grid_based_searches.py 47.3 KB
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""" Searches using grid-based methods """
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import os
import logging
import itertools
from collections import OrderedDict
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import datetime
import getpass
import socket
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import numpy as np
import matplotlib
import matplotlib.pyplot as plt
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from scipy.misc import logsumexp
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import pyfstat.helper_functions as helper_functions
from pyfstat.core import (BaseSearchClass, ComputeFstat,
                          SemiCoherentGlitchSearch, SemiCoherentSearch, tqdm,
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                          args, read_par)
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import lalpulsar
import lal
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class GridSearch(BaseSearchClass):
    """ Gridded search using ComputeFstat """
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    tex_labels = {'F0': '$f$', 'F1': '$\dot{f}$', 'F2': '$\ddot{f}$',
                  'Alpha': r'$\alpha$', 'Delta': r'$\delta$'}
    tex_labels0 = {'F0': '$-f_0$', 'F1': '$-\dot{f}_0$', 'F2': '$-\ddot{f}_0$',
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                   'Alpha': r'$-\alpha_0$', 'Delta': r'$-\delta_0$'}
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    search_labels = ['minStartTime', 'maxStartTime', 'F0s', 'F1s', 'F2s',
                     'Alphas', 'Deltas']
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    @helper_functions.initializer
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    def __init__(self, label, outdir, sftfilepattern, F0s, F1s, F2s, Alphas,
                 Deltas, tref=None, minStartTime=None, maxStartTime=None,
                 nsegs=1, BSGL=False, minCoverFreq=None, maxCoverFreq=None,
                 detectors=None, SSBprec=None, injectSources=None,
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                 input_arrays=False, assumeSqrtSX=None):
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        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
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        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
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        F0s, F1s, F2s, delta_F0s, delta_F1s, tglitchs, Alphas, Deltas: tuple
            Length 3 tuple describing the grid for each parameter, e.g
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            [F0min, F0max, dF0], for a fixed value simply give [F0]. Unless
            input_arrays == True, then these are the values to search at.
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        tref, minStartTime, maxStartTime: int
            GPS seconds of the reference time, start time and end time
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        input_arrays: bool
            if true, use the F0s, F1s, etc as is
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        For all other parameters, see `pyfstat.ComputeFStat` for details
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        Note: if a large number of grid points are used, checks against cached
        data may be slow as the array is loaded into memory. To avoid this, run
        with the `clean` option which uses a generator instead.
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        """

        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
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        self.set_out_file()
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        self.keys = ['_', '_', 'F0', 'F1', 'F2', 'Alpha', 'Delta']
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        self.search_keys = [x+'s' for x in self.keys[2:]]
        for k in self.search_keys:
            setattr(self, k, np.atleast_1d(getattr(self, k)))
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    def inititate_search_object(self):
        logging.info('Setting up search object')
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        if self.nsegs == 1:
            self.search = ComputeFstat(
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                tref=self.tref, sftfilepattern=self.sftfilepattern,
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                minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
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                detectors=self.detectors,
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                minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
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                BSGL=self.BSGL, SSBprec=self.SSBprec,
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                injectSources=self.injectSources,
                assumeSqrtSX=self.assumeSqrtSX)
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            self.search.get_det_stat = self.search.get_fullycoherent_twoF
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        else:
            self.search = SemiCoherentSearch(
                label=self.label, outdir=self.outdir, tref=self.tref,
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                nsegs=self.nsegs, sftfilepattern=self.sftfilepattern,
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                BSGL=self.BSGL, minStartTime=self.minStartTime,
                maxStartTime=self.maxStartTime, minCoverFreq=self.minCoverFreq,
                maxCoverFreq=self.maxCoverFreq, detectors=self.detectors,
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                injectSources=self.injectSources)
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            def cut_out_tstart_tend(*vals):
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                return self.search.get_semicoherent_twoF(*vals[2:])
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            self.search.get_det_stat = cut_out_tstart_tend
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    def get_array_from_tuple(self, x):
        if len(x) == 1:
            return np.array(x)
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        elif len(x) == 3 and self.input_arrays is False:
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            return np.arange(x[0], x[1], x[2])
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        else:
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            logging.info('Using tuple as is')
            return np.array(x)
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    def get_input_data_array(self):
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        logging.info("Generating input data array")
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        coord_arrays = []
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        for sl in self.search_labels:
            coord_arrays.append(
                self.get_array_from_tuple(np.atleast_1d(getattr(self, sl))))
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        self.coord_arrays = coord_arrays
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        self.total_iterations = np.prod([len(ca) for ca in coord_arrays])

