#!/usr/bin/env python
# coding: utf-8

# # Interact Exercises

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get_ipython().run_line_magic('matplotlib', 'inline')


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from matplotlib import pyplot as plt
import numpy as np


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from ipywidgets import *
from IPython.display import display


# ## String sorting

# Write a `sort_string` function that takes a string as its input and prints a new string consisting of the original one, sorted.  Add a `reverse` keyword argument with a default of `False` to allow for the sorting to be done in reverse.
# 
# Then, use `interact` to create a user interface for exploring your `sort_string` function.

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get_ipython().run_line_magic('load', 'soln/string_sorting.py')


# ## Plotting with parameters

# Write a `plot_sin` function that plots $sin(ax+b)$ over the interval $[0,4\pi]$.
# 
# Then use `interact` to create a user interface for exploring your function:
# 
# * `a` should be a floating point number over the interval $[0.0,5.0]$.
# * `b` should be a floating point number over the interval $[-5.0,5.0]$.

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get_ipython().run_line_magic('load', 'soln/param_plot_1.py')


# In matplotlib, the line style and color can be set with a third argument to `plot`. Examples of this argument:
# 
# * dashed red: `r--`
# * blue circles: `bo`
# * dotted black: `k.`
# 
# Add a `style` argument to your `plot_sin` function that allows you to set the line style of the plot.
# 
# Use `interact` to create a UI for `plot_sin` that has a drop down menu for selecting the line style between a **dotted red** line and a **dashed black line**. This time use `interact` as a decorator.

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get_ipython().run_line_magic('load', 'soln/param_plot_2.py')


# ## Simple data explorer

# In this exercise, you will use interact to build a UI for exploring correlations between different features in the [Iris dataset](http://en.wikipedia.org/wiki/Iris_flower_data_set) in [sklearn]http://scikit-learn.org/stable/(http://scikit-learn.org/stable/). This data contains 4 different measurements (called features in this content) of 150 different iris flowers of three different species.
# 
# Load the dataset:

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from sklearn.datasets import load_iris
iris_data = load_iris()


# The actual data is stored as a NumPy array under the `data` attribute:

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iris_data.data.shape


# You can see the meanings of the 4 columns of data by looking at the `feature_names` attribute:

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iris_data.feature_names


# Write a `plot_iris` function that creates a scatter plot (using `plt.scatter`) of two columns of this dataset. Your function should have the following signature:
# 
# ```python
# def plot_iris(a, col1, col2):
#     ...
# ```
# 
# where `a` is the NumPy array of data and `col1/col2` are the two columns to use for the scatter plot.
# 
# Use `interact` to build a UI to explore the iris dataset using your `plot_iris` function. You will need to use the `fixed` function when passing the dataset to the function.

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get_ipython().run_line_magic('load', 'soln/data_explorer.py')