Refactoring the pyfstat code

Transforms the single pyfstat.py into a python module splitting the
relevant code into separate sub-files in pyfstat. This should result in
improved readability.

README.md

@@ -40,11 +40,7 @@ are provided in the links.
 
 ### Dependencies
 
-`pyfstat` makes use of a variety python modules listed as the
-`imports` in the top of `pyfstat.py`. The first set are core modules (such as
-`os`, `sys`) while the second set are external and need to be installed for
-`pyfstat` to work properly. Please install the following widely available
-modules:
+`pyfstat` makes uses the following external python modules:
 
 * [numpy](http://www.numpy.org/)
 * [matplotlib](http://matplotlib.org/)

pyfstat/__init__.py

+from __future__ import division
+
+from .core import BaseSearchClass, ComputeFstat, Writer
+from .mcmc_based_searches import *
+from .grid_based_searches import *
+

pyfstat/grid_based_searches.py

+""" Searches using grid-based methods """
+
+import os
+import logging
+import itertools
+from collections import OrderedDict
+
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+
+import helper_functions
+from core import BaseSearchClass, ComputeFstat, SemiCoherentGlitchSearch
+from core import tqdm, args, earth_ephem, sun_ephem
+
+
+class GridSearch(BaseSearchClass):
+    """ Gridded search using ComputeFstat """
+    @helper_functions.initializer
+    def __init__(self, label, outdir, sftfilepath, F0s=[0], F1s=[0], F2s=[0],
+                 Alphas=[0], Deltas=[0], tref=None, minStartTime=None,
+                 maxStartTime=None, BSGL=False, minCoverFreq=None,
+                 maxCoverFreq=None, earth_ephem=None, sun_ephem=None,
+                 detector=None):
+        """
+        Parameters
+        ----------
+        label, outdir: str
+            A label and directory to read/write data from/to
+        sftfilepath: str
+            File patern to match SFTs
+        F0s, F1s, F2s, delta_F0s, delta_F1s, tglitchs, Alphas, Deltas: tuple
+            Length 3 tuple describing the grid for each parameter, e.g
+            [F0min, F0max, dF0], for a fixed value simply give [F0].
+        tref, minStartTime, maxStartTime: int
+            GPS seconds of the reference time, start time and end time
+
+        For all other parameters, see `pyfstat.ComputeFStat` for details
+        """
+
+        if earth_ephem is None:
+            self.earth_ephem = self.earth_ephem_default
+        if sun_ephem is None:
+            self.sun_ephem = self.sun_ephem_default
+
+        if os.path.isdir(outdir) is False:
+            os.mkdir(outdir)
+        self.out_file = '{}/{}_gridFS.txt'.format(self.outdir, self.label)
+        self.keys = ['_', '_', 'F0', 'F1', 'F2', 'Alpha', 'Delta']
+
+    def inititate_search_object(self):
+        logging.info('Setting up search object')
+        self.search = ComputeFstat(
+            tref=self.tref, sftfilepath=self.sftfilepath,
+            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
+            earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
+            detector=self.detector, transient=False,
+            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
+            BSGL=self.BSGL)
+
+    def get_array_from_tuple(self, x):
+        if len(x) == 1:
+            return np.array(x)
+        else:
+            return np.arange(x[0], x[1]*(1+1e-15), x[2])
+
+    def get_input_data_array(self):
+        arrays = []
+        for tup in ([self.minStartTime], [self.maxStartTime], self.F0s, self.F1s, self.F2s,
+                    self.Alphas, self.Deltas):
+            arrays.append(self.get_array_from_tuple(tup))
+
+        input_data = []
+        for vals in itertools.product(*arrays):
+            input_data.append(vals)
+
+        self.arrays = arrays
+        self.input_data = np.array(input_data)
+
+    def check_old_data_is_okay_to_use(self):
+        if args.clean:
