This lesson is being piloted (Beta version)

Plotting and Pandas

Overview

Teaching: 65 min
Exercises: 20 min
Questions
• How do we make scatter plots in Matplotlib? How do we store data in a Pandas `DataFrame`?

Objectives
• Select rows and columns from an Astropy `Table`.

• Use Matplotlib to make a scatter plot.

• Use Gala to transform coordinates.

• Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.

• Save a `DataFrame` in an HDF5 file.

In the previous episode, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.

Now we will read that data back in and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1.

Outline

1. We will read back the results from the previous lesson, which we saved in a FITS file.

2. Then we will transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.

3. We will put those results into a Pandas `DataFrame`, which we will use to select stars near the centerline of GD-1.

4. Plotting the proper motion of those stars, we will identify a region of proper motion for stars that are likely to be in GD-1.

5. Finally, we will select and plot the stars whose proper motion is in that region.

Starting from this episode

If you are starting a new notebook for this episode, expand this section for information you will need to get started.

In the previous episode, we ran a query on the Gaia server, downloaded data for roughly 140,000 stars, and saved the data in a FITS file. We will use that data for this episode. Whether you are working from a new notebook or coming back from a checkpoint, reloading the data will save you from having to run the query again.

If you are starting this episode here or starting this episode in a new notebook, you will need to run the following lines of code.

This imports previously imported functions:

``````import astropy.units as u
from astropy.coordinates import SkyCoord
from gala.coordinates import GD1Koposov10
from astropy.table import Table

from episode_functions import *
``````

This loads in the data (instructions for downloading data can be found in the setup instructions):

``````filename = 'gd1_results.fits'

gd1_frame = GD1Koposov10()
``````

Selecting rows and columns

In the previous episode, we selected spatial and proper motion information from the Gaia catalog for stars around a small part of GD-1. The output was returned as an Astropy `Table`. We can use `info` to check the contents.

``````polygon_results.info()
``````
``````<Table length=140339>
name    dtype    unit                              description
--------- ------- -------- ------------------------------------------------------------------
source_id   int64          Unique source identifier (unique within a particular Data Release)
ra float64      deg                                                    Right ascension
dec float64      deg                                                        Declination
pmra float64 mas / yr                         Proper motion in right ascension direction
pmdec float64 mas / yr                             Proper motion in declination direction
parallax float64      mas                                                           Parallax
``````

In this episode, we will see operations for selecting columns and rows from an Astropy `Table`. You can find more information about these operations in the Astropy documentation.

We can get the names of the columns like this:

``````polygon_results.colnames
``````
``````['source_id', 'ra', 'dec', 'pmra', 'pmdec', 'parallax']
``````

And select an individual column like this:

``````polygon_results['ra']
``````
``````<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140339>
142.48301935991023
142.25452941346344
142.64528557468074
142.57739430926034
142.58913564478618
141.81762228999614
143.18339801317677
142.9347319464589
142.26769745823267
142.89551292869012
[Output truncated]
``````

The result is a `Column` object that contains the data, and also the data type, units, and name of the column.

``````type(polygon_results['ra'])
``````
``````astropy.table.column.Column
``````

The rows in the `Table` are numbered from 0 to `n-1`, where `n` is the number of rows. We can select the first row like this:

``````polygon_results[0]
``````
``````<Row index=0>
source_id              ra                dec                pmra              pmdec             parallax
deg                deg              mas / yr           mas / yr             mas
int64             float64            float64            float64            float64            float64
------------------ ------------------ ----------------- ------------------- ----------------- -------------------
637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354
``````

The result is a `Row` object.

``````type(polygon_results[0])
``````
``````astropy.table.row.Row
``````

Notice that the bracket operator can be used to select both columns and rows. You might wonder how it knows which to select. If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.

If you apply the bracket operator twice, you can select a column and then an element from the column.

``````polygon_results['ra'][0]
``````
``````142.48301935991023
``````

Or you can select a row and then an element from the row.

``````polygon_results[0]['ra']
``````
``````142.48301935991023
``````

You get the same result either way.

Scatter plot

To see what the results look like, we will use a scatter plot. The library we will use is Matplotlib, which is the most widely-used plotting library for Python. The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.

We will import like this:

``````import matplotlib.pyplot as plt
``````

Pyplot is part of the Matplotlib library. It is conventional to import it using the shortened name `plt`.

