Bokeh

BokenのTabular data(カラムナデータ)

2018年5月27日 by 河副太智 Leave a Comment

アメリカの州別の病気発生件数を可視化（百日咳と麻疹）

import numpy as np
import pandas as pd
import holoviews as hv
import codecs
hv.extension('bokeh', 'matplotlib')
import holoviews as hv


diseases = pd.read_csv('holoviews-master/examples/assets/diseases.csv.gz')

vdims = [('measles', 'Measles Incidence'), ('pertussis', 'Pertussis Incidence')]
ds = hv.Dataset(diseases, ["State","Year"], vdims)

ds = ds.aggregate(function=np.mean)

%opts Curve [width=600 height=250] {+framewise}
(ds.to(hv.Curve, 'Year', 'measles') + ds.to(hv.Curve, 'Year', 'pertussis')).cols(1)

import numpy as np

import pandas as pd

import holoviews as hv

import codecs

hv.extension('bokeh', 'matplotlib')

import holoviews as hv

diseases = pd.read_csv('holoviews-master/examples/assets/diseases.csv.gz')

vdims = [('measles', 'Measles Incidence'), ('pertussis', 'Pertussis Incidence')]

ds = hv.Dataset(diseases, ["State","Year"], vdims)

ds = ds.aggregate(function=np.mean)

%opts Curve [width=600 height=250] {+framewise}

(ds.to(hv.Curve, 'Year', 'measles') + ds.to(hv.Curve, 'Year', 'pertussis')).cols(1)

BokehとHoloViewsを学ぶ

2018年5月10日 by 河副太智 Leave a Comment

HoloViews.orgにて学んだ内容をまとめていきます。

HoloViewsとはオープンソースpythonライブラリーで
データ分析、ビジュアライゼーションを簡単に素早く行う事が可能

HoloViewsサンプル

HoloViews.orgに掲載されているサンプル
コードは驚くほどシンプルな構造

更にGalleryページにはBoken,Matplotlib,Plotlyを使用した
サンプルグラフがある

各グラフのコードのサンプルもHoloViews.orgにて紹介している

Holoviewのインストール

condaがお勧め

conda install -c ioam holoviews bokeh

1	conda install -c ioam holoviews bokeh

上記で紹介したサンプルのグラフも含まれている

holoviews --install-examples
cd holoviews-examples

1 2	holoviews --install-examples cd holoviews-examples

NY地下鉄の駅名情報を可視化

pandas,numpyを使用する
hv.extension(“bokeh”)でBokehのエクステンションをロードし
Bokehを使った可視化ができるようになる

他のプロットライブラリーも組み合わせ可能(matplotlib等)

import pandas as pd
import numpy as np
import holoviews as hv
hv.extension('bokeh')

import pandas as pd

import numpy as np

import holoviews as hv

hv.extension('bokeh')

次にニューヨークの地下鉄の駅名やその他情報を
含むデータセットを読み込む
csvファイルはこちら

station_info = pd.read_csv('/station_info.csv')
station_info.head()

1 2	station_info = pd.read_csv('/station_info.csv') station_info.head()

可視化の為のオブジェクト

スキャッタープロット

hv.Scatterオブジェクトを使用して
駅の数と利用者数（百万単位）の２つをスキャッタープロット表示

scatter = hv.Scatter(station_info, 'services', 'ridership')
scatter

1 2	scatter = hv.Scatter(station_info, 'services', 'ridership') scatter

※画像のリンク先でグラフをドラッグで動かせる

csvのデータ(station_info)をhv.Scatterに渡し、scatterオブジェクトを作成
これ以外のエレメントはReference Galleryにて紹介

ヒストグラム

hv.Histogramオブジェクトを使用して地下鉄の駅開設日を
ヒストグラムにする

layout = hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened'])
layout

1 2	layout = hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened']) layout

＋を使用して複数のグラフ表示

スキャッタープロットとヒストグラムなど複数のオブジェクトを
使用したグラフを横並びに表示する

layout = scatter + hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened'])
layout

1 2	layout = scatter + hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened']) layout

以下ここまでのコードまとめ

import pandas as pd
import numpy as np
import holoviews as hv
hv.extension('bokeh')

station_info = pd.read_csv('holoviews-master/examples/assets/station_info.csv')
scatter = hv.Scatter(station_info, 'services', 'ridership')

layout = scatter + hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened'])
layout

import pandas as pd

import numpy as np

import holoviews as hv

hv.extension('bokeh')

station_info = pd.read_csv('holoviews-master/examples/assets/station_info.csv')

scatter = hv.Scatter(station_info, 'services', 'ridership')

layout = scatter + hv.Histogram(np.histogram(station_info['opened'], bins=24), kdims=['opened'])

layout

神経スパイク列を可視化

脳のニューロン発火現象を記録したデータセットを用いて
発火時間（milliseconds)
神経細胞が発火する頻度をヘルツ(Hertz)
の２つのデータセットを可視化

csvファイル(spike_train.csv.gz)はこちら

pandasとholoviewsをインポートし、spike_trainにcsvを読み込ませる

import pandas as pd
import holoviews as hv
spike_train = pd.read_csv('/spike_train.csv.gz')

import pandas as pd

import holoviews as hv

spike_train = pd.read_csv('/spike_train.csv.gz')

