1 說明：
=====
1.1 Bokeh是專門針對Web瀏覽器的交互式、可視化Python繪圖庫。
1.2 Bokeh，可以做出像D3.js簡潔漂亮的交互可視化效果，但是使用難度低于D3.js。
1.3 不需要使用JAVAscript。
2 官網：
======
https://docs.bokeh.org/en/latest/
https://github.com/bokeh/bokeh
3 安裝：
=====
pip install bokeh
#本機安裝
sudo pip3.8 install bokeh
4 環境：
=====
華為筆記本電腦、深度deepin-linux操作系統、python3.8和微軟vscode編輯器。
5 靜態基本作圖：
============
5.1 柱狀圖：
5.1.1 代碼：
from bokeh.io import output_file, show
from bokeh.plotting import figure
#數據，支持中文
fruits = ['蘋果', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
#繪圖
p = figure(x_range=fruits, plot_height=350, title="Fruit Counts",
toolbar_location=None, tools="")
#柱狀圖，vbar是指垂直柱狀圖
p.vbar(x=fruits, top=counts, width=0.9)
#導出文件：文件名和指定路徑，
#注意沒有這一行，也會自動在代碼所在的生成同名的html文件
#output_file("/home/xgj/Desktop/bokeh/bar_basic.html")
#展示圖
show(p)
5.1.2 圖：
5.2 折線圖
5.2.1 代碼：
from bokeh.io import output_file, show
from bokeh.plotting import figure
#數據，支持中文
fruits = ['蘋果', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
#繪圖
p = figure(x_range=fruits, plot_height=350, title="Fruit Counts",
toolbar_location=None, tools="")
#柱狀圖
p.line(x=fruits, y=counts)
#展示圖
show(p)
5.2.2 圖：
5.3 散點圖：
5.3.1 代碼：
#from bokeh.io import output_file, show
from bokeh.plotting import figure,output_file, show #同上
#數據，支持中文
fruits = ['蘋果', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
counts = [5, 3, 4, 2, 4, 6]
#繪圖
p = figure(x_range=fruits, plot_height=350, title="Fruit Counts",
toolbar_location=None, tools="")
#柱狀圖
p.scatter(x=fruits, y=counts,size=20, fill_color="#74add1")
#展示圖
show(p)
5.3.2 圖：
===基本作圖方便，優美；比matplotlib簡單，暫時介紹到這里===
6 高級作圖：
=========
6.1 js_events：調用js事件
6.2 代碼：
import numpy as np
from bokeh import events
from bokeh.io import output_file, show
from bokeh.layouts import column, row
from bokeh.models import Button, CustomJS, Div
from bokeh.plotting import figure
#定義js和事件
def display_event(div, attributes=[]):
style = 'float: left; clear: left; font-size: 13px'
return CustomJS(args=dict(div=div), code="""
var attrs = %s;
var args = [];
for (var i = 0; i < attrs.length; i++) {
var val = JSON.stringify(cb_obj[attrs[i]], function(key, val) {
return val.toFixed ? Number(val.toFixed(2)) : val;
})
args.push(attrs[i] + '=' + val)
}
var line = "<span style=%r><b>" + cb_obj.event_name + "</b>(" + args.join(", ") + ")</span>\n";
var text = div.text.concat(line);
var lines = text.split("\n")
if (lines.length > 35)
lines.shift();
div.text = lines.join("\n");
""" % (attributes, style))
#數據
N = 4000
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [
"#%02x%02x%02x" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)
]
p = figure(tools="pan,wheel_zoom,zoom_in,zoom_out,reset,tap,lasso_select,box_select")
#調用散點圖
p.scatter(x, y, radius=radii,
fill_color=colors, fill_alpha=0.6,
line_color=None)
#容器實例化，寬
div = Div(width=1000)
button = Button(label="Button", button_type="success", width=300)
layout = column(button, row(p, div))
#注冊事件回調
#按鈕事件
button.js_on_event(events.ButtonClick, display_event(div))
# LOD事件
p.js_on_event(events.LODStart, display_event(div))
p.js_on_event(events.LODEnd, display_event(div))
# Point events點事件
point_attributes = ['x','y','sx','sy']
p.js_on_event(events.Tap, display_event(div, attributes=point_attributes))
p.js_on_event(events.DoubleTap, display_event(div, attributes=point_attributes))
p.js_on_event(events.Press, display_event(div, attributes=point_attributes))
p.js_on_event(events.PressUp, display_event(div, attributes=point_attributes))
# Mouse wheel event
p.js_on_event(events.MouseWheel, display_event(div,attributes=point_attributes+['delta']))
# Mouse move, enter and leave
p.js_on_event(events.MouseMove, display_event(div, attributes=point_attributes))
p.js_on_event(events.MouseEnter, display_event(div, attributes=point_attributes))
p.js_on_event(events.MouseLeave, display_event(div, attributes=point_attributes))
# Pan events
pan_attributes = point_attributes + ['delta_x', 'delta_y']
p.js_on_event(events.Pan, display_event(div, attributes=pan_attributes))
p.js_on_event(events.PanStart, display_event(div, attributes=point_attributes))
p.js_on_event(events.PanEnd, display_event(div, attributes=point_attributes))
# Pinch events
pinch_attributes = point_attributes + ['scale']
p.js_on_event(events.Pinch, display_event(div, attributes=pinch_attributes))
p.js_on_event(events.PinchStart, display_event(div, attributes=point_attributes))
p.js_on_event(events.PinchEnd, display_event(div, attributes=point_attributes))
# Selection events
p.js_on_event(events.SelectionGeometry, display_event(div, attributes=['geometry', 'final']))
show(layout)
6.3 效果圖：
6.4 圖形總體
6.4.1 代碼：
from bokeh.core.enums import MarkerType
from bokeh.layouts import row
from bokeh.models import ColumnDataSource, Panel, Tabs
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.iris import flowers
source = ColumnDataSource(flowers)
def make_plot(title, marker, backend):
p = figure(title=title, plot_width=350, plot_height=350, output_backend=backend)
p.scatter("petal_length", "petal_width", source=source,
color='blue', fill_alpha=0.2, size=12, marker=marker)
return p
tabs = []
for marker in MarkerType:
p1 = make_plot(marker, marker, "canvas")
p2 = make_plot(marker + ' SVG', marker, "svg")
p3 = make_plot(marker + ' GL', marker, "webgl")
tabs.Append(Panel(child=row(p1, p2, p3), title=marker))
#output_file("marker_compare.html", title="Compare regular, SVG, and WebGL markers")
show(Tabs(tabs=tabs))
6.4.2 效果圖
===一般基本作圖是小白和普通人需要的掌握的，下次重點講===
7 機器學習：scikit-learn project
========================
7.1 代碼：
import numpy as np
from sklearn import cluster, datasets
from sklearn.preprocessing import StandardScaler
from bokeh.layouts import column, row
from bokeh.plotting import figure, output_file, show
print("nn*** This example may take several seconds to run before displaying. ***nn")
print("nn*** 該示例展示前需要等待幾秒. ***nn")
N = 50000
PLOT_SIZE = 400
# generate datasets.
np.random.seed(0)
noisy_circles = datasets.make_circles(n_samples=N, factor=.5, noise=.04)
noisy_moons = datasets.make_moons(n_samples=N, noise=.05)
centers = [(-2, 3), (2, 3), (-2, -3), (2, -3)]
blobs1 = datasets.make_blobs(centers=centers, n_samples=N, cluster_std=0.4, random_state=8)
blobs2 = datasets.make_blobs(centers=centers, n_samples=N, cluster_std=0.7, random_state=8)
colors = np.array([x for x in ('#00f', '#0f0', '#f00', '#0ff', '#f0f', '#ff0')])
colors = np.hstack([colors] * 20)
# create clustering algorithms
dbscan = cluster.DBSCAN(eps=.2)
birch = cluster.Birch(n_clusters=2)
means = cluster.MiniBatchKMeans(n_clusters=2)
spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack', affinity="nearest_neighbors")
affinity = cluster.AffinityPropagation(damping=.9, preference=-200)
# change here, to select clustering algorithm (note: spectral is slow)
algorithm = dbscan # <- SELECT ALG
plots =[]
for dataset in (noisy_circles, noisy_moons, blobs1, blobs2):
X, y = dataset
X = StandardScaler().fit_transform(X)
# predict cluster memberships
algorithm.fit(X)
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
p = figure(output_backend="webgl", title=algorithm.__class__.__name__,
plot_width=PLOT_SIZE, plot_height=PLOT_SIZE)
p.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), alpha=0.1,)
plots.append(p)
# generate layout for the plots
layout = column(row(plots[:2]), row(plots[2:]))
output_file("clustering.html", title="clustering with sklearn")
show(layout)
7.2 效果圖：
===自己整理并分享出來===
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