Keeping plots in the notebook

In recent versions of Jupyter, plots appear “inline”; that is, they are part of the notebook. In some older versions, plots appear in a new window. If your plots appear in a new window, you might want to run the following Jupyter magic command in a notebook cell:

``````%matplotlib inline
``````

Pyplot provides two functions that can make scatter plots, plt.scatter and plt.plot.

• `scatter` is more versatile; for example, you can make every point in a scatter plot a different color.

• `plot` is more limited, but for simple cases, it can be substantially faster.

Jake Vanderplas explains these differences in The Python Data Science Handbook.

Since we are plotting more than 100,000 points and they are all the same size and color, we will use `plot`.

Here is a scatter plot of the stars we selected in the GD-1 region with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees.

``````x = polygon_results['ra']
y = polygon_results['dec']
plt.plot(x, y, 'ko')

plt.xlabel('ra (degree ICRS)')
plt.ylabel('dec (degree ICRS)')
``````
``````<Figure size 432x288 with 1 Axes>
``````

The arguments to `plt.plot` are `x`, `y`, and a string that specifies the style. In this case, the letters `ko` indicate that we want a black, round marker (`k` is for black because `b` is for blue). The functions `xlabel` and `ylabel` put labels on the axes.

Looking at this plot, we can see that the region we selected, which is a rectangle in GD-1 coordinates, is a non-rectanglar region in ICRS coordinates.

However, this scatter plot has a problem. It is “overplotted”, which means that there are so many overlapping points, we can’t distinguish between high and low density areas.

To fix this, we can provide optional arguments to control the size and transparency of the points.

Exercise (5 minutes)

In the call to `plt.plot`, use the keyword argument `markersize` to make the markers smaller.

Then add the keyword argument `alpha` to make the markers partly transparent.

Adjust these arguments until you think the figure shows the data most clearly.

Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars. These stripes are caused by the way Gaia scans the sky, which you can read about here. The dataset we are using, Gaia Data Release 2, covers 22 months of observations; during this time, some parts of the sky were scanned more than others.

Solution

``````x = polygon_results['ra']
y = polygon_results['dec']
plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)

plt.xlabel('ra (degree ICRS)')
plt.ylabel('dec (degree ICRS)')
``````

Transform back

Remember that we selected data from a rectangle of coordinates in the GD-1 frame, then transformed them to ICRS when we constructed the query. The coordinates in the query results are in ICRS.

To plot them, we will transform them back to the GD-1 frame; that way, the axes of the figure are aligned with the orbit of GD-1, which is useful for two reasons:

• By transforming the coordinates, we can identify stars that are likely to be in GD-1 by selecting stars near the centerline of the stream, where φ2 is close to 0.

• By transforming the proper motions, we can identify stars with non-zero proper motion along the φ1 axis, which are likely to be part of GD-1.

To do the transformation, we will put the results into a `SkyCoord` object. In a previous episode, we created a `SkyCoord` object like this:

``````skycoord = SkyCoord(ra=polygon_results['ra'], dec=polygon_results['dec'])
``````

Notice that we did not specify the reference frame. That is because when using `ra` and `dec` in `SkyCoord`, the `ICRS` frame is assumed by default.

The `SkyCoord` object can keep track not just of location, but also proper motions. This means that we can initialize a `SkyCoord` object with location and proper motions, then use all of these quantities together to transform into the GD-1 frame.

Now we are going to do something similar, but now we will take advantage of the `SkyCoord` object’s capacity to include and track space motion information in addition to `ra` and `dec`. We will now also include:

• `pmra` and `pmdec`, which are proper motion in the `ICRS` frame, and

• `distance` and `radial_velocity`, which are important for the reflex correction and will be discussed in that section.

``````distance = 8 * u.kpc
radial_velocity= 0 * u.km/u.s

skycoord = SkyCoord(ra=polygon_results['ra'],
dec=polygon_results['dec'],
pm_ra_cosdec=polygon_results['pmra'],
pm_dec=polygon_results['pmdec'],
distance=distance,
``````

For the first four arguments, we use columns from `polygon_results`.

For `distance` and `radial_velocity` we use constants, which we explain in the section on reflex correction.

The result is an Astropy `SkyCoord` object, which we can transform to the GD-1 frame.

``````transformed = skycoord.transform_to(gd1_frame)
``````

The result is another `SkyCoord` object, now in the GD-1 frame.

