{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as sps\n", "import scipy.optimize as spo\n", "import scipy.signal as spg\n", "\n", "from collections import namedtuple\n", "import pandas as pd\n", "import os\n", "from scipy.stats import t\n", "\n", "import sys\n", "import scipy.io as io\n", "from scipy.optimize import curve_fit" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def expfunc(x, a, b, c):\n", " return a * np.exp(-b * x) + c\n", "\n", "def getz(alpha):\n", " z = sps.norm.ppf(1 - alpha/2)\n", " return z\n", "\n", "def gett(alpha, n):\n", " tinv = lambda p, df: abs(t.ppf(p/2, df))\n", " ts = tinv(alpha, n-2)\n", " return ts\n", "\n", "def corrCI(x, y, alpha): #from Zou 2007, comparing r values\n", " r, p = sps.pearsonr(x,y)\n", " z = getz(alpha)\n", " r_z = np.arctanh(r)\n", " se= 1/np.sqrt(x.size-3)\n", " z = sps.norm.ppf(1 - alpha/2)\n", " \n", " lo_z, up_z = r_z - z*se, r_z + z*se\n", " lo, up = np.tanh((lo_z, up_z))\n", " return [r, p, lo, up]\n", "\n", "def corrCIdiff(r1, l1, u1, r2, l2, u2): #from Zou 2007\n", " \n", " ldiff = r1 - r2 - np.sqrt( (r1 - l1)**2 + (u2 - r2)**2 )\n", " udiff = r1 - r2 + np.sqrt( (u1 - r1)**2 + (r2 - l2)**2 )\n", " rdiff = r1 - r2\n", " return [rdiff, ldiff, udiff]\n", "\n", "def regresiduals(x, y):\n", " xsum = sum((x - np.mean(x))**2)\n", " ysum = sum((y - np.mean(y))**2)\n", " xysum = sum((x - np.mean(x))* (y-np.mean(y)))\n", " return [xsum, xysum, ysum]\n", "\n", "def regCI(x,y,n, alpha):\n", " linreg0 = sps.stats.linregress(np.abs(x), y)\n", " res0 = regresiduals(x,y)\n", " ts = gett(alpha, n)\n", " xi = np.linspace(0, max(x))\n", " syi = np.empty(len(xi))\n", " for xp, c in enumerate(xi):\n", " syi[xp] = np.sqrt(linreg0.stderr**2 * res0[0] * (1/n + (c - np.mean(x))**2 / res0[0]))\n", " yi = linreg0.intercept + linreg0.slope*xi\n", " y1 = linreg0.intercept + linreg0.slope*xi + ts*syi\n", " y2 = linreg0.intercept + linreg0.slope*xi - ts*syi\n", " return [xi, yi, y1, y2]\n", "\n", "def slopediff(x1, y1, x2, y2, n1, n2):\n", " #Zar pg 363\n", " linreg1 = sps.stats.linregress(np.abs(x1), np.abs(y1))\n", " linreg2 = sps.stats.linregress(np.abs(x2), np.abs(y2))\n", " res1 = regresiduals(np.abs(x1), np.abs(y1))\n", " resSS1 = res1[2] - (res1[1]**2/res1[0] )\n", " res2 = regresiduals(np.abs(x2), np.abs(y2))\n", " resSS2 = res2[2] - (res2[1]**2/res2[0] )\n", " \n", " #calculate S_squared and Sb1 - Sb2 \n", " ssq = (resSS1 + resSS2)/ (n1 + n2)\n", " sbminusb = np.sqrt((ssq/res1[0]) + (ssq/res2[0]) )\n", " \n", " #calculate difference between slopes, the p value, and confidence intervals, t test\n", " slpt = (linreg1.slope - linreg2.slope )/ sbminusb \n", " slpp = sps.norm.sf(abs(slpt))*2 #formula to get a p value from a t test\n", " return [slpt, slpp]\n", "\n", "def elediff(x1, y1, x2, y2, n1, n2):\n", " #Zar pg 367\n", " linreg1 = sps.stats.linregress(np.abs(x1), np.abs(y1))\n", " linreg2 = sps.stats.linregress(np.abs(x2), np.abs(y2))\n", " res1 = regresiduals(np.abs(x1), np.abs(y1))\n", " resSS1 = res1[2] - (res1[1]**2/res1[0] )\n", " res2 = regresiduals(np.abs(x2), np.abs(y2))\n", " resSS2 = res2[2] - (res2[1]**2/res2[0] )\n", " \n", " #calculate S_squared and Sb1 - Sb2 \n", " ssq = (resSS1 + resSS2)/ (n1 + n2)\n", " sbminusb = np.sqrt((ssq/res1[0]) + (ssq/res2[0]) )\n", " \n", " s2yxc = ((res1[2] + res2[2]) - ( (res1[1] + res2[1])**2 / (res1[0] + res2[0]) ))/ \\\n", " (n1 + n2 - 3)\n", " bc = (res1[1]+ res2[1]) / (res1[0] + res2[0])\n", " elvt = ( (np.abs(y1).mean() - np.abs(y2).mean()) - \\\n", " ( (res1[1] + res2[1]) /(res1[0] + res2[0])*(np.abs(x1).mean() - np.abs(x2).mean()) ))\\\n", " / np.sqrt( s2yxc *(n1**-1 + n2**-1 + \\\n", " ( (np.abs(x1).mean() - np.abs(x2).mean())**2/(res1[0] + res2[0]) ) ) )\n", " return [elvt, sps.norm.sf(abs(elvt))*2] \n", "\n", "def yintdiff(x1, y1, x2, y2, n1, n2):\n", " #Zar pg 371\n", " linreg1 = sps.stats.linregress(np.abs(x1), np.abs(y1))\n", " linreg2 = sps.stats.linregress(np.abs(x2), np.abs(y2))\n", " res1 = regresiduals(np.abs(x1), np.abs(y1))\n", " resSS1 = res1[2] - (res1[1]**2/res1[0] )\n", " res2 = regresiduals(np.abs(x2), np.abs(y2))\n", " resSS2 = res2[2] - (res2[1]**2/res2[0] )\n", " \n", " #calculate S_squared and Sb1 - Sb2 \n", " ssq = (resSS1 + resSS2)/ (n1 + n2)\n", " sbminusb = np.sqrt((ssq/res1[0]) + (ssq/res2[0]) )\n", " \n", " bdt = res1[1]/res1[0]\n", " adt = np.abs(y1).mean() - bdt* np.abs(x1).mean()\n", " bcz = res2[1]/res2[0]\n", " acz = np.abs(y2).mean() - bcz* np.abs(x2).mean()\n", " s2yxp = (resSS1 + resSS2)/ ((n1 - 2 + n2 - 2))\n", " \n", " yintt = (adt - acz) / np.sqrt( s2yxp* (n1**-1 + n2**-1 + \\\n", " (np.abs(x1).mean()**2/ res1[0]) + \\\n", " (np.abs(x2).mean()**2/ res2[0]) ) )\n", " yintp = sps.norm.sf(abs(yintt))*2\n", " return [yintt, yintp]\n", "\n", "def MWUefsize(a, b):\n", " #effect size for Mann-Whitney U test\n", " [u, p] = sps.mannwhitneyu(a,b)\n", " n1 = a.size\n", " n2 = b.size\n", " comm = u/(n1*n2) #common language, just percentage\n", " rkbiscorr = np.abs(2*u/(n1*n2) - 1) #rank biserial correlation, effect increases as r -> 1\n", " return [u,p,comm, rkbiscorr]\n", "\n", "def find_nearest(array, value):\n", " array = np.asarray(array)\n", " idx = (np.abs(array - value)).argmin()\n", " return [idx]\n", "\n", "def Gaussian(t, ga, gb, gc, gd):\n", " return ga + np.abs(gb) * np.exp( - ( (t-gc)**2 / gd**2) )\n", "\n", "def Sigmoidal(t, sa, sb, sc, sd, se):\n", " return sa / (1 + np.exp(-sb*(t - sc)/sd)) + se\n", "\n", "def within(tval, tmin, tmax):\n", " return np.sum((tval > tmin) & (tval < tmax))\n", "\n", "def between(val, array):\n", " return array[0] <= val <= array[1]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# matplotlib settings\n", "mpl.rcParams['mathtext.default'] = 'regular'\n", "mpl.rcParams['font.family'] = 'sans-serif'\n", "mpl.rcParams['font.sans-serif'] = 'Arial'\n", "\n", "\n", "# Reproducible