diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..84034dfc --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,90 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project Overview + +The `zdm` package is a Python library for Fast Radio Burst (FRB) redshift-dispersion measure (z-DM) calculations. It implements statistical models for analyzing FRB populations, computing likelihoods, and constraining cosmological and FRB population parameters. + +## Build and Installation + +```bash +# Install in development mode +pip install -e .[dev] + +# Install dependencies for external FRB packages +pip install git+https://github.com/FRBs/ne2001.git#egg=ne2001 +pip install git+https://github.com/FRBs/FRB.git#egg=frb +``` + +## Running Tests + +```bash +# Run all tests with pytest +pytest + +# Run tests with tox +tox -e test-alldeps + +# Run a single test file +pytest zdm/tests/test_energetics.py + +# Run a specific test +pytest zdm/tests/test_energetics.py::test_init_gamma +``` + +## Architecture + +### Core Classes + +- **`parameters.State`** ([parameters.py](zdm/parameters.py)): Central configuration object containing all model parameters organized in dataclasses (`CosmoParams`, `FRBDemoParams`, `EnergyParams`, `HostParams`, `MWParams`, etc.). Use `state.update_param_dict(vparams)` to update parameters. + +- **`survey.Survey`** ([survey.py](zdm/survey.py)): Represents an FRB survey with instrument properties, beam patterns, and detected FRB data. Handles efficiency calculations, DM processing, and FRB metadata. + +- **`grid.Grid`** ([grid.py](zdm/grid.py)): Core computational class that builds 2D z-DM grids representing probability distributions of FRB detection rates. Takes a Survey and State, computes thresholds, detection efficiencies, and expected rates across the z-DM plane. + +- **`repeat_grid.repeat_Grid`** ([repeat_grid.py](zdm/repeat_grid.py)): Extension of Grid for handling repeating FRBs with additional rate parameters. + +### Key Modules + +- **`cosmology`** ([cosmology.py](zdm/cosmology.py)): Lambda CDM cosmology calculations including distance measures and volume elements. + +- **`pcosmic`** ([pcosmic.py](zdm/pcosmic.py)): Computes p(DM|z) - the probability distribution of cosmic DM given redshift, implementing the Macquart relation. + +- **`energetics`** ([energetics.py](zdm/energetics.py)): FRB luminosity function implementations using incomplete gamma functions with spline interpolation for efficiency. + +- **`iteration`** ([iteration.py](zdm/iteration.py)): Likelihood calculation routines. Key function `get_log_likelihood(grid, survey)` computes total log-likelihood for a grid given survey data. + +- **`loading`** ([loading.py](zdm/loading.py)): High-level functions to load surveys and initialize states. `set_state()` creates default parameter configurations, `load_CHIME()` loads CHIME survey data. + +- **`MCMC`** ([MCMC.py](zdm/MCMC.py)): MCMC parameter estimation using emcee. `calc_log_posterior()` is the main posterior evaluation function. + +### Data Flow + +1. Initialize a `State` with desired parameters via `loading.set_state()` or `parameters.State()` +2. Load survey data via `Survey` class pointing to survey files in `zdm/data/Surveys/` +3. Build a `Grid` from the survey and state +4. Compute likelihoods using `iteration.get_log_likelihood(grid, survey)` +5. For parameter estimation, use MCMC functions to explore parameter space + +### Data Files + +Survey data and beam patterns are stored in `zdm/data/`: +- `Surveys/`: Survey definition files (ECSV format) for CHIME, ASKAP, Parkes, etc. +- `BeamData/`: Beam response histograms for different telescopes +- `Cube/`: Precomputed parameter grids + +## Console Scripts + +```bash +zdm_build_cube # Build parameter cubes (zdm/scripts/build_cube.py) +zdm_pzdm # Compute p(z|DM) distributions (zdm/scripts/pzdm.py) +``` + +## Dependencies + +Key external dependencies: +- `frb`: FRB utilities from the FRBs organization +- `ne2001`: Galactic electron density model +- `emcee`: MCMC sampling +- `astropy`: Cosmology and astronomical utilities diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300.ecsv new file mode 100644 index 00000000..b6d2408c --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div2.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div2.ecsv new file mode 100644 index 00000000..049f4dc3 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div2.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask_div2.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, +# "TRES": 6.9, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 6.9 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div4.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div4.ecsv new file mode 100644 index 00000000..b73adc92 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div4.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask_div4.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, +# "TRES": 3.4, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 3.4 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div8.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div8.ecsv new file mode 100644 index 00000000..f79ffae0 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_1300_imp2.1_div8.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask_div8.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, +# "TRES": 1.7, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 1.7 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900.ecsv new file mode 100644 index 00000000..ba6ea0cc --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp1.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp1.ecsv new file mode 100644 index 00000000..19b075d2 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp1.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 13.8, "THRESH": 0.309} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.309 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div2.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div2.ecsv new file mode 100644 index 00000000..943b34e6 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div2.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask_div2.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, +# "TRES": 6.9, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 6.9 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div4.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div4.ecsv new file mode 100644 index 00000000..2ad7a67a --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div4.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask_div4.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, +# "TRES": 3.4, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 3.4 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div8.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div8.ecsv new file mode 100644 index 00000000..0b11f845 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.1_div8.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask_div8.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, +# "TRES": 1.7, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 1.7 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div2.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div2.ecsv new file mode 100644 index 00000000..9f91a21e --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div2.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask_div2.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 6.9, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 6.9 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div4.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div4.ecsv new file mode 100644 index 00000000..cc067e82 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div4.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask_div4.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 3.4, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 3.4 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div8.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div8.ecsv new file mode 100644 index 00000000..c639486c --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.2_div8.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask_div8.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 1.7, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 1.7 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div2.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div2.ecsv new file mode 100644 index 00000000..086e21ae --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div2.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 6.9, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 6.9 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div4.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div4.ecsv new file mode 100644 index 00000000..d23a01d7 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div4.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 3.4, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 3.4 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div8.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div8.ecsv new file mode 100644 index 00000000..c29e8ff4 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.3_div8.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 1.7, "THRESH": 0.99} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 1.7 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div2.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div2.ecsv new file mode 100644 index 00000000..abd9ae28 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div2.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 6.9, "THRESH": 0.99, "FRES": 0.167} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 0.167 "" "" 1 27.9 9.5 0.99 6.9 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div4.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div4.ecsv new file mode 100644 index 00000000..bbbeba90 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div4.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 3.4, "THRESH": 0.99, "FRES": 0.167} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 0.167 "" "" 1 27.9 9.5 0.99 3.4 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div8.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div8.ecsv new file mode 100644 index 00000000..22cf6b37 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div8.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 1.7, "THRESH": 0.99, "FRES": 0.167} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 0.167 "" "" 1 27.9 9.5 0.99 1.7 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div8_primary.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div8_primary.ecsv new file mode 100644 index 00000000..ec5984a6 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp2.4_div8_primary.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900_primary", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 1.728, "THRESH": 0.99, "FRES": 0.167} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 0.167 "" "" 1 27.9 9.5 0.99 1.728 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp3.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp3.ecsv new file mode 100644 index 00000000..5a1b953a --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp3.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 0.167 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp4.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp4.ecsv new file mode 100644 index 00000000..3991af8b --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp4.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900_primary", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp_all.ecsv b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp_all.ecsv new file mode 100644 index 00000000..d9d94349 --- /dev/null +++ b/papers/CRACO/ImprovedSurveys/CRAFT_CRACO_900_imp_all.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"BEAM": "CRACO_900_primary", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 1.728, "THRESH": 0.309} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 0.167 "" "" 1 27.9 9.5 0.309 1.728 4.0 "" "" 0.1 + diff --git a/papers/CRACO/Logs/CRACO_13.8ms_zdm.dmgal.altaz.mjd.csv b/papers/CRACO/Logs/CRACO_13.8ms_zdm.dmgal.altaz.mjd.csv new file mode 100644 index 00000000..d23722c0 --- /dev/null +++ b/papers/CRACO/Logs/CRACO_13.8ms_zdm.dmgal.altaz.mjd.csv @@ -0,0 +1,19 @@ +FRB,Voltage,SBID,Beam,Time,scan,DM,RA,Decl,z,SNR_craco,width,width_craco,Gl,Gb,Alt,Az,DM_NE2001,DM_YMW16,mjd +20231230A,,56346,16,2023-12-30 17:00:51.410,00/20231230165827,676.5,07h55m02.63s,08d30m51.80s,0.506,14.3,69.1,2,212.4371146896605,17.97891877866665,53.9263298264164,14.146588958005973,80.50362396240234,97.6689910888672,60308.70892835648 +20240117A,,57570,4,2024-01-17 15:51:39.260,00/20240117154642,881.0,03h35m30.73s,-15d51m08.98s,0.644,14.2,,2,205.01080436620737,-50.39993154457293,36.21989798514117,269.492398050258,35.474143981933594,36.91987991333008,60326.660871064814 +20240203A,,58571,17,2024-02-03 09:44:26.639,00/20240203093553,454.0,00h35m35.42s,15d50m26.65s,0.243,12.2,,1,117.3535873375361,-46.85929113491701,40.1111877832552,325.72875621933207,36.25816345214844,27.870031356811523,60343.40586387731 +20240208A,Y,58855,29,2024-02-08 20:00:53.981,00/20240208194747,260.2,10h36m55.08s,-00d57m15.72s,,10.3,4.3,1,248.39427311155248,47.16666857378167,47.17742992288276,300.7695275151878,38.96824645996094,27.51456069946289,60348.83395811343 +20240210B,,58971,33,2024-02-10 16:31:32.693 ,00/20240210161859,396.3,03h40m55.98s,-41d51m43.60s,0.325,10.1,,7,247.28221000499613,-52.5575874035369,17.890048001096456,231.5760475076721,34.113372802734375,26.36427879333496,60350.68857283565 +20240216A,,59133,32,2024-02-16 17:42:14.158 ,00/20240216174001,311.62,10h12m19.32s,14d02m40.69s,,10.9,,3,224.22729076375333,50.73104237970733,46.84336438903016,338.45375835417354,37.20788192749024,25.882566452026367,60356.737663865744 +20240310A,Y,59891,31,2024-03-10 07:39:08.871 ,00/20240310072445,601.8,01h10m29.27s,-44d26m19.58s,0.127,25.7,4.7,1,291.70592096754683,-72.27189918064167,65.0296582652478,219.43012772064307,30.100830078125,19.824995040893555,60379.31885267361 +20240312A,,59967,22,2024-03-12 06:36:49.824,00/20240312062255,330.0,03h26m29.61s,-54d36m03.54s,0.049,12.2,,7,268.01165096949404,-50.68493697121322,56.482997054493126,153.0531845491635,35.75310134887695,26.72962951660156,60381.27557666667 +20240316A,,60112,9,2024-03-16 21:29:29.392,06/20240316212642,667.0,19h55m51.23s,-65d26m07.42s,,11.5,,1,330.7532816993134,-31.15406482289452,41.99627413815936,156.3959896217338,64.36654663085938,47.882904052734375,60385.895479074075 +20240408A,,61035,8,2024-04-08 16:06:34.682 ,00/20240408154646,520.0,14h39m28.11s,-01d08m58.44s,,11.6,,1,349.88796162182496,51.59355014256265,55.34600428925305,46.81607231483956,30.16599464416504,26.98432731628418,60408.67123474537 +20240615A,,62923,25,2024-06-15 04:35:14.167 ,00/20240615043134,200.96,02h15m51.28s,-14d37m28.19s,0.073,67.0,,1,184.71911880791924,-66.71093379365679,37.36266176141692,271.7833563363435,31.05978202819824,25.47529983520508,60476.19113619213 +20240629A,Y,63380,17,2024-06-29 06:41:53.054,00/20240629063633,534.4,05h34m11.56s,-59d40m25.09s,,17.0,4.6,1,268.38290627337244,-32.83337968297496,42.04902394829363,212.08369409549752,50.80693435668945,50.25376892089844,60490.27908627315 +20240114A,Y,64245,14,2024-07-28 21:03:15.000,00/20240728210315,530.2,21h27m39.82s,04d19m45.84s,,90.5,,1,57.48099757577732,-31.651154757828376,26.57263037913117,290.3860768138203,49.6728630065918,38.81753540039063,60519.87725694444 +20240807A,Y,64415,5,2024-08-07 18:03:05.037 ,00/20240807175219,530.0,01h33m12.66s,-06d59m04.74s,0.21,11.6,5.0,3,151.0373834218158,-67.5478915278965,47.60022525776493,69.3550897371466,31.662885665893555,21.701465606689453,60529.75214163194 +20240826A,,65177,17,2024-08-26 18:48:24.546 ,00/20240826184119,387.0,22h38m27.84s,-30d08m18.23s,0.203,9.6,,0,19.06359304029001,-60.76944779953969,59.765880798780266,255.58766170314144,34.577857971191406,21.78661727905273,60548.783617430556 +20241005A,,66354,3,2024-10-05 11:57:50.099,00/20241005115201,764.6,19h39m12.19s,18d23m33.74s,,28.6,,1,54.67966081291331,-1.768683987037983,42.359308853764695,339.71633761570644,500.8161926269531,509.5898132324219,60588.4984965162 +20241014A,Y,66560,3,2024-10-14 22:08:05.382,00/20241014215659,496.0,03h19m21.47s,-58d19m15.94s,,22.9,2.3,1,273.76857034851463,-49.5888838906299,36.75883470791185,215.69635889120136,36.63035583496094,27.725454330444336,60597.92228451389 +20241027A,Y,67190,32,2024-10-27 19:05:01.536 ,00/20241027185312,320.0,02h24m06.96s,-20d42m12.71s,0.036,13.5,18.2,3,201.0414550879246,-67.9067678235051,50.14735441598143,269.32374450759187,30.4564266204834,25.284761428833008,60610.79515666667 diff --git a/papers/CRACO/Logs/configs.csv b/papers/CRACO/Logs/configs.csv new file mode 100644 index 00000000..c1e93ece --- /dev/null +++ b/papers/CRACO/Logs/configs.csv @@ -0,0 +1,28 @@ +footprint,pitch,fbar,Ttot,Teff,bfactors +closepack36,0.9,871.9907407407406,133.3827995848654,55.20311431592166,0.0037731053707122275 +closepack36,0.9,895.9907407407406,147.15617304808544,67.01859395219616,0.005723011627396993 +closepack36,0.9,943.9907407407406,959.3280934073424,586.7566543325207,0.005641908947673345 +closepack36,0.9,967.9907407407406,115.69741826598936,71.27771573413347,0.005622304890550736 +closepack36,0.9,991.9907407407406,25.41551620284714,6.7739871149550135,0.005626505885804575 +closepack36,0.9,1015.9907407407406,1.6927948799999997,0.668027325036817,0.005655854888775091 +closepack36,0.9,1295.9907407407406,95.41779457375038,22.92445732583923,0.005649859108163827 +closepack36,0.9,1367.9907407407406,26.333675519999996,9.210186330159706,0.004878491960906488 +closepack36,0.9,1391.9907407407406,0.0884736,0.0335790071273974,0.004605062214219177 +square_6x6,0.9,1247.9907407407406,9.818603536552844,0.2654696510787416,0.006173456942363615 +square_6x6,0.9,1295.9907407407406,107.04912389278248,42.57796021535586,0.005809831361325069 +square_6x6,0.9,1319.9907407407406,79.0560768,41.03431720760534,0.005563450053741847 +square_6x6,0.9,1343.9907407407406,0.02752512,0.013668886780754,0.005290686533634565 +square_6x6,0.9,1367.9907407407406,215.80578815999996,102.89281485500076,0.0050058732420725375 +square_6x6,0.9,1391.9907407407406,21.73599744000001,8.493462050861021,0.004721822885657397 +square_6x6,1.05,783.9907407407406,8.884715534978431,4.770632121717766,0.005954054290638123 +square_6x6,1.05,803.9907407407406,2.86752768,1.2777744796078987,0.00616244453545766 +square_6x6,1.05,807.9907407407406,90.96757263335908,57.53456811026801,0.006193068398545405 +square_6x6,1.05,815.9907407407406,61.91579146438168,33.34769662750901,0.006245519067227571 +square_6x6,1.05,839.9907407407406,114.00216595219229,60.43401962925271,0.00633323463187037 +square_6x6,1.05,855.9907407407406,44.97309696341232,33.582278385887214,0.0063458871918863555 +square_6x6,1.05,879.9907407407406,171.54883583999998,114.51053915240712,0.006322290079615022 +square_6x6,1.05,887.9907407407406,473.2619986853123,289.5395008602978,0.006307197397418742 +square_6x6,1.05,911.9907407407406,248.25446408733168,162.5343074318601,0.006252554834112624 +square_6x6,1.05,927.9907407407406,0.4708761599999999,0.1158861157654993,0.006217028281193489 +square_6x6,1.05,935.9907407407406,0.7697203212976449,0.1555878692011555,0.006200822904753574 +square_6x6,1.05,959.9907407407406,15.84365568,7.718796695003251,0.006163997889180536 diff --git a/papers/CRACO/Logs/craco_13ms_survey_db.weight.altaz.csv b/papers/CRACO/Logs/craco_13ms_survey_db.weight.altaz.csv new file mode 100644 index 00000000..9868155f --- /dev/null +++ b/papers/CRACO/Logs/craco_13ms_survey_db.weight.altaz.csv @@ -0,0 +1,23076 @@ +sbid,scan,nbeam,fch1,foff,nchans,nsamples,tsamp,tstart,srcra,srcdec,goodfrac,nant,ndm,trigger_threshold,pointra,pointdec,footprint,pitch,obsfreq,maxwidth,fmin,fmax,maxdm,obsconfig,w_bandwidth,w_goodfrac,w_nant,tobs,w_tot,t_eff,fbar,bfactors,alt,az +55063,00/20231129045710,36,799.9907407407406,1.0,144.0,39424.0,0.0138240000233054,60277.206962471166,255.74291942631285,-46.24902965937981,0.9998661483246732,24.0,100.0,9.0,255.73476198102236,-46.26925328962947,closepack36,0.9,920.5,8,799.9907407407406,943.9907407407406,756.4612007925376,closepack36_0.9_920MHz,0.5946035575013605,0.9997992292057614,1.0,544.9973769187922,0.5944841784728638,323.9923178874339,871.9907407407406,0.9732016877861472,70.14611015134096,188.03450458384728 +55064,00/20231129050941,36,799.9907407407406,1.0,144.0,39680.0,0.0138240000233054,60277.21560631114,302.008751475491,-63.87009704845884,0.9999197088179776,24.0,100.0,9.0,301.9848572686922,-63.91017706211133,closepack36,0.9,920.5,8,799.9907407407406,943.9907407407406,756.4612007925376,closepack36_0.9_920MHz,0.5946035575013605,0.9998795656445015,1.0,548.5363209247583,0.5945319468051358,326.1223667727233,871.9907407407406,0.9732016877861472,45.080751645242,156.7198776003333 +55234,00/20231208040925,36,743.9907407407406,1.0,288.0,45312.0,0.0138240000233054,60286.17380407107,238.23529166666663,-56.26410071723426,0.9937343802672376,24.0,250.0,9.0,238.23529166666663,-56.30629444444444,square_6x6,1.05,864.5,8,743.9907407407406,1031.9907407407406,959.801401472086,square_6x6_1.05_864MHz,1.0,0.9906163075570575,1.0,626.3930890560143,0.9906163075570575,620.515208959928,887.9907407407406,1.0186933540333152,57.491579370958014,199.06031931345677 +55236,00/20231208063810,36,743.9907407407406,1.0,288.0,46592.0,0.0138240000233054,60286.27705271103,274.90908750000006,-18.853019223347108,0.991440878775562,24.0,250.0,9.0,274.90908749999994,-18.862619444444444,square_6x6,1.05,864.5,8,743.9907407407406,1031.9907407407406,959.801401472086,square_6x6_1.05_864MHz,1.0,0.9871888294375232,1.0,644.0878090858453,0.9871888294375232,635.8362903064345,887.9907407407406,1.0186933540333152,70.64192862456771,289.8859767414167 +55237,00/20231208065121,36,743.9907407407406,1.0,288.0,47616.0,0.0138240000233054,60286.28626103121,277.89473333333336,-12.579412674210232,0.9836991941167678,24.0,250.0,9.0,277.8947333333333,-12.585686111111112,square_6x6,1.05,864.5,8,743.9907407407406,1031.9907407407406,959.801401472086,square_6x6_1.05_864MHz,1.0,0.9756487071575787,1.0,658.2435851097099,0.9756487071575787,642.214502807058,887.9907407407406,1.0186933540333152,66.8028169281726,303.7156189603284 +55257,00/20231208192323,36,743.9907407407406,1.0,288.0,43520.0,0.0138240000233054,60286.80847031091,125.58139166666668,-43.84557552091056,0.9924462682962673,24.0,250.0,9.0,125.58139166666666,-43.87260277777778,square_6x6,1.05,864.5,8,743.9907407407406,1031.9907407407406,959.801401472086,square_6x6_1.05_864MHz,1.0,0.9886908265324493,1.0,601.620481014251,0.9886908265324493,594.8166506328296,887.9907407407406,1.0186933540333152,72.74865738030323,181.07508795529944 +55540,00/20231220080923,36,799.9907407407406,1.0,288.0,60416.0,0.0138240000233054,60298.34039479102,318.85172130971347,-60.284708817998165,0.927277009960234,23.0,250.0,9.0,318.8571416666666,-60.321713888888894,closepack36,0.9,920.5,8,799.9907407407406,1087.9907407407406,1160.2611200536217,closepack36_0.9_920MHz,1.0,0.892923470682849,0.9368252320701606,835.1907854080191,0.8365132376433533,698.6481479515571,943.9907407407406,0.9822652712976302,55.74056798008651,189.0451792554662 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+21480,21480,74400,00/20250611052922,36,799.9907407407406,1.0,288.0,159744.0,0.003456,60837.229605591085,39.271261577459846,-37.23246307936929,0.8383825108309869,22.0,1000.0,10.0,39.272724999999994,-37.24849,closepack36,0.9,920.5,7,799.9907407407406,1087.9907407407406,1160.2611180975764,closepack36_0.9_920MHz,1.0,0.7676501076002639,0.8750385237975271,552.075264,0.6717234169475478,370.84188274629946,943.9907407407406,0.9401793605934324 +21481,21481,74400,00/20250611054825,36,799.9907407407406,1.0,288.0,118528.0,0.003456,60837.24278959109,39.271261577459846,-37.23246307936929,0.842898262312192,22.0,1000.0,10.0,39.272724999999994,-37.24849,closepack36,0.9,920.5,7,799.9907407407406,1087.9907407407406,1160.2611180975764,closepack36_0.9_920MHz,1.0,0.7738606036084256,0.8750385237975271,409.632768,0.67715784020658,277.38604045672304,943.9907407407406,0.9401793605934324 diff --git a/papers/CRACO/README.txt b/papers/CRACO/README.txt new file mode 100644 index 00000000..4a227f2c --- /dev/null +++ b/papers/CRACO/README.txt @@ -0,0 +1,47 @@ +These files relate to the CRACO paper modelling, and for effective +ICS observations for CRAFT repetition modelling. + +They process logs from CRACO observation files (in "Logs/"), and generate necessary inputs to zDM + +The scripts are currently set up to do this for both the 13.8 ms and 3.4ms survey data. + +Please regenerate the data by running through in the following order: + + +#1: get_configs +This routine reads in the log file, updates it with derived data, and re-saves it under Logs. It then looks up unique observing configurations, and prints them to screen + +Produces "Logs/configs.csv" and "Logs/3ms_configs.csv" + +#2: sim_configs +The routine loops over all previously identified configs (above), and generates +CRACO beams for them. + + +#3: weight_configs.py +This loads in beam histograms, and weights them according to weights derived in configs.dat +It then generates final beam patters for CRACO 900 and CRACO 1300 MHz observations + +MANUAL: Copy these to the CRAFT beam directory zdm/data/BeamData/ + +This also outputs the total effective beam sensitivity (\int B^-1.5 d \Omega). + +#4: plot_beams +This simply plots the previously generated beams. It also generates numerous plots of the individual components, and a plot including the primary beamshape only. + + +#5: make_dm_response +Calculates a DM mask, which represents the different limitations of maximum DM over the survey. This should be copied to zdm/data/Efficiencies/ + + +#6 plot_ASKAP_CRACO.py +This script plots the rate of FRB detections for these different surveys + +#7 gen_diagnostics.py +This generates plots of mean frequency etc etc, and averaged weightings +factors over the entire survey + +#8 plot_[900 or 1300]_{alternatives or improvements].py +Calculates total rates, and plots zDM curves, for various alternative configurations of CRACO, in order to evalaute the effect of various inefficiencies or future improvements + + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300.ecsv new file mode 100644 index 00000000..b6d2408c --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms.ecsv new file mode 100644 index 00000000..35509e0e --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 7.75,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_1300", "DMMASK": "3ms_craco_1300_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1372, +# "TRES": 3.456, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 387.0 22:38:27.84 -30:08:18.23 0.203 9.6 -1 0 19.0 -60.76 34.57 21.78 1320 240 diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_alldm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_alldm.ecsv new file mode 100644 index 00000000..0e3638f3 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_alldm.