# healpy getting-started tutorial¶

See the Jupyter Notebook version of this tutorial at https://github.com/healpy/healpy/blob/master/doc/healpy_tutorial.ipynb

See a executed version of the notebook with embedded plots at https://gist.github.com/zonca/9c114608e0903a3b8ea0bfe41c96f255

Choose the `inline`

backend of `matplotlib`

to display the plots
inside the Jupyter Notebook

```
import matplotlib.pyplot as plt
%matplotlib inline
```

```
import numpy as np
import healpy as hp
```

## NSIDE and ordering¶

Maps are simply numpy arrays, where each array element refers to a location in the sky as defined by the Healpix pixelization schemes (see the healpix website).

Note: Running the code below in a regular Python session will not display the maps; it’s recommended to use an IPython shell or a Jupyter notebook.

The resolution of the map is defined by the *NSIDE* parameter, which is
generally a power of 2.

```
NSIDE = 32
print(
"Approximate resolution at NSIDE {} is {:.2} deg".format(
NSIDE, hp.nside2resol(NSIDE, arcmin=True) / 60
)
)
```

The function `healpy.pixelfunc.nside2npix`

gives the number of pixels
*NPIX* of the map:

```
NPIX = hp.nside2npix(NSIDE)
print(NPIX)
```

The same pixels in the map can be ordered in 2 ways, either RING, where they are numbered in the array in horizontal rings starting from the North pole:

```
m = np.arange(NPIX)
hp.mollview(m, title="Mollview image RING")
hp.graticule()
```

The standard coordinates are the **colatitude** \(\theta\),
\(0\) at the North Pole, \(\pi/2\) at the equator and
\(\pi\) at the South Pole and the **longitude** \(\phi\) between
\(0\) and \(2\pi\) eastward, in a Mollview projection,
\(\phi=0\) is at the center and increases eastward toward the left
of the map.

We can also use vectors to represent coordinates, for example `vec`

is
the normalized vector that points to \(\theta=\pi/2, \phi=3/4\pi\):

```
vec = hp.ang2vec(np.pi / 2, np.pi * 3 / 4)
print(vec)
```

We can find the indices of all the pixels within \(10\) degrees of that point and then change the value of the map at those indices:

```
ipix_disc = hp.query_disc(nside=32, vec=vec, radius=np.radians(10))
```

```
m = np.arange(NPIX)
m[ipix_disc] = m.max()
hp.mollview(m, title="Mollview image RING")
```

We can retrieve colatitude and longitude of each pixel using
`pix2ang`

, in this case we notice that the first 4 pixels cover the
North Pole with pixel centers just ~\(1.5\) degrees South of the
Pole all at the same latitude. The fifth pixel is already part of
another ring of pixels.

```
theta, phi = np.degrees(hp.pix2ang(nside=32, ipix=[0, 1, 2, 3, 4]))
```

```
theta
```

```
phi
```

The RING ordering is necessary for the Spherical Harmonics transforms, the other option is NESTED ordering which is very efficient for map domain operations because scaling up and down maps is achieved just multiplying and rounding pixel indices. See below how pixel are ordered in the NESTED scheme, notice the structure of the 12 HEALPix base pixels (NSIDE 1):

```
m = np.arange(NPIX)
hp.mollview(m, nest=True, title="Mollview image NESTED")
```

All `healpy`

routines assume RING ordering, in fact as soon as you
read a map with `read_map`

, even if it was stored as NESTED, it is
transformed to RING. However, you can work in NESTED ordering passing
the `nest=True`

argument to most `healpy`

routines.

## Reading and writing maps to file¶

For the following section, it is required to download larger maps by
executing from the terminal the bash script `healpy_get_wmap_maps.sh`

which should be available in your path.

This will download the higher resolution WMAP data into the current directory.

```
!healpy_get_wmap_maps.sh
```

```
wmap_map_I = hp.read_map("wmap_band_iqumap_r9_7yr_W_v4.fits")
```

By default, input maps are converted to *RING* ordering, if they are in
*NESTED* ordering. You can otherwise specify `nest=True`

to retrieve a
map is NESTED ordering, or `nest=None`

to keep the ordering unchanged.

By default, `read_map`

loads the first column, for reading other
columns you can specify the `field`

keyword.

`write_map`

writes a map to disk in FITS format, if the input map is a
list of 3 maps, they are written to a single file as I,Q,U polarization
components:

```
hp.write_map("my_map.fits", wmap_map_I, overwrite=True)
```

## Visualization¶

As shown above, mollweide projection with `mollview`

is the most
common visualization tool for HEALPIX maps. It also supports coordinate
transformation, `coord`

does Galactic to ecliptic coordinate
transformation, `norm='hist'`

sets a histogram equalized color scale
and `xsize`

increases the size of the image. `graticule`

adds
meridians and parallels.

```
hp.mollview(
wmap_map_I,
coord=["G", "E"],
title="Histogram equalized Ecliptic",
unit="mK",
norm="hist",
min=-1,
max=1,
)
hp.graticule()
```

`gnomview`

instead provides gnomonic projection around a position
specified by `rot`

, for example you can plot a projection of the
galactic center, `xsize`

and `ysize`

change the dimension of the sky
patch.

```
hp.gnomview(wmap_map_I, rot=[0, 0.3], title="GnomView", unit="mK", format="%.2g")
```

`mollzoom`

is a powerful tool for interactive inspection of a map, it
provides a mollweide projection where you can click to set the center of
the adjacent gnomview panel. ## Masked map, partial maps

By convention, HEALPIX uses \(-1.6375 * 10^{30}\) to mark invalid or
unseen pixels. This is stored in healpy as the constant `UNSEEN`

.

All `healpy`

functions automatically deal with maps with `UNSEEN`

pixels, for example `mollview`

marks in grey those sections of a map.

There is an alternative way of dealing with UNSEEN pixel based on the
numpy`MaskedArray`

class, `hp.ma`

loads a map as a masked array,
by convention the mask is 0 where the data are masked, while numpy
defines data masked when the mask is True, so it is necessary to flip
the mask.

```
mask = hp.read_map("wmap_temperature_analysis_mask_r9_7yr_v4.fits").astype(np.bool)
wmap_map_I_masked = hp.ma(wmap_map_I)
wmap_map_I_masked.mask = np.logical_not(mask)
```

Filling a masked array fills in the `UNSEEN`

value and return a
standard array that can be used by `mollview`

. `compressed()`

instead removes all the masked pixels and returns a standard array that
can be used for examples by the matplotlib `hist()`

function:

```
hp.mollview(wmap_map_I_masked.filled())
```

```
plt.hist(wmap_map_I_masked.compressed(), bins=1000);
```

## Spherical Harmonics transforms¶

`healpy`

provides bindings to the C++ HEALPIX library for performing
spherical harmonic transforms. `hp.anafast`

computes the angular power
spectrum of a map:

```
LMAX = 1024
cl = hp.anafast(wmap_map_I_masked.filled(), lmax=LMAX)
ell = np.arange(len(cl))
```

therefore we can plot a normalized CMB spectrum and write it to disk:

```
plt.figure(figsize=(10, 5))
plt.plot(ell, ell * (ell + 1) * cl)
plt.xlabel("$\ell$")
plt.ylabel("$\ell(\ell+1)C_{\ell}$")
plt.grid()
hp.write_cl("cl.fits", cl, overwrite=True)
```

Gaussian beam map smoothing is provided by `hp.smoothing`

:

```
wmap_map_I_smoothed = hp.smoothing(wmap_map_I, fwhm=np.radians(1.))
hp.mollview(wmap_map_I_smoothed, min=-1, max=1, title="Map smoothed 1 deg")
```

For more information see the HEALPix primer