SVG files (including xml ids):\n", "mpl.rcParams['svg.hashsalt'] = '123'\n", "mpl.rcParams['savefig.dpi'] = '300'\n", "\n", "# Font sizes:\n", "mpl.rcParams['font.size'] = 12\n", "mpl.rcParams['axes.labelsize'] = 12 # default: 'medium' == 10\n", "mpl.rcParams['xtick.labelsize'] = 10 # default: 'medium' == 10\n", "mpl.rcParams['ytick.labelsize'] = 10 # default: 'medium' == 10\n", "mpl.rcParams['legend.fontsize'] = 10\n", "mpl.rcParams['xtick.major.size'] = 2\n", "mpl.rcParams['ytick.major.size'] = 2\n", "\n", "\n", "# Other texts:\n", "in_panel_fontsize = 12\n", "bc_indicator_size = in_panel_fontsize\n", "\n", "fildir = 'F:\\\\Various Files\\\\My Papers\\\\Shadron and Pena 2022\\\\eLife Figures\\\\'\n", "#fildir = 'F:\\\\Various Files\\\\SAC Meetings\\\\2022\\\\'" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#read excel file\n", "dt = pd.read_excel('F:/Shadron_Pena_2022/Shadron_Pena_2022_ruff_removed.xlsx', header = 0, comment = \"#\")\n", "jv = pd.read_excel('F:/Shadron_Pena_2022/Shadron_Pena_2022_juvenile.xlsx', header = 0, comment = \"#\")\n", "cz = pd.read_excel('F:/Shadron_Pena_2022/Cazettes_2014_normal.xlsx', header = 0, comment = \"#\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#only use units that are good, and are tuned to ITD\n", "dt = dt[dt['Use?'] == 'yes']\n", "allunits = dt.index[~dt['Best ITD'].isnull()].tolist()\n", "#use only the for sure ICx units\n", "icxlist = dt[dt['Nucleus'] == 'ICx']\n", "icx = icxlist.index[~icxlist['Best ITD'].isnull()].tolist()\n", "#set use to be allunits or icx\n", "use = icx\n", "\n", "jv = jv[jv['Nucleus'] == 'ICx']\n", "\n", "czneurons = np.arange(0, cz.shape[0])\n", "jvneurons = np.arange(0, jv.shape[0])\n", "\n", "jvn = jv['Best ITD'].size\n", "czn = cz['Best ITD'].size\n", "dtn = dt['Best ITD'][use].size" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "linreg = sps.stats.linregress(np.abs(dt['Best ITD'][use]), np.abs(dt['Best Freq'][use]))\n", "czlinreg = sps.stats.linregress(np.abs(cz['Best ITD']), np.abs(cz['Best Freq']))\n", "jvlinreg = sps.stats.linregress(np.abs(jv['Best ITD']), np.abs(jv['Best Freq']))\n", "\n", "\n", "randnu = np.empty_like(dt['Best ITD'][use])\n", "for n in range(randnu.size):\n", " randnu[n] = np.random.randint(-8,8,1)\n", "\n", "jvrand = np.empty_like(jv['Best ITD'])\n", "for n in range(jvrand.size):\n", " jvrand[n] = np.random.randint(-8,8,1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "czxi, czyi, czy1, czy2 = regCI(cz['Best ITD'], cz['Best Freq'], czn, 0.05)\n", "dtxi, dtyi, dty1, dty2 = regCI(dt['Best ITD'][use], dt['Best Freq'][use], dtn, 0.05)\n", "jvxi, jvyi, jvy1, jvy2 = regCI(jv['Best ITD'], jv['Best Freq'], jvn, 0.05)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = [5*1.2,5], facecolor = 'white')\n", "\n", "ax.scatter(cz['Best ITD'], cz['Low Freq'], marker = 'v', color = [0.6, 0.6, 0.6], s=30, linewidths=.7,zorder=5)\n", "ax.scatter(cz['Best ITD'], cz['High Freq'], marker = '^', color = [0.8, 0.8, 0.8], s=30, linewidths=.7,zorder=5)\n", "czsc = ax.scatter(cz['Best ITD'], cz['Best Freq'], marker = 'o', color = [0.7, 0.7, 0.7], s=30, linewidths=.7,zorder=5, \\\n", " label = f'Normal Owls, n = {czn}')\n", "for ITD in czneurons:\n", " ax.bar(cz['Best ITD'][ITD], cz['High Freq'][ITD] - cz['Low Freq'][ITD], .7, cz['Low Freq'][ITD], \\\n", " color = [0.7, 0.7, 0.7 ], zorder= 4)\n", "\n", " \n", "ax.scatter(np.abs(dt['Best ITD'][use] + randnu), dt['Low Freq'][use], marker = 'v', color = [0.6, 0, 0.25], \\\n", " linewidths = .7, edgecolor = 'black', s=30, zorder=7)\n", "ax.scatter(np.abs(dt['Best ITD'][use] + randnu), dt['High Freq'][use], marker = '^', color = [0.9, 0.7, 0.7], \\\n", " linewidths = .7, edgecolor = 'black', s=30, zorder=7)\n", "dtsc = ax.scatter(np.abs(dt['Best ITD'][use] + randnu), dt['Best Freq'][use], marker = 'o', color = [0.8, 0.1, 0.1], \\\n", " linewidths = .7, edgecolor = 'black', s=30, zorder=7, label = f'Ruff-Removed Owls, n = {dtn}')\n", "for nurep, ITD in enumerate(use):\n", " ax.bar(np.abs(dt['Best ITD'][ITD] + randnu[nurep]), dt['High Freq'][ITD] - dt['Low Freq'][ITD], .7, \\\n", " dt['Low Freq'][ITD], color = [0.9, .1, 0 ], zorder = 6) \n", " \n", "\n", "czline, = ax.plot(czxi, czyi, color = [0.1, 0.25, 1], linewidth = 2, \\\n", " label = f'Cazettes 2014, r = {czlinreg.rvalue:.2g}, p = {czlinreg.pvalue:.1e}', zorder = 9)\n", "ax.fill_between(czxi, czy1, czy2, color = [0.8, 0.8, .95], zorder = 1)\n", "\n", "dtline, = ax.plot(dtxi, dtyi, color = [0.25, 0.05, 0.05], linewidth = 2, \\\n", " label = f'Ruff-Removed Owls, r = {linreg.rvalue:.2g}, p = {linreg.pvalue:.1e} ', zorder = 8)\n", "ax.fill_between(dtxi, dty1, dty2, color = [0.95, 0.8, 0.8], zorder = 3)\n", "\n", "ax.set_ylim([1000, 8000])\n", "ax.set_xlim([0, cz['Best ITD'].max() + 10])\n", "ax.set_yticks(np.linspace(1000,8000,8))\n", "ax.set_yticklabels([1,2,3,4,5,6,7,8])\n", "\n", "\n", "plt.ylabel('Frequency (kHz)')\n", "plt.xlabel('ITD (μs)')\n", "legend1 = plt.gca().legend(handles = [dtline, czline], loc= [.31, .875])\n", "ax.add_artist(legend1)\n", "legend2 = plt.gca().legend(handles = [dtsc, czsc], loc = [0.52, 0.785], borderpad = .05)\n", "plt.show()\n", "file_name = 'Haircut_Freq_w_Cazettes_overlay.png'\n", "filname = os.path.join(fildir, file_name)\n", "fig.savefig(filname, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = [4*1.2,4], facecolor='white')\n", "\n", "jvx = np.linspace(0,np.max(jv['Best ITD']),40)\n", "\n", "ax.scatter(np.abs(jv['Best ITD']) + jvrand, jv['Low Freq'], marker = 'v', color = [0.2, 0.6, 0.5], s=30, \\\n", " edgecolor = 'black', linewidths = 0.7, zorder=7)\n", "ax.scatter(np.abs(jv['Best ITD']) + jvrand, jv['High Freq'], marker = '^', color = [0.2, 1.0, 0.5], s=30, \\\n", " edgecolor = 'black', linewidths = 0.7, zorder=7)\n", "jvsc = ax.scatter(np.abs(jv['Best ITD']) + jvrand, jv['Best Freq'], marker = 'o', color = [0.2, 0.8, 0.5], s =30, \\\n", " edgecolor = 'black', linewidths = 0.7, zorder=7, label = f'Juvenile Owls, n = {jvn}')\n", "for ITD in jvneurons:\n", " ax.bar(np.abs(jv['Best ITD'][ITD+1]) + jvrand[ITD], jv['High Freq'][ITD+1] - jv['Low Freq'][ITD+1], .7, jv['Low Freq'][ITD+1], \\\n", " color = [0.2, 0.9, 0.4 ], zorder= 6)\n", " \n", "jvline, = ax.plot(jvxi, jvyi, color = [0.2, 1, 0.5], linewidth = 2, \\\n", " label = f'Juvenile Owls, r = {jvlinreg.rvalue:.2g}, p = {jvlinreg.pvalue:.1e} ', zorder = 5)\n", "ax.fill_between(jvxi, jvy1, jvy2, color = [0.85, 0.95, 0.85], zorder = 4)\n", "\n", "ax.set_ylim([1000, 8000])\n", "ax.set_xlim([0, jv['Best ITD'].max() + 10])\n", "ax.set_yticks(np.linspace(1000,8000,8))\n", "ax.set_yticklabels([1,2,3,4,5,6,7,8])\n", "ax.set_facecolor(\"white\")\n", "plt.ylabel('Frequency (kHz)')\n", "plt.xlabel('ITD (μs)')\n", "legend1 = plt.gca().legend(handles = [jvline], loc= [.24, .91])\n", "ax.add_artist(legend1)\n", "legend2 = plt.gca().legend(handles = [jvsc], loc = [0.45, 0.86], borderpad = .05)\n", "plt.show()\n", "file_name = 'Juvenile_only.png'\n", "filname = os.path.join(fildir, file_name)\n", "fig.savefig(filname, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#MWU Comparisons" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "ww = np.abs(dt['Best ITD'][use]) <=30\n", "dtbest = dt['Best Freq'][use][ww]\n", "czbest = cz[cz['Best ITD']<= 30]['Best Freq']\n", "jvbest = jv[jv['Best ITD']<= 30]['Best Freq']\n", "\n", "\n", "dthi = dt['High Freq'][use][ww]\n", "czhi = cz[cz['Best ITD']<= 30]['High Freq']\n", "jvhi = jv[jv['Best ITD']<= 30]['High Freq']\n", "\n", "dtlo = dt['Low Freq'][use][ww]\n", "czlo = cz[cz['Best ITD']<= 30]['Low Freq']\n", "jvlo = jv[jv['Best ITD']<= 30]['Low Freq']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [], "source": [ "cjbest = MWUefsize(czbest, jvbest)\n", "djbest = MWUefsize(dtbest, jvbest)\n", "cdbest = MWUefsize(czbest, dtbest)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#All one Graph" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "cjhi = MWUefsize(czhi, jvhi)\n", "djhi = MWUefsize(dthi, jvhi)\n", "cdhi = MWUefsize(czhi, dthi)\n", "cjlo = MWUefsize(czlo, jvlo)\n", "djlo = MWUefsize(dtlo, jvlo)\n", "cdlo = MWUefsize(czlo, dtlo)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "##Freq range" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3.4647717459095255, 0.0005306816858342246]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#freqrange = np.log2(dt['High Freq'][use] / dt['Low Freq'][use])\n", "freqrange = dt['High Freq'][use] - dt['Low Freq'][use]\n", "#czfreqrange = np.log2(cz['High Freq'] / cz['Low Freq'])\n", "czfreqrange = (cz['High Freq'] - cz['Low Freq'])\n", "#jvfreqrange = np.log2(jv['High Freq'] / jv['Low Freq'])\n", "jvfreqrange = (jv['High Freq'] - jv['Low Freq'])\n", "\n", "rangelin = sps.stats.linregress( np.abs(dt['Best ITD'][use]), freqrange)\n", "czrangelin = sps.stats.linregress( cz['Best ITD'], czfreqrange)\n", "jvrangelin = sps.stats.linregress(np.abs(jv['Best ITD']), jvfreqrange)\n", "\n", "slopediff(dt['Best ITD'][use], freqrange, cz['Best ITD'], czfreqrange, dtn, czn )" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "##Frequency Range" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "djurg,djprg = sps.mannwhitneyu(freqrange, jvfreqrange)\n", "cdurg,cdprg = sps.mannwhitneyu(czfreqrange, freqrange)\n", "cjurg,cjprg = sps.mannwhitneyu(czfreqrange, jvfreqrange)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KruskalResult(statistic=16.020349930826416, pvalue=0.0003320666137286004)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ww = np.abs(dt['Best ITD'][use]) <=30\n", "dtfrtrg = freqrange[ww]\n", "czfrtrg = czfreqrange[cz['Best ITD']<= 30]\n", "jvfrtrg = jvfreqrange[jv['Best ITD']<= 30]\n", "\n", "djrg = MWUefsize(dtfrtrg, jvfrtrg)\n", "cdrg = MWUefsize(czfrtrg, dtfrtrg)\n", "cjrg = MWUefsize(czfrtrg, jvfrtrg)\n", "sps.kruskal(dtfrtrg,czfrtrg,jvfrtrg)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "jvlo.reset_index(inplace=True, drop=True)\n", "jvbest.reset_index(inplace=True, drop=True)\n", "jvhi.reset_index(inplace=True, drop=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "#ITD Width" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "itdwid = dt['High ITD'][use] - dt['Low ITD'][use]\n", "czitdwid = cz['ITD Width']\n", "jvitdwid = (jv['High ITD'] - jv['Low ITD'])\n", "\n", "widlin = sps.stats.linregress( np.abs(dt['Best ITD'][use]), itdwid)\n", "czwidlin = sps.stats.linregress( cz['Best ITD'], czitdwid)\n", "jvwidlin = sps.stats.linregress(np.abs(jv['Best ITD']), jvitdwid)\n", "\n", "ww = np.abs(dt['Best ITD'][use]) <=30\n", "dtfrtwid = itdwid[ww]\n", "czfrtwid = czitdwid[cz['Best ITD']<= 30]\n", "jvfrtwid = jvitdwid[jv['Best ITD']<= 30]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "KruskalResult(statistic=27.37680057830061, pvalue=1.1355422257725387e-06)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "djwid = MWUefsize(dtfrtwid, jvfrtwid)\n", "cdwid = MWUefsize(czfrtwid, dtfrtwid)\n", "cjwid = MWUefsize(czfrtwid, jvfrtwid)\n", "sps.kruskal(dtfrtwid,czfrtwid,jvfrtwid)" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "image/png": 