# Starting Glue from Python¶

In addition to using Glue as a standalone program, you can import glue as a library from Python. There are (at least) two good reasons to do this:

1. You are working with multidimensional data in python, and want to use Glue for quick interactive visualization.
2. You find yourself repeatedly loading the same sets of data each time you run Glue. You want to write a startup script to automate this process.

## Quickly send data to Glue with qglue¶

The easiest way to send python variables to Glue is to use qglue():

from glue import qglue


For example, say you are working with a Pandas DataFrame:

>>> df
<class 'pandas.core.frame.DataFrame'>
Int64Index: 500 entries, 0 to 499
Data columns (total 3 columns):
x    500  non-null values
y    500  non-null values
z    500  non-null values
dtypes: float64(3)


You can easily start up Glue with this data using:

>>> app = qglue(xyz=df)


This will send this data to Glue, and label it xyz.

qglue() accepts many data types as inputs. Let’s see some examples:

import numpy as np
import pandas as pd
from astropy.table import Table

x = [1, 2, 3]
y = [2, 3, 4]

u = [10, 20, 30, 40]
v = [20, 40, 60, 80]

pandas_data = pd.DataFrame({'x': x, 'y': y})
dict_data = {'u': u, 'v': v}
recarray_data = np.rec.array([(0, 1), (2, 3)],
dtype=[('a', 'i'), ('b', 'i')])
astropy_table = Table({'x': x, 'y': y})

• qglue(xy=pandas_data):
constructs a dataset labeled xy, with two components (x and y)
• qglue(uv=dict_data):
construct a dataset labeled uv, with two components (u and v)
• qglue(xy=pandas_data, uv=dict_data):
constructs both of the previous two data sets.
• qglue(rec=recarray_data, astro=astropy_table):
constructs two datasets: rec (components a and b), and astro (components x and y)
• qglue(bad=bad_data):
doesn’t work, because the two components x and u have different shapes.

Note

Reminder: in Glue, Data sets are collections of one or more Component objects. Components in a dataset are bascially arrays of the same shape. For more information, see Working with Data objects

Note

Datasets cannot be given the label links.

### Linking data with qglue¶

The Data Linking tutorial discusses how Glue uses the concept of links to compare different datasets. From the GUI, links are defined using the Link Manager. It is also possible to define some of these links with qglue.

The links keyword for qglue accepts a list of link descriptions. Each link description has the following format:

(component_list_a, component_set_b, forward_func, back_func)

• component_list_a and component_list_b are lists of component names. In the first example above, the x component in the xyz dataset is named 'xyz.x'.
• forward_func is a function which accepts one or more numpy arrays as input, and returns one or more numpy arrays as output. It computes the quantities in component_set_b, given the quantities in component_list_a.
• back_func performs the reverse operastion.

Here’s an example:

def pounds_to_kilos(lbs):
return lbs / 2.2

def kilos_to_pounds(kilos):
return kilos * 2.2

def lengths_to_area(width, height):
return width * height

link1 = (['data1.m_lb'], ['data_2.m_kg'], pounds_to_kilos, kilos_to_pounds)
link2 = (['data1.width', 'data1.height'], ['data2.area'], lengths_to_area)


The first link converts between the masses in two different data sets, recorded in different units. The second link is a 1-way link that computes the area of items in dataset 1, based on their width and height (there is no way to compute the width and height from the area measurements in dataset 2, so the reverse function is not provided). These links would enable the following interaction, for example:

1. Overplot histograms of the mass distribution of both datasets
2. Define a region in a plot of mass vs area for data 2, and apply that filter to dataset 1

Note

If you start Glue from a non-notebook IPython session, you will encounter an error like Multiple incompatible subclass instances of IPKernelApp are being created. The solution to this is to start Glue from a non-IPython shell, or from the notebook (see next section).

### Using qglue with the IPython/Jupyter Notebook¶

You can call qglue() from the IPython/Jupyter notebook normally. However, the default behavior is for Glue to block the execution of the notebook while the UI is running. If you would like to be able to use the notebook and Glue at the same time, run this cell before starting glue:

%gui qt


This must be executed in a separate cell, before starting Glue.

### Adding data to glue when started using qglue¶

Once glue has been launched, you can continue to add data to it using the add_data() method:

>>> app = qglue(data1=array1)


You can also pass filenames to add_data():

>>> app.add_data('myimage.fits')


## Manual data construction¶

If qglue is not flexible enough for your needs, you can build data objects using the general Glue data API described in Working with Data objects.

Here’s a simple script to load data and pass it to Glue:

from glue.core.data_factories import load_data
from glue.core import DataCollection
from glue.app.qt.application import GlueApplication

dc = DataCollection([image, catalog])

#start Glue
app = GlueApplication(dc)
app.start()


Some remarks:

• load_data() constructs Glue Data objects from files. It uses the file extension as a hint for file type
• Individual data objects are bundled inside a DataCollection
• The LinkSame function indicates that two attributes in different data sets descirbe the same quantity
• GlueApplication takes a DataCollection as input, and starts the GUI via start()

## Starting Glue from a script¶

If you call glue with a python script as input, Glue will simply run that script:

\$ glue startup_script.py


Likewise, if you are using the pre-built Mac application, you can right-click on a script and open the file with Glue.