Reflex Correction

The next step is to correct the proper motion measurements for the effect of the motion of our solar system around the Galactic center.

When we created `skycoord`, we provided constant values for `distance` and `radial_velocity` rather than measurements from Gaia.

That might seem like a strange thing to do, but here is the motivation:

• Because the stars in GD-1 are so far away, parallaxes measured by Gaia are negligible, making the distance estimates unreliable.
So we replace them with our current best estimate of the mean distance to GD-1, about 8 kpc. See Koposov, Rix, and Hogg, 2010.

• For the other stars in the table, this distance estimate will be inaccurate, so reflex correction will not be correct. But that should have only a small effect on our ability to identify stars with the proper motion we expect for GD-1.

• The measurement of radial velocity has no effect on the correction for proper motion, but we have to provide a value to avoid errors in the reflex correction calculation. So we provide `0` as an arbitrary place-keeper.

With this preparation, we can use `reflex_correct` from Gala (documentation here) to correct for the motion of the solar system.

``````from gala.coordinates import reflex_correct

skycoord_gd1 = reflex_correct(transformed)
``````

The result is a `SkyCoord` object that contains

• `phi1` and `phi2`, which represent the transformed coordinates in the GD-1 frame.

• `pm_phi1_cosphi2` and `pm_phi2`, which represent the transformed proper motions that have been corrected for the motion of the solar system around the Galactic center.

We can select the coordinates and plot them like this:

``````x = skycoord_gd1.phi1
y = skycoord_gd1.phi2
plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)

plt.xlabel('phi1 (degree GD1)')
plt.ylabel('phi2 (degree GD1)')
``````
``````<Figure size 432x288 with 1 Axes>
``````

We started with a rectangle in the GD-1 frame. When transformed to the ICRS frame, it is a non-rectangular region. Now, transformed back to the GD-1 frame, it is a rectangle again.

Pandas DataFrame

At this point we have two objects containing different sets of the data relating to identifying stars in GD-1. `polygon_results` is the Astropy `Table` we downloaded from Gaia.

``````type(polygon_results)
``````
``````astropy.table.table.Table
``````

And `skycoord_gd1` is a `SkyCoord` object that contains the transformed coordinates and proper motions.

``````type(skycoord_gd1)
``````
``````astropy.coordinates.sky_coordinate.SkyCoord
``````

On one hand, this division of labor makes sense because each object provides different capabilities. But working with multiple object types can be awkward. It will be more convenient to choose one object and get all of the data into it.

Now we can extract the columns we want from `skycoord_gd1` and add them as columns in the Astropy `Table` `polygon_results`. `phi1` and `phi2` contain the transformed coordinates.

``````polygon_results['phi1'] = skycoord_gd1.phi1
polygon_results['phi2'] = skycoord_gd1.phi2
polygon_results.info()
``````
``````<Table length=140339>
name    dtype    unit                              description
--------- ------- -------- ------------------------------------------------------------------
source_id   int64          Unique source identifier (unique within a particular Data Release)
ra float64      deg                                                    Right ascension
dec float64      deg                                                        Declination
pmra float64 mas / yr                         Proper motion in right ascension direction
pmdec float64 mas / yr                             Proper motion in declination direction
parallax float64      mas                                                           Parallax
phi1 float64      deg
phi2 float64      deg
``````

`pm_phi1_cosphi2` and `pm_phi2` contain the components of proper motion in the transformed frame.

``````polygon_results['pm_phi1'] = skycoord_gd1.pm_phi1_cosphi2
polygon_results['pm_phi2'] = skycoord_gd1.pm_phi2
polygon_results.info()
``````
``````<Table length=140339>
name    dtype    unit                              description
--------- ------- -------- ------------------------------------------------------------------
source_id   int64          Unique source identifier (unique within a particular Data Release)
ra float64      deg                                                    Right ascension
dec float64      deg                                                        Declination
pmra float64 mas / yr                         Proper motion in right ascension direction
pmdec float64 mas / yr                             Proper motion in declination direction
parallax float64      mas                                                           Parallax
phi1 float64      deg
phi2 float64      deg
pm_phi1 float64 mas / yr
pm_phi2 float64 mas / yr
``````

Detail If you notice that `SkyCoord` has an attribute called `proper_motion`, you might wonder why we are not using it.