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 7.75,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_1300", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1372, +# "TRES": 3.456, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 387.0 22:38:27.84 -30:08:18.23 0.203 9.6 -1 0 19.0 -60.76 34.57 21.78 1320 240 diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_alldm_zDM.pdf b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_alldm_zDM.pdf new file mode 100644 index 00000000..7ca3e9c4 Binary files /dev/null and b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_alldm_zDM.pdf differ diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_at_1.28_alldm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_at_1.28_alldm.ecsv new file mode 100644 index 00000000..8d7d5341 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_at_1.28_alldm.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 7.75,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_1300", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1372, +# "TRES": 1.28, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 387.0 22:38:27.84 -30:08:18.23 0.203 9.6 -1 0 19.0 -60.76 34.57 21.78 1320 240 diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_at_1.28_alldm_zDM.pdf b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_at_1.28_alldm_zDM.pdf new file mode 100644 index 00000000..c2b84ce5 Binary files /dev/null and b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_at_1.28_alldm_zDM.pdf differ diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_zDM.pdf b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_zDM.pdf new file mode 100644 index 00000000..7ca3e9c4 Binary files /dev/null and b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_3ms_zDM.pdf differ diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_icsdm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_icsdm.ecsv new file mode 100644 index 00000000..8640eeb2 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_icsdm.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "mid_ICS_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_nodm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_nodm.ecsv new file mode 100644 index 00000000..60d23f6f --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_nodm.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_nodm_w1.28.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_nodm_w1.28.ecsv new file mode 100644 index 00000000..112367cf --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_nodm_w1.28.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 12, "MAXWMETH": 1}, +# "telescope": {"BEAM": "CRACO_1300", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 1.28} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 1.28 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_w1.28.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_w1.28.ecsv new file mode 100644 index 00000000..b6f99393 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_1300_w1.28.ecsv @@ -0,0 +1,29 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 12, "MAXWMETH": 1}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 1.28} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 1300.5 1.0 "" "" 1 27.9 9.5 0.99 1.28 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900.ecsv new file mode 100644 index 00000000..ba6ea0cc --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_1.28.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_1.28.ecsv new file mode 100644 index 00000000..92259fbc --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_1.28.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 12, "MAXWMETH": 1}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 1.28} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 1.28 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms.ecsv new file mode 100644 index 00000000..4c63ec65 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 38.2,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_900", "DMMASK": "3ms_craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 935.2, +# "TRES": 3.456, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 676.5 07:55:02.63 08:30:51.80 0.506 14.3 69.1 2 212.4 17.97 35 97.66 888 288 diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_alldm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_alldm.ecsv new file mode 100644 index 00000000..507a20ed --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_alldm.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 38.2,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 935.2, +# "TRES": 3.456, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 676.5 07:55:02.63 08:30:51.80 0.506 14.3 69.1 2 212.4 17.97 35 97.66 888 288 diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_alldm_zDM.pdf b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_alldm_zDM.pdf new file mode 100644 index 00000000..edd5a915 Binary files /dev/null and b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_alldm_zDM.pdf differ diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_at_1.28_alldm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_at_1.28_alldm.ecsv new file mode 100644 index 00000000..69053160 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_at_1.28_alldm.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 38.2,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 935.2, +# "TRES": 1.28, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 676.5 07:55:02.63 08:30:51.80 0.506 14.3 69.1 2 212.4 17.97 35 97.66 888 288 diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_zDM.pdf b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_zDM.pdf new file mode 100644 index 00000000..edd5a915 Binary files /dev/null and b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_3ms_zDM.pdf differ diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_alldm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_alldm.ecsv new file mode 100644 index 00000000..98cd1600 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_alldm.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_alldm_1.28.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_alldm_1.28.ecsv new file mode 100644 index 00000000..579f5676 --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_alldm_1.28.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 12, "MAXWMETH": 1}, +# "telescope": {"BEAM": "CRACO_900", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 1.28} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 1.28 4.0 "" "" 0.1 + diff --git a/papers/CRACO/TestSurveys/CRAFT_CRACO_900_icsdm.ecsv b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_icsdm.ecsv new file mode 100644 index 00000000..8bdf414b --- /dev/null +++ b/papers/CRACO/TestSurveys/CRAFT_CRACO_900_icsdm.ecsv @@ -0,0 +1,28 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: BW, datatype: float64} +# - {name: DM, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: FRES, datatype: float64} +# - {name: Gb, datatype: string, subtype: 'float64[null]'} +# - {name: Gl, datatype: string, subtype: 'float64[null]'} +# - {name: NREP, datatype: int64} +# - {name: SNR, datatype: float64} +# - {name: SNRTHRESH, datatype: float64} +# - {name: THRESH, datatype: float64} +# - {name: TRES, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XDec, datatype: string, subtype: 'float64[null]'} +# - {name: XRA, datatype: string, subtype: 'float64[null]'} +# - {name: Z, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "low_ICS_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, "TRES": 13.8} }'} +# schema: astropy-2.0 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 288.0 401.4 35.0 906 1.0 "" "" 1 27.9 9.5 0.99 13.8 4.0 "" "" 0.1 + diff --git a/papers/CRACO/gen_diagnostics.py b/papers/CRACO/gen_diagnostics.py new file mode 100644 index 00000000..af3ce6d1 --- /dev/null +++ b/papers/CRACO/gen_diagnostics.py @@ -0,0 +1,351 @@ +""" +This file prints average weighting factors for CRACO observations + +It also plots a whole bunch of diagnostic plots +""" + +import numpy as np +import pandas as pd +from matplotlib import pyplot as plt +from zdm import misc_functions as mf +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +def main(): + """ + main file, to iterate over different CRACO surveys + """ + + #13.8ms survey + print_metrics("Logs/craco_13ms_survey_db.weight.altaz.csv",frblog="Logs/CRACO_13.8ms_zdm.dmgal.altaz.mjd.csv") + + #3.4ms survey + print_metrics("Logs/craco_3ms_survey_db.csv",prefix="3ms_") + +def print_metrics(logfile,prefix="",frblog=None): + """ + Prints metrics for given logfile. + + Args: + logfile [string]: name of observation logfile. + prefix [string]: prefix to append to outouts + frblog [string or None]: if present, generate a cumulative plot + of FRB detection rates vs observation time + """ + + df = pd.read_csv(logfile) + print(df.columns) + + LOW = np.where(df["fbar"] < 1000)[0] + HIGH = np.where(df["fbar"] > 1000)[0] + + # prints some characteristic values + print_mean_values(df,LOW,HIGH) + + # produces some basic plots + do_basic_plots(df,LOW,HIGH,prefix) + + # plots example of sampling time effet + plot_tsamp() + + if frblog is not None: + frbs = pd.read_csv(frblog) + + # produces plot of cumulative effective and normal time vs detected FRBs + plot_cumulative(df,LOW,HIGH,frbs,ks=True) + + # load_frbs + match_values(df,frbs) + +def match_values(df,frbs): + """ + For each frb, get slices corresponding to which observation they were found in + + Args: + df: pandas dataframe containing logfile info + frbs: pandas dataframe containing observed FRB info + """ + scans = frbs["scan"] + for i,scan in enumerate(scans): + j = np.where(df["scan"] == scan)[0] + fbar = df["fbar"][j].to_string(index=False, header=False) + nchan = df["nchans"][j].to_string(index=False, header=False) + print(i,fbar,nchan) + + + +def print_mean_values(df,LOW,HIGH): + """ + Prints mean values of various quantities + + args: + df: pandas dataframe containing logfile info + LOW [list]: indices of data frame corresponding to 900 MHz sample + HIGH [list]: indices of data frame corresponding to 1300 MHz sample + """ + + + Ttot = np.sum(df["tobs"]) + LTtot = np.sum(df["tobs"][LOW]) + HTtot = np.sum(df["tobs"][HIGH]) + LTeff = np.sum(df["t_eff"][LOW]) + HTeff = np.sum(df["t_eff"][HIGH]) + print("Total time is ",Ttot/3600," with ",LTtot/3600," at low, and ",HTtot/3600," at high") + print("Effective time includes bandwidth, Nant, and gf; NOT beam, DM, or width") + print("Total effective time is ",LTeff/3600," at low, and ",HTeff/3600," at high") + print("Mean high frequency is ",np.sum(df["tobs"][HIGH]*df["fbar"][HIGH])/HTtot) + print("Mean low frequency is ",np.sum(df["tobs"][LOW]*df["fbar"][LOW])/LTtot,"\n\n\n") + + + # on average, we have lost 0.913 due to bandwidth + print("mean bandwidth ",np.sum(df['nchans']*df["tobs"])/Ttot) + print("Mean bandwidth loss ",np.sum(df['w_bandwidth']*df["tobs"])/Ttot) + print("Mean HIGH bandwidth loss ",np.sum(df['w_bandwidth'][HIGH]*df["tobs"][HIGH])/HTtot) + print("Mean LOW bandwidth loss ",np.sum(df['w_bandwidth'][LOW]*df["tobs"][LOW])/LTtot,"\n\n") + + + # then we lose 70% due to "goodfrac" + print("Mean goodfrac ",np.sum(df['goodfrac']*df["tobs"])/Ttot) + print("Mean gf loss ",np.sum(df['w_goodfrac']*df["tobs"])/Ttot) + print("Optimal gf loss ",np.sum(df['goodfrac']**0.75*df["tobs"])/Ttot) + print("Mean LOW gf loss ",np.sum(df['w_goodfrac'][LOW]*df["tobs"][LOW])/LTtot) + print("Mean HIGH gf loss ",np.sum(df['w_goodfrac'][HIGH]*df["tobs"][HIGH])/HTtot,"\n\n") + + # then we lose 91% due to less than 25 antennas + print("Max number of antennas is ",np.max(df['nant'])) + print("Mean antennas ",np.sum(df['nant']*df["tobs"])/Ttot) + print("Mean nant loss ",np.sum(df['w_nant']*df["tobs"])/Ttot) + print("Low nant loss ",np.sum(df['w_nant'][LOW]*df["tobs"][LOW])/LTtot) + print("High nant loss ",np.sum(df['w_nant'][HIGH]*df["tobs"][HIGH])/HTtot,"\n\n") + + + + +def plot_cumulative(df,LOW,HIGH,frbs,ks=True): + """ + Generates some cumulative plots + + args: + df: pandas dataframe containing logfile info + LOW [list]: indices of data frame corresponding to 900 MHz sample + HIGH [list]: indices of data frame corresponding to 1300 MHz sample + frbs: pandas dataframe giving FRB info + ks [bool]: if True, perform a ks test on the two distributions + for consistency + """ + + teff = df["t_eff"]*df["bfactors"] + + ctraw = np.cumsum(df["tobs"]/3600) + Lctraw = np.cumsum(df["tobs"][LOW]/3600) + Hctraw = np.cumsum(df["tobs"][HIGH]/3600) + + cteff = np.cumsum(teff/3600) + Lcteff = np.cumsum(teff[LOW]/3600) + Hcteff = np.cumsum(teff[HIGH]/3600) + + frbxs,frbys=mf.make_cum_dist(frbs["mjd"]) + frbys *= len(frbs["mjd"]) + + + if ks: + from scipy.stats import kstest + from scipy.interpolate import interp1d + rvs = frbs["mjd"] + cum_func = interp1d(df["tstart"],cteff/cteff.values[-1],kind="linear",assume_sorted=True) + result = kstest(rvs,cum_func,mode="exact",alternative="two-sided") + print(result) + + plt.figure() + + #plt.plot(df["tstart"],ctraw) + plt.plot(df["tstart"],cteff,label="Total") + plt.plot(df["tstart"][LOW],Lcteff,label="900 MHz",linestyle="--") + plt.plot(df["tstart"][HIGH],Hcteff,label="1300 MHz",linestyle=":") + + ax = plt.gca() + # FRBs + ax2 = ax.twinx() + ax2.plot(frbxs,frbys,linestyle="-.",color="black") + ax2.set_ylabel("$N_{\\rm FRB}$") + plt.ylim(0,18) + + plt.sca(ax) + + # does a dummy plot + plt.plot([-1e9,-1e8],[-100,-100],linestyle="-.",color="black",label="CRACO FRBs") + plt.xlim(60280,60650) + plt.ylim(0,cteff.values[-1]) + plt.xlabel("mjd") + plt.ylabel("Cumulative $T_{\\rm eff}$ [hr]") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/eff_cumulative_fig.png") + plt.close() + + plt.figure() + + #plt.plot(df["tstart"],ctraw) + plt.plot(df["tstart"],ctraw,label="Total") + plt.plot(df["tstart"][LOW],Lctraw,label="900 MHz",linestyle="--") + plt.plot(df["tstart"][HIGH],Hctraw,label="1300 MHz",linestyle=":") + + # FRBs + ax = plt.gca() + ax2 = ax.twinx() + ax2.plot(frbxs,frbys) + ax2.set_ylabel("$N_{\\rm FRB}$") + plt.ylim(0,18) + + plt.sca(ax) + plt.ylim(0,ctraw.values[-1]) + plt.xlabel("mjd") + plt.ylabel("Cumulative $T_{\\rm obs}$ [hr]") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/raw_cumulative_fig.png") + plt.close() + +def do_basic_plots(df,LOW,HIGH,prefix): + """ + Produces basic plots + + Args: + df: pandas dataframe containing info + LOW: indices corresponding to low frequencies + HIGH: indices corresponding to high frequencies + prefix [string]: prefix for outputs + + """ + + #### Number of antennas #### + plt.figure() + plt.xlabel("Number of antennas") + plt.ylabel("Total obs time [hr]") + bins = np.linspace(0.5,36.5,37) + plt.hist(df["nant"],bins=bins,weights=df["tobs"]/3600) + plt.xlim(14,26) + plt.tight_layout() + plt.savefig("Plots/"+prefix+"Nant_hist.png") + plt.close() + + #### Bandwidth #### + plt.figure() + plt.xlabel("Bandwidth [MHz]") + plt.ylabel("Total obs time [hr]") + bins = np.linspace(5,345,35) + + + plt.hist(df["nchans"],bins=bins,weights=df["tobs"]/3600) + #plt.xlim(14,26) + plt.tight_layout() + plt.savefig("Plots/"+prefix+"bw_hist.png") + plt.close() + + ##### central frequency ### + plt.figure() + plt.xlabel("Central frequency [MHz]") + plt.ylabel("Total obs time [hr]") + bins = np.linspace(0,2000,201) + plt.hist(df["fbar"],bins=bins,weights=df["tobs"]/3600) + plt.xlim(750,1500) + plt.tight_layout() + plt.savefig("Plots/"+prefix+"Fbar_hist.png") + plt.close() + + + + + ##### good fraction ###### + plt.figure() + plt.xlabel("Fraction of good data") + plt.ylabel("Total obs time [hr]") + bins = np.linspace(0,1,101) + plt.hist(df["goodfrac"],bins=bins,weights=df["nsamples"]*df["tsamp"]/3600) + #plt.xlim(14,26) + plt.tight_layout() + plt.savefig("Plots/"+prefix+"gf_hist.png") + plt.close() + + plt.figure() + plt.xlabel("Fraction of good data") + plt.ylabel("Total obs time [hr]") + bins = np.linspace(0,1,101) + plt.hist(df["goodfrac"][LOW],bins=bins,weights=df["nsamples"][LOW]*df["tsamp"][LOW]/3600,label="900 MHz") + plt.hist(df["goodfrac"][HIGH],bins=bins,weights=df["nsamples"][HIGH]*df["tsamp"][HIGH]/3600,label="1300 MHz") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/"+prefix+"gf_hist_by_freq.png") + plt.close() + + + +def plot_tsamp(): + """ + Produces a dummy plot of the sampling time effect + """ + + NW=61 + wvals = np.logspace(-2,3,NW) + ICSthresh = 4.4 + ICS = np.full([NW],4.4*(1.28**0.5)) + CRACOthresh = ICSthresh * 25**0.5 / (24*23)**0.5 * (336/288)**0.5 + print("CRACO thresh is ",CRACOthresh) # why 0.99? + CRACO = np.full([NW],CRACOthresh*(13.8**0.5)) + + + OK = np.where(wvals > 1.28)[0] + ICS[OK] *= (wvals[OK]/1.28)**0.5 + BAD = np.where(wvals > 1.28*12.5)[0] + ICS[BAD] *= 1000 + + OK = np.where(wvals > 13.8)[0] + CRACO[OK] *= (wvals[OK]/13.8)**0.5 + OK = np.where(wvals > 13.8*8)[0] + CRACO[OK] *= (wvals[OK]/(13.8*8))**0.5 + + + plt.figure() + plt.xlabel("FRB effective width [ms]") + plt.ylabel("$F_{\\rm th}$ [Jy ms]") + plt.ylim(1,50) + plt.plot(wvals,ICS,label="ICS",linestyle="--") + c1 = plt.gca().lines[-1].get_color() + plt.plot(wvals,CRACO,label="CRACO") + c2 = plt.gca().lines[-1].get_color() + + + plt.plot([13.8,13.8],[0.1,CRACO[0]],color="black",linestyle=":") + plt.plot([1.28,1.28],[0.1,ICS[0]],color="black",linestyle=":") + + plt.plot([13.8*8,13.8*8],[0.1,CRACO[0]*2.83],color="black",linestyle=":") + plt.plot([1.28*12,1.28*12],[0.1,ICS[0]*3.46],color="black",linestyle=":") + + plt.text(2,5.5,"$F_{\\rm th} \\sim w^{0.5}$",rotation=45,color=c1,fontsize=12) + plt.text(25,3.9,"$F_{\\rm th} \\sim w^{0.5}$",rotation=45,color=c2,fontsize=12) + plt.text(200,15,"$F_{\\rm th} \\sim w$",rotation=66,color=c2,fontsize=12) + + + plt.text(0.8,1.2,"$t_{\\rm obs} = 1.28$ ms",rotation=90,color=c1,fontsize=12) + plt.text(9,1.2,"$t_{\\rm obs} = 13.8$ ms",rotation=90,color=c2,fontsize=12) + + + plt.text(18,1.4,"$12 \\times t_{\\rm obs}$",rotation=90,color=c1,fontsize=12) + plt.text(120,2,"$8 \\times t_{\\rm obs}$",rotation=90,color=c2,fontsize=12) + + plt.xscale("log") + plt.yscale("log") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/width_threshold_sketch.png") + plt.close() + + +main() diff --git a/papers/CRACO/get_configs.py b/papers/CRACO/get_configs.py new file mode 100644 index 00000000..a4db6bee --- /dev/null +++ b/papers/CRACO/get_configs.py @@ -0,0 +1,162 @@ + + +import numpy as np +import pandas as pd +import os +from matplotlib import pyplot as plt + +def main(): + """ + Reads in CRACO log file, extracts unique configurations, + produces observation time for each unique configuration + """ + infile = "Logs/craco_13ms_survey_db.weight.altaz.csv" + df = read_logfile(infile = infile) + + # adds statistical weighting factors + # turn on if extra info is not yet generated + df = add_columns(df,infile) + + # gets unique configurations + logfile="Logs/configs.csv" + if not os.path.exists(logfile): + get_unique(df,logfile) + + + ##### 3.4ms survey #### + + infile = "Logs/craco_3ms_survey_db.csv" + df = read_logfile(infile = infile) + + # adds statistical weighting factors + # turn on if extra info is not yet generated + df = add_columns(df,infile) + + # gets unique configurations + # gets unique configurations + logfile="Logs/3ms_configs.csv" + if not os.path.exists(logfile): + get_unique(df,logfile) + + + +def add_columns(df,opfile): + """ + Adds statistical weighting factors and other derived data + Args: + df: pandas dataframe containing observation info + opfile [string]: name of the output logfile + """ + + + # sens is proportional to square root of bandwidth + # rate goes as ^1.5. Hence, power of 0.75 + maxchan = np.max(df["nchans"]) + print("maxchan is ",maxchan) + print("Max channels is ",maxchan) + w_bw = (df["nchans"]/maxchan)**0.75 + df["w_bandwidth"] = w_bw + + # just go linearly with this + w_gf = df["goodfrac"]**1.5 # we lose signal linearly with bandwidth + df["w_goodfrac"] = w_gf + + # sensitivity to the power of 1.5 + maxant = np.max(df["nant"]) + sense = (df["nant"]*(df["nant"]-1))**0.5 + maxsense = (maxant*(maxant-1))**0.5 + w_nant = (sense/maxsense)**1.5 + df["w_nant"] = w_nant + + # observation time + df["tobs"] = df["nsamples"] * df["tsamp"] + + # total weight including all factors + df["w_tot"] = df["w_nant"] * df["w_goodfrac"] * df["w_bandwidth"] + + # total effective time (seconds) + df["t_eff"] = df["tobs"] * df["w_tot"] + + + df["fbar"] = (df["fmin"] + df["fmax"])/2. + + df.to_csv(opfile,index=False) + return df + +def read_logfile(infile = "Logs/craco_13ms_survey_db.csv"): + """ + read logfile + Args: + infile [string]: name of logfile + + Returns: + df: pandas dataframe containing log info + """ + + df = pd.read_csv(infile) + + #print(df.columns) + return df + +def get_unique(df,opfile): + """ + gets unique combinations of footprint, frequency, and pitch + + Args: + df: pandas dataframe containing log info + opfile: name of output file + """ + + footprints = np.unique(df["footprint"]) + + + config_fp=[] + config_pitch=[] + config_fb=[] + config_total=[] + config_wtotal=[] + + for fp in footprints: + OK = np.where(df["footprint"] == fp)[0] + fppitch = df["pitch"][OK] + pitches = np.unique(fppitch) + for pitch in pitches: + OK2 = np.where(fppitch[OK]==pitch) + # all original indices matching this + indices2 = OK[OK2] + fp_p_fbar = df["fbar"][indices2] + + fbars = np.unique(fp_p_fbar) + for fb in fbars: + OK3 = np.where(fp_p_fbar == fb)[0] + indices3 = indices2[OK3] + fp_p_fb_tobs = df["tobs"][indices3] + fp_p_fb_Nant = df["nant"][indices3] + weights = df["w_tot"][indices3] + + # normalising sensitivity according to Nant + # relative to Nant = 24, which is max in data + total = np.sum(fp_p_fb_tobs)/3600 + wtotal = np.sum(fp_p_fb_tobs*weights)/3600 + print(fp,pitch,fb,total,wtotal) + config_fp.append(fp) + config_pitch.append(pitch) + config_fb.append(fb) + config_total.append(total) + config_wtotal.append(wtotal) + fp = np.array(config_fp) + pitch = np.array(config_pitch) + fb = np.array(config_fb) + total = np.array(config_total) + wtotal = np.array(config_wtotal) + data = {"footprint": fp, + "pitch": pitch, + "fbar": fb, + "Ttot": total, + "Teff": wtotal} + + df = pd.DataFrame(data) + df.to_csv(opfile,index=False) + + +main() diff --git a/papers/CRACO/make_dm_response.py b/papers/CRACO/make_dm_response.py new file mode 100644 index 00000000..148ebf5f --- /dev/null +++ b/papers/CRACO/make_dm_response.py @@ -0,0 +1,152 @@ +""" +Generates mean DM response curves for each of the CRACO observations +These are single curves giving the average max DM observed + +Saves these as "masks" in zDM, and creates plots of said masks +""" + +import numpy as np +import pandas as pd +from matplotlib import pyplot as plt +import importlib.resources as resources +import os + +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + + +def main(): + """ + A wrapper around analyseDM, just to run it for both + 3.4ms and 13.8ms surveys + """ + + analyseDM("craco_13ms_survey_db.weight.altaz.csv") + analyseDM("craco_3ms_survey_db.csv",prefix="3ms_") + +def analyseDM(logfile,prefix=""): + """ + Main program to run masks and plotting for + - low and mid frequencies + - raw and effective observation time + + Args: + logfile [string]: name of logfile + prefix [string]: prefix for output + """ + + #logfile="craco_13ms_survey_db.weight.altaz.csv" + #logfile="craco_3ms_survey_db.csv" + + df = pd.read_csv("Logs/"+logfile) + + fcut = 1100 # threshold frequency between low and high + low = np.where(df["fbar"] < fcut)[0] + mid = np.where(df["fbar"] >= fcut)[0] + + + savedir = os.path.join(resources.files('zdm'), 'data','Efficiencies') + dmvals = np.linspace(0,10000,1000) + low_max_dm = np.array(df["maxdm"][low]) + mid_max_dm = np.array(df["maxdm"][mid]) + teffs = np.array(df["t_eff"]*df["bfactors"])/3600. + lteffs=teffs[low] + mteffs=teffs[mid] + + # effective observation times + l_max_dm_sorted,l_cum_teffs_sorted = make_dm_mask(dmvals,lteffs,low_max_dm,savedir,prefix+'craco_900_mask.npy') + m_max_dm_sorted,m_cum_teffs_sorted = make_dm_mask(dmvals,mteffs,mid_max_dm,savedir,prefix+'craco_1300_mask.npy') + + plot_dm_masks([l_max_dm_sorted,m_max_dm_sorted],[l_cum_teffs_sorted,m_cum_teffs_sorted], + ["900 MHz","1300 MHz"],"Plots/"+prefix+"max_searched_dm.png","$T_{\\rm eff}$ [hr]") + + + # raw observation times + ltraws=np.array(df["tobs"][low])/3600 + mtraws=np.array(df["tobs"][mid])/3600 + l_max_dm_sorted,l_cum_traws_sorted = make_dm_mask(dmvals,ltraws,low_max_dm,savedir,None) + m_max_dm_sorted,m_cum_traws_sorted = make_dm_mask(dmvals,mtraws,mid_max_dm,savedir,None) + + plot_dm_masks([l_max_dm_sorted,m_max_dm_sorted],[l_cum_traws_sorted,m_cum_traws_sorted], + ["900 MHz","1300 MHz"],"Plots/"+prefix+"raw_max_searched_dm.png","$T_{\\rm obs}$ [hr]") + +def make_dm_mask(dmvals,teffs,max_dms,savedir,savename): + """ + Makes a "DM mask" - cumulative distribution of fraction of time + a given DM has been searched. + + Args: + dmvals (np.ndarray): dispersion measure values to create mask at + teffs (np.ndarray): list of effective observations times + max_dms (np.ndarray): list of maximum searched dms corresponding to teffs + savedir: directory to save data in + savename: name of save file + + Returns: + cumulative distribution of max DMs + + """ + + #### orders from highest to lowest ### + order = np.argsort(-max_dms) + max_dm_sorted = max_dms[order] + + teffs_sorted = teffs[order] + cum_teffs_sorted = np.cumsum(teffs_sorted) + + ##### Interpolating and saving ###### + + # increments of 10 in DM + ndm = dmvals.size + dm_mask = np.zeros([2,ndm]) + + # switches to be lowest DM to highest DM + idm_mask = np.interp(dmvals, max_dm_sorted[::-1], cum_teffs_sorted[::-1]) + dm_mask[0,:] = dmvals + dm_mask[1,:] = idm_mask/idm_mask[0] + if savename is not None: + savefile = os.path.join(savedir,savename) + np.save(savefile,dm_mask) + + return max_dm_sorted,cum_teffs_sorted + +def plot_dm_masks(max_dms,cum_teffs_sorteds,labels,savefile,ylabel): + """ + Makes a plot of obsrvation time for each maximum DM + + Args: + max_dms (list of np.ndarray): list of maximum dispersion measures + cum_teffs_sorted (list of np.ndarray): list of effective observation times + labels (list of strings): labels for each item in list when plotted + savefile (string): name of plot file to save + ylabel (string): y axis label + """ + ######## plots the results ####### + + styles=["-","--",":","-."] + + plt.figure() + plt.ylim(0,3000) + plt.xlim(0,5000) + plt.ylabel(ylabel) + plt.xlabel("DM [pc cm$^{-3}$]") + + for i,max_dm_sorted in enumerate(max_dms): + max_dm_sorted = np.concatenate((max_dm_sorted,np.array([0]))) + cum_teffs_sorted = cum_teffs_sorteds[i] + cum_teffs_sorted = np.concatenate((cum_teffs_sorted,np.array([cum_teffs_sorted[-1]]))) + + plt.plot(max_dm_sorted,cum_teffs_sorted,label=labels[i],linestyle = styles[i]) + plt.legend() + plt.tight_layout() + plt.savefig(savefile) + plt.close() + +main() diff --git a/papers/CRACO/plot_1300_alternatives.py b/papers/CRACO/plot_1300_alternatives.py new file mode 100644 index 00000000..de4103d2 --- /dev/null +++ b/papers/CRACO/plot_1300_alternatives.py @@ -0,0 +1,230 @@ +""" +This script plots the resulting redshift and dm distributions +from various alternative hypothertical CRACO setups, +in order to estimate the effects on the system + +This one is limited to 1300 MHz observations + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +def main(): + """ + Main function - it loads in various surveys, and calculates their rates. + """ + # in case you wish to switch to another output directory + + opdir="TestSurveys/" + + # approximate best-fit values from recent analysis + # best-fit from Jordan et al + #state = states.load_state("HoffmannHalo25",scat="CHIME",rep=None) #scat="updated",rep=None) + state = states.load_state("HoffmannHalo25",scat="updated",rep=None) #scat="updated",rep=None) + #state = states.load_state("JamesH022",scat="CHIME",rep=None) #scat="updated",rep=None) + + #check_FE(state) + + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('../papers/CRACO/TestSurveys') + names=['CRAFT_CRACO_1300','CRAFT_CRACO_1300_w1.28','CRAFT_CRACO_1300_nodm', + 'CRAFT_CRACO_1300_3ms','CRAFT_CRACO_1300_3ms_alldm','CRAFT_CRACO_1300_3ms_at_1.28_alldm'] + labels = ["CRACO 1300 MHz","$t_{\\rm samp} = 1.28$ ms","All DMs searched", + "3.4 ms","3.4ms all DM"," at 1.28ms"] + linestyles=["-","-.","--",":","-","-.","--",":","-"] + nz=400 + zmax=4 + ndm=500 + dmmax=5000 + + ss,gs = loading.surveys_and_grids(survey_names=names,repeaters=False, + init_state=state,sdir=sdir, + zmax=zmax,nz=nz,dmmax=dmmax,ndm=ndm) + + ##### prints total relative rates ##### + for i,n in enumerate(names): + print("Total rate for survey ",n," is ",np.sum(gs[i].rates)/np.sum(gs[0].rates)) + + + ######### plots total DM and z distribution ####### + # set limits for plots - will be LARGE! + DMmax=4000 + zmax=4. + + plt.figure() + ax1 = plt.gca() + plt.xlabel("redshift $z$") + plt.ylabel("p(z) [a.u.]") + plt.xlim(0.01,3) + plt.ylim(0,1) + + plt.figure() + ax2 = plt.gca() + plt.xlabel("DM [pc cm$^{-3}$]") + plt.ylabel("p(DM) [a.u.]") + plt.ylim(0,1) + plt.xlim(0,4000) + + zvals = gs[0].zvals + dz = zvals[1]-zvals[0] + dmvals = gs[0].dmvals + ddm = dmvals[1]-dmvals[0] + + pzs=[] + pdms=[] + # chooses the first arbitrarily to extract zvals etc from + for i,g in enumerate(gs): + + s=ss[i] + g=gs[i] + name = names[i] + figures.plot_grid(gs[i].rates,g.zvals,g.dmvals, + name=opdir+name+"_zDM.pdf",norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + project=False,ylabel='${\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host}$', + zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5]) + + rates = gs[i].rates * 10**g.state.FRBdemo.lC + rate = np.sum(rates) + print("Relative rate for ",name," is ",rate," per day") + pz = np.sum(rates,axis=1) + pz /= dz + + pdm = np.sum(rates,axis=0) + pdm /= ddm + + pzs.append(pz) + pdms.append(pdm) + + znorm = np.max(pzs[1]) + dmnorm = np.max(pdms[1]) + + for i,g in enumerate(gs): + pz = pzs[i]/znorm + pdm = pdms[i]/dmnorm + + plt.sca(ax1) + plt.plot(zvals,pz,label=labels[i],linestyle=linestyles[i]) + + if i==1: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO1300_zcomparison1.png") + if i==2: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO1300_zcomparison2.png") + + plt.sca(ax2) + plt.plot(dmvals,pdm,label=labels[i],linestyle=linestyles[i]) + + if i==1: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO1300_dmcomparison1.png") + if i==2: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO1300_dmcomparison2.png") + + plt.sca(ax1) + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO1300_zcomparison.png") + plt.close() + + plt.sca(ax2) + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO1300_dmcomparison.png") + plt.close() + +def plot_efficiencies(gs,ss): + """ + Makes some efficiency plots + + Args: + gs [list of grids]: grid objects to plot + ss [ list of surveys]: survey objects to plot + """ + ###### plots efficiencies ###### + plt.figure() + for i,s in enumerate(ss): + + for j in np.arange(s.NWbins): + if j==0: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],label=labels[i]) + else: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color()) + plt.xlabel("DM") + plt.ylabel("Efficiency") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/efficiency.png") + plt.close() + + + ##### Plots an example of the threshold ###### + plt.figure() + for i,g in enumerate(gs): + print("Survey weights are ",ss[i].wlist,ss[i].wplist) + for j in np.arange(g.nthresh): + if j==0: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],label=labels[i],linewidth=0.2) + else: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color(),linewidth=j) + plt.xlabel("DM") + plt.ylabel("Threshold (erg)") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/g_thresholds.png") + plt.close() + + +def check_FE(state): + """ + Checks FRB rate compared to Fly's Eye rate, which is the most reliable and consistent + + Args: + state: zDM state object, to be used for Fly's Eye rate calculation + """ + ###### Checks normalisation ###### + ss,gs = loading.surveys_and_grids( + survey_names=["CRAFT_class_I_and_II"],repeaters=False,init_state=state) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + rate = np.sum(gs[0].rates) * 10**gs[0].state.FRBdemo.lC * ss[0].TOBS + print("Expected number for Fly's Eys is ",rate," per day") + print("c.f. actual number: ",ss[0].NORM_FRB) + + + +main() diff --git a/papers/CRACO/plot_1300_improvements.py b/papers/CRACO/plot_1300_improvements.py new file mode 100644 index 00000000..a2c3fb24 --- /dev/null +++ b/papers/CRACO/plot_1300_improvements.py @@ -0,0 +1,207 @@ +""" +This script creates zdm grids for ASKAP incoherent sum observations. + +It exists partly to calculate relative rates from surveys + +For CHIME 1.28, it's 2.54 +For updated, it's 1.88 + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +def main(): + + # in case you wish to switch to another output directory + + opdir="ImprovedSurveys/" + + # approximate best-fit values from recent analysis + # best-fit from Jordan et al + state = states.load_state("HoffmannHalo25",scat="updated",rep=None) + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('../papers/CRACO/ImprovedSurveys') + names=['CRAFT_CRACO_1300', + 'CRAFT_CRACO_1300_imp2.1_div2','CRAFT_CRACO_1300_imp2.1_div4','CRAFT_CRACO_1300_imp2.1_div8' + ] + labels = ["CRACO 1300 MHz", + "2.1 $t_{\\rm rs}=6.8$\\,ms","2.1 $t_{\\rm rs}=3.4$\\,ms","2.1 $t_{\\rm rs}=1.7$\\,ms" + ] + linestyles=["-","-.","--",":"] + nz=400 + zmax=4 + ndm=500 + dmmax=5000 + + ss,gs = loading.surveys_and_grids(survey_names=names,repeaters=False, + init_state=state,sdir=sdir, + zmax=zmax,nz=nz,dmmax=dmmax,ndm=ndm) + + + ##### prints total relative rates ##### + for i,n in enumerate(names): + print("Total rate for survey ",labels[i]," is ",np.sum(gs[i].rates)/np.sum(gs[0].rates)) + + + + ######### plots total DM and z distribution ####### + # set limits for plots - will be LARGE! + DMmax=4000 + zmax=4. + + plt.figure() + ax1 = plt.gca() + plt.xlabel("redshift $z$") + plt.ylabel("p(z) [a.u.]") + plt.xlim(0.01,3) + plt.ylim(0,1) + #plt.ylim(0,80) + + plt.figure() + ax2 = plt.gca() + plt.xlabel("DM pc cm$^{-3}$") + plt.ylabel("p(DM) [a.u.]") + plt.xlim(0,3000) + plt.ylim(0,1) + #plt.ylim(0,0.0009) + + zvals = gs[0].zvals + dz = zvals[1]-zvals[0] + dmvals = gs[0].dmvals + ddm = dmvals[1]-dmvals[0] + + pzs=[] + pdms=[] + allrates=[] + # chooses the first arbitrarily to extract zvals etc from + for i,g in enumerate(gs): + + s=ss[i] + g=gs[i] + name = names[i] + #figures.plot_grid(gs[i].rates,g.zvals,g.dmvals, + # name=opdir+name+"_zDM.pdf",norm=3,log=True, + # label='$\\log_{10} p({\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + # project=False,ylabel='${\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host}$', + # zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5]) + + rates = gs[i].get_rates() #gs[i].rates * 10**g.state.FRBdemo.lC + rate = np.sum(rates) + allrates.append(rate) + pz = np.sum(rates,axis=1) + pz /= dz + + pdm = np.sum(rates,axis=0) + pdm /= ddm + + pzs.append(pz) + pdms.append(pdm) + + for i,g in enumerate(gs): + pz = pzs[i]/np.max(pzs[1]) + pdm = pdms[i]/np.max(pdms[1]) + + print("Relative rate for ",names[i]," is ",allrates[i]/allrates[0]," per day") + + plt.sca(ax1) + plt.plot(zvals,pz,label=labels[i],linestyle=linestyles[i]) + + plt.sca(ax2) + plt.plot(dmvals,pdm,label=labels[i],linestyle=linestyles[i]) + + + + plt.sca(ax1) + plt.legend(fontsize=6) + plt.tight_layout() + plt.savefig("ImprovedSurveys/improved_1300_zs.png") + plt.close() + + plt.sca(ax2) + plt.legend(fontsize=6) + plt.tight_layout() + plt.savefig("ImprovedSurveys/improved_1300_dms.png") + plt.close() + +def plot_efficiencies(gs,ss): + """ + Does some efficiency plots + """ + ###### plots efficiencies ###### + plt.figure() + for i,s in enumerate(ss): + + for j in np.arange(s.NWbins): + if j==0: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],label=labels[i]) + else: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color()) + plt.xlabel("DM") + plt.ylabel("Efficiency") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/efficiency.png") + plt.close() + + + ##### Plots an example of the threshold ###### + plt.figure() + for i,g in enumerate(gs): + print("Survey weights are ",ss[i].wlist,ss[i].wplist) + for j in np.arange(g.nthresh): + if j==0: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],label=labels[i],linewidth=0.2) + else: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color(),linewidth=j) + plt.xlabel("DM") + plt.ylabel("Threshold (erg)") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/g_thresholds.png") + plt.close() + + +def check_FE(state): + """ + Checks FRB rate compared to Fly's Eye rate, which is the most reliable and consistent + """ + ###### Checks normalisation ###### + ss,gs = loading.surveys_and_grids( + survey_names=["CRAFT_class_I_and_II"],repeaters=False,init_state=state) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + rate = np.sum(gs[0].rates) * 10**gs[0].state.FRBdemo.lC * ss[0].TOBS + print("Expected number for Fly's Eys is ",rate," per day") + print("c.f. actual number: ",ss[0].NORM_FRB) + + + +main() diff --git a/papers/CRACO/plot_900_alternatives.py b/papers/CRACO/plot_900_alternatives.py new file mode 100644 index 00000000..1db0848a --- /dev/null +++ b/papers/CRACO/plot_900_alternatives.py @@ -0,0 +1,256 @@ +""" +This script plots the resulting redshift and dm distributions +from various alternative hypothertical CRACO setups, +in order to estimate the effects on the system + +This one is limited to 900 MHz observations + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +def main(): + """ + Main function - it loads in various surveys, and calculates their rates. + """ + + # in case you wish to switch to another output directory + + opdir="TestSurveys/" + + # approximate best-fit values from recent analysis + # best-fit from Jordan et al + #state = states.load_state("HoffmannHalo25",scat="CHIME",rep=None) #scat="updated",rep=None) + state = states.load_state("HoffmannHalo25",scat="updated",rep=None) #scat="updated",rep=None) + #state = states.load_state("JamesH022",scat="CHIME",rep=None) #scat="updated",rep=None) + + #check_FE(state) + + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('../papers/CRACO/TestSurveys') + names=['CRAFT_CRACO_900','CRAFT_CRACO_900_alldm','CRAFT_CRACO_900_icsdm', + 'CRAFT_CRACO_900_1.28','CRAFT_CRACO_900_alldm_1.28', + 'CRAFT_CRACO_900_3ms','CRAFT_CRACO_900_3ms_alldm','CRAFT_CRACO_900_3ms_at_1.28_alldm'] + labels = ["CRACO 900 MHz","All DMs searched","ICS DM limits","$t_{\\rm res} = 1.28$ ms", + "$t_{\\rm res} = 1.28$ ms, all DMs","CRACO 3.4ms", " allDMs", " at 1.28ms"] + linestyles=["-","-.","--",":","-","-.","--",":"] + nz=400 + zmax=4 + ndm=500 + dmmax=5000 + + ss,gs = loading.surveys_and_grids(survey_names=names,repeaters=False, + init_state=state,sdir=sdir, + zmax=zmax,nz=nz,dmmax=dmmax,ndm=ndm) + + + ##### prints total relative rates ##### + for i,n in enumerate(names): + print("Total rate for survey ",n," is ",np.sum(gs[i].rates)/np.sum(gs[0].rates)) + + + + ######### plots total DM and z distribution ####### + # set limits for plots - will be LARGE! + DMmax=4000 + zmax=4. + + plt.figure() + ax1 = plt.gca() + plt.xlabel("redshift $z$") + plt.ylabel("p(z) [a.u.]") + plt.xlim(0.01,3) + plt.ylim(0,1) + #plt.ylim(0,80) + + plt.figure() + ax2 = plt.gca() + plt.xlabel("DM pc cm$^{-3}$") + plt.ylabel("p(DM) [a.u.]") + plt.xlim(0,3000) + plt.ylim(0,1) + #plt.ylim(0,0.0009) + + zvals = gs[0].zvals + dz = zvals[1]-zvals[0] + dmvals = gs[0].dmvals + ddm = dmvals[1]-dmvals[0] + + pzs=[] + pdms=[] + allrates=[] + # chooses the first arbitrarily to extract zvals etc from + for i,g in enumerate(gs): + + s=ss[i] + g=gs[i] + name = names[i] + figures.plot_grid(gs[i].rates,g.zvals,g.dmvals, + name=opdir+name+"_zDM.pdf",norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + project=False,ylabel='${\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host}$', + zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5]) + + rates = gs[i].get_rates() #gs[i].rates * 10**g.state.FRBdemo.lC + rate = np.sum(rates) + allrates.append(rate) + pz = np.sum(rates,axis=1) + pz /= dz + + pdm = np.sum(rates,axis=0) + pdm /= ddm + + pzs.append(pz) + pdms.append(pdm) + + for i,g in enumerate(gs): + pz = pzs[i]/np.max(pzs[4]) + pdm = pdms[i]/np.max(pdms[4]) + + print("Relative rate for ",names[i]," is ",allrates[i]/allrates[0]," per day") + + plt.sca(ax1) + plt.plot(zvals,pz,label=labels[i],linestyle=linestyles[i]) + + if i==1: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_zcomparison1.png") + if i==2: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_zcomparison2.png") + if i==3: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_zcomparison3.png") + if i==4: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_zcomparison4.png") + + plt.sca(ax2) + plt.plot(dmvals,pdm,label=labels[i],linestyle=linestyles[i]) + + if i==1: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_dmcomparison1.png") + if i==2: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_dmcomparison2.png") + if i==3: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_dmcomparison3.png") + if i==4: + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_dmcomparison4.png") + + + + + plt.sca(ax1) + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_dmcomparison.png") + plt.close() + + plt.sca(ax2) + plt.legend() + plt.tight_layout() + plt.savefig("Plots/CRACO900_zcomparison.png") + plt.close() + +def plot_efficiencies(gs,ss): + """ + Does some efficiency plots + + Args: + gs [list of grids]: grid objects to plot + ss [ list of surveys]: survey objects to plot + """ + ###### plots efficiencies ###### + plt.figure() + for i,s in enumerate(ss): + + for j in np.arange(s.NWbins): + if j==0: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],label=labels[i]) + else: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color()) + plt.xlabel("DM") + plt.ylabel("Efficiency") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/efficiency.png") + plt.close() + + + ##### Plots an example of the threshold ###### + plt.figure() + for i,g in enumerate(gs): + print("Survey weights are ",ss[i].wlist,ss[i].wplist) + for j in np.arange(g.nthresh): + if j==0: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],label=labels[i],linewidth=0.2) + else: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color(),linewidth=j) + plt.xlabel("DM") + plt.ylabel("Threshold (erg)") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/g_thresholds.png") + plt.close() + + +def check_FE(state): + """ + Checks FRB rate compared to Fly's Eye rate, which is the most reliable and consistent + + Args: + state: zDM state object, to be used for Fly's Eye rate calculation + + """ + ###### Checks normalisation ###### + ss,gs = loading.surveys_and_grids( + survey_names=["CRAFT_class_I_and_II"],repeaters=False,init_state=state) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + rate = np.sum(gs[0].rates) * 10**gs[0].state.FRBdemo.lC * ss[0].TOBS + print("Expected number for Fly's Eys is ",rate," per day") + print("c.f. actual number: ",ss[0].NORM_FRB) + + + +main() diff --git a/papers/CRACO/plot_900_improvements.py b/papers/CRACO/plot_900_improvements.py new file mode 100644 index 00000000..fe1edc6d --- /dev/null +++ b/papers/CRACO/plot_900_improvements.py @@ -0,0 +1,249 @@ +""" +This script creates zdm grids for ASKAP incoherent sum observations. + +It exists partly to calculate relative rates from surveys + +For CHIME 1.28, it's 2.54 +For updated, it's 1.88 + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +def main(): + + # in case you wish to switch to another output directory + + opdir="ImprovedSurveys/" + + # approximate best-fit values from recent analysis + # best-fit from Jordan et al + state = states.load_state("HoffmannHalo25",scat="updated",rep=None) + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('../papers/CRACO/ImprovedSurveys') + """ + ORIGINAL INVESTIGATION + names=['CRAFT_CRACO_900','CRAFT_CRACO_900_imp1', + 'CRAFT_CRACO_900_imp2.1_div2','CRAFT_CRACO_900_imp2.1_div4','CRAFT_CRACO_900_imp2.1_div8', + 'CRAFT_CRACO_900_imp2.2_div2','CRAFT_CRACO_900_imp2.2_div4','CRAFT_CRACO_900_imp2.2_div8', + 'CRAFT_CRACO_900_imp2.3_div2','CRAFT_CRACO_900_imp2.3_div4','CRAFT_CRACO_900_imp2.3_div8', + 'CRAFT_CRACO_900_imp3' + ] + labels = ["CRACO 900 MHz","1: $T_{\\rm sys}=25^{\\circ}$ K", + "2.1 $t_{\\rm rs}=6.8$\\,ms","2.1 $t_{\\rm rs}=3.4$\\,ms","2.1 $t_{\\rm rs}=1.7$\\,ms", + "2.2 $t_{\\rm rs}=6.8$\\,ms","2.2 $t_{\\rm rs}=3.4$\\,ms","2.2 $t_{\\rm rs}=1.7$\\,ms", + "2.3 $t_{\\rm rs}=6.8$\\,ms","2.3 $t_{\\rm rs}=3.4$\\,ms","2.3 $t_{\\rm rs}=1.7$\\,ms", + "3: $\\nu_{\\rm res}=167\\,kHz" + ] + linestyles=["-","-","-.","-.","-.","--","--","--",":",":",":","-"] + """ + + """ + names=['CRAFT_CRACO_900', + 'CRAFT_CRACO_900_imp2.3_div2','CRAFT_CRACO_900_imp2.3_div4','CRAFT_CRACO_900_imp2.3_div8', + 'CRAFT_CRACO_900_imp2.4_div2','CRAFT_CRACO_900_imp2.4_div4','CRAFT_CRACO_900_imp2.4_div8', + 'CRAFT_CRACO_900_imp2.4_div8_primary', + #'CRAFT_CRACO_900_imp1', + 'CRAFT_CRACO_900_imp_all' + ] + labels = ["CRACO 900 MHz", + "1: $t_{\\rm rs}=6.8$\\,ms","2: $t_{\\rm rs}=3.4$\\,ms","2: $t_{\\rm rs}=1.7$\\,ms", + "2: $t_{\\rm rs}=6.8$\\,ms","3: $t_{\\rm rs}=3.4$\\,ms","3: $t_{\\rm rs}=1.7$\\,ms", + "3: Perfect imaging", + #"4: $T_{\\rm sys}=25^{\\circ}$ K", + "4: $T_{\\rm sys}=25^{\\circ}$ K"] + linestyles=["-",":",":",":","--","--","--","-.","-"] + """ + + + names=['CRAFT_CRACO_900', + 'CRAFT_CRACO_900_imp2.3_div8', + 'CRAFT_CRACO_900_imp2.4_div8', + 'CRAFT_CRACO_900_imp2.4_div8_primary', + 'CRAFT_CRACO_900_imp_all' + ] + labels = ["CRACO 900 MHz", + "1: $t_{\\rm res}=1.7$ ms", + "2: $\\nu_{\\rm res} = 167$ kHz", + "3: Perfect imaging", + "4: $T_{\\rm sys}=25^{\\circ}$ K"] + linestyles=["-",":","--","-.","-"] + + nz=400 + zmax=4 + ndm=500 + dmmax=5000 + + ss,gs = loading.surveys_and_grids(survey_names=names,repeaters=False, + init_state=state,sdir=sdir, + zmax=zmax,nz=nz,dmmax=dmmax,ndm=ndm) + + + ##### prints total relative rates ##### + for i,n in enumerate(names): + print("Total rate for survey ",labels[i]," is ",np.sum(gs[i].rates)/np.sum(gs[0].rates)) + + + + ######### plots total DM and z distribution ####### + # set limits for plots - will be LARGE! + DMmax=4000 + zmax=4. + + plt.figure() + ax1 = plt.gca() + plt.xlabel("redshift $z$") + plt.ylabel("p(z) [a.u.]") + plt.xlim(0.01,3) + plt.ylim(0,1) + #plt.ylim(0,80) + + plt.figure() + ax2 = plt.gca() + plt.xlabel("DM pc cm$^{-3}$") + plt.ylabel("p(DM) [a.u.]") + plt.xlim(0,3000) + plt.ylim(0,1) + #plt.ylim(0,0.0009) + + zvals = gs[0].zvals + dz = zvals[1]-zvals[0] + dmvals = gs[0].dmvals + ddm = dmvals[1]-dmvals[0] + + pzs=[] + pdms=[] + allrates=[] + # chooses the first arbitrarily to extract zvals etc from + for i,g in enumerate(gs): + + s=ss[i] + g=gs[i] + name = names[i] + #figures.plot_grid(gs[i].rates,g.zvals,g.dmvals, + # name=opdir+name+"_zDM.pdf",norm=3,log=True, + # label='$\\log_{10} p({\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + # project=False,ylabel='${\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host}$', + # zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5]) + + rates = gs[i].get_rates() #gs[i].rates * 10**g.state.FRBdemo.lC + rate = np.sum(rates) + allrates.append(rate) + pz = np.sum(rates,axis=1) + pz /= dz + + pdm = np.sum(rates,axis=0) + pdm /= ddm + + pzs.append(pz) + pdms.append(pdm) + + inorm=4 + for i,g in enumerate(gs): + pz = pzs[i]/np.max(pzs[inorm]) + pdm = pdms[i]/np.max(pdms[inorm]) + + print("Relative rate for ",names[i]," is ",allrates[i]/allrates[0]," per day") + + plt.sca(ax1) + plt.plot(zvals,pz,label=labels[i],linestyle=linestyles[i]) + + plt.sca(ax2) + plt.plot(dmvals,pdm,label=labels[i],linestyle=linestyles[i]) + + + + plt.sca(ax1) + plt.legend(fontsize=12) + plt.tight_layout() + plt.savefig("ImprovedSurveys/improved_zs.png") + plt.close() + + plt.sca(ax2) + plt.legend(fontsize=12) + plt.tight_layout() + plt.savefig("ImprovedSurveys/improved_dms.png") + plt.close() + +def plot_efficiencies(gs,ss): + """ + Does some efficiency plots + """ + ###### plots efficiencies ###### + plt.figure() + for i,s in enumerate(ss): + + for j in np.arange(s.NWbins): + if j==0: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],label=labels[i]) + else: + plt.plot(s.dmvals,s.efficiencies[j,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color()) + plt.xlabel("DM") + plt.ylabel("Efficiency") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/efficiency.png") + plt.close() + + + ##### Plots an example of the threshold ###### + plt.figure() + for i,g in enumerate(gs): + print("Survey weights are ",ss[i].wlist,ss[i].wplist) + for j in np.arange(g.nthresh): + if j==0: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],label=labels[i],linewidth=0.2) + else: + plt.plot(g.dmvals,g.thresholds[j,10,:],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color(),linewidth=j) + plt.xlabel("DM") + plt.ylabel("Threshold (erg)") + plt.legend() + plt.tight_layout() + plt.savefig("Plots/g_thresholds.png") + plt.close() + + +def check_FE(state): + """ + Checks FRB rate compared to Fly's Eye rate, which is the most reliable and consistent + """ + ###### Checks normalisation ###### + ss,gs = loading.surveys_and_grids( + survey_names=["CRAFT_class_I_and_II"],repeaters=False,init_state=state) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + rate = np.sum(gs[0].rates) * 10**gs[0].state.FRBdemo.lC * ss[0].TOBS + print("Expected number for Fly's Eys is ",rate," per day") + print("c.f. actual number: ",ss[0].NORM_FRB) + + + +main() diff --git a/papers/CRACO/plot_CRACO_zDM_grid.py b/papers/CRACO/plot_CRACO_zDM_grid.py new file mode 100644 index 00000000..5c20b662 --- /dev/null +++ b/papers/CRACO/plot_CRACO_zDM_grid.py @@ -0,0 +1,101 @@ +""" +This script creates zdm grids for CRACO observations. + +It exists partly to calculate relative rates from surveys + +It also calculates expected p(DM) distributions + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +def main(): + + # in case you wish to switch to another output directory + name="zDMPlots" + opdir=name+"/" + + # approximate best-fit values from recent analysis + # best-fit from Jordan et al + state = states.load_state("HoffmannHalo25",scat="updated",rep=None) + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('data/Surveys') + names=['CRAFT_CRACO_900','CRAFT_CRACO_1300'] + + ss,gs = loading.surveys_and_grids( + survey_names=names,repeaters=False,init_state=state,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + # set limits for plots - will be LARGE! + DMmax=3000 + zmax=2.5 + + plt.figure() + ax1 = plt.gca() + + plt.figure() + ax2 = plt.gca() + + # ids of FRBs with voltage data and redshift + voltage = np.array([3, 6, 11, 12, 16],dtype="int") + + # chooses the first arbitrarily to extract zvals etc from + for i,g in enumerate(gs): + s=ss[i] + g=gs[i] + name = names[i] + + noz=s.nozlist + z=s.zlist + + if noz is not None: + DMlines = s.DMEGs[noz] + else: + DMlines=None + + if z is not None: + FRBDMs = s.DMEGs[z] + FRBZs=s.Zs[z] + else: + FRBDMs=None + FRBZs=None + print(s.DMEGs[voltage]) + print(s.Zs[voltage]) + exit() + special = [s.DMEGs[voltage],s.Zs[voltage]] + + figures.plot_grid(gs[i].get_rates(),g.zvals,g.dmvals, + name=opdir+name+"_zDM.pdf",norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm cosmic} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + project=False,ylabel='${\\rm DM}_{\\rm cosmic} + {\\rm DM}_{\\rm host}$', + zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5], + FRBDMs=FRBDMs,FRBZs=FRBZs, + DMlines = DMlines,special=special + ) + + rate = np.sum(gs[i].rates) * 10**g.state.FRBdemo.lC + print("Relative rate for ",name," is ",rate," per day") + + + + +main() diff --git a/papers/CRACO/plot_beams.py b/papers/CRACO/plot_beams.py new file mode 100644 index 00000000..5e5597b5 --- /dev/null +++ b/papers/CRACO/plot_beams.py @@ -0,0 +1,219 @@ +""" +Script to plot inverse beamshapes \Omega(B) vs B + +""" +import numpy as np +from matplotlib import pyplot as plt +import importlib.resources as resources +import os +import matplotlib +import pandas as pd + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + +def main(): + """ + Loads in beam data and plots it + """ + + prefix="" + #prefix="3ms_" + + # loads in data + indir = os.path.join(resources.files('zdm'), 'data','BeamData') + + beams = ["CRACO_900","CRACO_1300","3ms_CRACO_900","3ms_CRACO_1300"]#,"ASKAP_892","ASKAP_1300"] + labels=["CRACO 900 13.8ms","CRACO 1300 13.8ms","CRACO 900 3.4ms","CRACO 1300 3.4ms"]#,"ICS 892","ICS 1300"] + linestyles=["-","--",":","-.","-","--",":","-."] + + Senses = [] + bfiles = [] + + plt.figure() + for i,beam in enumerate(beams): + bfile=beam+"_bins.npy" + hfile=beam+"_hist.npy" + + bfile = os.path.join(indir,bfile) + hfile = os.path.join(indir,hfile) + h=np.load(hfile) + b=np.load(bfile) + + + # divides hist file by log-scaling in b + bwidth = b[1]/b[0] + lbwidth = np.log10(bwidth) + b=b[:-1] * bwidth**0.5 + + # need to sum before normalisation + Sens = np.sum(h*b**1.5) + Senses.append(Sens) + bfiles.append(bfile) + + h /= lbwidth + # get bin centres + + if i == 0: + plt.plot(b,h,label=labels[i],linestyle=linestyles[i]) + else: + plt.plot(b,h,label=labels[i],linestyle=linestyles[i],color=plt.gca().lines[-1].get_color()) + + + plt.xlabel("$B$") + plt.ylabel("$\\Omega(B)$") + plt.legend() + plt.tight_layout() + + # plots data + plt.savefig("Plots/"+prefix+"craco_beams.png") + + + ###### Adds primary beams ##### + + # adds plots of primary beam response + # just knows that the b values are identical to previous + bfile="PrimaryBeams/"+prefix+"CRACO_900_hist.npy" + h = np.load(bfile) + + + # need to sum before normalisation + Sens = np.sum(h*b**1.5) + Senses.append(Sens) + bfiles.append(bfile) + + + h /= lbwidth + plt.plot(b,h,label="Primary 900",linestyle="-") + + bfile="PrimaryBeams/"+prefix+"CRACO_1300_hist.npy" + h = np.load(bfile) + + # need to sum before normalisation + Sens = np.sum(h*b**1.5) + Senses.append(Sens) + bfiles.append(bfile) + + h /= lbwidth + plt.plot(b,h,label="Primary 1300",linestyle="--",color=plt.gca().lines[-1].get_color()) + plt.legend() + plt.tight_layout() + plt.savefig("Plots/"+prefix+"primary_askap_beams.png") + + ###### Adds primary beams ##### + + # adds plots of primary beam response + # just knows that the b values are identical to previous + bfile=indir+"/ASKAP_892_hist.npy" + h = np.load(bfile) + b = np.load(indir+"/ASKAP_892_bins.npy") + + + bwidth = b[1]/b[0] + b = b[:-1]*bwidth**0.5 + + # need to sum before normalisation + Sens = np.sum(h*b**1.5) + Senses.append(Sens) + bfiles.append(bfile) + + lbwidth = np.log10(bwidth) + h /= lbwidth + + plt.plot(b,h,label="ICS 900",linestyle="-") + + bfile=indir+"/ASKAP_1300_hist.npy" + h = np.load(bfile) + b = np.load(indir+"/ASKAP_1300_bins.npy") + bwidth = b[1]/b[0] + b = b[:-1]*bwidth**0.5 + + # need to sum before normalisation + Sens = np.sum(h*b**1.5) + Senses.append(Sens) + bfiles.append(bfile) + + lbwidth = np.log10(bwidth) + h /= lbwidth + + plt.plot(b,h,label="ICS 1300",linestyle="--",color=plt.gca().lines[-1].get_color()) + plt.ylim(0,0.07) + plt.xlim(0,1) + plt.legend() + plt.tight_layout() + plt.savefig("Plots/"+prefix+"comparison_askap_beams.png") + + + plt.close() + + # prints relative sensitivitiesa compared to + #labels=["CRACO 900", "CRACO 1300", "Primary 900", "Primary 1300", "ICS 900", "ICS 1300"] + labels = labels + ["Primary 900", "Primary 1300", "ICS 900", "ICS 1300"] + mult = (180./np.pi)**2 + for i in np.arange(4): + print("Sensitivity of ",labels[2*i]," is ",Senses[2*i]*mult, " cf ICS: ",Senses[2*i]/Senses[-2]) + print("Sensitivity of ",labels[2*i+1]," is ",Senses[2*i+1]*mult, " cf ICS: ",Senses[2*i+1]/Senses[-1]) + + + ##### plots all components ##### + configs = pd.read_csv("Logs/"+prefix+"configs.csv")# np.loadtxt("configs.dat",dtype="str") + nconfigs = len(configs) + + b = np.load("BeamHistograms/craco_histogram_bins.npy") + bwidth = np.log10(b[1]/b[0]) + # get bin centres + b=b[:-1] * bwidth**0.5 + + plt.figure() + for i in np.arange(nconfigs): + + fp = configs["footprint"][i] + pitch = configs["pitch"][i] + fpitch = float(pitch) + spitch = str(pitch) + fbar = configs["fbar"][i] + tobs = configs["Ttot"][i] + teff = configs["Teff"][i] + freq = float(fbar) + sfreq = str(fbar) + + if freq > 1050: + continue + if fp=="closepack36": + footprint="closepack" + elif fp == "square_6x6": + footprint="square" + continue + else: + print("Unrecognised footprint ",fp) + exit() + + gsize = 10. + gpix = 2560 + basename = f"BeamHistograms/hist_craco_{footprint}_p{fpitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}_.npy" + + if os.path.exists(basename): + h = np.load(basename) + + else: + print("Cannot find ",basename) + exit() + + plt.plot(b,h/bwidth,label=footprint+" "+sfreq[0:4]+"MHz " + spitch) + + + plt.xlabel("$B$") + plt.ylabel("$\\Omega(B)$") + plt.legend(fontsize=8,loc="upper left") + plt.tight_layout() + + # plots data + plt.savefig("Plots/"+prefix+"closepack_lowf_component_beams.png") + plt.close() + + +main() diff --git a/papers/CRACO/sim_configs.py b/papers/CRACO/sim_configs.py new file mode 100644 index 00000000..e86fd7b3 --- /dev/null +++ b/papers/CRACO/sim_configs.py @@ -0,0 +1,77 @@ +""" +This script iterates over available beam configurations +and simulates their beam patterns +""" + +import numpy as np +import importlib.resources as resources +import os +import pandas as pd + +def main(): + """ + Loads in unique configs, and generates beamfiles for them + """ + + configfile="Logs/configs.csv" + sim_all_configs("BeamHistograms/",configfile,False) + sim_all_configs("PrimaryBeams/",configfile,True) + + configfile="Logs/3ms_configs.csv" + sim_all_configs("BeamHistograms/",configfile,False) + sim_all_configs("PrimaryBeams/",configfile,True) + +def sim_all_configs(Bdir,configfile,primary=False): + """ + Args: + Bdir [string]: directory for beam data + configfile [string]: file for telescope configuration data + primary [bool]: if True, simulate primary beam only + """ + configs = pd.read_csv(configfile)# np.loadtxt("configs.dat",dtype="str") + nconfigs = len(configs) + + pyfile = os.path.join(resources.files('zdm'), 'beam_generator','sim_craco_beam.py') + + for i in np.arange(nconfigs): + + fp = configs["footprint"][i] + pitch = configs["pitch"][i] + fpitch = float(pitch) + spitch = str(pitch) + fbar = configs["fbar"][i] + tobs = configs["Ttot"][i] + teff = configs["Teff"][i] + freq = float(fbar) + sfreq = str(fbar) + + if fp=="closepack36": + footprint="closepack" + elif fp == "square_6x6": + footprint="square" + else: + print("Unrecognised footprint ",fp) + exit() + + gsize = 10. + gpix = 2560 + basename = f"{Bdir}/hist_craco_{footprint}_p{fpitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}_.npy" + basename2 = f"./hist_craco_{footprint}_p{fpitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}_.npy" + basename3 = f"./craco_{footprint}_p{fpitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}.npy" + if os.path.exists(basename): + print("Found ",basename) + else: + command = "python "+pyfile +" -fp " + footprint + " -p " + spitch + " -f " + sfreq[0:7] + " --primary="+str(primary) + + os.system(command) + + command = "mv "+basename2+" "+Bdir + os.system(command) + + command = "mv "+basename3+" "+Bdir + os.system(command) + + + + +main() diff --git a/papers/CRACO/weight_configs.py b/papers/CRACO/weight_configs.py new file mode 100644 index 00000000..05ec0b82 --- /dev/null +++ b/papers/CRACO/weight_configs.py @@ -0,0 +1,176 @@ +""" +This script takes in all simulated configs, +and adds them together with appropriate weights to +create an average beamshape. + +It also adds the beam sensitivity values to the logfile +""" + +import numpy as np +import importlib.resources as resources +import os +import pandas as pd +def main(): + """ + Loads in unique configs, and generates beamfiles for them, + weighted by the time on sky + """ + + print("######## Generating beamfile for 13.8ms mode ######## ") + configfile="Logs/configs.csv" + logfile="Logs/craco_13ms_survey_db.weight.altaz.csv" + # turn on add to add beamfactors column + gen_weighted_beams("BeamHistograms/","FinalBeams/",configfile,logfile,add=False) + # turn on this to generate files for the primary beam + gen_weighted_beams("PrimaryBeams/","PrimaryBeams/",configfile,logfile,add=False) + + #### 3ms background ##### + print("######## Generating beamfile for 3.4ms mode ######## ") + configfile="Logs/3ms_configs.csv" + logfile="Logs/craco_3ms_survey_db.csv" + # turn on add to add beamfactors column + gen_weighted_beams("BeamHistograms/","FinalBeams/",configfile,logfile,add=True,prefix="3ms_") + # turn on this to generate files for the primary beam + gen_weighted_beams("PrimaryBeams/","PrimaryBeams/",configfile,logfile,add=False,prefix="3ms_") + +def gen_weighted_beams(indir,opdir,configfile,logfile,add=False,prefix=""): + """ + Generates summed beams over all configurations + + Args: + indir [string]: input directory for the beam footprint data + opdir [string]: output directory + configfile [string]: name of file containing configuration logs + logfile [string]: name of file containing logs of all observations + add [bool]: if true, add weights to the log file + prefix [string]: prefix to apply to output + """ + configs = pd.read_csv(configfile)# np.loadtxt("configs.dat",dtype="str") + nconfigs = len(configs) + + pyfile = os.path.join(resources.files('zdm'), 'beam_generator','sim_craco_beam.py') + + bins = np.load("BeamHistograms/craco_histogram_bins.npy") + nbins=bins.size-1 + bcentres = bins[0:-1] * (bins[1]/bins[0])**0.5 + # relative rate per solid angle: Euclidean expectation + bfactors = bcentres**1.5 + + fcut = 1100 + name1=prefix+"CRACO_900_hist.npy" + name2=prefix+"CRACO_1300_hist.npy" + t1=0. + t2=0. + h1 = np.zeros([nbins]) + h2 = np.zeros([nbins]) + fbar1=0. + fbar2=0. + + bfs=[] + + for i in np.arange(nconfigs): + + fp = configs["footprint"][i] + pitch = configs["pitch"][i] + fpitch = float(pitch) + spitch = str(pitch) + fbar = configs["fbar"][i] + tobs = configs["Ttot"][i] + teff = configs["Teff"][i] + freq = float(fbar) + sfreq = str(fbar) + + if fp=="closepack36": + footprint="closepack" + elif fp == "square_6x6": + footprint="square" + else: + print("Unrecognised footprint ",fp) + exit() + + gsize = 10. + gpix = 2560 + basename = f"{indir}hist_craco_{footprint}_p{fpitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}_.npy" + + if os.path.exists(basename): + hist = np.load(basename) + else: + print("Cannot find ",basename) + exit() + + Bfactor = np.sum(bfactors * hist) + bfs.append(Bfactor) + print(i," ",footprint," ",spitch," ",sfreq," beam factor is ",Bfactor) + + + if freq < fcut: + # low band + t1 += teff + h1 += hist*teff + fbar1 += freq*teff + else: + # L-band + t2 += teff + h2 += hist*teff + fbar2 += freq*teff + + h1 /= t1 + h2 /= t2 + fbar1 /= t1 + fbar2 /= t2 + print("Total time t1 is ",t1," mean freq of ",fbar1) + print("Total time t1 is ",t2," mean freq of ",fbar2) + + np.save(opdir+name1,h1) + np.save(opdir+name2,h2) + + print("Total effective sensitivity of 900 MHz beam is ",np.sum(bfactors*h1)) + print("Total effective sensitivity of 1300 MHz beam is ",np.sum(bfactors*h2)) + + bfs = np.array(bfs) + df = pd.read_csv(configfile) + df["bfactors"]=bfs + df.to_csv(configfile,index=False) + + # adds beam factors to the configs file + if add: + add_beamfactors(configfile,logfile) + +def add_beamfactors(configfile,logfile): + """ + Adds relative beam factors as weighting to data + + Args: + configfile [string]: name of file containing configuration logs + logfile [string]: name of file containing logs of all observations + """ + + dfc = pd.read_csv(configfile) + df = pd.read_csv(logfile) + nobs = len(df) + bfs = np.zeros([nobs]) + + nconfig = len(dfc) + + for i in np.arange(nconfig): + footprint = dfc["footprint"][i] + pitch = dfc["pitch"][i] + fbar = dfc["fbar"][i] + bf = dfc["bfactors"][i] + + OK1 = np.where(df["footprint"] == footprint)[0] + OK2 = np.where(df["pitch"] == pitch)[0] + OK3 = np.where(df["fbar"] == fbar)[0] + + OK = np.intersect1d(OK1,OK2) + OK = np.intersect1d(OK,OK3) + + bfs[OK3] = bf + + mean_bf = np.sum(df["t_eff"]*bfs)/np.sum(df["t_eff"]) + bfs /= mean_bf + + df["bfactors"]=bfs + + df.to_csv(logfile) +main() diff --git a/papers/FitRepetition2025/ICS_Survey_Analysis/perform_complex_analysis.py b/papers/FitRepetition2025/ICS_Survey_Analysis/perform_complex_analysis.py index 5c900e20..ced484c6 100644 --- a/papers/FitRepetition2025/ICS_Survey_Analysis/perform_complex_analysis.py +++ b/papers/FitRepetition2025/ICS_Survey_Analysis/perform_complex_analysis.py @@ -195,6 +195,16 @@ def main(alpha=-1.5): LOW=np.where(cfmid < 1000.)