.conda-env.Dockerfile

-FROM conda/miniconda3-centos7 AS build
+FROM continuumio/miniconda3 AS build
 ARG BUILD_DATE
 ARG CI_COMMIT_SHA
 
@@ -12,24 +12,24 @@ LABEL org.label-schema.build-date="${BUILD_DATE}"
 LABEL org.label-schema.vcs-ref="${CI_COMMIT_SHA}"
 
 ## Build bayeswave conda environment
-RUN conda update -yq -nbase conda
+RUN conda update -yq -n base conda
 COPY environment.yaml .
 RUN conda env create -f environment.yaml
 SHELL ["conda", "run", "-n", "bayeswave", "/bin/bash", "-c"]
 
 ## Use conda-pack to create a standalone enviornment
 ## in /venv:
- RUN conda install -c conda-forge conda-pack
- RUN conda-pack -n bayeswave -o /tmp/env.tar && \
-   mkdir /venv && cd /venv && tar xf /tmp/env.tar && \
-   rm /tmp/env.tar
-  
+RUN conda install -c conda-forge conda-pack
+RUN conda-pack -n bayeswave -o /tmp/env.tar && \
+  mkdir /venv && cd /venv && tar xf /tmp/env.tar && \
+  rm /tmp/env.tar
+
 ## We've put venv in same path it'll be in final image,
 ## so now fix up paths:
 RUN /venv/bin/conda-unpack
   
 ## Next stage
-FROM centos:7
+FROM rockylinux/rockylinux:latest
 
 ## Essentials
 RUN yum install -y git which && \

.gitignore

@@ -7,6 +7,7 @@
 __pycache__/
 *.egg-info/
 *.eggs/
+src/.ipynb_checkpoints/*
 
 # built objects
 *.o
@@ -19,6 +20,9 @@ BayesWaveToLALPSD
 src/version.h
 src/bayeswave.pc
 
+# dist dir
+dist/
+
 # autotools stuff
 .libs/
 .deps/

.gitlab-ci.yml

@@ -7,11 +7,15 @@
 # 4. Build & push the bayeswave runtime container from BayesWave source on top of the
 # conda env dependencies.
 # 5. Push documentation for tagged commits only
+#
+# TODO: build the env image only for master
+# TODO: use the upstream for the env image
 
 variables:
   BRANCH: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME
   COMMIT: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
   TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
+  UPSTREAM_REGISTRY: containers.ligo.org/lscsoft/bayeswave
   CONDA_ENV_IMAGE: conda-env
   BUILD_DIR: test-install
   BUILD_TARGET: $CI_PROJECT_DIR/$BUILD_DIR
@@ -27,7 +31,7 @@ stages:
   image: docker
   before_script:
     - echo "Logging in"
-    - docker login -u gitlab-ci-token -p $CI_BUILD_TOKEN $CI_REGISTRY
+    - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
   script:
     - if [ -z $CI_COMMIT_TAG ]; then IMAGE_TAG="latest"; else IMAGE_TAG="$CI_COMMIT_TAG"; fi
     - if [ -z $IMAGE_NAME ]; then IMAGE="$CI_REGISTRY_IMAGE:$IMAGE_TAG"; else IMAGE="$CI_REGISTRY_IMAGE/$IMAGE_NAME:$IMAGE_TAG" ; fi
@@ -35,20 +39,12 @@ stages:
     - docker build --no-cache
         --build-arg CI_COMMIT_SHA=${CI_COMMIT_SHA}
         --build-arg BUILD_DATE=$(date -u +'%Y-%m-%dT%H:%M:%SZ')
-        --build-arg BUILD_IMAGE=$CI_REGISTRY_IMAGE/conda-env:$IMAGE_TAG
+        --build-arg BUILD_IMAGE=$UPSTREAM_REGISTRY/$CONDA_ENV_IMAGE:$IMAGE_TAG
         -t $IMAGE --file $DOCKERFILE .
     - docker push $IMAGE
 
 # Build the conda environment dependencies
-conda-env:manual:
-  stage: docker
-  <<: *docker_deploy
-  variables:
-    IMAGE_NAME: $CONDA_ENV_IMAGE
-    DOCKERFILE: .conda-env.Dockerfile
-  when: manual
-
-conda-env:schedule:
+conda-env:
   stage: docker
   <<: *docker_deploy
   variables:
@@ -56,17 +52,18 @@ conda-env:schedule:
     DOCKERFILE: .conda-env.Dockerfile
   only:
     refs:
-      - schedules
+      - tags
+      - master@lscsoft/bayeswave
 
-conda-env:tag:
+# FIXME:  reconcile with above - SHORT TERM HACK (seriously - this could break paperwork)
+# Build the conda environment dependencies
+conda-env-manual:
   stage: docker
   <<: *docker_deploy
   variables:
     IMAGE_NAME: $CONDA_ENV_IMAGE
     DOCKERFILE: .conda-env.Dockerfile
-  only:
-    refs:
-      - tags
+  when: manual
 
 # -------------------------------------------------------
 # BUILD
@@ -74,7 +71,7 @@ conda-env:tag:
 # Build bayeswave from source
 bayeswave:
   stage: build
-  image: $CI_REGISTRY_IMAGE/$CONDA_ENV_IMAGE:latest
+  image: $UPSTREAM_REGISTRY/$CONDA_ENV_IMAGE:latest
   script:
     - mkdir -p build
     - cmake . -DCMAKE_BUILD_TYPE=Debug -DCMAKE_EXPORT_COMPILE_COMMANDS=true -DCMAKE_INSTALL_PREFIX=$BUILD_DIR
@@ -87,7 +84,7 @@ bayeswave:
 
 BayesWaveUtils:
   stage: build
-  image: $CI_REGISTRY_IMAGE/$CONDA_ENV_IMAGE:latest
+  image: $UPSTREAM_REGISTRY/$CONDA_ENV_IMAGE:latest
   script: 
     - pushd BayesWaveUtils
     - python setup.py install --prefix $BUILD_TARGET
@@ -138,7 +135,7 @@ docs:
 # Test bayeswave functionality
 BayesWave:
   stage: test
-  image: $CI_REGISTRY_IMAGE/$CONDA_ENV_IMAGE:latest
+  image: $UPSTREAM_REGISTRY/$CONDA_ENV_IMAGE:latest
   script:
     - source $BUILD_DIR/bayeswave-user-env.sh
     - cat $BUILD_DIR/bayeswave-user-env.sh
@@ -156,7 +153,7 @@ BayesWave:
 # Test bayeswavePost functionality
 BayesWavePost:
   stage: test
-  image: $CI_REGISTRY_IMAGE/$CONDA_ENV_IMAGE:latest
+  image: $UPSTREAM_REGISTRY/$CONDA_ENV_IMAGE:latest
   script:
     - source $BUILD_DIR/bayeswave-user-env.sh
     - cat $BUILD_DIR/bayeswave-user-env.sh
@@ -168,7 +165,7 @@ BayesWavePost:
 
 bayeswave_pipe:
   stage: test
-  image: $CI_REGISTRY_IMAGE/$CONDA_ENV_IMAGE:latest
+  image: $UPSTREAM_REGISTRY/$CONDA_ENV_IMAGE:latest
   script:
     - ls $BUILD_DIR
     - source $BUILD_DIR/bayeswave-user-env.sh
@@ -211,8 +208,8 @@ bayeswave-runtime:latest:
   <<: *docker_deploy
   variables:
     DOCKERFILE: Dockerfile
-  except:
-      - tags
+  only:
+      - master@lscsoft/bayeswave
 
 bayeswave-runtime:tag:
   stage: deploy

BayesWaveUtils/bayeswave_plot/__init__.py

@@ -17,4 +17,4 @@
 """
 """
 
-__all__=['bayeswave_utils']
+__all__=['bayeswave_utils', "BW_Flags", "wavelets"]

BayesWaveUtils/bayeswave_plot/wavelets.py

+#!/usr/bin/env python
+import numpy as np
+import matplotlib.pyplot as plt 
+ifoColors = ['darkgoldenrod','darkkhaki','darkseagreen','olive','cadetblue','green','slategray','darkcyan']
+
+
+def plot_repeats(runFlag, figpath = None, chain = 0):
+    # Takes in chain file name, computes wavelets repeated and makes a plot
+    # Plots histogram of that information
+    # should only be run when the glitch model is on (and no chirplets)
+
+    fig, ax = plt.subplots()
+    for i, ifo in enumerate(runFlag.ifoNames):
+        print("chain name = ", runFlag.get_chain_name())
+        try:
+            wf = get_wavelet_params(f"{runFlag.trigdir}/chains/glitch_params_{ifo}.dat.{chain}", model = 'glitch', 
+            burnin = 'half') # I think it's ok to hardcode glitch into the filename because this is only done for the glitch model
+        except:
+            try:
+                wf = get_wavelet_params(f"{runFlag.trigdir}/chains/full_params_{ifo}.dat.{chain}", model = 'glitch', 
+                burnin = 'half') # for full model
+            except:
+                try:
+                    wf = get_wavelet_params(f"{runFlag.trigdir}/chains/cbc_params_{ifo}.dat.{chain}", model = 'glitch', 
+                    burnin = 'half') # for CBC glitch runs
+                except:
+                    print("Couldn't get glitch files for repeat plot.\n")
+                    return(1)
+        repeats, percent_unique = get_repeats(wf)
+        # Get weighted average
+        sum_z2 = 0
+        for r in repeats:
+            sum_z2 += r ** 2
+        weight_avg = sum_z2 / sum(repeats)
+
+        if max(repeats) < 1:
+            bins = 1
+        else:
+            bins = np.arange(-0.5, max(repeats) + 0.5, 1)
+        ax.hist(repeats, bins = bins, density = False, label = "%s %.4g percent unique wavelets" % (ifo,percent_unique), alpha = 0.5, color = ifoColors[i])
+    ax.set_xlabel("Number of repeats")
+    ax.set_ylabel("Number of wavelets")
+    ax.set_title("Rate of glitch wavelet repitition")
+    ax.legend()
+
+    if figpath is not None:
+        plt.savefig(figpath)
+    return
+
+
+def get_repeats(glitch_dict):
+    """
+    Takes in output of the get_wavelet_params function 
+    Outputs list containing the number of repeated values for each glitch waveform
+    # TODO, will not work for chirplets 
+    """
+    sum_D = sum(glitch_dict['D'])
+    wavelet_params = fill_wavelet_matrix(glitch_dict)
+
+    percent_unique = 100 * get_unique_wavelet_list(wavelet_params) / sum_D
+    print(f"Percent unique {percent_unique}%")
+
+    # Holds onto info about whether that value has been repeated 
+    been_repeated = np.zeros(sum_D)        
+    repeats = []
+    for i in range(sum_D):
+        if i%10000 == 0:
+            print(f"Repeats is {len(repeats)} long and is {100 * sum(repeats) / sum_D} % done")
+            print(f"{i} of {sum_D}")
+        if been_repeated[i] != 0:
+            continue
+        w = np.where(np.all(np.isclose(wavelet_params.T - wavelet_params[:, i], 0), axis=1))
+        
+        repeats.append(len(w[0]))
+        been_repeated[w] = np.ones(len(w[0]))
+        
+    return repeats, percent_unique
+
+def fill_wavelet_matrix(glitch_dict):
+    sum_D = sum(glitch_dict['D'])
+    wavelet_params = np.zeros((5, sum_D))
+    i = 0
+    for key in glitch_dict.keys():
+        if key == 'D':
+            continue
+        wavelet_params[i] = glitch_dict[key]
+        i += 1
+    return wavelet_params
+
+
+def get_unique_wavelet_list(wavelet_params):
+    unique_rows = np.unique(wavelet_params, axis=1)
+    print(f"get_unique_wavelet_list {np.shape(unique_rows)}")
+    
+    return np.shape(unique_rows)[1] 
+
+def get_wavelet_params(filename, model, chirpflag=False, O1version=False, **keyword_parameters):
+    """
+    Read in chain file and get all wavelet params
+
+    arguments
+    ---------
+    filename (str): the chain file
+
+    model (str): signal or glitch
+
+    optional, chirpflag: True if using chirplets
+    optional, O1version: True if using O1 era chains
+    optional, restrict (int): line number if you only want one draw from the chain
+    optional, burnin: skip first N set of wavelets (or burnin = 'half' slips the first half)
+
+    outputs
+    -------
+    dictionary of the wavelet params
+    """
+    NW = 5 # number of intrinsic parameters (changes for chirplets)
+    NE = 6 # number of extrinsic parameters
+    start = 1
+
+    labels = ['t','f','Q','logA','phi_int'] # parameters of individual wavelets
+    extlabels = ['alpha','sindelta','psi','elip', 'phi_ext','scale'] # Common extrinsic parameters
+    if chirpflag:
+        NW = 6
+        labels.append('beta')
+
+    data = {}
+    for l in labels:
+        data[l] = []
+
+    if model == 'signal': # get extrinsic parameters
+        for l in extlabels:
+            data[l] = []
+
+    data['D'] = []
+
+    infile = open(filename)
+    lines = infile.readlines()
+
+    if ('restrict' in keyword_parameters):
+        restrict = int(keyword_parameters['restrict'])
+        rn = [restrict]
+    elif ('burnin' in keyword_parameters):
+        if keyword_parameters['burnin'] == 'half':
+            burnin = int(len(lines) // 2)
+        else:
+            burnin = keyword_parameters['burnin']
+        rn = np.arange(burnin, len(lines))
+    else:
+        rn = np.arange(0,len(lines))
+
+
+    for j in rn:
+        line = lines[j]
+        spl = line.split()
+        waveletnumber = int(spl[0]) # how many wavelets
+        data['D'].append(waveletnumber)
+        if model == 'signal':
+            if waveletnumber > 0: # only do this if there are signal wavelets active
+                start = NE+1 # extra parameters
+                if O1version:
+                    start += 1
+                for l in range(0,NE):
+                    data[extlabels[l]].append(float(spl[l+1]))
+        for i in range(0,waveletnumber):
+            for l in range(0,NW):
+                if labels[l] == 'logA':
+                    data[labels[l]].append(np.log10(float(spl[start+i*NW+l])))
+                else:
+                    data[labels[l]].append(float(spl[start+i*NW+l]))
+
+    return data
+

BayesWaveUtils/bayeswave_plot_data/navigate.js

@@ -13,6 +13,32 @@ $(document).ready(function(){
     $("#main").load("./html/glitch.html");
   });
 });
+$(document).ready(function(){
+    $("#cbc").click(function(){
+    $("#main").load("./html/cbc.html");
+  });
+});
+$(document).ready(function(){
+    $("#cbcglitch").click(function(){
+    $("#main").load("./html/cbcglitch.html");
+  });
+});
+$(document).ready(function(){
+    $("#cbcsignal").click(function(){
+    $("#main").load("./html/cbcsignal.html");
+  });
+});
+$(document).ready(function(){
+    $("#cbcmoments").click(function(){
+    $("#main").load("./html/cbcmoments.html");
+  });
+});
+$(document).ready(function(){
+    $("#cbcparams").click(function(){
+    $("#main").load("./html/cbcparams.html");
+  });
+});
+
 $(document).ready(function(){
     $("#full").click(function(){
     $("#main").load("./html/full.html");
@@ -93,11 +119,22 @@ $(document).ready(function(){
     $("#main").load("./html/diagnostics.html");
   });
 });
+$(document).ready(function(){
+    $("#verbose").click(function(){
+    $("#main").load("./html/verbose.html");
+  });
+});
 $(document).ready(function(){
     $("#skymap").click(function(){
     $("#main").load("./html/skymap.html");
   });
 });
+$(document).ready(function(){
+    $("#stokes").click(function(){
+    $("#main").load("./html/stokes.html");
+  });
+});
+
 $(document).ready(function(){
     $("#injections").click(function(){
     $("#main").load("./html/injections.html");
@@ -108,6 +145,21 @@ $(document).ready(function(){
     $("#main").load("./html/snr.html");
   });
 });
+$(document).ready(function(){
+    $("#clean").click(function(){
+    $("#main").load("./html/clean.html");
+  });
+});
+$(document).ready(function(){
+    $("#cleanmoments").click(function(){
+    $("#main").load("./html/cleanmoments.html");
+  });
+});
+$(document).ready(function(){
+    $("#cleaning").click(function(){
+    $("#main").load("./html/cleaning.html");
+  });
+});
 function toggle(showHideDiv, switchTextDiv) {
 	var ele = document.getElementById(showHideDiv);
 	var text = document.getElementById(switchTextDiv);

BayesWaveUtils/requirements.txt

+matplotlib
+numpy
+astropy
+gwpy
+scipy
+glue
\ No newline at end of file

BayesWaveUtils/scripts/cp_files.py

+#!/usr/bin/env python
+import shutil, sys, os, subprocess
+from bayeswave_plot import BW_Flags as bwf
+
+def keep_which_directory(temp, local):
+    # read in from each what model is currently being run, and decide from there which one to keep
+    # If one is in the cleaning stage, while the other is not, it will keep the one not being cleaned
+    # if the 2 are in the same state, it will keep the version with more iterations
+
+    # returns winner, loser
+
+    # Read in trigger directory name
+    trigdir =  str(sys.argv[1])
+    runFlag_local = bwf.Flags(local)
+    runFlag_temp = bwf.Flags(temp)
+
+    runFlag_dict = {'temp':runFlag_temp, 
+                    'local':runFlag_local}
+    dirs = { 'temp'  : temp, 
+             'local' : local}
+    #dirs = [temp, local]
+
+    # If temp bayeswave.run doesn't exist, then just keep the local version
+    # if local bayeswave.run doesn't exist (and temp does), then keep the temp version!
+    for i, key in enumerate(runFlag_dict.keys()):
+        try:
+            bwf.readbwb(runFlag_dict[key])
+        except FileNotFoundError as e:
+            print(e)
+            # if we did not find the bayeswave.run file, then choose the other key as the winner
+            print("WARNING: " + runFlag_dict[key].trigdir + '/bayeswave.run' + " has yet to be created")
+            if key == 'local':
+                # winner, loser 
+                return dirs['temp'], dirs['local']
+            return dirs['local'], dirs['temp']
+
+    # checkpointing isn't even on, so there is no reason to check checkpointing files, exiting this function
+    if not runFlag_dict['local'].checkpoint:
+        sys.exit("--checkpoint has not been turned on, returning")
+
+    models = {'temp':None, 'local':None}
+    lengths = {}
+
+    # now actually read in the model and iterations
+    for i, key in enumerate(runFlag_dict.keys()):
+        ### Read in model 
+        try:
+            # read in the model name
+            f = open(runFlag_dict[key].trigdir + '/checkpoint/state.dat')
+        except FileNotFoundError:
+            print("FileNotFound in {0}/checkpoint/state.dat".format(runFlag_dict[key].trigdir))
+            # if checkpointing file not found, then return the other directory
+            if key == 'local':
+                # winner, loser 
+                return dirs['temp'], dirs['local']
+            return dirs['local'], dirs['temp']
+        # read in model from first word of first line in the state file
+        model = str(f.readlines()[0].split()[0])
+        models[key] = model
+        f.close()
+
+
+        ### Read in iterations 
+        try:
+            f = open(runFlag_dict[key].trigdir + '/checkpoint/temperature.dat')
+        except FileNotFoundError:
+            # if checkpointing file not found, then return the other directory
+            if key == 'local':
+                # winner, loser 
+                return dirs['temp'], dirs['local']
+            return dirs['local'], dirs['temp']
+
+        # Keep track of file length 
+        file_length  = int(f.readlines()[0]) // runFlag_dict[key].Ncycle
+        lengths[key] = file_length
+        f.close()
+
+    # now we confirmed we have 2 directories that both have stuff in them, let's decide which one is more full.
+    if models['temp'] == models['local']:
+        # the 2 models are the same, return the directory corresponding to the larger checkpoint
+        if lengths['temp'] > lengths['local']:
+            return dirs['temp'], dirs['local']
+        # if they are the same length, keep the local directory 
+        return dirs['local'], dirs['temp']
+    # the 2 models are different, prioritize whichever one is not currently in the cleaning phase
+    if models['local'] == 'clean':
+        return dirs['temp'], dirs['local']
+    if models['temp'] == 'clean':
+        return dirs['local'], dirs['temp']
+
+    # when in doubt, just keep the local directory
+    # winner, loser
+    return dirs['local'], dirs['temp']
+
+
+def switch_and_delete_dirs(winner, loser, dirname):
+    # winner becomes dirname, loser gets deleted
+
+    # To make sure we don't delete something important, we make sure that the loser directory
+    # is something that we actually do want to delete
+    # directories we are deleteing should be of the form: WHATEVER/trigtime_WHATEVER or temp
+    deletenames = ['trigtime', 'temp']
+    delete_loser = False
+    for delete in deletenames:
+        if delete in loser:
+            delete_loser = True
+    if not delete_loser:
+        print("I AM NOT DELETING THE {loser} DIRECTORY!".format(loser = loser))
+        print("ERROR switch_and_delete_dirs: name does not contain temp or trigtime, returning")
+        return
+    else:
+        # loser gets deleted
+        shutil.rmtree(loser)
+    # winner gets moved
+    shutil.move(winner, dirname)
+    return
+
+# Make sure that directory string does not have a trailing '/'
+def clean_input(dir_string):
+    if dir_string[-1] == '/':
+        return dir_string[:-1]
+    else:
+        return(dir_string)
+
+# Call to cp_files is 
+#       cp_files.py local temp
+
+workdir = os.getcwd()
+print("cwd is {0}".format(workdir))
+
+# read in the directory local directory, then we make a temp directory from that directory
+# copy over the working directory to the remote machine
+tempdir =  clean_input(str(sys.argv[2]))
+temp = './temp'
+#shutil.copytree(tempdir, temp)
+print("Copying over files from tempdir {0} to tempdir {1}".format(tempdir, temp))
+p = subprocess.Popen('cp -r {0} {1}'.format(tempdir, temp), shell = True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
+p.wait()
+
+# localdir
+outputdir = clean_input(str(sys.argv[1]))
+print("localdir is {0}".format(outputdir))
+try:
+    process = subprocess.run('head {0}/checkpoint/temperature.dat'.format(outputdir), shell = True)
+except:
+    pass
+
+winner, loser = keep_which_directory(temp, outputdir)
+print("Keeping winner {0} \n Deleting loser {1}".format(winner, loser))
+switch_and_delete_dirs(winner, loser, outputdir)

BayesWaveUtils/scripts/delete_corruption.py

+#!/usr/bin/env python
+import subprocess, os, sys
+from bayeswave_plot import BW_Flags as bwf
+
+def delete_lines(filename, file_length):
+    # delete all lines except the first `file_length` lines
+
+    # get number of lines currently in the file 
+    try:
+        N_lines = int(subprocess.check_output("wc -l <{filename}".format(filename = filename), shell=True))
+    except subprocess.CalledProcessError:
+        # returns if file does not exist
+        return
+    if N_lines == file_length:
+        return
+    elif N_lines < file_length:
+        print("ERROR!! File {filename} too short! Should be {file_length} but is {N_lines}".format(filename = filename, file_length = file_length, N_lines = N_lines))
+    else:
+        print("Deleting {0} lines from {filename} ".format(N_lines - file_length, filename = filename))
+    # deletes from file_length (exclusive) to end of file
+    subprocess.run("sed -i '{file_length},$ d' {filename}".format(filename = filename, file_length = file_length+1), shell = True)
+
+# Read in trigger directory name 
+trigdir =  str(sys.argv[1])
+runFlags = bwf.Flags(trigdir)
+try:
+    bwf.readbwb(runFlags)
+except FileNotFoundError:
+    sys.exit("WARNING: " + runFlags.trigdir + '/bayeswave.run' + " has yet to be created, returning")
+
+if not runFlags.checkpoint:
+    sys.exit("--checkpoint has not been turned on, returning")
+
+# read in the model flag to see what checkpoint iteration we should be at
+f = open(runFlags.trigdir + '/checkpoint/temperature.dat')
+file_length = int(f.readlines()[0]) // runFlags.Ncycle 
+f.close()
+
+# get name of model
+try:
+    f = open(trigdir + '/checkpoint/state.dat', 'r')
+except:
+    sys.exit(runFlags.trigdir + '/checkpoint/state.dat' + " has yet to be created, returning")
+model = f.readlines()[0].split()[0] # first entry is what model we are on
+f.close()
+print("Model is {model}".format(model = model))
+
+# Get files relating to model 
+chaindir = runFlags.trigdir + '/chains'
+onlyfiles = [f for f in os.listdir(chaindir) if os.path.isfile(os.path.join(chaindir, f))]
+model_files = [f for f in onlyfiles if (model in f)]
+
+# if model is cbc, cbc_skychain.dat should not be considered part of the cbc files
+if model == 'cbc':
+    model_files.remove('cbc_skychain.dat')
+
+# delete extra lines from files 
+for f in model_files:
+     delete_lines(runFlags.trigdir + '/chains/' + f, file_length)

BayesWaveUtils/scripts/megasky.py

 #!/usr/bin/env python
 
+
 from __future__ import print_function
 
+print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\
+        Warning! megasky.py is now deprecated. Sky maps are now made within megaploy.py\n\
+        Use ONLY IF you need to make a fits file and know exactly what you're doing.\n\
+!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
+
 import lal
 import matplotlib
 matplotlib.use('Agg')
@@ -208,12 +214,26 @@ def make_skyview(directory='.', mdc=None, NSIDE=128, inj=None, npost=5000, geo=F
 
     # -- Input skymap data
     print("Extracting RA/DEC samples")
+    cbc_run = False
     try:
        filename = './chains/' + 'signal_params_h0.dat.0'
+       print("trying to read %s"%(filename))
        data = np.loadtxt(filename, unpack=True,usecols=(0,1,2))
     except:
-       filename = './chains/' + 'full_params_h0.dat.0'
-       data = np.loadtxt(filename, unpack=True,usecols=(0,1,2))
+       try:
+           print("trying to read %s"%(filename))
+           filename = './chains/' + 'full_params_h0.dat.0'
+           data = np.loadtxt(filename, unpack=True,usecols=(0,1,2))
+       except:
+            try:
+               print("trying to read %s"%(filename))
+               filename = './chains/' + 'cbc_params.dat.0'
+               data = np.loadtxt(filename, unpack=True,usecols=(0,11,12))
+               cbc_run = True
+            except:
+                print("Was not able to find anything to read, is this a glitchOnly run?")
+                pass
+
     ralist = data[1]
     sin_dec = data[2]
     print("Total samples are {0}".format(ralist.size))
@@ -378,13 +398,30 @@ if __name__ == "__main__":
       if(opts.eventnum is None):
         print("Provide event num if giving injfile", file=sys.stderr)
         sys.exit()
-      print("Loading xml")    
-      xmldoc = utils.load_filename(
-           opts.inj, contenthandler=LIGOLWContentHandler)
+      print("Loading xml")
+      print('xml filename', opts.inj, type(opts.inj))
+      try:
+          print('trying to open', opts.inj)
+          xmldoc = utils.load_filename(
+               opts.inj, contenthandler=LIGOLWContentHandler)
+      except:
+          try:
+              print('xml file not found, try looking a directory back')
+              print('trying to open', topdir + '/' + opts.inj)
+              xmldoc = utils.load_filename(
+                   topdir + '/' + opts.inj, contenthandler=LIGOLWContentHandler)
+          except:
+              print("didn't work either")
+
+              try:
+                  print('trying to open just the regular way, with open()', opts.inj)
+                  xmldoc = open(opts.inj)
+              except:
+                  print("Nothing is working here, watch me crash")
       try:
         print('Checking if using a sim_inspiral table...')
         injs = table.get_table(
-            xmldoc, lsctables.SimInspiralTable.tableName)
+                xmldoc, lsctables.SimInspiralTable.tableName)
         inj = injs[opts.eventnum]
         injpos = {
             'ra': inj.longitude,

BayesWaveUtils/setup.py

@@ -27,17 +27,37 @@ def package_files(directory):
             paths.append(os.path.join("..", path, filename))
     return paths
 
+def get_requirements(kind=None):
+    '''
+    '' This helper function is borrowed from Bilby 
+    ''    (https://git.ligo.org/lscsoft/bilby/-/blob/master/setup.py)
+    '''
+    if kind is None:
+        fname = "requirements.txt"
+    else:
+        fname = f"{kind}_requirements.txt"
+    with open(fname, "r") as ff:
+        requirements = ff.readlines()
+    print(requirements)
+    return requirements
+
+
 bayeswave_plot_data = package_files("bayeswave_plot_data") 
 bayeswave_pipe_examples = package_files("bayeswave_pipe_examples") 
 
 setup(
     name='BayesWaveUtils',
     version='0.1dev',
+    author='James Clark, Sophie Hourihane, Meg Millhouse, Sudarhsan Ghonge',
+    author_email="bayeswave@sympa.ligo.org",
+    description='Python tools for BayesWave.',
     packages=['bayeswave_plot','bayeswave_pipe'],
-    scripts=['scripts/megaplot.py', 'scripts/megasky.py', 'scripts/bayeswave_pipe'],
+    scripts=['scripts/megaplot.py', 'scripts/megasky.py', 'scripts/bayeswave_pipe', 'scripts/delete_corruption.py', 'scripts/cp_files.py'],
     package_data = {'bayeswave_plot': bayeswave_plot_data, 
         'bayeswave_pipe': bayeswave_pipe_examples},
     license='GPL',
     long_description=open('README.md').read(),
+    url='https://git.ligo.org/lscsoft/bayeswave/',
+
     )
 

BayesWaveUtils/wavelet_params/get_wavelet_params.py

@@ -68,7 +68,7 @@ def get_wavelet_params(filename, model, chirpflag=False, O1version=False, **keyw
 
     return data
 
-def wt(wave_params,psdfile):
+def wt(wave_params,psdfile, ID):
   
   """
   Makes a waveform from a set of wavelets
@@ -79,6 +79,8 @@ def wt(wave_params,psdfile):
 
   psdfile (str): data file of the PSD
 
+  ID (int): the number of the sample for the suggested reconstruction
+
   outputs
   -------
   array of the waveform
@@ -98,19 +100,18 @@ def wt(wave_params,psdfile):
 
   fmin = int(psd[0,0]*Tobs)
 
-
-  wavenumber = wave_params['D'][0]
+  wavenumber = wave_params['D'][ID]
+  # the starting index in order to export the right set of parameters
+  start = sum(wave_params['D'][:ID])
 
   for j in range(0,wavenumber):
 
-      t0 = wave_params['t'][j]
-      f0 = wave_params['f'][j]
-      Q = wave_params['Q'][j]
-      A = wave_params['A'][j]
-      phi0 = wave_params['phi'][j]
+      t0 = wave_params['t'][j+start]
+      f0 = wave_params['f'][j+start]
+      Q = wave_params['Q'][j+start]
+      A = wave_params['A'][j+start]
+      phi0 = wave_params['phi'][j+start]
       
-      i = int(f0*Tobs)
-      fac = 1.0/math.sqrt(psd[i-fmin,1])
       
       tau = Q/(2*np.pi*f0)
       
@@ -125,7 +126,6 @@ def wt(wave_params,psdfile):
       for i in range(imin,imax):
           t = float(i)/Nsamp*Tobs
           sf = A*np.exp(-((t-t0)**2)/(tau**2))
-          sf *= fac
           hs[i] += sf*np.cos(2*np.pi*f0*(t-t0)+phi0)
 
   return hs

CMakeLists.txt

 #
 # CMake packaging for BayesWave
-# Copyright 2019  James Alexander Clark <james.clark@ligo.org>
+# Copyright 2019  James Alexander Clark <james.clark@ligo.org>, Meg Millhouse <meg.millhouse@ligo.org>
 # Based on CMake packaging for libframe & Frv by Duncan MacLeod <duncan.macleod@ligo.org>
 #
 
@@ -10,7 +10,7 @@ cmake_minimum_required(VERSION 3.12.0 FATAL_ERROR)
 project(
     bayeswave
     LANGUAGES C
-    VERSION 1.0.6
+    VERSION 1.1.1
     DESCRIPTION "LIGO/VIRGO burst analysis algorithm"
     HOMEPAGE_URL "https://git.ligo.org/lscsoft/bayeswave"
 )
@@ -19,6 +19,33 @@ include(GNUInstallDirs)
 include(CheckFunctionExists)
 find_package(PkgConfig)
 find_package(Git)
+find_package(OpenMP)
+
+#-----------------------------------------------------------------------
+# Set rpath so you don't have to set $LD_LIBRARY_PATH before running
+# instructions from:
+#   https://gitlab.kitware.com/cmake/community/-/wikis/doc/cmake/RPATH-handling
+
+IF(DEFINED ENV{CONDA_PREFIX})
+    MESSAGE(STATUS "CONDA_PREFIX: --[$ENV{CONDA_PREFIX}]--")
+ELSE()
+    MESSAGE(STATUS "Warning: could not find conda environment")
+ENDIF()
+
+
+# use, i.e. don't skip the full RPATH for the build tree
+set(CMAKE_SKIP_BUILD_RPATH FALSE)
+
+# when building, don't use the install RPATH already
+# (but later on when installing)
+set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE)
+
+# In order to be able to install into a user specified directory, need to make sure the RPATH has the install directory first, and then the conda prefix.
+# So make sure to remove environment rpath, and then explicitly add conda library back in.
+# May be a more elegant way to do this
+set(CMAKE_INSTALL_REMOVE_ENVIRONMENT_RPATH TRUE)
+set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/${CMAKE_INSTALL_LIBDIR}:$ENV{CONDA_PREFIX}/lib") # 
+
 
 # -- build components -------
 
@@ -55,6 +82,7 @@ set(CPACK_SOURCE_IGNORE_FILES
     "\\\\.svn/"
     "\\\\.git"
     "build/"
+    "dist/"
     "CMakeFiles/"
     "CMakeCache.txt"
     "_CPack_Packages/"

README.md

@@ -9,7 +9,9 @@ signals and instrumental glitches, allowing robust, morphology-independent wavef
 See [arXiv:1410.3835](https://arxiv.org/abs/1410.3835) for an introduction to the BayesWave 
 algorithm.
 
-*LSC/Virgo members*: See the [BayesWave wiki](https://wiki.ligo.org/Bursts/BayesWave) for further info
+Work in progress documentation can be found [here](https://ldas-jobs.ligo.caltech.edu/~meg.millhouse/Docs/).
+
+*LSC/Virgo members*: See the [BayesWave wiki](https://wiki.ligo.org/Bursts/BayesWave) for further info.
 
 ## Citations
 

build.sh

 #!/bin/bash
 set -e
 
-INSTALL_PREFIX=$1
+INSTALL_PREFIX=${1:-${CONDA_PREFIX}}
+
+echo ${INSTALL_PREFIX}
 
 # Get cmake
 if hash cmake 2>/dev/null; then

cmake-build-debug/CMakeLists.txt

+#
+# CMake packaging for BayesWave
+# Copyright 2019  James Alexander Clark <james.clark@ligo.org>
+# Based on CMake packaging for libframe & Frv by Duncan MacLeod <duncan.macleod@ligo.org>
+#
+
+# -- package info -----------
+
+cmake_minimum_required(VERSION 3.12.0 FATAL_ERROR)
+project(
+    bayeswave
+    LANGUAGES C
+    VERSION 1.0.6
+    DESCRIPTION "LIGO/VIRGO burst analysis algorithm"
+    HOMEPAGE_URL "https://git.ligo.org/lscsoft/bayeswave"
+)
+
+include(GNUInstallDirs)
+include(CheckFunctionExists)
+find_package(PkgConfig)
+find_package(Git)
+find_package(OpenMP)
+
+# -- build components -------
+
+# C library | enable/disable with -DENABLE_C={yes,no} (default yes)
+add_subdirectory(src)
+#add_subdirectory(doc)
+
+# -- packaging components ---
+
+set(SPEC_IN "${CMAKE_CURRENT_SOURCE_DIR}/bayeswave.spec.in")
+set(SPEC    "${CMAKE_CURRENT_SOURCE_DIR}/bayeswave.spec")
+configure_file(${SPEC_IN} ${SPEC} @ONLY)
+
+# -- build tarball ----------
+#
+# to build a source tarball:
+#
+# mkdir dist
+# pushd dist
+# cmake ..
+# cmake --build . --target package_source
+#
+
+set(CPACK_PACKAGE_MAJOR ${${PROJECT_NAME}_MAJOR_VERSION})
+set(CPACK_PACKAGE_MINOR ${${PROJECT_NAME}_MINOR_VERSION})
+set(CPACK_PACKAGE_PATCH ${${PROJECT_NAME}_PATCH_VERSION})
+set(CPACK_PACKAGE_VERSION "${CPACK_PACKAGE_MAJOR}.${CPACK_PACKAGE_MINOR}.${CPACK_PACKAGE_PATCH}")
+
+set(CPACK_SOURCE_GENERATOR TXZ)
+set(CPACK_SOURCE_PACKAGE_FILE_NAME ${PROJECT_NAME}-${${PROJECT_NAME}_VERSION})
+set(CPACK_SOURCE_IGNORE_FILES
+    "/.*~$/"
+    ".*~$"
+    "\\\\.svn/"
+    "\\\\.git"
+    "build/"
+    "CMakeFiles/"
+    "CMakeCache.txt"
+    "_CPack_Packages/"
+    "\\\\.cmake"
+    "Makefile"
+    "\\\\.deps/"
+    "autom4te.cache/"
+    "\\\\.tar\\\\.xz"
+)
+include(CPack)