We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format.

Pandas `DataFrame`s versus Astropy `Table`s

Two common choices are the Pandas `DataFrame` and Astropy `Table`. Pandas `DataFrame`s and Astropy `Table`s share many of the same characteristics and most of the manipulations that we do can be done with either. As you become more familiar with each, you will develop a sense of which one you prefer for different tasks. For instance you may choose to use Astropy `Table`s to read in data, especially astronomy specific data formats, but Pandas `DataFrame`s to inspect the data. Fortunately, Astropy makes it easy to convert between the two data types. We will choose to use Pandas `DataFrame`, for two reasons:

1. It provides capabilities that are (almost) a superset of the other data structures, so it’s the all-in-one solution.

2. Pandas is a general-purpose tool that is useful in many domains, especially data science. If you are going to develop expertise in one tool, Pandas is a good choice.

However, compared to an Astropy `Table`, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns. Nevertheless, we think its a useful data type to be familiar with.

It is straightforward to convert an Astropy `Table` to a Pandas `DataFrame`.

``````import pandas as pd

results_df = polygon_results.to_pandas()
``````

`DataFrame` provides `shape`, which shows the number of rows and columns.

``````results_df.shape
``````
``````(140339, 10)
``````

It also provides `head`, which displays the first few rows. `head` is useful for spot-checking large results as you go along.

``````results_df.head()
``````
``````            source_id          ra        dec       pmra       pmdec   parallax        phi1       phi2    pm_phi1     pm_phi2
0  637987125186749568  142.483019  21.757716  -2.516838    2.941813  -0.257345  -54.975623  -3.659349   6.429945    6.518157
1  638285195917112960  142.254529  22.476168   2.662702  -12.165984   0.422728  -54.498247  -3.081524  -3.168637   -6.206795
2  638073505568978688  142.645286  22.166932  18.306747   -7.950660   0.103640  -54.551634  -3.554229   9.129447  -16.819570
3  638086386175786752  142.577394  22.227920   0.987786   -2.584105  -0.857327  -54.536457  -3.467966   3.837120    0.526461
4  638049655615392384  142.589136  22.110783   0.244439   -4.941079   0.099625  -54.627448  -3.542738   1.466103   -0.185292

``````

Attributes vs functions

`shape` is an attribute, so we display its value without calling it as a function.

`head` is a function, so we need the parentheses.

Before we go any further, we will take all of the steps that we have done and consolidate them into a single function that we can use to take the coordinates and proper motion that we get as an Astropy `Table` from our Gaia query, add columns representing the reflex corrected GD-1 coordinates and proper motions, and transform it into a Pandas `DataFrame`. This is a general function that we will use multiple times as we build different queries so we want to write it once and then call the function rather than having to copy and paste the code over and over again.

``````def make_dataframe(table):
"""Transform coordinates from ICRS to GD-1 frame.

table: Astropy Table

returns: Pandas DataFrame
"""
#Create a SkyCoord object with the coordinates and proper motions
# in the input table
skycoord = SkyCoord(
ra=table['ra'],
dec=table['dec'],
pm_ra_cosdec=table['pmra'],
pm_dec=table['pmdec'],
distance=8*u.kpc,

# Define the GD-1 reference frame
gd1_frame = GD1Koposov10()

# Transform input coordinates to the GD-1 reference frame
transformed = skycoord.transform_to(gd1_frame)

# Correct GD-1 coordinates for solar system motion around galactic center
skycoord_gd1 = reflex_correct(transformed)

#Add GD-1 reference frame columns for coordinates and proper motions
table['phi1'] = skycoord_gd1.phi1
table['phi2'] = skycoord_gd1.phi2
table['pm_phi1'] = skycoord_gd1.pm_phi1_cosphi2
table['pm_phi2'] = skycoord_gd1.pm_phi2

# Create DataFrame
df = table.to_pandas()

return df
``````

Here is how we use the function:

``````results_df = make_dataframe(polygon_results)
``````

Exploring data

One benefit of using Pandas is that it provides functions for exploring the data and checking for problems. One of the most useful of these functions is `describe`, which computes summary statistics for each column.

``````results_df.describe()
``````
``````          source_id             ra            dec           pmra  \
count  1.403390e+05  140339.000000  140339.000000  140339.000000
mean   6.792399e+17     143.823122      26.780285      -2.484404
std    3.792177e+16       3.697850       3.052592       5.913939
min    6.214900e+17     135.425699      19.286617    -106.755260
25%    6.443517e+17     140.967966      24.592490      -5.038789
50%    6.888060e+17     143.734409      26.746261      -1.834943
75%    6.976579e+17     146.607350      28.990500       0.452893
max    7.974418e+17     152.777393      34.285481     104.319923

pmdec       parallax           phi1           phi2  \
[Output truncated]
``````

Exercise (10 minutes)

Review the summary statistics in this table.