[0] HIGH=np.where(cfmid > 1400)[0] MID=np.where(np.abs(cfmid-1200) < 200)[0] + + + CP = np.where(cfootprint[LOW] == 1) + S = np.where(cfootprint[LOW]==2) + print("Respective low times between closepack and square: ",np.sum(ctime[LOW][CP]),np.sum(ctime[LOW][S])) + + CP = np.where(cfootprint[MID] == 1) + S = np.where(cfootprint[MID]==2) + print("Respective mid times between closepack and square: ",np.sum(ctime[MID][CP]),np.sum(ctime[MID][S])) + labels=["low","mid","high"] if True: # one per frequency bin @@ -228,8 +238,39 @@ def main(alpha=-1.5): ##### generates a single effective sensitivity curve ##### # weights by time and efficiency here if True: - mean_curve = get_dm_efficiencies(ctsamp,cfmid[OK],ceff[OK]*ctime[OK],outfile="mean_ICS_efficiency.npy") - + mean_curve,mask = get_dm_efficiencies(ctsamp[OK],cfmid[OK],ceff[OK]*ctime[OK], + outfile="mean_ICS_efficiency.npy", + maskname="mean_ICS_mask.npy") + + lmean_curve,lmask = get_dm_efficiencies(ctsamp[LOW],cfmid[LOW],ceff[LOW]*ctime[LOW], + outfile="low_ICS_efficiency.npy", + maskname="low_ICS_mask.npy") + + mmean_curve,mmask = get_dm_efficiencies(ctsamp[MID],cfmid[MID],ceff[MID]*ctime[MID], + outfile="mid_ICS_efficiency.npy", + maskname="mid_ICS_mask.npy") + + hmean_curve,hmask = get_dm_efficiencies(ctsamp[HIGH],cfmid[HIGH],ceff[HIGH]*ctime[HIGH], + outfile="high_ICS_efficiency.npy", + maskname="high_ICS_mask.npy") + + # plots these masks + plt.figure() + plt.xlabel("DM [pc cm$^{-3}$]") + plt.ylabel("Fractional coverage") + plt.plot(lmask[0,:],lmask[1,:],label="low") + plt.plot(mmask[0,:],mmask[1,:],label="mid") + plt.plot(hmask[0,:],hmask[1,:],label="high") + plt.plot(mask[0,:],mask[1,:],label="mean") + plt.legend() + plt.tight_layout() + plt.ylim(0,1) + plt.xlim(0,7000) + plt.savefig("masks.png") + plt.close() + + + exit() ############ cumulative rate plots vs efficiencies ########## @@ -409,7 +450,7 @@ def make_histograms(ICStimes,ICSfmids,ICSfootprints,ICSpitches,Nants,label="",sf return -def get_dm_efficiencies(tsamps,flists,weights,outfile,plot=True): +def get_dm_efficiencies(tsamps,flists,weights,outfile,maskname,plot=False): """ Returns a total efficiency due to sampling time and frequency @@ -418,15 +459,18 @@ def get_dm_efficiencies(tsamps,flists,weights,outfile,plot=True): tsamps: list of samplin times weights: list of weights """ - DMs = np.linspace(0.,7000.,701) + nDM=701 + DMs = np.linspace(0.,7000.,nDM) tlist = np.unique(tsamps) flist = np.unique(flists) - curves = get_sensitivity_curves(DMs,tlist,flist) + curves,DM_maxes = get_sensitivity_curves(DMs,tlist,flist) + effs = np.zeros([tsamps.size]) csum = 0. + DM_weights = np.zeros([tlist.size,flist.size]) for i,t in enumerate(tlist): OK1 = np.where(tsamps == t)[0] @@ -435,6 +479,20 @@ def get_dm_efficiencies(tsamps,flists,weights,outfile,plot=True): if len(OK2)>0: total_weight = np.sum(weights[OK1[OK2]]) csum += total_weight * curves[i][j] + DM_weights[i,j] = total_weight + + # we now make the DM mask + DM_weights = DM_weights.flatten() + DM_maxes = np.array(DM_maxes).flatten() + mask = np.zeros([2,nDM]) + for i, dm in enumerate(DMs): + + OK = np.where(DM_maxes > dm)[0] + total = np.sum(DM_weights[OK]) + mask[1,i]=total + + mask[0,:] = DMs + mask[1,:] /= mask[1,0] csum /= np.sum(weights) @@ -453,7 +511,9 @@ def get_dm_efficiencies(tsamps,flists,weights,outfile,plot=True): print("shape is ",tosave.shape) np.save(outfile,tosave) - return csum + np.save(maskname,mask) + + return csum,mask @@ -476,10 +536,13 @@ def get_sensitivity_curves(DMs,tlist,flist,nu_res = 1,iDM = 4096): """ ########## initiualise lognormal width distribution #######3 from zdm import parameters - state = parameters.State() + #state = parameters.State() import os from pkg_resources import resource_filename from zdm import survey + from zdm import states + + state = states.load_state("HoffmannEmin25") # sets path to ASKAP surveys sdir = os.path.join(resource_filename('zdm', 'data'), 'Surveys/') @@ -495,12 +558,16 @@ def get_sensitivity_curves(DMs,tlist,flist,nu_res = 1,iDM = 4096): df = 336. curves = [] + DM_maxes = [] for i,tres in enumerate(tlist): curves.append([]) + DM_maxes.append([]) for j,fbar in enumerate(flist): s.meta['FBAR'] = fbar s.meta['TRES'] = tres - widths,weights = survey.make_widths(s,state) + s.make_widths() + widths = s.wlist + weights = s.wplist rel_s_bar = 0. for k,w in enumerate(widths): @@ -509,14 +576,20 @@ def get_sensitivity_curves(DMs,tlist,flist,nu_res = 1,iDM = 4096): rel_s_bar += rel_s * weights[k] # sets the curves to zero after a maximum DM - # solves 4096 * tres = Delta t = 4.196e-3 * DM * (numax^-2 - numin^-2) - DM_max = iDM * tres / ((fbar/1e3 - df/2e3)**-2 - (fbar/1e3+df/2e3)**-2) / 4.149 + # solves 4096 * tres = Delta t = 4.196e-3 * DM * (numax^-2 - numin^-2) but in milliseconds + numaxGHz = fbar/1e3 + df/2e3 + numinGHz = fbar/1e3 - df/2e3 + dm_mult = 4.196 * (numinGHz**-2 - numaxGHz**-2) + DM_max = iDM * tres / dm_mult + #DM_max = iDM * tres / ((fbar/1e3 - df/2e3)**-2 - (fbar/1e3+df/2e3)**-2) / 4.149 + + DM_maxes[i].append(DM_max) bad = np.where(DMs > DM_max)[0] rel_s_bar[bad]=0. curves[i].append(rel_s_bar) - return curves + return curves,DM_maxes def get_width_efficiencies(tsamps,flists,maxDM=False,normF=1272,normT=1.182): """ diff --git a/papers/SKA_science/README.txt b/papers/SKA_science/README.txt new file mode 100644 index 00000000..bb32ebd7 --- /dev/null +++ b/papers/SKA_science/README.txt @@ -0,0 +1,25 @@ +### Simulations performed for SKA science chapters +# Inputs: courtesy of Evan Keane + + +# METHOD + +#1: sim_SKA_configs.py +This program reads in the inputs from Evan Keane. +They contain fields of view, and sensitivities, +when considering tied beams from the innermost N +antennas (mid) or stations (low). + +The program produces out files in Configs/, which saves +redshift and dm distributionbs, thresholds, observing times +(because it's easier to scale TOBS than FOV, i.e. this is +a proxy for the tied array beam size, which reduces FOV +as more stations are included), total number of observed FRBs, +and calculated detection thresholds. + +#2: make_zdists.py +This program reads in the configs from above. +It then chooses the one with the best N (highest number of FRBs +detected in total), and proceeds to calculate the zDM distribution +for 100 iterations of the Hoffmann et al paper. + diff --git a/papers/SKA_science/make_zdists.py b/papers/SKA_science/make_zdists.py index 8d7096fa..1cbd5597 100644 --- a/papers/SKA_science/make_zdists.py +++ b/papers/SKA_science/make_zdists.py @@ -25,7 +25,7 @@ from matplotlib import pyplot as plt import importlib.resources as resources -def main(): +def main(configdir = "Configs/"): """ Main program to loop over SKA frequency ranges and deployment milestones @@ -38,11 +38,12 @@ def main(): zDMgrid, zvals, dmvals = mf.get_zdm_grid( state, new=True, plot=False, method='analytic', - datdir=resources.files('zdm').joinpath('GridData') + datdir=resources.files('zdm').joinpath('GridData')) ####### sample MCMC parameter sets ###### - infile = resources.files('zdm').joinpath('scripts/MCMC/')+"/H0_prior10.h5" + infile = resources.files('zdm').joinpath('scripts/MCMC/H0_prior10.h5') + infile = str(infile) nsets=100 sample, params, pconfig = get_samples(infile,nsets) @@ -60,12 +61,12 @@ def main(): infile = "inputs/"+tel+telconfig+"_ID_radius_AonT_FoVdeg2" label = tel+"_"+telconfig generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, bw, survey_name, - sample, params, pconfig) + sample, params, pconfig,configdir=configdir) def generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, bw, survey_name, samples, params, pconfig, - opdir = "outputs/", plotdir = "plotdir/"): + configdir="Configs/", plotdir = "plotdir/"): """ generates a plot of FRB rate vs Nelements used @@ -104,12 +105,12 @@ def generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, Ngrids = radius.size # defines name of output files - opfile1 = opdir+label+"_N.npy" - opfile2 = opdir+label+"_pdm.npy" - opfile3 = opdir+label+"_pz.npy" - opfile4 = opdir+label+"_Nz.npy" - opfile5 = opdir+label+"_Tobs.npy" - opfile6 = opdir+label+"_thresh.npy" + opfile1 = configdir+label+"_N.npy" + opfile2 = configdir+label+"_pdm.npy" + opfile3 = configdir+label+"_pz.npy" + opfile4 = configdir+label+"_Nz.npy" + opfile5 = configdir+label+"_Tobs.npy" + opfile6 = configdir+label+"_thresh.npy" oldNs = np.load(opfile1) #pdms = np.load(opfile2) #pzs = np.load(opfile3) @@ -120,7 +121,10 @@ def generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, # finds the best ibest = np.argmax(oldNs) survey_dict = {"THRESH": thresh_Jyms[ibest], "TOBS": TOBS[ibest], "FBAR": freq, "BW": bw} - + print(infile) + print(ibest,survey_dict) + print("\n\n") + return Nsamples = samples.shape[0] Nz = zvals.size Ndm = dmvals.size @@ -129,13 +133,13 @@ def generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, pzs = np.zeros([Nsamples,Nz]) Ns = np.zeros([Nsamples]) - opdir = "sys_outputs/" - opfile7 = opdir+label+"_sys_N.npy" - opfile8 = opdir+label+"_sys_pz.npy" - opfile9 = opdir+label+"_sys_pdm.npy" + opdir = "outputs/" + opfile7 = opdir+label+"_N.npy" + opfile8 = opdir+label+"_pz.npy" + opfile9 = opdir+label+"_pdm.npy" - np.save("sysplotdir/zvals.npy",zvals) - np.save("sysplotdir/dmvals.npy",dmvals) + np.save("plotdir/zvals.npy",zvals) + np.save("plotdir/dmvals.npy",dmvals) #load=True load=False diff --git a/papers/SKA_science/sim_SKA_configs.py b/papers/SKA_science/sim_SKA_configs.py index a822510f..21897a5d 100644 --- a/papers/SKA_science/sim_SKA_configs.py +++ b/papers/SKA_science/sim_SKA_configs.py @@ -27,6 +27,8 @@ def main(): """ + opdir="Configs/" + #### Initialises general zDM stuff ##### state = parameters.State() # approximate best-fit values from recent analysis @@ -51,14 +53,20 @@ def main(): for config in ["AA4","AAstar"]: infile = "inputs/"+tel+config+"_ID_radius_AonT_FoVdeg2" label = tel+"_"+config - generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, bw) + generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, bw, + opdir=opdir,plotdir=opdir) def generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, bw, - opdir = "outputs/", plotdir = "plotdir/"): + opdir = "outputs/", plotdir = "plotdir/",load=False): """ generates a plot of FRB rate vs Nelements used """ + if not os.path.exists(opdir): + os.mkdir(opdir) + if not os.path.exists(plotdir): + os.mkdir(plotdir) + data=np.loadtxt(infile,dtype=str) radius = data[:,1].astype(float) sense_m2K = data[:,2].astype(float) @@ -86,7 +94,7 @@ def generate_sensitivity_plot(infile,state,zDMgrid, zvals, dmvals, label, freq, TOBS0 = 365.25 #calendar year TOBS = fov*TOBS0/fov[0] - if True: + if False: # produces a plot of sensitivity plt.figure() p1,=plt.plot(thresh_Jyms,label="Thresh",color="blue") diff --git a/papers/Vvmax/bvals.txt b/papers/Vvmax/bvals.txt new file mode 100644 index 00000000..c04e4d3d --- /dev/null +++ b/papers/Vvmax/bvals.txt @@ -0,0 +1,63 @@ +20170107.0 0.9495076331059649 +20170416.0 0.8065851884562867 +20170428.0 0.9967272804184072 +20170712.0 0.8620568437356246 +20170707.0 0.9672942924618126 +20170906.0 0.8997946149062184 +20171003.0 0.8023479222965371 +20171004.0 0.9253475485054687 +20171019.0 0.7630445701680714 +20171020.0 0.4736626120079286 +20171116.0 0.7982014651783024 +20171213.0 0.6092789132757906 +20171216.0 0.6351506006418929 +20180110.0 0.706014957103128 +20180119.0 0.8460346578986082 +20180128.0 0.9540866797208655 +20180128.0 0.7443499804538325 +20180130.0 0.7619549577301109 +20180131.0 0.749885040057768 +20180212.0 0.749885040057768 +20180315.0 0.7454593282568399 +20180324.0 0.6316267515934872 +20180417.0 0.6737262995448366 +20180430.0 0.7487803841690103 +20180515.0 0.9336213933974347 +20180525.0 0.6128095983569383 +20180924.0 0.9020445412853781 +20181112.0 0.8325403717943239 +20190102.0 0.909540678104508 +20190608.0 0.7890440574376807 +20190611.0 0.6581578264397666 +20190711.0 0.9887794322805494 +20190714.0 0.6843661312652278 +20191001.0 0.7283428568433659 +20191228.0 0.6524293613926698 +20200430.0 0.8281351538685899 +20200627.0 0.8962443727128535 +20200906.0 0.7621944071094687 +20210117.0 0.85713406712891 +20210214.0 0.702418138716084 +20210320.0 0.3336447617441035 +20210407.0 0.780733812012832 +20210807.0 0.7093453372193912 +20210809.0 0.9663702497438525 +20210912.0 0.6650190534831782 +20211127.0 0.9542149968098768 +20211203.0 0.9583053823038917 +20211212.0 0.16926287077269583 +20220105.0 0.5571554862748298 +20220501.0 0.8008961148072369 +20220531.0 0.32355074392623423 +20220610.0 0.9904112100254796 +20220725.0 0.21584469794607605 +20220918.0 0.6854992554893625 +20221106.0 0.6784531085713513 +20230521.0 0.883085030874285 +20230526.0 0.7670372255224207 +20230708.0 0.6642948874899768 +20230718.0 0.8338611462333758 +20230731.0 0.6248323834234126 +20230902.0 0.7357321651996569 +20231006.0 0.7883727571520244 +20231226.0 0.6341966084221008 diff --git a/papers/Vvmax/calc_beamvals.py b/papers/Vvmax/calc_beamvals.py new file mode 100644 index 00000000..25a3397a --- /dev/null +++ b/papers/Vvmax/calc_beamvals.py @@ -0,0 +1,243 @@ +""" +Script to calculate V and Vmax for CRAFT FRBs for the "VVmax" paper (Arcus et al) +""" + +# imports from zdm +from zdm import pcosmic +from zdm import loading as loading +from zdm import vvmax + +# standard python imports +from scipy import interpolate +import numpy as np +import os +from matplotlib import pyplot as plt +from scipy.special import jv as BessJ +from scipy import constants +from pkg_resources import resource_filename +from zdm import cosmology as cos +from scipy.integrate import quad + + + +# initialises cosmology and distance measures + + +def main(): + """ + + Main program to calculate V and Vmax values for the FRBs in fname + + INPUTS: + fname [string]: data file containing the FRBs of interest + outfile [string]: output file base for writing output + Nsfr [float]: scaling with star-formation rate + alpha [float]: frequency scaling of FRBs: F~nu^alpha + loc [bool]: True if using localised sample + False if z should be estimated through Macquart relation + minz [None or float]: if float, minimum redshift for FRBs + with low DM + write [bool]: if True, write data to outfile + laststrings [None or list of strings]: list of strings + to append for writing output + LimZ [None or float]: maximum redshift for V/Vmax calculations + zfrac [float]: if using minz, fractional distance to assume + between minz and maximum redshift for FRBs with otherwise + negative Macquart redshifts + + RETURNS: + laststrings: returns strings which otherwise would have been written + if laststrings is None, or otherwise returns input value + """ + # initialises cosmology and distance measures + #state = init_cos() + + # loads unlocalised data, gets Macquart z + names,DM,freqMHz,SNR,SNRth,DMG,tsamp,width,BeamThetaDeg,newFnu,zloc,Nants = load_unloc_frb_data("Data/all_unloc_frb_data.dat") + + # loads beamshape data + bvals,omegab = load_beamshape() + Bvals = calc_Bvals(BeamThetaDeg,freqMHz) + + for i,name in enumerate(names): + print(name,Bvals[i]) + + + +def load_data(fname): + """ + Edits the data used for the V/Vmax calculation according to various criteria + + Returns edited FRB properties + """ + + names,DM,freqMHz,SNR,SNRth,DMG,tsamp,width,BeamThetaDeg,Fnu,zDM,Nants \ + = load_loc_frb_data(fname) + + return names,freqMHz,BeamThetaDeg,Fnu + +def integrate_volume(zmax,Nsfr=0.): + """ + Integrates dV/dz from 0 to z + Weights according to SFR**n + """ + result = quad(dVdtaudz,0,zmax,args=[Nsfr]) + return result[0] + +def dVdtaudz(z,Nsfr): + """ + Volume element per dz per solid angle + """ + weighted_dVdtau = cos.dVdtau(z) * cos.sfr_evolution(z,Nsfr[0]) + return weighted_dVdtau + +def plot_fluence(DM,Fluences,Fnu): + """ + Creates a plot of the "Fluences" calculated by Clancy + vs the "Fnu" calculated by Wayne + """ + plt.figure() + plt.scatter(DM, Fluences/Fnu) + plt.xlabel("DM") + plt.ylabel("James F / Arcus F") + plt.ylim(0,2) + #plt.xscale("log") + #plt.yscale("log") + plt.savefig("frb_fluences.pdf") + plt.close() + +def calc_fluence(F0,Bvals,SNR,w,tsamp,DM,freq,Nants): + """ + Calculates fluence of the burst + Uses dodgy 4.15 ms dispersion measure calc + """ + SNRthresh = 9.5 + + dmsmear = DM * 4.15 * 1e6 * ((freq-0.5)**-2 - (freq+0.5)**-2) + + #ArcusEfficiency + #eta0=1. + #efficiency = eta0 * (1 + (tsamp/w)**2 + (dmsmear/w)**2)**-0.5 + + # only if we forget that the measured width is actually inclusive + # of these factors + #weff = (w**2 + tsamp**2 + dmsmear**2)**0.5 + #efficiency = weff**-0.5 + + # realises that we already include tsamp and dmsmear in w + # but wmin *must* be at least equal to dm smearing and tsamp + wmin = (dmsmear**2 + tsamp**2)**0.5 + efficiency = np.zeros([wmin.size]) + toolow = np.where(w=wmin)[0] + efficiency[toolow] = wmin[toolow]**-0.5 + efficiency[OK] = w[OK]**-0.5 + #if w < wmin: + # efficiency = wmin**-0.5 + #else: + # efficiency = w**-0.5 + + #efficiency=1. + + #efficiency = 1. + F = F0 * (SNR/SNRthresh) /Nants**0.5 /Bvals / efficiency + return F + +def calc_Bvals(thetasDeg,freqMHz): + """ + Calculates the FRB fluence assuming a certain threshold, SNR, and + beam centre distance + """ + # coefficient of Airy function is lambda/D + wavelength = constants.speed_of_light/1e6/freqMHz + radius = 6 # ASKAP diameter in m + Diam = radius * 2. + thetasRad = thetasDeg * np.pi/180. + stheta = np.sin(thetasRad) + k = 2. * np.pi/wavelength + # uses an airy function we should use a Gaussian like everybody else... + sigma = 1.1 * constants.speed_of_light/Diam/freqMHz/1e6/(2 * (2*np.log(2))**0.5) + + Bvals = np.exp(-0.5*(thetasRad/sigma)**2) + #inputs = stheta * k * radius + #Bvals = airy(inputs) + return Bvals + + +def plot_beamshape(theta,B): + """ + plots beamshape + """ + plt.figure() + plt.scatter(theta,B) + plt.xlabel("Offset (deg)") + plt.ylabel("B") + plt.tight_layout() + plt.savefig("frb_b_values.pdf") + plt.close() + +def airy(x): + """ + airy disk function + """ + + val = (2. * BessJ(1,x)/x)**2 + return val + +def load_beamshape(): + """ + loads askap beamshape info + """ + import os + sdir = os.path.join(resource_filename('zdm', 'data'), 'BeamData/') + bvals = np.load(sdir+"lat50_log_bins.npy") + omegab = np.load(sdir + "lat50_log_hist.npy") + return bvals,omegab + +def load_loc_frb_data(fname): + """ + Loads table of localised FRBs + """ + data = np.loadtxt(fname) + names = data[:,0] + DM = data[:,2] + freqMHz = data[:,4] + SNR = data[:,1] + #SNRth = data[:,4] + DMG = data[:,3] + tsamp = data[:,5] + width = data[:,6] + BeamThetaDeg = data[:,7] + oldFnu = data[:,8] + zloc = data[:,9] + Nants = data[:,10] + newFnu = data[:,11] + + SNRth = np.full([newFnu.size],9.5) + + return names,DM,freqMHz,SNR,SNRth,DMG,tsamp,width,BeamThetaDeg,newFnu,zloc,Nants + + +def load_unloc_frb_data(filename): + """ + Loads table of unlocalised FRBs + + Uses new fluence data from Ryan + """ + data = np.loadtxt(filename) + names = data[:,0] + DM = data[:,1] + freqMHz = data[:,2] + SNR = data[:,3] + SNRth = data[:,4] + DMG = data[:,5] + tsamp = data[:,6] + width = data[:,7] + BeamThetaDeg = data[:,8] + oldFnu = data[:,9] + zDM = data[:,10] + Nants = data[:,11] + newFnu = data[:,12] + return names,DM,freqMHz,SNR,SNRth,DMG,tsamp,width,BeamThetaDeg,newFnu,zDM,Nants + +main() diff --git a/papers/lsst/README.txt b/papers/lsst/README.txt new file mode 100644 index 00000000..162624af --- /dev/null +++ b/papers/lsst/README.txt @@ -0,0 +1,7 @@ +Directory for paper estimating p(z) for LSST + +We begin with theree instruments: + - CRAFT/CRACO + - MeerTRAP coherent + - SKA mid + diff --git a/papers/lsst/meerkat_mr.txt b/papers/lsst/meerkat_mr.txt new file mode 100644 index 00000000..327c8558 --- /dev/null +++ b/papers/lsst/meerkat_mr.txt @@ -0,0 +1,13 @@ +# r-band magnitudes of FRB hosts from Table B1 of Pastor-Morales et al https://arxiv.org/pdf/2507.05982 +# FRB zspec mr loc +20220222 0.853 23.86 +20220224 0.6271 21.63 +20230125 0.3265 22.12 +20230503 -0.32 20.11 +20230613 0.3923 20.132 +20230827 0 22.03 +20230827 0 25.7 +20230907 0.4638 19.83 +20231010 -0.61 21.24 +20231020 0.4775 0.46 +20231210 -0.5 21.19 diff --git a/papers/lsst/sim_pz.py b/papers/lsst/sim_pz.py new file mode 100644 index 00000000..f3051c0b --- /dev/null +++ b/papers/lsst/sim_pz.py @@ -0,0 +1,430 @@ +""" +Simulates p(z) for lSST paper +""" + +""" +This script creates zdm grids for MeerTRAP + eerTRAPcoherent'] +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states +from zdm import optical +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources +from scipy.interpolate import CubicSpline +from scipy import stats +import matplotlib + +defaultsize=18 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + +#r-band limits 24.7, 27.5(single visit, 10 year, these are 5 sigma limits) + +def main(opdir="Data/"): + + meerkat_z,meerkat_mr = read_meerkat() + + Load=True + repeaters=False + + Rlim0 = 19.8 # existing magnitude limits + Rlim1 = 24.7 + Rlim2 = 27.5 + + names=['CRAFT_CRACO_1300','MeerTRAPcoherent','SKA_mid'] + labels=["ASKAP CRACO", "MeerKAT/SKA-Mid","SKA-Mid"] + prefixes=["CRACO","MeerTRAP","SKA_Mid"] + linestyles = ["-","--",":"] + imax=2 # because SKA and mid are so similar + + if not os.path.exists(opdir): + os.mkdir(opdir) + + Rs,Rrmss,Rzvals,sbar,srms = process_rbands() + + plot_R(Rs,Rrmss,Rzvals,sbar,srms,opdir,Rlim1,Rlim2) + + if not Load: + #gs,ss = get_surveys_grids(names,opdir,repeaters=True,Test=False) + ss,gs = get_surveys_grids(names,opdir,repeaters=False,Test=False) + + plot_efficiencies(gs,ss,opdir,prefixes) + + plot_beams(ss,labels,opdir) + # plots telescope efficiencies at z=0 + + zvals = gs[0].zvals + else: + zvals = np.load(opdir+"zvals.npy") + + nz = zvals.size + + if not Load: + ##### gets mr distribution for each z ##### + NR=400 + Rvals = np.linspace(0,40,NR+1) # 401 values - defining bin edges + Rbars = (Rvals[:-1]+Rvals[1:])/2. + Rhist = np.zeros([nz,NR]) + fz0 = np.zeros([nz]) + fz1 = np.zeros([nz]) + fz2 = np.zeros([nz]) + iz0 = np.where(Rbars < Rlim0)[-1] + iz1 = np.where(Rbars < Rlim1)[-1] + iz2 = np.where(Rbars < Rlim2)[-1] + + for i,z in enumerate(zvals): + if z < Rzvals[0]: + continue + elif z > Rzvals[-1]: + continue + else: + Rbar = sbar(z) + Rrms = srms(z) + norm = stats.Normal(mu=Rbar,sigma=Rrms) + vals = norm.cdf(Rvals) + dv = vals[1:] - vals[:-1] + + # contains contributions from that redshift range + Rhist[i,:] = dv + + # fractions up to that redshift + fz0[i] = norm.cdf(Rlim0) + fz1[i] = norm.cdf(Rlim1) + fz2[i] = norm.cdf(Rlim2) + np.save(opdir+"fz_19.8.npy",fz0) + np.save(opdir+"fz_24.7.npy",fz1) + np.save(opdir+"fz_27.5.npy",fz2) + np.save(opdir+"Rhist.npy",Rhist) + np.save(opdir+"Rvals.npy",Rvals) + np.save(opdir+"Rbars.npy",Rbars) + else: + fz0 = np.load(opdir+"fz_19.8.npy") + fz1 = np.load(opdir+"fz_24.7.npy") + fz2 = np.load(opdir+"fz_27.5.npy") + Rhist = np.load(opdir+"Rhist.npy") + Rvals = np.load(opdir+"Rvals.npy") + Rbars = np.load(opdir+"Rbars.npy") + + + plt.figure() + plt.xlabel("z") + plt.ylabel("$f_{m_r}$") + plt.plot(zvals,fz1,label="$f_{24.7}$") + plt.plot(zvals,fz2,label="$f_{27.5}$") + plt.legend() + plt.tight_layout() + plt.savefig(opdir+"fraction_visible.png") + plt.close() + + DPplot(zvals,[fz1],["$m_r = 24.7$"],opdir + "DP_fraction_visible.png",color="orange") + + ####### p(z) plot ##### + plt.figure() + plt.xlabel("z") + plt.ylabel("N(z) [a.u.]") + ax1 = plt.gca() + + ####### p(R) plot ##### + plt.figure() + plt.xlabel("$m_r$") + plt.ylabel("$p(m_r)$ [a.u.]") + ax2 = plt.gca() + + for i,prefix in enumerate(prefixes): + if i==imax: + break + if Load: + pz = np.load(opdir+prefixes[i]+"_pz.npy") + pz0 = np.load(opdir+prefixes[i]+"_fpz0.npy") + pz1 = np.load(opdir+prefixes[i]+"_fpz1.npy") + pz2 = np.load(opdir+prefixes[i]+"_fpz2.npy") + else: + s=ss[i] + g=gs[i] + ########## gets p(z) ########## + if repeaters: + # plots individual sources + rates = (g.exact_singles+g.exact_reps)*g.Rc + else: + rates = g.rates*s.TOBS * 10**g.state.FRBdemo.lC + pz = np.sum(rates,axis=1) + dz = g.zvals[1]-g.zvals[0] + + # Norm is 7 days per week, with per redshift bin factored in + norm = 7./dz + pz *= norm + + pz0 = pz*fz0 + pz1 = pz*fz1 + pz2 = pz*fz2 + + np.save(opdir+"zvals.npy",g.zvals) + np.save(opdir+prefixes[i]+"_pz.npy",pz) + np.save(opdir+prefixes[i]+"_fpz0.npy",pz0) + np.save(opdir+prefixes[i]+"_fpz1.npy",pz1) + np.save(opdir+prefixes[i]+"_fpz2.npy",pz2) + + # plots p(z) + plt.sca(ax1) + norm = np.max(pz) + print(i,"Norm is ",norm) + + plt.plot(zvals,pz/norm,label=labels[i],linestyle="-") + plt.plot(zvals,pz0/norm,linestyle="-.",color=plt.gca().lines[-1].get_color()) + plt.plot(zvals,pz1/norm,linestyle="--",color=plt.gca().lines[-1].get_color()) + plt.plot(zvals,pz2/norm,linestyle=":",color=plt.gca().lines[-1].get_color()) + + print("For survey ",prefixes[i]," number of FRBs will be ",np.sum(pz),np.sum(pz0),np.sum(pz1),\ + np.sum(pz2),np.sum(pz0)/np.sum(pz),np.sum(pz1)/np.sum(pz),np.sum(pz2)/np.sum(pz)) + + # plots total magnitude distribution + pzR = (Rhist.T*pz).T + mag_hist = np.sum(pzR,axis=0) + + np.save(opdir+prefixes[i]+"mag_hist.npy",mag_hist) + + plt.sca(ax2) + norm = np.max(mag_hist) + + plt.plot(Rbars,mag_hist/norm,label=labels[i]) + + + plt.sca(ax1) + plt.ylim(0,1.02) + plt.xlim(0,6) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+"lsst_pz.png") + plt.close() + + plt.sca(ax2) + plt.xlim(10,35) + plt.ylim(0,1.02) + + #meerkat_z,meerkat_mr + mrbins=np.linspace(10,30,21) + weights = np.full([11],0.25) + + plt.hist(meerkat_mr,bins=mrbins,weights=weights,label="MK 2023 data") + + + plt.plot([Rlim1,Rlim1],[0,1],linestyle=":",color="black") + plt.plot([Rlim2,Rlim2],[0,1],linestyle=":",color="black") + plt.text(Rlim1+0.1,0.1,"$m_r=$"+str(Rlim1),rotation=90) + plt.text(Rlim2+0.1,0.1,"$m_r=$"+str(Rlim2),rotation=90) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+"lsst_pR.png") + plt.close() + + DPplot(zvals,[pz,pz1],["all FRBs","LSST"],opdir + "DP_pz.png",color="orange",legend=False) + DPplot(zvals,[pz,pz1,pz0],["all FRBs","LSST","Now"],opdir + "DP_pz0.png",color="orange",legend=False) + + DPplot(zvals,[pz1,pz0],["LSST","Now"],opdir + "DP_lsst_vs_now.png",color="orange",legend=False) + +def DPplot(zvals,yvals,labels,outfile,color="orange",legend=True): + + fig = plt.figure() + + linestyles=["-","--",":","-."] + # Plot in orange + Norm=-1 + for i,yval in enumerate(yvals): + norm = np.max(yval) + if norm > Norm: + Norm=norm + + for i,yval in enumerate(yvals): + plt.plot(zvals, yval/Norm, label=labels[i], color=color,linestyle=linestyles[i]) + + # Labels in orange + plt.xlabel("redshift", color=color) + plt.ylabel("fraction visible", color=color) + + # Axis limits + plt.xlim(0, 3) + plt.ylim(0, 1.05) + + # Make tick labels orange + plt.tick_params(axis='both', colors=color) + + # Make the axes spines orange + for spine in plt.gca().spines.values(): + spine.set_color(color) + + if legend: + # Legend text + frame in orange + leg = plt.legend() + for text in leg.get_texts(): + text.set_color(color) + leg.get_frame().set_edgecolor(color) + + fig.set_facecolor("none") + plt.gca().set_facecolor("none") + + plt.tight_layout() + plt.savefig(outfile,transparent=True) + plt.close() + +def plot_beams(ss,labels,opdir): + # plots telescope beams + plt.figure() + for i,s in enumerate(ss): + plt.scatter(s.beam_b,s.beam_o,label=labels[i]) + plt.xlabel("B") + plt.ylabel("$\\Omega(B)$") + plt.xscale("log") + plt.yscale("log") + plt.legend() + plt.tight_layout() + plt.savefig(opdir+"beams.png") + plt.close() + +def read_meerkat(): + """ + returns z and mr data from Pastor-Morales et al + https://arxiv.org/pdf/2507.05982 + """ + + data=np.loadtxt("meerkat_mr.txt",comments='#') + z=data[:,1] + mr = data[:,2] + z = np.abs(z) + return z,mr + +def plot_R(Rbars,Rrmss,Rzvals,sbar,srms,opdir,Rlim1,Rlim2): + # plot of mean and rms from Gaussian assumption + plt.figure() + plt.xlabel("z") + plt.ylabel("$m_r$") + plt.plot(Rzvals,Rbars,label="$\\mu_r$") + plt.plot(Rzvals,Rbars+Rrmss,linestyle="--",label="$\\mu_r \\pm \\sigma_r$") + plt.plot(Rzvals,Rbars-Rrmss,linestyle="--",color=plt.gca().lines[-1].get_color()) + plt.plot([0,6],[Rlim1,Rlim1],linestyle=":",color="black") + plt.plot([0,6],[Rlim2,Rlim2],linestyle=":",color="black") + + plt.text(3,Rlim1-1.5,"$m_r=$"+str(Rlim1)) + plt.text(0.01,Rlim2+0.2,"$m_r=$"+str(Rlim2)) + plt.legend() + plt.xlim(0,6) + plt.tight_layout() + plt.savefig(opdir+"Rbar_rms_z.png") + plt.close() + + +def plot_efficiencies(gs,ss,opdir,prefixes): + for i,s in enumerate(ss): + plt.figure() + g=gs[i] + + for j,w in enumerate(s.wlist): + plt.plot(g.dmvals,s.efficiencies[j,0,:],label="w="+str(w)[0:5]) # at z=0 + plt.xlabel("DM") + plt.ylabel("$\\epsilon$") + plt.yscale("log") + plt.ylim(0.1,2) + plt.legend(fontsize=8,loc="upper right") + plt.tight_layout() + plt.savefig(opdir+prefixes[i]+"_efficiencies.png") + plt.close() + +def get_surveys_grids(names,opdir,repeaters=True,Test=False): + + # approximate best-fit values from recent analysis + # load states from Hoffman et al 2025 + # use b or d for rep + state = states.load_state("HoffmannEmin25",scat="updated",rep='b') + + + # artificially add repeater data - we can't actually know this, + # because we don't have time per field. Just using one day for now + survey_dict={} + survey_dict["TFIELD"] = 24. + survey_dict["TOBS"] = 24. + survey_dict["NFIELDS"] = 1 + + survey_dict["NORM_REPS"] = 0 + survey_dict["NORM_SINGLES"] = 0 + survey_dict["NORM_FRB"] = 0 + + survey_dict["NBINS"] = 10 + survey_dict["BTHRESH"] = 0.01 + + + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('data/Surveys') + #names=['SKA_mid'] + + + # simple vs complex + if Test: + ndm=50 + nz=50 + dmmax=4000 + zmax=4 + survey_dict["WMETHOD"] = 2 + else: + ndm=1400 + nz=600 + dmmax=7000 + zmax=6 + # uses redshift-dependent scattering. This takes longer + # - by a factor of a few! + survey_dict["WMETHOD"] = 3 + + ss,gs = loading.surveys_and_grids(survey_names=names,repeaters=repeaters,init_state=state, + sdir=sdir,survey_dict=survey_dict,nz=nz,zmax=zmax,ndm=ndm,dmmax=dmmax) + return ss,gs + +def process_rbands(): + """ + Returns parameters of the host magnitude distribution as a function of redshift + """ + #FRBlist=["FRB20180301A FRB20180916B FRB20190520B FRB20201124A FRB20210410D FRB20121102A FRB20180924B FRB20181112A FRB20190102C FRB20190608B FRB20190611B FRB20190711A FRB20190714A FRB20191001A FRB20200430A FRB20200906A FRB20210117A FRB20210320C FRB20210807D FRB20211127I FRB20211203C FRB20211212A FRB20220105A] + table = optical.load_marnoch_data() + colnames = table.colnames + # gets FRBs + frblist=[] + for name in colnames: + if name[0:3]=="FRB": + frblist.append(name) + zlist = table["z"] + nz = zlist.size + nfrb = len(frblist) + Rmags = np.zeros([nfrb,nz]) + + for i,frb in enumerate(frblist): + + Rmags[i,:] = table[frb] + + # gets mean and rms + Rbar = np.average(Rmags,axis=0) + Rrms = (np.sum((Rmags - Rbar)**2,axis=0)/(nfrb-1))**0.5 + + sbar = CubicSpline(zlist,Rbar) + srms = CubicSpline(zlist,Rrms) + + + return Rbar,Rrms,zlist,sbar,srms +main() diff --git a/zdm/beam_generator/askap_tsys_polyval.npy b/zdm/beam_generator/askap_tsys_polyval.npy new file mode 100644 index 00000000..b324089f Binary files /dev/null and b/zdm/beam_generator/askap_tsys_polyval.npy differ diff --git a/zdm/beam_generator/sim_MeerTRAP_beams.py b/zdm/beam_generator/sim_MeerTRAP_beams.py index d963846b..87b97415 100644 --- a/zdm/beam_generator/sim_MeerTRAP_beams.py +++ b/zdm/beam_generator/sim_MeerTRAP_beams.py @@ -13,7 +13,7 @@ from matplotlib import pyplot as plt -def main(plot=True,freq=1284,gsize=800,spacing=0.001): +def main(plot=True,freq_MHz=1284,gsize=800,spacing=0.001): """ Frequency: @@ -34,12 +34,21 @@ def main(plot=True,freq=1284,gsize=800,spacing=0.001): # loops through beams, filling the grid Diam = 13.5 - beamw = get_beamwidth(freq,Diam,const=1.09) + beamw = get_beamwidth(freq_MHz,Diam,const=1.09) + primary_beam = grid_beam(xgrid,ygrid,0,0,beamw) + extent=[xgrid[0,0],xgrid[0,-1],ygrid[0,0],ygrid[-1,0]] - #plt.figure() - #plt.imshow(beams,origin='lower') - #plt.show() + if False: + # plot primary beamwidth + plt.figure() + image = plt.imshow(primary_beam,origin='lower',extent=extent) + cbar = plt.colorbar() + image.set_clim(0,1) + plt.xlabel("x [deg]") + plt.ylabel("y [deg]") + plt.tight_layout() + plt.show() ############# Get tied beam specifications ############ @@ -82,7 +91,7 @@ def main(plot=True,freq=1284,gsize=800,spacing=0.001): #plt.imshow(envelope,origin='lower') #plt.savefig("MeerTRAP_beam.pdf") #plt.close() - plot_envelope(envelope,xgrid[0,:],outfile="MeerTRAP_beam.pdf") + plot_envelope(envelope,xgrid[0,:],outfile="MeerTRAP_beam.png") # makes a beam histogram bins = np.logspace(-4,0,401) @@ -133,7 +142,7 @@ def gen_triangular_grid(Nx=32,Ny=24,sep=0.01): return x,y -def get_beamwidth(freq,D,const=1.09): +def get_beamwidth(freq_MHz,D,const=1.09): """ Gets beamwidth with frequency in Hz returns Gaussian sigma in degrees @@ -141,7 +150,7 @@ def get_beamwidth(freq,D,const=1.09): freq [float]: frequency in HHz """ c_light = 299792458. - HPBW=const*(c_light/(freq))/D # from RACS + HPBW=const*(c_light/(freq_MHz*1e6))/D # from RACS sigma=(HPBW/2.)*(2*np.log(2))**-0.5 deg_sigma = sigma * 180./np.pi return deg_sigma @@ -191,7 +200,9 @@ def plot_envelope(env,coords,outfile): themax = coords[-1] + dc/2. plt.figure() - plt.imshow(env,extent=(themin,themax,themin,themax),origin='lower') + image = plt.imshow(env,extent=(themin,themax,themin,themax),origin='lower') + plt.colorbar() + image.set_clim(0,1) plt.xlabel('x [deg]') plt.ylabel('y [deg]') plt.tight_layout() diff --git a/zdm/beam_generator/sim_craco_beam.py b/zdm/beam_generator/sim_craco_beam.py index 378ae8cd..92b7a11d 100644 --- a/zdm/beam_generator/sim_craco_beam.py +++ b/zdm/beam_generator/sim_craco_beam.py @@ -9,6 +9,7 @@ import os import logging from matplotlib import pyplot as plt +import importlib.resources as resources logging.basicConfig( level = logging.INFO, @@ -57,9 +58,9 @@ def hist_craco_beams(footprint, pitch, freq, gsize, gpix,plot=False): bcs = 10**np.linspace(bmin + db/2.,bmax - db/2.,nbins) - basename = f"./hist_craco_{footprint}_p{pitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}.npy" + basename = f"./hist_craco_{footprint}_p{pitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}" - np.save(basename,h) + np.save(basename+"_.npy",h) np.save("craco_histogram_bins.npy",binedges) if plot: @@ -95,11 +96,17 @@ def __init__( self.avals = self._load_aval() def _load_tsys(self): - coeffs = np.load("askap_tsys_polyval.npy") - return np.polyval(coeffs, self.freq) + """ + Tsys from Hotan et al + """ + return get_tsys(self.freq) + #infile = os.path.join(resources.files('zdm'),'beam_generator','askap_tsys_polyval.npy') + #coeffs = np.load(infile) + #return np.polyval(coeffs, self.freq) def _load_aval(self): - data = np.loadtxt("avals.dat") + afile = os.path.join(resources.files('zdm'),'beam_generator','avals.dat') + data = np.loadtxt(afile) x = data[:,0] y = data[:,1] newx = np.concatenate([x,-x[::-1]]) @@ -150,6 +157,10 @@ def primary_response(self, xpoints, ypoints, beamx, beamy, beamw=None): if beamw is None: beamw = self.beamwidth return Gauss(offsets, beamw) +def get_tsys(freq): + infile = os.path.join(resources.files('zdm'),'beam_generator','askap_tsys_polyval.npy') + coeffs = np.load(infile) + return np.polyval(coeffs, freq) class CracoBeam: @@ -218,7 +229,7 @@ def _make_response_grid(gsize=10., gpix=2560.): ypoints = np.linspace(-gsize/2, gsize/2, gpix) return xpoints, ypoints -def _sim_one_beam(ibeam, xpoints, ypoints, askapbeam, cracobeam, ): +def _sim_one_beam(ibeam, xpoints, ypoints, askapbeam, cracobeam, primary ): logger.info(f"start simulating craco response for beam{ibeam}...") beamx = askapbeam.beamscentre[0][ibeam] beamy = askapbeam.beamscentre[1][ibeam] @@ -228,6 +239,9 @@ def _sim_one_beam(ibeam, xpoints, ypoints, askapbeam, cracobeam, ): ### primary beam response logger.info(f"get primary beam response for beam{ibeam}...") primresponse = askapbeam.primary_response(xpoints, ypoints, beamx, beamy, ) + if primary: + return beamaval * primresponse + ### craco related response logger.info(f"get primary beam response for beam{ibeam}...") gridresponse = cracobeam.grid_response(xpoints, ypoints, beamx, beamy, ) @@ -240,7 +254,7 @@ def _sim_one_beam(ibeam, xpoints, ypoints, askapbeam, cracobeam, ): def get_craco_allbeams( footprint="square", pitch=0.9, freq=920.5, # askap footprint related, fov=1.1, npix=256, sbeam=40./3600., # craco related - gsize=10., gpix=2560 # response gridding params + gsize=10., gpix=2560, primary=False # response gridding params ): xpoints, ypoints = _make_response_grid(gsize=gsize, gpix=gpix) askapbeam = AskapBeam(footprint=footprint, pitch=pitch, freq=freq) @@ -248,8 +262,16 @@ def get_craco_allbeams( beamsresponse = np.zeros((36, gpix, gpix)) for ibeam in range(36): # 36 beams - beamsresponse[ibeam] = _sim_one_beam(ibeam, xpoints, ypoints, askapbeam, cracobeam, ) - + beamsresponse[ibeam] = _sim_one_beam(ibeam, xpoints, ypoints, askapbeam, cracobeam, primary) + + # multiplies by Tsys factor + tsys = askapbeam.tsys + tnorm = get_tsys(1250) # relative to 1.25 GHz + # relative to min Tsys of 80 K + fweight = (tnorm/tsys) + beamsresponse *= fweight + print("Freq ",freq," tsys ",tsys," fweights ",fweight) + savefname = f"./craco_{footprint}_p{pitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}.npy" np.save(savefname, beamsresponse) logger.info(f"save data to {savefname}...") @@ -265,6 +287,7 @@ def main(): parser.add_argument("-sb", "--sbeam", help="size of the synthesized beam", type=float, default=40./3600.) parser.add_argument("-gs", "--gridsize", help="grid size in the unit of degree", type=float, default=10.) parser.add_argument("-gn", "--gridnpix", help="number of pixel in the grid", type=int, default=2560) + parser.add_argument("--primary", help="simulate the primary beam only", type=bool, default=False) values = parser.parse_args() footprint=values.footprint @@ -275,16 +298,16 @@ def main(): sbeam=values.sbeam gsize=values.gridsize gpix=values.gridnpix + primary=values.primary opfile = f"./craco_{footprint}_p{pitch:.2f}_f{freq:.1f}MHz_f{gsize:.1f}d_npix{gpix}.npy" if not os.path.exists(opfile): get_craco_allbeams( footprint=footprint, pitch=pitch, freq=freq, fov=values.fov, npix=values.npix, sbeam=sbeam, gsize=gsize, - gpix=gpix + gpix=gpix,primary=primary ) - else: - hist_craco_beams(footprint, pitch, freq, gsize, gpix,plot=True) + hist_craco_beams(footprint, pitch, freq, gsize, gpix,plot=False) main() diff --git a/zdm/data/BeamData/3ms_CRACO_1300_bins.npy b/zdm/data/BeamData/3ms_CRACO_1300_bins.npy new file mode 100644 index 00000000..ecd42be1 Binary files /dev/null and b/zdm/data/BeamData/3ms_CRACO_1300_bins.npy differ diff --git a/zdm/data/BeamData/3ms_CRACO_1300_hist.npy b/zdm/data/BeamData/3ms_CRACO_1300_hist.npy new file mode 100644 index 00000000..b1565888 Binary files /dev/null and b/zdm/data/BeamData/3ms_CRACO_1300_hist.npy differ diff --git a/zdm/data/BeamData/3ms_CRACO_900_bins.npy b/zdm/data/BeamData/3ms_CRACO_900_bins.npy new file mode 100644 index 00000000..ecd42be1 Binary files /dev/null and b/zdm/data/BeamData/3ms_CRACO_900_bins.npy differ diff --git a/zdm/data/BeamData/3ms_CRACO_900_hist.npy b/zdm/data/BeamData/3ms_CRACO_900_hist.npy new file mode 100644 index 00000000..e7673a3a Binary files /dev/null and b/zdm/data/BeamData/3ms_CRACO_900_hist.npy differ diff --git a/zdm/data/BeamData/CRACO_1300_bins.npy b/zdm/data/BeamData/CRACO_1300_bins.npy new file mode 100644 index 00000000..ecd42be1 Binary files /dev/null and b/zdm/data/BeamData/CRACO_1300_bins.npy differ diff --git a/zdm/data/BeamData/CRACO_1300_hist.npy b/zdm/data/BeamData/CRACO_1300_hist.npy new file mode 100644 index 00000000..fe724009 Binary files /dev/null and b/zdm/data/BeamData/CRACO_1300_hist.npy differ diff --git a/zdm/data/BeamData/CRACO_900_bins.npy b/zdm/data/BeamData/CRACO_900_bins.npy new file mode 100644 index 00000000..ecd42be1 Binary files /dev/null and b/zdm/data/BeamData/CRACO_900_bins.npy differ diff --git a/zdm/data/BeamData/CRACO_900_hist.npy b/zdm/data/BeamData/CRACO_900_hist.npy new file mode 100644 index 00000000..0939c35e Binary files /dev/null and b/zdm/data/BeamData/CRACO_900_hist.npy differ diff --git a/zdm/data/BeamData/craco_square_p0.90_f1300.0MHz_f10.0d_npix2560_bins.npy b/zdm/data/BeamData/craco_square_p0.90_f1300.0MHz_f10.0d_npix2560_bins.npy new file mode 100644 index 00000000..ecd42be1 Binary files /dev/null and b/zdm/data/BeamData/craco_square_p0.90_f1300.0MHz_f10.0d_npix2560_bins.npy differ diff --git a/zdm/data/BeamData/craco_square_p0.90_f1300.0MHz_f10.0d_npix2560_hist.npy b/zdm/data/BeamData/craco_square_p0.90_f1300.0MHz_f10.0d_npix2560_hist.npy new file mode 100644 index 00000000..990c1e59 Binary files /dev/null and b/zdm/data/BeamData/craco_square_p0.90_f1300.0MHz_f10.0d_npix2560_hist.npy differ diff --git a/zdm/data/Efficiencies/3ms_craco_1300_mask.npy b/zdm/data/Efficiencies/3ms_craco_1300_mask.npy new file mode 100644 index 00000000..a88941f4 Binary files /dev/null and b/zdm/data/Efficiencies/3ms_craco_1300_mask.npy differ diff --git a/zdm/data/Efficiencies/3ms_craco_900_mask.npy b/zdm/data/Efficiencies/3ms_craco_900_mask.npy new file mode 100644 index 00000000..be67d437 Binary files /dev/null and b/zdm/data/Efficiencies/3ms_craco_900_mask.npy differ diff --git a/zdm/data/Efficiencies/craco_1300_mask.npy b/zdm/data/Efficiencies/craco_1300_mask.npy new file mode 100644 index 00000000..96b758b9 Binary files /dev/null and b/zdm/data/Efficiencies/craco_1300_mask.npy differ diff --git a/zdm/data/Efficiencies/craco_900_mask.npy b/zdm/data/Efficiencies/craco_900_mask.npy new file mode 100644 index 00000000..e58cb2af Binary files /dev/null and b/zdm/data/Efficiencies/craco_900_mask.npy differ diff --git a/zdm/data/Efficiencies/high_ICS_mask.npy b/zdm/data/Efficiencies/high_ICS_mask.npy new file mode 100644 index 00000000..333781cb Binary files /dev/null and b/zdm/data/Efficiencies/high_ICS_mask.npy differ diff --git a/zdm/data/Efficiencies/low_ICS_mask.npy b/zdm/data/Efficiencies/low_ICS_mask.npy new file mode 100644 index 00000000..823d1d63 Binary files /dev/null and b/zdm/data/Efficiencies/low_ICS_mask.npy differ diff --git a/zdm/data/Efficiencies/mean_ICS_mask.npy b/zdm/data/Efficiencies/mean_ICS_mask.npy new file mode 100644 index 00000000..961577a3 Binary files /dev/null and b/zdm/data/Efficiencies/mean_ICS_mask.npy differ diff --git a/zdm/data/Efficiencies/mid_ICS_mask.npy b/zdm/data/Efficiencies/mid_ICS_mask.npy new file mode 100644 index 00000000..13df54dd Binary files /dev/null and b/zdm/data/Efficiencies/mid_ICS_mask.npy differ diff --git a/zdm/data/Surveys/CHIME/CHIME_decbin_0_of_6.ecsv b/zdm/data/Surveys/CHIME/CHIME_decbin_0_of_6.ecsv index 7a78239b..2dc952fc 100644 --- a/zdm/data/Surveys/CHIME/CHIME_decbin_0_of_6.ecsv +++ b/zdm/data/Surveys/CHIME/CHIME_decbin_0_of_6.ecsv @@ -23,7 +23,7 @@ # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 4,\n \"NORM_REPS\": 0,\n \"TOBS\": 0.6783198770426007\n },\n \ # \"telescope\": {\n \"BEAM\": \"CHIME/Nbounds6/chime_bound_0_of_6\",\n \"BMETHOD\": 3,\n \ # \ \"DIAM\": -1.0,\n \"NBEAMS\": 1,\n \"DRIFT_SCAN\": 2,\n \ -# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\",\n \"WMETHOD\": 0\n }\n}"} +# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\"}\n}"} # schema: astropy-2.0 TNS BTHRESH BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20181118A 0 400.0 557.4 31.5 600.0 0.0244 "" "" 1 10.8 9.0 5.0 0.983 1.27 "" "" "" diff --git a/zdm/data/Surveys/CHIME/CHIME_decbin_1_of_6.ecsv b/zdm/data/Surveys/CHIME/CHIME_decbin_1_of_6.ecsv index 3ca70b94..12d06f49 100644 --- a/zdm/data/Surveys/CHIME/CHIME_decbin_1_of_6.ecsv +++ b/zdm/data/Surveys/CHIME/CHIME_decbin_1_of_6.ecsv @@ -23,7 +23,7 @@ # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 23,\n \"NORM_REPS\": 0,\n \"TOBS\": 1.1465105476038173\n },\n \ # \"telescope\": {\n \"BEAM\": \"CHIME/Nbounds6/chime_bound_1_of_6\",\n \"BMETHOD\": 3,\n \ # \ \"DIAM\": -1.0,\n \"NBEAMS\": 1,\n \"DRIFT_SCAN\": 2,\n \ -# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\",\n \"WMETHOD\": 0\n }\n}"} +# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\"}\n}"} # schema: astropy-2.0 TNS BTHRESH BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20180919B 0 400.0 560.2 32.8 600.0 0.0244 "" "" 1 16.1 9.0 5.0 0.983 0.408 "" "" "" diff --git a/zdm/data/Surveys/CHIME/CHIME_decbin_2_of_6.ecsv b/zdm/data/Surveys/CHIME/CHIME_decbin_2_of_6.ecsv index aea38082..3a66b6b1 100644 --- a/zdm/data/Surveys/CHIME/CHIME_decbin_2_of_6.ecsv +++ b/zdm/data/Surveys/CHIME/CHIME_decbin_2_of_6.ecsv @@ -23,7 +23,7 @@ # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 154,\n \"NORM_REPS\": 3,\n \"TOBS\": 3.262931916550108\n },\n \ # \"telescope\": {\n \"BEAM\": \"CHIME/Nbounds6/chime_bound_2_of_6\",\n \"BMETHOD\": 3,\n \ # \ \"DIAM\": -1.0,\n \"NBEAMS\": 1,\n \"DRIFT_SCAN\": 2,\n \ -# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\",\n \"WMETHOD\": 0\n }\n}"} +# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\"}\n}"} # schema: astropy-2.0 TNS BTHRESH BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20180727A 0 400.0 642.1 21.2 600.0 0.0244 "" "" 1 10.4 9.0 5.0 0.983 1.39 "" "" "" diff --git a/zdm/data/Surveys/CHIME/CHIME_decbin_3_of_6.ecsv b/zdm/data/Surveys/CHIME/CHIME_decbin_3_of_6.ecsv index ef109383..7d11de49 100644 --- a/zdm/data/Surveys/CHIME/CHIME_decbin_3_of_6.ecsv +++ b/zdm/data/Surveys/CHIME/CHIME_decbin_3_of_6.ecsv @@ -23,7 +23,7 @@ # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 234,\n \"NORM_REPS\": 12,\n \"TOBS\": 2.2319607791102194\n },\n \ # \"telescope\": {\n \"BEAM\": \"CHIME/Nbounds6/chime_bound_3_of_6\",\n \"BMETHOD\": 3,\n \ # \ \"DIAM\": -1.0,\n \"NBEAMS\": 1,\n \"DRIFT_SCAN\": 2,\n \ -# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\",\n \"WMETHOD\": 0\n }\n}"} +# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\"}\n}"} # schema: astropy-2.0 TNS BTHRESH BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20180725A 0 400.0 715.8 71.6 600.0 0.0244 "" "" 1 19.2 9.0 5.0 0.983 0.296 "" "" "" diff --git a/zdm/data/Surveys/CHIME/CHIME_decbin_4_of_6.ecsv b/zdm/data/Surveys/CHIME/CHIME_decbin_4_of_6.ecsv index 70904bf8..c7ca7e5b 100644 --- a/zdm/data/Surveys/CHIME/CHIME_decbin_4_of_6.ecsv +++ b/zdm/data/Surveys/CHIME/CHIME_decbin_4_of_6.ecsv @@ -23,7 +23,7 @@ # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 50,\n \"NORM_REPS\": 1,\n \"TOBS\": 0.10646560758593053\n },\n \ # \"telescope\": {\n \"BEAM\": \"CHIME/Nbounds6/chime_bound_4_of_6\",\n \"BMETHOD\": 3,\n \ # \ \"DIAM\": -1.0,\n \"NBEAMS\": 1,\n \"DRIFT_SCAN\": 2,\n \ -# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\",\n \"WMETHOD\": 0\n }\n}"} +# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\"}\n}"} # schema: astropy-2.0 TNS BTHRESH BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20180810B 0 400.0 169.1 45.8 600.0 0.0244 "" "" 1 38.6 9.0 5.0 0.983 0.311 "" "" "" diff --git a/zdm/data/Surveys/CHIME/CHIME_decbin_5_of_6.ecsv b/zdm/data/Surveys/CHIME/CHIME_decbin_5_of_6.ecsv index dff8303c..0f15d189 100644 --- a/zdm/data/Surveys/CHIME/CHIME_decbin_5_of_6.ecsv +++ b/zdm/data/Surveys/CHIME/CHIME_decbin_5_of_6.ecsv @@ -23,7 +23,7 @@ # - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 27,\n \"NORM_REPS\": 0,\n \"TOBS\": 0.01590415418623882\n },\n \ # \"telescope\": {\n \"BEAM\": \"CHIME/Nbounds6/chime_bound_5_of_6\",\n \"BMETHOD\": 3,\n \ # \ \"DIAM\": -1.0,\n \"NBEAMS\": 1,\n \"DRIFT_SCAN\": 2,\n \ -# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\",\n \"WMETHOD\": 0\n }\n}"} +# \ \"NBINS\": 1,\n \"WBIAS\": \"CHIME\"}\n}"} # schema: astropy-2.0 TNS BTHRESH BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20180730A 0 400.0 848.9 59.2 600.0 0.0244 "" "" 1 69.5 9.0 5.0 0.983 0.468 "" "" "" diff --git a/zdm/data/Surveys/CRAFT_CRACO_1300.ecsv b/zdm/data/Surveys/CRAFT_CRACO_1300.ecsv new file mode 100644 index 00000000..acc41f99 --- /dev/null +++ b/zdm/data/Surveys/CRAFT_CRACO_1300.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 9.5,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_1300", "DMMASK": "craco_1300_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1392, +# "TRES": 13.8, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +20240826A 387.0 22:38:27.84 -30:08:18.23 0.203 9.6 -1 0 19.0 -60.76 34.57 21.78 1320 240 diff --git a/zdm/data/Surveys/CRAFT_CRACO_1300_3ms.ecsv b/zdm/data/Surveys/CRAFT_CRACO_1300_3ms.ecsv new file mode 100644 index 00000000..35509e0e --- /dev/null +++ b/zdm/data/Surveys/CRAFT_CRACO_1300_3ms.