- {
- “cells”: [
- {
“cell_type”: “markdown”, “id”: “7bf73e77”, “metadata”: {}, “source”: [
“# Priors for Bayesian analysisn”, “n”, “Astromodels supports the definition of priors for all parameters inn”, “your model. You can use as prior any function (although of course notn”, “all functions should be used this way, but the choice is up to you).n”, “n”, “First let’s define a simple model containing one point source (see then”, ““Model tutorial” for more info):”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “id”: “ea0188e7”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:15.541101Z”, “iopub.status.busy”: “2021-08-17T08:10:15.540004Z”, “iopub.status.idle”: “2021-08-17T08:10:18.095714Z”, “shell.execute_reply”: “2021-08-17T08:10:18.096363Z”
}, “lines_to_next_cell”: 2
}, “outputs”: [], “source”: [
“%%capturen”, “from astromodels import *n”, “n”, “# Create a point source named "pts1"n”, “pts1 = PointSource(‘pts1’,ra=125.23, dec=17.98, spectral_shape=Powerlaw())n”, “n”, “# Create the modeln”, “my_model = Model(pts1)”
]
}, {
“cell_type”: “markdown”, “id”: “9705eedc”, “metadata”: {}, “source”: [
“Now let’s assign uniform priors to the parameters of the powerlawn”, “function. The function uniform_prior is defined like this:n”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “id”: “cf1437d4”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:18.119756Z”, “iopub.status.busy”: “2021-08-17T08:10:18.118050Z”, “iopub.status.idle”: “2021-08-17T08:10:18.124757Z”, “shell.execute_reply”: “2021-08-17T08:10:18.125872Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“<ul>n”, “n”, “<li>description: A function which is constant on the interval lower_bound - upper_bound and 0 outside the interval. The extremes of the interval are counted as part of the interval.</li>n”, “n”, “<li>formula: $ f(x)=\begin{cases}0 & x < \text{lower_bound} \\\text{value} & \text{lower_bound} \le x \le \text{upper_bound} \\ 0 & x > \text{upper_bound} \end{cases}$</li>n”, “n”, “<li>default parameters: n”, “<ul>n”, “n”, “<li>lower_bound: n”, “<ul>n”, “n”, “<li>value: 0.0</li>n”, “n”, “<li>desc: Lower bound for the interval</li>n”, “n”, “<li>min_value: -inf</li>n”, “n”, “<li>max_value: inf</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>upper_bound: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Upper bound for the interval</li>n”, “n”, “<li>min_value: -inf</li>n”, “n”, “<li>max_value: inf</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “<li>value: n”, “<ul>n”, “n”, “<li>value: 1.0</li>n”, “n”, “<li>desc: Value in the interval</li>n”, “n”, “<li>min_value: None</li>n”, “n”, “<li>max_value: None</li>n”, “n”, “<li>unit: </li>n”, “n”, “<li>is_normalization: False</li>n”, “n”, “<li>delta: 0.1</li>n”, “n”, “<li>free: True</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”, “n”, “</li>n”, “n”, “</ul>n”
], “text/plain”: [
“<IPython.core.display.HTML object>”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“Uniform_prior.info()”
]
}, {
“cell_type”: “markdown”, “id”: “76224d75”, “metadata”: {}, “source”: [
“We can use it as such:”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “id”: “ea7f9eeb”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:18.137469Z”, “iopub.status.busy”: “2021-08-17T08:10:18.135230Z”, “iopub.status.idle”: “2021-08-17T08:10:18.144613Z”, “shell.execute_reply”: “2021-08-17T08:10:18.145375Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“Parameter K = 1.0 [1 / (cm2 keV s)]n”, “(min_value = 1e-30, max_value = 1000.0, delta = 0.1, free = True) [prior: Uniform_prior]”
], “text/plain”: [
“Parameter K = 1.0 [1 / (cm2 keV s)]n”, “(min_value = 1e-30, max_value = 1000.0, delta = 0.1, free = True) [prior: Uniform_prior]”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“# Set ‘lower_bound’ to 0, ‘upper bound’ to 10, and leave the ‘value’ parametern”, “# to the default valuen”, “pts1.spectrum.main.Powerlaw.K.prior = Uniform_prior(lower_bound = 0, upper_bound=10)n”, “n”, “# Display itn”, “pts1.spectrum.main.Powerlaw.K.display()n”
]
}, {
“cell_type”: “markdown”, “id”: “de853643”, “metadata”: {}, “source”: [
“Now, lets’s set a Gaussian prior on the spectral index”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “id”: “5da12de7”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:18.155872Z”, “iopub.status.busy”: “2021-08-17T08:10:18.154636Z”, “iopub.status.idle”: “2021-08-17T08:10:18.163796Z”, “shell.execute_reply”: “2021-08-17T08:10:18.165260Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/html”: [
“Parameter index = -2.01 []n”, “(min_value = -10.0, max_value = 10.0, delta = 0.20099999999999998, free = True) [prior: Gaussian]”
], “text/plain”: [
“Parameter index = -2.01 []n”, “(min_value = -10.0, max_value = 10.0, delta = 0.20099999999999998, free = True) [prior: Gaussian]”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“n”, “pts1.spectrum.main.Powerlaw.index.prior = Gaussian(mu=-2, sigma=1)n”, “n”, “pts1.spectrum.main.Powerlaw.index.display()”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “id”: “3e22c1d4”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:18.177377Z”, “iopub.status.busy”: “2021-08-17T08:10:18.176335Z”, “iopub.status.idle”: “2021-08-17T08:10:18.754235Z”, “shell.execute_reply”: “2021-08-17T08:10:18.753396Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“Text(0, 0.5, ‘Prior’)”
]
}, “execution_count”: 5, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: “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n”, “text/plain”: [
“<Figure size 748.8x655.2 with 1 Axes>”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“# Let’s get 500 points uniformly distributed between -20 and 20n”, “import numpy as npn”, “import matplotlib.pyplot as pltn”, “%matplotlib inlinen”, “from jupyterthemes import jtplotn”, “jtplot.style(context="talk", fscale=1, ticks=True, grid=False)n”, “n”, “n”, “n”, “n”, “n”, “random_points = np.random.uniform(-10,20,100)n”, “n”, “fig, ax = plt.subplots()n”, “n”, “ax.plot(random_points,pts1.spectrum.main.Powerlaw.K.prior(random_points), ‘.’ )n”, “n”, “ax.set_ylim([-0.1,1.2])n”, “ax.set_xlabel("value of K")n”, “ax.set_ylabel("Prior")”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “id”: “5d517f64”, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-08-17T08:10:18.790263Z”, “iopub.status.busy”: “2021-08-17T08:10:18.763946Z”, “iopub.status.idle”: “2021-08-17T08:10:19.023679Z”, “shell.execute_reply”: “2021-08-17T08:10:19.025055Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“Text(0, 0.5, ‘Prior’)”
]
}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: “iVBORw0KGgoAAAANSUhEUgAAAoYAAAIqCAYAAAC5VIvsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA0TklEQVR4nO3deXCUdYLG8ae7YyCBsAREW8JhNCIeQ0SJiigajiTaUcaIwGCQmt0dXUd3VndXZSkBjy0cV5nacrDw2ioSs0jcZRhKw6KuEUFHdHTWDHEkAZWrJQfQAQKE2Ol3//BNm+5cDUn328f3U0UV75HOr99+0/3077QZhmEIAAAACc9udQEAAAAQHQiGAAAAkEQwBAAAgIlgCAAAAEkEQwAAAJgIhgAAAJBkcTAsKSlRbm6uZsyYoYqKik7HX3nlFd10003Kz8/Xpk2bLCghAABA4kiy6hfv2rVL5eXlqqioUEtLi4qKijR16lSlpaVJkv70pz/pv//7v/Xmm2/q+PHjmjt3rvLy8mS3U8kJAAAQDpalrMrKSrlcLqWmpmrYsGHKycnRtm3b/MffeecdzZ49W2lpaXI6nXrppZfk8/msKi4AAEDcs6zG0O12a+LEif5tp9OphoYG//bu3bvV1tamOXPm6OTJk7r//vs1fvz4kB7b4/GoqakpYN/hw4clSUlJlj1lAAAAS3m9XtlsNl155ZVdHrcsJdnt9oCQZrfb5XA4/NstLS365ptv9Nprr+ngwYOaM2eOrr76ag0bNqzXxy4rK9PKlSsD9r322ms0QwMAgITX02rIlgVDp9Opuro6/3Z9fb0mTZrk3x46dKiuvvpqDRgwQBkZGcrKypLb7Q4pGBYXF6uwsDBgn8fjkd1uV3Z2dv89CQAAgBhSVVXV43HLqtCmT5+ujRs3qrW1VfX19aqqqlJOTo7/+JQpU7R161a1tbXp0KFDcrvdyszMDOmx09PTlZmZGfDP4XDIZrOF6+kAAADEPMtqDLOysuRyueRyuWS327V48WLV1tZqxYoVKikpUVFRkb7++mvdcsstGjhwoJYsWaLBgwdbVVwAAIC4ZzN6amiOI+1VpzQlAwCARNVbHmI0BgAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGBKsvKXl5SUaPXq1XI4HHrooYfkcrn8x06ePKm8vDwNGjRIknThhRfqhRdesKqoAAAAcc+yYLhr1y6Vl5eroqJCLS0tKioq0tSpU5WWliZJcrvdmjx5sv7t3/7NqiICAAAkFMuakisrK+VyuZSamqphw4YpJydH27Zt8x93u90aOXKkVcUDAABIOJbVGLrdbk2cONG/7XQ61dDQ4N/ev3+/tm7dqvfee08pKSn653/+Z1199dUhPbbH41FTU1PAvra2NtntdKkEAADojmXB0G63KykpKWDb4XD4tzMyMrRw4UIVFhaqurpav/zlL/XOO+8oNTW118cuKyvTypUrA/atWrVKKSkp/fcEAAAA4oxlwdDpdKqurs6/XV9fr0mTJvm3c3JylJKSIrvdrgkTJujcc8/VgQMHdOGFF/b62MXFxSosLAzY5/F4qDEEAADogWVJafr06dq4caNaW1tVX1+vqqoq5eTk+I8vXrxYr7/+uiTp22+/VVNTk0aPHh3SY6enpyszMzPgn8PhkM1mC8tzAQAAiAeW1RhmZWXJ5XLJ5XLJbrdr8eLFqq2t1YoVK1RSUqKHH35Yjz76qEpLSzVo0CA988wzSk5Otqq4AAAAcc9mGIZhdSEioaqqSpKUnZ1tcUkAAACs0VseotMdAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYLI0GJaUlCg3N1czZsxQRUVFl+e0tbVp9uzZ2rRpU4RLBwAAkFiSrPrFu3btUnl5uSoqKtTS0qKioiJNnTpVaWlpAee9/PLL2rdvn0WlBAAASByWBcPKykq5XC6lpqYqNTVVOTk52rZtm2bOnOk/p6amRl988YVyc3NP67E9Ho+ampoC9rW1tclup+UcgHV8Xq/2rlmr5tqdGpR1oSSpeecu+b7/XvazzlLaxeM0Zv48SfKfN3jcRRozf57sSZa9XQNIIJa907jdbk2cONG/7XQ61dDQ4N/+/vvv9dRTT+mZZ57Rb3/729N67LKyMq1cuTJg36pVq5SSktK3QgNAiDqGwPZwt3fNWrnXrZckHdle3elnjlZ/KZvNJsMwAs8zDMlmIygCCDvL3lnsdruSOryx2e12ORwO//ZLL72kW265RRkZGaf92MXFxSosLAzY5/F4qDEE0O/aA+CxmtqAmj/D59N36zdI+iHc2cxg15tjtTt/CIIdHNz6kU41NgY81tgFd/X/kwGQ8CwLhk6nU3V1df7t+vp6TZo0yb/9ySefqKGhQaWlpWpsbNRHH30kn8+nW265pdfHTk9PV3p6esC+o0eP9l/hAcDUsRaw3dHqLzVgxNkB+46ZtX1d1RR2lDbuIhmG0eN5x7oJmF3VUlKzCOB0WPaOMX36dD3yyCO6++675fF4VFVVpccff9x//LXXXvP/f9GiRbrppptUUFBgQUkBoHvd1wLaArbSxl2k0T+bK5vN9kNINPsYHtu5S0Zrq2zJyRpy8TiN/tncH37aPC9t3EXytbX5ax/bH6srwU3VNptNo382l7AIIGSWvTtkZWXJ5XLJ5XLJbrdr8eLFqq2t1YoVK1RSUmJVsQBA0g+1b3vK1ujghx/JJmn49VM0tnh+p1DVXS3g2TdMkc1u94e70T+bK3tSUshNwB3P83m9sjscAY/VleCQeqx2Z5dhkWZoAN2xGUZQZ5Y4VVVVJUnKzs62uCQAYsHu0rJOTcSjZhd1ClU+r1f7Xi/X0ZraTjV/ka6ZCy7zqNlFOlZTGxBch/zkcqWNu4gaRCBB9ZaHeDcAgC501UTcVd++06kFDLcx8+cFNEG3NyN3DIa+1lZqEAF0i2AIAF3oqom4u7590aKrkBocFo/uqAk43t1AFgCJiWAIAF0YM3+eZBg6uPUjST/0Geyub180Cw6Lu0vLdLT6S/92tIddAJFFMASQMEIdUCL9EKjOX7hA5y9cEPmChlFXzc0A0I5gCCAh+LxebV+8RM01tf59363fILvDkVB97KKpTySA6MNSIAASwt41awNCYTv62AHAj6gxBJAQupuImj52vWNFFSBx8JcNICEEjzJ2pKTIeXM+fexCEDxJ9tHqL3X58qcIh0Ac4q8aQELoatAFwSY0nVZUqanVn/7ufp099QZqD4E4w18zgITAoIsz19WcjqcaD8q9br0ObvlQZ0+9noAIxAkGnwAAejRm/jylXTyuy2OnGhvlXrde+14vj3CpAIQDwRAA0CN7UpIuX/6URs0u0oARI7o8p/GDrap+bJl2l5bJ5/VGuIQA+gv1/gCAXrU3xY/+2Vzte71cjR9s1anGRv/xU42NOtXYyPrLQIwjGAKIOUyfYp3ggHisdqdaDtQFhMQD//O2DMPgdQFiEH+xAGJO8PQp1FBFXsfBPLtLy/yvhyS1HT8u97r1vC5ADCIYAog5naZPYfUSS7VPBXTgf95W2/Hj/v28LkDsIRgCiHrBTceDsi4MmD6F1Uus1V57aBhGQM0hrwsQewiGAKJecNNxRtFPNWp2UcBk1bBeV5OIA4gtBEMAUS+46bh519e6/KnHrSkMusUk4kDsYx5DAFFvcFCTJE2UABAe1BgCiHo0UQJAZBAMAUQ9migBIDIIhgAASzBRORB9+AsEYCnCQeJionIg+vDuC8BShIPEFTzanKX0AOsxKhmApVjFJHEFjzZvX0pv3+vlFpUIAMEQgKWYiiZxjZk/T6NmF8kxaFDAfr4cANahrh5ARAX3KRw1ZzZT0SQoltIDog/BEEBE0acQwZinEogeBEMAEUWfQgRjnkogetDHEEBE0acQAKIXNYYAwoo+hQAQOwiGAMKKPoUAEDtoSgYQVvQpBIDYQTAEEFb0KQSA2EFTMoCwYioSAIgdBEMAYcVUJAAQO2hKBgAAgCRqDAEAMSx4OqQx8+fJnsRHG3Cm+OsBAMQspkMC+hdNyQCAmMV0SED/IhgCAGIW0yEB/YumZABAzAqeDinjzju0u7SMPofAGeKvBQAQs4KnQ9pdWkafQ6APaEoGAMQN+hwCfUMwBADEDfocAn1DUzIAIG6wBCPQNwRDAEDcYAlGoG8IhgCAuMbqKEDo+MsA0CU+TBEvWB0FCB3v8gC6xIcp4gUjlYHQMSoZQJf4MEW8CB6p3HLggHaXlsnn9VpUIiB6EQwBdIlpPxAvxsyfp1GzizRgxAhJ0qnGg3KvW699r5dbXDIg+tCUDMCvY7/CQVkXKqPop2re9TXTfiCmtY9UPlZTq1ONjf79+3/3ezVtr9ZlTy5T0sCBFpYQiB7UGALwa+9XeGR7tb5bv0E2u12XP/W4xi64i4EniHnBteDy+dRcU6u/LH3CmgIBUYh3egB+9CtEPGuf/Hr/734v+Xz+/SfcbusKBUQZagwB+NGvEPGsvUl58EVZAftTMzIsKhEQfagxBODHcmJIBJc9uUx/WfqETrjdSs3I0KVPLrO6SEDUIBgC8GM5MSSCpIEDNeHfnra6GEBUoikZAAAAkgiGAAAAMFkaDEtKSpSbm6sZM2aooqIi4NixY8f0d3/3d8rPz1dhYaHef/99i0oJAACQGCzrY7hr1y6Vl5eroqJCLS0tKioq0tSpU5WWliZJevHFFzVhwgS9+OKL2rlzp+69917l5uZaVVwAAIC4Z1kwrKyslMvlUmpqqlJTU5WTk6Nt27Zp5syZkqTc3FxlZmZKkg4fPuwPjADOTMdVTQaPu0hj5s9j0moAQADLPhXcbrcmTpzo33Y6nWpoaPBvT5o0SZI0b948/d///Z+WLl0a8mN7PB41NTUF7Gtra5PdTpdKJK72VU0k6cj2atlsNkYgAwACWBYM7Xa7kjrUVtjtdjkcjk7nrV27VrW1tVqwYIFcLpeGDh3a62OXlZVp5cqVAftWrVqllJSUPpcbiDXtNYV1m94J2M+qJgCAYJYFQ6fTqbq6Ov92fX29v5ZQkv7lX/5FDz30kM455xyNGzdOo0eP1qFDh0IKhsXFxSosLAzY5/F4qDFEQupYU9gRq5oAoaEbBhKJZUlp+vTp2rhxo1pbW1VfX6+qqirl5OQEnNM+Uvnbb7+Vx+PR6NGjQ3rs9PR0ZWZmBvxzOByy2Wz9/jyAaBe8/rFj8CCNml3EqiZAiNq/XB3ZXi33uvXa93q51UUCwsayYJiVlSWXyyWXy6W7775bixYtUm1trRYuXChJevDBB/X+++8rPz9f//RP/6Snn35aycnJVhUXiFnB6x+fV5CvsQvuosYDCFHwl6vGD7bK5/VaVBogvGyGYRhWFyISqqqqJEnZ2dkWlwSILJ/Xq32vlwesf0woBEK3u7SsU3eMUbOLGLyFmNRbHuLTAYhzrH8M9M2Y+fN0cMtWnWo86N/H4C3EK0ZjAHHG5/Vqd2mZqh9bpt2lZTR5AX1kT0rS2VNvCNjH4C3EK2oMgTjDfIVA/xszf55sNpuO1e7U4KwL5WtrU/VjyxiljLjDnQzEmeCO8jR5AX3XsUvG7tIyffe730viyxfiD03JQJwJHoVMkxfQv/jyhXhGjSEQ44In3x01Z7a/yat9FDKA/jN43EU6sr3av82XL8QTgiEQ4+hTCERWx/6GfPlCvCEYAjHM5/WqccvWgH00awHhxRRQiGf0MQRi2N41a9XaYW41iWYtAMCZIxgCMSy4E/yAESNo1gIAnDGCIRDDgkcgj7jxBuZTAwCcMT5BgBjCCGQAQDgRDIEYwghkAEA40ZQMxBAm1gUAhBM1hkAMYWJdIHYEd/1gTWXEAu5QIIYwsS4QO+j6gVhEMARiCBPrArGDrh+IRfQxBAAgDIKnk6LrB2IBNYYAAIRBx64fg7MulK+tTdWPLaO/IaJar3elYRg6fPiwhg8fHonyAAAQFzp2/dhdWqbvfvd7SfQ3RHTrtSnZZrOpoKBAx48fj0R5gITn83q1u7RM1Y8t0+7SMvm8XquLBKCP6G+IWBFSH8P7779fy5Yt07fffqtDhw4F/APQv9pHMh7ZXi33uvXa93q51UUC0Ef0N0SsCKmDw69//WtJ0ltvvRWw32az6auvvur/UgEJjJoFIP4w1RRiRUjBcMeOHeEuBwATk1gD8YepphArQh4S9fHHH+vtt9/W4cOHlZGRoVmzZmn8+PHhLBuQkKhZAABYJaQ+huXl5Vq0aJGcTqeuv/562Ww2/c3f/I3eeOONcJcPSDjtNQuXP/W4xi64iyktAAARE9InziuvvKIXX3xRl1xyiX/fzTffrL//+7/XnDlzwlY4IBGwnioAIFqE9Olz7NgxjRw5MmDf2LFjdfTo0bAUCkgkrKcKAIgWITUlT5s2TUuXLtX+/fslSQ0NDVq+fLkmT54c1sIBiYBRyACAaBFSMFyyZImGDBkil8ulyy67TLm5uTpx4oSeeuqpcJcPiHvMbwYAiBY2wzCMUE9uXx7vr/7qr5QUY32gqqqqJEnZ2dkWlwQI5PN6te/18oBRyPQxBACEQ295qMdPn1/84hd65ZVX9Ld/+7ey2WxdnvPKK6/0sYhAYmN+MwBAtOgxGN5yyy2SJJfLFZHCAPGOEcgAgGjW4yfS7bffLp/Pp7ffflsvvPCCHA5HpMoFxCVGIAMAolmvg0/sdrtsNpsqKysjUR4gLvm8Xu0uLVPdpncC9jMCGQAQTUJqwzIMQw8++KAuv/xyDRkyJOAYfQyBnvm8Xm1fvETNNbWdjjECGQAQTUIKhvn5+crPzw93WYC4tHfN2k6h0DF4kM4ryGcdZABAVOk1GH799dcaNGiQxo8frzFjxkSiTEBcCZ7AWpLOK8inbyEAIOr02Mdw7dq1mj17tl566SUVFRVp3bp1kSoXEDc6TWB98ThqCgEAUanHGsOXXnpJr776qq666ip98sknWrRoke64445IlQ2IC2Pmz5PNZmMCawBA1Ovx06murk5XXXWVJGnSpEmqq6uLSKGAeMIE1gCAWBHSWsmSmMMQAAAgzvXannX48GEZhqH2JZXbt9sNHz48fKUDYgwrmwAAYlmPn1iGYWjKlCkBQfC6667z/99ms+mrr74KX+mAGBI8XyErmwAIF76EIlx6vIt27NgRqXIAMa+r+QpZ2QRAOLC8JsKFrxdAH7V/cw9e7k5iZRMA4RE8PypfQtFfCIZAH/S43B3zFQIIk8HjLtKR7dX+bb6Eor8QDIE+6G25O/r8AAiHruZHBfoDn1pAH7DcHQArMD8qwoVgCJyB9n6FJ4Mmfaf5GAAQywiGwBnoOCJQkgaMGKERN95A8zEAIKbxCQacgeAm5IHnOWnWARAVmOMQfcGdApwBRgQCiFbMcYi+IBgCZ4ARgQCiFXMcoi8IhsAZYEQggGhFiwb6gmAIAEAcoUUDfUEwBAAgjnRs0fB5vdpTtkYHP/xINknDr5+iscXzGYyCbnFnAD1gdB+AWLZ3zVp9t36Df/u79RtkdzjoCoNu8QkH9IDRfQBiWVerMzEYBT2xW10AIJoxug9ALBvcxcCTlgN12l1aJp/Xa0GJEO0IhkAPgt9UGd0HIJaMmT9PGUU/1YARI+RISZEknWpslHvdeu17vdzi0iEa0ZQM9IDRfQBimT0pSecvXKDzFy5Q9WPLAqaxoQUEXbE0GJaUlGj16tVyOBx66KGH5HK5/Me8Xq8ee+wxffzxxxo0aJAefPBB5eXlWVhaJCLmKwQQL5jfEKGwLBju2rVL5eXlqqioUEtLi4qKijR16lSlpaVJkt588001NTXp/fffV2Njo+bOnavrr79eqampVhUZAICY1bEFZHDWhfK1tan6sWXMuIAAlvUxrKyslMvlUmpqqoYNG6acnBxt27bNf/zgwYOaPXu27Ha7zj33XA0bNkwHDhywqrgAAMS09haQy596XLLZ9N36DTqyvVrudeu1ffESBqNAkoU1hm63WxMnTvRvO51ONTQ0+Ld/8Ytf+P9fWVmpQ4cOafTo0SE9tsfjUVNTU8C+trY22e2MtQEAIHjGheaaWu17vZyuM7AuGNrtdiV1qLa22+1yOBwB55w4cULPPfecKisr9fzzzys5OTmkxy4rK9PKlSsD9q1atUop5ogsAAASWXB/Q4nBKPiBZcHQ6XSqrq7Ov11fX69Jkyb5t5ubmzV//nxdccUVevPNN/19D0NRXFyswsLCgH0ej4caQwAA9EN/wyPVX6q5pta/j8EokCzsYzh9+nRt3LhRra2tqq+vV1VVlXJycvzHV69erauvvlpPPvnkaYVCSUpPT1dmZmbAP4fDIZvN1t9PAwCAmGNPStJPlj+lUbOL9FcTfqJRs4uYjguSLKwxzMrKksvlksvlkt1u1+LFi1VbW6sVK1aopKRE27dv144dO/Thhx/6f+bFF1/U+eefb1WRAQCIG0zHha7YDMMwrC5EJFRVVUmSsrOzLS4JAACANXrLQ3S6AwAAgCSCIQAAAEwEQwAAAEgiGAIAAMDEwogAAKBLPq9Xe9esVXPtTtZUThC8ugAAoEt716yVe916SdKR7dWy2WxMcRPnaEoGAABdCl5TmWXz4h/BEAAAdGlw0DJ5LJsX/2hKBgAAXRozf55sNpuO1e5U2riLWDYvARAMAQBAl1g2L/HQlAwAAABJBEMAAACYCIYAAACQRDAEAACAiWAIAAAASYxKBgAAfcTSefGDVw0AAPQJS+fFD4IhYhLfTgEgerB0XvzgkxQxiW+nABA9Bo+7SEe2V/u3WTovdhEMEZP4dgoA0YOl8+IHwRAxiW+nABA9WDovfhAMEZP4dgoAQP8jGCIm8e0UAID+xwTXAAAAkESNIaIc09IAABA5fMIiqjEtDQAAkUNTMqIa09IAABA5BENEtcFB09AwLQ0AAOFDUzKiGtPSAAAQOQRDRDWmpQEAIHIIhgAAIOKYdSI68QrAcrw5AEDiYdaJ6MSnLyzHmwMAJB5mnYhOjEqGZXxer3aXlqlu0zsB+3lzAID4x6wT0YkaQ1imY01hR7w5AED8Y9aJ6EQwhGWCmxEcgwfpvIJ83hwAIAEw60R0IhgiojoONGlrbQ04dl5BPm8SAABYiGCIiNpTtkbfrd/g3x48bpwcAwfQjAAAQBQgGCKiDn34UcD29x6Psl990aLSAACAjhiVjIgyrC4AAADoFsEQEXX29VMCt2+Y0s2ZAAAg0mhKRkSNLZ4vu8PB9AQAAEQhgiEiiukJAABnguVTI4MrCgAAoh7Lp0YGfQwBAEDUY23lyCAYAgCAqBe8tnLLgQPaXVomn9drUYniE03J6Bf0/QAAhFP72sqNH2zVqcZGnWo8KPe69TQp9zM+udEv6PsBAAin9sGLx2pqdaqx0b+fJuX+RVMy+gV9PwAAkRDcpJwWtI2+ocYQ/WLwuIt0ZHu1f5s/VABAOLQ3KTMfbngQDNEv+EMFAEQC8+GGF8EQZ6SrwSb8oQIAENsIhjhtPq9X2xcvUXNNrSQGmwAAEC8YfILTtnfNWn8obMdgEwAAYh/BEKcteASyxGATAADiAU3JCEnHPoVtra0Bx9IuHsdgEwAA4gDBECHpOIG19EMYtA8Y4B+BzConAADEPj7NEZLg5mP7gAG6/KnHrSkMAACngWVbQ8dVQUiYwBoAEKtYtjV0BEOEhAmsAQCximVbQ0cwREiYaR4AEKto9QodwRAAAMQ1Wr1CZ2kwLCkp0erVq+VwOPTQQw/J5XJ1OqeyslIffvihli5dakEJAQBArOuu1YtBKZ1Z9ux37dql8vJyVVRUqKWlRUVFRZo6darS0tIkSSdOnNBzzz2nioqKLgMjAABAXzAopTPLgmFlZaVcLpdSU1OVmpqqnJwcbdu2TTNnzvyhYElJuu6662S32+Xz+U7rsT0ej5qamgL2tbW1yW5noRcAAPADBqV0ZlkwdLvdmjhxon/b6XSqoaHBv52cnKwZM2bo6NGjqq6u7uohulVWVqaVK1cG7Fu1apVSUlL6VmgAABA3GJTSmWXB0G63K6lDO77dbpfD4eiXxy4uLlZhYWHAPo/HQ42hiT4VAAAwKKUrlqUBp9Opuro6/3Z9fb0mTZrUL4+dnp6u9PT0gH1Hjx7tl8eOdT6vV9sXL1FzTa0k+lQAABIXU7F1ZlkV2vTp07Vx40a1traqvr5eVVVVysnJsao4CSE4FLajTwUAAJAsrDHMysqSy+WSy+WS3W7X4sWLVVtbqxUrVqikpMSqYsW1vWvWdgqFEn0qAADoSiJ2vbIZhmFYXYhIqKqqkiRlZ2dbXBLrVD+2LKCTrSSlXTxOly9/Ku5vdAAATtfu0jL/dDaSNGp2Ucw3PfeWhxiNkUAGB9UMEgoBAOhe8HQ2B/7nbe0uLZPP67WoROFHIkggXY2+IhQCANC14Ols2o4fl3vdeh2p/lI/idOKlfh7RugWo68AAAhde4XKgf95W23Hj/v3N9fUat/r5XH5mUpTMgAAQBfaK1ScBXmdjsXrjB4EQwAAgB6MmT9Pgy8eF7AvXmf0oCkZAACgB/akJP1k+VPa93p53K+SQjAEAADoRaL00ycYAgAA9EE8TYQdm6UGAACIEnvXrPVPhH1ke7VsNlvM1i4SDAEAAPogeCLsxg+26lhNbUzWHsZOSQEAAKJQ8ETYpxobdaqxMSZrDwmGMSCe+i4AABBvOq4s1nKgTqcaG/3HYm2+Q9JFlPO2tOhP9z2g7w97JMV+3wUAAOJNxxHLu0vL/P0NJWlw1oX6dnWpDn74kWyShl8/RWOL50dtBU90lgp+Xy59wh8K28Xatw8AABJFx9rDtHEXydfWpu/Wb/Af/279Bh39y1dRu9Zy9JUIAU7ud3faF6+zrQMAEOuC5zusfmxZp3Oiea1llsSLcimjMgK2k4cNi9vZ1gEAiDeDu6nMidbWP4JhlLvsyWVKu3icHIMHKe3icZq46rdRWfUMAAA6GzN/njKKfipHakrA/mht/bMZhmFYXYhIqKqqkiRlZ2dbXBIAAJBofF5vp7WWrajo6S0PUfVkAaafAQAgsfS01nI05QLSiAXiaekcAADQN9GUC+hjaIHgpXOitQMqAAAIv2jKBQRDCwSPUIrWDqgAACD8oikX0JRsgeDJL5l+BgCAxBVNuYBgaIGeOqACAIDEEk25gGDYz6JpZBEAAMDpILH0s2gaWQQAAHA6GHzSz6JpZBEAAMDpIBj2s2gaWQQAAHA6aEruZ9E0sggAAOB0EAz7WTSNLAIAADgdNCUDAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACQRDAEAAGAiGAIAAEASwRAAAAAmgiEAAAAkEQwBAABgIhgCAABAEsEQAAAAJoIhAAAAJBEMAQAAYCIYAgAAQBLBEAAAACaCIQAAACRZHAxLSkqUm5urGTNmqKKiotPxZ599VjfddJMKCgq0bds2C0oIAACQOJKs+sW7du1SeXm5Kioq1NLSoqKiIk2dOlVpaWmSpA8++EB//vOf9d5772nPnj2655579M4778hup5ITAAAgHCxLWZWVlXK5XEpNTdWwYcOUk5MTUCtYWVmpO+64Qw6HQxdccIHOOecc7dixw6riAgAAxD3LagzdbrcmTpzo33Y6nWpoaAg4XlhY2On4pZde2utjezweNTU1Bexra2ujthEAAKAHlgVDu92upKSkgG2Hw9HtcZvNFnC8J2VlZVq5cmXAvlWrViklJaWPpQYAAIhflgVDp9Opuro6/3Z9fb0mTZrU7fGGhgZlZGSE9NjFxcUBtY3SD7WI1BgCAAB0z7KkNH36dG3cuFGtra2qr69XVVWVcnJyAo5v2LBBPp9PtbW1OnbsmC644IKQHjs9PV2ZmZkB/xwOh2w2W7ieDgAAQMyzrMYwKytLLpdLLpdLdrtdixcvVm1trVasWKGSkhLdeOON+uijj5SXl6eBAwdq+fLlVhUVAAAgIdgMwzCsLkQkVFVVSZKys7MtLgkAAIA1estDdLoDAACAJIIhAAAATARDAAAASCIYAgAAwEQwBAAAgCSCIQAAAEwEQwAAAEgiGAIAAMBEMAQAAIAkgiEAAABMBEMAAABIIhgCAADARDAEAACAJIIhAAAATARDAAAASCIYAgAAwEQwBAAAgCSCIQAAAEwEQwAAAEgiGAIAAMBEMAQAAIAkgiEAAABMBEMAAABIIhgCAADARDAEAACAJIIhAAAATARDAAAASCIYAgAAwEQwBAAAgCSCIQAAAEwEQwAAAEgiGAIAAMBEMAQAAIAkgiEAAABMBEMAAABIIhgCAADARDAEAACAJIIhAAAATARDAAAASCIYAgAAwEQwBAAAgCSCIQAAAEwEQwAAAEgiGAIAAMBEMAQAAIAkgiEAAABMBEMAAABIIhgCAADARDAEAACAJIIhAAAATARDAAAASCIYAgAAwEQwBAAAgCSCIQAAAEwEQwAAAEgiGAIAAMBEMAQAAIAkgiEAAABMBEMAAABIIhgCAADARDAEAACAJIIhAAAATJYFw40bN2ratGmaNm2aVq9e3e15NTU1uvfeeyNXMAAAgARlSTD0eDx67rnnVF5ero0bN+qNN97QN9980+m8Z599llAIAAAQIUlW/NKPP/5Y1157rUaMGCFJysvL05YtW3TBBRcEnHfVVVdp0KBBqqqqsqKYAAAACcWSYLh//36NHDnSv+10OrVnz55O502bNu2MgqHH41FTU1PAPq/XK0mETAAAkLDa81B3LAmGdrtdZ511VsB2UlL/FaWsrEwrV64M2FdSUtKvv6M7hmHo5MmTSklJkc1mC/vvi2Zci0Bcjx9xLQJxPQJxPX7EtQjE9fhRX65FT+eHPSmVlpbqtddeC9h34MAB3Xnnnf7t+vp6ZWRk9NvvLC4uVmFhYaf9Q4cOVXp6er/9nq58++23mjt3rjZt2qTMzMyw/q5ox7UIxPX4EdciENcjENfjR1yLQFyPH4XrWoQ9GN599926++67A/Y1NTVpzpw5Onr0qOx2u/73f/9XL730Ur/9zvT09LAHQAAAgHhjyajkoUOH6oEHHtCdd96pWbNmacGCBXI6naqvr1dBQYEVRQIAAEh4lvQxlKTbbrtNt912W8C+c889V5s2bQrYd8011+iaa66JZNEAAAASEiufAAAAQBLBsN+1N5MPHTrU6qJYjmsRiOvxI65FIK5HIK7Hj7gWgbgePwrXtbAZhmH06yMCAAAgJlFjCAAAAEkEQwAAAJgIhgAAAJBEMAQAAICJYAgAAABJBEMAAACYCIYAAACQRDDsV08//bSefPLJLo+VlJQoNzdXM2bMUEVFRYRLFhmGYeiZZ55Rfn6+8vLytGrVqk7nnDx5UjfccIMKCgpUUFCg+++/34KSRkYo10OSnn32Wd10000qKCjQtm3bIlzKyCopKdFNN92k/Px8/eY3v+l0PJHuj96uhZRY94YkeTweFRUVdXvc5XL5743Zs2dHsGSR19u1SITPlHYbN27UtGnTNG3aNK1evbrLc+L93ujt9e7X9woD/eKPf/yjcc011xhPPPFEp2M7d+40br75ZuP48ePGoUOHjBtvvNE4evSoBaUMr82bNxt//dd/bXi9XuP48eNGbm6usX///oBzdu7caTz88MMWlTCyQrkemzdvNoqLiw2v12t8/fXXxvTp0422tjaLShxee/fuNXJzcw2Px2OcOnXKmDdvnvHpp58GnJMo90co1yKR7g3DMIxXXnnFuPnmmw2Xy9Xl8RMnThh33XVXhEtljd6uRaJ8phiGYRw+fNjIzc01GhoajJMnTxo333yz8fXXXwecE+/3Rm+vd3+/V1Bj2A9OnjypFStW6J577unyeGVlpVwul1JTUzVs2DDl5OTE5bf/MWPG6JFHHpHD4VBzc7MMw9DAgQMDznG73Ro5cqRFJYysUK5HZWWl7rjjDjkcDl1wwQU655xztGPHDotKHF5ut1u33nqrhg4dquTkZF1xxRXatWtXp3MS4f4I5Vok0r0hSZdddpkWLlzY7XG3263zzjsvgiWyTm/XIlE+UyTp448/1rXXXqsRI0Zo4MCBysvL05YtWwLOifd7o7fXu7/fKwiG/eC5557Tz3/+827XK3S73crIyPBvO51ONTQ0RKh0kZOZmamLL77YX6WdnZ2t4cOHB5yzf/9+bd26VbfeeqvmzJmjTz/91KLShl8o1yNR7g1Juvbaa/XQQw9Jkvbu3au33npLEydODDgnUe6PUK5FIt0bkjR58mRNmTKl2+Nut1s7duzQrFmzNGvWLL399tsRLF1khXItEuXe2L9/f8CXRafTqfr6+oBz4v3e6O317u/7gWDYR5988okOHz6svLy8bs+x2+1KSkoK2HY4HJEoniUefvhh/eEPf9CePXu0efPmgGMZGRlauHChNmzYoMcee0z/+I//qBMnTlhT0Ajp6XoE3xs2my2u7w1JWrNmjebOnatf/epXGj9+fMCxRLs/eroWiXhv9GTIkCGaN2+e1q1bp5UrV2r58uXav3+/1cWyRCJ9ptjtdp111lkB2x2fuxT/90Zvr3d/v1ck9X4K2pWWluq1114L2Ddy5Ejt379fBQUFam5u1qlTp5ScnKxFixb5z3E6naqrq/Nv19fXa9KkSRErdzh0dS327t2rl19+WTfeeKOGDh2qyZMn68CBAwHn5OTkKCUlRXa7XRMmTNC5556rAwcO6MILL4xk8fvdmV6P4HujoaEh4JtfrOrqeowZM0bnnHOO9uzZo7Vr12rs2LGdfi4e748zvRaJdm/8x3/8R48/N378eP3kJz9RUlKSRo8erezsbH3zzTcaNWpUOIsbVmd6LeLxM0Xq+nocOHBAd955p3+7vr6+099BPN4bHfX2evf7e0Xfu0Wi3bp167odfHL77bcbp06dMurq6oyCggLj5MmTFpQwvH73u98Z9913n+Hz+Yzm5mbj9ttvN7788suAc371q18ZZWVlhmEYxjfffGNMmzbNOHXqlBXFDbtQrsfmzZuNe++912hrazNqamqMWbNmWVPYCPjkk0+M22+/3Whtbe32nES5P0K5Fol0b7Tbt29ftwMunnnmGePXv/61YRiGcfDgQWP69OlGY2NjJIsXUT1di0T5TDEMw/B4PMbMmTONI0eOGMeOHTNuu+0248CBAwHnxPu90dvr3d/vFdQYhkl9fb0WLlyoTZs2KSsrSy6XSy6XS3a7XYsXL+40CCEeFBYW6rPPPtPMmTOVnJysu+66S5deeqn+/Oc/a8WKFSopKdHDDz+sRx99VKWlpRo0aJCeeeYZJScnW130sAjletx444366KOPlJeXp4EDB2r58uVWFztstm/f7h900e6Xv/ylzj///IS7P0K5Fol0b3Sn4/vovffeq0ceeUQzZ87UwIED9cgjj+jss8+2uogRk4ifKZI0dOhQPfDAA7rzzjvl9Xp13333+fsZJsq90dXrXVtbG7b3CpthGEY/lR0AAAAxjMEnAAAAkEQwBAAAgIlgCAAAAEkEQwAAAJgIhgAAAJBEMASAM/bb3/5WS5cuDfvvaW1t1c9+9jNdfvnlamtr67EMXq9XDzzwgG677TZ5PJ6wlw1AfGEeQwCIco2Njfriiy/02Wef9bjUlc/n06JFi7Rnzx6VlJQoPT09gqUEEA+oMQQASfPmzdPq1av92yUlJbr33nslSf/1X/+l3NxcTZgwQXPnztWOHTs6/fy0adP0xRdfdLn91Vdfac6cObriiiv085//XG63u8sybN++XXfeeacmTpyo4uJi7dy50/9YPp9PV155pbxeb7fPYdmyZfrqq6+0evVqDRs27DSvAAAQDAFAklRQUKDNmzf7t9977z3dfPPNam5u1r/+67/qhRde0CeffKIJEyZo5cqVIT/u8ePHdc899+iee+7Rli1bdOmll2rx4sWdzmtubtY999yjBQsWaMuWLcrLy9N9992n1tZWvffee0pOTlZNTY2Skrpu6Hn66af11ltvafXq1Ro+fPhpP38AkAiGACBJysvL0+eff67m5mYdOXJE27dv14wZM3TWWWepvLxcl1xyiY4cOaIBAwboyJEjIT/u5s2bdckll2jGjBkaMmSI/uEf/kFffPGFjh492um8zMxM3XbbbUpLS9Pdd98tu92uv/zlL73+jrffflubN29WcnKy3n333dN+7gDQjj6GACBp5MiRuuSSS/SHP/xBLS0tuu666zR48GC1trZq9erV2rZtm0aNGqWUlJSQHs/n80mS3G63PvjgA1188cUBxxsbGzVkyBD/9nfffaexY8cGnDNs2DAdOnSo13VfhwwZotLSUn366adaunSppkyZ0umxACAU1BgCgCk/P1+bN2/We++9p1tuuUWStGHDBu3evVvvvvuuysrKNG3atC5/1maz+cNgW1ubmpqaJElnn322CgoKVFNTo5qaGu3YsUPr1q1TZmZmwM8PHz5cdXV1/m2fz6e9e/cqIyOj13JPnjxZ5557rm699VZdd911evTRR/1lAYDTQTAEAFN+fr62bNmizz//XLm5uZKk77//Xj6fTy0tLaqpqdF//ud/qrW1tVPwcjqdqq6uliS9+eabOnnypCRp6tSp+vzzz/XZZ5/p+PHjevnll7VkyRLZ7YFvv1OnTlVVVZXeffddNTc369VXX9U555yj8ePHn9ZzeOKJJ/Ttt9/q1VdfPdPLACCBEQwBwDRq1Cg5nU5deeWVSk1NlSTNmjVLaWlpuv766/XEE09oyZIl2rt3r9asWRPws4sWLdIbb7yhW2+9VX/84x81btw4ST/UGD777LN6/PHHdd1112nLli36zW9+0+l3jxgxQs8//7z+/d//XZMnT1ZlZaWee+65034OZ599tpYsWaLnn3++y9HTANATm2EYhtWFAAAAgPWoMQQAAIAkgiEAAABMBEMAAABIIhgCAADARDAEAACAJIIhAAAATARDAAAASCIYAgAAwEQwBAAAgCTp/wG3SuT1GoIUWAAAAABJRU5ErkJggg==n”, “text/plain”: [
“<Figure size 748.8x655.2 with 1 Axes>”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“random_points = np.random.uniform(-4,0,100)n”, “n”, “fig, ax = plt.subplots()n”, “n”, “ax.plot(random_points,pts1.spectrum.main.Powerlaw.index.prior(random_points), ‘r.’ )n”, “n”, “ax.set_ylim([-0.1,0.6])n”, “ax.set_xlabel("value of K")n”, “ax.set_ylabel("Prior")”
]
}, {
“cell_type”: “code”, “execution_count”: null, “id”: “7d9ab91b”, “metadata”: {}, “outputs”: [], “source”: []
}
], “metadata”: {
- “jupytext”: {
“formats”: “ipynb,md”
}, “kernelspec”: {
“display_name”: “Python 3”, “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”: “3.7.11”
}
}, “nbformat”: 4, “nbformat_minor”: 5
}