03_02_bokeh.py

# -*- coding: utf-8 -*-
from bokeh.plotting import figure, output_file, show

# Datos de los puntos
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# Salida a fichero html
#output_file("lines.html")

# Creado nuevo plot con título y etiquetas de ejes
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')

# Añade una línea entre los puntos fijados
p.line(x, y, legend_label="Temp.", line_width=2)

# Muestra los resultados
show(p)


from bokeh.models import ColumnDataSource

#Genera los datos
data = {'x_values': [1, 2, 3, 4, 5],
        'y_values': [6, 7, 2, 3, 6]}

#Crea la fuente de datos
source = ColumnDataSource(data=data)

#Crea la figura
p = figure()
#Crea un círculo
p.circle(x='x_values', y='y_values', source=source)
#Muestra la gráfica
show(p)


# Prepara los datos
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y0 = [i**2 for i in x]
y1 = [10**i for i in x]
y2 = [10**(i**2) for i in x]

# Fichero de salida
#output_file("log_lines.html")

# Crea la figura
p = figure(
   tools="pan,box_zoom,reset,save",
   y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
   x_axis_label='sections', y_axis_label='particles'
)

# añade los renders
p.line(x, x, legend_label="y=x")
p.circle(x, x, legend_label="y=x", fill_color="white", size=8)
p.line(x, y0, legend_label="y=x^2", line_width=3)
p.line(x, y1, legend_label="y=10^x", line_color="red")
p.circle(x, y1, legend_label="y=10^x", fill_color="red", line_color="red", size=6)
p.line(x, y2, legend_label="y=10^x^2", line_color="orange", line_dash="4 4")

# muestra los resultados
show(p)



import numpy as np


# prepare some data
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)
]

# output to static HTML file (with CDN resources)
output_file("./figures/color_scatter.html", title="color_scatter.py example", mode="cdn")

TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,box_select,lasso_select"

# create a new plot with the tools above, and explicit ranges
p = figure(tools=TOOLS, x_range=(0,100), y_range=(0,100))

# add a circle renderer with vectorized colors and sizes
p.circle(x,y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)

# show the results
show(p)



from bokeh.document import Document
from bokeh.embed import file_html
from bokeh.layouts import gridplot
from bokeh.models.glyphs import Circle
from bokeh.models import (BasicTicker, ColumnDataSource, Grid, LinearAxis,
                         DataRange1d, PanTool, Plot, WheelZoomTool)
from bokeh.resources import INLINE
from bokeh.sampledata.iris import flowers
from bokeh.util.browser import view

colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}

flowers['color'] = flowers['species'].map(lambda x: colormap[x])


source = ColumnDataSource(
    data=dict(
        petal_length=flowers['petal_length'],
        petal_width=flowers['petal_width'],
        sepal_length=flowers['sepal_length'],
        sepal_width=flowers['sepal_width'],
        color=flowers['color']
    )
)

xdr = DataRange1d(bounds=None)
ydr = DataRange1d(bounds=None)

def make_plot(xname, yname, xax=False, yax=False):
    mbl = 40 if yax else 0
    mbb = 40 if xax else 0
    plot = Plot(
        x_range=xdr, y_range=ydr, background_fill_color="#efe8e2",
        border_fill_color='white', plot_width=200 + mbl, plot_height=200 + mbb,
        min_border_left=2+mbl, min_border_right=2, min_border_top=2, min_border_bottom=2+mbb)

    circle = Circle(x=xname, y=yname, fill_color="color", fill_alpha=0.2, size=4, line_color="color")
    r = plot.add_glyph(source, circle)

    xdr.renderers.append(r)
    ydr.renderers.append(r)

    xticker = BasicTicker()
    if xax:
        xaxis = LinearAxis()
        xaxis.axis_label = xname
        plot.add_layout(xaxis, 'below')
        xticker = xaxis.ticker
    plot.add_layout(Grid(dimension=0, ticker=xticker))

    yticker = BasicTicker()
    if yax:
        yaxis = LinearAxis()
        yaxis.axis_label = yname
        yaxis.major_label_orientation = 'vertical'
        plot.add_layout(yaxis, 'left')
        yticker = yaxis.ticker
    plot.add_layout(Grid(dimension=1, ticker=yticker))

    plot.add_tools(PanTool(), WheelZoomTool())

    return plot

xattrs = ["petal_length", "petal_width", "sepal_width", "sepal_length"]
yattrs = list(reversed(xattrs))
plots = []

for y in yattrs:
    row = []
    for x in xattrs:
        xax = (y == yattrs[-1])
        yax = (x == xattrs[0])
        plot = make_plot(x, y, xax, yax)
        row.append(plot)
    plots.append(row)

grid = gridplot(plots)

doc = Document()
doc.add_root(grid)

if __name__ == "__main__":
    doc.validate()
    filename = "./figures/iris_splom.html"
    with open(filename, "w") as f:
        f.write(file_html(doc, INLINE, "Iris Data SPLOM"))
    print("Wrote %s" % filename)
    view(filename)



import numpy as np
import pandas as pd

from bokeh.plotting import figure, show, output_file

# generate some synthetic time series for six different categories
cats = list("abcdef")
yy = np.random.randn(2000)
g = np.random.choice(cats, 2000)
for i, l in enumerate(cats):
    yy[g == l] += i // 2
df = pd.DataFrame(dict(score=yy, group=g))

# find the quartiles and IQR for each category
groups = df.groupby('group')
q1 = groups.quantile(q=0.25)
q2 = groups.quantile(q=0.5)
q3 = groups.quantile(q=0.75)
iqr = q3 - q1
upper = q3 + 1.5*iqr
lower = q1 - 1.5*iqr

# find the outliers for each category
def outliers(group):
    cat = group.name
    return group[(group.score > upper.loc[cat]['score']) | (group.score < lower.loc[cat]['score'])]['score']
out = groups.apply(outliers).dropna()

# prepare outlier data for plotting, we need coordinates for every outlier.
if not out.empty:
    outx = []
    outy = []
    for cat in cats:
        # only add outliers if they exist
        if not out.loc[cat].empty:
            for value in out[cat]:
                outx.append(cat)
                outy.append(value)

p = figure(tools="save", background_fill_color="#EFE8E2", title="", x_range=cats)

# if no outliers, shrink lengths of stems to be no longer than the minimums or maximums
qmin = groups.quantile(q=0.00)
qmax = groups.quantile(q=1.00)
upper.score = [min([x,y]) for (x,y) in zip(list(qmax.loc[:,'score']),upper.score)]
lower.score = [max([x,y]) for (x,y) in zip(list(qmin.loc[:,'score']),lower.score)]

# stems
p.segment(cats, upper.score, cats, q3.score, line_color="black")
p.segment(cats, lower.score, cats, q1.score, line_color="black")

# boxes
p.vbar(cats, 0.7, q2.score, q3.score, fill_color="#E08E79", line_color="black")
p.vbar(cats, 0.7, q1.score, q2.score, fill_color="#3B8686", line_color="black")

# whiskers (almost-0 height rects simpler than segments)
p.rect(cats, lower.score, 0.2, 0.01, line_color="black")
p.rect(cats, upper.score, 0.2, 0.01, line_color="black")

# outliers
if not out.empty:
    p.circle(outx, outy, size=6, color="#F38630", fill_alpha=0.6)

p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = "white"
p.grid.grid_line_width = 2
p.xaxis.major_label_text_font_size="12pt"

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

show(p)