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 7.75,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_1300", "DMMASK": "3ms_craco_1300_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 1372, +# "TRES": 3.456, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 387.0 22:38:27.84 -30:08:18.23 0.203 9.6 -1 0 19.0 -60.76 34.57 21.78 1320 240 diff --git a/zdm/data/Surveys/CRAFT_CRACO_900.ecsv b/zdm/data/Surveys/CRAFT_CRACO_900.ecsv new file mode 100644 index 00000000..8f2d14aa --- /dev/null +++ b/zdm/data/Surveys/CRAFT_CRACO_900.ecsv @@ -0,0 +1,41 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 17,"TOBS": 64.68,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "CRACO_900", "DMMASK": "craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 906, +# "TRES": 13.8, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +20231230A 676.5 07:55:02.63 08:30:51.80 0.506 14.3 69.1 2 212.4 17.97 80.50 97.66 888 288 +20240117A 881.0 03:35:30.73 -15:51:08.98 0.644 14.2 -1 2 205.0 -50.39 35.47 36.91 888 288 +20240203A 454.0 00:35:35.42 15:50:26.65 0.243 12.2 -1 1 117.3 -46.85 36.25 27.87 888 288 +20240208A 260.2 10:36:55.08 -00:57:15.72 -1 10.3 4.3 1 248.3 47.16 38.96 27.51 888 288 +20240210B 396.3 03:40:55.98 -41:51:43.60 0.325 10.1 -1 7 247.2 -52.55 34.11 26.36 888 288 +20240216A 311.62 10:12:19.32 14:02:40.69 -1 10.9 -1 3 224.2 50.73 37.20 25.88 944 288 +20240310A 601.8 01:10:29.27 -44:26:19.58 0.127 25.7 4.7 1 291.7 -72.27 30.10 19.82 896 192 +20240312A 330.0 03:26:29.61 -54:36:03.54 0.049 12.2 -1 7 268.0 -50.68 35.75 26.72 808 192 +20240316A 667.0 19:55:51.23 -65:26:07.42 -1 11.5 -1 1 330.7 -31.15 64.36 47.88 896 192 +20240408A 520.0 14:39:28.11 -01:08:58.44 -1 11.6 -1 1 349.8 51.59 30.16 26.98 896 192 +20240615A 200.96 02:15:51.28 -14:37:28.19 0.073 67.0 -1 1 184.7 -66.71 31.05 25.47 880 240 +20240629A 534.4 05:34:11.56 -59:40:25.09 -1 17.0 4.6 1 268.3 -32.83 50.80 50.25 880 240 +20240114A 530.2 21:27:39.82 04:19:45.84 -1 90.5 -1 1 57.4 -31.65 49.67 38.81 944 288 +20240807A 530.0 01:33:12.66 -06:59:04.74 0.21 11.6 5.0 3 151.0 -67.54 31.66 21.70 944 288 +20241005A 764.6 19:39:12.19 18:23:33.74 -1 28.6 -1 1 54.6 -1.76 500.81 509.58 912 240 +20241014A 496.0 03:19:21.47 -58:19:15.94 -1 22.9 2.3 1 273.7 -49.58 36.63 27.72 888 288 +20241027A 320.0 02:24:06.96 -20:42:12.71 0.036 13.5 18.2 3 201.0 -67.90 30.45 25.28 856 288 diff --git a/zdm/data/Surveys/CRAFT_CRACO_900_3ms.ecsv b/zdm/data/Surveys/CRAFT_CRACO_900_3ms.ecsv new file mode 100644 index 00000000..4c63ec65 --- /dev/null +++ b/zdm/data/Surveys/CRAFT_CRACO_900_3ms.ecsv @@ -0,0 +1,25 @@ +# %ECSV 1.0 +# --- +# datatype: +# - {name: TNS, datatype: string} +# - {name: DM, datatype: float64} +# - {name: RA, datatype: string} +# - {name: DEC, datatype: string} +# - {name: Z, datatype: float64} +# - {name: SNR, datatype: float64} +# - {name: WIDTH, datatype: float64} +# - {name: XWc, datatype: float64} +# - {name: Gl, unit: deg, datatype: float64} +# - {name: Gb, unit: deg, datatype: float64} +# - {name: DMG, datatype: float64} +# - {name: XDMG, datatype: float64} +# - {name: FBAR, datatype: float64} +# - {name: BW, datatype: float64} +# meta: !!omap +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 38.2,"MAX_IW": 8, "MAXWMETH": 2}, +# "telescope": {"BEAM": "3ms_CRACO_900", "DMMASK": "3ms_craco_900_mask.npy", +# "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5, "FBAR": 935.2, +# "TRES": 3.456, "FRES": 1.0, "THRESH": 1.01, "SNRTHRESH": 9.5}}'} +# schema: astropy-2.0 +TNS DM RA DEC Z SNR WIDTH XWc Gl Gb DMG XDMG FBAR BW +DUMMY 676.5 07:55:02.63 08:30:51.80 0.506 14.3 69.1 2 212.4 17.97 35 97.66 888 288 diff --git a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv index b8a60e45..e9f372d2 100644 --- a/zdm/data/Surveys/CRAFT_ICS_1632.ecsv +++ b/zdm/data/Surveys/CRAFT_ICS_1632.ecsv @@ -25,8 +25,6 @@ # "telescope": {"BEAM": "ASKAP_1632", "DIAM": 12.0, "NBEAMS": 1, "NBINS": 5}}'} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XW95 TAU DEC RA Z B -DUMMY 336.0 206.0 27.1 1631.5 1.0 -61.10615576177682 118.0697870677333 12.8 9.0 4.4 1.182 2.1 5.628 1.8 01:40:36.8 10:30:40.7 -1 0.169 -DUMMY 336.0 206.0 27.1 1631.5 1.0 -61.10615576177682 118.0697870677333 12.8 9.0 4.4 1.182 2.1 5.628 1.8 01:40:36.8 10:30:40.7 -1 0.169 20211212A 336.0 206.0 27.1 1631.5 1.0 -61.10615576177682 118.0697870677333 12.8 9.0 4.4 1.182 2.1 5.628 1.8 01:40:36.8 10:30:40.7 0.0715 0.169 20220105A 336.0 583.0 22.0 1631.5 1.0 -40.39508677874226 124.21912132806385 9.8 9.0 4.4 1.182 0.95 2.25 0.43 22:27:57.8 13:55:13.01 0.2785 0.557 20221106A 336.0 343.8 34.8 1631.5 1.0 -81.71820282415577 41.73623647983608 19.5 9.0 4.4 1.182 5.33 6.895 0.182 -25:34:10.5166 03:46:49.0982 0.204 0.678 diff --git a/zdm/data/Surveys/CRAFT_average_ICS.ecsv b/zdm/data/Surveys/CRAFT_average_ICS.ecsv index 55fe43e0..109c5aa9 100644 --- a/zdm/data/Surveys/CRAFT_average_ICS.ecsv +++ b/zdm/data/Surveys/CRAFT_average_ICS.ecsv @@ -20,7 +20,7 @@ # meta: !!omap # - {survey_data: '{"observing": {"NORM_FRB": 28, "NORM_SINGLES": 28, "TOBS": 522.5, "TFIELD": 3.66}, # "telescope": {"BEAM": "ASKAP_average", "DIAM": 12.0, "NBEAMS": 36, "NBINS": 5, -# "WBIAS":"mean_ICS_efficiency", "DRIFT_SCAN": 1}}'} +# "DRIFT_SCAN": 1,"DMMASK": "mean_ICS_mask.npy"}}'} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20200430A 336.0 380.1 27.0 864.5 1.0 -50.423867191684614 126.69623499918922 16.0 9.0 4.4 1.728 6.5 12:22:34.007 15:18:49.581 0.161 diff --git a/zdm/data/Surveys/DSA.ecsv b/zdm/data/Surveys/DSA.ecsv index 14a6f50f..3cedb839 100644 --- a/zdm/data/Surveys/DSA.ecsv +++ b/zdm/data/Surveys/DSA.ecsv @@ -19,8 +19,10 @@ # - {name: XRA, datatype: float64} # - {name: Z, datatype: float64} # meta: !!omap -# - {survey_data: '{"observing": {"MAX_LOC_DMEG": -1, "FBAR": 1405.0, "MAX_DM": 1500}, "telescope": {"BMETHOD": 0, "BTHRESH": 0.001, "DIAM": 4.65, "NBEAMS": 1, -# "NBINS": 10}}'} +# - {survey_data: '{"observing": {"MAX_LOC_DMEG": -1, "FBAR": 1405.0, "MAX_DM": 1500}, +# "telescope": {"BMETHOD": 0, "BTHRESH": 0.001, "DIAM": 4.65, +# "NBEAMS": 1,"NBINS": 10, "FBAR": 1405, "FRES": 0.244141, +# "TRES":0.262144,"SNRTHRESH":8.5}}'} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 20240229A 187.5 491.554 37.37271394885186 1405.0 0.244141 44.55831358432649 133.33265375649617 1 93.8694 8.5 1.9 0.262144 0.524288 70.67622222 169.98354167 0.2870 diff --git a/zdm/data/Surveys/FAST.ecsv b/zdm/data/Surveys/FAST.ecsv index e65c0f76..a3a711cd 100644 --- a/zdm/data/Surveys/FAST.ecsv +++ b/zdm/data/Surveys/FAST.ecsv @@ -19,8 +19,10 @@ # - {name: XRA, datatype: string, subtype: 'float64[null]'} # - {name: Z, datatype: string, subtype: 'float64[null]'} # meta: !!omap -# - {survey_data: '{"observing": {"NORM_FRB": 9, "TOBS": 108.38, "MAX_DM": 4350}, "telescope": {"BEAM": "FAST_log", "DIAM": 300.0, "NBEAMS": 19, -# "NBINS": 10}}'} +# - {survey_data: '{"observing": {"NORM_FRB": 9, "TOBS": 108.38, "MAX_DM": 4350}, +# "telescope": {"BEAM": "FAST_log", "DIAM": 300.0, "NBEAMS": 19, +# "NBINS": 10, "THRESH":0.0146, "TRES":0.196608, +# "FRES":0.122,"FBAR":1250,"SNRTHRESH":7.0}}'} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 181017 500.0 1845.2 34.96 1250.0 0.122 -51.4 117.9 1 17.0 7.0 0.0146 0.196608 1.43 "" "" "" @@ -31,4 +33,4 @@ TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z 210208 500.0 1448.4 432.0 1250.0 0.122 3.6008 46.1672 1 52.54 7.0 0.0146 0.196608 2.4 "" "" "" 210705 500.0 2011.6 484.0 1250.0 0.122 2.7112 76.0306 1 23.82 7.0 0.0146 0.196608 10.9 "" "" "" 211005 500.0 2765.2 528.0 1250.0 0.122 -0.1694 60.3770 1 20.06 7.0 0.0146 0.196608 5.7 "" "" "" -220306 500.0 1273.9 454.0 1250.0 0.122 -1.0170 59.5450 1 20.98 7.0 0.0146 0.196608 4.2 "" "" "" \ No newline at end of file +220306 500.0 1273.9 454.0 1250.0 0.122 -1.0170 59.5450 1 20.98 7.0 0.0146 0.196608 4.2 "" "" "" diff --git a/zdm/data/Surveys/MeerTRAPcoherent.ecsv b/zdm/data/Surveys/MeerTRAPcoherent.ecsv index 94cacffc..accc2daf 100644 --- a/zdm/data/Surveys/MeerTRAPcoherent.ecsv +++ b/zdm/data/Surveys/MeerTRAPcoherent.ecsv @@ -16,7 +16,10 @@ # - {name: Z, datatype: float64} # meta: !!omap # - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 317.5}, -# "telescope": {"BEAM": "MeerTRAP_coherent_log","BTHRESH": 0.25,"NBEAMS": 1,"NBINS": 5}}'} +# "telescope": {"BEAM": "MeerTRAP_coherent_log", +# "BTHRESH": 0.25,"NBEAMS": 1,"NBINS": 5, +# "MAX_IDT": 1078, "FRES":0.836,"THRESH":0.069, +# "TRES": 0.30624, "FBAR":1284}}'} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES NREP SNR SNRTHRESH THRESH TRES WIDTH Z -DUMMY 770 200 35.0 1284 0.836 1 10. 10. 0.66 0.30624 1.0 -1.0 +DUMMY 770 200 35.0 1284 0.836 1 10. 10. 0.069 0.30624 1.0 -1.0 diff --git a/zdm/data/Surveys/MeerTRAPincoherent.ecsv b/zdm/data/Surveys/MeerTRAPincoherent.ecsv index 57bb6de2..8a922834 100644 --- a/zdm/data/Surveys/MeerTRAPincoherent.ecsv +++ b/zdm/data/Surveys/MeerTRAPincoherent.ecsv @@ -15,7 +15,10 @@ # - {name: WIDTH, datatype: float64} # meta: !!omap # - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 317.5}, -# "telescope": {"BEAM": "MeerTRAP_coherent_log","BTHRESH": 0.001,"NBEAMS": 1,"NBINS": 5}}'} +# "telescope": {"BEAM": "MeerTRAP_coherent_log", +# "BTHRESH": 0.001,"NBEAMS": 1,"NBINS": 5, +# "FRES":0.836,"TRES": 0.30624, "FBAR":1284, +# "THRESH":0.345}}'} # schema: astropy-2.0 TNS BW DM DMG FBAR FRES NREP SNR SNRTHRESH THRESH TRES WIDTH -DUMMY 770 200 35.0 1284 0.836 1 10. 10. 3.3 0.30624 1.0 +DUMMY 770 200 35.0 1284 0.836 1 10. 10. 0.345 0.30624 1.0 diff --git a/zdm/data/Surveys/SKA_mid.ecsv b/zdm/data/Surveys/SKA_mid.ecsv index d4f06b8f..3bbb080e 100644 --- a/zdm/data/Surveys/SKA_mid.ecsv +++ b/zdm/data/Surveys/SKA_mid.ecsv @@ -19,8 +19,9 @@ # - {name: XRA, datatype: string, subtype: 'float64[null]'} # - {name: Z, datatype: float64} # meta: !!omap -# - {survey_data: "{\n \"observing\": {\n \"NORM_FRB\": 1,\n \"TOBS\": 1\n },\n \"telescope\": {\n \ -# \ \"DIAM\": 15,\n \"BMETHOD\": 0,\n \"NBEAMS\": 1,\n \"NBINS\": 10\n }\n}"} +# - {survey_data: '{"observing": {"NORM_FRB": 1,"TOBS": 1}, +# "telescope": {"DIAM": 15, "BMETHOD": 0, "NBEAMS": 1, +# "NBINS": 10,"BTHRESH": 0.5}}'} # schema: astropy-2.0 -TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z -DUMMY 300 200 35.0 1284 0.1075 "" "" 1 10. 10. 0.2 0.069 1.0 "" "" 0.276 +TNS BW DM DMG FBAR FRES Gb Gl NREP SNR SNRTHRESH THRESH TRES WIDTH XDec XRA Z +DUMMY 300 200 35.0 1400 0.1075 "" "" 1 10. 10. 0.06 0.069 1.0 "" "" 0.276 diff --git a/zdm/grid.py b/zdm/grid.py index 885035fd..fa6eefd2 100644 --- a/zdm/grid.py +++ b/zdm/grid.py @@ -86,11 +86,10 @@ def __init__(self, survey, state, zDMgrid, zvals, dmvals, smear_mask, wdist=None self.calc_thresholds(survey.meta["THRESH"], efficiencies,weights=weights) else: - # if this is the case, why calc thresholds again below? + # this is called when the grid is not iterating over widths internally efficiencies = survey.mean_efficiencies # one dimension weights = None self.calc_thresholds(survey.meta["THRESH"], efficiencies, weights=weights) - efficiencies=survey.mean_efficiencies # Calculate self.calc_pdv() @@ -495,6 +494,23 @@ def calc_rates(self): self.sfr_smear = np.multiply(self.smear_grid.T, self.sfr).T self.rates = self.pdv * self.sfr_smear + + def get_rates(self): + """ + Returns rates, multiplied by the relevant constant, + and accounting for any DM preference via a DM mask + """ + + rates = np.zeros(self.rates.shape) + rates[:,:] = self.rates * 10**self.state.FRBdemo.lC + # multiplies by DM mask if applicable + if self.survey.dm_mask is not None: + rates = rates*self.survey.dm_mask + elif self.survey.max_dm is not None: + # in case a maximum DM is set in survey + if self.survey.max_idm < self.dmvals.size-1: + rates[:,self.survey.max_idm+1:]=0. + return rates def calc_thresholds(self, F0:float, eff_table, diff --git a/zdm/misc_functions.py b/zdm/misc_functions.py index ae7e4641..c0042e59 100644 --- a/zdm/misc_functions.py +++ b/zdm/misc_functions.py @@ -50,7 +50,7 @@ def get_w_tau_dist(grid,norm=True): state=grid.state # for shorter variable names survey = grid.survey - Wmethod = survey.meta["WMETHOD"] + Wmethod = survey.width_method if Wmethod == 0: print("WARNING: trivial width distribution: all 1ms") widths = np.array([1]) diff --git a/zdm/optical.py b/zdm/optical.py index b87ea3e1..26ac15cd 100644 --- a/zdm/optical.py +++ b/zdm/optical.py @@ -809,3 +809,16 @@ def plot_frb(name,ralist,declist,plist,opfile): plt.savefig(opfile) plt.tight_layout() plt.close() + + + + +def load_marnoch_data(): + """ + Loads the Marnoch et al data on r-band magnitudes from FRB hosts + """ + from astropy.table import Table + datafile="magnitudes_and_probabilities_vlt-fors2_R-SPECIAL.ecsv" + infile = os.path.join(resources.files('zdm'), 'data', 'optical', datafile) + table = Table.read(infile, format='ascii.ecsv') + return table diff --git a/zdm/parameters.py b/zdm/parameters.py index 582757cc..73fcfdae 100644 --- a/zdm/parameters.py +++ b/zdm/parameters.py @@ -116,7 +116,7 @@ class FRBDemoParams(data_class.myDataClass): ) lC: float = field( default=3.3249, - metadata={"help": "log10 constant in number per Gpc^-3 yr^-1 at z=0"}, + metadata={"help": "log10 constant in number per Gpc^-3 day^-1 at z=0"}, ) @@ -235,6 +235,12 @@ class WidthParams(data_class.myDataClass): "Notation": "", }, ) + Wmethod: int = field( + default=2, + metadata={'help': "Code for width method. 0: ignore it (all 1ms), 1: intrinsic lognormal, 2: include scattering, 3: scat & z-dependence, 4: specific FRB", + 'unit': '', + 'Notation': '', + }) Wlogmean: float = field( default=-0.29, metadata={ diff --git a/zdm/scripts/MCMC/est_pz.py b/zdm/scripts/MCMC/est_pz.py new file mode 100644 index 00000000..5051a23a --- /dev/null +++ b/zdm/scripts/MCMC/est_pz.py @@ -0,0 +1,273 @@ +""" +This file shows how to estimate p(z) for an FRB with a given DM, +from a given survey. +""" + + +import emcee +import argparse +import importlib.resources as resources +import os +import json +from zdm import survey +import numpy as np +from zdm import parameters +from astropy.cosmology import Planck18 +from zdm import figures +from zdm import misc_functions as mf +from zdm import grid as zdm_grid +import copy +from zdm import pcosmic +from matplotlib import pyplot as plt +import corner + + +# run with python est_pz.py --survey=MeerTRAPincoherent -b 500 -d "./" -o "Zest/" -i "H0_prior10" -n 100 +# these are now defaults + +#python est_pz.py --survey=MeerTRAPincoherent -b 500 -d "./" -o "Zest/" -i "H0_prior10" -n "100" -t "MTPC_1000" -D "1000." -l + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('-i', '--infile',default="H0_prior10",type=str,help="Input HDF5 file containing MCMC results") + parser.add_argument('-s', '--survey', default='MeerTRAPcoherent') + parser.add_argument('-d', '--directory', default="../../zdm/scripts/MCMC/", type=str, help="Directory containing the HDF5 file. Defaults to zdm/mcmc/") + parser.add_argument('-o', '--opdir', default="z2limit/", type=str, help="Output directory for files") + parser.add_argument('-n', default=10, type=int, help="Number of parameter sets to calculate") + parser.add_argument('-b', '--burnin', default=500, type=int, help="Burnin to discard from infile") + parser.add_argument('-l', '--load', default=False, action='store_true', help="Load previous data?") + parser.add_argument('-t', '--tag', default="", type=str, help="tag for output") + parser.add_argument('-D', '--DM', default=2500, type=float, help="DMeg limit") + + args = parser.parse_args() + + if args.directory == None: + args.directory = resources.files('zdm').joinpath('mcmc') + + return args + +def main(): + # Parse command line arguments + args = parse_args() + # Read MCMC output file + samples, params, config,labels = get_samples(args) + + if args.opdir != "": + opdir = args.opdir+"/" + if not os.path.exists(opdir): + os.mkdir(opdir) + + else: + opdir = "./" + dmmax = 7000.0 + #ndm = 1400 + #nz = 500 + dmmax = 4000 + zmax = 4 + ndm = 400 + nz = 200 + + load = args.load + tag = args.tag + DM = args.DM + + # Set up state + state = parameters.State() + state.set_astropy_cosmo(Planck18) + if config is not None: + state.update_params(config) + + # We explicitly load this to allow the "average grids" routine + # to more speedily calculate many grids. This only works because we + # are not changing H0 or F + grid_vals = mf.get_zdm_grid( + state, new=True, plot=False, method='analytic', + nz=nz, ndm=ndm, dmmax=dmmax, zmax = zmax, + datdir=resources.files('zdm').joinpath('GridData')) + + zvals = grid_vals[1] + dmvals = grid_vals[2] + + # Load survey + # If the survey is not specified, then the default is to use CRAFT_ICS_1300 + s = survey.load_survey(args.survey, state, grid_vals[2]) + + # Calculate and average grids + print("Calculating average grids...") + if load: + pdms = np.load(opdir+tag+"pdms.npy") + zdists = np.load(opdir+tag+"zdists.npy") + else: + pdms,zdists = average_grids(samples, params, s, state, grid_vals,opdir,DMFRB=DM) + np.save(opdir+tag+"pdms.npy",pdms) + np.save(opdir+tag+"zdists.npy",zdists) + + bins = np.linspace(0,0.1,101) + plt.figure() + plt.xlabel("$p({\\rm DM}_{\\rm EG} > "+str(DM)+")$") + plt.ylabel("Counts") + plt.hist(pdms,bins=bins) + plt.tight_layout() + plt.savefig(opdir+tag+"histogram.png") + plt.close() + + + # p(z) plot + Nsamples = len(pdms) + plt.figure() + avg = np.zeros([zvals.size]) + for i,zdist in enumerate(zdists): + plt.plot(zvals,zdist,color="grey") + avg += zdist + avg /= Nsamples + plt.plot(zvals,zdist,color="black",linewidth=3) + plt.xlabel("Redshift z") + plt.ylabel("p(z|DM_{\\rm EG}="+str(DM)+")") + plt.tight_layout() + plt.savefig(opdir+tag+"pz.png") + plt.close() + + # cumulative p(z) plot + + Nsamples = len(pdms) + plt.figure() + cavg = np.zeros([zvals.size]) + for i,zdist in enumerate(zdists): + czdist = np.cumsum(zdist) + czdist /= czdist[-1] + plt.plot(zvals,czdist,color="grey") + cavg += czdist + cavg /= Nsamples + plt.plot(zvals,cavg,color="black",linewidth=3) + plt.xlabel("Redshift z") + plt.ylabel("p(z|DM_{\\rm EG}="+str(DM)+")") + plt.tight_layout() + plt.savefig(opdir+tag+"cum_pz.png") + plt.close() + +def do_cornerplot(samples,labels,savefile): + """ + Args: + samples (np.array): + Array of samples of dimensions nwalkers x nparameters. + Used to create the cornerplot + labels (list of strings): + Text labels for each variable, in order of appearance in samples + savefile (string): filename for saved cornerplot + + """ + fig = plt.figure(figsize=(12,12)) + + titles = ['' for i in range(samples.shape[1])] # no titles for the plots + truths = None + corner.corner(samples,labels=labels, show_titles=True, titles=titles, + fig=fig,title_kwargs={"fontsize": 15},label_kwargs={"fontsize": 15}, + quantiles=[0.16,0.5,0.84], truths=truths); + + plt.savefig(savefile) + +def get_samples(args): + # Read in the MCMC results without the burnin + infile = os.path.join(args.directory, args.infile) + reader = emcee.backends.HDFBackend(infile + '.h5') + sample = reader.get_chain(discard=args.burnin, flat=True) + + # Thin the results + step = len(sample) // args.n + sample = sample[::step,:] + + # Get the corresponding parameters + # If there is a corresponding .out file, it will contain all the necessary information that was used during that run, + # otherwise parameters must be specified manually + if os.path.exists(infile + '.out'): + with open(infile + '.out', 'r') as f: + # Get configuration + line = f.readline() + + while not line.startswith('Config') and line: + line = f.readline() + if not line: + raise ValueError("No 'Config' line found in the .out file.") + config = json.loads(line[9:].replace("'", '"')) + + # Read down to parameter lines + while ('priors' not in line) and line: + line = f.readline() + + # Read parameters + params = [] + while ('priors' in line) and line: + vals = line.split() + params.append(vals[0]) + line = f.readline() + + # If there is no .out file, then the parameters must be specified manually + else: + params = ["sfr_n", "alpha", "lmean", "lsigma", "lEmax", "lEmin", "gamma", "H0"] + labels = [r"$n_{\rm sfr}$", r"$\alpha$", r"$\log_{10}\mu_h$", r"$\log_{10}\sigma_h$", + r"$\log_{10} E_{\rm max}$",r"$\log_{10}E_{\rm min}$", r"$\gamma$", r"$H_0$"] + config = None + + return sample, params, config, labels + +def average_grids(samples, params, s, state, grid_vals, opdir, DMFRB = 2500, pmin=0.01,log_halo=False,verbose=False): + + zDMgrid = grid_vals[0] + zvals = grid_vals[1] + dmvals = grid_vals[2] + + av_rates = np.zeros([len(zvals), len(dmvals)]) + rates = [] + + pdms=[] + zdists=[] + + # Load a grid for each parameter set + for i in range(samples.shape[0]): + if verbose: + print("Sampling parameter set ",i,". Params: ") + vals = samples[i,:] + + + dict = {} + for j in range(len(vals)): + dict[params[j]] = vals[j] + if verbose: + print(" ",params[j],": ",vals[j]) + + state.update_params(dict) + if 'DMhalo' in params: + if log_halo: + DMhalo = 10**dict['DMhalo'] + else: + DMhalo = dict['DMhalo'] + s.init_DMEG(DMhalo) + s.get_efficiency_from_wlist(s.DMlist,s.wlist,s.wplist,model=s.meta['WBIAS']) + + mask = pcosmic.get_dm_mask( + dmvals, (state.host.lmean, state.host.lsigma), zvals, plot=True + ) + grid = zdm_grid.Grid( + s, copy.deepcopy(state), zDMgrid, zvals, dmvals, mask, wdist=True + ) + + # we take all grid values where z_grid < z_FRB, but we use the highest one + # as "including" the FRB, so that we round conservatively + iDM = np.where(grid.dmvals < DMFRB)[0][-1] + + # get redshift distribution + zdist = grid.rates[:,iDM] + zdist /= np.sum(zdist) + + # DM distribution + pdm = np.sum(grid.rates,axis=0) + + # probability of that DM or higher + prob = np.sum(pdm[iDM:])/np.sum(pdm) + + pdms.append(prob) + zdists.append(zdist) + + return pdms,zdists + +main() diff --git a/zdm/scripts/PlotIndividualExperiments/CHIME.py b/zdm/scripts/PlotIndividualExperiments/CHIME.py index ee98993a..deb86558 100644 --- a/zdm/scripts/PlotIndividualExperiments/CHIME.py +++ b/zdm/scripts/PlotIndividualExperiments/CHIME.py @@ -8,7 +8,8 @@ from astropy.cosmology import Planck18 from matplotlib import pyplot as plt import os -from pkg_resources import resource_filename +from zdm import states +import importlib.resources as resources # zdm imports from zdm import loading @@ -28,11 +29,7 @@ def main(): os.mkdir(opdir) # sets basic state and cosmology - state = parameters.State() - state.set_astropy_cosmo(Planck18) - - # shortest method to load CHIME grids. Slightly expanded below - #ss,rgs,all_rates, all_singles, all_reps = loading.load_CHIME(state=state) + state = states.load_state("HoffmannEmin25",scat="updated",rep="b") # defines CHIME grids to load NDECBINS=6 @@ -40,8 +37,8 @@ def main(): for i in np.arange(NDECBINS): name="CHIME_decbin_"+str(i)+"_of_6" names.append(name) - survey_dir = os.path.join(resource_filename('zdm', 'data'), 'Surveys/CHIME/') - ss,gs = loading.surveys_and_grids(survey_names=names, init_state=state, rand_DMG=False,sdir = survey_dir, repeaters=True) + sdir = resources.files('zdm').joinpath('data/Surveys/CHIME') + ss,gs = loading.surveys_and_grids(survey_names=names, init_state=state, rand_DMG=False,sdir = sdir, repeaters=True) # compiles sums over all six declination bins rates = gs[0].rates * 10**gs[0].state.FRBdemo.lC * ss[0].TOBS diff --git a/zdm/scripts/PlotIndividualExperiments/plot_ASKAP_CRACO.py b/zdm/scripts/PlotIndividualExperiments/plot_ASKAP_CRACO.py new file mode 100644 index 00000000..edaf2609 --- /dev/null +++ b/zdm/scripts/PlotIndividualExperiments/plot_ASKAP_CRACO.py @@ -0,0 +1,126 @@ +""" +This script creates zdm grids for ASKAP incoherent sum observations. + + + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +def main(): + + # in case you wish to switch to another output directory + name="CRACO" + opdir=name+"/" + + # approximate best-fit values from recent analysis + # best-fit from Jordan et al + # plot scat "updated" if better, but takes ages! + state = states.load_state("HoffmannHalo25",scat="updated",rep=None) + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('data/Surveys') + names=['CRAFT_CRACO_1300','CRAFT_CRACO_900'] + + ss,gs = loading.surveys_and_grids( + survey_names=names,repeaters=False,init_state=state,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + # set limits for plots - will be LARGE! + DMmax=3000 + zmax=3. + + plt.figure() + ax1 = plt.gca() + + plt.figure() + ax2 = plt.gca() + + # chooses the first arbitrarily to extract zvals etc from + for i,g in enumerate(gs): + s=ss[i] + name = names[i] + + noz = np.where(s.frbs['Z'].values < 0.) + + figures.plot_grid(g.get_rates(),g.zvals,g.dmvals, + name=opdir+name+"_zDM.pdf",norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm EX},z)$ [a.u.]', + project=False,ylabel='${\\rm DM}_{\\rm EX}$', + zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5], + FRBDMs=s.frbs['DMEG'].values,FRBZs=s.frbs['Z'].values, + DMlines = s.frbs['DMEG'].values[noz]) + exit() + + pz = np.sum(mean_rates,axis=1) + pz /= np.max(pz) + ax1.plot(g.zvals,pz,label=name) + + cpz = np.cumsum(pz) + cpz /= cpz[-1] + + pdm = np.sum(mean_rates,axis=0) + pdm /= np.max(pdm) + ax2.plot(g.dmvals,pdm,label=name) + + total = np.sum(mean_rates) + print(name," expected