# Running from terminal¶

There is an automatic script that will obtain focus from a folder containing a focus sequence.

If you have fits files you can simply run.

goodman-focus

It will run with the following defaults:

Default values for arguments

Argument

Default Value

Options

--data-path <input>

Current Working Directory

Any valid path

--file-pattern <input>

*.fits

Any

--features-model <input>

gaussian

moffat

--plot-results

False

True

--debug

False

True

Where <input> is what you type.

To get some help and a full list of options use:

goodman-focus -h

# Using it as a library¶

After installing Install Using PYPI you can also import the class and instantiate it providing a set of arguments and values or using default ones.

from goodman_focus.goodman_focus import GoodmanFocus

If no argument is provided it will instantiate with the default values.

The list of arguments can be defined as follow:

import os
from goodman_focus.goodman_focus import GoodmanFocus

goodman_focus = GoodmanFocus(data_path=os.getcwd(),
file_pattern='*.fits',
obstype='FOCUS',
features_model='gaussian',
plot_results=False,
debug=False)


Which is equivalent to:

from goodman_focus.goodman_focus import GoodmanFocus

goodman_focus = GoodmanFocus()


features_model is the function or model to fit to each detected line. gaussian will use a Gaussian1D which provide more consistent results. and moffat will use a Moffat1D model which fits the profile better but is harder to control and results are less consistent than when using a gaussian.

Finally you need to call the instance, here is a full example.

from goodman_focus.goodman_focus import GoodmanFocus

goodman_focus = GoodmanFocus()

results = goodman_focus()


However since version 0.3.0 you can pass a list of files and all will only check that all files exists

# Interpreting Results¶

The terminal version will print a message like this

[17:16:06][INFO]: Best Focus for mode SP_Red_400m2_GG455 is -1032.6413206603302

Using it as a library will return a dictionary with the following values. Combination of settings for which the code is the same is called a mode, so the keys of the dictionary are the mode name, how the name is constructed is explained in decoding-mode-name

{'IM_Red_g-SDSS': -571.4837418709354,
'IM_Red_i-SDSS': -802.567783891946,
'IM_Red_r-SDSS': -573.8694347173587,
'IM_Red_z-SDSS': -1161.5072536268135,
'SP_Red_400m1_NOFILTER': -492.0760380190095,
'SP_Red_400m2_GG455': -1032.6413206603302}


It is also possible to obtain a plot, from terminal, use --plot-results. Below is a repreduction of results obtained with test data.

## Decoding de mode name¶

The mode name is constructed using two letters to define the observing technique (Imaging or Spectroscopy) and values obtained from the header. The characters <, > and blanks are removed.

The mode name is different for Imaging and Spectroscopy, since for imaging the important settings are the instrument and the filter and for spectroscopy the important values come from the instrument, the grating and observing mode and filter from second filter wheel. Below, the word inside the parenthesis represents a kewyword from the header.

For imaging:

IM_(INSTCONF)_(FILTER)

for example:

IM_Red_g-SDSS

For spectroscopy:

SP_(INSTCONF)_(WAVMODE)_(FILTER2)

for example:

SP_Red_400m2_GG455