データフレームを表示

spike_train

1	spike_train

hv.Curveでグラフ作成

可視化のオブジェクトにhv.Curveを使用
先ほど使用したhv.Scatter やhv.Histogramの仲間

hv.Curveの引数を左からcsv格納変数、インデックス名２つ、group=でグラフタイトル

curve = hv.Curve(spike_train, 'milliseconds', 'Hertz', group='Firing Rate')

1	curve = hv.Curve(spike_train, 'milliseconds', 'Hertz', group='Firing Rate')

bokehとmatplotlibをロード

hv.extension('bokeh', 'matplotlib')
curve

1 2	hv.extension('bokeh', 'matplotlib') curve

hv.Spikesでグラフ作成

spikes = hv.Spikes(spike_train.sample(300), kdims='milliseconds', vdims=[], group='Spike Train')
spikes

1 2	spikes = hv.Spikes(spike_train.sample(300), kdims='milliseconds', vdims=[], group='Spike Train') spikes

2つのグラフを並べる

curve + spikes

1	curve + spikes

更にカスタマイズして見やすいグラフ作成
グラフ上にカーソルを合わせると各データをインタラクティブに表示

%%output size=150
%%opts Curve  [height=100 width=600 xaxis=None tools=['hover']]
%%opts Curve (color='red' line_width=1.5)
%%opts Spikes [height=100 width=600 yaxis=None] (color='grey' line_width=0.25)
curve  = hv.Curve( spike_train, 'milliseconds', 'Hertz')
spikes = hv.Spikes(spike_train, 'milliseconds', [])
(curve + spikes).cols(1)

%%output size=150

%%opts Curve [height=100 width=600 xaxis=None tools=['hover']]

%%opts Curve (color='red' line_width=1.5)

%%opts Spikes [height=100 width=600 yaxis=None] (color='grey' line_width=0.25)

curve = hv.Curve( spike_train, 'milliseconds', 'Hertz')

spikes = hv.Spikes(spike_train, 'milliseconds', [])

(curve + spikes).cols(1)

%%output size=150で全体のサイズ
%%opts Curve でcurveグラフ個別の詳細設定

以下のグラフは画像ですのでインタラクティブ操作できません
実際に動かすにはこちらのグラフをご覧ください。

Bokeh と Holoviewsで月食グラフをhtmlに変換

2018年5月7日 by 河副太智 Leave a Comment

Bokeh と Holoviewsで月食グラフをhtmlに変換

import pandas as pd
import holoviews as hv
from bokeh.io import output_file, save, show
hv.extension('bokeh', 'matplotlib')

eclipses = pd.read_csv('../data/eclipses_21C.csv', parse_dates=['date'])
eclipses.head()

hv.Curve(eclipses)

curve= hv.Curve(eclipses)
renderer = hv.renderer('bokeh')
 
# rendererで保存
renderer.save(curve, 'graph')
 
# boken形式に変換
plot = renderer.get_plot(curve).state
 

# graph.htmlの名でhtmlファイルを出力
output_file('graph.html')

# プロット
show(plot)

import pandas as pd

import holoviews as hv

from bokeh.io import output_file, save, show

hv.extension('bokeh', 'matplotlib')

eclipses = pd.read_csv('../data/eclipses_21C.csv', parse_dates=['date'])

eclipses.head()

hv.Curve(eclipses)

curve= hv.Curve(eclipses)

renderer = hv.renderer('bokeh')

# rendererで保存

renderer.save(curve, 'graph')

# boken形式に変換

plot = renderer.get_plot(curve).state

# graph.htmlの名でhtmlファイルを出力

output_file('graph.html')

# プロット

show(plot)

BokehとHoloviewsでグラフをhtmlにする

2018年4月23日 by 河副太智 Leave a Comment

import holoviews as hv
curve = hv.Curve(range(10)) # Could be any HoloViews object

renderer = hv.renderer('bokeh')

# Using renderer save
renderer.save(curve, 'graph.html')

# Convert to bokeh figure then save using bokeh
plot = renderer.get_plot(curve).state

from bokeh.io import output_file, save, show

output_file("graph.html")
show(plot)

import holoviews as hv

curve = hv.Curve(range(10)) # Could be any HoloViews object

renderer = hv.renderer('bokeh')

# Using renderer save

renderer.save(curve, 'graph.html')