• Do the values make sense based on what you know about the context?

• Do you see any values that seem problematic, or evidence of other data issues?

Solution

The most noticeable issue is that some of the parallax values are negative, which seems non-physical.

Negative parallaxes in the Gaia database can arise from a number of causes like source confusion (high negative values) and the parallax zero point with systematic errors (low negative values).

Fortunately, we don’t use the parallax measurements in the analysis (one of the reasons we used constant distance for reflex correction).

Plot proper motion

Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows components of proper motion as a scatter plot:

In this figure, the shaded area identifies stars that are likely to be in GD-1 because:

• Due to the nature of tidal streams, we expect the proper motion for stars in GD-1 to be along the axis of the stream; that is, we expect motion in the direction of `phi2` to be near 0.

• In the direction of `phi1`, we don’t have a prior expectation for proper motion, except that it should form a cluster at a non-zero value.

By plotting proper motion in the GD-1 frame, we hope to find this cluster. Then we will use the bounds of the cluster to select stars that are more likely to be in GD-1.

The following figure is a scatter plot of proper motion, in the GD-1 frame, for the stars in `results_df`.

``````x = results_df['pm_phi1']
y = results_df['pm_phi2']
plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)

plt.xlabel('Proper motion phi1 (mas/yr GD1 frame)')
plt.ylabel('Proper motion phi2 (mas/yr GD1 frame)')
``````
``````<Figure size 432x288 with 1 Axes>
``````

Most of the proper motions are near the origin, but there are a few extreme values. Following the example in the paper, we will use `xlim` and `ylim` to zoom in on the region near the origin.

``````x = results_df['pm_phi1']
y = results_df['pm_phi2']
plt.plot(x, y, 'ko', markersize=0.1, alpha=0.1)

plt.xlabel('Proper motion phi1 (mas/yr GD1 frame)')
plt.ylabel('Proper motion phi2 (mas/yr GD1 frame)')

plt.xlim(-12, 8)
plt.ylim(-10, 10)
``````
``````<Figure size 432x288 with 1 Axes>
``````

There is a hint of an overdense region near (-7.5, 0), but if you didn’t know where to look, you would miss it.

To see the cluster more clearly, we need a sample that contains a higher proportion of stars in GD-1. We will do that by selecting stars close to the centerline.

Selecting the centerline

As we can see in the following figure, many stars in GD-1 are less than 1 degree from the line `phi2=0`.

Stars near this line have the highest probability of being in GD-1.

To select them, we will use a “Boolean mask”. We wil start by selecting the `phi2` column from the `DataFrame`:

``````phi2 = results_df['phi2']
type(phi2)
``````
``````pandas.core.series.Series
``````

The result is a `Series`, which is the structure Pandas uses to represent columns.

We can use a comparison operator, `>`, to compare the values in a `Series` to a constant.

``````phi2_min = -1.0 * u.degree
phi2_max = 1.0 * u.degree

mask = (phi2 > phi2_min)
``````
``````pandas.core.series.Series
``````

The result is a `Series` of Boolean values, that is, `True` and `False`.

``````mask.head()
``````
``````0    False
1    False
2    False
3    False
4    False
Name: phi2, dtype: bool
``````

To select values that fall between `phi2_min` and `phi2_max`, we’ll use the `&` operator, which computes “logical AND”. The result is true where elements from both Boolean `Series` are true.

``````mask = (phi2 > phi2_min) & (phi2 < phi2_max)
``````

Logical operators

Python’s logical operators (`and`, `or`, and `not`) don’t work with NumPy or Pandas. Both libraries use the bitwise operators (`&`, `|`, and `~`) to do elementwise logical operations (explanation here).