        if args.clean is False:
            input_data = []
            for vals in itertools.product(*coord_arrays):
                    input_data.append(vals)
            self.input_data = np.array(input_data)
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    def check_old_data_is_okay_to_use(self):
        if args.clean:
            return False
        if os.path.isfile(self.out_file) is False:
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            logging.info(
                'No old data found in "{:s}", continuing with grid search'
                .format(self.out_file))
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            return False
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        if self.sftfilepattern is not None:
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            oldest_sft = min([os.path.getmtime(f) for f in
                              self._get_list_of_matching_sfts()])
            if os.path.getmtime(self.out_file) < oldest_sft:
                logging.info('Search output data outdates sft files,'
                             + ' continuing with grid search')
                return False
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        data = np.atleast_2d(np.genfromtxt(self.out_file, delimiter=' '))
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        if np.all(data[:, 0: len(self.coord_arrays)] ==
                  self.input_data[:, 0:len(self.coord_arrays)]):
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            logging.info(
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                'Old data found in "{:s}" with matching input, no search '
                'performed'.format(self.out_file))
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            return data
        else:
            logging.info(
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                'Old data found in "{:s}", input differs, continuing with '
                'grid search'.format(self.out_file))
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            return False
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        return False
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    def run(self, return_data=False):
        self.get_input_data_array()
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        if args.clean:
            iterable = itertools.product(*self.coord_arrays)
        else:
            old_data = self.check_old_data_is_okay_to_use()
            iterable = self.input_data

            if old_data is not False:
                self.data = old_data
                return
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        if hasattr(self, 'search') is False:
            self.inititate_search_object()
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        data = []
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        for vals in tqdm(iterable,
                         total=getattr(self, 'total_iterations', None)):
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            detstat = self.search.get_det_stat(*vals)
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            thisCand = list(vals) + [detstat]
            data.append(thisCand)
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        data = np.array(data, dtype=np.float)
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        if return_data:
            return data
        else:
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            self.save_array_to_disk(data)
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            self.data = data

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    def get_header(self):
        header = ';'.join(['date:{}'.format(str(datetime.datetime.now())),
                           'user:{}'.format(getpass.getuser()),
                           'hostname:{}'.format(socket.gethostname())])
        header += '\n' + ' '.join(self.keys)
        return header

    def save_array_to_disk(self, data):
        logging.info('Saving data to {}'.format(self.out_file))
        header = self.get_header()
        np.savetxt(self.out_file, data, delimiter=' ', header=header)

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    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])

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    def plot_1D(self, xkey, ax=None, x0=None, xrescale=1, savefig=True,
                xlabel=None, ylabel='$\widetilde{2\mathcal{F}}$'):
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        if ax is None:
            fig, ax = plt.subplots()
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        xidx = self.keys.index(xkey)
        x = np.unique(self.data[:, xidx])
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        if x0:
            x = x - x0
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        x = x * xrescale
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        z = self.data[:, -1]
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        ax.plot(x, z)
        if x0:
            ax.set_xlabel(self.tex_labels[xkey]+self.tex_labels0[xkey])
        else:
            ax.set_xlabel(self.tex_labels[xkey])
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        if xlabel:
            ax.set_xlabel(xlabel)

        ax.set_ylabel(ylabel)
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        if savefig:
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            fig.tight_layout()
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            fig.savefig('{}/{}_1D.png'.format(self.outdir, self.label))
        else:
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            return ax
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    def plot_2D(self, xkey, ykey, ax=None, save=True, vmin=None, vmax=None,
                add_mismatch=None, xN=None, yN=None, flat_keys=[],
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                rel_flat_idxs=[], flatten_method=np.max, title=None,
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                predicted_twoF=None, cm=None, cbarkwargs={}, x0=None, y0=None,
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                colorbar=False, xrescale=1, yrescale=1):
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        """ 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])
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        if x0:
            x = x-x0
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        y = np.unique(self.data[:, yidx])
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        if y0:
            y = y-y0
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        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

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        pax = ax.pcolormesh(
            X*xrescale, Y*yrescale, Z, cmap=cm, vmin=vmin, vmax=vmax)
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        if colorbar:
            cb = plt.colorbar(pax, ax=ax, **cbarkwargs)
            cb.set_label('$2\mathcal{F}$')
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        if add_mismatch:
            self.add_mismatch_to_ax(ax, x, y, xkey, ykey, *add_mismatch)

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        ax.set_xlim(x[0]*xrescale, x[-1]*xrescale)
        ax.set_ylim(y[0]*yrescale, y[-1]*yrescale)
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        if x0:
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            ax.set_xlabel(self.tex_labels[xkey]+self.tex_labels0[xkey])
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        else:
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            ax.set_xlabel(self.tex_labels[xkey])
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        if y0:
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            ax.set_ylabel(self.tex_labels[ykey]+self.tex_labels0[ykey])
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        else:
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            ax.set_ylabel(self.tex_labels[ykey])
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        if title:
            ax.set_title(title)

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        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):
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        """ Get the maximum twoF over the grid

        Returns
        -------
        d: dict
            Dictionary containing, 'minStartTime', 'maxStartTime', 'F0', 'F1',
            'F2', 'Alpha', 'Delta' and 'twoF' of maximum

        """

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        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))
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        for k, v in d.items():
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            print('  {}={}'.format(k, v))

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    def set_out_file(self, extra_label=None):
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        if self.detectors:
            dets = self.detectors.replace(',', '')
        else:
            dets = 'NA'
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        if extra_label:
            self.out_file = '{}/{}_{}_{}_{}.txt'.format(
                self.outdir, self.label, dets, type(self).__name__,
                extra_label)
        else:
            self.out_file = '{}/{}_{}_{}.txt'.format(
                self.outdir, self.label, dets, type(self).__name__)

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class TransientGridSearch(GridSearch):
    """ Gridded transient-continous search using ComputeFstat """

    @helper_functions.initializer
    def __init__(self, label, outdir, sftfilepattern, F0s, F1s, F2s, Alphas,
                 Deltas, tref=None, minStartTime=None, maxStartTime=None,
                 BSGL=False, minCoverFreq=None, maxCoverFreq=None,
                 detectors=None, SSBprec=None, injectSources=None,
                 input_arrays=False, assumeSqrtSX=None,
                 transientWindowType=None, t0Band=None, tauBand=None,
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                 tauMin = None,
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                 dt0=None, dtau=None,
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                 outputTransientFstatMap=False,
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                 outputAtoms=False,
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                 tCWFstatMapVersion='lal', cudaDeviceName=None):
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        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
        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]. Unless
            input_arrays == True, then these are the values to search at.
        tref, minStartTime, maxStartTime: int
            GPS seconds of the reference time, start time and end time
        input_arrays: bool
            if true, use the F0s, F1s, etc as is
        transientWindowType: str
            If 'rect' or 'exp', compute atoms so that a transient (t0,tau) map
            can later be computed.  ('none' instead of None explicitly calls
            the transient-window function, but with the full range, for
            debugging). Currently only supported for nsegs=1.
        t0Band, tauBand: int
            if >0, search t0 in (minStartTime,minStartTime+t0Band)
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                   and tau in (tauMin,2*Tsft+tauBand).
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            if =0, only compute CW Fstat with t0=minStartTime,
                   tau=maxStartTime-minStartTime.
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        tauMin: int
            defaults to 2*Tsft
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        dt0, dtau: int
            grid resolutions in transient start-time and duration,
            both default to Tsft
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        outputTransientFstatMap: bool
            if true, write output files for (t0,tau) Fstat maps
            (one file for each doppler grid point!)
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        tCWFstatMapVersion: str
            Choose between standard 'lal' implementation,
            'pycuda' for gpu, and some others for devel/debug.
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        cudaDeviceName: str
            GPU name to be matched against drv.Device output.
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        For all other parameters, see `pyfstat.ComputeFStat` for details
        """

        self.nsegs = 1
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
        self.set_out_file()
        self.keys = ['_', '_', 'F0', 'F1', 'F2', 'Alpha', 'Delta']
        self.search_keys = [x+'s' for x in self.keys[2:]]
        for k in self.search_keys:
            setattr(self, k, np.atleast_1d(getattr(self, k)))

    def inititate_search_object(self):
        logging.info('Setting up search object')
        self.search = ComputeFstat(
            tref=self.tref, sftfilepattern=self.sftfilepattern,
            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
            detectors=self.detectors,
            transientWindowType=self.transientWindowType,
            t0Band=self.t0Band, tauBand=self.tauBand,
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            tauMin=self.tauMin,
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            dt0=self.dt0, dtau=self.dtau,
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            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
            BSGL=self.BSGL, SSBprec=self.SSBprec,
            injectSources=self.injectSources,
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            assumeSqrtSX=self.assumeSqrtSX,
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            tCWFstatMapVersion=self.tCWFstatMapVersion,
            cudaDeviceName=self.cudaDeviceName)
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        self.search.get_det_stat = self.search.get_fullycoherent_twoF

