diff --git a/README.md b/README.md index 09cc6ad..a10bfe5 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ fitburst ======== -This repo contains functions and objects for modeling dynamic spectra of dispersed astrophysical signals at radio frequencies. +This repository contains functions, objects, and scripts for modeling dynamic spectra of dispersed astrophysical signals at radio frequencies. ## Installation @@ -13,28 +13,15 @@ pc> cd fitburst pc/fitburst> pip install . # add the --user option if you're looking to install in your local environment. ``` -## Usage +## Usage and Documentation +Please refer to the documentation linked above to find desciptions on the codebase and examples for interacting with it. -Once installed, the `fitburst` functionality can be imported as a Python package for various custom purposes. For example, if you wish to simply read in CHIME/FRB metadata only (e.g., parameter estimates made by the various online pipelines) for a specific event, you could do the following: +## Publication +The theory behind the modeling and analysis routines is presented in a paper currently under review, but available on the arXiv. This paper includes a variety of fitting examples and discussions on the treatment of biasing effects (e.g., intra-channel smearing from pulse dispersion) that can be accounted for within `fitburst`. If you use this codebase and publish results obtained with it, we ask that you cite the `fitburst` paper using the following BibTex citation: -``` -pc> python ->>> from fitburst.backend.chimefrb import DataReader ->>> data = DataReader(48851362) -.... ->>> print(data.burst_parameters) # a dictionary of pipeline-specific parameters ->>> print(data.files) # a list of filenames for the total-intensity data set -``` - -There are also several example scripts available in the `fitburst/pipelines` section of the repo that utilize the `fitburst` package in slightly different ways. The current script used to analyze CHIME/FRB data (`fitburst/pipelines/fitburst_example_chimefrb.py`) comes with a variety of options for interacting with the full algorithm at the command line. For example, if you wish to run the full `fitburst` pipeline on an event (i.e., data I/O and pre-processing, setup and fitting of model against pre-processed intensity data) using the intensity-DM pipeline parameters as your initial guess, and ignoring the fit of scattering parameters, you should run: -``` -pc> python /path/to/fitburst_example_chimefrb.py 48851362 --pipeline dm -``` - -If you wish to change the size of the windowed spectrum, fit for a scattering timescale of a thin-screen model, and toggle its initial guess to a value of your choosing, you should instead run: +... COMING SOON! :sweat-smile -``` -pc> python /path/to/fitburst_example_chimefrb.py 48851362 --pipeline dm --window 0.12 --fit scattering_timescale --scattering_timescale 0.05 -``` +## Credits +All authors of the `fitburst` papers are the founding developers of `fitburst`, with Emmanuel Fonseca leading the development team. We welcome novel and meaningful contributions from interested users! -Use the `-h` option in the above script to see all available options and units for various numerical quantities. +This package was built using [namoona](https://github.com/CHIMEFRB/namoona). diff --git a/docs/installation.md b/docs/installation.md index 983ea8c..1ef1723 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -1,22 +1,28 @@ # fitburst - Installation -## Prerequisites -* Python 3.7 or greater -* [astropy](https://docs.astropy.org/en/stable/install.html) -* [numpy](https://numpy.org/install/) -* [matplotlib](https://matplotlib.org/users/installing.html) -* [mkdocs](https://www.mkdocs.org/#installation) -* [PyYAML](https://pyyaml.org/wiki/PyYAMLDocumentation) -* [scipy](https://www.scipy.org/install.html) - -## Using `pip` +## Building from `PyPI` -One day, `fitburst` will be retrievable from `pip`! Believe it! +`fitburst` will soon be retrievable from the Python Package Index ([PyPI](https://pypi.org)) via [pip](https://pypi.org/project/pip/). ## Building from Source -The current `fitburst` codebase uses `setuptools` for building the distribution and grabbing the above dependencies. To build from source, run the following commands: +The `fitburst` codebase uses either `pip` or [poetry](https://python-poetry.org) for building the distribution and grabbing dependencies. The easiest route to building `fitburst` is to use `pip`: > git clone https://github.com/CHIMEFRB/fitburst.git > cd fitburst/ - > pip install -e . + > pip install . + +## Dependencies +For out-of-the-box use, `fitburst` currently depends on the external Python packages listed below. We encourage interested developers to contribute software and/or replace existing functionality with new dependencies; however, we request that any additional dependency be open-source, meaningfully used, and accessible via `pip`. + +* Python 3.8 or greater +* [matplotlib](https://matplotlib.org/users/installing.html) +* [mkdocs](https://www.mkdocs.org/#installation) +* [numpy](https://numpy.org/install/) +* [pandas](https://pandas.pydata.org/docs/index.html) +* [pytz](https://pythonhosted.org/pytz/) +* [pyyaml](https://pyyaml.org/wiki/PyYAMLDocumentation) +* [scipy](https://www.scipy.org/install.html) + +### Developer Dependencies +There are additional dependencies for folks who wish to contribute and build code, tests, and/or documentation. These dependenices are listed in the `pyproject.toml` file under the `tools.poetry.group.*.dependencies` attributes. However, these dependencies currently can only be installed using `poetry` due to way in which `pip` understands the `pyproject.toml` file. diff --git a/docs/usage/creating_models.md b/docs/usage/creating_models.md index fd18ff4..09945b9 100644 --- a/docs/usage/creating_models.md +++ b/docs/usage/creating_models.md @@ -1,70 +1,120 @@ -We have developed a Python class, called the `SpectrumModeler`, for generating data models in a manner suitable for interaction with downstreaming fitting routines. The simplest version of a call to the model class is, +We have developed a Python class, called the `SpectrumModeler`, for generating models of dynamic spectra. The `SpectrumModeler` is designed for interaction with downstream fitting routines; however, it can nonetheless be used as a standalone object. The simplest version of a call to the `SpectrumModeler` is given here: ``` python ->>> from fitburst.analysis.model import SpectrumModeler ->>> model = SpectrumModeler(num_freq, num_time) +from fitburst.analysis.model import SpectrumModeler +freqs = ... # array of frequency labels, in MHz +times = ... # array of timestamps, in seconds +model = SpectrumModeler(freqs, times) ``` -where the quantities (`num_freq`, `num_time`) define the dimensions of the model spectrum. +where (`freqs`, `times`) define the centers of frequency channels and time bins, respectively. ## Parameters of the Model Object -The exact list of parameters will depend on the assumed shape of the spectral energy distribution (SED). By default, the `SpectrumModeler` assumes a "running power-law" model for the SED, and so the spectral parameters will be the `spectral_index` and `spectral_running`. The full list of parameters can be retrieved as follows: - -``` python ->>> print(model.parameters) -['amplitude', 'arrival_time', 'burst_width', 'dm', 'dm_index', 'scattering_timescale', 'scattering_index', 'spectral_index', 'spectral_running'] -``` - -If a Gaussian SED is instead desired, then you can instantiate the `SpectrumModeler` and set the correct option to indicate this choice: +As described in the `fitburst` paper, the `SpectrumModeler` is a function of nine fittable parameters. A tenth parameter, called `ref_freq` cannot be fitted as it serves as a frequency to which the amplitude and SED parameters are referenced. The full list of parameters can be retrieved as follows: ``` python ->>> model = SpectrumModeler(num_freq, num_time, freq_model = "gaussian") >>> print(model.parameters) -['amplitude', 'arrival_time', 'burst_width', 'dm', 'dm_index', 'scattering_timescale', 'scattering_index', 'freq_mean', 'freq_width'] +['amplitude', 'arrival_time', 'burst_width', 'dm', 'dm_index', 'ref_freq', 'scattering_timescale', 'scattering_index', 'spectral_index', 'spectral_running'] ``` -Notice that all but two parameters have changed, and that the `freq_mean` and `freq_width` now characterize the (Gaussian) shape of the SED. So far, only the `powerlaw` and `gaussian` SED models are available in `fitburst`. +Please refer to Section 2 and Table 1 of the `fitburst` paper for a description of these parameters. ## Loading Parameter Values into the Model Object -The above calls to the `SpectrumModeler` object yield an "empty" model object; the object is configured but contains no information on the model parameters. In order to load parameter values, we use the `update_parameters()` method of the `SpectrumModeler`: +In order to load parameter values, we use the `update_parameters()` method of the `SpectrumModeler`: ``` python # define dictiomary containing parameter values. burst_parameters = { - "amplitude" : [0.0], - "arrival_time" : [0.5], - "burst_width" : [0.005], - "dm" : [557.0], - "dm_index" : [-2.0], - "ref_freq" : [600.0], - "scattering_index" : [-4.0], - "scattering_timescale" : [0.0], - "freq_mean" : [450.0], - "freq_width" : [43.0], + "amplitude" : [0.], + "arrival_time" : [0.04], + "burst_width" : [0.003], + "dm" : [349.5], + "dm_index" : [-2.], + "ref_freq" : [1400.], + "scattering_index" : [-4.], + "scattering_timescale" : [0.], + "spectral_index" : [10.], + "spectral_running" : [-100.], } -# now update Gaussian-SED model object to use the above values. +# now instantiate the SpectrumModeler +model = SpectrumModeler(freqs, times) + +# update the SpectrumModeler to use the above values. model.update_parameters(burst_parameters) -# adjust the DM value while leaving all others unchanged in the model object. -model.update_parameters({"DM": [557.5]}) +# slightly adjust the DM only, leaving all others unchanged in the model object. +model.update_parameters({"dm": [348.95]}) ``` -The `update_parameters()` method is able to receive a dictionary that contains only one or a partial