Imports

import csv

import numpy as np
import numpy.random

import bokeh.plotting
import bokeh.layouts
import bokeh.embed

Temperature and CO$_{2}$

File io & data loading

Temp

temp_file_path = './temp'
temp_file_handle = open(temp_file_path, 'r')
temp_file_reader = csv.reader(temp_file_handle, delimiter = ' ')

temp_years = []
temp_temps = []
for i, row in enumerate(temp_file_reader):
    temp_years.append(row[0])
    temp_temps.append(row[1])

CO$_{2}$

co2_file_path = './co2'
co2_file_handle = open(co2_file_path, 'r')
co2_file_reader = csv.reader(co2_file_handle, delimiter = ' ')

co2_years = []
co2_co2s = []
for i, row in enumerate(co2_file_reader):
    row = [ele for ele in row if ele != '']
    if row[3] != '-99.99':
        co2_years.append(row[2])
        co2_co2s.append(row[3])

Create interactive plot

# Create static html file as output
bokeh.plotting.output_file('temp_co2.html', title = 'NASA historical temperature and CO2 annual mean records')

# Create the two figures
temp_figure = bokeh.plotting.figure(title = 'temperature', x_axis_label='year', y_axis_label = 'avg temp anomaly (deg C)', width = 500, height = 250)

co2_figure = bokeh.plotting.figure(title = 'co2', x_axis_label = 'year', y_axis_label = 'avg conc. co2 (ppm)', width = 500, height = 250, x_range = temp_figure.x_range)


# Plot in the two figures
temp_figure.line(temp_years, temp_temps, legend = 'temp', line_width=2)
co2_figure.line(co2_years, co2_co2s, legend = 'co2')

# Create the combined figure
figure = bokeh.layouts.column(temp_figure, co2_figure)

# Display results
bokeh.plotting.show(figure)

# Save results
bokeh.plotting.save(figure, './co2_temp.html')

Scatter

Create data

# Cluster one
mu_one = [1, 1]
covar_one = np.array([[1,.9],[.9,1]])
cluster_one = numpy.random.multivariate_normal(mu_one, covar_one, 200)

# Cluster two
mu_two = [-5,-4]
covar_two = np.array([[4,1], [1,4]])
cluster_two = numpy.random.multivariate_normal(mu_two, covar_two, 300)

# Cluster three
mu_three = [5,-3]
covar_three = np.array([[2,1.5], [1.5,2]])
cluster_three = numpy.random.multivariate_normal(mu_three, covar_three, 100)

# X Histogram
data_x = np.concatenate((cluster_one[:,0], cluster_two[:,0], cluster_three[:,0]), axis = 0)
vals_x, edges_x = np.histogram(data_x, bins = 10)

data_y = np.concatenate((cluster_one[:,1], cluster_two[:,1], cluster_three[:,1]), axis = 0)
vals_y, edges_y = np.histogram(data_y, bins = 10)

Create interactive plot

bokeh.plotting.output_file('scatter_plot.html', title = 'Bivariate gaussians')

##############
# Scatter plot
##############

scatter_figure = bokeh.plotting.figure(plot_width = 320, plot_height = 320,\
                                       tools = "pan,wheel_zoom,box_select,lasso_select,reset", title = 'Bivariate Gaussians')

# Cluster one
scatter_figure.circle(x = cluster_one[:,0], y = cluster_one[:,1], color = 'red', size = 3)

# Cluster two
scatter_figure.circle(x = cluster_two[:,0], y = cluster_two[:,1], color = 'blue', size = 3)

# Cluster three
scatter_figure.circle(x = cluster_three[:,0], y = cluster_three[:,1], color = 'green', size = 3)

###############
# Histogram
###############

x_histogram_figure = bokeh.plotting.figure(toolbar_location = None, plot_width = scatter_figure.plot_width, plot_height = 200, x_range=scatter_figure.x_range,\
                            min_border = 10, y_axis_location = 'right')

y_histogram_figure = bokeh.plotting.figure(toolbar_location = None, plot_width = 200, plot_height = scatter_figure.plot_height, y_range = scatter_figure.y_range,\
                                          min_border = 10, x_axis_location = 'above', x_range = (vals_y.min(), vals_y.max()))

# x- Histogram
x_histogram = x_histogram_figure.quad(top = vals_x, bottom = 0, left = edges_x[:-1], right = edges_x[1:], fill_color = '#c8c8c8', line_color = '#727272')

# y- Histogram
y_histogram = y_histogram_figure.quad(top = edges_y[:-1], bottom = edges_y[1:], left = 0, right = vals_y, fill_color = '#c8c8c8', line_color = '#727272')


figure = bokeh.layouts.column(bokeh.layouts.row(scatter_figure, y_histogram_figure),                   bokeh.layouts.row(x_histogram_figure, bokeh.layouts.Spacer(width=200, height=200)))

# Display the figure
bokeh.plotting.show(figure)

# Save figure
bokeh.plotting.save(figure, './scatterplot.html')