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}, “tags”: []

}, “source”: [

“# Band Calderone”

]

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“execution”: {

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}, “tags”: []

}, “outputs”: [], “source”: [

“%%capturen”, “n”, “import numpy as npn”, “n”, “import matplotlib.pyplot as pltn”, “n”, “import warningsn”, “warnings.simplefilter("ignore")n”, “n”, “from astromodels.functions.function import _known_functionsn”, “n”, “n”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “%matplotlib inline”

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“execution”: {

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}, “tags”: [

“parameters”

]

}, “outputs”: [], “source”: [

“func_name = "TbAbs"n”, “n”, “x_scale="log"n”, “y_scale="log"n”, “n”, “linear_range = Falsen”, “n”, “wide_energy_range = False”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “e15cceb0”, “metadata”: {

“execution”: {

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}, “tags”: [

“injected-parameters”

]

}, “outputs”: [], “source”: [

“# Parametersn”, “func_name = "Band_Calderone"n”, “wide_energy_range = Truen”, “x_scale = "log"n”, “y_scale = "log"n”, “linear_range = Falsen”

]

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“execution”: {

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}, “tags”: []

}, “outputs”: [], “source”: [

“func = _known_functions[func_name]()n”, “n”, “if wide_energy_range:n”, “n”, ” energy_grid = np.geomspace(1e2,1e4,500)n”, ” n”, “else:n”, ” n”, ” energy_grid = np.geomspace(2e-1,1e1,1000)n”, “n”, “if linear_range:n”, “n”, “tenergy_grid = np.linspace(-5,5,1000)n”, “n”, ” n”, “blue = "#4152E3"n”, “red = "#E3414B"n”, “green = "#41E39E"”

]

}, {

“cell_type”: “markdown”, “id”: “a2063840”, “metadata”: {

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}, “tags”: []

}, “source”: [

“## Description”

]

}, {

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“execution”: {

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” * description: The Band model from Band et al. 1993, implemented however in a way whichn”, ” * reduces the covariances between the parameters (Calderone et al., MNRAS, 448, 403C,n”, ” * 2015)n”, ” * formula: $ \text{(Calderone et al., MNRAS, 448, 403C, 2015)} $n”, ” * parameters:n”, ” * alpha:n”, ” * value: -1.0n”, ” * desc: The index for x smaller than the x peakn”, ” * min_value: -10.0n”, ” * max_value: 10.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: truen”, ” * beta:n”, ” * value: -2.2n”, ” * desc: index for x greater than the x peak (only if opt=1, i.e., for the Band model)n”, ” * min_value: -7.0n”, ” * max_value: -1.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.22000000000000003n”, ” * free: truen”, ” * xp:n”, ” * value: 200.0n”, ” * desc: position of the peak in the x*x*f(x) space (if x is energy, this is then”, ” * nuFnu or SED space)n”, ” * min_value: 0.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 20.0n”, ” * free: truen”, ” * F:n”, ” * value: 1.0e-06n”, ” * desc: integral in the band defined by a and bn”, ” * min_value: nulln”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: truen”, ” * delta: 1.0e-07n”, ” * free: truen”, ” * a:n”, ” * value: 1.0n”, ” * desc: lower limit of the band in which the integral will be computedn”, ” * min_value: 0.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: falsen”, ” * b:n”, ” * value: 10000.0n”, ” * desc: upper limit of the band in which the integral will be computedn”, ” * min_value: 0.0n”, ” * max_value: nulln”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 1000.0n”, ” * free: falsen”, ” * opt:n”, ” * value: 1.0n”, ” * desc: option to select the spectral model (0 corresponds to a cutoff power law,n”, ” * 1 to the Band model)n”, ” * min_value: 0.0n”, ” * max_value: 1.0n”, ” * unit: ‘’n”, ” * is_normalization: falsen”, ” * delta: 0.1n”, ” * free: false”

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“func.display()”

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}, “tags”: []

}, “source”: [

“## Shape n”, “n”, “The shape of the function. n”, “n”, “If this is not a photon model but a prior or linear function then ignore the units as these docs are auto-generated”

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“nbsphinx-thumbnail”

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“data”: {

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n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “n”, “ax.plot(energy_grid, func(energy_grid), color=blue)n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel("photon flux")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”

]

}, {

“cell_type”: “markdown”, “id”: “c88c699d”, “metadata”: {

“lines_to_next_cell”: 0, “papermill”: {

“duration”: 0.012494, “end_time”: “2021-08-17T08:14:28.914923”, “exception”: false, “start_time”: “2021-08-17T08:14:28.902429”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## F$_{\nu}$n”, “n”, “The F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “8f1f38ae”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:29.121449Z”, “iopub.status.busy”: “2021-08-17T08:14:28.998720Z”, “iopub.status.idle”: “2021-08-17T08:14:29.736571Z”, “shell.execute_reply”: “2021-08-17T08:14:29.737391Z”

}, “papermill”: {

“duration”: 0.809605, “end_time”: “2021-08-17T08:14:29.737853”, “exception”: false, “start_time”: “2021-08-17T08:14:28.928248”, “status”: “completed”

}, “tags”: []

}, “outputs”: [

{

“data”: {

“image/png”: 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n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid * func(energy_grid), red)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"energy flux (F$_{\nu}$)")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”, “n”

]

}, {

“cell_type”: “markdown”, “id”: “1649fd54”, “metadata”: {

“papermill”: {

“duration”: 0.013758, “end_time”: “2021-08-17T08:14:29.765810”, “exception”: false, “start_time”: “2021-08-17T08:14:29.752052”, “status”: “completed”

}, “tags”: []

}, “source”: [

“## $\nu$F$_{\nu}$n”, “n”, “The $\nu$F$_{\nu}$ shape of the photon modeln”, “if this is not a photon model, please ignore this auto-generated plot”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “fd3a1bbe”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2021-08-17T08:14:29.850909Z”, “iopub.status.busy”: “2021-08-17T08:14:29.843401Z”, “iopub.status.idle”: “2021-08-17T08:14:30.506097Z”, “shell.execute_reply”: “2021-08-17T08:14:30.507024Z”

}, “papermill”: {

“duration”: 0.728265, “end_time”: “2021-08-17T08:14:30.507422”, “exception”: false, “start_time”: “2021-08-17T08:14:29.779157”, “status”: “completed”

}, “tags”: []

}, “outputs”: [

{

“data”: {

“image/png”: 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OzdUv8rumv+lRMDKgqFhEEizb4+fO4gv4bt44ABY2vMXdtatpi3S6nEwkPlQsIi5wHIev5p3E/SWTyfeks7ptK9cGl7C9s8HtaCKHTMUi4qJTMwazMDANk1bCtnAD1waX8FrbVrdjiRwSFYuIy8p8OcwLTGFq9ihao53cUfMqjzds0OiUkrJULCJJwO/4uLFwArcUTiANL882vcsPqldS39XudjSRg6ZiEUkiU7JH8UjgUsq8ObzdsZuZwcW8H6pyO5bIQVGxiCSZUf4SFgbKOS19MMGuFq4PLmdps9UtyZIyVCwiSSjfm8FPSy7i67kn0UmEufXreLBuLR1RjU4pyU/FIpKkvI6Hb+eP4+7iSWQ7aaxo/ZDvB5fzuUanlCSnYhFJchMyh7EgUM7RvgI+7KxhZnAJb7XvdDuWyH6lXLEYY841xvyu+2ui23lEEmFoWj7zA1O5IHMEjZEObq1eydONFRqdUpJSUhWLMWaMMWZpj+nZxphtxpiPjTFXds8+F/hX4DvAVW7kFHFDpieNOUXnMiv/TDw4PNm4idtqVtGs0SklySRNsRhj7gOW9ZgeDcwARgPjgfuNMfnW2h8DBcD9wBMuRBVxjeM4XJ47mrmlUyjyZLKu/TOuCS7hk85at6OJ/J3P7QA9vAE0ESsRgHLgGWttC9BijFkDXGCMaex+bY619qDGejXGFAPFPef95je/8Xq93kMOL5JIY9LLeKxsGnfWvMrfQkFmBZdxU+EEJmWNcDuaSPIcsVhrlxArlz2GA9t6TO8ABgPfBwqBnxljrj7IzVwH2J5fzc3NxW1tbX2NLeKaYm8Wc0uncHnO8bRHw9xTu4b59esJ61Ew4rJkOmLZWwTo3Gs6bK2dfgjrfAT4bc8ZOTk56/x+f/F+3i+S1HyOh1kF4zneX8rP6tbxfPP7fBiq5bbiiRR7s9yOJ4epZC6WHcDQHtNDgNcOZYXdp87+4fTZxo0buxzHOZTVirhuUtZIjk4r5PaaV3g3VMmMysXcXnw+Y9LL3I4mh6GkORW2D4uArxhj0o0xQ4AzOcRiERnIRqQVsSBQzlkZR1IbaePGqhU837RZj4KRhOtTsRhjzjHGfMsYMyregfaw1m4GngXeA14FbrLW6mKIyBfI8aRzd/EkvpV3KhGizG94k5/UvqbRKSWhnL78a8YY80PgE2AasMVae2e8gyXKxo0bK30+X2Ds2LFuRxGJq7fad3JP7RoaIx0c7SvgzuILGJqW73YsGQAqKioIh8PBcePG7fNca5+OWKy1P7XW/l9r7deB3caYnx1SShGJu9MzhrAwUM6xacV8Gq7nmuBS1rVtdzuWHAb6dPHeGPMLYBBwBJAGaDQikSR0hC+XhwOXMq9uPS+0fsiPalbxjdyxXJV3Ml4nmS+xSirr611hjwK7gUprrW6aF0li6Y6PWwonMNpfyiP163m6qYIPQlXMKTqPfG+G2/FkAOpTsVhrN8U7iIj0H8dxmJpjGOkv4o6aV9jQsYuZwSXcWXw+o/wlbseTAeYLj4WNMdv3mp7Wv3FEpD8d7y/lscB0TkkfRGVXM9cFl7OiZYvbsWSAOdBJ1qF7Tf+xv4KISGIUeDO4v2QyV+aOoZMuHqhby9y6tYSiXW5HkwHiQMWy973I+oi6yADgdTxcnX8adxSfT5aTxtKWLcwOLicYbnY7mgwAB3tbiD7CKzKAnJs5nEcDUznKl4/trGZGcDGb2ne5HUtS3AEv3htjSokdqTh7TQNgrQ32WzoR6XfD0gr4eaCcB+reYE3bVn5QvZJv553Klblj0HP0pC8OdMTiAJ93f+3qnq7sMW93v6YTkYTI8qRxW9FEZuafDsATjRu5veYVWiIhl5NJKvrCIxZrrQfAGJPN/x+A63VrrfY2kQHGcRz+JfdERqUVc3ftat5o38624FLuLL6A4WkFbseTFNLbayxbgduAnwI7jTFX9FsiEXHVyRmDWBiYxmh/KZ+FG7g2uITVrZ+6HUtSSG+LZaS19jxr7RnAFOBOY8yt/ZhLRFxU6svmodIpTM8+jvZomLtqV7Og/i90aXRK6YVeFYu1trHH9xuAM4BJxph/769gIuKuNMfL7MKzuLXwHPx4ea75PW6uepHaLo1eIV+sV490McZcTGwExz1fQ7u/7gZ+0m/pRMR1k7OPYUT36JQVoc+ZUbmYO4rP54T0gNvRJEn19lTYAuBbwBigDVgD/BiY1E+5RCSJHOMvZmHZNM7MGEpNpJUbqlawqPl9jU4p+9SrIxZr7Yj+DiIiyS3Xk849xRfym6Z3eKrxHebVr+f9UBXXF3yJDE9fH5QuA5EGZBCRXvM4DlflncI9xReS4/hZ2fox11UtY1e4ye1okkQGbLEYY3zGmIuMMQ+4nUVkoBmfeSQLysoZmVbEx521zKxczPq2z9yOJUkiocXS/cv+cWOMNcZsNsZM7cM6xhhjlu41b7YxZpsx5mNjzJXds0cAE9GDM0X6xRBfHo+UXsZFWSNpjoaYU/Myv254m4iuuxz2En3EchWQBRwHXATM7/5Uf68YY+4Dlu01bzQwAxhN7OkA9xtj8q21W4An4hVcRP6nDI+PWwvPYXbBeLx4eKrpHebUvExTpMPtaOKiRBfLqcAL1tqotXYnseeOnQVgjMkzxpyz543GmMHGmHF7Lf8G8Phe88qBZ6y1LdbaKmJ3rF1woCDGmC8bY37b2tqaFwrpCTUifeU4DtNzjueh0ikUe7J4s30HMyuX8FGoxu1o4pJEF8tm4BJjjNcYcxxwArDnZvgC4BfGmLONMSXAC8Dwngtba5cQK5eehgPbekzvAAYfKIi19g/W2q9lZWU1+v3+PvwoItLTCekBHiubxlj/EezuamJWcBkrWz5yO5a4INHF8gTQBPwNeAjYCLQAWGu3A9OBXwGrgLnW2j/0Yp0RoHOv6XD3Ordaa2+OW3oR+UJF3kweKL2YK3JOIEQX99a9zry6P9Op0SkPK4kulqHAPdba4621U4gdrfytx+vbgRpgELChl+vcwT8OoTyE2EMzRcQFPsfDNQVncFvRRDIcH4taPuCGqhVUdbW4HU0SJNHFch5wP4Ax5jKg1Vr7cfd0GvB7YDlwBbDIGDOyF+tcBHzFGJNujBkCnAm81h/hRaT3JmYdzaOBcob68tgcqmJm5WIqOj53O5YkQKKL5b8BnzHmU+Au4H/3eG0EsNFae5e1dg0wi15chLfWbgaeBd4DXgVustbqKXkiSWB4WgELAuWcnTGMukg7N1W9wHNNf9OjYAY453D/C964cWOlz+cLjB071u0oIgNWNBrl2aZ3+WXjJiJEmZg5nFsKzybTk+Z2NOmDiooKwuFwcNy4cWX7en3AfvJeRJKH4zh8Ne8k7iuZTL4nndVtW7k2uITtnQ1uR5N+oGIRkYQZlzGYhYFpmLQStnWPTvla21a3Y0mcqVhEJKHKfDnMC0xhavYoWqOd3FHzKo83bNDolAOIikVEEs7v+LixcAK3FE4gDS/PNr3Lv1WvpL6r3e1oEgcqFhFxzZTsUTwSuJQybw6bOnYzM7iY90NVbseSQ6RiERFXjfKXsDBQzrj0wQS7Wrg+uJylzVa3JKcwFYuIuC7fm8G9JRfx9dyT6CTC3Pp1PFi3lo5o2O1o0gcqFhFJCl7Hw7fzx3F38SSynTRWtH7I94PL+VyjU6YcFYuIJJUJmcNYECjnaF8BH3bWMDO4hLfad7odSw6CikVEks7QtHzmB6ZyQeYIGiMd3Fq9kqcbKzQ6ZYpQsYhIUsr0pDGn6Fy+l38GHhyebNzEbTWraNbolElPxSIiSctxHL6cewI/K72EIk8m69o/45rgEj7prHU7mnwBFYuIJL2T0o/gsbJpnOgPsDMcG51yVesnbseS/VCxiEhKKPZmMbd0CpfnHE97NMw9tWuYX/8mYT0KJumoWEQkZfgcD7MKxjOn6FwyHB/PN2/mpqoXqOlqdTua9KBiEZGUMylrJPMDlzHYm8u7oUpmVC7m3Y5Kt2NJNxWLiKSkEWlFLCwr56yMI6mNtHFj1Qqeb9qsR8EkARWLiKSsHE86dxdP4lt5pxIhyvyGN/lJ7Wu0RTrdjnZYU7GISErzOA7fyBvLvSWTyfOks6rtE2YFl7JDo1O6RsUiIgPC6RlDWBgo59i0Yj4N13NNcCnr2ra7HeuwNGCLxRjjM8ZcZIx5wO0sIpIYR/hyeThwKZdkHUtLNMSPalbxZMMmjU6ZYAkvFmPMQ8aYD4wxW4wx3+7D8mOMMUv3mjfbGLPNGPOxMebK7tkjgImAc+ipRSRVpDs+bimcwI0FXyIND083VfDD6pdo0OiUCZPQYjHGTALGAKOBs4B7jTG5B7H8fcCyveaNBmZ0r3M8cL8xJt9auwV4Il7ZRSR1OI7D1BzDfwYupdSbxYaOXcwMLmFLqNrtaIeFRB+xdAEZgB/IAUJAJ4AxJs8Yc86eNxpjBhtjxu21/BvA43vNKweesda2WGurgDXABf2UX0RSyPH+Uh4LTOeU9EFUdjVzXXA5K1q2uB1rwEt0sawBIkAl8BHwlLV2z/FpAfALY8zZxpgS4AVgeM+FrbVLiJVLT8OBbT2mdwCDDxTEGPNlY8xvW1tb80Kh0MH/JCKSEgq8GdxfMpkrc8fQSRcP1K1lbt1aQtEut6MNWIkulu8A9UCAWCF8tftUFtba7cB04FfAKmCutfYPvVhnhO6jnh7T4e51brXW3ryvhay1f7DWfi0rK6vR7/f37acRkZTgdTxcnX8adxSfT6bjY2nLFmYHlxMMN7sdbUBKdLGcCjxnre2w1u4EXgFO6PH6dqAGGARs6OU6dwBDe0wPAbYeelQRGWjOzRzOgkA5R/nysZ3VzAguZlP7LrdjDTiJLpYKYIoxxmOMyQFO756HMSYN+D2wHLgCWGSMGdmLdS4CvmKMSTfGDAHOBF7rl/QikvKGpRXw80A552UOpyHSwQ+qV/JM41/1KJg4SnSxPAHUAR8AbwE/7757C2K3B2+01t5lrV0DzKIXF+GttZuBZ4H3gFeBm6y1bf0RXkQGhixPGrcVTWRm/ukAPNG4kdtrXqElouut8eAc7i29cePGSp/PFxg7dqzbUUTEBe+07+bu2tXURdo50pfPncUXMDytwO1YSa2iooJwOBwcN25c2b5eH7CfvBcR6Y2TMwaxMDCN0f5SPgs3cG1wCatbP3U7VkpTsYjIYa/Ul83c0ilMzz6O9miYu2pXs6D+L3oUTB+pWEREAL/jZXbhWdxaeA5+vDzX/B43V71IbZcu2R4sFYuISA+Ts49hfuAyBnlzqAh9zozKxWzuCLodK6WoWERE9nKMv5iFZdM4M2MoNZFWrq9awaLm93VLci+pWERE9iHXk849xRfyr7knEybCvPr13Ff3Ou2RsNvRkp6KRURkPzyOwzfzT+EnxReS4/hZ2fox11UtY1e4ye1oSU3FIiJyAOMzj2RBWTkj04r4uLOWmZWLWd/2mduxkpaKRUSkF4b48nik9DIuyhpJczTEnJqX+a/Gt4nousv/oGIREemlDI+PWwvPYXbBeLx4+K/Gd5hT8zJNkQ63oyUVFYuIyEFwHIfpOcfzUOkUij1ZvNm+g5mVS/goVON2tKShYhER6YMT0gM8VlbOSf4ydnc1MSu4jJUtH7kdKymoWERE+qjIm8WDpZdwRc4JhOji3rrXmVf3ZzoP89EpVSwiIofA53i4puAMbiuaSIbjY1HLB9xQtYKqrha3o7lGxSIiEgcTs47m0UA5Q315bA5VMbNyMRUdn7sdyxUqFhGROBmeVsCCQDlnZwyjLtLOTVUv8FzT3w67R8GoWERE4ijb4+fO4gv4bt44ABY0vMXdtatpi3S6nCxxVCwiInHmOA5fzTuJ+0omk+9JZ3XbVq4NLmF7Z4Pb0RJCxSIi0k/GZQxmYWAaJq2Ebd2jU77ets3tWP1OxSIi0o/KfDnMC0xhavYoWqOd3F7zCk80bBjQo1OqWERE+pnf8XFj4QRuKZxAGl6eaXqXf6teSX1Xu9vR+oWKRUQkQaZkj+LhwKUEvNls6tjNzOBi3g9VuR0r7lQsIiIJZPwlPBaYxrj0wQS7Wrg+uJylzXZA3ZKsYhERSbB8bwb3llzE13NPopMIc+vX8WDdWkLRgTE6pYpFRMQFXsfDt/PHcXfxJLKdNFa0fsj3g8v5fACMTjlgi8UY4zPGXGSMecDtLCIi+zMhcxgLAuUc7StgS2cNM4NLeKt9p9uxDokvkRszxtwNXNFjVjbwkLV27kGsYwzwU2vt1B7zZgM3AmFgjrX2WWAEMBFw4hBdRKTfDE3LZ35gKg/WreXVtk+5tXol/yfvVL6WexIeJ/V+hSX0iMVa+x/W2uOstccBZwK7gad6u7wx5j5g2V7zRgMzgNHAeOB+Y0y+tXYL8ETcwouI9KNMTxo/KjqP7+WfgQeHJxs3cVvNKppTcHRKN0+F/Rh41FpbDWCMyTPGnLPnRWPMYGPMuL2WeQN4fK955cAz1toWa20VsAa44EAbN8Z82Rjz29bW1rxQKHRIP4iISDw4jsOXc0/gZ6WXUOTJZF37Z1wTXMInnbVuRzsorhSLMWYYcCHwdI/ZBcAvjDFnG2NKgBeA4T2Xs9YuIVYuPQ0Hej4jYQcw+EAZrLV/sNZ+LSsrq9Hv9x/sjyAi0m9OSj+Cx8qmcaI/wM5wbHTKVa2fuB2r19w6YpkJ/Nxa+/d766y124HpwK+AVcBca+0ferGuCNC513S4e51brbU3xy21iEiCFHuzmFs6hctzjqc9Guae2jXMr3+TcAo8CibhxWKM8QLfBH63j5e3AzXAIGBDL1e5AxjaY3oIsLXvCUVEkoPP8TCrYDz/XnQu6Y6X55s3c1PVC9R0tbod7Qu5ccRyCvCptTbYc6YxJg34PbCc2J1ji4wxI3uxvkXAV4wx6caYIcRuCngtzplFRFxzYdZI5pdOZbA3l3dDlcyoXMy7HZVux9ovN4rlHOCtfcwfAWy01t5lrV0DzKIXF+GttZuBZ4H3gFeBm6y1bXHMKyLiupH+IhaWlXNWxpHURtq4sWoFzzdtTspHwTjJGCqRNm7cWOnz+QJjx451O4qIyAFFolF+2/RXftW4iSgwKXMENxZ+iUxPWsIyVFRUEA6Hg+PGjSvb1+sD9pP3IiIDkcdx+EbeWO4tmUyeJ51VbZ9wXdUydoYb3Y72dyoWEZEUdHrGEBYEyjkmrYhPOuuYWbmEdW3b3Y4FqFhERFLWIF8ujwQu45KsY2mJhvhRzSqebNjk+uiUKhYRkRSW7vi4pXACNxZ8iTQ8PN1UwQ+rX6LBxdEpVSwiIinOcRym5hj+M3Appd4sNnTsYmZwCVtC1a7kUbGIiAwQx/tLeSwwnVPSB1HZ1cx1weWsaPkw4TlULCIiA0iBN4P7SyZzZc6JdNLFA3VvMLduLaFoV8IyqFhERAYYr+Ph6oLTuaPofDIdH0tbtjA7uJxguDkh21exiIgMUOdmDWdBoJyjfPnYzmpmBBezqX1Xv29XxSIiMoANSyvg54FyzsscTkOkgx9Ur+SZxr/266NgVCwiIgNclieN24omMjP/dACeaNzIHbWv0hLpn0EOVSwiIocBx3H4l9wTebDkYgo8Gbzeto1rg0v75VEwKhYRkcPIyRmDeCwwjdH+UjqjXeQ48R9B1xf3NYqISFIr9WUzt3QK1V2t5Hsz4r5+HbGIiByG/I6Xwb7cflm3ikVEROJKxSIiInGlYhERkbhSsYiISFypWEREJK5ULCIiElf6HAsUh8NhKioq3M4hIpISwuEwQPH+XlexQDgajTrhcHi/Q62FQqF0v9/fsff8SCTihEKhLL/f3+rxePrviW79YH8/UzJv51DWdbDL9vb9vXnfgd6j/St5ttXXdfXX/tWb97q0fxUD4f2+Go1GD/uvUaNG/bYvr4+KiY4aNWqU2z9DvH/mZNzOoazrYJft7ft78z7tX6mzrb6uq7/2r968Nxn3L11jERGRuFKxxPzhEF9PRYn6meK5nUNZ18Eu29v39+Z92r9SZ1t9XVd/7V+9eW/S7V9ONJpSp26TijFmFGABY63d4nYeGVi0f0l/6s/9S0csIiISVyqWQ1MD3Nn9X5F40/4l/anf9i+dChMRkbjSEYuIiMSVikVEROJKxSIiInGlYhERkbhSsYiISFypWEREJK5ULCIiElcqFhERiSsVi4iIxJWKRURE4kojSMaRMeZcYFb35KPW2tUuxpEBxhgzCbgayAJ+Yq39s8uRZIAxxmQDG4DTrbXNfV2Pjlh6yRgzxhiztMf0bGPMNmPMx8aYK7tnnwv8K/Ad4Co3ckpq6uX+dSLwdeB24EI3ckpq6uX+BXAb8Mmhbk/F0gvGmPuAZT2mRwMzgNHAeOB+Y0y+tfbHQAFwP/CEC1ElBR3E/jUPmAD8AnjDjaySenq7fxljvgm8AFQd6jZ1Kqx33gCaiP0lAJQDz1hrW4AWY8wa4AJjTGP3a3OstXrUufRWb/evQmvtk8aY8cB/Aa+6E1dSTK/2L+B8oA44g9gp/Xv7ukEdsfSCtXYJ//gvxOHAth7TO4DBwPeBQuBnxpirExZQUtpB7F+OMeZp4LfAMwkLKCmtt/uXtfYqa+31wF+A+YeyTR2x9E0E6NxrOmytne5SHhlY9rd//RL4pTuRZADZ5/61Z8Ja+81D3YCOWPpmBzC0x/QQYKs7UWQA0v4l/anf9y8VS98sAr5ijEk3xgwBzgReczmTDBzav6Q/9fv+pWLpA2vtZuBZ4D1iF1Bvsta2uZtKBgrtX9KfErF/acx7ERGJKx2xiIhIXKlYREQkrlQsIiISVyoWERGJKxWLiIjElYpFRETiSo90EUlhxpjZwOdAJbDQWnvcQSz7BrDKWnt7j3kOsB24g9iTlH9srT3kx6jL4UVHLCIJZozxxmk9ecTG//l9H1fxDHD5XvPOAALAH4CHiQ0BIXJQdMQish/dIzY+BIwkNp7FTGttrTHm10CQ2L/oxwIrga9Za9uNMcOJjcVzFrFPNl9tra3oHuviy0CU2D/ophpjbgD+DWgD/hs4G5hGbDyME621H3bnWAv80lr75F4Rvwv8yVrbZYzpmbsIeB143Fo7zxhzMrAQGAOsA75jrd0GPAfMM8Ycu2dbxIpmhbW2HnjHGHOUMeYEa+17h/anKYcTHbGI7IMx5khij6e/FjgKCAEP9HjLVd2vjSD2C/tKY4wHWAw8T+wx9/9N7F/+e1wKPAX8szHmfGA2sTKZCPwTgLW2EXil+70YY4qB07rXu7cvd7+3Z+4sYiX4p+5SyemevpfYwwY3AU92bysIrOpezx7/zD8+kv9VQE/tloOiYhHZt68Dv7fWvmGtrQbmEPulu8dvrLUV3b+c1xJ7WuwZgNdau8Ba22itfRjwGmNO6l5mg7X299baTuBrxI4oPuo+epjbY91/BC7r/v4SYG13hr/rLrHTiR0V7eEjdlqs3lo7p3veVOAda+2fuo9C/gM4yxhT0P3630+HGWPGAIP4xxKrIHb0JdJrOhUmsm9HATONMdf2nGmMyej+tucv+nZi/y8dBYw2xuz9AL7B3f+t6zFvGLCmx/SOHt8vAv7TGJNN7Mjl+X3kKyb2D8OGHvNGAjXAMcaYImttbXemS/eR6QignliJLeg+QrscWLTXAwlriR3piPSajlhE9q0SeMBa61hrHSADOMVa236AZd7as0z3cmP5xwLZo4XYRfI9hu35xlpbCbwNTO7+WrSP5X2A0/21xw5ip9bWA3f3yPRcjzweYqfWtnRvqwFYQaxULud/jkwZJTYQlEiv6YhFZN9+Dyw3xvyG2O23PyF25PHPX7DMeiBgjPkn4CViv6jvA47ex3tXAbOMMb8n9v/hzcRuG97jj8DtwCfW2s/2sXwVsYv+BcSOKgBarLWdxphbgE3GmAXESuOnxpizgXeA64ArrLWn9ljXM90/XyGxGxF6KiB2o4JIr+mIRWQfrLV/I3bH1p+AXXSfGjvAMu3ELsL/kNgv/huAy621Hft4+2PAi8SuYbza/X24x+t/JHa087v9bCsMvAWctI/XNgO/AuZ1H/18g9hdYUFgCnDlXossJXZqbM/1n55OJFaYIr2m8VhEXGCMKYO/n/bCGDMDON9ae2X3tI/Y9ZLR1tqd+1nHLUCetfY/+jHnWmK3Wb/bX9uQgUdHLCLumAIsM8aUGWOOJXbr8UsAxphM4KvE7gbbZ6l0ewK4rLuE4s4YcyJQp1KRg6ViEXHH08Qu0G8B3iR2KuzX3a89DPyU2K3B+9V9+/Dj/M9TW/FyPbHTgSIHRafCREQkrnTEIiIicaViERGRuFKxiIhIXKlYREQkrlQsIiISV/8PoEBLpDi0dzgAAAAASUVORK5CYII=n”, “text/plain”: [

“<Figure size 432x288 with 1 Axes>”

]

}, “metadata”: {

“needs_background”: “light”

}, “output_type”: “display_data”

}

], “source”: [

“fig, ax = plt.subplots()n”, “n”, “ax.plot(energy_grid, energy_grid**2 * func(energy_grid), color=green)n”, “n”, “n”, “ax.set_xlabel("energy (keV)")n”, “ax.set_ylabel(r"$\nu$F$_{\nu}$")n”, “ax.set_xscale(x_scale)n”, “ax.set_yscale(y_scale)n”

]

}

], “metadata”: {

“jupytext”: {

“formats”: “ipynb,md”

}, “kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

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