx8fODi4lLk85WUlDBgwACsX78et2/fxtat\nW1GrVi38+uuvaNOmDZydneHj44OYmJgSvX5F8fXOIt/q3bs3fvjhByxcuFCiMdy5cwcjRozAsmXL\nYGNjI9G+iIgUGZNsyM5M9p07d2BpaSkTsUhbQSc9UsEaN26Mbdu2wcPDA//884/o8Vu3buH+/fsY\nNWpUrvsbNWqEli1bFrtG+tixY+jfvz/69euHc+fO4cqVK2jZsiWmTp0KgUCArl27liheJSUlmJiY\nYNq0aTh27Bhu3LgBBwcHPHjwAHZ2dujcuTMWLVpUoWa4o6Oj0ahRI6ioqOR7fcGCBQgODsalS5ck\n0v/NmzcxatQorF69Gv369ZNIH0REFQWTbHzZEi02NhYfPnyQWgwfPnxAbGwsHB0dOZMN6e4sIs/a\nt2+PKVOmYMKECcjIyIBQKMSyZcswa9YsqKmp5bnfycmpyG31Pn78CE9PT8yfPx979uzBxIkToaSk\nhBo1amDixIkICwvD8ePHy7y1oZaWFqytrbFmzRrcunULO3bswIsXL2BnZ4eXL1+WqW15kV+pyNc0\nNDSwZs0aeHh44O3bt2Lt+9q1axg7dix+/fVX9OrVS6xtExFVRDKTZE+ePBk//vgjPD09MXv2bADA\n+/fvsWjRIkyfPh2LFy+WWBKsqqoKIyMjqSa3d+/ehZGREUxNTXH37t0Kf9T7rVu3iqzvpfy5uLhA\nX18f8+fPR1BQEF69egVbW9t87+3bty8ePnyI2NjYfK9HRUWhX79+SEtLQ1hYGNq0aZPnHiUlJVSt\nWlWcLwECgQAtW7bEb7/9BktLS1hbW+PRo0di7UMWFZVkA0CXLl3Qv39//PLLL6XqI78djEJDQzF+\n/Hhs3rwZFhYWpWqXiIhyk5kkGwDmz5+PlStXYtmyZQCAwMBAfPfdd9iwYQNMTEwQGBgosb7btm2L\n27dvS6z9okRERKBt27aoXbs2qlWrhmfPnkktFmkLCgrCtWvX4OjoKO1Q5JJAIMDatWsRGhqKGTNm\nwMvLC8rK+R/uWrlyZQwdOjTPgkmhUAhfX18MGzYMU6dOxcaNG6GhoVEe4eciEAgwc+ZMeHl5wd7e\nHmFhYeUeQ3n6do/sgvz000+IiIjIc2BSUVJTU2FsbIzevXtj9+7dePPmDYKCgjB58mRs3769xCU/\nRERUMJlKsr+dvb116xa6desGALCwsMDNmzcl1nefPn0QEBCArKwsifVRmDt37qBt27YAvpxCKQsL\nMaUhOTkZs2bNwpo1a6ClpSXtcOSWhoYGtm/fjt69exdZWzt8+HAEBAQgMzMTAPDmzRuMGTMGhw4d\nwokTJwqcBS9PQ4YMwapVqzB37lyFfpfn2z2yC6Kuro4NGzbg559/LlEpTXBwMNq1awd3d3dcu3YN\nnTp1wtSpU+Hj44MffvihLKETEdE3ZCbJFggEWLRoEby9vXHhwgUAQEpKimhnBy0tLaSkpEis/06d\nOkFTU1NqB2Xcvn2bSTaAn3/+Gf369YO5ubm0Q5F7LVu2xKpVq4qslW7atCkaNmyICxcu4MqVK+jd\nuzeaNGmCwMBAGBgYlFO0RevVqxfS09MV9nsjLS0NiYmJxV6L0K5dOzg4OMDT07PYv3hcvHgRffr0\nQb9+/bBlyxbcvHkTFy9ehKmpaRkiJyKi/OT/HrIULFq0CNWrV8e7d++waNEi6Onp5bpe1kVVRREI\nBJg6dSrWrVuH/v37S7y/r7148QKfPn0SHbzSpk0b/PHHH+XWv6w4duwY7t69m+8WdCRZTk5O+Pnn\nnyEUCrF27VqZrMtVUlKCg4MDDh48KPqFVJFER0fDwMCgwNKe/MycORNWVlY4dOgQ7O3tC703Ozsb\nly9fhru7u+gxDQ0NqZQBERFVBDKTZFevXh0AoKmpiQ4dOiA6OhpaWlpITk6GtrY23r59m2/5QFRU\nFKKiokSf29vbl/qHRs5b0jdu3EDPnj1L90JKISgoCO3btxcdR92pUyc8evQIampqBW7lpWgSExPh\n4eEBPz8/6OjoSDucCsfR0RFxcXGYMmVKgf/+qqqqUk/IxowZgx9++AGrV69GlSpVpBqLuMXFxaFl\ny5Yl+jfW0NDAzp07YWNjg969exd6eFN4eDhq1KgBY2NjmRhLKjmOm+LgWMqv/MYuICBA9LGxsTGM\njY0ByEiSnZGRgezsbKirqyM9PR337t2Dra0t2rdvj6CgIAwaNAjBwcH5vqX59YvJkZqaWupYJk2a\nhJUrV8LMzEz0mFAoxJMnT1C5cmXUqFEDGhoaYp3p/uuvv9CqVatccTdo0AA3b95Eq1atxNaPrBIK\nhXBzc8PIkSNhZGRUpvGj0vPw8ABQ8PePhoaG1MdGU1MT33//Pfz9/WWiVlyc7t27BwMDgxL/Gzdq\n1AguLi6YOHEi/Pz8oKSUfxXgyZMn0b17d6SmpsrEWFLJcdwUB8dSfn07dhoaGgW+kygTNdkpKSmY\nO3cuPDw8MGfOHHz//fdo3bo1Bg0ahPv372P69OmIjIzEoEGDJB6LjY0NEhISRIssX716hfHjx8PJ\nyQlOTk7o0KEDDAwM4OTkJLZvkK/rsXO0adOmwhxK4+/vj4SEBHh7e0s7FJIDw4YNw8GDB6Udhtg9\nfvy4WIse8zNp0iS8f/8ee/bsKfCeCxcuoEePHqUNj4iISkggVMCl+gkJCWV6/t69e3H+/HnY2dnh\nl19+gYODA2bOnCk64jsjIwMLFizAnTt3sH//ftSoUaPUfWVlZaFly5a4du2aqGQmJ4Y7d+5g7dq1\nZXotsu7ff/9F//79ERAQgA4dOvA3exkmKzMvnz59Qvv27XH8+HGZWphZFpmZmejYsSMCAgLQpEmT\nUrURHR2NQYMG4cSJE3n2mE9MTET37t1x9+5dqKioyMxYUslw3BQHx1J+fTt2urq6Bd4rEzPZssbO\nzg6RkZFYtWoVfHx8MHv2bFGCDXzZW3jJkiXo1q0bhgwZghcvXpS6r6dPn6J27dq5Emzgy77dirqL\nQo7s7Gy4u7vDzc0NRkZG0g6H5ISqqiqGDBkCf39/aYciNvv27UOzZs3KdACToaEh3N3dMWPGjDxb\nkV6+fBldu3atMGs8iIhkAZPsfKipqeH48eM4e/Ys2rVrl+89AoEA3t7esLe3x+DBg0t9eMzX+2N/\nrXnz5vjnn3/w/v37Itt49+4dwsPDcfDgQSxatAjOzs7o3LkzTp8+XeBz8jv1rbz5+Pjg8+fPmDBh\ngrRDITnj6OiIQ4cOSW1fe3FKSUnB+vXrMXfu3DKv9RgzZgwqV66MVatW5drW7+LFi+W6mJuIiGRk\n4aMsKmyV/tcmTZoETU1N2NraYv/+/WjRokWJ+ikoyVZRUUHLli1x7949dOrUCcCXQ0Kio6Px5MkT\nPHnyBE+fPsWTJ0/w7t07GBoaomnTpmjWrBmcnZ2RlpaGJUuWoHfv3nlmr/z9/bFr1y6cOXOmXLcq\n/NrTp0+xYcMGnDx5EpUqVZJKDCS/mjdvDl1dXSxevBgeHh5yvdPIr7/+it69e4vl3RwlJSVs3LgR\nzs7OeP36NZYuXQqhUIiwsDAsX75cDNESEVFxMckWgxEjRkBDQwPDhg2Dr69vifbwvX37doHHh7dp\n0wYrVqyAiooKnj59ioyMDFEi3bRpU1hYWKBZs2bQ1dXNd0eBffv24ciRIxg2bJjosffv32PFihXI\nzMzE3bt30aZNm5K/4DLKzMzE9OnT4enpqTA1tVT+tm/fjiVLlqBr166YPXs2hgwZUuDOGrIqLi4O\n/v7+uHTpktjarFu3Lo4dO4ZJkyZhxIgRGDlyJJo0aYKaNWuKrQ8iIioaFz6K0YULFzBz5kxs2bIF\nnTt3LvL+lJQUmJqaIjIyEqqqqnmuR0dHIzg4GE2bNkXTpk1Rt27dEs08X79+HTNmzEBISIhoNnvZ\nsmWiU+USExOxbNmy4r9AMVm3bh1u3bqFffv25Xo9XAgi22R1fG7duoV58+ZBRUUFAQEB+X4vyaoJ\nEybAyMgIM2bMEHvbWVlZWLBgAXx9fTFz5sw8h9DI4lhS4ThuioNjKb+48FFKevbsiW3btsHNza1Y\npxbeunULrVu3LjApMDQ0xLhx42Bubo569eqVuLTDzMwMDRo0wKFDhwB82clj37598Pb2hp2dHU6c\nOIG0tLQStVlW9+7dg6+vL1avXi21UhVSLO3bt8fJkyeRlZWF0NBQaYdTbDdv3sTt27cltiahUqVK\nWLhwITZv3gwnJyeJ9EFERAVjki1mP/zwA/bu3QtPT08cPXq00Htv3LiR69AbSfjxxx+xYcMGfPr0\nCUuWLIGLiwvq1q0LPT09fPfddzh79qxE+/9aeno6pk+fjgULFqBevXrl1i8pPiUlJdH2dfJAKBRi\n8eLF8PT0hLq6ukT7GjBgAOrUqSPRPoiIKC8m2RLQunVrBAQEYOnSpfj9998LvO/69evo0KGDRGMx\nNTVFkyZN4O3tjfDwcEycOFF0zcHBoVy3Qdu4cSOaNm1aLocKUcVjbW2NCxcuID09XdqhFOn8+fP4\n8OEDhgwZIu1QiIhIQphkS0izZs1w9OhR7NixAzt27MhzPT09HVFRUQVuEShOs2bNgr+/P7y9vXPN\nmvXp0wf37t1DfHy8xGMAgCtXrmD06NEsEyGJqFOnDoyMjBAcHCztUAqVlZWF5cuXw8vLizvrEBEp\nMCbZEtSgQQMcPHgQ69evz1P7HBERgWbNmqFq1aoSj6Ndu3YICAjA4MGDcz2urq6OAQMGiGq2JS0m\nJqbUp9kRFYeNjQ2OHz8u7TAKdfToUWhqanLfaiIiBcckW8Lq16+PVq1a4cKFC7kev3HjhsRLRb7W\nuXPnfLc3GzZsGAICApCdnS3R/pOTk5Geng4dHR2J9kMVm5WVFS5fvlzuC3qLKyMjA6tXr8bs2bP5\njg4RkYJjkl0OhgwZgiNHjuR6rDwWPRZH69atoaamhqtXr0q0n2fPnqFx48ZMLEiiatasibZt2+b5\npVZW5ByfLgvf+0REJFlMsstB//79cf36dbx58wbAl5rM8PBwmJqaSjmyL8fDz5gxA1OnTsXdu3cl\n1k9MTAwaN24ssfaJctjY2MjULiNCoRDJycm4f/8+Nm7cCG9vb2mHRERE5YAnPpaDatWqwdLSEidO\nnMDo0aPx8OFD6OjoyMwJbDY2NlBVVcWIESPw66+/onv37mLvg0k2lZe+ffti/vz5eP/+PapVqybx\n/j5//ozExETEx8cjPj4ez58/F32c87lAIIC+vj6cnZ1hbGws8ZiIiEj6mGSXkyFDhmD9+vUYPXo0\nrl+/LnNvF/ft2xc1a9aEi4sL5syZA3t7e7G2HxMTg169eom1TaL8aGtro0OHDjh37pzEt8i7cOEC\nxo8fjxo1akBPTw96enrQ19dHixYtYGlpCX19fejp6UFTU5OlUkREFQyT7HJibm6OmTNn4tmzZ7h+\n/Tp69+4t7ZDyMDU1xeHDhzFixAj8999/mDp1qtgSA85kU3kaOHAg/Pz8MHjwYIkmtyEhIfDy8sq1\n/zwRERHAmuxyo6KiAhsbGxw9elRmFj3mp2nTpjh+/DhOnjyJOXPmICsrq8xtCoVCJtlUrmxsbPD+\n/XuJH7b04MEDtGzZUqJ9EBGRfGKSXY6GDh2KXbt2QVlZGfr6+tIOp0B169bFkSNHEB0dDVdX1zJv\nh5aYmIiqVatCU1NTTBESFU5FRQVr167FkiVLkJCQIJE+hEIhHjx4wBprIiLKF5PsctS6dWvUqFED\nZmZmMl+fqampiX379kFNTQ3Dhg0T7YxSGn///TdnsanctWzZEmPHjoWXlxeEQqHY24+Pj4e6urrM\nLGAmIiLZwiS7HAkEAnh4eMDR0VHaoRSLqqoqNm7cCFNTUwwePBjPnz8vVTssFSFpmTJlChITExEQ\nECD2tqOiolgqQkREBWKSXc5sbGzQpUsXaYdRbEpKSvj555/h7OyMgQMHIjIyEsCXt8ofP36Mffv2\nFTnLzSSbpEVFRQXr1q3DkiVL8OLFC7G2zXpsIiIqDHcXoWJxcXGBjo4OnJyc0L17d1y5cgXKysrQ\n09PDuXPnsHv37gJLYGJiYmR2oScpPmNjY4wePRpeXl6Ffp2WVFRUFAYMGCCWtoiISPFwJpuKzcbG\nBr6+vmjbti38/f1x9epVHDhwAC9fvsTevXsLfB5nsknapkyZghcvXuDw4cNia5OLHomIqDCcyaYS\nadeuHdq1ayf6PKdue/DgwejUqRMMDQ1z3Z+ZmYn4+Hg0bNiwvEMlElFVVcW6devg5OSErl27om7d\numVqLzU1FS9fvoSBgYGYIiQiIkXDmWwqM0NDQ8yaNQvTpk1DZmZmrmv//vsv6tSpg8qVK0spOqIv\nTExMMHLkSLHsNvLw4UM0b94clSpVElN0RESkaJhkk1iMGjUKNWvWxPr163M9HhMTgyZNmkgpKqLc\npk2bhvj4eBw9erRM7XBnESIiKgqTbBILgUCANWvWYO/evXj06JHocdZjkyzJKRtZsGABEhMTS90O\n67GJiKgoTLJJbHR0dDBjxgzMmzdP9HY8k2ySNa1atcKIESPg7e1d6rIRzmQTEVFRZCrJzs7Ohqen\nJ5YvXw4ACAgIwMSJE+Hp6QlPT09ERERIOUIqirOzM5KSknD27FkAPO2RZNP06dPxzz//IDAwsMTP\n/fz5M548eQIjIyMJREZERIpCpnYX+eOPP6Cvr4+0tDQAX0oQrK2tYW1tLeXIqLhUVFSwYMECeHt7\nw3rMyVIAACAASURBVMLCgjPZJJMqV66MtWvXYuTIkejcuTN0dHQKvHffvn0wNzdHgwYNAHx5d6ZO\nnTqoVq1aeYVLRERySGZmsl+/fo07d+7A0tJS9BauUCgs8y4AVP7Mzc3RokULbNiwAcnJydDV1ZV2\nSER5tG7dGo6Ojpg9e3aB/888f/4cc+fOxdixY/Hx40cAPOmRiIiKR2aS7N27d2PEiBFQUvpfSAKB\nAGfOnIGHhwe2bNmCDx8+SDFCKom5c+di+/btaNSoUa4xJZIl7u7uiImJwYkTJ/K9vmvXLowePRom\nJiaYOXMmhEIhoqKiuOiRiIiKJBPlIuHh4dDU1ISBgQGioqJEj/fu3Ru2trYAAH9/f+zZswdubm65\nnhsVFZXrOfb29tDQ0CifwKlArVq1wuTJkxEXF1fs8VBVVeXYyTBFHB8NDQ1s27YNDg4O6NevH2rW\nrCm6lpqaioCAAISGhkJHRwd9+vTBrl278PjxY7i6usr1v4UijmVFwHFTHBxL+ZXf2AUEBIg+NjY2\nFk3ECIQyUI/h5+eH0NBQKCkpITMzE2lpaTAzM8OUKVNE9yQlJWHFihVYs2ZNke0lJCRIMlwqJqFQ\niPT0dKirqxfrfg0NDaSmpko4KiotRR6fefPmISUlJdc+7z4+Prhx4wa2bdsGAIiPj8eAAQOQkpKC\nkJAQ6OnpSSvcMlPksVRkHDfFwbGUX9+OXWElsTLxPr6TkxO2bNmC3377DTNmzICxsTGmTJmCt2/f\niu65ceOGaOERyQeBQFDsBJtImjw9PfHXX38hNDQUAJCVlYWdO3di/Pjxonv09PSwZcsWGBoacp0B\nEREVSSbKRb4mFAohEAgAfFnVHxcXB4FAgNq1a8PV1VXK0RGRIqpatSoWL14Mb29vXLhwAUFBQahV\nqxbat2+f6z4zMzPR9pRERESFkYlyEXFjuYh84ttnsq0ijI+rqysaN26MGzduYPTo0bCxsZF2SBJR\nEcZSEXHcFAfHUn6VpFxE5mayiYikZdGiRejRoweqVKmC/v37SzscIiKSY0yyiYj+X506dbBixQpk\nZ2dDWZn/PRIRUenxpwgR0VesrKykHQIRESkAmdhdhIiIiIhIkTDJJiIiIiISMybZRERERERixiSb\niIiIiEjMmGQTEREREYkZk2wiIiIiIjFjkk1EREREJGZMsomIiIiIxIxJNhERERGRmDHJJiIiIiIS\nMybZRERERERixiSbiIiIiEjMmGQTEREREYkZk2wiIiIiIjFjkk1EREREJGZMsomIiIiIxIxJNhER\nERGRmDHJJiIiIiISMybZRERERERixiSbiIiIiEjMmGQTEREREYkZk2wiIiIiIjFjkk1EREREJGZM\nsomIiIiIxExZ2gF8LTs7G97e3qhRowa8vb3x/v17rFu3Dq9evULt2rXh7u6OqlWrSjtMIiIiIqJC\nydRM9h9//AF9fX0IBAIAQGBgIL777jts2LABJiYmCAwMlHKERERERERFk5kk+/Xr17hz5w4sLS0h\nFAoBALdu3UK3bt0AABYWFrh586Y0QyQiIiIiKhaZSbJ3796NESNGQEnpfyGlpKRAW1sbAKClpYWU\nlBRphUdEREREVGwykWSHh4dDU1MTBgYGolnsb+WUkBARERERyTqZWPj4+PFjhIeH486dO8jMzERa\nWho2btwILS0tJCcnQ1tbG2/fvoWWllae50ZFRSEqKkr0ub29PXR1dcszfBIjDQ0NaYdAheD4KA6O\npXziuCkOjqX8+nbsAgIC/o+9Ow9r6sz+AP5lX8MS9lVRcAEFF9Ra3EqtrVp3pVO101qdztjWtTOt\ndcal1rH+nKoVte3Y3WXacSnaotYVEWutS0EEXBDZd0hACDvk9wdzb5OQ5Sa5IQHO53l8Hkly733J\nDcnJuec9L/v/sLAwhIWFATCRTPb8+fPxySefYO/evVi5ciXCwsKwbNkyREZG4tKlSwCAxMREjBgx\nosO2YWFhiImJYf/J/qKka6Fzx42xnic6P92HqZ9LUx+fsdDz0n1s2LDB2EMgOlL2dygbhzIBNmAi\nQbYipjRk5syZuHPnDlasWIG0tDTMnDlT47ayvxwh3RG9xkl3R69x0t15eHgYewikE5hEuYis0NBQ\nhIaGAgAcHR2xbt06rbanN2fS3dFrnHR39Bon3Z2np6exh0A6gUlmsknPRB+spo3OT/dB57JrovPW\nfdC57Lq0OXdmUlXtPAghhBBCCCE6oUw2IYQQQgghPKMgmxBCCCGEEJ5RkE0IIYQQQgjPKMgmhBBC\nCCGEZxRkE0IIIYQQwjMKsgkhhBBCCOEZBdmEEEIIIYTwjIJsQgghhBBCeEZBNiGEEEIIITyjIJsQ\nQgghhBCeUZBNCCGEEEIIzyjIJoQQQgghhGcUZBNCCCGEEMIzCrIJIYQQQgjhGQXZhBBCCCGE8IyC\nbEIIIYQQQnhGQTYhhBBCCCE8oyCbEEIIIYQQnlGQTQghhBBCCM8oyCaEEEIIIYRnFGQTQgghhBDC\nMwqyCSGEEEII4RkF2YQQQgghhPCMgmxCCCGEEEJ4RkE2IYQQQgghPKMgmxBCCCGEEJ5ZanpAUVER\nPvroI/bn0tJSvPDCCxg3bhx27tyJiooKeHh4YNWqVXBwcAAAxMXFISEhAebm5li0aBEiIiIAAI8e\nPcLevXvR3NyMoUOHYtGiRQCA5uZm7NmzB9nZ2RAIBFi5ciU8PDwAAJcuXUJcXBwAYPbs2Rg/fjy/\nzwAhhBBCCCE805jJ9vX1xbZt27Bt2zZs3boVNjY2GDlyJI4fP47w8HDs2rULgwYNwvHjxwEABQUF\nuHr1Knbs2IG1a9fi888/h1QqBQB89tlnWLp0KWJjY1FSUoKUlBQAwMWLFyEQCBAbG4upU6fi0KFD\nAIDa2locO3YMW7ZswZYtW3D06FFIJBJDPRfEyNLT0409BKIGnZ/ug85l10Tnrfugc9l1aXPutCoX\nuXPnDry9veHu7o6bN2+yWeUJEybgxo0bAIAbN24gKioKlpaW8PT0hLe3NzIzMyEWi9HQ0IDg4GAA\nwLhx43D9+nUAkNvXqFGjcOfOHQBASkoKwsPD4eDgAAcHBwwePJgNzEn3Q286po3OT/dB57JrovPW\nfdC57LoMFmT//PPPiIqKAgBUV1fDxcUFAODs7Izq6moAgFgshpubG7uNm5sbRCIRxGIxhEIhe7tQ\nKIRIJAIAiEQidhsLCwvY29ujpqZG5b7UoRcu6e7oNU66O3qNk+6urKzM2EMgnYBzkN3S0oJbt25h\n9OjRHe4zMzPjdVD6oDdn0t3Ra5x0d/QaJ91deXm5sYdAOoHGiY+M5ORk9OnTB05OTgDas9dVVVVw\ncXGBWCyGs7MzgPYMdWVlJbtdZWUl3Nzc5DLXsrcz21RUVEAoFKK1tRV1dXUQCAQQCoVyb7aVlZUY\nNGiQ3LjS09PlHhMTE6PN709MCJ07boz1PNH56T5M/Vya+viMhZ6X7uO9994z9hCIjpT9HR4+fJj9\nf1hYGMLCwgBoEWTLlooAQGRkJC5duoSZM2ciMTERI0aMYG/ftWsXnn/+eYhEIpSUlCA4OBhmZmaw\ns7NDZmYmgoODkZSUhMmTJ7PbJCYmol+/frh27RoGDx4MAIiIiMC3334LiUQCqVSK1NRULFiwQG5c\nsr8Mo6ioiOuvRUyIQCBATU2NsYdBVKDz033Queya6Lx1H3Quuy7Fc+fr66vyCzCnILuhoQF37tzB\nn//8Z/a2mTNnYufOnUhISGBb+AGAv78/Ro8ejVWrVsHCwgKLFy9my0mWLFmCvXv3oqmpCUOHDsWQ\nIUMAANHR0di9ezeWL18OgUCAFStWAAAcHR0xZ84cvPvuuwCAuXPnsm0CCSGEEEIIMVVmUqa/XjdC\nmeyuib7ZmzY6P90Hncuuic5b90HnsutSlslWhVZ8JIQQQgghhGcUZBNCCCGEEMIzzhMfCSGEEEKI\n6RIIBMYeQrembYkPBdmEEEIIId0E1Xobhi5fYKhchBBCCCGEEJ5RkE0IIYQQQgjPKMgmhBBCCCGE\nZxRkE0IIIYQQk3P16lVERkYaexg6oyCbEEIIIYQYzIIFC/Dhhx92uP3MmTMYOnQo2trajDAqw6Mg\nmxBCCCGEGExMTAy+//77DrcfPXoUs2fPhrl59wxHu+dvRQghhBBCTMKkSZMgFovx66+/srdVVVXh\n4sWLmDNnDtavX4/hw4dj+PDh2LBhA5qampTux9/fH7m5uezPK1euxLZt2wC0l5YMHz4cn3zyCcLD\nwzFs2DD89NNPuHDhAsaMGYOwsDDs2bOH3VYqlWLPnj2IiorCoEGD8Je//AVVVVW8/t4UZBNCCCGE\nEIOxs7PDtGnTcPToUfa2H3/8EX379sXJkyeRnJyMc+fO4dy5c0hJScGuXbs47dfMzAxmZmbszxUV\nFWhsbERycjL++te/4q9//Svi4uJw9uxZxMXFYefOnSgoKAAAfPHFFzh79iyOHTuG5ORkODs74+9/\n/zuvvzcF2aRL2Lt3LzXYJ4QQQvTg5+fHyz9dzJs3DydPnmSz1EePHsW8efNw/PhxrF69GkKhEEKh\nEKtXr8axY8c471cqlbL/t7S0xIoVK2BhYYHp06ejqqoKS5Ysgb29Pfr164d+/fohIyMDAHDgwAG8\n/fbb8Pb2hpWVFVavXo2TJ0/yWh9OKz6SLuGTTz5BWFgYJkyYYOyhEEIIIV1SYWGh0Y49YsQIuLq6\n4vTp04iIiMDt27fxxRdfYOvWrfD392cf5+fnh9LSUp2O4erqyma2bW1tAQAeHh7s/ba2tpBIJACA\ngoICLFmyRK4e3MLCAuXl5fDy8tLp+IooyCYmr7m5GWKxGHfv3qUgmxBCCOmi5s6di6NHjyIrKwsT\nJkyAu7s7vL29kZ+fj5CQEADtXwRUBbl2dnaor69nfy4rK4Ovr69OY/Hz88OOHTsM2iKQykWIyaus\nrAQA9hIPIYQQQrqeefPmISkpCf/5z38wb948AMCMGTOwa9cuiEQiiEQi7Ny5E3PmzFG6fVhYGOLi\n4tDa2oqEhARcu3ZN57G89NJL2Lp1K5vdr6ysxNmzZ3XenzIUZBOTV15eDhsbGwqyCSGEkC7M398f\nkZGRqK+vx6RJkwAAK1asQEREBCZOnIiJEyciPDwcK1asYLeRndi4adMmnDt3DqGhoYiLi8PkyZPl\n9i/7WGU/y1qyZAkmTZqEF198Ef3798f06dORnJzMx6/5+/GlshXj3URRUZGxh0B0IBAIlE5uvHjx\nIj7++GMkJycjIyMDNjY2RhgdUXV+SNdD57JrovPWfRjqXNJrxHCY51bxOVZXrkKZbGLyysvL4e/v\nj8DAQGRmZhp7OIQQQgghGlGQTUxeeXk5PDw8EBoaSiUjhBBCCOkSqLsIMXllZWXw9/eHi4sLBdmE\nEEII6RIok01MXkVFBTw8PDBw4EAKsgkhhBDSJXDKZEskEnz66afsUpSvv/46fHx8sHPnTjYAWrVq\nFRwcHAAAcXFxSEhIgLm5ORYtWoSIiAgAwKNHj7B37140Nzdj6NChWLRoEYD2Psh79uxBdnY2BAIB\nVq5cyTYPv3TpEuLi4gAAs2fPxvjx4/l9BojJKy8vh7u7O0JCQpCRkQGpVKp2xjAhhBBCiLFxymR/\n9dVXGDp0KHbu3IkPP/wQfn5+OH78OMLDw7Fr1y4MGjQIx48fB9C+gs7Vq1exY8cOrF27Fp9//jm7\n5OVnn32GpUuXIjY2FiUlJUhJSQHQ3j1CIBAgNjYWU6dOxaFDhwAAtbW1OHbsGLZs2YItW7bg6NGj\n7Eo9pOcoLy+Hp6cnvLy8YGZmpvNKUIQQQgghnUVjJruurg737t3Dm2++CaB9yUl7e3vcvHkTGzdu\nBABMmDABGzduxIIFC3Djxg1ERUXB0tISnp6e8Pb2RmZmJjw8PNDQ0IDg4GAAwLhx43D9+nUMGTIE\nN2/eRExMDABg1KhR+OKLLwAAKSkpCA8PZzPkgwcPRkpKCqKionh/IojpYiY+mpmZsZMfvb29jT0s\nQgghxOQIBAJjD4H8j8Ygu6ysDE5OTvj444+Rm5uLoKAgvPLKK6iuroaLiwsAwNnZGdXV1QAAsVjM\nLo0JAG5ubhCJRLC0tIRQKGRvFwqFEIlEAACRSAQ3NzcAvwfxNTU1EIvF7O2y+yI9R1NTEyQSCfta\nY+qyo6OjjTwyQgghxLRQj2zTojHIbm1tRXZ2Nl599VUEBwfj66+/ZktDGMasj01PT0d6ejr7c0xM\nDH2L66Ksra07nLvCwkK4u7vD2dkZADBs2DBcuHBB7nGVlZVobGxU2xCe6E/Z+SFdE53LronOW/dB\n57LrUnbuDh8+zP4/LCwMYWFhADgE2W5ubhAKhWyZxxNPPIG4uDi4uLigqqoKLi4uEIvFbBAkFApR\nWVnJbl9ZWcnuQzYLzdzObFNRUQGhUIjW1lbU1dVBIBBAKBTKBdCVlZUYNGiQ3PhkfxkGfZPrmpSt\nVJWdnQ03Nzf29j59+mDnzp3sz1KpFAsWLEBWVhaOHDmCvn37dvq4ewpaSaz7oHPZNdF56z7oXHZd\niudOIBCwJc+KNE58dHFxgbu7O7tUeWpqKgICAjB8+HBcunQJAJCYmIgRI0YAACIjI/Hzzz+jpaUF\nZWVlKCkpQXBwMFxcXGBnZ4fMzExIpVIkJSXJbZOYmAgAuHbtGgYPHgwAiIiIQGpqKiQSCWpra5Ga\nmsp2KiE9AzPpkRESEoK8vDw0NDQAAI4cOYKamhqsXr0aL7zwAnJycow0UkIIIYSQ33Fq4bdo0SLs\n3r0bLS0t8PLywuuvv462tjbs3LkTCQkJbAs/APD398fo0aOxatUqWFhYYPHixWw5yZIlS7B37140\nNTVh6NChGDJkCAAgOjoau3fvxvLlyyEQCLBixQoAgKOjI+bMmYN3330XADB37lx2EiTpGSoqKuDu\n7s7+bGNjg969e+PBgwfw9/fHP//5Txw8eJD9YhYTE4OjR48iMDDQWEMmhBBCCIGZlOmv140wWXfS\ntSi7fBYbGwuJRMJ+0QKAZcuWISoqCr/88gtcXV3ZLjcA8PXXX+PTTz9FXFwcfHx8OmvoPQJd3uw+\n6Fx2TXTeug86l12X4rlTNx+MllUnJq2iogIBAQFyt4WGhmL//v2oqKhAQkKC3H2vvPIKxGIx3njj\nDRw+fBiWlvQSJ4QQQkjno2XViUkrKytjV/9khIaG4vbt29i8ebPS8qEVK1bAxsYG27dv76xhEkII\nIYTIoSCbmLSKiooOQfaIESPwr3/9C5MmTVK6jbm5OWJjY3H48GFcvny5M4ZJCCGEECKHgmxi0pRl\nsu3t7TF//ny123l4eGDXrl1YuXIlLcNOCCGEkE5HQTYxacoy2VyNGTMGCxYswLJly9Da2srzyAgh\nhBBCVKMgm5isxsZG1NfXs0uq62LlypVoa2tDbGys3uNpbm6mYJ0QQgghnFCQTUxWRUUF3Nzc2D7r\nurCwsMCePXuwf/9+XL16Va/xrFu3Dvv379drH4QQQgjpGSjIJiarvLxc51IRWd7e3vjoo4+wbNky\nVFRU6LQPqVSKCxcuID8/X+/xEEIIIaT7oyCbmKyysjK51R71MX78eMydOxfLly9HW1ub1ttnZWWh\nqKgI5eXlvIyHEEIIId0bBdnEZFVUVMDT05O3/f3tb39DfX099u7dq/W2ly5dQkBAAHUqIUSNtrY2\nXL9+HevXr8drr71m7OEQQohRUZBNTFZ5eTlvmWwAsLS0xN69e/HFF1/g+vXrWm2bmJiIuXPnUiab\nEBW+/PJLREZGYu3atXB1dcW5c+fQ3Nxs7GERQojRUJBNTFZ5eTmvmWwA8PX1xYcffog33ngDIpGI\n0zaNjY24fv06Zs2ahbKyMl7HQ0h3cfDgQezcuRPnz5/HqlWr4OHhgZKSEmMPixBCjIaCbGKy+M5k\nMyZOnIjp06ez7f00uXnzJkJCQhAUFASJRILGxkbex0RIV1dbW4u+ffuyP/v5+aGwsNCIIyKEEOOi\nINvIuGZTeyK+uosos2bNGojFYuzbt0/jYy9fvozx48fD3Nwcbm5uOncoIaQ7q62thaOjI/szBdmE\nkJ6OgmwjampqwtixY3Hjxg1jD8UkGaJchGFlZYWPP/4Yn3zyCdLS0tQ+NjExEePHjwcAeHl50eRH\nQhRIpVIKsgkhRAEF2UZ09epVVFVV4fLly8YeikkyVLkIIyAgAH/6059w4MABlY+prKxEbm4uhg4d\nCgDw8PCgyY+EKKivr4eVlRUsLS3Z23x9fSnIJoT0aBRkG9HJkycxbtw4XLlyxdhDMTkNDQ1obGyE\ns7OzQY8zY8YMnDp1SmUXhKSkJIwePRpWVlYA2jPZNPmREHk1NTUQCARyt1EmmxDS01GQbSQtLS34\n6aefsHHjRqSnp0MikRh7SCaloqIC7u7uei2pzkVAQAD69Omj8mpCYmIixo0bx/7s4eFBQTYhCmpq\nauRKRQAKsgkhhIJsI7l27Rr8/PzQv39/hIeH49dffzX2kExKWVmZwSY9Kpo1axaOHz/e4XapVIrL\nly9jwoQJ7G0UZBPSUW1trcpMtlQqNdKoCCHEuCjINpJTp05h6tSpAICoqCgkJSUZeUSmpaKiotOC\n7Oeffx7nz59HfX293O3p6emwsbFB79692duoXISQjhQnPQKAk5MTzM3NUV1dbaRREUKIcVGQbQRt\nbW04ffo0pkyZAgAYM2YM1WUr6MxMtru7O4YOHYqzZ8+yt7W1tWH9+vV49dVX5R7Lx8RHsViMrVu3\n6rUP0r01Nzfj5s2bxh4GZ8oy2QCVjBBCejZLzQ8B3njjDdjZ2cHc3BwWFhb44IMPUFtbi507d7IZ\nx1WrVsHBwQEAEBcXh4SEBJibm2PRokWIiIgAADx69Ah79+5Fc3Mzhg4dikWLFgFo/0DZs2cPsrOz\nIRAIsHLlSjbAunTpEuLi4gAAs2fPZlupdWU3b96Em5sbu3DDkCFDkJ+fj8rKSri5uRl5dKbBkD2y\nlZk5cyZOnDiBGTNmAGhfIloqlXYIsvnIZG/duhVHjhzBmjVr9NoP6b5SUlKwcuVK/Pzzz8YeCifK\narKB3zuMhIWFGWFUhBBiXJwz2Rs3bsS2bdvwwQcfAACOHz+O8PBw7Nq1C4MGDWJrWgsKCnD16lXs\n2LEDa9euxeeff87W5H322WdYunQpYmNjUVJSgpSUFADAxYsXIRAIEBsbi6lTp+LQoUMA2rMjx44d\nw5YtW7BlyxYcPXq0W0wQjI+PZ7PYQHvP5lGjRnWZD1RDa21txYULFzBgwIBOO+bkyZPZlorZ2dn4\n6KOPsH37dpiby/+JuLu7o7y8XOc605SUFJw9exYtLS0qO5oQUlJS0qVaRSorFwHaM9lFRUUGO25T\nUxOuX79usP0TQog+OAfZikHFzZs32azyhAkT2AVVbty4gaioKFhaWsLT0xPe3t7IzMyEWCxGQ0MD\ngoODAQDjxo1j3xxl9zVq1CjcuXMHQHtAEh4eDgcHBzg4OGDw4MFsYN5VtbW1ydVjM6hk5HcHDhyA\nlZUVpk2b1mnHFAgEGDt2LE6ePIm33noLy5cvR58+fTo8zs7ODra2tqiqqtL6GK2trXj33Xfx7rvv\nwsHBAXV1dXwMnXRDJSUlkEgkXeY1oqyFH2D4cpFr165h5cqVBts/IYTog1OQbWZmhvfffx9r1qzB\n+fPnAQDV1dVwcXEBADg7O7OTW8RisVzJg5ubG0QiEcRiMYRCIXu7UChklxQXiUTsNhYWFrC3t0dN\nTY3KfXVlKSkpsLe3R79+/eRupyC7XVFREbZv345t27Z1yCIb2syZM7Fp0ya0tbVh8eLFKh/n6emp\nU8nIgQMHYGdnh3nz5sHe3r5bXJUhhlFSUgIAXSabrS6TbcggOysrC0VFRWhtbTXYMQghRFecarLf\nf/99uLq64vHjx3j//ffh5+cnd7+hexl3J+fOncPkyZM7PGcDBgyARCJBfn4+AgICjDQ645JKpfjH\nP/6BV155BSEhIZ1+/KeffhrBwcHYvn07LCwsVD6OaePXv39/zvuuqKjA9u3bcfjwYZiZmVEmm6hV\nWloKoH0CcK9evYw8Gs1qamrg6+vb4fbOCLKbm5tRUlLS4XOJEEKMjVOQ7erqCqC9JdPIkSPx8OFD\nODs7o6qqCi4uLhCLxezKfEKhEJWVley2zGQ+2cy17O3MNhUVFRAKhWhtbUVdXR0EAgGEQiHS09Pl\nthk0aJDc2NLT0+UeExMTo/Sypal48OABFi5cqHSMEyZMwM2bNxEaGmqEkRnfqVOnkJ2djYMHD8LG\nxqbTjy8QCDgtce/n56fy8rgqf/vb3zB//nyMHDkSQPvfklQqNenXqiJra+suNd6urLy8HE5OTpBI\nJAZ5zvk+lw0NDXB3d++wz379+qG4uNhgr5ucnByYm5tDJBJ16hwOY6G/we6DzmXXpezcHT58mP1/\nWFgYO9lbY5Dd2NiItrY22NnZoaGhAampqZg7dy4iIyNx6dIlzJw5E4mJiRgxYgQAIDIyErt27cLz\nzz8PkUiEkpISBAcHw8zMDHZ2dsjMzERwcDCSkpIwefJkdpvExET069cP165dw+DBgwEAERER+Pbb\nbyGRSCCVSpGamooFCxbIjU/2l2HU1NRo+5x1mnv37sHf31/pGJ944gmcP38es2bNMsLIjOvx48dY\nvXo19uzZg6amJjQ1NRl7SCq5uroiLy+P8+vs+vXruHjxIhITE9ltbG1tUV5ebtKvVUUCgaBLjbcr\nYzpy5ObmGuQ55/tcVlVVwcrKqsM+HR0dUVZWBpFIBCsrK96Ox7h//z6GDh2K+/fvs58b3Rn9DXYf\ndC67LsVzJxAIEBMTo/SxGoPs6upq/Otf/wLQPmlvzJgxiIiIQN++fbFz504kJCSwLfwAwN/fH6NH\nj8aqVatgYWGBxYsXs6URS5Yswd69e9HU1IShQ4diyJAhAIDo6Gjs3r0by5cvh0AgwIoVKwC0MVXh\nmAAAIABJREFUv0HPmTMH7777LgBg7ty5bJvArqihoQHFxcUqL/9GRkZiz549nTwq07BlyxY8++yz\nGDVqlLGHopE2bfxaWlqwdu1arF+/Xq5mlWqyiSpSqRQlJSWYOHEiKioqjD0cTlS18LOysoK7uztK\nS0vh7+/P6zHr6uogEokwa9Ys5Ofn87pvQgjhg8Yg29PTkw2yZTk6OmLdunVKt5k9ezZmz57d4fY+\nffpg+/btHW63srLC6tWrle7rqaeewlNPPaVpmLxqbm7GwYMHMX36dF77VmdnZ8Pf3x/W1tZK73dy\ncuqRdbrXr1/HuXPnukwrLg8PD7kSJXW+/PJLuLu7Y/r06XK3U002UeXx48cwNzdHUFAQMjIyjD0c\nTlQtRgP8XpfNd5D96NEjBAUFoVevXrh16xav+yaEED7Qio8K7t+/j2nTpuH999/nvdtHZmam2gl9\ndnZ2PS7wamxsxNtvv4333nuP7VZj6rh2FykpKUFsbCw2b97cYaKrg4MDZbKJUqWlpfD29uZlddHO\nUlNTo/Iqo7+/v0EmPz58+BB9+/ZFQEAAZbIJISaJ08THnqC1tRWfffYZ9u7dizVr1iAnJwe5ubm8\nHiMrK4vtE66Mvb096urqIJVKe0zHlo8//hi9e/fu0DfclHl6enIKfjZt2oSFCxcqPedULkJUKSkp\n6XJBNpdMNt+ysrLYILugoID3/RNCiL4ok/0/Bw4cwIkTJ3Dy5EksWLAAvXr1Ql5eHq/H0JTJtrS0\nhKWlJRobG3k9rql6+PAhvvjiC/zzn//sUl8quGSyk5KScOvWLXZ+gSJDZbL1XfKdGF9xcTG8vLw4\nf5kzBeqCbGZpdb49fPgQwcHB8PX1RUlJCVpaWng/BiGE6IOC7P+5e/cuXnjhBQQGBgIAevXqhZyc\nHF6PoSnIBtoznPX19bwe1xS1tbXh7bffxurVq7tcf1sXFxdIJBKVX4aamprw97//HZs2bYKdnZ3S\nxxiiJruxsRETJkzg/QoM6VylpaXw8fFhM9mKq+2amqamJjQ3N8PW1lbp/YbOZNvY2EAoFLIL+BBC\niKmgIPt/cnJyEBQUxP7cu3dvXoOV1tZWPHr0CH379lX7OFtb2x5Rl/3tt9+iqakJL7/8srGHojVz\nc3O4u7urzDL++9//Ru/evTFp0iSV+zBEJjsxMRHV1dVyfepJ18OUi9jb28PCwgK1tbXGHpJaTBZb\n1dUoPz8/FBUV8XrMtrY2uffTwMBAqssmhJgcCrL/JycnB71792Z/9vHxQWVlJW+lG4WFhXB1dVXa\n5kpWT8hkl5aWYuvWrdi2bZvalRVNmaqSkYKCAnz66ad4//331ZbAGKImOz4+HkB7203SdZWWlsLL\nywvA76uLmjKJRKL2fc0Qmezi4mI4Ozuzx/X39+e9vI8QQvRFQTbaL7OXl5fLlS1YWlrCx8eHt+wI\nl1IRoGcE2Rs2bMD8+fO79MqWquplN2zYgCVLlmhcCpvvTHZjYyPOnz+PUaNGGT3IPnHiBK5du2bU\nMXRlTCYbaA+yTb1XtqbVT5nVTR8/fszbMZnOIgya/EgIMUUUZAPIz8+Hr68vLC3lm62oKhnJzMzE\nv//9b61qJbkG2d29jd+5c+dw584drFy50thD0YunpydKS0vlbrtw4QLu3buHpUuXatye75rsy5cv\no3///ujXrx+qqqo4bZORkYHm5mbexsCIj49HQkIC7/vtKRSDbFPPZNfW1qrNZJuZmfGezWbqsRlU\nLkJ6imPHjuHEiRN670cqlVIypBNQkI3fFzVQFBgYqDTIPnnyJDZv3oxNmzZxDrSZmfCaMG389HXk\nyBHcvXtX7/3wqba2FmvXrsXWrVtVTgjsKhQz2fX19Vi3bh02b96scgKYLL4z2fHx8Zg2bRqcnZ05\nZbLr6+sxd+5cnDt3jrcxMPLz82nypY5aW1tRWVkJT09PANzbRRqTqtUeZfEdZCtmsv39/SnIJj1C\nYmIiDhw4oPd+8vPzMWfOHDQ0NPAwKqIKBdnoWI/NUNVhJD09He+//z6uXbuGDRs2cAq0tSkX4SPI\n/vrrr/HLL7/ovR8+7d69G1FRURg7dqyxh6I3Dw8PuUz2xx9/jLCwMM6rk/JZk82UikyZMgUuLi6c\nguz4+HhUV1cjNTWVlzHIysvLo4BHR+Xl5XBxcYGVlRUAqJ1gqw9mIvbp06fx66+/6rUvTZlsgP82\nfoprDtCCNMQUdEYnoMLCQly/fp3zFUtVmFVSqSuPYVGQjY6dRRi9e/dWOpkmPT0dY8aMwXfffYdb\nt25h/fr1av+4pFIp50y2nZ2d3jXZLS0tuHfvnsn98aSnp+P555839jB44eXlxQY/OTk5+Oqrr7Bx\n40bO2/NZLpKUlIR+/frB29ubcyb74MGDePHFF3kPsqurq1FbW0uT0HQkWyoC8J/Jbmtrw4IFC+Dn\n54f58+fj4MGDWLp0qV49pjXVZAP8dxhRzGT7+vqivLzcIOVPhHDx3XffYf369QY/Tn5+Pvr27YvE\nxES99sME2Xx3/iHyKMiG+ky24mXvx48fo6KiAkFBQXB2dsZ//vMfJCcn4x//+IfKQJtpqebu7q5x\nLHzUZD98+BANDQ0mF2SLRCK4ubkZexi8kO1hvG7dOrz++uta9fvms1zkxx9/ZL+8cAmy7927h4KC\nAqxatQq3b9/mNfuSn5+PkJAQ1NXVmXzrOVMk21kEAO+rPpaWliItLQ2ZmZm4du0aDh06BF9fX70+\nsLlksoOCgvDgwQOdj6F4vKqqKrm/NysrK3h4eKC4uJiXYxCiraysLFy5csWgx2hubkZ5eTleeukl\nvUv9bt26hYCAAAqyDYyCbKgOsgMDA5GXl4e2tjb2tvT0dAwYMIBtPccE2qmpqVi7dq3cYxmZmZkI\nDg7mtKohH+Ui6enpcHV1NckgWygUGnsYvGAmPv7000/Iz8/HkiVLtNqeryBbtlQEaH89arqMyGSx\n/fz8YGNjw2tXhry8PAQGBrJ/O0Q7xcXFcplsvoPsnJwc9OnTRy7zHBMTg//+978675NLJnvYsGG4\ndesWL1/omDk05ubyH18BAQH0miNGU1xcjAcPHuhdxqHpGB4eHnjuueeQkJCg8xWouro6ZGZmYtKk\nSRRkG1iPD7KbmppQUlICf3//Dvc5ODhAIBDI1d6mpaVh0KBBco9zcnLCf/7zH2RkZGDNmjUdAm2u\n9dgAPy380tLSEB0d3aH7hbF1pyCbCX42bNiALVu2wNraWqvt7ezs0NDQoPRLmTaSkpIQEhICHx8f\nANBYk11XV4e4uDi8+OKLAIDw8HDcvn1brzHIYoJsqpHVTWlpaYdyET67i2RnZ3dIKMyYMQNJSUkQ\niUQ67ZNLJtvPzw9WVla8TIhVrMdm+Pv7Uxs/YjTFxcWws7PDb7/9ZrBjFBQUwN/fH76+vvD398eN\nGzd02s/t27cxYMAABAUF0dUfA+vxQXZBQQG8vb1VBkm9evWSy44oC7IBQCAQ4NChQ8jMzMQ777wj\nFzxxrccG+Auyn376aZPKZDc0NKC5uVnjh3FXYWtrC3t7e4wcORJPPvmk1tubm5vD1tZW73MdHx8v\nV+euqVzkhx9+wIgRI9hL7eHh4bhz545eY5Alm8mmDiPaU6zJdnNzQ2VlJW8lPcqu2jk7OyM6OhrH\njx/XaZ/Mio+aDB8+nK0D1YdiPTaD2vjp7siRIzh8+DAePnyo9xf/nqq4uBjPPPOMzoEvF/n5+WxC\n8JlnnsH58+d12s+tW7cwfPhw+Pr6UibbwHp8kK0ssyMrMDBQrsNIeno6wsLClD7W0dERBw8eRFZW\nFv7617+yb1YPHz7knMnWtyZbKpUiIyMDTz75JFpaWkymLpbJYnMpmekqli1bptdEF31LRpqamnDu\n3Dm2VATQHGQfPHgQCxcuZH/mO5Odn5+PgIAACnh0pBhk29raws7OjrdL0NnZ2Uoneb/wwgs6l4zU\n1NTAwcFB4+P4CrLVZbKpXER7+/btw+7du5GQkICXXnoJYWFhWL58ead0yugupFIpSkpK8Pzzz+Pm\nzZsGO05BQQECAgIAABMnTtS5LpsJsn18fCjINrAeH2SrqsdmyC5I09DQgOzsbPTv31/l4x0cHHDg\nwAHk5eVh9erVaG1tZWuyudC3JruwsBA2Njbw8PCAt7e3yWSzRSIRXF1djT0MXi1dupTtZ6wLfYNs\nplTE19eXvc3R0RH19fVKa/XS0tJQVlYm12aQyWTz9YFKNdn6UZz4CPBbl60qyI6KioJIJEJ6errW\n+zSVTDat+qi9EydOYN++ffj222/xySef4JdffsGVK1dw/fp1nV4LPZVIJIK9vT2ioqJw+/Ztvbr1\nqCObyQ4PD0dNTQ0ePXqk1T6kUillsjsRBdkagmzZcpEHDx4gKChI42IjDg4O2L9/PwoLC/Hmm29C\nJBIprflWRt9ykbS0NDbT7u3trbQuu6GhAWlpaTofQxfdqR6bL3Z2dnoF2YqlIkB7GYpAIFC6hPX5\n8+cxbdo0dtIu0F7za2trq1VA3NDQgLi4uA6BuVQqZTMtNAlNN4qZbIC/IFsqlSI3N1fp+52FhQXm\nzZunUzaby2I0ADB48GBkZWXplURoaGhATk6O0iuDdPVEO1evXsW6deuwf/9+uU4tbm5umDlzJr7/\n/nu9j5GVlaVzrX9XUlxcDB8fH7i4uMDPz89gC8ExNdlA+3v9xIkTtS4Zyc3NhbW1Nfz8/ODm5ob6\n+nq9yxaJahRkcwiymXIR2QBWE3t7e+zfvx8ikQh9+/aVC2zU0bdcRDHIVpbJvnz5Mqelv/kkFosp\nyFagT6/spqYmnD17Vq5UhOHi4qK0vKC8vFxpm8GIiAjOJSM5OTmYMWMGli1bhuzsbLn7ysrK4Ojo\nCAcHBzbgoUvO3NXX16OhoaHDFR++guyysjLY29urzDrHxMTg+PHjaGpq0mq/XDPZNjY2GDhwIFJS\nUrTav6w7d+4gODhY6Yqx3t7eqKys1Hr8PdGDBw+wdOlSfPzxxwgNDe1w/+zZs3HixAm0trbqfAyp\nVIrXX38d3377rT5D7RJkuwJFRkYarGREtlwEaK/L1rZk5ObNmxg+fDgAwMzMDD4+PrwuFEXk9fgg\nOzs7G3369FF5v2yvbFWTHlWxs7PDN998g3379mm1jT5Bdnp6OjtGLy8vpZns7OxsPHr0qFMzDJWV\nlRRkK9CnXCQpKQnBwcFypSIMVXXZ5eXlSnu1Dx48mNPkx/j4eEyfPh0xMTGYMmUKkpOT5e7Py8tj\nPwAEAgFsbW1RUVHB9Vfq8UpKSuDl5dVh3gJfQbam+Se9e/dGSEiI1pkxLi38GPqWjDCXuZWxtLSE\nl5cXBQwcfPjhh3jjjTcwZswYpff369cP7u7ueq0anJycjLS0NNy7d0/nfXQVTCYbaA+yDTH5saWl\nBaWlpXLv+WPGjEFqaiqnBcgYin9DVJdtWD06yG5paUFRUZHcN0NFHh4eaGhoQE1NjdZBNtA+cUnd\nB5siPspFmDGqymQzmXlDTtBQ1J0WouGLPkG2slIRhqogu6KiQmmQrSmT3dzcjE2bNuGdd97B119/\njcWLF2PYsGFKg+zAwED2Z+owoh1lpSIAf0G2pqt2gG49syUSCeeuQfoG2b/99huGDRum8n5qHalZ\ndXU1Ll++jJiYGLWPmzVrll4lI/v378e0adMMVjphSmSD7OHDhxvks7WkpARCoVCuExrT4erSpUuc\n96MYZPv6+lIbPwPq0UF2QUEBPDw8YGNjo/IxZmZm6NWrF7Kzs3H37l3O5SK60ifIFolEqKmpYQMd\nLy8vlUH20KFDeZmEpM3YKJMtz97eXqcgmykVmTp1qtL71QXZHh4eHW5XN/mxqKgI8+bNQ2ZmJi5f\nvswGOEOHDlWbyQaoRlZbij2yGXz1ylY16VHW888/jxs3bnA+nlQq5dQnm8EE2bqWEanLZAOdN/lx\nx44dOHnypMGPYwinTp3C2LFj4eLiovZxM2bMwJkzZ9DQ0KD1McRiMc6cOYN169YhOzu72y93X1xc\nzGaY+/bti7q6Ot6zw4qlIgxtWvlJJBI8evRILllIkx8Ni1OQ3dbWhrfffhtbt24F0F6D9/7772PF\nihXYvHmzXKAQFxeH5cuXY+XKlXLZsUePHuGtt97C8uXL8dVXX7G3Nzc3Y+fOnVi+fDn+/ve/y2Vs\nLl26hBUrVmDFihV6LfurCpfMDtAeLFy6dAnu7u5wcnLifRyy9CkXSU9PR2hoKLsSmo+Pj9JykZyc\nHMybN6/TM9kUZMvTtSb7ypUr6Nu3r9JSEUD1qo+qMtnu7u5wdHSUa1UJtNfuT506FU8//TS++eYb\nuSsR4eHhuH//PhobG9nb8vPzO2SydZn82NLSApFIhEePHvWobhFMuYgid3d3XspuuATZDg4OeO65\n53Ds2DFO+6yrq4OtrS3nOSe+vr6wsbHp8FrjoqioCM3NzejVq5fKx/j5+XVKuci1a9fw66+/8r7f\ntLQ0nD17lvf9yjp27Bhmz56t8XE+Pj4YNGiQTr2YDx8+jIkTJ8LPzw++vr7IysrSZahdhmwm28zM\nDJGRkbwnsZj2qIomTpyIixcvcupokpKSgtDQULnEIgXZhsUpyD516hT8/f3ZWsHjx48jPDwcu3bt\nwqBBg9hFDAoKCnD16lXs2LEDa9euxeeff85mLD777DMsXboUsbGxKCkpYSe/XLx4EQKBALGxsZg6\ndSoOHToEoD2QP3bsGLZs2YItW7bg6NGjvCxDLYtrkN2rVy+cOnVK61IRXejTwk+2HhtQnsluampC\naWkppk2bhtTU1E6bJFRZWdntWvjpS9dyEXWlIoDyVR8bGxtRV1cHZ2dnpdtEREQgNTUVANDa2ort\n27dj1apV2Lt3L5YtW9ZhCWs7Ozv06dNHrs2XYiZbWYeRhoYGfPrpp9i0aRPeeustLFmyBHPnzsUz\nzzyDkSNHon///ggKCsLYsWOxcOFCTJo0ySBfsE2R4pLqDL4y2Vzf75ie2VyyzVw7i8jStWTk1q1b\nGDZsmNpe+66urgZd1pqRk5OD+/fv877fxMREHDhwgPf9MgoLC3H37l1ER0dzevzs2bO1Lhlpa2vD\ngQMH8Mc//hEAMHDgwG5fl11SUsIG2YBhJj/KdhaR5evrCz8/P07Hu3nzJiIjIztsT+UihqMxyK6s\nrERycjKio6PZN92bN29i/PjxAIAJEyawRf43btxAVFQULC0t4enpCW9vb2RmZkIsFqOhoYHtFT1u\n3Dhcv369w75GjRrFTsBKSUlBeHg4HBwc4ODggMGDB+s1K10ZLpkdoD3IvnPnjsFLRQD9gmzF7ide\nXl4oKyuT+7DMz8+Ht7c3hEIhevXq1Wm9UMViMdVkK9ClXKS5uRlnzpxRWSoCKC8XqayshJubW4dg\nmTF48GCkpqaisrISCxcuxC+//ILTp0+rXc1SsWQkLy9PLsuouFoqAJw8eRLHjh2Du7s7hg0bhpkz\nZ2LFihXYsWMHjh49iuvXryM3Nxfp6em4evUqvv76ayxbtqzDh3RbWxsOHjzYrTLdqspFPDw89M5k\nS6VSzkH2yJEj0dzczOn9VptSEYY+Qba6UhFA82JMfGhoaEBxcTEyMzN533dZWZlBA9Ljx49j6tSp\nakskZU2ePBlXr16FWCzmfIwrV67A1taWDeYGDhzYreuypVIpioqK5P52DZXJVtUKmGuXEWVzGmji\no2FpDLK/+eYbLFy4UO7Dubq6mq3nkn1TUwyk3NzcIBKJOrRvEwqFbGcL2QlxFhYWsLe3R01Njcp9\n8UmbTDaATslk29nZob6+XqeaRcUgm1n6W/Z5k+2Ta8hWQ4qoXKQjXTLZTKmIslZ8DGWBhqpSEUZE\nRATOnj2LZ599FhEREfjuu+80LrQjG2Q3NzejvLxcroRF2SS0kydP4rXXXsPrr7+OBQsW4Pnnn8fY\nsWMxePBgBAYGwtnZWe69ZuTIkdi4cSNeeeUVtpSsrKwM8+fPx3vvvYcjR46oHWNXomriI7O0uj7t\n1MrLy2FjY6PySoYsMzMzzhMgtekswtA1yP7tt99MIshmyqIkEgnvWfPy8nIUFRUp7XOvL6lUimPH\njmHOnDmct3FycsL48eO1qj8/cOAAXnrpJfaKQ3cPsmtqatj1CRgRERG4d+8er/2nVdVkA9zrstPS\n0hAeHi53G5WLGJalujtv3boFJycnBAUFqcx4GnuZ7PT0dLmxxcTEcH7Tz8vLQ1hYmMbHM31En3ji\nCa0/UHRhbW0NKysrpb1gVamrq0NBQQGGDx8uN/vYz88PNTU1bGBdXFyMkJAQCAQCjB07FqdPnzb4\n7ySVSiESiRAYGKh2IR9ra+tOeX5NhZubG7Kzs7X6nc+cOYM5c+ao3cbb2xsSiUTuMbW1tfD29la5\nHXMFateuXXjuueeUPkbx/IwZMwZ79uyBQCDAo0eP2CskjAEDBqCkpAS2trawsrLC48eP8csvv+Cz\nzz7T6nd++eWXUVRUhMWLF+Ott97CqlWr8Morr2DZsmXYtWuXXkvbm5KysjL07dtX6XPj7OyM5uZm\njZPVVElLS5Pbt6a/tVdeeQVRUVH48MMP1b4Ptba2wsXFRavzOXr0aGRnZ8PMzIxzFryxsRF3795F\nVFSU2m18fX11Cvy1UVpaipCQELi7u6sNfHQhEolgbm6OgoICjBo1qsP9+rxH3rlzB/X19YiOjlZ5\nRUuZBQsWYM+ePZzWViguLsbVq1fl/saHDx+OjRs3dtv39oKCAvj5+cn9fgKBAAMHDkRmZiaioqKU\nbqftuSwsLET//v2VbhMVFYWamhr2PUSZyspK1NXVITQ0VC5uc3R0RGtrK6RSqcHnnHUXys7d4cOH\n2f+HhYWxCU+1Qfb9+/dx69YtJCcno7m5GfX19di9ezc7scrFxQVisZjNjgiFQlRWVrLbM5eoZTPX\nsrcz21RUVEAoFKK1tRV1dXUQCAQQCoVywXNlZaXSTLLsL8OoqalR/wyh/cMhLy8P7u7uGh8vFArx\n2muvwcHBgdO+9WVnZ4eysjKtMr+//fYb+vbti8bGRrnJaB4eHsjKymKD7Pv378Pf3x81NTUYNGgQ\n1q9fj8ePHxv0y1JNTQ2sra3R3Nysdpa5QCDolOfXVFhYWKCqqorz79zc3Iz4+HgsW7ZM7TbW1tYo\nLy+Xe0x+fj5cXFxUbmdlZYWEhAQAqv9+FM+Pj48PKioqkJubi7t377Jf6GR5enri/v376NWrF44f\nP44RI0bAwsJC6/P8xhtv4MGDB1i9ejX27t2L0aNHo66uDsnJySgtLYW9vb3c4+/evYt9+/Zhw4YN\nOgemnam5uRnFxcVwdnZW+tx4eHggOztb42qzqqSnpyMwMJDdt6a/NWdnZ4SHh+Po0aOYOXOmyseV\nlZXBzs5O6/MZGhqK3bt34y9/+QssLdV+DAFof38LCgqCVCpVeywrKyu2y5KhZGRkICAgAG5ubkhO\nTub1CmdxcTEiIiKQnJysdJEYfd4jDxw4gBkzZmh99eyJJ57A66+/jrt372pcuTg+Ph5PPfUUgN/f\nR5grMQUFBZyupHQ1Dx8+hJeXV4fzMmzYMFy+fLlD5pihzblsbW1FUVGR2vfw6OhoHD9+HK+99prS\n+69fv44BAwagtra2w30+Pj548OAB+vfvz2k82qiursa2bdvwz3/+k/d9G4viuRMIBCpbYqr9Ojt/\n/nx88skn2Lt3L1auXImwsDAsW7YMkZGRbF/GxMREjBgxAkB7+cHPP/+MlpYWlJWVoaSkBMHBwXBx\ncYGdnR0yMzMhlUqRlJQktw0zsenatWsYPHgwgN8nYkkkEtTW1iI1NRURERH6PTMycnNz4erqyilb\nbGVlhQ0bNnRa1l6XNn6qVqNUXJBGdkGKwMBAtLa2Gnw2vuyXKvI7bWuyr1y5gqCgILWlIoDyiY+q\n2vfpw8LCAuHh4UhJSenQWYQh22EkPj5ebS25OmZmZvjoo49w5coVjB49GkD78zd48GClXR6+/fZb\nJCcnY8qUKZ0270AfhYWF8PT0lLsKJcvd3V2vXtlc55/IYiZAqqNLTTYAfPDBB7h06RKio6Nx+vRp\njeVxzKRHTTqjXIQpM+zXrx8ePHjA677Ly8sxZswY3idVtra24vjx45y6iiiytrbG1KlT2QYH6qSn\np7Of4Qxzc3P079/fIBNFTYFsZxFZfJZjlpSUwNXVVW0tvaaSkfT0dJXzygxZMpKXl4cffvjBIPvu\nCrTqk80EmTNnzsSdO3ewYsUKpKWlsZkOf39/jB49GqtWrcKWLVuwePFidpslS5bg008/xfLly+Hl\n5YUhQ4YAaP/2VVNTg+XLl+PUqVOYP38+gPZLGHPmzMG7776LtWvXYu7cuXBwcODtFz979iwmTJjA\n2/74pMvkR8XOIgzFBWlk69DNzMwwYsQIg9dlUz22ctq28NPUVYShLNBQtdqjvpi6bMWFaBhMh5Ha\n2lpcuXIFzz77rM7HMjc37/CleMyYMbhy5YrcbW1tbTh16hT27duHt99+G3/4wx84t6QzltzcXLWt\n6fTtMMJ1/omsSZMmITU1Ve2XcK5LqisKDQ3FkSNHsHHjRmzfvh3Tp09X2xKPy6RH4PfXvq59uLnI\nyclBUFAQ74FjQ0MD6uvrMXLkSN4nP2ZkZEAgEKBfv346bc90GdH0vKpK9gwcOBAZGRk6HdvUqQqy\nmUVp+HgtquosImvs2LG4ffu2ynr+jIwMpVdHAMMG2WKxWGn2vKfQfJ3uf0JDQ9kT5OjoiHXr1il9\n3OzZs5V+W+7Tpw+2b9/e4XYrKyusXr1a6b6eeuop9tIT33788Ue88847Btm3vnTplZ2enq50QouX\nlxebyWtpaUFhYaFcMMS8Eai7JKwvCrKV02biI9NVRNXfiixVEx8NMXF36NChOHjwIAQCAZ555pkO\n9zOZ7PPnz2PkyJG8l26MGTOmw3tRSkoKHB0d0a9fP/Tr1w/9+/fHkiVLkJycjPXr16vncfx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VVlkvXYAD/lIhKJBC4uLkhKSmI/TFX1G+ejw4hsZxEGkyFXtcIiV2KxmPdyPRsbG0ydOhUnTpzA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\nPD09bfwpHJOzZ8+yYcMG5s2bh6urK5s3b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PBEGwj2rVq2VrjAtCtuhwCL11A8bf/L3Topyhswt298Ll1zgtiVAkKjm+0cJa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UbjBqaR4QND0A1rOW5tkhfYJKMqKlGfuh3zxDHMFzZgPPh1zLCOvu811JnnoGbUQc0MqJgC3jLw\neMDQIRGHeAL0eF8znQGmbv0P1nsH/Mv8+xLCKd0xwzouDVNnReJGhiNav66YTiJ92Kg8UPR3p+iK\n6VTGXYDcfD0eJtVoKvv372fDhg185jOf4Yc//CFXX331iH3G7buu7csJ6bNZQX9fU7v7hqfvJrWY\nYV1i0hTJ4YZcCp599lluuOGG5DpSl5P+mi3TOjM9ty+B2Wfddszrfz1wnUNJ36b0GGKGNRScJ6UD\nU7zvtTJX/3N7e3/z62f54A03DLgcY5jWP3ffMhKG9Tg95vSbHYeapCC9jMAq42uuu2HAthimFRdA\nbwJ0rMrDXoy9Se6UbStzWZ/v0a04E31vdiurZdOlWe+P9V3u++36Z3nvB27ABRh9y0+krSeV1vc3\nF+B1WfHZ5aMboPri1RQDWsXzPSJAavl5+8oj9bux2WVjf//xvn3e6xr4Ht20yihuWtvs0azPmGb/\nNsZNqyyeX/8s7/vADQPKSwF+j7X8mG6VLVjlpvrKLNb3Pi8QxypvO1St77nZ91hLWa7RF2OFB575\n1bMsf/8Nyb8pBT73wP0nYfQ/tv+39xMY/Lu3yyCT1H37N79+luuuv2HAb8/+35uyT6b/BjPVCcPd\njJX+G7d/o8O9b6RlpL8vff9Mr/PGyjRNfv2Ln3Nd/QzY34J54hicPA7dXRDrhXgMXG5wu8HtAZfL\neq5p1j+Xy9r54jHo7bU+09sDus5vfDVc501AuQ/KfNb/5T5U2vPUf6qsvP+5vdxYDOJxiPdiJuL8\n+o8hrm88o++1GOiJvhg91smD25OMVblcoPXFrIBEHNP+XPJfHIBf7z3E9fMbrIJxu/u21dP32CoD\nZT9Olonbeo/LNfB53+d//bvfc/37rk6ptEy74FMe288Z+bW+x79+bhPXL78i/cvM+vPoOvREINJt\ndVmKdEN3mF+/tZfrq/2Y3WHoDlv7QXcYTAO1+BK0T92eXN0LL7zAFVdYMQy3j9tSj6m2kX4bmepJ\nsOqXD1x/w6Bl9eoD64vUz6UeT232a/Zy7BhdavCy7Nd+u96qX5LbpVvHLrseyVR3J/qWax/b7Rwg\nfV12nm+/z+5e49b667b3fcBat6agJ2H97+s7pnXHreNkuda/jF7d+t8+vrq1/m3qSVh7RIWnP1b7\n/VpfXC5lfWbDb6ztDieser/S07+99vvt5dvbkfqafd7et3g0rGONl/7ji0vrjzWa6D++/G79s1x3\nww3JeHxDNG+n152ZTKpkHKzRVPbv38+8efP41Kc+5XQ4QgghhBBCDGnSJeO5ykfrYzAYJBwOj/ty\nC22dst6xG6oFJt8t406V33ivO5uWr3ytO1eTZZ+Fgfunblrd6KIpN1+Oh3yVlyy3uGLNdblOjjNe\nivWLrDu3dQ/VMj6p+owLUQiyTRCzucFIZGZfKpbhtZxl39DmdUe5dLZPvgMhhBgFScaFyNFI/Qkl\nwc6v1JF23JpK9kkXE083rdFqlAa6Kcm4EEKMhiTjQuQgkdDHLdnOx5CFpSaWMNMHwhETrCdh4JLR\na4QQYtSKIhlvbW3l+9//PpqmMWfOHG699Vaefvpptm3bRk1NDatWrcLlcrF582bWr19PIBDg9ttv\nx+fzOR26KAAjjQwxntJnhEy/6z49ntQRZQopIR9NmY22bHP9nF3GccPqpiJ5oHNcCqb5PbhcroLa\nf4UQopgUxQycdXV13HPPPaxdu5Z4PM6ePXsIhUKsW7eOuXPnsnXrVhKJBBs3bmTt2rUsX76cDRs2\nOB22KAB2l4YXD3USM/ofZzMrVqbZs9xuF5c3VA7bKm6/nmldMcPqY/vKO+HkLGe5xDQRRlNm9mfs\n7RpOIqEPGNovffnZzlr2yuFOytwqOUW0mHjRBLzbHeNoZzQ5y6kQQojcFEUy7nL1D4wZi8XYu3cv\nTU1NQP+098eOHaO+vh5N01i8eDEtLS1OhSsKhJ3U2VP4DvX39Mf286GS0OHGeE5lrzd1WtxXj4Tp\n6NHZ3x7jpQJKwEdib0eq9DLzuBQHRtiumAG/3X1qQLna467by0w9ERipfOK6OWi8ejFxdNMaE7gn\nIeUvhBCjVRTdVAC2bdvGo48+yplnnkkgEEDTrPMIv99Pd3c3kUgEf9+slT6fj0hkfIfZEsXFTuoM\nE7wuZfUthuS0xakjcaRP+W1/frTrtfUmTAwTth/rIhI3WFZfSVw3mV3poavXSE6ZW2j9xtP7si+r\nr+SlQ5283NqZvCKQ6UbVi+uC9Cas11K73aSWyatHwrR26MwMaMm/VXg14mmzxxl9Jy72VNPpk0Hp\nJgNaxO3v+j0NlQMm3BD5ZQKVZVrysRBCiNwVTTK+ZMkSlixZwoMPPkh5eTltbW1A/7T3fr8/mYBH\no9FkYp4qFAoRCoWSz5ubm0ecQn40vF5vXpZbaOss5PUmEjpeTy+GYeDRFK2dPex4N8ayuVPYvP80\nHk3hdnnQNI2Az4fX0wtAebmPzftPA3BN40zcmsLtdpFI6Mn1+nzWvuV2uwat8/d72wG48oypeN1R\nWk/30BWH2ZXlTAn4uW6B9dmEYfL83jZeO9bL1fOrBi3Laemzy2XTL9urWcnwq0fCycQd+pN2+2Tk\nrGl+Fs3wopuw7Z0wB0/HmFflTa7LPmGyT5BS6Sa88k6YA+0xaoMerpxrrcMw4Wg4zqtHwlwyOzjm\nE5t831swWehAZ6+RfCyEECJ3RZGMJxIJ3G4rVLslfOfOnaxYsYIdO3bQ2NhIbW0tra2tGIbB9u3b\naWxsHLScpqamZPcWm9MTFBTzOgt9vUtrywB76t444Z4ewhEvsXgvpktxUW0Qjwbx3igXzbLe29MT\nJRbvTT5ObwW+pnEmv205DgweTUU3IRbvxTChOxplSZ0PRYLehMmiGd4Bk6LoJiT0OOjQFYkMSvqc\nOMEZSqZRXzK9Zk/jHh/isoL9mQq/n65IhM0HOzkajjN3qpeL64IDlp3VSDN9LeSpJwFDrTvVSIm2\nDE+ZPTfQE9eTj4UQQuSuKOrPN954g2eeeQbTNJkxYwbNzc2cPn2a1atXU1NTw4033ojL5eKaa65h\nzZo1ydFURGlLTaIurA3y+tEwrxzu5JI5lbx2JMzrR61W1PQuK8vqKzFGMcKJ3aUjtWX4ktnBQbGk\nvjfT3wpRphhTX4sZ8EToFCbwoYXTKEtp6E9Pqu2rAJqC2qCHJbODGbuWDLXOS2YHubAWXj8a5tUj\n4WQib/+f3j0mdTnZDE2p9/Vhl1FaRqYBAa8r+VgIIUTuiiIZt7uopFq5ciUrV64c8Nry5ctZvnz5\nRIYmClxq33G7jzbAwdMxAC6u60/WPC7FS4c6qfBqtJzqRQE3NU3Dq/UnlOVe97Atti41sGV4uNbX\nyTZ7pAl0x3R2vtvF/9/emYdHUWX9/3urlySddEJIwCSENRhENhXCAIYgOuor+goOCPoiI4zPOG4/\nddx1hAxBdEZG30FERXReXHEEFFdmxBHDPhAVDQkMEJYkspOELB2S7q77+6NTTXenu9NLVVd19/k8\nDw+d6qp77lJd9a1T5547PCvFb/vCybGuY3Aqv8P17WizNiLBwJzx5UDo3m1pXBL0zM1bT3jHBoDz\n858JgiCI4IkKMU4Q4SK4eE4Bh0cWcA+JkLKd6L24RD1jqCVcva9SmIbrxMJAvOuxEJ9sFBwe8YN1\nFjS1e48e9paVJhg8++nS7PMTRgM9Tq/XBfRGQsrSQvhHD6BfeoLzM0EQBBE8dP0kYhJXAebNAytN\n/HONUwYcGVA4FzFlcAYEPykMPTOyjM5NxSd7zqCxzY4hPZJwWS8zdtR698xKdZMeALZ2eHDH941e\nD7mdO/J+H22yol83Y6d+d/VUS5NYuypPOlb6W3rDMTo3FT8ed8SHS8La8/nJ20TQy/ukwmazu4Uk\neaappFVRg4MDqDzlmAtxWY525jkQBEH4g7e1AT8fBs61At0zgQt6gTH1LvokxomYw1X4SWJNwlN8\ne5Kgd+TKBprQbucQmHcx7ZY2sUM5CoxBYAxNVhF7TrZAFAHBI5BWOtagY7g02yFejjVZnd9FowD0\nleM71Pzfrv3rmqpQ5EBjmx3r9tWhzcZxYUYCtlY34niz4wGAdYQISeMl9aXoxb6/1IlaXRVVi7QD\nSOmIGW9XtyoEQRBdwmsPgX+5Gry8DOiZAySZgFPHAL0B7PoZYGMnqiLKSYwTMYnIAXTk+P650YoW\nq4icjlR4/gSWFPrQ1W/RoGPgHWKcdaT+65VqwEXGRFSebEVNQxvG9E7BwIzOXmCDjuFksw0fVZxB\nltmACzMSYBXVD4sIJVzG88Fn94lmZ85wz7ht6cEokDSO3lIVjumditLDZ3G8uQ0ZSXokGQQca7YC\n4A4XrZe3D4D7XAEpTEXs8OQH0qZAF3mKV5IMNHWTIAhtw9vawNe+A75jI9g1N0GYeTdYcorjO86B\n/RUQ//4m+E87IMz5PVhCQkTrR2KckA0txT4n6Bm4CCTqBSQaBLRYRWcqPF8ZNCQBNs6Lp9STNptD\nAEreWMCRO7zZakdmigGWBhE/N1lxuqURggA3b+3IHDPKaptwsqUVp1qAcX0yYBDU7Tc50vkJDLBY\nRZ/lS+Eh1yb7D1PRMe+pCg0CkKBjGNojCUY9g03kaG0XYRAEXNbLkabSzgGr6LAldAj4Ntv5MroK\nU3HFn/ecOM+xRqvaVSAIgvAJrzsN8a/FYLn9IMx/GSzF/Y05YwzIHwrhiT+Dv7UE4isLIdz3NJjB\nGLE6khgnZEFruZnbbBxnz9lR09iOHskGTBmcgR+PN7mtIumLQDyhAgPgkU7PNSTmR2Mz9ALD2XOd\nJzMaBWBUrhlGPYNV5KoL8XDwjLH2jBN3nczqib8JnUYBndJC6tj5WH9pwSAwoIdJD8k5u7W6EWfP\n2XHOJuKCFMckXVfPuKc9Xxlx/C08RJxHB6C7Sef8TBAEoSX4iaMQ/3ce2MRJEK79ld99mcEI/OZB\n8GWLwN97DWz2/RGqJYlxIgaRPKs/HW/G/jNtEBigF3wvRiMdE+jEPV8L3mytbgQ4MLp3KlraRRxt\ntiI/IwHDs1I6lWkUgEuzU5x/qx2fHHaqQY/Png9nwHlhrtfr3GLn22zeY/O7ym1uEzn6dDPi0myz\nm7C3WEUM7pGIi3okd0o1qdfrAmqna5adrvaNZ3SAM6SLxDhBEFqC1x6CuHg+2H/fCqHo2oCOYYIO\nwpwHIC58COL2DRDGTFS4lg5IjBOyoDXhIoldSQgHUr9g6u05ofO7o0041WJD/Tkb2kQOg8DAAL+x\n4J5ZRtR+o6CUbc+MJnLg6r3eWu1YybNfuhHj+jjiwbfXNGJHreMtiLfVQ4OxQ/jnTIv3VJYEQRBq\nwav2Qly6EOzWOyEUjA/qWJaYBOG3j0L8azH4xZeCpXZTqJbnITFOyIbawsVbzLprKIKS9bPaHdk9\nDjcw1FlsuOli99SIanu+5SLQeQGuDz+Bft9Vme0d4ehSdhVfnnMdc091GAv9rlX0ApCfmQB0vH0i\nCIJQG77nR4jL/wJhzgNgw0Z1fYAXWJ8BYGOuAF+9Auw3D8pcw86QGCdiAm9hEUp6nD1FqRSCcXHP\nZADnBWNXnm+tvVHwR7BefNfvJS+2r++7ol0E1lScAXB+VVSpjPF9U50PO1KZ3uwR8iMwoKVdBMA6\n5XonCIKINHzXdohvL4Vw1+Ng+UPDKovdeCvEp+8GrzkE1ru/TDX0DvkyiKgnlFzW4ZQjidJNRxpx\nzu74LIVhGIXzQjxQ4iV13vaaRmypboTNFl5Yg+gxTjrm6HPPPpTsKSHK5TrntGYrWGwicKLFihMt\nVti8J9IhCIKICOK2DRDffRXCA8VhC3EAYIkmsP+aCvGT92SonX/IM05ENf5yQgfjcQ7W6yvlwf7+\naBMMOuZ3cmis5KpWw4svdatRcHjEXfODqxVjH8k4fy3NKfBFaoIOGqwWQRBxhLjhC/B1ayA8tAAs\np49s5bIJ/wX+1VrwQ/vA+ufLVq4nJMaJmMFV8HqLbXZdVdGXqDHoWJfx3VIaw+4zC9cAACAASURB\nVN0nmmGxihidm+oWHy7ZsopwLoIzupc54HhrLePZRs9tvr5zFfLeFv3x3F8aK9eJn652RO47Dl8q\nK5AHh0DGRK5xs9nsMTN3QEJgQP9uiYBHnD5BEEQk4JxD/OJD8C1fQ3j0WbAeWbKWzwxGsGumgP/z\nY7C7Hpe1bFeiQozv378fb7/9NhhjyMvLw+23345PP/0UZWVlyMzMxL333gudTodNmzbhq6++QnJy\nMh544AEkJSWpXXVCYbx5a715E+0c2PFzEw7XtyPby0qcksDe2hFy4s8Lae/wzoodebRdPbXS91J2\nlZPNVqQl6nFptvoe3UAIVHj68tj6E9H++tNbvL/IHTnCrXbuVubo3FT8cMx7zvhgPMmB7Ottn1De\nENg5sKGqHu3WtqDGX+tzCs6JwI/HWwAAgy9Igf/lnAiCIOSDiyLOvf0K+E87ITz2J7Bu3RWxwwp/\nCfGzD8DPnATL6KmIjaiIGe/RoweKi4tRUlKCs2fPorKyEhUVFSgpKUHfvn2xc+dO2Gw2fP3115g/\nfz6Kioqwfv16tasdl6gR3xpMCIjJIMDoY2fPLBxdIe1r6ChPEm5bqxth6PhS5IDINRrw64FU/1Dj\nrF3bbzIIzn4JBbHD/sgccyfxKjD/OeMDqWe452gkw460HOLEASQadEg06BAdZzlBELEAt1nB3/xf\n2A7+B8KjzykmxIGO2PFxV4F/84ViNqLCM96t2/kcj3q9HrW1tRgyZAgAYNiwYdi8eTNyc3PRu3dv\nCIKA4cOH47XXXlOrunGLVuJbvXkTdcwh7LYeaUS7DyUWqBfSNcf19hqHB3ecS3sFBgzPSsHQC86H\nqRgEbXs4g8VfXyXoGZrbRAQaSOxZlp2fXzXT1xwAX7a7GkObze52jgaSe16OcdMxYGJeOpotlpgY\nf4kEl/FJiKF2EQShXXirBeKyPwN6A1L+sAjNbe2K22RX3QDxmYfAb/wfsIQE2cuPCjEuceTIETQ2\nNsJkMoF1LPtmMpnQ0tICi8UCk8nxkjQpKQkWi6XT8RUVFaioqHD+PX36dJjNZtnraTQaFSlXazY9\n7dpsdhgNbTAIDImJSUg0BnZ6Sdk1vMUSB2I30HJsNjsSE9oAACkmU1D2vNmV2iuKIpKTHO299iKT\nWx3Gp5hgEzn0AgvJXiQJVnj6EsKBLCPvLabc8ztP77dnWJG3cqTPrvMD/NUhEK+zXOJZr9fFlBAH\nHLnFE/UCGKM84wRBKA8/cdSxmM+goWC33AlmTAAiIcYzLwD6Xwj+wzawMVfIXn7UiPHm5mb87W9/\nw0MPPYSqqirU1dUBACwWC5KTk2EymZwCvLW11SnMXRkyZIjToy7R1NQke13NZrMi5WrNpje7l2Ul\nYGt1I/6171xA3vFQvelmsxkNjQ67kvgKpJyCbMcTbWtr54e1QO16tve7o034174TnWKnJaRl30fm\nmANOe6jGgxUQvvAMJKba0zvtKcQ93zb4imP3Fmsu4at8vV7X6YHB9dhYE8tKYxOBBP35z9D28yZB\nEFEM3/09xL/9L9jkmRAm/FfE7QuX/xJi6T8ABcR4VPgy7HY7lixZglmzZiEtLQ15eXmorKwEAJSX\nlyM/Px/Z2dmoqamBKIr46aefkJ+vXAoawjfe4q79xeja+fn4YF94O14SdFJ8cyDlSPWTU3DpvMQv\ne8ZeG3QMh+vbsVWhnNehomR8f7j9LGWnCTSOPZh4d8+6+Ts23D5SYw5FpHOgVzdYUd1g1dS5TRBE\n7MCtVoir/g/iWy9BuOsJVYQ4AGDEL4Daw+CnjstedFR4xrdt24aqqiq8954j8fqtt96KwYMHY968\necjMzMQNN9wAnU6Hq666CsXFxc5sKkTk8Rb/25UXNEHv8BoHms3C2z5dlaMUrqEZvr4fmWNGm61R\nU6nf/HmnlcbVO+2Jt/PHF677+isjmGNdCXcOhD/PvVJEet4GA5CaIDg/EwRByAk/Wg3xjReAjJ4Q\n5r0EZo7MtdQbzGAA+8UE8C1fg025Tdayo0KMFxYWorCw0G1bfn4+Jk+e7LatqKgIRUVFkawa4YVg\nBYDVzoM+RhJ04ZYTLL5yRXum8vMMfRjfN3Ymb8qFrxzinqE+voS1Z/hKsBMtXfeLpcm1kURgQJJR\nAIM284zHQm5/gohHeHsb+JerwEvXgd00C2z8tc65gmrCxl0J8dU/gU+eKWu5USHGCW1i5+cnTfq6\n6fnydAYSW9yVGPMnppS4CQebK9oz/jnQePFI4Powo5RQ8TUG0gNNV8f68/D68jqH2hZfx3lb0Mmz\nHr6O7+qtiRLIlf0lUIw6IDVBD4DBqLF4ca1kdyIIIjj47u8gvr8MrE+ewxuenqF2lc7TewCg0wOH\nDwDDL5OtWBLjREhINzqjoQ2XZSX49HIC/hfj8SdyAglPkcr3FgPs71g58RRA7SKwtcO+UcdwpKEd\n3x1twuhekQ2h6Qol6+JvUSDpgWZMb//nQDC2pPNPjjKlmOvvjvpeJEraL5DzTKqblGlHaSJ5jrXa\ngEP1bR2fU2E0Rs42QRCxBT+0H+LHbwNnTkG49Xdgw0aqXaVOMMbACgrBd24kMU4QasQ8+8sV7So2\nvzvahGNNVmSbDbgsxwzGmsJapCZW8Seau/Lweov5Fjv63mrnIZ8TksAWOZCipVcZGoUDsHWc21o7\nwyP9loAgiNDgx2shrn0XqNoL9t+3gI37JZheu/KUjRoPcfEfwefcL1uZ2m0toSlcvdCu4SMpJhNa\nWy1BLZbT1X7B7ivnsV3hL1e05FG12jn6pRudqQxH9zIrUhct42sM/D3QeCsj0O8DzW/uir8QE2nh\npuFZKT4fGoKdKBpKnnnPOmotBtqkB4ZnmZyftYZW+okgiM7wutPgn38A/sN2sGtvApvze0UW1JEb\n1qsPkGSCfV8F0KufLGVq8PJJaA3X1/Fjeqe6haRIAqOryXUSwdwc/e3bVcxzJG/Ckgj3FSoRr4LA\nV7u7WvwmFMEZyBwETxve3qwEWkYwdQx1/L3FxWstBlrkwIlmq/MzQRBEV/Cms+DrVoNv/QZs/DUQ\nnnkNLDlF7WoFBSsYj/ZtG4Bpc2Qpj97DEl0SaA5v1/0DzfnsekywkRzBxgbLkTNamrDqum1LdSO+\nO9rk7CM54qBjiWD6PZRzx5VI9H24dfQsy1sZauQnDwWbCLTbRbTbRceiPwRBED7glhaIn7wHce49\ngM0G4Y9LIEy9PeqEOACwUZfDumMjuCjPhY8844RfPHN4GwX5Q0AiMeFSrpzRRkMbCrITnOE6rku3\nj+tiUmo8IufYyhmi4csDHsnJv/4mubq+iXI9p7QWA8050C7FjEfBwwNBEJGH2+3gGz4H/3I12LBR\nEJ5+0bG8fBTDsnKBpGTgyAGgf/iLTIYtxg8cOICBAwf6/P6bb77BlVdeGa4ZQmVcc3gHE+8NoFNe\n7kjFvSppx1MwSXa0IpKiFX8iedMRR397y2wSqq1Qj5MrZaHBSyWkN1FSSkXPmHstwRhg73glpIEU\nwARBaAx+eD/Ed5YCphQIjywEy+mjdpVkw1BQiPYftoNpQYwvXLgQEydOxC233AKjS16r48ePY9my\nZWhoaCAxHsWEMxnSNY5a8vz5Sm+olLfdNcd3ODakOiYmJuHcuVYADsFk1DG024GfjjfBJoaexSPa\nCSbPfCC56T3LtorAsSar829vfRzsw5e3/YM5F/2l8wyUNpu7opd7NVlvoVVyM6B7oqLlEwQRffBz\nFvC174Hv3AQ2bQ7YmCs0sWiPnBhGXY62V/4E/OrXYZcVdsz4okWL8PPPP+ORRx5BRUUFRFHE2rVr\n8fjjj2PQoEFYtGhR2JUk1CWU2Owt1Y3YWt3o1fMnh41AELlDwH13tMkp4MK1selQgzNWOMUo4OdG\nK/5efgqHG9qg1+IShBGgqxhqqd+l/TZU1aNdDCzuWjpme00jLsxIQL90o08hHkwct7/9I/mGQ2De\nV66UYzVZ1/5WKv68nQN7T7Vi76lWtFOYCkEQAPgP2yHOuw841wph/ssQxk6MOSEOALq8iwBLM/iJ\no2GXFbZnPDMzE08++SS+/fZb/OUvf4HJZEJaWhoWLFiAPn1i53UEETwCg9O7F2y2inDRMYdHXMo7\nLTfSZE2Rc3DOkWLU4zIZPJmEd6RUg76Esp07Qj4ilc9djvPYWxmR+n3IhR7nw1NoAhJBxDe87hTE\nla8Dx3+GcMdDYIOGql0lRWGCADZiNPiuf4Nde1NYZcly/bRarTh69ChEUYTRaIRer4dewwnbCWXx\nFprgGloQKZEhR45vz1CG8f27ocnS6gxRmDzYsUyv4OL9jQYRJSeBCkhpP3+56ds7JqYbBfd89v7K\nlkI7pJAkf2k2bTa7c4zkENPhomRaTs/+VoJkA3B5HzMYBCQbFDFBEITG4XY7xK8/Af/iQ7Cr/hvs\nzsfADPFxQWCXjIH45SpAbTG+d+9eLFu2DN26dcPzzz+PzMxMrF27Fk899RQmT56MyZMnQxDCi4ap\nr6/Hn/70J9TW1uKdd96BIAj49NNPUVZWhszMTNx7773Q6XTYtGkTvvrqKyQnJ+OBBx5AUlJSuM0j\nQsRbZgg14qnDzd4h1X10bip+ONYEo74dbdY25wqbBsE9BAPo3E6tLdSiBIG2zfW88DymXQTWVJwB\nANx0cQZ21AZ33gheyu6UI7+qHu3WtriJ7dex0BYbChSLFdha3QTAETtuNnZxAEEQMQU/cgDN770K\nbkyE8PjzYFm91K5SZLloOLD8L+CNDWCp3UIuJuyY8T//+c+4/vrrUVxcjAsuuAA6nQ5Tp07FM888\ng7KyMjzxxBPhmkBKSgrmzZuH/HzHjNWzZ8+ioqICJSUl6Nu3L3bu3AmbzYavv/4a8+fPR1FREdav\nXx+2XaIzgeQ/jpYcyYEicuD7o004XN8OveBI5dYv3ej0wvprr5w5qWMBOwc2VNWH3R+ufS55gKUF\nqTYdaXR62L0hxtj5qSYcjv4UueMzQRDxAT9ngfjBcogvlSDhuqkQHn4m/oQ44HgDcPEI8PKysMoJ\n2zP+wgsvoHv37p225+bmYsGCBVi3bl24JmAwGGBweeVRVVWFIUOGAACGDRuGzZs3Izc3F71794Yg\nCBg+fDhee+21sO26Eg/eza4IxMvtbZ9oioF1xTWF3faaRmSbDRjVOw3nzp13/7WLvlfejBUide4b\nBWDqkAznZ1/nja+MPID7pN3Rvcydzr/x/bthY9VJfHe0yZk3nwgdgQFZZoPzc1fQdZQgohvOOfD9\nVogfvgk2eASEP74MY04vtDU1qV011WDDC8B/3Alc/suQywhbjHsT4hK1tbWoq6sL10QnLBaLMwTF\nZDKhpaUFFosFJpMJAJCUlASLRb4YSbVDLdRArptmtN98JaEn5RJPNOphbXNPnZigZ87sF5Kn3PVY\nOR5G1OpHuc591/pPzEtHs8XitSxXcRxsqI+3Sbuex0kZbw7Xt6PN1ihbzvJ4RWCO8x9gEJj/cQr0\nYd7X8QRBqAs/Vgtx5TKgscExQTM/tidoBgobOhL8gzfAbVYwfWix8rLPsmxsbMTmzZtRWlqKw4cP\n45JLLpHbBEwmk1PkWywWJCcnw2QyOQV4a2urU5i7UlFRgYqKCuff06dPh9ls7tKezWaH0dAGAEhM\nTIJeYH7jMI1GY0DlyomcNm02OzZU1QNwCCfXtl57kaNfpW3e7Er7APBZTrhEso9tNju2d7TjmvRu\nMJvNsNns0Osc+caL8ro7zwl/fRcq0f4w6Fl/qU8Cnewa6NsWqTxp0i7Q+Ti9XoeROWa02RoD8uQS\n/hFYx8qbHX0pxyq30vEEQWgDXnca/MsPwb/bAnb9dLArrgejJB1OWGo3IKsXsL8SGDwipDJk6U2b\nzYaysjKUlpZi165d6NmzJ06fPo3nnnsOAwYMkMOEG3l5efjqq69w4403ory8HPn5+cjOzkZNTQ1E\nUcRPP/3kjC93ZciQIc7wFommAF+tFGQnwM6Bf+07AcD/zcZsNgdcrlzIadPOgXar4+HDlwczELvB\nlBMskexj13ZY29vR2mqBnQMMNgDAuXOtbhMG5WhzpB/mfKFEmJHNZg/74SKQ0BVfceFGwbGKp7dy\niOAQOVB7th0Ac6b79EU0h6wRRDzCq6vAv10H/t1WsPHXQCh5FcxMD8reYMNHgf9UBqaWGH/jjTew\ndetWmM1mjBkzBjNmzEC/fv1w5513IiMjI9ziAQB2ux3PPvssDh8+jIULF+LWW2/F4MGDMW/ePGRm\nZuKGG26ATqfDVVddheLiYmc2FTmJp5tHqDdNz1fMsXTzleLBXT3dnjmtA03FFyxq92O4NuWov9T/\nQOAedX8e9HDqQpxHLwB9uiVAEAQYdefDuXz1bVdhRl0dTxBER9x2zSHwqj1A7WHwulNoarXA3t4G\nGIxAQiJYWnegeyaQngHWvQeQ0RPo3gMsqXPUgLPctjag9hB4xffgP+4AmhvBxl8LYcFSsNT0CLYw\n+mDDCiAu/wsw446Qjg9bjK9fvx69e/fGzTffjEsvvRRGo/y5rXQ6HebOneu2beDAgZg8ebLbtqKi\nIhQVFclu31kPLzf3WL2xh/uKOdI5xZXCs13e8lQD5ydyGnRMkYmBavaj6zkux5Lzer0uYHHeKTWh\njyXofQlvz/Ll8MorTTRdU3QMSE3UQS84HlJ9jU8gSBOlpeMJgnCH150GL10Hvn0DoNODXTQc6N0f\nwojRSOqZBUtbG2C1Am3nwBvqgPpTQPVBiLv+DdSdBs6cBPR6oHsPIDUd0OkAQQBamoHGBqDhNHBB\nL7CLhkOYfgdw4cVggnKpUWOKPgOAc63gJ46CXZAT9OFhi/ElS5agtLQU7777LpYuXYqRI0di7Nix\nEEU/ucWiFM+bZLTH8sYqSogZkQNW0RED75qnWjoHDDoGvcBibmJgoGK4q2O9iWdfx4RKrPW3mteU\nQH9DrTbg4Jk2MADDevr2uBEEETq86Sz4p++D79gENuYKCPcXAzl93JaY15vNYC6hm95+upxzoKUJ\nOHMKaGoA7CLA7UBSCpCaBvTICnkCYrzDGAMbNhK8fCfYBZO7PsCDsMV4z549cfPNN2PatGnYu3cv\nSktL8corr6C1tRUrV67E9ddfj969e4drRnW0cpPUKmqHUkjIPU7Sq/Pvjjbhh2NN4D5+MlY7x+jc\nVLTbG7vMKhHNGBRskLcMNZf3SXU7r8I9x4LxyscrwfyGRA5YbA7HC0d446OVawhBaAXOOfjWb8BX\n/x/YLyZAeHYZWHLo84kYY0BKquMfITtseAHEDV8Av1RBjDsrwRgGDx6MwYMH4ze/+Q127NiB0tJS\nPPbYY1i5cqVcZjRFvN48fAnNWO0DHTsfHz5hYDra2s75jIsf3ze10+v2aO4Xz3CcNlvgrmvPvgnV\n6x2IRz3U8lzRwgNUtF1T9AKQ2FFRvSDP/AKCIABuaQZ/eyn48VrHgjq5/dSuEtEVg0cAf/tf8HMW\nsMTg3hQqkpvGaDSisLAQhYWFiuQZV4NAY1JjHa2nH1NCzEhl2jmw7cjZTsupx/r54BqWFWw6QG8h\nXa6pLz339TynItWfWnrzpfY5FMxvSGBASoIA1pFnnCCI8OGnjkNcsgBs0DAId/wezCD/XDxCflhi\nEjBgEFD5I3DZ2KCODVuMr1q1CowxRyySa6VcYpmmTZsWrhlN4C2dmrfthLooMR7BlBlt3s1AiUS7\nwi2XfpPyEGj/CQww6gQITCAxThAywA/th7j0GbBJN0O48ga1q0MECRs2CvynnWCRFuOrV69GTk6O\n13zinHM3UR5LaMmTFkliVWgGgo75Xz3Sc99YJNR2uZ43ci7+5Eo4v8l4Pq/DQceA3FQDDHoj9RtB\nhAk/tA/ikgUQfn0f2CW/ULs6RAiw4QUQ/7EGXBTBhMDTqoUtxm+//XaUlpbi0KFDKCoqwvjx42XL\nL6411PC6adHTp6W6+EOuvnMtR6/XRU371cBfn4fSb8GOockQek5JGtfQ0DEGmTN5EkTc4RTit98P\nNqJA7eoQIcJ6ZgNJJqDmINB3YMDHhS3GJ02ahEmTJqGmpgalpaWYO3cusrOzMWHCBIwZM0aRvONq\nEOiS3ErbJAIj3L5znWzoK8844U4ofe5PbAdbnp0D+844Vj8dnpUSUl50Ijja7MD3x1oAMOR17wET\nrZBNEEHDj/8M8eVnSIjHCI5QlTKwIMS4bA6N3r1747bbbsOSJUvQr18/LF26FPv27ZOreE2iY/J4\nXcPJrUzIjyQCt1Q3uo2NnTvyjHtu91cOja1vpH7edKQR7TItS8BwPr+ur3H0VRcaq+Cxi4ClncPS\nLsIee0tLEITi8MYGiC/NB5tyGwnxGIENGwVeXhbUMbL5MWpra1FaWootW7YgKysLd999N/Lz8+Uq\nXnW68oSH4oHz5vlzLcebTfL0BYZcby68ZfjwhedKlfH2VkPKyS59DgSRA8earPjuaBNG9zJ3ykwT\nzBgaBWDqkAznZ2k8xA6h7S+loevCRnI8ZHdFrPyOdQKQmawDGIPOh2snVtpKEHLD29ocWVN+MQHC\n+GvUrg4hFxdeDJz4GbyxHiw1PaBDwhbjX375JTZu3Ii2tjYUFRWhpKQEmZmZ4RarSQK5mYeT7s+b\ngHO1GY8CLxzkmGzo2f/j+3fDuXOtXjPraDnlo5z4ElfB5lfXMWBcH8eCSlYfbulgx9DoIgilh4Pv\njjZhe01jl+Ni0DFsrXYs2qTk7yuWfsciB8wJeudnT2KprQQhJ5xz8HdfAeuZA3bj/6hdHUJGmN4A\nDB4BXv492OVXBXRM2GL8rbfeQnZ2NvLy8lBbW4uVK1e6pTpkjOG+++4L10xMEsqiKN5ueEoTbZ4t\nOerrS2wbDW0oyE4I6PhYzM4ht7gyCsDoXo4V5YJJHRrIGEv7+BL6Eq555KWHCSIwBAa02uwA5Rkn\niKDg334JXnsIwhOLYjbrXDzDhhWAl+8EIiXGp06d6nYi+cs3HquEI7yCfS2foD//RaihMcEcE22e\nLX/1DVWkBxJP7G3stN5XShBKmEewkzcDOSeDDT3xFRbmjXAf9vy9eQmnXKXoql6nW+w+jw0ldIlw\nhyaNxx78wB7wzz6A8MSfwRK6du4Q0Qcbdhn4h2+A26wOT3kXhC3Gc3JyUFhYGG4xsrFixQocOnQI\n/fv3x+zZsyNmN9RsHZ7HdVWO5OXzDAkI1GY0CWs5CSUzh4SrqEtLNqG11eJ133joT39CMtgwnUj1\nm6sQ70rYBHJeyPEb6irMKZi3BErSVb1EDiR0bPQVpuLtOhUPvxU5kCaNe676S0QvvLEB4rLnHZlT\neuaoXR1CIVhqOthNswCrFYiEGH/99dc1I8YPHjyItrY2zJ8/H2+88QaqqqqQl5endrU6EeoNPZjJ\nhHIRbeEWctTX06vqWrbngjXx+IAjRxu76jd/4xjIGPua/ByNwkbL55jAgEQDAwsgTCXYOQUEEWtw\nziG+/TLYmCsoc0ocIFwxKeB9Yyor7IEDBzBixAgAwLBhw7Bv3z5FxXig3io5vVquZQQrOkN9PR5t\nN01v9Q1VpPsSdcGWE0t4a78SD23SPApvnuxAbCgxPoG00zOkKdC6avGht6t66QUgQRDABAF6ofO5\noYYDIZbQscBX/SW0D9/0T6D+NNhdj6tdFUJjhC3GRVHE7t27/e4zdOjQcM0EREtLC3r27AkAMJlM\nqKmpUcxWoN4qXxlS5Eq7F+4xgcbfhmpPS8jx0HKu3RbxxZ+0hL/zJdSHHKncUM7NYJBL2Pj7rUve\nX4OOoc3Gg8rMEoyXP5L4sylywGhgYIzBJgLbf+48Xl05EGLl+qIUtOpvbMCP/wz+8bsQHnsuoBhi\nIr4IW4xbrVa8+uqrfvdZunRpuGYCwmQyobW1FQBgsViQnJzs9n1FRQUqKiqcf0+fPh1ms9lneTab\nY2KSZ2iC9J3R4FjtL8VkctvHaDQ6y/W3n5y42gyGrupns9mxoaoegEPIeH4fqt1wibRdqR8SDFbo\ndQYIgtCpv/ydL/FEoOJK8nyHEssfqjhRSthI7RC5+yRrudCqGBM5UG+xA7AHlOlJ6QcugtAi3GaD\n+OaLYP99C1h2b7WrQ2iQsMV4QkJCxMR2V+Tn52P9+vUYO3YsysvLMXHiRLfvhwwZgiFDhrhta2pq\n8lpWIDcJKcVds8UxoU/ax2w2u5Ur7ec58U9OPG0GgiRsfNVP8vS1Wx1i3ZtHMRS7chBpu+f7IQGj\ncpKgY+79JbeoUOMBJxC68tLK2Q+ettRYnMcb/h4IBAaMzOm8eFGk7EcagQF2zsHAoBfi6y0RQQQK\n/+LvQIoZbOL1aleF0CgxFTPev39/GI1GFBcXo1+/fopP3tQxoF1ElwuFaPHGFEj2Bi0In0jjK95X\nEoYpps7ZVKKVUEWdnOdBV+Lec5tBx2AQ3BfnUaJevvD1u4lUKInWPMk2EWi3cTDGYROBxI6XQv4W\nhnLdrnYIDkEoDa/aC77xnxDm/jUuUj0ToRG2GPfMK642cqUzDHSi1ndHm3CsyYpsc2zEgLWLjv89\nPXvRdqMMNQe7a6iB1c47CS5fISj+zhcteTIlbDa7IqIuFHHlb6KmhPR9m42jjXMkGBzjo4XUeVoc\n30ghMKCbSQcGyUvuP+2qrwcZgohF+DkLxDdfhDDzbrBu3dWuDqFhwhbj77zzjhz10CSB3CSsdo5+\n6cZOr6a1jjfR1C4CayrOgAP41cUZUeuxUst7qHTYRrQQbBsDfUtj6PhCEDqHgkj7eUudJ3c2I1+h\nM5EYX615kgUGcA6gw+MnjZMoAmDuY+KaJpQg4gH+wRtgg4aBXTZW7aoQGiemwlQijRZujF2tDOkP\nb3XmAFra7dh9ohmXZqdo4oYfKaTxdBVvobQ/Gjyler0uYmEVctoY0zsVBsF7hg5f9oNdjKgr1B5X\nuezLMTYCAxJ1AgThfJ7xNhtHSoIAq8eMTi1cL4nYIBqusfz7reD7dkOYi1B+MwAAIABJREFUt1jt\nqhBRAInxMFHzYuAqNK69yBR2eUbB4RHffaIZFqsYdnlqEe5NP9iVTV3x5inVqgBRuj6Beo0DiRsf\n0zsVW6sbsaO2sdO4dJU6T0m0PL7+kNOjbxU5hI5Ff6SHWek3FK39Q2iXaHjbyBvqIL73GoR7ngJL\nTFK7OkQUQGKccCNRB1yanQIgum+eodbdzh2TBK0BvHIIJoUf4ZtA+lHH0OUKj97KCEcM0vgGhlHH\nwAQBwPm3SWoK8GjwmhKxC+cc4orFYBP+CyzvIrWrQ0QJJMajGNebnpy5reP1JiZNyOX8fAYZf/v6\nmowmxcbGaz9KdCXKvE32C9V77lqm5z6hhhpp3fsWDnIJZh0Depj00On0Yfe5HAQzbiTaoxO1H/a6\ngn/zBWBpAZs0Xe2qEFEEifEoR4sXI63S1c3XzoHD9e0AgFG9vO9js9l9xukHIy7jBdf2u/abt8w1\n/ghEONk5sONnR+750b2ia0K1GsjRP3YOHG5ohyBYcWn2+dfxWu/7WH/YinW0Ol72mkPgn38A4cnn\nwfQkr4jAobOFcCNWvUWB3Hx1DMhOMTg/S8dJf9s5sKGqHu3WNlzeJ9VrVo1AxWW84Wt+g+siOf4e\nknyNnef4SA9TI3O8j6G347yhde+bVhC5Y9Efu92Rc3z7z8pms+kKGrf4Qkv3K25tR8uSZ8Cm3g7W\nM0ft6hBRBolxF7T0w1aDePAWidzdQ+vZxgTD+Q1dZeLw1j+BiMtoRq7fiD/R5JpzPNhQISnfv6sQ\n9BzDYCaWEl2TkiBAgPfOUvKa4utcDMQGifboR2v3K/7RO9Bn5cJ++S/VrQgRlZAY70BrP2w1EV3E\nUKwgxXJ/d7QJ3x1tQpuNe1011Z9HW8eAiXnpaLZYgp4oGAsPeuH+RqQ4fGl+g69+2vFzEw7XtyPb\nbMD4voFnpNExYHzfwASWSC8uZKO+1Q7mkk0F0E6mHn9E82+R0Ba88gfw77Yg6fk30OLjwZQg/EFi\nnHDiKli31zTG3EOJjjnEtsFHo7yJPs+/9Xpd0J7UeH/QkyPXt6+wIs/xCfQhKUEfZ4OgECIHbCIH\n6/gsZzYbgvCHVs4t3tQI8f9egvCbByGY04CmJvUqQ0QtJMZdkDx38XzTkARrLOJ68XbdFszf/gg0\nvCJaCffmZzII6Cp9vY45Jl9KMd9dxXkHGpLgSaye45FGLwD53ZMA5vjsDSV+C1oRYoS6qD32XBQh\n/u1FsF9MABs8Qt3KEFENiXEos0pftBKLN7lwV9QM1MamI4041mRFv3SjWzaPWOrTUMMBRuem4qPK\nM9h/pg2/HuV/EQxv4xToJM5A6xMr46E2IgeOt7SDMRbx0B8aO0Jt+JergHPnwKbcpnZViChH82L8\nhx9+wNtvvw2z2YySkhIAgN1uxyuvvIJTp07hsssuw5QpUwAAK1aswKFDh9C/f3/Mnj1bxVpHN7F0\nk9NKiEgs9WkoCAyKRFKGOr7xPh5yIXKgzmIDVBDjBKEmfM+P4N9+CeEPL1IaQyJsfLxY1A75+flY\ntGiR27aysjLk5uaipKQEe/fuRUNDAw4ePIi2tjbMnz8fNpsNVVVVAduQPGXxGM9LnA8vCQdp8uDU\nIRmU49oLRgGYOiQDU4dkINEY/I3L12/Uzmkyppok6ICLeiRh6AUpSAhx3TE5fn8EEUl4wxmIb/4v\nhDseAkvPULs6RAyg+ce55OTkTtv279+PsWPHAgCGDh2KAwcOoK6uDiNGOGK2hg0bhn379iEvLy9g\nOySeYpNAspwEE/4gLfrja0IhnUe+MYb56O85HtICSwl65kwnSUQWHQPG9UlFUmIiuPVc0Mdr5c0V\nQQQKb2uD+PJCsImTKE6ckA3Ne8a9YbFYkJTkiDs1mUywWCxoaWlBYmKic1tLS4uaVSQ0hKtIDtQL\nJ4mELdWNzv2lRX9ct0UC8hwC7SKwpuIM1lScQbvHJFCrnZOIU4l2Efi48gw+2HW807jEM/SbjU24\nKIKvWAyW1Qts0s1qV4eIITTjGW9oaMDixYvdtqWlpeHBBx/stK8kwAGHMM/KykJraytaW1ud27x5\n1CsqKlBRUeH8e/r06TCbzXI2AwBgNBoVKVdrNqPNrs1mx4aqegCOfOFSvmtpRUjpb2lfo6ENAJBi\nMkGv18FmsyPBYIVBSHRuUxryHPpGeutBokddhDDeCMXiZFr6zcYu/PMPwOtOQXhkIRijgSXkQzNi\nvFu3biguLg5o3/z8fOzevRsDBw5ERUUFCgsLkZGRgfXr12Ps2LEoLy/HxIkTOx03ZMgQDBkyxG1b\nkwI5Qc1msyLlas1mtNm1c6Dd6hDY3hbu8aQgOwEA0NpqcR7f2mZDu80a0PHhoMYDjpaRYs6lz65s\nryHhoxY6BvROM8KgM4QlyAlC64ibvgLf+g2EpxaBGYxqV4eIMTQfpnLw4EEsWLAANTU1eOaZZ2Cz\n2TBy5EhUV1dj3rx5GDRoELp164b+/fvDaDSiuLgYOp0uqHhxIj4IdqKutzzXCXodhAiKB5pcfB6j\nEHzcOYULKA9jgCBo/lYSMeg3G3vw77aAf/I+hAfng6Wmq10dIgbRjGfcFwMGDMDcuXM7bb///vs7\nbaN0hkRXdHVz9JWzWposqNcZnItDRQq6ofvGV5iDJMLJa648nAOiSAHjrtC5Fjvw3d9DfO81hxDP\n6qV2dYgYRfNinCAiRSCxnoIg0I1WY/haIEjkjmXvabVN5bBz4EhDOwTBjhFZifTbIGIK/uNOiG+9\nBOGeJ8H6DFC7OkQMQ2KcIAJA8sCmmEzOGHJC2wgMzpSHJBKVQceAbLMBRn3oMeMEoUV42WaI7y+D\n8P/mgvXPV7s6RIxDYpwgOugqs4OOISIZVIjwiMUMHVpFWuyKHlKJWEL85nPwL1dD+H0JWO/+aleH\niANIjBMEfMeKE9og2PGhcSQIIli43Q7+9+Xge8shPP4nsB5ZaleJiBNIjBNxD+UF1jY0PtpFGhuj\noQ0F2Qk0NkTUws/WQ3zzRUCng/DE82CmzmuVEIRSkBgnCIIgCCJu4bu/g7jiJbDx14LdMANMR+GI\nRGQhMU7EPRRjrG1ofLQLTWwmohl+zgL+0Tvgu/4N4bePgA0apnaViDiFxDgR8wQSbyx9R7HjyuCv\nX4MZH0J72DlgEyl9JBFd8F3/hrhyGdjgERDm/RUsJVXtKhFxDIlxIqYJJt6YYpOVwbNf/X1HfR5d\ntIvAmoozEAQdbhrcLegVUgki0vDqKoirVwB1pyHMeRDsouFqV4kgSIwTBEEQBBHb8OO14F98CL7n\nR7DrZ4CNvwZMTxKI0AZ0JhIxTTDxxhSbrAz++pX6PLoxCsDUIRlITkqCta1V7eoQRCd41V6I//wI\nOLAHbOL1EGbeBZZoUrtaBOEGiXEi5glG5JEgVAaKB49djAKQaNTD2qZ2TQjCHf79Nogfvgl2zRSw\nOx4GS0hQu0oE4RUS40RcQBMztYPNZoed01jECnbuGFOC0BzDCyCMGE2pCgnNo3kx/vXXX2PDhg0A\ngOuuuw6FhYWw2+145ZVXcOrUKVx22WWYMmUKAGDFihU4dOgQ+vfvj9mzZ6tYa0JL0CRB7WDnwIaq\nerRb22gsYgBa9IfQMhQTTkQLmp/7PmLECCxcuBAlJSX4/PPPAQBlZWXIzc1FSUkJ9u7di4aGBhw8\neBBtbW2YP38+bDYbqqqqVK45QRAEQRAEQfhH84+NPXr0AAAIggBdx6um/fv3Y+zYsQCAoUOH4sCB\nA6irq8OIESMAAMOGDcO+ffuQl5enTqUJTUGTBLWDjgET89LRbLHQWMQAtOgPQRBE+GjeMy6xfv16\nFBQUAAAsFguSkpIAACaTCRaLBS0tLUhMTHRua2lpUa2uhPbQMRLiWkGv19FYxBA65hhTgiAIIjQ0\n4xlvaGjA4sWL3bZ169YNDzzwAPbv349du3bh0UcfBXBegAMOYZ6VlYXW1la0trY6tyUnJ3eyUVFR\ngYqKCuff06dPR05OjiLtMZvNipSrNZtkVzmUOjcl1Oo/sh0bdj3PTyVsKNVfVG501TXYcpW+dvoj\nHq8vZDt8NOMZ79atG4qLi93+PfDAA6irq8M777yDe+65B4w53Gn5+fnYvXs3AIfAHjhwIPLz81Fe\nXg4AKC8vR35+ficbQ4YMwfTp053/lOLDDz9UrGwt2SS70Yua7SDbsWdXCRtK1ZvKja66BltuPP7G\nyXb029aMGPfF6tWrcfbsWbzwwguYP38+rFYrRo4cierqasybNw+DBg1Ct27d0L9/fxiNRhQXF0On\n01G8OEEQBEEQBKF5NBOm4os777zT6/b777+/0zZKZ0gQBEEQBEFEE7o//vGPf1S7ErFIz54948Im\n2Y1e1GwH2Y49u0rYUKreVG501TXYcuPxN062o9s245xzWUoiCIIgCIIgCCIoNB8zThAEQRAEQRCx\nColxgiAIgiAIglAJEuMEQRAEQRAEoRKaz6ZCEIQ8tLa2oqWlBSkpKc7VamPddjy2WWnbVVVV2Ldv\nH1paWpCcnIz8/HxKJUuohprnY7zaJuSHJnCGgd1ux86dOzv9IAoKCqDTKbc8dDzZVautra2tWL9+\nPfbt2weLxQKTyYT8/HxcffXVSEpKUsyuEpSXl+Ojjz5CYmIiTCaTc7Xam266CcOHD49J2/HY5kjY\nXrFiBWw2G4YNG+ZcCbm8vBw6nQ5z5swJuVylfudKlBtNdQWUuZYpdX0MtlylzsdAiFfbat4b1bId\nEbucCJnFixfztWvX8qqqKn7s2DFeVVXF165dyxcvXkx2o9gm55w/99xzfMuWLbypqYnbbDbe1NTE\nt2zZwp977jlF7SrB008/zVtbW922tba28j/84Q8xazse2xwJ2/PmzQtqe6Ao9TtXotxoqivnylzL\nlLo+BluuUudjIMSrbTXvjWrZjoRdClMJg9OnT3dafGjAgAGYN28e2Y1imwDQ0tKCMWPGQBAc0ypS\nUlIwZswYrFu3TlG7SqDX61FdXY38/HznturqahiNxpi1HY9tjoTt/v37Y9myZRgxYgQSExPR2tqK\n8vJy9O/fP6xylfqdK1FuNNUVUOZaptT1MdhylTofAyFebat5b1TLdiTsUphKGHz66aeoqKjAkCFD\nkJSUhNbWVlRWVmLw4MGYPHky2Y1SmwCwadMmfP311+jdu7fzNWBNTQ2uuuoqFBUVKWZXCerq6rB2\n7VrU1NRAFEUIgoA+ffpg8uTJ6N69e0zajsc2R8r2wYMHceDAAbS0tDhf14YrApT6nStRbjTVFVDm\nWqbU9TGUcpU4HwMlHm2reW9Uy3Yk7JIYD4O6ujrodDpUVVXBYrHg5MmTyMzMxLhx46DXK/fSYefO\nnejfvz9qamqcP8S8vDykpaUpZhMAbDYbNm/ejNOnTyMrKwt2ux2nTp3Cddddh+TkZMXsVlZWOtua\nnJyMkSNHIjMzUzF7EjabDcePH3fGiGVnZysap04Q8UxlZSVqa2thMpmc17QTJ064eflDobGxEVVV\nVc5r5cGDBzFt2rSQy1Pquq/k9VWJa5lUpnRdzsrKkuW+51luV3VtaGjAgQMHnG0bOHAgunXrFnY9\nAkFN22qi5r1RLdtK2yUxHgbz589HcXEx/va3vyEhIQFDhw7FoUOHcPDgQTz00EOK2f3d736HzMxM\npKWlYfTo0Rg1ahRSUlIUsyfx/PPPY+DAgWhpacHBgwdx2WWXwWw2Y8uWLfjDH/6giM13330X7e3t\n6NevH3bv3g2j0QhBEDBo0CBMmDBBEZuAehNHlaC6uhoffPABLBYLOOcQBAFJSUmYMWMG+vbtG5O2\n47HNatsOh7feegtnz56FXq9HY2Mj7r77bqSlpTmvsaEyb948MMbgepurqalBnz59MH/+/JDKVOq6\nr9T1VYlrmSiK+OGHH6DT6TB8+HDn6/udO3eioKAg5Lp64+9//ztmzJjh9bu1a9di7969GDp0qNNj\nWVFRgUGDBmHKlCmy1kNLttVEzXtjTCevkC36PA4pKSlx+1/ij3/8o6J2pfKPHz/OP/nkE15cXMwX\nLFjA//GPf0TELuecP/TQQ163K2mT8/N9PX/+fMVscq7exFElePrpp/mZM2fctp05c4bPnTs3Zm3H\nY5vVth0OrvU7fPgwnzdvHt+/f3/Y15bPPvuMv/zyy7y8vNy5beHChWGVqdR1X6nrq1KTWFeuXMk/\n/PBD/vTTT/Pa2lpZ6nrXXXfxp59+ms+dO9f5b/bs2T4nJvo6ryNxvqtpW03UvDfGcvIKmsAZBkVF\nRXjttdeQkZGBl156CRdffDGOHDmCAQMGRMT+BRdcgBtvvBE33ngjGhoaUFZWpqi9pKQkrFmzBi0t\nLUhNTcVnn32G5ORkRZ9I09LSsHbtWvTp0weVlZXo1asXALh5upRArYmjkUTpPtSi7Xhss9q2A4Fz\nDpvNBr1ej759++LRRx/FkiVLUFNTE1a5N9xwA6xWK7755husX78ehYWFYfeFUtd9pa6vSlzLzpw5\n4yzzqquuwiuvvIJrr702rHoCwOzZs7F9+3YMHz4c48ePh16vx7PPPounnnrK6/49evTAxx9/jOHD\nhyMpKQkWiwW7d++OSBijmrbVRM17Yywnr6AwlTCpq6vDrl27cPbsWeeri379+ilqc9euXbjkkksU\nteENq9WKXbt2wWw2Iz8/Hxs3bkRrayvGjx+vWJiM3W7Hjh07cPLkSeTk5GDkyJEQBAF1dXWKToZT\na+KoElRXV+PDDz9ES0sLRFEEYwwpKSmYPn06+vTpE5O247HNatsOh/3796NHjx5u8bZ2ux3btm1D\nYWGhLDZsNhs2btyIY8eOYebMmWGVpcR1X6nrqxLXsuLiYjzxxBPOHMtWqxXLli3Dzp078dZbb4Vc\nV4nvv/8eGzduRH5+PsrKynyKHrvdjrKyMuzfv98t//PIkSMjEjKhlm01UfPeGMvJK0iME4QPzp49\n65ykJU0oO3XqFAYOHKh21QiCIAJG7mvZyZMnkZyc3GliaWVlJS6++GI5qgwA2L17tzNVZzB1tVqt\nMBgMstUjGKQ3PLGMmvdGpSZ6d8WBAwdQVVWF5ORkJCYmoqWlBePHj3fOlwgXEuME4QVRFL1uX7hw\nIebOnRvh2oRHXV0dPv74Y9TW1jrT3eXm5mLKlCnIyMiISdvx2Ga1bRPaRIlrmVLXx2DL3bx5Mz7/\n/HPodDoUFBRg8uTJYIyFPfE3HJ555hk8/fTTqtiOBGreG5Wa6N0Vy5cvh9VqRXt7OwwGA5KSkmAy\nmXDmzBnce++9stiI7cc3ggiRWbNmeX3SPnz4cOQrEyZLlizBzJkz3bwWBw4cwNKlSxWPtVPLdjy2\nWW3bhDZR4lqm1PUx2HLXrVuHhQsXQhAErF+/HosWLcI999wTVh0CxdfvKdx5DlpHzXtjVVUVSkpK\nAABHjhzBiy++iFmzZilut7a21pl96eGHH8YLL7wAALI+AJAYJwgv5Obm4pFHHun0GnbBggUq1Sh0\nrFYrcnNz3bbl5uaivb09Zm3HY5vVtk1oEyWuZUpdH0MpV4rPvuaaa9C/f388//zzOHv2bFj1CISm\npiYsWrSoU0hKNN4jgkHNe6NSE727wvVtwK233ur8zBiTzQaFqRCEF+rr65GSktIp7tBut0fd5Jzd\nu3djzZo1MBqNzskn7e3t+NWvfoVhw4bFpO14bLPatgltosS1TKnrY7Dlfv311xgxYgR69Ojh3FZX\nV4fVq1fjzjvvDLkegfDDDz8gPz+/kyg9ePBgxDKqqYGa98ZITPT2Rk1NDXJyctzaZ7PZsGvXLowa\nNUoWGyTGCSJOaG9vd65CmJCQEBe247HNatsmCLVYvHgxHnjgAVVs//Wvf8WDDz6oim0i+qEwFYKI\ncVpbW7F+/Xrs27fPLQXX1Vdf7UxNFmu247HNatsmCLVpaGhQzXYkQmOI2IXEOEHEOIsXL0ZRURGu\nvPJKZ+jCTz/9hMWLF+OJJ56ISdvx2Ga1bRMEQRChIU+CRIIgNEtLSwvGjBmDlJQU6HQ6pKSkYMyY\nMWhpaYlZ2/HYZrVtEwRBEKFBnnEiJB5++GHccccdAS3wcO+99+Kuu+6iCWQqcc0112D+/Pno3bs3\nTCYTLBYLampqcPXVV8es7Xhss9q2CYIgiNCgCZyEV7wJ6G+//RbffPONM89nMGXdfffdGDp0qNzV\nJALEZrPh+PHjzjji7OzsiGWFUct2PLZZbdvxxt69e/Huu++itrYWgiCgV69emD17NmpqavDqq69i\n5syZuPHGG53733XXXbj//vudToza2lq899572LNnDzjnGDBgAG699VZnHueFCxdi6NChziW36+rq\ncPfdd+N//ud/Om17/fXXkZaWFuEe0BYNDQ1umTbixTYR/ZBnnPCJnDk0CfWw2+0oKyvDvn370NLS\nguTkZOTn56OgoEBxkaaW7Xhss9q24w2LxYI//elPuPPOOzF27FhYrVbs3bvXmfItJSUFn3zyCa65\n5hokJiYCcL+mHj9+HHPnzsW1116L++67DzqdDhs2bHCu4Jifn4/Bgwdjz549TuFdWVmJnJycTtuy\ns7PjXogDUFUMkxAnwoFixomQuPfee1FeXg7AkUbt5Zdfxpw5c/D73/8en3zyCe6++263/Q8dOoRH\nH30Us2fPxl//+ldYrVY1qh2XLF26FCdOnEBhYSFuuukmFBYW4sSJE1i6dGnM2o7HNqttO944duwY\nGGMYN24cGGMwGo0YPnw4+vTpAwDo1asXBg0ahM8//9zr8atWrcKgQYNwyy23IDk5GYmJibjuuutQ\nVFSE9957DwAwePBg/Oc//3Ees3fvXlx//fWoqqpy2zZ48GAFW0oQhNKQGCd80lUEk+TlWbVqFc6c\nOYOlS5di7ty52LRpU6d9t2/fjj/84Q94+eWXceTIEXz77bdKVJnwwunTpzF58mQMGDAAWVlZGDBg\nACZPnozTp0/HrO14bLPatuONnJwcCIKApUuXYteuXWhubu60z/Tp0/HFF194nUBbXl6OMWPGdNo+\nZswY/Oc//4HVasXAgQNhtVqdS43v2bMHw4cPR1ZWlts2EuMEEd1QmArhk0WLFnVaccrbymLbt2/H\nb3/7W5hMJphMJkyaNAmrVq1y2+e6665zvsYbOXKk80ZCKM+oUaPw3HPPYciQIc50d5WVlRg5cmTM\n2o7HNqttO95ISkpCSUkJPvnkEyxbtgwNDQ249NJL8bvf/c65T79+/TB8+HCsXbsWM2fOdDu+qakJ\n6enpncpNT08H5xzNzc1IT0/HhRdeiMrKSmRmZsJisaBnz54YPHiwc1ttbW1AE+kJgtAuJMYJnzz2\n2GNuky6lCZye1NXVISMjw/l39+7dO+3jGk9nNBpRX18vc20JX9x4442YMGECqqqqYLFYkJGRgQkT\nJuDUqVMRsT1w4EDU1tYiKSnJafvEiROK27344otRVVWFpKQkpKenIy0tDePHj1fUrmT7iiuuQFVV\nFVpaWpCRkYH29nZnjK/StidMmICDBw+iqqoKWVlZmDZtGgYOHKi47XikV69euOeeewAAR48exZIl\nS7BixQqMGDHCuc+MGTPw5JNP4oYbbnA71mw2e70O1tfXgzHmXGZdihvv2bMnBg0aBAC46KKLsGHD\nBvTs2ROZmZnIzMxUqokEQUQAClMhwiY9PR1nzpxx/u362Rs0MTSyiKIIs9mMSy65BOPGjcMll1wC\ns9mMlStXKm77rbfewtdff40DBw5g8+bNyMvLQ1pamuK2ly9fjq+++gp79uzBjz/+iJ9++gnHjh3D\nq6++qqhdAJg3bx5eeOEFfPzxx/jqq6+wdu1afPHFFyguLlbc9sKFC5GWloajR4/iwIEDsFgsWLdu\nHd5//33Fbcc7OTk5mDBhAmpqajpt/8UvfoE1a9a4bR82bBi2bdvWqZxt27Zh0KBBMBqNABxifO/e\nvaisrHR6wPPz8/Gf//wHlZWVFKJC+GTWrFn49a9/jV//+teYMWMGbrvtNuffmzdvxocffogZM2bg\nyy+/dDvuyy+/xIwZMzq94T558iRmzJiBN954w217XV0d5syZg7179zq3nT59GnPmzMGBAweUa2AM\nQZ5xImzGjh2Ljz/+GHl5eWhra8M//vEPv/tTNs3IMmvWLGeqNFciESpUVVXlTIV55MgRvPjii5g1\na5bidmtrazF//nwAjpz4L7zwAgBERBCPHj0aR44cwYQJE5xvlp599lk89dRTitu22WwAgB07dqC4\nuBiCIOCaa67B3LlzFbcdbxw9ehTff/89xo0bh+7du+P06dPYsmWL19/atGnT8Mgjj7htu/nmm/Hk\nk0/igw8+wA033ACdTodvv/0WGzdudBuv/Px8NDc3Y9OmTc7tKSkpMJvN2LRpE2699VZlG0pELe+8\n847zs7cUw6tWrUJ2djY2btyISZMmObeXlpYiJyenk+OstLQUvXv3xtatWzF79mzo9Q4J2b17d8yc\nORPLli3D888/D4PBgOXLl2PixIn0Vi5ASIwTQeHNqz1t2jQsX74c9913H9LT01FYWIgNGzb4LYO8\n45EjNzcXjzzyiPO1t8SCBQsUt805h81mg16vR9++ffHoo49iyZIlnbyHciOKovOzq1iJxHl3ww03\nwGq14ptvvsH69etRWFgYsQfQ2tpaLFmyBCdOnIDNZnN6Vyl7kfwkJiZi//79+Pzzz51pJEeOHIlZ\ns2Zh+/btbudaz549UVRUhPXr1zu3ZWVloaSkBO+99x7uvfdecM6Rl5fnTGsokZCQgLy8PBw9etSZ\nqQVweMzXr19PnnEiLPLy8nDw4EHU1tYiNzcXNTU1sFqtGDBggNt1i3OOjRs34rbbbsP777+PsrIy\ntwnIv/zlL7FlyxasWrUKOTk5OHbsGB5++GE1mhSV0KI/hOx89dVX2LZtW0S8kETX1NfXIyUlxZn/\nWMJutyuee3r//v3o0aOH25wBu92Obdu2obCwUDG7NTU1yMnJ6TTuVOFNAAAGJklEQVQBedeuXRg1\napRidj2x2WzYuHEjjh071mkCnxKcPHnS+bl79+7Q6/U4d+4c9uzZg0svvVRx+wRBaBNfnvHjx48j\nNzcXFosFM2fOxLvvvouUlBTU1NQgKysLN998MwBH1p7nnnsOb7zxBtasWYPq6mo8/vjjbjZOnDiB\nxx57DIIg4JFHHsGQIUMi2sZohmLGibBpaGjA3r17IYoijh49is8//xwFBQVqV4voID09vZMQBxCR\nRWAuvPDCToth6HQ6RYU4APTu3btT+/R6fUSFuGTzyiuvjIgQBxweWOmf9Ao5MTGRhDhBEJ2QfLHj\nx4/Hli1bYLfbsXXrVq8T3UtLSzFy5EgYjUaMHTsWu3btQmNjo9s+mZmZ6N69O0wmE72xCRIS40TY\n2Gw2LF++HLfffjtKSkpQUFCAa6+9Vu1qEQRBEAThB8YYMjMzkZWVhffffx/Z2dlu2dEAx8J+27dv\nx9ixYwE4Unb27NkTmzdvdttv7dq1MJvNSEtLw6effhqxNsQCFDNOhE1mZqZzghxBEARBENGB5B0v\nKirCq6++invvvdf5nTTvYceOHWhtbcXrr7/uzKTS0tKC0tJS58TP2tpafPbZZ3j22Wdhs9kwd+5c\njBkzBllZWRFuUXRCYpwgCIIgCCKOGTduHDIzM52Th12nE5aWlmLixIluk+HPnDmDJ598EtXV1cjN\nzcWrr76KyZMnIycnB4Bjob9ly5bR3LEAoTAVgiCinoqKCtx9991qV4MgCCJqcM1sZjQaMXToUGcG\nJml7XV0ddu/ejeuvvx5paWnOfwMGDMAll1yC0tJSrFu3Dlar1W1hs2nTpqGhocHrQoFEZyibCkEQ\nmmHhwoW48MILMX36dLftO3fuxPLly/Haa69BEDr7ECoqKvDyyy9HZFEfgiAIgpAT8owTBKEZrrji\nCmzatKnT9o0bN2L8+PFehThBEARBRDMUM04QhGYoKCjA8uXLsWfPHmdqrObmZnz//fd49tlnsWLF\nCucS4mPHjsVtt93mTOHnyowZM/DSSy/hggsuAAAsXboUGRkZuOWWW1BRUYElS5Zg0qRJ+PTTT6HT\n6XDHHXdAr9fjrbfeQlNTE2688UZMmTIFgCN28pNPPsG//vUvWCwWDB06FL/97W+RkpISoV4hCIIg\nYhlyMxEEoRmkHLalpaXObdu2bUOvXr2wfft27N+/H4sWLcKiRYtQVVWFNWvWBFSu56qvZ8+ehdVq\nxeuvv47p06dj2bJl2Lx5M/785z+jpKQEq1evxqlTpwAA69atQ1lZGebPn49ly5YhOTkZb775prwN\nJwiCIOIWEuMEQWiKCRMm4N///jdsNhsAR4jKhAkTsHnzZtx8881ITU1Famoqpk2bho0bNwZcruv0\nGJ1Oh1/96lcQBAHjxo1Dc3MzJk2ahMTEROTm5iI3NxdHjhwBAKxfvx633HKLc0XLm2++Gdu3b4co\nivI2nCAIgohLKEyFIAhNcdFFF8FsNmPHjh0YMGAADhw4gEceeQQrV65EZmamc7/MzEzU19eHZMNs\nNrtlEQDgtlKo0WjEuXPnAACnTp3CokWL3OLVdTodzp49i/T09JDsEwRBEIQEiXGCIDRHUVERSktL\n8fPPP+OSSy5BWloa0tPTcerUKeTm5gIATp8+7VMMG41GtLW1Of+ur6/vtKpcoGRmZuKee+5x5t8l\nCIIgCDmhMBWCIDTHhAkT8NNPP+Gbb77BhAkTAACXX345PvroIzQ2NqKxsRGrV69GUVGR1+P79euH\nzZs3QxRF7Nq1C3v27Am5LldffTVWrlyJ06dPAwAaGxtRVlYWcnkEQRAE4Qp5xgmC0Bw9evTARRdd\nhCNHjmDUqFEAgKlTp6K1tRWPPvooAEc2lalTp3o9fs6cOVi6dCn++c9/oqCgAKNHjw65LpMmTQLn\nHM888wzq6+uRmpqKyy+/3FkvgiAIgggHWvSHIAiCIAiCIFSCwlQIgiAIgiAIQiVIjBMEQRAEQRCE\nSpAYJwiCIAiCIAiVIDFOEARBEARBECpBYpwgCIIgCIIgVILEOEEQBEEQBEGoBIlxgiAIgiAIglAJ\nEuMEQRAEQRAEoRIkxgmCIAiCIAhCJf4/INs5/hz4wYoAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pandas.tools.plotting import scatter_matrix\n", + "scatter_matrix(wdf.ix['2014', ['High', 'Volume', 'SNOW', 'TMAX']], diagonal='kde');" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2015-07-02', '2015-07-01', '2015-06-30', '2015-06-29',\n", + " '2015-06-26', '2015-06-25', '2015-06-24', '2015-06-23',\n", + " '2015-06-22', '2015-06-19', \n", + " ...\n", + " '1983-04-13', '1983-04-12', '1983-04-11', '1983-04-08',\n", + " '1983-04-07', '1983-04-06', '1983-04-05', '1983-04-04',\n", + " '1983-03-31', '1983-03-30'],\n", + " dtype='datetime64[ns]', name=u'Date', length=8092, freq=None, tz=None)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wdf.index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/pandas-pyweb-il/gendb.py b/pandas-pyweb-il/gendb.py new file mode 100755 index 0000000..386d494 --- /dev/null +++ b/pandas-pyweb-il/gendb.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python + +import pandas as pd +import sqlite3 +from os import remove + + +def read_csv(path): + return pd.read_csv(path, dtype={'DATE': str}) + + +df = pd.concat([ + read_csv('564288.csv'), + read_csv('564289.csv'), + pd.read_csv('564290.csv'), +]) + +dbfile = 'weather.db' + +df = df[df['STATION'] == 'GHCND:USW00094846'] +try: + remove(dbfile) +except OSError: + pass +conn = sqlite3.connect(dbfile) +df.to_sql('weather', conn) diff --git a/pandas-pyweb-il/weather.db b/pandas-pyweb-il/weather.db new file mode 100644 index 0000000..a9aded0 Binary files /dev/null and b/pandas-pyweb-il/weather.db differ diff --git a/py-descriptors/desc-dot.ipynb b/py-descriptors/desc-dot.ipynb new file mode 100644 index 0000000..4fef8ed --- /dev/null +++ b/py-descriptors/desc-dot.ipynb @@ -0,0 +1,1302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Descriptors and Python's Dot Operator\n", + "\n", + "Miki Tebeka .:. [353solutions](http://353solutions.com) .:. Python, Scientific Python and Go workshops & consulting.\n", + "\n", + "## References\n", + "* [`__getattr__`](https://docs.python.org/3/reference/datamodel.html#object.__getattr__) and [`__getattribute__`](https://docs.python.org/3/reference/datamodel.html#object.__getattribute__)\n", + "* [Implementing Descriptors](https://docs.python.org/3/reference/datamodel.html#implementing-descriptors)\n", + "* [Descriptors HowTo](https://docs.python.org/3/howto/descriptor.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'r'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp = open('/dev/random')\n", + "fp.mode" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'r'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "getattr(fp, 'mode')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'r'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__getattribute__('mode')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mode': 'r'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dot lookup first looks in object __dict__\n", + "fp.__dict__" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mode': 'r'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# vars is shorthand for __dict__\n", + "vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'mode' in vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "UnicodeDecodeError", + "evalue": "'utf-8' codec can't decode byte 0xeb in position 3: invalid continuation byte", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m/opt/anaconda/lib/python3.5/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m 319\u001b[0m \u001b[1;31m# decode input (taking the buffer into account)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 322\u001b[0m \u001b[1;31m# keep undecoded input until the next call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xeb in position 3: invalid continuation byte" + ] + } + ], + "source": [ + "# opended in text mode, can't read\n", + "fp.read(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<_io.BufferedReader name='/dev/random'>" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp = open('/dev/random', 'rb')\n", + "fp" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "b'm\\x1b\\xccH\\xb8\\xb5\\xd8n\\x93\\xd3'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.read(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'rb'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.mode" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mode not in object\n", + "'mode' in vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# . lookup looks next in class\n", + "'mode' in vars(fp.__class__)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.readlines" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'readlines' in vars(fp.__class__)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(_io._BufferedIOBase,)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__class__.__bases__" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'readlines' in vars(fp.__class__.__bases__[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(_io.BufferedReader, _io._BufferedIOBase, _io._IOBase, object)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__class__.__mro__" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# . lookup goes over the MRO (method resolution order)\n", + "for cls in fp.__class__.__mro__:\n", + " if 'readlines' in vars(cls):\n", + " print(cls)\n", + " break\n", + "else:\n", + " print('not found')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'_io.BufferedReader' object has no attribute 'no_such_attr'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_such_attr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m: '_io.BufferedReader' object has no attribute 'no_such_attr'" + ] + } + ], + "source": [ + "# if . lookup don't find - it raises AttributeError\n", + "fp.no_such_attr" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# A descriptor is class implementing __get__ (can also have __set__ and __del__)\n", + "class Desc:\n", + " def __get__(self, inst, owner):\n", + " print('__get__: inst: %r, owner: %r' % (inst, owner))\n", + " def __set__(self, inst, value):\n", + " print('__set__: inst: %r, value: %r' % (inst, value))\n", + " \n", + "class Stock:\n", + " symbol = Desc()\n", + " \n", + "s = Stock()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__get__: inst: <__main__.Stock object at 0x7f7d1513a390>, owner: \n" + ] + } + ], + "source": [ + "s.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__get__: inst: None, owner: \n" + ] + } + ], + "source": [ + "Stock.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__set__: inst: <__main__.Stock object at 0x7f7d1513a390>, value: 'BRK.A'\n" + ] + } + ], + "source": [ + "s.symbol = 'BRK.A'" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216298.8)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Field:\n", + " def __init__(self):\n", + " self._value = None\n", + " \n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return self._value\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " self._value = value\n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + " \n", + " \n", + "class SymbolField(Field):\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "class PriceField(Field):\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, not %s' % type(value))\n", + " if value <= 0:\n", + " raise ValueError('price must be > 0, got %.2f' % value)\n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " \n", + " def __init__(self, symbol, price):\n", + " self.symbol, self.price = symbol, price\n", + " def __repr__(self):\n", + " return 'Stock(%r, %r)' % (self.symbol, self.price)\n", + " \n", + "brka = Stock('BRK.A', 216298.80)\n", + "brka\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "price must be > 0, got -3.20", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbrka\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m3.2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36m__set__\u001b[1;34m(self, inst, value)\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__set__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massert_valid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36massert_valid\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'price must be float, not %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'price must be > 0, got %.2f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 29\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mStock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: price must be > 0, got -3.20" + ] + } + ], + "source": [ + "brka.price = -3.2" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# DON'T DO, price won't be a descriptor any more\n", + "# Stock.price = 7" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('V', 97.48)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = Stock('V', 97.48)\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('V', 97.48)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# _value is class attribute, shared among instances = Fields\n", + "brka" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock(216298.8, 216298.8)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2'nd try = store in object\n", + "class Field:\n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, '_field')\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, '_field', value)\n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + " \n", + " \n", + "class SymbolField(Field):\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "class PriceField(Field):\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, not %s' % type(value))\n", + " if value <= 0:\n", + " raise ValueError('price must be > 0, got %.2f' % value)\n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " \n", + " def __init__(self, symbol, price):\n", + " self.symbol, self.price = symbol, price\n", + " \n", + " def __repr__(self):\n", + " return 'Stock(%r, %r)' % (self.symbol, self.price)\n", + "\n", + "brka = Stock('BRK.A', 216298.80)\n", + "brka\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_field': 216298.8}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Every descriptior is using _field, need unique name\n", + "vars(brka)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216298.8)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 3'rd try - use _attr per class\n", + "class Field:\n", + " _attr = None\n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, self._attr)\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, self._attr, value)\n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + " \n", + " \n", + "class SymbolField(Field):\n", + " _attr = '_symbol'\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "class PriceField(Field):\n", + " _attr = '_price'\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, not %s' % type(value))\n", + " if value <= 0:\n", + " raise ValueError('price must be > 0, got %.2f' % value)\n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " \n", + " def __init__(self, symbol, price):\n", + " self.symbol, self.price = symbol, price\n", + " \n", + " def __repr__(self):\n", + " return 'Stock(%r, %r)' % (self.symbol, self.price)\n", + "\n", + "brka = Stock('BRK.A', 216298.80)\n", + "brka\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_price': 216298.8, '_symbol': 'BRK.A'}" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars(brka)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216297.8, 216297.8)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Field:\n", + " _attr = None\n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, self._attr)\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, self._attr, value)\n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + " \n", + " \n", + "class SymbolField(Field):\n", + " _attr = '_symbol'\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "class PriceField(Field):\n", + " _attr = '_price'\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, not %s' % type(value))\n", + " if value <= 0:\n", + " raise ValueError('price must be > 0, got %.2f' % value)\n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " low = PriceField()\n", + " \n", + " def __init__(self, symbol, price, low):\n", + " self.symbol, self.price, self.low = symbol, price, low\n", + " \n", + " def __repr__(self):\n", + " return 'Stock(%r, %r, %r)' % (self.symbol, self.price, self.low)\n", + "\n", + "brka = Stock('BRK.A', 216298.80, 216297.80)\n", + "brka\n", + "# Both price and low use _price in the object" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216298.8, 216297.8)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# final version - using unique name per field\n", + "# some people use a dict inside the object to hold all the descriptor fields - so __dict__ has only one extra key\n", + "from itertools import count\n", + "class Field:\n", + " _next_id = count().__next__\n", + " \n", + " def __init__(self):\n", + " self._attr = '_%s_%d' % (self.__class__.__name__, self._next_id())\n", + " \n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, self._attr)\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, self._attr, value)\n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + " \n", + " \n", + "class SymbolField(Field):\n", + " _attr = '_symbol'\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "class PriceField(Field):\n", + " _attr = '_price'\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, not %s' % type(value))\n", + " if value <= 0:\n", + " raise ValueError('price must be > 0, got %.2f' % value)\n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " low = PriceField()\n", + " \n", + " def __init__(self, symbol, price, low):\n", + " self.symbol, self.price, self.low = symbol, price, low\n", + " \n", + " def __repr__(self):\n", + " return 'Stock(%r, %r, %r)' % (self.symbol, self.price, self.low)\n", + "\n", + "brka = Stock('BRK.A', 216298.80, 216297.80)\n", + "brka\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_PriceField_1': 216298.8,\n", + " '_PriceField_2': 216297.8,\n", + " '_SymbolField_0': 'BRK.A'}" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars(brka)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Point(1.0, 2.0)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Python has classmethod for alternate initializer\n", + "class Point:\n", + " def __init__(self, x, y):\n", + " self.x, self.y = x, y\n", + " \n", + " @classmethod\n", + " def from_str(cls, value):\n", + " \"\"\"From string in format 'x,y'\"\"\"\n", + " x, y = map(float, value.split(\",\"))\n", + " return cls(x, y)\n", + " \n", + " def __repr__(self):\n", + " return '%s(%r, %r)' % (self.__class__.__name__, self.x, self.y)\n", + " \n", + "p = Point.from_str('1, 2')\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "117" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# partial demo\n", + "from functools import partial\n", + "def add(x, y):\n", + " return x + y\n", + "\n", + "add7 = partial(add, 7)\n", + "add7(110)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Point(1.0, 2.0)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Write our own classmethod using descriptor\n", + "class ClassMethod:\n", + " def __init__(self, func):\n", + " self.func = func\n", + " \n", + " def __get__(self, inst, owner):\n", + " return partial(self.func, owner)\n", + " \n", + "class Point:\n", + " def __init__(self, x, y):\n", + " self.x, self.y = x, y\n", + " \n", + " @ClassMethod\n", + " def from_str(cls, value):\n", + " \"\"\"From string in format 'x,y'\"\"\"\n", + " x, y = map(float, value.split(\",\"))\n", + " return cls(x, y)\n", + " \n", + " def __repr__(self):\n", + " return '%s(%r, %r)' % (self.__class__.__name__, self.x, self.y)\n", + " \n", + "p = Point.from_str('1, 2')\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hi, I am Tim. How are you?'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Person:\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " def greet(self):\n", + " return 'Hi, I am %s. How are you?' % self.name\n", + " \n", + "p = Person('Tim')\n", + "p.greet()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Regular methods are descriptors\n", + "p.greet.__get__" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hi, I am Tim. How are you?'" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meth = p.greet.__get__(p, Person)\n", + "meth()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__del__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__enter__',\n", + " '__eq__',\n", + " '__exit__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getstate__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__next__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '_checkClosed',\n", + " '_checkReadable',\n", + " '_checkSeekable',\n", + " '_checkWritable',\n", + " '_dealloc_warn',\n", + " '_finalizing',\n", + " 'close',\n", + " 'closed',\n", + " 'detach',\n", + " 'fileno',\n", + " 'flush',\n", + " 'isatty',\n", + " 'mode',\n", + " 'name',\n", + " 'peek',\n", + " 'raw',\n", + " 'read',\n", + " 'read1',\n", + " 'readable',\n", + " 'readinto',\n", + " 'readinto1',\n", + " 'readline',\n", + " 'readlines',\n", + " 'seek',\n", + " 'seekable',\n", + " 'tell',\n", + " 'truncate',\n", + " 'writable',\n", + " 'write',\n", + " 'writelines']" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dir show all available attributes (unless you go funky with __getattr__ or __getattribute__)\n", + "dir(fp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thank You!" + ] + }, + { + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/py-descriptors/desc-solution.ipynb b/py-descriptors/desc-solution.ipynb new file mode 100644 index 0000000..b814c97 --- /dev/null +++ b/py-descriptors/desc-solution.ipynb @@ -0,0 +1,1459 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Descriptors and the Python dot operator\n", + "\n", + "Miki Tebeka, [353Solutions](http://353solutions.com)\n", + "\n", + "* https://docs.python.org/3/reference/datamodel.html#object.__getattr__\n", + "* https://docs.python.org/3/reference/datamodel.html#implementing-descriptors\n", + "* https://docs.python.org/3/howto/descriptor.html" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<_io.TextIOWrapper name='/dev/random' mode='r' encoding='UTF-8'>" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp = open('/dev/random')\n", + "fp" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'r'" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.mode" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'r'" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "getattr(fp, 'mode')" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'r'" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__getattribute__('mode')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mode': 'r'}" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__dict__" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mode': 'r'}" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'mode' in vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "UnicodeDecodeError", + "evalue": "'utf-8' codec can't decode byte 0xeb in position 0: invalid continuation byte", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m/opt/anaconda/lib/python3.5/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m 319\u001b[0m \u001b[1;31m# decode input (taking the buffer into account)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 322\u001b[0m \u001b[1;31m# keep undecoded input until the next call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xeb in position 0: invalid continuation byte" + ] + } + ], + "source": [ + "fp.read(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<_io.BufferedReader name='/dev/random'>" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp = open('/dev/random', 'rb')\n", + "fp" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "b'\\x926\\xf9r\\x9c\\xb2\\x15\\xc6C\\x1b'" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.read(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'rb'" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.mode" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'mode' in vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'mode' in vars(fp.__class__)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.readlines" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'readlines' in vars(fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'readlines' in vars(fp.__class__)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(_io._BufferedIOBase,)" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__class__.__bases__" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'readlines' in vars(fp.__class__.__bases__[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(_io.BufferedReader, _io._BufferedIOBase, _io._IOBase, object)" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp.__class__.__mro__" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "for cls in fp.__class__.__mro__:\n", + " if 'readlines' in vars(cls):\n", + " print(cls)\n", + " break\n", + "else:\n", + " print('no idea where \"readlines\" comes from')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Descriptors" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Desc:\n", + " def __get__(self, inst, owner):\n", + " print('__get__: inst=%r, owner=%r' % (inst, owner))\n", + " \n", + " def __set__(self, inst, value):\n", + " print('__set__: inst=%r, value=%r' % (inst, value))\n", + " \n", + "\n", + "class Stock:\n", + " symbol = Desc()\n", + " \n", + "s = Stock()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__get__: inst=<__main__.Stock object at 0x7fa16f426e48>, owner=\n" + ] + } + ], + "source": [ + "s.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__get__: inst=None, owner=\n" + ] + } + ], + "source": [ + "Stock.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__set__: inst=<__main__.Stock object at 0x7fa16f426e48>, value='brk.a'\n" + ] + } + ], + "source": [ + "s.symbol = 'brk.a'" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'brk.a'" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Stock.symbol = 'brk.a'\n", + "Stock.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'brk.a'" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216298.0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Field:\n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return self._value\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " self._value = value\n", + " \n", + " def assert_valid(self, value): # Mention abc\n", + " pass\n", + "\n", + "\n", + "class SymbolField(Field):\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "\n", + "class PriceField(Field):\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, got %s' % type(value))\n", + " \n", + " if value <= 0:\n", + " raise ValueError('price most be positive, got %s' % price)\n", + " \n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " \n", + " def __init__(self, symbol, price):\n", + " self.symbol = symbol\n", + " self.price = price\n", + " \n", + " def __repr__(self):\n", + " cls = self.__class__.__name__\n", + " return '%s(%r, %r)' % (cls, self.symbol, self.price)\n", + "\n", + "brka = Stock('BRK.A', 216298.00)\n", + "brka" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "97.48" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = Stock('V', 97.48)\n", + "v.price" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "97.48" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brka.price" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock(216298.0, 216298.0)" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Field:\n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, '_field')\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, '_field', value) \n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + "\n", + "\n", + "class SymbolField(Field):\n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "\n", + "class PriceField(Field):\n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, got %s' % type(value))\n", + " \n", + " if value <= 0:\n", + " raise ValueError('price most be positive, got %s' % price)\n", + " \n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " \n", + " def __init__(self, symbol, price):\n", + " self.symbol = symbol\n", + " self.price = price\n", + " \n", + " def __repr__(self):\n", + " cls = self.__class__.__name__\n", + " return '%s(%r, %r)' % (cls, self.symbol, self.price)\n", + "\n", + "brka = Stock('BRK.A', 216298.00)\n", + "brka" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216298.0)" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Field:\n", + " _attr = None\n", + " \n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, self._attr)\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, self._attr, value) \n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + "\n", + "\n", + "class SymbolField(Field):\n", + " _attr = '_symbol'\n", + " \n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "\n", + "class PriceField(Field):\n", + " _attr = '_price'\n", + " \n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, got %s' % type(value))\n", + " \n", + " if value <= 0:\n", + " raise ValueError('price most be positive, got %s' % price)\n", + " \n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " \n", + " def __init__(self, symbol, price):\n", + " self.symbol = symbol\n", + " self.price = price\n", + " \n", + " def __repr__(self):\n", + " cls = self.__class__.__name__\n", + " return '%s(%r, %r)' % (cls, self.symbol, self.price)\n", + "\n", + "brka = Stock('BRK.A', 216298.00)\n", + "brka" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_price': 216298.0, '_symbol': 'BRK.A'}" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars(brka)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "symbol must be upper case, got 'v'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mStock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m74.48\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, symbol, price)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msymbol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msymbol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msymbol\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 42\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprice\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m__set__\u001b[1;34m(self, inst, value)\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__set__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massert_valid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0msetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_attr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36massert_valid\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0massert_valid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misupper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'symbol must be upper case, got %r'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: symbol must be upper case, got 'v'" + ] + } + ], + "source": [ + "Stock('v', 74.48)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216297.0, 216297.0)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Field:\n", + " _attr = None\n", + " \n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, self._attr)\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, self._attr, value) \n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + "\n", + "\n", + "class SymbolField(Field):\n", + " _attr = '_symbol'\n", + " \n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "\n", + "class PriceField(Field):\n", + " _attr = '_price'\n", + " \n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, got %s' % type(value))\n", + " \n", + " if value <= 0:\n", + " raise ValueError('price most be positive, got %s' % price)\n", + " \n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " low = PriceField()\n", + " \n", + " def __init__(self, symbol, price, low):\n", + " self.symbol = symbol\n", + " self.price = price\n", + " self.low = low\n", + " \n", + " def __repr__(self):\n", + " cls = self.__class__.__name__\n", + " return '%s(%r, %r, %r)' % (cls, self.symbol, self.price, self.low)\n", + "\n", + "brka = Stock('BRK.A', 216298.00, 216297.00)\n", + "brka" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Stock('BRK.A', 216298.0, 216297.0)" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from itertools import count\n", + "class Field:\n", + " _next_id = count().__next__\n", + "\n", + " def __init__(self):\n", + " self._attr = '_%s_%d' % (self.__class__.__name__, self._next_id())\n", + " \n", + " def __get__(self, inst, owner):\n", + " if inst is None:\n", + " return self\n", + " return getattr(inst, self._attr)\n", + " \n", + " def __set__(self, inst, value):\n", + " self.assert_valid(value)\n", + " setattr(inst, self._attr, value) \n", + " \n", + " def assert_valid(self, value):\n", + " pass\n", + " \n", + "\n", + "class SymbolField(Field):\n", + " _attr = '_symbol'\n", + " \n", + " def assert_valid(self, value):\n", + " if not str.isupper(value):\n", + " raise ValueError('symbol must be upper case, got %r' % value)\n", + " \n", + "\n", + "class PriceField(Field):\n", + " _attr = '_price'\n", + " \n", + " def assert_valid(self, value):\n", + " if not isinstance(value, float):\n", + " raise TypeError('price must be float, got %s' % type(value))\n", + " \n", + " if value <= 0:\n", + " raise ValueError('price most be positive, got %s' % price)\n", + " \n", + " \n", + "class Stock:\n", + " symbol = SymbolField()\n", + " price = PriceField()\n", + " low = PriceField()\n", + " \n", + " def __init__(self, symbol, price, low):\n", + " self.symbol = symbol\n", + " self.price = price\n", + " self.low = low\n", + " \n", + " def __repr__(self):\n", + " cls = self.__class__.__name__\n", + " return '%s(%r, %r, %r)' % (cls, self.symbol, self.price, self.low)\n", + "\n", + "brka = Stock('BRK.A', 216298.00, 216297.00)\n", + "brka" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_PriceField_1': 216298.0,\n", + " '_PriceField_2': 216297.0,\n", + " '_SymbolField_0': 'BRK.A'}" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vars(brka)" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-10" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Math:\n", + " @staticmethod\n", + " def neg(val):\n", + " return -val\n", + " \n", + "Math.neg(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-10" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = Math()\n", + "m.neg(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class StaticMethod:\n", + " def __init__(self, func):\n", + " self.func = func\n", + " \n", + " def __get__(self, inst, owner):\n", + " return self.func\n", + " \n", + " \n", + "class Math:\n", + " @StaticMethod\n", + " def neg(val):\n", + " return -val" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-10" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = Math()\n", + "m.neg(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-10" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Math.neg(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Point(2.3, 3.4)" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Point:\n", + " def __init__(self, x, y):\n", + " self.x, self.y = x, y\n", + " \n", + " @classmethod\n", + " def from_str(cls, val):\n", + " \"\"\"From string in format 'x,y'\"\"\"\n", + " x, y = map(float, val.split(','))\n", + " return cls(x, y)\n", + " \n", + " def __repr__(self):\n", + " return '%s(%r, %r)' % (self.__class__.__name__, self.x, self.y)\n", + " \n", + "p = Point.from_str('2.3, 3.4')\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Point(2.3, 3.4)" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from functools import partial\n", + "\n", + "class ClassMethod:\n", + " def __init__(self, func):\n", + " self.func = func\n", + " \n", + " def __get__(self, inst, owner):\n", + " return partial(self.func, owner)\n", + " \n", + " \n", + "class Point:\n", + " def __init__(self, x, y):\n", + " self.x, self.y = x, y\n", + " \n", + " @ClassMethod\n", + " def from_str(cls, val):\n", + " \"\"\"From string in format 'x,y'\"\"\"\n", + " # print(cls)\n", + " x, y = map(float, val.split(','))\n", + " return cls(x, y)\n", + " \n", + " def __repr__(self):\n", + " return '%s(%r, %r)' % (self.__class__.__name__, self.x, self.y)\n", + "\n", + "p = Point.from_str('2.3, 3.4')\n", + "p" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Point(1.1, 2.2)" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.from_str('1.1,2.2')" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Tim Peters'" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Person:\n", + " def __init__(self, first, last):\n", + " self.first = first\n", + " self.last = last\n", + " \n", + " @property\n", + " def name(self):\n", + " return '%s %s' % (self.first, self.last)\n", + " \n", + "p = Person('Tim', 'Peters')\n", + "p.name" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Tim Peters'" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Property:\n", + " def __init__(self, func):\n", + " self.func = func\n", + " \n", + " def __get__(self, inst, value):\n", + " if not inst:\n", + " return self\n", + " \n", + " return self.func(inst)\n", + "\n", + "class Person:\n", + " def __init__(self, first, last):\n", + " self.first = first\n", + " self.last = last\n", + " \n", + " @Property\n", + " def name(self):\n", + " return '%s %s' % (self.first, self.last)\n", + " \n", + "p = Person('Tim', 'Peters')\n", + "p.name" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Hi, I'm Elvis. How are you?\"" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Person:\n", + " def __init__(self, name):\n", + " self.name = name\n", + " \n", + " def greet(self):\n", + " return \"Hi, I'm %s. How are you?\" % self.name\n", + " \n", + "p = Person('Elvis')\n", + "p.greet()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p.greet.__get__" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Hi, I'm Elvis. How are you?\"" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meth = p.greet.__get__(p, Person)\n", + "meth()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/py-fns/Functions-CI.ipynb b/py-fns/Functions-CI.ipynb new file mode 100644 index 0000000..fc1a6db --- /dev/null +++ b/py-fns/Functions-CI.ipynb @@ -0,0 +1,744 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining and Calling Python Functions\n", + "### Miki Tebeka : CEO, CTO & UFO @ 353Solutions\n", + "\n", + "We're going to cover the many ways you can define and call functions in Python. I hope each of you will learn something new." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11/02/2015 22:00:05\n", + "\n", + "CPython 3.5.0\n", + "IPython 4.0.0\n", + "\n", + "compiler : GCC 5.2.0\n", + "system : Linux\n", + "release : 4.2.5-1-ARCH\n", + "machine : x86_64\n", + "processor : \n", + "CPU cores : 4\n", + "interpreter: 64bit\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sub(x, y):\n", + " \"\"\"Return the y's predecessor of x\"\"\"\n", + " return x - y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n", + "-6\n", + "-6\n" + ] + } + ], + "source": [ + "print(sub(1, 7)) # \"regular\" call\n", + "print(sub(y=7, x=1)) # call with keyword arguments\n", + "print(sub(1, y=7)) # mix & match" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "positional argument follows keyword argument (, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m print(sub(y=7, 1))\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n" + ] + } + ], + "source": [ + "# however you can't\n", + "print(sub(y=7, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n" + ] + } + ], + "source": [ + "args = (1, 7)\n", + "print(sub(*args))\n", + "\n", + "# * in function call unpacks sequence to positional" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "()\n", + "(1, 2, 3)\n", + "(1, 2, 3, 4)\n" + ] + } + ], + "source": [ + "def vargs(*args):\n", + " print(args)\n", + " \n", + "vargs()\n", + "vargs(1,2,3)\n", + "\n", + "a, b = (1, 2), (3, 4)\n", + "vargs(*a, *b) # New in 3.5 (used to be: vargs(*(a+b)))\n", + "\n", + "# * in function definition packs positional to tuple" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n" + ] + } + ], + "source": [ + "kw = {'x': 1, 'y': 7}\n", + "print(sub(**kw))\n", + "\n", + "# ** in function call unpacks mapping to keyword args" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n", + "{'y': 2, 'x': 1}\n", + "{'y': 2, 'a': 10, 'x': 1, 'b': 20}\n" + ] + } + ], + "source": [ + "def kwargs(**kw):\n", + " print(kw)\n", + " \n", + "kwargs()\n", + "kwargs(x=1, y=2)\n", + "\n", + "kw1, kw2 = {'x': 1, 'y': 2}, {'a': 10, 'b': 20}\n", + "kwargs(**kw1, **kw2) # New in 3.5 (can't have duplicate keys)\n", + "\n", + "# ** in function definition packs keyword args to dict" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "()\n", + "{}\n", + "('bugs',)\n", + "{}\n", + "()\n", + "{'taz': 'brown'}\n", + "('daffy', 'taz')\n", + "{'colors': ['black', 'brown']}\n" + ] + } + ], + "source": [ + "def eat_all(*args, **kw):\n", + " print(args)\n", + " print(kw)\n", + "\n", + "eat_all()\n", + "eat_all('bugs')\n", + "eat_all(taz='brown')\n", + "eat_all('daffy', 'taz', colors=['black', 'brown'])\n", + "\n", + "# very bad design, mostly used in decorators" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n", + "-6\n" + ] + } + ], + "source": [ + "def sub(x, y=7):\n", + " return x - y\n", + "\n", + "print(sub(1, 7))\n", + "print(sub(1))\n", + "\n", + "# use x=y to speficy default arguments" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n", + "[1, 2]\n" + ] + } + ], + "source": [ + "def prappend(n, vals=[]):\n", + " vals.append(n)\n", + " print(vals)\n", + "prappend(1)\n", + "prappend(2)\n", + "\n", + "# default arguments are computed at definition time\n", + "# NEVER USE MUTABLE DEFAULT ARGUMENTS" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n", + "[2]\n" + ] + } + ], + "source": [ + "# fix to the above\n", + "def prappend(n, vals=None):\n", + " vals = [] if vals is None else vals\n", + " vals.append(n)\n", + " print(vals)\n", + "prappend(1)\n", + "prappend(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class subber:\n", + " def __init__(self, n):\n", + " self.n = n\n", + "\n", + " def __call__(self, x):\n", + " return self.n + x\n", + "\n", + "sub10 = subber(10)\n", + "sub10(7)\n", + "\n", + "# any object which implements __call__ can be used as a function\n", + "# (yes, functions objects have __call__ as well)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-6" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def make_subber(n):\n", + " def subber(x):\n", + " return x - n\n", + " return subber\n", + "\n", + "sub7 = make_subber(7)\n", + "sub7(1)\n", + "\n", + "# closure" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "UnboundLocalError", + "evalue": "local variable 'ncalls' referenced before assignment", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msub\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# UnboundLocalError, can't reassign to global\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36msub\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mncalls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msub\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mncalls\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'ncalls' referenced before assignment" + ] + } + ], + "source": [ + "ncalls = 0\n", + "def sub(x, y):\n", + " ncalls += 1\n", + " return x + y\n", + "\n", + "sub(1, 2) # UnboundLocalError, can't reassign to global" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n", + "1\n" + ] + } + ], + "source": [ + "ncalls = 0\n", + "def sub(x, y):\n", + " global ncalls\n", + " ncalls += 1\n", + " return x - y\n", + "print(sub(1, 7))\n", + "print(ncalls)\n", + "\n", + "# Use the \"global\" keyword for such things" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1446494434.27457]\n" + ] + } + ], + "source": [ + "from time import time\n", + "call_times = []\n", + "def sub(x, y):\n", + " call_times.append(time())\n", + " return x - y\n", + "sub(1, 7)\n", + "print(call_times)\n", + "\n", + "# However you *can* mutate an object without using \"global\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n", + "1 - 7 calculated\n", + "-6\n" + ] + } + ], + "source": [ + "def sub(x, y, *, verbose=False):\n", + " if verbose:\n", + " print('%s - %s calculated' % (x, y))\n", + " return x - y\n", + "\n", + "print(sub(1, 7))\n", + "print(sub(1, 7, verbose=True))\n", + "\n", + "# * forces keyword argument only (see below), new in 3.5" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "sub() takes 2 positional arguments but 3 were given", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msub\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: sub() takes 2 positional arguments but 3 were given" + ] + } + ], + "source": [ + "sub(1, 7, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-6\n" + ] + }, + { + "data": { + "text/plain": [ + "{'return': int, 'x': int, 'y': int}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sub(x: int, y: int) -> int:\n", + " return x - y\n", + "\n", + "print(sub(1, 7))\n", + "sub.__annotations__\n", + "\n", + "# type annotation, just store information in __annotations__\n", + "# new in 3.5" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: coroutine 'sub' was never awaited\n" + ] + } + ], + "source": [ + "async def sub(x, y):\n", + " return x - y\n", + "\n", + "print(sub(1, 7))\n", + "\n", + "# new in 3.5, see PEP 492" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TestResults(failed=0, attempted=2)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sub(x, y):\n", + " \"\"\"Returns the y's predecessor to x\n", + "\n", + " >>> sub(10, 3)\n", + " 7\n", + " >>> sub(5, 22)\n", + " -17\n", + " \"\"\"\n", + " return x - y\n", + "\n", + "import doctest\n", + "doctest.testmod()\n", + "\n", + "# You can write test in your docstring\n", + "# nose, py.test and Sphinx can test your documentation" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<_sre.SRE_Match object; span=(0, 2), match=' #'>\n" + ] + } + ], + "source": [ + "import re\n", + "\n", + "is_comment = re.compile('^\\s*#').match\n", + "print(is_comment(' # this is a comment'))\n", + "\n", + "# You can use bound methods as functions" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'how are you?'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from functools import partial\n", + "\n", + "compact_ws = partial(re.compile('\\s+').sub, ' ')\n", + "compact_ws('how are \\t you?')\n", + "\n", + "# You can use functool.partial to create new functions\n", + "# (AKA currying)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sub took 0.10sec\n", + "-6\n" + ] + } + ], + "source": [ + "from functools import wraps\n", + "from time import time, sleep\n", + "\n", + "def timed(fn):\n", + " @wraps(fn)\n", + " def wrapper(*args, **kw):\n", + " start = time()\n", + " try:\n", + " return fn(*args, **kw)\n", + " finally:\n", + " print('%s took %.2fsec' % (fn.__name__, time() - start))\n", + " return wrapper\n", + " \n", + "@timed\n", + "def sub(x, y):\n", + " sleep(0.1)\n", + " return x - y\n", + "\n", + "print(sub(1, 7))\n", + "\n", + "# you can use decorators to add functionality to functions" + ] + }, + { + "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/py-fns/Functions.ipynb b/py-fns/Functions.ipynb new file mode 100644 index 0000000..87772fd --- /dev/null +++ b/py-fns/Functions.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining and Calling Python Functions\n", + "### Miki Tebeka : CEO, CTO & UFO @ 353Solutions\n", + "\n", + "We're going to cover the many ways you can define and call functions in Python. I hope each of you will learn something new." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11/02/2015 16:49:49\n", + "\n", + "CPython 3.5.0\n", + "IPython 4.0.0\n", + "\n", + "compiler : GCC 5.2.0\n", + "system : Linux\n", + "release : 4.2.5-1-ARCH\n", + "machine : x86_64\n", + "processor : \n", + "CPU cores : 4\n", + "interpreter: 64bit\n" + ] + } + ], + "source": [ + "%watermark" + ] + }, + { + "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/py-fns/fns.py b/py-fns/fns.py new file mode 100644 index 0000000..52697a0 --- /dev/null +++ b/py-fns/fns.py @@ -0,0 +1,265 @@ +def sub(x, y): + """Returns the y's predecessor to x""" + return x - y + + +sub(1, 7) +sub(y=7, x=1) +sub(1, y=7) +sub(x=1, 7) # syntax error + +args = 1, 7 +sub(*args) +# * in call unpacks sequence to positional + +a, b = (1, ), (7, ) +sub(*(a + b)) +# new in 3.5 +sub(*a, *b) + +def vargs(*args): + print(args) + +vargs() +vargs(1, 2, 3) + +# * in definition packs positional to tuple + +kw = {'x': 1, 'y': 7} +sub(**kw) +# ** in call unpacks mapping to keyword arguments + +k = {'x': 1} +w = {'y': 7} +kw = k.copy() +kw.update(w) +sub(**kw) + +# new in 3.5, beware of doubles +sub(**k, **w) + + +def kwargs(**kw): + print(kw) + +kwargs() +kwargs(name='daffy', age=78) +# ** in function call packs keyword arguments to dict + + +def eat_all(*args, **kw): + print(args) + print(kw) + +eat_all() +eat_all('bugs') +eat_all(taz='brown') +eat_all('daffy', 'taz', colors=['black', 'brown']) + +# very bad design, mostly used in decorators + + +def sub(x, y=1): + return x - y + +sub(1, 7) +sub(8) + +# Use var=value to specify default arguments +# NEVER USE MUTABLE DEFAULT ARGUMENTS + + +class subber: + def __init__(self, n): + self.n = n + + def __call__(self, x): + return x - self.n + +sub7 = subber(7) +sub7(1) + +# any object which implements __call__ can be used as a function +sub.__call__(1, 7) + + +def sub(x: int, y: int) -> int: + return x - y + +sub(1, 2) +help(sub) +sub.__annotations__ + + +def sub(x, y, *, verbose=False): + if verbose: + print('subing %s to %s' % (x, y)) + return x - y + +# ensure keyword only parameters (start without * and show sub(1, 2, True) not +# that readable) + +sub(1, 7) +sub(1, 7, True) # Error +sub(1, 7, verbose=True) + + +# PEP 492 +async def sub(x, y): + return x - y +sub(1, 7) # return coroutine + + +cat = ''.join +cat(['a', 'b', 'c']) + +# You can use bound methods as functions + +from functools import partial + +compact_ws = partial(re.compile('\s+').sub, ' ') +compact_ws('how are \t you?') + +# You can use functool.partial to create new functions + +sub = lambda x, y: x - y +sub(1, 7) + +# You can use lambda to create adhoc functions. Very limited capability + +fns = [lambda: n for n in range(5)] +vals = [fn() for fn in fns] +print(vals) + +fns = [lambda n=n: n for n in range(5)] +vals = [fn() for fn in fns] +print(vals) + +# default arguments computed at definition time + +def prappend(n, vals=[]): + vals.append(n) + print(vals) +prappend(1) +prappend(2) + +def prappend(n, vals=None): + vals = [] if vals is None else vals + vals.append(n) + print(vals) +prappend(1) +prappend(2) + +# never use mutable default arguments + + +def make_subber(n): + def subber(x): + return x - n + return subber + +# closure + +sub7 = make_subber(7) +sub7(1) + + + +ncalls = 0 +def sub(x, y): + ncalls += 1 + return x - y + +sub(1, 7) # UnboundLocalError + + +ncalls = 0 +def sub(x, y): + global ncalls + ncalls += 1 + return x - y +sub(1, 7) +print(ncalls) + + +from time import time +call_times = [] +def sub(x, y): + call_times.append(time()) + return x - y +sub(1, 7) +print(call_times) + + +def sub(x, y): + """Returns the y's predecessor to x + + >>> sub(10, 3) + 7 + >>> sub(5, 22) + -17 + """ + return x - y + +import doctest +doctest.testmod() + +# Sphinx can run doctest - test your documentation! +# can run tests of text files +# nose & py.test can run doctest tests as well + +from functools import wraps +from time import time, sleep + +def timed(fn): + @wraps(fn) + def wrapper(*args, **kw): + start = time() + try: + return fn(*args, **kw) + finally: + print('%s took %.2fsec' % (fn.__name__, time() - start)) + return wrapper + +@timed +def sub(x, y): + sleep(0.1) + return x - y + +sub(1, 7) + +def ncalls(fn): + count = 0 + @wraps(fn) + def wrapper(*args, **kw): + + count += 1 + print('%s called %d times' % (fn.__name__, count)) + return fn(*args, **kw) + return wrapper + + +@ncalls +def sub(x, y): + return x - y + +sub(1, 6) +# UnboundLocalError + +def ncalls(fn): + count = 0 + @wraps(fn) + def wrapper(*args, **kw): + nonlocal count + + count += 1 + print('%s called %d times' % (fn.__name__, count)) + return fn(*args, **kw) + return wrapper + +sub(1, 6) +sub(1, 6) + +# you can use decorators to add functionality to functions + +# https://www.youtube.com/watch?v=aintdHnqaio diff --git a/py-fns/fns.txt b/py-fns/fns.txt new file mode 100644 index 0000000..f6fadd1 --- /dev/null +++ b/py-fns/fns.txt @@ -0,0 +1,300 @@ +Python 3.5.1 (default, Dec 7 2015, 12:58:09) +[GCC 5.2.0] on linux +Type "copyright", "credits" or "license()" for more information. +>>> # Defining and Calling Python Functions +>>> ## Miki Tebkea :: 353Solutions - world class Python, Scientific Python and Go workshops +>>> def sub(x, y): + """Return's the y's predecessor of x""" + return x - y + +>>> sub(1, 7) +-6 +>>> sub(y=7, x=1) +-6 +>>> sub(1, y=7) +-6 +>>> sub(x=1, y) +SyntaxError: positional argument follows keyword argument +>>> sub(x=1, 7) +SyntaxError: positional argument follows keyword argument +>>> args = (1, 7) +>>> args = 1, 7 +>>> sub(*args) +-6 +>>> # * in function calls unpacks sequence to positional arguments +>>> a, b = (1,), (7,) +>>> sub(*(a + b)) +-6 +>>> a + b +(1, 7) +>>> sub(*a, *b) +-6 +>>> def vargs(*args): + print(args) + + +>>> vargs() +() +>>> vargs(1, 2, 3) +(1, 2, 3) +>>> # * in function definition packs positional to tuple +>>> kw = {'x': 1, 'y': 7} +>>> sub(**kw) +-6 +>>> k, w = {'x': 1}, {'y': 7} +>>> # Python < 3.5 +>>> kw = k.copy() +>>> kw.update(w) +>>> sub(**kw) +-6 +>>> sub(**k, **w) +-6 +>>> sub(**k, **k, **w) +Traceback (most recent call last): + File "", line 1, in + sub(**k, **k, **w) +TypeError: sub() got multiple values for keyword argument 'x' +>>> def kwargs(**kw): + print(kw) + + +>>> kwargs() +{} +>>> kwargs(x=1, y=7) +{'x': 1, 'y': 7} +>>> # ** in function definition packs keyword arguments to a dict +>>> def eat_all(*args, **kw): + print(args) + print(kw) + + +>>> eat_all() +() +{} +>>> eat_all(1,2) +(1, 2) +{} +>>> eat_all(1, 2, z=17) +(1, 2) +{'z': 17} +>>> def sub(x, y=1): + return x - y + +>>> sub(1, 7) +-6 +>>> sub(1) +0 +>>> # var=value in function definition is a default value and is evaluated at function definition time +>>> # NEVER USE MUTABLE DEFAULT ARGUMENTS +>>> def append(obj, items=[]): + items.append(obj) + print(items) + + +>>> append(1) +[1] +>>> append(2) +[1, 2] +>>> append(3) +[1, 2, 3] +>>> def append(obj, items=None): + items = items if items is None else [] + items.append(obj) + print(items) + + +>>> append(3) +Traceback (most recent call last): + File "", line 1, in + append(3) + File "", line 3, in append + items.append(obj) +AttributeError: 'NoneType' object has no attribute 'append' +>>> def append(obj, items=None): + items = [] if items is None else items + items.append(obj) + print(items) + + +>>> append(3) +[3] +>>> append(2) +[2] +>>> class subber: + def __init__(self, n): + self.n = n + def __call__(self, x): + return x - self.n + + +>>> sub3 = subber(3) +>>> sub3(1) +-2 +>>> sub + +>>> sub.__call__(1, 7) +-6 +>>> def sub(x, y, verbose=False): + if verbose: + print('%s - %s' % (x, y)) + return x - y + +>>> sub(1, 7) +-6 +>>> sub(1, 7, True) +1 - 7 +-6 +>>> sub(1, 7, verbose=True) +1 - 7 +-6 +>>> def sub(x, y, *, verbose=False): + if verbose: + print('%s - %s' % (x, y)) + return x - y + +>>> sub(1, 7, verbose=True) +1 - 7 +-6 +>>> sub(1, 7, True) +Traceback (most recent call last): + File "", line 1, in + sub(1, 7, True) +TypeError: sub() takes 2 positional arguments but 3 were given +>>> sub(1, 7, 9, True) +Traceback (most recent call last): + File "", line 1, in + sub(1, 7, 9, True) +TypeError: sub() takes 2 positional arguments but 4 were given +>>> def sub(*, x, y): + return x-y + +>>> sub(1, 2) +Traceback (most recent call last): + File "", line 1, in + sub(1, 2) +TypeError: sub() takes 0 positional arguments but 2 were given +>>> sub(x=1, y=7) +-6 +>>> cat = ''.join +>>> cat(['a', 'b', 'c']) +'abc' +>>> import re +>>> match_comment = re.compile('#.*').match +>>> match_comment('hi there') +>>> match_comment('# hi there') +<_sre.SRE_Match object; span=(0, 10), match='# hi there'> +>>> if match_comment('# hi there'): + print('comment') + + +comment +>>> from functools import partial +>>> compact_ws = partial(re.compile('\s+').sub, ' ') +>>> compact_ws('hello there PyWeb ') +'hello there PyWeb ' +>>> sub = lambda x, y: x - y +>>> sub(1, 7) +-6 +>>> fns = [lambda: n for n in range(5)] +>>> [fn() for fn in fns] +[4, 4, 4, 4, 4] +>>> n +Traceback (most recent call last): + File "", line 1, in + n +NameError: name 'n' is not defined +>>> fns = [lambda n=n: n for n in range(5)] +>>> [fn() for fn in fns] +[0, 1, 2, 3, 4] +>>> async def sub(x, y): + return x - y + +>>> sub (1, 7) + +>>> def sub(x: int, y: int) -> int: + return x - y + +>>> sub(1, 7) + +Warning (from warnings module): + File "/usr/lib/python3.5/idlelib/rpc.py", line 610 + builtins._ = None +RuntimeWarning: coroutine 'sub' was never awaited +-6 +>>> sub.__annotations__ +{'x': , 'y': , 'return': } +>>> ncalls = 0 +>>> def sub(x, y): + ncalls -= 1 + return x - y + +>>> sub(1, 7) +Traceback (most recent call last): + File "", line 1, in + sub(1, 7) + File "", line 2, in sub + ncalls -= 1 +UnboundLocalError: local variable 'ncalls' referenced before assignment +>>> def sub(x, y): + global ncalls + + ncalls -= 1 + return x - y + +>>> sub(1, 7) +-6 +>>> ncalls +-1 +>>> from functools import wraps +>>> def timed(fn): + @wraps(fn) + def wrapper(*args, **kw): + start = time() + try: + return fn(*args, **kw) + finally: + print('%s took %ssec' % (fn.__name__, time() - start)) + + +>>> from time import time +>>> @timed +def sub(x, y): + sleep(0. 1) + +SyntaxError: invalid syntax +>>> @timed +def sub(x, y): + sleep(0.1) + return x - y + +>>> from time import sleep +>>> sub(1, 7) +Traceback (most recent call last): + File "", line 1, in + sub(1, 7) +TypeError: 'NoneType' object is not callable +>>> def timed(fn): + @wraps(fn) + def wrapper(*args, **kw): + start = time() + try: + return fn(*args, **kw) + finally: + print('%s took %ssec' % (fn.__name__, time() - start)) + return wrapper + +>>> @timed +def sub(x, y): + sleep(0.1) + return x - y + +>>> sub(1, 7) +sub took 0.10054755210876465sec +-6 +>>> help(sub) +Help on function sub in module __main__: + +sub(x, y) + +>>> # nonlocal +>>> diff --git a/python-gotchas/src/colors.py b/python-gotchas/src/colors.py new file mode 100644 index 0000000..3e3eb3d --- /dev/null +++ b/python-gotchas/src/colors.py @@ -0,0 +1,6 @@ +colors = [ + 'red', + 'gree', + 'blue' + 'yellow' +] diff --git a/python-gotchas/src/colors_fixed.py b/python-gotchas/src/colors_fixed.py new file mode 100644 index 0000000..ed65a7c --- /dev/null +++ b/python-gotchas/src/colors_fixed.py @@ -0,0 +1,6 @@ +colors = [ + 'red', + 'gree', + 'blue', + 'yellow', # <1> +] diff --git a/teaching-geeks/README.md b/teaching-geeks/README.md new file mode 100644 index 0000000..7c09f33 --- /dev/null +++ b/teaching-geeks/README.md @@ -0,0 +1,49 @@ +# Teaching Geeks +Reversim Summit 2016 + + +## Outline +* Why should you teach? + * Fun + * Best way to learn a subject + ("הרבה למדתי מרבותי, ומחבירי – יותר מרבותי, ומתלמידי – יותר מכולן" ) + * Fame (personal brand) + * Sell other services +* Choose concrete examples + * Learn by doing + * Cone of Experience + * Engineers like תכלס + * They know how to read - exercise +* However give good foundation + * Teach a man to fish... +* Don't burn them with TMI + * Build a man a fire, and he'll be warm for a day. Set a man on fire, and + he'll be warm for the rest of his life. +* Encourage mistakes and playing + * Ask to make predictions + * People who make mistakes learn most +* Repetitive learning +* Rule the class + * Study show best teachers control the class +* Sell yourself and the course + * We don't like it but it's effective + * האמת לא רק צריכה להעשות אלא גם להראות +* Use breaks to feel the class + * Ask informal questions + * Answer questions +* Use humor + * A/B testing my jokes on them + * People remember what you make them feel + * How to make friends and influence people +* Be yourself + * Everyone have their own teaching style +* Practice, practice, practice + * Video yourself +* Simple environment +* Talk on "not a show" in teaching + * http://seriouspony.com/blog/2013/10/4/presentation-skills-considered-harmful +* Prepare + * Bathroom + * Water + * Projector + * Class setup