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Gregory Ashton
PyFstat
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35b898da
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35b898da
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Dec 23, 2016
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Gregory Ashton
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README.md
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# PyFstat
This is a python package containing basic wrappers of the
`lalpulsar`
module
with capabilities to perform a variety of searches, primarily focusing on
semi-coherent glitch searches.
This is a python package providing an interface to perform F-statistic based
continuous gravitational wave (CW) searches. At its core, this is a simple
wrapper of selected tools in
[
'lalpulsar'
](
http://software.ligo.org/docs/lalsuite/lalpulsar/
)
. The general
idea is to allow easy scripting of new search pipelines, we focus
primarily on interfacing the CW routines with
[
emcee
](
http://dan.iel.fm/emcee/current/
)
a python MCMC sampler.
## Examples
...
...
@@ -20,6 +25,20 @@ to have run the [script to generate fake data](examples/make_fake_data.py).
## Installation
### `python` installation
The scripts are written in
`python 2.7+`
and therefore require a working
`python`
installation. While many systems come with a system wide python
installation, it can often be easier to manage a user-specific python
installation. This way one does not require root access to install or remove
modules. One method to do this, is to use the
`conda`
system, either through
the stripped down
[
miniconda
](
http://conda.pydata.org/miniconda.html
)
installation, or the full-featured
[
anaconda
](
https://www.continuum.io/downloads
)
(
these
are essentially the
same, but the
`anaconda`
version installs a variety of useful packages such as
`numpy`
and
`scipy`
by default.
### `pyfstat` installation
The script can be installed system wide via
```
$ python setup.py install
...
...
@@ -29,13 +48,14 @@ was successful, run
```
$ python -c 'import pyfstat'
```
if no error message is output, then you have installed
`pyfstat`
.
if no error message is output, then you have installed
`pyfstat`
. Note that
the module will be installed to whichever python executable you call it from.
### Ephemeris installation
The scripts require a path to ephemeris files in order to use the
`lalpulsar.ComputeFstat`
module. This can either be specified when initialising
each search, or more simply by placing a file
`~/pyfstat.conf`
into your home
each search, or more simply by placing a file
`~/
.
pyfstat.conf`
into your home
directory which looks like
```
...
...
@@ -46,17 +66,32 @@ here, we use the default ephemeris files provided with `lalsuite`.
### Dependencies
*
swig-enabled lalpulsar, a minimal configuration is given by
The installation above will complete succesfully without the following
dependencies, but you will subsequently find various
`ImportError`
messages
when running
`pyfstat`
scripts if you haven't installed the following modules.
*
swig-enabled
[
`lalapps`
](
http://software.ligo.org/docs/lalsuite/lalsuite/
)
with
at least
`lalpulsar`
. A minimal confuration line to use when installing
`lalapps`
is
```
$ ./configure --prefix=${HOME}/lalsuite-install --disable-all-lal --enable-lalpulsar --enable-lalapps --enable-swig
```
*
[
emcee
](
http://dan.iel.fm/emcee/current/
)[
^1
]
*
[
corner
](
https://pypi.python.org/pypi/corner/
)[
^1
]
*
[
dill
](
https://pypi.python.org/pypi/dill
)[
^1
]
*
[
tqdm
](
https://pypi.python.org/pypi/tqdm
)[
^1
]
(optional), if installed, this
*
If using the MCMC tools, you will need to install the following python
modules
*
[
numpy
](
http://www.numpy.org/
)
*
[
scipy
](
https://www.scipy.org/
)
*
[
emcee
](
http://dan.iel.fm/emcee/current/
)
*
[
corner
](
https://pypi.python.org/pypi/corner/
)
*
[
dill
](
https://pypi.python.org/pypi/dill
)
*
[
tqdm
](
https://pypi.python.org/pypi/tqdm
)(
optional
)
, if installed, this
provides a useful progress bar and estimate of the remaining run-time.
[
^1
]:
Most
easily installed using either
`conda`
or
`pip`
.
To install all of these modules, run
```
$ pip install -r /PATH/TO/THIS/DIRECTORY/requirements.txt
```
where
`pip`
is the python package installer, if you have installed python
from conda then it can be installed via
`conda install pip`
.
docs/fully_coherent_search_using_MCMC.md
View file @
35b898da
# Fully coherent search using MCMC
In this example, we will show the basics of setting up and running a
MCMC
search for a
fully-coherent search. This is based on the example
In this example, we will show the basics of setting up and running a
fully-coherent
MCMC
search. This is based on the example
[
fully_coherent_search_using_MCMC.py
](
../example/fully_coherent_search_using_MCMC.py
)
.
We will run the search on the
`basic`
data generated in the
[
make_fake_data
](
make_fake_data.md
)
example.
...
...
@@ -21,8 +21,8 @@ in the data, and the start and end times:
F0
=
30.0
F1
=
-
1e-10
F2
=
0
Alpha
=
5e-3
Delta
=
6e-2
Alpha
=
np
.
radians
(
83.6292
)
Delta
=
np
.
radians
(
22.0144
)
tref
=
362750407.0
tstart
=
1000000000
...
...
@@ -30,7 +30,7 @@ duration = 100*86400
tend
=
tstart
=
duration
```
Now, we need to specify ou
t
prior. This is a dictionary containing keys for
Now, we need to specify ou
r
prior. This is a dictionary containing keys for
each variable (in the
`MCMCSearch`
these are
`F0`
,
`F1`
,
`F2`
,
`Alpha`
, and
`Delta`
). In this example, we choose a uniform box in
`F0`
and
`F1`
:
...
...
docs/make_fake_data.md
View file @
35b898da
...
...
@@ -13,14 +13,15 @@ fake data, define the Crab parameters and create an instant of the `Writer`
called
`data`
```
python
import
numpy
as
np
from
pyfstat
import
Writer
# Define parameters of the Crab pulsar
F0
=
30.0
F1
=
-
1e-10
F2
=
0
Alpha
=
5e-3
Delta
=
6e-2
Alpha
=
np
.
radians
(
83.6292
)
Delta
=
np
.
radians
(
22.0144
)
tref
=
362750407.0
# Signal strength
...
...
@@ -168,3 +169,7 @@ two_glitch_data = Writer(
two_glitch_data
.
make_data
()
```
So, having run
`$ python make_fake_data.py`
(from the
`examples`
directory), we
will see that in the sub-directory
`examples/data/`
there are three
`.sft`
files. These will be used throughout the other examples.
examples/fully_coherent_search_using_MCMC.py
View file @
35b898da
...
...
@@ -11,8 +11,8 @@ tend = tstart + duration
F0
=
30.0
F1
=
-
1e-10
F2
=
0
Alpha
=
5e-3
Delta
=
6e-2
Alpha
=
np
.
radians
(
83.6292
)
Delta
=
np
.
radians
(
22.0144
)
tref
=
.
5
*
(
tstart
+
tend
)
depth
=
10
...
...
examples/make_fake_data.py
View file @
35b898da
from
pyfstat
import
Writer
import
numpy
as
np
# First, we generate data with a reasonably strong smooth signal
...
...
@@ -6,8 +7,8 @@ from pyfstat import Writer
F0
=
30.0
F1
=
-
1e-10
F2
=
0
Alpha
=
5e-3
Delta
=
6e-2
Alpha
=
np
.
radians
(
83.6292
)
Delta
=
np
.
radians
(
22.0144
)
tref
=
362750407.0
# Signal strength
...
...
requirements.txt
0 → 100644
View file @
35b898da
numpy
matplotlib
scipy
emcee
corner
dill
tqdm
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