jupyterteam@ucdavis.edu

%matplotlib widget

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

# Fixing random state for reproducibility
np.random.seed(19680801)


def gen_rand_line(length, dims=2):
    """
    Create a line using a random walk algorithm.

    Parameters
    ----------
    length : int
        The number of points of the line.
    dims : int
        The number of dimensions of the line.
    """
    line_data = np.empty((dims, length))
    line_data[:, 0] = np.random.rand(dims)
    for index in range(1, length):
        # scaling the random numbers by 0.1 so
        # movement is small compared to position.
        # subtraction by 0.5 is to change the range to [-0.5, 0.5]
        # to allow a line to move backwards.
        step = (np.random.rand(dims) - 0.5) * 0.1
        line_data[:, index] = line_data[:, index - 1] + step
    return line_data


def update_lines(num, data_lines, lines):
    for line, data in zip(lines, data_lines):
        # NOTE: there is no .set_data() for 3 dim data...
        line.set_data(data[0:2, :num])
        line.set_3d_properties(data[2, :num])
    return lines


# Attaching 3D axis to the figure
fig = plt.figure()
ax = fig.add_subplot(projection="3d")

# Fifty lines of random 3-D lines
data = [gen_rand_line(25, 3) for index in range(50)]

# Creating fifty line objects.
# NOTE: Can't pass empty arrays into 3d version of plot()
lines = [ax.plot(dat[0, 0:1], dat[1, 0:1], dat[2, 0:1])[0] for dat in data]

# Setting the axes properties
ax.set_xlim3d([0.0, 1.0])
ax.set_xlabel('X')

ax.set_ylim3d([0.0, 1.0])
ax.set_ylabel('Y')

ax.set_zlim3d([0.0, 1.0])
ax.set_zlabel('Z')

ax.set_title('3D Test')

# Creating the Animation object
line_ani = animation.FuncAnimation(
    fig, update_lines, 50, fargs=(data, lines), interval=50)

plt.show()

import pandas as pd
iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris

#You can scroll this code!
import pandas as pd
import numpy as np
import os

from bqplot import (
    LogScale, LinearScale, OrdinalColorScale, ColorAxis,
    Axis, Scatter, Lines, CATEGORY10, Label, Figure, Tooltip
)

from ipywidgets import HBox, VBox, IntSlider, Play, jslink
initial_year = 1800
data = pd.read_json('https://raw.githubusercontent.com/bqplot/bqplot/958b0679cbccbc1411688cc6f8a5a22642377c68/examples/Applications/Wealth%20Of%20Nations/nations.json')
def clean_data(data):
    for column in ['income', 'lifeExpectancy', 'population']:
        data = data.drop(data[data[column].apply(len) <= 4].index)
    return data

def extrap_interp(data):
    data = np.array(data)
    x_range = np.arange(1800, 2009, 1.)
    y_range = np.interp(x_range, data[:, 0], data[:, 1])
    return y_range

def extrap_data(data):
    for column in ['income', 'lifeExpectancy', 'population']:
        data[column] = data[column].apply(extrap_interp)
    return data
data = clean_data(data)
data = extrap_data(data)
income_min, income_max = np.min(data['income'].apply(np.min)), np.max(data['income'].apply(np.max))
life_exp_min, life_exp_max = np.min(data['lifeExpectancy'].apply(np.min)), np.max(data['lifeExpectancy'].apply(np.max))
pop_min, pop_max = np.min(data['population'].apply(np.min)), np.max(data['population'].apply(np.max))
def get_data(year):
    year_index = year - 1800
    income = data['income'].apply(lambda x: x[year_index])
    life_exp = data['lifeExpectancy'].apply(lambda x: x[year_index])
    pop =  data['population'].apply(lambda x: x[year_index])
    return income, life_exp, pop
tt = Tooltip(fields=['name', 'x', 'y'], labels=['Country Name', 'Income per Capita', 'Life Expectancy'])
year_label = Label(x=[0.75], y=[0.10], default_size=46, font_weight='bolder', colors=['orange'],
                   text=[str(initial_year)], enable_move=True)
x_sc = LogScale(min=income_min, max=income_max)
y_sc = LinearScale(min=life_exp_min, max=life_exp_max)
c_sc = OrdinalColorScale(domain=data['region'].unique().tolist(), colors=CATEGORY10[:6])
size_sc = LinearScale(min=pop_min, max=pop_max)
ax_y = Axis(label='Life Expectancy', scale=y_sc, orientation='vertical', side='left', grid_lines='solid')
ax_x = Axis(label='Income per Capita', scale=x_sc, grid_lines='solid')
# Start with the first year's data
cap_income, life_exp, pop = get_data(initial_year)
wealth_scat = Scatter(x=cap_income, y=life_exp, color=data['region'], size=pop,
                      names=data['name'], display_names=False,
                      scales={'x': x_sc, 'y': y_sc, 'color': c_sc, 'size': size_sc},
                      default_size=4112, tooltip=tt, animate=True, stroke='Black',
                      unhovered_style={'opacity': 0.5})
nation_line = Lines(x=data['income'][0], y=data['lifeExpectancy'][0], colors=['Gray'],
                       scales={'x': x_sc, 'y': y_sc}, visible=False)
time_interval = 10
fig = Figure(marks=[wealth_scat, year_label, nation_line], axes=[ax_x, ax_y],
             title='Health and Wealth of Nations', animation_duration=time_interval)
year_slider = IntSlider(min=1800, max=2008, step=1, description='Year', value=initial_year)
def hover_changed(change):
    if change.new is not None:
        nation_line.x = data[data['name'] == wealth_scat.names[change.new]]['income'].values[0]
        nation_line.y = data[data['name'] == wealth_scat.names[change.new]]['lifeExpectancy'].values[0]
        nation_line.visible = True
    else:
        nation_line.visible = False
        
wealth_scat.observe(hover_changed, 'hovered_point')
def year_changed(change):
    wealth_scat.x, wealth_scat.y, wealth_scat.size = get_data(year_slider.value)
    year_label.text = [str(year_slider.value)]

year_slider.observe(year_changed, 'value')
play_button = Play(min=1800, max=2008, interval=time_interval)
jslink((play_button, 'value'), (year_slider, 'value'))
VBox([HBox([play_button, year_slider]), fig])

import numpy as np
from scipy.integrate import odeint

from bokeh.plotting import figure, output_file, show

sigma = 10
rho = 28
beta = 8.0/3
theta = 3 * np.pi / 4

def lorenz(xyz, t):
    x, y, z = xyz
    x_dot = sigma * (y - x)
    y_dot = x * rho - x * z - y
    z_dot = x * y - beta* z
    return [x_dot, y_dot, z_dot]

initial = (-10, -7, 35)
t = np.arange(0, 100, 0.006)

solution = odeint(lorenz, initial, t)

x = solution[:, 0]
y = solution[:, 1]
z = solution[:, 2]
xprime = np.cos(theta) * x - np.sin(theta) * y

colors = ["#C6DBEF", "#9ECAE1", "#6BAED6", "#4292C6", "#2171B5", "#08519C", "#08306B",]

p = figure(title="Lorenz attractor example", background_fill_color="#fafafa")

p.multi_line(np.array_split(xprime, 7), np.array_split(z, 7),
             line_color=colors, line_alpha=0.8, line_width=1.5)

output_file("lorenz.html", title="lorenz.py example")

show(p)

%matplotlib widget
import ipywidgets as widgets
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10)

def sine_func(x, w, amp):
    return amp*np.sin(w*x)

@widgets.interact(w=(0, 4, 0.25), amp=(0, 4, .1))
def update(w = 1, amp = 1):
    plt.clf()
    plt.ylim(-4, 4)
    plt.plot(x, sine_func(x, w, amp))