set of the full parameter list, as shown in the second method call above. This feature is important when fitting models to data where one or more model parameters are desired to be fixed to pre-determined values. +The `update_parameters()` method receives a dictionary with one or more parameters with values loaded in Python lists, as shown in the second method call above. This feature exists to allow for flexibility in generating models for fitting where one or more parameters are fixed to pre-determined values. + +## Generating Mulit-Component Models + +The `SpectrumModeler` is also capable of generating models of a multi-component spectrum, i.e., a dynamic spectrum with $N$ distinct pulses. Such models can be created with the same code above, but with values of the `burst_parameters` dicitionary that are lists of length $N$. For example, the following code will overwrite the above parameters to instantiate a model with 3 components: + +```python +# define dictiomary containing parameter values. +burst_parameters = { + "amplitude" : [0., 0., 0.], + "arrival_time" : [0.03, 0.04, 0.05], + "burst_width" : [0.001, 0.003, 0.0005], + "dm" : [349.5, 349.5, 349.5], + "dm_index" : [-2., -2., -2.], + "ref_freq" : [1400., 1400., 1400.], + "scattering_index" : [-4., -4., -4.], + "scattering_timescale" : [0., 0., 0.], + "spectral_index" : [10., 0., -10.], + "spectral_running" : [-100., -100., -100.], +} + +# now instantiate the SpectrumModeler for a three-component model. +num_components = len(burst_parameters["dm"]) +model = SpectrumModeler(freqs, times, num_components = num_components) + +# now update Gaussian-SED model object to use the above values. +model.update_parameters(burst_parameters) -The `SpectrumModeler` is also capable of generating models of a multi-component spectrum. The only changed needed for this to occur is for the values of the above `burst_parameters` dicitionary to be lists of length greater than 1, where the $i$th element for each list corresponds to parameters of "sub-burst" $i$. +``` ## Creating Models for De-dispersed Data -Once the above configuration is done, we can then compute a model spectrum with the `compute_mode()` method within the `SpectrumModeler`. +Users will typically want to fit models of dynamic spectra against data that are already de-dispersed. The `SpectrumModeler` can be used as shown above, but with one caveat: the `dm` parameter is treated as a "DM offset" for de-dispersed spectra, instead of the "full" DM whose values are supplied in the above examples. Once this configuration is done, we can then compute a model spectrum with the `compute_mode()` method within the `SpectrumModeler`. + +The following code with use the latest example above and perform the adjustment needed for generating a de-dispersed dynamic spectum: ``` python -freqs = ... # array of frequency labels, in MHz -times = ... # array of timestamps, in seconds +# indicate whether the spectrum is de-dispersed or not. +is_dedispersed = True + +# define dictiomary containing parameter values. +burst_parameters = { + "amplitude" : [0., 0., 0.], + "arrival_time" : [0.03, 0.04, 0.05], + "burst_width" : [0.001, 0.003, 0.0005], + "dm" : [349.5, 349.5, 349.5], + "dm_index" : [-2., -2., -2.], + "ref_freq" : [1400., 1400., 1400.], + "scattering_index" : [-4., -4., -4.], + "scattering_timescale" : [0., 0., 0.], + "spectral_index" : [10., 0., -10.], + "spectral_running" : [-100., -100., -100.], +} + +# adjust DM value to zero offset, if necessary. +num_components = len(burst_parameters["dm"]) + +if is_dedispersed: + burst_parameters["dm"] = [0.] * num_components + +# now instantiate the SpectrumModeler for a three-component model. +model = SpectrumModeler(freqs, times, num_components = num_components) -spectrum_model = model.compute_model(times, freqs) +# grab the model spectrum. +spectrum_model = model.compute_model() ``` -If you're using a `DataReader` to load and prepare data, then the above arrays can be accessed through the `freqs` and `times` attributes. +The above call with return a NumPy `ndarray` with shape `(num_freq, num_time)`. ## Creating Models for Dispersed Data +In rare or simulation cases, it may be desired to create a dispersed dynamic spectrum. This spectrum can be generated using the latest example above, but instead setting `is_dedispersed = False`. diff --git a/docs/usage/formatting_data_generic.md b/docs/usage/formatting_data_generic.md index cde30f8..f83eb97 100644 --- a/docs/usage/formatting_data_generic.md +++ b/docs/usage/formatting_data_generic.md @@ -1,4 +1,4 @@ -We have defined a `fitburst`-compliant ("generic") data format for loading all required data into the `fitburst` data-reading object. Users can adopt this generic format to ensure initialization of required variables and arrays. The generic-format data are stored in and read from a Python3 Numpy `.npz` file. +For ease of use, we have defined a `fitburst`-compliant ("generic") data format for loading all required data into the `fitburst` data-reading object. Users can adopt this generic format to ensure initialization of required variables and arrays used within `fitburst`. The generic-format data are stored in and read from a Python3 Numpy `.npz` file. ## Concept of Generic Format A generic-compatible data file, e.g., "input\_data.npz", is assumed to contain three entries: diff --git a/docs/usage/getting_started.md b/docs/usage/getting_started.md new file mode 100644 index 0000000..12aceba --- /dev/null +++ b/docs/usage/getting_started.md @@ -0,0 +1,97 @@ +The `fitburst` codebase can be used in two different ways: as a Python package; or through Python3 scripts provided in the repository. This page describes how to interact with the two usage modes. + +## `fitburst` as a Python package +Once properly installed, `fitburst` can be immediate accessed through a Python interpreter as an importable package: + +``` python +user@pc > python +>>> import fitburst +>>> +``` + +All underlying objects and functions can be accessed in this package format, with Python docstrings that emulate the `numpy` documentation format. + +## Example `fitburst` Pipelines +Any "full `fitburst` pipeline" consists of the following major sections, in order of operation: + +0. command-line interface +1. data I/O +2. declaration of initial guess for burst parameters +3. configuration of `fitburst` model object +4. configuration of `fitburst` "fitter" object +5. execution of fit +6. generation of figures, fit-summary files, etc. + +Of course, some users may be interested in a "simulation pipeline" which simulates radio pulses with features they wish to model in the presence of controllable noise. We provide two scripts for interested users to get started in using `fitburst` under these conditions. Both scripts are located in the pipeline subdirectory of the `fitburst` codebase. + +One of these scripts will create a simulated, de-dispersed dynamic spectrum. For example, execute the following lines after compiling `fitburst`: + +``` python +user@pc> cd /path/to/fitburst/fitburst/pipelines/ +user@pc | pipelines> python simulate_burst.py +``` + +The output of this script is a plot printed to the screen for reference, and a file in the "fitburst-generic" format (`simulated_data.npz`), described in a separate documentation page, that is saved to the local area. This file contains the simulated spectrum shown in the plot, various parameters that describe the context of the spectrum, and an initial guess of model parameters. + +The second script is an example of a "full pipeline" version of fitburst that performs I/O, model instantiation, and least-squares fitting: + +``` python +user@pc | pipelines> python fitburst_pipeline.py simulated_data.npz --verbose +``` + +The output of this second script consists of three items: a `.png` file contain a three-panel plot showing the data, best-fit model, and their difference; a JSON file that contains the best-fit parameters and statistics of the fit; and terminal output that shows similar information due to the use of the `--verbose` option: + +``` python +INFO: no solution file found or provided; proceeding with fit... +INFO: there are 256 frequencies and 128 time samples. +INFO: there are 256 good frequencies... +INFO: input data cube is already dedispersed! +INFO: setting 'dm' entry to 0, now considered a dm-offset parameter... +INFO: initial guess for 3-component model: + * amplitude: [0.0, 0.0, 0.0] + * arrival_time: [0.03, 0.04, 0.05] + * burst_width: [0.001, 0.002, 0.0005] + * dm: [0.0, 0.0, 0.0] + * dm_index: [-2.0, -2.0, -2.0] + * ref_freq: [1500.0, 1400.0, 1300.0] + * scattering_index: [-4.0, -4.0, -4.0] + * scattering_timescale: [0.0, 0.0, 0.0] + * spectral_index: [0.0, 0.0, 0.0] + * spectral_running: [-300.0, -300.0, -300.0] +INFO: computing dedispersion-index matrix +INFO: initializing model +INFO: removing the following parameters: dm_index, scattering_index, scattering_timescale +INFO: new list of fit parameters: amplitude, arrival_time, burst_width, dm, spectral_index, spectral_running +0.00000 0.00000 0.03000 -4.00000 0.00000 0.00100 0.00000 -300.00000 +0.00000 0.00000 0.04000 -4.00000 0.00000 0.00200 0.00000 -300.00000 +0.00000 0.00000 0.05000 -4.00000 0.00000 0.00050 0.00000 -300.00000 +0.02077 0.00435 0.02999 -4.00000 0.00000 0.00096 -0.06705 -301.05914 +0.02077 -0.01179 0.04000 -4.00000 0.00000 0.00204 0.83079 -295.33102 +0.02077 0.02074 0.05001 -4.00000 0.00000 0.00045 0.08439 -272.67249 +0.03633 0.00446 0.02999 -4.00000 0.00000 0.00096 -0.06086 -301.33863 +0.03633 -0.01175 0.04000 -4.00000 0.00000 0.00204 0.83347 -294.97891 +0.03633 0.02339 0.05001 -4.00000 0.00000 0.00046 -0.00992 -275.70777 +0.03592 0.00446 0.02999 -4.00000 0.00000 0.00096 -0.06146 -301.29372 +0.03592 -0.01178 0.04000 -4.00000 0.00000 0.00204 0.83464 -294.98868 +0.03592 0.02348 0.05001 -4.00000 0.00000 0.00046 0.00581 -275.27610 +0.03615 0.00446 0.02999 -4.00000 0.00000 0.00096 -0.06141 -301.29554 +0.03615 -0.01178 0.04000 -4.00000 0.00000 0.00204 0.83460 -294.98740 +0.03615 0.02349 0.05001 -4.00000 0.00000 0.00046 0.00438 -275.31794 +INFO: fit successful! +INFO: computing hessian matrix with best-fit parameters. +0.03615 0.00446 0.02999 -4.00000 0.00000 0.00096 -0.06141 -301.29554 +0.03615 -0.01178 0.04000 -4.00000 0.00000 0.00204 0.83460 -294.98740 +0.03615 0.02349 0.05001 -4.00000 0.00000 0.00046 0.00438 -275.31794 +0.03615 0.00446 0.02999 -4.00000 0.00000 0.00096 -0.06141 -301.29554 +0.03615 -0.01178 0.04000 -4.00000 0.00000 0.00204 0.83460 -294.98740 +0.03615 0.02349 0.05001 -4.00000 0.00000 0.00046 0.00438 -275.31794 +INFO: best-fit estimate for 3-component model: + * amplitude: [0.004458347857993017, -0.01178052452614616, 0.023488538876660896] +/- [0.011361366063813588, 0.0090246635765475, 0.01652378379027083] + * arrival_time: [0.029986353743391356, 0.040002393748902666, 0.050007330750880916] +/- [2.443033563379835e-05, 4.181234443170404e-05, 1.518622689210296e-05] + * burst_width: [0.0009574508453823643, 0.002039008450473889, 0.00045508475860427344] +/- [2.403509547560637e-05, 4.033352991181347e-05, 1.906271029135856e-05] + * dm: [0.036151607836687895] +/- [0.09274487453599654] + * spectral_index: [-0.06141150018163158, 0.8345952746331111, 0.00437634181414019] +/- [0.6464300408655368, 0.4603917717520832, 0.8428930404305951] + * spectral_running: [-301.2955381690263, -294.9873984865791, -275.31793520453783] +/- [17.684251952258453, 11.120116046037642, 21.199888485781074] +``` + +The `fitburst_pipeline.py` script comes with a variety of options that may be useful when working with "real" data, but aren't necessary to use for the data simulated above. This script will work with real data so long as the input file matches the "fitburst-generic" format of the simulated data; explore these options on real data as you see fit! diff --git a/docs/usage/using_fitburst.md b/docs/usage/using_fitburst.md deleted file mode 100644 index baeac59..0000000 --- a/docs/usage/using_fitburst.md +++ /dev/null @@ -1,59 +0,0 @@ -The `fitburst` codebase can be used in two different ways: as a Python package; or through Python3 scripts provided in the repository. This page describes how to interact with the two usage modes. - -## `fitburst` as a Python package -Once properly installed, `fitburst` can be immediate accessed through a Python interpreter as an importable package: - -``` python -user@pc > python ->>> import fitburst ->>> -``` - -All underlying objects and functions can be accessed in this package format, with Python docstrings that emulate the `numpy` documentation format. - -## Example `fitburst` Pipelines -The full `fitburst` pipeline consists of the following major sections, in order of operation: - -0. command-line interface -1. data I/O -2. declaration of initial guess for burst parameters -3. configuration of `fitburst` model object -4. configuration of `fitburst` "fitter" object -5. execution of fit -6. generation of figures, fit-summary files, and/or convergence status. - -We have provided several "pipeline" scripts as demonstrations of `fitburst` as an importable package. These Python3 scripts will perform a weighted least-squares fit of a model against different types of existing data formats. For example, a user with data in the `fitburst`-compliant "generic" format (outlined in this documation) can run the `fitburst_example_generic.py` script with the `--verbose` option if more output is desired: - -``` python -> python /path/to/fitburst_example_generic.py /location/of/input_data.npz --verbose -INFO: no solution file found or provided; proceeding with fit... -INFO: input data cube is already dedispersed! -INFO: setting 'dm' entry to 0, now considered a dm-offset parameter... -INFO: initial guess for 1-component model: - * ref_freq: [406.95] - * arrival_time: [0.5] - * dm: [0.0] - * burst_width: [0.05] - * amplitude: [-3.0] - * scattering_timescale: [0.0] - * dm_index: [-2.0] - * scattering_index: [-4.0] - * spectral_index: [-1.0] - * spectral_running: [1.0] -INFO: computing dedispersion-index matrix -INFO: initializing model -INFO: dimensions of model spectrum set to (32, 256) -INFO: removing the following parameters: dm_index, scattering_index, scattering_timescale -INFO: new list of fit parameters: amplitude, arrival_time, burst_width, dm, spectral_index, spectral_running -INFO: fit successful! -INFO: derived uncertainties and fit statistics -INFO: best-fit estimate for 1-component model: - * amplitude: [-6.324027826884258] +/- [0.06858211715116933] - * arrival_time: [0.09509827951169918] +/- [0.004102709304261897] - * burst_width: [0.02668654193357436] +/- [0.0011340937579950628] - * dm: [0.7976612513974163] +/- [0.28790218747669244] - * spectral_index: [3.0854722262265004] +/- [0.8459390426969531] - * spectral_running: [-3.555893925137327] +/- [1.0570358282340104] -Data vmin, vmax = -0.00, 0.00 -Model vmin, vmax = -0.00, 0.00 -``` diff --git a/fitburst/backend/__init__.py b/fitburst/backend/__init__.py index 66f21ed..35562b4 100644 --- a/fitburst/backend/__init__.py +++ b/fitburst/backend/__init__.py @@ -23,6 +23,5 @@ Loader=yaml.FullLoader ) -# now import the project-specific routines. +# now import the format-specific module files. from . import generic -from . import chimefrb diff --git a/fitburst/backend/chimefrb.py b/fitburst/backend/chimefrb.py deleted file mode 100644 index 5f79670..0000000 --- a/fitburst/backend/chimefrb.py +++ /dev/null @@ -1,392 +0,0 @@ -import numpy as np -import datetime -import requests -import glob -import pytz -import sys - -# now import some fitburst-specific packages. -from fitburst.utilities import bases -from . import telescopes - - -class DataReader(bases.ReaderBaseClass): - """ - A child class of I/O and processing for CHIME/FRB data, inheriting the basic - structure defined in ReaderBaseClass(). - """ - - def __init__(self, eventid, beam_id: int = 0, data_location: str = "/data/frb-archiver"): - - # before anything else, initialize superclass. - super().__init__() - - # now, ensure eventid makes sense before retrieving data. - self.eventid = eventid - assert isinstance(self.eventid, int) - print("CHIMEFRBReader executed:") - - # define CHIME/FRB-specific parameters to be updated by data-retrieval method. - self.beam_id = beam_id - self.downsample_factor = None - self.files = [] - self.fpga_count_start = None - self.fpga_count_total = None - self.fpga_frame0_nano = None - self.frbmaster_request_status = None - self.rfi_freq_count = None - self.rfi_mask = None - - # as a default, grab data from FRBMaster. - print("... grabbing metadata for eventID {0}".format(self.eventid)) - self._retrieve_metadata_frbmaster(self.eventid, beam_id=self.beam_id) - - def get_parameters(self, pipeline: str = "L1") -> dict: - """ - Returns a dictionary containing parameters as keys and their FRBMaster entries - stored as values. - - Parameters - ---------- - pipeline: str, optional - The name of CHIME/FRB pipeline for which to grab locked parameters. - Current options are: L1, dm, fitburst. - - Returns - ------- - parameter_dict : dict - A python dicitonary containing parameters of dynamic spectra, with available - pipeline values replacin fitburst default values. - """ - - parameter_dict = {} - - ### if fitburst results exist and are desired, grab those. - if bool(self.burst_parameters["fitburst"]) and pipeline == "fitburst": - current_round = "round_2" - - if "scattering_timescale" in self.burst_parameters["fitburst"]["round_3"]: - current_round = "round_3" - - for current_key in self.burst_parameters["fitburst"][current_round].keys(): - parameter_dict[current_key] = self.burst_parameters["fitburst"][ - current_round - ][current_key] - - # adjust certain FRBMaster entries if burst has multiple components. - num_components = len(parameter_dict["arrival_time"]) - parameter_dict["ref_freq"] = parameter_dict["ref_freq"] * num_components - - # add parameters that are not reported in FRBMaster here. - parameter_dict["dm_index"] = [-2.0] * num_components - parameter_dict["scattering_index"] = [-4.0] * num_components - - - ### if instead the DM-pipeline results exist and are desired, grab those. - elif bool(self.burst_parameters["dm-pipeline"]) and pipeline == "dm": - print("woohoo DM pipleine") - parameter_dict["amplitude"] = [-3.0] - parameter_dict["burst_width"] = self.burst_parameters["dm-pipeline"]["width"] - parameter_dict["dm"] = self.burst_parameters["dm-pipeline"]["dm"] - parameter_dict["dm_index"] = [-2.0] - parameter_dict["ref_freq"] = [telescopes["chimefrb"]["pivot_freq"]["spectrum"]] - parameter_dict["scattering_index"] = [-4.0] - parameter_dict["scattering_timescale"] = [0.0] - parameter_dict["spectral_index"] = [-1.0] - parameter_dict["spectral_running"] = [0.0] - - if self.fpga_frame0_nano is not None: - parameter_dict["arrival_time"] = [ - pytz.utc.localize(self.burst_parameters["dm-pipeline"]["timestamp_utc"][0]).timestamp() - \ - (self.fpga_frame0_nano * 1e-9) - ] - - ### if the default mode is chosen, just grab the parameters determined by L1. - elif bool(self.burst_parameters["L1"]) and pipeline == "L1": - print("ok at least there is L1") - - # L1 only estimates parameters for one component, so just create a dictionary - # corresponding to one burst component. Use guesses for values not estimated by L1. - parameter_dict["amplitude"] = [-3.0] - parameter_dict["arrival_time"] = [ - self.burst_parameters["L1"]["timestamp_fpga"] * - telescopes["chimefrb"]["fpga"]["time_per_sample"] - ] - parameter_dict["burst_width"] = [0.05] - parameter_dict["dm"] = [self.burst_parameters["L1"]["dm"]] - parameter_dict["dm_index"] = [-2.0] - parameter_dict["ref_freq"] = [telescopes["chimefrb"]["pivot_freq"]["spectrum"]] - parameter_dict["scattering_index"] = [-4.0] - parameter_dict["scattering_timescale"] = [0.0] - parameter_dict["spectral_index"] = [-1.0] - parameter_dict["spectral_running"] = [0.0] - - - else: - print("ERROR: no parameters retrieved from FRBMaster!") - - ### return parameter dictionary. - return parameter_dict - - def load_data(self, files: list) -> None: - """ - Load data from CHIME/FRB msgpack data files. - - Parameters - ---------- - files: list - A list of msgpack files to load - - """ - - try: - from cfod.analysis.intensity.chime_intensity import unpack_datafiles - - except ImportError as err: - print("Unable to import from cfod") - print("Please ensure this package is installed.") - print(err) - - unpacked_data_set = unpack_datafiles(files) - self.data_full = unpacked_data_set[0] - self.data_weights = unpacked_data_set[1] - self.fpga_count_start = unpacked_data_set[2] - self.fpga_count_total = unpacked_data_set[3] - self.downsample_factor = unpacked_data_set[4] - self.rfi_mask = unpacked_data_set[5] - self.fpga_frame0_nano = (unpacked_data_set[6])[0] - - # derive time information from loaded data. - n_freqs, n_times = self.data_full.shape - times = np.arange(n_times, dtype=int) + (self.downsample_factor // 2) - times *= ( - telescopes["chimefrb"]["num_frames_per_sample"] - * telescopes["chimefrb"]["num_factor_upchannel"] - ) - times += self.fpga_count_start[0] - self.times = times * telescopes["chimefrb"]["fpga"]["time_per_sample"] - - # now derive frequency information. - freqs = np.arange(n_freqs, dtype=np.float64) - freqs *= -( - telescopes["chimefrb"]["bandwidth"] / telescopes["chimefrb"]["num_channels"] - ) - freqs += ( - telescopes["chimefrb"]["fpga"]["freq_top"] - + telescopes["chimefrb"]["bandwidth"] - / telescopes["chimefrb"]["fpga"]["num_channels"] - / 2 - ) - self.freqs = freqs[::-1] - - # define index values before exiting. - self.num_freq = len(self.freqs) - self.num_time = len(self.times) - self.res_freq = self.freqs[1] - self.freqs[0] - self.res_time = self.times[1] - self.times[0] - - def _retrieve_metadata_frbmaster( - self, eventid: str, beam_id: int = 0, mountpoint: str = "/data/chime" - ) -> None: - """ - This internal methods executes CHIME/FRB-specific actions for retrieving the necessary - metadata from the FRBMaster database, and requires direct network access to raw intensity - data (i.e., should be run at the CHIME or CANFAR sites). - - Parameters - ---------- - eventid : str - The CHIME/FRB ID for the event of interest. - - beam_id : int, optional - The index of a list of recorded beams, corresponding to the desired data set. - (This list is ordered in decreasing S/N; beam_id = 0 corresponds to the highest-S/N beam.) - - mountpoint : str, optional - The local root directory where raw intensity data are stored. - - Returns - ------- - None : None - This method sets a large of number of attributes that comprise the DataReader object. - """ - - try: - from cfod.analysis.intensity.chime_intensity import natural_keys - from chime_frb_api.backends.frb_master import FRBMaster - - except ImportError as err: - print("Unable to import from cfod and/or chime_frb_api") - print("Please ensure thoses packagea are installed.") - print(err) - - # perform an initial get of data from the L4 database in order to - master = FRBMaster() - event_L4 = master.events.get_event(eventid, full_header=True) - ids, snrs = [], [] - - for current_entry_L4 in event_L4["event_beam_header"]: - ids += [int(current_entry_L4["beam_no"])] - snrs += [float(current_entry_L4["snr"])] - - # now order id list in descending order based on S/N values. - snrs = np.array(snrs) - ids_sorted = [ids[idx] for idx in np.argsort(-snrs).tolist()] - beam_no = ids_sorted[beam_id] - - # next, perform a GET to retrieve FRBMaster data. - event = master.events.get_event(eventid) - entry_realtime = None - - for current_entry in event["measured_parameters"]: - if current_entry["pipeline"]["name"] == "realtime": - entry_realtime = current_entry - - print("realtime entry:", entry_realtime) - - # grab l1 data of basic properties, stash into parameter attribute. - timestamp_substr = entry_realtime["datetime"] - - if "UTC" in timestamp_substr: - elems = timestamp_substr.split() - timestamp_substr = " ".join(elems[:len(elems)-1]) - - self.burst_parameters["L1"] = {} - self.burst_parameters["L1"]["dm"] = entry_realtime["dm"] - self.burst_parameters["L1"]["dm_range"] = entry_realtime["dm_error"] - self.burst_parameters["L1"]["time_range"] = 0.01 - self.burst_parameters["L1"]["timestamp_fpga"] = event["fpga_time"] - self.burst_parameters["L1"]["timestamp_utc"] = datetime.datetime.strptime( - timestamp_substr, "%Y-%m-%d %H:%M:%S.%f" - ) - - # try getting data from frb-vsop.chime. - print( - "... trying to grab chime/frb data from fitburst/dm-pipeline results...", - end="", - ) - - # establish connection to fRBMaster. - locked_id_dm = None - locked_id_fitburst = None - self.burst_parameters["dm-pipeline"] = {} - self.burst_parameters["fitburst"] = {} - - try: - if "intensity-dm-pipeline" in event["locked"].keys(): - locked_id_dm = event["locked"]["intensity-dm-pipeline"] - - - for current_measurement in event["measured_parameters"]: - # if there are locked DM-pipeline results, grab and stash those. - - if ( - current_measurement["pipeline"]["name"] == "intensity-dm-pipeline" - and current_measurement["measurement_id"] == locked_id_dm - ): - - self.burst_parameters["dm-pipeline"]["snr"] = [ - current_measurement["snr"] - ] - self.burst_parameters["dm-pipeline"]["beam_number"] = [ - current_measurement["beam_number"] - ] - self.burst_parameters["dm-pipeline"]["dm"] = [ - current_measurement["dm_snr"] - ] - self.burst_parameters["dm-pipeline"]["width"] = [ - current_measurement["width"] - ] - - # get timestamp and avoid error with UTC substring. - timestamp_substr = current_measurement["datetime"] - - if "UTC" in timestamp_substr: - elems = timestamp_substr.split() - timestamp_substr = " ".join(elems[:len(elems)-1]) - - self.burst_parameters["dm-pipeline"]["timestamp_utc"] = \ - [datetime.datetime.strptime( - str(timestamp_substr), "%Y-%m-%d %H:%M:%S.%f") - ] - - - - except Exception as exc: - print( - "WARNING: unable to retrieve locked parameters for DM pipeline from FRBMaster\n", - "Exception: ", - exc - ) - - try: - if "intensity-fitburst" in event["locked"].keys(): - locked_id_fitburst = event["locked"]["intensity-fitburst"] - - for current_measurement in event["measured_parameters"]: - - if ( - current_measurement["pipeline"]["name"] == "intensity-fitburst" - and current_measurement["measurement_id"] == locked_id_fitburst - ): - - # determine which fitburst round it is and stash separately. - current_round = "round_1" - - if "Round 1" in current_measurement["pipeline"]["logs"]: - pass - - elif "Round 2" in current_measurement["pipeline"]["logs"]: - current_round = "round_2" - - elif "Round 3" in current_measurement["pipeline"]["logs"]: - current_round = "round_3" - - # now stash fitburst parameters. - self.burst_parameters["fitburst"][current_round] = {} - self.burst_parameters["fitburst"][current_round][ - "dm" - ] = current_measurement["sub_burst_dm"] - self.burst_parameters["fitburst"][current_round][ - "burst_width" - ] = current_measurement["sub_burst_width"] - self.burst_parameters["fitburst"][current_round][ - "amplitude" - ] = np.log10(current_measurement["sub_burst_fluence"]).tolist() - self.burst_parameters["fitburst"][current_round][ - "arrival_time" - ] = current_measurement["sub_burst_timestamp"] - self.burst_parameters["fitburst"][current_round][ - "spectral_index" - ] = current_measurement["sub_burst_spectral_index"] - self.burst_parameters["fitburst"][current_round][ - "spectral_running" - ] = current_measurement["sub_burst_spectral_running"] - self.burst_parameters["fitburst"][current_round][ - "ref_freq" - ] = [current_measurement["fitburst_reference_frequency"]] - - # if current round has scattering timescale, stash it as well. - if "sub_burst_scattering_timescale" in current_measurement: - self.burst_parameters["fitburst"][current_round][ - "scattering_timescale" - ] = current_measurement["sub_burst_scattering_timescale"] - print("success!") - - except Exception as exc: - print( - "WARNING: unable to retrieve locked parameters from frb-vsop.chime:8001\n", - "Exception: ", - exc - ) - - # now grab filenames. - print("... now grabbing locations on the CHIME/FRB archivers...", end="") - date_string = self.burst_parameters["L1"]["timestamp_utc"].strftime("%Y/%m/%d") - path_to_data = "{0}/intensity/raw/{1}/astro_{2}/{3:04d}".format( - mountpoint, date_string, eventid, beam_no - ) - - self.files = glob.glob("{0}/*.msgpack".format(path_to_data)) - self.files.sort(key=natural_keys) - print("success!") diff --git a/fitburst/backend/telescopes.yaml b/fitburst/backend/telescopes.yaml index 6688097..88ba22a 100644 --- a/fitburst/backend/telescopes.yaml +++ b/fitburst/backend/telescopes.yaml @@ -1,8 +1,10 @@ # Configuration values for CHIME/FRB data. -# +# TODO: create module file to read CHIME/FRB total intensity data, +# which will use the values below. # Notes: # - all time entries have units of seconds # - all freq entires have units of MHz + chimefrb: bandwidth: 400.0 num_channels: 16384 diff --git a/fitburst/pipelines/fitburst_example_chimefrb.py b/fitburst/pipelines/fitburst_example_chimefrb.py deleted file mode 100644 index f65efc4..0000000 --- a/fitburst/pipelines/fitburst_example_chimefrb.py +++ /dev/null @@ -1,541 +0,0 @@ -#! /user/bin/env python - -### import and configure logger to only report warnings or worse for non-fitburst packages. -import datetime -import logging -right_now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") -logging.basicConfig(filename=f"fitburst_run_{right_now}.log", level=logging.DEBUG) -logging.getLogger('cfod').setLevel(logging.WARNING) -logging.getLogger('chime_frb_api').setLevel(logging.WARNING) -logging.getLogger('matplotlib').setLevel(logging.WARNING) -logging.getLogger('numpy').setLevel(logging.WARNING) -logging.getLogger('urllib3').setLevel(logging.WARNING) -log = logging.getLogger("fitburst") - -from fitburst.analysis.peak_finder import FindPeak -from fitburst.analysis.fitter import LSFitter -import fitburst.backend.chimefrb as chimefrb -import fitburst.analysis.model as mod -import chime_frb_constants as const -import fitburst.utilities as ut -import fitburst.routines as rt -import numpy as np -import copy -import json -import time -import sys -import os - -### import and configure matplotlib. -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt - -### import and configure argparse. -import argparse - -parser = argparse.ArgumentParser(description= - "A Python3 script that uses fitburst API to read, pre-process, window, and fit CHIME/FRB data " + - "against a model of the dynamic spectrum." -) - -parser.add_argument( - "eventIDs", action="store", nargs="+", type=int, - help="One or more CHIME/FRB event IDs." -) - -parser.add_argument( - "--amplitude", action="store", dest="amplitude", default=None, nargs="+", type=float, - help="Initial guess for burst amplitude, in dex." -) - -parser.add_argument( - "--arrival_time", action="store", dest="arrival_time", default=None, nargs="+", type=float, - help="Initial guess for arrival time, in seconds." -) - -parser.add_argument( - "--beam", action="store", dest="beam", default=0, type=int, - help="Index of beam list.." -) - -parser.add_argument( - "--dm", action="store", dest="dm", default=None, nargs="+", type=float, - help="Initial guess for dispersion measure (DM), in pc/cc." -) - -parser.add_argument( - "--downsample_freq", action="store", dest="factor_freq_downsample", default=1, type=int, - help="Downsample the raw spectrum along the frequency axis by a specified integer." -) - -parser.add_argument( - "--downsample_time", action="store", dest="factor_time_downsample", default=1, type=int, - help="Downsample the raw spectrum along the time axis by a specified integer." -) - -parser.add_argument( - "--fit", action="store", dest="parameters_to_fit", default=[], nargs="+", type=str, - help="A list of model parameters to fit during least-squares estimation." -) - -parser.add_argument( - "--fix", action="store", dest="parameters_to_fix", default=[], nargs="+", type=str, - help="A list of model parameters to hold fixed to initial values." -) - -parser.add_argument( - "--iterations", action="store", dest="num_iterations", default=1, type=int, - help="Integer number of fit iterations." -) - -parser.add_argument( - "--latest", action="store", default=None, dest="latest_solution_location", type=str, - help="If set, use existing solution if present." -) - -parser.add_argument( - "--no_fit", action="store_true", dest="no_fit", - help="If set, then skip fit and create state file using input parameters." -) - -parser.add_argument( - "--normalize_variance", action="store_true", dest="normalize_variance", - help="If set, then normalize variance during preprocessing of spectrum." -) - -parser.add_argument( - "--offset_dm", action="store", dest="offset_dm", default=0.0, type=float, - help="Offset applied to initial dispersion measure, in pc/cc." -) - -parser.add_argument( - "--offset_time", action="store", dest="offset_time", default=0.0, type=float, - help="Offset applied to initial arrival time, in seconds." -) - -parser.add_argument( - "--peakfind_dist", action="store", dest="peakfind_dist", default=5, type=int, - help="Separation used for peak-finding algorithm (for multi-component fitting)." -) - -parser.add_argument( - "--peakfind_rms", action="store", dest="peakfind_rms", default=None, type=float, - help="RMS used for peak-finding algorithm (for multi-component fitting)." -) - -parser.add_argument( - "--pipeline", action="store", dest="pipeline", default="L1", type=str, - help="Name of CHIME/FRB pipeline whose results will be used as initial guesses." -) - -parser.add_argument( - "--ref_freq", action="store", dest="ref_freq_override", default=None, type=float, - help="Override of reference frequency." -) - -parser.add_argument( - "--save", action="store_true", dest="save_results", - help="If set, save best-fit results to a JSON file." -) - -parser.add_argument( - "--scattering_timescale", action="store", dest="scattering_timescale", default=None, nargs="+", type=float, - help="Initial guess for scattering index." -) - -parser.add_argument( - "--scintillation", action="store_true", dest="scintillation", - help="If set, then enable per-channel amplitude estimation in cases of scintillation." -) - -parser.add_argument( - "--spectral_index", action="store", dest="spectral_index", default=None, nargs="+", type=float, - help="Initial guess for spectral index." -) - -parser.add_argument( - "--spectral_running", action="store", dest="spectral_running", default=None, nargs="+", type=float, - help="Initial guess for spectral running." -) - -parser.add_argument( - "--upsample_freq", action="store", dest="factor_freq_upsample", default=8, type=int, - help="Upsample the raw spectrum along the frequency axis by a specified integer." -) - -parser.add_argument( - "--upsample_time", action="store", dest="factor_time_upsample", default=4, type=int, - help="Upsample the raw spectrum along the time axis by a specified integer." -) - -parser.add_argument( - "--variance_range", action="store", dest="variance_range", default=[0.95, 1.05], - nargs=2, type=float, help="Range of variance values used to designate channels with RFI." -) - -parser.add_argument( - "--variance_weight", action="store", dest="variance_weight", default=(1. / const.L0_NUM_FRAMES_SAMPLE / 2), - type=float, help="Scaling value applied to variances in preprocessing step." -) - -parser.add_argument( - "--verbose", action="store_true", dest="verbose", - help="If set, then print more information during pipeline execution." -) - -parser.add_argument( - "--width", action="store", dest="width", default=None, nargs="+", type=float, - help="Initial guess for burst width, in seconds." -) - -parser.add_argument( - "--window", action="store", dest="window", default=0.08, type=float, - help="Half of size of data window, in seconds." -) - -# grab CLI inputs from argparse. -args = parser.parse_args() -amplitude = args.amplitude -arrival_time = args.arrival_time -beam = args.beam -dm = args.dm -eventIDs = args.eventIDs -factor_freq_downsample = args.factor_freq_downsample -factor_time_downsample = args.factor_time_downsample -factor_freq_upsample = args.factor_freq_upsample -factor_time_upsample = args.factor_time_upsample -latest_solution_location = args.latest_solution_location -normalize_variance = args.normalize_variance -no_fit = args.no_fit -num_iterations = args.num_iterations -offset_dm = args.offset_dm -offset_time = args.offset_time -parameters_to_fit = args.parameters_to_fit -parameters_to_fix = args.parameters_to_fix -peakfind_rms = args.peakfind_rms -peakfind_dist = args.peakfind_dist -pipeline = args.pipeline -ref_freq_override = args.ref_freq_override -save_results = args.save_results -scattering_timescale = args.scattering_timescale -scintillation = args.scintillation -spectral_index = args.spectral_index -spectral_running = args.spectral_running -variance_range = args.variance_range -variance_weight = args.variance_weight -verbose = args.verbose -width = args.width -window_orig = args.window - -# before looping over events, suss out model parameters to fit and/or hold fixed. -parameters_to_fix += ["dm_index", "scattering_index", "scattering_timescale"] - -for current_fit_parameter in parameters_to_fit: - if current_fit_parameter in parameters_to_fix: - parameters_to_fix.remove(current_fit_parameter) - log.info(f"the parameter '{current_fit_parameter}' is now a fit parameter") - -# loop over all CHIME/FRB events supplied at command line. -for current_event_id in eventIDs: - log.info(f"now preparing to fit spectrum for {current_event_id}") - - # grab initial parameters to check if pipeline-specific parameters exist. - try: - data = chimefrb.DataReader(current_event_id, beam_id=beam) - - except: - log.error(f"ERROR: {current_event_id} fails at DB-parsing stage, moving on to next event...") - continue - - initial_parameters = data.get_parameters(pipeline=pipeline) - - # if returned parameter dictionary is empty, move on to next event. - if bool(initial_parameters): - log.info(f"successfully grabbed parameter data from CHIME/FRB {pipeline} pipeline") - - else: - log.error(f"couldn't grab CHIME/FRB {pipeline} pipeline data for event {current_event_id}") - continue - - window = window_orig - - # load data into memory and pre-process. - try: - data.load_data(data.files) - log.info(f"successfully read raw msgpack data for event {current_event_id}") - - except Exception as exc: - log.error(f"couldn't read raw msgpack data for event {current_event_id}") - continue - - # now that frame0-nano value is available after loading of data, grab parameters - # to obtain timestamp info. - initial_parameters = data.get_parameters(pipeline=pipeline) - - # if a JSON file containing results already exists, then read that in. - latest_solution_file = f"{latest_solution_location}/results_fitburst_{current_event_id}.json" - results = None - - if ( - latest_solution_location is not None and os.path.isfile(latest_solution_file) - ): - log.info(f"loading data from results file for event {current_event_id}") - results = json.load(open(latest_solution_file, "r")) - initial_parameters = results["model_parameters"] - - try: - if window_orig == 0.08: - window = results["fit_logistics"]["spectrum_window"] - - except: - log.warning(f"window size not found in file '{latest_solution_file}'") - - - log.info(f"window size for {current_event_id} adjusted to +/- {0:.1f} ms, from input JSON data".format(window * 1e3)) - - else: - pass - #initial_parameters["burst_width"] = [window / 10.] - - # if scattering timescale is a fit parameter, initially set to width. - if ( - initial_parameters["scattering_timescale"][0] == 0. and - "scattering_timescale" not in parameters_to_fix - ): - initial_parameters["scattering_timescale"] = copy.deepcopy( - (np.fabs(np.array(initial_parameters["burst_width"])) * 1.).tolist() - ) - initial_parameters["burst_width"] = (np.array(initial_parameters["burst_width"]) / 1.).tolist() - - # if guesses are provided through CLI, overload them into the initial-guess dictionary. - initial_parameters["dm"][0] += offset_dm - initial_parameters["arrival_time"][0] += offset_time - - if amplitude is not None: - initial_parameters["amplitude"] = amplitude - - if arrival_time is not None: - initial_parameters["arrival_time"] = arrival_time - - if dm is not None: - initial_parameters["dm"] = dm - - if scattering_timescale is not None: - initial_parameters["scattering_timescale"] = scattering_timescale - - if spectral_index is not None: - initial_parameters["spectral_index"] = spectral_index - - if spectral_running is not None: - initial_parameters["spectral_running"] = spectral_running - - if width is not None: - initial_parameters["burst_width"] = width - - # now, clean and normalize data. - data.preprocess_data( - normalize_variance=normalize_variance, - variance_range=variance_range, - variance_weight=variance_weight - ) - - # if desired, downsample data prior to extraction. - #data.downsample(factor_freq_downsample, factor_time_upsample) - log.info(f"downsampled raw data by factors of (ds_freq, ds_time) = ({factor_freq_downsample}, {factor_time_downsample})") - - # if the number of RFI-flagged channels is "too large", skip this event altogether. - num_bad_freq = data.num_freq - np.sum(data.good_freq) - - if (num_bad_freq / data.num_freq) > 0.7: - log.error( - f" {num_bad_freq} out of {data.num_freq} frequencies masked for event {current_event_id}" - ) - continue - - # now compute dedisperse matrix for data, given initial DM, and grab windowed data. - freq_min = min(data.freqs) - freq_max = max(data.freqs) - - log.info(f"computing dedispersion-index matrix for {current_event_id}") - log.info(f"dedispersing data for {current_event_id} over freq range ({freq_min}, {freq_max}) MHz") - params = initial_parameters.copy()#data.burst_parameters["fitburst"]["round_2"] - data.dedisperse( - params["dm"][0], - np.mean(params["arrival_time"]), - ref_freq=params["ref_freq"][0] - ) - - # before doing anything, check if window size doesn't extend beyond data set. - # if it does, adjust down by an appropriate amount. - window_max = data.times[-1] - np.mean(initial_parameters["arrival_time"]) - - if window > window_max: - window = window_max - 0.005 - log.warning(f"window size for {current_event_id} adjusted to +/- {window * 1e3} ms") - - if window_max < 0.: - log.error(f"{current_event_id} has a negative widnow size, initial guess for TOA is too far off...") - continue - - data_windowed, times_windowed = data.window_data(np.mean(params["arrival_time"]), window=window) - - # check if there are any lingering zero-weighted channels. - weird_chan = 0 - for current_chan in range(data.num_freq): - if data.good_freq[current_chan]: - if data_windowed[current_chan, :].sum() == 0.: - data.good_freq[current_chan] = False - weird_chan += 1 - - if weird_chan > 0: - log.warning(f"WARNING: there are {weird_chan} weird channels") - - #plt.pcolormesh(rt.manipulate.downsample_2d(data_windowed * data.good_freq[:, None], 64, 1)) - #plt.savefig("test.png") - - # before defining model, adjust model parameters with peak-finding algorithm. - if peakfind_rms is not None: - log.info(f"running FindPeak on {current_event_id} to isolate burst components...") - peaks = FindPeak(data_windowed, times_windowed, data.freqs, rms=peakfind_rms) - peaks.find_peak(distance=peakfind_dist) - initial_parameters = peaks.get_parameters_dict(initial_parameters) - - # now create initial model. - # since CHIME/FRB data are in msgpack format, define a few things - # so that this version of fitburst works similar to the original version on site. - log.info(f"initializing spectrum model for {current_event_id}") - num_components = len(initial_parameters["amplitude"]) - initial_parameters["dm"] = [0.] * num_components - - if ref_freq_override is not None: - initial_parameters["ref_freq"] = [ref_freq_override] - initial_parameters["arrival_time"][0] = initial_parameters["arrival_time"][0] +\ - rt.ism.compute_time_dm_delay( - initial_parameters["dm"][0], - 4149.3775, - -2., - ref_freq_override, - freq2 = initial_parameters["ref_freq"][0], - ) - - - model = mod.SpectrumModeler( - data.freqs, - times_windowed, - dm_incoherent=params["dm"][0], - factor_freq_upsample=factor_freq_upsample, - factor_time_upsample=factor_time_upsample, - num_components=num_components, - is_dedispersed=True, - is_folded=False, - scintillation=scintillation, - verbose=verbose, - ) - - model.update_parameters(initial_parameters) - bestfit_model = model.compute_model(data=data_windowed) * data.good_freq[:, None] - bestfit_params = model.get_parameters_dict() - bestfit_params["dm"] = [params["dm"][0] + x for x in bestfit_params["dm"]] - #print(bestfit_params["dm"]) - #sys.exit() - bestfit_residuals = data_windowed - bestfit_model - fit_is_successful = False - fit_statistics = None - - ### now set up fitter and execute least-squares fitting, if desired. - if not no_fit: - - for current_iteration in range(num_iterations): - log.info(f"fitting model for {current_event_id}, loop #{current_iteration + 1}") - fitter = LSFitter(data_windowed, model, good_freq=data.good_freq, weighted_fit=True) - fitter.fix_parameter(parameters_to_fix) - start = time.time() - fitter.fit(exact_jacobian=True) - - # before executing the fitting loop, overload model class with best-fit parameters. - if fitter.results.success: - stop = time.time() - log.info(f"LSFitter.fit() took {stop - start} seconds to run.") - model.update_parameters(fitter.fit_statistics["bestfit_parameters"]) - bestfit_model = model.compute_model(data=data_windowed) * data.good_freq[:, None] - bestfit_params = model.get_parameters_dict() - bestfit_params["dm"] = [params["dm"][0] + x for x in bestfit_params["dm"]] - bestfit_residuals = data_windowed - bestfit_model - fit_is_successful = True - fit_statistics = fitter.fit_statistics - - # TODO: for now, stash covariance data for offline comparison; remove at some point. - np.savez( - f"covariance_matrices_{current_event_id}.npz", - covariance_approx = fitter.covariance_approx, - covariance_exact = fitter.covariance, - covariance_labels = fitter.covariance_labels - ) - - else: - fit_statistics = results["fit_statistics"] - log.warning("skipping fit and creating state file using input parameters.") - - ### now compute best-fit model of spectrum and plot. - if fit_is_successful or no_fit: - - # create summary plot using original data. - data_grouped = ut.plotting.compute_downsampled_data( - times_windowed, data.freqs, data_windowed, data.good_freq, - spectrum_model = bestfit_model, factor_freq = int(64 / factor_freq_downsample), factor_time = 1 - ) - - ut.plotting.plot_summary_triptych( - data_grouped, output_name = f"summary.{current_event_id}.png", show=False - ) - - # create JSON file contain burst parameters and statistics. - timestamp = None - - if data.fpga_frame0_nano is not None: - timestamp = rt.times.compute_arrival_times(initial_parameters, data.fpga_frame0_nano * 1e-9) - - with open(f"results_fitburst_{current_event_id}.json", "w") as out: - json.dump( - { - "model_parameters": bestfit_params, - "fit_statistics": fit_statistics, - "fit_logistics" : { - "dm_incoherent" : params["dm"], - "factor_freq_upsample" : factor_freq_upsample, - "factor_time_upsample" : factor_time_upsample, - "is_repeater": None, - "normalize_variance" : normalize_variance, - "spectrum_window": window, - "variance_range" : variance_range, - "variance_weight" : variance_weight - }, - "derived_parameters" : { - "arrival_time_UTC" : timestamp - } - }, - out, - indent=4 - ) - - # finally, if desired, save spectrum and burst-parameter/metadata dictionaries. - bad_chans = np.where(data.good_freq == False) - - if save_results: - np.savez( - f"test_data_CHIMEFRB_{current_event_id}.npz", - burst_parameters = bestfit_params, - data_full = data_windowed, - metadata = { - "bad_chans" : bad_chans[0].tolist(), - "freqs_bin0" : data.freqs[0], - "is_dedispersed" : True, - "num_freq" : data.num_freq, - "num_time" : len(times_windowed), - "times_bin0" : times_windowed[0], - "res_freq" : data.res_freq, - "res_time" : data.res_time, - } - - ) diff --git a/fitburst/pipelines/fitburst_example_chimefrb_baseband.py b/fitburst/pipelines/fitburst_example_chimefrb_baseband.py deleted file mode 100644 index 91da416..0000000 --- a/fitburst/pipelines/fitburst_example_chimefrb_baseband.py +++ /dev/null @@ -1,121 +0,0 @@ -#! /usr/bin/env python - -# configure backend for matplotlib. -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt - -from fitburst.analysis.model import SpectrumModeler -from fitburst.backend.generic import DataReader -from fitburst.analysis.fitter import LSFitter -from copy import deepcopy -import fitburst.routines.manipulate as manip -import chime_frb_constants as const -import fitburst.utilities as ut -import numpy as np -import sys - -# read in data. -data = DataReader("fitburst_65547659.npz", data_location="/data/frb-baseband/baseband_test") - -# load data into memory and pre-process. -data.load_data() -data.good_freq = np.sum(data.data_weights, axis=1) // data.num_time - -# get parameters. -initial_parameters = data.burst_parameters -current_parameters = deepcopy(initial_parameters) - -if data.is_dedispersed: - print("INFO: input data cube is already dedispersed!") - print("INFO: setting 'dm' entry to 0, now considered a dm-offset parameter...") - current_parameters["dm"][0] = 0.0 - -# now compute dedisperse matrix for data, given initial DM, and grab windowed data. -print("INFO: computing dedispersion-index matrix") -data.dedisperse( - initial_parameters["dm"][0], - current_parameters["arrival_time"][0], - reference_freq=initial_parameters["ref_freq"][0], - dm_offset=0.0 -) - -# get windowed data. -data_windowed, times_windowed = data.window_data(current_parameters["arrival_time"][0], window=0.002) - -# now create model. -print("INFO: initializing model") -model = SpectrumModeler() -model.is_dedispersed = data.is_dedispersed -model.set_dimensions(data.num_freq, len(times_windowed)) - -# before instantiating model parameters, add a second (first-arriving) component. -# this step manually creates a two-component parameter dictionary that is needed -# for multi-component fitting. -num_components = 2 -new_parameters = {} - -for current_key, current_value in current_parameters.items(): - new_parameters[current_key] = current_parameters[current_key] * num_components - - # adjust arrival time of first/new burst. - if current_key == "arrival_time": - new_parameters[current_key][0] -= 0.0001 - -# add parameters to ensure all are set. -model.num_components = len(new_parameters["arrival_time"]) -model.update_parameters(new_parameters) -model.update_parameters({"amplitude": [np.log10(np.mean(data_windowed))] * num_components}) -model.update_parameters({"scattering_index": [-4.0] * num_components}) -model.update_parameters({"scattering_timescale": [0.0] * num_components}) -model.update_parameters({"spectral_index": [0.0] * num_components}) -model.update_parameters({"spectral_running": [0.0] * num_components}) - -current_model = model.compute_model(times_windowed, data.freqs) - -# now set up fitter and execute. -fitter = LSFitter(model) -fitter.fix_parameter(["dm", "dm_index", "scattering_timescale"]) -fitter.weighted_fit = True -fitter.fit(times_windowed, data.freqs, data_windowed) - -# extract best-fit data, create best-fit model and plot windowed data. -bestfit_parameters = fitter.load_fit_parameters_list(fitter.bestfit_results["parameters"]) -bestfit_uncertainties = fitter.load_fit_parameters_list(fitter.bestfit_results["uncertainties"]) -print(fitter.bestfit_results["solver"]) -print("Best-fit parameters:", bestfit_parameters) -print("Best-fit uncertaintes:", bestfit_uncertainties) - -# now compute best-fit model, residuals, and plot. -model.update_parameters(bestfit_parameters) -bestfit_model = model.compute_model(times_windowed, data.freqs) -bestfit_residuals = data_windowed - bestfit_model - -ut.plotting.plot_summary_triptych( - times_windowed, data.freqs, data_windowed, fitter.good_freq, model = bestfit_model, - residuals = bestfit_residuals -) - -# now re-dedisperse data and plot for comparison. -dm_offset = 0. - -if "dm" in bestfit_parameters: - dm_offset = bestfit_parameters["dm"][0] - -print("INFO: computing dedispersion-index matrix") -data.dedisperse( - initial_parameters["dm"][0], - current_parameters["arrival_time"][0], - reference_freq=initial_parameters["ref_freq"][0], - dm_offset=dm_offset -) -data_windowed_new, times_windowed_new = data.window_data(current_parameters["arrival_time"][0], window=0.002) - -plt.subplot(121) -plt.pcolormesh(manip.downsample_2d(data_windowed, 64, 1)) -plt.title("Original") -plt.subplot(122) -plt.title("Re-Dedispersed") -plt.pcolormesh(manip.downsample_2d(data_windowed_new, 64, 1)) -plt.tight_layout() -plt.savefig("spectra_comparisons.png", dpi=500, fmt="png") diff --git a/fitburst/pipelines/fitburst_example_chimefrb_downsample.py b/fitburst/pipelines/fitburst_example_chimefrb_downsample.py deleted file mode 100644 index 7f96932..0000000 --- a/fitburst/pipelines/fitburst_example_chimefrb_downsample.py +++ /dev/null @@ -1,63 +0,0 @@ -#! /user/bin/env python - -from fitburst.analysis.fitter import LSFitter -import fitburst.backend.chimefrb as chimefrb -import fitburst.analysis.model as mod -import chime_frb_constants as const -import fitburst.utilities as ut -import fitburst.routines as rt -import numpy as np -import sys - -# import and configure matplotlib. -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt - -# read in data. -# OG event: 37888771 -data = chimefrb.DataReader(37888771) -initial_parameters = data.get_parameters() - -# load data into memory and pre-process. -data.load_data(data.files) -data.preprocess_data(variance_weight=1/(const.L0_NUM_FRAMES_SAMPLE * 2)) -data.downsample(factor_freq=1, factor_time=4) - -# now compute dedisperse matrix for data, given initial DM, and grab windowed data. -print("INFO: computing dedispersion-index matrix") -params = data.burst_parameters["fitburst"]["round_2"] -data.dedisperse( - params["dm"][0], - params["arrival_time"][0], - reference_freq=params["reference_freq"][0] -) - -data_windowed, times_windowed = data.window_data(params["arrival_time"][0], window=0.04) - -# now create initial model. -print("INFO: initializing model") -model = mod.SpectrumModeler() -model.is_dedispersed = data.is_dedispersed -model.set_dimensions(data.num_freq, len(times_windowed)) -model.set_dedispersion_idx(data.dedispersion_idx) -model.update_parameters(initial_parameters) -model.dm_index = [-2.0] -model.scattering_timescale = [0.0] -model.scattering_index = [-4.0] - -# now set up fitter and create initial model. -fitter = LSFitter(model) -fitter.fix_parameter(["dm_index", "scattering_timescale"]) -fitter.fit(data.times, data.freqs, data_windowed) - -# now obtain and plot windowed data. -bestfit_parameters = fitter.load_fit_parameters_list(fitter.bestfit_results["parameters"]) -model.update_parameters(bestfit_parameters) -bestfit_model = model.compute_model(data.times, data.freqs) -bestfit_residuals = data_windowed - bestfit_model - -ut.plotting.plot_summary_triptych( - data.times, data.freqs, data_windowed, fitter.good_freq, model = bestfit_model, - residuals = bestfit_residuals * fitter.weights[:, None] -) diff --git a/fitburst/pipelines/fitburst_example_generic_gridding.py b/fitburst/pipelines/fitburst_example_generic_gridding.py deleted file mode 100644 index d29f11b..0000000 --- a/fitburst/pipelines/fitburst_example_generic_gridding.py +++ /dev/null @@ -1,184 +0,0 @@ -#! /usr/bin/env python - -# configure backend for matplotlib. -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt - -from fitburst.analysis.model import SpectrumModeler -from fitburst.backend.generic import DataReader -from fitburst.analysis.fitter import LSFitter -from copy import deepcopy -import fitburst.routines.manipulate as manip -import fitburst.utilities as ut -import numpy as np -import argparse -import json -import sys -import os - -parser = argparse.ArgumentParser(description= - "A Python3 script that uses fitburst API to read, preprocess, window, and fit CHIME/FRB data " + - "against a model of the dynamic spectrum." -) - -parser.add_argument( - "file", action="store", nargs="+", type=str, - help="Data file containing spectrum and metada in 'generic' format." -) - -parser.add_argument( - "-n", action="store", dest="num_grid", default=20, type=int, - help="Number of grid points along each dimension" -) - -parser.add_argument( - "--solution", action="store", dest="solution", nargs="+", type=str, - help="Data file containing spectrum and metada in 'generic' format." -) - -### grab CLI inputs from argparse. -args = parser.parse_args() -infiles = args.file -num_grid = args.num_grid -insolutions = args.solution -fit_for_scattering = True -use_stored_results = True -parameters_fixed = ["dm_index", "scattering_index", "scattering_timescale"] -snr_threshold = 10. - -# before proceeding, adjusted fixed-parameter list if necessary. -if fit_for_scattering: - parameters_fixed.remove("scattering_timescale") - -# now, loop over data files to read and perform fitting. -for current_file, current_solution in zip(infiles, insolutions): - current_file_base = current_file.split("/") - elems = current_file_base[-1].split("_") - print(current_file) - print(elems) - filename_substring = "{0}_{1}".format(elems[1], elems[4]) - - if os.path.isfile(current_solution) and use_stored_results: - results = json.load(open(current_solution, "r")) - - elif not use_stored_results: - pass - - else: - continue - - # now extract some numbers from previous fit. - if results["fit_statistics"]["snr"] >= snr_threshold or not use_stored_results: - - data = DataReader(current_file) - - # load data into memory and pre-process. - data.load_data() - data.good_freq = np.sum(data.data_weights, axis=1) // data.num_time - #data.preprocess_data(variance_range=[0., 0.8]) - - # get parameters. - initial_parameters = data.burst_parameters - current_parameters = deepcopy(initial_parameters) - - if fit_for_scattering and use_stored_results: - current_parameters = results["model_parameters"] - current_parameters["amplitude"] = [-3.] - current_parameters["arrival_time"] = [0.55] - current_parameters["burst_width"] = [0.008] - current_parameters["dm"] = [0.0] - current_parameters["scattering_timescale"] = [0.18] - current_parameters["spectral_index"] = [-1.0] - current_parameters["spectral_running"] = [1.0] - - else: - current_parameters["arrival_time"] = [0.5] - current_parameters["burst_width"] = [0.05] - current_parameters["scattering_timescale"] = [0.0] - current_parameters["spectral_index"] = [-1.0] - current_parameters["spectral_running"] = [1.0] - - if data.is_dedispersed and not use_stored_results: - print("INFO: input data cube is already dedispersed!") - print("INFO: setting 'dm' entry to 0, now considered a dm-offset parameter...") - current_parameters["dm"][0] = 0.0 - - # now compute dedisperse matrix for data, given initial DM, and grab windowed data. - print("INFO: computing dedispersion-index matrix") - data.dedisperse( - initial_parameters["dm"][0], - current_parameters["arrival_time"][0], - reference_freq=initial_parameters["ref_freq"][0], - dm_offset=0.0 - ) - - data_windowed = data.data_full - times_windowed = data.times - #plt.pcolormesh(data.times, data.freqs, data.data_full) - #plt.savefig("test.png") - - # now create model. - print("INFO: initializing model") - model = SpectrumModeler() - model.dm0 = initial_parameters["dm"][0] - model.is_dedispersed = data.is_dedispersed - model.is_folded = True - model.set_dimensions(data.num_freq, len(times_windowed)) - model.update_parameters(current_parameters) - current_model = model.compute_model(times_windowed, data.freqs) - - # now set up fitter and create initial model. - fitter = LSFitter(model) - fitter.fix_parameter(parameters_fixed) - fitter.weighted_fit = True - print("INFO: now executing least-squares fitting...") - fitter.fit(times_windowed, data.freqs, data_windowed) - - # extract best-fit data, create best-fit model and plot windowed data. - if fitter.success: - bestfit_parameters = fitter.fit_statistics["bestfit_parameters"] - bestfit_uncertainties = fitter.fit_statistics["bestfit_uncertainties"] - print(bestfit_parameters) - model.update_parameters(bestfit_parameters) - - # now define gridding bounds. - min_dm = bestfit_parameters["dm"][0] - 10 * bestfit_uncertainties["dm"][0] - max_dm = bestfit_parameters["dm"][0] + 10 * bestfit_uncertainties["dm"][0] - min_st = bestfit_parameters["scattering_timescale"][0] - 10 * bestfit_uncertainties["scattering_timescale"][0] - max_st = bestfit_parameters["scattering_timescale"][0] + 10 * bestfit_uncertainties["scattering_timescale"][0] - - if min_st < 0.: - min_st = 0. - - # next, define grid arrays. - array_dm = np.linspace(min_dm, max_dm, num=num_grid) - array_st = np.linspace(min_st, max_st, num=num_grid) - - # now, loop over dimensions and determine fit. - current_parameters_grid = deepcopy(fitter.fit_statistics["bestfit_parameters"]) - matrix_chisq = np.zeros((num_grid, num_grid)) - del fitter - - for ii in range(num_grid): - for jj in range(num_grid): - - # update model with adjusted values for gridded parameters. - current_parameters_grid["dm"] = [array_dm[ii]] - current_parameters_grid["scattering_timescale"] = [array_st[jj]] - model.update_parameters(current_parameters_grid) - - # now define fitter for gridded parameters. - fitter = LSFitter(model) - fitter.fix_parameter(parameters_fixed + ["dm", "scattering_timescale"]) - fitter.weighted_fit = True - fitter.fit(times_windowed, data.freqs, data_windowed) - matrix_chisq[ii, jj] = fitter.fit_statistics["chisq_final"] - - # now compute PDF map and plot. - print(matrix_chisq) - pdf_chisq = 0.5 * np.exp(-0.5 * (matrix_chisq - np.min(matrix_chisq))) - plt.pcolormesh(array_dm, array_st, pdf_chisq, cmap="Blues") - plt.xlabel(r"Dispersion Measure (pc cm$^{-3}$)") - plt.ylabel(r"Scattering Timescale @ 400 MHz (ms)") - plt.savefig("pdf_map.png", dpi=500, fmt="png") diff --git a/fitburst/pipelines/fitburst_example_simulated_data.py b/fitburst/pipelines/fitburst_example_simulated_data.py deleted file mode 100644 index b573ba2..0000000 --- a/fitburst/pipelines/fitburst_example_simulated_data.py +++ /dev/null @@ -1,35 +0,0 @@ -#! /user/bin/env python - -from fitburst.analysis.fitter import LSFitter -import fitburst.backend.chimefrb as chimefrb -import fitburst.analysis.model as mod -import chime_frb_constants as const -import numpy as np -import sys - -# import and configure matplotlib. -import matplotlib.pyplot as plt - -# read in data. -data = chimefrb.DataReader(37888771) -initial_parameters = data.get_parameters() -data.times = np.linspace(0., 0.08, num=100) -data.freqs = np.linspace(400., 800., num=1024) - -# set up model. -model = mod.SpectrumModeler() -model.update_parameters(initial_parameters) -model.update_parameters({"arrival_time": [0.04]}) -model.scattering_timescale = [0.0] -model.scattering_index = [-4.0] - -# now set up fitter and create initial model. -fake_data = model.compute_model(data.times, data.freqs) -fake_data += np.random.normal(size=fake_data.shape) -fitter = LSFitter(model) - -# fix DM and scattering-timescale at their above values. -fitter.fix_parameter(["dm", "scattering_timescale"]) - -# now fit! -fitter.fit(data.times, data.freqs, fake_data) diff --git a/fitburst/pipelines/fitburst_example_generic.py b/fitburst/pipelines/fitburst_pipeline.py similarity index 99% rename from fitburst/pipelines/fitburst_example_generic.py rename to fitburst/pipelines/fitburst_pipeline.py index 4f425e6..1760816 100644 --- a/fitburst/pipelines/fitburst_example_generic.py +++ b/fitburst/pipelines/fitburst_pipeline.py @@ -374,7 +374,7 @@ if preprocess_data: data.preprocess_data(normalize_variance=True, variance_range=variance_range) -print(f"There are {data.good_freq.sum()} good frequencies...") +print(f"INFO: there are {data.good_freq.sum()} good frequencies...") # now downsample after preprocessing, if desired. data.downsample(factor_freq_downsample, factor_time_downsample) @@ -407,7 +407,6 @@ initial_parameters[current_parameter] = basic_parameters[current_parameter] * num_components current_parameters = deepcopy(initial_parameters) -print(f"INFO: current parameters = {current_parameters}") # update DM value to use ("full" or DM offset) for dedispersion if # input data are already dedispersed or not. @@ -511,7 +510,6 @@ fitter = LSFitter(data_windowed, model, data.good_freq, weighted_fit=True, weight_range=weight_range) fitter.fix_parameter(parameters_to_fix) fitter.fit(exact_jacobian=True) - print(fitter.results) # extract best-fit data for next loop. if fitter.results.success: diff --git a/fitburst/pipelines/run_fitburst.py b/fitburst/pipelines/run_fitburst.py deleted file mode 100644 index f46afa1..0000000 --- a/fitburst/pipelines/run_fitburst.py +++ /dev/null @@ -1,93 +0,0 @@ -#! /usr/bin/env python - -# configure backend for matplotlib. -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt - -from fitburst.analysis.model import SpectrumModeler -from fitburst.backend.generic import DataReader -from fitburst.analysis.fitter import LSFitter -from copy import deepcopy -import fitburst.routines.manipulate as manip -import chime_frb_constants as const -import fitburst.utilities as ut -import numpy as np -import sys -import logging - -from profile_modeling import get_signal -from baseband_analysis.utilities import get_profile -# Logging Config -LOGGING_CONFIG = {} -logging_format = "[%(asctime)s] %(process)d-%(levelname)s " -logging_format += "%(module)s::%(funcName)s():l%(lineno)d: " -logging_format += "%(message)s" -logging.basicConfig(format=logging_format, level=logging.INFO) -log = logging.getLogger() - -def run_fitburst(fname, path): - # read in data. - data = DataReader(fname, data_location=path) - # load data into memory and pre-process. - data.load_data() - data.good_freq = np.sum(data.data_weights, axis=1) // data.num_time - # get parameters. - initial_parameters = data.burst_parameters - current_parameters = deepcopy(initial_parameters) - - # get windowed data. - data_windowed, times_windowed = data.data_full, np.linspace(0,data.num_time,data.num_time) * data.res_time - #data.window_data(np.mean(current_parameters["arrival_time"]), window=w) - - # now create model. - log.info("Initializing model...") - model = SpectrumModeler() - model.is_dedispersed = data.is_dedispersed - model.set_dimensions(data.num_freq, len(times_windowed)) - - # before instantiating model parameters, add a second (first-arriving) component. - # this step manually creates a two-component parameter dictionary that is needed - # for multi-component fitting. - num_components = len(current_parameters['arrival_time']) - - # add parameters to ensure all are set. - model.num_components = len(current_parameters["arrival_time"]) - model.update_parameters(current_parameters) - model.update_parameters({"amplitude": [np.log10(np.mean(data_windowed))] * num_components}) - model.update_parameters({"scattering_index": [-4.0] * num_components}) - model.update_parameters({"scattering_timescale": current_parameters["scattering_timescale"]}) - model.update_parameters({"spectral_index": current_parameters["spectral_index"]}) - model.update_parameters({"spectral_running": current_parameters["spectral_running"]}) - model.update_parameters({"ref_freq": current_parameters['ref_freq']}) - - current_model = model.compute_model(times_windowed, data.freqs) - - # now set up fitter and execute. - fitter = LSFitter(model) - fitter.fix_parameter(['dm', "dm_index", "scattering_index"]) - fitter.weighted_fit = True - fitter.fit(times_windowed, data.freqs, data_windowed) - - # extract best-fit data, create best-fit model and plot windowed data. - bestfit_parameters = fitter.load_fit_parameters_list(fitter.bestfit_results["parameters"]) - try: - bestfit_uncertainties = fitter.load_fit_parameters_list(fitter.bestfit_results["uncertainties"]) - print("Best-fit uncertaintes:", bestfit_uncertainties) - except: - pass - print(fitter.bestfit_results["solver"]) - print("Best-fit parameters:", bestfit_parameters) - - - # now compute best-fit model, residuals, and plot. - model.update_parameters(bestfit_parameters) - bestfit_model = model.compute_model(times_windowed, data.freqs) - bestfit_residuals = data_windowed - bestfit_model - - ut.plotting.plot_summary_triptych( - times_windowed, data.freqs, data_windowed, fitter.good_freq, num_std = 3, model = bestfit_model, - residuals = bestfit_residuals, show = True - ) - np.savez(path + 'RN1-2/' + fname, bestfit_parameters) - return \ No newline at end of file diff --git a/fitburst/pipelines/simulate_burst.py b/fitburst/pipelines/simulate_burst.py new file mode 100644 index 0000000..1e05525 --- /dev/null +++ b/fitburst/pipelines/simulate_burst.py @@ -0,0 +1,87 @@ +#! /bin/env/python + +import matplotlib +matplotlib.rcParams["font.family"] = "times" +matplotlib.rcParams["font.size"] = 15 +matplotlib.rcParams["xtick.labelsize"] = 12 +matplotlib.rcParams["ytick.labelsize"] = 12 + +from copy import deepcopy +import matplotlib.pyplot as plt +import fitburst as fb +import numpy as np +import sys + +# define dimensions of the data. +is_dedispersed = True +num_freq = 2 ** 8 +num_time = 2 ** 7 +freq_lo = 1200. +freq_hi = 1600. +time_lo = 0. +time_hi = 0.08 + +freqs = np.linspace(freq_lo, freq_hi, num = num_freq) +times = np.linspace(time_lo, time_hi, num = num_time) + +# define physical parameters for a dispersed burst to simulate. +params = { + "amplitude" : [0., 0., 0.], + "arrival_time" : [0.03, 0.04, 0.05], + "burst_width" : [0.001, 0.002, 0.0005], + "dm" : [349.5, 349.5, 349.5], + "dm_index" : [-2., -2., -2.], + "ref_freq" : [1500., 1400., 1300.], + "scattering_index" : [-4., -4., -4.], + "scattering_timescale" : [0., 0., 0.], + "spectral_index" : [0., 0., 0.], + "spectral_running" : [-300., -300., -300.], +} + +num_components = len(params["dm"]) + +# define and/or extract parameters. +new_params = deepcopy(params) + +if is_dedispersed: + new_params["dm"] = [0.] * num_components + +# define model object for CHIME/FRB data and load in parameter values. +model_obj = fb.analysis.model.SpectrumModeler( + freqs, + times, + is_dedispersed = is_dedispersed, + num_components = num_components, + verbose = True, + ) + +model_obj.update_parameters(new_params) + +# now compute model and add noise. +model = model_obj.compute_model() +model += np.random.normal(0., 0.2, size = model.shape) + +# plot. +plt.pcolormesh(times, freqs, model) +plt.xlabel("Time (s)") +plt.xlabel("Observing Frequency (MHz)") +plt.show() + +# finally, save data into fitburst-generic format. +metadata = { + "bad_chans" : [], + "freqs_bin0" : freqs[0], + "is_dedispersed" : is_dedispersed, + "num_freq" : num_freq, + "num_time" : num_time, + "times_bin0" : 0., + "res_freq" : freqs[1] - freqs[0], + "res_time" : times[1] - times[0] +} + +np.savez( + "simulated_data.npz", + data_full = model, + metadata = metadata, + burst_parameters = params, +) diff --git a/fitburst/utilities/plotting.py b/fitburst/utilities/plotting.py index e29cf4f..cc0aa30 100644 --- a/fitburst/utilities/plotting.py +++ b/fitburst/utilities/plotting.py @@ -10,7 +10,6 @@ # import and configure matplotlig for GUI-less node. import numpy as np import matplotlib -matplotlib.use("Agg") import matplotlib.pyplot as plt from matplotlib import gridspec diff --git a/mkdocs.yml b/mkdocs.yml index 85d1211..4594ca9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -69,13 +69,13 @@ extra_javascript: nav: - Home: index.md - - About: about.md - Installation: installation.md - Usage: - - Ways to Use fitburst: usage/using_fitburst.md + - Ways to Use fitburst: usage/getting_started.md - Format of Input Data: usage/formatting_data_generic.md - Working with Data Readers: usage/using_data_readers.md - Creating Models of Spectra: usage/creating_models.md + - 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