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from bqplot import *
import numpy as np
The colors for the ColorScale can be defined one of two ways:
colors attribute to a list of css colors. They can be either:'white''#000000''rgb(0, 0, 0)'.col_sc = ColorScale(colors=['yellow', 'red'])
bqplot's color-schemes. As of now we support all the colorbrewer schemes (link), as well as the matplotlib schemes 'viridis', 'magma', 'inferno' and 'plasma'.col_sc = ColorScale(scheme=['viridis'])
The color scale then linearly interpolates between its colors.
A ColorAxis, like other Axis types, takes a color scale as input. It can then be displayed in a Figure.
ax_col = ColorAxis(scale=col_sc)
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size = 100
np.random.seed(0)
x_data = range(size)
y_data = np.cumsum(np.random.randn(size) * 100.0)
def create_figure(color_scale, color_data):
scales = {'x': LinearScale(), 'y': LinearScale(), 'color': color_scale}
scatter = Scatter(x=x_data, y=y_data, color=color_data,
scales=scales,
stroke='black')
ax_y = Axis(label='Price', scale=scales['y'],
orientation='vertical', side='left')
ax_x = Axis(label='Day', scale=scales['x'], num_ticks=10, label_location='end')
ax_c = ColorAxis(scale=color_scale, label='Returns', orientation='vertical', side='right')
ax_y = Axis(scale=scales['y'],
orientation='vertical', side='left')
ax_x = Axis(scale=scales['x'], num_ticks=10, label_location='end')
ax_c = ColorAxis(scale=color_scale, orientation='vertical', side='right')
m_chart = dict(top=50, bottom=70, left=50, right=100)
return Figure(axes=[ax_x, ax_y, ax_c], title='Up and Down',
marks=[scatter], fig_margin=m_chart,)
col_sc = ColorScale()
create_figure(col_sc, y_data)
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col_sc.min = 0
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col_sc.reverse = True
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col_sc.min = None
col_sc.mid = 0
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import pandas as pd
date_col_sc = DateColorScale()
dates = pd.date_range(start='01-01-2007', periods=size)
create_figure(date_col_sc, dates)
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date_col_sc.min = pd.datetime(2007,2,28)
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from bqplot.market_map import MarketMap
from ipywidgets import IntSlider, SelectionSlider, Dropdown
from ipywidgets import VBox, HBox, Layout
from traitlets import link
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div_schemes = [
'Spectral',
'RdYlGn',
'RdBu',
'PiYG',
'PRGn',
'RdYlBu',
'BrBG',
'RdGy',
'PuOr',
]
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def scheme_inspector(fig, schemes, title=''):
'''
Takes a Figure and a list of schemes and returns the Figure along with
sliders to go through the different schemes.
Supposes that the
'''
# Get the color scale
col_sc = fig.marks[0].scales['color']
# Create the widgets to select the colorscheme
scheme_slider = SelectionSlider(description='',
options=schemes)
scheme_dd = Dropdown(description='Scheme',
options=schemes)
link((scheme_dd, 'value'), (scheme_slider, 'value'))
def update_scheme(*args):
col_sc.scheme = scheme_slider.value
# ax_c.label = scheme_slider.value
scheme_slider.observe(update_scheme, 'value')
scheme_dd.observe(update_scheme, 'value')
update_scheme()
return VBox([HBox([scheme_dd, scheme_slider]), fig])
scheme_inspector(create_figure(ColorScale(), y_data), div_schemes)
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lin_schemes = [
'OrRd',
'PuBu',
'BuPu',
'Oranges',
'BuGn',
'YlOrBr',
'YlGn',
'Reds',
'RdPu',
'Greens',
'YlGnBu',
'Purples',
'GnBu',
'Greys',
'YlOrRd',
'PuRd',
'Blues',
'PuBuGn',
'viridis',
'plasma',
'inferno',
'magma'
]
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scheme_inspector(create_figure(ColorScale(), y_data), lin_schemes,
title='Non-diverging schemes')
The OrdinalColorScale is a color scale for categorical data, i.e. data that does not have an intrinsic order.
The scale colors may be specified by the user, or chosen from a set scheme. As of now, the supported color schemes are the colorbrewer categorical schemes, listed here along with their maximum number of colors.
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ord_schemes = {
'Set2': 8,
'Accent': 8,
'Set1': 9,
'Set3': 12,
'Dark2': 8,
'Paired': 12,
'Pastel2': 8,
'Pastel1': 9,
}
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def partition(array, n_groups):
n_elements = len(array)
if n_groups > n_elements:
return np.arange(n_elements)
n_per_group = n_elements // n_groups + (n_elements % n_groups > 0)
return np.tile(range(1, n_groups+1), n_per_group)[:n_elements]
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# Define the control widgets
n_groups_slider = IntSlider(description='n colors', min=3)
scheme_slider = SelectionSlider(options=ord_schemes)
scheme_dd = Dropdown(description='Scheme',
options=ord_schemes)
link((scheme_dd, 'label'), (scheme_slider, 'label'))
def update_scheme(*args):
col_sc.scheme = scheme_slider.label
ax_c.label = scheme_slider.label
n_groups_slider.max = scheme_slider.value
def update_categories(*args):
groups = partition(names, n_groups_slider.value)
market_map.color = groups
market_map.groups = groups
n_groups_slider.observe(update_categories, 'value')
scheme_slider.observe(update_scheme, 'value')
# Define the bqplot marketmap
names = range(100)
col_sc = OrdinalColorScale()
ax_c = ColorAxis(scale=col_sc)
market_map = MarketMap(names=names,
display_text=['' for _ in names],
scales={'color': col_sc}, axes=[ax_c],
layout=Layout(min_width='800px', min_height='600px'))
update_scheme()
update_categories()
VBox([HBox([scheme_dd, scheme_slider, n_groups_slider]), market_map])
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