to detect ",total," in ",time," hr") + + plt.sca(ax1) + plt.xlabel("z") + plt.ylabel("p(z)") + plt.xlim(0.01,3) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+name+"_pz.pdf") + plt.close() + + np.save(opdir+name+"_pz.npy",pz) + np.save(opdir+"zvalues.npy",g.zvals) + + np.save(opdir+name+"_pDM.npy",pdm) + np.save(opdir+"DMvalues.npy",g.dmvals) + + zDM_norm = g.rates * s.TOBS * 10**g.state.FRBdemo.lC + np.save(opdir+name+"_zDM.npy",zDM_norm) + + + plt.figure() + plt.plot(g.zvals,cpz) + plt.xlabel('z') + plt.ylabel('$p(z_{\\rm FRB} > z)$') + plt.tight_layout() + plt.savefig(opdir+name+"_cum_pz.pdf") + plt.close() + + plt.sca(ax2) + plt.xlabel("DM") + plt.ylabel("p(DM)") + plt.xlim(0,4000) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+name+"_pdm.pdf") + plt.close() + + + + +main() diff --git a/zdm/scripts/PlotIndividualExperiments/plot_DSA.py b/zdm/scripts/PlotIndividualExperiments/plot_DSA.py new file mode 100644 index 00000000..c8c69031 --- /dev/null +++ b/zdm/scripts/PlotIndividualExperiments/plot_DSA.py @@ -0,0 +1,125 @@ +""" +This script creates zdm grids for MeerTRAP + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +def main(): + + # approximate best-fit values from recent analysis + # load states from Hoffman et al 2025 + state = states.load_state("HoffmannEmin25",scat="updated",rep=None) + opdir="DSA" + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('data/Surveys') + name="DSA" + names=[name] + + ss,gs = loading.surveys_and_grids( + survey_names=names,repeaters=False,init_state=state,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + # set limits for plots - will be LARGE! + DMmax=2000 + zmax=2. + + plt.figure() + ax1 = plt.gca() + + plt.figure() + ax2 = plt.gca() + + s=ss[0] + g=gs[0] + name = names[0] + figures.plot_grid(g.rates,g.zvals,g.dmvals, + name=opdir+name+"_zDM.pdf",norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + project=False,ylabel='${\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host}$', + zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5]) + + pz = np.sum(g.rates,axis=1) + pz /= np.max(pz) + ax1.plot(g.zvals,pz,label=name) + + cpz = np.cumsum(pz) + cpz /= cpz[-1] + + pdm = np.sum(g.rates,axis=0) + pdm /= np.max(pdm) + ax2.plot(g.dmvals,pdm,label=name) + + total = np.sum(g.get_rates()) + print(name," detects ",total," per day") + + plt.sca(ax1) + plt.xlabel("z") + plt.ylabel("p(z)") + plt.xlim(0.01,3) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+name+"_pz.pdf") + plt.close() + + np.save(opdir+name+"_pz.npy",pz) + np.save(opdir+name+"zvalues.npy",g.zvals) + + np.save(opdir+name+"_pDM.npy",pdm) + np.save(opdir+name+"DMvalues.npy",g.dmvals) + + zDM_norm = g.rates* 10**g.state.FRBdemo.lC + np.save(opdir+name+"_zDM.npy",zDM_norm) + + + plt.figure() + plt.plot(g.zvals,cpz) + plt.xlabel('z') + plt.xlim(0,zmax) + plt.ylabel('$p(z_{\\rm FRB} > z)$') + plt.tight_layout() + plt.savefig(opdir+name+"_cum_pz.pdf") + plt.close() + + plt.sca(ax2) + plt.xlabel("DM") + plt.ylabel("p(DM)") + plt.xlim(0,DMmax) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+name+"_pdm.pdf") + plt.close() + + + + +main() diff --git a/zdm/scripts/PlotIndividualExperiments/plot_FAST.py b/zdm/scripts/PlotIndividualExperiments/plot_FAST.py new file mode 100644 index 00000000..b7509595 --- /dev/null +++ b/zdm/scripts/PlotIndividualExperiments/plot_FAST.py @@ -0,0 +1,125 @@ +""" +This script creates zdm grids for MeerTRAP + +""" +import os + +from astropy.cosmology import Planck18 +from zdm import cosmology as cos +from zdm import figures +from zdm import parameters +from zdm import survey +from zdm import pcosmic +from zdm import iteration as it +from zdm import loading +from zdm import io +from zdm import optical as opt +from zdm import states + +import numpy as np +from zdm import survey +from matplotlib import pyplot as plt +import importlib.resources as resources + +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + +def main(): + + # approximate best-fit values from recent analysis + # load states from Hoffman et al 2025 + state = states.load_state("HoffmannEmin25",scat="updated",rep=None) + opdir="FAST" + + if not os.path.exists(opdir): + os.mkdir(opdir) + + # Initialise surveys and grids + sdir = resources.files('zdm').joinpath('data/Surveys') + name="FAST" + names=[name] + + ss,gs = loading.surveys_and_grids( + survey_names=names,repeaters=False,init_state=state,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter + + # set limits for plots - will be LARGE! + DMmax=5000 + zmax=5. + + plt.figure() + ax1 = plt.gca() + + plt.figure() + ax2 = plt.gca() + + s=ss[0] + g=gs[0] + name = names[0] + figures.plot_grid(g.rates,g.zvals,g.dmvals, + name=opdir+name+"_zDM.pdf",norm=3,log=True, + label='$\\log_{10} p({\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host},z)$ [a.u.]', + project=False,ylabel='${\\rm DM}_{\\rm IGM} + {\\rm DM}_{\\rm host}$', + zmax=zmax,DMmax=DMmax,Aconts=[0.01,0.1,0.5]) + + pz = np.sum(g.rates,axis=1) + pz /= np.max(pz) + ax1.plot(g.zvals,pz,label=name) + + cpz = np.cumsum(pz) + cpz /= cpz[-1] + + pdm = np.sum(g.rates,axis=0) + pdm /= np.max(pdm) + ax2.plot(g.dmvals,pdm,label=name) + + total = np.sum(g.rates) * s.TOBS * 10**g.state.FRBdemo.lC + print(name," detects ",total," in 317.5 hr") + + plt.sca(ax1) + plt.xlabel("z") + plt.ylabel("p(z)") + plt.xlim(0.01,3) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+name+"_pz.pdf") + plt.close() + + np.save(opdir+name+"_pz.npy",pz) + np.save(opdir+name+"zvalues.npy",g.zvals) + + np.save(opdir+name+"_pDM.npy",pdm) + np.save(opdir+name+"DMvalues.npy",g.dmvals) + + zDM_norm = g.rates * s.TOBS * 10**g.state.FRBdemo.lC + np.save(opdir+name+"_zDM.npy",zDM_norm) + + + plt.figure() + plt.plot(g.zvals,cpz) + plt.xlabel('z') + plt.xlim(0,zmax) + plt.ylabel('$p(z_{\\rm FRB} > z)$') + plt.tight_layout() + plt.savefig(opdir+name+"_cum_pz.pdf") + plt.close() + + plt.sca(ax2) + plt.xlabel("DM") + plt.ylabel("p(DM)") + plt.xlim(0,DMmax) + plt.legend() + plt.tight_layout() + plt.savefig(opdir+name+"_pdm.pdf") + plt.close() + + + + +main() diff --git a/zdm/scripts/PlotIndividualExperiments/plot_Meertrap.py b/zdm/scripts/PlotIndividualExperiments/plot_Meertrap.py index 35e83d2b..89d68449 100644 --- a/zdm/scripts/PlotIndividualExperiments/plot_Meertrap.py +++ b/zdm/scripts/PlotIndividualExperiments/plot_Meertrap.py @@ -14,41 +14,37 @@ from zdm import loading from zdm import io from zdm import optical as opt +from zdm import states import numpy as np from zdm import survey from matplotlib import pyplot as plt -from pkg_resources import resource_filename +import importlib.resources as resources + +import matplotlib + +defaultsize=14 +ds=4 +font = {'family' : 'Helvetica', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + + def main(): - # in case you wish to switch to another output directory - - - # approximate best-fit values from recent analysis - # best-fit from Jordan et al - if True: - # approximate best-fit values from recent analysis - param_dict={'sfr_n': 0.21, 'alpha': 0.11, 'lmean': 2.18, 'lsigma': 0.42, 'lEmax': 41.37, - 'lEmin': 39.47, 'gamma': -1.04, 'H0': 70.23, 'halo_method': 0, 'sigmaDMG': 0.0, 'sigmaHalo': 0.0,'lC': -7.61} - opdir='MeerTRAP/' - else: - # best fit from James et al - param_dict={'sfr_n': 1.13, 'alpha': 0.99, 'lmean': 2.27, 'lsigma': 0.55, 'lEmax': 41.26, - 'lEmin': 32, 'gamma': -0.95, 'H0': 73, 'halo_method': 0, 'sigmaDMG': 0.0, 'sigmaHalo': 0.0,'lC': -0.76} - opdir='MeerTRAP2/' + # load states from Hoffman et al 2025 + state = states.load_state("HoffmannEmin25",scat="updated",rep=None) + opdir="MeerTRAP" if not os.path.exists(opdir): os.mkdir(opdir) # Initialise surveys and grids - sdir = os.path.join(resource_filename('zdm', 'data'), 'Surveys') + sdir = resources.files('zdm').joinpath('data/Surveys') names=['MeerTRAPcoherent'] - state = parameters.State() - state.set_astropy_cosmo(Planck18) - state.update_params(param_dict) - ss,gs = loading.surveys_and_grids( survey_names=names,repeaters=False,init_state=state,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter diff --git a/zdm/scripts/likelihood_components.py b/zdm/scripts/likelihood_components.py index fef75ad2..96d6077a 100644 --- a/zdm/scripts/likelihood_components.py +++ b/zdm/scripts/likelihood_components.py @@ -34,7 +34,6 @@ def main(): # add estimation from ptauw state.scat.Sbackproject=True - state.scat.Sbackproject=True if not os.path.exists(opdir): os.mkdir(opdir) diff --git a/zdm/scripts/write_macquart_relation.py b/zdm/scripts/write_macquart_relation.py index e24d4360..9dde07f8 100644 --- a/zdm/scripts/write_macquart_relation.py +++ b/zdm/scripts/write_macquart_relation.py @@ -1,35 +1,28 @@ """ -Simply plots the Macquart relation - +Simply writes out the Macquart relation """ - from zdm import pcosmic -from zdm.MC_sample import loading +from zdm import loading +from zdm import states import numpy as np + def main(): # Initialise surveys and grids name='CRAFT_ICS_1300' # approximate best-fit values from recent analysis - vparams = {} - vparams['H0'] = 70 # choose your preferred value! - + state=states.load_state() sdir='../data/Surveys/' - nozlist=[] - s,g = loading.survey_and_grid( - survey_name=name,NFRB=None,sdir=sdir) # should be equal to actual number of FRBs, but for this purpose it doesn't matter - - # set up new parameters - g.update(vparams) + #print(state) zvals=np.linspace(0.1,5.,50) - macquart_relation=pcosmic.get_mean_DM(zvals, g.state) + macquart_relation=pcosmic.get_mean_DM(zvals, state) for i,z in enumerate(zvals): print(z,macquart_relation[i]) - #print(macquart_relation) + main() diff --git a/zdm/states.py b/zdm/states.py index 7e8876c2..afb04ebc 100644 --- a/zdm/states.py +++ b/zdm/states.py @@ -50,6 +50,12 @@ def load_state(case="HoffmannHalo25",scat=None,rep=None): # just use the user-defined scattering version. Check if this exists! if not scat in scats: raise ValueError("Case ",scat," undefined, please choose from ",scats) + elif scat=="orig": + vparams = set_orig_scat(vparams) + elif scat=="CHIME": + vparams = set_chime_scat(vparams) + elif scat=="updated": + vparams = set_updated_scat(vparams) elif case == "JamesSFR22": scat="orig" vparams = set_orig_scat(vparams) @@ -115,7 +121,6 @@ def set_fit_params(vparams,case): sets best-fit standard fit parameters, as returned from the MCMC - """ @@ -284,6 +289,7 @@ def set_orig_scat(vparams): vparams['width']['WNbins'] = 5 vparams['width']['WidthFunction'] = 1 # lognormal vparams['width']['Wthresh'] = 0.5 + vparams['width']['Wmethod'] = 1 # intrinsic width only # approximately the same width treatment - # the functionality is changed, so it's not *quite* # identical @@ -323,6 +329,8 @@ def set_chime_scat(vparams): vparams['width']['WNbins'] = 5 vparams['width']['WidthFunction'] = 1 # lognormal vparams['width']['Wthresh'] = 0.5 + vparams['width']['Wmethod'] = 2 # width and scattering, no z-dependence + # approximately the same width treatment - # the functionality is changed, so it's not *quite* # identical @@ -332,7 +340,7 @@ def set_chime_scat(vparams): if not 'scat' in vparams: - vparams['width'] = {} + vparams['scat'] = {} vparams['scat'] = {} vparams['scat']['Slogmean'] = 0.305 vparams['scat']['Slogsigma'] = 0.75 @@ -349,6 +357,9 @@ def set_updated_scat(vparams): James et al 2025, which focusses on width and scattering """ + # first-order correction compared to CHIME scattering + #vparams['FRBdemo']['lC'] = vparams['FRBdemo']['lC'] + np.log10(20./13.6) + if not 'width' in vparams: vparams['width'] = {} vparams['width']['Wlogmean'] = -0.29 @@ -356,6 +367,7 @@ def set_updated_scat(vparams): vparams['width']['WNbins'] = 12 vparams['width']['WidthFunction'] = 2 # half-lognormal vparams['width']['Wthresh'] = 0.5 + vparams['width']['Wmethod'] = 3 # width and scattering, z-dependence # approximately the same width treatment - # the functionality is changed, so it's not *quite* # identical diff --git a/zdm/survey.py b/zdm/survey.py index 440ca81c..6a4d6f99 100644 --- a/zdm/survey.py +++ b/zdm/survey.py @@ -120,7 +120,16 @@ def init_widths(self,state=None): self.thresh = self.state.width.Wthresh self.wlogmean = self.state.width.Wlogmean self.wlogsigma = self.state.width.Wlogsigma - self.width_method = self.meta["WMETHOD"] + if self.meta['WBIAS'] == 'Quadrature' or self.meta['WBIAS'] == 'Sammons' or self.meta['WBIAS']=="StdDev": + # these allow width-dependent sensitivities + self.width_method = self.state.width.Wmethod + else: + # the sensitivity is a fixed function of DM. Don't waste + # time on multiple width bins! (though maybe we should...) + # so just set to a constant width of 1, i.e. a single bin. + self.width_method = 0 + + if self.width_method == 3 and self.zvals is None: raise ValueError("Width method 3 requires z-values to be set") self.NInternalBins=self.state.width.WNInternalBins @@ -158,7 +167,7 @@ def init_widths(self,state=None): raise ValueError("state parameter scat.ScatFunction ",SF," not implemented, use 0-2 only") # sets n width bins equal to zero for this survey - if self.meta['WMETHOD'] == 0 or self.meta['WMETHOD'] == 4: + if self.width_method == 0 or self.width_method == 4: self.NWbins = 1 ###### calculate width bins. We fix these here ###### @@ -194,7 +203,7 @@ def init_widths(self,state=None): self.internal_logwvals = np.linspace(minval,maxval,self.NInternalBins) # initialise probability bins - if self.meta['WMETHOD'] == 3: + if self.width_method == 3: # evaluate efficiencies at each redshift self.efficiencies = np.zeros([self.NWbins,self.NZ,self.NDM]) self.wplist = np.zeros([self.NWbins,self.NZ]) @@ -219,13 +228,42 @@ def init_widths(self,state=None): #_ = self.get_efficiency_from_wlist(self.wlist,self.wplist, # model=self.meta['WBIAS'], edir=self.edir, iz=None) self.do_efficiencies() + + def process_dm_mask(self,edir=None): + """ + Processes a DM mask into units of local DM + """ + # loads the mask + if edir is None: + edir = resources.files('zdm').joinpath('data/Efficiencies') + filename = os.path.expanduser(os.path.join(edir, self.meta['DMMASK'])) + + if not os.path.exists(filename): + raise ValueError("Could not find DM mask " + filename + " in directory "+edir) + + # Should contain DM in the first row and efficiencies in the second row + sensitivity_array = np.load(filename) + if self.DMGs is not None: + effective_vals = self.dmvals + self.state.MW.DMhalo + np.median(self.DMGs) + else: + effective_vals = self.dmvals + self.state.MW.DMhalo + 30 + dm_mask = np.interp(effective_vals, sensitivity_array[0,:], sensitivity_array[1,:], left=1.,right=0) + self.dm_mask = dm_mask + def do_efficiencies(self): """ Function to handle calculation of efficiencies. Allows this to be called externally, e.g. if DM halo model changes """ - if self.meta['WMETHOD'] == 3: + + # process DM mask to get mask in units of used DM values + if self.meta['DMMASK'] is not None: + self.process_dm_mask() + else: + self.dm_mask = None + + if self.width_method == 3: for iz,z in enumerate(self.zvals): _ = self.get_efficiency_from_wlist(self.wlist,self.wplist[:,iz], model=self.meta['WBIAS'], edir=self.edir, iz=iz) @@ -238,7 +276,7 @@ def make_widths(self,iz=None): is required to estimate detection efficiency, and (potentially) calculate p(w,tau). - The functionality depends on self.meta['WMETHOD'], which + The functionality depends on self.width_method, which is set via WMETHOD [int: 0-4]: @@ -257,13 +295,13 @@ def make_widths(self,iz=None): for individual FRB-by-FRB analyses. """ - if self.meta['WMETHOD'] == 0: + if self.width_method == 0: # do not take a distribution, just use 1ms for everything # this is done for tests, for complex surveys such as CHIME, # or for estimating the properties of a single FRB self.wlist[0] = 1. self.wplist[0] = 1. - elif self.meta['WMETHOD'] == 1: + elif self.width_method == 1: # take intrinsic width function only for i in np.arange(self.NWbins): norm=(2.*np.pi)**-0.5/self.wlogsigma @@ -271,12 +309,12 @@ def make_widths(self,iz=None): weight,err=quad(self.WidthFunction, np.log10(self.wbins[i]),np.log10(self.wbins[i+1]),args=args) self.wplist[i]=weight - elif self.meta['WMETHOD'] == 2 or self.meta['WMETHOD'] == 3: + elif self.width_method == 2 or self.width_method == 3: # include scattering distribution. 3 means include z-dependence #gets cumulative hist and bin edges if iz is not None: - if self.meta['WMETHOD'] == 2: + if self.width_method == 2: print("Trying to calculate redshift dependence for redshift ",\ "independent WMETHOD=2. Internally inconsistent.") exit() @@ -318,7 +356,7 @@ def make_widths(self,iz=None): self.pws=pw self.ptaus=ptau - elif self.meta['WMETHOD'] == 4: + elif self.width_method == 4: # use specific width of FRB. This requires there to be only a single FRB in the survey if s.meta['NFRB'] != 1: raise ValueError("If width method in make_widths is 4 only one FRB should be specified in the survey but ", str(s.meta['NFRB']), " FRBs were specified") @@ -561,6 +599,11 @@ def get_w_coeffs(self,wlist): dkws2=kws-iws1 # applies to izs2 dkws1 = 1. - dkws2 + # in case iws1 is in the largets bin, then + # iws2 will be too large + toobig1 = np.where(iws2 >= self.wlist.size)[0] + iws2[toobig1]=self.wlist.size-1 + # checks for values which are too large toobigw = np.where(wlist > self.WMax)[0] if len(toobigw) > 0: @@ -821,16 +864,18 @@ def process_survey_file(self,filename:str, # Survey Data self.survey_data = survey_data.SurveyData.from_jsonstr( frb_tbl.meta['survey_data']) + + # Meta -- for convenience for now; best to migrate away from this for key in self.survey_data.params: DC = self.survey_data.params[key] self.meta[key] = getattr(self.survey_data[DC],key) - + # Get default values from default frb data default_frb = survey_data.FRB() # we now populate missing fields with the default values - for field in fields(default_frb): + for field in fields(default_frb):\ # checks to see if this is a field in metadata: if so, takes priority if survey_dict is not None and field.name in survey_dict.keys(): default_vaue = survey_dict[field.name] @@ -907,23 +952,27 @@ def process_survey_file(self,filename:str, if len(self.frbs) > 0: # first, replacing missing values with survey values + # only do this for values which are otherwise missing! # replace default values with observed media values # it's unclear if median or mean is the best here - self.meta['SNRTHRESH'] = np.median(self.frbs['SNRTHRESH']) - self.meta['THRESH'] = np.median(self.frbs['THRESH']) - self.meta['BW'] = np.median(self.frbs['BW']) - self.meta['FBAR'] = np.median(self.frbs['FBAR']) - self.meta['FRES'] = np.median(self.frbs['FRES']) - self.meta['TRES'] = np.median(self.frbs['TRES']) - self.meta['WIDTH'] = np.median(self.frbs['WIDTH']) - self.meta['DMG'] = np.mean(self.frbs['DMG']) - + keylist = ['SNRTHRESH','THRESH','BW','FBAR','FRES','TRES','WIDTH','DMG'] + for key in keylist: + if not key in self.meta: + self.meta[key] = np.median(self.frbs[key]) + #self.meta['SNRTHRESH'] = np.median(self.frbs['SNRTHRESH']) + #self.meta['THRESH'] = np.median(self.frbs['THRESH']) + #self.meta['BW'] = np.median(self.frbs['BW']) + #self.meta['FBAR'] = np.median(self.frbs['FBAR']) + #self.meta['FRES'] = np.median(self.frbs['FRES']) + #self.meta['TRES'] = np.median(self.frbs['TRES']) + #self.meta['WIDTH'] = np.median(self.frbs['WIDTH']) + #self.meta['DMG'] = np.mean(self.frbs['DMG']) # over-rides survey data if applicable if survey_dict is not None: for key in survey_dict: self.meta[key] = survey_dict[key] - + print(self.meta[key], "overidden by ",survey_dict[key]) ### processes galactic contributions self.process_dmg() @@ -968,7 +1017,7 @@ def process_survey_file(self,filename:str, print("FRB survey sucessfully initialised with ",self.NFRB," FRBs starting from", self.iFRB) - + def fix_coordinates(self,verbose=False): """ @@ -1094,6 +1143,14 @@ def calc_max_dm(self): max_dm = max_dt / (k_DM * ((f_low/1e3)**(-2) - (f_high/1e3)**(-2))) self.max_dm = max_dm + + # calculates max iDM value + if self.max_dm is not None: + max_dmeg = max_dm - np.median(self.DMhalos + self.DMGs) + max_idm = np.where(self.dmvals < max_dmeg)[0][-1] + self.max_idm = max_idm + else: + self.max_idm = None def get_efficiency_from_wlist(self,wlist,plist, model="Quadrature", @@ -1148,8 +1205,9 @@ def get_efficiency_from_wlist(self,wlist,plist, edir=edir, max_iw=self.meta['MAX_IW'], max_meth = self.meta['MAXWMETH']) - # keep an internal record of this + + # keep an internal record of this if iz is None: self.efficiencies=efficiencies self.DMlist=DMlist @@ -1160,6 +1218,7 @@ def get_efficiency_from_wlist(self,wlist,plist, self.DMlist[iz,:]=DMlist mean_efficiencies=np.mean(efficiencies,axis=0) self.mean_efficiencies[iz,:]=mean_efficiencies #be careful here!!! This may not be what we want! + return efficiencies def __repr__(self): @@ -1250,8 +1309,9 @@ def calc_relative_sensitivity(DM_frb,DM,w,fbar,t_res,nu_res,Nchan=336,max_idt=No uw=w if model == "StdDev": - totalw = (uw**2 + dm_smearing**2/3. + t_res**2/3.)**0.5 - nosmearw = (uw**2 + t_res**2/3.)**0.5 + # 2* to be +- one standard deviation. But we're now fixing Tres to be unity + totalw = (uw**2 + dm_smearing**2/3. + t_res**2)**0.5 + nosmearw = (uw**2 + t_res**2)**0.5 else: totalw = (uw**2 + dm_smearing**2 + t_res**2)**0.5 nosmearw = (uw**2 + t_res**2)**0.5 @@ -1284,7 +1344,7 @@ def calc_relative_sensitivity(DM_frb,DM,w,fbar,t_res,nu_res,Nchan=336,max_idt=No # we have already reduced it by \sqrt{t} # we thus add a further sqrt{t} factor sensitivity[toolong] *= (max_w / totalw[toolong])**0.5 - + # If model not CHIME, Quadrature or Sammons assume it is a filename else: if edir is None: @@ -1297,7 +1357,7 @@ def calc_relative_sensitivity(DM_frb,DM,w,fbar,t_res,nu_res,Nchan=336,max_idt=No # Should contain DM in the first row and efficiencies in the second row sensitivity_array = np.load(filename) sensitivity = np.interp(DM, sensitivity_array[0,:], sensitivity_array[1,:], right=1e-2) - + return sensitivity diff --git a/zdm/survey_data.py b/zdm/survey_data.py index fed9ac34..35ff2b11 100644 --- a/zdm/survey_data.py +++ b/zdm/survey_data.py @@ -75,7 +75,7 @@ class FRB(data_class.myDataClass): 'Notation': '', }) SNRTHRESH: float = field( - default=0., + default=10., metadata={'help': "S/N threshold to detect an FRB", 'unit': '', 'Notation': '', @@ -157,12 +157,6 @@ class Telescope(data_class.myDataClass): 'unit': '', 'Notation': '', }) - WMETHOD: int = field( - default=2, - metadata={'help': "Code for width method. 0: ignore it (all 1ms), 1: intrinsic lognormal, 2: include scattering, 3: scat & z-dependence, 4: specific FRB", - 'unit': '', - 'Notation': '', - }) WDATA: int = field( default=2, metadata={'help': "What does the WIDTH column include? 0 intrinsic, 1: also scattering, 2: also DM smearing", @@ -175,6 +169,12 @@ class Telescope(data_class.myDataClass): 'unit': '', 'Notation': '', }) + DMMASK: str = field( + default=None, + metadata={'help': "Mask to apply multiplicatively in DM space over standard Wbias model", + 'unit': '', + 'Notation': '', + }) BMETHOD: int = field( default=2, metadata={'help': "Method for beam calculation. See beams.py:simplify_beam()", @@ -201,6 +201,12 @@ class Telescope(data_class.myDataClass): 'unit': 'Jy ms', 'Notation': '', }) + SNRTHRESH: float = field( + default=10., + metadata={'help': "S/N threshold to detect an FRB", + 'unit': '', + 'Notation': '', + }) FBAR: float = field( default=1300., metadata={'help': "Mean frequency", @@ -271,7 +277,7 @@ class Observing(data_class.myDataClass): }) MAX_IDT: int = field( default=None, - metadata={'help': "Maximum number of time samples seaarched (4096 for CRAFT ICS)", + metadata={'help': "Maximum number of time samples searched (4096 for CRAFT ICS)", 'unit': '', 'Notation': '', })