diff --git a/docs/notebooks.rst b/docs/notebooks.rst index 1e611ac0..5ca4e40c 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -78,6 +78,7 @@ LightCurveLynx. .. toctree:: :maxdepth: 1 + Wrapping Bagle Models Wrapping Redback Models diff --git a/docs/notebooks/pre_executed/wrapping_bagle.ipynb b/docs/notebooks/pre_executed/wrapping_bagle.ipynb new file mode 100644 index 00000000..068bbf5a --- /dev/null +++ b/docs/notebooks/pre_executed/wrapping_bagle.ipynb @@ -0,0 +1,823 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "27226089", + "metadata": {}, + "source": [ + "# Wrapping Libraries - Bagle Example\n", + "\n", + "This notebook is part of a series demonstrating how users can add support for their own simulation packages by adding simple wrappers. The example covered in this notebook is the [Bagle Microlensing package](https://github.com/MovingUniverseLab/bagle_microlensing). To run this notebook you will need to install Bagle. See the instructions [here](https://bagle.readthedocs.io/en/latest/installation.html).\n", + "\n", + "This notebook is based on the [lightcurvelynx_BAGLE.ipynb notebook](https://github.com/LSST-TVSSC/microlensing/blob/main/lightcurvelynx_simulations/lightcurvelynx_BAGLE.ipynb) by Natasha Abrams and Katarzyna Kruszynska (2025).\n", + "\n", + "\n", + "## Bagle\n", + "\n", + "[Bagle](https://github.com/MovingUniverseLab/bagle_microlensing) is a package for simulating and analyzing gravitational lensing effects, which includes functions to model the output of such event. For an introduction to bagle, see their tutorial notebook: [https://github.com/MovingUniverseLab/BAGLE_Microlensing/blob/main/BAGLE_TUTORIAL.ipynb](https://github.com/MovingUniverseLab/BAGLE_Microlensing/blob/main/BAGLE_TUTORIAL.ipynb).\n", + "\n", + "Bagle uses the `bagle.model` object to store the information and perform the computations for microlensing events. This is the object that we will want to wrap within a LightCurveLynx ``BasePhysicalModel`` so we can use it in the computation flow.\n", + "\n", + "Let's start by looking at how we can create and query a standard bagle model. The object's constructor takes in the parameters as (order dependent) arguments. For example, we can create a `PSPL_PhotAstrom_Par_Param1` (point source, point lens) object as:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4450a840", + "metadata": {}, + "outputs": [], + "source": [ + "from bagle import model\n", + "\n", + "# Create the event model with given parameter.\n", + "event1 = model.PSPL_PhotAstrom_Par_Param1(\n", + " 1.0, # mL (msun)\n", + " 61600, # t0 (MJD)\n", + " 0.1, # beta (mas)\n", + " 4000.0, # dL (pc)\n", + " 4000.0 / 8000.0, # dL/dS\n", + " 0.0, # xS0_E (mas/yr)\n", + " 0.0, # xS0_N (mas/yr)\n", + " 5.0, # muL_E (mas/yr)\n", + " 10.0, # muL_N (mas/yr)\n", + " 0.0, # muS_E (mas/yr)\n", + " 0.0, # muS_N (mas/yr)\n", + " [1, 1, 1, 1, 1, 1], # b_sff\n", + " [23, 22, 21.5, 21, 20.5, 19.5], # mag_src\n", + " raL=270.66679, # RA (deg)\n", + " decL=-35.70483, # Dec (deg)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e79c51f7", + "metadata": {}, + "source": [ + "Given the model, we can query for outputs, such as the amplification level, over given times. Here we use 200.0 days before the given t0 to 200.0 days after it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "26a180bb-de40-4353-a493-ff31964feaab", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-22T13:03:53.184752Z", + "iopub.status.busy": "2025-01-22T13:03:53.184473Z", + "iopub.status.idle": "2025-01-22T13:03:54.978720Z", + "shell.execute_reply": "2025-01-22T13:03:54.978343Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Amplification')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "t = np.arange(61400.0, 61800.0, 1.0)\n", + "\n", + "A = event1.get_amplification(t)\n", + "plt.plot(t, A)\n", + "plt.xlabel(\"Time (MJD)\")\n", + "plt.ylabel(\"Amplification\")" + ] + }, + { + "cell_type": "markdown", + "id": "e0b1cad1", + "metadata": {}, + "source": [ + "Most important for our case will be the model’s ``get_photometry()`` function which will return the *magnitudes* for a given filter at a given array of times. We can uses this as the basis for LightCurveLynx’s computation function with a few modifications. Most notably, LightCurveLynx uses fluxes in nJy, so we will need to convert the magnitudes.\n", + "\n", + "## Wrapping Bagle Models\n", + "\n", + "To wrap a bagle model we need to design a subclass of LightCurveLynx's `BasePhysicalModel` that:\n", + "1) constructs the bagle model,\n", + "2) sets its parameters, and \n", + "3) queries the flux that it produces (returning the results in nJy). \n", + "Since bagle models operate on the bandflux level, we start by subclassing LightCuvreLynx's `BandfluxModel` class.\n", + "\n", + "We start using an alternative approach for creating a model object from the model name (given in Abrams and Kruszynska's notebook) that loads the model from the library (using Python's ``getattr()`` function) and assemble the parameters as a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "54e94aea", + "metadata": {}, + "outputs": [], + "source": [ + "# Give the name of the class we will create.\n", + "model_name = \"PSPL_PhotAstrom_Par_Param1\"\n", + "\n", + "# Define the parameters as a dictionary.\n", + "dL = 4000 # Distance to lens\n", + "dS = 8000 # Distance to source\n", + "parameter_dict = {\n", + " \"raL\": 270.66679,\n", + " \"decL\": -35.70483,\n", + " \"mL\": 1, # Msun (Primary lens current mass)\n", + " \"t0\": 61600, # mjd\n", + " \"beta\": 0.1,\n", + " \"dL\": dL,\n", + " \"dL_dS\": dL / dS,\n", + " \"xS0_E\": 0.0, # arbitrary offset (arcsec)\n", + " \"xS0_N\": 0.0, # arbitrary offset (arcsec)\n", + " \"muL_E\": 5.0,\n", + " \"muL_N\": 10.0,\n", + " \"muS_E\": 0.0,\n", + " \"muS_N\": 0.0,\n", + " \"b_sff\": [1, 1, 1, 1, 1, 1],\n", + " \"mag_src\": [23, 22, 21.5, 21, 20.5, 19.5],\n", + "}\n", + "\n", + "# Create the model object, by creating the model class by name and\n", + "# passing the parameters as a dictionary.\n", + "mod_class = getattr(model, model_name)\n", + "mod = mod_class(**parameter_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "c4ed88cf", + "metadata": {}, + "source": [ + "To wrap this as a LightCurveLynx model, we will need two functions:\n", + "* ``__init__()`` - Set up the internal data and register the (settable) parameters.\n", + "* ``compute_bandflux()`` - Compute the flux values (in nJy) at given times and filters.\n", + "\n", + "The model above specifies the ``b_sff`` and ``mag_src`` for the filters in the order: u, g, r, i, z, y. To make the mappings easier, we also define a class variable `_filter_idx` with dictionary mapping filter name to its index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da09419a-cf38-4d6f-b44c-e1110caaf541", + "metadata": { + "execution": { + "iopub.execute_input": "2025-01-22T13:03:54.980555Z", + "iopub.status.busy": "2025-01-22T13:03:54.980382Z", + "iopub.status.idle": "2025-01-22T13:04:14.827058Z", + "shell.execute_reply": "2025-01-22T13:04:14.826455Z" + } + }, + "outputs": [], + "source": [ + "from lightcurvelynx.astro_utils.mag_flux import mag2flux\n", + "from lightcurvelynx.models.physical_model import BandfluxModel\n", + "\n", + "\n", + "class FixedBagleModel(BandfluxModel):\n", + " \"\"\"A wrapper for a Bagle model with a fixed set of parameters.\"\"\"\n", + "\n", + " # Convenience mapping from filter name to index in the parameter list.\n", + " _filter_idx = {\"u\": 0, \"g\": 1, \"r\": 2, \"i\": 3, \"z\": 4, \"y\": 5}\n", + "\n", + " def __init__(self, model_name, parameter_dict, **kwargs):\n", + " # We start by extracting the parameter information needed for a general\n", + " # physical model (base class).\n", + " ra = parameter_dict[\"raL\"]\n", + " dec = parameter_dict[\"decL\"]\n", + " t0 = parameter_dict[\"t0\"]\n", + "\n", + " # Call the parent class constructor to set up the model.\n", + " super().__init__(ra=ra, dec=dec, t0=t0, **kwargs)\n", + "\n", + " # The create the bagle model object and set the parameters.\n", + " self.model_name = model_name\n", + " self.parameter_dict = parameter_dict\n", + " mod_class = getattr(model, model_name)\n", + " self.model_obj = mod_class(**parameter_dict)\n", + "\n", + " def compute_bandflux(self, times, filter, state):\n", + " \"\"\"Evaluate the model at the passband level for a single, given graph state and filter.\n", + "\n", + " Parameters\n", + " ----------\n", + " times : numpy.ndarray\n", + " A length T array of observer frame timestamps in MJD.\n", + " filter : str\n", + " The name of the filter.\n", + " state : GraphState\n", + " An object mapping graph parameters to their values with num_samples=1.\n", + " This is not used in this model, but is required for the function signature.\n", + "\n", + " Returns\n", + " -------\n", + " bandflux : numpy.ndarray\n", + " A length T array of band fluxes for this model in this filter.\n", + " \"\"\"\n", + " mags = self.model_obj.get_photometry(times, self._filter_idx[filter])\n", + "\n", + " # Convert mags to fluxes.\n", + " bandflux = mag2flux(mags)\n", + " return bandflux\n", + "\n", + "\n", + "# Create the source object.\n", + "source = FixedBagleModel(model_name, parameter_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "4f0746aa", + "metadata": {}, + "source": [ + "### The Init Function\n", + "\n", + "The ``__init__()`` function starts by extracting the parameters needed by the base class (RA, dec, and t0). The actual code we would want to use in production is a bit more complex because we also want to check `kwargs` for these values **and** bagle can use other parameters to compute t0. We keep the code simple here for illustration purposes.\n", + "\n", + "The init function then uses the earlier approach to load the class from its name and instantiate the object with the parameter dictionary. This object is saved in the ``model_obj`` attribute for use in computation.\n", + "\n", + "\n", + "### The Compute Function\n", + " \n", + "The ``compute_bandflux()`` simply uses the saved model to compute the bandflux values for the given times and filter. Since bagle models use an integer index for each of the different filters, we use the filter to index map to query the correct part of the model.\n", + "\n", + "Note that the logic here is pretty simple. The bagle library does all the heavy lifting. The compute function only needs to know how to map the inputs into the format bagle is expecting and map the outputs to what LightCurveLynx is expecting.\n", + "\n", + "\n", + "## Using the Model in a Simulation\n", + "\n", + "Once we define a ``FixedBagleModel``, we can use it in the general LightCurveLynx simulation framework.\n", + "\n", + "First, as always, we load the OpSim and filter information we want to use for the simulation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e607e925", + "metadata": {}, + "outputs": [], + "source": [ + "from lightcurvelynx import _LIGHTCURVELYNX_BASE_DATA_DIR\n", + "from lightcurvelynx.astro_utils.passbands import PassbandGroup\n", + "from lightcurvelynx.obstable.opsim import OpSim\n", + "\n", + "opsim_db = OpSim.from_db(_LIGHTCURVELYNX_BASE_DATA_DIR / \"opsim\" / \"baseline_v3.4_10yrs.db\")\n", + "\n", + "table_dir = _LIGHTCURVELYNX_BASE_DATA_DIR / \"passbands\" / \"LSST\"\n", + "passband_group = PassbandGroup.from_preset(\n", + " preset=\"LSST\",\n", + " units=\"nm\",\n", + " trim_quantile=0.001,\n", + " delta_wave=1,\n", + " table_dir=table_dir,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a4b25603", + "metadata": {}, + "source": [ + "Then we run the simulation, passing the wrapped object as the source we want to simulate.\n", + "\n", + "This is relatively fast because we are only evaluating the bagle model at the points where it is actually observed. In this case that means those times when Rubin's field of view included (270.66679, -35.70483)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "05331c73", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Simulating: 100%|██████████| 3/3 [00:00<00:00, 171.88obj/s]\n" + ] + } + ], + "source": [ + "from lightcurvelynx.simulate import simulate_lightcurves\n", + "\n", + "# Peform three simulations of the source given the opsim and passband group.\n", + "lightcurves = simulate_lightcurves(source, 3, opsim_db, passband_group)" + ] + }, + { + "cell_type": "markdown", + "id": "2a2b50b8", + "metadata": {}, + "source": [ + "We can plot the simulated observations using LightCurveLynx's ``plot_lightcurves`` function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a5ee7b73", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from lightcurvelynx.utils.plotting import plot_lightcurves\n", + "\n", + "for idx in range(3):\n", + " # Extract the row for this object.\n", + " lc = lightcurves.loc[idx]\n", + "\n", + " # Plot the lightcurves.\n", + " ax = plot_lightcurves(\n", + " fluxes=np.asarray(lc[\"lightcurve\"][\"flux\"], dtype=float),\n", + " times=np.asarray(lc[\"lightcurve\"][\"mjd\"], dtype=float),\n", + " fluxerrs=np.asarray(lc[\"lightcurve\"][\"fluxerr\"], dtype=float),\n", + " filters=np.asarray(lc[\"lightcurve\"][\"filter\"], dtype=str),\n", + " )\n", + " ax.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e9a601c4", + "metadata": {}, + "source": [ + "The **good news** is that the microlensing event shows up where expected (t0=61600). Unfortunately the **bad news** is that all three simulated events are identical, because we created the model using a fixed set of parameters. We just simulated the same microlensing event three times.\n", + "\n", + "## Adding LightCurveLynx Dynamic Sampling\n", + "\n", + "We can make the LightCurveLynx wrapper more powerful by allowing the parameters to be set by the package’s sampling framework. As shown in other tutorial notebooks, parameters of a LightCurveLynx model can be set from a variety of sources, including constants or parameters from other nodes. In order to support a dynamic parameter, we use the ``add_parameter()`` function to add it to the model object. It will then be incorporated into the sampling flow.\n", + "\n", + "Let's start by modifying our model wrapper class to register and use each of the bagle model's parameter. Since they are passed as a dictionary, we can iterate over the items in the dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f09d673", + "metadata": {}, + "outputs": [], + "source": [ + "class BagleModel(BandfluxModel):\n", + " \"\"\"A wrapper for a Bagle model with a flexible set of parameters.\"\"\"\n", + "\n", + " # Convenience mapping from filter name to index in the parameter list.\n", + " _filter_idx = {\"u\": 0, \"g\": 1, \"r\": 2, \"i\": 3, \"z\": 4, \"y\": 5}\n", + "\n", + " def __init__(self, model_name, parameter_dict, **kwargs):\n", + " # We start by extracting the parameter information needed for a general physical model.\n", + " ra = parameter_dict[\"raL\"]\n", + " dec = parameter_dict[\"decL\"]\n", + " t0 = parameter_dict[\"t0\"]\n", + "\n", + " # Call the parent class constructor to set up the model.\n", + " super().__init__(ra=ra, dec=dec, t0=t0, **kwargs)\n", + "\n", + " # Add all of the parameters in the dictionary as settable parameters (if they are not\n", + " # already set by the parent class) and save their names (in order) for later use.\n", + " self.parameter_names = []\n", + " for param_name, param_value in parameter_dict.items():\n", + " self.parameter_names.append(param_name)\n", + " if param_name not in self.list_params():\n", + " self.add_parameter(param_name, param_value)\n", + "\n", + " # Save the model name and class, but DO NOT create the model object yet.\n", + " self.model_name = model_name\n", + " self.model_class = getattr(model, model_name)\n", + "\n", + " def compute_bandflux(self, times, filter, state):\n", + " \"\"\"Evaluate the model at the passband level for a single, given graph state and filter.\n", + "\n", + " Parameters\n", + " ----------\n", + " times : numpy.ndarray\n", + " A length T array of observer frame timestamps in MJD.