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# Bokeh Tutorial
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# 01. Basic Plotting Interface
# This section of the tutorial covers the [`bokeh.plotting`](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html)
# interface. This interface is a "mid-level" interface, and the main idea can be described by the statement:
#
# **Starting from simple default figures (with sensible default tools, grids and axes), add markers and other shapes whose visual attributes are tied to directly data.**
#
# We will see that it is possible to customize and change all of the defaults, but having them means that it is possible to get up and running very quickly.
# # Imports and Setup
#
# When using the [`bokeh.plotting`](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html) interface, there are a few common imports:
# * Use the [`figure`](http://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure) function to create new plot objects to work with.
# * Call the functions [`output_file`](http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh.io.output_file), [`output_notebook`](http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh.io.output_notebook), and [`output_server`](http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh.io.output_server) (possibly in combination) to tell Bokeh how to display or save output.
# * Execute [`show`](http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh.io.show) and [`save`](http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh.io.save) to display or save plots and layouts.
# In[2]:
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
# In this case, we are in the Jupyter notebook, so call `output_notebook()`. We only need to call this once, and all subsequent calls to `show()` will display inline in the notebook.
# In[3]:
output_notebook()
# # Some Basic Scatter Plots
#
# In this section you will see how to use Bokeh's various marker types to create simple scatter plots.
# In[4]:
# create a new plot with default tools, using figure
p = figure(plot_width=400, plot_height=400)
# add a circle renderer with a size, color, and alpha
p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=15, line_color="navy", fill_color="orange", fill_alpha=0.5)
show(p) # show the results
# All Bokeh markers accept `size` (measured in screen space units) as a property. Circles also have `radius` (measured in "data" space units).
# In[5]:
# EXERCISE: Try changing the example above to set a `radius` value instead of `size`
# To scatter square markers instead of circles, you can use the `square` method on figures.
# In[6]:
# create a new plot using figure
p = figure(plot_width=400, plot_height=400)
# add a square renderer with a size, color, alpha, and sizes
p.square([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=[10, 15, 20, 25, 30], color="firebrick", alpha=0.6)
show(p) # show the results
# Note that in the example above, we are also specifying different sizes for each individual marker. ***In general, all of a glyph's properties can be "vectorized" in this fashion. *** Also note that we have passed ``color`` as a shorthand to set both the line and fill colors easily at the same time. This is a convenience specific to ``bokeh.plotting``.
#
# There are many marker types available in Bokeh, you can see details and
# example plots for all of them in the reference guide by clicking on entries in the list below:
#
# In[7]:
# EXERCISE: Plot some different markers in this cell
# # Basic Line Plots
# In[8]:
# create a new plot (with a title) using figure
p = figure(plot_width=400, plot_height=400, title="My Line Plot")
# add a line renderer
p.line([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], line_width=2)
show(p) # show the results
# # Images
# The example below shows how to use the `image_rgba` method to display raw RGBA data.
#
# **Note**: This example makes use of the NumPy library
# In[9]:
from __future__ import division
import numpy as np
# set up some data
N = 20
img = np.empty((N,N), dtype=np.uint32)
view = img.view(dtype=np.uint8).reshape((N, N, 4))
for i in range(N):
for j in range(N):
view[i, j, 0] = int(i/N*255) # red
view[i, j, 1] = 158 # green
view[i, j, 2] = int(j/N*255) # blue
view[i, j, 3] = 255 # alpha
# create a new plot (with a fixed range) using figure
p = figure(x_range=[0,10], y_range=[0,10])
# add an RGBA image renderer
p.image_rgba(image=[img], x=[0], y=[0], dw=[10], dh=[10])
show(p) # show the results
# # Other Kinds of Glyphs
#
# Bokeh supports many other kinds of glyphs. You can click on the User Guide links below to see how to create plots with these glyphs using the [`bokeh.plotting`](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html) interface.
#
# * [Rectangles and Ovals](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html#rectangles-and-ovals)
# * [Segments and Rays](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html#segments-and-rays)
# * [Wedges and Arcs](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html#wedges-and-arcs)
# * [Specialized Curves](http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html#specialized-curves)
#
# In[10]:
# EXERCISE: Plot some of the other glyph types, following the examples in the User Guide.
# # Plots with Multiple Glyphs
#
# It is possible to combine more than one glyph on a single figure. You just need to call multiple glyph methods on one figure object:
# In[11]:
# set up some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 8, 7, 3]
# create a new plot with figure
p = figure(plot_width=400, plot_height=400)
# add both a line and circles on the same plot
p.line(x, y, line_width=2)
p.circle(x, y, fill_color="white", size=8)
show(p) # show the results
# In[12]:
# EXERCISE: create your own plot combining multiple glyphs together