From 06b381f4ed18db56ec5a5c303d975079ddf724b7 Mon Sep 17 00:00:00 2001 From: Kern Lab Date: Fri, 17 Nov 2017 14:53:06 -0500 Subject: [PATCH] more cleanup --- .../compareTwoVersions-checkpoint.ipynb | 351 ------------------ compareTwoVersions.ipynb | 351 ------------------ 2 files changed, 702 deletions(-) delete mode 100644 .ipynb_checkpoints/compareTwoVersions-checkpoint.ipynb delete mode 100644 compareTwoVersions.ipynb diff --git a/.ipynb_checkpoints/compareTwoVersions-checkpoint.ipynb b/.ipynb_checkpoints/compareTwoVersions-checkpoint.ipynb deleted file mode 100644 index be9ba99..0000000 --- a/.ipynb_checkpoints/compareTwoVersions-checkpoint.ipynb +++ /dev/null @@ -1,351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quick comparison of the old discoal with the new trajectory update" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#first simulate without selection\n", - "!./discoal 10 1000 10000 -r 20 -t 10 -en 0.05 0 0.1 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 10000 -r 20 -t 10 -en 0.05 0 0.1 | niceStats > testUpdate_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fetching package metadata ...........\n", - "Solving package specifications: .\n", - "\n", - "Package plan for installation in environment /Users/adk/anaconda:\n", - "\n", - "The following packages will be UPDATED:\n", - "\n", - " conda: 4.3.27-py36hb556a21_0 --> 4.3.30-py36h173c244_0\n", - "\n", - "The following packages will be SUPERSEDED by a higher-priority channel:\n", - "\n", - " conda-env: 2.6.0-0 --> 2.6.0-h36134e3_0 \n", - " seaborn: 0.8-py36_0 --> 0.8.0-py36h74df97e_0 \n", - "\n", - "conda-env-2.6. 100% |################################| Time: 0:00:00 2.76 MB/s\n", - "conda-4.3.30-p 100% |################################| Time: 0:00:00 9.28 MB/s\n", - "seaborn-0.8.0- 100% |################################| Time: 0:00:00 12.51 MB/s\n" - ] - } - ], - "source": [ - "#make boxplots in the seaborn style, do some install and imports\n", - "!conda install seaborn --yes\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame()\n", - "import numpy as np\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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w1nhzAAQLQc5jrAtVmuLxeO9B011dXZ6ePcqbA4wl3hwAwUKQA8ZAU1NT1nFF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\npImWAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pylab as plt\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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/dTUfv9x66615429+85uOZgKgEnEnMoBCTlnmMcbMlnSHpHOttdcaYy6RdJW1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\n3HKLJOmuu+6a0GOPvNg7obg4tWg0qtWrV7ueBoA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# simulate with selection, but equilibrium\n", - "#!./discoal 10 1000 10000 -r 20 -t 100 -ws 0 -a 10000 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 10000 -r 20 -t 100 -ws 0 -a 10000 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,10)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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bj6D3rpUpv8516hgAINarb8r50CsPe0+4sXhraq890Nr+PUdRfuqPw/eecGN4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\nkpvrPlw7XH6ae7oPk3wmyffq7MmdSd4wvNCeMpKllAeAvwJ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# simulate with selection, and single popnSize change\n", - "#!./discoal 10 1000 10000 -r 20 -t 100 -ws 0 -a 1000 -en 0.01 0 0.1 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 10000 -r 20 -t 100 -ws 0 -a 1000 -en 0.01 0 0.1 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,10)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ecamw1yoQLBUZ5Ni8HgBGNny5EZYfAfzNTa/eh+LxuDo7O+nGAfCl6upq9fT0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\nU6fq9KpeffnCN5zVvGP3zIK2ugJQODp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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#simulate under CEU model taken from /san/personal/dan/spatialSVM/testingModels/simLaunchScripts/\n", - "#but with fixed alpha, rho, theta, etc\n", - "!./discoal 10 1000 10000 -r 82 -t 18 -ws 0 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 40 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 10000 -r 82 -t 18 -ws 0 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 40 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,25)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SNBodEOY6OjrGffjDH67PZDImSV/84hcHDHBVV1e7b3/72x1XXHHFO3t6enTS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\nEFIEOQAAgJAiyAEAAIQUQQ4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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#simulate soft sweeps under CEU model taken from /san/personal/dan/spatialSVM/testingModels/simLaunchScripts/\n", - "\n", - "!./discoal 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.05 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.05 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,25)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SicS2zs7O8N69e6t37ty57aWXXnrhU5/6VL+tmrq7u+3jH//4H3z3u9/9zUsv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\n0kj9BZbi+WvGtnjiAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#even softer\n", - "#!./discoal 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.2 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.2 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,35)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AZeiyyy7bs2jRopN2795dtWvXrqpFixYNv+yyyw5V2z70oQ/t/uUvfzkinU5r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\ncePG5WwDCBchDgAi4PHHH8/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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#hard range\n", - "#!./discoal 10 1000 100000 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -i 4 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 100000 198 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "#plt.ylim(-2.7,50)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3SfqepPvM7HJJmyVdLEnuvtbM7pO0TskrW691966o3jWS7pZUIunR6AEAADBkZSzEufvT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\nrLRVsmGplNiTlbYAZA/zxAE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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#soft range\n", - "!./discoal 10 1000 100000 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -Pf 0 0.2 -i 4 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 100000 198 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -Pf 0 0.2 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "#plt.ylim(-2.7,50)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/compareTwoVersions.ipynb b/compareTwoVersions.ipynb deleted file mode 100644 index be9ba99..0000000 --- a/compareTwoVersions.ipynb +++ /dev/null @@ -1,351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quick comparison of the old discoal with the new trajectory update" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#first simulate without selection\n", - "!./discoal 10 1000 10000 -r 20 -t 10 -en 0.05 0 0.1 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 10000 -r 20 -t 10 -en 0.05 0 0.1 | niceStats > testUpdate_stats" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fetching package metadata ...........\n", - "Solving package specifications: .\n", - "\n", - "Package plan for installation in environment /Users/adk/anaconda:\n", - "\n", - "The following packages will be UPDATED:\n", - "\n", - " conda: 4.3.27-py36hb556a21_0 --> 4.3.30-py36h173c244_0\n", - "\n", - "The following packages will be SUPERSEDED by a higher-priority channel:\n", - "\n", - " conda-env: 2.6.0-0 --> 2.6.0-h36134e3_0 \n", - " seaborn: 0.8-py36_0 --> 0.8.0-py36h74df97e_0 \n", - "\n", - "conda-env-2.6. 100% |################################| Time: 0:00:00 2.76 MB/s\n", - "conda-4.3.30-p 100% |################################| Time: 0:00:00 9.28 MB/s\n", - "seaborn-0.8.0- 100% |################################| Time: 0:00:00 12.51 MB/s\n" - ] - } - ], - "source": [ - "#make boxplots in the seaborn style, do some install and imports\n", - "!conda install seaborn --yes\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame()\n", - "import numpy as np\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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w1nhzAAQLQc5jrAtVmuLxeO9B011dXZ6ePcqbA4wl3hwAwUKQA8ZAU1NT1nFF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\npImWAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pylab as plt\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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/dTUfv9x66615429+85uOZgKgEnEnMoBCTlnmMcbMlnSHpHOttdcaYy6RdJW1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\n3HKLJOmuu+6a0GOPvNg7obg4tWg0qtWrV7ueBoA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# simulate with selection, but equilibrium\n", - "#!./discoal 10 1000 10000 -r 20 -t 100 -ws 0 -a 10000 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 10000 -r 20 -t 100 -ws 0 -a 10000 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,10)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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bj6D3rpUpv8516hgAINarb8r50CsPe0+4sXhraq890Nr+PUdRfuqPw/eecGN4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\nkpvrPlw7XH6ae7oPk3wmyffq7MmdSd4wvNCeMpKllAeAvwJ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# simulate with selection, and single popnSize change\n", - "#!./discoal 10 1000 10000 -r 20 -t 100 -ws 0 -a 1000 -en 0.01 0 0.1 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 10000 -r 20 -t 100 -ws 0 -a 1000 -en 0.01 0 0.1 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,10)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ecamw1yoQLBUZ5Ni8HgBGNny5EZYfAfzNTa/eh+LxuDo7O+nGAfCl6upq9fT0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\nU6fq9KpeffnCN5zVvGP3zIK2ugJQODp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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#simulate under CEU model taken from /san/personal/dan/spatialSVM/testingModels/simLaunchScripts/\n", - "#but with fixed alpha, rho, theta, etc\n", - "!./discoal 10 1000 10000 -r 82 -t 18 -ws 0 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 40 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 10000 -r 82 -t 18 -ws 0 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 40 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,25)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SNBodEOY6OjrGffjDH67PZDImSV/84hcHDHBVV1e7b3/72x1XXHHFO3t6enTS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\nEFIEOQAAgJAiyAEAAIQUQQ4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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#simulate soft sweeps under CEU model taken from /san/personal/dan/spatialSVM/testingModels/simLaunchScripts/\n", - "\n", - "!./discoal 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.05 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.05 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,25)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SicS2zs7O8N69e6t37ty57aWXXnrhU5/6VL+tmrq7u+3jH//4H3z3u9/9zUsv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\n0kj9BZbi+WvGtnjiAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#even softer\n", - "#!./discoal 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.2 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 10000 -r 82 -t 18 -ws 0 -f 0.2 -a 1000 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "plt.ylim(-2.7,35)\n", - "plt.show()\n", - "#this looks very similar, presumably because all trajectories have to fix during small popnSize" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AZeiyyy7bs2jRopN2795dtWvXrqpFixYNv+yyyw5V2z70oQ/t/uUvfzkinU5r\n48aNtU8//fQJuZ4vH4WcEwcAAIDDuPjii7s/+clPps4777wzpczEhosuumhf3/033HDDn5YuXTqs\nqanprNGjR+8/99xz9xxvn4Q4oIhyLffR1ZWZk9PYePhVcljuAwCK5+Thww7kM6O0oOc7irlz526b\nO3futuxt3d3dz0qZ1QLa2tpeK9qARIgDArNv376jPwgAUBSltqabHwhxQBGx3AcAICj5XHYLAAAA\nJYYQBwAAEEGEOAAAgAgixAEAAEQQIQ4AACCCmJ0KAADKzhdvuemMPX9KDSrW89WfFDvwL/92b0kt\nW0KIAwAAZWfPn1KDvnRGsmgh7ht5xLeXXnpp0FVXXTX+ggsu2LNy5cr6hoaGA7/5zW+SGzduHPT5\nz3/+lB07dtQMGTKk99577904adKkt0499dRJmzZt6tyxY0d1Q0PDOY8++uhLV1111Z7Jkyefcd99\n922YNGnS/lz9cTgVAACgSF577bUhs2bNeiOZTK458cQTe9ra2obfdNNNp95zzz2vrVmz5sVvf/vb\nm//+7//+lJqaGr373e9+a9WqVUM6OjrqzzzzzO7ly5fX79u3z7Zs2TLoaAFOohIHAABQNI2Njfsv\nvPDCfZJ07rnndm/YsGHws88+W3/ddde9p+8xBw4cMEm68MILdy9duvSEV199dfA//dM/bfnxj388\ncsWKFXvOPvvsvfn0RSUOAACgSAYNGuT6bldXV7sdO3ZUn3DCCel169at7ft65ZVX1kjSZZddtufJ\nJ5+sX7Vq1Tuuu+66P+/atat66dKlJ1x00UV78umLEAcAAOCTYcOG9Y4ZM+bAT37yk+GS1Nvbq6ee\nemqoJH3oQx/au2rVqvqqqipXV1fnJk6c2N3W1jbywx/+8O58npsQBwAA4KOf/exnr9x3330nn3HG\nGRPGjx8/8Ve/+tVJkjR06FD3rne968DkyZP3StIll1yyZ+/evVUXXHDBvnyel3PiAABA2ak/KXYg\nnxmlhTzf0R5zxhlnHFi/fv2avvadd965re/2E088sf5w+zzzzDOHRvn5z39+x+c///kd+Y6JEAcA\nAMpOqa3p5gcOpwIAAEQQIQ4AACCCCHEAAKAc9Pb29lrYg/CL97P1Zm8jxAEAgHKwevv27SeWY5Dr\n7e217du3nyhpdfZ2JjYAAIDIS6fTN23duvXerVu3nqXyK1L1SlqdTqdvyt5IiAMAAJF3/vnnvyFp\natjjCFIZ/TZFAAAgAElEQVS5JVUAAICKQIgDAACIIEIcAABABBHiAAAAIogQBwAAEEGEuDylUinN\nmjVLqVQq7KEAAAAQ4vKVSCTU2dmptra2sIcCAIHhAyxQughxeUilUmpvb5dzTu3t7YG9mfHmCSBs\nfIAFShchLg+JREK9vZnLlfX09AT2ZrZgwQK98MILWrBgQSD9AUC2sD7AAsgPV2zIw5IlS5ROpyVJ\n6XRaHR0dmjNnjq99plIpdXR0SJI6Ojo0ffp0xWIxX/sM0/z585VMJgver2+f2bNnF7xvU1OTZs6c\nWfB+QKU43AdYv9/7AOSPEJeH5uZmLVq0SOl0WjU1NWppafG9zwULFhx68+zt7dWCBQt02223+d5v\nWJLJpNaveVan1PcUtN+gg5li8v6NKwva77U91QU9HqhEYXyABZA/Qlwe4vG42tvbJUnV1dWaNm2a\n730uXbr0be1yDnGSdEp9j7503q5A+vrGqmGB9ANEWRgfYAHkj3Pi8hCLxdTa2iozU2trayCHNZ1z\nOdsA4Ld4PK6qqsy/iaA+wALIHyEuT/F4XJMmTQrsTezyyy/v125ubg6kXwDoE8YHWAD5I8TlKRaL\nad68eYG9ic2YMePQJ+CqqipNnz49kH4BIFvQH2AB5I9z4kpULBZTc3OzFi9erJaWlkA/AYcxU7Sr\nq0snF7wXAL/1fYAFUHoIcSVsxowZ2rp1a+BVuGQyqedWv6ieuhEF7Vd1IHPe3jOvbCtov+ruHaof\nUivVFrQbAAAVjRBXwsL8BNxTN0L73vuRQPoaum6R1Ls7kL4AACgXnBMHAAAQQYS4PHEdUwAAUEoI\ncXkK4yLQyWRSH/3oR49pkgEAAChvhLg8ZF8E+rHHHgusGnfXXXdp7969uuuuuwLpDwAARAchLg+J\nREIHDx6UJB08eDCQalwymdSGDRskSRs2bKAaBwAA+iHE5aGjo+PQZa+cc1q8eLHvfQ6svlGNAwAA\n2QhxeWhoaMjZ9kNfFe5IbQAAUNkIcXnYtm1bzrYfxo0bl7MNAAAqGyEuDy0tLf3aV1xxhe993n77\n7TnbAACgshHi8hCPx2VmkiQzC+RC0E1NTaqvr5ck1dfXq6mpyfc+AQBAdBDi8pQd4oKQSqX01ltv\nSZLeeustFhkGAAD9EOLykEgkVFWVeamqqqoCWWIkkUiop6dHktTT0xPoIsMAAKD0EeLysGTJEqXT\naUlSOp1WR0eH732GsawJAACIDkJcHpqbm1VTUyNJqqmpedtEBz+EsawJAACIDkJcHuLx+KHDqdXV\n1YFMbAhjWRMAABAdhLg8xGIxtba2yszU2tqqWCzme59hLGsCAACigxCXp6lTp6qurk5XX311IP3F\n4/F+7SCqfwAAIDoIcXlauHChuru79cgjjwTS386dO3O2ASAIqVRKs2bNYpkjoAQR4vKQSqXU3t4u\n55za29sDeTMbeMH7gW0ACEIikVBnZyfLHAEliBCXh0Qiod7eXknBrdk28IL3A9sA4LcwPsACyJ9v\nIc7MfmJmb5jZ6qxtI8ysw8zWe9+HZ913m5klzewlM7sya/v5Ztbp3TfPgrpkQpYw1omrq6vL2QYA\nv4XxARZA/vysxP1UUuuAbf8saalzbrykpV5bZjZB0vWSJnr73GNm1d4+P5D0OUnjva+Bz+m75ubm\nfpfdCmKduO7u7pxtAPBbGB9gAeTPtxDnnFshaceAzddISni3E5I+lrX95865/c65VyUlJV1gZqMk\nDXPOPe0yly9oy9onMFOnTu139YSgZqgCQJjCWOgcQP6CPieuwTm3xbu9VVLfZQgaJW3Ketxmb1uj\nd3vg9kAtXLiwXyUuiBmqQ4cOzdkGAL+FsdA5gPyFNrHBq6y5Yj6nmU03s5VmtnL79u1Fe94lS5b0\nq8QFcUhh3759OdsA4LcwFjoHkL+gQ9w27xCpvO9veNu7JI3NetwYb1uXd3vg9sNyzi1wzk12zk0e\nOXJk0Qbd3Nzcr80hBQCVIh6Pa9KkSVThgBIUdIhbKKnvUgRxSQ9nbb/ezAab2WnKTGD4vXfodZeZ\nfcCblTota5/AXHrppTnbAFCuYrGY5s2bRxUOKEF+LjHyM0lPSTrDzDab2WclfUtSi5mtl9TsteWc\nWyPpIUlrJbVLusU51+M91c2S7lVmssPLkh7za8xH8v3vf79fe/78+b732XceypHaAACgstX49cTO\nub87wl2XH+HxX5f09cNsXynprCIOrWBhLLw7aNAgvfXWW/3aAAAAfSjv5KG6ujpn2w/ZAe5wbQAA\nUNkIcXno6enJ2QYAAAgaIQ4AACCCCHEl6oMf/GDONgAAqGyEuBI1ePDgnG0AAFDZCHElasWKFTnb\nAACgshHi8nDeeef1a59//vm+99nb25uzDQAAKhshLg8DL+F18sknhzQSAACADEJcHji0CQAASg0h\nLg8NDQ05237gslsAACAXkkEetm3blrPth4GHcAe2AVSeVCqlWbNmKZVKhT0UACXAt2unlpOWlhYt\nXLjwUPuKK64o2nPPnz9fyWTybdsPFxxnz57db1tTU5NmzpxZtLEAKG2JREKdnZ1qa2vTnDlzwh4O\ngJBRicvDpZdemrMNAH5LpVJqb2+Xc07t7e2BVeOo/gGli0pcHr7zne+8rf3ggw8W5bmPVEn79Kc/\nrc2bNx9qjxkzRnfffXdR+gQQPYlE4tB1m9PpdGDVOKp/QOmiEpeHLVu29Gu//vrrvvc5d+7cnG0A\nlWXJkiWHQlxPT486Ojp87zOs6h+A/BDiSlRTU5MGDRokKVOFa2pqCnlEAMJ08cUX92tfcsklvveZ\nSCQOLTTe09OjtrY23/sEkD9CXAk79dRTVVVVRRUOgMws8D6XLFmidDotKXMIN4jqH4D8EeJKWF1d\nnSZNmkQVDoAef/zxnG0/NDc3q6Ymc+p0TU2NWlpafO8TQP6Y2JDlSMt9HM7A5T4klvwA4J/q6uqc\nbT/E43G1t7cf6m/atGm+9wkgf1Ti8tB3btqR2gDgt7179+Zs+yEWi6m1tVVmptbWVsViMd/7BJA/\nKnFZjlRFSyaTuummmw6177nnHg5xAqgIU6dO1dKlS3X11VeHPRQAA1CJy0P2TNGxY8cS4AAEbuDE\nhqAmOixcuFDd3d165JFHAukPQP4IcXnqmyn6la98JeyhAKhAzrmcbT+wThxQ2ghxeWKmKIAwhVGJ\nY504oLQR4gAgAhobG/u1x4wZ43ufrBMHlDZCHABEwMBDmW+++abvfTY3Nx+q+JkZ68QBJYYQBwAR\n0NLS0i9QXXHFFb73OXXq1EPn3jnnmKEKlBhCHABEQDwe7zeZIYiFdxcuXNivzQxVoLQQ4gAgAnbu\n3HnotnOuX9svA8+BW7x4se99AsgfIQ4AIuCuu+7K2fZDQ0NDzjaAcBHiACACNmzYkLPth23btuVs\nAwgXIQ4AIqC+vj5n2w8DZ6MGMZkCQP4IcQAQAX3rtR2p7Yd4PN6vHcRkCgD5I8QBQAQMrIJdeeWV\nvvc5cPJEEJMpAOSPEAcAEZBdFTOzQKpiYUymAJA/QhwAREz2enF+CmMyBYD8EeIAIALmz5+fs+2H\ncePG5WwDCBchDgAi4PHHH8/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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#hard range\n", - "#!./discoal 10 1000 100000 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -i 4 | niceStats > test_stats\n", - "#!./discoalTajUpdate 10 1000 100000 198 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "#plt.ylim(-2.7,50)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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3SfqepPvM7HJJmyVdLEnuvtbM7pO0TskrW691966o3jWS7pZUIunR6AEAADBkZSzEufvT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\nrLRVsmGplNiTlbYAZA/zxAE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AQIAIcQAAAAH6f2szy6iQ6V0+AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#soft range\n", - "!./discoal 10 1000 100000 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -Pf 0 0.2 -i 4 | niceStats > test_stats\n", - "!./discoalTajUpdate 10 1000 100000 198 -Pt 180 1800 -Pre 825 2475 -en 0.0126605 0 0.196553 -en 0.0165436 0 0.196553 -en 0.0207647 0 0.147053 -en 0.0253532 0 0.147053 -en 0.0303409 0 0.191351 -en 0.0357627 0 0.191351 -en 0.0416566 0 0.317217 -en 0.048063 0 0.317217 -en 0.0550275 0 0.584728 -en 0.0625978 0 0.584728 -en 0.0708278 0 0.983724 -en 0.0797711 0 0.983724 -en 0.08949 0 1.36141 -en 0.100054 0 1.36141 -en 0.111537 0 1.6075 -en 0.124021 0 1.6075 -en 0.137595 0 1.68853 -en 0.152349 0 1.68853 -en 0.168386 0 1.58871 -en 0.185813 0 1.58871 -en 0.204774 0 1.36773 -en 0.225377 0 1.36773 -en 0.247772 0 1.14413 -en 0.272115 0 1.14413 -en 0.298577 0 0.970856 -en 0.327341 0 0.970856 -en 0.358617 0 0.856995 -en 0.392654 0 0.856995 -en 0.429657 0 0.791563 -en 0.469887 0 0.791563 -en 0.513583 0 0.758969 -en 0.561122 0 0.758969 -en 0.612772 0 0.755267 -en 0.668917 0 0.755267 -en 0.729922 0 0.775539 -en 0.796139 0 0.775539 -en 0.868178 0 0.830728 -en 0.946578 0 0.830728 -en 1.03179 0 0.941172 -en 1.12433 0 0.941172 -en 1.22492 0 1.12891 -en 1.33438 0 1.12891 -en 1.45369 0 1.375 -en 1.58312 0 1.375 -en 1.72379 0 1.59459 -en 1.8767 0 1.59459 -en 2.04269 0 1.67968 -en 2.22306 0 1.67968 -en 2.41911 0 1.60582 -en 2.63256 0 1.60582 -en 2.86466 0 1.42703 -en 3.11698 0 1.42703 -en 3.39128 0 1.42703 -en 3.68909 0 1.42703 -ws 0 -Pa 187.5 3750 -Pu 0 0.0266667 -Pf 0 0.2 -i 4 | niceStats > testUpdate_stats\n", - "X1 = pd.read_table(\"test_stats\")\n", - "X1=X1.assign(version=[\"old\"]*1000)\n", - "X2 = pd.read_table(\"testUpdate_stats\")\n", - "X2=X2.assign(version=[\"new\"]*1000)\n", - "both = pd.concat((X1,X2))\n", - "df_long = pd.melt(both, \"version\", var_name=\"stat\", value_name=\"value\")\n", - "sns.factorplot(\"stat\", hue=\"version\", y=\"value\", data=df_long, kind=\"box\",size=8)\n", - "#plt.ylim(-2.7,50)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}