Also, we need the parentheses around the conditions; otherwise the order of operations is incorrect.

The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region.

``````mask.sum()
``````
``````25084
``````

A Boolean `Series` is sometimes called a “mask” because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:

``````centerline_df = results_df[mask]
type(centerline_df)
``````
``````pandas.core.frame.DataFrame
``````

`centerline_df` is a `DataFrame` that contains only the rows from `results_df` that correspond to `True` values in `mask`. So it contains the stars near the centerline of GD-1.

We can use `len` to see how many rows are in `centerline_df`:

``````len(centerline_df)
``````
``````25084
``````

And what fraction of the rows we have selected.

``````len(centerline_df) / len(results_df)
``````
``````0.1787386257562046
``````

There are about 25,000 stars in this region, about 18% of the total.

Plotting proper motion

This is the second time we are plotting proper motion, and we can imagine we might do it a few more times. Instead of copying and pasting the previous code, we will write a function that we can reuse on any dataframe.

``````def plot_proper_motion(df):
"""Plot proper motion.

df: DataFrame with `pm_phi1` and `pm_phi2`
"""
x = df['pm_phi1']
y = df['pm_phi2']
plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)

plt.xlabel('Proper motion phi1 (mas/yr)')
plt.ylabel('Proper motion phi2 (mas/yr)')

plt.xlim(-12, 8)
plt.ylim(-10, 10)
``````

And we can call it like this:

``````plot_proper_motion(centerline_df)
``````
``````<Figure size 432x288 with 1 Axes>
``````

Now we can see more clearly that there is a cluster near (-7.5, 0).

You might notice that our figure is less dense than the one in the paper. That’s because we started with a set of stars from a relatively small region. The figure in the paper is based on a region about 10 times bigger.

In the next episode we will go back and select stars from a larger region. But first we will use the proper motion data to identify stars likely to be in GD-1.

Filtering based on proper motion

The next step is to select stars in the “overdense” region of proper motion, which are candidates to be in GD-1.

In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.

We will use a simple rectangle for now, but in a later lesson we will see how to select a polygonal region as well.

Here are bounds on proper motion we chose by eye:

``````pm1_min = -8.9
pm1_max = -6.9
pm2_min = -2.2
pm2_max =  1.0
``````

To draw these bounds, we will use the `make_rectangle` function we wrote in episode 2 to make two lists containing the coordinates of the corners of the rectangle.

``````pm1_rect, pm2_rect = make_rectangle(
pm1_min, pm1_max, pm2_min, pm2_max)
``````

Here is what the plot looks like with the bounds we chose.

``````plot_proper_motion(centerline_df)
plt.plot(pm1_rect, pm2_rect, '-')
``````
``````<Figure size 432x288 with 1 Axes>
``````

Now that we have identified the bounds of the cluster in proper motion, we will use it to select rows from `results_df`.

We will use the following function, which uses Pandas operators to make a mask that selects rows where `series` falls between `low` and `high`.

``````def between(series, low, high):
"""Check whether values are between `low` and `high`."""
return (series > low) & (series < high)
``````

The following mask selects stars with proper motion in the region we chose.

``````pm1 = results_df['pm_phi1']
pm2 = results_df['pm_phi2']

pm_mask = (between(pm1, pm1_min, pm1_max) &
between(pm2, pm2_min, pm2_max))
``````

Again, the sum of a Boolean series is the number of `TRUE` values.

``````pm_mask.sum()
``````
``````1049
``````

Now we can use this mask to select rows from `results_df`.

``````selected_df = results_df[pm_mask]
len(selected_df)
``````
``````1049
``````

These are the stars we think are likely to be in GD-1. We can inspect these stars, plotting their coordinates (not their proper motion).

``````x = selected_df['phi1']
y = selected_df['phi2']
plt.plot(x, y, 'ko', markersize=1, alpha=1)

plt.xlabel('phi1 (degree GD1)')
plt.ylabel('phi2 (degree GD1)')
``````
``````<Figure size 432x288 with 1 Axes>
``````

Now that is starting to look like a tidal stream!

To clean up the plot a little bit we can add two new Matplotlib commands:

• `axis` with the parameter `equal` sets up the axes so a unit is the same size along the `x` and `y` axes.

• `title` puts the input string as a title at the top of the plot. The `fontsize` keyword sets the `fontsize` to be `medium`, a little smaller than the default `large`.