+            return False
+        if os.path.isfile(self.out_file) is False:
+            logging.info('No old data found, continuing with grid search')
+            return False
+        data = np.atleast_2d(np.genfromtxt(self.out_file, delimiter=' '))
+        if np.all(data[:, 0:-1] == self.input_data):
+            logging.info(
+                'Old data found with matching input, no search performed')
+            return data
+        else:
+            logging.info(
+                'Old data found, input differs, continuing with grid search')
+            return False
+
+    def run(self, return_data=False):
+        self.get_input_data_array()
+        old_data = self.check_old_data_is_okay_to_use()
+        if old_data is not False:
+            self.data = old_data
+            return
+
+        self.inititate_search_object()
+
+        logging.info('Total number of grid points is {}'.format(
+            len(self.input_data)))
+
+        data = []
+        for vals in tqdm(self.input_data):
+            FS = self.search.run_computefstatistic_single_point(*vals)
+            data.append(list(vals) + [FS])
+
+        data = np.array(data)
+        if return_data:
+            return data
+        else:
+            logging.info('Saving data to {}'.format(self.out_file))
+            np.savetxt(self.out_file, data, delimiter=' ')
+            self.data = data
+
+    def convert_F0_to_mismatch(self, F0, F0hat, Tseg):
+        DeltaF0 = F0[1] - F0[0]
+        m_spacing = (np.pi*Tseg*DeltaF0)**2 / 12.
+        N = len(F0)
+        return np.arange(-N*m_spacing/2., N*m_spacing/2., m_spacing)
+
+    def convert_F1_to_mismatch(self, F1, F1hat, Tseg):
+        DeltaF1 = F1[1] - F1[0]
+        m_spacing = (np.pi*Tseg**2*DeltaF1)**2 / 720.
+        N = len(F1)
+        return np.arange(-N*m_spacing/2., N*m_spacing/2., m_spacing)
+
+    def add_mismatch_to_ax(self, ax, x, y, xkey, ykey, xhat, yhat, Tseg):
+        axX = ax.twiny()
+        axX.zorder = -10
+        axY = ax.twinx()
+        axY.zorder = -10
+
+        if xkey == 'F0':
+            m = self.convert_F0_to_mismatch(x, xhat, Tseg)
+            axX.set_xlim(m[0], m[-1])
+
+        if ykey == 'F1':
+            m = self.convert_F1_to_mismatch(y, yhat, Tseg)
+            axY.set_ylim(m[0], m[-1])
+
+    def plot_1D(self, xkey):
+        fig, ax = plt.subplots()
+        xidx = self.keys.index(xkey)
+        x = np.unique(self.data[:, xidx])
+        z = self.data[:, -1]
+        plt.plot(x, z)
+        fig.savefig('{}/{}_1D.png'.format(self.outdir, self.label))
+
+    def plot_2D(self, xkey, ykey, ax=None, save=True, vmin=None, vmax=None,
+                add_mismatch=None, xN=None, yN=None, flat_keys=[],
+                rel_flat_idxs=[], flatten_method=np.max,
+                predicted_twoF=None, cm=None, cbarkwargs={}):
+        """ Plots a 2D grid of 2F values
+
+        Parameters
+        ----------
+        add_mismatch: tuple (xhat, yhat, Tseg)
+            If not None, add a secondary axis with the metric mismatch from the
+            point xhat, yhat with duration Tseg
+        flatten_method: np.max
+            Function to use in flattening the flat_keys
+        """
+        if ax is None:
+            fig, ax = plt.subplots()
+        xidx = self.keys.index(xkey)
+        yidx = self.keys.index(ykey)
+        flat_idxs = [self.keys.index(k) for k in flat_keys]
+
+        x = np.unique(self.data[:, xidx])
+        y = np.unique(self.data[:, yidx])
+        flat_vals = [np.unique(self.data[:, j]) for j in flat_idxs]
+        z = self.data[:, -1]
+
+        Y, X = np.meshgrid(y, x)
+        shape = [len(x), len(y)] + [len(v) for v in flat_vals]
+        Z = z.reshape(shape)
+
+        if len(rel_flat_idxs) > 0:
+            Z = flatten_method(Z, axis=tuple(rel_flat_idxs))
+
+        if predicted_twoF:
+            Z = (predicted_twoF - Z) / (predicted_twoF + 4)
+            if cm is None:
+                cm = plt.cm.viridis_r
+        else:
+            if cm is None:
+                cm = plt.cm.viridis
+
+        pax = ax.pcolormesh(X, Y, Z, cmap=cm, vmin=vmin, vmax=vmax)
+        cb = plt.colorbar(pax, ax=ax, **cbarkwargs)
+        cb.set_label('$2\mathcal{F}$')
+
+        if add_mismatch:
+            self.add_mismatch_to_ax(ax, x, y, xkey, ykey, *add_mismatch)
+
+        ax.set_xlim(x[0], x[-1])
+        ax.set_ylim(y[0], y[-1])
+        labels = {'F0': '$f$', 'F1': '$\dot{f}$'}
+        ax.set_xlabel(labels[xkey])
+        ax.set_ylabel(labels[ykey])
+
+        if xN:
+            ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(xN))
+        if yN:
+            ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(yN))
+
+        if save:
+            fig.tight_layout()
+            fig.savefig('{}/{}_2D.png'.format(self.outdir, self.label))
+        else:
+            return ax
+
+    def get_max_twoF(self):
+        twoF = self.data[:, -1]
+        idx = np.argmax(twoF)
+        v = self.data[idx, :]
+        d = OrderedDict(minStartTime=v[0], maxStartTime=v[1], F0=v[2], F1=v[3],
+                        F2=v[4], Alpha=v[5], Delta=v[6], twoF=v[7])
+        return d
+
+    def print_max_twoF(self):
+        d = self.get_max_twoF()
+        print('Max twoF values for {}:'.format(self.label))
+        for k, v in d.iteritems():
+            print('  {}={}'.format(k, v))
+
+
+class GridGlitchSearch(GridSearch):
+    """ Grid search using the SemiCoherentGlitchSearch """
+    @helper_functions.initializer
+    def __init__(self, label, outdir, sftfilepath=None, F0s=[0],
+                 F1s=[0], F2s=[0], delta_F0s=[0], delta_F1s=[0], tglitchs=None,
+                 Alphas=[0], Deltas=[0], tref=None, minStartTime=None,
+                 maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
+                 write_after=1000, earth_ephem=None, sun_ephem=None):
+
+        """
+        Parameters
+        ----------
+        label, outdir: str
+            A label and directory to read/write data from/to
+        sftfilepath: str
+            File patern to match SFTs
+        F0s, F1s, F2s, delta_F0s, delta_F1s, tglitchs, Alphas, Deltas: tuple
+            Length 3 tuple describing the grid for each parameter, e.g
+            [F0min, F0max, dF0], for a fixed value simply give [F0].
+        tref, minStartTime, maxStartTime: int
+            GPS seconds of the reference time, start time and end time
+
+        For all other parameters, see pyfstat.ComputeFStat.
+        """
+        if tglitchs is None:
+            self.tglitchs = [self.maxStartTime]
+        if earth_ephem is None:
+            self.earth_ephem = self.earth_ephem_default
+        if sun_ephem is None:
+            self.sun_ephem = self.sun_ephem_default
+
+        self.search = SemiCoherentGlitchSearch(
+            label=label, outdir=outdir, sftfilepath=self.sftfilepath,
+            tref=tref, minStartTime=minStartTime, maxStartTime=maxStartTime,
+            minCoverFreq=minCoverFreq, maxCoverFreq=maxCoverFreq,
+            earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
+            BSGL=self.BSGL)
+
+        if os.path.isdir(outdir) is False:
+            os.mkdir(outdir)
+        self.out_file = '{}/{}_gridFS.txt'.format(self.outdir, self.label)
+        self.keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta', 'delta_F0',
+                     'delta_F1', 'tglitch']
+
+    def get_input_data_array(self):
+        arrays = []
+        for tup in (self.F0s, self.F1s, self.F2s, self.Alphas, self.Deltas,
+                    self.delta_F0s, self.delta_F1s, self.tglitchs):
+            arrays.append(self.get_array_from_tuple(tup))
+
+        input_data = []
+        for vals in itertools.product(*arrays):
+            input_data.append(vals)
+
+        self.arrays = arrays
+        self.input_data = np.array(input_data)
+
+
+

pyfstat/helper_functions.py

+"""
+Provides helpful functions to facilitate ease-of-use of pyfstat
+"""
+
+import os
+import sys
+import argparse
+import logging
+import inspect
+from functools import wraps
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def set_up_optional_tqdm():
+    try:
+        from tqdm import tqdm
+    except ImportError:
+        def tqdm(x, *args, **kwargs):
+            return x
+    return tqdm
+
+
+def set_up_matplotlib_defaults():
+    plt.switch_backend('Agg')
+    plt.rcParams['text.usetex'] = True
+    plt.rcParams['axes.formatter.useoffset'] = False
+
+
+def set_up_command_line_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-q", "--quite", help="Decrease output verbosity",
+                        action="store_true")
+    parser.add_argument("--no-interactive", help="Don't use interactive",
+                        action="store_true")
+    parser.add_argument("-c", "--clean", help="Don't use cached data",
+                        action="store_true")
+    parser.add_argument("-u", "--use-old-data", action="store_true")
+    parser.add_argument('-s', "--setup-only", action="store_true")
+    parser.add_argument('-n', "--no-template-counting", action="store_true")
+    parser.add_argument('unittest_args', nargs='*')
+    args, unknown = parser.parse_known_args()
+    sys.argv[1:] = args.unittest_args
+    if args.quite or args.no_interactive:
+        def tqdm(x, *args, **kwargs):
+            return x
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+    stream_handler = logging.StreamHandler()
+    if args.quite:
+        stream_handler.setLevel(logging.WARNING)
+    else:
+        stream_handler.setLevel(logging.DEBUG)
+    stream_handler.setFormatter(logging.Formatter(
+        '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
+    logger.addHandler(stream_handler)
+    return args
+
+
+def set_up_ephemeris_configuration():
+    config_file = os.path.expanduser('~')+'/.pyfstat.conf'
+    if os.path.isfile(config_file):
+        d = {}
+        with open(config_file, 'r') as f:
+            for line in f:
+                k, v = line.split('=')
+                k = k.replace(' ', '')
+                for item in [' ', "'", '"', '\n']:
+                    v = v.replace(item, '')
+                d[k] = v
+        earth_ephem = d['earth_ephem']
+        sun_ephem = d['sun_ephem']
+    else:
+        logging.warning('No ~/.pyfstat.conf file found please provide the '
+                        'paths when initialising searches')
+        earth_ephem = None
+        sun_ephem = None
+    return earth_ephem, sun_ephem
+
+
+def round_to_n(x, n):
+    if not x:
+        return 0
+    power = -int(np.floor(np.log10(abs(x)))) + (n - 1)
+    factor = (10 ** power)
+    return round(x * factor) / factor
+
+
+def texify_float(x, d=2):
+    if type(x) == str:
+        return x
+    x = round_to_n(x, d)
+    if 0.01 < abs(x) < 100:
+        return str(x)
+    else:
+        power = int(np.floor(np.log10(abs(x))))
+        stem = np.round(x / 10**power, d)
+        if d == 1:
+            stem = int(stem)
+        return r'${}{{\times}}10^{{{}}}$'.format(stem, power)
+
+
+def initializer(func):
+    """ Decorator function to automatically assign the parameters to self """
+    names, varargs, keywords, defaults = inspect.getargspec(func)
+
+    @wraps(func)
+    def wrapper(self, *args, **kargs):
+        for name, arg in list(zip(names[1:], args)) + list(kargs.items()):
+            setattr(self, name, arg)
+
+        for name, default in zip(reversed(names), reversed(defaults)):
+            if not hasattr(self, name):
+                setattr(self, name, default)
+
+        func(self, *args, **kargs)
+
+    return wrapper
+

pyfstat/optimal_setup_functions.py

+"""
+
+Provides functions to aid in calculating the optimal setup based on the metric
+volume estimates.
+
+"""
+
+import logging
+import numpy as np
+import scipy.optimize
+import lal
+import lalpulsar
+
+
+def get_optimal_setup(
+        R, Nsegs0, tref, minStartTime, maxStartTime, DeltaOmega,
+        DeltaFs, fiducial_freq, detector_names, earth_ephem, sun_ephem):
+    logging.info('Calculating optimal setup for R={}, Nsegs0={}'.format(
+        R, Nsegs0))
+
+    V_0 = get_V_estimate(
+        Nsegs0, tref, minStartTime, maxStartTime, DeltaOmega, DeltaFs,
+        fiducial_freq, detector_names, earth_ephem, sun_ephem)
+    logging.info('Stage {}, nsegs={}, V={}'.format(0, Nsegs0, V_0))
+
+    nsegs_vals = [Nsegs0]
+    V_vals = [V_0]
+
+    i = 0
+    nsegs_i = Nsegs0
+    while nsegs_i > 1:
+        nsegs_i, V_i = get_nsegs_ip1(
+            nsegs_i, R, tref, minStartTime, maxStartTime, DeltaOmega,
+            DeltaFs, fiducial_freq, detector_names, earth_ephem, sun_ephem)
+        nsegs_vals.append(nsegs_i)
+        V_vals.append(V_i)
+        i += 1
+        logging.info(
+            'Stage {}, nsegs={}, V={}'.format(i, nsegs_i, V_i))
+
+    return nsegs_vals, V_vals
+
+
+def get_nsegs_ip1(
+        nsegs_i, R, tref, minStartTime, maxStartTime, DeltaOmega,
+        DeltaFs, fiducial_freq, detector_names, earth_ephem, sun_ephem):
+
+    log10R = np.log10(R)
+    log10Vi = np.log10(get_V_estimate(
+        nsegs_i, tref, minStartTime, maxStartTime, DeltaOmega, DeltaFs,
+        fiducial_freq, detector_names, earth_ephem, sun_ephem))
+
+    def f(nsegs_ip1):
+        if nsegs_ip1[0] > nsegs_i:
+            return 1e6
+        if nsegs_ip1[0] < 0:
+            return 1e6
+        nsegs_ip1 = int(nsegs_ip1[0])
+        if nsegs_ip1 == 0:
+            nsegs_ip1 = 1
+        Vip1 = get_V_estimate(
+            nsegs_ip1, tref, minStartTime, maxStartTime, DeltaOmega,
+            DeltaFs, fiducial_freq, detector_names, earth_ephem, sun_ephem)
+        if Vip1[0] is None:
+            return 1e6
+        else:
+            log10Vip1 = np.log10(Vip1)
+            return np.abs(log10Vi[0] + log10R - log10Vip1[0])
+    res = scipy.optimize.minimize(f, .5*nsegs_i, method='Powell', tol=0.1,
+                                  options={'maxiter': 10})
+    nsegs_ip1 = int(res.x)
+    if nsegs_ip1 == 0:
+        nsegs_ip1 = 1
+    if res.success:
+        return nsegs_ip1, get_V_estimate(
+            nsegs_ip1, tref, minStartTime, maxStartTime, DeltaOmega, DeltaFs,
+            fiducial_freq, detector_names, earth_ephem, sun_ephem)
+    else:
+        raise ValueError('Optimisation unsuccesful')
+
+
+def get_V_estimate(
+        nsegs, tref, minStartTime, maxStartTime, DeltaOmega, DeltaFs,
+        fiducial_freq, detector_names, earth_ephem, sun_ephem):
+    """ Returns V, Vsky, Vpe estimated from the super-sky metric
+
+    Parameters
+    ----------
+    nsegs: int
+        Number of semi-coherent segments
+    tref: int
+        Reference time in GPS seconds
+    minStartTime, maxStartTime: int
+        Minimum and maximum SFT timestamps
+    DeltaOmega: float
+        Solid angle of the sky-patch
+    DeltaFs: array
+        Array of [DeltaF0, DeltaF1, ...], length determines the number of
+        spin-down terms.
+    fiducial_freq: float
+        Fidicual frequency
+    detector_names: array
+        Array of detectors to average over
+    earth_ephem, sun_ephem: st
+        Paths to the ephemeris files
+
+    """
+    spindowns = len(DeltaFs) - 1
+    tboundaries = np.linspace(minStartTime, maxStartTime, nsegs+1)
+
+    ref_time = lal.LIGOTimeGPS(tref)
+    segments = lal.SegListCreate()
+    for j in range(len(tboundaries)-1):
+        seg = lal.SegCreate(lal.LIGOTimeGPS(tboundaries[j]),
+                            lal.LIGOTimeGPS(tboundaries[j+1]),
+                            j)
+        lal.SegListAppend(segments, seg)
+    detNames = lal.CreateStringVector(*detector_names)
+    detectors = lalpulsar.MultiLALDetector()
+    lalpulsar.ParseMultiLALDetector(detectors, detNames)
+    detector_weights = None
+    detector_motion = (lalpulsar.DETMOTION_SPIN
+                       + lalpulsar.DETMOTION_ORBIT)
+    ephemeris = lalpulsar.InitBarycenter(earth_ephem, sun_ephem)
+    try:
+        SSkyMetric = lalpulsar.ComputeSuperskyMetrics(
+            spindowns, ref_time, segments, fiducial_freq, detectors,
+            detector_weights, detector_motion, ephemeris)
+    except RuntimeError as e:
+        logging.debug('Encountered run-time error {}'.format(e))
+        return None, None, None
+
+    sqrtdetG_SKY = np.sqrt(np.linalg.det(
+        SSkyMetric.semi_rssky_metric.data[:2, :2]))
+    sqrtdetG_PE = np.sqrt(np.linalg.det(
+        SSkyMetric.semi_rssky_metric.data[2:, 2:]))
+
+    Vsky = .5*sqrtdetG_SKY*DeltaOmega
+    Vpe = sqrtdetG_PE * np.prod(DeltaFs)
+    if Vsky == 0:
+        Vsky = 1
+    if Vpe == 0:
+        Vpe = 1
+    return (Vsky * Vpe, Vsky, Vpe)

setup.py

@@ -3,8 +3,8 @@
 from distutils.core import setup
 
 setup(name='PyFstat',
-      version='0.1',
+      version='0.2',
       author='Gregory Ashton',
       author_email='gregory.ashton@ligo.org',
-      py_modules=['pyfstat'],
+      packages=['pyfstat'],
       )

tests.py

@@ -208,7 +208,7 @@ class TestAuxillaryFunctions(Test):
 
     def test_get_V_estimate_sky_F0_F1(self):
 
-        out = pyfstat.get_V_estimate(
+        out = pyfstat.optimal_setup_functions.get_V_estimate(
             self.nsegs, self.tref, self.minStartTime, self.maxStartTime,
             self.DeltaOmega, self.DeltaFs, self.fiducial_freq,
             self.detector_names, self.earth_ephem, self.sun_ephem)
@@ -217,7 +217,7 @@ class TestAuxillaryFunctions(Test):
         self.__class__.Vpe_COMPUTED_WITH_SKY = Vpe
 
     def test_get_V_estimate_F0_F1(self):
-        out = pyfstat.get_V_estimate(
+        out = pyfstat.optimal_setup_functions.get_V_estimate(
             self.nsegs, self.tref, self.minStartTime, self.maxStartTime,
             self.DeltaOmega, self.DeltaFs, self.fiducial_freq,
             self.detector_names, self.earth_ephem, self.sun_ephem)