    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

        if hasattr(self, 'search') is False:
            self.inititate_search_object()

        data = []
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        if self.outputTransientFstatMap:
            tCWfilebase = os.path.splitext(self.out_file)[0] + '_tCW_'
            logging.info('Will save per-Doppler Fstatmap' \
                         ' results to {}*.dat'.format(tCWfilebase))
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        self.timingFstatMap = 0.
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        for vals in tqdm(self.input_data):
            detstat = self.search.get_det_stat(*vals)
            windowRange = getattr(self.search, 'windowRange', None)
            FstatMap = getattr(self.search, 'FstatMap', None)
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            self.timingFstatMap += getattr(self.search, 'timingFstatMap', None)
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            thisCand = list(vals) + [detstat]
            if getattr(self, 'transientWindowType', None):
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                if self.tCWFstatMapVersion == 'lal':
                    F_mn = FstatMap.F_mn.data
                else:
                    F_mn = FstatMap.F_mn
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                if self.outputTransientFstatMap:
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                    # per-Doppler filename convention:
                    # freq alpha delta f1dot f2dot
                    tCWfile = ( tCWfilebase
                                + '%.16f_%.16f_%.16f_%.16g_%.16g.dat' %
                                (vals[2],vals[5],vals[6],vals[3],vals[4]) )
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                    if self.tCWFstatMapVersion == 'lal':
                        fo = lal.FileOpen(tCWfile, 'w')
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                        lalpulsar.write_transientFstatMap_to_fp (
                            fo, FstatMap, windowRange, None )
                        # instead of lal.FileClose(),
                        # which is not SWIG-exported:
                        del fo
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                    else:
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                        self.write_F_mn ( tCWfile, F_mn, windowRange)
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                maxidx = np.unravel_index(F_mn.argmax(), F_mn.shape)
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                thisCand += [windowRange.t0+maxidx[0]*windowRange.dt0,
                             windowRange.tau+maxidx[1]*windowRange.dtau]
            data.append(thisCand)
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            if self.outputAtoms:
                self.search.write_atoms_to_file(os.path.splitext(self.out_file)[0])
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        logging.info('Total time spent computing transient F-stat maps: {:.2f}s'.format(self.timingFstatMap))

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        data = np.array(data, dtype=np.float)
        if return_data:
            return data
        else:
            self.save_array_to_disk(data)
            self.data = data

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    def write_F_mn (self, tCWfile, F_mn, windowRange ):
        with open(tCWfile, 'w') as tfp:
            tfp.write('# t0 [s]     tau [s]     2F\n')
            for m, F_m in enumerate(F_mn):
                this_t0 = windowRange.t0 + m * windowRange.dt0
                for n, this_F in enumerate(F_m):
                    this_tau = windowRange.tau + n * windowRange.dtau;
                    tfp.write('  %10d %10d %- 11.8g\n' % (this_t0, this_tau, 2.0*this_F))

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    def __del__(self):
        if hasattr(self,'search'):
            self.search.__del__()

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class SliceGridSearch(GridSearch):
    """ Slice gridded search using ComputeFstat """
    @helper_functions.initializer
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    def __init__(self, label, outdir, sftfilepattern, F0s, F1s, F2s, Alphas,
                 Deltas, tref=None, minStartTime=None, maxStartTime=None,
                 nsegs=1, BSGL=False, minCoverFreq=None, maxCoverFreq=None,
                 detectors=None, SSBprec=None, injectSources=None,
                 input_arrays=False, assumeSqrtSX=None, Lambda0=None):
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        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
        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]. Unless
            input_arrays == True, then these are the values to search at.
        tref, minStartTime, maxStartTime: int
            GPS seconds of the reference time, start time and end time
        input_arrays: bool
            if true, use the F0s, F1s, etc as is