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JiTkkJSnnn7+AnJzTSUoyQ6C+SCibG7uLI5MXInIqcDVwGbAeeBw4D3gWa40r6lBVXnjhLoYPf5n4+AyamirZu3cOP/7xXwJB+g19Dw3j5sbWODF58TYwClgGFNrrWojIX4CDobYfLpqaaomPzwlMVAwYMILExOE0N9f3u4mK/kQ4Nze2xoke62HgWbV2EMf5ZwlV1ScizoRYCgOJiQPxeKqCPCu83j39dqKivxDY3OhpoKmmLtBjObK5sRU9DiYTaEDkG8AiVR1r7yJ+A/ieqq53QsBjDSbTva5cSEgYhss1GK/3MImJw2hqqqSlpapb54gFp1SDRXvBgsorKvjh1bM4XF9LatbxDGjUHtlY4Yrd7ue3wBwAVd0iIt/G6sU6DSgTTjpbIFZVliy5mOxsaxG4peVzqqsv4eab3+2WbdUfFoj7OgX5+Xy88cOwbl51QrGSVHWj/42qbvTvIo5G2tpWCQnHkZDwFWNb9TP8m1fDhRPTX/Uicp7/jYhMA+o6+XxESUpKw+utDloENraVwWmc6LFuAp4XEb9bkw/oNHdWJNmz5wNaWurZvv0cEhJycLlqufjiX5j1KoOjOLFAvMGe/TsdywNjm1qho6MOn8/H88/fEVi7am7exb59V5Od3TZ8h8EQGk6sY6Vg5R0+Disf8Tn2TExURWmyvNhvwes9Pij4psuV0y+dbA3hxYmh4ApgJFZaHv+8ZlTNR3/hxT4QkWazdmUIexJBJxTr68Cp0bx1xO/FnpSUSFbWjezYcRkJCVk0Nn7AD36w0NhX/Yzyigrm3HQdSVmZNO09zOOLlzjqJwjOKNYuB9oIK34v9uZmITFxBKec8mrAvho5clykxTP0Iu3FE3TaTxCcUawPgH+KyGtAIJtZNNlYIsJ3v3snzz13q5kN7Oe43W6SsjLDFk/QjxOKlY4V+mxUq7KosbFUlU8/Xc9f//pbBgw4mfj4SmbMuIGRI882StUJ4bZBIkVaWhqN1YeC/ASdjCfox4np9jkAIpKhqjUhS+Qg/ngWjY21jBlTGpiw+OtfrTgWhvbpqzEF/fuwDhw6xO4Zt5A2bAjJtp9g1Lk0icgpwAvAIBEp5Asn3K2hth0KwfEsRgXFsYiPzzZT7B3QV2MKtt6H9b3X7idxUCp1uw+wef4S8s90PomgE0PBB7G8L36jqtUi8ntgKTDZgbaPmWHDhgccZVNTx9nxLPYGTbEfPPgeCxeO7nH7sUBPk5lHe0zBnBG57Kk8tliw/nsxtGAMablDA/ZVWu5QDnkbg5yvs4cPo3pn93Y5dIYTinW8qv7DL7yqPmxnvO8UEfkXcMR++6l/SBkqVVU7A/EMCgu/w9691eTk3M6OHZfhcg2luXkz69Y9T2FhgV+OPrkNpKNr6up609LS2N3cHLUxBfdU7ia//M5jPs7n8/HptSsDSeYC9pW3mTPLvohrUlFw7G23hxOKpSIyAHvCQkSygPjODrA/j6oWO3D+IFpnCvF6rbTGlZXzSUoaRlpaJX/+89MBpTIEU1FWxtzvf5/DBw4wraqK3NRUGgYO5OFnnonpYWDD1mp2/+o1fO4m6tTNCzN+SmrOYBpra8m69ZyoXSB+GCs31lA7jvulWMkSOmMskCIif7dl+JmqvhuqIG0zhYwe/XQgnkVj49V8/PF7Jp5FB6gqN82axYAjR1gzaRIZCQnsamxk3u7dnOnw4mlvoqrsvf9NUlIHcu5zvyZxUCq1Vft5bd4vOXHZFWH7PTgxK/iYiOwAvg0kAHNV9R9dHNYA/A54BDgZ+KuIjG7tvWEPJ68ZPrz7Nk1HmUIGDBhBc3MudXV1UWEnRCNut5shcXG4UlIC9tXwlBSy4uOjxr7qCb76JlKHZJI0IDlgW6V/5QTShg1BG5ph4ICwnNeRKE2qugZYcwyHbAc+sfO4bheRQ0A2rbw4VHUpsLSgoKDbBpDJFNJz0tLS2OfxQENDkH21z+eL6fsWl5pE/YHDeOLrg2yr+n2fMyQ1fPtxnZhur6WdBWFV7ewv7kdY20yuF5EcrEXmPQ7IwpNPLuaiiywbqz9mCukJfttq3969AEx7+21yBgygNiWFZatXx/R9+/y1D2jeW0OLxFm2VfZgGuvCZ1v5caLHOq3V6yQsG6u+i2MeBZbbodMU+JFTTrytk3H78wf3Ne8BJ/HbVkk1NYxITuapwkLiRahpaeHa6uqYtq98Ph+fP7aei//x+4Bt9colP2PUc/OIj+90fi1knLCx2ua0uUtENmDZUB0d04wV3DMshDueQV/Cb1tpcjIJcXEcl2QNjwYlJsa8feXZ7yZ9RFaQbTXoxGF4D9YRf0J4fx+OT4mIyBjgBKfb7S6xlDc3GvDbVvsaG9lt21dAn7CvXEPTce/cG5RnuLZyL64h4b8mp22sOKyZwdtCbbcnlJVVcPnl8xHJRnUPK1cuMlnuO6GtbRWPZV8NS0mhMS0t5tevjm7fi1eU1d/6T9JHZOGu3MfxV53dK0suTttYCtSoqtuBdo8JVeXyy+eTnLwyMBNYUlLC1q1vxfSPI1x0ZFsdbm5mblUV67dujek1P1Vl36J/cvEr95OQnkLN9l28fsv9HHde78Q3cUKxvpQXq/UPWVXfcuAcXeJ2uxHJDnK2hayAjdCZcnVUFwvDyRHZ2VTaPU538V/vuIwMstqxreIPHQoY98Ozsti5J+QJ217HV9/EwOzBAfvquDEjSMsZgq++ifgwrV21xgnF+jVQCGwGWoA8rKlz/xDx6w6co0vS09NR3RO0dgV7AzZCLChJT6jcu5fynGOPzquqXO92sw9L0VqvXXmamynLzkZEKKjuftrZaEJSEqmtPhC8drX3UFjXrlrjhGJ9Btyqqm8DiMhY4E5V/Z4DbXcbEWHlykWUlJQAWcBeVq5cZIaBHfBaYyN7vF7E9qec9vbbDBswAPfRo/w0OTmm71tr38De8AtsDycU61S/UgGo6vsiMtKBdo+ZgoJ8tm59y+TN7QKfz8fy5mbWTZlCRkIClQ0NzFi3jp/HxcX8ml97voF1uw/w2nW/Inl0dq/J4YRiNYjIlVhh0ASYBxzboN9B/AvCho7Z7/MxMjU14BM4IjWVk9LSOCrCoBhQqq62dgwtGENqzuCgfVdJriQ2Ft7VC9JZOKFYVwNPYTnU+oC3gVkOtGsIA6pKsiqf1tcH2VU7GxoYEiN/SK33Y6kq3rqjARtaVdl165+73HfVEVGzH0tV/w2MFZHjgUZVbQhdLEM42NrczK/r66lV5ajPx5S1azkxJYWdDQ3MSUyMuen1gC1V2wQ+HwA+jwckHk98ba/6BrbFiQXiLCzfv5OBSSLyPHClqsbeHG0fRlVZ3NhIalISA0V4+qyzGORysbWujhvKyjgvOTnSIh4TflsqOSUVUlIDZUcPufneX+7tdd/Atji10fEF4AbgMLAJa1j4HQfaNjhEvSpDBwzAlZhIQlxcwL76ano6Oamp1KsyMAbsq+zhwwLDtaEFY0gemhmo87V4SByYEuwbOCKbTeP+55jadwInFGukqi4TketVtQX4LxH5wIF2DQ6SKsL+o0eJ93q/tG61t7GR1BjxCfQHelFVTp90Fgf3Hw7U+Xus1raVe+devF5vrw9znVAsn4gEpBaRNMLg3GsIDRHhpuRkFto2VqyvW4kIjy9ewg+umsWhmsP4fNZ6nLfFE/ANrN+1n9rKfRGxHZ1QrNXASqy4gvOwZgmfdaBdQxhIjotjeFISexobKfH5OCNG163KKyq49JorqWmoZeCwISQ1eHnk/gc584wzUFXq6uoYNmxYr9tWfpyYFfyliFyB1UudgxVT8JFQ2zU4i6rywNGjrJo0KTAEvGTdOh4aEH6/OadpHXzz/Jd+Gxj23TT3vwLJDTIzM7tuKIw4MSv4hKrOwlogNkQp9arkJCcHBeLMTk6OmUmL1rjdbhIGp5EalxKU3CBhyKCo2ZjpxOAzT2JxLNFPUFXcHg9HWlqoarORcU9jI6kx9tWpKj6fj6b9RwKLwGBtYmw5cCRqNmY6YWNVA1tE5F2gzl+oqjc60LYhBLY2N3NXbS2NIsRhbTWI5UmLQFKDzw/h8/oA5YUZPyVt2NCwJTfoKU4o1nr7YYgiVJVFDQ144+PJTUxE7EVhfyDOOe++y+iEhEiL2W38dlWdy0fKCcdx7vL/G1gE3vSTh9iyoSKqPEd6rFgicqaqblTV3vNsNLRLR3um8gcNYrjLxaA2i8LDU1JISEqiMIY2MPrtqmRvIvEJrqBF4OSc46MuGGsoPdYjwJkAIrJAVe9xRiTDsdJ6o6OqUufz4fV6ubW+nsqWFoa2syjcejNjZ0TLRsf09HRaDtbS2FKPiAQtAkeTbeUnFMVq/Y1cCBjFijB+J9vPvV6IiyMOaPF6qfT5vggUE6P21YonV1JZWWlHLYpe28pPKIrVeq97dF1VP8TvZJuSmEh9SwsnJCUFbKqd9fVcvn499yUnEx8fz8AYWxT2+XzM//n/4cK/Lw7YVa9ecjubX3yTjIyMqLwWR2K3E0U5h/sbw7OyAsO1cRkZHJ+URLLLxeABAwI21ciBAxmYksL0gwd71H6kqaqqIm14m8Cb/zGM+vr6wEJwtAULCkWxckXkgXZeA2a6vbfwR1Dy+XxMOvVUDu3bx4GWFpp8viCbKu6EE/AdPEhcXFzMBdbJzc2ltnJvkF1VV7mPnDa2ZTQRimI91MFrQy9TUVbGFRdcgPvwYcuu8vnY6fP1meQGcXFxLL57ITfNuIWBw0+grnIfi+9eGFXT622RaNP0thQUFGh5eXmkxYhaVJVJp57Kvqoq3rQTxlU2NPCdd99lzZYtZGRkMGjQoIBSxXJqWJ/PR3V1NTk5OVGhVCJSoartpgd1ysYyRAi3200mkNIqYdyI1FROHjgQl8tFRkZGROVzkri4OHJzcyMtRreIvNobQiI9PZ3DwKdt/AA/O3o0yAYx9C6mx4pxNpaX09jURKPXGwgOU9nUxF0PPRQVw6X+ihPbRv6qqt9qU/auqo4PtW1D56gq82fP5oXRo8k47TR21tczZ+dOtm3fHrENfgaLUHwFVwGnACeJyOZWVQlAU6iCGbrG7XaT7XIFrVedmJpKfX19p4n3YjkJRKwQSo91CzASWAb8pFW5B/h3dxoQkaFABXCOqm4NQZZ+SXp6Ons8nuDAMB4PaWlpRkkiTI8VS1U/Az4TkdGq6gOwE3WfpKqHOz3Y+mwC8Eegsacy9HdEhEVPPEHJrFlkuVzs9XhY9MQTMbte1ZdwYvLiGhGZDNwIbASOiMhqVf3vLo77HfAHoKvPGTohv6CAt7ZsMYkgogwnpo2uBv4TuAR4CfgaVlCZDrGTKBxQ1b918plrRKT8wIEDDojYt/EngjBKFT04oViqqvuA6cDrqurBSmfbGT8CzhGRUqxEdU/YoapbN7pUVQuGDBnigIgGQ+/ixFCwSURuA6YAV4vIdUB9Zweo6mT/a1u5rlXViKX+MRicxoke6yqsaffZ9qTFJKzhocHQb3EiYOc2EfkJMMoOgzb3WFL5qGpxqDIYDNFGyD2WiIwHdgB/AYYBu0RkQqjtGgyxjBNDwd9iTVwcUtUq4ApgsQPtGgwxixOKlWJndQRAVV8lBp17FyxYEGkRwk51dTU+O/NhXyYavksnFKBFRDKx416IyGgH2ux1tm3bxowZMyItRljZsmULJ554IikpKZEWJawkR0F2SicU6x5gDZAlIk8B3wSucaDdXuW5556LtAhhZ9WqVZxzzjmdOuganMGJWcFXRGQrlrdFPHC3qn4UsmQGx7n44osjLUK/wRFbSFU/EZEW4CygxYk2DYZYpseTFyIyQUS2i0ipiJwLfAj8DHhPRC5yTELDMbFw4ULGjRvHeeedx6FDh4Lqdu3aRVFRERMmTGDp0qXHVPb6668zatQoiouLKS4uxuv18tFHHzF+/HgmTpzIo48+2rsXGu2oao8eWBlGLgGuA44C0+zyU4BNPW237SM/P18N3aOqqkqnT5+uqqqrV6/W22+/Pah+3rx5Wlpaqh6PR4uKirSmpqbbZffee6+uWrUqqL3zzz9f33nnHfX5fHreeefpwYMHe+1aowGgXDv43YYy3Z6qqs+p6hJgr6q+YSvqdkxk3B6zfPlyLrjgAoqLi5k8eTL79+8Pqvf3GP7Hpk2bAnXl5eUUFRUBMH36dNauXRt07MaNGykqKiI+Pp7CwkI2btzY7bLNmzezbNkyioqKePzxxwHYuXMnZ599NiLC2LFjef/998N7c2KIUGwsb6vXbTc2GsUKgczMTF588UVWrFjBkiVLuOOOOwJ1paWlHR7ndrsDWTdSU1Opq6sLqvd4PIEAM/767pZNmjSJqVOnkpOTw9SpU5k2bRqjRo1izZo1nH322axZsyag1AbnkiIYRXIQ/w+0sLCQV199NaiuuLg46P2iRYvIy8sDrK36n332GUC7+aJcLhc+n4+4uLhAfXfLJk+eHJimLyoqYuvWrfzud7/j2muvJTk5mdNOO43jjjvO+ZsRo4QyFPy6iLhFxN36tYjUAqc7JF+/xD+kKi8v55RTTgmqKy0tDXr4lQogPz+ftWvXoqq88cYbjB8fHCgrLy+PdevW4fV62bBhA2PHju122fTp06mqqgqUffWrX+XNN9/k0UcfZdWqVezevZuxY8eG/d7ECqH0WCc5JoUhiI0bNzJ16lQSExN56qmnun1cbm4u06ZNY8KECSQnJ/Pss88CcOGFF7J69WoWLFjA7Nmzqa2t5aqrriIjI6PbZffddx8XXXQRLpeLK664gtzcXE488URmzpxJUlISN998c5/36DgWTOz2KGP58uXU1NQwf/78SIti6ILOYrebUKkGQxiIOS/0vs6VV14ZaREMDmB6LIMhDBjFMhjCgFEsgyEM9CnFUlWOHDnSb+KWqyqHDx9m586d/WJncCzRZxSrrKyCMWMmM27cXMaMmUx5eUWkRQorFWVlfG3YMPJzc5lTWMgpGRmsXLEi0mIZbPrEOpaqMmbMZJKTV+JyZeLxHKaxsYStW9/qk2GXtU3eYX+mkanr17P98GGTcK6X6PPrWG63G5FsXK5MAPs5i9ra2sgKFib8eYdPbJV3ODMxkZEDBlBdXR1Z4QxAH1Gs9PR0VPfg8VhO9tbz3oCnd1/D5B2OfvrEArGIsHLlIkpKSoAsYC8rVy7qk8NAsK73gf/9Xy4//3yK165lpMk7HHX0CRvLj6r2qzxR/lnQ2tpahg0bZpSql+nMxuoTPZYff56o/oKIkJGRQUZGRqRFMbTB/MUZDGHAKJbBEAaMYhkMYcAolsEQBoxiGQxhICKKJSLxIvKYiKwTkbdExMTPMPQpItVjzQRQ1YnAz4H7IiSHwRAWIrKOpaoviMgr9tsRwL5IyGEwhIuILRCrqkdE/gR8D/hSfhkRuQYrz1adiGwLoyiDgYNhbD/aMNfrHCM6qoi4S5OIZAEbgK+qan0Ezl/ekVtKX8Rcb+8QqcmLK0Tkv+23DYCP4FjwvcnSCJ03Upjr7QUi0mOJSCrwOJYregKwUFVf7HVBDIYwEfGhoMHQFzELxAZDGIhZxRKRkSKiInJVm/JbRGR5L8tyZavlg4hg34sPRGSTiPxLRLaJSJmIdGm4i0ieiOwQkQoRKRKRD+12zu4N2TuQSUVkcC+dK0dE3rFf3ykiD4baZswqlo0PuFdERkdakCjhG6qap6pnqOpo4Bng99047nzgn6qaD5yIlaEzT1XXh1PYaEFVq1V1gpNtxrpiNQL3Ak+KSGLrChEZJCL/a//7fiAivxERl13XJCLP2v/qBSJyVER+KSLvici/ReT7IvKciGwVkTftyRZE5EcissHuEXaKyHW9f8ndw77W4cDn9vugf2L/exEpAa4HLhCR3cA9QKGI/DMScrehWEQ+9L8RkWL7+4wXkV0ikt+q7hn/9yEit4vIRrvXfUFEcuzyUhH5le1G95mIPCIicfbop67tyUVkmIg8b/fkm0XkZ90VPNYVC+AXQB3wyzblDwCHsJLgFQBjgVvsukTgZVUdrarlQBLWv/RZwJ+AR4D5wFeBQVg/uoHAXODbqnoG8APgN2G8rp7wT/sHUA1st8vmdHaAqq4E/gA8o6rDsFzM1qrqN8Iras9RVS/wGPa1iUgmMB3rD3YW1nd+lqrmAa9ifZ9+TgKKga8D3wKmdHKqFcBjdk9+FjBdRL7fHRljfmu+qvpE5HJgk4j8rVXVt4CJdnbzJhH5A5ayLLTrgzNfw5/t5x3AB6q6G0BEPgWOU9U6EZkBfEdETgbygIHhuKYQ+IaqHhSRM7F+UP9U1f1dHRSjPAaUicjNwKXAS6p6xP6OzgLK7bgn8UDrjHgvq6oPcIvIJ8BxwKdtG7dHKVOA40Tkf+zigVjf+7NdCRfzigWgqrtEZB5Wb/OEXRxHcG7kOKw1Mz9tu/6mVq9b2p5DRHKB9VgLjm8Dq4AZoUkeHlR1o4j8J7BcRP6lqp9h3YvWEXYS2z04QojIGYBHVT8QCUQCOkAHMqvqThHZiPUdzMH60wRLkX6tqkvsdpOAzFZtNLZ63faetCberpugqg12W4OBo925nr4wFARAVVcBf+WLG/w34AaxSMLyO/xHCKcowPqi7wH+jq1UIhIfQpthQ1WfAt4D7reLDgD59v1II/r+FGYAt9mvz8SS9yAwXESG2sr2wzbHLAP+C0hV1XV22d+Aq0XEH1Xobqwh3TGhqm7gXeBmABHJANYBF3Tn+D6jWDY3AjtbvR4KfGA/tmHZYz3l70CV3c5HWBMDB4BRIbQZbm4Avi0i5wIrseT9GHgFWBNJwdrhQWCwPVnxJHC1qm4B/giUY/3I2w7ZXgJGEmxDPYJ1fe+KyBYsW+rKHsp0GTBeRD7A8md9yrZJu8R4XhgMYaCv9VgGQ1RgFMtgCAN9YlawryAiCnyItYVGsaaJ3cB19npbVCMitwCnqeqVkZYl0hjFij6+oaqBHa/2j/X3QMT89gzHjhkKRjFt3ZLsss7cde4VK/LVxyJyq/2+XEQ+EpHT7c/lisjLtpvXhyJyq13+SxH5favzfEtENtivJ4jIWvu8ZfYiLCKSICJLROQTsZxYJ/bazYl2VNU8ouSBNfz7ANgMVAP/D8s1a6hdPwt4GnDZ768BXrVflwJ/tl+Ps9uaab+/H1hqv14D3Gy/HgS8j7U+9B9Y0/GJdt0zwNVYi6vbgJF2eQ6wC0vhbwLewFq4TQUqgOWRvo/R8DBDweijM7ekrtx1VtvPO+zn11q9L7bddCYC3wRQywVoOfAtVX1aRDYD54vIG8BU4CpgMpANvPCFQwSKtT40HXhSVZuBZhFZaZf3e4xiRSnavltSV+46TW3aaOuaFceXXXhau3otw+oVTwBeUMs/Mh74SFXH+Q+wh58HgHlt2vMc84X2UYyNFcXol92SQnLXUdVaLA+GH4O1tQZLkfyuXs8D+Vhe/MvssneBk0Vksn1MHpb3xjAsF7JZIjJARAZgefwbMD1WLHADsNl2S3oE6wf9rj01X8mxu+uUAA+JyBws2+hJYDmAqjaJyDPAdFV9zy47ICIXAb+1lScOuEJVPxORP2K5dH2ItUXn45CutA9hXJoMhjBghoIGQxgwimUwhAGjWAZDGDCKZTCEAaNYBkMYMIplMIQBo1gGQxgwimUwhIH/D6D6t9TbW732AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fig7a(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [3,3])\n", " else:\n", " standalone = False\n", " \n", " posit = [1,2,3]\n", " bplot = ax.boxplot([czbest, dtbest, jvbest], patch_artist = True, sym = \"\", widths = 0.4,\\\n", " positions = posit, labels = ['Normal Owls', 'Ruffcut Owls', 'Juvenile Owls'], zorder = 5)\n", "\n", " colors = [[0.2, 0.2, 0.95], [0.95, 0.3, 0.3], [0.4, 0.9, 0.7]]\n", " for i, (sc) in enumerate((czbest,dtbest,jvbest)):\n", " ax.scatter(np.linspace(posit[i]-.2,posit[i]+.2, num = len(sc)), np.sort(sc), zorder = 6, \\\n", " linewidth = .7, s = 20, edgecolors = 'black', color = colors[i])\n", "\n", " colors = [[0.5, 0.5, 0.9], [0.8, 0.1, 0.1], [0.2, 0.8, 0.5]]\n", " for patch, color in zip(bplot['boxes'], colors):\n", " patch.set_facecolor(color)\n", " for median in bplot['medians']:\n", " median.set_color('black')\n", " ax.set_ylabel('Best Frequency (kHz)')\n", " ax.set_ylim([3000, 7000])\n", " ax.set_yticks(np.arange(3000,7001,1000))\n", " ax.set_yticklabels([3,4,5,6,7])\n", " ax.set_xticklabels(['Normal', 'Ruff\\nRemoved', 'Juvenile'], fontsize = 12)\n", "\n", " plt.xlim([0.6,3.4])\n", " ax.text(0.05,7000, 'a', fontsize = 12, fontweight = 'bold' )\n", "\n", " ax.annotate(f'p = {cdbest[1]:.2g}', xy=(1.5, 6500), xytext=(1.5, 6600), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=.1', lw=.7))\n", " ax.annotate(f'p = {djbest[1]:.2g}', xy=(2.5, 6000), xytext=(2.5, 6100), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=.1', lw=.7))\n", " ax.annotate(f'p = {cjbest[1]:.2g}', xy=(2.0, 3800), xytext=(2.0, 3500), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=6.4, lengthB=.1', lw=.7))\n", "\n", " \n", " if standalone:\n", " plt.show()\n", " file_name = 'boxplots_best.png'\n", " filname = os.path.join(fildir, file_name)\n", " fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "fig7a()" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fig7b(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [3,3])\n", " else:\n", " standalone = False\n", " \n", " posit_hi = [.75,2,3.25]\n", " posit_lo = [1.25,2.5,3.75]\n", "\n", "\n", " bplot = ax.boxplot([czhi, dthi, jvhi], bootstrap = 1000, patch_artist = True, sym = \"\",\\\n", " positions = posit_hi, widths = 0.4, zorder = 5)\n", "\n", " colors = [[0.3, 0.3, 0.9], [0.9, 0.3, 0.3], [0.4, 0.8, 0.3]]\n", " for i, (sc) in enumerate((czhi,dthi,jvhi)):\n", " ax.scatter(np.linspace(posit_hi[i]-0.2,posit_hi[i]+0.2, num = len(sc)), np.sort(sc), zorder = 6, \\\n", " linewidth = .7, s = 20, edgecolors = 'black', color = colors[i])\n", "\n", " colors = [[0.7, 0.7, 0.9], [0.9, 0.7, 0.7], [0.2, 1.0, 0.5]]\n", " for patch, color in zip(bplot['boxes'], colors):\n", " patch.set_facecolor(color)\n", " for median in bplot['medians']:\n", " median.set_color('black') \n", "\n", "\n", " bplot = ax.boxplot([czlo, dtlo, jvlo], bootstrap = 1000, patch_artist = True, sym = \"\",\\\n", " positions = posit_lo, widths = 0.4, zorder = 5)\n", " colors = [[0.4, 0.1, 0.7], [0.8, 0, 0.1], [0.3, 0.8, 0.4]]\n", " for i, (sc) in enumerate((czlo,dtlo,jvlo)):\n", " ax.scatter(np.linspace(posit_lo[i]-0.2,posit_lo[i]+0.2, num = len(sc)), np.sort(sc), zorder = 6, \\\n", " linewidth = .7, s = 20, edgecolors = 'black', color = colors[i])\n", "\n", " colors = [[0.3, 0.3, 0.65], [0.6, 0, 0.25], [0.2, 0.6, 0.5]]\n", " for patch, color in zip(bplot['boxes'], colors):\n", " patch.set_facecolor(color)\n", " for median in bplot['medians']:\n", " median.set_color('black')\n", "\n", " ax.set_ylabel('Frequency (kHz)')\n", " ax.set_xticks([1, 2.25, 3.5])\n", " ax.set_xticklabels(['Normal', 'Ruff\\nRemoved', 'Juvenile'], fontsize = 12)\n", " ax.set_yticks(np.arange(1000,8400,1000))\n", " ax.set_yticklabels([1,2,3,4,5,6,7,8])\n", "\n", " ax.set_ylim([800, 8400])\n", " ax.set_xlim([0.5, 4])\n", "\n", " for hi in posit_hi:\n", " ax.text(hi ,900, 'High\\nBound', horizontalalignment = 'center', ma = 'center', fontsize = 8)\n", " for lo in posit_lo:\n", " ax.text(lo ,900, 'Low\\nBound', horizontalalignment = 'center', ma = 'center', fontsize = 8)\n", "\n", "\n", " ax.annotate(f'p = {cdhi[1]:.2g}', xy=(1.375, 7200), xytext=(1.375, 7300), xycoords='data', \n", " fontsize=8, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=0.1', lw=.7))\n", " ax.annotate(f'p = {djhi[1]:.2g}', xy=(2.625, 7400), xytext=(2.625, 7500), xycoords='data', \n", " fontsize=8, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=0.1', lw=.7))\n", " ax.annotate(f'p = {cjhi[1]:.2g}', xy=(2.0, 7950), xytext=(2.0, 8050), xycoords='data', \n", " fontsize=8, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=7.5, lengthB=0.1', lw=.7))\n", "\n", " ax.annotate(f'p = {cdlo[1]:.2g}', xy=(1.875, 2500), xytext=(1.875, 2400), xycoords='data', \n", " fontsize=8, ha='center', va='top',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=0.1', lw=.7))\n", " ax.annotate(f'p = {djlo[1]:.2g}', xy=(3.125, 2800), xytext=(3.125, 2700), xycoords='data', \n", " fontsize=8, ha='center', va='top',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=0.1', lw=.7))\n", " ax.annotate(f'p = {cjlo[1]:.2g}', xy=(2.5, 2100), xytext=(2.5, 2000), xycoords='data', \n", " fontsize=8, ha='center', va='top',\n", " arrowprops=dict(arrowstyle='-[, widthB=7.5, lengthB=0.1', lw=.7))\n", " ax.text(-.1,8400, 'b', fontsize = 12, fontweight = 'bold' )\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'boxplots_sum.png'\n", " filname = os.path.join(fildir, file_name)\n", " fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "fig7b()" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fig7c(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [3,3])\n", " else:\n", " standalone = False\n", "\n", " posit = [1, 2, 3]\n", " bplot = ax.boxplot([czfrtrg, dtfrtrg, jvfrtrg], patch_artist = True, sym = \"\",\\\n", " positions = posit, labels = ['Normal Owls', 'Ruffcut Owls', 'Juvenile Owls'])\n", "\n", " colors = [[0.2, 0.2, 0.95], [0.95, 0.3, 0.3], [0.4, 0.9, 0.7]]\n", " for i, (sc) in enumerate((czfrtrg, dtfrtrg, jvfrtrg)):\n", " ax.scatter(np.linspace(posit[i]-.2,posit[i]+.2, num = len(sc)), np.sort(sc), zorder = 6, \\\n", " linewidth = .7, s = 20, edgecolors = 'black', color = colors[i])\n", "\n", " colors = [[0.5, 0.5, 0.9], [0.8, 0.1, 0.1], [0.2, 0.8, 0.5]]\n", " for patch, color in zip(bplot['boxes'], colors):\n", " patch.set_facecolor(color)\n", " for median in bplot['medians']:\n", " median.set_color('black')\n", " ax.set_ylabel('Frequency Range (kHz)')\n", " plt.xlim([0.6,3.4])\n", " ax.text(0.05,5000, 'c', fontsize = 12, fontweight = 'bold' )\n", "\n", " ax.set_yticks(np.arange(1000,5001,1000))\n", " ax.set_xticklabels(['Normal', 'Ruff\\nRemoved', 'Juvenile'], fontsize = 12)\n", " ax.set_yticklabels([1,2,3,4,5])\n", " \n", "\n", " ax.annotate(f'p = {cdrg[1]:.2g}', xy=(1.5, 3600), xytext=(1.5, 3700), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=.1', lw=1))\n", " ax.annotate(f'p = {djrg[1]:.2g}', xy=(2.5, 4000), xytext=(2.5, 4100), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=.1', lw=1))\n", " ax.annotate(f'p = {cjrg[1]:.2g}', xy=(2.0, 4600), xytext=(2.0, 4700), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=6.4, lengthB=.1', lw=1))\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'freq_box_Hz.png'\n", " filname = os.path.join(fildir, file_name)\n", " fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "\n", "fig7c()" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fig7d(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [3,3])\n", " else:\n", " standalone = False\n", " \n", " posit = [1, 2, 3]\n", " bplot = ax.boxplot([czfrtwid, dtfrtwid, jvfrtwid], patch_artist = True, sym = \"\",\\\n", " positions = posit, labels = ['Normal Owls', 'Ruff-Removed Owls', 'Juvenile Owls'])\n", "\n", " colors = [[0.2, 0.2, 0.95], [0.95, 0.3, 0.3], [0.4, 0.9, 0.7]]\n", " for i, (sc) in enumerate((czfrtwid, dtfrtwid, jvfrtwid)):\n", " ax.scatter(np.linspace(posit[i]-.2,posit[i]+.2, num = len(sc)), np.sort(sc), zorder = 6, \\\n", " linewidth = .7, s = 20, edgecolors = 'black', color = colors[i])\n", "\n", " colors = [[0.5, 0.5, 0.9], [0.8, 0.1, 0.1], [0.2, 0.8, 0.5]]\n", " for patch, color in zip(bplot['boxes'], colors):\n", " patch.set_facecolor(color)\n", " for median in bplot['medians']:\n", " median.set_color('black')\n", " ax.set_ylabel('ITD Width (μs)', va = 'center')\n", " plt.xlim([0.6,3.4])\n", " ax.set_xticklabels(['Normal', 'Ruff\\nRemoved', 'Juvenile'], fontsize = 12)\n", " ax.text(0.05,180, 'd', fontsize = 