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from traits.api import HasTraits, Property, Dict, cached_property, Any, Str, Int


class MyTraits(HasTraits):

    a = Any

    b = Property(Dict(key_trait=Str, value_trait=Int), depends_on=['a'])

    @cached_property

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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 jaidev <jaidev@newton>
#
# Distributed under terms of the MIT license.

from sklearn.metrics.pairwise import check_pairwise_arrays
from sklearn.preprocessing import normalize

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jaidevd

from PySide import QtGui, QtCore
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import datetime

IRIS_PATH = "/tmp/access"

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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 jaidev <jaidev@newton>
#
# Distributed under terms of the MIT license.

"""

jaidevd

SciPy India 2014 will feature a lot of hands-on, practical tutorial sessions that require the participants to have some software on their respective computers. Please follow the instructions here before you come to the conference, so that we may start the tutorials without any delay.

Installing Canopy

Canopy is the primary Python distribution that we will be using during the conference. You may use Anaconda, or the system Python distribution, or any other Python interpreter for the conference. However, please note that we may not be able to support any other distribution other than Canopy. This is because the Python packages we will be providing for the tutorials are customized for Canopy.

Please visit this link https://store.enthought.com/downloads/ and click on the "Download Canopy Express" button to download the Canopy installer. Run this installer to install Canopy.

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import pandas as pd
import numpy as np

#attendance_data = pd.read_csv('AttendanceData.csv',index_col=2,
#                              parse_dates=True)
#attendance_data.columns = ['event_id', 'user_id']

event_data = pd.read_csv('EventData.csv',#skipfooter=1,
                          index_col=0)
event_data.columns = ['latitude', 'longitude']

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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################################################################################
# Description:
# -------------
# This is a TraistUI (http://docs.enthought.com/traitsui) application that 
# is used for performing some exploratory data analysis on the training data
# from Kaggle's Galaxy Zoo competition. 
# (http://kaggle.com/c/galaxy-zoo-the-galaxy-challenge)
# Broadly, it can be thought of as a browser for the data, which shows the 
# training images, the target distribution functions and some metrics related 
# to the training data.

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\documentclass{beamer}
%
% Choose how your presentation looks.
%
% For more themes, color themes and font themes, see:
% http://deic.uab.es/~iblanes/beamer_gallery/index_by_theme.html
%
\mode<presentation>
{
  \usetheme{Warsaw}      % or try Darmstadt, Madrid, Warsaw, ...