# Convert to bokeh figure then save using bokeh

plot = renderer.get_plot(curve).state

from bokeh.io import output_file, save, show

output_file("graph.html")

show(plot)

周期表をbokehでhtml出力

2018年4月18日 by 河副太智 Leave a Comment

from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
from bokeh.sampledata.periodic_table import elements
from bokeh.transform import dodge, factor_cmap

output_file("period.html")

periods = ["I", "II", "III", "IV", "V", "VI", "VII"]
groups = [str(x) for x in range(1, 19)]

df = elements.copy()
df["atomic mass"] = df["atomic mass"].astype(str)
df["group"] = df["group"].astype(str)
df["period"] = [periods[x-1] for x in df.period]
df = df[df.group != "-"]
df = df[df.symbol != "Lr"]
df = df[df.symbol != "Lu"]

cmap = {
    "alkali metal"         : "#a6cee3",
    "alkaline earth metal" : "#1f78b4",
    "metal"                : "#d93b43",
    "halogen"              : "#999d9a",
    "metalloid"            : "#e08d49",
    "noble gas"            : "#eaeaea",
    "nonmetal"             : "#f1d4Af",
    "transition metal"     : "#599d7A",
}

source = ColumnDataSource(df)

p = figure(title="Periodic Table (omitting LA and AC Series)", plot_width=1000, plot_height=450,
           tools="", toolbar_location=None,
           x_range=groups, y_range=list(reversed(periods)))

p.rect("group", "period", 0.95, 0.95, source=source, fill_alpha=0.6, legend="metal",
       color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))

text_props = {"source": source, "text_align": "left", "text_baseline": "middle"}

x = dodge("group", -0.4, range=p.x_range)

r = p.text(x=x, y="period", text="symbol", **text_props)
r.glyph.text_font_style="bold"

r = p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number", **text_props)
r.glyph.text_font_size="8pt"

r = p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name", **text_props)
r.glyph.text_font_size="5pt"

r = p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass", **text_props)
r.glyph.text_font_size="5pt"

p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle")

p.add_tools(HoverTool(tooltips = [
    ("Name", "@name"),
    ("Atomic number", "@{atomic number}"),
    ("Atomic mass", "@{atomic mass}"),
    ("Type", "@metal"),
    ("CPK color", "$color[hex, swatch]:CPK"),
    ("Electronic configuration", "@{electronic configuration}"),
]))

p.outline_line_color = None
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_standoff = 0
p.legend.orientation = "horizontal"
p.legend.location ="top_center"

show(p)

from bokeh.io import output_file, show

from bokeh.models import ColumnDataSource, HoverTool

from bokeh.plotting import figure

from bokeh.sampledata.periodic_table import elements

from bokeh.transform import dodge, factor_cmap

output_file("period.html")

periods = ["I", "II", "III", "IV", "V", "VI", "VII"]

groups = [str(x) for x in range(1, 19)]

df = elements.copy()

df["atomic mass"] = df["atomic mass"].astype(str)

df["group"] = df["group"].astype(str)

df["period"] = [periods[x-1] for x in df.period]

df = df[df.group != "-"]

df = df[df.symbol != "Lr"]

df = df[df.symbol != "Lu"]

cmap = {

"alkali metal" : "#a6cee3",

"alkaline earth metal" : "#1f78b4",

"metal" : "#d93b43",

"halogen" : "#999d9a",

"metalloid" : "#e08d49",

"noble gas" : "#eaeaea",

"nonmetal" : "#f1d4Af",

"transition metal" : "#599d7A",

}

source = ColumnDataSource(df)

p = figure(title="Periodic Table (omitting LA and AC Series)", plot_width=1000, plot_height=450,

tools="", toolbar_location=None,

x_range=groups, y_range=list(reversed(periods)))

p.rect("group", "period", 0.95, 0.95, source=source, fill_alpha=0.6, legend="metal",

color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))

text_props = {"source": source, "text_align": "left", "text_baseline": "middle"}

x = dodge("group", -0.4, range=p.x_range)

r = p.text(x=x, y="period", text="symbol", **text_props)

r.glyph.text_font_style="bold"

r = p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number", **text_props)

r.glyph.text_font_size="8pt"

r = p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name", **text_props)

r.glyph.text_font_size="5pt"

r = p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass", **text_props)

r.glyph.text_font_size="5pt"

p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle")

p.add_tools(HoverTool(tooltips = [

("Name", "@name"),

("Atomic number", "@{atomic number}"),

("Atomic mass", "@{atomic mass}"),

("Type", "@metal"),

("CPK color", "$color[hex, swatch]:CPK"),

("Electronic configuration", "@{electronic configuration}"),

]))

p.outline_line_color = None

p.grid.grid_line_color = None

p.axis.axis_line_color = None

p.axis.major_tick_line_color = None

p.axis.major_label_standoff = 0

p.legend.orientation = "horizontal"

p.legend.location ="top_center"

show(p)