{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sb\n", "import os\n", "import scipy.io\n", "from scipy import stats\n", "from astropy import stats as astrostats\n", "\n", "import octopus as oct" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import astropy" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Import data\n", "T4T5c_R_deg = np.load('T4T5c_R_deg.npy')\n", "T4T5d_R_deg = np.load('T4T5d_R_deg.npy')\n", "T4T5d_T4T5c_block_R_deg = np.load('T4T5d_T4T5c_block_R_deg.npy')\n", "\n", "rotations = [0,45,90,135,180,225,270,315,360]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make Scatter Plot of R_deg\n", "\n", "pos = [0,1,2]\n", "\n", "fig = plt.figure(figsize = (0.5*len(pos), 2.3))\n", "\n", "ax = fig.add_subplot(111)\n", "w = 0.6\n", "wb = 0.6\n", "data = [np.radians(T4T5c_R_deg), np.radians(T4T5d_R_deg), np.radians(T4T5d_T4T5c_block_R_deg)]\n", "colors=['C3', 'C0', '0.2']\n", "\n", "for j in range (len(data)-1):\n", " ax.plot([pos[j]-w/2, pos[j]+w/2],[stats.circmean(data[j]),stats.circmean(data[j])],\n", " color=colors[j], linestyle='-', linewidth = 4)\n", " ax.errorbar(pos[j],stats.circmean(data[j]),yerr=[stats.circstd(data[j])/(np.sqrt(len(data[j])))], color='k',zorder = 0) \n", " \n", "for j in range (len(data)): \n", " for i in range(len(data[j])):\n", " ax.scatter(pos[j] + np.random.random(1) * w/2 - w/4, data[j][i], s=25, color=colors[j], alpha=0.5, zorder = 2) \n", " \n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "ax.spines['left'].set_position(('outward', 7))\n", "ax.spines['bottom'].set_position(('outward', 7))\n", "ax.set_xticks(pos)\n", "ax.set_xticklabels(('T4/T5c', 'T4/T5d', 'T4/T5d \\nblock'), rotation=45, size=10)\n", "ax.set_yticks(np.radians(rotations[::2]))\n", "ax.set_yticklabels(rotations[::2])\n", "ax.set_ylabel(u'PD of suppression [°]', size=10)\n", "plt.ylim([0,np.pi*2+0.01])\n", "ax.tick_params(axis='both', which='major', labelsize=10)\n", "ax.tick_params(axis='both', which='major', labelsize=10)\n", "\n", "bbox_inches = 'tight'\n", "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n", "#Fig5_PD_of_Inhibition.pdf',bbox_inches='tight', dpi=600, transparent=True)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#POLAR PLOT\n", "fig = plt.figure(figsize = (2.1,2.1))\n", "for i in range(len(T4T5c_R_deg)):\n", " plt.polar([0,np.radians(T4T5c_R_deg[i])],[0,1],linewidth=1.75, color='C3', alpha=0.4)\n", "plt.polar([0,stats.circmean(np.radians(T4T5c_R_deg))],[0,1],linewidth=3.5, color='C3')\n", "plt.yticks([])\n", "#plt.xticks([0])\n", "plt.ylim([0,1])\n", "plt.tick_params(axis='both', which='major', labelsize=10)\n", "\n", "bbox_inches = 'tight'\n", "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n", "#Fig5_T4T5c_polar_wo_arrow.pdf',bbox_inches='tight', dpi=600, transparent=True)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#POLAR PLOT\n", "fig = plt.figure(figsize = (2.1,2.1))\n", "for i in range(len(T4T5d_R_deg)): \n", " plt.polar([0,np.radians(T4T5d_R_deg[i])],[0,1],linewidth=1.75, color='C0', alpha=0.4)\n", "plt.polar([0,stats.circmean(np.radians(T4T5d_R_deg))],[0,1],linewidth=3.5, color='C0')\n", "plt.yticks([])\n", "#plt.xticks([0])\n", "plt.ylim([0,1])\n", "plt.tick_params(axis='both', which='major', labelsize=10)\n", "\n", "bbox_inches = 'tight'\n", "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n", "#Fig5_T4T5d_polar_wo_arrow.pdf',bbox_inches='tight', dpi=600, transparent=True)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#POLAR PLOT\n", "fig = plt.figure(figsize = (2.1,2.1))\n", "for i in range(len(T4T5d_T4T5c_block_R_deg)):\n", " plt.polar([0,np.radians(T4T5d_T4T5c_block_R_deg[i])],[0,1],linewidth=1.75, color='0.2', alpha=0.5)\n", "#plt.polar([0,stats.circmean(np.radians(T4T5_D_TNT_R_deg))],[0,1],linewidth=4, color='k')\n", "plt.yticks([])\n", "plt.ylim([0,0.95])\n", "plt.tick_params(axis='both', which='major', labelsize=10)\n", "\n", "bbox_inches = 'tight'\n", "#plt.savefig('C:\\\\Users\\\\gammer\\\\Desktop\\\\DATA Surface\\\\LPi Opponency\\\\plots_LPi_ms\\\\\\\n", "#Fig5_T4T5d_T4T5cTNT_polar.pdf',bbox_inches='tight', dpi=600, transparent=True)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.8690879344940186, 0.09754662960767746)\n", "(0.8260365128517151, 0.018810812383890152)\n", "(0.931892991065979, 0.5334630608558655)\n" ] } ], "source": [ "print(stats.shapiro(T4T5c_R_deg))\n", "print(stats.shapiro(T4T5d_R_deg))\n", "print(stats.shapiro(T4T5d_T4T5c_block_R_deg))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.028338216814149362\n", "0.022866569716960078\n", "0.6618565819602494\n" ] } ], "source": [ "print(astrostats.rayleightest(np.radians(T4T5c_R_deg)))\n", "print(astrostats.rayleightest(np.radians(T4T5d_R_deg)))\n", "print(astrostats.rayleightest(np.radians(T4T5d_T4T5c_block_R_deg)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }