{ "cells": [ { "cell_type": "markdown", "id": "e40c9735", "metadata": { "tags": [] }, "source": [ "# Neural-Network–Based Continuum Background Inpainting for COSI\n", "\n", "## Overview\n", "\n", "This notebook provides an introduction to neural-network–based background inpainting for COSI data using the `ContinuumEstimationNN` class in cosipy. The purpose of this tutorial is to demonstrate how machine learning methods can be used to estimate the continuum background. Specifically, here we use a graph-based convolutional neural network. This approach trains a neural network in order to reconstruct (inpaint) the background for regions of the Compton data space (CDS) occupied by a source of interest (currently only tested for point sources). The resulting background estimate can be used directly in COSI analysis.\n", "\n", "**What This Method Does**\n", "\n", "At a high level, the inpainting procedure consists of the following steps:\n", "\n", "1. Project COSI data into the 3D CDS spanned by measured energy (Em), Compton scatter angle (Phi), and sky coordinates (PsiChi)\n", "\n", "2. Mask region in the CDS dominated by a point source using the point source response and angular resolution measure (ARM). \n", "\n", "3. Train a neural network on the unmasked sky pixels\n", "\n", "4. Predict the background in the masked region and merge the result with the observed data\n", "\n", "The sky is discretized using HEALPix, which naturally defines a graph where each pixel is connected to its neighbors. This allows the neural network to propagate information across the sphere while respecting the underlying sky geometry.\n", "\n", "**Training Modes**\n", "\n", "The inpainting algorithm supports three complementary training modes:\n", "\n", "- **Self-supervised** (default) \n", " No background model is required. The network learns by randomly masking a fraction of the sky and training itself to reconstruct those pixels.\n", "\n", "- **Supervised** \n", " A background model is provided and treated as ground truth in the masked regions.\n", "\n", "- **Hybrid** \n", " A weighted combination of supervised and self-supervised training. This mode leverages an existing background model while remaining robust to modeling uncertainties.\n", "\n", "The training mode is selected with a single keyword argument, making it easy to experiment with different strategies.\n", "\n", "**Neural Network Architecture**\n", "\n", "Two graph-based neural network architectures are available:\n", "\n", "- **GCN (Graph Convolutional Network)** \n", " A lightweight, four-layer architecture optimized for stability and speed.\n", "\n", "- **Graph U-Net** \n", " A deeper architecture that captures larger-scale structure through pooling and unpooling operations.\n", "\n", "Both models operate directly on HEALPix graphs and are implemented using PyTorch Geometric. GPU acceleration is used automatically when available, but the method also runs on CPUs for smaller datasets and testing.\n", "\n", "**Outputs and Diagnostics**\n", "\n", "This workflow produces:\n", "\n", "- **Inpainted background histogram**: compatible with cosipy\n", "- **Mollweide sky maps**: optional, showing the true, masked, and inpainted data for a given bin of Em and Phi\n", "- **Training-loss diagnostics**: saved during optimization\n", "- **Accuracy evaluation plots**: when a reference background model is available\n", "\n", "These diagnostics are designed to help assess both the quality of the final background estimate and the behavior of the neural network during training.\n", "\n", "**Limitations of the Method**\n", "\n", "The source region in the CDS is masked based on the ARM. However, for the COSI response, the ARM of a source has a long tail. This limits the accuracy of the estimated background, since the source photons in the tail cannot be masked. \n", "\n", "In terms of training the network, most of the infrastructure is in place. However, further development is needed to optimize the network and training. \n", "\n", "**Tutorial Outline**\n", "\n", "There are three main parts to this tutorial. First, we will demonstrate the basic functionality of the continuum background estimation class, and use it to get an estimate of the background using the GCN method. Then, instead of a NN-based algorithm, we'll estimate the background using a simple interpolation method (same as was used for DC3). Finally, we will then use the estimated background to perform a spectral fit. This example uses the Crab and total background from DC3. " ] }, { "cell_type": "markdown", "id": "2b003b53-24da-42ed-a376-b52eb3efcd0f", "metadata": { "tags": [] }, "source": [ "## Estimating the background with NN-based inpainting" ] }, { "cell_type": "code", "execution_count": 1, "id": "8a0f4b8b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
13:52:54 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:45\n",
       "                  available                                                                                        \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m13:52:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=197598;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=915349;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#45\u001b\\\u001b[2m45\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mavailable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  functions.py:67\n",
       "                  will not be available.                                                                           \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=714807;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=357844;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#67\u001b\\\u001b[2m67\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mwill not be available. \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
13:52:55 WARNING   The ebltable package is not available. Models that depend on it will not be     absorption.py:34\n",
       "                  available                                                                                        \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m13:52:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=659854;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=341913;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#34\u001b\\\u001b[2m34\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mavailable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
13:52:55 INFO      Starting 3ML!                                                                     __init__.py:44\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m13:52:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m Starting 3ML! \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=889928;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=220172;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   WARNINGs here are NOT errors                                                      __init__.py:45\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m WARNINGs here are \u001b[0m\u001b[1;31mNOT\u001b[0m\u001b[1;38;5;251m errors \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=693707;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=878349;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#45\u001b\\\u001b[2m45\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   but are inform you about optional packages that can be installed                  __init__.py:46\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=636168;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=547946;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#46\u001b\\\u001b[2m46\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING    to disable these messages, turn off start_warning in your config file            __init__.py:47\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=309393;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=824037;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#47\u001b\\\u001b[2m47\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   no display variable set. using backend for graphics without display (agg)         __init__.py:53\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m no display variable set. using backend for graphics without display \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251magg\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=432374;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=78752;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#53\u001b\\\u001b[2m53\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
13:52:56 WARNING   ROOT minimizer not available                                                minimization.py:1208\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m13:52:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=933613;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=323093;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1208\u001b\\\u001b[2m1208\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   Multinest minimizer not available                                           minimization.py:1218\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=578630;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=654870;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1218\u001b\\\u001b[2m1218\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   PyGMO is not available                                                      minimization.py:1228\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=513591;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=636269;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/minimizer/minimization.py#1228\u001b\\\u001b[2m1228\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The cthreeML package is not installed. You will not be able to use plugins which  __init__.py:95\n",
       "                  require the C/C++ interface (currently HAWC)                                                     \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=971687;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=848540;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#95\u001b\\\u001b[2m95\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   Could not import plugin FermiLATLike.py. Do you have the relative instrument     __init__.py:136\n",
       "                  software installed and configured?                                                               \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=73737;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=804291;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#136\u001b\\\u001b[2m136\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   Could not import plugin HAWCLike.py. Do you have the relative instrument         __init__.py:136\n",
       "                  software installed and configured?                                                               \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=975241;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=788350;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#136\u001b\\\u001b[2m136\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   No fermitools installed                                              lat_transient_builder.py:44\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=200886;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=462424;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
13:52:57 WARNING   Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:345\n",
       "                  performances in 3ML                                                                              \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m13:52:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=909389;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=639610;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#345\u001b\\\u001b[2m345\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal     __init__.py:345\n",
       "                  performances in 3ML                                                                              \n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=7735;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=877590;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/__init__.py#345\u001b\\\u001b[2m345\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING UserWarning: An issue occurred while importing 'pyg-lib'. Disabling its usage. Stacktrace: /lib64/libm.so.6: version `GLIBC_2.29' not found (required by /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/libpyg.so)\n", "\n", "\n", "WARNING UserWarning: An issue occurred while importing 'torch-scatter'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_scatter/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n", "\n", "\n", "WARNING UserWarning: An issue occurred while importing 'torch-cluster'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_cluster/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n", "\n", "\n", "WARNING UserWarning: An issue occurred while importing 'torch-spline-conv'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_spline_conv/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n", "\n", "\n", "WARNING UserWarning: An issue occurred while importing 'torch-sparse'. Disabling its usage. Stacktrace: /project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/torch_sparse/_version_cuda.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKSs\n", "\n" ] } ], "source": [ "# Imports:\n", "from cosipy.background_estimation import ContinuumEstimationInterp\n", "from cosipy.background_estimation.ml import ContinuumEstimationNN\n", "from cosipy.util import fetch_wasabi_file\n", "import logging\n", "logging.basicConfig(level=logging.INFO)\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "22076637", "metadata": {}, "source": [ "The first part of the notebook requires the following files:\n", "1) crab_and_bg_combined_binned_data.h5\n", "2) crab_binned_data.hdf5\n", "3) crab_psr.h5\n", "4) Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\n", "\n", "They can be downloaded using the cells below." ] }, { "cell_type": "code", "execution_count": 4, "id": "ec8e59c4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_and_bg_combined_binned_data.h5\n" ] } ], "source": [ "# crab_and_bg_combined_binned_data.h5\n", "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_and_bg_combined_binned_data.h5', checksum = 'f5ee6f356ebc30a3168a1697c4ec2bdc')" ] }, { "cell_type": "code", "execution_count": 5, "id": "475a448c-5f4b-48b8-ba7e-b26f4f2c8b02", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_binned_data.hdf5\n" ] } ], "source": [ "# crab_binned_data.hdf5\n", "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_binned_data.hdf5', checksum = '405862396dea2be79d7892d6d5bb50d8')" ] }, { "cell_type": "code", "execution_count": 6, "id": "f7966690-f4c4-4d4a-9888-9c02ccea1dca", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_psr.h5\n" ] } ], "source": [ "# crab_psr.h5\n", "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_psr.h5', checksum = '700ea171dd52a989deb0810b20302b0d')" ] }, { "cell_type": "code", "execution_count": 8, "id": "b5a1eb28-5a0b-4d64-a1f4-9081a5a1f5f8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/DC3/Data/Backgrounds/Ge/Binned_BG_Files/Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\n" ] } ], "source": [ "# Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\n", "fetch_wasabi_file('COSI-SMEX/DC3/Data/Backgrounds/Ge/Binned_BG_Files/Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5', checksum = '270ba55f0da6bc25d4919709ae6a31c2')" ] }, { "cell_type": "markdown", "id": "42d1d040", "metadata": {}, "source": [ "Define input files:" ] }, { "cell_type": "code", "execution_count": 2, "id": "e2d285d2-7d77-4a79-bd75-06254da25cec", "metadata": { "tags": [] }, "outputs": [], "source": [ "data_path = \"./\"\n", "input_data = data_path + \"crab_and_bg_combined_binned_data.h5\"\n", "crab_data = data_path + \"crab_binned_data.hdf5\"\n", "psr_file = data_path + \"crab_psr.h5\"\n", "background_model = data_path + \"Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\"" ] }, { "cell_type": "markdown", "id": "8831e312-e5b8-4cb9-a6d3-fcc855f377f9", "metadata": {}, "source": [ "Define instance of class:" ] }, { "cell_type": "code", "execution_count": 3, "id": "6ade7276", "metadata": { "tags": [] }, "outputs": [], "source": [ "instance = ContinuumEstimationNN()" ] }, { "cell_type": "markdown", "id": "3e1c3fda-124a-4584-84cb-1a37f17516af", "metadata": {}, "source": [ "There is a convenience function that allows you to run the background estimation algorithm with a single command. The command is shown below with all available options. At minimum, you need to pass the input data and point source response file. All optional inputs are shown with their default values, except for `epochs`, which we set to 1 in order to speed up the runtime. See cosipy documentation for more information on the input parameters.\n", "\n", "Note: this example notebook is being run with 1 GPU. The code detects this automatically and will utilize GPU acceleration. If no GPUs are detected it will default to CPU. " ] }, { "cell_type": "code", "execution_count": 4, "id": "36e86160-c3a9-446c-9468-324ded91bcbd", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:...loading the pre-computed point source response ...\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:--> done\n", "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:GPUs available: 1\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GPU 0: NVIDIA A100-PCIE-40GB (capability 8.0)\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GCN model.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "98b5c64411124721a0f01eb5d2bb9510", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Inpainting (Em, Phi): 0%| | 0/300 [00:00 done\n", "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:GPUs available: 1\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GPU 0: NVIDIA A100-PCIE-40GB (capability 8.0)\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GCN model.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d54ce61c6d0449adb5414de7952654f9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Inpainting (Em, Phi): 0%| | 0/300 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_supervised_600_epochs_0p6containment_true_E2_Phi4.pdf\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_supervised_600_epochs_0p6containment_masked_E2_Phi4.pdf\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_supervised_600_epochs_0p6containment_inpainted_E2_Phi4.pdf\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAHDCAYAAADvDfQIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5s0lEQVR4nO3de1iUdf7/8RenQQHNAgMDs69QLrRtWB4yV8N1QFyTtXA1M8trU0vb3LYtUbetNFt1tbKfbXphbnRYE1s7UGsexjxl5WaFlhie8dDigRIZUYbD/P5wmSJAhnsGZhiej+vqupjP/bk/8769vPPFfX/uz+1nt9vtAgAAQKP5e7oAAACAloogBQAAYBBBCgAAwCCCFAAAgEEEKQAAAIMIUgAAAAYRpAAAAAwiSAEAABjUKoLUwYMH9fvf/16pqakaM2aMvvzyS0+XBAAAfIDPB6mKigpNnz5dSUlJ+ve//60//OEPevzxx1VcXOzp0gAAQAvn80Hq8OHDKikp0fDhwxUQEKAePXro6quv1pYtWzxdGgAAaOECPV3AT5WWlmr58uXKy8vT7t27VVJSomnTpmnw4MG1+tpsNi1dulRr165VSUmJYmNjNW7cOPXs2bNGv5++TtBut+vgwYNNehwAAMD3ed0VqeLiYmVlZamgoEBxcXEX7Tt79mytWLFCycnJmjx5svz9/TVlyhTt3LnT0efKK69UWFiYsrOzVVFRoW3btik3N1fnz59v6kMBAAA+zs/+08s1Hmaz2VRSUqLw8HB98803mjBhQp1XpPLy8nT//fdr4sSJGjVqlCSprKxMY8eOVYcOHbRo0SJH3/3792vBggU6ePCgunXrpg4dOqhz584aO3Zscx4aAADwMV53a89kMik8PLzBfps2bVJAQIDS0tIcbcHBwRoyZIgyMzN1/PhxRUZGSpJiY2O1cOFCR7+JEycqJSXF/cUDAIBWxetu7Tlr7969iomJUWhoaI32+Ph4SdK+ffscbfv371dZWZnOnz+vN954Q3a7Xb17927WegEAgO/xuitSzioqKqrzylV126lTpxxtq1at0gcffKCqqir16NFDTz/9dL3jnjp1SkVFRY7PZWVlOnfunK6//nq1adPGjUcAAABauhYbpMrKyhQUFFSr3WQyObZXe/DBB/Xggw86NW5OTo6ysrJqtS9ZskTdunUzViwAAPBJLTZIBQcHq7y8vFa7zWZzbDciLS1Nffv2dXwuKCjQrFmzjBUJAAB8WosNUuHh4Tp58mSt9urbchEREYbGjYiIMLwvAABoXVrsZPO4uDgdPXpUZ8+erdGel5fn2A4AANCUWmyQSkpKUmVlpXJychxtNptNq1atUkJCgmPpA6MsFoumTp1aY9kEAACAH/PKW3srV66U1Wp13KbbunWrTpw4IUlKT09XWFiYEhISNGDAAGVmZur06dOKjo7W6tWrVVhYqIyMDJdrMJvNMpvNys/P1/jx410eDwAA+B6vDFLZ2dkqLCx0fN68ebM2b94sSUpJSVFYWJgkafr06YqMjNSaNWtktVrVtWtXzZ07V4mJiZ4oGwAAtDJe94oYb1N9RYrlDwAAwE+12DlSAAAAnuaVt/a8gcVikcVikdVq9XQpAABvUVUpHdsiWf8rhXWSovtJ/gGergoeRJCqB5PNAQA17H1L+vAPkvXoD21hMdKvnpeuvt3l4a+66ioFBwerbdu2KisrU/fu3bVkyRLHO2XXrVunp556SkePHtVll10mf39/jRs3ThMmTKg11saNG3X+/Hmlpqa6XBcujlt7AAA0ZO9bUs7wmiFKkqzHLrTvfcstX5Odna3c3Fzt2rVLxcXFjleWrV27Vvfcc4/mzJmjAwcOaPv27Xrrrbd07NixOsfZuHGjVq9eXe/3VFRUuKVeEKQAALi4qsoLV6JU17NZ/2vb8NCFfm5is9lUWlqqSy+9VJI0c+ZMPf7447r55psdfWJiYjRjxoxa++bm5mrx4sX65z//qcTERM2cOVOHDh1Shw4dlJGRoRtuuEEvvPCCnnzyST300EOO/V544QWNHTvW8Xn+/Pnq1auXbrjhBqWmpqqgoMBtx+dLCFIAAFzMsS21r0TVYJdKjlzo56KRI0cqMTFRUVFR8vf314gRIyRJX3zxhXr37u3UGImJibr//vs1evRo5ebm6vHHH5ckFRcX69prr9UXX3xRI0DVZdmyZcrPz9cnn3yiL774QqNHj9akSZNcOjZfxRypejDZHAAg6cLEcnf2u4js7GwlJiaqoqJC9913nzIyMvTMM8/U6jd69Gjt2rVLhYWF2rNnj9q3b9/g2EFBQbrrrrucquOdd97RZ599phtvvFGSVFnpvqttvoYrUvUwm82aM2eOHnzwQU+XAgDwpLBO7u3nhMDAQKWnpzvmOXXv3l3/+c9/HNv/+c9/Kjc3V8ePH1dVVZVTY4aEhMjf/4d/9gMDA2sEpPPnzzt+ttvtmjZtmnJzc5Wbm6uvvvpKX331lauH5ZMIUgAAXEx0vwtP58mvng5+UrvOF/q50YcffuhYCPovf/mLZs6cqU8//dSx/ezZs/Xu2759exUXF190/Li4OG3fvl2VlZUqLS3VypUrHduGDRumxYsX67vvvpMklZeX68svv3TlcHwWt/YAALgY/4ALSxzkDNeFMPXjSef/C1cDFrhlPamRI0eqbdu2qqioUJcuXbR48WJJUmpqqpYuXapHH31U3377rTp27CiTyaSFCxeqXbt2tca57bbb9NprrykxMVG333677r777lp9br/9dr355puKj49XTEyMunfvrtLSUkkXbh0WFRVpwIABki485fe73/1O3bt3d/kYfQ2viGkAr4gBAEiqex2pdp0vhCg3rCOFlokrUgAAOOPq26XY37CyOWogSAEA4Cz/AKlzkqergBchSNWD5Q8AAEBDCFL14F17AACgISx/AAAAYBBBCgAAwCBu7QEA0ICikkpZz9e/WlBYGz+Ft+PpvdaIIAUAwEUUlVTqsWXFqrjI6+YCA6RZd17iUpi66qqrFBwcrLZt26qsrEzdu3fXkiVLFBoaKklat26dnnrqKR09elSXXXaZ/P39NW7cOE2YMMHwd/7YggULdMcddygqKkqStHjxYpWUlOjRRx91y/jShXf4RUVF6aabbmr0vu+//77mz5+vjRs3uq0ed+DWHgAAF2E9b79oiJKkikpd9IqVs7Kzs5Wbm6tdu3apuLhYWVlZkqS1a9fqnnvu0Zw5c3TgwAFt375db731lo4dO+byd1ZbsGCBCgsLHZ/vv/9+t4Yo6UKQ+vFrbnwBQaoeFotFU6dO1cKFCz1dCgCglbHZbCotLdWll14qSZo5c6Yef/xx3XzzzY4+MTExmjFjRp37l5eXa+rUqerVq5cSExM1YsQIff/995Kkl156SQkJCUpMTNR1112nbdu2aebMmfr22281cuRIJSYmKjc3V08++aQeeughSVJWVpbMZrNGjRqlhIQE3XzzzcrLy9Ntt92m+Ph4paSkOJYLWr9+vfr06aPu3bvr2muv1dKlSyVJq1atUk5OjubNm6fExES99NJLkqTXXntNvXv31g033KD+/ftrx44djmOYNGmSrr76avXq1UsbNmxw/x+0G3Brrx4sfwAAaG7V79o7dOiQbrzxRo0YMUKS9MUXXzTqF/t58+YpNDRU//nPfyRJTz31lB577DH9/e9/15/+9Cd988036tSpk8rLy1VWVqbevXvrH//4h7Kzs5WYmCjpwtWjH/vss8/01Vdf6corr9SYMWM0dOhQffzxx4qMjNStt96qV155RQ888IBuuOEGffTRRwoICNB3332n7t27a9CgQfr1r3+ttLQ0JSYmOgLa1q1b9cYbb2jz5s0KDg7Wli1bdOedd2rXrl3KzMxUfn6+du3aJUkaNGiQa3+4TYQgBQCAl6gOMhUVFbrvvvuUkZGhZ555pla/0aNHa9euXSosLNSePXvUvn37GtvfeecdFRcXa+XKlZIuXOG66qqrJEkDBw50BKHBgwfrmmuucaq2Pn366Morr5Qk9ejRQ+Xl5YqMjJQk9ezZU3v37pUkFRUV6d5779WePXsUGBiooqIiff3114qJiak15rvvvqsdO3aod+/ejrbvvvtO586d0/r163X33XfLZDJJkn73u985rm55E27tAQDgZQIDA5Wenq7Vq1dLkrp37+64uiRJ//znP5Wbm6vjx4+rqqqq1v52u10LFy5Ubm6ucnNzlZeXp1WrVkmSVq5cqTlz5qi8vFy//vWvtXz5cqdqatOmjePngICAWp8rKiokXZhb9ctf/lJfffWVcnNzdc011+j8+fN1jmm323XPPfc46szNzdV///tftW3btlZfPz8/p+psbgQpAAC80Icffqhu3bpJkv7yl79o5syZNSZqnz17tt59hw0bpueee06lpaWSpNLSUu3atUsVFRXav3+/evTooUceeUTDhw93BLT27duruLjY5bq///57denSRX5+ftq8ebNjzlNd35GWlqbXX39dhw8fliRVVVVp+/btki5MsXn99ddVXl4um82ml19+2eXamgK39gAA8BLVc6QqKirUpUsXLV68WJKUmpqqpUuX6tFHH9W3336rjh07ymQyaeHChWrXrl2tcTIyMhxzn6qv5GRkZCguLk6/+93v9N133ykwMFAdO3Z0BJTJkydr/PjxCgkJcTwtaMScOXM0adIkPfXUU0pMTKxx227MmDEaO3as3nnnHT3wwAMaN26c/va3v+m2225TRUWFbDabhgwZoh49emj8+PH6+uuvlZCQoEsvvVT9+vXT559/briupuJnt9tdf17Th1VPNl+yZInjNwMAQOvRXOtIoWXiihQAABcR3i5As+68hJXNUSeCFAAADQhvF6Dw2nfQAIJUfSwWiywWi2OBMQAAgJ8iSNWDBTkBAEBDWP4AAADAIIIUAACAQQQpAAAAgwhSAAAABhGkAAAADCJIAQAAGESQAgAAMIggBQAAYBBBCgAAwCBWNq8Hr4gBAAANIUjVg1fEAACAhnBrDwAAwCCCFAAAgEEEKQAAAIMIUgAAAAYRpAAAAAwiSAEAABhEkAIAADCIIAUAAGAQQQoAAMAgghQAAIBBBCkAAACDCFIAAAAGEaQAAAAMIkgBAAAYRJACAAAwKNDTBXgri8Uii8Uiq9Xq6VIAAICXIkjVw2w2y2w2Kz8/X+PHj/d0OQAAwAtxaw8AAMAgghQAAIBBBCkAAACDCFIAAAAGEaQAAAAMIkgBAAAYRJACAAAwiCAFAABgEEEKAADAIIIUAACAQQQpAAAAgwhSAAAABhGkAAAADCJIAQAAGESQAgAAMIggBQAAYFCgpwtoDnv37tVzzz2nAwcO6JJLLtGYMWN06623erosAADQwrWKK1KzZs1Sr169tGrVKs2cOVMLFy7UoUOHPF0WAABo4VpFkCosLNTAgQPl7++vbt26qUuXLjp8+LCnywIAAC2c1wWp0tJS/eMf/9AjjzyiIUOGqH///vrggw/q7Guz2bRo0SLddtttMpvNuu+++/TZZ5/V6peenq5169apoqJCeXl5OnHihBISEpr6UAAAgI/zuiBVXFysrKwsFRQUKC4u7qJ9Z8+erRUrVig5OVmTJ0+Wv7+/pkyZop07d9bo17t3b61evVrJycl64IEHdN999ykiIqIpDwMAALQCXhekwsPD9fbbb+vNN9/UxIkT6+2Xl5en9evXa8KECZo0aZLS0tK0YMECRUVFadGiRY5+Z86cUUZGhiZNmiSLxaKXXnpJmZmZys/Pb47DAQAAPszrgpTJZFJ4eHiD/TZt2qSAgAClpaU52oKDgzVkyBDt2rVLx48flyQdO3ZMbdq0UVJSkgICAhQbG6uf//zn2rFjR5MdAwAAaB28Lkg5a+/evYqJiVFoaGiN9vj4eEnSvn37JEmdO3dWWVmZtmzZIrvdrkOHDmnnzp3q2rVrs9cMAAB8S4tdR6qoqKjOK1fVbadOnZIkhYWFacaMGVq8eLFmzZql9u3ba8SIEerRo0ed4546dUpFRUWOzwUFBU1QPQAA8AUtNkiVlZUpKCioVrvJZHJsr9arVy/16tXLqXFzcnKUlZXllhoBAIBva7FBKjg4WOXl5bXabTabY7sRaWlp6tu3r+NzQUGBZs2aZaxIAADg01pskAoPD9fJkydrtVffljO6vEFERARLIwAAAKe02MnmcXFxOnr0qM6ePVujPS8vz7EdAACgKbXYIJWUlKTKykrl5OQ42mw2m1atWqWEhARFRkZ6sDoAANAaeOWtvZUrV8pqtTpu023dulUnTpyQdOF1L2FhYUpISNCAAQOUmZmp06dPKzo6WqtXr1ZhYaEyMjJcrsFischischqtbo8FgAA8E1+drvd7ukifmrEiBEqLCysc1t2drY6deok6cKTeUuXLtXatWtltVrVtWtXjRs3zukn9JyRn5+v8ePHa8mSJerWrZvbxgUAAC2fVwYpb0KQAgAA9Wmxc6QAAAA8zSvnSHkD5kgBAICGEKTqYTabZTabHbf2AAAAfopbewAAAAYRpAAAAAwiSAEAABhEkAIAADCIyeb14Kk9AADQEIJUPXhqDwAANIRbewAAAAYRpAAAAAzi1h7cYn9huU6eqap3e8f2/oqNCmrGigAAaHoEKbhsf2G55rxV0mC/qbe3I0wBAHwKQaoePLXnvItdifppv9ioJi4GAIBmRJCqB0/tAQCAhhCkWpG1uee0bsd5t49bXl4pZ55bWL65RCs/KXXpu5Kvb6OUxLYujQEAgLsQpFqR84c+0emziW4f98rKHTob0L3BfuHnduiwreF+F3P+0CdS4q9cGgMAAHchSLUibVSiDvZv3T5uuP2IDsuJIGU/ojP2SJe+q40antQOAEBzIUi1Iikx+5Vy/AG3j/tF1U36UmkN9rvJ731NCnzMtS+Ledi1/RvgWMahskKmghz5lx5TVUi0bF3SpIBAlnEAANRAkGpNejx84T83s+0+K20oa7if+RUpPtTt3+8utZdxGPTDjwfKJF04RpZxAABUI0jVg+UPnNfxUpP87KWy+wXU28fPXqmOl5qasarGYxkHAEBjEaTqwfIHzouNClJGeged2bZYlxx4VQF2m2NbpZ9JxV3vVvve93v/VZzKikb0C27SUgAALQNBCm4RGxUk/eZBqeI+aceL0un9UodY6fpJUqD7rkStfW+11h3p4rbxfuzyyj1SQL8G+22xWLRywzUufVdy5wKlDE11aQwAgOcRpOBegSbpxoeabPjzNrtOy7Un/+oTbf/aqX5BdpvLNZy3HXJpfwCAdyBIoUVpY/JTBx1vkrHL/Zy7clbuZ3K5hjYmP5f2BwB4B4IUWpSUoalKaaKxP919pfY48fRhP7NZN7n89GG8i/sDALxBw+/1AFqLACd/r3C2HwDA5xGkgP/p2N6508HZfgAA38ev1sD/xEYFaert7VjZHADgNIJUPViQs3WKjQr632KbwVL8KE+XYxivugGA5uFnt9vtni7Cm1UvyLlkyRJ169bN0+UADar9qpu68aobAHAdkz0AH9OYV90AAFxDkAJ8TaNedQMAcAVzpAAP4FU3AOAbCFKAB/CqGwDwDQQpwAN41Q0A+AaCFOABvOoGAHwDk80BX8OrbgCg2RCkAB/Dq24AoPnwKyngY3zlVTeszg6gJSBIAT6opb/qpvbq7IN++PFAmaQLc8BYnR2ApxGk6sG79gDPaczq7BcCIwB4BkGqHmazWWaz2fGuPQDNqFGrswc3aSkAcDEEKS/AXBC0VE21Qrsvrc7O+Q34NoKUhzEXBC1ZU63Q7iurs3N+A76PIOVhzAVBS9ZUK7T7yursnN+A7yNIeRpzQdCCNdUK7T6zOjvnN+DzCFJOev7NQwq9zLnfLhvDl+aCAG4TEKjq214N93NNU83zkji/gdaAIOWkEl2mcuaCAM2iOVdnb6p5XhLnN9AaEKSc1E7fKVRhbh/XV+aCAO7UnKuzN9U8L4nzG2gN/Ox2u93TRXiz6nWklixZom7durl9/E93n9VSJ+aC3Dsg2A1zQQA0J85vwPfx1lJPc3aOhxvmggBoZpzfgM8jSHlYc84FAdC8OL8B38evQR7WnHNBADQvzm/A9xGkvEBsVND/FuMLluJHebocAG7E+Q34Nq4nAwAAGESQAgAAMIhbe/WwWCyyWCyyWq2eLgUAAHgpglQ9zGazzGazYx0pAACAnzJ8a+/zzz/XG2+8UaPt3//+t4YPH67f/OY3WrhwoSorK10uEAAAwFsZDlIvv/yy9u3b5/i8f/9+zZ8/Xx06dFBiYqJWrlyp5cuXu6VIAAAAb2Q4SBUUFNR4ZcratWsVGhqqF154QTNmzNCtt96qNWvWuKVIAAAAb2Q4SJ07d06hoT+8G2rbtm3q1auX2rRpI0n62c9+puPHm+ZFoAAAAN7A8GTzyy+/XN98842GDBmio0eP6uDBgxo5cqRje0lJiYKCWK0XAFq6/YXlrM4O1MNwkEpOTtYrr7yikydP6tChQ2rXrp1++ctfOrbn5+erc+fObikSAOAZ+wvLNeetkh+1DPrhxwNlksokSVNvb0eYQqtkOEiNGTNGFRUV+vTTTxUZGalp06apXbt2kqQzZ84oNzdXw4cPd1uhAIDmd/JMldP9LrwKB2hdDAepwMBAjR8/vs41ltq3b6933nnHlboAAN6gsqIR/YKbtBTAGxkOUn/4wx90991368Ybb6xz+xdffKFXXnlFzz//vOHiAADOWfveaq070sXt415euUcK6Ndgvy0Wi1ZuuMal70ruXKCUoakujQE0N8NBKjc3V7feemu927///nvt2LHD6PAAgEY4b7PrtCLdPm60/Wun+gXZbS5//3nbIZf2BzzBpVfE+Pn51bvt2LFjCgkJcWV4AICT2pj81EHuX3Km3M/kdD9Xv7+Nqf5/UwBv1agg9cEHH2j16tWOz6+++qree++9Wv2sVqsOHDig3r17u14hAKBBKUNTldIE4366+0rt2VDWYL9+ZrNuig9tsN/Fxbu4P9D8GhWkysrKdPr0acfn0tLSWlel/Pz81LZtW6WlpWns2LHuqBEA4CkBgape4qDhfkDr06i/+cOGDdOwYcMkSSNGjNDkyZNrrB0FAPAtHds79wIMZ/sBvsbwrxArVqxwZx0AAC8UGxWkqbe3Y2VzoB4uX4stLS1VYWGhSkpKZLfba21PTEx09SsAAB4UGxX0v8U2g6X4UZ4uB/AqhoPU6dOn9fzzz2vTpk2qqqq98q3dbpefn582btzoSn0AAABey3CQmjdvnj7++GOlp6fr+uuvd7wexhsNGjSoxufz589r4sSJuuOOOzxUEQAA8AWGg9T27ds1YsQITZw40Z31NIk1a9Y4fj516pR++9vfqn///h6sCAAA+ALDj1kEBwcrKqrlvaFy3bp1uvbaa3XFFVd4uhQAANDCGQ5SKSkp2rJliztrkXRh8vo//vEPPfLIIxoyZIj69++vDz74oM6+NptNixYt0m233Saz2az77rtPn3322UXHX7t2ba1bfQAAAEYYDlK33HKLzpw5o0ceeUSbNm3S7t27lZ+fX+u/xiouLlZWVpYKCgoUFxd30b6zZ8/WihUrlJycrMmTJ8vf319TpkzRzp076+y/f/9+HTlyRElJSY2uCwAA4KcMz5H6/e9/7/h5+/bttbYbfWovPDxcb7/9tsLDw/XNN99owoQJdfbLy8vT+vXrNXHiRI0adeFx3EGDBmns2LFatGiRFi1aVGufNWvWqG/fvl49MR4AALQchoPU1KlT3VmHg8lkUnh4eIP9Nm3apICAAKWlpTnagoODNWTIEGVmZur48eOKjPzhTeRVVVWyWCz605/+1CR1AwCA1sdwkBo8eLA762i0vXv3KiYmRqGhNV+SGR9/4aWX+/btqxGkPv/8c1VUVPAiZQAA4DYt9i2TRUVFdV65qm47depUjfa1a9dq4MCBCgy8+CGfOnVKRUVFjs8FBQVuqBYAAPgiw0Fqzpw5TvVrqluAZWVlCgqq/W4nk8nk2P5jf/7zn50aNycnR1lZWS7XBwAAfJ/hIPXFF1/UaquqqlJRUZGqqqrUoUMHtWnTxqXiLiY4OFjl5eW12m02m2O7EWlpaerbt6/jc0FBgWbNmmWsSAAA4NMMB6kVK1bU2V5RUaF3331X//rXv/TMM88YLqwh4eHhOnnyZK326ttyERERhsaNiIgwvC8AAGhdDK8jVZ/AwEClp6erZ8+eWrBggbuHd4iLi9PRo0d19uzZGu15eXmO7QAAAE3J7UGqWmxsrHbs2NFUwyspKUmVlZXKyclxtNlsNq1atUoJCQk1ntgzwmKxaOrUqVq4cKGrpQIAAB/VZE/tbd++3fAcqZUrV8pqtTpu023dulUnTpyQJKWnpyssLEwJCQkaMGCAMjMzdfr0aUVHR2v16tUqLCxURkaGy/WbzWaZzWbl5+dr/PjxLo8HAAB8j+EgVd+TbVarVTt27NCePXs0evRoQ2NnZ2ersLDQ8Xnz5s3avHmzpAvv+AsLC5MkTZ8+XZGRkVqzZo2sVqu6du2quXPnKjEx0dD3AgAANIaf3W63G9nxlltuqbO9Xbt2uuKKK3Trrbdq6NCh8vPzc6lAT6u+IrVkyRJ169bN0+UAAAAvYviK1KZNm9xZBwAAQIvTYlc2b2oWi0UWi0VWq9XTpQAAAC/lcpDKzc3VJ5984pjTFBUVpT59+rT4eUpMNgcAAA0xHKTKy8s1Y8YMffTRR7Lb7Y4J4FarVdnZ2erXr5+eeOKJBt9tBwAA0FK59NTeli1bdMcdd2jkyJG67LLLJEnff/+9li9fruXLlysrK0vjxo1zW7EAAADexPCCnOvWrVNqaqomTpzoCFGSdOmll2rixIkaNGiQ1q5d65YiAQAAvJHhK1LfffedEhIS6t2ekJCgDz/80OjwHsdkcwAA0BDDV6Q6duyoL7/8st7tubm56tixo9HhPc5sNmvOnDl68MEHPV0KAADwUoaDVGpqqjZs2KD58+fr8OHDqqysVFVVlQ4fPqxnnnlGGzduVGpqqjtrBQAA8CqGb+3dddddOnbsmN577z29//77jhXM7Xa77Ha7UlNTNWbMGLcVCgAA4G0MB6mAgABNnz5dI0eO1CeffKLjx49LkiIjI9WnTx/Fxsa6rUgAAABv1KggVVZWpoULF+r//u//lJ6eLkmKjY2tFZr+9a9/6d1339XkyZNZRwoAAPisRqWc9957T6tXr9arr7560X59+vTR4sWL1bVrVw0bNsyV+jyGp/YAwLfsLyzXyTNVUmWFTAU58i89pqqQaNm6pEkBgerY3l+xUUGeLhMtTKOC1IYNG9S/f39dccUVF+0XHR2tpKQkWSyWFhukeEUMAPiO/YXlmvNWyY9aBv3w44EySWWSpKm3tyNMoVEa9dTegQMH9Itf/MKpvj//+c914MABQ0UBAOBOJ89UubUfUK1RQaq8vNzpOU+BgYGy2WyGigIAwK0qK9zbD/ifRt3ai4iI0MGDB53qe/DgQUVERBgqCgDQ+qx9b7XWHenSJGNfXrlHCujXYL8tFotWbrjGpe9K7lyglKGso9haNCpI3XjjjVqzZo3uuusuXXrppfX2+/7777VmzRolJSW5Wh8AoJU4b7PrtCKbZOxo+9dO9Quy21yu4bztkEv7o2VpVJAaPXq01q1bp4ceekgZGRl1vmsvLy9Pc+fOlc1m06hRo9xWKADAt7Ux+amDjjfJ2OV+Jqf7uVpDG5OfS/ujZWlUkLriiis0Y8YMzZgxQ5MmTVKnTp3UtWtXhYSEqLS0VAcPHtS3336r4OBgPfHEE4qOjm6qugEAPiZlaKpSmmjsT3dfqT0byhrs189s1k3xoS5+W7yL+6MlafRqmX369NHLL7+sZcuW6eOPP9ZHH33k2BYREaFbb71Vd955Z4NLJHg71pECAB8SEKjqJQ4a7gc4z89ut9tdGaC0tFRnz55VaGioQkJC3FWX16heR2rJkiXq1q2bp8sBABhQex2purGOFBrL5egdEhLikwEKAOA7YqOCNPX2dqxsDrfjGiYAoFWIjQpSbJQkBUvxPAwF92jUgpwAAAD4AUEKAADAIIIUAACAQQQpAAAAgwhSAAAABvHUXj1YkBMAADSEIFUPs9kss9nsWJATAADgp7i1BwAAYBBBCgAAwCCCFAAAgEEEKQAAAIMIUgAAAAYRpAAAAAwiSAEAABhEkAIAADCIIAUAAGAQK5vXg1fEAACAhhCk6sErYgAAQEO4tQcAAGAQQQoAAMAgghQAAIBBBCkAAACDCFIAAAAGEaQAAAAMIkgBAAAYRJACAAAwiCAFAABgEEEKAADAIIIUAACAQQQpAAAAgwhSAAAABhGkAAAADAr0dAHeymKxyGKxyGq1eroUAADgpQhS9TCbzTKbzcrPz9f48eM9XQ4AAPBC3NoDAAAwiCAFAABgEEEKAADAIIIUAACAQQQpAAAAgwhSAAAABhGkAAAADCJIAQAAGESQAgAAMIggBQAAYBBBCgAAwCCCFAAAgEEEKQAAAIMIUgAAAAYRpAAAAAwiSAEAABhEkAIAADAo0NMFNJdly5Zp5cqVslqtiomJ0cKFCxUSEuLpsgAAQAvWKoLUW2+9pW3btunFF1/U5ZdfrgMHDigwsFUcOgAAaEI+nyYqKyv12muv6YUXXlBkZKQkKTY21sNVAQAAX+B1Qaq0tFTLly9XXl6edu/erZKSEk2bNk2DBw+u1ddms2np0qVau3atSkpKFBsbq3Hjxqlnz56OPidPnlRZWZk2btyoFStWKCwsTHfccYeGDh3anIcFAAB8kNdNNi8uLlZWVpYKCgoUFxd30b6zZ8/WihUrlJycrMmTJ8vf319TpkzRzp07HX1Onjwpq9WqI0eOaMWKFZo5c6YyMzO1Y8eOpj4UAADg47wuSIWHh+vtt9/Wm2++qYkTJ9bbLy8vT+vXr9eECRM0adIkpaWlacGCBYqKitKiRYsc/YKDgyVJY8eOVXBwsGJjYzVw4EB9+umnTX4sAADAt3ldkDKZTAoPD2+w36ZNmxQQEKC0tDRHW3BwsIYMGaJdu3bp+PHjkqTOnTsrKChIfn5+jn4//hkAAMAorwtSztq7d69iYmIUGhpaoz0+Pl6StG/fPklS27Ztdcstt+jVV1+VzWbToUOH9OGHH+qmm25q9poBAIBv8brJ5s4qKiqq88pVddupU6ccbX/84x81d+5cDR06VJdcconuvfdeXX/99XWOe+rUKRUVFTk+FxQUuLlyAACM219YrpNnqqTKCpkKcuRfekxVIdGydUmTAgLVsb2/YqOCPF1mq9Fig1RZWZmCgmr/RTGZTI7t1dq1a6dZs2Y5NW5OTo6ysrLcUiMAAO60v7Bcc94q+VHLoB9+PFAm6cK/fVNvb0eYaiYtNkgFBwervLy8VrvNZnNsNyItLU19+/Z1fC4oKHA6hAEA0JROnqlyul9sVBMXA0ktOEiFh4fr5MmTtdqrb8tFREQYGjciIsLwvgAANKnKikb0M3ZBAY3TYoNUXFycvvzyS509e7bGhPO8vDzHdgAAmtva91Zr3ZEuTTL25ZV7pIB+DfbbYrFo5YZrXPqu5M4FShma6tIYrUGLDVJJSUlavny5cnJyNGrUKEkXbuutWrVKCQkJjtfBGGWxWGSxWGS1Wt1RLgCglThvs+u0XPs3qD7R9q+d6hdkt7lcw3nbIZf2by28MkitXLlSVqvVcZtu69atOnHihCQpPT1dYWFhSkhI0IABA5SZmanTp08rOjpaq1evVmFhoTIyMlyuwWw2y2w2Kz8/X+PHj3d5PABA69DG5KcOOt4kY5f7mZzu52oNbUysuegMrwxS2dnZKiwsdHzevHmzNm/eLElKSUlRWFiYJGn69OmKjIzUmjVrZLVa1bVrV82dO1eJiYmeKBsAAKUMTVVKE4396e4rtWdDWYP9+pnNuik+tMF+Fxfv4v6tg1cGqRUrVjjVLzg4WJMmTdKkSZOauCIAALxAQKCqlzhouB+aQ4td2RwAgNamY3vn/tl2th9cR2StB5PNAQDeJjYqSFNvb8fK5l7Ez2632z1dhDernmy+ZMkSdevWzdPlAAAAL8K1PwAAAIMIUgAAAAYRpAAAAAwiSAEAABjEU3v14Kk9AADQEIJUPXhFDAAAaAi39gAAAAwiSAEAABhEkAIAADCIIAUAAGAQk83rwVN7AACgIQSpevDUHgAAaAi39gAAAAwiSAEAABhEkAIAADCIIAUAAGAQQQoAAMAgntqrB8sfAACAhhCk6sHyBwAAoCHc2gMAADCIIAUAAGAQQQoAAMAgghQAAIBBBCkAAACDCFIAAAAGEaQAAAAMYh2perAgJwAAaAhBqh4syAkAABrCrT0AAACDCFIAAAAGEaQAAAAMIkgBAAAYRJACAAAwiCAFAABgEEEKAADAIIIUAACAQQQpAAAAgwhSAAAABvGKmHrwrj0AANAQglQ9eNceAABoCLf2AAAADCJIAQAAGESQAgAAMIggBQAAYBBBCgAAwCCCFAAAgEEEKQAAAIMIUgAAAAYRpAAAAAwiSAEAABhEkAIAADCIIAUAAGAQQQoAAMAgghQAAIBBgZ4uwFtZLBZZLBZZrVZPlwIAALwUQaoeZrNZZrNZ+fn5Gj9+vKfLAQAAXohbewAAAAYRpAAAAAwiSAEAABhEkAIAADCIIAUAAGAQQQoAAMAgghQAAIBBBCkAAACDCFIAAAAGEaQAAAAMIkgBAAAYRJACAAAwiCAFAABgEEEKAADAIIIUAACAQQQpAAAAgwhSAAAABgV6uoDmMHnyZOXl5SkgIECS9Itf/ELz5s3zcFUAAKClaxVBSpKmTJmilJQUT5cBAAB8CLf2AAAADPK6K1KlpaVavny58vLytHv3bpWUlGjatGkaPHhwrb42m01Lly7V2rVrVVJSotjYWI0bN049e/as1XfhwoVauHChrr76aj3wwAOKjY1tjsMBAAA+zOuuSBUXFysrK0sFBQWKi4u7aN/Zs2drxYoVSk5O1uTJk+Xv768pU6Zo586dNfrdf//9ys7O1r/+9S/16NFDjz76qEpLS5vyMAAAQCvgdUEqPDxcb7/9tt58801NnDix3n55eXlav369JkyYoEmTJiktLU0LFixQVFSUFi1aVKNvQkKCQkJCFBwcrDvvvFMhISHatWtXUx8KAADwcV4XpEwmk8LDwxvst2nTJgUEBCgtLc3RFhwcrCFDhmjXrl06fvx4vfv6+fnJbre7pV4AANB6eV2QctbevXsVExOj0NDQGu3x8fGSpH379kmSSkpK9Nlnn8lms6m8vFwrVqxQSUmJEhISmr1mAADgW7xusrmzioqK6rxyVd126tQpSVJlZaUyMzN1+PBhBQYGKi4uTnPnzlVYWFid4546dUpFRUWOz9WBrKCgwN2HAAAAmliXLl3Upk2bJhu/xQapsrIyBQUF1Wo3mUyO7ZLUoUMHLVmyxOlxc3JylJWVVat91qxZxgoFAAAeM2/ePPXu3bvJxm+xQSo4OFjl5eW12m02m2O7EWlpaerbt6/j8+7du/Xss88qIyOjwacIcWGZiQcffNDTZTjNk/U29Xe7c3x3jGV0DCP7ObtPQUGBZs2apccee0xdunRpdG2tDee393w357fz53fbtm0bXVdjtNggFR4erpMnT9Zqr74tFxERYWjciIiIOveNi4tTt27dDI3ZmoSFhbWoPydP1tvU3+3O8d0xltExjOzX2H26dOnSov7eegrnt/d8N+e38/sYvbDirBY72TwuLk5Hjx7V2bNna7Tn5eU5tqP5mc1mT5fQKJ6st6m/253ju2Mso2MY2a+l/T1sKVranyvnd/ON1ZrP7xYbpJKSklRZWamcnBxHm81m06pVq5SQkKDIyEgPVtd6edtf8IbwP9rmG6s1/4/WV7S0P1fO7+YbqzWf3155a2/lypWyWq2O23Rbt27ViRMnJEnp6ekKCwtTQkKCBgwYoMzMTJ0+fVrR0dFavXq1CgsLlZGR4bZawsPDNXbsWKfWtgLQsnB+A76ruc5vP7sXrkw5YsQIFRYW1rktOztbnTp1knThybzqd+1ZrVZ17dpV48aNU69evZqzXAAA0Ep5ZZACAABoCVrsHClvYbPZNGfOHA0fPlypqam6//779fXXX3u6LABuMm/ePA0bNkypqam65557tHXrVk+XBMDNvv76a91yyy165ZVXGr0vV6RcdO7cOWVnZ2vw4MHq2LGjNmzYoAULFig7O1shISGeLg+AiwoKCtSpUyeZTCbt3r1bDz/8sJYvX65LLrnE06UBcIOqqipNmjRJdrtdN998s+65555G7c8VKRe1bdtWY8eOVWRkpPz9/TVw4EAFBgbqyJEjni4NgBt06dLF8cYEPz8/lZeXO15BBaDle++99xQfH294UV6vfGqvKZWWlmr58uXKy8vT7t27VVJSomnTpmnw4MG1+tpsNsdk9pKSEsXGxmrcuHHq2bNnveMfOXJEJSUlio6ObsrDAFCHpjq/n332Wa1atUo2m0033XSTunbt2hyHA+BHmuL8Li4u1ptvvqlFixZp4cKFhupqdVekiouLlZWVpYKCggYX7Zw9e7ZWrFih5ORkTZ48Wf7+/poyZYp27txZZ/+ysjLNmjVLo0ePrvelyACaTlOd3w8//LDWrFmj5557Tj179pSfn19THQKAejTF+b1kyRL99re/Vbt27YwXZm9lysrK7KdOnbLb7Xb77t277f369bOvWrWqVr9du3bZ+/XrZ1+2bJmj7fz58/Y77rjDfv/999fqX15ebp8yZYp9xowZ9qqqqqY7AAD1aqrz+8cyMjLsH3/8sXsLB9Agd5/f+fn59nvvvddeUVFht9vt9qefftqelZXV6Lpa3RUpk8nk1OJcmzZtUkBAgNLS0hxtwcHBGjJkiHbt2qXjx4872quqqjRr1iz5+flp+vTp/LYKeEhTnN8/VVlZqWPHjrmlXgDOc/f5nZubqyNHjig9PV3Dhg3Thx9+qGXLlmn27NmNqqvVzZFy1t69exUTE6PQ0NAa7fHx8ZKkffv2OV5DM3/+fBUVFWn+/PkKDOSPFPB2zp7fVqtVn3zyifr27SuTyaQtW7boyy+/1IQJEzxRNgAnOHt+p6WlaeDAgY7t/+///T916tRJo0ePbtT38a9+PYqKiupMvtVt1U/tFBYW6v3335fJZKqRfv/2t7/p+uuvb55iATSKs+e3n5+f3n//fT333HOy2+2Kjo7WX/7yF1199dXNWi8A5zl7frdp00Zt2rRxbA8ODlbbtm0bPV+KIFWPsrIyBQUF1Wqvfgy6rKxMkhQVFaXNmzc3a20AXOPs+R0aGqrnn3++WWsD4Bpnz++fmj59uqHva3VzpJwVHBys8vLyWu02m82xHUDLxPkN+K7mPr8JUvUIDw9XUVFRrfbqtoiIiOYuCYCbcH4Dvqu5z2+CVD3i4uJ09OhRnT17tkZ7Xl6eYzuAlonzG/BdzX1+E6TqkZSUpMrKSuXk5DjabDabVq1apYSEBMcTewBaHs5vwHc19/ndKiebr1y5Ular1XGZb+vWrTpx4oQkKT09XWFhYUpISNCAAQOUmZmp06dPKzo6WqtXr1ZhYaEyMjI8WT6Ai+D8BnyXN57ffna73e72Ub3ciBEjVFhYWOe27OxsderUSdKFmf3V7+qxWq3q2rWrxo0bp169ejVnuQAagfMb8F3eeH63yiAFAADgDsyRAgAAMIggBQAAYBBBCgAAwCCCFAAAgEEEKQAAAIMIUgAAAAYRpAAAAAwiSAEAABhEkAIAADCIIAUAAGAQQQpAqzF58mT1799f/fv3r/Hy0v/+97/q37+/3njjjWavacuWLY6a+vfvr2+++abZawBgXKCnCwDQenzwwQeaPXt2vdsXLVqka6+9tklruPLKK3X33XerY8eObh+7oqJCt912m6688kr9/e9/r7OP3W7X8OHD1aFDBy1dulTdunXTY489ph07dui9995ze00AmhZBCkCzu/feex1vaf+x6OjoJv/uyy67TCkpKU0ydmBgoJKSkpSTk6PCwkJFRUXV6rNjxw6dPHlSI0aMkCRdfvnlSklJUWVlJUEKaIEIUgCaXe/evfWzn/3M02U0ieTkZL377ruyWCy66667am1ft26d/P39NXDgQA9UB8DdmCMFwOv8eM7SW2+9pZEjRyo5OVkPP/ywjh8/LrvdrldeeUXp6ekym82aNm2azpw549Ya7Ha75s2bp1/96lfatGmTo33t2rUaN26czGazhgwZoieffFLHjx93bL/uuusUFRUli8VSa8yKigpt2rRJ3bt3V0REhFvrBeAZBCkAze7s2bM6ffp0jf+Ki4tr9bNYLHrnnXeUnp6ukSNHaseOHXryySf10ksvadu2bbrzzjs1dOhQffzxx3rxxRfdVl9lZaX++te/as2aNXr66ad1yy23SJJeffVVPf3004qJidHvf/97/fa3v9Xnn3+uBx98UCUlJZIkPz8/JScn68CBAzp48GCNcbdt26YzZ84oOTnZbbUC8Cxu7QFodn/84x9rtZlMplpXcU6ePKlly5YpLCxMklRVVaXXX39dZWVlyszMVGDghf+FFRcXa926dXr44YdlMplcqq2iokKzZs3S1q1b9de//lW9evWSJBUWFurll1/WuHHjNGbMGEf//v37695779U777zjaE9OTtZrr72mdevWacKECY6+FotFJpPJEcwAtHwEKQDN7o9//KM6d+5co83fv/YF8qSkJEeIkqT4+HhJF4JKdYiqbrdYLDp16pSuuOIKw3VVVFToiSee0Pbt2/W3v/1N3bt3d2zbvHmzqqqqNGDAAJ0+fdrRftlllykmJkZffvmlI0hdddVVuvrqq7V+/XpHkDp37py2bt2qm2++WaGhoYZrBOBdCFIAml18fLxTk80jIyNrfK4OVZdffnmd7dW314x6/fXXde7cOc2bN69GiJKko0ePym63684776xz3x8HO+lC2HvxxRf11Vdf6brrrtOWLVt0/vx5busBPoYgBcBr1XWVSpICAgLqbLfb7S59X69evfSf//xHy5YtU2JiooKDgx3bqqqq5Ofnp3nz5tVZV9u2bWt8NpvNWrx4sSwWi6677jpZLBa1a9dON910k0s1AvAuBCkA+J+EhAT95je/0dSpU/XEE09o1qxZjitN0dHRstvt6tSpU63bknWJiIhQ9+7dtXHjRt1zzz3avn27Bg8erKCgoKY+DADNiKf2AOBHevTooSeeeELbtm3T008/raqqKkkXJpUHBATo5ZdfrnXly2631/nUYXJysr7//nvNnz9fFRUV3NYDfBBXpAA0u23btunw4cO12n/+85+7NFncXfr166dp06bp6aefVkhIiB599FFFR0fr3nvvVWZmpgoLC9WvXz+FhITo22+/1ZYtWzR06FCNGjWqxji33HKLnn32WX300Ue6/PLLdf3113voiAA0FYIUgGa3dOnSOtunTZvmFUFKklJSUlRaWqpnn31WoaGhmjRpku666y517txZb775prKysiRJHTt2VM+ePfXLX/6y1hihoaHq27evNmzYoIEDB8rPz6+ZjwJAU/Ozuzo7EwBaiMmTJ6uiokJ//etfFRQU5BXLEJSXl+vs2bNav369nn/+eWVmZvrs63MAX8QVKQCtytdff620tDT16dNHc+fO9XQ5+vTTT/XnP//Z02UAMIgrUgBajfz8fMdaUx06dFBcXJyHK5JOnz6tffv2OT4nJCQoJCTEgxUBaAyCFAAAgEEsfwAAAGAQQQoAAMAgghQAAIBBBCkAAACDCFIAAAAGEaQAAAAMIkgBAAAYRJACAAAwiCAFAABgEEEKAADAoP8PfCr8F8xO3MYAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n", "\n", "WARNING RuntimeWarning: divide by zero encountered in divide\n", "\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Accuracy plots saved with prefix 'inpainted_supervised_600_epochs_0p6containment_...'\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Total time elapsed: 604.48 seconds\n" ] } ], "source": [ "instance.estimate_bg(input_data, psr_file, background_model=background_model,\n", " training_mode=\"supervised\", containment=0.6, epochs=600, prefix=\"inpainted_supervised_600_epochs_0p6containment\", \n", " evaluate=True, show_plots=True, visualize=True, verbose=False)" ] }, { "cell_type": "markdown", "id": "06bc6593-fb1e-4587-9513-a0cbaef4bd59", "metadata": {}, "source": [ "Producing the estimted BG map took 604.5 seconds (10.1 minutes). From the diagnostic plots, we see that the agreement with the model is generally within 1% (when projected onto a given axis).\n", "\n", "Let's now take a look at the training loss for one of the energy bins:" ] }, { "cell_type": "code", "execution_count": 6, "id": "4c26ac85-3f7e-4faa-a7a0-27e13b5497e2", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for E in range(2,3):\n", " instance.plot_training_loss(\"inpainting_nn_model_training_loss.npy\",E,\"training_loss\",vmax=70000)" ] }, { "cell_type": "markdown", "id": "c759b718-aec7-4dd7-b53b-2b00b6897c27", "metadata": {}, "source": [ "The NN model is saved, including the weights, optimizer state, epochs, and training loss. Let's run the estimate again, but this time we'll laod the model from file. We just want to demonstrate the funcionality here, and so we'll set `epochs` to 1. " ] }, { "cell_type": "code", "execution_count": 7, "id": "4cfea996-3930-4eb3-89bc-2e0c05e76c73", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:...loading the pre-computed point source response ...\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:--> done\n", "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:GPUs available: 1\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Using GPU 0: NVIDIA A100-PCIE-40GB (capability 8.0)\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:loading NN model...\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Loaded NN model (weights only) from inpainting_nn_model.pth\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d5ed3f0e15c54b7ab9f4fcf7b406930e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Inpainting (Em, Phi): 0%| | 0/300 [00:00 done\n", "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Inpainted histogram saved to inpainted_simple_estimated_bg.h5\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_simple_true_E2_Phi4.pdf\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_simple_masked_E2_Phi4.pdf\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Saved visualization: inpainted_simple_inpainted_E2_Phi4.pdf\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: invalid value encountered in divide\n", "\n", "\n", "WARNING RuntimeWarning: divide by zero encountered in divide\n", "\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.background_estimation.ContinuumEstimationNN:Accuracy plots saved with prefix 'inpainted_simple_...'\n", "INFO:cosipy.background_estimation.ContinuumEstimationNN:Total time elapsed: 3.29 seconds\n" ] } ], "source": [ "instance = ContinuumEstimationInterp()\n", "instance.estimate_bg(input_data, psr_file, background_model=background_model,\n", " prefix=\"inpainted_simple\", evaluate=True, show_plots=True, visualize=True, verbose=False)" ] }, { "cell_type": "markdown", "id": "0d4cdffd-8afe-4265-b175-a515c7f75f61", "metadata": {}, "source": [ "The agreement seems to be a bit worse compared to the NN-based method (within ~2%). " ] }, { "cell_type": "markdown", "id": "f3db8dc1-743d-4bbd-8bda-73144f7e219d", "metadata": {}, "source": [ "## Spectral fit using the estimated background\n", "This example is nearly identical to the spectral fit example in docs/api/interfaces/examples/crab. The main difference is that instead of the ideal BG file, we will use the estimated BG calculated in the previous section.\n", "\n", "Imports:" ] }, { "cell_type": "code", "execution_count": 9, "id": "cec2141e-a22f-464b-ae40-2293bb5f6575", "metadata": {}, "outputs": [], "source": [ "from cosipy import test_data, BinnedData\n", "from cosipy.spacecraftfile import SpacecraftHistory\n", "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n", "from cosipy.util import fetch_wasabi_file\n", "\n", "from cosipy.statistics import PoissonLikelihood\n", "from cosipy.background_estimation import FreeNormBinnedBackground\n", "from cosipy.interfaces import ThreeMLPluginInterface\n", "from cosipy.response import BinnedThreeMLModelFolding, BinnedInstrumentResponse, BinnedThreeMLPointSourceResponse\n", "from cosipy.data_io import EmCDSBinnedData\n", "\n", "import sys\n", "\n", "from scoords import SpacecraftFrame\n", "\n", "from astropy.time import Time\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord, Galactic\n", "\n", "import astromodels\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n", "from astromodels import Parameter, Powerlaw\n", "\n", "from pathlib import Path\n", "\n", "import os" ] }, { "cell_type": "markdown", "id": "9d7ea122-fb89-4e51-aac9-72643033c241", "metadata": {}, "source": [ "In addition to the files above, you will need the following files:\n", "\n", "1. inputs_crab.yaml\n", "2. crab_DC2_astromodel.yaml\n", "2. DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits\n", "3. ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\n", "\n", "These can be downloaded using the cells below:" ] }, { "cell_type": "code", "execution_count": 18, "id": "64a1136a-f49f-4f22-9d9e-41e87bbd9b6a", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/inputs_crab.yaml\n" ] } ], "source": [ "# inputs_crab.yaml\n", "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/inputs_crab.yaml', checksum = 'dd30283e1809ccc51d75ad22cf20e766')" ] }, { "cell_type": "code", "execution_count": 19, "id": "a17f0833-2c79-44d8-9652-4ee35b4d9e35", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_DC2_astromodel.yaml\n" ] } ], "source": [ "# crab_DC2_astromodel.yaml\n", "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/background_estimationNN/crab_DC2_astromodel.yaml', checksum = 'b8366b8e09ede0f29581d1ec466628c6')" ] }, { "cell_type": "code", "execution_count": 20, "id": "1fc66b8e-9bf6-4257-991c-827811c56dcd", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/DC3/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\n" ] } ], "source": [ "# DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits\n", "fetch_wasabi_file('COSI-SMEX/develop/Data/Orientation/DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits', checksum = 'ca94ff1d7a73c1f41479aaf598807673')" ] }, { "cell_type": "code", "execution_count": 23, "id": "472b0bb0-7f47-4e6c-99a4-b599999a5cf6", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.util.data_fetching:Downloading cosi-pipeline-public/COSI-SMEX/develop/Data/Responses/ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\n" ] } ], "source": [ "# ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5 \n", "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5', checksum = '7121f094be50e7bfe9b31e53015b0e85')" ] }, { "cell_type": "markdown", "id": "6b65cfcd-910f-43ce-a612-229bd6d0c037", "metadata": {}, "source": [ "Define all input files:" ] }, { "cell_type": "code", "execution_count": 10, "id": "30dd030f-aadb-4085-978d-0fc5080c4441", "metadata": {}, "outputs": [], "source": [ "data_path = \"./\"\n", "total_data = os.path.join(data_path,\"crab_and_bg_combined_binned_data.h5\")\n", "crab_data = os.path.join(data_path, \"crab_binned_data.hdf5\")\n", "psr_file = os.path.join(data_path,\"crab_psr.h5\")\n", "background_model = os.path.join(data_path,\"Total_BG_3months_binned_for_continuum_filtered_with_SAAcut.hdf5\")\n", "input_yaml = os.path.join(data_path,\"inputs_crab.yaml\")\n", "ori_file = os.path.join(data_path,\"DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.fits\")\n", "dr_file = os.path.join(data_path,\"ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\")" ] }, { "cell_type": "markdown", "id": "904c2322-78b4-468c-b97a-804e4393912c", "metadata": { "tags": [] }, "source": [ "The estimated BG file was calculated at the last step:" ] }, { "cell_type": "code", "execution_count": 11, "id": "3e378bfd-e1b2-4a01-b3a7-ef64747cda7d", "metadata": { "tags": [] }, "outputs": [], "source": [ "estimated_bg = \"inpainted_supervised_600_epochs_0p6containment_estimated_bg.h5\"\n", "estimated_bg_simple = \"inpainted_simple_estimated_bg.h5\"" ] }, { "cell_type": "markdown", "id": "c00eb73f-6abd-4ef9-bb97-8800c2586acb", "metadata": {}, "source": [ "Load files:" ] }, { "cell_type": "code", "execution_count": 12, "id": "7edb9275-e105-4e52-8da8-e91cc60bae4c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n" ] } ], "source": [ "# Orientation file:\n", "sc_orientation = SpacecraftHistory.open(ori_file)\n", "\n", "# Detector response:\n", "dr = FullDetectorResponse.open(dr_file)\n", "\n", "# Load Crab data:\n", "crab = BinnedData(input_yaml)\n", "crab.load_binned_data_from_hdf5(binned_data=crab_data)\n", "\n", "# Load BG model true:\n", "bg = BinnedData(input_yaml)\n", "bg.load_binned_data_from_hdf5(binned_data=background_model)\n", "\n", "# Load BG model estimated:\n", "bg_est = BinnedData(input_yaml)\n", "bg_est.load_binned_data_from_hdf5(binned_data=estimated_bg)\n", "\n", "# Load BG model estimated simple:\n", "bg_est_simple = BinnedData(input_yaml)\n", "bg_est_simple.load_binned_data_from_hdf5(binned_data=estimated_bg_simple)\n", "\n", "# Load total data:\n", "total = BinnedData(input_yaml)\n", "total.load_binned_data_from_hdf5(binned_data=total_data)" ] }, { "cell_type": "markdown", "id": "016814a8-7d13-47e6-9305-03a3b66efa56", "metadata": {}, "source": [ "Define interfaces below. Here we will use the FreeNormBinnedBackground interface. The only difference is that we will use the estimated BG instead of the true BG. " ] }, { "cell_type": "code", "execution_count": 13, "id": "fbefb778-8d3a-4395-937d-d4138160f5c1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "WARNING RuntimeWarning: divide by zero encountered in divide\n", "\n" ] } ], "source": [ "# ------- Interfaces ----------\n", "\n", "# Total data:\n", "data = EmCDSBinnedData(total.binned_data.project('Em', 'Phi', 'PsiChi'))\n", "\n", "# Response\n", "instrument_response = BinnedInstrumentResponse(dr, data)\n", "\n", "# BG:\n", "bkg_dist = {\"total_bkg\":bg_est.binned_data.project('Em', 'Phi', 'PsiChi')}\n", "\n", "#for bckfile in bkg_dist.keys():\n", "# bkg_dist[bckfile] += sys.float_info.min\n", " \n", "bkg = FreeNormBinnedBackground(bkg_dist,\n", " sc_history=sc_orientation,\n", " copy = False)\n", "\n", "psr = BinnedThreeMLPointSourceResponse(data = data,\n", " instrument_response = instrument_response,\n", " sc_history=sc_orientation,\n", " energy_axis = dr.axes['Ei'],\n", " polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n", " nside = 2*data.axes['PsiChi'].nside)\n", "\n", "response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n", "\n", "like_fun = PoissonLikelihood(data, response, bkg)\n", "\n", "cosi = ThreeMLPluginInterface('cosi',\n", " like_fun,\n", " response,\n", " bkg)" ] }, { "cell_type": "markdown", "id": "a83c40ec-7378-409c-a380-79b0837120b5", "metadata": {}, "source": [ "Nuisance parameters, model, and plugin:" ] }, { "cell_type": "code", "execution_count": 14, "id": "1add1e17-cf35-49aa-9e2a-e6e30e9a69ee", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
14:06:39 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:06:39\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=408782;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=842630;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Nuisance parameter guess, bounds, etc.\n", "for bkg_label in bkg_dist.keys():\n", " cosi.bkg_parameter[bkg_label] = Parameter(bkg_label, # background parameter\n", " 1, # initial value of parameter\n", " min_value=0, # minimum value of parameter\n", " max_value= 100, # maximum value of parameter\n", " delta=0.05, # initial step used by fitting engine\n", " unit = u.Hz\n", " )\n", " \n", "# Load the astromodel for the Crab (this is the same model as \n", "# used in the DC2 Crab tutorial):\n", "model = astromodels.load_model('./crab_DC2_astromodel.yaml')\n", "\n", "\n", "# Define plugin and fit:\n", "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...) " ] }, { "cell_type": "markdown", "id": "b66d5cae-e5b6-4ad2-95e0-85f129fb780e", "metadata": { "tags": [] }, "source": [ "Perform likelihood fit:" ] }, { "cell_type": "code", "execution_count": 15, "id": "d0d9e7f8-dce5-4abf-94a5-05784352e666", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
14:06:43 INFO      set the minimizer to minuit                                              joint_likelihood.py:994\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:06:43\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=701321;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=617277;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.response.threeml_point_source_response:... Calculating point source response ...\n", "INFO:cosipy.response.threeml_point_source_response:--> done (source name : source)\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", " the requested tolerance from being achieved. The error may be \n", " underestimated.\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n", " the requested tolerance from being achieved. The error may be \n", " underestimated.\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: overflow encountered in _pow\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n" ] }, { "data": { "text/html": [ "
14:07:13 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:07:13\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=230012;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=607988;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Best fit values:\n",
       "\n",
       "
\n" ], "text/plain": [ "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
resultunit
parameter
source.spectrum.main.Band.K(3.243 +/- 0.009) x 10^-51 / (keV s cm2)
total_bkg(3.57217 +/- 0.00031) x 10Hz
\n", "
" ], "text/plain": [ " result unit\n", "parameter \n", "source.spectrum.main.Band.K (3.243 +/- 0.009) x 10^-5 1 / (keV s cm2)\n", "total_bkg (3.57217 +/- 0.00031) x 10 Hz" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Correlation matrix:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "
1.00-0.65
-0.651.00
" ], "text/plain": [ " 1.00 -0.65\n", "-0.65 1.00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Values of -log(likelihood) at the minimum:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
-log(likelihood)
cosi-1591385619.4884508
total-1591385619.4884508
\n", "
" ], "text/plain": [ " -log(likelihood)\n", "cosi -1591385619.4884508\n", "total -1591385619.4884508" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Values of statistical measures:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
statistical measures
AIC-3182771234.9768496
BIC-3182771214.281757
\n", "
" ], "text/plain": [ " statistical measures\n", "AIC -3182771234.9768496\n", "BIC -3182771214.281757" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "( value negative_error positive_error \\\n", " source.spectrum.main.Band.K 0.000032 -8.268109e-08 8.567800e-08 \n", " total_bkg 35.721728 -3.062963e-03 3.050569e-03 \n", " \n", " error unit \n", " source.spectrum.main.Band.K 8.417955e-08 1 / (keV s cm2) \n", " total_bkg 3.056766e-03 Hz ,\n", " -log(likelihood)\n", " cosi -1591385619.4884508\n", " total -1591385619.4884508)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "like = JointLikelihood(model, plugins, verbose = False)\n", "\n", "like.fit()" ] }, { "cell_type": "markdown", "id": "49a925f2-683e-488b-91b9-ba931cb7ad7a", "metadata": {}, "source": [ "Compare best-fit to injected source:" ] }, { "cell_type": "code", "execution_count": 16, "id": "ce946e23-5c79-400e-8210-80eef76a577a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
14:07:29 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:07:29\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=350057;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=401087;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=702322;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=607582;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get expectation\n", "\n", "results = like.results\n", "\n", "\n", "#print(results.display())\n", "\n", "parameters = {par.name:results.get_variates(par.path)\n", " for par in results.optimized_model[\"source\"].parameters.values()\n", " if par.free}\n", "\n", "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", "\n", "#print(results.optimized_model[\"source\"])\n", " \n", "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", "\n", "flux_lo = np.zeros_like(energy)\n", "flux_median = np.zeros_like(energy)\n", "flux_hi = np.zeros_like(energy)\n", "flux_inj = np.zeros_like(energy)\n", "\n", "for i, e in enumerate(energy):\n", " flux = results_err(e)\n", " flux_median[i] = flux.median\n", " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", " #flux_inj[i] = spectrum_inj.evaluate_at(e)\n", "\n", "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n", "binned_energy = np.array([])\n", "bin_sizes = np.array([])\n", "\n", "for i in range(len(binned_energy_edges)-1):\n", " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", "\n", "expectation = response.expectation(copy = True) \n", "exp_est = expectation # for later comparison\n", "\n", "# Plot the fitted and injected spectra\n", "fig,ax = plt.subplots()\n", "\n", "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", "#ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", "\n", "ax.legend()\n", "\n", "ax.set_ylim(.1,100)\n", "plt.show()\n", "\n", "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", "#ax.stairs(expectation_inj.project('Em').todense().contents, binned_energy_edges, color='blue', label = \"Injected spectrum convolved with response\")\n", "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "\n", "plt.show()\n", "\n", "# Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts \n", "expectation_bkg = bkg.expectation(copy = True)\n", "\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(expectation.project('Em').todense().contents + expectation_bkg.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "ax.stairs(data.data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", "ax.errorbar(binned_energy, data.data.project('Em').todense().contents, yerr=np.sqrt(data.data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ed584e47-887b-47e7-8c8b-4f48f375e71f", "metadata": {}, "source": [ "Let's do the same thing for the simple inpainting. It's the same procedure, and so we'll run everything together. " ] }, { "cell_type": "code", "execution_count": 17, "id": "e7771329-f14c-4a9b-a5e1-e7b0e152f9be", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n" ] }, { "data": { "text/html": [ "
14:08:35 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:08:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=114718;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=208865;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
14:08:35 INFO      set the minimizer to minuit                                              joint_likelihood.py:994\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:08:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=421350;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=512278;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.response.threeml_point_source_response:... Calculating point source response ...\n", "INFO:cosipy.response.threeml_point_source_response:--> done (source name : source)\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n", "\n", "WARNING RuntimeWarning: invalid value encountered in log\n", "\n" ] }, { "data": { "text/html": [ "
14:09:03 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:09:03\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=868136;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=229769;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Best fit values:\n",
       "\n",
       "
\n" ], "text/plain": [ "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
resultunit
parameter
source.spectrum.main.Band.K(3.589 +/- 0.009) x 10^-51 / (keV s cm2)
total_bkg(3.56374 +/- 0.00031) x 10Hz
\n", "
" ], "text/plain": [ " result unit\n", "parameter \n", "source.spectrum.main.Band.K (3.589 +/- 0.009) x 10^-5 1 / (keV s cm2)\n", "total_bkg (3.56374 +/- 0.00031) x 10 Hz" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Correlation matrix:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "
1.00-0.66
-0.661.00
" ], "text/plain": [ " 1.00 -0.66\n", "-0.66 1.00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Values of -log(likelihood) at the minimum:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
-log(likelihood)
cosi-1591401274.5105834
total-1591401274.5105834
\n", "
" ], "text/plain": [ " -log(likelihood)\n", "cosi -1591401274.5105834\n", "total -1591401274.5105834" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Values of statistical measures:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
statistical measures
AIC-3182802545.021115
BIC-3182802524.326022
\n", "
" ], "text/plain": [ " statistical measures\n", "AIC -3182802545.021115\n", "BIC -3182802524.326022" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=814663;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=34050;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=710629;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=150383;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Orientation file:\n", "sc_orientation = SpacecraftHistory.open(ori_file)\n", "\n", "# Detector response:\n", "dr = FullDetectorResponse.open(dr_file)\n", "\n", "# Load Crab data:\n", "crab = BinnedData(input_yaml)\n", "crab.load_binned_data_from_hdf5(binned_data=crab_data)\n", "\n", "# Load BG model true:\n", "bg = BinnedData(input_yaml)\n", "bg.load_binned_data_from_hdf5(binned_data=background_model)\n", "\n", "# Load BG model estimated:\n", "bg_est = BinnedData(input_yaml)\n", "bg_est.load_binned_data_from_hdf5(binned_data=estimated_bg)\n", "\n", "# Load BG model estimated simple:\n", "bg_est_simple = BinnedData(input_yaml)\n", "bg_est_simple.load_binned_data_from_hdf5(binned_data=estimated_bg_simple)\n", "\n", "# Load total data:\n", "total = BinnedData(input_yaml)\n", "total.load_binned_data_from_hdf5(binned_data=total_data)\n", "\n", "# ------- Interfaces ----------\n", "\n", "# Total data:\n", "data = EmCDSBinnedData(total.binned_data.project('Em', 'Phi', 'PsiChi'))\n", "\n", "# Response\n", "instrument_response = BinnedInstrumentResponse(dr, data)\n", "\n", "# BG:\n", "bkg_dist = {\"total_bkg\":bg_est_simple.binned_data.project('Em', 'Phi', 'PsiChi')}\n", "#for bckfile in bkg_dist.keys():\n", "# bkg_dist[bckfile] += sys.float_info.min\n", " \n", "bkg = FreeNormBinnedBackground(bkg_dist,\n", " sc_history=sc_orientation,\n", " copy = False)\n", "\n", "psr = BinnedThreeMLPointSourceResponse(data = data,\n", " instrument_response = instrument_response,\n", " sc_history=sc_orientation,\n", " energy_axis = dr.axes['Ei'],\n", " polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n", " nside = 2*data.axes['PsiChi'].nside)\n", "\n", "response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n", "\n", "like_fun = PoissonLikelihood(data, response, bkg)\n", "\n", "cosi = ThreeMLPluginInterface('cosi',\n", " like_fun,\n", " response,\n", " bkg)\n", "\n", "# Nuisance parameter guess, bounds, etc.\n", "for bkg_label in bkg_dist.keys():\n", " cosi.bkg_parameter[bkg_label] = Parameter(bkg_label, # background parameter\n", " 1, # initial value of parameter\n", " min_value=0, # minimum value of parameter\n", " max_value= 100, # maximum value of parameter\n", " delta=0.05, # initial step used by fitting engine\n", " unit = u.Hz\n", " )\n", " \n", "# Load the astromodel for the Crab (this is the same model as \n", "# used in the DC2 Crab tutorial):\n", "model = astromodels.load_model('./crab_DC2_astromodel.yaml')\n", "\n", "\n", "# Define plugin and fit:\n", "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...) \n", "\n", "like = JointLikelihood(model, plugins, verbose = False)\n", "\n", "like.fit()\n", "\n", "# Get expectation\n", "\n", "results = like.results\n", "\n", "\n", "#print(results.display())\n", "\n", "parameters = {par.name:results.get_variates(par.path)\n", " for par in results.optimized_model[\"source\"].parameters.values()\n", " if par.free}\n", "\n", "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", "\n", "#print(results.optimized_model[\"source\"])\n", " \n", "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", "\n", "flux_lo = np.zeros_like(energy)\n", "flux_median = np.zeros_like(energy)\n", "flux_hi = np.zeros_like(energy)\n", "flux_inj = np.zeros_like(energy)\n", "\n", "for i, e in enumerate(energy):\n", " flux = results_err(e)\n", " flux_median[i] = flux.median\n", " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", " #flux_inj[i] = spectrum_inj.evaluate_at(e)\n", "\n", "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n", "binned_energy = np.array([])\n", "bin_sizes = np.array([])\n", "\n", "for i in range(len(binned_energy_edges)-1):\n", " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", "\n", "expectation = response.expectation(copy = True) \n", "exp_est_simple = expectation # for later comparison\n", "\n", "# Plot the fitted and injected spectra\n", "fig,ax = plt.subplots()\n", "\n", "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", "#ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", "\n", "ax.legend()\n", "\n", "ax.set_ylim(.1,100)\n", "plt.show()\n", "\n", "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", "#ax.stairs(expectation_inj.project('Em').todense().contents, binned_energy_edges, color='blue', label = \"Injected spectrum convolved with response\")\n", "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "\n", "plt.show()\n", "\n", "# Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts \n", "expectation_bkg = bkg.expectation(copy = True)\n", "\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(expectation.project('Em').todense().contents + expectation_bkg.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "ax.stairs(data.data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", "ax.errorbar(binned_energy, data.data.project('Em').todense().contents, yerr=np.sqrt(data.data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4958f127-b1f0-47fd-b05e-3c3ccdb9d6df", "metadata": {}, "source": [ "Finally, let's see what we get using the ideal BG. " ] }, { "cell_type": "code", "execution_count": 18, "id": "323ef337-4621-47ed-b87b-09c4dd874ce1", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n", "INFO:yayc.configurator:Using configuration file at ./inputs_crab.yaml\n" ] }, { "data": { "text/html": [ "
14:11:05 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:11:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=346398;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=32617;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
14:11:05 INFO      set the minimizer to minuit                                              joint_likelihood.py:994\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:11:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=558430;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=263220;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/threeML/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:cosipy.response.threeml_point_source_response:... Calculating point source response ...\n", "INFO:cosipy.response.threeml_point_source_response:--> done (source name : source)\n" ] }, { "data": { "text/html": [ "
14:11:35 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m14:11:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=573625;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=315275;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Best fit values:\n",
       "\n",
       "
\n" ], "text/plain": [ "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
resultunit
parameter
source.spectrum.main.Band.K(2.927 +/- 0.009) x 10^-51 / (keV s cm2)
total_bkg(3.57954 +/- 0.00031) x 10Hz
\n", "
" ], "text/plain": [ " result unit\n", "parameter \n", "source.spectrum.main.Band.K (2.927 +/- 0.009) x 10^-5 1 / (keV s cm2)\n", "total_bkg (3.57954 +/- 0.00031) x 10 Hz" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Correlation matrix:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", "
1.00-0.66
-0.661.00
" ], "text/plain": [ " 1.00 -0.66\n", "-0.66 1.00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Values of -log(likelihood) at the minimum:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
-log(likelihood)
cosi-1591705157.1250365
total-1591705157.1250365
\n", "
" ], "text/plain": [ " -log(likelihood)\n", "cosi -1591705157.1250365\n", "total -1591705157.1250365" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n",
       "Values of statistical measures:\n",
       "\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
statistical measures
AIC-3183410310.250021
BIC-3183410289.5549283
\n", "
" ], "text/plain": [ " statistical measures\n", "AIC -3183410310.250021\n", "BIC -3183410289.5549283" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=878335;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=548787;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:810\n",
       "
\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=923149;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=26314;file:///project/ckarwin/astro/chris/my_envs/cosipy_pr_IM/lib/python3.10/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Orientation file:\n", "sc_orientation = SpacecraftHistory.open(ori_file)\n", "\n", "# Detector response:\n", "dr = FullDetectorResponse.open(dr_file)\n", "\n", "# Load Crab data:\n", "crab = BinnedData(input_yaml)\n", "crab.load_binned_data_from_hdf5(binned_data=crab_data)\n", "\n", "# Load BG model true:\n", "bg = BinnedData(input_yaml)\n", "bg.load_binned_data_from_hdf5(binned_data=background_model)\n", "\n", "# Load BG model estimated:\n", "bg_est = BinnedData(input_yaml)\n", "bg_est.load_binned_data_from_hdf5(binned_data=estimated_bg)\n", "\n", "# Load BG model estimated simple:\n", "bg_est_simple = BinnedData(input_yaml)\n", "bg_est_simple.load_binned_data_from_hdf5(binned_data=estimated_bg_simple)\n", "\n", "# Load total data:\n", "total = BinnedData(input_yaml)\n", "total.load_binned_data_from_hdf5(binned_data=total_data)\n", "\n", "# ------- Interfaces ----------\n", "\n", "# Total data:\n", "data = EmCDSBinnedData(total.binned_data.project('Em', 'Phi', 'PsiChi'))\n", "\n", "# Response\n", "instrument_response = BinnedInstrumentResponse(dr, data)\n", "\n", "# BG:\n", "bkg_dist = {\"total_bkg\":bg.binned_data.project('Em', 'Phi', 'PsiChi')}\n", "\n", "#for bckfile in bkg_dist.keys():\n", "# bkg_dist[bckfile] += sys.float_info.min\n", " \n", "bkg = FreeNormBinnedBackground(bkg_dist,\n", " sc_history=sc_orientation,\n", " copy = False)\n", "\n", "psr = BinnedThreeMLPointSourceResponse(data = data,\n", " instrument_response = instrument_response,\n", " sc_history=sc_orientation,\n", " energy_axis = dr.axes['Ei'],\n", " polarization_axis = dr.axes['Pol'] if 'Pol' in dr.axes.labels else None,\n", " nside = 2*data.axes['PsiChi'].nside)\n", "\n", "response = BinnedThreeMLModelFolding(data = data, point_source_response = psr)\n", "\n", "like_fun = PoissonLikelihood(data, response, bkg)\n", "\n", "cosi = ThreeMLPluginInterface('cosi',\n", " like_fun,\n", " response,\n", " bkg)\n", "\n", "# Nuisance parameter guess, bounds, etc.\n", "for bkg_label in bkg_dist.keys():\n", " cosi.bkg_parameter[bkg_label] = Parameter(bkg_label, # background parameter\n", " 1, # initial value of parameter\n", " min_value=0, # minimum value of parameter\n", " max_value= 100, # maximum value of parameter\n", " delta=0.05, # initial step used by fitting engine\n", " unit = u.Hz\n", " )\n", " \n", "# Load the astromodel for the Crab (this is the same model as \n", "# used in the DC2 Crab tutorial):\n", "model = astromodels.load_model('./crab_DC2_astromodel.yaml')\n", "\n", "\n", "# Define plugin and fit:\n", "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...) \n", "\n", "like = JointLikelihood(model, plugins, verbose = False)\n", "\n", "like.fit()\n", "\n", "# Get expectation\n", "\n", "results = like.results\n", "\n", "\n", "#print(results.display())\n", "\n", "parameters = {par.name:results.get_variates(par.path)\n", " for par in results.optimized_model[\"source\"].parameters.values()\n", " if par.free}\n", "\n", "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", "\n", "#print(results.optimized_model[\"source\"])\n", " \n", "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", "\n", "flux_lo = np.zeros_like(energy)\n", "flux_median = np.zeros_like(energy)\n", "flux_hi = np.zeros_like(energy)\n", "flux_inj = np.zeros_like(energy)\n", "\n", "for i, e in enumerate(energy):\n", " flux = results_err(e)\n", " flux_median[i] = flux.median\n", " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", " #flux_inj[i] = spectrum_inj.evaluate_at(e)\n", "\n", "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n", "binned_energy = np.array([])\n", "bin_sizes = np.array([])\n", "\n", "for i in range(len(binned_energy_edges)-1):\n", " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", "\n", "expectation = response.expectation(copy = True) \n", "exp_true = expectation # for later comparison\n", "\n", "# Plot the fitted and injected spectra\n", "fig,ax = plt.subplots()\n", "\n", "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", "#ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", "\n", "ax.legend()\n", "\n", "ax.set_ylim(.1,100)\n", "plt.show()\n", "\n", "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", "#ax.stairs(expectation_inj.project('Em').todense().contents, binned_energy_edges, color='blue', label = \"Injected spectrum convolved with response\")\n", "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "\n", "plt.show()\n", "\n", "# Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts \n", "expectation_bkg = bkg.expectation(copy = True)\n", "\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(expectation.project('Em').todense().contents + expectation_bkg.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents+expectation_bkg.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "ax.stairs(data.data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", "ax.errorbar(binned_energy, data.data.project('Em').todense().contents, yerr=np.sqrt(data.data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "474e919e-bb5d-4a2a-8f25-b527b158d1ce", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "code", "execution_count": 47, "id": "8270b35f-2c62-46bf-bd05-2ff9b684b518", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the fitted spectrum convolved with the response, as well as the simulated source counts\n", "fig,ax = plt.subplots()\n", "\n", "ax.stairs(exp_est.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response (NN)\")\n", "ax.errorbar(binned_energy, exp_est.project('Em').todense().contents, yerr=np.sqrt(exp_est.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", "\n", "ax.stairs(exp_est_simple.project('Em').todense().contents, binned_energy_edges, color='red', label = \"Best fit convolved with response (Simple)\")\n", "ax.errorbar(binned_energy, exp_est_simple.project('Em').todense().contents, yerr=np.sqrt(exp_est_simple.project('Em').todense().contents), color='red', linewidth=0, elinewidth=1)\n", "\n", "ax.stairs(exp_true.project('Em').todense().contents, binned_energy_edges, color='cyan', label = \"Best fit convolved with response (true)\")\n", "ax.errorbar(binned_energy, exp_true.project('Em').todense().contents, yerr=np.sqrt(exp_est.project('Em').todense().contents), color='cyan', linewidth=0, elinewidth=1)\n", "\n", "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_yscale(\"log\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"Counts\")\n", "\n", "ax.legend()\n", "plt.title(\"Crab\")\n", "plt.show()\n", "\n", "# fractional residuals:\n", "fig,ax = plt.subplots()\n", "\n", "xerr = crab.binned_data.axes['Em'].widths.value / 2.0\n", "\n", "# NN inpainted:\n", "x = (crab.binned_data.project('Em').todense().contents - exp_est.project('Em').todense().contents) / crab.binned_data.project('Em').todense().contents\n", "plt.semilogx(binned_energy, x, label=\"NN inpainting\", ls=\"\", marker=\"s\", color=\"purple\")\n", "plt.errorbar(binned_energy, x, xerr=xerr, ls=\"\", marker=\"s\", color=\"purple\")\n", "\n", "# simple inpainted:\n", "x = (crab.binned_data.project('Em').todense().contents - exp_est_simple.project('Em').todense().contents) / crab.binned_data.project('Em').todense().contents\n", "plt.semilogx(binned_energy, x, label=\"Simple inpainting\", ls=\"\", marker=\"s\", color=\"red\")\n", "plt.errorbar(binned_energy, x, xerr=xerr, ls=\"\", marker=\"s\", color=\"red\")\n", "\n", "# True:\n", "x = (crab.binned_data.project('Em').todense().contents - exp_true.project('Em').todense().contents) / crab.binned_data.project('Em').todense().contents\n", "plt.semilogx(binned_energy, x, label=\"Ideal BG\", ls=\"\", marker=\"s\", color=\"cyan\")\n", "plt.errorbar(binned_energy, x, xerr=xerr, ls=\"\", marker=\"s\", color=\"cyan\")\n", "\n", "ax.set_xlabel(\"Energy (keV)\")\n", "ax.set_ylabel(\"(true - recovered)/true\")\n", "\n", "plt.legend(loc=1)\n", "plt.ylim(-0.5,0.5)\n", "plt.xlim(1e2,1e4)\n", "plt.grid(ls=\":\",color=\"grey\",alpha=0.4)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "52e9c8da-2020-4174-96fa-f9982ba6fda6", "metadata": {}, "source": [ "input: norm = 3.07e-5 \n", "\n", "estimated BG NN: norm = (3.243 +/- 0.009)e-5, logL = 1591385619.4884508 \n", "\n", "estimated BG simple: norm = (3.589 +/- 0.009)e-5, logL = 1591401274.5105834 \n", "\n", "ideal BG: norm = (2.927 +/- 0.009)e-5, logL = 1591705157.1250365 \n", "\n", "Comparing estimated BG (simple) to true BG:\n", "$2 \\Delta \\mathrm{logL}$ = 607765.2 $\\implies \\sigma = 780$ (true BG is preferred)\n", "\n", "Comparing estimated BG simple to GCN:\n", "$2 \\Delta \\mathrm{logL}$ = 31310.0 $\\implies \\sigma = 177$ (simple inpainting is preferred)\n", "\n", "Thus, the ideal BG performs significanlty better than the estimated, as expected. The NN estimate gives a normalization closer to the true value. The counts also look better at most energies (except for the last 2 bins). However, the simple alogirthm gives a significantly better TS compared to the NN-based inpainting. The next step is to improve the estimation algorithm to get closer to the ideal BG." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 }