{

“cells”: [

{

“cell_type”: “markdown”, “id”: “0703e08d”, “metadata”: {}, “source”: [

“# Creating PPC from GBM SpectrumLike fitsn”, “n”, “A very useful way to check the quality of Bayesian fits is through posterior predicitve checks.n”, “We can create these with the machinery inside of 3ML for certain plugins (perhaps more in the future).n”, “n”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “id”: “ca29fe49”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:33.262786Z”, “iopub.status.busy”: “2021-08-24T15:09:33.259376Z”, “iopub.status.idle”: “2021-08-24T15:09:37.781301Z”, “shell.execute_reply”: “2021-08-24T15:09:37.780481Z”

}, “lines_to_next_cell”: 2

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m The naima package is not available. Models that depend on it will not be availableu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it will not be available.u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m The ebltable package is not available. Models that depend on it will not be availableu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m Starting 3ML!u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m no display variable set. using backend for graphics without display (agg)u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m ROOT minimizer not availableu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m Multinest minimizer not availableu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m PyGMO is not availableu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m The cthreeML package is not installed. You will not be able to use plugins which require the C/C++ interface (currently HAWC)u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m Could not import plugin FermiLATLike.py. Do you have the relative instrument software installed and configured?u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m Could not import plugin HAWCLike.py. Do you have the relative instrument software installed and configured?u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal performances in 3MLu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal performances in 3MLu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[35mWARNING u001b[0m]u001b[35m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal performances in 3MLu001b[0mn”

]

}

], “source”: [

“import numpy as npn”, “from threeML.io.package_data import get_path_of_data_filen”, “from threeML.utils.OGIP.response import OGIPResponsen”, “from threeML import (n”, ” silence_warnings,n”, ” DispersionSpectrumLike,n”, ” display_spectrum_model_counts,n”, “)n”, “from threeML import set_threeML_style, BayesianAnalysis, DataList, threeML_confign”, “from astromodels import Blackbody, Powerlaw, PointSource, Model, Log_normaln”, “n”, “threeML_config.plugins.ogip.data_plot.counts_color = "#FCE902"n”, “threeML_config.plugins.ogip.data_plot.background_color = "#CC0000"”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “a8cff239”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:37.790102Z”, “iopub.status.busy”: “2021-08-24T15:09:37.789456Z”, “iopub.status.idle”: “2021-08-24T15:09:37.801153Z”, “shell.execute_reply”: “2021-08-24T15:09:37.801820Z”

}, “lines_to_next_cell”: 0, “nbsphinx”: “hidden”

}, “outputs”: [], “source”: [

“from jupyterthemes import jtplotn”, “n”, “%matplotlib inlinen”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “n”, “silence_warnings()n”, “n”, “set_threeML_style()n”, “n”, “threeML_config.plugins.ogip.data_plot.counts_color = "#FCE902"n”, “threeML_config.plugins.ogip.data_plot.background_color = "#CC0000"n”, “n”, “n”, “threeML_config.plugins.ogip.fit_plot.background_mpl_kwargs = dict(ls="-", lw=0.7)n”, “threeML_config.interface.multi_progress_cmap = "Reds"”

]

}, {

“cell_type”: “markdown”, “id”: “19a1e337”, “metadata”: {}, “source”: [

“## Generate some synthetic datan”, “n”, “First, lets use 3ML to generate some fake data. Here we will have a background spectrum that is a power law and a source that is a black body.n”, “n”, “### Grab a demo response from the 3ML library”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “9394d05a”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:37.805957Z”, “iopub.status.busy”: “2021-08-24T15:09:37.804589Z”, “iopub.status.idle”: “2021-08-24T15:09:37.830106Z”, “shell.execute_reply”: “2021-08-24T15:09:37.828956Z”

}

}, “outputs”: [], “source”: [

“# we will use a demo responsen”, “response = OGIPResponse(get_path_of_data_file("datasets/ogip_powerlaw.rsp"))”

]

}, {

“cell_type”: “markdown”, “id”: “5d6a3b84”, “metadata”: {}, “source”: [

“### Simulate the datan”, “n”, “We will mimic the some normal c-stat style data.”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “56d4e417”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:37.837371Z”, “iopub.status.busy”: “2021-08-24T15:09:37.836579Z”, “iopub.status.idle”: “2021-08-24T15:09:42.337891Z”, “shell.execute_reply”: “2021-08-24T15:09:42.338380Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m Auto-probed noise models:u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - observation: poissonu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - background: Noneu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m Auto-probed noise models:u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - observation: poissonu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - background: Noneu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m Auto-probed noise models:u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - observation: poissonu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - background: poissonu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m Auto-probed noise models:u001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - observation: poissonu001b[0mn”

]

}, {

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m - background: poissonu001b[0mn”

]

}, {

“data”: {

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n”, “text/plain”: [

“<Figure size 748.8x655.2 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“np.random.seed(1234)n”, “n”, “# rescale the functions for the responsen”, “source_function = Blackbody(K=1e-7, kT=500.0)n”, “background_function = Powerlaw(K=1, index=-1.5, piv=1.0e3)n”, “spectrum_generator = DispersionSpectrumLike.from_function(n”, ” "fake",n”, ” source_function=source_function,n”, ” background_function=background_function,n”, ” response=response,n”, “)n”, “n”, “fig = spectrum_generator.view_count_spectrum()”

]

}, {

“cell_type”: “markdown”, “id”: “1cc09071”, “metadata”: {}, “source”: [

“## Fit the datan”, “n”, “We will quickly fit the data via Bayesian posterior sampling… After all you need a posterior to do posterior predictive checks!n”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “456a47df”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:42.347075Z”, “iopub.status.busy”: “2021-08-24T15:09:42.346432Z”, “iopub.status.idle”: “2021-08-24T15:09:42.349790Z”, “shell.execute_reply”: “2021-08-24T15:09:42.349202Z”

}

}, “outputs”: [], “source”: [

“source_function.K.prior = Log_normal(mu=np.log(1e-7), sigma=1)n”, “source_function.kT.prior = Log_normal(mu=np.log(300), sigma=2)n”, “n”, “ps = PointSource("demo", 0, 0, spectral_shape=source_function)n”, “n”, “model = Model(ps)”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “9ba5280d”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:42.356400Z”, “iopub.status.busy”: “2021-08-24T15:09:42.354819Z”, “iopub.status.idle”: “2021-08-24T15:09:42.357066Z”, “shell.execute_reply”: “2021-08-24T15:09:42.357574Z”

}

}, “outputs”: [], “source”: [

“ba = BayesianAnalysis(model, DataList(spectrum_generator))”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “7d0a8182”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:09:42.362900Z”, “iopub.status.busy”: “2021-08-24T15:09:42.362244Z”, “iopub.status.idle”: “2021-08-24T15:10:09.148475Z”, “shell.execute_reply”: “2021-08-24T15:10:09.149092Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“[u001b[32mINFO u001b[0m]u001b[32m sampler set to emceeu001b[0mn”

]

}, {

“data”: {

“application/vnd.jupyter.widget-view+json”: {

“model_id”: “321b8709103540cc9100bc59f347c8ad”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

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n”, “text/plain”: [

“<Figure size 748.8x655.2 with 2 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“ba.restore_median_fit()n”, “fig = display_spectrum_model_counts(n”, ” ba,n”, ” data_color="#CC0000",n”, ” model_color="#FCE902",n”, ” background_color="k",n”, ” show_background=True,n”, ” source_only=False,n”, ” min_rate=10,n”, “)n”, “ax = fig.get_axes()[0]n”, “n”, “_ = ax.set_ylim(1e-3)”

]

}, {

“cell_type”: “markdown”, “id”: “c11d68cc”, “metadata”: {}, “source”: [

“## Compute the PPCsn”, “n”, “We want to check the validiity of the fit via posterior predicitive checks (PPCs).n”, “Essentially:n”, “n”, “$$\pi\left(y^{\mathrm{rep}} \mid y\right)=\int \mathrm{d} \theta \pi(\theta \mid y) \pi\left(y^{\mathrm{rep}} \mid \theta\right)$$n”, “n”, “n”, “So, we will randomly sample the posterior, fold those point of the posterior model throught the likelihood and sample new counts. As this is an expensive process, we will want to save this to disk (in an HDF5 file).n”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “id”: “a5d5f626”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:10:14.977254Z”, “iopub.status.busy”: “2021-08-24T15:10:14.976548Z”, “iopub.status.idle”: “2021-08-24T15:10:14.988626Z”, “shell.execute_reply”: “2021-08-24T15:10:14.989137Z”

}

}, “outputs”: [], “source”: [

“from twopc import compute_ppc”

]

}, {

“cell_type”: “code”, “execution_count”: 10, “id”: “b88d6e2a”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:10:14.994430Z”, “iopub.status.busy”: “2021-08-24T15:10:14.993817Z”, “iopub.status.idle”: “2021-08-24T15:10:31.477080Z”, “shell.execute_reply”: “2021-08-24T15:10:31.476432Z”

}

}, “outputs”: [

{

“data”: {

“application/vnd.jupyter.widget-view+json”: {

“model_id”: “ce8c91244c6c4d0983371a04d96487ce”, “version_major”: 2, “version_minor”: 0

}, “text/plain”: [

“sampling posterior: 0%| | 0/1000 [00:00<?, ?it/s]”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“ppc = compute_ppc(n”, ” ba, ba.results, n_sims=1000, file_name="my_ppc.h5", overwrite=True, return_ppc=Truen”, “)”

]

}, {

“cell_type”: “markdown”, “id”: “2da199b0”, “metadata”: {}, “source”: [

“Lets take a look at the results of the background substracted rate”

]

}, {

“cell_type”: “code”, “execution_count”: 11, “id”: “f0f29a60”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:10:31.504484Z”, “iopub.status.busy”: “2021-08-24T15:10:31.503632Z”, “iopub.status.idle”: “2021-08-24T15:10:34.161219Z”, “shell.execute_reply”: “2021-08-24T15:10:34.161837Z”

}

}, “outputs”: [

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 748.8x655.2 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“fig = ppc.fake.plot(bkg_subtract=True, colors=["#FF1919", "#CC0000", "#7F0000"])”

]

}, {

“cell_type”: “markdown”, “id”: “d7982521”, “metadata”: {}, “source”: [

“And the PPCs with source + background”

]

}, {

“cell_type”: “code”, “execution_count”: 12, “id”: “23e714b6”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-24T15:10:34.188100Z”, “iopub.status.busy”: “2021-08-24T15:10:34.186766Z”, “iopub.status.idle”: “2021-08-24T15:10:37.196659Z”, “shell.execute_reply”: “2021-08-24T15:10:37.196118Z”

}

}, “outputs”: [

{

“data”: {

“image/png”: 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“text/plain”: [

“<Figure size 748.8x655.2 with 1 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“fig = ppc.fake.plot(bkg_subtract=False, colors=["#FF1919", "#CC0000", "#7F0000"])”

]

}, {

“cell_type”: “markdown”, “id”: “20bb5644”, “metadata”: {}, “source”: [

“** AWESOME! ** It looks like our fit and accurately produce future data!”

]

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}

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