In an example like this, where `x` and `y` represent coordinates in space, equal axes ensures that the distance between points is represented accurately. Since we are now constraining the relative proportions of our axes, the data may not fill the entire figure.

``````x = selected_df['phi1']
y = selected_df['phi2']

plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)

plt.xlabel('phi1 [deg]')
plt.ylabel('phi2 [deg]')
plt.title('Proper motion selection', fontsize='medium')

plt.axis('equal')
``````
``````<Figure size 432x288 with 1 Axes>
``````

Before we go any further, we will put the code we wrote to make one of the panel figures into a function that we will use in future episodes to recreate this entire plot with a single line of code.

``````def plot_pm_selection(df):
"""Plot in GD-1 spatial coordinates the location of the stars
selected by proper motion
"""
x = df['phi1']
y = df['phi2']

plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)

plt.xlabel('phi1 [deg]')
plt.ylabel('phi2 [deg]')
plt.title('Proper motion selection', fontsize='medium')

plt.axis('equal')
``````

Now our one line plot command is:

``````plot_pm_selection(selected_df)
``````

Saving the DataFrame

At this point we have run a successful query and cleaned up the results. This is a good time to save the data.

To save a Pandas `DataFrame`, one option is to convert it to an Astropy `Table`, like this:

``````from astropy.table import Table

selected_table = Table.from_pandas(selected_df)
type(selected_table)
``````
``````astropy.table.table.Table
``````

Then we could write the `Table` to a FITS file, as we did in the previous lesson.

But Pandas provides functions to write DataFrames in other formats; to see what they are find the functions here that begin with `to_`.

One of the best options is HDF5, which is Version 5 of Hierarchical Data Format.

HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).

An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset. (Multi-extension FITS files can also contain more than one table.)

And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).

Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS).

We can write a Pandas `DataFrame` to an HDF5 file like this:

``````filename = 'gd1_data.hdf'

selected_df.to_hdf(filename, 'selected_df', mode='w')
``````

Because an HDF5 file can contain more than one Dataset, we have to provide a name, or “key”, that identifies the Dataset in the file.

We could use any string as the key, but it is generally a good practice to use a descriptive name (just like your `DataFrame` variable name) so we will give the Dataset in the file the same name (key) as the `DataFrame`.

By default, writing a `DataFrame` appends a new dataset to an existing HDF5 file. We will use the argument `mode='w'` to overwrite the file if it already exists rather than append another dataset to it.

Exercise (5 minutes)

We are going to need `centerline_df` later as well. Write a line of code to add it as a second Dataset in the HDF5 file.

Hint: Since the file already exists, you should not use `mode='w'`.

Solution

``````centerline_df.to_hdf(filename, 'centerline_df')
``````

We can use `getsize` to confirm that the file exists and check the size. `getsize` returns a value in bytes. For the size files we’re looking at, it will be useful to view their size in MegaBytes (MB), so we will divide by 1024*1024.

``````from os.path import getsize

MB = 1024 * 1024
getsize(filename) / MB
``````
``````2.2084197998046875
``````

If you forget what the names of the Datasets in the file are, you can read them back like this:

``````with pd.HDFStore(filename) as hdf:
print(hdf.keys())
``````
``````['/centerline_df', '/selected_df']
``````

Context Managers

We use a `with` statement here to open the file before the print statement and (automatically) close it after. Read more about context managers.

The keys are the names of the Datasets which makes it easy for us to remember which `DataFrame` is in which Dataset.

Summary

In this episode, we re-loaded the Gaia data we saved from a previous query.

We transformed the coordinates and proper motion from ICRS to a frame aligned with the orbit of GD-1, and stored the results in a Pandas `DataFrame`.

Then we replicated the selection process from the Price-Whelan and Bonaca paper:

• We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.

• We identified a region of proper motion that contains stars likely to be in GD-1.

• We used a Boolean `Series` as a mask to select stars whose proper motion is in that region.

So far, we have used data from a relatively small region of the sky. In the next lesson, we will write a query that selects stars based on proper motion, which will allow us to explore a larger region.

Key Points

• When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.

• For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.

• An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions. They have pros and cons, but for many projects, either one would be a reasonable choice.

• To store data from a Pandas `DataFrame`, a good option is an HDF5 file, which can contain multiple Datasets.