\n", + " filter : str\n", + " The name of the filter.\n", + " state : GraphState\n", + " An object mapping graph parameters to their values with num_samples=1.\n", + " This is not used in this model, but is required for the function signature.\n", + "\n", + " Returns\n", + " -------\n", + " bandflux : numpy.ndarray\n", + " A length T array of band fluxes for this model in this filter.\n", + " \"\"\"\n", + " # Extract the local parameters for this object from the full state object.\n", + " local_params = self.get_local_params(state)\n", + "\n", + " # Create the bagle model object and set the parameters from the current state. We do this\n", + " # here because the parameters saved in `state` will be different in each run.\n", + " current_params = {param_name: local_params[param_name] for param_name in self.parameter_names}\n", + " model_obj = self.model_class(**current_params)\n", + "\n", + " # Use the newly created model object with the current parameters to compute the photometry.\n", + " mags = model_obj.get_photometry(times, self._filter_idx[filter])\n", + " bandflux = mag2flux(mags)\n", + " return bandflux" + ] + }, + { + "cell_type": "markdown", + "id": "f8a9cf37", + "metadata": {}, + "source": [ + "### The Init Function\n", + "\n", + "The ``__init__()`` function is similar to the earlier example. It starts by extracting the parameters needed by the base class (RA, dec, and t0) and calling that class’s constructor. Again the actual code we would want to use in production is a bit more complex because we also want to check `kwargs` for these values **and** bagle can use other parameters to compute t0. We keep the code simple here for illustration purposes. For a productionized version of this code see models/bagle_models.py, which handles several edge cases.\n", + "\n", + "The init function then iterates through the given dictionary saving a list of all the keys and adding any unknown parameters to the model. Note that we need to check whether a parameter already exists because the ``t0`` parameter may already be added by the base class’s constructor.\n", + "\n", + "Unlike the previous implementation, this constructor does not create the object. As we will see next, this is because we need to create a new object for each set of parameters we have sampled.\n", + "\n", + "\n", + "### The Compute Function\n", + " \n", + "The ``compute_bandflux()`` extracts the sampled parameters from the ``state`` object and constructs a dictionary from them. The ``state`` object holds all the sampled parameter data for each run of the simulation, allowing users to replay all or part of the simulation. These objects are created when sampling the model’s parameters and their values filled in according to the recipes that define each parameter.\n", + "\n", + "Once the function has constructed the dictionary object, it instantiates a new instance of the bagle model and uses it to generate the fluxes. While this might seem wasteful (we only use the object once), it is necessary because we need each of the objects to have different sets of parameters.\n", + "\n", + "### Modifying the Simulation\n", + "\n", + "To use the newly supported settable parameters, let's expand out the parameter dictionary so that three of the parameters are randomly chosen:\n", + "\n", + "* ``mL`` will be sampled uniformly from [1,10) solar masses.\n", + "* ``t0`` will be sampled uniformly from [61000, 63000)\n", + "* ``beta`` be sampled from a normal distribution with mean=0.6 and std=0.1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c66da2f2", + "metadata": {}, + "outputs": [], + "source": [ + "from lightcurvelynx.math_nodes.np_random import NumpyRandomFunc\n", + "\n", + "dL = 4000 # Distance to lens\n", + "dS = 8000 # Distance to source\n", + "parameter_dict2 = {\n", + " \"raL\": 270.66679,\n", + " \"decL\": -35.70483,\n", + " \"mL\": NumpyRandomFunc(\"uniform\", low=1, high=20),\n", + " \"t0\": NumpyRandomFunc(\"uniform\", low=61000, high=63000),\n", + " \"beta\": NumpyRandomFunc(\"normal\", loc=0.6, scale=0.1),\n", + " \"dL\": dL,\n", + " \"dL_dS\": dL / dS,\n", + " \"xS0_E\": 0.0, # arbitrary offset (arcsec)\n", + " \"xS0_N\": 0.0, # arbitrary offset (arcsec)\n", + " \"muL_E\": 5.0,\n", + " \"muL_N\": 10.0,\n", + " \"muS_E\": 0.0,\n", + " \"muS_N\": 0.0,\n", + " \"b_sff\": [1, 1, 1, 1, 1, 1],\n", + " \"mag_src\": [23, 22, 21.5, 21, 20.5, 19.5],\n", + "}\n", + "\n", + "# Create the source object.\n", + "source2 = BagleModel(model_name, parameter_dict2)" + ] + }, + { + "cell_type": "markdown", + "id": "2c4f6699", + "metadata": {}, + "source": [ + "Once again, we sample and simulate the new objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f24d678", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Simulating: 100%|██████████| 5/5 [00:00<00:00, 139.00obj/s]\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lightcurves = simulate_lightcurves(\n", + " source2,\n", + " 5,\n", + " opsim_db,\n", + " passband_group,\n", + " param_cols=[\"BagleModel_0.mL\", \"BagleModel_0.beta\"],\n", + ")\n", + "\n", + "for idx in range(5):\n", + " # Extract the row for this object.\n", + " lc = lightcurves.loc[idx]\n", + "\n", + " # Plot the lightcurves.\n", + " ax = plot_lightcurves(\n", + " fluxes=np.asarray(lc[\"lightcurve\"][\"flux\"], dtype=float),\n", + " times=np.asarray(lc[\"lightcurve\"][\"mjd\"], dtype=float),\n", + " fluxerrs=np.asarray(lc[\"lightcurve\"][\"fluxerr\"], dtype=float),\n", + " filters=np.asarray(lc[\"lightcurve\"][\"filter\"], dtype=str),\n", + " title=(f\"t0={lc['t0']:.2f}, mL={lc['BagleModel_0_mL']:.2f} beta={lc['BagleModel_0_beta']:.2f}\"),\n", + " )\n", + " ax.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8549a60d", + "metadata": {}, + "source": [ + "## Presampled Values\n", + "\n", + "If the user has their own sampling infrastructure, they might not be interested in using the random number generators provided by LightCurveLynx. In that case, we can still use the same approach in constructing our wrapper model, but change how the parameters are feed into the model.\n", + "\n", + "Let’s consider the case where *some* of the model parameters are pre-sampled and provided as an astropy Table (note other data structures, such as a pandas DataFrame could also be used).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "010599d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mL t0 ... xS0_N mag_src \n", + "--- ----- ... ---------------------- ----------------------------------------\n", + "1.0 61510 ... 0.058944183818574226 23.041715470725205 .. 19.803960783010716\n", + "2.0 61520 ... -0.020379822858091774 23.982802122444703 .. 19.86090593677731\n", + "2.5 61530 ... -0.011305866774735683 22.669975495051954 .. 19.790028044190933\n", + "3.0 61540 ... -0.020954962013930465 22.69492608845565 .. 19.38739905858954\n", + "3.1 61550 ... -0.0076015406212712605 23.10803029122737 .. 19.170782194017903\n", + "4.0 61560 ... 0.13779443636415814 23.293308947319805 .. 19.943759504281683\n", + "5.0 61570 ... -0.017100204084893683 23.104765717956596 .. 19.24286128426326\n", + "6.0 61580 ... -0.02320294693724652 23.3576425636409 .. 19.503333724226607\n", + "7.0 61590 ... -0.17482475185251556 23.479748213215952 .. 19.076582380216905\n", + "8.0 61600 ... -0.1534792101882318 22.65750216828863 .. 19.830850764048407\n" + ] + } + ], + "source": [ + "from astropy.table import Table\n", + "\n", + "num_samples = 10\n", + "\n", + "# Build a list of different mag_src values for each sample. Draw each\n", + "# list of magnitudes from a normal distribution around given mean values.\n", + "mag_list = []\n", + "mean_mags = np.array([23, 22, 21.5, 21, 20.5, 19.5])\n", + "for i in range(num_samples):\n", + " mag_list.append(np.random.normal(mean_mags, 0.5))\n", + "\n", + "param_table = Table(\n", + " {\n", + " \"mL\": [1.0, 2.0, 2.5, 3.0, 3.1, 4.0, 5.0, 6.0, 7.0, 8.0],\n", + " \"t0\": [61510, 61520, 61530, 61540, 61550, 61560, 61570, 61580, 61590, 61600],\n", + " \"beta\": [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1],\n", + " \"xS0_E\": np.random.normal(0.0, 0.1, num_samples).tolist(),\n", + " \"xS0_N\": np.random.normal(0.0, 0.1, num_samples).tolist(),\n", + " \"mag_src\": mag_list,\n", + " }\n", + ")\n", + "print(param_table)" + ] + }, + { + "cell_type": "markdown", + "id": "bbc6f7f2", + "metadata": {}, + "source": [ + "We can use the ``TableSampler`` class provided in the ``math_nodes/given_sampler.py`` which reads paramters out of a table. By setting ``in_order=True`` the rows will be used in-order. Otherwise the values will be randomly sampled. **NOTE**: The ``TableSampler``does not support in-order traversal when performing distributed computation.\n", + "\n", + "Users can access the parameters from the table sampler using the dot notation with the parameter name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c554d2f", + "metadata": {}, + "outputs": [], + "source": [ + "from lightcurvelynx.math_nodes.given_sampler import TableSampler\n", + "\n", + "table_sampler = TableSampler(param_table, in_order=False)\n", + "\n", + "parameter_dict2 = {\n", + " \"raL\": 270.66679, # Constant RA\n", + " \"decL\": -35.70483, # Constant Dec\n", + " \"mL\": table_sampler.mL, # The mL values from the table (in order)\n", + " \"t0\": table_sampler.t0, # The t0 values from the table (in order)\n", + " \"beta\": table_sampler.beta, # The beta values from the table (in order)\n", + " \"dL\": dL, # Constant dL\n", + " \"dL_dS\": dL / dS, # Constant dL/dS\n", + " \"xS0_E\": table_sampler.xS0_E, # The xS0_E values from the table (in order)\n", + " \"xS0_N\": table_sampler.xS0_N, # The xS0_N values from the table (in order)\n", + " \"muL_E\": 5.0, # Constant muL_E\n", + " \"muL_N\": 10.0, # Constant muL_N\n", + " \"muS_E\": 0.0, # Constant muS_E\n", + " \"muS_N\": 0.0, # Constant muS_N\n", + " \"b_sff\": [1, 1, 1, 1, 1, 1],\n", + " \"mag_src\": table_sampler.mag_src, # A different mag_src list for each sample\n", + "}\n", + "\n", + "# Create the source object.\n", + "source3 = BagleModel(model_name, parameter_dict2)" + ] + }, + { + "cell_type": "markdown", + "id": "94b3e5cf", + "metadata": {}, + "source": [ + "When we run the simulation, the values used to create the bagle models will be a combination of the constant values and the values from the table. The ``TableSampler`` ensures that the same row is used for all the parameters in a sample, so they are consistent.\n", + "\n", + "We can print some of these values from the results to see what the simulation looks like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5627786", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Simulating: 100%|██████████| 10/10 [00:00<00:00, 173.19obj/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: (270.66679, -35.70483): t0=61590, mL=7.0\n", + "1: (270.66679, -35.70483): t0=61530, mL=2.5\n", + "2: (270.66679, -35.70483): t0=61570, mL=5.0\n", + "3: (270.66679, -35.70483): t0=61550, mL=3.1\n", + "4: (270.66679, -35.70483): t0=61580, mL=6.0\n", + "5: (270.66679, -35.70483): t0=61590, mL=7.0\n", + "6: (270.66679, -35.70483): t0=61580, mL=6.0\n", + "7: (270.66679, -35.70483): t0=61520, mL=2.0\n", + "8: (270.66679, -35.70483): t0=61560, mL=4.0\n", + "9: (270.66679, -35.70483): t0=61590, mL=7.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "lightcurves = simulate_lightcurves(source3, 10, opsim_db, passband_group, param_cols=[\"BagleModel_0.mL\"])\n", + "\n", + "for idx in range(10):\n", + " row = lightcurves.loc[idx]\n", + " print(f\"{idx}: ({row['ra']}, {row['dec']}): t0={row['t0']}, mL={row['BagleModel_0_mL']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "57ebcbcf", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook shows how we can create a basic wrapper for the bagle models. A full featured version can be found in ``models/bagle_models.py``, including:\n", + "* The ability to set a custom filter map.\n", + "* Conditional imports for the bagle package.\n", + "* Various helper functions." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lightcurvelynx", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.8" + }, + "nbsphinx": { + "execute": "never" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/lightcurvelynx/models/bagle_models.py b/src/lightcurvelynx/models/bagle_models.py new file mode 100644 index 00000000..ab616509 --- /dev/null +++ b/src/lightcurvelynx/models/bagle_models.py @@ -0,0 +1,163 @@ +"""Wrappers for the models defined in bagle. + +https://github.com/MovingUniverseLab/BAGLE_Microlensing +""" + +import numpy as np +from citation_compass import CiteClass + +from lightcurvelynx.astro_utils.mag_flux import mag2flux +from lightcurvelynx.models.physical_model import BandfluxModel + + +class BagleWrapperModel(BandfluxModel, CiteClass): + """A wrapper for bagle models. + + Parameterized values include: + * dec - The object's declination in degrees. [from BasePhysicalModel] + * distance - The object's luminosity distance in pc. [from BasePhysicalModel] + * ra - The object's right ascension in degrees. [from BasePhysicalModel] + * redshift - The object's redshift. [from BasePhysicalModel] + * t0 - The t0 of the zero phase, date. [from BasePhysicalModel] + Additional parameterized values are used for specific bagle models. + + References + ---------- + * Lu et al., “The BAGLE Python Package for Bayesian Analysis of Gravitational Lensing Events”, + AAS Journals, submitted + * Bhadra et al., “Modeling Binary Lenses and Sources with the BAGLE Python Package”, AAS Journals, + submitted + * Chen et al., “Adjusting Gaussian Process Priors for BAGLE's Gravitational Microlensing Model Fits”, + in prep. + + Parameters + ---------- + model_info : str or class + The name of the bagle model class to use in the simulation or the class itself. + parameter_dict : dict + A dictionary of parameter names and values to use for the model. The keys should + match the parameter names expected by the bagle model. + filter_idx : dict, optional + A mapping from filter names to indices expected by the bagle model. If not provided, + a default mapping for ugrizy filters will be used. + **kwargs : dict, optional + Any additional keyword arguments. + """ + + # Convenience mapping from filter name to index in the parameter list. + _default_filter_idx = {"u": 0, "g": 1, "r": 2, "i": 3, "z": 4, "y": 5} + + def __init__(self, model_info, parameter_dict, filter_idx=None, **kwargs): + # We start by extracting the parameter information needed for a general physical model. + # We check the parameter dictionary first, falling back to kwargs if needed. + ra = parameter_dict.get("raL", None) + if "ra" in kwargs: + if ra is None: + ra = kwargs.pop("ra") + else: + raise ValueError( + "The 'ra' parameter is specified in both parameter_dict (as 'raL') " + " and kwargs (as 'ra'). Please only use the parameter_dict." + ) + + dec = parameter_dict.get("decL", None) + if "dec" in kwargs: + if dec is None: + dec = kwargs.pop("dec") + else: + raise ValueError( + "The 'dec' parameter is specified in both parameter_dict (as 'decL') " + " and kwargs (as 'dec'). Please only use the parameter_dict." + ) + + # The t0 parameter can be specified different ways from the bagle model, + # including being computed from other parameters. We need a base value to use + # for the time bounds, so we use t0 if it is in the parameter_dict or otherwise + # anchor on any parameter that starts with "t0" (under the assumption that it will + # indicate the same general range of times). + t0 = parameter_dict.get("t0", None) + if t0 is None: + for key, value in parameter_dict.items(): + if key.startswith("t0"): + t0 = value + break + if "t0" in kwargs: + raise ValueError( + "The 't0' parameter must be specified in only the 'parameter_dict' for the BagleWrapperModel." + ) + + super().__init__(ra=ra, dec=dec, t0=t0, **kwargs) + + # Add all of the parameters in the dictionary as settable parameters (if they are not + # already set by the parent class) and save their names (in order) for later use. + self._parameter_names = [] + for param_name, param_value in parameter_dict.items(): + self._parameter_names.append(param_name) + if param_name in self.list_params(): + self.set_parameter(param_name, param_value) + else: + self.add_parameter(param_name, param_value) + + # Save the model class, but DO NOT create the model object yet. We allow the + # user to pass in a class to simplify testing when bagle is not installed. + if isinstance(model_info, str): + try: + from bagle import model + except ImportError as err: # pragma: no cover + raise ImportError( + "The bagle package is required to use the BagleWrapperModel. Please install it. " + "See https://bagle.readthedocs.io/en/latest/installation.html for instructions." + ) from err + self._model_class = getattr(model, model_info) + else: + self._model_class = model_info + + # Save the filter index mapping, using the default if none is provided. + if filter_idx is None: + self._filter_idx = self._default_filter_idx + else: + self._filter_idx = filter_idx + + @property + def parameter_names(self): + """The names of the parameters for this model.""" + return self._parameter_names + + def compute_bandflux(self, times, filter, state): + """Evaluate the model at the passband level for a single, given graph state and filter. + + Parameters + ---------- + times : numpy.ndarray + A length T array of observer frame timestamps in MJD. + filter : str + The name of the filter. + state : GraphState + An object mapping graph parameters to their values with num_samples=1. + This is not used in this model, but is required for the function signature. + + Returns + ------- + bandflux : numpy.ndarray + A length T array of band fluxes for this model in this filter. + """ + # Extract the local parameters for this object from the full state object. + local_params = self.get_local_params(state) + + # Create the bagle model object and set the parameters from the current state. We do this + # here because the parameters saved in `state` will be different in each run. + current_params = {param_name: local_params[param_name] for param_name in self.parameter_names} + model_obj = self._model_class(**current_params) + + # If the model computes a t0 that is different from the one in the graph state, save the new one. + if hasattr(model_obj, "t0") and "t0" in local_params: + base_t0 = local_params["t0"] + computed_t0 = model_obj.t0 + if np.abs(computed_t0 - base_t0) > 1e-8: + node_name = str(self) + state.set(node_name, "t0", computed_t0) + + # Use the newly created model object with the current parameters to compute the photometry. + mags = model_obj.get_photometry(times, self._filter_idx[filter]) + bandflux = mag2flux(mags) + return bandflux diff --git a/tests/lightcurvelynx/models/test_bagle_model.py b/tests/lightcurvelynx/models/test_bagle_model.py new file mode 100644 index 00000000..db952868 --- /dev/null +++ b/tests/lightcurvelynx/models/test_bagle_model.py @@ -0,0 +1,241 @@ +import numpy as np +import pytest +from lightcurvelynx.astro_utils.mag_flux import mag2flux +from lightcurvelynx.models.bagle_models import BagleWrapperModel + + +class _FlatBagleModel: + """A flat dummy bagle model for testing purposes. + + Attributes + ---------- + scale : float + A scaling factor for the fluxes. + filter_mags : list + A list of magnitudes for each filter. + """ + + def __init__(self, scale, filter_mags): + self.scale = scale + self.filter_mags = filter_mags + + def get_photometry(self, times, filter_idx): + """Compute dummy fluxes based on parameters and times. + + Parameters + ---------- + times : numpy.ndarray + An array of times. + filter_idx : int + The index of the filter. + """ + return np.ones_like(times) * self.scale * self.filter_mags[filter_idx] + + +class _GaussianBagleModel: + """A Gaussian-shaped dummy bagle model for testing purposes. + + Attributes + ---------- + scale : float + A scaling factor for the fluxes. + filter_mags : list + A list of magnitudes for each filter. + """ + + def __init__(self, scale, filter_mags, **kwargs): + self.scale = scale + self.filter_mags = filter_mags + + # Simulate handling a t0 that is computed based on arguments. + self.t0 = 0.0 + if "t0" in kwargs: + self.t0 = kwargs["t0"] + elif "t0_com" in kwargs: + self.t0 = kwargs["t0_com"] - 2.0 + + def get_photometry(self, times, filter_idx): + """Compute dummy fluxes based on parameters and times. Simulates + a Gaussian light curve shape. + + Parameters + ---------- + times : numpy.ndarray + An array of times. + filter_idx : int + The index of the filter. + """ + # Since we are computing magnitudes we subtract out the Gaussian shape. + base = -np.exp(-0.5 * ((times - self.t0) / 50.0) ** 2) + return base * self.scale + self.filter_mags[filter_idx] + + +def test_bagle_wrapper_model() -> None: + """Test that we can create and query a BagleWrapperModel object.""" + parameter_dict = { + "scale": 2.0, + "filter_mags": [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], # ugrizy + } + model = BagleWrapperModel(_FlatBagleModel, parameter_dict, node_label="model") + + # Check that we have the given parameters. + all_params = model.list_params() + assert "scale" in all_params + assert "filter_mags" in all_params + assert model.parameter_names == ["scale", "filter_mags"] + + # Check that we have the standard physical model parameters. + assert "ra" in all_params + assert "dec" in all_params + assert "t0" in all_params + assert "redshift" in all_params + assert "distance" in all_params + + # Test that we can query the model for fluxes. + graph_state = model.sample_parameters(num_samples=1) + assert graph_state["model"]["scale"] == 2.0 + assert np.array_equal( + graph_state["model"]["filter_mags"], + [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], + ) + assert graph_state["model"]["ra"] is None + assert graph_state["model"]["dec"] is None + + query_times = np.array([0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 20.0, 21.0]) + query_filters = np.array(["u", "r", "u", "r", "u", "r", "u", "r"]) + + fluxes = model.evaluate_bandfluxes(None, query_times, query_filters, graph_state) + expected_mags = 2.0 * np.array([20.0, 22.0, 20.0, 22.0, 20.0, 22.0, 20.0, 22.0]) + expected_fluxes = mag2flux(expected_mags) + assert np.allclose(fluxes, expected_fluxes) + + +def test_bagle_wrapper_model_ra_dec() -> None: + """Test that we can create and query a BagleWrapperModel object + with different settings for RA, dec.""" + # No RA/dec given. + parameter_dict = { + "scale": 2.0, + "filter_mags": [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], # ugrizy + } + model1 = BagleWrapperModel( + _FlatBagleModel, + parameter_dict, + node_label="model", + ) + graph_state1 = model1.sample_parameters(num_samples=1) + assert graph_state1["model"]["ra"] is None + assert graph_state1["model"]["dec"] is None + + # Specify RA and dec as kwargs to the wrapper model. + model2 = BagleWrapperModel( + _FlatBagleModel, + parameter_dict, + ra=21.0, + dec=-11.0, + node_label="model", + ) + graph_state2 = model2.sample_parameters(num_samples=1) + assert graph_state2["model"]["ra"] == 21.0 + assert graph_state2["model"]["dec"] == -11.0 + + # Specify RA and dec as part of the parameter_dict (to the bagle model). + parameter_dict2 = { + "scale": 2.0, + "filter_mags": [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], # ugrizy + "raL": 150.0, + "decL": 2.0, + } + model3 = BagleWrapperModel( + _FlatBagleModel, + parameter_dict2, + node_label="model", + ) + graph_state3 = model3.sample_parameters(num_samples=1) + assert graph_state3["model"]["ra"] == 150.0 + assert graph_state3["model"]["dec"] == 2.0 + + # Using both kwargs and parameter_dict should raise an error. + with pytest.raises(ValueError): + _ = BagleWrapperModel( + _FlatBagleModel, + parameter_dict2, + ra=21.0, + dec=-11.0, + node_label="model", + ) + + +def test_bagle_wrapper_model_t0() -> None: + """Test that we can create and query a BagleWrapperModel object with t0 set.""" + parameter_dict = { + "scale": 5.0, + "filter_mags": [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], # ugrizy + "t0": 10.0, + } + model = BagleWrapperModel( + _GaussianBagleModel, + parameter_dict, + node_label="model", + ) + + # Check that we can query the model for fluxes. + graph_state = model.sample_parameters(num_samples=1) + assert graph_state["model"]["t0"] == 10.0 + + query_times = np.arange(-5.0, 25.0, 0.1) + query_filters = np.array(["r"] * len(query_times)) + fluxes = model.evaluate_bandfluxes(None, query_times, query_filters, graph_state) + + # The max flux should be at t0 = 10.0 + max_idx = np.int64(150) # index of time = 10.0 + assert np.argmax(fluxes) == max_idx + + # We do not allow specifying t0 via kwargs to the wrapper model. + with pytest.raises(ValueError): + _ = BagleWrapperModel( + _GaussianBagleModel, + parameter_dict, + t0=10.0, + node_label="model", + ) + + # We can specify t0 as a computed parameter. + parameter_dict2 = { + "scale": 2.0, + "filter_mags": [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], # ugrizy + "t0_com": 12.0, # t0 will be computed as t0_com - 2.0 = 10.0 + } + model2 = BagleWrapperModel( + _GaussianBagleModel, + parameter_dict2, + node_label="model", + ) + + # The wrapper model will save the base value of t0 (not the computed one). + # However the correct translation (computed value) will be used for the fluxes. + graph_state2 = model2.sample_parameters(num_samples=1) + assert graph_state2["model"]["t0"] == 12.0 # base value + fluxes2 = model2.evaluate_bandfluxes(None, query_times, query_filters, graph_state2) + assert graph_state2["model"]["t0"] == 10.0 # updated value + assert np.argmax(fluxes2) == max_idx + + +def test_bagle_wrapper_model_filter_idx() -> None: + """Test that we can create and query a BagleWrapperModel object with filter indices.""" + parameter_dict = { + "scale": 2.0, + "filter_mags": [20.0, 21.0, 22.0, 23.0, 24.0, 25.0], # ugrizy + } + custom_filter_idx = {"u": 5, "g": 4, "r": 3, "i": 2, "z": 1, "y": 0} + model = BagleWrapperModel(_FlatBagleModel, parameter_dict, filter_idx=custom_filter_idx) + + # Test that we can query the model for fluxes. + graph_state = model.sample_parameters(num_samples=1) + query_times = np.array([0.0, 0.5, 1.0, 2.0, 3.0, 4.0, 20.0, 21.0]) + query_filters = np.array(["u", "r", "u", "r", "u", "r", "u", "r"]) + + fluxes = model.evaluate_bandfluxes(None, query_times, query_filters, graph_state) + expected_mags = 2.0 * np.array([25.0, 23.0, 25.0, 23.0, 25.0, 23.0, 25.0, 23.0]) + expected_fluxes = mag2flux(expected_mags) + assert np.allclose(fluxes, expected_fluxes)