Merge branch 'add_sky_sensitivity' into 'master'

_version.py

+# Copyright (C) 2018  Charlie Hoy <charlie.hoy@ligo.org>
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+__version__ = "beta"

main.py

@@ -65,6 +65,9 @@ def command_line():
     parser.add_argument("-c", "--config", dest="config",
                         help="configuration file associcated with each samples file.", nargs='+',
                         default=None)
+    parser.add_argument("--sensitivity", action="store_true",
+                        help="generate sky sensitivities for HL, HLV",
+                        default=False)
     return parser
 
 def run_checks(opts):
@@ -218,6 +221,16 @@ def make_plots(opts, colors=None):
             fig = plot._1d_histogram_plot(j, param_samples, latex_labels[j])
             plt.savefig("%s/plots/1d_posterior_%s_%s.png" %(opts.webdir, i, j))
             plt.close()
+        if opts.sensitivity:
+            fig = plot._sky_sensitivity(["H1", "L1"], 0.2,
+                                        combined_maxL[num])
+            plt.savefig("%s/plots/%s_sky_sensitivity_HL" %(opts.webdir, i))
+            plt.close()
+            fig = plot._sky_sensitivity(["H1", "L1", "V1"], 0.2,
+                                        combined_maxL[num])
+            plt.savefig("%s/plots/%s_sky_sensitivity_HLV" %(opts.webdir, i))
+            plt.close()
+         
     # if len(approximants) > 1, then we need to do comparison plots
     if len(opts.approximant) > 1:
         for ind, j in enumerate(parameters):
@@ -376,6 +389,20 @@ def make_comparison_pages(opts, approximants, samples, colors, parameters):
                  "{}/plots/compare_waveforms.png".format(opts.baseurl)]]
     html_file.make_table_of_images(headings=["sky_map", "waveform"],
                                    contents=contents)
+    if opts.sensitivity:
+        html_file.add_content("<div class='row justify-content-center' "
+                              "style='margin=top: 2.0em;'>"
+                              "<p>To see the sky sensitivity for the following "
+                              "networks, click the button</p></div>")
+        html_file.add_content("<div class='row justify-content-center' "
+                              "style='margin-top: 0.2em;'>"
+                              "<button type='button' class='btn btn-light' "
+                              "onclick='%s.src=\"%s/plots/combined_skymap.png\"'"
+                              "style='margin-left:0.25em; margin-right:0.25em'>Sky Map</button>"
+                              "<button type='button' class='btn btn-light' "
+                              "onclick='%s.src=\"%s/plots/IMRPhenomPv2_sky_sensitivity_HL.png\"'"
+                              "style='margin-left:0.25em; margin-right:0.25em'>HL</button>"
+                               %("combined_skymap", opts.baseurl, "combined_skymap", opts.baseurl))
     html_file.make_footer(user=os.environ["USER"], rundir=opts.webdir)
     # edit all of the comparison pages
     pages = ["Comparison_{}".format(i) for i in parameters]

plot.py

@@ -153,8 +153,8 @@ def _waveform_plot(maxL_params, **kwargs):
     """
     logging.info("Generating the maximum likelihood waveform plot for H1") 
     if not LALSIMULATION:
-        raise Exception("LALSimulation could not be imported. Please install "
-                        "LALSuite to be able to use all features")
+        raise exception("lalsimulation could not be imported. please install "
+                        "lalsuite to be able to use all features")
     delta_frequency = kwargs.get("delta_f", 1./256)
     minimum_frequency = kwargs.get("f_min", 10.)
     maximum_frequency = kwargs.get("f_max", 1000.)
@@ -264,6 +264,9 @@ def _sky_map_plot(ra, dec, **kwargs):
     ax = plt.subplot(111, projection="hammer")
     ax.cla()
     ax.grid()
+    ax.set_xticklabels([r"$22^{h}$", r"$20^{h}$", r"$18^{h}$", r"$16^{h}$",
+                        r"$14^{h}$", r"$12^{h}$", r"$10^{h}$", r"$8^{h}$",
+                        r"$6^{h}$", r"$4^{h}$", r"$2^{h}$"]) 
     levels = [1.0 - np.exp(-0.5), 1 - np.exp(-2), 1-np.exp(-9./2.)]
 
     H,X,Y = np.histogram2d(ra, dec, bins=50)
@@ -328,6 +331,9 @@ def _sky_map_comparison_plot(ra_list, dec_list, approximants, colors, **kwargs):
     ax = plt.subplot(111, projection="hammer")
     ax.cla()
     ax.grid()
+    ax.set_xticklabels([r"$22^{h}$", r"$20^{h}$", r"$18^{h}$", r"$16^{h}$",
+                        r"$14^{h}$", r"$12^{h}$", r"$10^{h}$", r"$8^{h}$",
+                        r"$6^{h}$", r"$4^{h}$", r"$2^{h}$"])
     levels = [1.0 - np.exp(-0.5), 1 - np.exp(-2), 1-np.exp(-9./2.)]
 
     for num, i in enumerate(ra_list):
@@ -415,3 +421,117 @@ def _make_corner_plot(opts, samples, params, approximant, latex_labels,
     height *= figure.dpi
     np.savetxt("{}/plots/corner/{}_axes.txt".format(opts.webdir, approximant), extent)
     return figure
+
+def __get_cutoff_indices(flow, fhigh, df, N):
+    """
+    Gets the indices of a frequency series at which to stop an overlap
+    calculation.
+
+    Parameters
+    ----------
+    flow: float
+        The frequency (in Hz) of the lower index.
+    fhigh: float
+        The frequency (in Hz) of the upper index.
+    df: float
+        The frequency step (in Hz) of the frequency series.
+    N: int
+        The number of points in the **time** series. Can be odd
+        or even.
+
+    Returns
+    -------
+    kmin: int
+    kmax: int
+    """
+    if flow:
+        kmin = int(flow / df)
+    else:
+        kmin = 1
+    if fhigh:
+        kmax = int(fhigh / df )
+    else:
+        kmax = int((N + 1)/2.)
+    return kmin,kmax
+
+def _sky_sensitivity(network, resolution, maxL_params, **kwargs):
+    """Generate the sky sensitivity for a given network
+
+    Parameters
+    ----------
+    network: list
+        list of detectors you want included in your sky sensitivity plot
+    resolution: float
+        resolution of the skymap
+    maxL_params: dict
+        dictionary of waveform parameters for the maximum likelihood waveform
+    """
+    logging.info("Generating the sky sensitivity for %s" %(network)) 
+    if not LALSIMULATION:
+        raise Exception("LALSimulation could not be imported. Please install "
+                        "LALSuite to be able to use all features")
+    delta_frequency = kwargs.get("delta_f", 1./256)
+    minimum_frequency = kwargs.get("f_min", 20.)
+    maximum_frequency = kwargs.get("f_max", 1000.)
+    frequency_array = np.arange(minimum_frequency, maximum_frequency,
+                                delta_frequency)
+
+    approx = lalsim.GetApproximantFromString(maxL_params["approximant"])
+    mass_1 = maxL_params["mass_1"]*MSUN_SI
+    mass_2 = maxL_params["mass_2"]*MSUN_SI
+    luminosity_distance = maxL_params["luminosity_distance"]*PC_SI*10**6
+    iota, S1x, S1y, S1z, S2x, S2y, S2z = \
+        lalsim.SimInspiralTransformPrecessingNewInitialConditions(
+            maxL_params["iota"], maxL_params["phi_jl"], maxL_params["tilt_1"],
+            maxL_params["tilt_2"], maxL_params["phi_12"], maxL_params["a_1"],
+            maxL_params["a_2"], mass_1, mass_2, kwargs.get("f_ref", 10.),
+            maxL_params["phase"])
+    h_plus, h_cross = lalsim.SimInspiralChooseFDWaveform(mass_1, mass_2, S1x,
+                          S1y, S1z, S2x, S2y, S2z,
+                          luminosity_distance, iota,
+                          maxL_params["phase"], 0.0, 0.0, 0.0, delta_frequency,
+                          minimum_frequency, maximum_frequency,
+                          kwargs.get("f_ref", 10.), None, approx)
+    h_plus = h_plus.data.data
+    h_cross = h_cross.data.data
+    h_plus = h_plus[:len(frequency_array)]
+    h_cross = h_cross[:len(frequency_array)]
+    psd = {}
+    psd["H1"] = psd["L1"] = np.array([lalsim.SimNoisePSDaLIGOZeroDetHighPower(i) for i in frequency_array])
+    psd["V1"] = np.array([lalsim.SimNoisePSDVirgo(i) for i in frequency_array])
+    kmin, kmax = __get_cutoff_indices(minimum_frequency, maximum_frequency,
+                                      delta_frequency, (len(h_plus)-1)*2)
+    ra = np.arange(-np.pi, np.pi, resolution)
+    dec = np.arange(-np.pi, np.pi, resolution)
+    X,Y = np.meshgrid(ra, dec)
+    N = np.zeros([len(dec), len(ra)])
+    
+    indices = np.ndindex(len(ra), len(dec))
+    for ind in indices:
+        ar = {}
+        SNR = {}
+        for i in network:
+            ard = __antenna_response(i, ra[ind[0]], dec[ind[1]],
+                                     maxL_params["psi"], maxL_params["geocent_time"])
+            ar[i] = [ard[0], ard[1]]
+            strain = np.array(h_plus*ar[i][0]+h_cross*ar[i][1])
+            integrand = np.conj(strain[kmin:kmax])*strain[kmin:kmax]/psd[i][kmin:kmax]
+            integrand = integrand[:-1]
+            SNR[i] = np.sqrt(4*delta_frequency*np.sum(integrand).real)
+            ar[i][0] *= SNR[i]
+            ar[i][1] *= SNR[i]
+        numerator = 0.0
+        denominator = 0.0
+        for i in network:
+            numerator += sum(i**2 for i in ar[i])
+            denominator += SNR[i]**2
+        N[ind[1]][ind[0]] = (((numerator/denominator)**0.5))
+    fig = plt.figure()
+    ax = plt.subplot(111, projection="hammer")
+    ax.cla()
+    ax.grid()
+    plt.pcolormesh(X,Y,N)
+    ax.set_xticklabels([r"$22^{h}$", r"$20^{h}$", r"$18^{h}$", r"$16^{h}$",
+                        r"$14^{h}$", r"$12^{h}$", r"$10^{h}$", r"$8^{h}$",
+                        r"$6^{h}$", r"$4^{h}$", r"$2^{h}$"])
+    return fig

webpage.py

@@ -14,6 +14,7 @@
 # 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
 import utils
+from _version import __version__
 import sys
 from pygments import highlight
 from pygments.lexers import get_lexer_by_name
@@ -105,6 +106,8 @@ class page():
         """
         self.add_content("<div class='jumbotron text-center' style='background-color: {}; margin-bottom:0'>\n".format(colour))
         self.add_content("  <h1 id={}>{}</h1>\n".format(approximant, title))
+        self.add_content("<h4><span class='badge badge-info'>Code Version: %s"
+                         "</span></h4>\n" %(__version__), indent=2)
         self.add_content("</div>\n")
 
     def _footer(self, user, rundir):
@@ -294,9 +297,12 @@ class page():
         self.add_content("<tbody>\n", indent=6)
 
         for i in contents:
+            self.add_content("<tr>\n", indent=8)
             for j in i:
-                self.add_content("<th><img src='{}' ".format(self.base_url+"/plots/"+j.split("/")[-1]) +
-                                 "alt='No image available' style='width:{}px;'></td>\n".format(1050./len(i)), indent=8)
+                self.add_content("<td><img src='{}' ".format(self.base_url+"/plots/"+j.split("/")[-1]) +
+                                 "alt='No image available' style='width:{}px;' "
+                                 "id='{}'></td>\n".format(1050./len(i),j.split("/")[-1][:-4]), indent=10)
+            self.add_content("</tr>\n", indent=8)
 
         self.add_content("</tbody>\n", indent=6)
         self.add_content("</table>\n", indent=4)