        For all other parameters, see `pyfstat.ComputeFStat` for details
        """

        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
        self.set_out_file()
        self.keys = ['_', '_', 'F0', 'F1', 'F2', 'Alpha', 'Delta']
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        self.ndim = 0
        self.thetas = [F0s, F1s, Alphas, Deltas]
        self.ndim = 4
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        self.search_keys = ['F0', 'F1', 'Alpha', 'Delta']
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        if self.Lambda0 is None:
            raise ValueError('Lambda0 undefined')
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        if len(self.Lambda0) != len(self.search_keys):
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            raise ValueError(
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                'Lambda0 must be of length {}'.format(len(self.search_keys)))
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        self.Lambda0 = np.array(Lambda0)
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    def run(self, factor=2, max_n_ticks=4, whspace=0.07, save=True,
            **kwargs):
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        lbdim = 0.5 * factor   # size of left/bottom margin
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        trdim = 0.4 * factor   # size of top/right margin
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        plotdim = factor * self.ndim + factor * (self.ndim - 1.) * whspace
        dim = lbdim + plotdim + trdim

        fig, axes = plt.subplots(self.ndim, self.ndim, figsize=(dim, dim))

        # Format the figure.
        lb = lbdim / dim
        tr = (lbdim + plotdim) / dim
        fig.subplots_adjust(left=lb, bottom=lb, right=tr, top=tr,
                            wspace=whspace, hspace=whspace)

        search = GridSearch(
            self.label, self.outdir, self.sftfilepattern,
            F0s=self.Lambda0[0], F1s=self.Lambda0[1], F2s=self.F2s[0],
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            Alphas=self.Lambda0[2], Deltas=self.Lambda0[3], tref=self.tref,
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime)
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        for i, ikey in enumerate(self.search_keys):
            setattr(search, ikey+'s', self.thetas[i])
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            search.label = '{}_{}'.format(self.label, ikey)
            search.set_out_file()
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            search.run()
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            axes[i, i] = search.plot_1D(ikey, ax=axes[i, i], savefig=False,
                                        x0=self.Lambda0[i]
                                        )
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            setattr(search, ikey+'s', [self.Lambda0[i]])
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            axes[i, i].yaxis.tick_right()
            axes[i, i].yaxis.set_label_position("right")
            axes[i, i].set_xlabel('')
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            for j, jkey in enumerate(self.search_keys):
                ax = axes[i, j]

                if j > i:
                    ax.set_frame_on(False)
                    ax.set_xticks([])
                    ax.set_yticks([])
                    continue

                ax.get_shared_x_axes().join(axes[self.ndim-1, j], ax)
                if i < self.ndim - 1:
                    ax.set_xticklabels([])
                if j < i:
                    ax.get_shared_y_axes().join(axes[i, i-1], ax)
                    if j > 0:
                        ax.set_yticklabels([])
                if j == i:
                    continue

                ax.xaxis.set_major_locator(
                    matplotlib.ticker.MaxNLocator(max_n_ticks, prune="upper"))
                ax.yaxis.set_major_locator(
                    matplotlib.ticker.MaxNLocator(max_n_ticks, prune="upper"))

                setattr(search, ikey+'s', self.thetas[i])
                setattr(search, jkey+'s', self.thetas[j])
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                search.label = '{}_{}'.format(self.label, ikey+jkey)
                search.set_out_file()
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                search.run()
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                ax = search.plot_2D(jkey, ikey, ax=ax, save=False,
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                                    y0=self.Lambda0[i], x0=self.Lambda0[j],
                                    **kwargs)
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                setattr(search, ikey+'s', [self.Lambda0[i]])
                setattr(search, jkey+'s', [self.Lambda0[j]])

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                ax.grid(lw=0.2, ls='--', zorder=10)
                ax.set_xlabel('')
                ax.set_ylabel('')

        for i, ikey in enumerate(self.search_keys):
            axes[-1, i].set_xlabel(
                self.tex_labels[ikey]+self.tex_labels0[ikey])
            if i > 0:
                axes[i, 0].set_ylabel(
                    self.tex_labels[ikey]+self.tex_labels0[ikey])
            axes[i, i].set_ylabel("$2\mathcal{F}$")

        if save:
            fig.savefig(
                '{}/{}_slice_projection.png'.format(self.outdir, self.label))
        else:
            return fig, axes
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class GridUniformPriorSearch():
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    @helper_functions.initializer
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    def __init__(self, theta_prior, NF0, NF1, label, outdir, sftfilepattern,
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                 tref, minStartTime, maxStartTime, minCoverFreq=None,
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                 maxCoverFreq=None, BSGL=False, detectors=None, nsegs=1,
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                 SSBprec=None, injectSources=None):
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        dF0 = (theta_prior['F0']['upper'] - theta_prior['F0']['lower'])/NF0
        dF1 = (theta_prior['F1']['upper'] - theta_prior['F1']['lower'])/NF1
        F0s = [theta_prior['F0']['lower'], theta_prior['F0']['upper'], dF0]
        F1s = [theta_prior['F1']['lower'], theta_prior['F1']['upper'], dF1]
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        self.search = GridSearch(
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            label, outdir, sftfilepattern, F0s=F0s, F1s=F1s, tref=tref,
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            Alphas=[theta_prior['Alpha']], Deltas=[theta_prior['Delta']],
            minStartTime=minStartTime, maxStartTime=maxStartTime, BSGL=BSGL,
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            detectors=detectors, minCoverFreq=minCoverFreq,
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            injectSources=injectSources, maxCoverFreq=maxCoverFreq,
            nsegs=nsegs, SSBprec=SSBprec)
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    def run(self):
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        self.search.run()
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    def get_2D_plot(self, **kwargs):
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        return self.search.plot_2D('F0', 'F1', **kwargs)
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class GridGlitchSearch(GridSearch):
    """ Grid search using the SemiCoherentGlitchSearch """
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    search_labels = ['F0s', 'F1s', 'F2s', 'Alphas', 'Deltas', 'delta_F0s',
                     'delta_F1s', 'tglitchs']

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    @helper_functions.initializer
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    def __init__(self, label, outdir='data', sftfilepattern=None, F0s=[0],
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                 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,
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                 detectors=None):
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        """
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        Run a single-glitch grid search

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        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
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        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
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        F0s, F1s, F2s, delta_F0s, delta_F1s, tglitchs, Alphas, Deltas: tuple
            Length 3 tuple describing the grid for each parameter, e.g
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            [F0min, F0max, dF0], for a fixed value simply give [F0]. Note that
            tglitchs is referenced to zero at minStartTime.
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        tref, minStartTime, maxStartTime: int
            GPS seconds of the reference time, start time and end time

        For all other parameters, see pyfstat.ComputeFStat.
        """
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        self.BSGL = False
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        self.input_arrays = False
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        if tglitchs is None:
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            raise ValueError('You must specify `tglitchs`')
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        self.search = SemiCoherentGlitchSearch(
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            label=label, outdir=outdir, sftfilepattern=self.sftfilepattern,
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            tref=tref, minStartTime=minStartTime, maxStartTime=maxStartTime,
            minCoverFreq=minCoverFreq, maxCoverFreq=maxCoverFreq,
            BSGL=self.BSGL)
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        self.search.get_det_stat = self.search.get_semicoherent_nglitch_twoF
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        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
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        self.set_out_file()
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        self.keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta', 'delta_F0',
                     'delta_F1', 'tglitch']


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class SlidingWindow(GridSearch):
    @helper_functions.initializer
    def __init__(self, label, outdir, sftfilepattern, F0, F1, F2,
                 Alpha, Delta, tref, minStartTime=None,
                 maxStartTime=None, window_size=10*86400, window_delta=86400,
                 BSGL=False, minCoverFreq=None, maxCoverFreq=None,
                 detectors=None, SSBprec=None, injectSources=None):
        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
        F0, F1, F2, Alpha, Delta: float
            Fixed values to compute output over
        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 os.path.isdir(outdir) is False:
            os.mkdir(outdir)
        self.set_out_file()
        self.nsegs = 1

        self.tstarts = [self.minStartTime]
        while self.tstarts[-1] + self.window_size < self.maxStartTime:
            self.tstarts.append(self.tstarts[-1]+self.window_delta)
        self.tmids = np.array(self.tstarts) + .5 * self.window_size

    def inititate_search_object(self):
        logging.info('Setting up search object')
        self.search = ComputeFstat(
            tref=self.tref, sftfilepattern=self.sftfilepattern,
            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
            detectors=self.detectors, transient=True,
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
            BSGL=self.BSGL, SSBprec=self.SSBprec,
            injectSources=self.injectSources)

    def check_old_data_is_okay_to_use(self, out_file):
        if os.path.isfile(out_file):
            tmids, vals, errvals = np.loadtxt(out_file).T
            if len(tmids) == len(self.tmids) and (
                    tmids[0] == self.tmids[0]):
                self.vals = vals
                self.errvals = errvals
                return True
        return False

    def run(self, key='h0', errkey='dh0'):
        self.key = key
        self.errkey = errkey
        out_file = '{}/{}_{}-sliding-window.txt'.format(
            self.outdir, self.label, key)

        if self.check_old_data_is_okay_to_use(out_file) is False:
            self.inititate_search_object()
            vals = []
            errvals = []
            for ts in self.tstarts:
                loudest = self.search.get_full_CFSv2_output(
                        ts, ts+self.window_size, self.F0, self.F1, self.F2,
                        self.Alpha, self.Delta, self.tref)
                vals.append(loudest[key])
                errvals.append(loudest[errkey])

            np.savetxt(out_file, np.array([self.tmids, vals, errvals]).T)
            self.vals = np.array(vals)
            self.errvals = np.array(errvals)

    def plot_sliding_window(self, factor=1, fig=None, ax=None):
        if ax is None:
            fig, ax = plt.subplots()
        days = (self.tmids-self.minStartTime) / 86400
        ax.errorbar(days, self.vals*factor, yerr=self.errvals*factor)
        ax.set_ylabel(self.key)
        ax.set_xlabel(
            r'Mid-point (days after $t_\mathrm{{start}}$={})'.format(
                self.minStartTime))
        ax.set_title(
            'Sliding window of {} days in increments of {} days'
            .format(self.window_size/86400, self.window_delta/86400),
            )

        if fig:
            fig.savefig('{}/{}_{}-sliding-window.png'.format(
                self.outdir, self.label, self.key))
        else:
            return ax


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class FrequencySlidingWindow(GridSearch):
    """ A sliding-window search over the Frequency """
    @helper_functions.initializer
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    def __init__(self, label, outdir, sftfilepattern, F0s, F1, F2,
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                 Alpha, Delta, tref, minStartTime=None,
                 maxStartTime=None, window_size=10*86400, window_delta=86400,
                 BSGL=False, minCoverFreq=None, maxCoverFreq=None,
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                 detectors=None, SSBprec=None, injectSources=None):
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        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
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        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
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        F0s: array
            Frequency range
        F1, F2, Alpha, Delta: float
            Fixed values to compute twoF(F) over
        tref, minStartTime, maxStartTime: int
            GPS seconds of the reference time, start time and end time

        For all other parameters, see `pyfstat.ComputeFStat` for details
        """

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        self.transientWindowType = 'rect'
        self.nsegs = 1
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        self.t0Band = None
        self.tauBand = None
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        self.tauMin = None
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        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
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        self.set_out_file()
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        self.F1s = [F1]
        self.F2s = [F2]
        self.Alphas = [Alpha]
        self.Deltas = [Delta]
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        self.input_arrays = False
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        self.keys = ['_', '_', 'F0', 'F1', 'F2', 'Alpha', 'Delta']
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    def inititate_search_object(self):
        logging.info('Setting up search object')
        self.search = ComputeFstat(
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            tref=self.tref, sftfilepattern=self.sftfilepattern,
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            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
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            detectors=self.detectors, transientWindowType=self.transientWindowType,
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            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
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            BSGL=self.BSGL, SSBprec=self.SSBprec,
            injectSources=self.injectSources)
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        self.search.get_det_stat = (
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            self.search.get_fullycoherent_twoF)
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    def get_input_data_array(self):
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        coord_arrays = []
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        tstarts = [self.minStartTime]
        while tstarts[-1] + self.window_size < self.maxStartTime:
            tstarts.append(tstarts[-1]+self.window_delta)
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        coord_arrays = [tstarts]
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        for tup in (self.F0s, self.F1s, self.F2s,
                    self.Alphas, self.Deltas):
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            coord_arrays.append(self.get_array_from_tuple(tup))
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        input_data = []
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        for vals in itertools.product(*coord_arrays):
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            input_data.append(vals)

        input_data = np.array(input_data)
        input_data = np.insert(
            input_data, 1, input_data[:, 0] + self.window_size, axis=1)

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        self.coord_arrays = coord_arrays
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        self.input_data = np.array(input_data)

    def plot_sliding_window(self, F0=None, ax=None, savefig=True,
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                            colorbar=True, timestamps=False,
                            F0rescale=1, **kwargs):
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        data = self.data
        if ax is None:
            ax = plt.subplot()
        tstarts = np.unique(data[:, 0])
        tends = np.unique(data[:, 1])
        frequencies = np.unique(data[:, 2])
        twoF = data[:, -1]
        tmids = (tstarts + tends) / 2.0
        dts = (tmids - self.minStartTime) / 86400.
        if F0:
            frequencies = frequencies - F0
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            ax.set_ylabel('Frequency - $f_0$ [Hz] \n $f_0={:0.2f}$'.format(F0))
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        else:
            ax.set_ylabel('Frequency [Hz]')
        twoF = twoF.reshape((len(tmids), len(frequencies)))
        Y, X = np.meshgrid(frequencies, dts)
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        pax = ax.pcolormesh(X, Y*F0rescale, twoF, **kwargs)
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        if colorbar:
            cb = plt.colorbar(pax, ax=ax)
            cb.set_label('$2\mathcal{F}$')
        ax.set_xlabel(
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            r'Mid-point (days after $t_\mathrm{{start}}$={})'.format(
                self.minStartTime))
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        ax.set_title(
            'Sliding window length = {} days in increments of {} days'
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            .format(self.window_size/86400, self.window_delta/86400),
            )
        if timestamps:
            axT = ax.twiny()
            axT.set_xlim(tmids[0]*1e-9, tmids[-1]*1e-9)
            axT.set_xlabel('Mid-point timestamp [GPS $10^{9}$ s]')
            ax.set_title(ax.get_title(), y=1.18)
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        if savefig:
            plt.tight_layout()
            plt.savefig(
                '{}/{}_sliding_window.png'.format(self.outdir, self.label))
        else:
            return ax
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class EarthTest(GridSearch):
    """ """
    tex_labels = {'deltaRadius': '$\Delta R$ [m]',
                  'phaseOffset': 'phase-offset [rad]',
                  'deltaPspin': '$\Delta P_\mathrm{spin}$ [s]'}

    @helper_functions.initializer
    def __init__(self, label, outdir, sftfilepattern, deltaRadius,
                 phaseOffset, deltaPspin, F0, F1, F2, Alpha,
                 Delta, tref=None, minStartTime=None, maxStartTime=None,
                 BSGL=False, minCoverFreq=None, maxCoverFreq=None,
                 detectors=None, injectSources=None,
                 assumeSqrtSX=None):
        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
        F0, F1, F2, Alpha, Delta: float
        tref, minStartTime, maxStartTime: int
            GPS seconds of the reference time, start time and end time

        For all other parameters, see `pyfstat.ComputeFStat` for details
        """
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        self.transientWindowType = None
        self.t0Band = None
        self.tauBand = None
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        self.tauMin = None
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        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
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        self.nsegs = 1
        self.F0s = [F0]
        self.F1s = [F1]
        self.F2s = [F2]
        self.Alphas = [Alpha]
        self.Deltas = [Delta]
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        self.duration = maxStartTime - minStartTime
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        self.deltaRadius = np.atleast_1d(deltaRadius)
        self.phaseOffset = np.atleast_1d(phaseOffset)
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        self.phaseOffset = self.phaseOffset + 1e-12  # Hack to stop cached data being used
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        self.deltaPspin = np.atleast_1d(deltaPspin)
        self.set_out_file()
        self.SSBprec = lalpulsar.SSBPREC_RELATIVISTIC
        self.keys = ['deltaRadius', 'phaseOffset', 'deltaPspin']

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        self.prior_widths = [
            np.max(self.deltaRadius)-np.min(self.deltaRadius),
            np.max(self.phaseOffset)-np.min(self.phaseOffset),
            np.max(self.deltaPspin)-np.min(self.deltaPspin)]

        if hasattr(self, 'search') is False:
            self.inititate_search_object()

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    def get_input_data_array(self):
        logging.info("Generating input data array")
        coord_arrays = [self.deltaRadius, self.phaseOffset, self.deltaPspin]
        input_data = []
        for vals in itertools.product(*coord_arrays):