12, fontweight = 'bold' )\n", " ax.set_yticks(np.arange(40,181,20))\n", " ax.set_yticklabels(np.arange(40,181,20), horizontalalignment = 'right')\n", "\n", " ax.annotate(f'p = {cdwid[1]:.2g}', xy=(1.5, 147), xytext=(1.5, 150), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=0.1', lw=1))\n", " ax.annotate(f'p = {djwid[1]:.2g}', xy=(2.5, 147), xytext=(2.5, 150), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=3.2, lengthB=0.1', lw=1))\n", " ax.annotate(f'p = {cjwid[1]:.2g}', xy=(2.0, 166), xytext=(2.0, 169), xycoords='data', \n", " fontsize=9, ha='center', va='bottom',\n", " arrowprops=dict(arrowstyle='-[, widthB=6.4, lengthB=0.1', lw=1))\n", "\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'itdwidth_box_Hz.png'\n", " filname = os.path.join(fildir, file_name)\n", " fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "fig7d()" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ((ax1, ax2),(ax3,ax4)) = plt.subplots(2, 2, figsize= (6, 6))\n", "\n", "fig7a(ax1)\n", "fig7b(ax2)\n", "fig7c(ax3)\n", "fig7d(ax4)\n", "\n", "plt.tight_layout()\n", "plt.subplots_adjust( bottom=None, right=None, top = None, wspace=.25, hspace=.2)\n", "\n", "\n", "file_name = 'fig7.png'\n", "filname = os.path.join(fildir, file_name)\n", "fig.savefig(filname, bbox_inches = 'tight')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "#ICCls" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#only use units that are good, and are tuned to ITD\n", "dt = dt[dt['Use?'] == 'yes']\n", "allunits = dt.index[~dt['Best ITD'].isnull()].tolist()\n", "#use only the for sure ICx units\n", "iccllist = dt[dt['Nucleus'] == 'ICCls']\n", "iccl = iccllist.index[~iccllist['Best ITD'].isnull()].tolist()\n", "#set use to be allunits or icx\n", "iccluse = iccl\n", "iccln = dt['Best ITD'][iccluse].size" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "icclrandnu = np.empty_like(dt['Best ITD'][iccluse])\n", "for n in range(icclrandnu.size):\n", " icclrandnu[n] = np.random.randint(-12,12,1)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = [4*1.2,4])\n", "\n", "ax.scatter(np.abs(dt['Best ITD'][iccluse] + icclrandnu), dt['Low Freq'][iccluse], marker = 'v', color = [0.25, 0, 0.6], edgecolor = 'black', \\\n", " linewidth = 0.7, s=30, zorder=12)\n", "ax.scatter(np.abs(dt['Best ITD'][iccluse] + icclrandnu), dt['High Freq'][iccluse], marker = '^', color = [0.6, 0.7, 0.9], edgecolor = 'black', \\\n", " linewidth = 0.7, s=30, zorder=12)\n", "ax.scatter(np.abs(dt['Best ITD'][iccluse] + icclrandnu), dt['Best Freq'][iccluse], marker = 'o', color = [0.1, 0.1, 0.8], edgecolor = 'black', \\\n", " linewidth = 0.7, s =30, zorder=12, \\\n", " label = f'ICCls units, n = {iccln} ')\n", "for nurep, ITD in enumerate(iccluse):\n", " ax.bar(np.abs(dt['Best ITD'][ITD] + icclrandnu[nurep]), dt['High Freq'][ITD] - dt['Low Freq'][ITD], 1, dt['Low Freq'][ITD], color = [0.1, .1, 0.9 ]) \n", " \n", "ax.set_yticks(np.linspace(1000,8000,8))\n", "ax.set_yticklabels([1,2,3,4,5,6,7,8])\n", " \n", "#ax.plot(np.abs(cz['Best ITD']), linreg.intercept + linreg.slope*np.abs(cz['Best ITD']), 'r', linewidth = 5)\n", "#ax.plot(np.abs(cz['Best ITD']), 5758 + -17.11*np.abs(cz['Best ITD']), 'b', linewidth = 5)\n", "ax.set_ylim([1000, 8000])\n", "plt.ylabel('Frequency (kHz)')\n", "plt.xlabel('ITD (μs)')\n", "plt.gca().legend(loc=\"upper right\")\n", "plt.show()\n", "file_name = 'Haircut_ICCls.png'\n", "filname = os.path.join(fildir, file_name)\n", "#fig.savefig(filname, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "##Example unit" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "d = '2021-1013'\n", "m = '44.07'\n", "n = '0' + m\n", "\n", "abifile = pedman.pyxdphys.XDdata(fr'F:\\xdphys\\{d}\\{m}\\{n}.0.abi')\n", "ildfile = pedman.pyxdphys.XDdata(fr'F:\\xdphys\\{d}\\{m}\\{n}.1.iid')\n", "bcfile = pedman.pyxdphys.XDdata(fr'F:\\xdphys\\{d}\\{m}\\{n}.2.gen')\n", "itdfile = pedman.pyxdphys.XDdata(fr'F:\\xdphys\\{d}\\{m}\\{n}.3.itd')\n", "bffile = pedman.pyxdphys.XDdata(fr'F:\\xdphys\\{d}\\{m}\\{n}.4.bf')\n", "#sptfile = pedman.pyxdphys.XDdata(r'D:\\xdphys\\2021-0811\\046.00\\046.00.5.iid')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[100.85, 20.0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[-297.29, -22.0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[499.0, 62.0]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "best ITD:\n" ] }, { "data": { "text/plain": [ "[20.0, -22.0, 62.0, 0.94]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "best freq:\n" ] }, { "data": { "text/plain": [ "[3300.0, 2600.0, 4000.0]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "best ILD:\n" ] }, { "data": { "text/plain": [ "array([0.])" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gaussian or Sigmoidal\n" ] }, { "data": { "text/plain": [ "array([0.95, 0.19])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%run ./QuickXDPhys-BasicFrequencyTuning.ipynb" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Best ILD\n", "def fig2c(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [1.5*1.2,1.5])\n", " else:\n", " standalone = False\n", "\n", " ax.errorbar(ildx, ildy, sps.stats.sem(ildspikecounts, axis=1), linewidth=1,marker = 'o', markersize = 1)\n", " ax.set_xlabel('ILD (dB)', va = 'center')\n", " #ax.set_ylabel('Spike Count')\n", " ax.tick_params(axis='both', length = 1)\n", " ax.set_yticks([5, 10])\n", " ax.set_yticklabels([5, 10], ha = 'center')\n", " \n", " ax.text(-0.15,.95, 'c', fontsize = 12, fontweight = 'bold' , transform = ax.transAxes)\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'ex_ILD.png'\n", " filname = os.path.join(fildir, file_name)\n", " #fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "\n", "fig2c()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Best Freq\n", "\n", "def fig2b(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [1.5*1.2,1.5])\n", " else:\n", " standalone = False\n", " \n", " ax.errorbar(bffile.params['bf']['range'], bfspikecounts.mean(axis=1), sps.stats.sem(bfspikecounts, axis=1),\\\n", " linewidth = 1, markersize = 1)\n", " ax.set_xlabel('Frequency (kHz)', va = 'center')\n", " #ax.set_ylabel('Spike Count')\n", " ax.hlines((np.max(bfy) - np.min(bfy))/2 + np.min(bfy), 400, 10000, color = 'black', linewidths = 2)\n", " ax.scatter([freq_lo, freq_hi], [lofr, hifr], color = 'black', s = 10)\n", " ax.scatter([best_freq], [np.max(bfy)], marker = 'o',color = 'red', zorder = 1, s = 10)\n", " ax.set_yticks([ 5, 10])\n", " ax.set_yticklabels([ 5, 10], ha = 'center')\n", " ax.set_xticks([ 0, 5000, 10000])\n", " ax.set_xticklabels([ 0, 5, 10], ha = 'center')\n", " \n", " ax.text(-0.15,.95, 'b', fontsize = 12, fontweight = 'bold' , transform = ax.transAxes)\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'ex_ILD.png'\n", " filname = os.path.join(fildir, file_name)\n", " #fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "fig2b()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Best ITD Graph\n", "\n", "def fig2a(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [1.5*1.2,1.5])\n", " else:\n", " standalone = False \n", " ax.errorbar(itdfile.params['itd']['range'], itdspikecounts.mean(axis=1), sps.stats.sem(itdspikecounts, axis=1), \\\n", " linewidth = 1, markersize = 1)\n", " ax.plot(np.arange(-80,121,10), Gaussian(np.arange(-80,121,10), *ITDopt), zorder = 5, linewidth = 1.5)\n", " ax.set_xlabel('ITD (µs)', va = 'center')\n", " ax.set_ylabel('Spike Count', va = 'center')\n", " ax.set_yticks([ 5, 10, 15])\n", " ax.set_yticklabels([ 5, 10, 15], ha = 'center')\n", " \n", " ax.text(-0.45,.95, 'a', fontsize = 12, fontweight = 'bold' , transform = ax.transAxes)\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'ex_ITD.png'\n", " filname = os.path.join(fildir, file_name)\n", " #fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "fig2a()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize= (4.5*1.2, 1.5))\n", "\n", "fig2a(ax1)\n", "fig2b(ax2)\n", "fig2c(ax3)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "#ILD" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "czxildi, czyildi, czyild1, czyild2 = regCI(cz['Best ITD'], cz['ILD'], czn, 0.05)\n", "dtxildi, dtyildi, dtyild1, dtyild2 = regCI(dt['Best ITD'][use], dt['ILD'][use], dtn, 0.05)\n", "jvxildi, jvyildi, jvyild1, jvyild2 = regCI(jv['Best ITD'], jv['ILD'], jvn, 0.05)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3.679724719557728, 0.00023348586084959703]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ildlinreg = sps.stats.linregress(np.abs(dt['Best ITD'][use]), dt['ILD'][use])\n", "czildlinreg = sps.stats.linregress(np.abs(cz['Best ITD']), cz['ILD'])\n", "jvildlinreg = sps.stats.linregress(np.abs(jv['Best ITD']), jv['ILD'])\n", "\n", "\n", "slopediff(dt['Best ITD'][use], dt['ILD'][use], cz['Best ITD'], cz['ILD'], dtn, czn)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fig2d(ax = None):\n", " if ax is None:\n", " standalone = True\n", " fig, ax = plt.subplots(figsize = [4.5,3])\n", " else:\n", " standalone = False \n", " \n", " ax.scatter(np.abs(dt['Best ITD'][use] + randnu), dt['ILD'][use], marker = 'o', color = [0.8, 0.1, 0.1], \\\n", " linewidths = 0.7, edgecolor = 'black', s =20, zorder=11)\n", " ax.scatter(np.abs(cz['Best ITD']), cz['ILD'], marker = 'o', color = [0.5, 0.5, 1], s = 20, zorder = 10)\n", " #ax.scatter(np.abs(jv['Best ITD']), jv['ILD'], marker = 'o', color = [0.2, 0.9, 0.5], s = 100)\n", " \n", " czline, = ax.plot(czxildi, czyildi, color = [0.1, 0.25, 1], linewidth = 2.5, \\\n", " label = f'Cazettes 2014, r = {czildlinreg.rvalue:.2g}, p = {czildlinreg.pvalue:.2g}', zorder = 9)\n", " ax.fill_between(czxildi, czyild1, czyild2, color = [0.8, 0.8, .95], zorder = 1)\n", " \n", " dtline, = ax.plot(dtxildi, dtyildi, color = [0.25, 0.05, 0.05], linewidth = 2.5, \\\n", " label = f'Ruff-Removed Owls, r = {ildlinreg.rvalue:.2g}, p = {ildlinreg.pvalue:.2g} ', zorder = 8)\n", " ax.fill_between(dtxildi, dtyild1, dtyild2, color = [0.95, 0.8, 0.8], zorder = 3)\n", " \n", " ax.text(-0.15,.95, 'd', fontsize = 12, fontweight = 'bold' , transform = ax.transAxes)\n", " \n", " plt.ylabel('ILD (dB)')\n", " plt.xlabel('ITD (μs)')\n", " ax.set_ylim([-25, 25])\n", " #legend1 = plt.gca().legend(handles = [czline], loc = [.35, .01])\n", " #ax.add_artist(legend1)\n", " #legend2 = plt.gca().legend(handles = [dtline], loc = [.25, .87])\n", " plt.gca().legend( loc = [.04, .01])\n", "\n", "\n", " if standalone:\n", " plt.show()\n", " file_name = 'ruffcut_ILD.png'\n", " filname = os.path.join(fildir, file_name)\n", " #fig.savefig(filname, bbox_inches='tight')\n", " return ax\n", "fig2d()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(facecolor= 'white')\n", "fig.set_figheight(4.5)\n", "fig.set_figwidth(4.5)\n", "\n", "ax1 = plt.subplot2grid(shape=(3,3), loc = (0,0), colspan = 1) \n", "ax2 = plt.subplot2grid(shape=(3,3), loc = (0,1), colspan = 1) \n", "ax3 = plt.subplot2grid(shape=(3,3), loc = (0,2), colspan = 1) \n", "ax4 = plt.subplot2grid(shape=(3,3), loc = (1,0), colspan = 3, rowspan = 2) \n", "\n", "fig2a(ax1)\n", "fig2b(ax2)\n", "fig2c(ax3)\n", "fig2d(ax4)\n", "\n", "#plt.tight_layout()\n", "plt.subplots_adjust( bottom=None, right=None, top = None, wspace=.2, hspace=.6)\n", "\n", "file_name = 'Fig2_whole.png'\n", "filname = os.path.join(fildir, file_name)\n", "fig.savefig(filname, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#fig2 = plt.figure()\n", "#fig2.tight_layout()\n", "#\n", "#ax1 = plt.subplot(2,3,1)\n", "#fig2a(ax1)\n", "#ax2 = plt.subplot(2,3,2)\n", "#fig2b(ax2)\n", "#ax3 = plt.subplot(2,3,3)\n", "#fig2c(ax3)\n", "#ax4 = plt.subplot(2,1,2)\n", "#fig2d(ax4)\n", "#\n", "#plt.subplots_adjust( bottom=None, right=None, top=None, wspace=None, hspace=.4)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }