Haukekoehn train fluxes

benchmarks/GRB/benchmark_afterglowpy_tophat.py

@@ -10,29 +10,40 @@
 model_dir = f"../../trained_models/GRB/afterglowpy/{name}/"
 FILTERS = ["radio-6GHz", "radio-3GHz"]#["radio-3GHz", "radio-6GHz", "bessellv", "X-ray-1keV"]
 
+
 for metric_name in ["$\\mathcal{L}_2$", "$\\mathcal{L}_\infty$"]:    
     if metric_name == "$\\mathcal{L}_2$":
         file_ending = "L2"
     else:
         file_ending = "Linf"
 
-
-    B = Benchmarker(name = "tophat",
+    B = Benchmarker(name = name,
+                parameter_grid = parameter_grid,
                 model_dir = model_dir,
+                MODEL = AfterglowpyLightcurvemodel,
                 filters = FILTERS,
                 n_test_data = 2000,
                 metric_name = metric_name,
-                remake_test_data = True,
+                remake_test_data = False,
                 jet_type = -1,
                 )
+
+    fig, ax = B.plot_error_distribution("radio-6GHz")
+
 
     for filt in FILTERS:
 
-        fig, ax = B.plot_lightcurves_mismatch(filter =filt)
-        fig.savefig(f"./figures/benchmark_{filt}_{file_ending}.pdf", dpi = 200)
+        fig, ax = B.plot_lightcurves_mismatch(filter =filt, parameter_labels = ["$\\iota$", "$\log_{10}(E_0)$", "$\\theta_{\\mathrm{core}}$", "$\log_{10}(n_{\mathrm{ism}})$", "$p$", "$\\epsilon_E$", "$\\epsilon_B$"])
+        fig.savefig(f"./benchmarks/{name}/benchmark_{filt}_{file_ending}.pdf", dpi = 200)
 
         B.print_correlations(filter = filt)
+
+
+        if metric_name == "$\\mathcal{L}_\infty$":
+            fig, ax = B.plot_error_distribution(filt)
+            fig.savefig(f"./benchmarks/{name}/error_distribution_{filt}.pdf", dpi = 200)
 
 
         fig, ax = B.plot_worst_lightcurve(filter = filt)
         fig.savefig(f"./figures/worst_lightcurve_{filt}_{file_ending}.pdf", dpi = 200)
+

benchmarks/KN/benchmark_Bu2019lm.py

@@ -29,11 +29,14 @@
         fig.savefig(f"./figures/benchmark_{filt}_{file_ending}.pdf", dpi = 200)
 
         B.print_correlations(filter = filt)
-
-
-        fig, ax = B.plot_worst_lightcurve(filter = filt)
-        fig.savefig(f"./figures/worst_lightcurve_{filt}_{file_ending}.pdf", dpi = 200)
 
 
+        if metric_name == "$\\mathcal{L}_\infty$":
+            fig, ax = B.plot_error_distribution(filt)
+            fig.savefig(f"./benchmarks/{name}/error_distribution_{filt}.pdf", dpi = 200)
+
+
+    fig, ax = B.plot_worst_lightcurves()
+    fig.savefig(f"./benchmarks/{name}/worst_lightcurves_{file_ending}.pdf", dpi = 200)
 
 

examples/.gitignore

@@ -1 +1 @@
-figures/
+results_*.npz

examples/GRB/bash.sh

@@ -0,0 +1,27 @@
+#!/bin/bash -l
+#Set job requirements
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH -p gpu
+#SBATCH -t 00:30:00
+#SBATCH --gpus-per-node=1
+#SBATCH --cpus-per-gpu=1
+#SBATCH --mem-per-gpu=5G
+#SBATCH --output=outdir_GRB170817_tophat/log.out
+#SBATCH --job-name=GRB170817
+
+now=$(date)
+echo "$now"
+
+# Loading modules
+# module load 2024
+# module load Python/3.10.4-GCCcore-11.3.0
+conda activate /home/twouters2/miniconda3/envs/ninjax
+
+# Display GPU name
+nvidia-smi --query-gpu=name --format=csv,noheader
+
+# Run the script
+python run_GRB170817_tophat.py
+
+echo "DONE"

examples/GRB/injection_gaussian.py

@@ -0,0 +1,213 @@
+"""Injection runs with afterglowpy gaussian"""
+
+import os
+import jax
+print(f"GPU found? {jax.devices()}")
+import jax.numpy as jnp
+jax.config.update("jax_enable_x64", True)
+import numpy as np
+import matplotlib.pyplot as plt
+import corner
+
+from fiesta.inference.lightcurve_model import AfterglowpyPCA, PCALightcurveModel
+from fiesta.inference.injection import InjectionRecoveryAfterglowpy
+from fiesta.inference.likelihood import EMLikelihood
+from fiesta.inference.prior import Uniform, CompositePrior, Constraint
+from fiesta.inference.prior_dict import ConstrainedPrior
+from fiesta.inference.fiesta import Fiesta
+from fiesta.utils import load_event_data, write_event_data
+
+import time
+start_time = time.time()
+
+################
+### Preamble ###
+################
+
+jax.config.update("jax_enable_x64", True)
+
+params = {"axes.grid": True,
+        "text.usetex" : True,
+        "font.family" : "serif",
+        "ytick.color" : "black",
+        "xtick.color" : "black",
+        "axes.labelcolor" : "black",
+        "axes.edgecolor" : "black",
+        "font.serif" : ["Computer Modern Serif"],
+        "xtick.labelsize": 16,
+        "ytick.labelsize": 16,
+        "axes.labelsize": 16,
+        "legend.fontsize": 16,
+        "legend.title_fontsize": 16,
+        "figure.titlesize": 16}
+
+plt.rcParams.update(params)
+
+default_corner_kwargs = dict(bins=40, 
+                        smooth=1., 
+                        label_kwargs=dict(fontsize=16),
+                        title_kwargs=dict(fontsize=16), 
+                        color="blue",
+                        # quantiles=[],
+                        # levels=[0.9],
+                        plot_density=True, 
+                        plot_datapoints=False, 
+                        fill_contours=True,
+                        max_n_ticks=4, 
+                        min_n_ticks=3,
+                        save=False,
+                        truth_color="red")
+
+
+##############
+### MODEL  ###
+##############
+
+name = "gaussian"
+model_dir = f"../../flux_models/afterglowpy_{name}/model"
+FILTERS = ["radio-3GHz", "radio-6GHz", "X-ray-1keV", "bessellv"]
+
+model = AfterglowpyPCA(name,
+                            model_dir, 
+                            filters = FILTERS)
+
+
+###################
+###    INJECT   ###
+### AFTERGLOWPY ###
+###################
+
+trigger_time = 58849 # 01-01-2020 in mjd
+remake_injection = False
+injection_dict = {"inclination_EM": 0.174, "log10_E0": 54.4, "thetaCore": 0.14, "alphaWing": 3, "p": 2.6, "log10_n0": -2, "log10_epsilon_e": -2.06, "log10_epsilon_B": -4.2, "luminosity_distance": 40.0}
+
+if remake_injection:
+    injection = InjectionRecoveryAfterglowpy(injection_dict, jet_type = 0, filters = FILTERS, N_datapoints = 70, error_budget = 0.5, tmin = 1, tmax = 2000, trigger_time = trigger_time)
+    injection.create_injection()
+    data = injection.data
+    write_event_data("./injection_gaussian/injection_gaussian.dat", data)
+
+data = load_event_data("./injection_gaussian/injection_gaussian.dat")
+#############################
+### PRIORS AND LIKELIHOOD ###
+#############################
+
+inclination_EM = Uniform(xmin=0.0, xmax=np.pi/2, naming=['inclination_EM'])
+log10_E0 = Uniform(xmin=47.0, xmax=57.0, naming=['log10_E0'])
+thetaCore = Uniform(xmin=0.01, xmax=np.pi/5, naming=['thetaCore'])
+alphaWing = Uniform(xmin = 0.2, xmax = 3.5, naming= ["alphaWing"])
+thetaWing = Constraint(xmin = 0, xmax = np.pi/2, naming = ["thetaWing"])
+log10_n0 = Uniform(xmin=-6.0, xmax=2.0, naming=['log10_n0'])
+p = Uniform(xmin=2.01, xmax=3.0, naming=['p'])
+log10_epsilon_e = Uniform(xmin=-4.0, xmax=0.0, naming=['log10_epsilon_e'])
+log10_epsilon_B = Uniform(xmin=-8.0, xmax=0.0, naming=['log10_epsilon_B'])
+epsilon_tot = Constraint(xmin = 0, xmax = 1, naming = ["epsilon_tot"])
+
+# luminosity_distance = Uniform(xmin=30.0, xmax=50.0, naming=['luminosity_distance'])
+def conversion_function(sample):
+    converted_sample = sample
+    converted_sample["thetaWing"] = converted_sample["thetaCore"] * converted_sample["alphaWing"]
+    converted_sample["epsilon_tot"] = 10**(converted_sample["log10_epsilon_B"]) + 10**(converted_sample["log10_epsilon_e"]) 
+    return converted_sample
+
+prior_list = [inclination_EM, 
+              log10_E0, 
+              thetaCore,
+              alphaWing,
+              log10_n0, 
+              p, 
+              log10_epsilon_e, 
+              log10_epsilon_B,
+              thetaWing,
+              epsilon_tot]
+
+prior = ConstrainedPrior(prior_list, conversion_function)
+
+detection_limit = None
+likelihood = EMLikelihood(model,
+                          data,
+                          FILTERS,
+                          tmax = 2000.0,
+                          trigger_time=trigger_time,
+                          detection_limit = detection_limit,
+                          fixed_params={"luminosity_distance": 40.0},
+                          error_budget = 1e-5)
+
+
+##############
+### FIESTA ###
+##############
+
+mass_matrix = jnp.eye(prior.n_dim)
+eps = 5e-3
+local_sampler_arg = {"step_size": mass_matrix * eps}
+
+# Save for postprocessing
+outdir = f"./injection_{name}/"
+if not os.path.exists(outdir):
+    os.makedirs(outdir)
+
+fiesta = Fiesta(likelihood,
+                prior,
+                n_chains = 1_000,
+                n_loop_training = 7,
+                n_loop_production = 3,
+                num_layers = 4,
+                hidden_size = [64, 64],
+                n_epochs = 20,
+                n_local_steps = 50,
+                n_global_steps = 200,
+                local_sampler_arg=local_sampler_arg,
+                outdir = outdir)
+
+fiesta.sample(jax.random.PRNGKey(42))
+
+fiesta.print_summary()
+
+name = outdir + f'results_training.npz'
+print(f"Saving samples to {name}")
+state = fiesta.Sampler.get_sampler_state(training=True)
+chains, log_prob, local_accs, global_accs, loss_vals = state["chains"], state[
+"log_prob"], state["local_accs"], state["global_accs"], state["loss_vals"]
+local_accs = jnp.mean(local_accs, axis=0)
+global_accs = jnp.mean(global_accs, axis=0)
+np.savez(name, log_prob=log_prob, local_accs=local_accs,
+        global_accs=global_accs, loss_vals=loss_vals)
+
+#  - production phase
+name = outdir + f'results_production.npz'
+print(f"Saving samples to {name}")
+state = fiesta.Sampler.get_sampler_state(training=False)
+chains, log_prob, local_accs, global_accs = state["chains"], state[
+    "log_prob"], state["local_accs"], state["global_accs"]
+local_accs = jnp.mean(local_accs, axis=0)
+global_accs = jnp.mean(global_accs, axis=0)
+np.savez(name, chains=chains, log_prob=log_prob,
+            local_accs=local_accs, global_accs=global_accs)
+
+################
+### PLOTTING ###
+################
+# Fixed names: do not include them in the plotting, as will break corner
+parameter_names = prior.naming
+truths = [injection_dict[key] for key in parameter_names]
+
+n_chains, n_steps, n_dim = np.shape(chains)
+samples = np.reshape(chains, (n_chains * n_steps, n_dim))
+samples = np.asarray(samples) # convert from jax.numpy array to numpy array for corner consumption
+
+corner.corner(samples, labels = parameter_names, hist_kwargs={'density': True}, truths = truths, **default_corner_kwargs)
+plt.savefig(os.path.join(outdir, "corner.png"), bbox_inches = 'tight')
+plt.close()
+
+end_time = time.time()
+runtime_seconds = end_time - start_time
+number_of_minutes = runtime_seconds // 60
+number_of_seconds = np.round(runtime_seconds % 60, 2)
+print(f"Total runtime: {number_of_minutes} m {number_of_seconds} s")
+
+print("Plotting lightcurves")
+fiesta.plot_lightcurves()
+print("Plotting lightcurves . . . done")
+
+print("DONE")

examples/GRB/injection_gaussian/.gitignore

@@ -0,0 +1 @@
+

examples/GRB/injection_gaussian/injection_gaussian.dat

@@ -0,0 +1,70 @@
+2020-01-02T00:00:00.000 radio-3GHz 6.473406 0.500000
+2020-04-10T22:48:00.000 radio-3GHz 12.246090 0.500000
+2020-07-19T21:36:00.000 radio-3GHz 13.860922 0.500000
+2020-10-27T20:24:00.000 radio-3GHz 15.145347 0.500000
+2021-02-04T19:12:00.000 radio-3GHz 16.200114 0.500000
+2021-05-15T18:00:00.000 radio-3GHz 17.037075 0.500000
+2021-08-23T16:48:00.000 radio-3GHz 17.709738 0.500000
+2021-12-01T15:36:00.000 radio-3GHz 18.265443 0.500000
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