From 42ec8562996caf7ce6a9e5ab207ecc190d62eb45 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:20:56 -0400 Subject: [PATCH 001/153] Add files via upload --- FiberFit-toshare-6.5.19.ipynb | 476 ++++++++++++++++++++++++++++++++++ 1 file changed, 476 insertions(+) create mode 100644 FiberFit-toshare-6.5.19.ipynb diff --git a/FiberFit-toshare-6.5.19.ipynb b/FiberFit-toshare-6.5.19.ipynb new file mode 100644 index 0000000..3e3167a --- /dev/null +++ b/FiberFit-toshare-6.5.19.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Import necessary Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", + "import skimage.io as io\n", + "from skimage import data\n", + "from skimage.filters import sobel \n", + "from skimage.morphology import watershed\n", + "from skimage import feature\n", + "from skimage.color import rgb2gray\n", + "from skimage.feature import corner_harris,corner_peaks\n", + "from skimage.exposure import equalize_hist\n", + "from skimage import measure\n", + "from skimage.draw import ellipse\n", + "from skimage.measure import label, regionprops\n", + "from skimage.transform import rotate\n", + "from skimage.feature import corner_orientations\n", + "from skimage.filters import threshold_otsu\n", + "from skimage.segmentation import clear_border\n", + "from skimage.measure import label\n", + "from skimage.morphology import closing, square\n", + "from skimage.measure import regionprops\n", + "from skimage.color import label2rgb\n", + "from __future__ import division\n", + "import seaborn\n", + "import pandas\n", + "# try:\n", + "# from runtime import *\n", + "# except ImportError:\n", + "# from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "from os import listdir\n", + "from os.path import isfile, join\n", + "import numpy\n", + "import cv2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Input Variables " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "## File Path with binary square images of fibers to analyze\n", + "mypath='C:/Users/Maggie/Desktop/fiberFit'\n", + "\n", + "##Path to folder where you would like images to be saved\n", + "savepath = 'C:/Users/Maggie/Desktop/fiberFitOutput/'\n", + "## NOTE - we can also output raw data. let me know if you would prefer that.\n", + "\n", + "## Variables for settings\n", + "CO_lower = 32 #upper fiber thickness, pixels\n", + "CO_upper = 2 #lower fiber thickness, pixels\n", + "angleInc = 1 #angle increment\n", + "radStep = 0.5 #radial step size\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, Run the code below. Figures will be saved in specified output folder" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Maggie\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n", + "C:\\Users\\Maggie\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:66: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", + "C:\\Users\\Maggie\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:70: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n" + ] + } + ], + "source": [ + "## Upload Files\n", + "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", + "images = numpy.empty(len(onlyfiles), dtype=object)\n", + "names = numpy.empty(len(onlyfiles), dtype=object)\n", + "for n in range(0, len(onlyfiles)):\n", + " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", + " names[n]= (onlyfiles[n])\n", + "img = numpy.empty(len(images), dtype=object)\n", + "for n in range(0,len(images)):\n", + " img[n]= np.invert(images[n])\n", + " \n", + "## Set up empty Matrices (can clean this up later )\n", + "ell_data_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", + "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", + "normPower_test = numpy.empty(len(img),dtype=object)\n", + "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", + "A_test = numpy.empty(len(img),dtype=object)\n", + "centroid_test = numpy.empty(len(img),dtype=object)\n", + "t_new_test = numpy.empty(len(img),dtype=object)\n", + "im = numpy.empty(len(img), dtype=object)\n", + "PF = numpy.empty(len(img),dtype=object)\n", + "PS = numpy.empty(len(img),dtype=object)\n", + "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", + "PowerY_test = numpy.empty(len(img),dtype=object)\n", + "\n", + "## begin analysis\n", + "for n in range(0,len(img)):\n", + " im=img[n][:,:,1] # select first image\n", + " M= im.shape[0] #get image shape\n", + " N1 = im.shape[1] \n", + " n1 = np.round(N1/ 2) - 1\n", + " freq = np.arange(-n1, n1 + 1, 1)\n", + " x, y = freq, freq\n", + " \n", + " ## Fourier Transform\n", + " fft_result = np.fft.fft2(im)\n", + " Fshift = np.fft.fftshift(fft_result)\n", + " \n", + " ## adjust to power spectrum to remove imaginary components\n", + " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", + " Pabs = np.abs(Fshift) ** 2\n", + " \n", + " M= im.shape[0]\n", + " N1=im.shape[1]\n", + "\n", + " # shift in terms of image because power specrum is the mirroR of lines so\n", + " # misrroring back in terms of image would give right allignment\n", + " PabsFlip1 = np.rot90(Pabs)\n", + " PabsFlip = np.flipud(PabsFlip1)\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", + " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", + " logScale = plt.figure(frameon=False, figsize=(10, 10))\n", + " ax = plt.Axes(logScale, [0., 0., 1., 1.])\n", + " ax.set_axis_off()\n", + " logScale.add_axes(ax)\n", + " plt.axis('off')\n", + " plt.imshow(log(PabsFlip), cmap='gray', aspect='auto')\n", + " plt.savefig(savepath+'FFT '+names[n])\n", + " #plt.show() \n", + " plt.close()\n", + "\n", + " # Set up polar coordinates prior to summing the spectrum\n", + " theta1Rad = np.linspace(0.0, 2 * math.pi, num=360/angleInc)\n", + " theta1Rad_test[n]=theta1Rad\n", + " f1 = np.round_(N1 / (2 * CO_lower))\n", + " f2 = np.round_(N1 / (2 * CO_upper))\n", + " rho1 = np.linspace(f1, f2, num=(f2 - f1)/radStep) # frequency band\n", + " PowerX = np.zeros((theta1Rad.size, theta1Rad.size))\n", + " PowerY = np.zeros((theta1Rad.size))\n", + " PF[n] = PabsFlip\n", + " \n", + " # Interpolate using a Spine\n", + " PowerSpline = scipy.interpolate.RectBivariateSpline(y=y, x=x, z=PabsFlip)\n", + " PS[n] = PowerSpline\n", + " n_dx = 0.001\n", + "\n", + " for p in range(0, theta1Rad.size):\n", + " # converting theta1Rad and rho1 to cartesian coordinates\n", + " xfinal = rho1 * math.cos(theta1Rad[p])\n", + " yfinal = rho1 * math.sin(theta1Rad[p])\n", + "\n", + " # Evaluate spin on path\n", + " px = PowerSpline.ev(yfinal, xfinal)\n", + " PowerY[p] = np.sum(px)\n", + " #print(PowerY)\n", + " PowerY_test[n]=PowerY\n", + " theta1Rad = theta1Rad_test[n]\n", + " PowerY = PowerY_test[n]\n", + " #print(PowerY)\n", + " # Only use the data in the first two quadrants (Spectrum is symmetric)\n", + " num = len(theta1Rad)\n", + " PowerYFinal = PowerY[0:num // 2]\n", + " theta1RadFinal = theta1Rad[0:num // 2]\n", + " theta1RadFinal_test[n]= theta1RadFinal\n", + " power_area = np.trapz(PowerYFinal, theta1RadFinal)\n", + " normPower = PowerYFinal / power_area\n", + " normPower_test[n]=normPower\n", + " \n", + " # Combine data into [XY] to fit to an ellipse\n", + " Mirtheta1RadFinal1 = np.concatenate([theta1RadFinal.T, (theta1RadFinal + np.pi).T])\n", + " MirnormPower = np.concatenate([normPower.T, normPower.T])\n", + " theta = Mirtheta1RadFinal1\n", + " radius = MirnormPower\n", + " ydata = radius * np.cos(theta)\n", + " xdata = radius * np.sin(theta)\n", + " ell_data = np.vstack([xdata, ydata])\n", + " ell_data = ell_data.T\n", + " ell_data_test[n] = ell_data\n", + " Mirtheta1RadFinal_test[n] = Mirtheta1RadFinal1\n", + " MirnormPower_test[n]= MirnormPower\n", + "\n", + " # Python fitting function, see EllipseDirectFit\n", + " XY = ell_data_test[n]\n", + " centroid = np.mean(XY, axis=0)\n", + " e1 = (XY[:, 0] - centroid[0]) ** 2\n", + " e2 = (XY[:, 0] - centroid[0]) * ((XY[:, 1] - centroid[1]))\n", + " e3 = (XY[:, 1] - centroid[1]) ** 2\n", + " D1 = np.vstack(([e1, e2, e3])).T\n", + " f1 = XY[:, 0] - centroid[0]\n", + " f2 = XY[:, 1] - centroid[1]\n", + " f3 = np.ones((1, XY.shape[0]))\n", + " D2 = vstack(([f1, f2, f3])).T\n", + " S1 = dot(D1.T, D1)\n", + " S2 = dot(D1.T, D2)\n", + " S3 = dot(D2.T, D2)\n", + " T = dot(-linalg.inv(S3), S2.T)\n", + " M = S1 + (dot(S2, T))\n", + " M = np.vstack(([M[2, :] / 2, - M[1, :], M[0, :] / 2]))\n", + " _eval, evec = linalg.eig(M)\n", + " cond = (4 * evec[0, :] * evec[2, :]) - (evec[1, :] ** 2)\n", + " A1 = evec[:, nonzero(cond > 0)[0]]\n", + " A = np.vstack((A1, dot(T, A1)))\n", + " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", + " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", + " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", + " 0] - A[4] * centroid[1]\n", + " A[3] = A3\n", + " A[4] = A4\n", + " A[5] = A5\n", + " A = A / linalg.norm(A)\n", + " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", + " if ((A[0]) <= (A[2])):\n", + " # Ellipse is horizontal\n", + " angle = 0;\n", + " major = sqrt(1 / A[0])\n", + " minor = sqrt(1 / A[2])\n", + " else:\n", + " angle = np.pi/2;\n", + " major = sqrt(1 / A[2])\n", + " minor = sqrt(1 / A[0])\n", + " else:\n", + " R = ((A[2] - A[0]) / A[1])\n", + " tg = R - sqrt((R * R) + 1)\n", + " angle = math.atan(tg)\n", + " P = (2 * tg) / (1 + (tg * tg))\n", + "\n", + " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", + " if (angle < (-pi / 4)):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle\n", + " elif ((A[1] / P <= (-A[1] / P))):\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", + " if (angle < 0):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle - np.pi\n", + " else:\n", + " # Switch\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + "\n", + " t_New = (angle) #angle\n", + " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", + " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", + " MirnormPower = MirnormPower_test[n]\n", + " t = t_new_test[n]\n", + " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", + " th = np.zeros(len(r_line))\n", + " for i in range(0, len(r_line)):\n", + " th[i] = t\n", + " th = np.concatenate([th, (th + 180)])\n", + " r_line = np.concatenate([r_line, r_line])\n", + " ## Polar Graph\n", + " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"notebook\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'14',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " plt.figure(frameon=False, figsize=(15, 10))\n", + " plt.polar(Mirtheta1RadFinal1, MirnormPower, 'o')\n", + " plt.suptitle(abs(t)-180, fontsize=20)\n", + " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", + " plt.savefig(savepath+'polar '+names[n])\n", + "\n", + " if (max(MirnormPower)<2):\n", + " inc = 0.5\n", + " elif (max(MirnormPower)<5):\n", + " inc = 1\n", + " elif max(MirnormPower)<20:\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", + "\n", + " #plt.show()\n", + " t_final_rad = t*pi/180\n", + " def fitted_func(thetas, c):\n", + " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", + " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", + " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", + " c0 = 15\n", + " xdata = theta1RadFinal_test[n]\n", + " theta1RadFinal = theta1RadFinal_test[n]\n", + " ydata= normPower_test[n]\n", + " normPower = normPower_test[n]\n", + " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", + "\n", + " # Shift data for plotting purposes\n", + "\n", + " diff = abs(theta1RadFinal - (t * pi / 180))\n", + " centerLoc = find(diff == min(diff))\n", + "\n", + " num_angles = len(theta1RadFinal)\n", + " shift = (round(num_angles / 2) - (num_angles - centerLoc))\n", + "\n", + " normPower1 = np.roll(normPower, -shift)\n", + " theta1RadFinal1 = np.roll(theta1RadFinal, -shift)\n", + "\n", + " if (shift > 0):\n", + " s = num_angles - shift\n", + " for k in range(s, num_angles):\n", + " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", + " elif (shift < 0):\n", + " for k in range(0,90):\n", + " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", + " \n", + " ## Normalized Distribution Graph\n", + " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", + "\n", + " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", + " plt.xlim([-95, 95])\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " h3, = plt.plot(theta1RadFinal1 * 180 / pi, p_act, linewidth=4,color=\"#CE7E56\")\n", + " plt.xlabel('Angle (°)', **csfont)\n", + " plt.ylabel('Normalized Intensity', **csfont)\n", + "\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", + " plt.ylim([0, max(normPower1) + .3])\n", + " plt.savefig(savepath+'Normdist '+names[n])\n", + " #plt.show()\n", + " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", + " plt.close()\n", + " t_final=t\n", + " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", + " #print(rValue**2)\n", + " # Rounding results for Title of Figure\n", + " krnd = math.ceil(k * 1000) / 1000\n", + " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", + " krnd = math.trunc(krnd * 100) / 100\n", + " thrnd = math.trunc(thrnd * 100) / 100\n", + " a = 32.02\n", + " b= -12.43\n", + " c = 47.06\n", + " d = -0.9185\n", + " e = 19.43\n", + " f = -0.07693\n", + " x = kappa[0]\n", + " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", + " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", + " \n", + " ## Combined plot figure\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"notebook\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'18',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " fig = plt.figure(frameon=False,figsize=(20,12))\n", + " fig.subplots_adjust(top=0.95)\n", + " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", + " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", + " cart_dist = plt.subplot(grid[1, 0:2])\n", + " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", + " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " cart_dist.set_xlim([-92, 92])\n", + " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", + " cart_dist.set_ylim([0, 1])\n", + " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", + "\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " h3, = cart_dist.plot(theta1RadFinal1 * 180 / pi, p_act, linewidth=4,color=\"#CE7E56\")\n", + "\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + "\n", + " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", + " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower, 'o');\n", + " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " polar_dist.text(np.radians(-110),1.65, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " orig_fig = plt.subplot(grid[0, 0]); \n", + " orig_fig.set_title('Processed Binary Image',fontsize=20).set_position([.5, 1.0])\n", + " orig_fig.axis('off');\n", + " fft_fig = plt.subplot(grid[0, 1])\n", + " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", + " fft_fig.set_axis_off()\n", + " fft_fig.axis('off')\n", + " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", + " orig_fig.imshow(images[1],cmap=plt.cm.viridis )\n", + " #plt.tight_layout()\n", + " plt.savefig(savepath+'Grouped'+names[n])\n", + " #plt.show()\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda root]", + "language": "python", + "name": "conda-root-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 7ea442edbb32edbf6b23b8a0fa3286e4bab6e548 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:23:39 -0400 Subject: [PATCH 002/153] Create requirements.txt --- requirements.txt | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3b67469 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +skimage.io +skimage +__future__ +seaborn +pandas +numpy +scipy.ndimage +pylab +os +os.path +cv2 From 943929285a5a7a630c15de25c8ce06476d3cb7a9 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:25:27 -0400 Subject: [PATCH 003/153] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3b67469..22cc9d3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ skimage.io skimage -__future__ seaborn pandas numpy From e9d4bd1d359d02422b26535587d61a6a16bc0342 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:26:20 -0400 Subject: [PATCH 004/153] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 22cc9d3..24ff1ad 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -skimage.io skimage seaborn pandas From 0fe031f4ada1d4df44ccc3daabadcd77e600edd3 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:27:20 -0400 Subject: [PATCH 005/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 24ff1ad..10c93d5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -skimage +scikit-image seaborn pandas numpy From be75ad1c9e30c13cac6b15c429df55718c753e42 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:28:11 -0400 Subject: [PATCH 006/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 10c93d5..8ee6713 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ scikit-image seaborn pandas numpy -scipy.ndimage +scipy pylab os os.path From 95d38d78f08ecd4780f58d7e8d64268140911bc2 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:29:05 -0400 Subject: [PATCH 007/153] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8ee6713..dcea406 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,7 +3,6 @@ seaborn pandas numpy scipy -pylab os os.path cv2 From b73623c3073bddfc847c1693f178687b86bb62fd Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:30:45 -0400 Subject: [PATCH 008/153] Update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index dcea406..4f1fd80 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,6 @@ seaborn pandas numpy scipy -os -os.path +#os +#os.path cv2 From 51142ac1fa848ae0724fb21c464f1719f788b98a Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:32:32 -0400 Subject: [PATCH 009/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4f1fd80..c0ad9e1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,4 @@ numpy scipy #os #os.path -cv2 +#cv2 From e8eebfeb559a29d99684bfb2695e79f43cd1c9b8 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:41:08 -0400 Subject: [PATCH 010/153] Update requirements.txt --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c0ad9e1..4016b99 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,5 @@ numpy scipy #os #os.path -#cv2 +opencv +enriquesmarquez cv2 From 9aac3b2c39c34632344c6b549dea31e5907a9c34 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:41:49 -0400 Subject: [PATCH 011/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4016b99..66058c7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,4 @@ scipy #os #os.path opencv -enriquesmarquez cv2 + From d71170a15243a195b03d36fb8c31d5bd09fdb9b7 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 00:52:08 -0400 Subject: [PATCH 012/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 66058c7..7147b0b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,5 +5,5 @@ numpy scipy #os #os.path -opencv +opencv-python From e0e22973466548f07b9f2b185b22074d06737be6 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 01:01:35 -0400 Subject: [PATCH 013/153] Update requirements.txt --- requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements.txt b/requirements.txt index 7147b0b..7c0d388 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,6 @@ scipy #os #os.path opencv-python +matplotlib + From c9737deae450df88b4f8f4cd635e8c4a7961d416 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 02:59:36 -0400 Subject: [PATCH 014/153] Update requirements.txt --- requirements.txt | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7c0d388..02d94cc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,5 +7,9 @@ scipy #os.path opencv-python matplotlib - +pathlib +sys +threading +random +shutil From e453d2c262e8394d449bf60a8c9986ed2b88108e Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 03:01:07 -0400 Subject: [PATCH 015/153] Update requirements.txt --- requirements.txt | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 02d94cc..18a54e9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,8 +8,7 @@ scipy opencv-python matplotlib pathlib -sys threading random shutil - +csv From bfa21227354e5fbe7849529a54231bd64fe4041c Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 03:03:26 -0400 Subject: [PATCH 016/153] Update requirements.txt --- requirements.txt | 3 --- 1 file changed, 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 18a54e9..3b86bdd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,7 +8,4 @@ scipy opencv-python matplotlib pathlib -threading -random -shutil csv From fbd69e9d8bbb8d4ad7deb1736d54b137fb4e069c Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 03:04:32 -0400 Subject: [PATCH 017/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3b86bdd..d50e8f4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,4 @@ scipy opencv-python matplotlib pathlib -csv + From 5b54ce1b92b4738b3a4900fcec41e7b8afacd067 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 20:20:36 -0400 Subject: [PATCH 018/153] Update requirements.txt --- requirements.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d50e8f4..4005759 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,7 @@ scipy opencv-python matplotlib pathlib - +PyQt5 +os-win +os-sys +syspath From 6d360b261e8c0f8517e6a078384ed39022b6a389 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 6 Jun 2019 20:24:01 -0400 Subject: [PATCH 019/153] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 4005759..261c499 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,3 +12,4 @@ PyQt5 os-win os-sys syspath +PyQt5 From 3c662a98479d0384eebefcfa4d1cc3746a83d9d1 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 31 Jul 2019 17:22:22 -0400 Subject: [PATCH 020/153] Add files via upload --- FiberFit-toshare-7.31.19.ipynb | 2444 ++++++++++++++++++++++++++++++++ 1 file changed, 2444 insertions(+) create mode 100644 FiberFit-toshare-7.31.19.ipynb diff --git a/FiberFit-toshare-7.31.19.ipynb b/FiberFit-toshare-7.31.19.ipynb new file mode 100644 index 0000000..7c59adf --- /dev/null +++ b/FiberFit-toshare-7.31.19.ipynb @@ -0,0 +1,2444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Import necessary Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4a2025c0e430485fb865809c20bd009e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Press Me', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from __future__ import division\n", + "from __future__ import print_function\n", + "from __future__ import unicode_literals\n", + "from __future__ import division\n", + "from __future__ import absolute_import\n", + "\n", + "import argparse, re, os, glob, sys, pprint, itertools\n", + "import inspect\n", + "\n", + "import numpy as np\n", + "# try:\n", + "# from runtime import *\n", + "# except ImportError:\n", + "# from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + " \n", + "btn = widgets.Button(description='Press Me')\n", + "display(btn)\n", + "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", + "import skimage.io as io\n", + "from skimage import data\n", + "from skimage.filters import sobel \n", + "from skimage.morphology import watershed\n", + "from skimage import feature\n", + "from skimage.color import rgb2gray\n", + "from skimage.feature import corner_harris,corner_peaks\n", + "from skimage.exposure import equalize_hist\n", + "from skimage import measure\n", + "from skimage.draw import ellipse\n", + "from skimage.measure import label, regionprops\n", + "from skimage.transform import rotate\n", + "from skimage.feature import corner_orientations\n", + "from skimage.filters import threshold_otsu\n", + "from skimage.segmentation import clear_border\n", + "from skimage.measure import label\n", + "from skimage.morphology import closing, square\n", + "from skimage.measure import regionprops\n", + "from skimage.color import label2rgb\n", + "import seaborn\n", + "import pandas\n", + "try:\n", + " from runtime import *\n", + "except ImportError:\n", + " from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "from os import listdir\n", + "from os.path import isfile, join\n", + "import numpy\n", + "import cv2\n", + "\n", + "\n", + "#test\n", + " \n", + "import sys\n", + "import matplotlib\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.ndimage\n", + "import scipy.interpolate\n", + "import scipy.optimize\n", + "import scipy.integrate\n", + "import scipy.stats\n", + "import math\n", + "import time\n", + "import glob\n", + "from pylab import *\n", + "from pandas import DataFrame\n", + "from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from src.fiberfit_model.EllipseDirectFit import*\n", + "from src.fiberfit_model import helpers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Input Variables " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "## File Path with binary square images of fibers to analyze\n", + "mypath='C:/Users/mepre/OneDrive/Desktop/Desktop/output/fiberfit'\n", + "\n", + "##Path to folder where you would like images to be saved\n", + "savepath = 'C:/Users/mepre/OneDrive/Pictures/FiberFitOutput2/Output/'\n", + "## NOTE - we can also output raw data. let me know if you would prefer that.\n", + "\n", + "## Variables for settings\n", + "CO_lower = 20 #upper fiber thickness, pixels\n", + "CO_upper = 1 #lower fiber thickness, pixels\n", + "angleInc = 1 #angle increment\n", + "radStep = 0.5 #radial step size\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, Run the code below. Figures will be saved in specified output folder" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length image 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:73: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:77: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 n\n", + "1 Index\n", + "1 n\n", + "2 Index\n", + "2 n\n", + "3 Index\n", + "3 n\n", + "4 Index\n", + "4 n\n", + "5 Index\n", + "5 n\n", + "6 Index\n", + "6 n\n", + "7 Index\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:371: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:62: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:214: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:268: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:325: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 n\n", + "8 Index\n", + "8 n\n", + "9 Index\n", + "9 n\n", + "10 Index\n", + "10 n\n", + "11 Index\n", + "11 n\n", + "12 Index\n", + "12 n\n", + "13 Index\n", + "13 n\n", + "14 Index\n", + "14 n\n", + "15 Index\n", + "15 n\n", + "16 Index\n", + "16 n\n", + "17 Index\n", + "17 n\n", + "18 Index\n", + "18 n\n", + "19 Index\n", + "19 n\n", + "20 Index\n", + "20 n\n", + "21 Index\n", + "21 n\n", + "22 Index\n", + "22 n\n", + "23 Index\n", + "23 n\n", + "24 Index\n", + "24 n\n", + "25 Index\n", + "25 n\n", + "26 Index\n", + "26 n\n", + "27 Index\n", + "27 n\n", + "28 Index\n" + ] + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m 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bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[1;34m(self, renderer, bbox_extra_artists)\u001b[0m\n\u001b[0;32m 2359\u001b[0m \u001b[0mbb\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 245\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Upload Files\n", + "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", + "images = numpy.empty(len(onlyfiles), dtype=object)\n", + "names = numpy.empty(len(onlyfiles), dtype=object)\n", + "for n in range(0, len(onlyfiles)):\n", + " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", + " names[n]= (onlyfiles[n])\n", + "img = numpy.empty(len(images), dtype=object)\n", + "for n in range(0,len(images)):\n", + " img[n]= np.invert(images[n])\n", + " \n", + "## Set up empty Matrices (can clean this up later )\n", + "ell_data_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", + "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", + "normPower_test = numpy.empty(len(img),dtype=object)\n", + "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", + "A_test = numpy.empty(len(img),dtype=object)\n", + "centroid_test = numpy.empty(len(img),dtype=object)\n", + "t_new_test = numpy.empty(len(img),dtype=object)\n", + "im = numpy.empty(len(img), dtype=object)\n", + "PF = numpy.empty(len(img),dtype=object)\n", + "PS = numpy.empty(len(img),dtype=object)\n", + "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", + "PowerY_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal1_plot= numpy.empty(len(img),dtype=object)\n", + "MirnormPower_plot=numpy.empty(len(img),dtype=object)\n", + "th_plot=numpy.empty(len(img),dtype=object)\n", + "r_line_plot=numpy.empty(len(img),dtype=object)\n", + "num=0\n", + "index=num\n", + "\n", + "## begin analysis\n", + "print('length image',len(img))\n", + "for n in range(0,len(img)):\n", + " im=img[n][:,:,1] # select first image\n", + " M= im.shape[0] #get image shape\n", + " N1 = im.shape[1] \n", + " n1 = np.round(N1/ 2) - 1\n", + " freq = np.arange(-n1, n1 + 1, 1)\n", + " x, y = freq, freq\n", + " \n", + " ## Fourier Transform\n", + " fft_result = np.fft.fft2(im)\n", + " Fshift = np.fft.fftshift(fft_result)\n", + " \n", + " ## adjust to power spectrum to remove imaginary components\n", + " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", + " Pabs = np.abs(Fshift) ** 2\n", + " \n", + " M= im.shape[0]\n", + " N1=im.shape[1]\n", + "\n", + " # shift in terms of image because power spectrum is the mirroR of lines so\n", + " # mirroring back in terms of image would give right allignment\n", + " PabsFlip1 = np.rot90(Pabs)\n", + " PabsFlip = np.flipud(PabsFlip1)\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", + " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", + " logScale = plt.figure(frameon=False, figsize=(10, 10))\n", + " ax = plt.Axes(logScale, [0., 0., 1., 1.])\n", + " ax.set_axis_off()\n", + " logScale.add_axes(ax)\n", + " plt.axis('off')\n", + " plt.imshow(log(PabsFlip), cmap='gray', aspect='auto')\n", + " plt.savefig(savepath+'FFT '+names[n])\n", + " #plt.show() \n", + " plt.close()\n", + "\n", + " # Set up polar coordinates prior to summing the spectrum\n", + " theta1Rad = np.linspace(0.0, 2 * math.pi, num=360/angleInc)\n", + " theta1Rad_test[n]=theta1Rad\n", + " f1 = np.round_(N1 / (2 * CO_lower))\n", + " f2 = np.round_(N1 / (2 * CO_upper))\n", + " rho1 = np.linspace(f1, f2, num=(f2 - f1)/radStep) # frequency band\n", + " PowerX = np.zeros((theta1Rad.size, theta1Rad.size))\n", + " PowerY = np.zeros((theta1Rad.size))\n", + " PF[n] = PabsFlip\n", + " \n", + " # Interpolate using a Spine\n", + " PowerSpline = scipy.interpolate.RectBivariateSpline(y=y, x=x, z=PabsFlip)\n", + " PS[n] = PowerSpline\n", + " n_dx = 0.001\n", + "\n", + " for p in range(0, theta1Rad.size):\n", + " # converting theta1Rad and rho1 to cartesian coordinates\n", + " xfinal = rho1 * math.cos(theta1Rad[p])\n", + " yfinal = rho1 * math.sin(theta1Rad[p])\n", + "\n", + " # Evaluate spin on path\n", + " px = PowerSpline.ev(yfinal, xfinal)\n", + " PowerY[p] = np.sum(px)\n", + " #print(PowerY)\n", + " PowerY_test[n]=PowerY\n", + " theta1Rad = theta1Rad_test[n]\n", + " PowerY = PowerY_test[n]\n", + " #print(PowerY)\n", + " # Only use the data in the first two quadrants (Spectrum is symmetric)\n", + " num = len(theta1Rad)\n", + " PowerYFinal = PowerY[0:num // 2]\n", + " theta1RadFinal = theta1Rad[0:num // 2]\n", + " theta1RadFinal_test[n]= theta1RadFinal\n", + " power_area = np.trapz(PowerYFinal, theta1RadFinal)\n", + " normPower = PowerYFinal / power_area\n", + " normPower_test[n]=normPower\n", + " \n", + " # Combine data into [XY] to fit to an ellipse\n", + " Mirtheta1RadFinal1 = np.concatenate([theta1RadFinal.T, (theta1RadFinal + np.pi).T])\n", + " #print('round1',Mirtheta1RadFinal1)\n", + " MirnormPower = np.concatenate([normPower.T, normPower.T])\n", + " theta = Mirtheta1RadFinal1\n", + " #print('theta',theta)\n", + " radius = MirnormPower\n", + " #print('radius',MirnormPower)\n", + " ydata = radius * np.cos(theta)\n", + " xdata = radius * np.sin(theta)\n", + " ell_data = np.vstack([xdata, ydata])\n", + " ell_data = ell_data.T\n", + " ell_data_test[n] = ell_data\n", + " Mirtheta1RadFinal_test[n] = Mirtheta1RadFinal1\n", + " MirnormPower_test[n]= MirnormPower\n", + "\n", + " # Python fitting function, see EllipseDirectFit\n", + " XY = ell_data_test[n]\n", + " centroid = np.mean(XY, axis=0)\n", + " e1 = (XY[:, 0] - centroid[0]) ** 2\n", + " e2 = (XY[:, 0] - centroid[0]) * ((XY[:, 1] - centroid[1]))\n", + " e3 = (XY[:, 1] - centroid[1]) ** 2\n", + " D1 = np.vstack(([e1, e2, e3])).T\n", + " f1 = XY[:, 0] - centroid[0]\n", + " f2 = XY[:, 1] - centroid[1]\n", + " f3 = np.ones((1, XY.shape[0]))\n", + " D2 = vstack(([f1, f2, f3])).T\n", + " S1 = dot(D1.T, D1)\n", + " S2 = dot(D1.T, D2)\n", + " S3 = dot(D2.T, D2)\n", + " T = dot(-linalg.inv(S3), S2.T)\n", + " M = S1 + (dot(S2, T))\n", + " M = np.vstack(([M[2, :] / 2, - M[1, :], M[0, :] / 2]))\n", + " _eval, evec = linalg.eig(M)\n", + " cond = (4 * evec[0, :] * evec[2, :]) - (evec[1, :] ** 2)\n", + " A1 = evec[:, nonzero(cond > 0)[0]]\n", + " A = np.vstack((A1, dot(T, A1)))\n", + " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", + " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", + " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", + " 0] - A[4] * centroid[1]\n", + " A[3] = A3\n", + " A[4] = A4\n", + " A[5] = A5\n", + " A = A / linalg.norm(A)\n", + " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", + " if ((A[0]) <= (A[2])):\n", + " # Ellipse is horizontal\n", + " angle = 0;\n", + " major = sqrt(1 / A[0])\n", + " minor = sqrt(1 / A[2])\n", + " else:\n", + " angle = np.pi/2;\n", + " major = sqrt(1 / A[2])\n", + " minor = sqrt(1 / A[0])\n", + " else:\n", + " R = ((A[2] - A[0]) / A[1])\n", + " tg = R - sqrt((R * R) + 1)\n", + " angle = math.atan(tg)\n", + " P = (2 * tg) / (1 + (tg * tg))\n", + "\n", + " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", + " if (angle < (-pi / 4)):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle\n", + " elif ((A[1] / P <= (-A[1] / P))):\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", + " if (angle < 0):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle - np.pi\n", + " else:\n", + " # Switch\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + "\n", + " t_New = (angle) #angle\n", + " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", + " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", + " #print('round2',Mirtheta1RadFinal1)\n", + " MirnormPower = MirnormPower_test[n]\n", + " #print('mirnormpower',MirnormPower)\n", + " t = t_new_test[n]\n", + " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", + " th = np.zeros(len(r_line))\n", + " for i in range(0, len(r_line)):\n", + " th[i] = t\n", + " th = np.concatenate([th, (th + 180)])\n", + " r_line = np.concatenate([r_line, r_line])\n", + " ## Polar Graph\n", + " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"poster\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'14',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " plt.figure(frameon=False, figsize=(15, 10))\n", + " plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5)\n", + " plt.suptitle(abs(t)-180, fontsize=20)\n", + " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", + " plt.savefig(savepath+'polarpositive'+names[n])\n", + " if (max(MirnormPower)<2):\n", + " inc = 0.5\n", + " elif (max(MirnormPower)<5):\n", + " inc = 1\n", + " elif max(MirnormPower)<20:\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", + "\n", + " #plt.show()\n", + " t_final_rad = t*pi/180\n", + " def fitted_func(thetas, c):\n", + " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", + " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", + " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", + " c0 = 15\n", + " xdata = theta1RadFinal_test[n]\n", + " theta1RadFinal = theta1RadFinal_test[n]\n", + " ydata= normPower_test[n]\n", + " normPower = normPower_test[n]\n", + " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", + "\n", + " # Shift data for plotting purposes\n", + "\n", + " diff = abs(theta1RadFinal - (t * pi / 180))\n", + " #print(diff,'diff')\n", + " #centerLoc=diff.find(diff==min(diff))\n", + " centerLoc = diff[np.argmin(diff)]#find(diff== min(diff))\n", + " #print(centerLoc,'centerLoc')\n", + "\n", + " num_angles = len(theta1RadFinal)\n", + " shift = ((round(num_angles / 2) - (num_angles - centerLoc)))\n", + " #print(shift,'shift')\n", + "\n", + " normPower1 = np.roll(normPower, -int(shift))\n", + " normPower1\n", + " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", + "\n", + " if (shift > 0):\n", + " s = num_angles - shift\n", + " s= np.asscalar(s)\n", + " for k in range(s, num_angles):\n", + " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", + " elif (shift < 0):\n", + " for k in range(0,90):\n", + " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", + " \n", + " ## Normalized Distribution Graph\n", + " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", + "\n", + " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", + " plt.xlim([-95, 95])\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " #print(p_act)\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", + " plt.xlabel('Angle (°)', **csfont)\n", + " plt.ylabel('Normalized Intensity', **csfont)\n", + " #print(max(normPower))\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", + " plt.ylim([0, max(normPower1) + .3])\n", + " plt.savefig(savepath+'Normdist '+names[n])\n", + " #plt.show()\n", + " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", + " plt.close()\n", + " t_final=t\n", + " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", + " #print(rValue**2)\n", + " # Rounding results for Title of Figure\n", + " krnd = math.ceil(k * 1000) / 1000\n", + " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", + " krnd = math.trunc(krnd * 100) / 100\n", + " thrnd = math.trunc(thrnd * 100) / 100\n", + " a = 32.02\n", + " b= -12.43\n", + " c = 47.06\n", + " d = -0.9185\n", + " e = 19.43\n", + " f = -0.07693\n", + " x = kappa[0]\n", + " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", + " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", + " sum_NP = sum(normPower1)\n", + " #normPower2 = normPower1/sum(normPower1)\n", + " #sum_NP2 = sum(normPower2)\n", + " #print(sum_NP2)\n", + " ## Combined plot figure\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"notebook\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'18',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " fig = plt.figure(frameon=False,figsize=(20,12))\n", + " fig.subplots_adjust(top=0.95)\n", + " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", + " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", + " cart_dist = plt.subplot(grid[1, 0:2])\n", + " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", + " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " cart_dist.set_xlim([-92, 92])\n", + " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", + " cart_dist.set_ylim([0, max(normPower1) + .3])\n", + " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", + "\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", + "\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + "\n", + " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", + " polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o');\n", + " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " fig.text(0.75, 0.05, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " #polar_dist.text(np.radians(-110),1.95, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " orig_fig = plt.subplot(grid[0, 0]); \n", + " orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0])\n", + " orig_fig.axis('off');\n", + " fft_fig = plt.subplot(grid[0, 1])\n", + " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", + " fft_fig.set_axis_off()\n", + " fft_fig.axis('off')\n", + " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", + " orig_fig.imshow(images[n],cmap=plt.cm.viridis )\n", + " #plt.tight_layout()\n", + " plt.savefig(savepath+'Grouped'+names[n])\n", + " print(n,'n')\n", + " #plt.show()\n", + " polar_dist_2 = plt.figure(figsize=(10, 10))\n", + " #print('linebeforeindex')\n", + " #print(index,'currentindex')\n", + " Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1;\n", + " MirnormPower_plot[index]=MirnormPower;\n", + " th_plot[index] = th * pi/180\n", + " #print(th_plot[index],'thplotindex')\n", + " r_line_plot[index]=r_line\n", + " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower);\n", + " polar_dist.plot(th * pi/180, r_line,linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " index=index+1\n", + " print(index,'Index')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + " theta1Rad = np.linspace(0.0, 2 * math.pi, num=360/angleInc)\n", + " #print(theta1Rad,'theta1rad')\n", + " theta1Rad_test[n]=theta1Rad\n", + " ## Normalized Distribution Graph\n", + " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", + " \n", + " for p in range(0, theta1Rad.size):\n", + " # converting theta1Rad and rho1 to cartesian coordinates\n", + " xfinal = rho1 * math.cos(theta1Rad[p])\n", + " yfinal = rho1 * math.sin(theta1Rad[p])\n", + "\n", + " # Evaluate spin on path\n", + " px = PowerSpline.ev(yfinal, xfinal)\n", + " PowerY[p] = np.sum(px)\n", + " #print(PowerY)\n", + " PowerY_test[n]=PowerY\n", + " theta1Rad = theta1Rad_test[n]\n", + " PowerY = PowerY_test[n]\n", + " #print(PowerY)\n", + " # Only use the data in the first two quadrants (Spectrum is symmetric)\n", + " num = len(theta1Rad)\n", + " print(num,'num')\n", + " PowerYFinal = PowerY[0:num // 2]\n", + " #print(PowerYFinal,'poweryfinal')\n", + " theta1RadFinal = theta1Rad[0:num // 2]\n", + " #print(theta1RadFinal,'theta1radfianl')\n", + " theta1RadFinal_test[n]= theta1RadFinal\n", + " power_area = np.trapz(PowerYFinal, theta1RadFinal)\n", + " normPower = PowerYFinal / power_area\n", + " normPower_test[n]=normPower\n", + " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", + " h2 = plt.bar((theta1RadFinal * 180 / pi)+shift, normPower1,color=\"#dedede\")\n", + " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", + " plt.xlim([-95, 95])\n", + " p_act = fitted_func(theta1RadFinal, kappa)\n", + "\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)+shift]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = plt.plot(b[:,1],b[:,0], linewidth=2,color=\"#CE7E56\")\n", + " plt.xlabel('Angle (°)', **csfont)\n", + " plt.ylabel('Normalized Intensity', **csfont)\n", + " #print(max(normPower1),'max normpower')\n", + " if (max(normPower1)<2):\n", + " inc = 0.5\n", + " elif (max(normPower1)<5):\n", + " inc = 2\n", + " elif (max(normPower1)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", + " plt.ylim([0, max(normPower1) + .3])\n", + " plt.savefig(savepath+'Normdist '+names[n])\n", + " #plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "int(shift)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "size(theta1RadFinal)\n", + "normPower1 = np.roll(normPower, 87)\n", + "#normPower1\n", + "theta1RadFinal1 = np.roll(theta1RadFinal, 87)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.array([[1,4,6,2,-5],[0.2,0.4,-0.2,0.3,1.2]]).T\n", + "test = (theta1RadFinal1 * 180 / pi)+shift\n", + "a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)+shift]])\n", + "\n", + "b = a[a[:,0].argsort()]#np.sort(a,axis=1)\n", + "b = b.T\n", + "test = b[:,0]\n", + "print(shape(test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b[:,1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9d0ecbe96882e7aac1baedf7428484c960877778 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 11 Dec 2019 10:18:00 -0500 Subject: [PATCH 021/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 261c499..e7abfc9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,7 +9,7 @@ opencv-python matplotlib pathlib PyQt5 -os-win +#os-win os-sys syspath PyQt5 From a1f6d69e71d266aea20252c07ad12f41ef564a4a Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 11 Dec 2019 10:20:53 -0500 Subject: [PATCH 022/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index e7abfc9..205e2e7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,4 @@ PyQt5 #os-win os-sys syspath -PyQt5 +#PyQt5 From 6bab151755bbc21ac0b033bfc2cae8c1c3ac8c87 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 11 Dec 2019 10:21:02 -0500 Subject: [PATCH 023/153] Update requirements.txt From 167e6fb5264257469a9ac326270663b407b8bd1c Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 11 Dec 2019 10:24:30 -0500 Subject: [PATCH 024/153] Update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 205e2e7..a559b5d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,6 +10,6 @@ matplotlib pathlib PyQt5 #os-win -os-sys +#os-sys syspath -#PyQt5 +PyQt5 From 61f8a5733bba91bdc1b623a09b3af9cb0e9dd0a0 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 20 May 2020 15:06:14 -0400 Subject: [PATCH 025/153] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index a559b5d..f40c826 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,7 @@ seaborn pandas numpy scipy +smop #os #os.path opencv-python From fad2da68a589d51cc656875dfdb170e33aa09870 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 20 May 2020 15:18:23 -0400 Subject: [PATCH 026/153] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index f40c826..a766efb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,6 +4,7 @@ pandas numpy scipy smop +runtime-0.1.4 #os #os.path opencv-python From 548819b461b8d47c06d315b20213c94c47ced575 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 20 May 2020 15:19:51 -0400 Subject: [PATCH 027/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a766efb..34ef47a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ pandas numpy scipy smop -runtime-0.1.4 +runtime #os #os.path opencv-python From 59abf2962c1cb752ad87d4c4574882f61058468b Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:01:26 -0400 Subject: [PATCH 028/153] Update requirements.txt --- requirements.txt | 42 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 34ef47a..5b046cf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,4 +14,44 @@ PyQt5 #os-win #os-sys syspath -PyQt5 + +cv +cv_utils +cv2studio +Cycler +imageio +ipykernel +ipython_genutils +ipython +ipywidgets +isapi_wsgi +jedi +Jinja +py-dateutil +decorator +ffmpy +imageio +more-itertools +mpl-toolkits.clifford +mpl4qt +mpl-tools +mpl-utils +orderedet +parso +PIMS +prometheus +prompt_toolkit +Pygments +pylab-sdk +pyswip +pytz +ply +PyVISA +pywin32 +pywinpty +six +Send2Trash +setuptools +skimage +slicerator +toolkits From bd30eef1f335aa0e548e6a83a8f9899d087d3f6d Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:17:16 -0400 Subject: [PATCH 029/153] Update requirements.txt --- requirements.txt | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/requirements.txt b/requirements.txt index 5b046cf..62caffc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -54,4 +54,28 @@ Send2Trash setuptools skimage slicerator +terminado +testpath +tests toolkits +tornado +traitlets +usb +wcwidth +webencodings +wheel +widgetsnbextension +zmq +pyzmq +pyrsistent +cycler +decorator +pipping-easy-install +entrypoints +ipykernal +mistune +pandocfilters +pickleshare +pylab-sdk +pyparsing +zipp From d80c65c93dd6c9b5ffca9fbf92f3602c67586b83 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:21:00 -0400 Subject: [PATCH 030/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 62caffc..9d3e418 100644 --- a/requirements.txt +++ b/requirements.txt @@ -26,7 +26,7 @@ ipython ipywidgets isapi_wsgi jedi -Jinja +#Jinja py-dateutil decorator ffmpy From cc8d8c0dbe58126aeabab5f75a2638cc9795de45 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:22:10 -0400 Subject: [PATCH 031/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 9d3e418..efded9e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -36,7 +36,7 @@ mpl-toolkits.clifford mpl4qt mpl-tools mpl-utils -orderedet +#orderedet parso PIMS prometheus From df0a47119367419374b36c4f81e992ae89a8fa4d Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:26:19 -0400 Subject: [PATCH 032/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index efded9e..a31254a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -39,7 +39,7 @@ mpl-utils #orderedet parso PIMS -prometheus +#prometheus prompt_toolkit Pygments pylab-sdk From 82b006cca019de5ee0717809a3b3e9c3078ae5c3 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:28:06 -0400 Subject: [PATCH 033/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a31254a..c4586e7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -47,7 +47,7 @@ pyswip pytz ply PyVISA -pywin32 +#pywin32 pywinpty six Send2Trash From e3f3c92bed7bb9e8ae9a0a1a0a42f1116d6711ac Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:29:50 -0400 Subject: [PATCH 034/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c4586e7..8dab02b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -52,7 +52,7 @@ pywinpty six Send2Trash setuptools -skimage +#skimage slicerator terminado testpath From e4e8038ed22ae3c051953c5ff28a7f5348eb1dc7 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:31:53 -0400 Subject: [PATCH 035/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8dab02b..f0f25cf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -72,7 +72,7 @@ cycler decorator pipping-easy-install entrypoints -ipykernal +#ipykernal mistune pandocfilters pickleshare From 1472dfd916790f85374004156447cd011f317c97 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:38:58 -0400 Subject: [PATCH 036/153] Update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index f0f25cf..d70764e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -70,8 +70,8 @@ pyzmq pyrsistent cycler decorator -pipping-easy-install -entrypoints +#pipping-easy-install +#entrypoints #ipykernal mistune pandocfilters From 5487019ae47a196d79a88db5b6493b9480aa9bf9 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:43:05 -0400 Subject: [PATCH 037/153] Update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index d70764e..712851a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -69,11 +69,11 @@ zmq pyzmq pyrsistent cycler -decorator +#decorator #pipping-easy-install #entrypoints #ipykernal -mistune +#mistune pandocfilters pickleshare pylab-sdk From b3ec24f9b8bf4f35ef23cf791b494b135b4920ee Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:46:44 -0400 Subject: [PATCH 038/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 712851a..5fd086f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -66,7 +66,7 @@ webencodings wheel widgetsnbextension zmq -pyzmq +#pyzmq pyrsistent cycler #decorator From e535c4d1b728a433f38837d6723361f793456e65 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:48:30 -0400 Subject: [PATCH 039/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5fd086f..edecd25 100644 --- a/requirements.txt +++ b/requirements.txt @@ -65,7 +65,7 @@ wcwidth webencodings wheel widgetsnbextension -zmq +#zmq #pyzmq pyrsistent cycler From 55182c7dc667a037895dcbc473d2443aed441f0f Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:50:37 -0400 Subject: [PATCH 040/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index edecd25..13083ba 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,7 +14,7 @@ PyQt5 #os-win #os-sys syspath - +esmre cv cv_utils cv2studio From 5a67cdf9101106d285bdf865013828557b7228e5 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:53:36 -0400 Subject: [PATCH 041/153] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 13083ba..8026219 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ +numba scikit-image seaborn pandas From d9f32c183d06d5588697bb8c5655c873a499c4f0 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:55:45 -0400 Subject: [PATCH 042/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8026219..36539ff 100644 --- a/requirements.txt +++ b/requirements.txt @@ -33,7 +33,7 @@ decorator ffmpy imageio more-itertools -mpl-toolkits.clifford +#mpl-toolkits.clifford mpl4qt mpl-tools mpl-utils From f027284caa70009f989c7b1e127c49c71eb2d3ea Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 11:58:53 -0400 Subject: [PATCH 043/153] Update requirements.txt --- requirements.txt | 65 +++--------------------------------------------- 1 file changed, 3 insertions(+), 62 deletions(-) diff --git a/requirements.txt b/requirements.txt index 36539ff..abe6295 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numba +#numba scikit-image seaborn pandas @@ -15,68 +15,9 @@ PyQt5 #os-win #os-sys syspath -esmre +#esmre cv cv_utils cv2studio Cycler -imageio -ipykernel -ipython_genutils -ipython -ipywidgets -isapi_wsgi -jedi -#Jinja -py-dateutil -decorator -ffmpy -imageio -more-itertools -#mpl-toolkits.clifford -mpl4qt -mpl-tools -mpl-utils -#orderedet -parso -PIMS -#prometheus -prompt_toolkit -Pygments -pylab-sdk -pyswip -pytz -ply -PyVISA -#pywin32 -pywinpty -six -Send2Trash -setuptools -#skimage -slicerator -terminado -testpath -tests -toolkits -tornado -traitlets -usb -wcwidth -webencodings -wheel -widgetsnbextension -#zmq -#pyzmq -pyrsistent -cycler -#decorator -#pipping-easy-install -#entrypoints -#ipykernal -#mistune -pandocfilters -pickleshare -pylab-sdk -pyparsing -zipp + From a6045e9ed420bfedc67ba65fb6819850085a6686 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 12:01:32 -0400 Subject: [PATCH 044/153] Update requirements.txt --- requirements.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/requirements.txt b/requirements.txt index abe6295..c0cb0cc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,3 +21,9 @@ cv_utils cv2studio Cycler +imageio +ipykernel +ipython_genutils +ipython +ipywidgets +isapi_wsgi From 20504c3857d9664d4cc09448b2accf9dfed1372a Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 12:07:25 -0400 Subject: [PATCH 045/153] Update requirements.txt --- requirements.txt | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/requirements.txt b/requirements.txt index c0cb0cc..e317b52 100644 --- a/requirements.txt +++ b/requirements.txt @@ -27,3 +27,16 @@ ipython_genutils ipython ipywidgets isapi_wsgi + + +jedi +#Jinja +py-dateutil +decorator +ffmpy +imageio +more-itertools +#mpl-toolkits.clifford +mpl4qt +mpl-tools +mpl-utils From 6fae7b7dfd49c41451a09d0dea436828f2cf99aa Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 12:11:34 -0400 Subject: [PATCH 046/153] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index e317b52..6171826 100644 --- a/requirements.txt +++ b/requirements.txt @@ -40,3 +40,4 @@ more-itertools mpl4qt mpl-tools mpl-utils +greenlet From 4ce98f5cd3fdee316b13ef05ae1697cd7fd7bf8a Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 13:56:37 -0400 Subject: [PATCH 047/153] Add files via upload --- FiberFit-toshare-7.31.19.ipynb | 1740 ++------------------------------ 1 file changed, 58 insertions(+), 1682 deletions(-) diff --git a/FiberFit-toshare-7.31.19.ipynb b/FiberFit-toshare-7.31.19.ipynb index 7c59adf..3705d13 100644 --- a/FiberFit-toshare-7.31.19.ipynb +++ b/FiberFit-toshare-7.31.19.ipynb @@ -13,13 +13,52 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: greenlet in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.4.15)\n", + "Requirement already satisfied: PyQt5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (5.14.2)\n", + "Requirement already satisfied: PyQt5-sip<13,>=12.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from PyQt5) (12.7.2)\n", + "\u001b[31mERROR: Could not find a version that satisfies the requirement PyQt4 (from versions: none)\u001b[0m\n", + "\u001b[31mERROR: No matching distribution found for PyQt4\u001b[0m\n", + "Requirement already satisfied: PyQt5-sip in /srv/conda/envs/notebook/lib/python3.7/site-packages (12.7.2)\n", + "\u001b[31mERROR: Could not find a version that satisfies the requirement 12.7.1 (from versions: none)\u001b[0m\n", + "\u001b[31mERROR: No matching distribution found for 12.7.1\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install greenlet\n", + "!{sys.executable} -m pip install PyQt5\n", + "import PyQt5" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams['backend'] = \"Qt5Agg\"\n", + "from matplotlib.figure import Figure\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a2025c0e430485fb865809c20bd009e", + "model_id": "f193c4ca5e5144a39a06c63a58c85a28", "version_major": 2, "version_minor": 0 }, @@ -112,7 +151,7 @@ "import glob\n", "from pylab import *\n", "from pandas import DataFrame\n", - "from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", + "#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", "import matplotlib.pyplot as plt\n", "\n", "from src.fiberfit_model.EllipseDirectFit import*\n", @@ -128,15 +167,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "## File Path with binary square images of fibers to analyze\n", - "mypath='C:/Users/mepre/OneDrive/Desktop/Desktop/output/fiberfit'\n", + "mypath='fibers'\n", "\n", "##Path to folder where you would like images to be saved\n", - "savepath = 'C:/Users/mepre/OneDrive/Pictures/FiberFitOutput2/Output/'\n", + "savepath = 'output/'\n", "## NOTE - we can also output raw data. let me know if you would prefer that.\n", "\n", "## Variables for settings\n", @@ -155,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": { "scrolled": false }, @@ -164,15 +203,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "length image 28\n" + "length image 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:73: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:77: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n" + "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n" ] }, { @@ -182,450 +221,12 @@ "0 n\n", "1 Index\n", "1 n\n", - "2 Index\n", - "2 n\n", - "3 Index\n", - "3 n\n", - "4 Index\n", - "4 n\n", - "5 Index\n", - "5 n\n", - "6 Index\n", - "6 n\n", - "7 Index\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:371: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:62: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:214: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:268: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - "C:\\Users\\mepre\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\ipykernel_launcher.py:325: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7 n\n", - "8 Index\n", - "8 n\n", - "9 Index\n", - "9 n\n", - "10 Index\n", - "10 n\n", - "11 Index\n", - "11 n\n", - "12 Index\n", - "12 n\n", - "13 Index\n", - "13 n\n", - "14 Index\n", - "14 n\n", - "15 Index\n", - "15 n\n", - "16 Index\n", - "16 n\n", - "17 Index\n", - "17 n\n", - "18 Index\n", - "18 n\n", - "19 Index\n", - "19 n\n", - "20 Index\n", - "20 n\n", - "21 Index\n", - "21 n\n", - "22 Index\n", - "22 n\n", - "23 Index\n", - "23 n\n", - "24 Index\n", - "24 n\n", - "25 Index\n", - "25 n\n", - "26 Index\n", - "26 n\n", - "27 Index\n", - "27 n\n", - "28 Index\n" - ] - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 245\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 127\u001b[0m \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 129\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[1;34m(self, renderer, bbox_extra_artists)\u001b[0m\n\u001b[0;32m 2359\u001b[0m \u001b[0mbb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2360\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mbbox_extra_artists\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2361\u001b[1;33m \u001b[0martists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2362\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2363\u001b[0m \u001b[0martists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbbox_extra_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" + "2 Index\n" ] }, { "data": { + "image/png": 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\n", 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" ] @@ -633,24 +234,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 245\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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\u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2363\u001b[0m \u001b[0martists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbbox_extra_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, { "data": { + "image/png": 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\n", 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" ] @@ -667,24 +253,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, { "data": { + "image/png": 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\n", 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" ] @@ -692,1204 +263,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 245\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2330\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2331\u001b[0m \u001b[1;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "list.remove(x): x not in list", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\fiberfit\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m 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bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m 2058\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2059\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n\u001b[1;32m-> 2060\u001b[1;33m bbox_extra_artists=bbox_artists)\n\u001b[0m\u001b[0;32m 2061\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"pad_inches\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2062\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpad\u001b[0m \u001b[1;32mis\u001b[0m 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calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2332\u001b[1;33m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2333\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: list.remove(x): x not in list" - ] - }, { "data": { + "image/png": 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" ] @@ -1980,11 +356,11 @@ " plt.close()\n", "\n", " # Set up polar coordinates prior to summing the spectrum\n", - " theta1Rad = np.linspace(0.0, 2 * math.pi, num=360/angleInc)\n", + " theta1Rad = np.linspace(0.0, 2 * math.pi, num=int(360/angleInc))\n", " theta1Rad_test[n]=theta1Rad\n", " f1 = np.round_(N1 / (2 * CO_lower))\n", " f2 = np.round_(N1 / (2 * CO_upper))\n", - " rho1 = np.linspace(f1, f2, num=(f2 - f1)/radStep) # frequency band\n", + " rho1 = np.linspace(f1, f2, num=int((f2 - f1)/radStep)) # frequency band\n", " PowerX = np.zeros((theta1Rad.size, theta1Rad.size))\n", " PowerY = np.zeros((theta1Rad.size))\n", " PF[n] = PabsFlip\n", @@ -2300,7 +676,7 @@ "metadata": {}, "outputs": [], "source": [ - " theta1Rad = np.linspace(0.0, 2 * math.pi, num=360/angleInc)\n", + " theta1Rad = np.linspace(0.0, 2 * math.pi, num=int(360/angleInc))\n", " #print(theta1Rad,'theta1rad')\n", " theta1Rad_test[n]=theta1Rad\n", " ## Normalized Distribution Graph\n", @@ -2436,7 +812,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" } }, "nbformat": 4, From b368c8d6f68d91eeef61cbe9664512c99370c9bd Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 21 May 2020 13:57:30 -0400 Subject: [PATCH 048/153] Rename FiberFit-toshare-7.31.19.ipynb to FiberFit-toshare-5.21.2020.ipynb --- ...rFit-toshare-7.31.19.ipynb => FiberFit-toshare-5.21.2020.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-7.31.19.ipynb => FiberFit-toshare-5.21.2020.ipynb (100%) diff --git a/FiberFit-toshare-7.31.19.ipynb b/FiberFit-toshare-5.21.2020.ipynb similarity index 100% rename from FiberFit-toshare-7.31.19.ipynb rename to FiberFit-toshare-5.21.2020.ipynb From e9dfac0eb907fc79d755ec81f98756cfc76038ba Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:51:09 -0400 Subject: [PATCH 049/153] Add files via upload --- FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb | 777 +++++++++++++++++++ 1 file changed, 777 insertions(+) create mode 100644 FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb diff --git a/FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb b/FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb new file mode 100644 index 0000000..16d31e4 --- /dev/null +++ b/FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb @@ -0,0 +1,777 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Import necessary Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: greenlet in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.4.15)\n", + "Requirement already satisfied: PyQt5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (5.14.2)\n", + "Requirement already satisfied: PyQt5-sip<13,>=12.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from PyQt5) (12.7.2)\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install greenlet\n", + "!{sys.executable} -m pip install PyQt5\n", + "import PyQt5" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams['backend'] = \"Qt5Agg\"\n", + "from matplotlib.figure import Figure\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c1ce96e32e0b411fb67c0b3abdcb1d26", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Press Me', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from __future__ import division\n", + "from __future__ import print_function\n", + "from __future__ import unicode_literals\n", + "from __future__ import division\n", + "from __future__ import absolute_import\n", + "\n", + "import argparse, re, os, glob, sys, pprint, itertools\n", + "import inspect\n", + "\n", + "import numpy as np\n", + "# try:\n", + "# from runtime import *\n", + "# except ImportError:\n", + "# from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + " \n", + "btn = widgets.Button(description='Press Me')\n", + "display(btn)\n", + "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", + "import skimage.io as io\n", + "from skimage import data\n", + "from skimage.filters import sobel \n", + "from skimage.morphology import watershed\n", + "from skimage import feature\n", + "from skimage.color import rgb2gray\n", + "from skimage.feature import corner_harris,corner_peaks\n", + "from skimage.exposure import equalize_hist\n", + "from skimage import measure\n", + "from skimage.draw import ellipse\n", + "from skimage.measure import label, regionprops\n", + "from skimage.transform import rotate\n", + "from skimage.feature import corner_orientations\n", + "from skimage.filters import threshold_otsu\n", + "from skimage.segmentation import clear_border\n", + "from skimage.measure import label\n", + "from skimage.morphology import closing, square\n", + "from skimage.measure import regionprops\n", + "from skimage.color import label2rgb\n", + "import seaborn\n", + "import pandas\n", + "try:\n", + " from runtime import *\n", + "except ImportError:\n", + " from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "from os import listdir\n", + "from os.path import isfile, join\n", + "import numpy\n", + "import cv2\n", + "\n", + "\n", + "#test\n", + " \n", + "import sys\n", + "import matplotlib\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.ndimage\n", + "import scipy.interpolate\n", + "import scipy.optimize\n", + "import scipy.integrate\n", + "import scipy.stats\n", + "import math\n", + "import time\n", + "import glob\n", + "from pylab import *\n", + "from pandas import DataFrame\n", + "#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from src.fiberfit_model.EllipseDirectFit import*\n", + "from src.fiberfit_model import helpers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Input Variables " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "## File Path with binary square images of fibers to analyze\n", + "mypath='test_images/'\n", + "\n", + "##Path to folder where you would like images to be saved\n", + "savepath = 'output/'\n", + "## NOTE - we can also output raw data. let me know if you would prefer that.\n", + "\n", + "## Variables for settings\n", + "CO_lower = 20 #upper fiber thickness, pixels\n", + "CO_upper = 1 #lower fiber thickness, pixels\n", + "angleInc = 1 #angle increment\n", + "radStep = 0.5 #radial step size\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, Run the code below. Figures will be saved in specified output folder" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length image 7\n", + "0 n\n", + "1 Index\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:241: RuntimeWarning: overflow encountered in exp\n", + "/srv/conda/envs/notebook/lib/python3.7/site-packages/scipy/integrate/quadrature.py:243: RuntimeWarning: invalid value encountered in double_scalars\n", + " err = abs(newval-val)\n", + "/srv/conda/envs/notebook/lib/python3.7/site-packages/scipy/integrate/quadrature.py:251: AccuracyWarning: maxiter (50) exceeded. 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Latest difference = 1.702365e+299\n", + " AccuracyWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 n\n", + "2 Index\n", + "2 n\n", + "3 Index\n", + "3 n\n", + "4 Index\n", + "4 n\n", + "5 Index\n", + "5 n\n", + "6 Index\n", + "6 n\n", + "7 Index\n" + ] + } + ], + "source": [ + "## Upload Files\n", + "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", + "images = numpy.empty(len(onlyfiles), dtype=object)\n", + "names = numpy.empty(len(onlyfiles), dtype=object)\n", + "for n in range(0, len(onlyfiles)):\n", + " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", + " names[n]= (onlyfiles[n])\n", + "img = numpy.empty(len(images), dtype=object)\n", + "for n in range(0,len(images)):\n", + " img[n]= np.invert(images[n])\n", + " \n", + "## Set up empty Matrices (can clean this up later )\n", + "ell_data_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", + "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", + "normPower_test = numpy.empty(len(img),dtype=object)\n", + "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", + "A_test = numpy.empty(len(img),dtype=object)\n", + "centroid_test = numpy.empty(len(img),dtype=object)\n", + "t_new_test = numpy.empty(len(img),dtype=object)\n", + "im = numpy.empty(len(img), dtype=object)\n", + "PF = numpy.empty(len(img),dtype=object)\n", + "PS = numpy.empty(len(img),dtype=object)\n", + "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", + "PowerY_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal1_plot= []\n", + "MirnormPower_plot=[]\n", + "th_plot=[]\n", + "r_line_plot=[]\n", + "normPower1_plot=[]\n", + "num=0\n", + "index=num\n", + "\n", + "plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output\n", + "\n", + "## begin analysis\n", + "print('length image',len(img))\n", + "for n in range(0,len(img)):\n", + " im=img[n][:,:,1] # select first image\n", + " M= im.shape[0] #get image shape\n", + " N1 = im.shape[1] \n", + " n1 = np.round(N1/ 2) - 1\n", + " freq = np.arange(-n1, n1 + 1, 1)\n", + " x, y = freq, freq\n", + " \n", + " ## Fourier Transform\n", + " fft_result = np.fft.fft2(im)\n", + " Fshift = np.fft.fftshift(fft_result)\n", + " \n", + " ## adjust to power spectrum to remove imaginary components\n", + " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", + " Pabs = np.abs(Fshift) ** 2\n", + " \n", + " M= im.shape[0]\n", + " N1=im.shape[1]\n", + "\n", + " # shift in terms of image because power spectrum is the mirroR of lines so\n", + " # mirroring back in terms of image would give right allignment\n", + " PabsFlip1 = np.rot90(Pabs)\n", + " PabsFlip = np.flipud(PabsFlip1)\n", + " while (PabsFlip.shape[0]>x.shape[0]):\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=0) \n", + " while (PabsFlip.shape[1]>y.shape[0]):\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", + " while (PabsFlip.shape[0] 0)[0]]\n", + " A = np.vstack((A1, dot(T, A1)))\n", + " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", + " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", + " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", + " 0] - A[4] * centroid[1]\n", + " A[3] = A3\n", + " A[4] = A4\n", + " A[5] = A5\n", + " A = A / linalg.norm(A)\n", + " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", + " if ((A[0]) <= (A[2])):\n", + " # Ellipse is horizontal\n", + " angle = 0;\n", + " major = sqrt(1 / A[0])\n", + " minor = sqrt(1 / A[2])\n", + " else:\n", + " angle = np.pi/2;\n", + " major = sqrt(1 / A[2])\n", + " minor = sqrt(1 / A[0])\n", + " else:\n", + " R = ((A[2] - A[0]) / A[1])\n", + " tg = R - sqrt((R * R) + 1)\n", + " angle = math.atan(tg)\n", + " P = (2 * tg) / (1 + (tg * tg))\n", + "\n", + " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", + " if (angle < (-pi / 4)):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle\n", + " elif ((A[1] / P <= (-A[1] / P))):\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", + " if (angle < 0):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle - np.pi\n", + " else:\n", + " # Switch\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + "\n", + " t_New = (angle) #angle\n", + " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", + " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", + " #print('round2',Mirtheta1RadFinal1)\n", + " MirnormPower = MirnormPower_test[n]\n", + " #print('mirnormpower',MirnormPower)\n", + " t = t_new_test[n]\n", + " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", + " th = np.zeros(len(r_line))\n", + " for i in range(0, len(r_line)):\n", + " th[i] = t\n", + " th = np.concatenate([th, (th + 180)])\n", + " r_line = np.concatenate([r_line, r_line])\n", + " ## Polar Graph\n", + " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"poster\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'14',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " plt.figure(frameon=False, figsize=(15, 10))\n", + " plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5)\n", + " plt.suptitle(abs(t)-180, fontsize=20)\n", + " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", + " plt.savefig(savepath+'polarpositive'+names[n])\n", + " if (max(MirnormPower)<2):\n", + " inc = 0.5\n", + " elif (max(MirnormPower)<5):\n", + " inc = 1\n", + " elif max(MirnormPower)<20:\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", + "\n", + " #plt.show()\n", + " t_final_rad = t*pi/180\n", + " def fitted_func(thetas, c):\n", + " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", + " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", + " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", + " c0 = 15\n", + " xdata = theta1RadFinal_test[n]\n", + " theta1RadFinal = theta1RadFinal_test[n]\n", + " ydata= normPower_test[n]\n", + " normPower = normPower_test[n]\n", + " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", + "\n", + " # Shift data for plotting purposes\n", + "\n", + " diff = abs(theta1RadFinal - (t * pi / 180))\n", + " #print(diff,'diff')\n", + " #centerLoc=diff.find(diff==min(diff))\n", + " centerLoc = diff[np.argmin(diff)]#find(diff== min(diff))\n", + " #print(centerLoc,'centerLoc')\n", + "\n", + " num_angles = len(theta1RadFinal)\n", + " shift = ((round(num_angles / 2) - (num_angles - centerLoc)))\n", + " #print(shift,'shift')\n", + "\n", + " normPower1 = np.roll(normPower, -int(shift))\n", + " normPower1\n", + " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", + "\n", + " if (shift > 0):\n", + " s = num_angles - shift\n", + " s= np.asscalar(s)\n", + " for k in range(s, num_angles):\n", + " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", + " elif (shift < 0):\n", + " for k in range(0,90):\n", + " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", + " \n", + " ## Normalized Distribution Graph\n", + " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", + "\n", + " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", + " plt.xlim([-95, 95])\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " #print(p_act)\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", + " plt.xlabel('Angle (°)', **csfont)\n", + " plt.ylabel('Normalized Intensity', **csfont)\n", + " #print(max(normPower))\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", + " plt.ylim([0, max(normPower1) + .3])\n", + " normPower1_plot.append(normPower1)\n", + " plt.savefig(savepath+'Normdist '+names[n])\n", + " #plt.show()\n", + " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", + " plt.close()\n", + " t_final=t\n", + " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", + " #print(rValue**2)\n", + " # Rounding results for Title of Figure\n", + " krnd = math.ceil(k * 1000) / 1000\n", + " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", + " krnd = math.trunc(krnd * 100) / 100\n", + " thrnd = math.trunc(thrnd * 100) / 100\n", + " a = 32.02\n", + " b= -12.43\n", + " c = 47.06\n", + " d = -0.9185\n", + " e = 19.43\n", + " f = -0.07693\n", + " x = kappa[0]\n", + " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", + " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", + " sum_NP = sum(normPower1)\n", + " #normPower2 = normPower1/sum(normPower1)\n", + " #sum_NP2 = sum(normPower2)\n", + " #print(sum_NP2)\n", + " ## Combined plot figure\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"notebook\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'18',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " fig = plt.figure(frameon=False,figsize=(20,12))\n", + " fig.subplots_adjust(top=0.95)\n", + " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", + " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", + " cart_dist = plt.subplot(grid[1, 0:2])\n", + " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", + " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " cart_dist.set_xlim([-92, 92])\n", + " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", + " cart_dist.set_ylim([0, max(normPower1) + .3])\n", + " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", + "\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", + "\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + "\n", + " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", + " polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o');\n", + " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " fig.text(0.75, 0.05, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " #polar_dist.text(np.radians(-110),1.95, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " orig_fig = plt.subplot(grid[0, 0]); \n", + " orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0])\n", + " orig_fig.axis('off');\n", + " fft_fig = plt.subplot(grid[0, 1])\n", + " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", + " fft_fig.set_axis_off()\n", + " fft_fig.axis('off')\n", + " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", + " orig_fig.imshow(images[n],cmap=plt.cm.viridis )\n", + " #plt.tight_layout()\n", + " plt.savefig(savepath+'Grouped'+names[n])\n", + " print(n,'n')\n", + " #plt.show()\n", + " polar_dist_2 = plt.figure(figsize=(10, 10))\n", + " #print('linebeforeindex')\n", + " #print(index,'currentindex')\n", + " #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1;\n", + " #MirnormPower_plot[index]=MirnormPower;\n", + " #th_plot[index] = th * pi/180\n", + " #print(th_plot[index],'thplotindex')\n", + " #r_line_plot[index]=r_line\n", + " Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1);\n", + " MirnormPower_plot.append(MirnormPower);\n", + " th_plot.append(th * pi/180)\n", + " r_line_plot.append(r_line)\n", + " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower);\n", + " polar_dist.plot(th * pi/180, r_line,linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " index=index+1\n", + " print(index,'Index')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "## export of cartesian coordinate data\n", + "arr = numpy.array(MirnormPower_plot) \n", + "arr2 = numpy.array(Mirtheta1RadFinal1_plot)\n", + "arr3 = numpy.array(normPower1_plot)\n", + "arr4 = numpy.array(theta1RadFinal1*180/pi)\n", + "a = numpy.transpose(arr3)\n", + "b = numpy.transpose(arr4)\n", + "b[b> 100] = 0\n", + "b[b<-100] = 0\n", + "export_data=np.hstack(((b[:,None]),a))\n", + "numpy.savetxt(\"data_export.csv\", export_data, delimiter=\",\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 971da95add96b02ebb310f6793ec62fc308d7e65 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:51:43 -0400 Subject: [PATCH 050/153] Rename FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb to FiberFit-toshare-5.22.2020.ipynb --- ....21.2020 (1) (2) (5).ipynb => FiberFit-toshare-5.22.2020.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb => FiberFit-toshare-5.22.2020.ipynb (100%) diff --git a/FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb b/FiberFit-toshare-5.22.2020.ipynb similarity index 100% rename from FiberFit-toshare-5.21.2020 (1) (2) (5).ipynb rename to FiberFit-toshare-5.22.2020.ipynb From 9da643c7dc03ffd850058a9dd8de7dafbaffa10c Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:53:22 -0400 Subject: [PATCH 051/153] Create create.txt --- output/create.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 output/create.txt diff --git a/output/create.txt b/output/create.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/output/create.txt @@ -0,0 +1 @@ + From 86e4e02e0a1791ff6e1e3e1c12ddb280cb63c7f8 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:53:40 -0400 Subject: [PATCH 052/153] Delete create.txt --- output/create.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 output/create.txt diff --git a/output/create.txt b/output/create.txt deleted file mode 100644 index 8b13789..0000000 --- a/output/create.txt +++ /dev/null @@ -1 +0,0 @@ - From 64111e0a4a4a6e86d74159d7daf39632ed574e9f Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:54:03 -0400 Subject: [PATCH 053/153] Create blank.txt --- output/blank.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 output/blank.txt diff --git a/output/blank.txt b/output/blank.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/output/blank.txt @@ -0,0 +1 @@ + From b96759eecce5cd0eea2170ceb8dfa95a205d3596 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:54:55 -0400 Subject: [PATCH 054/153] Rename FiberFit-toshare-5.21.2020.ipynb to old_versions/FiberFit-toshare-5.21.2020.ipynb --- .../FiberFit-toshare-5.21.2020.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-5.21.2020.ipynb => old_versions/FiberFit-toshare-5.21.2020.ipynb (100%) diff --git a/FiberFit-toshare-5.21.2020.ipynb b/old_versions/FiberFit-toshare-5.21.2020.ipynb similarity index 100% rename from FiberFit-toshare-5.21.2020.ipynb rename to old_versions/FiberFit-toshare-5.21.2020.ipynb From aede11b934492b47a761f5ead4ed5544719aeed6 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 14:55:57 -0400 Subject: [PATCH 055/153] Rename FiberFit-toshare-6.5.19.ipynb to old_versions/FiberFit-toshare-6.5.19.ipynb --- .../FiberFit-toshare-6.5.19.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-6.5.19.ipynb => old_versions/FiberFit-toshare-6.5.19.ipynb (100%) diff --git a/FiberFit-toshare-6.5.19.ipynb b/old_versions/FiberFit-toshare-6.5.19.ipynb similarity index 100% rename from FiberFit-toshare-6.5.19.ipynb rename to old_versions/FiberFit-toshare-6.5.19.ipynb From c847dc92075a5c6aff2d09cec89b4d218d261887 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 16:03:31 -0400 Subject: [PATCH 056/153] Add files via upload --- FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb | 682 +++++++++++++++++++ 1 file changed, 682 insertions(+) create mode 100644 FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb diff --git a/FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb b/FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb new file mode 100644 index 0000000..894aefa --- /dev/null +++ b/FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb @@ -0,0 +1,682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Import necessary Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: greenlet in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.4.15)\n", + "Requirement already satisfied: PyQt5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (5.14.2)\n", + "Requirement already satisfied: PyQt5-sip<13,>=12.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from PyQt5) (12.7.2)\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install greenlet\n", + "!{sys.executable} -m pip install PyQt5\n", + "import PyQt5" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams['backend'] = \"Qt5Agg\"\n", + "from matplotlib.figure import Figure\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2a7ec5a5845c4c22af718f4ce934d24f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Press Me', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from __future__ import division\n", + "from __future__ import print_function\n", + "from __future__ import unicode_literals\n", + "from __future__ import division\n", + "from __future__ import absolute_import\n", + "\n", + "import argparse, re, os, glob, sys, pprint, itertools\n", + "import inspect\n", + "\n", + "import numpy as np\n", + "# try:\n", + "# from runtime import *\n", + "# except ImportError:\n", + "# from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + " \n", + "btn = widgets.Button(description='Press Me')\n", + "display(btn)\n", + "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", + "import skimage.io as io\n", + "from skimage import data\n", + "from skimage.filters import sobel \n", + "from skimage.morphology import watershed\n", + "from skimage import feature\n", + "from skimage.color import rgb2gray\n", + "from skimage.feature import corner_harris,corner_peaks\n", + "from skimage.exposure import equalize_hist\n", + "from skimage import measure\n", + "from skimage.draw import ellipse\n", + "from skimage.measure import label, regionprops\n", + "from skimage.transform import rotate\n", + "from skimage.feature import corner_orientations\n", + "from skimage.filters import threshold_otsu\n", + "from skimage.segmentation import clear_border\n", + "from skimage.measure import label\n", + "from skimage.morphology import closing, square\n", + "from skimage.measure import regionprops\n", + "from skimage.color import label2rgb\n", + "import seaborn\n", + "import pandas\n", + "try:\n", + " from runtime import *\n", + "except ImportError:\n", + " from smop.runtime import *\n", + "\n", + "from src.fiberfit_model.helpers import debug\n", + "import numpy as np\n", + "from numpy.linalg import eig, inv\n", + "import scipy.ndimage\n", + "from pylab import *\n", + "from os import listdir\n", + "from os.path import isfile, join\n", + "import numpy\n", + "import cv2\n", + "\n", + "\n", + "#test\n", + " \n", + "import sys\n", + "import matplotlib\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.ndimage\n", + "import scipy.interpolate\n", + "import scipy.optimize\n", + "import scipy.integrate\n", + "import scipy.stats\n", + "import math\n", + "import time\n", + "import glob\n", + "from pylab import *\n", + "from pandas import DataFrame\n", + "#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from src.fiberfit_model.EllipseDirectFit import*\n", + "from src.fiberfit_model import helpers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Input Variables " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "## File Path with binary square images of fibers to analyze\n", + "mypath='test_images/'\n", + "\n", + "##Path to folder where you would like images to be saved\n", + "savepath = 'output/'\n", + "## NOTE - we can also output raw data. let me know if you would prefer that.\n", + "\n", + "## Variables for settings\n", + "CO_lower = 20 #upper fiber thickness, pixels\n", + "CO_upper = 1 #lower fiber thickness, pixels\n", + "angleInc = 1 #angle increment\n", + "radStep = 0.5 #radial step size\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, Run the code below. Figures will be saved in specified output folder" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length image 6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 n\n", + "1 Index\n", + "1 n\n", + "2 Index\n", + "2 n\n", + "3 Index\n", + "3 n\n", + "4 Index\n", + "4 n\n", + "5 Index\n", + "5 n\n", + "6 Index\n" + ] + } + ], + "source": [ + "## Upload Files\n", + "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", + "images = numpy.empty(len(onlyfiles), dtype=object)\n", + "names = numpy.empty(len(onlyfiles), dtype=object)\n", + "for n in range(0, len(onlyfiles)):\n", + " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", + " names[n]= (onlyfiles[n])\n", + "img = numpy.empty(len(images), dtype=object)\n", + "for n in range(0,len(images)):\n", + " img[n]= np.invert(images[n])\n", + " \n", + "## Set up empty Matrices (can clean this up later )\n", + "ell_data_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", + "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", + "normPower_test = numpy.empty(len(img),dtype=object)\n", + "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", + "A_test = numpy.empty(len(img),dtype=object)\n", + "centroid_test = numpy.empty(len(img),dtype=object)\n", + "t_new_test = numpy.empty(len(img),dtype=object)\n", + "im = numpy.empty(len(img), dtype=object)\n", + "PF = numpy.empty(len(img),dtype=object)\n", + "PS = numpy.empty(len(img),dtype=object)\n", + "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", + "PowerY_test = numpy.empty(len(img),dtype=object)\n", + "Mirtheta1RadFinal1_plot= []\n", + "MirnormPower_plot=[]\n", + "th_plot=[]\n", + "r_line_plot=[]\n", + "normPower1_plot=[]\n", + "num=0\n", + "index=num\n", + "\n", + "plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output\n", + "mpl.rcParams['figure.max_open_warning'] = 0\n", + "maxiter = 100\n", + "\n", + "## begin analysis\n", + "print('length image',len(img))\n", + "for n in range(0,len(img)):\n", + " im=img[n][:,:,1] # select first image\n", + " M= im.shape[0] #get image shape\n", + " N1 = im.shape[1] \n", + " n1 = np.round(N1/ 2) - 1\n", + " freq = np.arange(-n1, n1 + 1, 1)\n", + " x, y = freq, freq\n", + " \n", + " ## Fourier Transform\n", + " fft_result = np.fft.fft2(im)\n", + " Fshift = np.fft.fftshift(fft_result)\n", + " \n", + " ## adjust to power spectrum to remove imaginary components\n", + " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", + " Pabs = np.abs(Fshift) ** 2\n", + " \n", + " M= im.shape[0]\n", + " N1=im.shape[1]\n", + "\n", + " # shift in terms of image because power spectrum is the mirroR of lines so\n", + " # mirroring back in terms of image would give right allignment\n", + " PabsFlip1 = np.rot90(Pabs)\n", + " PabsFlip = np.flipud(PabsFlip1)\n", + " while (PabsFlip.shape[0]>x.shape[0]):\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=0) \n", + " while (PabsFlip.shape[1]>y.shape[0]):\n", + " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", + " while (PabsFlip.shape[0] 0)[0]]\n", + " A = np.vstack((A1, dot(T, A1)))\n", + " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", + " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", + " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", + " 0] - A[4] * centroid[1]\n", + " A[3] = A3\n", + " A[4] = A4\n", + " A[5] = A5\n", + " A = A / linalg.norm(A)\n", + " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", + " if ((A[0]) <= (A[2])):\n", + " # Ellipse is horizontal\n", + " angle = 0;\n", + " major = sqrt(1 / A[0])\n", + " minor = sqrt(1 / A[2])\n", + " else:\n", + " angle = np.pi/2;\n", + " major = sqrt(1 / A[2])\n", + " minor = sqrt(1 / A[0])\n", + " else:\n", + " R = ((A[2] - A[0]) / A[1])\n", + " tg = R - sqrt((R * R) + 1)\n", + " angle = math.atan(tg)\n", + " P = (2 * tg) / (1 + (tg * tg))\n", + "\n", + " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", + " if (angle < (-pi / 4)):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle\n", + " elif ((A[1] / P <= (-A[1] / P))):\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", + " if (angle < 0):\n", + " angle = angle + np.pi\n", + " else:\n", + " angle = angle - np.pi\n", + " else:\n", + " # Switch\n", + " if (angle < 0):\n", + " angle = angle + np.pi / 2\n", + " else:\n", + " angle = angle - np.pi / 2\n", + "\n", + " t_New = (angle) #angle\n", + " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", + " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", + " #print('round2',Mirtheta1RadFinal1)\n", + " MirnormPower = MirnormPower_test[n]\n", + " #print('mirnormpower',MirnormPower)\n", + " t = t_new_test[n]\n", + " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", + " th = np.zeros(len(r_line))\n", + " for i in range(0, len(r_line)):\n", + " th[i] = t\n", + " th = np.concatenate([th, (th + 180)])\n", + " r_line = np.concatenate([r_line, r_line])\n", + " ## Polar Graph\n", + " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"poster\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'14',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " plt.figure(frameon=False, figsize=(15, 10))\n", + " plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5)\n", + " plt.suptitle(abs(t)-180, fontsize=20)\n", + " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", + " plt.savefig(savepath+'polarpositive'+names[n])\n", + " if (max(MirnormPower)<2):\n", + " inc = 0.5\n", + " elif (max(MirnormPower)<5):\n", + " inc = 1\n", + " elif max(MirnormPower)<20:\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", + "\n", + " #plt.show()\n", + " t_final_rad = t*pi/180\n", + " def fitted_func(thetas, c):\n", + " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", + " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", + " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", + " c0 = 15\n", + " xdata = theta1RadFinal_test[n]\n", + " theta1RadFinal = theta1RadFinal_test[n]\n", + " ydata= normPower_test[n]\n", + " normPower = normPower_test[n]\n", + " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", + "\n", + " # Shift data for plotting purposes\n", + "\n", + " diff = abs(theta1RadFinal - (t * pi / 180))\n", + " #print(diff,'diff')\n", + " #centerLoc=diff.find(diff==min(diff))\n", + " centerLoc = diff[np.argmin(diff)]#find(diff== min(diff))\n", + " #print(centerLoc,'centerLoc')\n", + "\n", + " num_angles = len(theta1RadFinal)\n", + " shift = ((round(num_angles / 2) - (num_angles - centerLoc)))\n", + " #print(shift,'shift')\n", + "\n", + " normPower1 = np.roll(normPower, -int(shift))\n", + " normPower1\n", + " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", + "\n", + " if (shift > 0):\n", + " s = num_angles - shift\n", + " s= np.asscalar(s)\n", + " for k in range(s, num_angles):\n", + " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", + " elif (shift < 0):\n", + " for k in range(0,90):\n", + " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", + " \n", + " ## Normalized Distribution Graph\n", + " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", + "\n", + " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", + " plt.xlim([-95, 95])\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " #print(p_act)\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", + " plt.xlabel('Angle (°)', **csfont)\n", + " plt.ylabel('Normalized Intensity', **csfont)\n", + " #print(max(normPower))\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", + " plt.ylim([0, max(normPower1) + .3])\n", + " normPower1_plot.append(normPower1)\n", + " plt.savefig(savepath+'Normdist '+names[n])\n", + " #plt.show()\n", + " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", + " plt.close()\n", + " t_final=t\n", + " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", + " #print(rValue**2)\n", + " # Rounding results for Title of Figure\n", + " krnd = math.ceil(k * 1000) / 1000\n", + " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", + " krnd = math.trunc(krnd * 100) / 100\n", + " thrnd = math.trunc(thrnd * 100) / 100\n", + " a = 32.02\n", + " b= -12.43\n", + " c = 47.06\n", + " d = -0.9185\n", + " e = 19.43\n", + " f = -0.07693\n", + " x = kappa[0]\n", + " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", + " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", + " sum_NP = sum(normPower1)\n", + " #normPower2 = normPower1/sum(normPower1)\n", + " #sum_NP2 = sum(normPower2)\n", + " #print(sum_NP2)\n", + " ## Combined plot figure\n", + " palette = seaborn.set_palette(graphs);\n", + " seaborn.set_context(\"notebook\",font_scale=1.5);\n", + " seaborn.set_style('ticks')\n", + " csfont = {'fontname':'Times New Roman',\n", + " 'size':'18',\n", + " }\n", + " ticksfont = {'fontname':'Times New Roman'}\n", + " fig = plt.figure(frameon=False,figsize=(20,12))\n", + " fig.subplots_adjust(top=0.95)\n", + " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", + " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", + " cart_dist = plt.subplot(grid[1, 0:2])\n", + " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", + " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", + " cart_dist.set_xlim([-92, 92])\n", + " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", + " cart_dist.set_ylim([0, max(normPower1) + .3])\n", + " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", + "\n", + " p_act = fitted_func(theta1RadFinal1, kappa)\n", + " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", + " a= a.T\n", + " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", + " h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", + "\n", + " if (max(normPower)<2):\n", + " inc = 0.5\n", + " elif (max(normPower)<5):\n", + " inc = 1\n", + " elif (max(normPower)<20):\n", + " inc = 5\n", + " else:\n", + " inc = 10\n", + "\n", + " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", + " polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o');\n", + " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " fig.text(0.75, 0.05, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " #polar_dist.text(np.radians(-110),1.95, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", + " orig_fig = plt.subplot(grid[0, 0]); \n", + " orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0])\n", + " orig_fig.axis('off');\n", + " fft_fig = plt.subplot(grid[0, 1])\n", + " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", + " fft_fig.set_axis_off()\n", + " fft_fig.axis('off')\n", + " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", + " orig_fig.imshow(images[n],cmap=plt.cm.viridis )\n", + " #plt.tight_layout()\n", + " plt.savefig(savepath+'Grouped'+names[n])\n", + " print(n,'n')\n", + " #plt.show()\n", + " polar_dist_2 = plt.figure(figsize=(10, 10))\n", + " #print('linebeforeindex')\n", + " #print(index,'currentindex')\n", + " #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1;\n", + " #MirnormPower_plot[index]=MirnormPower;\n", + " #th_plot[index] = th * pi/180\n", + " #print(th_plot[index],'thplotindex')\n", + " #r_line_plot[index]=r_line\n", + " Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1);\n", + " MirnormPower_plot.append(MirnormPower);\n", + " th_plot.append(th * pi/180)\n", + " r_line_plot.append(r_line)\n", + " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower);\n", + " polar_dist.plot(th * pi/180, r_line,linewidth=4);\n", + " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", + " index=index+1\n", + " print(index,'Index')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "## export of cartesian coordinate data\n", + "arr = numpy.array(MirnormPower_plot) \n", + "arr2 = numpy.array(Mirtheta1RadFinal1_plot)\n", + "arr3 = numpy.array(normPower1_plot)\n", + "arr4 = numpy.array(theta1RadFinal1*180/pi)\n", + "a = numpy.transpose(arr3)\n", + "b = numpy.transpose(arr4)\n", + "b[b> 100] = 0\n", + "b[b<-100] = 0\n", + "export_data=np.hstack(((b[:,None]),a))\n", + "numpy.savetxt(savepath+\"data_export.csv\", export_data, delimiter=\",\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1168f5f79c28b74337115610060ef6e7c683d5b3 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 16:04:02 -0400 Subject: [PATCH 057/153] Rename FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb to FiberFit-toshare-5.22.2020_2.ipynb --- ...1.2020 (1) (2) (6).ipynb => FiberFit-toshare-5.22.2020_2.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb => FiberFit-toshare-5.22.2020_2.ipynb (100%) diff --git a/FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb b/FiberFit-toshare-5.22.2020_2.ipynb similarity index 100% rename from FiberFit-toshare-5.21.2020 (1) (2) (6).ipynb rename to FiberFit-toshare-5.22.2020_2.ipynb From 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a64e81e24234c9875ba6c5a6165c74e1212cd35e Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 16:53:31 -0400 Subject: [PATCH 060/153] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 872537c..6171826 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,4 @@ #numba -quanfima scikit-image seaborn pandas From 933f0f6640485a7f90b6ba89b7d13c3317dd0587 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 18:10:18 -0400 Subject: [PATCH 061/153] Update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 6171826..2dfd064 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ #numba +git996 scikit-image seaborn pandas From 3bcb56ba176f0b3089399789c13fe2b253897cf2 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 18:23:04 -0400 Subject: [PATCH 062/153] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2dfd064..6171826 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,4 @@ #numba -git996 scikit-image seaborn pandas From bd220da11c8cab042b392039e71a28d533deac53 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 22 May 2020 18:40:15 -0400 Subject: [PATCH 063/153] Update requirements.txt --- requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements.txt b/requirements.txt index 6171826..7f4b372 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,6 @@ #numba +pip==0.5.1 +quanfima scikit-image seaborn pandas From 8845230130aca828f7f8419e7c1c2f430bf42339 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 23 May 2020 23:13:25 -0400 Subject: [PATCH 064/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7f4b372..7fd2077 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ #numba pip==0.5.1 -quanfima +quanfima==0.1a1 scikit-image seaborn pandas From a395d3b0e5a409b89439c538975a6ae263d48fca Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 23 May 2020 23:14:37 -0400 Subject: [PATCH 065/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7fd2077..d6fb770 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ #numba pip==0.5.1 -quanfima==0.1a1 +git996 scikit-image seaborn pandas From afd27d9a9281b036f3bfbbbce601fdd14c5306c2 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 23 May 2020 23:15:31 -0400 Subject: [PATCH 066/153] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d6fb770..7c21e88 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ #numba pip==0.5.1 -git996 scikit-image seaborn pandas From 66dd1407fdd8a24136fe5f0c9651634d941fb385 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 23 May 2020 23:20:27 -0400 Subject: [PATCH 067/153] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7c21e88..b20c670 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ #numba -pip==0.5.1 +pip==19.3.1 scikit-image seaborn pandas From 19bcaa88a4c46bb490d4a9054056bc786666cc02 Mon Sep 17 00:00:00 2001 From: mprender Date: Sun, 24 May 2020 11:01:33 -0400 Subject: [PATCH 068/153] Update requirements.txt --- requirements.txt | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/requirements.txt b/requirements.txt index b20c670..4af6791 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,12 @@ #numba pip==19.3.1 +matplotlib==2.0.2 +nose==1.3.7 +numpy==1.13.3 +pandas==0.19.2 +scikit-image==0.12.3 +scikit-learn==0.18.1 +scipy==0.19.0 scikit-image seaborn pandas From fbe301f9bf5f43ec855ecda7bc57d74ae7e60cf9 Mon Sep 17 00:00:00 2001 From: mprender Date: Sun, 24 May 2020 11:05:27 -0400 Subject: [PATCH 069/153] Update requirements.txt --- requirements.txt | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/requirements.txt b/requirements.txt index 4af6791..433605e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,12 @@ #numba pip==19.3.1 -matplotlib==2.0.2 -nose==1.3.7 -numpy==1.13.3 -pandas==0.19.2 -scikit-image==0.12.3 -scikit-learn==0.18.1 -scipy==0.19.0 +#matplotlib==2.0.2 +#nose==1.3.7 +#numpy==1.13.3 +#pandas==0.19.2 +#scikit-image==0.12.3 +#scikit-learn==0.18.1 +#scipy==0.19.0 scikit-image seaborn pandas From f63339a2d96519b4d729e5ea0169b81d3c30811c Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 19:33:54 -0400 Subject: [PATCH 070/153] Update requirements.txt --- requirements.txt | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/requirements.txt b/requirements.txt index 433605e..346a842 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,14 @@ #numba + +greenlet +PyQt5 +GitPython +statsmodels +ipywidgets +!git clone https://github.com/rshkarin/quanfima.git +!jupyter nbextension enable --py --sys-prefix widgetsnbextension +!conda install -c ukoethe vigra=1.11.1 anaconda --yes; + pip==19.3.1 #matplotlib==2.0.2 #nose==1.3.7 From 37917c5f33646a676d7c2cd1e4af67c183441fcb Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 19:45:00 -0400 Subject: [PATCH 071/153] Update and rename requirements.txt to environment.yml --- environment.yml | 44 ++++++++++++++++++++++++++++++++++ requirements.txt | 61 ------------------------------------------------ 2 files changed, 44 insertions(+), 61 deletions(-) create mode 100644 environment.yml delete mode 100644 requirements.txt diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..b414baa --- /dev/null +++ b/environment.yml @@ -0,0 +1,44 @@ +name: test-environment + +dependencies: + - python==3.4 + - cv + - cv_utils + - cv2studio + - Cycler + - decorator + - ffmpy + - greenlet + - PyQt5 + - GitPython + - statsmodels + - imageio + - ipykernel + - ipython_genutils + - ipython + - ipywidgets + - isapi_wsgi + - jedi + - matplotlib + - more-itertools + - mpl4qt + - mpl-tools + - mpl-utils + - numpy + - opencv-python + - pandas + - pathlib + - pip==19.3.1 + - py-dateutil + - PyQt5 + - scikit-image + - scipy + - seaborn + - smop + - syspath + - runtime + - pip: + - git+https://github.com/rshkarin/quanfima.git + - jupyterlab + + diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 346a842..0000000 --- a/requirements.txt +++ /dev/null @@ -1,61 +0,0 @@ -#numba - -greenlet -PyQt5 -GitPython -statsmodels -ipywidgets -!git clone https://github.com/rshkarin/quanfima.git -!jupyter nbextension enable --py --sys-prefix widgetsnbextension -!conda install -c ukoethe vigra=1.11.1 anaconda --yes; - -pip==19.3.1 -#matplotlib==2.0.2 -#nose==1.3.7 -#numpy==1.13.3 -#pandas==0.19.2 -#scikit-image==0.12.3 -#scikit-learn==0.18.1 -#scipy==0.19.0 -scikit-image -seaborn -pandas -numpy -scipy -smop -runtime -#os -#os.path -opencv-python -matplotlib -pathlib -PyQt5 -#os-win -#os-sys -syspath -#esmre -cv -cv_utils -cv2studio -Cycler - -imageio -ipykernel -ipython_genutils -ipython -ipywidgets -isapi_wsgi - - -jedi -#Jinja -py-dateutil -decorator -ffmpy -imageio -more-itertools -#mpl-toolkits.clifford -mpl4qt -mpl-tools -mpl-utils -greenlet From 36d1da90e475fa61c509ffd5b5683ccfbd97742f Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 19:49:15 -0400 Subject: [PATCH 072/153] Create setup.py --- setup.py | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 setup.py diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..14a39ad --- /dev/null +++ b/setup.py @@ -0,0 +1,2 @@ +jupyter nbextension enable --py --sys-prefix widgetsnbextension +conda install -c ukoethe vigra=1.11.1 anaconda --yes; From 0865e10b10752b5a93832ee3245b1db5e8ffe2c7 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 19:57:23 -0400 Subject: [PATCH 073/153] Update environment.yml --- environment.yml | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/environment.yml b/environment.yml index b414baa..3f755c1 100644 --- a/environment.yml +++ b/environment.yml @@ -1,15 +1,10 @@ name: test-environment - +channels: + - conda-forge dependencies: - - python==3.4 - - cv - - cv_utils - - cv2studio - Cycler - decorator - - ffmpy - greenlet - - PyQt5 - GitPython - statsmodels - imageio @@ -17,28 +12,32 @@ dependencies: - ipython_genutils - ipython - ipywidgets - - isapi_wsgi - jedi - matplotlib - more-itertools - - mpl4qt - mpl-tools - - mpl-utils - numpy - - opencv-python - pandas - pathlib - pip==19.3.1 - - py-dateutil - - PyQt5 - scikit-image - scipy - seaborn - - smop - - syspath - - runtime - pip: - git+https://github.com/rshkarin/quanfima.git + - ffmpy + - isapi_wsgi - jupyterlab + - cv + - opencv-python + - mpl-utils + - mpl4qt + - PyQt5 + - py-dateutil + - runtime + - cv_utils + - cv2studio + - smop + - syspath From 7c2e17d5f8f32f755df697eed4179d36483f9939 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 19:59:09 -0400 Subject: [PATCH 074/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 3f755c1..e93cc0a 100644 --- a/environment.yml +++ b/environment.yml @@ -15,7 +15,6 @@ dependencies: - jedi - matplotlib - more-itertools - - mpl-tools - numpy - pandas - pathlib @@ -32,6 +31,7 @@ dependencies: - opencv-python - mpl-utils - mpl4qt + - mpl-tools - PyQt5 - py-dateutil - runtime From 74aa815312d12de4e091ed30a47cc25f85d1d7d8 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 20:06:46 -0400 Subject: [PATCH 075/153] Update environment.yml --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index e93cc0a..f4ca953 100644 --- a/environment.yml +++ b/environment.yml @@ -1,7 +1,9 @@ name: test-environment channels: - conda-forge + - defaults dependencies: + - python = 2.7 - Cycler - decorator - greenlet From 1b32828900aced0150afa9110b70e92be315b475 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 20:20:12 -0400 Subject: [PATCH 076/153] Update environment.yml --- environment.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/environment.yml b/environment.yml index f4ca953..c4ecf96 100644 --- a/environment.yml +++ b/environment.yml @@ -25,11 +25,9 @@ dependencies: - scipy - seaborn - pip: - - git+https://github.com/rshkarin/quanfima.git - ffmpy - isapi_wsgi - jupyterlab - - cv - opencv-python - mpl-utils - mpl4qt From 3f115105a6899cf34dbd0cafdd4d144ac559be4b Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 20:23:37 -0400 Subject: [PATCH 077/153] Update environment.yml --- environment.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index c4ecf96..cc03b60 100644 --- a/environment.yml +++ b/environment.yml @@ -2,8 +2,9 @@ name: test-environment channels: - conda-forge - defaults + - !!python/unicode dependencies: - - python = 2.7 + - python = 3.7.7 - Cycler - decorator - greenlet @@ -26,6 +27,7 @@ dependencies: - seaborn - pip: - ffmpy + - git+https://github.com/rshkarin/quanfima.git - isapi_wsgi - jupyterlab - opencv-python From 610b1bd11a3b35fb01c2e816581097d67a819ad1 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 20:25:45 -0400 Subject: [PATCH 078/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index cc03b60..a4a4ec4 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,7 @@ channels: - defaults - !!python/unicode dependencies: - - python = 3.7.7 + - python = 3.4.0 - Cycler - decorator - greenlet From b93895155708266e484c91deb030fadd7fcf0a38 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 20:27:39 -0400 Subject: [PATCH 079/153] Update environment.yml --- environment.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index a4a4ec4..077ccae 100644 --- a/environment.yml +++ b/environment.yml @@ -2,7 +2,6 @@ name: test-environment channels: - conda-forge - defaults - - !!python/unicode dependencies: - python = 3.4.0 - Cycler @@ -25,9 +24,9 @@ dependencies: - scikit-image - scipy - seaborn + - git+https://github.com/rshkarin/quanfima.git - pip: - ffmpy - - git+https://github.com/rshkarin/quanfima.git - isapi_wsgi - jupyterlab - opencv-python From bc4dc083cde120b665db345116ab25e368699420 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 20:29:00 -0400 Subject: [PATCH 080/153] Update environment.yml --- environment.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/environment.yml b/environment.yml index 077ccae..1749cac 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,6 @@ channels: - conda-forge - defaults dependencies: - - python = 3.4.0 - Cycler - decorator - greenlet @@ -24,7 +23,6 @@ dependencies: - scikit-image - scipy - seaborn - - git+https://github.com/rshkarin/quanfima.git - pip: - ffmpy - isapi_wsgi From a832f587e28a578132631daa3255fcd22ce5bfc6 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:46:31 -0400 Subject: [PATCH 081/153] Update environment.yml --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 1749cac..91bf530 100644 --- a/environment.yml +++ b/environment.yml @@ -23,6 +23,7 @@ dependencies: - scikit-image - scipy - seaborn + - vigra=1.11.1 - pip: - ffmpy - isapi_wsgi From 4f734672fa53f8813c60b33e0468fe216a53ebd6 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:47:42 -0400 Subject: [PATCH 082/153] Create test.txt --- quanfima/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 quanfima/test.txt diff --git a/quanfima/test.txt b/quanfima/test.txt new file mode 100644 index 0000000..9daeafb --- /dev/null +++ b/quanfima/test.txt @@ -0,0 +1 @@ +test From 7318f7c186f19b976c66f39e8468d8b607678764 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:48:17 -0400 Subject: [PATCH 083/153] Add files via upload --- quanfima/.gitignore | 9 ++++ quanfima/.travis.yml | 11 +++++ quanfima/LICENSE | 21 +++++++++ quanfima/MANIFEST.in | 1 + quanfima/README.rst | 95 +++++++++++++++++++++++++++++++++++++++ quanfima/requirements.txt | 7 +++ quanfima/setup.py | 36 +++++++++++++++ quanfima/tox.ini | 3 ++ 8 files changed, 183 insertions(+) create mode 100644 quanfima/.gitignore create mode 100644 quanfima/.travis.yml create mode 100644 quanfima/LICENSE create mode 100644 quanfima/MANIFEST.in create mode 100644 quanfima/README.rst create mode 100644 quanfima/requirements.txt create mode 100644 quanfima/setup.py create mode 100644 quanfima/tox.ini diff --git a/quanfima/.gitignore b/quanfima/.gitignore new file mode 100644 index 0000000..0d327a6 --- /dev/null +++ b/quanfima/.gitignore @@ -0,0 +1,9 @@ +# Compiled python modules. +*.pyc + +# Setuptools distribution folder. +/dist/ + +# Python egg metadata, regenerated from source files by setuptools. +/*.egg-info +/*.egg diff --git a/quanfima/.travis.yml b/quanfima/.travis.yml new file mode 100644 index 0000000..3a3d687 --- /dev/null +++ b/quanfima/.travis.yml @@ -0,0 +1,11 @@ +language: python + +python: + - "2.7" + +install: + - pip install -r requirements.txt + - pip install . + +script: + - nosetests diff --git a/quanfima/LICENSE b/quanfima/LICENSE new file mode 100644 index 0000000..9825b19 --- /dev/null +++ b/quanfima/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Roman Shkarin, Andrei Shkarin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/quanfima/MANIFEST.in b/quanfima/MANIFEST.in new file mode 100644 index 0000000..9561fb1 --- /dev/null +++ b/quanfima/MANIFEST.in @@ -0,0 +1 @@ +include README.rst diff --git a/quanfima/README.rst b/quanfima/README.rst new file mode 100644 index 0000000..083e361 --- /dev/null +++ b/quanfima/README.rst @@ -0,0 +1,95 @@ +.. image:: docs/source/_static/logo.png + :align: left + +----------- + +.. image:: https://travis-ci.org/rshkarin/quanfima.svg?branch=master + :target: https://travis-ci.org/rshkarin/quanfima + +.. image:: https://readthedocs.org/projects/quanfima/badge/?version=latest + :target: http://quanfima.readthedocs.io/en/latest/?badge=latest + :alt: Documentation Status + +.. image:: https://zenodo.org/badge/127795855.svg + :target: https://zenodo.org/badge/latestdoi/127795855 + + + + +*Quanfima* (**qu**\ antitative **an**\ alysis of **fi**\ brous **ma**\ terials) +is a collection of useful functions for morphological analysis and visualization +of 2D/3D data from various areas of material science. The aim is to simplify +the analysis process by providing functionality for frequently required tasks +in the same place. + +More examples of usage you can find in the documentation. + +- Analysis of fibrous structures by tensor-based method in 2D / 3D datasets. +- Estimation of structure diameters in 2D / 3D by a ray-casting method. +- Counting of particles in 2D / 3D datasets and providing a detailed report in + pandas.DataFrame format. +- Calculation of porosity measure for each material in 2D / 3D datasets. +- Visualization in 2D / 3D using matplotlib, visvis packages. + +Installation +------------ + +The easiest way to install the latest version is by using pip:: + + $ pip install quanfima + +You may also use Git to clone the repository and install it manually:: + + $ git clone https://github.com/rshkarin/quanfima.git + $ cd quanfima + $ python setup.py install + +Usage +----- +Open a grayscale image, perform segmentation, estimate porosity, analyze fiber +orientation and diameters, and plot the results. + +.. code-block:: python + + import numpy as np + from skimage import io, filters + from quanfima import morphology as mrph + from quanfima import visualization as vis + from quanfima import utils + + img = io.imread('../data/polymer_slice.tif') + + th_val = filters.threshold_otsu(img) + img_seg = (img > th_val).astype(np.uint8) + + # estimate porosity + pr = mrph.calc_porosity(img_seg) + for k,v in pr.items(): + print 'Porosity ({}): {}'.format(k, v) + + # prepare data and analyze fibers + data, skeleton, skeleton_thick = utils.prepare_data(img_seg) + cskel, fskel, omap, dmap, ovals, dvals = \ + mrph.estimate_fiber_properties(data, skeleton) + + # plot results + vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°', + figsize=(10,10), + name='2d_polymer', + output_dir='/path/to/output/dir') + vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow', + name='2d_polymer', + output_dir='/path/to/output/dir') + +.. code-block:: python + + >> Porosity (Material 1): 0.845488888889 + +.. image:: docs/source/_static/2d_polymer_data.png + :align: center + +.. image:: docs/source/_static/2d_polymer_orientation_map_600px.png + :align: center + +.. image:: docs/source/_static/2d_polymer_diameter_map_600px.png + :align: center diff --git a/quanfima/requirements.txt b/quanfima/requirements.txt new file mode 100644 index 0000000..40dca1a --- /dev/null +++ b/quanfima/requirements.txt @@ -0,0 +1,7 @@ +matplotlib==2.0.2 +nose==1.3.7 +numpy==1.13.3 +pandas==0.19.2 +scikit-image==0.12.3 +scikit-learn==0.18.1 +scipy==0.19.0 diff --git a/quanfima/setup.py b/quanfima/setup.py new file mode 100644 index 0000000..e40cd36 --- /dev/null +++ b/quanfima/setup.py @@ -0,0 +1,36 @@ +from setuptools import setup + +def readme(): + with open('README.rst') as f: + return f.read() + +setup(name='quanfima', + version='0.1a3', + description='The package for morphological analysis and visualization of fibrous materials.', + long_description=readme(), + classifiers=[ + 'Development Status :: 3 - Alpha', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 2.7', + 'Topic :: Scientific/Engineering :: Information Analysis', + ], + keywords='biomaterials fiber analysis material science visualization', + url='http://github.com/rshkarin/quanfima', + author='Roman Shkarin, Andrei Shkarin', + author_email='roman.v.shkarin@gmail.com, andrei.shkarin@gmail.com', + license='MIT', + packages=['quanfima', 'docs'], + python_requires='>=2.6,<3', + install_requires=[ + 'matplotlib==2.0.2', + 'numpy>=1.13.3', + 'pandas>=0.19.2', + 'scikit-image>=0.12.3', + 'scikit-learn>=0.18.1', + 'scipy>=0.19.0', + 'imageio>=2.3.0' + ], + test_suite='nose.collector', + tests_require=['nose', 'nose-cover3'], + include_package_data=True, + zip_safe=False) diff --git a/quanfima/tox.ini b/quanfima/tox.ini new file mode 100644 index 0000000..74b39f6 --- /dev/null +++ b/quanfima/tox.ini @@ -0,0 +1,3 @@ +[flake8] +ignore = W391 +max-line-length = 100 From 72205d7f09fa5523ad5fd6e6522b3797b2378b74 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:49:45 -0400 Subject: [PATCH 084/153] Create test.txt --- quanfima/quanfima/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 quanfima/quanfima/test.txt diff --git a/quanfima/quanfima/test.txt b/quanfima/quanfima/test.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/quanfima/quanfima/test.txt @@ -0,0 +1 @@ + From 5c696d51f7e29f91b298fccde86a0bd6bb1fbd7d Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:50:10 -0400 Subject: [PATCH 085/153] Add files via upload --- quanfima/quanfima/__init__.py | 19 + quanfima/quanfima/morphology.py | 1159 ++++++++++++++++++++++++++++ quanfima/quanfima/simulation.py | 676 ++++++++++++++++ quanfima/quanfima/utils.py | 150 ++++ quanfima/quanfima/visualization.py | 1082 ++++++++++++++++++++++++++ 5 files changed, 3086 insertions(+) create mode 100644 quanfima/quanfima/__init__.py create mode 100644 quanfima/quanfima/morphology.py create mode 100644 quanfima/quanfima/simulation.py create mode 100644 quanfima/quanfima/utils.py create mode 100644 quanfima/quanfima/visualization.py diff --git a/quanfima/quanfima/__init__.py b/quanfima/quanfima/__init__.py new file mode 100644 index 0000000..7a3aba5 --- /dev/null +++ b/quanfima/quanfima/__init__.py @@ -0,0 +1,19 @@ +"""Quanfima initialization.""" + +import logging + +__version__ = '0.1a' +__log_name__ = 'matscipy' +log = logging.getLogger(__log_name__) + +cuda_available = True +try: + import pycuda +except ImportError: + cuda_available = False + +visvis_available = True +try: + import visvis +except ImportError: + visvis_available = False diff --git a/quanfima/quanfima/morphology.py b/quanfima/quanfima/morphology.py new file mode 100644 index 0000000..4561ac8 --- /dev/null +++ b/quanfima/quanfima/morphology.py @@ -0,0 +1,1159 @@ +from __future__ import print_function +import math +import time +import os +import itertools +import numpy as np +from skimage import feature, measure, filters +from skimage.util.shape import view_as_blocks +from scipy import ndimage as ndi +from scipy.spatial import distance +import vigra +import pandas as pd +from multiprocessing import Pool +from quanfima import cuda_available + +if cuda_available: + import pycuda.autoinit + import pycuda.driver as cuda + from pycuda.compiler import SourceModule + from pycuda import gpuarray + +# Constants of object counter +_MEASUREMENTS = { + 'Label': 'label', + 'Area': 'area', + 'Perimeter': 'perimeter' +} + + +def cast_ray(theta, y0, x0, fiber_mask, ray_len=100): + """Computes a distance between two detected points at `fiber_mask`. + + Casts a ray from the point (x0, y0) at `fiber_mask` under angle `theta` of maximum + length of `ray_len`. The ray is casted towards to opposite directions, thus two points + (x1,y1) and (x2,y2) are detected at opposite borders of fiber. Finally, the Euclidean + distance between these points is calculated and returned. + + Parameters + ---------- + theta : float + Indicates the angle under which the ray is casted. + + y0 : integer + Indicates the y-axis component of the origin of ray emission. + + x0 : integer + Indicates the x-axis component of the origin of ray emission. + + fiber_mask : 2D array + Indicates the binary array of fibers. + + ray_len : integer + Indicates the maximum length of the ray. + + Returns + ------- + distance : float + The distance between (x1,y0) and (x2,y2) detected points. + """ + m = math.tan(theta) + y1, x1, y2, x2 = 0, 0, 0, 0 + + for i in range(-ray_len, 0)[::-1]: + if np.abs(m) <= 1.: + y = int(y0 - i * m) + x = int(x0 + i) + else: + y = int(y0 - i) + x = int(x0 + i * 1./m) + + if x < 0 or x >= fiber_mask.shape[1] or \ + y < 0 or y >= fiber_mask.shape[0]: + continue + + if fiber_mask[y, x] == 0: + break + + y1, x1 = y, x + + for i in range(ray_len): + if np.abs(m) <= 1.: + y = int(y0 - i * m) + x = int(x0 + i) + else: + y = int(y0 - i) + x = int(x0 + i * 1./m) + + if x < 0 or x >= fiber_mask.shape[1] or \ + y < 0 or y >= fiber_mask.shape[0]: + continue + + if fiber_mask[y, x] == 0: + break + + y2, x2 = y, x + + return distance.euclidean((y1, x1), (y2, x2)) + + +def scan_fiber_thickness(angle, patch, angular_step=1., tilt_range=range(-5, 6), ray_len=100): + """Computes an average diameter of a structure within a patch. + + Estimates an average distance from sequence of distances calculated with `cast_ray` + function for a range of angles `tilt_range`. + + Parameters + ---------- + orientation : float + Indicates the orientation angle of a structure centered within a patch. + + patch : 2D array + Indicates the patch with a centered structure having orientation `angle`. + + angular_step : float + Indicates the angular step of the scanning range in degrees. + + tilt_range : integer + Indicates the scanning range of steps to estimate the distance. + + ray_len : integer + Indicates the maximum length of the ray. + + Returns + ------- + average_diameter : float + The average diameter of a structure centered within a patch. + """ + thickness_dist = [] + + y0, x0 = patch.shape[0]/2, patch.shape[1]/2 + pi2 = np.pi/2. + rad_p_deg = np.deg2rad(angular_step) + + for tilt_amount in tilt_range: + theta = angle + pi2 + tilt_amount * rad_p_deg + thickness_dist.append(cast_ray(theta, y0, x0, patch, ray_len=ray_len)) + + average_distance = np.mean(thickness_dist) + return average_distance + + +def estimate_fiber_properties(fiber_mask, fiber_skel, paddding=25, window_radius=12, + orient_type='tensor', diameter_window_radius=12): + """Computes orientation and diameter of fibers at every point of a skeleton. + + Estimates orientation and diameter of fibers at every point of a skeleton. The orientation + is estimated using either tensor-based or PCA-based approach. The distance is evaluated + by scanning a mask of fibers in a speficied angular range. + + Parameters + ---------- + fiber_mask : 2D array + Indicates the binary data of fibers produced by the segmentation process. + + fiber_skel : 2D array + Indicates the skeleton produced by thinning of the binary data of fibers. + + paddding : integer + Indicates the amount of padding at the corners to prevent an estimation error. + + window_radius : integer + Indicates the radius of the local window of orientation calculation, which leads to + patches of size (`window_radius`*2+1) x (`window_radius`*2+1). + + orient_type : str + Indicates the type of algorithm for orientation estimation ('tensor' or 'pca'). + + diameter_window_radius : integer + Indicates the radius of the local window of diameter estimation, which leads to + patches of size (`diameter_window_radius`*2+1) x (`diameter_window_radius`*2+1). + + Returns + ------- + (clear_fiber_skel, fiber_skel, output_orientation_map, output_diameter_map, + orientation_vals, diameter_vals) : tuple of arrays + The skeleton with removed intersections, the skeleton, the orientation map, the + diameter map, the arrays of orientation and diameter values. + """ + orientation_vals, diameter_vals = [], [] + + padded_fiber_skel = np.pad(fiber_skel, pad_width=(paddding,), mode='constant', constant_values=0) + padded_fiber_mask = np.pad(fiber_mask, pad_width=(paddding,), mode='constant', constant_values=0) + + output_orientation_map, output_diameter_map = \ + np.zeros_like(padded_fiber_skel, dtype=np.float32), \ + np.zeros_like(padded_fiber_skel, dtype=np.float32) + + pcy, pcx = (window_radius*2+1)/2., (window_radius*2+1)/2. + + ycords, xcords = np.nonzero(padded_fiber_skel) + + clear_padded_fiber_skel = padded_fiber_skel.copy() + + method = feature.corner_harris(clear_padded_fiber_skel, sigma=1.5) + corner_points = feature.corner_peaks(method, min_distance=3) + + for i, (yy, xx) in enumerate(corner_points): + clear_padded_fiber_skel[yy-1:yy+2, xx-1:xx+2] = np.zeros((3, 3), + dtype=clear_padded_fiber_skel.dtype) + + # PCA-based approach + def calc_orientation_pca(patch): + lbls = ndi.label(patch, structure=ndi.generate_binary_structure(2, 2))[0] + + rgns = measure.regionprops(lbls) + dists = [(r.label, distance.euclidean((pcy, pcx), (r.centroid[0], r.centroid[1]))) for r in rgns] + dists = sorted(dists, key=lambda x: x[1]) + orintations = [r.orientation for r in rgns if r.label == dists[0][0]] + centroids = [r.centroid for r in rgns if r.label == dists[0][0]] + + orientation = orintations[0] + y0, x0 = centroids[0] + + return orientation, y0, x0 + + # Tensor-based approach + def calc_orientation_tensor(patch): + Axx, Axy, Ayy = feature.structure_tensor(patch, sigma=0.1) + tensor_vals = np.array([[np.mean(Axx), np.mean(Axy)], [np.mean(Axy), np.mean(Ayy)]]) + + w, v = np.linalg.eig(tensor_vals) + orientation = math.atan2(*v[:,np.argmax(w)]) + y0, x0 = patch.shape[0]/2, patch.shape[1]/2 + + return orientation, y0, x0 + + for i, (yy, xx) in enumerate(zip(ycords, xcords)): + patch_skel = padded_fiber_skel[(yy-window_radius):(yy+window_radius+1), + (xx-window_radius):(xx+window_radius+1)] + + patch_mask = padded_fiber_mask[(yy-window_radius):(yy+window_radius+1), + (xx-window_radius):(xx+window_radius+1)] + + if orient_type == 'tensor': + orientation, y0, x0 = calc_orientation_tensor(patch_skel) + elif orient_type == 'pca': + orientation, y0, x0 = calc_orientation_pca(patch_skel) + + final_orientation = orientation if orientation >= 0.0 else (np.pi - np.abs(orientation)) + orientation_vals.append(final_orientation) + output_orientation_map[yy, xx] = final_orientation + + if clear_padded_fiber_skel[yy, xx] != 0: + gy, gx = yy - window_radius + y0, xx - window_radius + x0 + + fiber_tpatch = padded_fiber_mask[yy-diameter_window_radius:yy+diameter_window_radius+1, + xx-diameter_window_radius:xx+diameter_window_radius+1] + + final_thickness = scan_fiber_thickness(orientation, patch_mask) + output_diameter_map[yy, xx] = final_thickness + diameter_vals.append(final_thickness) + + output_orientation_map = output_orientation_map[paddding:-paddding, paddding:-paddding] + output_diameter_map = output_diameter_map[paddding:-paddding, paddding:-paddding] + clear_fiber_skel = clear_padded_fiber_skel[paddding:-paddding, paddding:-paddding] + + return (clear_fiber_skel, fiber_skel, + output_orientation_map, output_diameter_map, + orientation_vals, diameter_vals) + +def estimate_fourier_orientation(data, grid_shape=(2,2), sigma=2., zoom=1., order=3): + """Computes orientation at every block of the subdivided image. + + Subdivides the image into the grid of blocks `grid_shape`, it computes + 2D FFT for every block, then the real components are segmented by + the Otsu thresholding and the PCA-based approach calculates the orientation + of structures within each block. + + Parameters + ---------- + data : ndarray + Indicates the grayscale 2D image. + + grid_shape : tuple + Indicates the number of blocks to subdivide the image. + + sigma : float + Indicates the sigma value of the Gaussian filter to smooth the real part + of 2D Fourier spectrum. + + zoom : float + Indicates the upscaling factor of each block before applying 2D FFT. + + order : str + Indicates the order of interpolation used in the upscaling procedure. + + Returns + ------- + (orient_blocks, block_shape) : tuple of arrays + The 2D array of the orientation angle within each block, + the shape of a block. + """ + block_shape = tuple([int(math.floor(d/float(gs))) for d, gs in + zip(data.shape, grid_shape)]) + data_blocks = view_as_blocks(data, block_shape=block_shape) + orient_blocks = np.zeros(grid_shape, dtype=np.float32) + + for i in xrange(grid_shape[0]): + for j in xrange(grid_shape[1]): + y0, x0 = block_shape[0] * j + block_shape[0]/2, \ + block_shape[1] * i + block_shape[1]/2 + dblock = data_blocks[i, j] + + if zoom != 1.: + dblock = ndi.interpolation.zoom(dblock, zoom, order=order) + + dblock_freq = np.abs(np.fft.fftshift(np.fft.fft2(dblock)).real) + dblock_freq = ndi.gaussian_filter(dblock_freq, sigma=sigma) + dblock_mask = (dblock_freq > filters.threshold_otsu(dblock_freq)).astype(np.uint8) + + lbls = ndi.label(dblock_mask, + structure=ndi.generate_binary_structure(2,2))[0] + rgns = measure.regionprops(lbls) + + dists = [(r.label, r.area) for r in rgns] + dists = sorted(dists, key=lambda x: x[1]) + + orient_blocks[i, j] = [r.orientation for r in rgns + if r.label == dists[0][0]][0] + + return (orient_blocks, block_shape) + + +def numpy3d_to_array(np_array, allow_surface_bind=True): + """Converts 3D numpy array to 3D device array. + """ + # numpy3d_to_array + # taken from pycuda mailing list (striped for C ordering only) + + d, h, w = np_array.shape + + descr = cuda.ArrayDescriptor3D() + descr.width = w + descr.height = h + descr.depth = d + descr.format = cuda.dtype_to_array_format(np_array.dtype) + descr.num_channels = 1 + descr.flags = 0 + + if allow_surface_bind: + descr.flags = cuda.array3d_flags.SURFACE_LDST + + device_array = cuda.Array(descr) + + copy = cuda.Memcpy3D() + copy.set_src_host(np_array) + copy.set_dst_array(device_array) + copy.width_in_bytes = copy.src_pitch = np_array.strides[1] + copy.src_height = copy.height = h + copy.depth = d + + copy() + + return device_array + + +def _filter_coords(data, ws, th_nval=0.8): + """Filters coordinates of 3D local windows by number of non-zero values speficied by `th_nval`. + """ + ws2 = ws / 2 + + def check_point(data, pt, ws2, th): + size = max(data.shape) + lim0 = pt - ws2 + lim1 = pt + ws2 + + if any(np.array(lim0) < 0) or any(np.array(lim1) > (size - 1)): + return False + + z0, y0, x0 = lim0 + z1, y1, x1 = lim1 + + vol = data[z0:z1, y0:y1, x0:x1] + nz_vals = np.count_nonzero(vol) + + if np.count_nonzero(vol) < ws2*2.*th: + return False + + return True + + filtered_coords = [pt for pt in np.transpose(np.nonzero(data)) if check_point(data, pt, ws2, th_nval)] + + return np.transpose(filtered_coords) + + +def extract_patch(data, pt, ws2): + """Extract a path from `data` of radius `ws2`. + + Parameters + ---------- + data : 3D array + Indicates the 3D data array. + + pt : array or tuple + Indicates the coordinates of a point within `data`. + + ws2 : integer + Indicates a half of the window size of the patch to be extracted. + + Returns + ------- + patch : 3D array + The 3D patch extracted from the local window around the point `pt` in the `data` + with radius of `ws2`. + """ + lim0 = pt - ws2 + lim1 = pt + ws2 + + if any(np.array(lim0) < 0) or any(np.array(lim1) > (data.shape[0] - 1)): + return None + + z0, y0, x0 = lim0 + z1, y1, x1 = lim1 + patch = data[z0:z1, y0:y1, x0:x1] + return patch + + +def orientation_3d_tensor_vigra(data, sigma=0.1): + """Computes 3D orientation from a 3D structure tensor of `data`. + + Parameters + ---------- + data : ndarray + Indicates the N-dimensional array. + + sigma : float + Indicates the sigma value of the Gaussian filter. + + Returns + ------- + (lat, azth) : tuple of floats + The latitude / elevation and azimuth component of 3D orientation of structures within + the patch `data`. + """ + img = vigra.filters.structureTensor(data, 1, 1, sigma_d=sigma) + Axx = img[:, :, :, 0] + Axy = img[:, :, :, 1] + Axz = img[:, :, :, 2] + Ayy = img[:, :, :, 3] + Ayz = img[:, :, :, 4] + Azz = img[:, :, :, 5] + + tensor_vals = np.array([[np.mean(Azz), np.mean(Ayz), np.mean(Axz)], + [np.mean(Ayz), np.mean(Ayy), np.mean(Axy)], + [np.mean(Axz), np.mean(Axy), np.mean(Axx)]]) + + tensor_vals = tensor_vals[::-1, ::-1] + + eps = 1e-8 + w, v = np.linalg.eig(tensor_vals) + + mv = v[:, np.argmin(w)] # z, y, x + mv[np.abs(mv) < eps] = 0 + + G = np.sqrt(mv[2]**2 + mv[1]**2) + lat = np.arcsin(np.around(G, decimals=3)) + azth = np.arctan(mv[1] / mv[2]) if mv[2] else np.pi/2. + + return (lat, azth) + + +def estimate_tensor(name, skel, data, window_size, output_dir, sigma=0.025, make_output=True): + """Computes 3D orientation at every point of a skeleton of data. + + Estimates 3D orientation at every point of the skeleton `skel` extracted from the binary + data `data` within a 3D local window of size `window_size` using the tensor-based approach + with a Gaussian smoothing of `sigma`. + + Parameters + ---------- + name : str + Indicates the name of the output npy file. + + skel : 3D array + Indicates the skeleton of the binary data. + + data : 3D array + Indicates the 3D binary data. + + window_size : integer + Indicates the size of the 3D local window. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + output_fmt : str + Indicates the format of + + sigma : float + Indicates the sigma value of the Gaussian filter. + + make_output : boolean + Specifies if the estimated data should be stored. + + Returns + ------- + output_props : dict + The dictionary of properties specifying the sample name, the algorithm name, the number + of processes, and the execution time. + """ + output_props = dict() + + Z, Y, X = skel.nonzero() + tens_lat_arr = np.zeros_like(skel, dtype=np.float32) + tens_azth_arr = np.zeros_like(skel, dtype=np.float32) + skel_est = np.zeros_like(skel, dtype=np.int32) + + ws = np.uint32(window_size) + ws2 = ws/2 + skel_shape = skel.shape + + output_props['sample_name'] = name + output_props['type'] = 'tensor' + output_props['n_processes'] = 1 + + ts = time.time() + + for idx, pt in enumerate(zip(Z, Y, X)): + lim0 = pt - ws2 + lim1 = pt + ws2 + + if any(np.array(lim0) < 0) or any(np.array(lim1) > (skel_shape[0] - 1)): + skel_est[pt] = -1 + continue + + z0, y0, x0 = lim0 + z1, y1, x1 = lim1 + + area = data[z0:z1, y0:y1, x0:x1] + + lat, azth = orientation_3d_tensor_vigra(area, sigma) + + tens_lat_arr[pt] = lat + tens_azth_arr[pt] = azth + skel_est[pt] = 255 + + te = time.time() + output_props['time'] = te-ts + + print("Tensor time: {}s" % (output_props['time'])) + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + if make_output: + opath = os.path.join(output_dir, '{}.npy').format(name) + output_props['output_path'] = opath + output = {'lat': tens_lat_arr, 'azth': tens_azth_arr, 'skeleton': skel_est, 'indices': skel_est > 0} + output['props'] = output_props + np.save(opath, output) + + return output_props + + +def execute_tensor(patch, sigma): + """Executes the tensor-based approach for a `patch` and `sigma` for Gaussian smoothing. + + Parameters + ---------- + patch : 3D aray + Indicates the data patch of some size. + + sigma : float + Indicates the sigma value of the Gaussian filter. + + Returns + ------- + (lat, azth, 255) : tuple + The latitude / elevation and azimuth components of 3D orientation with valid value + for the point at the skeleton. If the patch is None, then return (0, 0, -1) where + -1 indicates that this point of the skeleton is invalid. + """ + if patch is None: + return (0, 0, -1) + + lat, azth = orientation_3d_tensor_vigra(patch, sigma) + + return (lat, azth, 255) + + +def unpack_execute_tensor(args): + """Unpack input arguments and return result of `execute_tensor` function + """ + return execute_tensor(*args) + + +def estimate_tensor_parallel(name, skel, data, window_size, output_dir, sigma=0.025, + make_output=True, n_processes=4): + """Computes 3D orientation at every point of a skeleton of data in parallel processes. + + Estimates 3D orientation at every point of the skeleton `skel` extracted from the binary + data `data` within a 3D local window of size `window_size` using the tensor-based approach + with a Gaussian smoothing of `sigma`. The orientation is estimated simultaneously at + `n_processes` parallel processes. + + Parameters + ---------- + name : str + Indicates the name of the output npy file. + + skel : 3D array + Indicates the skeleton of the binary data. + + data : 3D array + Indicates the 3D binary data. + + window_size : integer + Indicates the size of the 3D local window. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + sigma : float + Indicates the sigma value of the Gaussian filter. + + make_output : boolean + Specifies if the estimated data should be stored. + + n_processes : integer + Indicates the number of the parallel processes. + + Returns + ------- + output_props : dict + The dictionary of properties specifying the sample name, the algorithm name, the number + of processes, and the execution time. + """ + output_props = dict() + + Z, Y, X = skel.nonzero() + tens_lat_arr = np.zeros_like(skel, dtype=np.float32) + tens_azth_arr = np.zeros_like(skel, dtype=np.float32) + skel_est = np.zeros_like(skel, dtype=np.int32) + + ws = np.uint32(window_size) + ws2 = ws/2 + + output_props['sample_name'] = name + output_props['type'] = 'tensor_parallel' + output_props['n_processes'] = n_processes + + ts = time.time() + + pts = zip(Z, Y, X) + data_patches = [extract_patch(data, pt, ws2) for pt in pts] + print(len(data_patches)) + args = zip(data_patches, itertools.repeat(sigma)) + + proc_pool = Pool(processes=n_processes) + results = np.array(proc_pool.map(unpack_execute_tensor, args)) + proc_pool.close() + proc_pool.join() + proc_pool.terminate() + + te = time.time() + output_props['time'] = te-ts + + lat_arr, azth_arr, skel_arr = results.T + tens_lat_arr[Z, Y, X] = lat_arr + tens_azth_arr[Z, Y, X] = azth_arr + skel_est[Z, Y, X] = skel_arr + + print("Tensor parallel time: %fs" % (output_props['time'])) + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + if make_output: + opath = os.path.join(output_dir, '{}.npy').format(name) + output_props['output_path'] = opath + output = {'lat': tens_lat_arr, 'azth': tens_azth_arr, 'skeleton': skel_est, 'indices': skel_est > 0} + output['props'] = output_props + np.save(opath, output) + + return output_props + + +def _diameter_kernel(): + """Returns the CUDA kernel to estimate 3D diameter of structures in a 3D local window. + """ + diameter_kernel_src = """ + + texture tex_data; + + __global__ void diameter3d (unsigned int width, + unsigned int height, + unsigned int depth, + const int n_points, + const float norm_factor, + const int max_iters, + const int n_scan_angles, + const int *X, + const int *Y, + const int *Z, + const float *scan_angl_arr, + const float *azth_data, + const float *lat_data, + float *radius_arr) + { + unsigned long blockId, idx; + blockId = blockIdx.x + blockIdx.y * gridDim.x; + idx = blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x; + + if (idx > n_points) { + return; + } + + float _x, _y, _z; + _x = (float)X[idx] + 0.5; + _y = (float)Y[idx] + 0.5; + _z = (float)Z[idx] + 0.5; + + // ----------------------------------------- + // Find the diameter + + float azth = azth_data[idx]; + azth += M_PI_2; + float lat = lat_data[idx]; + + float cy = cosf(lat), sy = sinf(lat); + float cz = cosf(azth), sz = sinf(azth); + //float cz = -sinf(azth), sz = cosf(azth); // taking into account azth + pi/2 + + //vector along fiber + //float dx = -sy * sz; + //float dy = cy* sz; + //float dz = cy; + float uvec[3] = {0.0, 0.0, 1.0}; + float fiber_vector_x[3] = {0.0, + uvec[2]*cy + uvec[3]*sy, + -uvec[2]*sy + uvec[3]*cy}; + + float fiber_vector_z[3] = {fiber_vector_x[0]*cz - fiber_vector_x[1]*sz, + fiber_vector_x[0]*sz + fiber_vector_x[1]*cz, + fiber_vector_x[2]}; + + float dx = fiber_vector_z[0], dy = fiber_vector_z[1], dz = fiber_vector_z[2]; + + //scan vector perpendicular to a fiber vector (rotation X -> Z) + float scan_vec[3] = {0, 1, 0}; // unit vector perpendicular to default (0,0,1) direction + float rot_scan_vec_x[3] = {0.0, + scan_vec[1]*cy + scan_vec[2]*sy, + -scan_vec[1]*sy + scan_vec[2]*cy}; + + + float rot_scan_vec_z[3] = {rot_scan_vec_x[0]*cz - rot_scan_vec_x[1]*sz, + rot_scan_vec_x[0]*sz + rot_scan_vec_x[1]*cz, + rot_scan_vec_x[2]}; + + float out_radius = 0; + + for (int scan_angl_idx = 0; scan_angl_idx < n_scan_angles; scan_angl_idx++) { + float theta = scan_angl_arr[scan_angl_idx]; + + float ct = cosf(theta), st = sinf(theta); + float x = rot_scan_vec_z[0], y = rot_scan_vec_z[1], z = rot_scan_vec_z[2]; + float u = dx, v = dy, w = dz; + + //rotation of point (x,y,z) around axis (u,v,w) + float scan_vec_coords[3] = + {u*(u*x + v*y + w*z)*(1.0f - ct) + x*ct + (-w*y + v*z)*st, + v*(u*x + v*y + w*z)*(1.0f - ct) + y*ct + (w*x - u*z)*st, + w*(u*x + v*y + w*z)*(1.0f - ct) + z*ct + (-v*x + u*y)*st}; + + float nc[3] = {_x, _y, _z}; + float p[3]; + + for (int i = 0; i < max_iters; i++) { + nc[0] += scan_vec_coords[0]; + nc[1] += scan_vec_coords[1]; + nc[2] += scan_vec_coords[2]; + + if (tex3D(tex_data, nc[0], nc[1], nc[2]) == 0) { + p[0] = nc[0]; + p[1] = nc[1]; + p[2] = nc[2]; + break; + } + } + + out_radius += norm3df(p[0] - _x, p[1] - _y, p[2] - _z); + } + + radius_arr[idx] = out_radius * norm_factor; + } + """ + + dm_program = SourceModule(diameter_kernel_src) + diameter3d = dm_program.get_function("diameter3d") + + return dm_program, diameter3d + + +def estimate_diameter_gpu(skel, data, lat_data, azth_data, n_scan_angles, max_iters=150, do_reshape=True): + """Computes 3D diameter at every point of a skeleton of data using GPU. + + Estimates 3D diameter at every point of the skeleton `skel` extracted from the binary + data `data` with help of orientation information provided by `lat_data` and `azth_data` + arrays. The diameter is evaluated with a ray casting approach `cast_ray` adapted for + a 3D case. + + Parameters + ---------- + skel : 3D array + Indicates the skeleton of the binary data. + + data : 3D array + Indicates the 3D binary data. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + n_scan_angles : int + Indicates the number of scanning angles on a range [0, 360] degrees. + + max_iters : int + Indicates the maximum length of a ray in each direction. + + do_reshape : boolean + Specifies if the output array should be reshaped immediately after estimation. + + Returns + ------- + out : dict + The dictionary of the 3D array of estimated diameter and the execution time. + """ + if not cuda_available: + print('The pycuda package is not found. The diameter estimation cannot be done.') + return None + + program, diameter3d = _diameter_kernel() + + Z, Y, X = np.int32(skel.nonzero()) + depth, height, width = np.uint32(skel.shape) + + scan_angl_arr = np.deg2rad(np.float32(np.linspace(0, 360, num=n_scan_angles, endpoint=False))) + radius_arr = np.zeros_like(Z, dtype=np.float32) + + lat_data_1d = lat_data[skel.nonzero()] + azth_data_1d = azth_data[skel.nonzero()] + + gpu_X = gpuarray.to_gpu(X) + gpu_Y = gpuarray.to_gpu(Y) + gpu_Z = gpuarray.to_gpu(Z) + + gpu_lat_data_1d = gpuarray.to_gpu(lat_data_1d) + gpu_azth_data_1d = gpuarray.to_gpu(azth_data_1d) + + gpu_radius_arr = gpuarray.to_gpu(radius_arr) + gpu_scan_angl_arr = gpuarray.to_gpu(scan_angl_arr) + + gpu_rad_tex = program.get_texref('tex_data') + gpu_data = numpy3d_to_array(data) + gpu_rad_tex.set_array(gpu_data) + + n_scan_angles = np.uint32(n_scan_angles) + n_points = np.uint32(len(Z)) + max_iters = np.uint32(max_iters) + norm_factor = np.float32(1. / n_scan_angles) + + block = (16, 16, 1) + n_blocks = np.ceil(float(n_points)/(block[0] * block[1])) + g_cols = 2 + g_rows = np.int(np.ceil(n_blocks / g_cols)) + grid = (g_rows, g_cols, 1) + + start = cuda.Event() + end = cuda.Event() + + start.record() # start timing + diameter3d(width, height, depth, + n_points, + norm_factor, + max_iters, + n_scan_angles, + gpu_X, + gpu_Y, + gpu_Z, + gpu_scan_angl_arr, + gpu_azth_data_1d, + gpu_lat_data_1d, + gpu_radius_arr, + block=block, + grid=grid) + end.record() # end timing + end.synchronize() + + dm_time = start.time_till(end)*1e-3 + print("Diameter estimation time: %fs" % (dm_time)) + + radius_arr = gpu_radius_arr.get() + + if do_reshape: + radius_arr = np.reshape(radius_arr, data.shape, order='C') + + out = {'diameter': radius_arr * 2., 'time': dm_time} + return out + + +def estimate_diameter_single_run(name, output_dir, data, skel, lat_data, azth_data, + n_scan_angles=32, make_output=True): + """Computes 3D diameter using GPU and stores result in a npy file. + + Parameters + ---------- + name : str + Indicates the name of the output npy file. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + data : 3D array + Indicates the 3D binary data. + + skel : 3D array + Indicates the skeleton of the binary data. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + n_scan_angles : int + Indicates the number of scanning angles on a range [0, 360] degrees. + + make_output : boolean + Specifies if the estimated data should be stored. + + Returns + ------- + output_props : dict + The dictionary of properties specifying the sample name, the algorithm name, + the number of processes, and the execution time. + """ + if not cuda_available: + print('The pycuda package is not found. The diameter estimation cannot be done.') + return None + + output = dict() + output_props = dict() + + output['diameter'] = np.zeros_like(data, dtype=np.float32) + + output_props['time'] = 0. + output_props['sample_name'] = name + output_props['type'] = 'diameter_gpu' + output_props['n_processes'] = 1 + + result = estimate_diameter_gpu(skel, data, lat_data, azth_data, n_scan_angles, do_reshape=False) + + output_props['time'] += result['time'] + output['diameter'][skel.nonzero()] = result['diameter'] + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + print('Total diameter execution time: {}'%format(output_props['time'])) + + if make_output: + opath = os.path.join(output_dir, '{}.npy').format(name) + output_props['output_path'] = opath + output['indices'] = skel > 0 + output['props'] = output_props + np.save(opath, output) + + return output_props + + +def estimate_diameter_batches(name, output_dir, data, skel, lat_data, azth_data, border_gap, + n_scan_angles=32, out_arr_names=['diameter'], make_output=True, + slices_per_batch=100): + """Computes 3D diameter using GPU in batches and stores result in a npy file. + + Parameters + ---------- + name : str + Indicates the name of the output npy file. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + data : 3D array + Indicates the 3D binary data. + + skel : 3D array + Indicates the skeleton of the binary data. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + border_gap : integer + Indicates the number of overlapping slices along z-axis, usually it should be more or + equal to a half of size of the 3D local window. + + n_scan_angles : int + Indicates the number of scanning angles on a range [0, 360] degrees. + + out_arr_names : array of str + Indicates the array of keys of the output dictionary. + + make_output : boolean + Specifies if the estimated data should be stored. + + slices_per_batch : integer + The number of slices along z-axis in a batch. + + Returns + ------- + output_props : dict + The dictionary of properties specifying the sample name, the algorithm name, + the number of processes, and the execution time. + """ + if not cuda_available: + print('The pycuda package is not found. The diameter estimation cannot be done.') + return None + + output = dict() + output_props = dict() + + for arr_name in out_arr_names: + output[arr_name] = np.zeros_like(data, dtype=np.float32) + + output_props['time'] = 0. + output_props['sample_name'] = name + output_props['type'] = 'diameter_gpu' + output_props['n_processes'] = 1 + + depth, height, width = data.shape + batches_idxs = np.array_split(np.arange(depth), np.ceil(depth / float(slices_per_batch))) + print(batches_idxs) + + for batch_idxs in batches_idxs: + batch_len = len(batch_idxs) + + if batch_idxs[0] == 0: + arr1, arr2 = None, np.arange(batch_idxs[-1]+1, batch_idxs[-1]+border_gap+1) + gaped_batch_idxs = np.concatenate((batch_idxs, arr2)) + elif batch_idxs[-1] == (depth - 1): + arr1, arr2 = np.arange(batch_idxs[0]-border_gap, batch_idxs[0]), None + gaped_batch_idxs = np.concatenate((arr1, batch_idxs)) + else: + arr1, arr2 = np.arange(batch_idxs[0]-border_gap, batch_idxs[0]), \ + np.arange(batch_idxs[-1], batch_idxs[-1]+border_gap+1) + gaped_batch_idxs = np.concatenate((arr1, batch_idxs, arr2)) + + batched_data = data[gaped_batch_idxs] + batched_skel = skel[gaped_batch_idxs] + batched_lat_data = lat_data[gaped_batch_idxs] + batched_azth_data = azth_data[gaped_batch_idxs] + + gaped_out_dict = estimate_diameter_gpu(batched_skel, + batched_data, + batched_lat_data, + batched_azth_data, + n_scan_angles, + do_reshape=False) + + output_props['time'] += gaped_out_dict['time'] + + for data_name in out_arr_names: + gaped_arr_values = gaped_out_dict[data_name] + gaped_arr = np.zeros_like(batched_data, dtype=np.float32) + gaped_arr[batched_skel.nonzero()] = gaped_arr_values + + if batch_idxs[0] == 0: + output[data_name][batch_idxs] = gaped_arr[:batch_len] + elif batch_idxs[-1] == (depth - 1): + output[data_name][batch_idxs] = gaped_arr[border_gap:] + else: + output[data_name][batch_idxs] = gaped_arr[border_gap:border_gap+batch_len] + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + print('Total diameter execution time: {}'%(output_props['time'])) + + if make_output: + opath = os.path.join(output_dir, '{}.npy').format(name) + output_props['output_path'] = opath + output['indices'] = output['diameter'] > 0 + output['props'] = output_props + np.save(opath, output) + + return output_props + + +def _calc_sphericity(volume, perimeter): + """Computes sphericity from volume and perimeter of an object. + """ + r = ((3.0 * volume) / (4.0 * np.pi)) ** (1.0/3.0) + return (4.0 * np.pi * (r*r)) / perimeter + + +def object_counter(stack_binary_data): + """Label and counts particles in a binary data. + + Parameters + ---------- + stack_binary_data : 3D array + Indicates the 3D binary data. + + Returns + ------- + (objects_stats, labeled_stack) : tuple + The tuple of a DataFrame object of counted partilces and the labeled 3D data. + """ + measurements_vals = _MEASUREMENTS.values() + + print('Object counting - Labeling...') + labeled_stack, num_labels = ndi.measurements.label(stack_binary_data) + objects_stats = pd.DataFrame(columns=measurements_vals) + + print('Object counting - Stats gathering...') + for slice_idx in np.arange(labeled_stack.shape[0]): + for region in measure.regionprops(labeled_stack[slice_idx]): + objects_stats = objects_stats.append({mname: region[mval] + for mname, mval in _MEASUREMENTS.items()}, + ignore_index=True) + + print('Object counting - Stats grouping...') + objects_stats = objects_stats.groupby('Label', as_index=False).sum() + objects_stats['Sphericity'] = _calc_sphericity(objects_stats['Area'], + objects_stats['Perimeter']) + + return (objects_stats, labeled_stack) + + +def calc_porosity(data): + """Computes porosity. + + Parameters + ---------- + data : 3D array + Indicates the labeled 3D data. + + Returns + ------- + out : dict + The dictionary of materials and corresponding porosity values. + """ + total_volume = data.size + mats = np.unique(data) + out = {'Material {}'.format(m): \ + 1. - (float(data[data == m].size) / total_volume) \ + for m in mats[mats > 0]} + return out diff --git a/quanfima/quanfima/simulation.py b/quanfima/quanfima/simulation.py new file mode 100644 index 0000000..1386896 --- /dev/null +++ b/quanfima/quanfima/simulation.py @@ -0,0 +1,676 @@ +from __future__ import print_function +import time +import os +import itertools +import numpy as np +from multiprocessing import Pool +from scipy import ndimage as ndi +from sklearn import metrics +from skimage import filters, morphology, data as skidata, exposure, draw + + +def random_in(rng, number=1): + """Returns a random value within a given range. + """ + start, end = rng + values = np.random.random_sample(number) * (end - start) + start + return values[0] if number == 1 else values + + +def mkfiber(dims_size, length, radius, azth, lat, offset_xyz): + """Computes fiber coordinates and its length. + + Computes a fiber of speficied `length`, `radius`, oriented under azimuth `azth` and + latitude / elevation `lat` angles shifted to `offset_xyz` from the center of a volume of + size `dims_size`. + + Parameters + ---------- + dims_size : tuple + Indicates the size of the volume. + + length : integer + Indicates the length of the simulated fiber. + + radius : integer + Indicates the radius of the simulated fiber. + + azth : float + Indicates the azimuth component of the orientation angles of the fiber in radians. + + lat : float + Indicates the latitude / elevation component of the orientation angles of the fiber + in radians. + + offset_xyz : tuple + Indicates the offset from the center of the volume where the fiber will be generated. + + Returns + ------- + fiber_pts, fiber_len : tuple of array and number + The array of fiber coordinates and the length. + """ + dims_size = np.array(dims_size) + + half_pi = np.pi / 2. + mx = np.array([[1., 0., 0], + [0., np.cos(lat), np.sin(lat)], + [0., -np.sin(lat), np.cos(lat)]]) + + azth += half_pi + mz = np.array([[np.cos(azth), -np.sin(azth), 0], + [np.sin(azth), np.cos(azth), 0], + [0., 0., 1.]]) + + # Directional vector + dl = 1 + dir_vec = np.array([0, 0, 1]) + dir_vec = np.dot(mx, dir_vec) + dir_vec = np.dot(mz, dir_vec) + dx, dy, dz = dir_vec[0], dir_vec[1], dir_vec[2] + + # Compute length + n_steps = np.round(length / dl) + half_steps = int(np.ceil(n_steps / 2.)) + steps = range(half_steps - int(n_steps), half_steps) + + # Draw circle perpedicular to the directional vector + X, Y = draw.circle(0, 0, radius) + Z = np.repeat(0, len(Y)) + circle_pts = np.array([X, Y, Z]) + circle_pts = np.dot(mx, circle_pts) + circle_pts = np.dot(mz, circle_pts) + + # Propogate the circle profile along the directional vector + slice_pts = np.array(zip(*circle_pts)) + dxyz = np.array([dx, dy, dz]) + step_shifts = np.array([step * dxyz for step in steps]) # [(dx,dy,dz), ...] for each step + center_shift = dims_size * 0.5 + offset_xyz # (x, y ,z) + slices_pts = np.round(np.array([slice_pts + step_shift + center_shift + for step_shift in step_shifts])) + + # Filter all the points which are outside the boundary + pt_filter = lambda pt: np.all(np.greater_equal(pt, (0, 0, 0))) and \ + np.all(np.less(np.array(pt), dims_size)) + n_slices = 0 + fiber_pts = None + for pts in slices_pts: + slice_pts_mask = [pt_filter(pt) for pt in pts] + slice_pts = pts[slice_pts_mask].astype(np.int32) + if len(slice_pts) > 0: + n_slices += 1 + fiber_pts = slice_pts if fiber_pts is None else \ + np.concatenate((fiber_pts, slice_pts)) + # number of slices, e.g. steps. + fiber_len = np.round(n_slices * dl).astype(np.int32) + fiber_pts = fiber_pts.astype(np.int32) + return fiber_pts, fiber_len + + +def simulate_fibers(volume_shape, n_fibers=1, radius_lim=(4, 10), length_lim=(0.2, 0.8), + lat_lim=(0, np.pi), azth_lim=(0, np.pi), gap_lim=(3, 10), + max_fails=10, max_len_loss=0.5, intersect=False): + """Simulates fibers in a volume. + + Simulates `n_fibers` of the radii and lengths in ranges `radius_lim` and `length_lim`, + oriented in a range of azimuth `azth_lim` and latitude \ elevation 'lat_lim' angles, + separated with a gap in a range of `gap_lim`. The simulation process stops if the number + of attempts to generate a fiber exceeds `max_fails`. + + Parameters + ---------- + volume_shape : tuple + Indicates the size of the volume. + + n_fibers : integer + Indicates the number of fibers to be generated. + + radius_lim : tuple + Indicates the range of radii for fibers to be generated. + + length_lim : tuple + Indicates the range of lengths for fibers to be generated. + + lat_lim : tuple + Indicates the range of latitude / elevation component of the orientation angles of + the fibers to be generated. + + azth_lim : tuple + Indicates the range of azimuth component of the orientation angles of the fibers to + be generated. + + gap_lim : tuple + Indicates the range of gaps separating the fibers from each other. + + max_fails : integer + Indicates the maximum number of failures during the simulation process. + + max_len_loss : float + Indicates the maximum fraction of the generated fiber placed out of volume, exceeding + which the fiber is counted as failed. + + intersect : boolean + Specifies if generated fibers can intersect. + + Returns + ------- + (volume, lat_ref, azth_ref, diameter, n_generated, elapsed_time) : tuple of arrays and numbers + The binary volume of generated fibers, the volumes of latitude / elevation and + azimuth angles at every fiber point, the volume of diameters at every fibers point, + the number of generated fibers and the simulation time. + """ + ts = time.time() + + volume = np.zeros(volume_shape, dtype=np.int32) + lat_ref = np.zeros_like(volume, dtype=np.float32) + azth_ref = np.zeros_like(volume, dtype=np.float32) + diameter = np.zeros_like(volume, dtype=np.float32) + + dims = np.array(volume.shape)[::-1] + offset_lim = zip(-dims / 2, dims / 2) + + n_generated = 0 + n_fails = 0 + while n_generated < n_fibers and n_fails < max_fails: + length = min(volume_shape) + length = np.floor(length * random_in(length_lim, number=1)).astype(np.int32) + + offset = [random_in(olim, number=1) for olim in offset_lim] + offset = np.round(offset).astype(np.int32) + + azth = random_in(azth_lim, number=1) + lat = random_in(lat_lim, number=1) + radius = random_in(radius_lim, number=1) + + gap = random_in(gap_lim, number=1) + gap = np.round(gap).astype(np.int32) + + fiber_pts, fiber_len = mkfiber(dims, length, radius, azth, lat, offset) + gap_fiber_pts, gap_fiber_len = mkfiber(dims, length, radius + gap, azth, lat, offset) + + # Length loss + if (1. - float(gap_fiber_len) / length) > max_len_loss: + n_fails = n_fails + 1 + continue + + # Intersection + if gap_fiber_pts.size: + X_gap, Y_gap, Z_gap = gap_fiber_pts[:, 0], gap_fiber_pts[:, 1], gap_fiber_pts[:, 2] + X, Y, Z = fiber_pts[:, 0], fiber_pts[:, 1], fiber_pts[:, 2] + + if not intersect: + if np.any(volume[Z_gap, Y_gap, X_gap]): + n_fails = n_fails + 1 + + if n_fails == max_fails: + print("The number of fails exceeded. Generated {} fibers".\ + format(n_generated)) + + continue + + # Fill the volume + volume[Z, Y, X] = 1 + lat_ref[Z, Y, X] = lat + azth_ref[Z, Y, X] = azth + diameter[Z, Y, X] = radius * 2 + n_generated = n_generated + 1 + n_fails = 0 + + te = time.time() + elapsed_time = te - ts + + return (volume, lat_ref, azth_ref, diameter, n_generated, elapsed_time) + + +def generate_datasets(volume_size=(512, 512, 512), n_fibers=50, radius_lim=(4, 10), + length_lim=(0.2, 0.8), gap_lim=(3, 10), max_fails=100, + median_rad=3, intersect=False, output_dir=None, params=None): + """Simulates speficied configurations of fibers and stores in a npy file. + + Simulates a number of fiber configurations speficied in `params` with `n_fibers` of the + radii and lengths in ranges `radius_lim` and `length_lim`, separated with gaps in a range + of `gap_lim`. The simulation process stops if the number of attempts to generate a fiber + exceeds `max_fails`. + + Parameters + ---------- + volume_size : tuple + Indicates the size of the volume. + + n_fibers : integer + Indicates the number of fibers to be generated. + + radius_lim : tuple + Indicates the range of radii for fibers to be generated. + + length_lim : tuple + Indicates the range of lengths for fibers to be generated. + + gap_lim : tuple + Indicates the range of gaps separating the fibers from each other. + + max_fails : integer + Indicates the maximum number of failures during the simulation process. + + median_rad : integer + Indicates the radius of median filter to fill holes occured due to rounding of + coordinates of the generated fibers. + + intersect : boolean + Specifies if generated fibers can intersect. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + params : dict + Indicates the configurations of orientation of fibers to be generated. + + Returns + ------- + out : dict + The dictionary of generated datasets of specified configurations. + """ + if params is None: + params = {'aligned': {'lat_rng': (15, 15), 'azth_rng': (27, 27)}, + 'medium': {'lat_rng': (0, 45), 'azth_rng': (-45, 45)}, + 'disordered': {'lat_rng': (0, 90), 'azth_rng': (-89, 90)}} + + out = {} + for name, config in params.items(): + data, lat_data, azth_data, diameter_data, n_gen_fibers, elapsed_time = \ + simulate_fibers(volume_size, + lat_lim=tuple([np.deg2rad(v) for v in config['lat_rng']]), + azth_lim=tuple([np.deg2rad(v) for v in config['azth_rng']]), + radius_lim=radius_lim, + n_fibers=n_fibers, + max_fails=max_fails, + gap_lim=gap_lim, + length_lim=length_lim, + intersect=intersect) + + data_8bit = data.astype(np.uint8) + data_8bit = ndi.binary_fill_holes(data_8bit) + data_8bit = ndi.median_filter(data_8bit, footprint=morphology.ball(median_rad)) + lat_data = ndi.median_filter(lat_data, footprint=morphology.ball(median_rad)) + azth_data = ndi.median_filter(azth_data, footprint=morphology.ball(median_rad)) + diameter_data = ndi.median_filter(diameter_data, footprint=morphology.ball(median_rad)) + + out[name] = {'data': data_8bit, + 'lat': lat_data, + 'azth': azth_data, + 'diameter': diameter_data, + 'skeleton': morphology.skeletonize_3d(data_8bit).astype(np.float32), + 'props': {'n_gen_fibers': n_gen_fibers, + 'time': elapsed_time, + 'intersect': intersect}} + + if output_dir is not None and not os.path.exists(output_dir): + os.makedirs(output_dir) + + np.save(os.path.join(output_dir, 'dataset_fibers_n{}_r{}_{}_g{}_{}_mr{}_i{}.npy'. + format(n_fibers, + radius_lim[0], radius_lim[-1], + gap_lim[0], gap_lim[-1], + median_rad, int(intersect))), out) + + return out + + +def simulate_particles(volume_shape, n_particles=1, radius_lim=(3, 30), max_fails=10): + """Simulates particles in a volume. + + Simulates `n_particles` of the radii in a range `radius_lim`. The simulation process + stops if the number of attempts to generate a particle exceeds `max_fails`. + + Parameters + ---------- + volume_shape : tuple + Indicates the size of the volume. + + n_particles : integer + Indicates the number of particles to be generated. + + radius_lim : tuple + Indicates the range of radii for particles to be generated. + + max_fails : integer + Indicates the maximum number of failures during the simulation process. + + Returns + ------- + (volume, diameter, n_generated, elapsed_time) : tuple of arrays and numbers + The binary volume of generated particles, the volume of diameters at every point of + particles, the number of generated particles and the simulation time. + """ + ts = time.time() + + volume = np.zeros(volume_shape, dtype=np.uint8) + diameter = np.zeros_like(volume, dtype=np.int32) + + dims = np.array(volume.shape) + offset_lim = zip(itertools.repeat(0), dims) + + n_generated = 0 + n_fails = 0 + while n_generated < n_particles and n_fails < max_fails: + if (n_generated % 100 == 0) or (n_generated == n_particles): + print('n_generated = {}/{}, n_fails = {}/{}'.format(n_generated, n_particles, + n_fails, max_fails)) + + offset = [random_in(olim, number=1) for olim in offset_lim] + offset = np.round(offset).astype(np.int32) + + radius = np.round(random_in(radius_lim, number=1)) + + gen_ball = morphology.ball(radius, dtype=np.int32) + Z, Y, X = gen_ball.nonzero() + + Z += offset[0] + Y += offset[1] + X += offset[2] + + if np.max(X) >= dims[0] or np.min(X) < 0 or \ + np.max(Y) >= dims[1] or np.min(Y) < 0 or \ + np.max(Z) >= dims[2] or np.min(Z) < 0: + + n_fails = n_fails + 1 + continue + + if np.any(volume[Z, Y, X]): + n_fails = n_fails + 1 + + if n_fails == max_fails: + print("The number of fails exceeded. Generated {} particles".\ + format(n_generated)) + + continue + + # Fill the volume + volume[Z, Y, X] = 1 + diameter[Z, Y, X] = radius * 2 + n_generated = n_generated + 1 + n_fails = 0 + + te = time.time() + elapsed_time = te - ts + + return (volume, diameter, n_generated, elapsed_time) + + +def generate_particle_dataset(volume_size=(512, 512, 512), n_particles=500, + radius_lim=(4, 10), max_fails=100, output_dir=None): + """Simulates a speficied number of particles and stores complete dataset in a npy file. + + Parameters + ---------- + volume_size : tuple + Indicates the size of the volume. + + n_particles : integer + Indicates the number of particles to be generated. + + radius_lim : tuple + Indicates the range of radii for particles to be generated. + + max_fails : integer + Indicates the maximum number of failures during the simulation process. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + Returns + ------- + out : dict + The dictionary of generated dataset. + """ + out = {} + + data, diameter_data, n_gen_particle, elapsed_time = \ + simulate_particles(volume_size, + n_particles=n_particles, + radius_lim=radius_lim, + max_fails=max_fails) + + out['normal'] = {'data': data, + 'diameter': diameter_data, + 'props': {'n_gen_particles': n_gen_particle, + 'time': elapsed_time}} + + if output_dir is not None and not os.path.exists(output_dir): + os.makedirs(output_dir) + + np.save(os.path.join(output_dir, + 'dataset_particles_n{}_r{}_{}.npy'.format(n_particles, + radius_lim[0], + radius_lim[-1])), out) + return out + + +def generate_blobs(volume_size, blob_size_fraction=0.1, transparency_ratio=0.5, sigma=90.): + """Generates random blobs smoothed with Gaussian filter in a volume. + + Generates several blobs of random size in a volume using function from scikit-image, + which are subsequently smoothed with a Gaussian filter of a large sigma to imitate + 3D uneven illumination of the volume. + + Parameters + ---------- + volume_size : tuple + Indicates the size of the volume. + + blob_size_fraction : float + Indicates the fraction of volume occupied by blobs. + + transparency_ratio : float + Indicates the transparency of blobs in a range [0, 1]. + + sigma : float + Indicates the sigma of Gaussian filter. + + Returns + ------- + blobs_smeared : ndarray + The volume with smoothed blobs of a specified transparency. + """ + blobs = skidata.binary_blobs(length=max(volume_size), + blob_size_fraction=blob_size_fraction, + n_dim=len(volume_size), + seed=1) + blobs = blobs.astype(np.float32) + + blobs_smeared = ndi.gaussian_filter(blobs, sigma) * transparency_ratio + return blobs_smeared + + +def generate_noised_data(datasets_path, noise_levels=[0.0, 0.15, 0.3], + smooth_levels=[0.0, 1.0, 2.0], blobs=None, use_median=True, + median_rad=3, output_dir=None, n_processes=9): + """Contaminates datasets with a speficied additive Gaussian noise and smoothing level. + + Contaminates the datasets (generated with `generate_datasets` function) with the specified + level of additive Gaussian noise and smoothing, uneven illumination can be added if + `blobs` is provided. The contaminating process can be performed in a parallel `n_processes` + processes. + + Parameters + ---------- + datasets_path : str + Indicates the path to dataset. + + noise_levels : array + Indicates the array of standard deviations of noise. + + smooth_levels : array + Indicates the array of sigma values of Gaussian filter. + + blobs : ndarray + Indicates the volume of uneven illumination generated by `generate_blobs`. + + use_median : boolean + Specifies if the median filter should be applied after addition of noise. + + median_rad : integer + Indicates the size of median filter. + + output_dir : str + Indicates the path to the output folder where the data will be stored. + + n_processes : integer + Indicates the number of parallel processes. + + Returns + ------- + results : array of dicts + The array of dictionaries containing paths to contaminated datasets, and other + properties. + """ + datasets_names = np.load(datasets_path).item().keys() + n_datasets = len(datasets_names) + + dataset_filename = os.path.basename(datasets_path) + dataset_filename = os.path.splitext(dataset_filename)[0] + output_dir = os.path.join(output_dir, dataset_filename) + + data_items = [(dname, dpath, blb, odir) + for dname, dpath, blb, odir in zip(datasets_names, + [datasets_path]*n_datasets, + [blobs]*n_datasets, + [output_dir]*n_datasets)] + + params = [data_items, noise_levels, smooth_levels] + params = [p for p in itertools.product(*params)] + + proc_pool = Pool(processes=n_processes) + results = proc_pool.map(unpack_additive_noise, params) + proc_pool.close() + proc_pool.join() + proc_pool.terminate() + + np.save(os.path.join(output_dir, 'params.npy'), results) + + return results + + +def unpack_additive_noise(args): + """Unpack arguments and return result of `additive_noise` function. + """ + return additive_noise(*args) + + +def additive_noise(params, noise_lvl, smooth_lvl, use_median=True, median_rad=3): + """Contaminates datasets with a speficied additive Gaussian noise and smoothing level. + + Contaminates the datasets (generated with `generate_datasets` function) with the specified + level of additive Gaussian noise and smoothing, uneven illumination can be added by + extracting `blobs` from `params` tuple with some other arguments. + + Parameters + ---------- + params : tuple + Contains `name`, `dataset_path`, `blobs`, `output_dir` arguments passed from + `generate_noised_data`. + + noise_level : float + Indicates the standard deviations of noise. + + smooth_level : float + Indicates the sigma value of Gaussian filter. + + use_median : boolean + Specifies if the median filter should be applied after addition of noise. + + median_rad : integer + Indicates the size of median filter. + + Returns + ------- + datasets_props : dict + The dictionary containing the path to the reference dataset, the path to the + contaminated dataset, the generated name, the SNR level, the precision, the recall + and f1-score, and the level of noise and smoothing. + """ + name, dataset_path, blobs, output_dir = params + + datasets = np.load(dataset_path).item() + data = datasets[name]['data'] + data_skel = datasets[name]['skeleton'] + + def median_fltr(data, footprint): + out = np.zeros_like(data, dtype=np.uint8) + + for i in xrange(data.shape[0]): + out[i] = filters.rank.median(data[i], selem=footprint) + + return out + + def threshold_dataset(data): + data_seg = np.zeros_like(data, dtype=np.uint8) + data_8bit = exposure.rescale_intensity(data, in_range='image', + out_range=np.uint8).astype(np.uint8) + + for i in xrange(data_seg.shape[0]): + dslice = data_8bit[i] + th_val = filters.threshold_otsu(dslice) + data_seg[i] = (dslice > th_val).astype(np.uint8) + + return data_seg + + print('{}: Noise: {} | Smooth: {}'.format(name, noise_lvl, smooth_lvl)) + + data_ref = data.astype(np.float32) + data_ref_skel = exposure.rescale_intensity(data_skel, + out_range=(0, 1)).astype(np.uint8) + + data_noised = data_ref + noise_lvl * np.random.randn(*data_ref.shape) + + if (blobs is not None) and (noise_lvl != 0.) and (smooth_lvl != 0.): + data_noised += blobs + + if smooth_lvl != 0: + data_noised = ndi.gaussian_filter(data_noised, smooth_lvl) + + snr = np.mean(data_noised[data_ref != 0]) / np.std(data_noised[data_ref == 0]) + data_noised = exposure.rescale_intensity(data_noised, out_range=np.uint8).astype(np.uint8) + + if use_median and (noise_lvl != 0.) and (smooth_lvl != 0.): + data_noised = median_fltr(data_noised, morphology.disk(median_rad)) + + data_noised_seg = threshold_dataset(data_noised) + data_noised_skel = morphology.skeletonize_3d(data_noised_seg) + + precision, recall, fbeta_score, support = \ + metrics.precision_recall_fscore_support(data_ref_skel.flatten(), + data_noised_skel.flatten(), + beta=1.0, + pos_label=1, + average='binary') + + print('Precision: {}, Recall: {}, F1-score: {}'.format(precision, recall, fbeta_score)) + + data_out = {'data': data_ref, + 'data_noised': data_noised, + 'skeleton': data_ref_skel, + 'skeleton_noised': data_noised_skel, + 'seg_noised': data_noised_seg} + + dataset_outpath = os.path.join(output_dir, + 'dataset_noised_fibers_{}_nl{}_sl{}.npy'. + format(name, noise_lvl, smooth_lvl)) + + datasets_props = {'ref_dataset_path': dataset_path, + 'dataset_path': dataset_outpath, + 'name': name, + 'snr': snr, + 'precision': precision, + 'recall': recall, + 'f1_score': fbeta_score, + 'adgauss_std': noise_lvl, + 'smooth_sigma': smooth_lvl} + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + np.save(dataset_outpath, data_out) + + return datasets_props diff --git a/quanfima/quanfima/utils.py b/quanfima/quanfima/utils.py new file mode 100644 index 0000000..1d21215 --- /dev/null +++ b/quanfima/quanfima/utils.py @@ -0,0 +1,150 @@ +from __future__ import print_function +import os +import operator +import numpy as np +from scipy import ndimage as ndi +from skimage import morphology +from matplotlib import colors +from statsmodels.stats.multicomp import MultiComparison +from statsmodels.stats.libqsturng import psturng + + +def prepare_data(data, dilate_iterations=1, sigma=0.5): + """Returns the given binary data, its skeleton and the thickened skeleton. + + The skeleton of a given 2D or 3D array is computed, then it is thickened + using morphological dilation with `dilate_iterations` and smoothed with + help of Gaussian filter of specified `sigma`. + + Parameters + ---------- + data : ndarray + 2D or 3D binary array which will be processed. + + dilate_iterations : integer + Indicates the number of iterations for thickenning the skeleton. + + sigma : float + Indicates the sigma of Gaussian filter used in smoothing of skeleton. + + Returns + ------- + arrays : tuple of 2D or 3D arrays + The original array, its skeleton and the thickened skeleton. + """ + data_8bit = data.astype(np.uint8) + data_8bit = ndi.binary_fill_holes(data_8bit).astype(np.uint8) + + if data.ndim == 3: + skeleton = morphology.skeletonize_3d(data_8bit) + elif data.ndim == 2: + skeleton = morphology.skeletonize(data_8bit) + else: + raise ValueError('Incorrect number of data dimensions, it supports from 2 to 3 dimensions.') + + skeleton_thick = ndi.binary_dilation(skeleton, iterations=dilate_iterations).astype(np.float32) + skeleton_thick = ndi.filters.gaussian_filter(skeleton_thick, sigma) + + return (data, skeleton, skeleton_thick) + + +def geo2rgb(lat, azth, azth_max=np.pi, lat_max=np.pi): + """Translates geo-coordinates to color in RGB color space. + + Parameters + ---------- + lat : float + Indicates latitude or elevation component of a given geo-coordinates. + + azth : float + Indicates azimuth component of a given geo-coordinates. + + azth_max : float + Indicates the normalization value for the azimuth component. + + lat_max : float + Indicates the normalization value for the latitude or elevation component. + + Returns + ------- + array : tuple of values + The tuple of RGB values [R, G, B]. + """ + return colors.hsv_to_rgb([azth/azth_max, lat/lat_max, 1.0]) + + +def calculate_tukey_posthoc(df, column, type_column='type', verbose=True, write=False, name=None, output_dir=None): + """Computes p-values using ANOVA with post-hoc Tukey HSD for a given DataFrame. + + Estimates p-values for a given DataFrame assuming that the sample + type is named as `type_column`. + + Parameters + ---------- + df : pandas DataFrame + Contains the table of values ans corresponding types or classes. + + column : str + Indicates the column of values. + + type_column : str + Indicates the column of sample kind. + + verbose : boolean + Specifies if the output should be printed into a terminal. + + write : boolean + Specifies if the output should be written into a text file. + + name : str + Indicates the name of the output file. + + output_dir : str + Indicates the output dir where the file will be written. + + Returns + ------- + dict : sample typles and p-values + The dict of sample types and cooresponding p-values. + """ + mc = MultiComparison(df[column], df[type_column]) + tt = mc.tukeyhsd() + st_range = np.abs(tt.meandiffs) / tt.std_pairs + + fout = None + if write and output_dir: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fout = open(os.path.join(output_dir, name + '.txt'), 'w') + print(os.path.join(output_dir, name + '.txt')) + + if write: + print('Tukey post-hoc ({0})'.format(column), end="", file=fout) + print(tt, end="", file=fout) + print(mc.groupsunique, end="", file=fout) + + if verbose: + print('Tukey post-hoc ({0})'%(column)) + print(tt) + print(mc.groupsunique) + + pvals = psturng(st_range, len(tt.groupsunique), tt.df_total) + + out = {} + groups = mc.groupsunique + g1idxs, g2idxs = mc.pairindices + + for g1i, g2i, p in zip(g1idxs, g2idxs, pvals): + gname = '{}-{}'.format(groups[g1i], groups[g2i]) + out[gname] = p + + min_item = min(out.iteritems(), key=operator.itemgetter(1)) + + for grp, p in out.items(): + if fout and write: + print >> fout, '{}: {}'.format(grp, p) + if verbose: + print(grp, ': ', p) + + return out, min_item diff --git a/quanfima/quanfima/visualization.py b/quanfima/quanfima/visualization.py new file mode 100644 index 0000000..7d3c156 --- /dev/null +++ b/quanfima/quanfima/visualization.py @@ -0,0 +1,1082 @@ +from __future__ import print_function +import os +import math +import numpy as np +import matplotlib as mpl +from skimage import io, filters +import matplotlib.colors as colors +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +import mpl_toolkits.axisartist.floating_axes as floating_axes +from matplotlib import cm +from scipy import ndimage as ndi, interpolate +from skimage import morphology +from matplotlib.projections import PolarAxes +from matplotlib.transforms import Affine2D +from mpl_toolkits.axisartist.grid_finder import DictFormatter, FixedLocator +from mpl_toolkits.axes_grid1 import make_axes_locatable +from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear +from utils import geo2rgb +from quanfima import visvis_available +if visvis_available: + import visvis as vv + + +def plot_orientation_map(orient_map, fiber_skel, radius_structure_elem=1, + figsize=(12, 12), cmap='hsv', dpi=200, min_label='0', + max_label='180', name=None, output_dir=None): + """Plots the orientation map with the color wheel. + + Plots the orientation map from the provided angles `orient_map` and skeleton + `fiber_skel` of size `figsize` using the colormap `cmap` and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + orient_map : ndarray + 2D array of orientation at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + dpi : integer + Indicates the DPI of the output image. + + min_label : str + Indicates the label of minimum degree. + + max_label : str + Indicates the label of minimum degree. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + orient_map = ndi.grey_dilation(orient_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + fig = plt.figure(figsize=figsize) + masked_orient_map = np.ma.masked_where(fiber_skel == 0, orient_map) + + quant_steps = 2056 + cmap_obj = cm.get_cmap(cmap, quant_steps) + cmap_obj.set_bad(color='black') + + omap_ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + omap_ax.set_axis_off() + omap_ax.imshow(masked_orient_map, cmap=cmap_obj, interpolation=None) + + display_axes = fig.add_axes([0.780, -0.076, 0.2, 0.2], projection='polar') + display_axes._direction = np.pi + + norm = mpl.colors.Normalize(0.0, np.pi) + cb = mpl.colorbar.ColorbarBase(display_axes, cmap=cmap_obj, norm=norm, orientation='horizontal') + + display_axes.text(0.09, 0.56, min_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + verticalalignment='center', transform=display_axes.transAxes) + + display_axes.text(0.85, 0.56, max_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + verticalalignment='center', transform=display_axes.transAxes) + + cb.outline.set_visible(False) + display_axes.set_axis_off() + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_orientation_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + + plt.show() + + +def plot_diameter_map(thickness_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', tmin=None, tmax=None, dpi=200, + labelsize=20, label='Diameter, [pixels]', name=None, + output_dir=None): + """Plots the diameter map with the colorbar. + + Plots the diameter map from the provided diameters `thickness_map` and + skeleton `fiber_skel` of size `figsize` using the colormap `cmap` and the + limits of the colorbar specified by `tmin` and `tmax`, and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + thickness_map : ndarray + 2D array of diameter at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + tmin : float + Indicates the minimum value of the colorbar. + + tmax : float + Indicates the maximum value of the colorbar. + + dpi : integer + Indicates the DPI of the output image. + + labelsize : integer + Indicates the fontsize of the label of the colorbar. + + label : str + Indicates the label of the colorbar. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + thickness_map = ndi.grey_dilation(thickness_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_thickness_map = np.ma.masked_where(fiber_skel == 0, thickness_map) + + cmap_obj = cm.get_cmap(cmap) + cmap_obj.set_bad(color='black') + + fig = plt.figure(figsize=figsize) + ax = plt.subplot(111) + ax.set_axis_off() + + im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size="2.5%", pad=0.05) + cbar = plt.colorbar(im, cax=cax) + cbar.ax.tick_params(labelsize=labelsize) + cbar.set_label(label, fontsize=labelsize) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + +def gather_polar_errors(datasets_path, estimation_path, + azth_rng=np.arange(-90, 91, step=3), + lat_rng=np.arange(0, 91, step=3)): + """Computes the absolute angular error in ranges between estimated datasets. + + Calculates the absolute angular error in ranges `azth_rng` and `lat_rng` + between estimated orientation datasets localed at paths `datasets_path` and + `estimation_path`. + + Parameters + ---------- + datasets_path : str + Indicates the path to the reference / estimated dataset. + + estimation_path : str + Indicates the path to the estimated / reference dataset. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude or elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + reference_dataset = np.load(datasets_path).item() + estimated_dataset = np.load(estimation_path).item() + idxs = estimated_dataset['indices'] + + print(reference_dataset.keys()) + print(estimated_dataset.keys()) + + ref_azth, ref_lat = reference_dataset['azth'][idxs], reference_dataset['lat'][idxs] + res_azth, res_lat = estimated_dataset['azth'][idxs], estimated_dataset['lat'][idxs] + + abs_err_azth, abs_err_lat = np.abs(ref_azth - res_azth), np.abs(ref_lat - res_lat) + + out = _angle_err2mean_abs_err(abs_err_azth, abs_err_lat, + ref_azth, ref_lat, + azth_rng=azth_rng, lat_rng=lat_rng) + + return out + + +def _angle_err2mean_abs_err(azth_err, lat_err, azth_ref, lat_ref, + azth_rng=np.arange(-90, 91, step=5), + lat_rng=np.arange(0, 91, step=5)): + """Computes the absolute angular error in ranges between estimated datasets. + + Parameters + ---------- + azth_err : 3D array + Indicates the error of azimuth components. + + lat_err : 3D array + Indicates the error of latitude / elevation components. + + azth_ref : 3D array + Indicates the reference dataset of estimated azimuth component. + + lat_ref : 3D array + Indicates the reference dataset of estimated latitude or elevation component. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude and elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + out = np.zeros((len(azth_rng) - 1, len(lat_rng) - 1), dtype=np.float32) + + for i in xrange(len(azth_rng) - 1): + for j in xrange(len(lat_rng) - 1): + rng_azth = (np.deg2rad(azth_rng[i]), np.deg2rad(azth_rng[i+1])) + rng_lat = (np.deg2rad(lat_rng[j]), np.deg2rad(lat_rng[j+1])) + + idxs_azth = np.where((azth_ref >= rng_azth[0]) & (azth_ref < rng_azth[1])) + idxs_lat = np.where((lat_ref >= rng_lat[0]) & (lat_ref < rng_lat[1])) + + idxs = np.intersect1d(idxs_azth, idxs_lat) + + if len(idxs): + mu_azth, mu_lat = np.rad2deg(np.mean(azth_err[idxs])), \ + np.rad2deg(np.mean(lat_err[idxs])) + + out[i, j] = mu_azth + mu_lat + else: + out[i, j] = 0.0 + + return out + + +def plot_polar_heatmap(data, name, interp_factor=5., color_limits=False, + hide_colorbar=False, vmin=None, vmax=None, log_scale=True, + dpi=200, output_dir=None): + """Plots the polar heatmap describing azimuth and latitude / elevation components. + + Plots the polar heatmap where each cell of the heatmap corresponds to + the specific element of the array provided by `gather_polar_errors` + function. + + Parameters + ---------- + data : 2D array + Indicates the array containing the sum of angular errors within the + specified angular ranges. It is usually provided by `gather_polar_errors` + function. + + name : str + Indicates the name of the output png file. + + interp_factor : float + Indicates the interpolation factor of the heatmap. + + color_limits : boolean + Specifies if the determined intensity limits should be returned. + + hide_colorbar : boolean + Specifies if the colorbar should be hidden. + + vmin : float + Indicates the minimum value of the colorbar. + + vmax : float + Indicates the maximum value of the colorbar. + + log_scale : float + Specifies if the heatmap sould be in the logarithmic scale. + + dpi : integer + Indicates the DPI of the output image. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + th0, th1 = 0., 180. + r0, r1 = 0, 90 + thlabel, rlabel = 'Azimuth', 'Elevation' + + tr_scale = Affine2D().scale(np.pi/180., 1.) + tr = tr_scale + PolarAxes.PolarTransform() + + lat_ticks = [(.0*90., '0$^{\circ}$'), + (.33*90., '30$^{\circ}$'), + (.66*90., '60$^{\circ}$'), + (1.*90., '90$^{\circ}$')] + r_grid_locator = FixedLocator([v for v, s in lat_ticks]) + r_grid_formatter = DictFormatter(dict(lat_ticks)) + + angle_ticks = [(0*180., '90$^{\circ}$'), + (.25*180., '45$^{\circ}$'), + (.5*180., '0$^{\circ}$'), + (.75*180., '-45$^{\circ}$'), + (1.*180., '-90$^{\circ}$')] + theta_grid_locator = FixedLocator([v for v, s in angle_ticks]) + theta_tick_formatter = DictFormatter(dict(angle_ticks)) + + grid_helper = GridHelperCurveLinear(tr, + extremes=(th0, th1, r0, r1), + grid_locator1=theta_grid_locator, + grid_locator2=r_grid_locator, + tick_formatter1=theta_tick_formatter, + tick_formatter2=r_grid_formatter) + + fig = plt.figure() + ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper) + fig.add_subplot(ax) + ax.set_facecolor('white') + + ax.axis["bottom"].set_visible(False) + ax.axis["top"].toggle(ticklabels=True, label=True) + ax.axis["top"].set_axis_direction("bottom") + ax.axis["top"].major_ticklabels.set_axis_direction("top") + ax.axis["top"].label.set_axis_direction("top") + + ax.axis["left"].set_axis_direction("bottom") + ax.axis["right"].set_axis_direction("top") + + ax.axis["top"].label.set_text(thlabel) + ax.axis["left"].label.set_text(rlabel) + + aux_ax = ax.get_aux_axes(tr) + aux_ax.patch = ax.patch + ax.patch.zorder = 0.9 + + rad = np.linspace(0, 90, data.shape[1]) + azm = np.linspace(0, 180, data.shape[0]) + + f = interpolate.interp2d(rad, azm, data, kind='linear', bounds_error=True, fill_value=0) + + new_rad = np.linspace(0, 90, 180*interp_factor) + new_azm = np.linspace(0, 180, 360*interp_factor) + new_data_angle_dist = f(new_rad, new_azm) + new_r, new_th = np.meshgrid(new_rad, new_azm) + new_data_angle_dist += 1. + + if log_scale: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', + norm=colors.LogNorm(vmin=1. if vmin is None else vmin, + vmax=new_data_angle_dist.max() if vmax is None else vmax)) + else: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', vmin=vmin, vmax=vmax) + + cbar = plt.colorbar(data_mesh, orientation='vertical', shrink=.88, pad=.1, aspect=15) + cbar.ax.set_ylabel('Absolute error, [deg.]') + + if hide_colorbar: + cbar.remove() + + ax.grid(False) + + plt.show() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + + if color_limits: + return 1., new_data_angle_dist.max() + + +def plot_histogram_fig(data, num_bins, xticks, color, splot_index=111, output_dir=None, + xlim=(None, None), ylim=(None, None), name=None, in_percent=False, + bar_width=0.8, ticks_pad=7, xticks_fontsize=22, + yticks_fontsize=22, xlabel=None, ylabel=None, + labels_fontsize=20, grid_alpha=0.3, title_fontsize=22, + exp_fontsize=15, type=None, figsize=(12, 8), dpi=200): + """Plots the histogram from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + num_bins : integer + Indicates the number of histogram bins. + + xticks : array + Indicates the array of ticks of the X-axis. + + color : str + Indicates the color of the histogram. + + Returns + ------- + tuple : tuple of Figure and Axis objects. + """ + fig = plt.figure(figsize=figsize) + ax = fig.add_subplot(splot_index) + + weights = np.ones_like(data)/float(len(data)) + + n, bins, patches = ax.hist(data, num_bins, color=color, + rwidth=bar_width, weights=weights) + + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='both', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.tick_params(axis='y', + labelsize=yticks_fontsize, + colors='#000000', + which='major', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='y', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.xaxis.set_minor_locator(ticker.MultipleLocator(5)) + ax.yaxis.set_minor_locator(ticker.MultipleLocator(5)) + + plt.xticks(xticks, xticks) + + ax.xaxis.offsetText.set_fontsize(exp_fontsize) + ax.yaxis.offsetText.set_fontsize(exp_fontsize) + + ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2), + fontsize=yticks_fontsize) + ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=6, min_n_ticks=6)) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + + ax.set_ylabel(ylabel, labelpad=2, fontsize=labels_fontsize, color='black') + ax.set_xlabel(xlabel, labelpad=2, fontsize=labels_fontsize, color='black') + + ax.set_ylim(ylim) + ax.set_xlim(xlim) + + if in_percent: + vals = ax.get_yticks() + ax.set_yticklabels(['{:3.0f}'.format(x*100) for x in vals]) + + ax.set_axisbelow(True) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, bbox_inches='tight', pad_inches=0.1, dpi=dpi) + + return fig, ax + + +def create_pie_chart(data, rngs, colors=['#244268', '#426084', '#67809F', '#95A9C1', '#C6D2E0'], + unit_scale=1.0, measure_quantity='m^3', figsize=(33, 15), + legend_loc=(0.383, -0.25), zebra_color=(False, 3), + legend_fontsize=50, chart_fontsize=60, dpi=72, name=None, + output_dir=None): + """Plots the piechart of from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + rngs : tuple of tuples + Indicates the ranges of the piechart. + + colors : array + Indicates the color for the region of the piechart corresponding to the + specific range. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + figsize : tuple of integers + Indicates the size of the output figure. + + legend_loc : tuple + Indicates the position of the legend of the figure. + + zebra_color : tuple + Allows to change the text color of the region to white from the first to + the speficied index of the region (True, reg_index). + + legend_fontsize : integer + Indicates the fontsize of the legend. + + chart_fontsize : integer + Indicates the fontsize of the figure. + + dpi : integer + Indicates the DPI of the output image. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + def number(val): + if val < 1000: + return '%d' % val + + sv = str(val) + return '$\mathregular{10^{%d}}$' % (len(sv)-2) if val % 10 == 0 else '%0.0e' % val + + def get_title(v1, v2, measure_quantity): + ftm = '%s $\minus$ %s %s' + return ftm % (number(v1), number(v2), measure_quantity) + + data_ranges = [] + df = data * unit_scale + for rng in rngs: + rng_min, rng_max = rng[0], rng[1] + data_rng = df[(df > rng_min) & (df < rng_max)] + data_ranges.append(data_rng) + + num_elem = [len(p) for p in data_ranges] + se = sum(num_elem) + + print('Num of particles: {}'%(se)) + + proc_particles = [n/float(se) * 100.0 for n in num_elem] + + for size, rng in zip(num_elem, rngs): + print('{}-{}: {}'%(rng[0], rng[1], size)) + + titles = [get_title(minv, maxv, measure_quantity) for minv, maxv in rngs] + + textprops = {'fontsize': chart_fontsize, + 'weight': 'normal', + 'family': 'sans-serif'} + pie_width = 0.5 + fig, ax = plt.subplots(figsize=figsize) + ax.axis('equal') + patches, texts, autotexts = ax.pie(proc_particles, + textprops=textprops, + colors=colors, + autopct='%1.1f%%', + radius=1, + pctdistance=1-pie_width/2) + + if (zebra_color is not None) and (zebra_color[0]): + for tt in autotexts[:zebra_color[1]]: + tt.set_color('white') + + plt.setp(patches, + width=pie_width, + edgecolor='white') + + plt.legend(patches, titles, loc=legend_loc, fontsize=legend_fontsize) + + _d, _offset, _di = [1, -1], [0.45, 0.45], 0 + + for t, p in zip(autotexts, proc_particles): + if p < 2.0: + pos = list(t.get_position()) + pos[0] = pos[0] + _d[_di] * _offset[_di] + + t.set_position(pos) + _di += 1 + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.1, dpi=dpi) + + +def _bbox_3D(img): + """Crops the non-zero part of a volume + """ + r = np.any(img, axis=(1, 2)) + c = np.any(img, axis=(0, 2)) + z = np.any(img, axis=(0, 1)) + + rmin, rmax = np.where(r)[0][[0, -1]] + cmin, cmax = np.where(c)[0][[0, -1]] + zmin, zmax = np.where(z)[0][[0, -1]] + + return rmin, rmax, cmin, cmax, zmin, zmax + + +def plot_3d_orientation_map(name, lat_data, azth_data, radius_structure_elem=1, + output_dir=None, width=512, height=512, + camera_azth=44.5, camera_elev=35.8, camera_roll=0.0, + camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k'): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_azth : float + Indicates the azimuth angle of the camera. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(azth_data) + + azth, lat = azth_data[rmin:rmax, cmin:cmax, zmin:zmax], \ + np.abs(lat_data[rmin:rmax, cmin:cmax, zmin:zmax]) + + skel = azth.copy().astype(np.float32) + skel[skel.nonzero()] = 1. + + azth = ndi.grey_dilation(azth, structure=morphology.ball(radius_structure_elem)) + lat = ndi.grey_dilation(lat, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)) + + Z, Y, X = skel.nonzero() + vol_orient = np.zeros(skel.shape + (3,), dtype=np.float32) + + print(vol_orient.size, vol_orient[skel.nonzero()].size) + + for z, y, x in zip(Z, Y, X): + vol_orient[z, y, x] = geo2rgb(lat[z, y, x], azth[z, y, x]) + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(vol_orient[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + # def mouseUp(event): + # print 'mouseUp!!' + # a = vv.gca() + # print a.camera.GetViewParams() + # + # a.eventMouseUp.Bind(mouseUp) + # fig.eventMouseUp.Bind(mouseUp) + # + # a.Draw() + # fig.DrawNow() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_orientation.png'.format(name)), + sf=1, bg=background_color) + app.Run() + + +def plot_3d_diameter_map(name, data, unit_scale=1.0, measure_quantity='vox', + radius_structure_elem=1, output_dir=None, width=512, + height=512, camera_azth=44.5, camera_elev=35.8, + camera_roll=0.0, camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k', + cb_x_offset=10): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + data : 3D array + Indicates the 3D array containing diameter at every point of the skeleton. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + camera_azth : float + Indicates the azimuth angle of the camera. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + + cb_x_offset : int + Indicates the offset of the colorbar from the right window side. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(data) + dmtr = data[rmin:rmax, cmin:cmax, zmin:zmax] * unit_scale + skel = np.zeros_like(dmtr, dtype=np.uint8) + skel[dmtr.nonzero()] = 1 + + dmtr = ndi.grey_dilation(dmtr, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)).astype(np.float32) + skel[skel.nonzero()] = 1. + + dmtr = dmtr * skel + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(dmtr[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + t.colormap = vv.CM_JET + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + cb = vv.colorbar() + cb.SetLabel('Diameter, [{}]'.format(measure_quantity)) + cb._label.position.x += cb_x_offset + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_diameter.png'.format(name)), + sf=1, bg='w') + app.Run() + + +def plot_color_wheel(name, output_dir=None, dpi=500, xlabel='Elevation', + ylabel='Azimuth', fontsize=10, num_xticks=4, yticks=(-90, 90)): + """Plots the color wheel for visualizations of 3D orintation. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + + xlabel : str + Indicates the text along the x-axis. + + ylabel : str + Indicates the text along the y-axis. + + fontsize : int + Indicates the font size of labels along axes. + + num_xticks : int + Indicates the number of ticks along axes. + + yticks : tuple + Indicates the range of minimum and maximum values along the y-axis. + """ + + azth, lat = np.linspace(0., 1., num=180), np.linspace(0., 1., num=90) + rgb_arr = np.zeros((len(azth), len(lat), 3)) + for i in xrange(len(azth)): + for j in xrange(len(lat)): + rgb_arr[i, j, :] = colors.hsv_to_rgb([azth[i], lat[j], 1.0]) + + fig, ax = plt.subplots(figsize=(2, 2)) + ax.set_facecolor('red') + + ax.set_xlim([0, 90]) + ax.set_ylim([0, 181]) + + ax.set_yticks(np.linspace(0, 180, num=7).astype(np.int32)) + ax.set_yticklabels(np.linspace(yticks[0], yticks[1], num=7).astype(np.int32)) + + ax.set_xlabel(xlabel, fontsize=fontsize, labelpad=0, color='w') + ax.set_ylabel(ylabel, fontsize=fontsize, labelpad=0, color='w') + + xmajorlocator = ticker.LinearLocator(num_xticks) + ax.xaxis.set_major_locator(xmajorlocator) + ax.tick_params(direction='out', length=2, width=0, labelsize=fontsize, pad=0, colors='w') + + ax.imshow(rgb_arr) + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_color_bar.png'.format(name)), + transparent=True, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + +def plot_fourier_orientation(data, orient_blocks, block_shape, figsize=(12,12), + cmap='gray', line_length=20, line_width=2.5, + line_color='red', line_style='-', name=None, + output_dir=None, dpi=200): + """Plots the orientation map in a block-wise manner. + + Plots the orientation vector over the image `data` at the center of each + block of the subdivided image. The orientation at every block 'orient_blocks' + and its size `block_shape` are specified by orientation estimation method. + The result can be and stored as a png file with DPI of `dpi` to the folder + specified by `output_dir`. + + Parameters + ---------- + data : ndarray + An image on top of which, the orientation will be plotted.. + + orient_blocks : ndarray + 2D array of orientation at every block of the subdivided image. + + block_shape : tuple + Indicates the block size within which the orientation is calculated. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used for image. + + line_length : integer + Indicates the length of the orientation vector at each block. + + line_width : float + Indicates the line width of the orientation vector at each block. + + line_color : str + Indicates the line color of the orientation vector at each block. + + line_style : str + Indicates the line style of the orientation vector at each block. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + """ + fig, ax = plt.subplots(figsize=figsize) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + ax.set_axis_off() + + ax.imshow(data, cmap=cmap) + + for i in xrange(orient_blocks.shape[0]): + for j in xrange(orient_blocks.shape[1]): + y0, x0 = block_shape[0] * j + block_shape[0]/2, \ + block_shape[1] * i + block_shape[1]/2 + + orientation = orient_blocks[i ,j] + + x2 = x0 - math.sin(orientation) * line_length + y2 = y0 - math.cos(orientation) * line_length + + x3 = x0 + math.sin(orientation) * line_length + y3 = y0 + math.cos(orientation) * line_length + + ax.plot((x2, x3), (y2, y3), linestyle=line_style, + linewidth=line_width, color=line_color) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_fourier_orientation_map.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.05, dpi=dpi) + + plt.show() From 5302aaec50533708c99abe338a6e35b047a2b7ed Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:50:50 -0400 Subject: [PATCH 086/153] Create test.txt --- quanfima/quanfima/test/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 quanfima/quanfima/test/test.txt diff --git a/quanfima/quanfima/test/test.txt b/quanfima/quanfima/test/test.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/quanfima/quanfima/test/test.txt @@ -0,0 +1 @@ + From b861a3f4cb370af40991828d8d722d7cd0d91430 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:51:49 -0400 Subject: [PATCH 087/153] Create test.txt --- quanfima/docs/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 quanfima/docs/test.txt diff --git a/quanfima/docs/test.txt b/quanfima/docs/test.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/quanfima/docs/test.txt @@ -0,0 +1 @@ + From c6f5768ff83c1d494d00add336b2d112d04d4eb9 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:53:21 -0400 Subject: [PATCH 088/153] Add files via upload --- quanfima/docs/Makefile | 20 ++++++++++++++++++++ quanfima/docs/make.bat | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 56 insertions(+) create mode 100644 quanfima/docs/Makefile create mode 100644 quanfima/docs/make.bat diff --git a/quanfima/docs/Makefile b/quanfima/docs/Makefile new file mode 100644 index 0000000..2c30804 --- /dev/null +++ b/quanfima/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = quanfima +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/quanfima/docs/make.bat b/quanfima/docs/make.bat new file mode 100644 index 0000000..41fc86d --- /dev/null +++ b/quanfima/docs/make.bat @@ -0,0 +1,36 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build +set SPHINXPROJ=quanfima + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd From 3320611aebd5d744bc228a91487fd76123e78f9f Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:54:24 -0400 Subject: [PATCH 089/153] Create test.txt --- quanfima/docs/source/static/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 quanfima/docs/source/static/test.txt diff --git a/quanfima/docs/source/static/test.txt b/quanfima/docs/source/static/test.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/quanfima/docs/source/static/test.txt @@ -0,0 +1 @@ + From 35c0204052b9299de8f01d1957a70eae7ec695b0 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:54:57 -0400 Subject: [PATCH 090/153] Add files via upload --- quanfima/docs/source/change_log.rst | 32 ++++ quanfima/docs/source/conf.py | 170 ++++++++++++++++++ quanfima/docs/source/index.rst | 29 +++ quanfima/docs/source/overview.rst | 28 +++ quanfima/docs/source/quanfima.rst | 35 ++++ quanfima/docs/source/quickstart.rst | 266 ++++++++++++++++++++++++++++ 6 files changed, 560 insertions(+) create mode 100644 quanfima/docs/source/change_log.rst create mode 100644 quanfima/docs/source/conf.py create mode 100644 quanfima/docs/source/index.rst create mode 100644 quanfima/docs/source/overview.rst create mode 100644 quanfima/docs/source/quanfima.rst create mode 100644 quanfima/docs/source/quickstart.rst diff --git a/quanfima/docs/source/change_log.rst b/quanfima/docs/source/change_log.rst new file mode 100644 index 0000000..27a61f8 --- /dev/null +++ b/quanfima/docs/source/change_log.rst @@ -0,0 +1,32 @@ +========= +ChangeLog +========= + +Version 0.1a1 +============== + +Features: + +- Orientation and diameter estimation for 2D / 3D data +- Counting of particles with subsequent estimation of properties +- Porosity calculation for ND data +- Visualization of 2D orientation and diameter with MPL package +- Visualization of 3D orientation and diameter with VisVis package +- Simulation of fibers and particles with various scenarios + + +Version 0.1a2 +============== + +Fix: + +- Incorrect display of measure units at a legend of a pie chart + + +Version 0.1a3 +============== + +New features: + +- Block-wise orientation estimation with 2D FFT and PCA-approach [estimate_fourier_orientation] +- Visualization of block-wise orientation with MPL package [plot_fourier_orientation] diff --git a/quanfima/docs/source/conf.py b/quanfima/docs/source/conf.py new file mode 100644 index 0000000..3483c70 --- /dev/null +++ b/quanfima/docs/source/conf.py @@ -0,0 +1,170 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/stable/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +sys.path.insert(0, os.path.abspath('../..')) +print os.path.abspath('../..') + + +# -- Project information ----------------------------------------------------- + +project = u'quanfima' +copyright = u'2018, Roman Shkarin, Andrei Shkarin' +author = u'Roman Shkarin, Andrei Shkarin' + +# The short X.Y version +version = u'' +# The full version, including alpha/beta/rc tags +release = u'0.1a1' + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.viewcode', + 'sphinx.ext.githubpages', + # 'autoapi.extension' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path . +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# autoapi_type = 'python' +# autoapi_dirs = [os.path.abspath('../../quanfima')] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'quanfimadoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'quanfima.tex', u'quanfima Documentation', + u'Roman Shkarin, Andrei Shkarin', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'quanfima', u'quanfima Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'quanfima', u'quanfima Documentation', + author, 'quanfima', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Extension configuration ------------------------------------------------- diff --git a/quanfima/docs/source/index.rst b/quanfima/docs/source/index.rst new file mode 100644 index 0000000..9d4626b --- /dev/null +++ b/quanfima/docs/source/index.rst @@ -0,0 +1,29 @@ +.. title:: Quanfima + +====================== +Quanfima Documentation +====================== + +*Quanfima* (**qu**\antitative **an**\alysis of **fi**\brous **ma**\terials) +is a collection of useful routines for morphological analysis and visualization +of 2D/3D data from various areas of material science. The aim is to simplify +the analysis process by providing functionality for frequently required tasks +in the same place. + +- Analysis of fibrous structures by tensor-based method in 2D / 3D datasets. +- Estimation of structure diameters in 2D / 3D by a ray-casting method. +- Counting of particles in 2D / 3D datasets and providing a detailed report in + pandas.DataFrame format. +- Calculation of porosity measure for each material in 2D / 3D datasets. +- Visualization in 2D / 3D using matplotlib, visvis packages. + +User Guide +========== + +.. toctree:: + :maxdepth: 2 + + overview + quickstart + quanfima + change_log diff --git a/quanfima/docs/source/overview.rst b/quanfima/docs/source/overview.rst new file mode 100644 index 0000000..d17cde9 --- /dev/null +++ b/quanfima/docs/source/overview.rst @@ -0,0 +1,28 @@ +======== +Overview +======== + +Requirements +============ + +- Python 2.7 +- PyCUDA >= 2017.1.1 +- Numpy >= 1.13 +- SciPy >= 0.19 +- scikit-image >= 0.12 +- scikit-learn >= 0.18 +- Pandas >= 0.19 +- Matplotlib >= 2.0 + +Installation +============ + +The easiest way to install the latest version is by using pip:: + + $ pip install quanfima + +You may also use Git to clone the repository and install it manually:: + + $ git clone https://github.com/rshkarin/quanfima.git + $ cd quanfima + $ python setup.py install diff --git a/quanfima/docs/source/quanfima.rst b/quanfima/docs/source/quanfima.rst new file mode 100644 index 0000000..08e2089 --- /dev/null +++ b/quanfima/docs/source/quanfima.rst @@ -0,0 +1,35 @@ +============= +API Reference +============= + +quanfima\.morphology module +--------------------------- + +.. automodule:: quanfima.morphology + :members: + :undoc-members: + :show-inheritance: + +quanfima\.simulation module +--------------------------- + +.. automodule:: quanfima.simulation + :members: + :undoc-members: + :show-inheritance: + +quanfima\.utils module +---------------------- + +.. automodule:: quanfima.utils + :members: + :undoc-members: + :show-inheritance: + +quanfima\.visualization module +------------------------------ + +.. automodule:: quanfima.visualization + :members: + :undoc-members: + :show-inheritance: diff --git a/quanfima/docs/source/quickstart.rst b/quanfima/docs/source/quickstart.rst new file mode 100644 index 0000000..e4e61cf --- /dev/null +++ b/quanfima/docs/source/quickstart.rst @@ -0,0 +1,266 @@ +========== +Quickstart +========== + +Analysis of fibrous 2D data +^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Open a grayscale image, perform segmentation, estimate porosity, analyze fiber +orientation and diameters, and plot the results. + +.. code-block:: python + + import numpy as np + from skimage import io, filters + from quanfima import morphology as mrph + from quanfima import visualization as vis + from quanfima import utils + + img = io.imread('../../data/polymer_slice.tif') + + th_val = filters.threshold_otsu(img) + img_seg = (img > th_val).astype(np.uint8) + + # estiamte porosity + pr = mrph.calc_porosity(img_seg) + for k,v in pr.items(): + print 'Porosity ({}): {}'.format(k, v) + + # prepare data and analyze fibers + data, skeleton, skeleton_thick = utils.prepare_data(img_seg) + cskel, fskel, omap, dmap, ovals, dvals = \ + mrph.estimate_fiber_properties(data, skeleton) + + # plot results + vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°', + figsize=(10,10), + name='2d_polymer', + output_dir='../../data/results') + vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow', + name='2d_polymer', + output_dir='../../data/results') + +.. code-block:: python + + >> Porosity (Material 1): 0.845488888889 + +.. image:: _static\2d_polymer_data.png + :width: 90 % + :align: center +.. image:: _static\2d_polymer_orientation_map.png + :width: 90 % + :align: center +.. image:: _static\2d_polymer_diameter_map.png + :width: 90 % + :align: center + +Analysis of 3D data of fibrous material +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Open a micro-CT dataset, perform slice-wise segmentation, estimate porosity, +analyze 3D fiber orientation and diameters, and visualize the results. + +.. code:: python + + import numpy as np + from skimage import filters + from quanfima import morphology as mrph + from quanfima import visualization as vis + from quanfima import utils + + data = np.memmap('../../data/polymer3d_8bit_128x128x128.raw', + shape=(128,128,128), dtype=np.uint8, mode='r') + + data_seg = np.zeros_like(data, dtype=np.uint8) + for i in xrange(data_seg.shape[0]): + th_val = filters.threshold_otsu(data[i]) + data_seg[i] = (data[i] > th_val).astype(np.uint8) + + # estimate porosity + pr = mrph.calc_porosity(data_seg) + for k,v in pr.items(): + print 'Porosity ({}): {}'.format(k, v) + + # prepare data and analyze fibers + pdata, pskel, pskel_thick = utils.prepare_data(data_seg) + oprops = mrph.estimate_tensor_parallel('polymer_orientation_w32', pskel, + pskel_thick, 32, + '../../data/results') + + odata = np.load(oprops['output_path']).item() + lat, azth, skel = odata['lat'], odata['azth'], odata['skeleton'] + + dprops = mrph.estimate_diameter_single_run('polymer_diameter', + '../../data/results', + pdata, skel, lat, azth) + dmtr = np.load(dprops['output_path']).item()['diameter'] + + # plot results + vis.plot_3d_orientation_map('polymer_w32', lat, azth, + output_dir='../../data/results', + camera_azth=40.47, + camera_elev=32.5, + camera_fov=35.0, + camera_loc=(40.85, 46.32, 28.85), + camera_zoom=0.005124) + + vis.plot_3d_diameter_map('polymer_w32', dmtr, + output_dir='../../data/results', + measure_quantity='vox', + camera_azth=40.47, + camera_elev=32.5, + camera_fov=35.0, + camera_loc=(40.85, 46.32, 28.85), + camera_zoom=0.005124, + cb_x_offset=5, + width=620) + +.. code:: python + + >> Porosity (Material 1): 0.855631351471 + +.. image:: _static\polymer_w32_3d_orientation.png + :width: 78 % + :align: center +.. image:: _static\polymer_w32_3d_diameter.png + :width: 80 % + :align: center + +Estimation of p-values +^^^^^^^^^^^^^^^^^^^^^^ +Estimate p-values between several groups of samples with corresponding +measurements of some material's property. + +.. code:: python + + import pandas as pd + from quanfima import utils + + prop_vals = [174.93, 182.42, 194.61, 234.6, 229.73, 242.6, 38.78, 37.79, + 32.06, 14.81, 15.23, 13.84] + mat_groups = ['PCL_cl', 'PCL_cl', 'PCL_cl', 'PCL_wa', 'PCL_wa', 'PCL_wa', + 'PCL_SiHA_cl', 'PCL_SiHA_cl', 'PCL_SiHA_cl', 'PCL_SiHA_wa', + 'PCL_SiHA_wa', 'PCL_SiHA_wa'] + df_elongation = pd.DataFrame({'elongation': prop_vals, 'type': mat_groups}) + + _, _ = utils.calculate_tukey_posthoc(df_elongation, 'elongation', + name='samples_elongation', + write=True, + output_dir='../../data/results') + +.. code:: python + + >> Tukey post-hoc (elongation) + >> Multiple Comparison of Means - Tukey HSD,FWER=0.05 + >> ========================================================= + >> group1 group2 meandiff lower upper reject + >> --------------------------------------------------------- + >> PCL_SiHA_cl PCL_SiHA_wa -21.5833 -37.8384 -5.3282 True + >> PCL_SiHA_cl PCL_cl 147.7767 131.5216 164.0318 True + >> PCL_SiHA_cl PCL_wa 199.4333 183.1782 215.6884 True + >> PCL_SiHA_wa PCL_cl 169.36 153.1049 185.6151 True + >> PCL_SiHA_wa PCL_wa 221.0167 204.7616 237.2718 True + >> PCL_cl PCL_wa 51.6567 35.4016 67.9118 True + >> --------------------------------------------------------- + >> ['PCL_SiHA_cl' 'PCL_SiHA_wa' 'PCL_cl' 'PCL_wa'] + >> PCL_SiHA_cl-PCL_SiHA_wa : 0.011919282004 + >> PCL_SiHA_wa-PCL_cl : 0.001 + >> PCL_SiHA_wa-PCL_wa : 0.001 + >> PCL_cl-PCL_wa : 0.001 + >> PCL_SiHA_cl-PCL_wa : 0.001 + >> PCL_SiHA_cl-PCL_cl : 0.001 + +Simulate and count particles in 3D data +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Count and estimate properties of particles in a generated dataset comprised +of spheres of varying radius. + +.. code:: python + + from quanfima import simulation + from quanfima import morphology as mrph + + volume, diameter, _, _ = simulation.simulate_particles((512,512,512), + n_particles=1000) + + stats, labeled_volume = mrph.object_counter(volume) + +.. code:: python + + >> Label Area Perimeter Sphericity + >> 0 1.0 20479.0 2896.958728 1.249533 + >> 1 2.0 5575.0 1184.028571 1.284158 + >> 2 3.0 57777.0 5816.142853 1.242660 + >> 3 4.0 17077.0 2545.194226 1.260001 + >> 4 5.0 5575.0 1184.028571 1.284158 + >> 5 6.0 65267.0 6348.926691 1.234752 + >> .. ... ... ... ... + >> 791 792.0 2109.0 605.185858 1.314154 + >> 792 793.0 257.0 134.225397 1.456369 + >> 793 794.0 257.0 134.225397 1.456369 + >> 794 795.0 123.0 78.627417 1.521179 + + >> [795 rows x 4 columns] + +Simulate fibers and estimate properties +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Simulate a 3D dataset containing some number of fibers, estimate their +properties and visualize. + +.. code:: python + + import numpy as np + from scipy import ndimage as ndi + from skimage import morphology + from quanfima import simulation + from quanfima import morphology as mrph + from quanfima import utils + from quanfima import visualization as vis + + volume, lat_ref, azth_ref, diameter, _, _ = \ + simulation.simulate_fibers((128,128,128), n_fibers=30, max_fails=100, + radius_lim=(2, 3), gap_lim=(3,5)) + volume = volume.astype(np.uint8) + volume = ndi.binary_fill_holes(volume) + volume = ndi.median_filter(volume, footprint=morphology.ball(2)) + lat_ref = ndi.median_filter(lat_ref, footprint=morphology.ball(2)) + azth_ref = ndi.median_filter(azth_ref, footprint=morphology.ball(2)) + + # prepare data and analyze fibers + pdata, pskel, pskel_thick = utils.prepare_data(volume) + oprops = mrph.estimate_tensor_parallel('dataset_orientation_w36', + pskel, pskel_thick, 36, + '../../data/results') + + odata = np.load(oprops['output_path']).item() + lat, azth, skel = odata['lat'], odata['azth'], odata['skeleton'] + + dprops = mrph.estimate_diameter_single_run('dataset_diameter', + '../../data/results', + pdata, skel, lat, azth) + dmtr = np.load(dprops['output_path']).item()['diameter'] + + # plot results + vis.plot_3d_orientation_map('dataset_w36', lat, azth, + output_dir='../../data/results', + camera_azth=40.47, + camera_elev=32.5, + camera_fov=35.0, + camera_loc=(40.85, 46.32, 28.85), + camera_zoom=0.005124) + + vis.plot_3d_diameter_map('dataset_w36', dmtr, + output_dir='../../data/results', + measure_quantity='vox', + camera_azth=40.47, + camera_elev=32.5, + camera_fov=35.0, + camera_loc=(40.85, 46.32, 28.85), + camera_zoom=0.005124, + cb_x_offset=5, + width=620) + +.. image:: _static\dataset_w36_3d_orientation.png + :width: 78 % + :align: center +.. image:: _static\dataset_w36_3d_diameter.png + :width: 80 % + :align: center From f661b82f565096049f063347c0cb59411263e231 Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:55:57 -0400 Subject: [PATCH 091/153] Add files via upload --- .../docs/source/static/2d_polymer_data.png | Bin 0 -> 315289 bytes .../source/static/2d_polymer_diameter_map.png 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zO-0_i(6boPJP8*XIMafr{p*FQvr~}88gty5z711tZ7ebtjnW;FEXFz!iA#P25=8%Y#_ConqCJYS~n|J6zSFV`Smua^ku3HR*n>$}^a8 zz#Qs=0Ff@}6%(e42Ys89BFYTF(|ZNnm{kd-z4RjNiGo|r}7@xDgo*<$?r zkO)ox^Z}Nr3(;Rm#`Juv6^*I3rmWan+J3{02F>%JYFfcGIw*>cn@7>T_s&!0OPA>E zta$ES{KiKl?WQ*XTsTQ$4 zQ!R8v{^9M!37SHR}vR)u*yqQEw`a?z_hYaEk2sZatzk{~vMFSA|LDJj1N=i>zKy!fu~nI5AIg z2vcOCw8#7I6=z5XG*YPkdb35%hn7lD`7Y?vaGK}534$!3qKP|bH-M!LAb$C=s0t#p*@ zN_K#*iGAd~sW;D%QSv%*I*0|fkcp+@kSoN$%_dHR_Jl$Z>L`FPFoKoFvFtU6P?YsY z0_%A*czK>M9tl)L_EIwN#4HFD=LA9Q6P{fNngiyR;bdUlZm`oA0!!~!@?((_bMEOe z2>0)W)SM`2_H8f|+7*s^Oh%A;)dkdIFGsdy3B#9^26Dc3lvwAuk^KH|L`6jc{tp>K B{=onM literal 0 HcmV?d00001 From 3d4fcf817a9c2ec155870c9696171edc12d04ffd Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 26 May 2020 23:59:44 -0400 Subject: [PATCH 094/153] Add files via upload --- quanfima/quanfima/visualization_testO.py | 1208 ++++++++++++++++++++++ 1 file changed, 1208 insertions(+) create mode 100644 quanfima/quanfima/visualization_testO.py diff --git a/quanfima/quanfima/visualization_testO.py b/quanfima/quanfima/visualization_testO.py new file mode 100644 index 0000000..09af8a8 --- /dev/null +++ b/quanfima/quanfima/visualization_testO.py @@ -0,0 +1,1208 @@ +from __future__ import print_function +import os +import math +import numpy as np +import matplotlib as mpl +from skimage import io, filters +import matplotlib.colors as colors +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +import mpl_toolkits.axisartist.floating_axes as floating_axes +from matplotlib import cm +from scipy import ndimage as ndi, interpolate +from skimage import morphology +from matplotlib.projections import PolarAxes +from matplotlib.transforms import Affine2D +from mpl_toolkits.axisartist.grid_finder import DictFormatter, FixedLocator +from mpl_toolkits.axes_grid1 import make_axes_locatable +from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear +from quanfima.quanfima.utils import geo2rgb +from quanfima.quanfima import visvis_available +if visvis_available: + import visvis as vv + +def plot_orientM_map(orient_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', min_label=None, max_label=None, dpi=200, + labelsize=20, label='Orientation, [pixels]', name=None, + output_dir=None): + """Plots the diameter map with the colorbar. + + Plots the diameter map from the provided diameters `thickness_map` and + skeleton `fiber_skel` of size `figsize` using the colormap `cmap` and the + limits of the colorbar specified by `tmin` and `tmax`, and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + thickness_map : ndarray + 2D array of diameter at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + tmin : float + Indicates the minimum value of the colorbar. + + tmax : float + Indicates the maximum value of the colorbar. + + dpi : integer + Indicates the DPI of the output image. + + labelsize : integer + Indicates the fontsize of the label of the colorbar. + + label : str + Indicates the label of the colorbar. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + thickness_map = ndi.grey_dilation(orient_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_thickness_map = np.ma.masked_where(fiber_skel == 0, thickness_map) + + cmap_obj = cm.get_cmap(cmap) + cmap_obj.set_bad(color='black') + + fig = plt.figure(figsize=figsize) + #ax = plt.subplot(111) + #ax.set_axis_off() + + #im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + #divider = make_axes_locatable(ax) + #cax = divider.append_axes('right', size="2.5%", pad=0.05) + #cbar = plt.colorbar(im, cax=cax) + #cbar.ax.tick_params(labelsize=labelsize) + #cbar.set_label(label, fontsize=labelsize) + + + quant_steps = 2056 + cmap_obj = cm.get_cmap(cmap, quant_steps) + cmap_obj.set_bad(color='black') + + omap_ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + omap_ax.set_axis_off() + omap_ax.imshow(masked_thickness_map, cmap=cmap_obj, interpolation=None) + + display_axes = fig.add_axes([0.780, -0.076, 0.2, 0.2], projection='polar') + display_axes_direction = np.pi + + norm = mpl.colors.Normalize(0.0, np.pi) + cb = mpl.colorbar.ColorbarBase(display_axes, cmap=cmap_obj, norm=norm, orientation='horizontal') + + display_axes.text(0.09, 0.56, min_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + verticalalignment='center', transform=display_axes.transAxes) + + display_axes.text(0.85, 0.56, max_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + verticalalignment='center', transform=display_axes.transAxes) + + cb.outline.set_visible(False) + display_axes.set_axis_off() + + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + + +def plot_orientation_test_map(orient_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', dpi=200, min_label='0', + max_label='180', name=None, output_dir=None): + """Plots the orientation map with the color wheel. + + Plots the orientation map from the provided angles `orient_map` and skeleton + `fiber_skel` of size `figsize` using the colormap `cmap` and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + orient_map : ndarray + 2D array of orientation at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + dpi : integer + Indicates the DPI of the output image. + + min_label : str + Indicates the label of minimum degree. + + max_label : str + Indicates the label of minimum degree. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + orient_map = ndi.grey_dilation(orient_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_orient_map = np.ma.masked_where(fiber_skel == 0, orient_map) + + fig = plt.figure(figsize=figsize) + ax = plt.subplot(111) + ax.set_axis_off() + + quant_steps = 2056 + + cmap_obj = cm.get_cmap(cmap, quant_steps) + cmap_obj.set_bad(color='black') + + im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size="2.5%", pad=0.05) + cbar = plt.colorbar(im, cax=cax) + cbar.ax.tick_params(labelsize=labelsize) + cbar.set_label(label, fontsize=labelsize) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_orient_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + #omap_ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + #omap_ax.set_axis_off() + #omap_ax.imshow(masked_orient_map, cmap=cmap_obj, interpolation=None) + + #display_axes = fig.add_axes([0.780, -0.076, 0.2, 0.2], projection='polar') + #display_axes._direction = np.pi + + #norm = mpl.colors.Normalize(0.0, np.pi) + #cb = mpl.colorbar.ColorbarBase(display_axes, cmap=cmap_obj, norm=norm, orientation='horizontal') + + #display_axes.text(0.09, 0.56, min_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + # verticalalignment='center', transform=display_axes.transAxes) + + #display_axes.text(0.85, 0.56, max_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + # verticalalignment='center', transform=display_axes.transAxes) + + #cb.outline.set_visible(False) + #display_axes.set_axis_off() + + #if (output_dir is not None) and (name is not None): + # if not os.path.exists(output_dir): + # os.makedirs(output_dir) + # fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + # transparent=False, + # bbox_inches='tight', + # pad_inches=0.1, + # dpi=dpi) + + #plt.show() + + +def plot_diameter_map_test(thickness_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', tmin=None, tmax=None, dpi=200, + labelsize=20, label='Diameter, [pixels]', name=None, + output_dir=None): + """Plots the diameter map with the colorbar. + + Plots the diameter map from the provided diameters `thickness_map` and + skeleton `fiber_skel` of size `figsize` using the colormap `cmap` and the + limits of the colorbar specified by `tmin` and `tmax`, and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + thickness_map : ndarray + 2D array of diameter at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + tmin : float + Indicates the minimum value of the colorbar. + + tmax : float + Indicates the maximum value of the colorbar. + + dpi : integer + Indicates the DPI of the output image. + + labelsize : integer + Indicates the fontsize of the label of the colorbar. + + label : str + Indicates the label of the colorbar. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + thickness_map = ndi.grey_dilation(thickness_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_thickness_map = np.ma.masked_where(fiber_skel == 0, thickness_map) + + cmap_obj = cm.get_cmap(cmap) + cmap_obj.set_bad(color='black') + + fig = plt.figure(figsize=figsize) + ax = plt.subplot(111) + ax.set_axis_off() + + im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size="2.5%", pad=0.05) + cbar = plt.colorbar(im, cax=cax) + cbar.ax.tick_params(labelsize=labelsize) + cbar.set_label(label, fontsize=labelsize) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + +def gather_polar_errors(datasets_path, estimation_path, + azth_rng=np.arange(-90, 91, step=3), + lat_rng=np.arange(0, 91, step=3)): + """Computes the absolute angular error in ranges between estimated datasets. + + Calculates the absolute angular error in ranges `azth_rng` and `lat_rng` + between estimated orientation datasets localed at paths `datasets_path` and + `estimation_path`. + + Parameters + ---------- + datasets_path : str + Indicates the path to the reference / estimated dataset. + + estimation_path : str + Indicates the path to the estimated / reference dataset. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude or elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + reference_dataset = np.load(datasets_path).item() + estimated_dataset = np.load(estimation_path).item() + idxs = estimated_dataset['indices'] + + print(reference_dataset.keys()) + print(estimated_dataset.keys()) + + ref_azth, ref_lat = reference_dataset['azth'][idxs], reference_dataset['lat'][idxs] + res_azth, res_lat = estimated_dataset['azth'][idxs], estimated_dataset['lat'][idxs] + + abs_err_azth, abs_err_lat = np.abs(ref_azth - res_azth), np.abs(ref_lat - res_lat) + + out = _angle_err2mean_abs_err(abs_err_azth, abs_err_lat, + ref_azth, ref_lat, + azth_rng=azth_rng, lat_rng=lat_rng) + + return out + + +def _angle_err2mean_abs_err(azth_err, lat_err, azth_ref, lat_ref, + azth_rng=np.arange(-90, 91, step=5), + lat_rng=np.arange(0, 91, step=5)): + """Computes the absolute angular error in ranges between estimated datasets. + + Parameters + ---------- + azth_err : 3D array + Indicates the error of azimuth components. + + lat_err : 3D array + Indicates the error of latitude / elevation components. + + azth_ref : 3D array + Indicates the reference dataset of estimated azimuth component. + + lat_ref : 3D array + Indicates the reference dataset of estimated latitude or elevation component. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude and elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + out = np.zeros((len(azth_rng) - 1, len(lat_rng) - 1), dtype=np.float32) + + for i in xrange(len(azth_rng) - 1): + for j in xrange(len(lat_rng) - 1): + rng_azth = (np.deg2rad(azth_rng[i]), np.deg2rad(azth_rng[i+1])) + rng_lat = (np.deg2rad(lat_rng[j]), np.deg2rad(lat_rng[j+1])) + + idxs_azth = np.where((azth_ref >= rng_azth[0]) & (azth_ref < rng_azth[1])) + idxs_lat = np.where((lat_ref >= rng_lat[0]) & (lat_ref < rng_lat[1])) + + idxs = np.intersect1d(idxs_azth, idxs_lat) + + if len(idxs): + mu_azth, mu_lat = np.rad2deg(np.mean(azth_err[idxs])), \ + np.rad2deg(np.mean(lat_err[idxs])) + + out[i, j] = mu_azth + mu_lat + else: + out[i, j] = 0.0 + + return out + + +def plot_polar_heatmap(data, name, interp_factor=5., color_limits=False, + hide_colorbar=False, vmin=None, vmax=None, log_scale=True, + dpi=200, output_dir=None): + """Plots the polar heatmap describing azimuth and latitude / elevation components. + + Plots the polar heatmap where each cell of the heatmap corresponds to + the specific element of the array provided by `gather_polar_errors` + function. + + Parameters + ---------- + data : 2D array + Indicates the array containing the sum of angular errors within the + specified angular ranges. It is usually provided by `gather_polar_errors` + function. + + name : str + Indicates the name of the output png file. + + interp_factor : float + Indicates the interpolation factor of the heatmap. + + color_limits : boolean + Specifies if the determined intensity limits should be returned. + + hide_colorbar : boolean + Specifies if the colorbar should be hidden. + + vmin : float + Indicates the minimum value of the colorbar. + + vmax : float + Indicates the maximum value of the colorbar. + + log_scale : float + Specifies if the heatmap sould be in the logarithmic scale. + + dpi : integer + Indicates the DPI of the output image. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + th0, th1 = 0., 180. + r0, r1 = 0, 90 + thlabel, rlabel = 'Azimuth', 'Elevation' + + tr_scale = Affine2D().scale(np.pi/180., 1.) + tr = tr_scale + PolarAxes.PolarTransform() + + lat_ticks = [(.0*90., '0$^{\circ}$'), + (.33*90., '30$^{\circ}$'), + (.66*90., '60$^{\circ}$'), + (1.*90., '90$^{\circ}$')] + r_grid_locator = FixedLocator([v for v, s in lat_ticks]) + r_grid_formatter = DictFormatter(dict(lat_ticks)) + + angle_ticks = [(0*180., '90$^{\circ}$'), + (.25*180., '45$^{\circ}$'), + (.5*180., '0$^{\circ}$'), + (.75*180., '-45$^{\circ}$'), + (1.*180., '-90$^{\circ}$')] + theta_grid_locator = FixedLocator([v for v, s in angle_ticks]) + theta_tick_formatter = DictFormatter(dict(angle_ticks)) + + grid_helper = GridHelperCurveLinear(tr, + extremes=(th0, th1, r0, r1), + grid_locator1=theta_grid_locator, + grid_locator2=r_grid_locator, + tick_formatter1=theta_tick_formatter, + tick_formatter2=r_grid_formatter) + + fig = plt.figure() + ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper) + fig.add_subplot(ax) + ax.set_facecolor('white') + + ax.axis["bottom"].set_visible(False) + ax.axis["top"].toggle(ticklabels=True, label=True) + ax.axis["top"].set_axis_direction("bottom") + ax.axis["top"].major_ticklabels.set_axis_direction("top") + ax.axis["top"].label.set_axis_direction("top") + + ax.axis["left"].set_axis_direction("bottom") + ax.axis["right"].set_axis_direction("top") + + ax.axis["top"].label.set_text(thlabel) + ax.axis["left"].label.set_text(rlabel) + + aux_ax = ax.get_aux_axes(tr) + aux_ax.patch = ax.patch + ax.patch.zorder = 0.9 + + rad = np.linspace(0, 90, data.shape[1]) + azm = np.linspace(0, 180, data.shape[0]) + + f = interpolate.interp2d(rad, azm, data, kind='linear', bounds_error=True, fill_value=0) + + new_rad = np.linspace(0, 90, 180*interp_factor) + new_azm = np.linspace(0, 180, 360*interp_factor) + new_data_angle_dist = f(new_rad, new_azm) + new_r, new_th = np.meshgrid(new_rad, new_azm) + new_data_angle_dist += 1. + + if log_scale: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', + norm=colors.LogNorm(vmin=1. if vmin is None else vmin, + vmax=new_data_angle_dist.max() if vmax is None else vmax)) + else: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', vmin=vmin, vmax=vmax) + + cbar = plt.colorbar(data_mesh, orientation='vertical', shrink=.88, pad=.1, aspect=15) + cbar.ax.set_ylabel('Absolute error, [deg.]') + + if hide_colorbar: + cbar.remove() + + ax.grid(False) + + plt.show() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + + if color_limits: + return 1., new_data_angle_dist.max() + + +def plot_histogram_fig(data, num_bins, xticks, color, splot_index=111, output_dir=None, + xlim=(None, None), ylim=(None, None), name=None, in_percent=False, + bar_width=0.8, ticks_pad=7, xticks_fontsize=22, + yticks_fontsize=22, xlabel=None, ylabel=None, + labels_fontsize=20, grid_alpha=0.3, title_fontsize=22, + exp_fontsize=15, type=None, figsize=(12, 8), dpi=200): + """Plots the histogram from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + num_bins : integer + Indicates the number of histogram bins. + + xticks : array + Indicates the array of ticks of the X-axis. + + color : str + Indicates the color of the histogram. + + Returns + ------- + tuple : tuple of Figure and Axis objects. + """ + fig = plt.figure(figsize=figsize) + ax = fig.add_subplot(splot_index) + + weights = np.ones_like(data)/float(len(data)) + + n, bins, patches = ax.hist(data, num_bins, color=color, + rwidth=bar_width, weights=weights) + + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='both', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.tick_params(axis='y', + labelsize=yticks_fontsize, + colors='#000000', + which='major', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='y', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.xaxis.set_minor_locator(ticker.MultipleLocator(5)) + ax.yaxis.set_minor_locator(ticker.MultipleLocator(5)) + + plt.xticks(xticks, xticks) + + ax.xaxis.offsetText.set_fontsize(exp_fontsize) + ax.yaxis.offsetText.set_fontsize(exp_fontsize) + + ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2), + fontsize=yticks_fontsize) + ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=6, min_n_ticks=6)) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + + ax.set_ylabel(ylabel, labelpad=2, fontsize=labels_fontsize, color='black') + ax.set_xlabel(xlabel, labelpad=2, fontsize=labels_fontsize, color='black') + + ax.set_ylim(ylim) + ax.set_xlim(xlim) + + if in_percent: + vals = ax.get_yticks() + ax.set_yticklabels(['{:3.0f}'.format(x*100) for x in vals]) + + ax.set_axisbelow(True) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, bbox_inches='tight', pad_inches=0.1, dpi=dpi) + + return fig, ax + + +def create_pie_chart(data, rngs, colors=['#244268', '#426084', '#67809F', '#95A9C1', '#C6D2E0'], + unit_scale=1.0, measure_quantity='m^3', figsize=(33, 15), + legend_loc=(0.383, -0.25), zebra_color=(False, 3), + legend_fontsize=50, chart_fontsize=60, dpi=72, name=None, + output_dir=None): + """Plots the piechart of from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + rngs : tuple of tuples + Indicates the ranges of the piechart. + + colors : array + Indicates the color for the region of the piechart corresponding to the + specific range. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + figsize : tuple of integers + Indicates the size of the output figure. + + legend_loc : tuple + Indicates the position of the legend of the figure. + + zebra_color : tuple + Allows to change the text color of the region to white from the first to + the speficied index of the region (True, reg_index). + + legend_fontsize : integer + Indicates the fontsize of the legend. + + chart_fontsize : integer + Indicates the fontsize of the figure. + + dpi : integer + Indicates the DPI of the output image. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + def number(val): + if val < 1000: + return '%d' % val + + sv = str(val) + return '$\mathregular{10^{%d}}$' % (len(sv)-2) if val % 10 == 0 else '%0.0e' % val + + def get_title(v1, v2, measure_quantity): + ftm = '%s $\minus$ %s %s' + return ftm % (number(v1), number(v2), measure_quantity) + + data_ranges = [] + df = data * unit_scale + for rng in rngs: + rng_min, rng_max = rng[0], rng[1] + data_rng = df[(df > rng_min) & (df < rng_max)] + data_ranges.append(data_rng) + + num_elem = [len(p) for p in data_ranges] + se = sum(num_elem) + + print('Num of particles: {}'%(se)) + + proc_particles = [n/float(se) * 100.0 for n in num_elem] + + for size, rng in zip(num_elem, rngs): + print('{}-{}: {}'%(rng[0], rng[1], size)) + + titles = [get_title(minv, maxv, measure_quantity) for minv, maxv in rngs] + + textprops = {'fontsize': chart_fontsize, + 'weight': 'normal', + 'family': 'sans-serif'} + pie_width = 0.5 + fig, ax = plt.subplots(figsize=figsize) + ax.axis('equal') + patches, texts, autotexts = ax.pie(proc_particles, + textprops=textprops, + colors=colors, + autopct='%1.1f%%', + radius=1, + pctdistance=1-pie_width/2) + + if (zebra_color is not None) and (zebra_color[0]): + for tt in autotexts[:zebra_color[1]]: + tt.set_color('white') + + plt.setp(patches, + width=pie_width, + edgecolor='white') + + plt.legend(patches, titles, loc=legend_loc, fontsize=legend_fontsize) + + _d, _offset, _di = [1, -1], [0.45, 0.45], 0 + + for t, p in zip(autotexts, proc_particles): + if p < 2.0: + pos = list(t.get_position()) + pos[0] = pos[0] + _d[_di] * _offset[_di] + + t.set_position(pos) + _di += 1 + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.1, dpi=dpi) + + +def _bbox_3D(img): + """Crops the non-zero part of a volume + """ + r = np.any(img, axis=(1, 2)) + c = np.any(img, axis=(0, 2)) + z = np.any(img, axis=(0, 1)) + + rmin, rmax = np.where(r)[0][[0, -1]] + cmin, cmax = np.where(c)[0][[0, -1]] + zmin, zmax = np.where(z)[0][[0, -1]] + + return rmin, rmax, cmin, cmax, zmin, zmax + + +def plot_3d_orientation_map(name, lat_data, azth_data, radius_structure_elem=1, + output_dir=None, width=512, height=512, + camera_azth=44.5, camera_elev=35.8, camera_roll=0.0, + camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k'): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_azth : float + Indicates the azimuth angle of the camera. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(azth_data) + + azth, lat = azth_data[rmin:rmax, cmin:cmax, zmin:zmax], \ + np.abs(lat_data[rmin:rmax, cmin:cmax, zmin:zmax]) + + skel = azth.copy().astype(np.float32) + skel[skel.nonzero()] = 1. + + azth = ndi.grey_dilation(azth, structure=morphology.ball(radius_structure_elem)) + lat = ndi.grey_dilation(lat, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)) + + Z, Y, X = skel.nonzero() + vol_orient = np.zeros(skel.shape + (3,), dtype=np.float32) + + print(vol_orient.size, vol_orient[skel.nonzero()].size) + + for z, y, x in zip(Z, Y, X): + vol_orient[z, y, x] = geo2rgb(lat[z, y, x], azth[z, y, x]) + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(vol_orient[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + # def mouseUp(event): + # print 'mouseUp!!' + # a = vv.gca() + # print a.camera.GetViewParams() + # + # a.eventMouseUp.Bind(mouseUp) + # fig.eventMouseUp.Bind(mouseUp) + # + # a.Draw() + # fig.DrawNow() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_orientation.png'.format(name)), + sf=1, bg=background_color) + app.Run() + + +def plot_3d_diameter_map(name, data, unit_scale=1.0, measure_quantity='vox', + radius_structure_elem=1, output_dir=None, width=512, + height=512, camera_azth=44.5, camera_elev=35.8, + camera_roll=0.0, camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k', + cb_x_offset=10): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + data : 3D array + Indicates the 3D array containing diameter at every point of the skeleton. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + camera_azth : float + Indicates the azimuth angle of the camera. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + + cb_x_offset : int + Indicates the offset of the colorbar from the right window side. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(data) + dmtr = data[rmin:rmax, cmin:cmax, zmin:zmax] * unit_scale + skel = np.zeros_like(dmtr, dtype=np.uint8) + skel[dmtr.nonzero()] = 1 + + dmtr = ndi.grey_dilation(dmtr, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)).astype(np.float32) + skel[skel.nonzero()] = 1. + + dmtr = dmtr * skel + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(dmtr[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + t.colormap = vv.CM_JET + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + cb = vv.colorbar() + cb.SetLabel('Diameter, [{}]'.format(measure_quantity)) + cb._label.position.x += cb_x_offset + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_diameter.png'.format(name)), + sf=1, bg='w') + app.Run() + + +def plot_color_wheel(name, output_dir=None, dpi=500, xlabel='Elevation', + ylabel='Azimuth', fontsize=10, num_xticks=4, yticks=(-90, 90)): + """Plots the color wheel for visualizations of 3D orintation. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + + xlabel : str + Indicates the text along the x-axis. + + ylabel : str + Indicates the text along the y-axis. + + fontsize : int + Indicates the font size of labels along axes. + + num_xticks : int + Indicates the number of ticks along axes. + + yticks : tuple + Indicates the range of minimum and maximum values along the y-axis. + """ + + azth, lat = np.linspace(0., 1., num=180), np.linspace(0., 1., num=90) + rgb_arr = np.zeros((len(azth), len(lat), 3)) + for i in xrange(len(azth)): + for j in xrange(len(lat)): + rgb_arr[i, j, :] = colors.hsv_to_rgb([azth[i], lat[j], 1.0]) + + fig, ax = plt.subplots(figsize=(2, 2)) + ax.set_facecolor('red') + + ax.set_xlim([0, 90]) + ax.set_ylim([0, 181]) + + ax.set_yticks(np.linspace(0, 180, num=7).astype(np.int32)) + ax.set_yticklabels(np.linspace(yticks[0], yticks[1], num=7).astype(np.int32)) + + ax.set_xlabel(xlabel, fontsize=fontsize, labelpad=0, color='w') + ax.set_ylabel(ylabel, fontsize=fontsize, labelpad=0, color='w') + + xmajorlocator = ticker.LinearLocator(num_xticks) + ax.xaxis.set_major_locator(xmajorlocator) + ax.tick_params(direction='out', length=2, width=0, labelsize=fontsize, pad=0, colors='w') + + ax.imshow(rgb_arr) + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_color_bar.png'.format(name)), + transparent=True, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + +def plot_fourier_orientation(data, orient_blocks, block_shape, figsize=(12,12), + cmap='gray', line_length=20, line_width=2.5, + line_color='red', line_style='-', name=None, + output_dir=None, dpi=200): + """Plots the orientation map in a block-wise manner. + + Plots the orientation vector over the image `data` at the center of each + block of the subdivided image. The orientation at every block 'orient_blocks' + and its size `block_shape` are specified by orientation estimation method. + The result can be and stored as a png file with DPI of `dpi` to the folder + specified by `output_dir`. + + Parameters + ---------- + data : ndarray + An image on top of which, the orientation will be plotted.. + + orient_blocks : ndarray + 2D array of orientation at every block of the subdivided image. + + block_shape : tuple + Indicates the block size within which the orientation is calculated. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used for image. + + line_length : integer + Indicates the length of the orientation vector at each block. + + line_width : float + Indicates the line width of the orientation vector at each block. + + line_color : str + Indicates the line color of the orientation vector at each block. + + line_style : str + Indicates the line style of the orientation vector at each block. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + """ + fig, ax = plt.subplots(figsize=figsize) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + ax.set_axis_off() + + ax.imshow(data, cmap=cmap) + + for i in xrange(orient_blocks.shape[0]): + for j in xrange(orient_blocks.shape[1]): + y0, x0 = block_shape[0] * j + block_shape[0]/2, \ + block_shape[1] * i + block_shape[1]/2 + + orientation = orient_blocks[i ,j] + + x2 = x0 - math.sin(orientation) * line_length + y2 = y0 - math.cos(orientation) * line_length + + x3 = x0 + math.sin(orientation) * line_length + y3 = y0 + math.cos(orientation) * line_length + + ax.plot((x2, x3), (y2, y3), linestyle=line_style, + linewidth=line_width, color=line_color) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_fourier_orientation_map.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.05, dpi=dpi) + + plt.show() From 6e11dfd2ef29a1147cf0da0745248a095db850cf Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 27 May 2020 00:00:42 -0400 Subject: [PATCH 095/153] Update morphology.py --- quanfima/quanfima/morphology.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quanfima/quanfima/morphology.py b/quanfima/quanfima/morphology.py index 4561ac8..9d383b2 100644 --- a/quanfima/quanfima/morphology.py +++ b/quanfima/quanfima/morphology.py @@ -11,7 +11,7 @@ import vigra import pandas as pd from multiprocessing import Pool -from quanfima import cuda_available +from quanfima.quanfima import cuda_available if cuda_available: import pycuda.autoinit From f61c79fa56d0a7f9df64d5996c5af4b2156edde8 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 27 May 2020 00:02:31 -0400 Subject: [PATCH 096/153] Add files via upload --- src/fibers_P.py | 497 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 497 insertions(+) create mode 100644 src/fibers_P.py diff --git a/src/fibers_P.py b/src/fibers_P.py new file mode 100644 index 0000000..23ba975 --- /dev/null +++ b/src/fibers_P.py @@ -0,0 +1,497 @@ +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from __future__ import division +from __future__ import absolute_import + +import argparse, re, os, glob, sys, pprint, itertools +import inspect + +import matplotlib +matplotlib.rcParams['backend'] = "Qt5Agg" +from matplotlib.figure import Figure +import matplotlib.pyplot as plt + + +import numpy as np +from src.fiberfit_model.helpers import debug +import numpy as np +from numpy.linalg import eig, inv +import scipy.ndimage +from pylab import * + +import ipywidgets as widgets +from IPython.display import display +#btn = widgets.Button(description='Press Me') +#display(btn) +#display(QPixmap(saved_images_dir_name + "/angDist_" + ".png").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation)) +import skimage.io as io +from skimage import data +from skimage.filters import sobel +from skimage.morphology import watershed +from skimage import feature +from skimage.color import rgb2gray +from skimage.feature import corner_harris,corner_peaks +from skimage.exposure import equalize_hist +from skimage import measure +from skimage.draw import ellipse +from skimage.measure import label, regionprops +from skimage.transform import rotate +from skimage.feature import corner_orientations +from skimage.filters import threshold_otsu +from skimage.segmentation import clear_border +from skimage.measure import label +from skimage.morphology import closing, square +from skimage.measure import regionprops +from skimage.color import label2rgb +import seaborn +import pandas +try: + from runtime import * +except ImportError: + from smop.runtime import * + +from src.fiberfit_model.helpers import debug +import numpy as np +from numpy.linalg import eig, inv +import scipy.ndimage +from pylab import * +from os import listdir +from os.path import isfile, join +import numpy +import cv2 + + + +import sys +import matplotlib +import numpy as np +import scipy +import scipy.ndimage +import scipy.interpolate +import scipy.optimize +import scipy.integrate +import scipy.stats +import math +import time +import glob +from pylab import * +from pandas import DataFrame +#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +import matplotlib.pyplot as plt +from src.foruploading5 import prepimages +from src.fiberfit_model.EllipseDirectFit import* +from src.fiberfit_model import helpers +from ipywidgets import IntProgress +from IPython.display import display +import time + +def fiber_analysisD(mypath,savepath, CO_lower,CO_upper,angleInc,radStep): + plt.ioff() + ## Upload Files + ## Upload Files + onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ] + images = numpy.empty(len(onlyfiles), dtype=object) + names = numpy.empty(len(onlyfiles), dtype=object) + for n in range(0, len(onlyfiles)): + images[n] = cv2.imread( join(mypath,onlyfiles[n]) ) + names[n]= (onlyfiles[n]) + img = numpy.empty(len(images), dtype=object) + for n in range(0,len(images)): + img[n]= np.invert(images[n]) + ## Set up empty Matrices (can clean this up later ) + ell_data_test = numpy.empty(len(img),dtype=object) + Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object) + MirnormPower_test = numpy.empty(len(img),dtype=object) + normPower_test = numpy.empty(len(img),dtype=object) + theta1RadFinal_test = numpy.empty(len(img),dtype=object) + A_test = numpy.empty(len(img),dtype=object) + centroid_test = numpy.empty(len(img),dtype=object) + t_new_test = numpy.empty(len(img),dtype=object) + im = numpy.empty(len(img), dtype=object) + PF = numpy.empty(len(img),dtype=object) + PS = numpy.empty(len(img),dtype=object) + theta1Rad_test = numpy.empty(len(img),dtype=object) + PowerY_test = numpy.empty(len(img),dtype=object) + Mirtheta1RadFinal1_plot= [] + MirnormPower_plot=[] + th_plot=[] + r_line_plot=[] + normPower1_plot=[] + num=0 + index=num + + plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output + mpl.rcParams['figure.max_open_warning'] = 0 + maxiter = 100 + + ## begin analysis + print('Number of Images:',len(img)) + max_count=100 + tot_sum_progress = len(img) + PROGRESSBAR = IntProgress(min=0, max=max_count,description='Progress') + PROGRESSBAR.bar_style='info' + display(PROGRESSBAR) # display the bar + for n in range(0,len(img)): + PROGRESSBAR.value = int(n/tot_sum_progress*100) # signal to increment the progress bar + im=img[n][:,:,1] # select first image + M= im.shape[0] #get image shape + N1 = im.shape[1] + n1 = np.round(N1/ 2) - 1 + freq = np.arange(-n1, n1 + 1, 1) + x, y = freq, freq + + ## Fourier Transform + fft_result = np.fft.fft2(im) + Fshift = np.fft.fftshift(fft_result) + + ## adjust to power spectrum to remove imaginary components + magnitude_spectrum= 20*np.log(np.abs(Fshift)) + Pabs = np.abs(Fshift) ** 2 + + M= im.shape[0] + N1=im.shape[1] + + # shift in terms of image because power spectrum is the mirroR of lines so + # mirroring back in terms of image would give right allignment + PabsFlip1 = np.rot90(Pabs) + PabsFlip = np.flipud(PabsFlip1) + while (PabsFlip.shape[0]>x.shape[0]): + PabsFlip = np.delete(PabsFlip, (0), axis=0) + while (PabsFlip.shape[1]>y.shape[0]): + PabsFlip = np.delete(PabsFlip, (0), axis=1) + while (PabsFlip.shape[0] 0)[0]] + A = np.vstack((A1, dot(T, A1))) + A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1] + A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0] + A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[ + 0] - A[4] * centroid[1] + A[3] = A3 + A[4] = A4 + A[5] = A5 + A = A / linalg.norm(A) + if (abs(A[1]) < (1 * 10 ^ (-15))): + if ((A[0]) <= (A[2])): + # Ellipse is horizontal + angle = 0; + major = sqrt(1 / A[0]) + minor = sqrt(1 / A[2]) + else: + angle = np.pi/2; + major = sqrt(1 / A[2]) + minor = sqrt(1 / A[0]) + else: + R = ((A[2] - A[0]) / A[1]) + tg = R - sqrt((R * R) + 1) + angle = math.atan(tg) + P = (2 * tg) / (1 + (tg * tg)) + + if ((A[0] > 0 and A[1] > 0 and A[2] > 0)): + if (angle < (-pi / 4)): + angle = angle + np.pi + else: + angle = angle + elif ((A[1] / P <= (-A[1] / P))): + if (angle < 0): + angle = angle + np.pi / 2 + else: + angle = angle - np.pi / 2 + elif (A[0] < 0 and A[1] < 0 and A[2] < 0): + if (angle < 0): + angle = angle + np.pi + else: + angle = angle - np.pi + else: + # Switch + if (angle < 0): + angle = angle + np.pi / 2 + else: + angle = angle - np.pi / 2 + + t_New = (angle) #angle + t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi) + Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n] + #print('round2',Mirtheta1RadFinal1) + MirnormPower = MirnormPower_test[n] + #print('mirnormpower',MirnormPower) + t = t_new_test[n] + r_line = np.arange(0, max(MirnormPower) + .5, .5) + th = np.zeros(len(r_line)) + for i in range(0, len(r_line)): + th[i] = t + th = np.concatenate([th, (th + 180)]) + r_line = np.concatenate([r_line, r_line]) + ## Polar Graph + graphs = ["#71C4BE","#BBDBA0","#CE7E56","#BC5C81","#84355A"]; + palette = seaborn.set_palette(graphs); + seaborn.set_context("poster",font_scale=1.5); + seaborn.set_style('ticks') + csfont = {'fontname':'Times New Roman', + 'size':'14', + } + ticksfont = {'fontname':'Times New Roman'} + plt.figure(frameon=False, figsize=(15, 10)); + plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5); + plt.suptitle(abs(t)-180, fontsize=20); + plt.polar(th * pi/180, r_line,color="#CE7E56",linewidth=4); + plt.savefig(savepath+'polarpositive'+names[n]); + if (max(MirnormPower)<2): + inc = 0.5 + elif (max(MirnormPower)<5): + inc = 1 + elif max(MirnormPower)<20: + inc = 5 + else: + inc = 10 + plt.yticks(np.arange(inc, max(MirnormPower), inc));#, **ticksfont) + + #plt.show() + t_final_rad = t*pi/180 + def fitted_func(thetas, c): + int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi) + return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \ + np.exp(c * np.cos(2 * (thetas - t_final_rad))) + c0 = 15 + xdata = theta1RadFinal_test[n] + theta1RadFinal = theta1RadFinal_test[n] + ydata= normPower_test[n] + normPower = normPower_test[n] + kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n]) + + # Shift data for plotting purposes + + diff = abs(theta1RadFinal - (t * pi / 180)) + #print(diff,'diff') + #centerLoc=diff.find(diff==min(diff)) + centerLoc = diff[np.argmin(diff)]#find(diff== min(diff)) + #print(centerLoc,'centerLoc') + + num_angles = len(theta1RadFinal) + shift = ((round(num_angles / 2) - (num_angles - centerLoc))) + #print(shift,'shift') + + normPower1 = np.roll(normPower, -int(shift)) + normPower1 + theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift)) + + if (shift > 0): + s = num_angles - shift + s= np.asscalar(s) + for k in range(s, num_angles): + theta1RadFinal1[k] = np.pi + theta1RadFinal1[k] + elif (shift < 0): + for k in range(0,90): + theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k] + + ## Normalized Distribution Graph + cartDist = plt.figure(figsize=(10, 10)); # Creates a figure containing cartesian distribution. + + h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); + plt.xticks(np.arange(-360, 360, 45,), **ticksfont); + plt.xlim([-95, 95]); + p_act = fitted_func(theta1RadFinal1, kappa); + #print(p_act) + a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]); + a= a.T + b = a[a[:,1].argsort()]#np.sort(a,axis=1) + h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); + plt.xlabel('Angle (°)', **csfont); + plt.ylabel('Normalized Intensity', **csfont); + #print(max(normPower)) + if (max(normPower)<2): + inc = 0.5 + elif (max(normPower)<5): + inc = 1 + elif (max(normPower)<20): + inc = 5 + else: + inc = 10 + plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont); + plt.ylim([0, max(normPower1) + .3]); + normPower1_plot.append(normPower1); + plt.savefig(savepath+'Normdist '+names[n]); + #plt.show() + #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight') + plt.close(); + t_final=t + slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1) + #print(rValue**2) + # Rounding results for Title of Figure + krnd = math.ceil(k * 1000) / 1000 + thrnd = abs(math.ceil(t_final * 1000) / 1000)-180 + krnd = math.trunc(krnd * 100) / 100 + thrnd = math.trunc(thrnd * 100) / 100 + a = 32.02 + b= -12.43 + c = 47.06 + d = -0.9185 + e = 19.43 + f = -0.07693 + x = kappa[0] + sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x) + #print("name",names[n],"kappa",kappa,"sigma",sig,"Rsq",rValue**2,"mu",t_final) + sum_NP = sum(normPower1) + #normPower2 = normPower1/sum(normPower1) + #sum_NP2 = sum(normPower2) + #print(sum_NP2) + ## Combined plot figure + palette = seaborn.set_palette(graphs); + seaborn.set_context("notebook",font_scale=1.5); + seaborn.set_style('ticks') + csfont = {'fontname':'Times New Roman', + 'size':'18', + } + ticksfont = {'fontname':'Times New Roman'} + fig = plt.figure(frameon=False,figsize=(20,12)); + fig.subplots_adjust(top=0.95); + fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold'); + grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1]); + cart_dist = plt.subplot(grid[1, 0:2]); + cart_dist.set_title('Von Mises Distribution (k)',fontsize=20); + h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); + cart_dist.set_xlim([-92, 92]); + cart_dist.set_xlabel('Angle (°)',fontsize=18); + cart_dist.set_ylim([0, max(normPower1) + .3]); + cart_dist.set_ylabel('Normalized Intensity',fontsize=18); + + p_act = fitted_func(theta1RadFinal1, kappa) + a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]) + a= a.T + b = a[a[:,1].argsort()]#np.sort(a,axis=1) + h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); + + if (max(normPower)<2): + inc = 0.5 + elif (max(normPower)<5): + inc = 1 + elif (max(normPower)<20): + inc = 5 + else: + inc = 10 + + polar_dist = plt.subplot(grid[0:2,3:5],projection='polar'); + polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o'); + polar_dist.plot(th * pi/180, r_line,color="#CE7E56",linewidth=4); + polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); + fig.text(0.75, 0.05, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}); + #polar_dist.text(np.radians(-110),1.95, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}) + orig_fig = plt.subplot(grid[0, 0]); + orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0]); + orig_fig.axis('off'); + fft_fig = plt.subplot(grid[0, 1]); + fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0]); + fft_fig.set_axis_off(); + fft_fig.axis('off'); + #fft_fig.imshow(log(PabsFlip), cmap='gray') + #orig_fig.imshow(images[n],cmap=plt.cm.viridis ) + #plt.tight_layout() + plt.savefig(savepath+'Grouped'+names[n]); + #print(n,'n') + #plt.show() + polar_dist_2 = plt.figure(figsize=(10, 10)); + #print('linebeforeindex') + #print(index,'currentindex') + #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1; + #MirnormPower_plot[index]=MirnormPower; + #th_plot[index] = th * pi/180 + #print(th_plot[index],'thplotindex') + #r_line_plot[index]=r_line + Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1); + MirnormPower_plot.append(MirnormPower); + th_plot.append(th * pi/180) + r_line_plot.append(r_line) + polar_dist.plot(Mirtheta1RadFinal1, MirnormPower); + polar_dist.plot(th * pi/180, r_line,linewidth=4); + polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); + plt.close('all') + #index=index+1 + #print(index,'Index') + From 77b65b15081a7d33caf4d612ce4591ed0ba1c7d5 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 27 May 2020 00:03:05 -0400 Subject: [PATCH 097/153] Create foruploading5.py --- src/foruploading5.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 src/foruploading5.py diff --git a/src/foruploading5.py b/src/foruploading5.py new file mode 100644 index 0000000..96adb8b --- /dev/null +++ b/src/foruploading5.py @@ -0,0 +1,32 @@ +import io +import ipywidgets as widgets +import numpy +import cv2 +import os +from os import listdir +from os.path import isfile, join +import numpy as np + +def ff(b): + for elem in b.new.values(): + #print(elem['metadata']['name']) + with open(os.path.join('Data/',elem['metadata']['name']), 'wb') as file: + file.write( elem['content']) + +def upload(s): + s.style.button_color = "lightblue" + s.observe(ff, names='value') + +def prepimages(mypath): + ##Path to folder where you would like images to be saved + savepath = 'output2/' + ## Upload Files + onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]; + images = numpy.empty(len(onlyfiles), dtype=object); + names = numpy.empty(len(onlyfiles), dtype=object); + for n in range(0, len(onlyfiles)): + images[n] = cv2.imread( join(mypath,onlyfiles[n]) ); + names[n]= (onlyfiles[n]); + img_tot = numpy.empty(len(images), dtype=object); + for n in range(0,len(images)): + img_tot[n]= np.invert(images[n]) From f2f83946e6e69f2df587b67c62e89e19d66f7c5a Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 27 May 2020 09:39:36 -0400 Subject: [PATCH 098/153] Create test.txt --- Data/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 Data/test.txt diff --git a/Data/test.txt b/Data/test.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Data/test.txt @@ -0,0 +1 @@ + From 51ae506c90dc37814b57a642c997e1b1ebb96dc8 Mon Sep 17 00:00:00 2001 From: mprender Date: Wed, 27 May 2020 09:40:50 -0400 Subject: [PATCH 099/153] Update fibers_P.py --- src/fibers_P.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/src/fibers_P.py b/src/fibers_P.py index 23ba975..b387e41 100644 --- a/src/fibers_P.py +++ b/src/fibers_P.py @@ -494,4 +494,16 @@ def fitted_func(thetas, c): plt.close('all') #index=index+1 #print(index,'Index') +## export of cartesian coordinate data + arr = numpy.array(MirnormPower_plot) + arr2 = numpy.array(Mirtheta1RadFinal1_plot) + arr3 = numpy.array(normPower1_plot) + arr4 = numpy.array(theta1RadFinal1*180/pi) + a = numpy.transpose(arr3) + b = numpy.transpose(arr4) + b[b> 100] = 0 + b[b<-100] = 0 + export_data=np.hstack(((b[:,None]),a)) + numpy.savetxt(savepath+"data_export.csv", export_data, delimiter=",") + return export_data From e8fd7993aad25ef1a8230a203398994822137622 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 1 Jun 2020 14:06:42 -0400 Subject: [PATCH 100/153] Rename FiberFit-toshare-5.22.2020.ipynb to old_versions/FiberFit-toshare-5.22.2020.ipynb --- .../FiberFit-toshare-5.22.2020.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-5.22.2020.ipynb => old_versions/FiberFit-toshare-5.22.2020.ipynb (100%) diff --git a/FiberFit-toshare-5.22.2020.ipynb b/old_versions/FiberFit-toshare-5.22.2020.ipynb similarity index 100% rename from FiberFit-toshare-5.22.2020.ipynb rename to old_versions/FiberFit-toshare-5.22.2020.ipynb From ed739b2dfb6d001c27bebfb43bf6432a9e4ada49 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 1 Jun 2020 14:39:42 -0400 Subject: [PATCH 101/153] Rename FiberFit-toshare-5.22.2020_2.ipynb to old_version/FiberFit-toshare-5.22.2020_2.ipynb --- .../FiberFit-toshare-5.22.2020_2.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-5.22.2020_2.ipynb => old_version/FiberFit-toshare-5.22.2020_2.ipynb (100%) diff --git a/FiberFit-toshare-5.22.2020_2.ipynb b/old_version/FiberFit-toshare-5.22.2020_2.ipynb similarity index 100% rename from FiberFit-toshare-5.22.2020_2.ipynb rename to old_version/FiberFit-toshare-5.22.2020_2.ipynb From 6414a4bb5a63a81a02ca9b53e0210850c138610e Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 1 Jun 2020 14:59:24 -0400 Subject: [PATCH 102/153] Add files via upload --- FiberFit-toshare-6.1.2020.ipynb | 274 ++++++++++++++++++++++++++++++++ quanfima_torun11.py | 100 ++++++++++++ widgetsmp.py | 66 ++++++++ 3 files changed, 440 insertions(+) create mode 100644 FiberFit-toshare-6.1.2020.ipynb create mode 100644 quanfima_torun11.py create mode 100644 widgetsmp.py diff --git a/FiberFit-toshare-6.1.2020.ipynb b/FiberFit-toshare-6.1.2020.ipynb new file mode 100644 index 0000000..d77f496 --- /dev/null +++ b/FiberFit-toshare-6.1.2020.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Upload your Images for analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\asus\\anaconda3\\envs\\test-environment\\lib\\site-packages\\IPython\\html.py:12: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", + " warn(\"The `IPython.html` package has been deprecated since IPython 4.0. \"\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6aca4500dd1842b392504e9ccac596de", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from widgetsmp import s,run\n", + "from src.foruploading5 import ff,upload,prepimages\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, Markdown, clear_output\n", + "from ipywidgets import interact, interact_manual\n", + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Run Code. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "acdaf7a966b3454b94cc665a3efb8510", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c3a4ae74bbc446e1baa0a268b7beca58", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness. Again, output will be in \"output\" folder. \n", + "###### Note: These color maps are from a program called Quanfima. To note - this is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results." + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "ename": "AttributeError", + "evalue": "'float' object has no attribute 'get_matrix'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2d_polymer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m output_dir='/path/to/output/dir')\n\u001b[1;32m---> 32\u001b[1;33m vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2d_polymer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\FiberFit\\quanfima\\quanfima\\visualization.py\u001b[0m in \u001b[0;36mplot_orientation_map\u001b[1;34m(orient_map, fiber_skel, radius_structure_elem, figsize, cmap, dpi, min_label, max_label, name, output_dir)\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m fig.savefig(os.path.join(output_dir, '{}_orientation_map.png'.format(name)),\n\u001b[0m\u001b[0;32m 102\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tight'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\envs\\test-environment\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36msavefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m 2201\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2203\u001b[1;33m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2080\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox_extra_artists\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2081\u001b[0m bbox_inches = self.figure.get_tightbbox(renderer,\n", + "\u001b[1;32m~\\anaconda3\\envs\\test-environment\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m 36\u001b[0m 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\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1122\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mset_rmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAttributeError\u001b[0m: 'float' object has no attribute 'get_matrix'" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#from quanfima_torun11 import colormaps\n", + "#%matplotlib inline\n", + "#colormaps(orient_type='tensor')\n", + "import numpy as np\n", + "from skimage import io, filters\n", + "from quanfima.quanfima import morphology as mrph\n", + "from quanfima.quanfima import visualization as vis\n", + "#from quanfima.quanfima import utils\n", + "\n", + "img_seg = cv2.imread('Data/segmented_square.png',0)\n", + "\n", + "#th_val = filters.threshold_otsu(img)\n", + "#img_seg = (img > th_val).astype(np.uint8)\n", + "\n", + "# estimate porosity\n", + "#pr = mrph.calc_porosity(img_seg)\n", + "#for k,v in pr.items():\n", + "# print 'Porosity ({}): {}'.format(k, v)\n", + "\n", + "# prepare data and analyze fibers\n", + "data = img_seg\n", + "skeleton = img_seg\n", + "skeleton_thick = img_seg\n", + "#skeleton, skeleton_thick = utils.prepare_data(img_seg)\n", + "cskel, fskel, omap, dmap, ovals, dvals = \\\n", + " mrph.estimate_fiber_properties(data, skeleton)\n", + "\n", + "# plot results\n", + "vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow',\n", + " name='DIAMETER_estimate',\n", + " output_dir='output/')\n", + "vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',\n", + " figsize=(10,10),\n", + " name='ORIENTATION_estimate',\n", + " output_dir='output/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Code below is for easier export of data - not ready yet." + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [], + "source": [ + "def csv_download_link(df, csv_file_name, delete_prompt=True):\n", + " \"\"\"Display a download link to load a data frame as csv from within a Jupyter notebook\"\"\"\n", + " numpy.savetxt(csv_file_name, df, delimiter=\",\")\n", + " from IPython.display import FileLink\n", + " display(FileLink(csv_file_name))\n", + " if delete_prompt:\n", + " a = input('Press enter to delete the file after you have downloaded it.')\n", + " import os\n", + " os.remove(csv_file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "df.csv
      " + ], + "text/plain": [ + "/home/jovyan/df.csv" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Press enter to delete the file after you have downloaded it.\n" + ] + } + ], + "source": [ + "csv_download_link(output, 'output.csv')" + ] + } + ], + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/quanfima_torun11.py b/quanfima_torun11.py new file mode 100644 index 0000000..30a199e --- /dev/null +++ b/quanfima_torun11.py @@ -0,0 +1,100 @@ +from __future__ import print_function +import numpy as np +from skimage import io, filters +from quanfima.quanfima import morphology as mrph +from quanfima.quanfima import visualization as vis +from quanfima.quanfima.visualization_testQ import plot_orientation_test_map +from quanfima.quanfima import utils2 +#from quanfima.quanfima import utils +from skimage.morphology import watershed +from skimage.feature import peak_local_max +from skimage import measure +from skimage.segmentation import random_walker +import matplotlib.pyplot as plt +from scipy import ndimage +import matplotlib.pyplot as plt +from skimage import data +from skimage.filters import threshold_otsu +import cv2 +import math +import time +import os +import itertools +import numpy as np +from skimage import feature, measure, filters +from skimage.util.shape import view_as_blocks +from scipy import ndimage as ndi +from scipy.spatial import distance +import vigra +import pandas as pd +from multiprocessing import Pool +from quanfima.quanfima import cuda_available + +if cuda_available: + import pycuda.autoinit + import pycuda.driver as cuda + from pycuda.compiler import SourceModule + from pycuda import gpuarray + + +def colormaps(orient_type,paddding=20,diameter_window_radius=12,window_radius=12): + img = cv2.imread('Data/segmented_square.png',0) + #img = np.invert(img) + #img_seg = img + #plt.imshow(img) + binary = img #>125 + #plt.imshow(binary) + img_seg=binary + #binary.ndim + skeleton = img_seg + data = skeleton + skeleton_thick=skeleton + #plt.imshow(skeleton) + fiber_mask= data + fiber_skel = skeleton + #paddding=50 + #window_radius=12, + #orient_type='pca' + #diameter_window_radius=12 + orientation_vals, diameter_vals = [], [] + + padded_fiber_skel = np.pad(fiber_skel, pad_width=(paddding,), mode='constant', constant_values=0) + padded_fiber_mask = np.pad(fiber_mask, pad_width=(paddding,), mode='constant', constant_values=0) + #plt.imshow(padded_fiber_skel) + pcy, pcx = (10*2+1)/2., (10*2+1)/2. + + ycords, xcords = np.nonzero(padded_fiber_skel) + + clear_padded_fiber_skel = padded_fiber_skel.copy() + method = feature.corner_harris(clear_padded_fiber_skel, sigma=1)#sigma=1.5 + #plt.imshow(method) + corner_points = feature.corner_peaks(method, min_distance=1)#min_distance =3 + image = img + #fig = plt.figure() + #plt.imshow(image) + # Convert coordinates to x and y lists + #y_corner,x_corner = zip(*corner_points) + #plt.plot(x_corner,y_corner,'o') # Plot corners + #if title: + #plt.title('title') + #plt.xlim(0,data.shape[1]) + #plt.ylim(data.shape[0],0) # Images use weird axes + #fig.set_size_inches(np.array(fig.get_size_inches()) * 1.5) + #plt.show() + #print "Number of corners:",len(corners) + for i, (yy, xx) in enumerate(corner_points): + clear_padded_fiber_skel[yy-2:yy+4, xx-2:xx+4] = np.zeros((6, 6), + dtype=clear_padded_fiber_skel.dtype) + from quanfima.quanfima import morphology7 as mrph + cskel, fskel, omap, dmap, ovals, dvals = \ + mrph.estimate_fiber_properties(data, skeleton) + #plt.imshow(clear_padded_fiber_skel) + from quanfima.quanfima.visualization_testQ import plot_orientM_map, plot_orientation_test_map + # plot results + plot_orientM_map(omap, cskel, min_label=u'0°', max_label=u'180°', + figsize=(10,10), + name='2d_polymer', + output_dir='output/') + vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow', + name='2d_polymer', + output_dir='/path/to/output/dir') diff --git a/widgetsmp.py b/widgetsmp.py new file mode 100644 index 0000000..7ee00cd --- /dev/null +++ b/widgetsmp.py @@ -0,0 +1,66 @@ +import pandas as pd +import matplotlib.pyplot as plt +#%matplotlib notebook +from ipywidgets import * +from IPython.display import display +from IPython.html import widgets +plt.style.use('ggplot') +from src.fibers_QQQQQQ_invert import fiber_analysis +#button = widgets.Button(description='My Button') +#out = widgets.Output() + +# linking button and function together using a button's method +#button.on_click(on_button_clicked) +# displaying button and its output together +#widgets.VBox([button,out]) + +from src.foruploading5 import ff,upload,prepimages +import ipywidgets as widgets +from IPython.display import display, Markdown, clear_output +from ipywidgets import interact, interact_manual +def s(_): + s = widgets.FileUpload(accept='', multiple=True) + return output + +def run(_): + + button = widgets.Button( + description='Run Fiber Analysis', + layout={'width': '300px'}) + out= widgets.Output() + def on_button_clicked(_): + # "linking function with output" + with out: + # what happens when we press the button + clear_output() + print('Program executing. Output in output folder on home page') + fiber_analysis( CO_lower=int(text1.value), + CO_upper=int(text2.value), + angleInc=int(text3.value), + radStep=float(text4.value), + mypath='Data/', + savepath='output/') + text1 = widgets.Text( + placeholder='20', + description="Max fiber Thickness (pixels)", + layout={'width':'300px'}, + style={'description_width':'175px'}) + text2 = widgets.Text( + placeholder='2', + description="Min fiber Thickness (pixels)", + layout={'width':'300px'}, + style={'description_width':'175px'}) + text3 = widgets.Text( + placeholder='1', + description="Angle Increment (degrees)", + layout={'width':'300px'}, + style={'description_width':'175px'}) + text4 = widgets.Text( + placeholder='0.5', + description="Radial Step Size (radians)", + layout={'width':'300 px'}, + style={'description_width':'175px'}) + button.on_click(on_button_clicked) + box=widgets.VBox([text1,text2,text3,text4,button]) + display(box,out) + \ No newline at end of file From 1d0ae93f5e2b8408eff4c1ac8583b1897d37cc9e Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 1 Jun 2020 15:01:34 -0400 Subject: [PATCH 103/153] Delete test.txt --- Data/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Data/test.txt diff --git a/Data/test.txt b/Data/test.txt deleted file mode 100644 index 8b13789..0000000 --- a/Data/test.txt +++ /dev/null @@ -1 +0,0 @@ - From d29f1c886b382d6002b9c6862c55bea4a11f5744 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 1 Jun 2020 15:02:58 -0400 Subject: [PATCH 104/153] Create .gitkeep --- Data/.gitkeep | 1 + 1 file changed, 1 insertion(+) create mode 100644 Data/.gitkeep diff --git a/Data/.gitkeep b/Data/.gitkeep new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Data/.gitkeep @@ -0,0 +1 @@ + From 4f74a995cf9d3c6cf879d89c9887da6250dd8c6c Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 1 Jun 2020 15:13:46 -0400 Subject: [PATCH 105/153] Add files via upload --- src/fibers_QQQQQQ_invert.py | 516 ++++++++++++++++++++++++++++++++++++ 1 file changed, 516 insertions(+) create mode 100644 src/fibers_QQQQQQ_invert.py diff --git a/src/fibers_QQQQQQ_invert.py b/src/fibers_QQQQQQ_invert.py new file mode 100644 index 0000000..a10ec8d --- /dev/null +++ b/src/fibers_QQQQQQ_invert.py @@ -0,0 +1,516 @@ +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from __future__ import division +from __future__ import absolute_import + +import argparse, re, os, glob, sys, pprint, itertools +import inspect + +import matplotlib +matplotlib.rcParams['backend'] = "Qt5Agg" +from matplotlib.figure import Figure +import matplotlib.pyplot as plt + + +import numpy as np +from src.fiberfit_model.helpers import debug +import numpy as np +from numpy.linalg import eig, inv +import scipy.ndimage +from pylab import * + +import ipywidgets as widgets +from IPython.display import display +#btn = widgets.Button(description='Press Me') +#display(btn) +#display(QPixmap(saved_images_dir_name + "/angDist_" + ".png").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation)) +import skimage.io as io +from skimage import data +from skimage.filters import sobel +from skimage.morphology import watershed +from skimage import feature +from skimage.color import rgb2gray +from skimage.feature import corner_harris,corner_peaks +from skimage.exposure import equalize_hist +from skimage import measure +from skimage.draw import ellipse +from skimage.measure import label, regionprops +from skimage.transform import rotate +from skimage.feature import corner_orientations +from skimage.filters import threshold_otsu +from skimage.segmentation import clear_border +from skimage.measure import label +from skimage.morphology import closing, square +from skimage.measure import regionprops +from skimage.color import label2rgb +import seaborn +import pandas +try: + from runtime import * +except ImportError: + from smop.runtime import * + +from src.fiberfit_model.helpers import debug +import numpy as np +from numpy.linalg import eig, inv +import scipy.ndimage +from pylab import * +from os import listdir +from os.path import isfile, join +import numpy +import cv2 + + + +import sys +import matplotlib +import numpy as np +import scipy +import scipy.ndimage +import scipy.interpolate +import scipy.optimize +import scipy.integrate +import scipy.stats +import math +import time +import glob +from pylab import * +from pandas import DataFrame +#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +import matplotlib.pyplot as plt +from src.foruploading5 import prepimages +from src.fiberfit_model.EllipseDirectFit import* +from src.fiberfit_model import helpers +from ipywidgets import IntProgress +from IPython.display import display +import time + +def fiber_analysis(mypath,savepath, CO_lower,CO_upper,angleInc,radStep): + plt.ioff() + ## Upload Files + ## Upload Files + onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ] + images = numpy.empty(len(onlyfiles), dtype=object) + names = numpy.empty(len(onlyfiles), dtype=object) + for n in range(0, len(onlyfiles)): + images[n] = cv2.imread( join(mypath,onlyfiles[n]) ) + names[n]= (onlyfiles[n]) + img = numpy.empty(len(images), dtype=object) + for n in range(0,len(images)): + img[n]= np.invert(images[n]) + ## Set up empty Matrices (can clean this up later ) + ell_data_test = numpy.empty(len(img),dtype=object) + Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object) + MirnormPower_test = numpy.empty(len(img),dtype=object) + normPower_test = numpy.empty(len(img),dtype=object) + theta1RadFinal_test = numpy.empty(len(img),dtype=object) + A_test = numpy.empty(len(img),dtype=object) + centroid_test = numpy.empty(len(img),dtype=object) + t_new_test = numpy.empty(len(img),dtype=object) + im = numpy.empty(len(img), dtype=object) + PF = numpy.empty(len(img),dtype=object) + PS = numpy.empty(len(img),dtype=object) + theta1Rad_test = numpy.empty(len(img),dtype=object) + PowerY_test = numpy.empty(len(img),dtype=object) + Mirtheta1RadFinal1_plot= [] + MirnormPower_plot=[] + th_plot=[] + r_line_plot=[] + normPower1_plot=[] + num=0 + index=num + + plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output + mpl.rcParams['figure.max_open_warning'] = 0 + maxiter = 100 + + ## begin analysis + print('Number of Images:',len(img)) + max_count=100 + tot_sum_progress = len(img) + PROGRESSBAR = IntProgress(min=0, max=max_count,description='Progress') + PROGRESSBAR.bar_style='info' + display(PROGRESSBAR) # display the bar + for n in range(0,len(img)): + PROGRESSBAR.value = int(n/tot_sum_progress*100) # signal to increment the progress bar + im=img[n][:,:,1] # select first image + M= im.shape[0] #get image shape + N1 = im.shape[1] + n1 = np.round(N1/ 2) - 1 + freq = np.arange(-n1, n1 + 1, 1) + x, y = freq, freq + + ## Fourier Transform + fft_result = np.fft.fft2(im) + Fshift = np.fft.fftshift(fft_result) + + ## adjust to power spectrum to remove imaginary components + magnitude_spectrum= 20*np.log(np.abs(Fshift)) + Pabs = np.abs(Fshift) ** 2 + + M= im.shape[0] + N1=im.shape[1] + + # shift in terms of image because power spectrum is the mirroR of lines so + # mirroring back in terms of image would give right allignment + PabsFlip1 = np.rot90(Pabs) + PabsFlip = np.flipud(PabsFlip1) + while (PabsFlip.shape[0]>x.shape[0]): + PabsFlip = np.delete(PabsFlip, (0), axis=0) + while (PabsFlip.shape[1]>y.shape[0]): + PabsFlip = np.delete(PabsFlip, (0), axis=1) + while (PabsFlip.shape[0] 0)[0]] + A = np.vstack((A1, dot(T, A1))) + A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1] + A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0] + A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[ + 0] - A[4] * centroid[1] + A[3] = A3 + A[4] = A4 + A[5] = A5 + A = A / linalg.norm(A) + if (abs(A[1]) < (1 * 10 ^ (-15))): + if ((A[0]) <= (A[2])): + # Ellipse is horizontal + angle = 0; + major = sqrt(1 / A[0]) + minor = sqrt(1 / A[2]) + else: + angle = np.pi/2; + major = sqrt(1 / A[2]) + minor = sqrt(1 / A[0]) + else: + R = ((A[2] - A[0]) / A[1]) + tg = R - sqrt((R * R) + 1) + angle = math.atan(tg) + P = (2 * tg) / (1 + (tg * tg)) + + if ((A[0] > 0 and A[1] > 0 and A[2] > 0)): + if (angle < (-pi / 4)): + angle = angle + np.pi + else: + angle = angle + elif ((A[1] / P <= (-A[1] / P))): + if (angle < 0): + angle = angle + np.pi / 2 + else: + angle = angle - np.pi / 2 + elif (A[0] < 0 and A[1] < 0 and A[2] < 0): + if (angle < 0): + angle = angle + np.pi + else: + angle = angle - np.pi + else: + # Switch + if (angle < 0): + angle = angle + np.pi / 2 + else: + angle = angle - np.pi / 2 + + t_New = (angle) #angle + t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi) + Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n] + #print('round2',Mirtheta1RadFinal1) + MirnormPower = MirnormPower_test[n] + #print('mirnormpower',MirnormPower) + t = t_new_test[n] + r_line = np.arange(0, max(MirnormPower) + .5, .5) + th = np.zeros(len(r_line)) + for i in range(0, len(r_line)): + th[i] = t + th = np.concatenate([th, (th + 180)]) + r_line = np.concatenate([r_line, r_line]) + ## Polar Graph + graphs = ["#71C4BE","#BBDBA0","#CE7E56","#BC5C81","#84355A"]; + palette = seaborn.set_palette(graphs); + seaborn.set_context("poster",font_scale=1.5); + seaborn.set_style('ticks') + csfont = {'fontname':'Times New Roman', + 'size':'14', + } + ticksfont = {'fontname':'Times New Roman'} + plt.figure(frameon=False, figsize=(15, 10)); + plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5); + plt.suptitle(abs(t)-180, fontsize=20); + plt.polar(th * pi/180, r_line,color="#CE7E56",linewidth=4); + plt.savefig(savepath+'polarpositive'+names[n]); + if (max(MirnormPower)<2): + inc = 0.5 + elif (max(MirnormPower)<5): + inc = 1 + elif max(MirnormPower)<20: + inc = 5 + else: + inc = 10 + plt.yticks(np.arange(inc, max(MirnormPower), inc));#, **ticksfont) + + #plt.show() + t_final_rad = t*pi/180 + def fitted_func(thetas, c): + int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi) + return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \ + np.exp(c * np.cos(2 * (thetas - t_final_rad))) + c0 = 15 + xdata = theta1RadFinal_test[n] + theta1RadFinal = theta1RadFinal_test[n] + ydata= normPower_test[n] + normPower = normPower_test[n] + kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n]) + + # Shift data for plotting purposes + + diff = abs(theta1RadFinal - (t * pi / 180)) + #print(diff,'diff') + #centerLoc=diff.find(diff==min(diff)) + centerLoc = diff[np.argmin(diff)]#find(diff== min(diff)) + #print(centerLoc,'centerLoc') + + num_angles = len(theta1RadFinal) + shift = ((round(num_angles / 2) - (num_angles - centerLoc))) + #print(shift,'shift') + + normPower1 = np.roll(normPower, -int(shift)) + normPower1 + theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift)) + + if (shift > 0): + s = num_angles - shift + s= np.asscalar(s) + for k in range(s, num_angles): + theta1RadFinal1[k] = np.pi + theta1RadFinal1[k] + elif (shift < 0): + for k in range(0,90): + theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k] + + ## Normalized Distribution Graph + cartDist = plt.figure(figsize=(10, 10)); # Creates a figure containing cartesian distribution. + + h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); + plt.xticks(np.arange(-360, 360, 45,), **ticksfont); + plt.xlim([-95, 95]); + p_act = fitted_func(theta1RadFinal1, kappa); + #print(p_act) + a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]); + a= a.T + b = a[a[:,1].argsort()]#np.sort(a,axis=1) + h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); + plt.xlabel('Angle (°)', **csfont); + plt.ylabel('Normalized Intensity', **csfont); + #print(max(normPower)) + if (max(normPower)<2): + inc = 0.5 + elif (max(normPower)<5): + inc = 1 + elif (max(normPower)<20): + inc = 5 + else: + inc = 10 + plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont); + plt.ylim([0, max(normPower1) + .3]); + normPower1_plot.append(normPower1); + plt.savefig(savepath+'Normdist '+names[n]); + #plt.show() + #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight') + plt.close(); + t_final=t + slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1) + #print(rValue**2) + # Rounding results for Title of Figure + krnd = math.ceil(k * 1000) / 1000 + thrnd = abs(math.ceil(t_final * 1000) / 1000)-180 + krnd = math.trunc(krnd * 100) / 100 + thrnd = math.trunc(thrnd * 100) / 100 + a = 32.02 + b= -12.43 + c = 47.06 + d = -0.9185 + e = 19.43 + f = -0.07693 + x = kappa[0] + sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x) + #print("name",names[n],"kappa",kappa,"sigma",sig,"Rsq",rValue**2,"mu",t_final) + sum_NP = sum(normPower1) + #normPower2 = normPower1/sum(normPower1) + #sum_NP2 = sum(normPower2) + #print(sum_NP2) + ## Combined plot figure + palette = seaborn.set_palette(graphs); + seaborn.set_context("notebook",font_scale=1.5); + seaborn.set_style('ticks') + csfont = {'fontname':'Times New Roman', + 'size':'18', + } + ticksfont = {'fontname':'Times New Roman'} + fig = plt.figure(frameon=False,figsize=(20,12)); + fig.subplots_adjust(top=0.95); + fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold'); + grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1]); + cart_dist = plt.subplot(grid[1, 0:2]); + cart_dist.set_title('Von Mises Distribution (k)',fontsize=20); + h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); + cart_dist.set_xlim([-92, 92]); + cart_dist.set_xlabel('Angle (°)',fontsize=18); + cart_dist.set_ylim([0, max(normPower1) + .3]); + cart_dist.set_ylabel('Normalized Intensity',fontsize=18); + + p_act = fitted_func(theta1RadFinal1, kappa) + a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]) + a= a.T + b = a[a[:,1].argsort()]#np.sort(a,axis=1) + h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); + + if (max(normPower)<2): + inc = 0.5 + elif (max(normPower)<5): + inc = 1 + elif (max(normPower)<20): + inc = 5 + else: + inc = 10 + + polar_dist = plt.subplot(grid[0:2,3:5],projection='polar'); + polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o'); + polar_dist.plot(th * pi/180, r_line,color="#CE7E56",linewidth=4); + polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); + fig.text(0.75, 0.05, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}); + #polar_dist.text(np.radians(-110),1.95, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}) + orig_fig = plt.subplot(grid[0, 0]); + orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0]); + orig_fig.axis('off'); + fft_fig = plt.subplot(grid[0, 1]); + fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0]); + fft_fig.set_axis_off(); + fft_fig.axis('off'); + #fft_fig.imshow(log(PabsFlip), cmap='gray') + #orig_fig.imshow(images[n],cmap=plt.cm.viridis ) + #plt.tight_layout() + plt.savefig(savepath+'Grouped'+names[n]); + #print(n,'n') + #plt.show() + polar_dist_2 = plt.figure(figsize=(10, 10)); + #print('linebeforeindex') + #print(index,'currentindex') + #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1; + #MirnormPower_plot[index]=MirnormPower; + #th_plot[index] = th * pi/180 + #print(th_plot[index],'thplotindex') + #r_line_plot[index]=r_line + Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1); + MirnormPower_plot.append(MirnormPower); + th_plot.append(th * pi/180) + r_line_plot.append(r_line) + polar_dist.plot(Mirtheta1RadFinal1, MirnormPower); + polar_dist.plot(th * pi/180, r_line,linewidth=4); + polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); + plt.close('all') + #index=index+1 + #print(index,'Index') + if n == (tot_sum_progress-1): + PROGRESSBAR.value = 100 # signal to increment the progress bar +## export of cartesian coordinate data + arr = numpy.array(MirnormPower_plot) + arr2 = numpy.array(Mirtheta1RadFinal1_plot) + arr3 = numpy.array(normPower1_plot) + cv2.imread('src/angles_csv.csv') + angles = np.genfromtxt('src/angles_csv.csv',delimiter=',') + arr4 = angles[:,0] + #arr4 = numpy.array(theta1RadFinal1*180/pi) + a = numpy.transpose(arr3) + b = numpy.transpose(arr4) + b[b> 100] = 0 + b[b<-100] = 0 + export_data=np.hstack(((b[:,None]),a)) + numpy.savetxt(savepath+"data_export.csv", export_data, delimiter=",") + return export_data + From 1cee5bd3a5923f4aa2062346789751b97061a7e5 Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 4 Mar 2021 18:38:19 -0500 Subject: [PATCH 106/153] Add files via upload --- FiberFit_toshare_3_4_2021.ipynb | 392 ++++++++++++++++++++++++++++++++ 1 file changed, 392 insertions(+) create mode 100644 FiberFit_toshare_3_4_2021.ipynb diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb new file mode 100644 index 0000000..496d0cc --- /dev/null +++ b/FiberFit_toshare_3_4_2021.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\asus\\anaconda3\\envs\\test-environment\\lib\\site-packages\\IPython\\html.py:12: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", + " warn(\"The `IPython.html` package has been deprecated since IPython 4.0. \"\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d80277056c9043a69b11a045f7ff94f8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from widgetsmp import s,run,run2,save,clear\n", + "from src.foruploading5 import ff,upload,prepimages\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, Markdown, clear_output\n", + "from ipywidgets import interact, interact_manual\n", + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7cb0f1a2432048a29252787e2e927245", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6975bd10c7144a07a943ecee559986ea", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Create zip file to download." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "a ../FiberFit/output\n", + "a ../FiberFit/output/data_export.csv\n", + "a ../FiberFit/output/FFT Norm Test Image_90_0.7_90.03_0.7_0.57297.png\n", + "a ../FiberFit/output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.57297.png\n", + "a ../FiberFit/output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.57297.png\n", + "a ../FiberFit/output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.57297.png\n", + "tar: *;: Couldn't find file: No such file or directory\n", + "tar: Error exit delayed from previous errors.\n" + ] + } + ], + "source": [ + "!tar chvfz notebook.tar.gz ../FiberFit/output *;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finally, download output file by clicking on download link. Files include images for analysis and bulk .csv file. Delete files in Data and output file by clicking on clear data button prior to running a new set of files." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c8d80d987bc14e1c8d05f95221ad87a7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \\n \\n …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e4c3e2549d9e4388ad4e37ee77a80ffe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "de69a172b496466b8a55eb4a7836e7dc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save(_)\n", + "clear(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness (code modified from [this paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215137). Again, output will be in \"output\" folder. \n", + "###### Note: These color maps are from a program called Quanfima. This is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results. PNG or JPEG files are suggested for speedier calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps: First, Upload a single image for analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "54aa62e0d1324968893f0c728a5a8d1a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps: Next, run analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9b897537eab1430499fadea380851c8e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Run Fiber Orientation Visualization', layout=Layout(width='300px'), style=B…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d4d4663a0f0e4484831c72dd2b4ade16", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run2(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create output file to download" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "a ../FiberFit/output\n", + "a ../FiberFit/output/DIAMETER_estimate_diameter_map.png\n", + "a ../FiberFit/output/orientation_map.png\n", + "tar: *;: Couldn't find file: No such file or directory\n", + "tar: Error exit delayed from previous errors.\n" + ] + } + ], + "source": [ + "!tar chvfz notebook.tar.gz ../FiberFit/output *;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download output file by clicking on download link. Files include images shown above. Click clear data to clear input and output data to run again." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "00de3e2d734f4f6fa63c0e80beb101fc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \\n \\n …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6d22eab1f2ba4b58af9881492698c0b8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "adf9ee61be174e20ba9e5e871607668b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save(_)\n", + "clear(_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ed503297045e2c21cf9f73a513002ba16eba50fa Mon Sep 17 00:00:00 2001 From: mprender Date: Thu, 4 Mar 2021 18:40:07 -0500 Subject: [PATCH 107/153] Rename oldFiberFit-toshare-6.1.2020.ipynb to oldversions/FiberFit-toshare-6.1.2020.ipynb --- .../FiberFit-toshare-6.1.2020.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit-toshare-6.1.2020.ipynb => oldversions/FiberFit-toshare-6.1.2020.ipynb (100%) diff --git a/FiberFit-toshare-6.1.2020.ipynb b/oldversions/FiberFit-toshare-6.1.2020.ipynb similarity index 100% rename from FiberFit-toshare-6.1.2020.ipynb rename to oldversions/FiberFit-toshare-6.1.2020.ipynb From fd336376553da4ed0f9b67a93183edb93aafb423 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:02:55 -0500 Subject: [PATCH 108/153] Delete old_version directory --- .../FiberFit-toshare-5.22.2020_2.ipynb | 682 ------------------ 1 file changed, 682 deletions(-) delete mode 100644 old_version/FiberFit-toshare-5.22.2020_2.ipynb diff --git a/old_version/FiberFit-toshare-5.22.2020_2.ipynb b/old_version/FiberFit-toshare-5.22.2020_2.ipynb deleted file mode 100644 index 894aefa..0000000 --- a/old_version/FiberFit-toshare-5.22.2020_2.ipynb +++ /dev/null @@ -1,682 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Import necessary Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: greenlet in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.4.15)\n", - "Requirement already satisfied: PyQt5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (5.14.2)\n", - "Requirement already satisfied: PyQt5-sip<13,>=12.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from PyQt5) (12.7.2)\n" - ] - } - ], - "source": [ - "import sys\n", - "!{sys.executable} -m pip install greenlet\n", - "!{sys.executable} -m pip install PyQt5\n", - "import PyQt5" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "matplotlib.rcParams['backend'] = \"Qt5Agg\"\n", - "from matplotlib.figure import Figure\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2a7ec5a5845c4c22af718f4ce934d24f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Press Me', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from __future__ import division\n", - "from __future__ import print_function\n", - "from __future__ import unicode_literals\n", - "from __future__ import division\n", - "from __future__ import absolute_import\n", - "\n", - "import argparse, re, os, glob, sys, pprint, itertools\n", - "import inspect\n", - "\n", - "import numpy as np\n", - "# try:\n", - "# from runtime import *\n", - "# except ImportError:\n", - "# from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - " \n", - "btn = widgets.Button(description='Press Me')\n", - "display(btn)\n", - "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", - "import skimage.io as io\n", - "from skimage import data\n", - "from skimage.filters import sobel \n", - "from skimage.morphology import watershed\n", - "from skimage import feature\n", - "from skimage.color import rgb2gray\n", - "from skimage.feature import corner_harris,corner_peaks\n", - "from skimage.exposure import equalize_hist\n", - "from skimage import measure\n", - "from skimage.draw import ellipse\n", - "from skimage.measure import label, regionprops\n", - "from skimage.transform import rotate\n", - "from skimage.feature import corner_orientations\n", - "from skimage.filters import threshold_otsu\n", - "from skimage.segmentation import clear_border\n", - "from skimage.measure import label\n", - "from skimage.morphology import closing, square\n", - "from skimage.measure import regionprops\n", - "from skimage.color import label2rgb\n", - "import seaborn\n", - "import pandas\n", - "try:\n", - " from runtime import *\n", - "except ImportError:\n", - " from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "from os import listdir\n", - "from os.path import isfile, join\n", - "import numpy\n", - "import cv2\n", - "\n", - "\n", - "#test\n", - " \n", - "import sys\n", - "import matplotlib\n", - "import numpy as np\n", - "import scipy\n", - "import scipy.ndimage\n", - "import scipy.interpolate\n", - "import scipy.optimize\n", - "import scipy.integrate\n", - "import scipy.stats\n", - "import math\n", - "import time\n", - "import glob\n", - "from pylab import *\n", - "from pandas import DataFrame\n", - "#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from src.fiberfit_model.EllipseDirectFit import*\n", - "from src.fiberfit_model import helpers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Input Variables " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "## File Path with binary square images of fibers to analyze\n", - "mypath='test_images/'\n", - "\n", - "##Path to folder where you would like images to be saved\n", - "savepath = 'output/'\n", - "## NOTE - we can also output raw data. let me know if you would prefer that.\n", - "\n", - "## Variables for settings\n", - "CO_lower = 20 #upper fiber thickness, pixels\n", - "CO_upper = 1 #lower fiber thickness, pixels\n", - "angleInc = 1 #angle increment\n", - "radStep = 0.5 #radial step size\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now, Run the code below. Figures will be saved in specified output folder" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "length image 6\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 n\n", - "1 Index\n", - "1 n\n", - "2 Index\n", - "2 n\n", - "3 Index\n", - "3 n\n", - "4 Index\n", - "4 n\n", - "5 Index\n", - "5 n\n", - "6 Index\n" - ] - } - ], - "source": [ - "## Upload Files\n", - "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", - "images = numpy.empty(len(onlyfiles), dtype=object)\n", - "names = numpy.empty(len(onlyfiles), dtype=object)\n", - "for n in range(0, len(onlyfiles)):\n", - " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", - " names[n]= (onlyfiles[n])\n", - "img = numpy.empty(len(images), dtype=object)\n", - "for n in range(0,len(images)):\n", - " img[n]= np.invert(images[n])\n", - " \n", - "## Set up empty Matrices (can clean this up later )\n", - "ell_data_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", - "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", - "normPower_test = numpy.empty(len(img),dtype=object)\n", - "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", - "A_test = numpy.empty(len(img),dtype=object)\n", - "centroid_test = numpy.empty(len(img),dtype=object)\n", - "t_new_test = numpy.empty(len(img),dtype=object)\n", - "im = numpy.empty(len(img), dtype=object)\n", - "PF = numpy.empty(len(img),dtype=object)\n", - "PS = numpy.empty(len(img),dtype=object)\n", - "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", - "PowerY_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal1_plot= []\n", - "MirnormPower_plot=[]\n", - "th_plot=[]\n", - "r_line_plot=[]\n", - "normPower1_plot=[]\n", - "num=0\n", - "index=num\n", - "\n", - "plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output\n", - "mpl.rcParams['figure.max_open_warning'] = 0\n", - "maxiter = 100\n", - "\n", - "## begin analysis\n", - "print('length image',len(img))\n", - "for n in range(0,len(img)):\n", - " im=img[n][:,:,1] # select first image\n", - " M= im.shape[0] #get image shape\n", - " N1 = im.shape[1] \n", - " n1 = np.round(N1/ 2) - 1\n", - " freq = np.arange(-n1, n1 + 1, 1)\n", - " x, y = freq, freq\n", - " \n", - " ## Fourier Transform\n", - " fft_result = np.fft.fft2(im)\n", - " Fshift = np.fft.fftshift(fft_result)\n", - " \n", - " ## adjust to power spectrum to remove imaginary components\n", - " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", - " Pabs = np.abs(Fshift) ** 2\n", - " \n", - " M= im.shape[0]\n", - " N1=im.shape[1]\n", - "\n", - " # shift in terms of image because power spectrum is the mirroR of lines so\n", - " # mirroring back in terms of image would give right allignment\n", - " PabsFlip1 = np.rot90(Pabs)\n", - " PabsFlip = np.flipud(PabsFlip1)\n", - " while (PabsFlip.shape[0]>x.shape[0]):\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=0) \n", - " while (PabsFlip.shape[1]>y.shape[0]):\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", - " while (PabsFlip.shape[0] 0)[0]]\n", - " A = np.vstack((A1, dot(T, A1)))\n", - " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", - " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", - " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", - " 0] - A[4] * centroid[1]\n", - " A[3] = A3\n", - " A[4] = A4\n", - " A[5] = A5\n", - " A = A / linalg.norm(A)\n", - " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", - " if ((A[0]) <= (A[2])):\n", - " # Ellipse is horizontal\n", - " angle = 0;\n", - " major = sqrt(1 / A[0])\n", - " minor = sqrt(1 / A[2])\n", - " else:\n", - " angle = np.pi/2;\n", - " major = sqrt(1 / A[2])\n", - " minor = sqrt(1 / A[0])\n", - " else:\n", - " R = ((A[2] - A[0]) / A[1])\n", - " tg = R - sqrt((R * R) + 1)\n", - " angle = math.atan(tg)\n", - " P = (2 * tg) / (1 + (tg * tg))\n", - "\n", - " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", - " if (angle < (-pi / 4)):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle\n", - " elif ((A[1] / P <= (-A[1] / P))):\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", - " if (angle < 0):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle - np.pi\n", - " else:\n", - " # Switch\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - "\n", - " t_New = (angle) #angle\n", - " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", - " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", - " #print('round2',Mirtheta1RadFinal1)\n", - " MirnormPower = MirnormPower_test[n]\n", - " #print('mirnormpower',MirnormPower)\n", - " t = t_new_test[n]\n", - " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", - " th = np.zeros(len(r_line))\n", - " for i in range(0, len(r_line)):\n", - " th[i] = t\n", - " th = np.concatenate([th, (th + 180)])\n", - " r_line = np.concatenate([r_line, r_line])\n", - " ## Polar Graph\n", - " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"poster\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'14',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " plt.figure(frameon=False, figsize=(15, 10))\n", - " plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5)\n", - " plt.suptitle(abs(t)-180, fontsize=20)\n", - " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", - " plt.savefig(savepath+'polarpositive'+names[n])\n", - " if (max(MirnormPower)<2):\n", - " inc = 0.5\n", - " elif (max(MirnormPower)<5):\n", - " inc = 1\n", - " elif max(MirnormPower)<20:\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", - "\n", - " #plt.show()\n", - " t_final_rad = t*pi/180\n", - " def fitted_func(thetas, c):\n", - " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", - " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", - " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", - " c0 = 15\n", - " xdata = theta1RadFinal_test[n]\n", - " theta1RadFinal = theta1RadFinal_test[n]\n", - " ydata= normPower_test[n]\n", - " normPower = normPower_test[n]\n", - " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", - "\n", - " # Shift data for plotting purposes\n", - "\n", - " diff = abs(theta1RadFinal - (t * pi / 180))\n", - " #print(diff,'diff')\n", - " #centerLoc=diff.find(diff==min(diff))\n", - " centerLoc = diff[np.argmin(diff)]#find(diff== min(diff))\n", - " #print(centerLoc,'centerLoc')\n", - "\n", - " num_angles = len(theta1RadFinal)\n", - " shift = ((round(num_angles / 2) - (num_angles - centerLoc)))\n", - " #print(shift,'shift')\n", - "\n", - " normPower1 = np.roll(normPower, -int(shift))\n", - " normPower1\n", - " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", - "\n", - " if (shift > 0):\n", - " s = num_angles - shift\n", - " s= np.asscalar(s)\n", - " for k in range(s, num_angles):\n", - " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", - " elif (shift < 0):\n", - " for k in range(0,90):\n", - " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", - " \n", - " ## Normalized Distribution Graph\n", - " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", - "\n", - " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", - " plt.xlim([-95, 95])\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " #print(p_act)\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", - " plt.xlabel('Angle (°)', **csfont)\n", - " plt.ylabel('Normalized Intensity', **csfont)\n", - " #print(max(normPower))\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", - " plt.ylim([0, max(normPower1) + .3])\n", - " normPower1_plot.append(normPower1)\n", - " plt.savefig(savepath+'Normdist '+names[n])\n", - " #plt.show()\n", - " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", - " plt.close()\n", - " t_final=t\n", - " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", - " #print(rValue**2)\n", - " # Rounding results for Title of Figure\n", - " krnd = math.ceil(k * 1000) / 1000\n", - " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", - " krnd = math.trunc(krnd * 100) / 100\n", - " thrnd = math.trunc(thrnd * 100) / 100\n", - " a = 32.02\n", - " b= -12.43\n", - " c = 47.06\n", - " d = -0.9185\n", - " e = 19.43\n", - " f = -0.07693\n", - " x = kappa[0]\n", - " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", - " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", - " sum_NP = sum(normPower1)\n", - " #normPower2 = normPower1/sum(normPower1)\n", - " #sum_NP2 = sum(normPower2)\n", - " #print(sum_NP2)\n", - " ## Combined plot figure\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"notebook\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'18',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " fig = plt.figure(frameon=False,figsize=(20,12))\n", - " fig.subplots_adjust(top=0.95)\n", - " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", - " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", - " cart_dist = plt.subplot(grid[1, 0:2])\n", - " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", - " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " cart_dist.set_xlim([-92, 92])\n", - " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", - " cart_dist.set_ylim([0, max(normPower1) + .3])\n", - " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", - "\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", - "\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - "\n", - " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", - " polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o');\n", - " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " fig.text(0.75, 0.05, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " #polar_dist.text(np.radians(-110),1.95, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " orig_fig = plt.subplot(grid[0, 0]); \n", - " orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0])\n", - " orig_fig.axis('off');\n", - " fft_fig = plt.subplot(grid[0, 1])\n", - " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", - " fft_fig.set_axis_off()\n", - " fft_fig.axis('off')\n", - " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", - " orig_fig.imshow(images[n],cmap=plt.cm.viridis )\n", - " #plt.tight_layout()\n", - " plt.savefig(savepath+'Grouped'+names[n])\n", - " print(n,'n')\n", - " #plt.show()\n", - " polar_dist_2 = plt.figure(figsize=(10, 10))\n", - " #print('linebeforeindex')\n", - " #print(index,'currentindex')\n", - " #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1;\n", - " #MirnormPower_plot[index]=MirnormPower;\n", - " #th_plot[index] = th * pi/180\n", - " #print(th_plot[index],'thplotindex')\n", - " #r_line_plot[index]=r_line\n", - " Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1);\n", - " MirnormPower_plot.append(MirnormPower);\n", - " th_plot.append(th * pi/180)\n", - " r_line_plot.append(r_line)\n", - " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower);\n", - " polar_dist.plot(th * pi/180, r_line,linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " index=index+1\n", - " print(index,'Index')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "## export of cartesian coordinate data\n", - "arr = numpy.array(MirnormPower_plot) \n", - "arr2 = numpy.array(Mirtheta1RadFinal1_plot)\n", - "arr3 = numpy.array(normPower1_plot)\n", - "arr4 = numpy.array(theta1RadFinal1*180/pi)\n", - "a = numpy.transpose(arr3)\n", - "b = numpy.transpose(arr4)\n", - "b[b> 100] = 0\n", - "b[b<-100] = 0\n", - "export_data=np.hstack(((b[:,None]),a))\n", - "numpy.savetxt(savepath+\"data_export.csv\", export_data, delimiter=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "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": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 33563e3a9d17e0ed77e5e7e36940b535e0769257 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:03:09 -0500 Subject: [PATCH 109/153] Delete old_versions directory --- old_versions/FiberFit-toshare-5.21.2020.ipynb | 820 ------------------ old_versions/FiberFit-toshare-5.22.2020.ipynb | 777 ----------------- old_versions/FiberFit-toshare-6.5.19.ipynb | 476 ---------- 3 files changed, 2073 deletions(-) delete mode 100644 old_versions/FiberFit-toshare-5.21.2020.ipynb delete mode 100644 old_versions/FiberFit-toshare-5.22.2020.ipynb delete mode 100644 old_versions/FiberFit-toshare-6.5.19.ipynb diff --git a/old_versions/FiberFit-toshare-5.21.2020.ipynb b/old_versions/FiberFit-toshare-5.21.2020.ipynb deleted file mode 100644 index 3705d13..0000000 --- a/old_versions/FiberFit-toshare-5.21.2020.ipynb +++ /dev/null @@ -1,820 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Import necessary Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: greenlet in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.4.15)\n", - "Requirement already satisfied: PyQt5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (5.14.2)\n", - "Requirement already satisfied: PyQt5-sip<13,>=12.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from PyQt5) (12.7.2)\n", - "\u001b[31mERROR: Could not find a version that satisfies the requirement PyQt4 (from versions: none)\u001b[0m\n", - "\u001b[31mERROR: No matching distribution found for PyQt4\u001b[0m\n", - "Requirement already satisfied: PyQt5-sip in /srv/conda/envs/notebook/lib/python3.7/site-packages (12.7.2)\n", - "\u001b[31mERROR: Could not find a version that satisfies the requirement 12.7.1 (from versions: none)\u001b[0m\n", - "\u001b[31mERROR: No matching distribution found for 12.7.1\u001b[0m\n" - ] - } - ], - "source": [ - "import sys\n", - "!{sys.executable} -m pip install greenlet\n", - "!{sys.executable} -m pip install PyQt5\n", - "import PyQt5" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "matplotlib.rcParams['backend'] = \"Qt5Agg\"\n", - "from matplotlib.figure import Figure\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f193c4ca5e5144a39a06c63a58c85a28", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Press Me', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from __future__ import division\n", - "from __future__ import print_function\n", - "from __future__ import unicode_literals\n", - "from __future__ import division\n", - "from __future__ import absolute_import\n", - "\n", - "import argparse, re, os, glob, sys, pprint, itertools\n", - "import inspect\n", - "\n", - "import numpy as np\n", - "# try:\n", - "# from runtime import *\n", - "# except ImportError:\n", - "# from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - " \n", - "btn = widgets.Button(description='Press Me')\n", - "display(btn)\n", - "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", - "import skimage.io as io\n", - "from skimage import data\n", - "from skimage.filters import sobel \n", - "from skimage.morphology import watershed\n", - "from skimage import feature\n", - "from skimage.color import rgb2gray\n", - "from skimage.feature import corner_harris,corner_peaks\n", - "from skimage.exposure import equalize_hist\n", - "from skimage import measure\n", - "from skimage.draw import ellipse\n", - "from skimage.measure import label, regionprops\n", - "from skimage.transform import rotate\n", - "from skimage.feature import corner_orientations\n", - "from skimage.filters import threshold_otsu\n", - "from skimage.segmentation import clear_border\n", - "from skimage.measure import label\n", - "from skimage.morphology import closing, square\n", - "from skimage.measure import regionprops\n", - "from skimage.color import label2rgb\n", - "import seaborn\n", - "import pandas\n", - "try:\n", - " from runtime import *\n", - "except ImportError:\n", - " from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "from os import listdir\n", - "from os.path import isfile, join\n", - "import numpy\n", - "import cv2\n", - "\n", - "\n", - "#test\n", - " \n", - "import sys\n", - "import matplotlib\n", - "import numpy as np\n", - "import scipy\n", - "import scipy.ndimage\n", - "import scipy.interpolate\n", - "import scipy.optimize\n", - "import scipy.integrate\n", - "import scipy.stats\n", - "import math\n", - "import time\n", - "import glob\n", - "from pylab import *\n", - "from pandas import DataFrame\n", - "#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from src.fiberfit_model.EllipseDirectFit import*\n", - "from src.fiberfit_model import helpers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Input Variables " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "## File Path with binary square images of fibers to analyze\n", - "mypath='fibers'\n", - "\n", - "##Path to folder where you would like images to be saved\n", - "savepath = 'output/'\n", - "## NOTE - we can also output raw data. let me know if you would prefer that.\n", - "\n", - "## Variables for settings\n", - "CO_lower = 20 #upper fiber thickness, pixels\n", - "CO_upper = 1 #lower fiber thickness, pixels\n", - "angleInc = 1 #angle increment\n", - "radStep = 0.5 #radial step size\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now, Run the code below. Figures will be saved in specified output folder" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "length image 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n", - "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 n\n", - "1 Index\n", - "1 n\n", - "2 Index\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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      " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Upload Files\n", - "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", - "images = numpy.empty(len(onlyfiles), dtype=object)\n", - "names = numpy.empty(len(onlyfiles), dtype=object)\n", - "for n in range(0, len(onlyfiles)):\n", - " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", - " names[n]= (onlyfiles[n])\n", - "img = numpy.empty(len(images), dtype=object)\n", - "for n in range(0,len(images)):\n", - " img[n]= np.invert(images[n])\n", - " \n", - "## Set up empty Matrices (can clean this up later )\n", - "ell_data_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", - "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", - "normPower_test = numpy.empty(len(img),dtype=object)\n", - "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", - "A_test = numpy.empty(len(img),dtype=object)\n", - "centroid_test = numpy.empty(len(img),dtype=object)\n", - "t_new_test = numpy.empty(len(img),dtype=object)\n", - "im = numpy.empty(len(img), dtype=object)\n", - "PF = numpy.empty(len(img),dtype=object)\n", - "PS = numpy.empty(len(img),dtype=object)\n", - "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", - "PowerY_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal1_plot= numpy.empty(len(img),dtype=object)\n", - "MirnormPower_plot=numpy.empty(len(img),dtype=object)\n", - "th_plot=numpy.empty(len(img),dtype=object)\n", - "r_line_plot=numpy.empty(len(img),dtype=object)\n", - "num=0\n", - "index=num\n", - "\n", - "## begin analysis\n", - "print('length image',len(img))\n", - "for n in range(0,len(img)):\n", - " im=img[n][:,:,1] # select first image\n", - " M= im.shape[0] #get image shape\n", - " N1 = im.shape[1] \n", - " n1 = np.round(N1/ 2) - 1\n", - " freq = np.arange(-n1, n1 + 1, 1)\n", - " x, y = freq, freq\n", - " \n", - " ## Fourier Transform\n", - " fft_result = np.fft.fft2(im)\n", - " Fshift = np.fft.fftshift(fft_result)\n", - " \n", - " ## adjust to power spectrum to remove imaginary components\n", - " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", - " Pabs = np.abs(Fshift) ** 2\n", - " \n", - " M= im.shape[0]\n", - " N1=im.shape[1]\n", - "\n", - " # shift in terms of image because power spectrum is the mirroR of lines so\n", - " # mirroring back in terms of image would give right allignment\n", - " PabsFlip1 = np.rot90(Pabs)\n", - " PabsFlip = np.flipud(PabsFlip1)\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", - " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", - " logScale = plt.figure(frameon=False, figsize=(10, 10))\n", - " ax = plt.Axes(logScale, [0., 0., 1., 1.])\n", - " ax.set_axis_off()\n", - " logScale.add_axes(ax)\n", - " plt.axis('off')\n", - " plt.imshow(log(PabsFlip), cmap='gray', aspect='auto')\n", - " plt.savefig(savepath+'FFT '+names[n])\n", - " #plt.show() \n", - " plt.close()\n", - "\n", - " # Set up polar coordinates prior to summing the spectrum\n", - " theta1Rad = np.linspace(0.0, 2 * math.pi, num=int(360/angleInc))\n", - " theta1Rad_test[n]=theta1Rad\n", - " f1 = np.round_(N1 / (2 * CO_lower))\n", - " f2 = np.round_(N1 / (2 * CO_upper))\n", - " rho1 = np.linspace(f1, f2, num=int((f2 - f1)/radStep)) # frequency band\n", - " PowerX = np.zeros((theta1Rad.size, theta1Rad.size))\n", - " PowerY = np.zeros((theta1Rad.size))\n", - " PF[n] = PabsFlip\n", - " \n", - " # Interpolate using a Spine\n", - " PowerSpline = scipy.interpolate.RectBivariateSpline(y=y, x=x, z=PabsFlip)\n", - " PS[n] = PowerSpline\n", - " n_dx = 0.001\n", - "\n", - " for p in range(0, theta1Rad.size):\n", - " # converting theta1Rad and rho1 to cartesian coordinates\n", - " xfinal = rho1 * math.cos(theta1Rad[p])\n", - " yfinal = rho1 * math.sin(theta1Rad[p])\n", - "\n", - " # Evaluate spin on path\n", - " px = PowerSpline.ev(yfinal, xfinal)\n", - " PowerY[p] = np.sum(px)\n", - " #print(PowerY)\n", - " PowerY_test[n]=PowerY\n", - " theta1Rad = theta1Rad_test[n]\n", - " PowerY = PowerY_test[n]\n", - " #print(PowerY)\n", - " # Only use the data in the first two quadrants (Spectrum is symmetric)\n", - " num = len(theta1Rad)\n", - " PowerYFinal = PowerY[0:num // 2]\n", - " theta1RadFinal = theta1Rad[0:num // 2]\n", - " theta1RadFinal_test[n]= theta1RadFinal\n", - " power_area = np.trapz(PowerYFinal, theta1RadFinal)\n", - " normPower = PowerYFinal / power_area\n", - " normPower_test[n]=normPower\n", - " \n", - " # Combine data into [XY] to fit to an ellipse\n", - " Mirtheta1RadFinal1 = np.concatenate([theta1RadFinal.T, (theta1RadFinal + np.pi).T])\n", - " #print('round1',Mirtheta1RadFinal1)\n", - " MirnormPower = np.concatenate([normPower.T, normPower.T])\n", - " theta = Mirtheta1RadFinal1\n", - " #print('theta',theta)\n", - " radius = MirnormPower\n", - " #print('radius',MirnormPower)\n", - " ydata = radius * np.cos(theta)\n", - " xdata = radius * np.sin(theta)\n", - " ell_data = np.vstack([xdata, ydata])\n", - " ell_data = ell_data.T\n", - " ell_data_test[n] = ell_data\n", - " Mirtheta1RadFinal_test[n] = Mirtheta1RadFinal1\n", - " MirnormPower_test[n]= MirnormPower\n", - "\n", - " # Python fitting function, see EllipseDirectFit\n", - " XY = ell_data_test[n]\n", - " centroid = np.mean(XY, axis=0)\n", - " e1 = (XY[:, 0] - centroid[0]) ** 2\n", - " e2 = (XY[:, 0] - centroid[0]) * ((XY[:, 1] - centroid[1]))\n", - " e3 = (XY[:, 1] - centroid[1]) ** 2\n", - " D1 = np.vstack(([e1, e2, e3])).T\n", - " f1 = XY[:, 0] - centroid[0]\n", - " f2 = XY[:, 1] - centroid[1]\n", - " f3 = np.ones((1, XY.shape[0]))\n", - " D2 = vstack(([f1, f2, f3])).T\n", - " S1 = dot(D1.T, D1)\n", - " S2 = dot(D1.T, D2)\n", - " S3 = dot(D2.T, D2)\n", - " T = dot(-linalg.inv(S3), S2.T)\n", - " M = S1 + (dot(S2, T))\n", - " M = np.vstack(([M[2, :] / 2, - M[1, :], M[0, :] / 2]))\n", - " _eval, evec = linalg.eig(M)\n", - " cond = (4 * evec[0, :] * evec[2, :]) - (evec[1, :] ** 2)\n", - " A1 = evec[:, nonzero(cond > 0)[0]]\n", - " A = np.vstack((A1, dot(T, A1)))\n", - " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", - " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", - " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", - " 0] - A[4] * centroid[1]\n", - " A[3] = A3\n", - " A[4] = A4\n", - " A[5] = A5\n", - " A = A / linalg.norm(A)\n", - " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", - " if ((A[0]) <= (A[2])):\n", - " # Ellipse is horizontal\n", - " angle = 0;\n", - " major = sqrt(1 / A[0])\n", - " minor = sqrt(1 / A[2])\n", - " else:\n", - " angle = np.pi/2;\n", - " major = sqrt(1 / A[2])\n", - " minor = sqrt(1 / A[0])\n", - " else:\n", - " R = ((A[2] - A[0]) / A[1])\n", - " tg = R - sqrt((R * R) + 1)\n", - " angle = math.atan(tg)\n", - " P = (2 * tg) / (1 + (tg * tg))\n", - "\n", - " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", - " if (angle < (-pi / 4)):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle\n", - " elif ((A[1] / P <= (-A[1] / P))):\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", - " if (angle < 0):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle - np.pi\n", - " else:\n", - " # Switch\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - "\n", - " t_New = (angle) #angle\n", - " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", - " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", - " #print('round2',Mirtheta1RadFinal1)\n", - " MirnormPower = MirnormPower_test[n]\n", - " #print('mirnormpower',MirnormPower)\n", - " t = t_new_test[n]\n", - " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", - " th = np.zeros(len(r_line))\n", - " for i in range(0, len(r_line)):\n", - " th[i] = t\n", - " th = np.concatenate([th, (th + 180)])\n", - " r_line = np.concatenate([r_line, r_line])\n", - " ## Polar Graph\n", - " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"poster\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'14',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " plt.figure(frameon=False, figsize=(15, 10))\n", - " plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5)\n", - " plt.suptitle(abs(t)-180, fontsize=20)\n", - " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", - " plt.savefig(savepath+'polarpositive'+names[n])\n", - " if (max(MirnormPower)<2):\n", - " inc = 0.5\n", - " elif (max(MirnormPower)<5):\n", - " inc = 1\n", - " elif max(MirnormPower)<20:\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", - "\n", - " #plt.show()\n", - " t_final_rad = t*pi/180\n", - " def fitted_func(thetas, c):\n", - " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", - " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", - " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", - " c0 = 15\n", - " xdata = theta1RadFinal_test[n]\n", - " theta1RadFinal = theta1RadFinal_test[n]\n", - " ydata= normPower_test[n]\n", - " normPower = normPower_test[n]\n", - " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", - "\n", - " # Shift data for plotting purposes\n", - "\n", - " diff = abs(theta1RadFinal - (t * pi / 180))\n", - " #print(diff,'diff')\n", - " #centerLoc=diff.find(diff==min(diff))\n", - " centerLoc = diff[np.argmin(diff)]#find(diff== min(diff))\n", - " #print(centerLoc,'centerLoc')\n", - "\n", - " num_angles = len(theta1RadFinal)\n", - " shift = ((round(num_angles / 2) - (num_angles - centerLoc)))\n", - " #print(shift,'shift')\n", - "\n", - " normPower1 = np.roll(normPower, -int(shift))\n", - " normPower1\n", - " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", - "\n", - " if (shift > 0):\n", - " s = num_angles - shift\n", - " s= np.asscalar(s)\n", - " for k in range(s, num_angles):\n", - " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", - " elif (shift < 0):\n", - " for k in range(0,90):\n", - " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", - " \n", - " ## Normalized Distribution Graph\n", - " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", - "\n", - " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", - " plt.xlim([-95, 95])\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " #print(p_act)\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", - " plt.xlabel('Angle (°)', **csfont)\n", - " plt.ylabel('Normalized Intensity', **csfont)\n", - " #print(max(normPower))\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", - " plt.ylim([0, max(normPower1) + .3])\n", - " plt.savefig(savepath+'Normdist '+names[n])\n", - " #plt.show()\n", - " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", - " plt.close()\n", - " t_final=t\n", - " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", - " #print(rValue**2)\n", - " # Rounding results for Title of Figure\n", - " krnd = math.ceil(k * 1000) / 1000\n", - " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", - " krnd = math.trunc(krnd * 100) / 100\n", - " thrnd = math.trunc(thrnd * 100) / 100\n", - " a = 32.02\n", - " b= -12.43\n", - " c = 47.06\n", - " d = -0.9185\n", - " e = 19.43\n", - " f = -0.07693\n", - " x = kappa[0]\n", - " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", - " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", - " sum_NP = sum(normPower1)\n", - " #normPower2 = normPower1/sum(normPower1)\n", - " #sum_NP2 = sum(normPower2)\n", - " #print(sum_NP2)\n", - " ## Combined plot figure\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"notebook\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'18',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " fig = plt.figure(frameon=False,figsize=(20,12))\n", - " fig.subplots_adjust(top=0.95)\n", - " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", - " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", - " cart_dist = plt.subplot(grid[1, 0:2])\n", - " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", - " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " cart_dist.set_xlim([-92, 92])\n", - " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", - " cart_dist.set_ylim([0, max(normPower1) + .3])\n", - " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", - "\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", - "\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - "\n", - " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", - " polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o');\n", - " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " fig.text(0.75, 0.05, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " #polar_dist.text(np.radians(-110),1.95, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " orig_fig = plt.subplot(grid[0, 0]); \n", - " orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0])\n", - " orig_fig.axis('off');\n", - " fft_fig = plt.subplot(grid[0, 1])\n", - " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", - " fft_fig.set_axis_off()\n", - " fft_fig.axis('off')\n", - " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", - " orig_fig.imshow(images[n],cmap=plt.cm.viridis )\n", - " #plt.tight_layout()\n", - " plt.savefig(savepath+'Grouped'+names[n])\n", - " print(n,'n')\n", - " #plt.show()\n", - " polar_dist_2 = plt.figure(figsize=(10, 10))\n", - " #print('linebeforeindex')\n", - " #print(index,'currentindex')\n", - " Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1;\n", - " MirnormPower_plot[index]=MirnormPower;\n", - " th_plot[index] = th * pi/180\n", - " #print(th_plot[index],'thplotindex')\n", - " r_line_plot[index]=r_line\n", - " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower);\n", - " polar_dist.plot(th * pi/180, r_line,linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " index=index+1\n", - " print(index,'Index')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " theta1Rad = np.linspace(0.0, 2 * math.pi, num=int(360/angleInc))\n", - " #print(theta1Rad,'theta1rad')\n", - " theta1Rad_test[n]=theta1Rad\n", - " ## Normalized Distribution Graph\n", - " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", - " \n", - " for p in range(0, theta1Rad.size):\n", - " # converting theta1Rad and rho1 to cartesian coordinates\n", - " xfinal = rho1 * math.cos(theta1Rad[p])\n", - " yfinal = rho1 * math.sin(theta1Rad[p])\n", - "\n", - " # Evaluate spin on path\n", - " px = PowerSpline.ev(yfinal, xfinal)\n", - " PowerY[p] = np.sum(px)\n", - " #print(PowerY)\n", - " PowerY_test[n]=PowerY\n", - " theta1Rad = theta1Rad_test[n]\n", - " PowerY = PowerY_test[n]\n", - " #print(PowerY)\n", - " # Only use the data in the first two quadrants (Spectrum is symmetric)\n", - " num = len(theta1Rad)\n", - " print(num,'num')\n", - " PowerYFinal = PowerY[0:num // 2]\n", - " #print(PowerYFinal,'poweryfinal')\n", - " theta1RadFinal = theta1Rad[0:num // 2]\n", - " #print(theta1RadFinal,'theta1radfianl')\n", - " theta1RadFinal_test[n]= theta1RadFinal\n", - " power_area = np.trapz(PowerYFinal, theta1RadFinal)\n", - " normPower = PowerYFinal / power_area\n", - " normPower_test[n]=normPower\n", - " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", - " h2 = plt.bar((theta1RadFinal * 180 / pi)+shift, normPower1,color=\"#dedede\")\n", - " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", - " plt.xlim([-95, 95])\n", - " p_act = fitted_func(theta1RadFinal, kappa)\n", - "\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)+shift]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = plt.plot(b[:,1],b[:,0], linewidth=2,color=\"#CE7E56\")\n", - " plt.xlabel('Angle (°)', **csfont)\n", - " plt.ylabel('Normalized Intensity', **csfont)\n", - " #print(max(normPower1),'max normpower')\n", - " if (max(normPower1)<2):\n", - " inc = 0.5\n", - " elif (max(normPower1)<5):\n", - " inc = 2\n", - " elif (max(normPower1)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", - " plt.ylim([0, max(normPower1) + .3])\n", - " plt.savefig(savepath+'Normdist '+names[n])\n", - " #plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "int(shift)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "size(theta1RadFinal)\n", - "normPower1 = np.roll(normPower, 87)\n", - "#normPower1\n", - "theta1RadFinal1 = np.roll(theta1RadFinal, 87)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr = np.array([[1,4,6,2,-5],[0.2,0.4,-0.2,0.3,1.2]]).T\n", - "test = (theta1RadFinal1 * 180 / pi)+shift\n", - "a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)+shift]])\n", - "\n", - "b = a[a[:,0].argsort()]#np.sort(a,axis=1)\n", - "b = b.T\n", - "test = b[:,0]\n", - "print(shape(test))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b[:,0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b[:,1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "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": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/old_versions/FiberFit-toshare-5.22.2020.ipynb b/old_versions/FiberFit-toshare-5.22.2020.ipynb deleted file mode 100644 index 16d31e4..0000000 --- a/old_versions/FiberFit-toshare-5.22.2020.ipynb +++ /dev/null @@ -1,777 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Import necessary Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: greenlet in /srv/conda/envs/notebook/lib/python3.7/site-packages (0.4.15)\n", - "Requirement already satisfied: PyQt5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (5.14.2)\n", - "Requirement already satisfied: PyQt5-sip<13,>=12.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from PyQt5) (12.7.2)\n" - ] - } - ], - "source": [ - "import sys\n", - "!{sys.executable} -m pip install greenlet\n", - "!{sys.executable} -m pip install PyQt5\n", - "import PyQt5" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "matplotlib.rcParams['backend'] = \"Qt5Agg\"\n", - "from matplotlib.figure import Figure\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c1ce96e32e0b411fb67c0b3abdcb1d26", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Press Me', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from __future__ import division\n", - "from __future__ import print_function\n", - "from __future__ import unicode_literals\n", - "from __future__ import division\n", - "from __future__ import absolute_import\n", - "\n", - "import argparse, re, os, glob, sys, pprint, itertools\n", - "import inspect\n", - "\n", - "import numpy as np\n", - "# try:\n", - "# from runtime import *\n", - "# except ImportError:\n", - "# from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - " \n", - "btn = widgets.Button(description='Press Me')\n", - "display(btn)\n", - "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", - "import skimage.io as io\n", - "from skimage import data\n", - "from skimage.filters import sobel \n", - "from skimage.morphology import watershed\n", - "from skimage import feature\n", - "from skimage.color import rgb2gray\n", - "from skimage.feature import corner_harris,corner_peaks\n", - "from skimage.exposure import equalize_hist\n", - "from skimage import measure\n", - "from skimage.draw import ellipse\n", - "from skimage.measure import label, regionprops\n", - "from skimage.transform import rotate\n", - "from skimage.feature import corner_orientations\n", - "from skimage.filters import threshold_otsu\n", - "from skimage.segmentation import clear_border\n", - "from skimage.measure import label\n", - "from skimage.morphology import closing, square\n", - "from skimage.measure import regionprops\n", - "from skimage.color import label2rgb\n", - "import seaborn\n", - "import pandas\n", - "try:\n", - " from runtime import *\n", - "except ImportError:\n", - " from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "from os import listdir\n", - "from os.path import isfile, join\n", - "import numpy\n", - "import cv2\n", - "\n", - "\n", - "#test\n", - " \n", - "import sys\n", - "import matplotlib\n", - "import numpy as np\n", - "import scipy\n", - "import scipy.ndimage\n", - "import scipy.interpolate\n", - "import scipy.optimize\n", - "import scipy.integrate\n", - "import scipy.stats\n", - "import math\n", - "import time\n", - "import glob\n", - "from pylab import *\n", - "from pandas import DataFrame\n", - "#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from src.fiberfit_model.EllipseDirectFit import*\n", - "from src.fiberfit_model import helpers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Input Variables " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "## File Path with binary square images of fibers to analyze\n", - "mypath='test_images/'\n", - "\n", - "##Path to folder where you would like images to be saved\n", - "savepath = 'output/'\n", - "## NOTE - we can also output raw data. let me know if you would prefer that.\n", - "\n", - "## Variables for settings\n", - "CO_lower = 20 #upper fiber thickness, pixels\n", - "CO_upper = 1 #lower fiber thickness, pixels\n", - "angleInc = 1 #angle increment\n", - "radStep = 0.5 #radial step size\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now, Run the code below. Figures will be saved in specified output folder" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "length image 7\n", - "0 n\n", - "1 Index\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:241: RuntimeWarning: overflow encountered in exp\n", - "/srv/conda/envs/notebook/lib/python3.7/site-packages/scipy/integrate/quadrature.py:243: RuntimeWarning: invalid value encountered in double_scalars\n", - " err = abs(newval-val)\n", - "/srv/conda/envs/notebook/lib/python3.7/site-packages/scipy/integrate/quadrature.py:251: AccuracyWarning: maxiter (50) exceeded. 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Latest difference = 1.702365e+299\n", - " AccuracyWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 n\n", - "2 Index\n", - "2 n\n", - "3 Index\n", - "3 n\n", - "4 Index\n", - "4 n\n", - "5 Index\n", - "5 n\n", - "6 Index\n", - "6 n\n", - "7 Index\n" - ] - } - ], - "source": [ - "## Upload Files\n", - "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", - "images = numpy.empty(len(onlyfiles), dtype=object)\n", - "names = numpy.empty(len(onlyfiles), dtype=object)\n", - "for n in range(0, len(onlyfiles)):\n", - " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", - " names[n]= (onlyfiles[n])\n", - "img = numpy.empty(len(images), dtype=object)\n", - "for n in range(0,len(images)):\n", - " img[n]= np.invert(images[n])\n", - " \n", - "## Set up empty Matrices (can clean this up later )\n", - "ell_data_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", - "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", - "normPower_test = numpy.empty(len(img),dtype=object)\n", - "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", - "A_test = numpy.empty(len(img),dtype=object)\n", - "centroid_test = numpy.empty(len(img),dtype=object)\n", - "t_new_test = numpy.empty(len(img),dtype=object)\n", - "im = numpy.empty(len(img), dtype=object)\n", - "PF = numpy.empty(len(img),dtype=object)\n", - "PS = numpy.empty(len(img),dtype=object)\n", - "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", - "PowerY_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal1_plot= []\n", - "MirnormPower_plot=[]\n", - "th_plot=[]\n", - "r_line_plot=[]\n", - "normPower1_plot=[]\n", - "num=0\n", - "index=num\n", - "\n", - "plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output\n", - "\n", - "## begin analysis\n", - "print('length image',len(img))\n", - "for n in range(0,len(img)):\n", - " im=img[n][:,:,1] # select first image\n", - " M= im.shape[0] #get image shape\n", - " N1 = im.shape[1] \n", - " n1 = np.round(N1/ 2) - 1\n", - " freq = np.arange(-n1, n1 + 1, 1)\n", - " x, y = freq, freq\n", - " \n", - " ## Fourier Transform\n", - " fft_result = np.fft.fft2(im)\n", - " Fshift = np.fft.fftshift(fft_result)\n", - " \n", - " ## adjust to power spectrum to remove imaginary components\n", - " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", - " Pabs = np.abs(Fshift) ** 2\n", - " \n", - " M= im.shape[0]\n", - " N1=im.shape[1]\n", - "\n", - " # shift in terms of image because power spectrum is the mirroR of lines so\n", - " # mirroring back in terms of image would give right allignment\n", - " PabsFlip1 = np.rot90(Pabs)\n", - " PabsFlip = np.flipud(PabsFlip1)\n", - " while (PabsFlip.shape[0]>x.shape[0]):\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=0) \n", - " while (PabsFlip.shape[1]>y.shape[0]):\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", - " while (PabsFlip.shape[0] 0)[0]]\n", - " A = np.vstack((A1, dot(T, A1)))\n", - " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", - " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", - " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", - " 0] - A[4] * centroid[1]\n", - " A[3] = A3\n", - " A[4] = A4\n", - " A[5] = A5\n", - " A = A / linalg.norm(A)\n", - " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", - " if ((A[0]) <= (A[2])):\n", - " # Ellipse is horizontal\n", - " angle = 0;\n", - " major = sqrt(1 / A[0])\n", - " minor = sqrt(1 / A[2])\n", - " else:\n", - " angle = np.pi/2;\n", - " major = sqrt(1 / A[2])\n", - " minor = sqrt(1 / A[0])\n", - " else:\n", - " R = ((A[2] - A[0]) / A[1])\n", - " tg = R - sqrt((R * R) + 1)\n", - " angle = math.atan(tg)\n", - " P = (2 * tg) / (1 + (tg * tg))\n", - "\n", - " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", - " if (angle < (-pi / 4)):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle\n", - " elif ((A[1] / P <= (-A[1] / P))):\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", - " if (angle < 0):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle - np.pi\n", - " else:\n", - " # Switch\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - "\n", - " t_New = (angle) #angle\n", - " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", - " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", - " #print('round2',Mirtheta1RadFinal1)\n", - " MirnormPower = MirnormPower_test[n]\n", - " #print('mirnormpower',MirnormPower)\n", - " t = t_new_test[n]\n", - " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", - " th = np.zeros(len(r_line))\n", - " for i in range(0, len(r_line)):\n", - " th[i] = t\n", - " th = np.concatenate([th, (th + 180)])\n", - " r_line = np.concatenate([r_line, r_line])\n", - " ## Polar Graph\n", - " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"poster\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'14',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " plt.figure(frameon=False, figsize=(15, 10))\n", - " plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5)\n", - " plt.suptitle(abs(t)-180, fontsize=20)\n", - " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", - " plt.savefig(savepath+'polarpositive'+names[n])\n", - " if (max(MirnormPower)<2):\n", - " inc = 0.5\n", - " elif (max(MirnormPower)<5):\n", - " inc = 1\n", - " elif max(MirnormPower)<20:\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", - "\n", - " #plt.show()\n", - " t_final_rad = t*pi/180\n", - " def fitted_func(thetas, c):\n", - " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", - " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", - " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", - " c0 = 15\n", - " xdata = theta1RadFinal_test[n]\n", - " theta1RadFinal = theta1RadFinal_test[n]\n", - " ydata= normPower_test[n]\n", - " normPower = normPower_test[n]\n", - " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", - "\n", - " # Shift data for plotting purposes\n", - "\n", - " diff = abs(theta1RadFinal - (t * pi / 180))\n", - " #print(diff,'diff')\n", - " #centerLoc=diff.find(diff==min(diff))\n", - " centerLoc = diff[np.argmin(diff)]#find(diff== min(diff))\n", - " #print(centerLoc,'centerLoc')\n", - "\n", - " num_angles = len(theta1RadFinal)\n", - " shift = ((round(num_angles / 2) - (num_angles - centerLoc)))\n", - " #print(shift,'shift')\n", - "\n", - " normPower1 = np.roll(normPower, -int(shift))\n", - " normPower1\n", - " theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift))\n", - "\n", - " if (shift > 0):\n", - " s = num_angles - shift\n", - " s= np.asscalar(s)\n", - " for k in range(s, num_angles):\n", - " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", - " elif (shift < 0):\n", - " for k in range(0,90):\n", - " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", - " \n", - " ## Normalized Distribution Graph\n", - " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", - "\n", - " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", - " plt.xlim([-95, 95])\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " #print(p_act)\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", - " plt.xlabel('Angle (°)', **csfont)\n", - " plt.ylabel('Normalized Intensity', **csfont)\n", - " #print(max(normPower))\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", - " plt.ylim([0, max(normPower1) + .3])\n", - " normPower1_plot.append(normPower1)\n", - " plt.savefig(savepath+'Normdist '+names[n])\n", - " #plt.show()\n", - " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", - " plt.close()\n", - " t_final=t\n", - " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", - " #print(rValue**2)\n", - " # Rounding results for Title of Figure\n", - " krnd = math.ceil(k * 1000) / 1000\n", - " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", - " krnd = math.trunc(krnd * 100) / 100\n", - " thrnd = math.trunc(thrnd * 100) / 100\n", - " a = 32.02\n", - " b= -12.43\n", - " c = 47.06\n", - " d = -0.9185\n", - " e = 19.43\n", - " f = -0.07693\n", - " x = kappa[0]\n", - " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", - " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", - " sum_NP = sum(normPower1)\n", - " #normPower2 = normPower1/sum(normPower1)\n", - " #sum_NP2 = sum(normPower2)\n", - " #print(sum_NP2)\n", - " ## Combined plot figure\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"notebook\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'18',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " fig = plt.figure(frameon=False,figsize=(20,12))\n", - " fig.subplots_adjust(top=0.95)\n", - " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", - " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", - " cart_dist = plt.subplot(grid[1, 0:2])\n", - " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", - " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " cart_dist.set_xlim([-92, 92])\n", - " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", - " cart_dist.set_ylim([0, max(normPower1) + .3])\n", - " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", - "\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]])\n", - " a= a.T\n", - " b = a[a[:,1].argsort()]#np.sort(a,axis=1)\n", - " h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color=\"#CE7E56\")\n", - "\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - "\n", - " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", - " polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o');\n", - " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " fig.text(0.75, 0.05, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " #polar_dist.text(np.radians(-110),1.95, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " orig_fig = plt.subplot(grid[0, 0]); \n", - " orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0])\n", - " orig_fig.axis('off');\n", - " fft_fig = plt.subplot(grid[0, 1])\n", - " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", - " fft_fig.set_axis_off()\n", - " fft_fig.axis('off')\n", - " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", - " orig_fig.imshow(images[n],cmap=plt.cm.viridis )\n", - " #plt.tight_layout()\n", - " plt.savefig(savepath+'Grouped'+names[n])\n", - " print(n,'n')\n", - " #plt.show()\n", - " polar_dist_2 = plt.figure(figsize=(10, 10))\n", - " #print('linebeforeindex')\n", - " #print(index,'currentindex')\n", - " #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1;\n", - " #MirnormPower_plot[index]=MirnormPower;\n", - " #th_plot[index] = th * pi/180\n", - " #print(th_plot[index],'thplotindex')\n", - " #r_line_plot[index]=r_line\n", - " Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1);\n", - " MirnormPower_plot.append(MirnormPower);\n", - " th_plot.append(th * pi/180)\n", - " r_line_plot.append(r_line)\n", - " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower);\n", - " polar_dist.plot(th * pi/180, r_line,linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " index=index+1\n", - " print(index,'Index')\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "## export of cartesian coordinate data\n", - "arr = numpy.array(MirnormPower_plot) \n", - "arr2 = numpy.array(Mirtheta1RadFinal1_plot)\n", - "arr3 = numpy.array(normPower1_plot)\n", - "arr4 = numpy.array(theta1RadFinal1*180/pi)\n", - "a = numpy.transpose(arr3)\n", - "b = numpy.transpose(arr4)\n", - "b[b> 100] = 0\n", - "b[b<-100] = 0\n", - "export_data=np.hstack(((b[:,None]),a))\n", - "numpy.savetxt(\"data_export.csv\", export_data, delimiter=\",\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "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": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/old_versions/FiberFit-toshare-6.5.19.ipynb b/old_versions/FiberFit-toshare-6.5.19.ipynb deleted file mode 100644 index 3e3167a..0000000 --- a/old_versions/FiberFit-toshare-6.5.19.ipynb +++ /dev/null @@ -1,476 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Import necessary Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#display(QPixmap(saved_images_dir_name + \"/angDist_\" + \".png\").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation))\n", - "import skimage.io as io\n", - "from skimage import data\n", - "from skimage.filters import sobel \n", - "from skimage.morphology import watershed\n", - "from skimage import feature\n", - "from skimage.color import rgb2gray\n", - "from skimage.feature import corner_harris,corner_peaks\n", - "from skimage.exposure import equalize_hist\n", - "from skimage import measure\n", - "from skimage.draw import ellipse\n", - "from skimage.measure import label, regionprops\n", - "from skimage.transform import rotate\n", - "from skimage.feature import corner_orientations\n", - "from skimage.filters import threshold_otsu\n", - "from skimage.segmentation import clear_border\n", - "from skimage.measure import label\n", - "from skimage.morphology import closing, square\n", - "from skimage.measure import regionprops\n", - "from skimage.color import label2rgb\n", - "from __future__ import division\n", - "import seaborn\n", - "import pandas\n", - "# try:\n", - "# from runtime import *\n", - "# except ImportError:\n", - "# from smop.runtime import *\n", - "\n", - "from src.fiberfit_model.helpers import debug\n", - "import numpy as np\n", - "from numpy.linalg import eig, inv\n", - "import scipy.ndimage\n", - "from pylab import *\n", - "from os import listdir\n", - "from os.path import isfile, join\n", - "import numpy\n", - "import cv2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Input Variables " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "## File Path with binary square images of fibers to analyze\n", - "mypath='C:/Users/Maggie/Desktop/fiberFit'\n", - "\n", - "##Path to folder where you would like images to be saved\n", - "savepath = 'C:/Users/Maggie/Desktop/fiberFitOutput/'\n", - "## NOTE - we can also output raw data. let me know if you would prefer that.\n", - "\n", - "## Variables for settings\n", - "CO_lower = 32 #upper fiber thickness, pixels\n", - "CO_upper = 2 #lower fiber thickness, pixels\n", - "angleInc = 1 #angle increment\n", - "radStep = 0.5 #radial step size\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Now, Run the code below. Figures will be saved in specified output folder" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Maggie\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " max_open_warning, RuntimeWarning)\n", - "C:\\Users\\Maggie\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:66: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", - "C:\\Users\\Maggie\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:70: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n" - ] - } - ], - "source": [ - "## Upload Files\n", - "onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]\n", - "images = numpy.empty(len(onlyfiles), dtype=object)\n", - "names = numpy.empty(len(onlyfiles), dtype=object)\n", - "for n in range(0, len(onlyfiles)):\n", - " images[n] = cv2.imread( join(mypath,onlyfiles[n]) )\n", - " names[n]= (onlyfiles[n])\n", - "img = numpy.empty(len(images), dtype=object)\n", - "for n in range(0,len(images)):\n", - " img[n]= np.invert(images[n])\n", - " \n", - "## Set up empty Matrices (can clean this up later )\n", - "ell_data_test = numpy.empty(len(img),dtype=object)\n", - "Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object)\n", - "MirnormPower_test = numpy.empty(len(img),dtype=object)\n", - "normPower_test = numpy.empty(len(img),dtype=object)\n", - "theta1RadFinal_test = numpy.empty(len(img),dtype=object)\n", - "A_test = numpy.empty(len(img),dtype=object)\n", - "centroid_test = numpy.empty(len(img),dtype=object)\n", - "t_new_test = numpy.empty(len(img),dtype=object)\n", - "im = numpy.empty(len(img), dtype=object)\n", - "PF = numpy.empty(len(img),dtype=object)\n", - "PS = numpy.empty(len(img),dtype=object)\n", - "theta1Rad_test = numpy.empty(len(img),dtype=object)\n", - "PowerY_test = numpy.empty(len(img),dtype=object)\n", - "\n", - "## begin analysis\n", - "for n in range(0,len(img)):\n", - " im=img[n][:,:,1] # select first image\n", - " M= im.shape[0] #get image shape\n", - " N1 = im.shape[1] \n", - " n1 = np.round(N1/ 2) - 1\n", - " freq = np.arange(-n1, n1 + 1, 1)\n", - " x, y = freq, freq\n", - " \n", - " ## Fourier Transform\n", - " fft_result = np.fft.fft2(im)\n", - " Fshift = np.fft.fftshift(fft_result)\n", - " \n", - " ## adjust to power spectrum to remove imaginary components\n", - " magnitude_spectrum= 20*np.log(np.abs(Fshift))\n", - " Pabs = np.abs(Fshift) ** 2\n", - " \n", - " M= im.shape[0]\n", - " N1=im.shape[1]\n", - "\n", - " # shift in terms of image because power specrum is the mirroR of lines so\n", - " # misrroring back in terms of image would give right allignment\n", - " PabsFlip1 = np.rot90(Pabs)\n", - " PabsFlip = np.flipud(PabsFlip1)\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", - " PabsFlip = np.delete(PabsFlip, (0), axis=0)\n", - " PabsFlip = np.delete(PabsFlip, (0), axis=1) \n", - " logScale = plt.figure(frameon=False, figsize=(10, 10))\n", - " ax = plt.Axes(logScale, [0., 0., 1., 1.])\n", - " ax.set_axis_off()\n", - " logScale.add_axes(ax)\n", - " plt.axis('off')\n", - " plt.imshow(log(PabsFlip), cmap='gray', aspect='auto')\n", - " plt.savefig(savepath+'FFT '+names[n])\n", - " #plt.show() \n", - " plt.close()\n", - "\n", - " # Set up polar coordinates prior to summing the spectrum\n", - " theta1Rad = np.linspace(0.0, 2 * math.pi, num=360/angleInc)\n", - " theta1Rad_test[n]=theta1Rad\n", - " f1 = np.round_(N1 / (2 * CO_lower))\n", - " f2 = np.round_(N1 / (2 * CO_upper))\n", - " rho1 = np.linspace(f1, f2, num=(f2 - f1)/radStep) # frequency band\n", - " PowerX = np.zeros((theta1Rad.size, theta1Rad.size))\n", - " PowerY = np.zeros((theta1Rad.size))\n", - " PF[n] = PabsFlip\n", - " \n", - " # Interpolate using a Spine\n", - " PowerSpline = scipy.interpolate.RectBivariateSpline(y=y, x=x, z=PabsFlip)\n", - " PS[n] = PowerSpline\n", - " n_dx = 0.001\n", - "\n", - " for p in range(0, theta1Rad.size):\n", - " # converting theta1Rad and rho1 to cartesian coordinates\n", - " xfinal = rho1 * math.cos(theta1Rad[p])\n", - " yfinal = rho1 * math.sin(theta1Rad[p])\n", - "\n", - " # Evaluate spin on path\n", - " px = PowerSpline.ev(yfinal, xfinal)\n", - " PowerY[p] = np.sum(px)\n", - " #print(PowerY)\n", - " PowerY_test[n]=PowerY\n", - " theta1Rad = theta1Rad_test[n]\n", - " PowerY = PowerY_test[n]\n", - " #print(PowerY)\n", - " # Only use the data in the first two quadrants (Spectrum is symmetric)\n", - " num = len(theta1Rad)\n", - " PowerYFinal = PowerY[0:num // 2]\n", - " theta1RadFinal = theta1Rad[0:num // 2]\n", - " theta1RadFinal_test[n]= theta1RadFinal\n", - " power_area = np.trapz(PowerYFinal, theta1RadFinal)\n", - " normPower = PowerYFinal / power_area\n", - " normPower_test[n]=normPower\n", - " \n", - " # Combine data into [XY] to fit to an ellipse\n", - " Mirtheta1RadFinal1 = np.concatenate([theta1RadFinal.T, (theta1RadFinal + np.pi).T])\n", - " MirnormPower = np.concatenate([normPower.T, normPower.T])\n", - " theta = Mirtheta1RadFinal1\n", - " radius = MirnormPower\n", - " ydata = radius * np.cos(theta)\n", - " xdata = radius * np.sin(theta)\n", - " ell_data = np.vstack([xdata, ydata])\n", - " ell_data = ell_data.T\n", - " ell_data_test[n] = ell_data\n", - " Mirtheta1RadFinal_test[n] = Mirtheta1RadFinal1\n", - " MirnormPower_test[n]= MirnormPower\n", - "\n", - " # Python fitting function, see EllipseDirectFit\n", - " XY = ell_data_test[n]\n", - " centroid = np.mean(XY, axis=0)\n", - " e1 = (XY[:, 0] - centroid[0]) ** 2\n", - " e2 = (XY[:, 0] - centroid[0]) * ((XY[:, 1] - centroid[1]))\n", - " e3 = (XY[:, 1] - centroid[1]) ** 2\n", - " D1 = np.vstack(([e1, e2, e3])).T\n", - " f1 = XY[:, 0] - centroid[0]\n", - " f2 = XY[:, 1] - centroid[1]\n", - " f3 = np.ones((1, XY.shape[0]))\n", - " D2 = vstack(([f1, f2, f3])).T\n", - " S1 = dot(D1.T, D1)\n", - " S2 = dot(D1.T, D2)\n", - " S3 = dot(D2.T, D2)\n", - " T = dot(-linalg.inv(S3), S2.T)\n", - " M = S1 + (dot(S2, T))\n", - " M = np.vstack(([M[2, :] / 2, - M[1, :], M[0, :] / 2]))\n", - " _eval, evec = linalg.eig(M)\n", - " cond = (4 * evec[0, :] * evec[2, :]) - (evec[1, :] ** 2)\n", - " A1 = evec[:, nonzero(cond > 0)[0]]\n", - " A = np.vstack((A1, dot(T, A1)))\n", - " A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1]\n", - " A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0]\n", - " A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[\n", - " 0] - A[4] * centroid[1]\n", - " A[3] = A3\n", - " A[4] = A4\n", - " A[5] = A5\n", - " A = A / linalg.norm(A)\n", - " if (abs(A[1]) < (1 * 10 ^ (-15))):\n", - " if ((A[0]) <= (A[2])):\n", - " # Ellipse is horizontal\n", - " angle = 0;\n", - " major = sqrt(1 / A[0])\n", - " minor = sqrt(1 / A[2])\n", - " else:\n", - " angle = np.pi/2;\n", - " major = sqrt(1 / A[2])\n", - " minor = sqrt(1 / A[0])\n", - " else:\n", - " R = ((A[2] - A[0]) / A[1])\n", - " tg = R - sqrt((R * R) + 1)\n", - " angle = math.atan(tg)\n", - " P = (2 * tg) / (1 + (tg * tg))\n", - "\n", - " if ((A[0] > 0 and A[1] > 0 and A[2] > 0)):\n", - " if (angle < (-pi / 4)):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle\n", - " elif ((A[1] / P <= (-A[1] / P))):\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - " elif (A[0] < 0 and A[1] < 0 and A[2] < 0):\n", - " if (angle < 0):\n", - " angle = angle + np.pi\n", - " else:\n", - " angle = angle - np.pi\n", - " else:\n", - " # Switch\n", - " if (angle < 0):\n", - " angle = angle + np.pi / 2\n", - " else:\n", - " angle = angle - np.pi / 2\n", - "\n", - " t_New = (angle) #angle\n", - " t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi)\n", - " Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n]\n", - " MirnormPower = MirnormPower_test[n]\n", - " t = t_new_test[n]\n", - " r_line = np.arange(0, max(MirnormPower) + .5, .5)\n", - " th = np.zeros(len(r_line))\n", - " for i in range(0, len(r_line)):\n", - " th[i] = t\n", - " th = np.concatenate([th, (th + 180)])\n", - " r_line = np.concatenate([r_line, r_line])\n", - " ## Polar Graph\n", - " graphs = [\"#71C4BE\",\"#BBDBA0\",\"#CE7E56\",\"#BC5C81\",\"#84355A\"];\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"notebook\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'14',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " plt.figure(frameon=False, figsize=(15, 10))\n", - " plt.polar(Mirtheta1RadFinal1, MirnormPower, 'o')\n", - " plt.suptitle(abs(t)-180, fontsize=20)\n", - " plt.polar(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4)\n", - " plt.savefig(savepath+'polar '+names[n])\n", - "\n", - " if (max(MirnormPower)<2):\n", - " inc = 0.5\n", - " elif (max(MirnormPower)<5):\n", - " inc = 1\n", - " elif max(MirnormPower)<20:\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(inc, max(MirnormPower), inc))#, **ticksfont)\n", - "\n", - " #plt.show()\n", - " t_final_rad = t*pi/180\n", - " def fitted_func(thetas, c):\n", - " int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi)\n", - " return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \\\n", - " np.exp(c * np.cos(2 * (thetas - t_final_rad)))\n", - " c0 = 15\n", - " xdata = theta1RadFinal_test[n]\n", - " theta1RadFinal = theta1RadFinal_test[n]\n", - " ydata= normPower_test[n]\n", - " normPower = normPower_test[n]\n", - " kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n])\n", - "\n", - " # Shift data for plotting purposes\n", - "\n", - " diff = abs(theta1RadFinal - (t * pi / 180))\n", - " centerLoc = find(diff == min(diff))\n", - "\n", - " num_angles = len(theta1RadFinal)\n", - " shift = (round(num_angles / 2) - (num_angles - centerLoc))\n", - "\n", - " normPower1 = np.roll(normPower, -shift)\n", - " theta1RadFinal1 = np.roll(theta1RadFinal, -shift)\n", - "\n", - " if (shift > 0):\n", - " s = num_angles - shift\n", - " for k in range(s, num_angles):\n", - " theta1RadFinal1[k] = np.pi + theta1RadFinal1[k]\n", - " elif (shift < 0):\n", - " for k in range(0,90):\n", - " theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k]\n", - " \n", - " ## Normalized Distribution Graph\n", - " cartDist = plt.figure(figsize=(10, 10)) # Creates a figure containing cartesian distribution.\n", - "\n", - " h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " plt.xticks(np.arange(-360, 360, 45,), **ticksfont)\n", - " plt.xlim([-95, 95])\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " h3, = plt.plot(theta1RadFinal1 * 180 / pi, p_act, linewidth=4,color=\"#CE7E56\")\n", - " plt.xlabel('Angle (°)', **csfont)\n", - " plt.ylabel('Normalized Intensity', **csfont)\n", - "\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - " plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont)\n", - " plt.ylim([0, max(normPower1) + .3])\n", - " plt.savefig(savepath+'Normdist '+names[n])\n", - " #plt.show()\n", - " #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight')\n", - " plt.close()\n", - " t_final=t\n", - " slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1)\n", - " #print(rValue**2)\n", - " # Rounding results for Title of Figure\n", - " krnd = math.ceil(k * 1000) / 1000\n", - " thrnd = abs(math.ceil(t_final * 1000) / 1000)-180\n", - " krnd = math.trunc(krnd * 100) / 100\n", - " thrnd = math.trunc(thrnd * 100) / 100\n", - " a = 32.02\n", - " b= -12.43\n", - " c = 47.06\n", - " d = -0.9185\n", - " e = 19.43\n", - " f = -0.07693\n", - " x = kappa[0]\n", - " sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x)\n", - " #print(\"name\",names[n],\"kappa\",kappa,\"sigma\",sig,\"Rsq\",rValue**2,\"mu\",t_final)\n", - " \n", - " ## Combined plot figure\n", - " palette = seaborn.set_palette(graphs);\n", - " seaborn.set_context(\"notebook\",font_scale=1.5);\n", - " seaborn.set_style('ticks')\n", - " csfont = {'fontname':'Times New Roman',\n", - " 'size':'18',\n", - " }\n", - " ticksfont = {'fontname':'Times New Roman'}\n", - " fig = plt.figure(frameon=False,figsize=(20,12))\n", - " fig.subplots_adjust(top=0.95)\n", - " fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold')\n", - " grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1])\n", - " cart_dist = plt.subplot(grid[1, 0:2])\n", - " cart_dist.set_title('Von Mises Distribution (k)',fontsize=20)\n", - " h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color=\"#dedede\")\n", - " cart_dist.set_xlim([-92, 92])\n", - " cart_dist.set_xlabel('Angle (°)',fontsize=18)\n", - " cart_dist.set_ylim([0, 1])\n", - " cart_dist.set_ylabel('Normalized Intensity',fontsize=18)\n", - "\n", - " p_act = fitted_func(theta1RadFinal1, kappa)\n", - " h3, = cart_dist.plot(theta1RadFinal1 * 180 / pi, p_act, linewidth=4,color=\"#CE7E56\")\n", - "\n", - " if (max(normPower)<2):\n", - " inc = 0.5\n", - " elif (max(normPower)<5):\n", - " inc = 1\n", - " elif (max(normPower)<20):\n", - " inc = 5\n", - " else:\n", - " inc = 10\n", - "\n", - " polar_dist = plt.subplot(grid[0:2,3:5],projection='polar');\n", - " polar_dist.plot(Mirtheta1RadFinal1, MirnormPower, 'o');\n", - " polar_dist.plot(th * pi/180, r_line,color=\"#CE7E56\",linewidth=4);\n", - " polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08])\n", - " polar_dist.text(np.radians(-110),1.65, ' \\n Kappa: %s '%(krnd)+' mu: %s \\n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \\n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4})\n", - " orig_fig = plt.subplot(grid[0, 0]); \n", - " orig_fig.set_title('Processed Binary Image',fontsize=20).set_position([.5, 1.0])\n", - " orig_fig.axis('off');\n", - " fft_fig = plt.subplot(grid[0, 1])\n", - " fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0])\n", - " fft_fig.set_axis_off()\n", - " fft_fig.axis('off')\n", - " fft_fig.imshow(log(PabsFlip), cmap='gray')\n", - " orig_fig.imshow(images[1],cmap=plt.cm.viridis )\n", - " #plt.tight_layout()\n", - " plt.savefig(savepath+'Grouped'+names[n])\n", - " #plt.show()\n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda root]", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From a926d5d77e0820629894de08b24a454a1a7bd5d7 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:03:23 -0500 Subject: [PATCH 110/153] Delete oldversions directory --- oldversions/FiberFit-toshare-6.1.2020.ipynb | 274 -------------------- 1 file changed, 274 deletions(-) delete mode 100644 oldversions/FiberFit-toshare-6.1.2020.ipynb diff --git a/oldversions/FiberFit-toshare-6.1.2020.ipynb b/oldversions/FiberFit-toshare-6.1.2020.ipynb deleted file mode 100644 index d77f496..0000000 --- a/oldversions/FiberFit-toshare-6.1.2020.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Upload your Images for analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\asus\\anaconda3\\envs\\test-environment\\lib\\site-packages\\IPython\\html.py:12: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", - " warn(\"The `IPython.html` package has been deprecated since IPython 4.0. \"\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6aca4500dd1842b392504e9ccac596de", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from widgetsmp import s,run\n", - "from src.foruploading5 import ff,upload,prepimages\n", - "import ipywidgets as widgets\n", - "from IPython.display import display, Markdown, clear_output\n", - "from ipywidgets import interact, interact_manual\n", - "s = widgets.FileUpload(accept='', multiple=True)\n", - "upload(s)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Run Code. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "acdaf7a966b3454b94cc665a3efb8510", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c3a4ae74bbc446e1baa0a268b7beca58", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "run(_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness. Again, output will be in \"output\" folder. \n", - "###### Note: These color maps are from a program called Quanfima. To note - this is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results." - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "ename": "AttributeError", - "evalue": "'float' object has no attribute 'get_matrix'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2d_polymer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m output_dir='/path/to/output/dir')\n\u001b[1;32m---> 32\u001b[1;33m vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',\n\u001b[0m\u001b[0;32m 33\u001b[0m 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'{}_orientation_map.png'.format(name)),\n\u001b[0m\u001b[0;32m 102\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tight'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\test-environment\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36msavefig\u001b[1;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[0;32m 2201\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2203\u001b[1;33m 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\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1122\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mset_rmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mAttributeError\u001b[0m: 'float' object has no attribute 'get_matrix'" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#from quanfima_torun11 import colormaps\n", - "#%matplotlib inline\n", - "#colormaps(orient_type='tensor')\n", - "import numpy as np\n", - "from skimage import io, filters\n", - "from quanfima.quanfima import morphology as mrph\n", - "from quanfima.quanfima import visualization as vis\n", - "#from quanfima.quanfima import utils\n", - "\n", - "img_seg = cv2.imread('Data/segmented_square.png',0)\n", - "\n", - "#th_val = filters.threshold_otsu(img)\n", - "#img_seg = (img > th_val).astype(np.uint8)\n", - "\n", - "# estimate porosity\n", - "#pr = mrph.calc_porosity(img_seg)\n", - "#for k,v in pr.items():\n", - "# print 'Porosity ({}): {}'.format(k, v)\n", - "\n", - "# prepare data and analyze fibers\n", - "data = img_seg\n", - "skeleton = img_seg\n", - "skeleton_thick = img_seg\n", - "#skeleton, skeleton_thick = utils.prepare_data(img_seg)\n", - "cskel, fskel, omap, dmap, ovals, dvals = \\\n", - " mrph.estimate_fiber_properties(data, skeleton)\n", - "\n", - "# plot results\n", - "vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow',\n", - " name='DIAMETER_estimate',\n", - " output_dir='output/')\n", - "vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',\n", - " figsize=(10,10),\n", - " name='ORIENTATION_estimate',\n", - " output_dir='output/')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Code below is for easier export of data - not ready yet." - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "def csv_download_link(df, csv_file_name, delete_prompt=True):\n", - " \"\"\"Display a download link to load a data frame as csv from within a Jupyter notebook\"\"\"\n", - " numpy.savetxt(csv_file_name, df, delimiter=\",\")\n", - " from IPython.display import FileLink\n", - " display(FileLink(csv_file_name))\n", - " if delete_prompt:\n", - " a = input('Press enter to delete the file after you have downloaded it.')\n", - " import os\n", - " os.remove(csv_file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "df.csv
      " - ], - "text/plain": [ - "/home/jovyan/df.csv" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Press enter to delete the file after you have downloaded it.\n" - ] - } - ], - "source": [ - "csv_download_link(output, 'output.csv')" - ] - } - ], - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 7cc56282d59eb642e4c7b4ef9a24395d08dc9a44 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:05:30 -0500 Subject: [PATCH 111/153] Add files via upload --- quanfima_torun.py | 40 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 quanfima_torun.py diff --git a/quanfima_torun.py b/quanfima_torun.py new file mode 100644 index 0000000..549b02f --- /dev/null +++ b/quanfima_torun.py @@ -0,0 +1,40 @@ +import numpy as np +from skimage import io, filters +import cv2 +from quanfima.quanfima import morphology as mrph +from quanfima.quanfima import visualizationDE as vis +from quanfima.quanfima import visualization_testQ as visb +import matplotlib.pyplot as plt +#img_seg = cv2.imread('Data/MAX_5p_10_2.tif',0) +from os import listdir +from os.path import isfile, join +import numpy +import cv2 + + +def fiber_visualization(): + plt.ioff() + mypath='Data/' + onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ] + images = numpy.empty(len(onlyfiles), dtype=object) + names = numpy.empty(len(onlyfiles), dtype=object) + for n in range(0, len(onlyfiles)): + images[n] = cv2.imread( join(mypath,onlyfiles[n]),0 ) + names[n]= (onlyfiles[n]) + img = numpy.empty(len(images), dtype=object) + for n in range(0,len(images)): + img_seg= images[n] + data = img_seg + skeleton = img_seg + skeleton_thick = img_seg + #skeleton, skeleton_thick = utils.prepare_data(img_seg) + cskel, fskel, omap, dmap, ovals, dvals = \ + mrph.estimate_fiber_properties(data, skeleton) + # plot results + vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow', + name='DIAMETER_estimate', + output_dir='output/') + vis.plot_orientation_map(omap, fskel, min_label=None, max_label=None, + figsize=(10,10), + name='ORIENTATION_estimate', + output_dir='output/') \ No newline at end of file From 55db4277474964ca95ee05e4f95bb81432cd55a0 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:22:12 -0500 Subject: [PATCH 112/153] Update environment.yml --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 91bf530..2d3bf17 100644 --- a/environment.yml +++ b/environment.yml @@ -24,6 +24,7 @@ dependencies: - scipy - seaborn - vigra=1.11.1 + - libgl1-mesa-glx - pip: - ffmpy - isapi_wsgi From 9f94859790e4cbded1d51f78b9a31720b2c9c338 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:24:57 -0500 Subject: [PATCH 113/153] Update environment.yml --- environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 2d3bf17..05f6851 100644 --- a/environment.yml +++ b/environment.yml @@ -2,6 +2,8 @@ name: test-environment channels: - conda-forge - defaults +packages: + - package: libgl1-mesa-dev dependencies: - Cycler - decorator @@ -24,7 +26,6 @@ dependencies: - scipy - seaborn - vigra=1.11.1 - - libgl1-mesa-glx - pip: - ffmpy - isapi_wsgi From 6b3398c7abcb64c135462502623a4ac29204f149 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:32:28 -0500 Subject: [PATCH 114/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 05f6851..9213cfe 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,7 @@ channels: - conda-forge - defaults packages: - - package: libgl1-mesa-dev + - package: libgl1-mesa-glx dependencies: - Cycler - decorator From a4b2313e1e38146737a604a3949f088956212c7e Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:46:49 -0500 Subject: [PATCH 115/153] Update environment.yml --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 9213cfe..392f24f 100644 --- a/environment.yml +++ b/environment.yml @@ -4,6 +4,7 @@ channels: - defaults packages: - package: libgl1-mesa-glx + - package: libgl1-mesa-dev dependencies: - Cycler - decorator From 76caa44acc7699b4a6a2a9e49ea5185cb763f198 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:52:49 -0500 Subject: [PATCH 116/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 392f24f..3b07159 100644 --- a/environment.yml +++ b/environment.yml @@ -31,7 +31,7 @@ dependencies: - ffmpy - isapi_wsgi - jupyterlab - - opencv-python + - opencv-python--headless - mpl-utils - mpl4qt - mpl-tools From 16becf122262eb672c4339ff4cf1b8d2e031b2a1 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:56:00 -0500 Subject: [PATCH 117/153] Create apt.txt --- apt.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 apt.txt diff --git a/apt.txt b/apt.txt new file mode 100644 index 0000000..f359f07 --- /dev/null +++ b/apt.txt @@ -0,0 +1 @@ +libgl1-mesa-glx From 2acd02231a011f651eff7c782190f20b0e2bfbce Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 22:56:36 -0500 Subject: [PATCH 118/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 3b07159..392f24f 100644 --- a/environment.yml +++ b/environment.yml @@ -31,7 +31,7 @@ dependencies: - ffmpy - isapi_wsgi - jupyterlab - - opencv-python--headless + - opencv-python - mpl-utils - mpl4qt - mpl-tools From 86ba73b4868e7bb67e6436ee94851aabe1a4e62f Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 23:38:07 -0500 Subject: [PATCH 119/153] Create empty.txt --- Data/empty.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 Data/empty.txt diff --git a/Data/empty.txt b/Data/empty.txt new file mode 100644 index 0000000..89e5656 --- /dev/null +++ b/Data/empty.txt @@ -0,0 +1 @@ +emptyfile From 2bc15120c8612451791ddb2e36dc6c7b662a79a4 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 23:38:21 -0500 Subject: [PATCH 120/153] Delete .gitkeep --- Data/.gitkeep | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Data/.gitkeep diff --git a/Data/.gitkeep b/Data/.gitkeep deleted file mode 100644 index 8b13789..0000000 --- a/Data/.gitkeep +++ /dev/null @@ -1 +0,0 @@ - From 83f21a4c4a458354f64217d0f747d0ee03272fa6 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 23:39:23 -0500 Subject: [PATCH 121/153] Add files via upload --- widgetsmp.py | 82 ++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 79 insertions(+), 3 deletions(-) diff --git a/widgetsmp.py b/widgetsmp.py index 7ee00cd..36bab6d 100644 --- a/widgetsmp.py +++ b/widgetsmp.py @@ -4,11 +4,17 @@ from ipywidgets import * from IPython.display import display from IPython.html import widgets +import base64 +res = 'computed results' plt.style.use('ggplot') from src.fibers_QQQQQQ_invert import fiber_analysis +from quanfima_torun import fiber_visualization +import os +import glob #button = widgets.Button(description='My Button') #out = widgets.Output() - +#import ipyvuetify as v +from IPython.display import display, FileLink # linking button and function together using a button's method #button.on_click(on_button_clicked) # displaying button and its output together @@ -20,7 +26,7 @@ from ipywidgets import interact, interact_manual def s(_): s = widgets.FileUpload(accept='', multiple=True) - return output + return s.data def run(_): @@ -63,4 +69,74 @@ def on_button_clicked(_): button.on_click(on_button_clicked) box=widgets.VBox([text1,text2,text3,text4,button]) display(box,out) - \ No newline at end of file +def run2(_): + + button = widgets.Button( + description='Run Fiber Orientation Visualization', + layout={'width': '300px'}) + out= widgets.Output() + def on_button_clicked(_): + # "linking function with output" + with out: + # what happens when we press the button + clear_output() + print('Program executing. Output in output folder on home page') + fiber_visualization() + + button.on_click(on_button_clicked) + box=widgets.VBox([button]) + display(box,out) + +from ipywidgets import HTML +HTML() + +def save(_): + filename = 'notebook.tar.gz' + b64 = base64.b64encode(res.encode()) + payload = b64.decode() + #BUTTONS + html_buttons = ''' + + + + + + + + + + ''' + html_button = html_buttons.format(payload=payload,filename=filename) + display(HTML(html_button)) + out= widgets.Output() + + +def clear(_): + button = widgets.Button( + description='Clear Data', + layout={'width': '300px'}) + out= widgets.Output() + def on_button_clicked(_): + # "linking function with output" + with out: + # what happens when we press the button + clear_output() + print('Program executing. Files in Data and output folder will be cleared') + files = glob.glob('../FiberFit/Data/*',recursive=True) + for f in files: + try: + os.remove(f) + except OSError as e: + print("Error: %s : %s" % (f, e.strerror)) + + files = glob.glob('../FiberFit/output/*',recursive=True) + + for f in files: + try: + os.remove(f) + except OSError as e: + print("Error: %s : %s" % (f, e.strerror)) + button.on_click(on_button_clicked) + box=widgets.VBox([button]) + display(box,out) + out= widgets.Output() \ No newline at end of file From 10c1102fb66a23938475e4185b6a5a1af548b9d0 Mon Sep 17 00:00:00 2001 From: mprender Date: Fri, 5 Mar 2021 23:42:46 -0500 Subject: [PATCH 122/153] Add files via upload --- quanfima/quanfima/utils2.py | 156 +++ quanfima/quanfima/visualizationDE.py | 1081 +++++++++++++++++++ quanfima/quanfima/visualization_testQ.py | 1208 ++++++++++++++++++++++ 3 files changed, 2445 insertions(+) create mode 100644 quanfima/quanfima/utils2.py create mode 100644 quanfima/quanfima/visualizationDE.py create mode 100644 quanfima/quanfima/visualization_testQ.py diff --git a/quanfima/quanfima/utils2.py b/quanfima/quanfima/utils2.py new file mode 100644 index 0000000..a5bb792 --- /dev/null +++ b/quanfima/quanfima/utils2.py @@ -0,0 +1,156 @@ +from __future__ import print_function +import os +import operator +import numpy as np +from scipy import ndimage as ndi +from skimage import morphology +from matplotlib import colors +from statsmodels.stats.multicomp import MultiComparison +from statsmodels.stats.libqsturng import psturng +import matplotlib.pyplot as plt +from skimage import data +from skimage.filters import threshold_otsu +import cv2 + + +def prepare_data(data, dilate_iterations=1, sigma=0.5): + """Returns the given binary data, its skeleton and the thickened skeleton. + + The skeleton of a given 2D or 3D array is computed, then it is thickened + using morphological dilation with `dilate_iterations` and smoothed with + help of Gaussian filter of specified `sigma`. + + Parameters + ---------- + data : ndarray + 2D or 3D binary array which will be processed. + + dilate_iterations : integer + Indicates the number of iterations for thickenning the skeleton. + + sigma : float + Indicates the sigma of Gaussian filter used in smoothing of skeleton. + + Returns + ------- + arrays : tuple of 2D or 3D arrays + The original array, its skeleton and the thickened skeleton. + """ + data_8bit = data.astype(np.uint8) + data_8bit = ndi.binary_fill_holes(data_8bit).astype(np.uint8) + + if data.ndim == 3: + skeleton = data#morphology.skeletonize_3d(data_8bit) + plt.imshow(skeleton) + elif data.ndim == 2: + skeleton = data# morphology.skeletonize(data_8bit) + else: + raise ValueError('Incorrect number of data dimensions, it supports from 2 to 3 dimensions.') + + skeleton_thick = ndi.binary_dilation(skeleton, iterations=dilate_iterations).astype(np.float32) + skeleton_thick = ndi.filters.gaussian_filter(skeleton_thick, sigma) + plt.imshow(skeleton_thick) + + return (data, skeleton, skeleton_thick) + + +def geo2rgb(lat, azth, azth_max=np.pi, lat_max=np.pi): + """Translates geo-coordinates to color in RGB color space. + + Parameters + ---------- + lat : float + Indicates latitude or elevation component of a given geo-coordinates. + + azth : float + Indicates azimuth component of a given geo-coordinates. + + azth_max : float + Indicates the normalization value for the azimuth component. + + lat_max : float + Indicates the normalization value for the latitude or elevation component. + + Returns + ------- + array : tuple of values + The tuple of RGB values [R, G, B]. + """ + return colors.hsv_to_rgb([azth/azth_max, lat/lat_max, 1.0]) + + +def calculate_tukey_posthoc(df, column, type_column='type', verbose=True, write=False, name=None, output_dir=None): + """Computes p-values using ANOVA with post-hoc Tukey HSD for a given DataFrame. + + Estimates p-values for a given DataFrame assuming that the sample + type is named as `type_column`. + + Parameters + ---------- + df : pandas DataFrame + Contains the table of values ans corresponding types or classes. + + column : str + Indicates the column of values. + + type_column : str + Indicates the column of sample kind. + + verbose : boolean + Specifies if the output should be printed into a terminal. + + write : boolean + Specifies if the output should be written into a text file. + + name : str + Indicates the name of the output file. + + output_dir : str + Indicates the output dir where the file will be written. + + Returns + ------- + dict : sample typles and p-values + The dict of sample types and cooresponding p-values. + """ + mc = MultiComparison(df[column], df[type_column]) + tt = mc.tukeyhsd() + st_range = np.abs(tt.meandiffs) / tt.std_pairs + + fout = None + if write and output_dir: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fout = open(os.path.join(output_dir, name + '.txt'), 'w') + print(os.path.join(output_dir, name + '.txt')) + + if write: + print('Tukey post-hoc ({0})'.format(column), end="", file=fout) + print(tt, end="", file=fout) + print(mc.groupsunique, end="", file=fout) + + if verbose: + print('Tukey post-hoc ({0})'%(column)) + print(tt) + print(mc.groupsunique) + + pvals = psturng(st_range, len(tt.groupsunique), tt.df_total) + + out = {} + groups = mc.groupsunique + g1idxs, g2idxs = mc.pairindices + + for g1i, g2i, p in zip(g1idxs, g2idxs, pvals): + gname = '{}-{}'.format(groups[g1i], groups[g2i]) + out[gname] = p + + min_item = min(out.iteritems(), key=operator.itemgetter(1)) + + for grp, p in out.items(): + if fout and write: + print >> fout, '{}: {}'.format(grp, p) + if verbose: + print(grp, ': ', p) + + return out, min_item diff --git a/quanfima/quanfima/visualizationDE.py b/quanfima/quanfima/visualizationDE.py new file mode 100644 index 0000000..168efe6 --- /dev/null +++ b/quanfima/quanfima/visualizationDE.py @@ -0,0 +1,1081 @@ +from __future__ import print_function +import os +import math +import numpy as np +import matplotlib as mpl +from skimage import io, filters +import matplotlib.colors as colors +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +import mpl_toolkits.axisartist.floating_axes as floating_axes +from matplotlib import cm +from scipy import ndimage as ndi, interpolate +from skimage import morphology +from matplotlib.projections import PolarAxes +from matplotlib.transforms import Affine2D +from mpl_toolkits.axisartist.grid_finder import DictFormatter, FixedLocator +from mpl_toolkits.axes_grid1 import make_axes_locatable +from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear +from quanfima.quanfima.utils2 import geo2rgb +from quanfima.quanfima import visvis_available +if visvis_available: + import visvis as vv + + +def plot_orientation_map(orient_map, fiber_skel, radius_structure_elem=1, + figsize=(20, 20), cmap='hsv', dpi=200, min_label=u'0°', + max_label=u'180°', name=None, output_dir=None): + """Plots the orientation map with the color wheel. + + Plots the orientation map from the provided angles `orient_map` and skeleton + `fiber_skel` of size `figsize` using the colormap `cmap` and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + orient_map : ndarray + 2D array of orientation at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + dpi : integer + Indicates the DPI of the output image. + + min_label : str + Indicates the label of minimum degree. + + max_label : str + Indicates the label of minimum degree. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + orient_map = ndi.grey_dilation(orient_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + fig = plt.figure(figsize=figsize) + masked_orient_map = np.ma.masked_where(fiber_skel == 0, orient_map) + + quant_steps = 2056 + cmap_obj = cm.get_cmap(cmap, quant_steps) + cmap_obj.set_bad(color='black') + + omap_ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + omap_ax.set_axis_off() + omap_ax.imshow(masked_orient_map, cmap=cmap_obj, interpolation=None) + + #display_axes = fig.add_axes([0.780, -0.076, 0.2, 0.2], projection='polar') + #display_axes._direction = np.pi + + norm = mpl.colors.Normalize(0.0, np.pi) + #cb = mpl.colorbar.ColorbarBase(display_axes, cmap=cmap_obj, norm=norm, orientation='horizontal') + + #display_axes.text(0.09, 0.56, min_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + # verticalalignment='center', transform=display_axes.transAxes) + + #display_axes.text(0.85, 0.56, max_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + # verticalalignment='center', transform=display_axes.transAxes) + + #cb.outline.set_visible(False) + #display_axes.set_axis_off() + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, 'orientation_map.png'), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + +def plot_diameter_map(thickness_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', tmin=None, tmax=None, dpi=200, + labelsize=20, label='Diameter, [pixels]', name=None, + output_dir=None): + """Plots the diameter map with the colorbar. + + Plots the diameter map from the provided diameters `thickness_map` and + skeleton `fiber_skel` of size `figsize` using the colormap `cmap` and the + limits of the colorbar specified by `tmin` and `tmax`, and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + thickness_map : ndarray + 2D array of diameter at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + tmin : float + Indicates the minimum value of the colorbar. + + tmax : float + Indicates the maximum value of the colorbar. + + dpi : integer + Indicates the DPI of the output image. + + labelsize : integer + Indicates the fontsize of the label of the colorbar. + + label : str + Indicates the label of the colorbar. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + thickness_map = ndi.grey_dilation(thickness_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_thickness_map = np.ma.masked_where(fiber_skel == 0, thickness_map) + + cmap_obj = cm.get_cmap(cmap) + cmap_obj.set_bad(color='black') + + fig = plt.figure(figsize=figsize) + ax = plt.subplot(111) + ax.set_axis_off() + + im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size="2.5%", pad=0.05) + cbar = plt.colorbar(im, cax=cax) + cbar.ax.tick_params(labelsize=labelsize) + cbar.set_label(label, fontsize=labelsize) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + +def gather_polar_errors(datasets_path, estimation_path, + azth_rng=np.arange(-90, 91, step=3), + lat_rng=np.arange(0, 91, step=3)): + """Computes the absolute angular error in ranges between estimated datasets. + + Calculates the absolute angular error in ranges `azth_rng` and `lat_rng` + between estimated orientation datasets localed at paths `datasets_path` and + `estimation_path`. + + Parameters + ---------- + datasets_path : str + Indicates the path to the reference / estimated dataset. + + estimation_path : str + Indicates the path to the estimated / reference dataset. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude or elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + reference_dataset = np.load(datasets_path).item() + estimated_dataset = np.load(estimation_path).item() + idxs = estimated_dataset['indices'] + + print(reference_dataset.keys()) + print(estimated_dataset.keys()) + + ref_azth, ref_lat = reference_dataset['azth'][idxs], reference_dataset['lat'][idxs] + res_azth, res_lat = estimated_dataset['azth'][idxs], estimated_dataset['lat'][idxs] + + abs_err_azth, abs_err_lat = np.abs(ref_azth - res_azth), np.abs(ref_lat - res_lat) + + out = _angle_err2mean_abs_err(abs_err_azth, abs_err_lat, + ref_azth, ref_lat, + azth_rng=azth_rng, lat_rng=lat_rng) + + return out + + +def _angle_err2mean_abs_err(azth_err, lat_err, azth_ref, lat_ref, + azth_rng=np.arange(-90, 91, step=5), + lat_rng=np.arange(0, 91, step=5)): + """Computes the absolute angular error in ranges between estimated datasets. + + Parameters + ---------- + azth_err : 3D array + Indicates the error of azimuth components. + + lat_err : 3D array + Indicates the error of latitude / elevation components. + + azth_ref : 3D array + Indicates the reference dataset of estimated azimuth component. + + lat_ref : 3D array + Indicates the reference dataset of estimated latitude or elevation component. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude and elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + out = np.zeros((len(azth_rng) - 1, len(lat_rng) - 1), dtype=np.float32) + + for i in xrange(len(azth_rng) - 1): + for j in xrange(len(lat_rng) - 1): + rng_azth = (np.deg2rad(azth_rng[i]), np.deg2rad(azth_rng[i+1])) + rng_lat = (np.deg2rad(lat_rng[j]), np.deg2rad(lat_rng[j+1])) + + idxs_azth = np.where((azth_ref >= rng_azth[0]) & (azth_ref < rng_azth[1])) + idxs_lat = np.where((lat_ref >= rng_lat[0]) & (lat_ref < rng_lat[1])) + + idxs = np.intersect1d(idxs_azth, idxs_lat) + + if len(idxs): + mu_azth, mu_lat = np.rad2deg(np.mean(azth_err[idxs])), \ + np.rad2deg(np.mean(lat_err[idxs])) + + out[i, j] = mu_azth + mu_lat + else: + out[i, j] = 0.0 + + return out + + +def plot_polar_heatmap(data, name, interp_factor=5., color_limits=False, + hide_colorbar=False, vmin=None, vmax=None, log_scale=True, + dpi=200, output_dir=None): + """Plots the polar heatmap describing azimuth and latitude / elevation components. + + Plots the polar heatmap where each cell of the heatmap corresponds to + the specific element of the array provided by `gather_polar_errors` + function. + + Parameters + ---------- + data : 2D array + Indicates the array containing the sum of angular errors within the + specified angular ranges. It is usually provided by `gather_polar_errors` + function. + + name : str + Indicates the name of the output png file. + + interp_factor : float + Indicates the interpolation factor of the heatmap. + + color_limits : boolean + Specifies if the determined intensity limits should be returned. + + hide_colorbar : boolean + Specifies if the colorbar should be hidden. + + vmin : float + Indicates the minimum value of the colorbar. + + vmax : float + Indicates the maximum value of the colorbar. + + log_scale : float + Specifies if the heatmap sould be in the logarithmic scale. + + dpi : integer + Indicates the DPI of the output image. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + th0, th1 = 0., 180. + r0, r1 = 0, 90 + thlabel, rlabel = 'Azimuth', 'Elevation' + + tr_scale = Affine2D().scale(np.pi/180., 1.) + tr = tr_scale + PolarAxes.PolarTransform() + + lat_ticks = [(.0*90., '0$^{\circ}$'), + (.33*90., '30$^{\circ}$'), + (.66*90., '60$^{\circ}$'), + (1.*90., '90$^{\circ}$')] + r_grid_locator = FixedLocator([v for v, s in lat_ticks]) + r_grid_formatter = DictFormatter(dict(lat_ticks)) + + angle_ticks = [(0*180., '90$^{\circ}$'), + (.25*180., '45$^{\circ}$'), + (.5*180., '0$^{\circ}$'), + (.75*180., '-45$^{\circ}$'), + (1.*180., '-90$^{\circ}$')] + theta_grid_locator = FixedLocator([v for v, s in angle_ticks]) + theta_tick_formatter = DictFormatter(dict(angle_ticks)) + + grid_helper = GridHelperCurveLinear(tr, + extremes=(th0, th1, r0, r1), + grid_locator1=theta_grid_locator, + grid_locator2=r_grid_locator, + tick_formatter1=theta_tick_formatter, + tick_formatter2=r_grid_formatter) + + fig = plt.figure() + ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper) + fig.add_subplot(ax) + ax.set_facecolor('white') + + ax.axis["bottom"].set_visible(False) + ax.axis["top"].toggle(ticklabels=True, label=True) + ax.axis["top"].set_axis_direction("bottom") + ax.axis["top"].major_ticklabels.set_axis_direction("top") + ax.axis["top"].label.set_axis_direction("top") + + ax.axis["left"].set_axis_direction("bottom") + ax.axis["right"].set_axis_direction("top") + + ax.axis["top"].label.set_text(thlabel) + ax.axis["left"].label.set_text(rlabel) + + aux_ax = ax.get_aux_axes(tr) + aux_ax.patch = ax.patch + ax.patch.zorder = 0.9 + + rad = np.linspace(0, 90, data.shape[1]) + azm = np.linspace(0, 180, data.shape[0]) + + f = interpolate.interp2d(rad, azm, data, kind='linear', bounds_error=True, fill_value=0) + + new_rad = np.linspace(0, 90, 180*interp_factor) + new_azm = np.linspace(0, 180, 360*interp_factor) + new_data_angle_dist = f(new_rad, new_azm) + new_r, new_th = np.meshgrid(new_rad, new_azm) + new_data_angle_dist += 1. + + if log_scale: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', + norm=colors.LogNorm(vmin=1. if vmin is None else vmin, + vmax=new_data_angle_dist.max() if vmax is None else vmax)) + else: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', vmin=vmin, vmax=vmax) + + cbar = plt.colorbar(data_mesh, orientation='vertical', shrink=.88, pad=.1, aspect=15) + cbar.ax.set_ylabel('Absolute error, [deg.]') + + if hide_colorbar: + cbar.remove() + + ax.grid(False) + + plt.show() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + + if color_limits: + return 1., new_data_angle_dist.max() + + +def plot_histogram_fig(data, num_bins, xticks, color, splot_index=111, output_dir=None, + xlim=(None, None), ylim=(None, None), name=None, in_percent=False, + bar_width=0.8, ticks_pad=7, xticks_fontsize=22, + yticks_fontsize=22, xlabel=None, ylabel=None, + labels_fontsize=20, grid_alpha=0.3, title_fontsize=22, + exp_fontsize=15, type=None, figsize=(12, 8), dpi=200): + """Plots the histogram from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + num_bins : integer + Indicates the number of histogram bins. + + xticks : array + Indicates the array of ticks of the X-axis. + + color : str + Indicates the color of the histogram. + + Returns + ------- + tuple : tuple of Figure and Axis objects. + """ + fig = plt.figure(figsize=figsize) + ax = fig.add_subplot(splot_index) + + weights = np.ones_like(data)/float(len(data)) + + n, bins, patches = ax.hist(data, num_bins, color=color, + rwidth=bar_width, weights=weights) + + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='both', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.tick_params(axis='y', + labelsize=yticks_fontsize, + colors='#000000', + which='major', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='y', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.xaxis.set_minor_locator(ticker.MultipleLocator(5)) + ax.yaxis.set_minor_locator(ticker.MultipleLocator(5)) + + plt.xticks(xticks, xticks) + + ax.xaxis.offsetText.set_fontsize(exp_fontsize) + ax.yaxis.offsetText.set_fontsize(exp_fontsize) + + ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2), + fontsize=yticks_fontsize) + ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=6, min_n_ticks=6)) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + + ax.set_ylabel(ylabel, labelpad=2, fontsize=labels_fontsize, color='black') + ax.set_xlabel(xlabel, labelpad=2, fontsize=labels_fontsize, color='black') + + ax.set_ylim(ylim) + ax.set_xlim(xlim) + + if in_percent: + vals = ax.get_yticks() + ax.set_yticklabels(['{:3.0f}'.format(x*100) for x in vals]) + + ax.set_axisbelow(True) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, bbox_inches='tight', pad_inches=0.1, dpi=dpi) + + return fig, ax + + +def create_pie_chart(data, rngs, colors=['#244268', '#426084', '#67809F', '#95A9C1', '#C6D2E0'], + unit_scale=1.0, measure_quantity='m^3', figsize=(33, 15), + legend_loc=(0.383, -0.25), zebra_color=(False, 3), + legend_fontsize=50, chart_fontsize=60, dpi=72, name=None, + output_dir=None): + """Plots the piechart of from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + rngs : tuple of tuples + Indicates the ranges of the piechart. + + colors : array + Indicates the color for the region of the piechart corresponding to the + specific range. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + figsize : tuple of integers + Indicates the size of the output figure. + + legend_loc : tuple + Indicates the position of the legend of the figure. + + zebra_color : tuple + Allows to change the text color of the region to white from the first to + the speficied index of the region (True, reg_index). + + legend_fontsize : integer + Indicates the fontsize of the legend. + + chart_fontsize : integer + Indicates the fontsize of the figure. + + dpi : integer + Indicates the DPI of the output image. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + def number(val): + if val < 1000: + return '%d' % val + + sv = str(val) + return '$\mathregular{10^{%d}}$' % (len(sv)-2) if val % 10 == 0 else '%0.0e' % val + + def get_title(v1, v2, measure_quantity): + ftm = '%s $\minus$ %s %s' + return ftm % (number(v1), number(v2), measure_quantity) + + data_ranges = [] + df = data * unit_scale + for rng in rngs: + rng_min, rng_max = rng[0], rng[1] + data_rng = df[(df > rng_min) & (df < rng_max)] + data_ranges.append(data_rng) + + num_elem = [len(p) for p in data_ranges] + se = sum(num_elem) + + print('Num of particles: {}'%(se)) + + proc_particles = [n/float(se) * 100.0 for n in num_elem] + + for size, rng in zip(num_elem, rngs): + print('{}-{}: {}'%(rng[0], rng[1], size)) + + titles = [get_title(minv, maxv, measure_quantity) for minv, maxv in rngs] + + textprops = {'fontsize': chart_fontsize, + 'weight': 'normal', + 'family': 'sans-serif'} + pie_width = 0.5 + fig, ax = plt.subplots(figsize=figsize) + ax.axis('equal') + patches, texts, autotexts = ax.pie(proc_particles, + textprops=textprops, + colors=colors, + autopct='%1.1f%%', + radius=1, + pctdistance=1-pie_width/2) + + if (zebra_color is not None) and (zebra_color[0]): + for tt in autotexts[:zebra_color[1]]: + tt.set_color('white') + + plt.setp(patches, + width=pie_width, + edgecolor='white') + + plt.legend(patches, titles, loc=legend_loc, fontsize=legend_fontsize) + + _d, _offset, _di = [1, -1], [0.45, 0.45], 0 + + for t, p in zip(autotexts, proc_particles): + if p < 2.0: + pos = list(t.get_position()) + pos[0] = pos[0] + _d[_di] * _offset[_di] + + t.set_position(pos) + _di += 1 + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.1, dpi=dpi) + + +def _bbox_3D(img): + """Crops the non-zero part of a volume + """ + r = np.any(img, axis=(1, 2)) + c = np.any(img, axis=(0, 2)) + z = np.any(img, axis=(0, 1)) + + rmin, rmax = np.where(r)[0][[0, -1]] + cmin, cmax = np.where(c)[0][[0, -1]] + zmin, zmax = np.where(z)[0][[0, -1]] + + return rmin, rmax, cmin, cmax, zmin, zmax + + +def plot_3d_orientation_map(name, lat_data, azth_data, radius_structure_elem=1, + output_dir=None, width=512, height=512, + camera_azth=44.5, camera_elev=35.8, camera_roll=0.0, + camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k'): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_azth : float + Indicates the azimuth angle of the camera. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(azth_data) + + azth, lat = azth_data[rmin:rmax, cmin:cmax, zmin:zmax], \ + np.abs(lat_data[rmin:rmax, cmin:cmax, zmin:zmax]) + + skel = azth.copy().astype(np.float32) + skel[skel.nonzero()] = 1. + + azth = ndi.grey_dilation(azth, structure=morphology.ball(radius_structure_elem)) + lat = ndi.grey_dilation(lat, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)) + + Z, Y, X = skel.nonzero() + vol_orient = np.zeros(skel.shape + (3,), dtype=np.float32) + + print(vol_orient.size, vol_orient[skel.nonzero()].size) + + for z, y, x in zip(Z, Y, X): + vol_orient[z, y, x] = geo2rgb(lat[z, y, x], azth[z, y, x]) + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(vol_orient[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + # def mouseUp(event): + # print 'mouseUp!!' + # a = vv.gca() + # print a.camera.GetViewParams() + # + # a.eventMouseUp.Bind(mouseUp) + # fig.eventMouseUp.Bind(mouseUp) + # + # a.Draw() + # fig.DrawNow() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_orientation.png'.format(name)), + sf=1, bg=background_color) + app.Run() + + +def plot_3d_diameter_map(name, data, unit_scale=1.0, measure_quantity='vox', + radius_structure_elem=1, output_dir=None, width=512, + height=512, camera_azth=44.5, camera_elev=35.8, + camera_roll=0.0, camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k', + cb_x_offset=10): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + data : 3D array + Indicates the 3D array containing diameter at every point of the skeleton. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + camera_azth : float + Indicates the azimuth angle of the camera. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + + cb_x_offset : int + Indicates the offset of the colorbar from the right window side. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(data) + dmtr = data[rmin:rmax, cmin:cmax, zmin:zmax] * unit_scale + skel = np.zeros_like(dmtr, dtype=np.uint8) + skel[dmtr.nonzero()] = 1 + + dmtr = ndi.grey_dilation(dmtr, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)).astype(np.float32) + skel[skel.nonzero()] = 1. + + dmtr = dmtr * skel + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(dmtr[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + t.colormap = vv.CM_JET + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + cb = vv.colorbar() + cb.SetLabel('Diameter, [{}]'.format(measure_quantity)) + cb._label.position.x += cb_x_offset + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_diameter.png'.format(name)), + sf=1, bg='w') + app.Run() + + +def plot_color_wheel(name, output_dir=None, dpi=500, xlabel='Elevation', + ylabel='Azimuth', fontsize=10, num_xticks=4, yticks=(-90, 90)): + """Plots the color wheel for visualizations of 3D orintation. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + + xlabel : str + Indicates the text along the x-axis. + + ylabel : str + Indicates the text along the y-axis. + + fontsize : int + Indicates the font size of labels along axes. + + num_xticks : int + Indicates the number of ticks along axes. + + yticks : tuple + Indicates the range of minimum and maximum values along the y-axis. + """ + + azth, lat = np.linspace(0., 1., num=180), np.linspace(0., 1., num=90) + rgb_arr = np.zeros((len(azth), len(lat), 3)) + for i in xrange(len(azth)): + for j in xrange(len(lat)): + rgb_arr[i, j, :] = colors.hsv_to_rgb([azth[i], lat[j], 1.0]) + + fig, ax = plt.subplots(figsize=(2, 2)) + ax.set_facecolor('red') + + ax.set_xlim([0, 90]) + ax.set_ylim([0, 181]) + + ax.set_yticks(np.linspace(0, 180, num=7).astype(np.int32)) + ax.set_yticklabels(np.linspace(yticks[0], yticks[1], num=7).astype(np.int32)) + + ax.set_xlabel(xlabel, fontsize=fontsize, labelpad=0, color='w') + ax.set_ylabel(ylabel, fontsize=fontsize, labelpad=0, color='w') + + xmajorlocator = ticker.LinearLocator(num_xticks) + ax.xaxis.set_major_locator(xmajorlocator) + ax.tick_params(direction='out', length=2, width=0, labelsize=fontsize, pad=0, colors='w') + + ax.imshow(rgb_arr) + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_color_bar.png'.format(name)), + transparent=True, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + +def plot_fourier_orientation(data, orient_blocks, block_shape, figsize=(12,12), + cmap='gray', line_length=20, line_width=2.5, + line_color='red', line_style='-', name=None, + output_dir=None, dpi=200): + """Plots the orientation map in a block-wise manner. + + Plots the orientation vector over the image `data` at the center of each + block of the subdivided image. The orientation at every block 'orient_blocks' + and its size `block_shape` are specified by orientation estimation method. + The result can be and stored as a png file with DPI of `dpi` to the folder + specified by `output_dir`. + + Parameters + ---------- + data : ndarray + An image on top of which, the orientation will be plotted.. + + orient_blocks : ndarray + 2D array of orientation at every block of the subdivided image. + + block_shape : tuple + Indicates the block size within which the orientation is calculated. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used for image. + + line_length : integer + Indicates the length of the orientation vector at each block. + + line_width : float + Indicates the line width of the orientation vector at each block. + + line_color : str + Indicates the line color of the orientation vector at each block. + + line_style : str + Indicates the line style of the orientation vector at each block. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + """ + fig, ax = plt.subplots(figsize=figsize) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + ax.set_axis_off() + + ax.imshow(data, cmap=cmap) + + for i in xrange(orient_blocks.shape[0]): + for j in xrange(orient_blocks.shape[1]): + y0, x0 = block_shape[0] * j + block_shape[0]/2, \ + block_shape[1] * i + block_shape[1]/2 + + orientation = orient_blocks[i ,j] + + x2 = x0 - math.sin(orientation) * line_length + y2 = y0 - math.cos(orientation) * line_length + + x3 = x0 + math.sin(orientation) * line_length + y3 = y0 + math.cos(orientation) * line_length + + ax.plot((x2, x3), (y2, y3), linestyle=line_style, + linewidth=line_width, color=line_color) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_fourier_orientation_map.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.05, dpi=dpi) + + plt.show() diff --git a/quanfima/quanfima/visualization_testQ.py b/quanfima/quanfima/visualization_testQ.py new file mode 100644 index 0000000..9e44485 --- /dev/null +++ b/quanfima/quanfima/visualization_testQ.py @@ -0,0 +1,1208 @@ +from __future__ import print_function +import os +import math +import numpy as np +import matplotlib as mpl +from skimage import io, filters +import matplotlib.colors as colors +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +import mpl_toolkits.axisartist.floating_axes as floating_axes +from matplotlib import cm +from scipy import ndimage as ndi, interpolate +from skimage import morphology +from matplotlib.projections import PolarAxes +from matplotlib.transforms import Affine2D +from mpl_toolkits.axisartist.grid_finder import DictFormatter, FixedLocator +from mpl_toolkits.axes_grid1 import make_axes_locatable +from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear +from quanfima.quanfima.utils2 import geo2rgb +from quanfima.quanfima import visvis_available +if visvis_available: + import visvis as vv + +def plot_orientM_map(orient_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', min_label=None, max_label=None, dpi=200, + labelsize=20, label='Orientation, [pixels]', name=None, + output_dir=None): + """Plots the diameter map with the colorbar. + + Plots the diameter map from the provided diameters `thickness_map` and + skeleton `fiber_skel` of size `figsize` using the colormap `cmap` and the + limits of the colorbar specified by `tmin` and `tmax`, and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + thickness_map : ndarray + 2D array of diameter at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + tmin : float + Indicates the minimum value of the colorbar. + + tmax : float + Indicates the maximum value of the colorbar. + + dpi : integer + Indicates the DPI of the output image. + + labelsize : integer + Indicates the fontsize of the label of the colorbar. + + label : str + Indicates the label of the colorbar. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + thickness_map = ndi.grey_dilation(orient_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_thickness_map = np.ma.masked_where(fiber_skel == 0, thickness_map) + + cmap_obj = cm.get_cmap(cmap) + cmap_obj.set_bad(color='black') + + fig = plt.figure(figsize=figsize) + #ax = plt.subplot(111) + #ax.set_axis_off() + + #im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + #divider = make_axes_locatable(ax) + #cax = divider.append_axes('right', size="2.5%", pad=0.05) + #cbar = plt.colorbar(im, cax=cax) + #cbar.ax.tick_params(labelsize=labelsize) + #cbar.set_label(label, fontsize=labelsize) + + + quant_steps = 2056 + cmap_obj = cm.get_cmap(cmap, quant_steps) + cmap_obj.set_bad(color='black') + + omap_ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + omap_ax.set_axis_off() + omap_ax.imshow(orient_map, cmap=cmap_obj, interpolation=None) + + display_axes = fig.add_axes([0.780, -0.076, 0.2, 0.2], projection='polar') + display_axes_direction = np.pi + + norm = mpl.colors.Normalize(0.0, np.pi) + cb = mpl.colorbar.ColorbarBase(display_axes, cmap=cmap_obj, norm=norm, orientation='horizontal') + + display_axes.text(0.09, 0.56, min_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + verticalalignment='center', transform=display_axes.transAxes) + + display_axes.text(0.85, 0.56, max_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + verticalalignment='center', transform=display_axes.transAxes) + + cb.outline.set_visible(False) + display_axes.set_axis_off() + + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + + +def plot_orientation_test_map(orient_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', dpi=200, min_label='0', + max_label='180', name=None, output_dir=None): + """Plots the orientation map with the color wheel. + + Plots the orientation map from the provided angles `orient_map` and skeleton + `fiber_skel` of size `figsize` using the colormap `cmap` and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + orient_map : ndarray + 2D array of orientation at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + dpi : integer + Indicates the DPI of the output image. + + min_label : str + Indicates the label of minimum degree. + + max_label : str + Indicates the label of minimum degree. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + orient_map = ndi.grey_dilation(orient_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_orient_map = np.ma.masked_where(fiber_skel == 0, orient_map) + + fig = plt.figure(figsize=figsize) + ax = plt.subplot(111) + ax.set_axis_off() + + quant_steps = 2056 + + cmap_obj = cm.get_cmap(cmap, quant_steps) + cmap_obj.set_bad(color='black') + + im = ax.imshow(orient_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size="2.5%", pad=0.05) + cbar = plt.colorbar(im, cax=cax) + cbar.ax.tick_params(labelsize=labelsize) + cbar.set_label(label, fontsize=labelsize) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_orient_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + #omap_ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + #omap_ax.set_axis_off() + #omap_ax.imshow(masked_orient_map, cmap=cmap_obj, interpolation=None) + + #display_axes = fig.add_axes([0.780, -0.076, 0.2, 0.2], projection='polar') + #display_axes._direction = np.pi + + #norm = mpl.colors.Normalize(0.0, np.pi) + #cb = mpl.colorbar.ColorbarBase(display_axes, cmap=cmap_obj, norm=norm, orientation='horizontal') + + #display_axes.text(0.09, 0.56, min_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + # verticalalignment='center', transform=display_axes.transAxes) + + #display_axes.text(0.85, 0.56, max_label, color='white', fontsize=20, weight='bold', horizontalalignment='center', + # verticalalignment='center', transform=display_axes.transAxes) + + #cb.outline.set_visible(False) + #display_axes.set_axis_off() + + #if (output_dir is not None) and (name is not None): + # if not os.path.exists(output_dir): + # os.makedirs(output_dir) + # fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + # transparent=False, + # bbox_inches='tight', + # pad_inches=0.1, + # dpi=dpi) + + #plt.show() + + +def plot_diameter_map_test(thickness_map, fiber_skel, radius_structure_elem=1, + figsize=(15, 15), cmap='hsv', tmin=None, tmax=None, dpi=200, + labelsize=20, label='Diameter, [pixels]', name=None, + output_dir=None): + """Plots the diameter map with the colorbar. + + Plots the diameter map from the provided diameters `thickness_map` and + skeleton `fiber_skel` of size `figsize` using the colormap `cmap` and the + limits of the colorbar specified by `tmin` and `tmax`, and writes as a png + file with DPI of `dpi` to the folder specified by `output_dir`. + + Parameters + ---------- + thickness_map : ndarray + 2D array of diameter at every point of the skeleton. + + fiber_skel : ndarray + The binary skeleton extracted from the binary data. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used to map angles to colors. + + tmin : float + Indicates the minimum value of the colorbar. + + tmax : float + Indicates the maximum value of the colorbar. + + dpi : integer + Indicates the DPI of the output image. + + labelsize : integer + Indicates the fontsize of the label of the colorbar. + + label : str + Indicates the label of the colorbar. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + disk = morphology.disk(radius_structure_elem) + thickness_map = ndi.grey_dilation(thickness_map, structure=disk).astype(np.float32) + fiber_skel = ndi.binary_dilation(fiber_skel, structure=disk).astype(np.float32) + + masked_thickness_map = np.ma.masked_where(fiber_skel == 0, thickness_map) + + cmap_obj = cm.get_cmap(cmap) + cmap_obj.set_bad(color='black') + + fig = plt.figure(figsize=figsize) + ax = plt.subplot(111) + ax.set_axis_off() + + im = ax.imshow(masked_thickness_map, cmap=cmap_obj, vmin=tmin, vmax=tmax) + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size="2.5%", pad=0.05) + cbar = plt.colorbar(im, cax=cax) + cbar.ax.tick_params(labelsize=labelsize) + cbar.set_label(label, fontsize=labelsize) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + fig.savefig(os.path.join(output_dir, '{}_diameter_map.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + + +def gather_polar_errors(datasets_path, estimation_path, + azth_rng=np.arange(-90, 91, step=3), + lat_rng=np.arange(0, 91, step=3)): + """Computes the absolute angular error in ranges between estimated datasets. + + Calculates the absolute angular error in ranges `azth_rng` and `lat_rng` + between estimated orientation datasets localed at paths `datasets_path` and + `estimation_path`. + + Parameters + ---------- + datasets_path : str + Indicates the path to the reference / estimated dataset. + + estimation_path : str + Indicates the path to the estimated / reference dataset. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude or elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + reference_dataset = np.load(datasets_path).item() + estimated_dataset = np.load(estimation_path).item() + idxs = estimated_dataset['indices'] + + print(reference_dataset.keys()) + print(estimated_dataset.keys()) + + ref_azth, ref_lat = reference_dataset['azth'][idxs], reference_dataset['lat'][idxs] + res_azth, res_lat = estimated_dataset['azth'][idxs], estimated_dataset['lat'][idxs] + + abs_err_azth, abs_err_lat = np.abs(ref_azth - res_azth), np.abs(ref_lat - res_lat) + + out = _angle_err2mean_abs_err(abs_err_azth, abs_err_lat, + ref_azth, ref_lat, + azth_rng=azth_rng, lat_rng=lat_rng) + + return out + + +def _angle_err2mean_abs_err(azth_err, lat_err, azth_ref, lat_ref, + azth_rng=np.arange(-90, 91, step=5), + lat_rng=np.arange(0, 91, step=5)): + """Computes the absolute angular error in ranges between estimated datasets. + + Parameters + ---------- + azth_err : 3D array + Indicates the error of azimuth components. + + lat_err : 3D array + Indicates the error of latitude / elevation components. + + azth_ref : 3D array + Indicates the reference dataset of estimated azimuth component. + + lat_ref : 3D array + Indicates the reference dataset of estimated latitude or elevation component. + + azth_rng : array + Indicates the ranges of azimuth angles where the error is accumulated. + + lat_rng : array + Indicates the ranges of latitude and elevation angles where the error + is accumulated. + + Returns + ------- + array : 2D array + The 2D array of accumulated errors within the combinations of the specified + angular ranges. + """ + out = np.zeros((len(azth_rng) - 1, len(lat_rng) - 1), dtype=np.float32) + + for i in xrange(len(azth_rng) - 1): + for j in xrange(len(lat_rng) - 1): + rng_azth = (np.deg2rad(azth_rng[i]), np.deg2rad(azth_rng[i+1])) + rng_lat = (np.deg2rad(lat_rng[j]), np.deg2rad(lat_rng[j+1])) + + idxs_azth = np.where((azth_ref >= rng_azth[0]) & (azth_ref < rng_azth[1])) + idxs_lat = np.where((lat_ref >= rng_lat[0]) & (lat_ref < rng_lat[1])) + + idxs = np.intersect1d(idxs_azth, idxs_lat) + + if len(idxs): + mu_azth, mu_lat = np.rad2deg(np.mean(azth_err[idxs])), \ + np.rad2deg(np.mean(lat_err[idxs])) + + out[i, j] = mu_azth + mu_lat + else: + out[i, j] = 0.0 + + return out + + +def plot_polar_heatmap(data, name, interp_factor=5., color_limits=False, + hide_colorbar=False, vmin=None, vmax=None, log_scale=True, + dpi=200, output_dir=None): + """Plots the polar heatmap describing azimuth and latitude / elevation components. + + Plots the polar heatmap where each cell of the heatmap corresponds to + the specific element of the array provided by `gather_polar_errors` + function. + + Parameters + ---------- + data : 2D array + Indicates the array containing the sum of angular errors within the + specified angular ranges. It is usually provided by `gather_polar_errors` + function. + + name : str + Indicates the name of the output png file. + + interp_factor : float + Indicates the interpolation factor of the heatmap. + + color_limits : boolean + Specifies if the determined intensity limits should be returned. + + hide_colorbar : boolean + Specifies if the colorbar should be hidden. + + vmin : float + Indicates the minimum value of the colorbar. + + vmax : float + Indicates the maximum value of the colorbar. + + log_scale : float + Specifies if the heatmap sould be in the logarithmic scale. + + dpi : integer + Indicates the DPI of the output image. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + th0, th1 = 0., 180. + r0, r1 = 0, 90 + thlabel, rlabel = 'Azimuth', 'Elevation' + + tr_scale = Affine2D().scale(np.pi/180., 1.) + tr = tr_scale + PolarAxes.PolarTransform() + + lat_ticks = [(.0*90., '0$^{\circ}$'), + (.33*90., '30$^{\circ}$'), + (.66*90., '60$^{\circ}$'), + (1.*90., '90$^{\circ}$')] + r_grid_locator = FixedLocator([v for v, s in lat_ticks]) + r_grid_formatter = DictFormatter(dict(lat_ticks)) + + angle_ticks = [(0*180., '90$^{\circ}$'), + (.25*180., '45$^{\circ}$'), + (.5*180., '0$^{\circ}$'), + (.75*180., '-45$^{\circ}$'), + (1.*180., '-90$^{\circ}$')] + theta_grid_locator = FixedLocator([v for v, s in angle_ticks]) + theta_tick_formatter = DictFormatter(dict(angle_ticks)) + + grid_helper = GridHelperCurveLinear(tr, + extremes=(th0, th1, r0, r1), + grid_locator1=theta_grid_locator, + grid_locator2=r_grid_locator, + tick_formatter1=theta_tick_formatter, + tick_formatter2=r_grid_formatter) + + fig = plt.figure() + ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper) + fig.add_subplot(ax) + ax.set_facecolor('white') + + ax.axis["bottom"].set_visible(False) + ax.axis["top"].toggle(ticklabels=True, label=True) + ax.axis["top"].set_axis_direction("bottom") + ax.axis["top"].major_ticklabels.set_axis_direction("top") + ax.axis["top"].label.set_axis_direction("top") + + ax.axis["left"].set_axis_direction("bottom") + ax.axis["right"].set_axis_direction("top") + + ax.axis["top"].label.set_text(thlabel) + ax.axis["left"].label.set_text(rlabel) + + aux_ax = ax.get_aux_axes(tr) + aux_ax.patch = ax.patch + ax.patch.zorder = 0.9 + + rad = np.linspace(0, 90, data.shape[1]) + azm = np.linspace(0, 180, data.shape[0]) + + f = interpolate.interp2d(rad, azm, data, kind='linear', bounds_error=True, fill_value=0) + + new_rad = np.linspace(0, 90, 180*interp_factor) + new_azm = np.linspace(0, 180, 360*interp_factor) + new_data_angle_dist = f(new_rad, new_azm) + new_r, new_th = np.meshgrid(new_rad, new_azm) + new_data_angle_dist += 1. + + if log_scale: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', + norm=colors.LogNorm(vmin=1. if vmin is None else vmin, + vmax=new_data_angle_dist.max() if vmax is None else vmax)) + else: + data_mesh = aux_ax.pcolormesh(new_th, new_r, new_data_angle_dist, cmap='jet', vmin=vmin, vmax=vmax) + + cbar = plt.colorbar(data_mesh, orientation='vertical', shrink=.88, pad=.1, aspect=15) + cbar.ax.set_ylabel('Absolute error, [deg.]') + + if hide_colorbar: + cbar.remove() + + ax.grid(False) + + plt.show() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + + if color_limits: + return 1., new_data_angle_dist.max() + + +def plot_histogram_fig(data, num_bins, xticks, color, splot_index=111, output_dir=None, + xlim=(None, None), ylim=(None, None), name=None, in_percent=False, + bar_width=0.8, ticks_pad=7, xticks_fontsize=22, + yticks_fontsize=22, xlabel=None, ylabel=None, + labels_fontsize=20, grid_alpha=0.3, title_fontsize=22, + exp_fontsize=15, type=None, figsize=(12, 8), dpi=200): + """Plots the histogram from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + num_bins : integer + Indicates the number of histogram bins. + + xticks : array + Indicates the array of ticks of the X-axis. + + color : str + Indicates the color of the histogram. + + Returns + ------- + tuple : tuple of Figure and Axis objects. + """ + fig = plt.figure(figsize=figsize) + ax = fig.add_subplot(splot_index) + + weights = np.ones_like(data)/float(len(data)) + + n, bins, patches = ax.hist(data, num_bins, color=color, + rwidth=bar_width, weights=weights) + + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='both', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='x', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.tick_params(axis='y', + labelsize=yticks_fontsize, + colors='#000000', + which='major', + direction='out', + length=6, + width=2, + pad=ticks_pad) + ax.tick_params(axis='y', + labelsize=xticks_fontsize, + colors='#000000', + which='minor', + direction='out', + length=4, + width=2, + pad=ticks_pad) + + ax.xaxis.set_minor_locator(ticker.MultipleLocator(5)) + ax.yaxis.set_minor_locator(ticker.MultipleLocator(5)) + + plt.xticks(xticks, xticks) + + ax.xaxis.offsetText.set_fontsize(exp_fontsize) + ax.yaxis.offsetText.set_fontsize(exp_fontsize) + + ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2), + fontsize=yticks_fontsize) + ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=6, min_n_ticks=6)) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + + ax.set_ylabel(ylabel, labelpad=2, fontsize=labels_fontsize, color='black') + ax.set_xlabel(xlabel, labelpad=2, fontsize=labels_fontsize, color='black') + + ax.set_ylim(ylim) + ax.set_xlim(xlim) + + if in_percent: + vals = ax.get_yticks() + ax.set_yticklabels(['{:3.0f}'.format(x*100) for x in vals]) + + ax.set_axisbelow(True) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + transparent=False, bbox_inches='tight', pad_inches=0.1, dpi=dpi) + + return fig, ax + + +def create_pie_chart(data, rngs, colors=['#244268', '#426084', '#67809F', '#95A9C1', '#C6D2E0'], + unit_scale=1.0, measure_quantity='m^3', figsize=(33, 15), + legend_loc=(0.383, -0.25), zebra_color=(False, 3), + legend_fontsize=50, chart_fontsize=60, dpi=72, name=None, + output_dir=None): + """Plots the piechart of from a given data. + + Parameters + ---------- + data : 1D array + Indicates the array containing the values. + + rngs : tuple of tuples + Indicates the ranges of the piechart. + + colors : array + Indicates the color for the region of the piechart corresponding to the + specific range. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + figsize : tuple of integers + Indicates the size of the output figure. + + legend_loc : tuple + Indicates the position of the legend of the figure. + + zebra_color : tuple + Allows to change the text color of the region to white from the first to + the speficied index of the region (True, reg_index). + + legend_fontsize : integer + Indicates the fontsize of the legend. + + chart_fontsize : integer + Indicates the fontsize of the figure. + + dpi : integer + Indicates the DPI of the output image. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + """ + def number(val): + if val < 1000: + return '%d' % val + + sv = str(val) + return '$\mathregular{10^{%d}}$' % (len(sv)-2) if val % 10 == 0 else '%0.0e' % val + + def get_title(v1, v2, measure_quantity): + ftm = '%s $\minus$ %s %s' + return ftm % (number(v1), number(v2), measure_quantity) + + data_ranges = [] + df = data * unit_scale + for rng in rngs: + rng_min, rng_max = rng[0], rng[1] + data_rng = df[(df > rng_min) & (df < rng_max)] + data_ranges.append(data_rng) + + num_elem = [len(p) for p in data_ranges] + se = sum(num_elem) + + print('Num of particles: {}'%(se)) + + proc_particles = [n/float(se) * 100.0 for n in num_elem] + + for size, rng in zip(num_elem, rngs): + print('{}-{}: {}'%(rng[0], rng[1], size)) + + titles = [get_title(minv, maxv, measure_quantity) for minv, maxv in rngs] + + textprops = {'fontsize': chart_fontsize, + 'weight': 'normal', + 'family': 'sans-serif'} + pie_width = 0.5 + fig, ax = plt.subplots(figsize=figsize) + ax.axis('equal') + patches, texts, autotexts = ax.pie(proc_particles, + textprops=textprops, + colors=colors, + autopct='%1.1f%%', + radius=1, + pctdistance=1-pie_width/2) + + if (zebra_color is not None) and (zebra_color[0]): + for tt in autotexts[:zebra_color[1]]: + tt.set_color('white') + + plt.setp(patches, + width=pie_width, + edgecolor='white') + + plt.legend(patches, titles, loc=legend_loc, fontsize=legend_fontsize) + + _d, _offset, _di = [1, -1], [0.45, 0.45], 0 + + for t, p in zip(autotexts, proc_particles): + if p < 2.0: + pos = list(t.get_position()) + pos[0] = pos[0] + _d[_di] * _offset[_di] + + t.set_position(pos) + _di += 1 + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_chart.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.1, dpi=dpi) + + +def _bbox_3D(img): + """Crops the non-zero part of a volume + """ + r = np.any(img, axis=(1, 2)) + c = np.any(img, axis=(0, 2)) + z = np.any(img, axis=(0, 1)) + + rmin, rmax = np.where(r)[0][[0, -1]] + cmin, cmax = np.where(c)[0][[0, -1]] + zmin, zmax = np.where(z)[0][[0, -1]] + + return rmin, rmax, cmin, cmax, zmin, zmax + + +def plot_3d_orientation_map(name, lat_data, azth_data, radius_structure_elem=1, + output_dir=None, width=512, height=512, + camera_azth=44.5, camera_elev=35.8, camera_roll=0.0, + camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k'): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + lat_data : 3D array + Indicates the 3D array containing latitude / elevation angle at every point of + the skeleton in radians. + + azth_data : 3D array + Indicates the 3D array containing azimuth angle at every point of the skeleton + in radians. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_azth : float + Indicates the azimuth angle of the camera. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(azth_data) + + azth, lat = azth_data[rmin:rmax, cmin:cmax, zmin:zmax], \ + np.abs(lat_data[rmin:rmax, cmin:cmax, zmin:zmax]) + + skel = azth.copy().astype(np.float32) + skel[skel.nonzero()] = 1. + + azth = ndi.grey_dilation(azth, structure=morphology.ball(radius_structure_elem)) + lat = ndi.grey_dilation(lat, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)) + + Z, Y, X = skel.nonzero() + vol_orient = np.zeros(skel.shape + (3,), dtype=np.float32) + + print(vol_orient.size, vol_orient[skel.nonzero()].size) + + for z, y, x in zip(Z, Y, X): + vol_orient[z, y, x] = geo2rgb(lat[z, y, x], azth[z, y, x]) + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(vol_orient[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + # def mouseUp(event): + # print 'mouseUp!!' + # a = vv.gca() + # print a.camera.GetViewParams() + # + # a.eventMouseUp.Bind(mouseUp) + # fig.eventMouseUp.Bind(mouseUp) + # + # a.Draw() + # fig.DrawNow() + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_orientation.png'.format(name)), + sf=1, bg=background_color) + app.Run() + + +def plot_3d_diameter_map(name, data, unit_scale=1.0, measure_quantity='vox', + radius_structure_elem=1, output_dir=None, width=512, + height=512, camera_azth=44.5, camera_elev=35.8, + camera_roll=0.0, camera_fov=35.0, camera_zoom=0.0035, + camera_loc=(67.0, 81.6, 45.2), xlabel='', ylabel='', + zlabel='', axis_color='w', background_color='k', + cb_x_offset=10): + """Renders orientation data in 3D with RGB angular color-coding. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + data : 3D array + Indicates the 3D array containing diameter at every point of the skeleton. + + unit_scale : float + Indicates the scale factor of the data values. + + measure_quantity : str + Indicates the name of measure of the values. + + radius_structure_elem : integer + Indicates the size of the structure element of the dilation process to + thicken the skeleton. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + camera_azth : float + Indicates the azimuth angle of the camera. + + width : int + Indicates the width of the visualization window. + + height : int + Indicates the width of the visualization window. + + camera_elev : float + Indicates the latitude / elevation angle of the camera. + + camera_roll : float + Indicates the roll angle of the camera. + + camera_fov : float + Indicates the field of view of the camera. + + camera_zoom : float + Indicates the zoom level of the camera. + + camera_loc : tuple + Indicates the camera location. + + xlabel : str + Indicates the label along the x-axis. + + ylabel : str + Indicates the label along the y-axis. + + zlabel : str + Indicates the label along the z-axis. + + axis_color : str + Indicates the color of axes. + + background_color : str + Indicates the background color of the figure. + + cb_x_offset : int + Indicates the offset of the colorbar from the right window side. + """ + if not visvis_available: + print('The visvis package is not found. The visualization cannot be done.') + return + + rmin, rmax, cmin, cmax, zmin, zmax = _bbox_3D(data) + dmtr = data[rmin:rmax, cmin:cmax, zmin:zmax] * unit_scale + skel = np.zeros_like(dmtr, dtype=np.uint8) + skel[dmtr.nonzero()] = 1 + + dmtr = ndi.grey_dilation(dmtr, structure=morphology.ball(radius_structure_elem)) + skel = ndi.binary_dilation(skel, structure=morphology.ball(radius_structure_elem)).astype(np.float32) + skel[skel.nonzero()] = 1. + + dmtr = dmtr * skel + + app = vv.use() + + fig = vv.figure() + fig._currentAxes = None + fig.relativeFontSize = 2. + fig.position.w = width + fig.position.h = height + + t = vv.volshow(dmtr[:, :, :], renderStyle='iso') + t.isoThreshold = 0.5 + t.colormap = vv.CM_JET + + a = vv.gca() + a.camera.azimuth = camera_azth + a.camera.elevation = camera_elev + a.camera.roll = camera_roll + a.camera.fov = camera_fov + a.camera.zoom = camera_zoom + a.camera.loc = camera_loc + + a.bgcolor = background_color + a.axis.axisColor = axis_color + a.axis.xLabel = xlabel + a.axis.yLabel = ylabel + a.axis.zLabel = zlabel + + cb = vv.colorbar() + cb.SetLabel('Diameter, [{}]'.format(measure_quantity)) + cb._label.position.x += cb_x_offset + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + vv.screenshot(os.path.join(output_dir, '{}_3d_diameter.png'.format(name)), + sf=1, bg='w') + app.Run() + + +def plot_color_wheel(name, output_dir=None, dpi=500, xlabel='Elevation', + ylabel='Azimuth', fontsize=10, num_xticks=4, yticks=(-90, 90)): + """Plots the color wheel for visualizations of 3D orintation. + + Parameters + ---------- + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + + xlabel : str + Indicates the text along the x-axis. + + ylabel : str + Indicates the text along the y-axis. + + fontsize : int + Indicates the font size of labels along axes. + + num_xticks : int + Indicates the number of ticks along axes. + + yticks : tuple + Indicates the range of minimum and maximum values along the y-axis. + """ + + azth, lat = np.linspace(0., 1., num=180), np.linspace(0., 1., num=90) + rgb_arr = np.zeros((len(azth), len(lat), 3)) + for i in xrange(len(azth)): + for j in xrange(len(lat)): + rgb_arr[i, j, :] = colors.hsv_to_rgb([azth[i], lat[j], 1.0]) + + fig, ax = plt.subplots(figsize=(2, 2)) + ax.set_facecolor('red') + + ax.set_xlim([0, 90]) + ax.set_ylim([0, 181]) + + ax.set_yticks(np.linspace(0, 180, num=7).astype(np.int32)) + ax.set_yticklabels(np.linspace(yticks[0], yticks[1], num=7).astype(np.int32)) + + ax.set_xlabel(xlabel, fontsize=fontsize, labelpad=0, color='w') + ax.set_ylabel(ylabel, fontsize=fontsize, labelpad=0, color='w') + + xmajorlocator = ticker.LinearLocator(num_xticks) + ax.xaxis.set_major_locator(xmajorlocator) + ax.tick_params(direction='out', length=2, width=0, labelsize=fontsize, pad=0, colors='w') + + ax.imshow(rgb_arr) + + if output_dir is not None: + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + fig.savefig(os.path.join(output_dir, '{}_color_bar.png'.format(name)), + transparent=True, + bbox_inches='tight', + pad_inches=0.1, + dpi=dpi) + plt.show() + +def plot_fourier_orientation(data, orient_blocks, block_shape, figsize=(12,12), + cmap='gray', line_length=20, line_width=2.5, + line_color='red', line_style='-', name=None, + output_dir=None, dpi=200): + """Plots the orientation map in a block-wise manner. + + Plots the orientation vector over the image `data` at the center of each + block of the subdivided image. The orientation at every block 'orient_blocks' + and its size `block_shape` are specified by orientation estimation method. + The result can be and stored as a png file with DPI of `dpi` to the folder + specified by `output_dir`. + + Parameters + ---------- + data : ndarray + An image on top of which, the orientation will be plotted.. + + orient_blocks : ndarray + 2D array of orientation at every block of the subdivided image. + + block_shape : tuple + Indicates the block size within which the orientation is calculated. + + figsize : tuple of integers + Indicates the size of the output figure. + + cmap : str + Indicates the name of a colormap used for image. + + line_length : integer + Indicates the length of the orientation vector at each block. + + line_width : float + Indicates the line width of the orientation vector at each block. + + line_color : str + Indicates the line color of the orientation vector at each block. + + line_style : str + Indicates the line style of the orientation vector at each block. + + name : str + Indicates the name of the output png file. + + output_dir : str + Indicates the path to the output folder where the image will be stored. + + dpi : integer + Indicates the DPI of the output image. + """ + fig, ax = plt.subplots(figsize=figsize) + + ax.xaxis.grid(False) + ax.yaxis.grid(False) + ax.set_axis_off() + + ax.imshow(data, cmap=cmap) + + for i in xrange(orient_blocks.shape[0]): + for j in xrange(orient_blocks.shape[1]): + y0, x0 = block_shape[0] * j + block_shape[0]/2, \ + block_shape[1] * i + block_shape[1]/2 + + orientation = orient_blocks[i ,j] + + x2 = x0 - math.sin(orientation) * line_length + y2 = y0 - math.cos(orientation) * line_length + + x3 = x0 + math.sin(orientation) * line_length + y3 = y0 + math.cos(orientation) * line_length + + ax.plot((x2, x3), (y2, y3), linestyle=line_style, + linewidth=line_width, color=line_color) + + if (output_dir is not None) and (name is not None): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + plt.tight_layout() + fig.savefig(os.path.join(output_dir, '{}_fourier_orientation_map.png'.format(name)), + bbox_inches='tight', transparent=True, pad_inches=0.05, dpi=dpi) + + plt.show() From a7bf3df7c32705ac2147a4d60cfe09a15b1d1050 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 00:07:35 -0500 Subject: [PATCH 123/153] Add files via upload --- FiberFit_toshare_3_4_2021 (1).ipynb | 396 ++++++++++++++++++++++++++++ 1 file changed, 396 insertions(+) create mode 100644 FiberFit_toshare_3_4_2021 (1).ipynb diff --git a/FiberFit_toshare_3_4_2021 (1).ipynb b/FiberFit_toshare_3_4_2021 (1).ipynb new file mode 100644 index 0000000..8eb8bb2 --- /dev/null +++ b/FiberFit_toshare_3_4_2021 (1).ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1219dbd5358a4a279143910349bcd993", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from widgetsmp import s,run,run2,save,clear\n", + "from src.foruploading5 import ff,upload,prepimages\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, Markdown, clear_output\n", + "from ipywidgets import interact, interact_manual\n", + "import os\n", + "import glob\n", + "## Remove blank file from output folder\n", + "files = glob.glob('Data/*',recursive=True)\n", + "for f in files:\n", + " try:\n", + " os.remove(f)\n", + " except OSError as e:\n", + " print(\"Error: %s : %s\" % (f, e.strerror))\n", + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "946208961e984985872728f647d5d0f8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5ad1c51d823b4ee79cf4b604d108fcff", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Create zip file to download." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output/blank.txt\r\n", + "output/data_export.csv\r\n", + "output/FFT Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", + "output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", + "output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", + "output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n" + ] + } + ], + "source": [ + "!tar chvfz notebook.tar.gz output/*;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finally, download output file by clicking on download link. Files include images for analysis and bulk .csv file. Delete files in Data and output file by clicking on clear data button prior to running a new set of files." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "49efbe4520b74682b4ffdd09e8144e08", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \\n \\n …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "88286a86a8b34902a9b4f03ddcf0d925", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4ba3b33e785d412287f14f2e0bcecaf3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save(_)\n", + "clear(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness (code modified from [this paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215137). Again, output will be in \"output\" folder. \n", + "###### Note: These color maps are from a program called Quanfima. This is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results. PNG or JPEG files are suggested for speedier calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps: First, Upload a single image for analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d6c9c56930834fe4b1de43240ffbf059", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps: Next, run analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fb442c2f98e54c3bb0b6ac739c1b7e4e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Run Fiber Orientation Visualization', layout=Layout(width='300px'), style=B…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dd6c1dc8b1de40b58b4888777f056456", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run2(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create output file to download" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output/blank.txt\r\n", + "output/data_export.csv\r\n", + "output/DIAMETER_estimate_diameter_map.png\r\n", + "output/FFT Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", + "output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", + "output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", + "output/orientation_map.png\r\n", + "output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n" + ] + } + ], + "source": [ + "!tar chvfz notebook.tar.gz output/*;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download output file by clicking on download link. Files include images shown above. Click clear data to clear input and output data to run again." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "174fbb910ab040e8a5fa987c4153fdeb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \\n \\n …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2064a8163800430889b35cf83acec5d9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a1ea08f6586e4c4fbfdf93246adcd69f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save(_)\n", + "clear(_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 351fd9e431f92889828f91df4485e2073dc0f2a4 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 00:08:05 -0500 Subject: [PATCH 124/153] Delete FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 392 -------------------------------- 1 file changed, 392 deletions(-) delete mode 100644 FiberFit_toshare_3_4_2021.ipynb diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb deleted file mode 100644 index 496d0cc..0000000 --- a/FiberFit_toshare_3_4_2021.ipynb +++ /dev/null @@ -1,392 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\asus\\anaconda3\\envs\\test-environment\\lib\\site-packages\\IPython\\html.py:12: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", - " warn(\"The `IPython.html` package has been deprecated since IPython 4.0. \"\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d80277056c9043a69b11a045f7ff94f8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from widgetsmp import s,run,run2,save,clear\n", - "from src.foruploading5 import ff,upload,prepimages\n", - "import ipywidgets as widgets\n", - "from IPython.display import display, Markdown, clear_output\n", - "from ipywidgets import interact, interact_manual\n", - "s = widgets.FileUpload(accept='', multiple=True)\n", - "upload(s)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7cb0f1a2432048a29252787e2e927245", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6975bd10c7144a07a943ecee559986ea", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Create zip file to download." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "a ../FiberFit/output\n", - "a ../FiberFit/output/data_export.csv\n", - "a ../FiberFit/output/FFT Norm Test Image_90_0.7_90.03_0.7_0.57297.png\n", - "a ../FiberFit/output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.57297.png\n", - "a ../FiberFit/output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.57297.png\n", - "a ../FiberFit/output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.57297.png\n", - "tar: *;: Couldn't find file: No such file or directory\n", - "tar: Error exit delayed from previous errors.\n" - ] - } - ], - "source": [ - "!tar chvfz notebook.tar.gz ../FiberFit/output *;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Finally, download output file by clicking on download link. Files include images for analysis and bulk .csv file. Delete files in Data and output file by clicking on clear data button prior to running a new set of files." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c8d80d987bc14e1c8d05f95221ad87a7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HTML(value='\\n \\n \\n …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e4c3e2549d9e4388ad4e37ee77a80ffe", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "de69a172b496466b8a55eb4a7836e7dc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "save(_)\n", - "clear(_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness (code modified from [this paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215137). Again, output will be in \"output\" folder. \n", - "###### Note: These color maps are from a program called Quanfima. This is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results. PNG or JPEG files are suggested for speedier calculations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps: First, Upload a single image for analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "54aa62e0d1324968893f0c728a5a8d1a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s = widgets.FileUpload(accept='', multiple=True)\n", - "upload(s)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps: Next, run analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9b897537eab1430499fadea380851c8e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Button(description='Run Fiber Orientation Visualization', layout=Layout(width='300px'), style=B…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d4d4663a0f0e4484831c72dd2b4ade16", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "run2(_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create output file to download" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "a ../FiberFit/output\n", - "a ../FiberFit/output/DIAMETER_estimate_diameter_map.png\n", - "a ../FiberFit/output/orientation_map.png\n", - "tar: *;: Couldn't find file: No such file or directory\n", - "tar: Error exit delayed from previous errors.\n" - ] - } - ], - "source": [ - "!tar chvfz notebook.tar.gz ../FiberFit/output *;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download output file by clicking on download link. Files include images shown above. Click clear data to clear input and output data to run again." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "00de3e2d734f4f6fa63c0e80beb101fc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HTML(value='\\n \\n \\n …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6d22eab1f2ba4b58af9881492698c0b8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "adf9ee61be174e20ba9e5e871607668b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "save(_)\n", - "clear(_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "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": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From bc136a74a4c330578e41ba992fffad9c976f3d86 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 00:08:26 -0500 Subject: [PATCH 125/153] Rename FiberFit_toshare_3_4_2021 (1).ipynb to FiberFit_toshare_3_4_2021.ipynb --- ..._toshare_3_4_2021 (1).ipynb => FiberFit_toshare_3_4_2021.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit_toshare_3_4_2021 (1).ipynb => FiberFit_toshare_3_4_2021.ipynb (100%) diff --git a/FiberFit_toshare_3_4_2021 (1).ipynb b/FiberFit_toshare_3_4_2021.ipynb similarity index 100% rename from FiberFit_toshare_3_4_2021 (1).ipynb rename to FiberFit_toshare_3_4_2021.ipynb From 6e01f3b00cf70d6f052d6d0f0cfd38466a2a5adc Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 00:09:20 -0500 Subject: [PATCH 126/153] Add files via upload From e5e6d1899d6fc57052e7a6e10ba05fe139462061 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 00:12:59 -0500 Subject: [PATCH 127/153] Add files via upload --- src/angles_csv.csv | 180 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 180 insertions(+) create mode 100644 src/angles_csv.csv diff --git a/src/angles_csv.csv b/src/angles_csv.csv new file mode 100644 index 0000000..592fccb --- /dev/null +++ b/src/angles_csv.csv @@ -0,0 +1,180 @@ +-89,, +-88,, +-87,, +-86,, +-85,, +-84,, +-83,, +-82,, +-81,, +-80,, +-79,, +-78,, +-77,, +-76,, +-75,, +-74,, +-73,, +-72,, +-71,, +-70,, +-69,, +-68,, +-67,, +-66,, +-65,, +-64,, +-63,, +-62,, +-61,, +-60,, +-59,, +-58,, +-57,, +-57,, +-56,, +-55,, +-54,, +-53,, +-52,, +-51,, +-50,, +-49,, +-48,, +-47,, +-46,, +-45,, +-44,, +-43,, +-42,, +-41,, +-40,, +-39,, +-38,, +-37,, +-36,, +-35,, +-34,, +-33,, +-32,, +-31,, +-30,, +-29,, +-28,, +-27,, +-26,, +-25,, +-24,, +-23,, +-22,, +-21,, +-20,, +-19,, +-18,, +-17,, +-16,, +-15,, +-14,, +-13,, +-12,, +-11,, +-10,, +-9,, +-8,, +-7,, +-6,, +-5,, +-4,, +-3,, +-2,, +-1,, +0,, +1,, +2,, +3,, +4,, +5,, +6,, +7,, +8,, +9,, +10,, +11,, +12,, +13,, +14,, +15,, +16,, +17,, +18,, +19,, +20,, +21,, +22,, +23,, +24,, +25,, +26,, +27,, +28,, +29,, +30,, +31,, +32,, +33,, +34,, +35,, +36,, +37,, +38,, +39,, +40,, +41,, +42,, +43,, +44,, +45,, +46,, +47,, +48,, +49,, +50,, +51,, +52,, +53,, +54,, +55,, +56,, +57,, +58,, +59,, +60,, +61,, +62,, +63,, +64,, +65,, +66,, +67,, +68,, +69,, +70,, +71,, +72,, +73,, +74,, +75,, +76,, +77,, +78,, +79,, +80,, +81,, +82,, +83,, +84,, +85,, +86,, +87,, +88,, +89,, From cc0e829ff0a25c3944c66fe552aa209cc165a096 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 00:21:03 -0500 Subject: [PATCH 128/153] Update widgetsmp.py --- widgetsmp.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/widgetsmp.py b/widgetsmp.py index 36bab6d..602d5c3 100644 --- a/widgetsmp.py +++ b/widgetsmp.py @@ -122,14 +122,14 @@ def on_button_clicked(_): # what happens when we press the button clear_output() print('Program executing. Files in Data and output folder will be cleared') - files = glob.glob('../FiberFit/Data/*',recursive=True) + files = glob.glob('Data/*',recursive=True) for f in files: try: os.remove(f) except OSError as e: print("Error: %s : %s" % (f, e.strerror)) - files = glob.glob('../FiberFit/output/*',recursive=True) + files = glob.glob('output/*',recursive=True) for f in files: try: @@ -139,4 +139,4 @@ def on_button_clicked(_): button.on_click(on_button_clicked) box=widgets.VBox([button]) display(box,out) - out= widgets.Output() \ No newline at end of file + out= widgets.Output() From c6d244322e9aec006047d8a9e94520daf195cbce Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:24:00 -0500 Subject: [PATCH 129/153] Add files via upload --- widgetsmp (2).py | 130 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 widgetsmp (2).py diff --git a/widgetsmp (2).py b/widgetsmp (2).py new file mode 100644 index 0000000..061d9fa --- /dev/null +++ b/widgetsmp (2).py @@ -0,0 +1,130 @@ +import pandas as pd +import matplotlib.pyplot as plt +#%matplotlib notebook +from ipywidgets import * +from IPython.display import display +from IPython.html import widgets +import base64 +res = 'computed results' +plt.style.use('ggplot') +from src.fibers_QQQQQQ_invert import fiber_analysis +from quanfima_torun import fiber_visualization +import os +import glob +#button = widgets.Button(description='My Button') +#out = widgets.Output() +#import ipyvuetify as v +from IPython.display import display, FileLink +# linking button and function together using a button's method +#button.on_click(on_button_clicked) +# displaying button and its output together +#widgets.VBox([button,out]) + +from src.foruploading5 import ff,upload,prepimages +import ipywidgets as widgets +from IPython.display import display, Markdown, clear_output +from ipywidgets import interact, interact_manual +def s(_): + s = widgets.FileUpload(accept='', multiple=True) + return s.data + +def run(_): + + button = widgets.Button( + description='Run Fiber Analysis', + layout={'width': '300px'}) + out= widgets.Output() + def on_button_clicked(_): + # "linking function with output" + with out: + # what happens when we press the button + clear_output() + print('Program executing. Output in output folder on home page') + fiber_analysis( CO_lower=int(text1.value), + CO_upper=int(text2.value), + mypath='Data/', + savepath='output/') + text1 = widgets.Text( + placeholder='20', + description="Max fiber Thickness (pixels)", + layout={'width':'300px'}, + style={'description_width':'175px'}) + text2 = widgets.Text( + placeholder='2', + description="Min fiber Thickness (pixels)", + layout={'width':'300px'}, + style={'description_width':'175px'}) + button.on_click(on_button_clicked) + box=widgets.VBox([text1,text2,button]) + display(box,out) +def run2(_): + + button = widgets.Button( + description='Run Fiber Orientation Visualization', + layout={'width': '300px'}) + out= widgets.Output() + def on_button_clicked(_): + # "linking function with output" + with out: + # what happens when we press the button + clear_output() + print('Program executing. Output in output folder on home page') + fiber_visualization() + + button.on_click(on_button_clicked) + box=widgets.VBox([button]) + display(box,out) + +from ipywidgets import HTML +HTML() + +def save(_): + filename = 'notebook.tar.gz' + b64 = base64.b64encode(res.encode()) + payload = b64.decode() + #BUTTONS + html_buttons = ''' + + + + + + + + + + ''' + html_button = html_buttons.format(payload=payload,filename=filename) + display(HTML(html_button)) + out= widgets.Output() + + +def clear(_): + button = widgets.Button( + description='Clear Data', + layout={'width': '300px'}) + out= widgets.Output() + def on_button_clicked(_): + # "linking function with output" + with out: + # what happens when we press the button + clear_output() + print('Program executing. Files in Data and output folder will be cleared') + files = glob.glob('Data/*',recursive=True) + for f in files: + try: + os.remove(f) + except OSError as e: + print("Error: %s : %s" % (f, e.strerror)) + + files = glob.glob('output/*',recursive=True) + + for f in files: + try: + os.remove(f) + except OSError as e: + print("Error: %s : %s" % (f, e.strerror)) + button.on_click(on_button_clicked) + box=widgets.VBox([button]) + display(box,out) + out= widgets.Output() From 7c5a5185b15573074ac5ee77fe92a3dc5b880a1e Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:24:26 -0500 Subject: [PATCH 130/153] Delete widgetsmp.py --- widgetsmp.py | 142 --------------------------------------------------- 1 file changed, 142 deletions(-) delete mode 100644 widgetsmp.py diff --git a/widgetsmp.py b/widgetsmp.py deleted file mode 100644 index 602d5c3..0000000 --- a/widgetsmp.py +++ /dev/null @@ -1,142 +0,0 @@ -import pandas as pd -import matplotlib.pyplot as plt -#%matplotlib notebook -from ipywidgets import * -from IPython.display import display -from IPython.html import widgets -import base64 -res = 'computed results' -plt.style.use('ggplot') -from src.fibers_QQQQQQ_invert import fiber_analysis -from quanfima_torun import fiber_visualization -import os -import glob -#button = widgets.Button(description='My Button') -#out = widgets.Output() -#import ipyvuetify as v -from IPython.display import display, FileLink -# linking button and function together using a button's method -#button.on_click(on_button_clicked) -# displaying button and its output together -#widgets.VBox([button,out]) - -from src.foruploading5 import ff,upload,prepimages -import ipywidgets as widgets -from IPython.display import display, Markdown, clear_output -from ipywidgets import interact, interact_manual -def s(_): - s = widgets.FileUpload(accept='', multiple=True) - return s.data - -def run(_): - - button = widgets.Button( - description='Run Fiber Analysis', - layout={'width': '300px'}) - out= widgets.Output() - def on_button_clicked(_): - # "linking function with output" - with out: - # what happens when we press the button - clear_output() - print('Program executing. Output in output folder on home page') - fiber_analysis( CO_lower=int(text1.value), - CO_upper=int(text2.value), - angleInc=int(text3.value), - radStep=float(text4.value), - mypath='Data/', - savepath='output/') - text1 = widgets.Text( - placeholder='20', - description="Max fiber Thickness (pixels)", - layout={'width':'300px'}, - style={'description_width':'175px'}) - text2 = widgets.Text( - placeholder='2', - description="Min fiber Thickness (pixels)", - layout={'width':'300px'}, - style={'description_width':'175px'}) - text3 = widgets.Text( - placeholder='1', - description="Angle Increment (degrees)", - layout={'width':'300px'}, - style={'description_width':'175px'}) - text4 = widgets.Text( - placeholder='0.5', - description="Radial Step Size (radians)", - layout={'width':'300 px'}, - style={'description_width':'175px'}) - button.on_click(on_button_clicked) - box=widgets.VBox([text1,text2,text3,text4,button]) - display(box,out) -def run2(_): - - button = widgets.Button( - description='Run Fiber Orientation Visualization', - layout={'width': '300px'}) - out= widgets.Output() - def on_button_clicked(_): - # "linking function with output" - with out: - # what happens when we press the button - clear_output() - print('Program executing. Output in output folder on home page') - fiber_visualization() - - button.on_click(on_button_clicked) - box=widgets.VBox([button]) - display(box,out) - -from ipywidgets import HTML -HTML() - -def save(_): - filename = 'notebook.tar.gz' - b64 = base64.b64encode(res.encode()) - payload = b64.decode() - #BUTTONS - html_buttons = ''' - - - - - - - - - - ''' - html_button = html_buttons.format(payload=payload,filename=filename) - display(HTML(html_button)) - out= widgets.Output() - - -def clear(_): - button = widgets.Button( - description='Clear Data', - layout={'width': '300px'}) - out= widgets.Output() - def on_button_clicked(_): - # "linking function with output" - with out: - # what happens when we press the button - clear_output() - print('Program executing. Files in Data and output folder will be cleared') - files = glob.glob('Data/*',recursive=True) - for f in files: - try: - os.remove(f) - except OSError as e: - print("Error: %s : %s" % (f, e.strerror)) - - files = glob.glob('output/*',recursive=True) - - for f in files: - try: - os.remove(f) - except OSError as e: - print("Error: %s : %s" % (f, e.strerror)) - button.on_click(on_button_clicked) - box=widgets.VBox([button]) - display(box,out) - out= widgets.Output() From 19d885412fd0911ac6093b25e2ced481940178b1 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:24:49 -0500 Subject: [PATCH 131/153] Rename widgetsmp (2).py to widgetsmp.py --- widgetsmp (2).py => widgetsmp.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename widgetsmp (2).py => widgetsmp.py (100%) diff --git a/widgetsmp (2).py b/widgetsmp.py similarity index 100% rename from widgetsmp (2).py rename to widgetsmp.py From f283060df42ab970bcf5b77baf0cf4a61aefd1d3 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:25:24 -0500 Subject: [PATCH 132/153] Add files via upload --- FiberFit_toshare_3_4_2021 (3).ipynb | 398 ++++++++++++++++++++++++++++ 1 file changed, 398 insertions(+) create mode 100644 FiberFit_toshare_3_4_2021 (3).ipynb diff --git a/FiberFit_toshare_3_4_2021 (3).ipynb b/FiberFit_toshare_3_4_2021 (3).ipynb new file mode 100644 index 0000000..d095ee0 --- /dev/null +++ b/FiberFit_toshare_3_4_2021 (3).ipynb @@ -0,0 +1,398 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Analysis; Modified from FiberFit Software\n", + "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "\n", + "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/srv/conda/envs/notebook/lib/python3.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", + " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "448a008d479d43e7acb8b816338c4bfe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from widgetsmp import s,run,run2,save,clear\n", + "from src.foruploading5 import ff,upload,prepimages\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, Markdown, clear_output\n", + "from ipywidgets import interact, interact_manual\n", + "import os\n", + "import glob\n", + "%matplotlib inline\n", + "## Remove blank file from output folder\n", + "files = glob.glob('Data/*',recursive=True)\n", + "for f in files:\n", + " try:\n", + " os.remove(f)\n", + " except OSError as e:\n", + " print(\"Error: %s : %s\" % (f, e.strerror))\n", + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "22a99f3dcad74e48b9f794dca567e39a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "17d380b8fb844c878356cacc729c1a94", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, Create zip file to download." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output/data_export.csv\r\n", + "output/FFT Norm Test Image_90_0.7_90.03_0.7_0.5729723.png\r\n", + "output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.5729723.png\r\n", + "output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.5729723.png\r\n", + "output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.5729723.png\r\n" + ] + } + ], + "source": [ + "!tar chvfz notebook.tar.gz output/*;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finally, download output file by clicking on download link. Files include images for analysis and bulk .csv file. Delete files in Data and output file by clicking on clear data button prior to running a new set of files." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0326fc2d463f4487ae43dc6b83407cb0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \\n \\n …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f0cacc9ed7d44e49a62ead2ffea602bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5ab496d2336e4ca0b3de20750368f736", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save(_)\n", + "clear(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness (code modified from [this paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215137). Again, output will be in \"output\" folder. \n", + "###### Note: These color maps are from a program called Quanfima. This is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results. PNG or JPEG files are suggested for speedier calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps: First, Upload a single image for analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02033485ccb64585b7172f35bed4c8b0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s = widgets.FileUpload(accept='', multiple=True)\n", + "upload(s)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps: Next, run analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "90747e3bec0f4fd98a67ebd80f4be938", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Run Fiber Orientation Visualization', layout=Layout(width='300px'), style=B…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb5c6f7237d1431a84ebbf21a8d8c89c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run2(_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create output file to download" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "output/DIAMETER_estimate_diameter_map.png\r\n", + "output/orientation_map.png\r\n" + ] + } + ], + "source": [ + "!tar chvfz notebook.tar.gz output/*;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download output file by clicking on download link. Files include images shown above. Click clear data to clear input and output data to run again." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "15b97f9033a74a96acdca2e904f7c1a6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n \\n \\n …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ff16db18b43242a591a73b8c9611b86b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "18a3abad11f4459999349b409fbaf7e7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save(_)\n", + "clear(_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1b6c36779217501150003bb02d127633786becec Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:25:43 -0500 Subject: [PATCH 133/153] Delete FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 396 -------------------------------- 1 file changed, 396 deletions(-) delete mode 100644 FiberFit_toshare_3_4_2021.ipynb diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb deleted file mode 100644 index 8eb8bb2..0000000 --- a/FiberFit_toshare_3_4_2021.ipynb +++ /dev/null @@ -1,396 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fiber Analysis; Modified from FiberFit Software\n", - "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", - "\n", - "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1219dbd5358a4a279143910349bcd993", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from widgetsmp import s,run,run2,save,clear\n", - "from src.foruploading5 import ff,upload,prepimages\n", - "import ipywidgets as widgets\n", - "from IPython.display import display, Markdown, clear_output\n", - "from ipywidgets import interact, interact_manual\n", - "import os\n", - "import glob\n", - "## Remove blank file from output folder\n", - "files = glob.glob('Data/*',recursive=True)\n", - "for f in files:\n", - " try:\n", - " os.remove(f)\n", - " except OSError as e:\n", - " print(\"Error: %s : %s\" % (f, e.strerror))\n", - "s = widgets.FileUpload(accept='', multiple=True)\n", - "upload(s)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "946208961e984985872728f647d5d0f8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Text(value='', description='Max fiber Thickness (pixels)', layout=Layout(width='300px'), placeh…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5ad1c51d823b4ee79cf4b604d108fcff", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, Create zip file to download." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/blank.txt\r\n", - "output/data_export.csv\r\n", - "output/FFT Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", - "output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", - "output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", - "output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n" - ] - } - ], - "source": [ - "!tar chvfz notebook.tar.gz output/*;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Finally, download output file by clicking on download link. Files include images for analysis and bulk .csv file. Delete files in Data and output file by clicking on clear data button prior to running a new set of files." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "49efbe4520b74682b4ffdd09e8144e08", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HTML(value='\\n \\n \\n …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "88286a86a8b34902a9b4f03ddcf0d925", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4ba3b33e785d412287f14f2e0bcecaf3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "save(_)\n", - "clear(_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next, if desired, run code below for color maps of fiber orientation and diameter thickness (code modified from [this paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215137). Again, output will be in \"output\" folder. \n", - "###### Note: These color maps are from a program called Quanfima. This is a method that utilizes alternative calculation method than above. It takes longer to calculate, but can be used qualitatively against csv + image outputs in above program to confirm if similar results. PNG or JPEG files are suggested for speedier calculations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps: First, Upload a single image for analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d6c9c56930834fe4b1de43240ffbf059", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FileUpload(value={}, description='Upload', multiple=True, style=ButtonStyle(button_color='lightblue'))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "s = widgets.FileUpload(accept='', multiple=True)\n", - "upload(s)\n", - "s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Steps: Next, run analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fb442c2f98e54c3bb0b6ac739c1b7e4e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Button(description='Run Fiber Orientation Visualization', layout=Layout(width='300px'), style=B…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dd6c1dc8b1de40b58b4888777f056456", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "run2(_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create output file to download" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/blank.txt\r\n", - "output/data_export.csv\r\n", - "output/DIAMETER_estimate_diameter_map.png\r\n", - "output/FFT Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", - "output/GroupedNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", - "output/Normdist Norm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n", - "output/orientation_map.png\r\n", - "output/polarpositiveNorm Test Image_90_0.7_90.03_0.7_0.572972.png\r\n" - ] - } - ], - "source": [ - "!tar chvfz notebook.tar.gz output/*;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download output file by clicking on download link. Files include images shown above. Click clear data to clear input and output data to run again." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "174fbb910ab040e8a5fa987c4153fdeb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HTML(value='\\n \\n \\n …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2064a8163800430889b35cf83acec5d9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Button(description='Clear Data', layout=Layout(width='300px'), style=ButtonStyle()),))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a1ea08f6586e4c4fbfdf93246adcd69f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "save(_)\n", - "clear(_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "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": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From dfc98a4ffedd49dd2685b4b6ce0b98cd80376307 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:26:16 -0500 Subject: [PATCH 134/153] Rename FiberFit_toshare_3_4_2021 (3).ipynb to FiberFit_toshare_3_4_2021.ipynb --- ..._toshare_3_4_2021 (3).ipynb => FiberFit_toshare_3_4_2021.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FiberFit_toshare_3_4_2021 (3).ipynb => FiberFit_toshare_3_4_2021.ipynb (100%) diff --git a/FiberFit_toshare_3_4_2021 (3).ipynb b/FiberFit_toshare_3_4_2021.ipynb similarity index 100% rename from FiberFit_toshare_3_4_2021 (3).ipynb rename to FiberFit_toshare_3_4_2021.ipynb From 944d8f2aaf09f09587557a7d86f672f245af5876 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:27:06 -0500 Subject: [PATCH 135/153] Update FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb index d095ee0..89e7bdf 100644 --- a/FiberFit_toshare_3_4_2021.ipynb +++ b/FiberFit_toshare_3_4_2021.ipynb @@ -6,7 +6,7 @@ "source": [ "# Fiber Analysis; Modified from FiberFit Software\n", "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", - "#### this software below is same as above, but it allows for bulk proccessing and provides potential for export of raw data and custom formatting of images\n", + "#### this software below is same as above, but it allows for bulk proccessing and provides export of raw data\n", "\n", "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." ] From d00367430aa0ab2f9bc1558562350396f5c83d1b Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:27:38 -0500 Subject: [PATCH 136/153] Update FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb index 89e7bdf..d87ade4 100644 --- a/FiberFit_toshare_3_4_2021.ipynb +++ b/FiberFit_toshare_3_4_2021.ipynb @@ -8,7 +8,7 @@ "## software is from [this paper](https://link.springer.com/article/10.1007/s10237-016-0776-3) and Github with original program can be viewed [here.](https://github.com/NTMatBoiseState/FiberFit/tree/master/src/fiberfit_model)\n", "#### this software below is same as above, but it allows for bulk proccessing and provides export of raw data\n", "\n", - "### Steps: First, Upload your Images for analysis. A minimum of two images is required for the first part of the program." + "### Steps: First, Upload your Images for analysis." ] }, { From 84eb72448e94c1313d8ee762ed1c586cb64d7605 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:28:57 -0500 Subject: [PATCH 137/153] Update FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb index d87ade4..11132c5 100644 --- a/FiberFit_toshare_3_4_2021.ipynb +++ b/FiberFit_toshare_3_4_2021.ipynb @@ -102,7 +102,7 @@ } ], "source": [ - "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" + "run(_)### Next, Run Code. Input maximum and minimum fiber thickness for fibers. Suggested values are shown in boxes, but still need to be typed in the box. Output will be in \"output\" folder on home page. Can Download images and .csv from there (working on including .zip file download)" ] }, { From d5d67c663d2707dc18f7c60ed475a2a08fec92cd Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:29:45 -0500 Subject: [PATCH 138/153] Update FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb index 11132c5..8c9d1b3 100644 --- a/FiberFit_toshare_3_4_2021.ipynb +++ b/FiberFit_toshare_3_4_2021.ipynb @@ -64,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Next, Run Code. Input maximum and minimum fiber thickness for fibers, desired angle increment and radial step size. Suggested values are shown in boxes. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." + "### Next, Run Code. Input maximum and minimum fiber thickness for fibers. Suggested values are shown in boxes, but still need to be typed in the box. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." ] }, { From 0e5e1b05d3d93554015893d5608156046bc929e9 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:30:38 -0500 Subject: [PATCH 139/153] Update FiberFit_toshare_3_4_2021.ipynb --- FiberFit_toshare_3_4_2021.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FiberFit_toshare_3_4_2021.ipynb b/FiberFit_toshare_3_4_2021.ipynb index 8c9d1b3..fbdb82e 100644 --- a/FiberFit_toshare_3_4_2021.ipynb +++ b/FiberFit_toshare_3_4_2021.ipynb @@ -64,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Next, Run Code. Input maximum and minimum fiber thickness for fibers. Suggested values are shown in boxes, but still need to be typed in the box. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." + "### Next, Run Code. Input maximum and minimum fiber thickness (unit - pixels) for fibers. Suggested values are shown in boxes, but still need to be typed in the box. Output will be in \"output\" folder on home page. CSV output provides alignment data in order of images uploaded." ] }, { From 567dd2425d8d3d555a77c461c05de7e75bf1aa82 Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:41:09 -0500 Subject: [PATCH 140/153] Add files via upload --- src/fibers_QQQQQQ_invert (2).py | 526 ++++++++++++++++++++++++++++++++ 1 file changed, 526 insertions(+) create mode 100644 src/fibers_QQQQQQ_invert (2).py diff --git a/src/fibers_QQQQQQ_invert (2).py b/src/fibers_QQQQQQ_invert (2).py new file mode 100644 index 0000000..9490ace --- /dev/null +++ b/src/fibers_QQQQQQ_invert (2).py @@ -0,0 +1,526 @@ +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals +from __future__ import division +from __future__ import absolute_import + +import argparse, re, os, glob, sys, pprint, itertools +import inspect + +import matplotlib +matplotlib.rcParams['backend'] = "Qt5Agg" +from matplotlib.figure import Figure +import matplotlib.pyplot as plt + +import pandas as pd +import numpy as np +from src.fiberfit_model.helpers import debug +import numpy as np +from numpy.linalg import eig, inv +import scipy.ndimage +from pylab import * + +import ipywidgets as widgets +from IPython.display import display +#btn = widgets.Button(description='Press Me') +#display(btn) +#display(QPixmap(saved_images_dir_name + "/angDist_" + ".png").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation)) +import skimage.io as io +from skimage import data +from skimage.filters import sobel +from skimage.morphology import watershed +from skimage import feature +from skimage.color import rgb2gray +from skimage.feature import corner_harris,corner_peaks +from skimage.exposure import equalize_hist +from skimage import measure +from skimage.draw import ellipse +from skimage.measure import label, regionprops +from skimage.transform import rotate +from skimage.feature import corner_orientations +from skimage.filters import threshold_otsu +from skimage.segmentation import clear_border +from skimage.measure import label +from skimage.morphology import closing, square +from skimage.measure import regionprops +from skimage.color import label2rgb +import seaborn +import pandas +try: + from runtime import * +except ImportError: + from smop.runtime import * + +from src.fiberfit_model.helpers import debug +import numpy as np +from numpy.linalg import eig, inv +import scipy.ndimage +from pylab import * +from os import listdir +from os.path import isfile, join +import numpy +import cv2 + + + +import sys +import matplotlib +import numpy as np +import scipy +import scipy.ndimage +import scipy.interpolate +import scipy.optimize +import scipy.integrate +import scipy.stats +import math +import time +import glob +from pylab import * +from pandas import DataFrame +#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +import matplotlib.pyplot as plt +from src.foruploading5 import prepimages +from src.fiberfit_model.EllipseDirectFit import* +from src.fiberfit_model import helpers +from ipywidgets import IntProgress +from IPython.display import display +import time + +def fiber_analysis(mypath,savepath, CO_lower,CO_upper): + angleInc = 1 + radStep = 0.5 + ##%matplotlib inline + ##plt.ioff() + ## Upload Files + ## Upload Files + onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ] + images = numpy.empty(len(onlyfiles), dtype=object) + names = numpy.empty(len(onlyfiles), dtype=object) + for n in range(0, len(onlyfiles)): + images[n] = cv2.imread( join(mypath,onlyfiles[n]) ) + names[n]= (onlyfiles[n]) + img = numpy.empty(len(images), dtype=object) + for n in range(0,len(images)): + img[n]= np.invert(images[n]) + ## Set up empty Matrices (can clean this up later ) + ell_data_test = numpy.empty(len(img),dtype=object) + Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object) + MirnormPower_test = numpy.empty(len(img),dtype=object) + normPower_test = numpy.empty(len(img),dtype=object) + theta1RadFinal_test = numpy.empty(len(img),dtype=object) + A_test = numpy.empty(len(img),dtype=object) + centroid_test = numpy.empty(len(img),dtype=object) + t_new_test = numpy.empty(len(img),dtype=object) + im = numpy.empty(len(img), dtype=object) + PF = numpy.empty(len(img),dtype=object) + PS = numpy.empty(len(img),dtype=object) + theta1Rad_test = numpy.empty(len(img),dtype=object) + PowerY_test = numpy.empty(len(img),dtype=object) + Mirtheta1RadFinal1_plot= [] + MirnormPower_plot=[] + th_plot=[] + r_line_plot=[] + normPower1_plot=[] + num=0 + index=num + + plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output + mpl.rcParams['figure.max_open_warning'] = 0 + maxiter = 100 + + ## begin analysis + print('Number of Images:',len(img)) + max_count=100 + tot_sum_progress = len(img) + PROGRESSBAR = IntProgress(min=0, max=max_count,description='Progress') + PROGRESSBAR.bar_style='info' + display(PROGRESSBAR) # display the bar + for n in range(0,len(img)): + PROGRESSBAR.value = int(n/tot_sum_progress*100) # signal to increment the progress bar + im=img[n][:,:,1] # select first image + M= im.shape[0] #get image shape + N1 = im.shape[1] + n1 = np.round(N1/ 2) - 1 + freq = np.arange(-n1, n1 + 1, 1) + x, y = freq, freq + + ## Fourier Transform + fft_result = np.fft.fft2(im) + Fshift = np.fft.fftshift(fft_result) + + ## adjust to power spectrum to remove imaginary components + magnitude_spectrum= 20*np.log(np.abs(Fshift)) + Pabs = np.abs(Fshift) ** 2 + + M= im.shape[0] + N1=im.shape[1] + + # shift in terms of image because power spectrum is the mirroR of lines so + # mirroring back in terms of image would give right allignment + PabsFlip1 = np.rot90(Pabs) + PabsFlip = np.flipud(PabsFlip1) + while (PabsFlip.shape[0]>x.shape[0]): + PabsFlip = np.delete(PabsFlip, (0), axis=0) + while (PabsFlip.shape[1]>y.shape[0]): + PabsFlip = np.delete(PabsFlip, (0), axis=1) + while (PabsFlip.shape[0] 0)[0]] + A = np.vstack((A1, dot(T, A1))) + A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1] + A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0] + A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[ + 0] - A[4] * centroid[1] + A[3] = A3 + A[4] = A4 + A[5] = A5 + A = A / linalg.norm(A) + if (abs(A[1]) < (1 * 10 ^ (-15))): + if ((A[0]) <= (A[2])): + # Ellipse is horizontal + angle = 0; + major = sqrt(1 / A[0]) + minor = sqrt(1 / A[2]) + else: + angle = np.pi/2; + major = sqrt(1 / A[2]) + minor = sqrt(1 / A[0]) + else: + R = ((A[2] - A[0]) / A[1]) + tg = R - sqrt((R * R) + 1) + angle = math.atan(tg) + P = (2 * tg) / (1 + (tg * tg)) + + if ((A[0] > 0 and A[1] > 0 and A[2] > 0)): + if (angle < (-pi / 4)): + angle = angle + np.pi + else: + angle = angle + elif ((A[1] / P <= (-A[1] / P))): + if (angle < 0): + angle = angle + np.pi / 2 + else: + angle = angle - np.pi / 2 + elif (A[0] < 0 and A[1] < 0 and A[2] < 0): + if (angle < 0): + angle = angle + np.pi + else: + angle = angle - np.pi + else: + # Switch + if (angle < 0): + angle = angle + np.pi / 2 + else: + angle = angle - np.pi / 2 + + t_New = (angle) #angle + t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi) + Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n] + #print('round2',Mirtheta1RadFinal1) + MirnormPower = MirnormPower_test[n] + #print('mirnormpower',MirnormPower) + t = t_new_test[n] + r_line = np.arange(0, max(MirnormPower) + .5, .5) + th = np.zeros(len(r_line)) + for i in range(0, len(r_line)): + th[i] = t + th = np.concatenate([th, (th + 180)]) + r_line = np.concatenate([r_line, r_line]) + ## Polar Graph + graphs = ["#71C4BE","#BBDBA0","#CE7E56","#BC5C81","#84355A"]; + palette = seaborn.set_palette(graphs); + seaborn.set_context("poster",font_scale=1.5); + seaborn.set_style('ticks') + csfont = {'fontname':'Times New Roman', + 'size':'14', + } + ticksfont = {'fontname':'Times New Roman'} + plt.figure(frameon=False, figsize=(15, 10)); + plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5); + plt.suptitle(abs(t)-180, fontsize=20); + plt.polar(th * pi/180, r_line,color="#CE7E56",linewidth=4); + plt.savefig(savepath+'polarpositive'+names[n]); + if (max(MirnormPower)<2): + inc = 0.5 + elif (max(MirnormPower)<5): + inc = 1 + elif max(MirnormPower)<20: + inc = 5 + else: + inc = 10 + plt.yticks(np.arange(inc, max(MirnormPower), inc));#, **ticksfont) + + #plt.show() + t_final_rad = t*pi/180 + def fitted_func(thetas, c): + int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi) + return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \ + np.exp(c * np.cos(2 * (thetas - t_final_rad))) + c0 = 15 + xdata = theta1RadFinal_test[n] + theta1RadFinal = theta1RadFinal_test[n] + ydata= normPower_test[n] + normPower = normPower_test[n] + kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n]) + + # Shift data for plotting purposes + + diff = abs(theta1RadFinal - (t * pi / 180)) + #print(diff,'diff') + #centerLoc=diff.find(diff==min(diff)) + centerLoc = diff[np.argmin(diff)]#find(diff== min(diff)) + #print(centerLoc,'centerLoc') + + num_angles = len(theta1RadFinal) + shift = ((round(num_angles / 2) - (num_angles - centerLoc))) + #print(shift,'shift') + + normPower1 = np.roll(normPower, -int(shift)) + normPower1 + theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift)) + + if (shift > 0): + s = num_angles - shift + s= np.asscalar(s) + for k in range(s, num_angles): + theta1RadFinal1[k] = np.pi + theta1RadFinal1[k] + elif (shift < 0): + for k in range(0,90): + theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k] + + ## Normalized Distribution Graph + cartDist = plt.figure(figsize=(10, 10)); # Creates a figure containing cartesian distribution. + + h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); + plt.xticks(np.arange(-360, 360, 45,), **ticksfont); + plt.xlim([-95, 95]); + p_act = fitted_func(theta1RadFinal1, kappa); + #print(p_act) + a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]); + a= a.T + b = a[a[:,1].argsort()]#np.sort(a,axis=1) + h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); + plt.xlabel('Angle (°)', **csfont); + plt.ylabel('Normalized Intensity', **csfont); + #print(max(normPower)) + if (max(normPower)<2): + inc = 0.5 + elif (max(normPower)<5): + inc = 1 + elif (max(normPower)<20): + inc = 5 + else: + inc = 10 + plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont); + plt.ylim([0, max(normPower1) + .3]); + normPower1_plot.append(normPower1); + plt.savefig(savepath+'Normdist '+names[n]); + #plt.show() + #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight') + plt.close(); + t_final=t + slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1) + #print(rValue**2) + # Rounding results for Title of Figure + krnd = math.ceil(k * 1000) / 1000 + thrnd = abs(math.ceil(t_final * 1000) / 1000)-180 + krnd = math.trunc(krnd * 100) / 100 + thrnd = math.trunc(thrnd * 100) / 100 + a = 32.02 + b= -12.43 + c = 47.06 + d = -0.9185 + e = 19.43 + f = -0.07693 + x = kappa[0] + sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x) + #print("name",names[n],"kappa",kappa,"sigma",sig,"Rsq",rValue**2,"mu",t_final) + sum_NP = sum(normPower1) + #normPower2 = normPower1/sum(normPower1) + #sum_NP2 = sum(normPower2) + #print(sum_NP2) + ## Combined plot figure + palette = seaborn.set_palette(graphs); + seaborn.set_context("notebook",font_scale=1.5); + seaborn.set_style('ticks') + csfont = {'fontname':'Times New Roman', + 'size':'18', + } + ticksfont = {'fontname':'Times New Roman'} + fig = plt.figure(frameon=False,figsize=(20,12)); + fig.subplots_adjust(top=0.95); + fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold'); + grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1]); + cart_dist = plt.subplot(grid[1, 0:2]); + cart_dist.set_title('Von Mises Distribution (k)',fontsize=20); + h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); + cart_dist.set_xlim([-92, 92]); + cart_dist.set_xlabel('Angle (°)',fontsize=18); + cart_dist.set_ylim([0, max(normPower1) + .3]); + cart_dist.set_ylabel('Normalized Intensity',fontsize=18); + + p_act = fitted_func(theta1RadFinal1, kappa) + a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]) + a= a.T + b = a[a[:,1].argsort()]#np.sort(a,axis=1) + h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); + + if (max(normPower)<2): + inc = 0.5 + elif (max(normPower)<5): + inc = 1 + elif (max(normPower)<20): + inc = 5 + else: + inc = 10 + + polar_dist = plt.subplot(grid[0:2,3:5],projection='polar'); + polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o'); + polar_dist.plot(th * pi/180, r_line,color="#CE7E56",linewidth=4); + polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); + fig.text(0.75, 0.05, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}); + #polar_dist.text(np.radians(-110),1.95, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}) + orig_fig = plt.subplot(grid[0, 0]); + orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0]); + orig_fig.axis('off'); + fft_fig = plt.subplot(grid[0, 1]); + fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0]); + fft_fig.set_axis_off(); + fft_fig.axis('off'); + fft_fig.imshow(log(PabsFlip), cmap='gray') + orig_fig.imshow(images[n],cmap=plt.cm.viridis ) + #plt.tight_layout() + plt.savefig(savepath+'Grouped'+names[n]); + #print(n,'n') + #plt.show() + polar_dist_2 = plt.figure(figsize=(10, 10)); + #print('linebeforeindex') + #print(index,'currentindex') + #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1; + #MirnormPower_plot[index]=MirnormPower; + #th_plot[index] = th * pi/180 + #print(th_plot[index],'thplotindex') + #r_line_plot[index]=r_line + Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1); + MirnormPower_plot.append(MirnormPower); + th_plot.append(th * pi/180) + r_line_plot.append(r_line) + polar_dist.plot(Mirtheta1RadFinal1, MirnormPower); + polar_dist.plot(th * pi/180, r_line,linewidth=4); + polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); + plt.close('all') + #index=index+1 + #print(index,'Index') + if n == (tot_sum_progress-1): + PROGRESSBAR.value = 100 # signal to increment the progress bar +## export of cartesian coordinate data + arr = numpy.array(MirnormPower_plot) + arr2 = numpy.array(Mirtheta1RadFinal1_plot) + arr3 = numpy.array(normPower1_plot) + cv2.imread('src/angles_csv.csv') + angles = np.genfromtxt('src/angles_csv.csv',delimiter=',') + arr4 = angles[:,0] + #arr4 = numpy.array(theta1RadFinal1*180/pi) + a = numpy.transpose(arr3) + b = numpy.transpose(arr4) + b[b> 100] = 0 + b[b<-100] = 0 + export_data=np.hstack(((b[:,None]),a)) + #numpy.savetxt(savepath+"data_export.csv", export_data, delimiter=",") + my_input=['Angle'] + for n in range(0,len(img)): + my_input.append(names[n]) + column_values = my_input + df = pd.DataFrame(data = export_data, + columns = column_values) + df.to_csv(savepath+"data_export.csv") + return export_data + From 5dc142fd5bfd4f1cd77582df3c2cd1509ceb0b8f Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:41:38 -0500 Subject: [PATCH 141/153] Delete fibers_QQQQQQ_invert.py --- src/fibers_QQQQQQ_invert.py | 516 ------------------------------------ 1 file changed, 516 deletions(-) delete mode 100644 src/fibers_QQQQQQ_invert.py diff --git a/src/fibers_QQQQQQ_invert.py b/src/fibers_QQQQQQ_invert.py deleted file mode 100644 index a10ec8d..0000000 --- a/src/fibers_QQQQQQ_invert.py +++ /dev/null @@ -1,516 +0,0 @@ -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals -from __future__ import division -from __future__ import absolute_import - -import argparse, re, os, glob, sys, pprint, itertools -import inspect - -import matplotlib -matplotlib.rcParams['backend'] = "Qt5Agg" -from matplotlib.figure import Figure -import matplotlib.pyplot as plt - - -import numpy as np -from src.fiberfit_model.helpers import debug -import numpy as np -from numpy.linalg import eig, inv -import scipy.ndimage -from pylab import * - -import ipywidgets as widgets -from IPython.display import display -#btn = widgets.Button(description='Press Me') -#display(btn) -#display(QPixmap(saved_images_dir_name + "/angDist_" + ".png").scaled(300, 400, Qt.KeepAspectRatio, Qt.SmoothTransformation)) -import skimage.io as io -from skimage import data -from skimage.filters import sobel -from skimage.morphology import watershed -from skimage import feature -from skimage.color import rgb2gray -from skimage.feature import corner_harris,corner_peaks -from skimage.exposure import equalize_hist -from skimage import measure -from skimage.draw import ellipse -from skimage.measure import label, regionprops -from skimage.transform import rotate -from skimage.feature import corner_orientations -from skimage.filters import threshold_otsu -from skimage.segmentation import clear_border -from skimage.measure import label -from skimage.morphology import closing, square -from skimage.measure import regionprops -from skimage.color import label2rgb -import seaborn -import pandas -try: - from runtime import * -except ImportError: - from smop.runtime import * - -from src.fiberfit_model.helpers import debug -import numpy as np -from numpy.linalg import eig, inv -import scipy.ndimage -from pylab import * -from os import listdir -from os.path import isfile, join -import numpy -import cv2 - - - -import sys -import matplotlib -import numpy as np -import scipy -import scipy.ndimage -import scipy.interpolate -import scipy.optimize -import scipy.integrate -import scipy.stats -import math -import time -import glob -from pylab import * -from pandas import DataFrame -#from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas -import matplotlib.pyplot as plt -from src.foruploading5 import prepimages -from src.fiberfit_model.EllipseDirectFit import* -from src.fiberfit_model import helpers -from ipywidgets import IntProgress -from IPython.display import display -import time - -def fiber_analysis(mypath,savepath, CO_lower,CO_upper,angleInc,radStep): - plt.ioff() - ## Upload Files - ## Upload Files - onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ] - images = numpy.empty(len(onlyfiles), dtype=object) - names = numpy.empty(len(onlyfiles), dtype=object) - for n in range(0, len(onlyfiles)): - images[n] = cv2.imread( join(mypath,onlyfiles[n]) ) - names[n]= (onlyfiles[n]) - img = numpy.empty(len(images), dtype=object) - for n in range(0,len(images)): - img[n]= np.invert(images[n]) - ## Set up empty Matrices (can clean this up later ) - ell_data_test = numpy.empty(len(img),dtype=object) - Mirtheta1RadFinal_test =numpy.empty(len(img),dtype=object) - MirnormPower_test = numpy.empty(len(img),dtype=object) - normPower_test = numpy.empty(len(img),dtype=object) - theta1RadFinal_test = numpy.empty(len(img),dtype=object) - A_test = numpy.empty(len(img),dtype=object) - centroid_test = numpy.empty(len(img),dtype=object) - t_new_test = numpy.empty(len(img),dtype=object) - im = numpy.empty(len(img), dtype=object) - PF = numpy.empty(len(img),dtype=object) - PS = numpy.empty(len(img),dtype=object) - theta1Rad_test = numpy.empty(len(img),dtype=object) - PowerY_test = numpy.empty(len(img),dtype=object) - Mirtheta1RadFinal1_plot= [] - MirnormPower_plot=[] - th_plot=[] - r_line_plot=[] - normPower1_plot=[] - num=0 - index=num - - plt.rcParams.update({'figure.max_open_warning': 0}) ## avoid warning output - mpl.rcParams['figure.max_open_warning'] = 0 - maxiter = 100 - - ## begin analysis - print('Number of Images:',len(img)) - max_count=100 - tot_sum_progress = len(img) - PROGRESSBAR = IntProgress(min=0, max=max_count,description='Progress') - PROGRESSBAR.bar_style='info' - display(PROGRESSBAR) # display the bar - for n in range(0,len(img)): - PROGRESSBAR.value = int(n/tot_sum_progress*100) # signal to increment the progress bar - im=img[n][:,:,1] # select first image - M= im.shape[0] #get image shape - N1 = im.shape[1] - n1 = np.round(N1/ 2) - 1 - freq = np.arange(-n1, n1 + 1, 1) - x, y = freq, freq - - ## Fourier Transform - fft_result = np.fft.fft2(im) - Fshift = np.fft.fftshift(fft_result) - - ## adjust to power spectrum to remove imaginary components - magnitude_spectrum= 20*np.log(np.abs(Fshift)) - Pabs = np.abs(Fshift) ** 2 - - M= im.shape[0] - N1=im.shape[1] - - # shift in terms of image because power spectrum is the mirroR of lines so - # mirroring back in terms of image would give right allignment - PabsFlip1 = np.rot90(Pabs) - PabsFlip = np.flipud(PabsFlip1) - while (PabsFlip.shape[0]>x.shape[0]): - PabsFlip = np.delete(PabsFlip, (0), axis=0) - while (PabsFlip.shape[1]>y.shape[0]): - PabsFlip = np.delete(PabsFlip, (0), axis=1) - while (PabsFlip.shape[0] 0)[0]] - A = np.vstack((A1, dot(T, A1))) - A3 = A[3] - 2 * A[0] * centroid[0] - A[1] * centroid[1] - A4 = A[4] - 2 * A[2] * centroid[1] - A[1] * centroid[0] - A5 = A[5] + A[0] * centroid[0] ** 2 + A[2] * centroid[1] ** 2 + A[1] * centroid[0] * centroid[1] - A[3] * centroid[ - 0] - A[4] * centroid[1] - A[3] = A3 - A[4] = A4 - A[5] = A5 - A = A / linalg.norm(A) - if (abs(A[1]) < (1 * 10 ^ (-15))): - if ((A[0]) <= (A[2])): - # Ellipse is horizontal - angle = 0; - major = sqrt(1 / A[0]) - minor = sqrt(1 / A[2]) - else: - angle = np.pi/2; - major = sqrt(1 / A[2]) - minor = sqrt(1 / A[0]) - else: - R = ((A[2] - A[0]) / A[1]) - tg = R - sqrt((R * R) + 1) - angle = math.atan(tg) - P = (2 * tg) / (1 + (tg * tg)) - - if ((A[0] > 0 and A[1] > 0 and A[2] > 0)): - if (angle < (-pi / 4)): - angle = angle + np.pi - else: - angle = angle - elif ((A[1] / P <= (-A[1] / P))): - if (angle < 0): - angle = angle + np.pi / 2 - else: - angle = angle - np.pi / 2 - elif (A[0] < 0 and A[1] < 0 and A[2] < 0): - if (angle < 0): - angle = angle + np.pi - else: - angle = angle - np.pi - else: - # Switch - if (angle < 0): - angle = angle + np.pi / 2 - else: - angle = angle - np.pi / 2 - - t_New = (angle) #angle - t_new_test[n] = abs((angle* 180/np.pi)-90)-(2*angle* 180/np.pi) - Mirtheta1RadFinal1= Mirtheta1RadFinal_test[n] - #print('round2',Mirtheta1RadFinal1) - MirnormPower = MirnormPower_test[n] - #print('mirnormpower',MirnormPower) - t = t_new_test[n] - r_line = np.arange(0, max(MirnormPower) + .5, .5) - th = np.zeros(len(r_line)) - for i in range(0, len(r_line)): - th[i] = t - th = np.concatenate([th, (th + 180)]) - r_line = np.concatenate([r_line, r_line]) - ## Polar Graph - graphs = ["#71C4BE","#BBDBA0","#CE7E56","#BC5C81","#84355A"]; - palette = seaborn.set_palette(graphs); - seaborn.set_context("poster",font_scale=1.5); - seaborn.set_style('ticks') - csfont = {'fontname':'Times New Roman', - 'size':'14', - } - ticksfont = {'fontname':'Times New Roman'} - plt.figure(frameon=False, figsize=(15, 10)); - plt.polar(Mirtheta1RadFinal1, abs(MirnormPower), 'o',ms=5); - plt.suptitle(abs(t)-180, fontsize=20); - plt.polar(th * pi/180, r_line,color="#CE7E56",linewidth=4); - plt.savefig(savepath+'polarpositive'+names[n]); - if (max(MirnormPower)<2): - inc = 0.5 - elif (max(MirnormPower)<5): - inc = 1 - elif max(MirnormPower)<20: - inc = 5 - else: - inc = 10 - plt.yticks(np.arange(inc, max(MirnormPower), inc));#, **ticksfont) - - #plt.show() - t_final_rad = t*pi/180 - def fitted_func(thetas, c): - int_value, int_err = scipy.integrate.quadrature(func=lambda x: exp(c * cos(x)), a=0.0, b=np.pi) - return ((np.pi * (1.0 / np.pi * (int_value))) ** - 1) * \ - np.exp(c * np.cos(2 * (thetas - t_final_rad))) - c0 = 15 - xdata = theta1RadFinal_test[n] - theta1RadFinal = theta1RadFinal_test[n] - ydata= normPower_test[n] - normPower = normPower_test[n] - kappa, kappa_pcov = scipy.optimize.curve_fit(f=fitted_func, p0=(c0,), xdata=theta1RadFinal_test[n], ydata=normPower_test[n]) - - # Shift data for plotting purposes - - diff = abs(theta1RadFinal - (t * pi / 180)) - #print(diff,'diff') - #centerLoc=diff.find(diff==min(diff)) - centerLoc = diff[np.argmin(diff)]#find(diff== min(diff)) - #print(centerLoc,'centerLoc') - - num_angles = len(theta1RadFinal) - shift = ((round(num_angles / 2) - (num_angles - centerLoc))) - #print(shift,'shift') - - normPower1 = np.roll(normPower, -int(shift)) - normPower1 - theta1RadFinal1 = np.roll(theta1RadFinal, -int(shift)) - - if (shift > 0): - s = num_angles - shift - s= np.asscalar(s) - for k in range(s, num_angles): - theta1RadFinal1[k] = np.pi + theta1RadFinal1[k] - elif (shift < 0): - for k in range(0,90): - theta1RadFinal1[k] = -np.pi + theta1RadFinal1[k] - - ## Normalized Distribution Graph - cartDist = plt.figure(figsize=(10, 10)); # Creates a figure containing cartesian distribution. - - h2 = plt.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); - plt.xticks(np.arange(-360, 360, 45,), **ticksfont); - plt.xlim([-95, 95]); - p_act = fitted_func(theta1RadFinal1, kappa); - #print(p_act) - a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]); - a= a.T - b = a[a[:,1].argsort()]#np.sort(a,axis=1) - h3, = plt.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); - plt.xlabel('Angle (°)', **csfont); - plt.ylabel('Normalized Intensity', **csfont); - #print(max(normPower)) - if (max(normPower)<2): - inc = 0.5 - elif (max(normPower)<5): - inc = 1 - elif (max(normPower)<20): - inc = 5 - else: - inc = 10 - plt.yticks(np.arange(0, max(normPower1) + .3, inc), **ticksfont); - plt.ylim([0, max(normPower1) + .3]); - normPower1_plot.append(normPower1); - plt.savefig(savepath+'Normdist '+names[n]); - #plt.show() - #cartDist.savefig(dir + 'cartDist_' + number.__str__(), bbox_inches='tight') - plt.close(); - t_final=t - slope, intercept, rValue, pValue, stderr = scipy.stats.linregress(p_act, normPower1) - #print(rValue**2) - # Rounding results for Title of Figure - krnd = math.ceil(k * 1000) / 1000 - thrnd = abs(math.ceil(t_final * 1000) / 1000)-180 - krnd = math.trunc(krnd * 100) / 100 - thrnd = math.trunc(thrnd * 100) / 100 - a = 32.02 - b= -12.43 - c = 47.06 - d = -0.9185 - e = 19.43 - f = -0.07693 - x = kappa[0] - sig = math.exp(b*x) + c*math.exp(d*x) + e*exp(f*x) - #print("name",names[n],"kappa",kappa,"sigma",sig,"Rsq",rValue**2,"mu",t_final) - sum_NP = sum(normPower1) - #normPower2 = normPower1/sum(normPower1) - #sum_NP2 = sum(normPower2) - #print(sum_NP2) - ## Combined plot figure - palette = seaborn.set_palette(graphs); - seaborn.set_context("notebook",font_scale=1.5); - seaborn.set_style('ticks') - csfont = {'fontname':'Times New Roman', - 'size':'18', - } - ticksfont = {'fontname':'Times New Roman'} - fig = plt.figure(frameon=False,figsize=(20,12)); - fig.subplots_adjust(top=0.95); - fig.suptitle('%s '%(names[n]), fontsize=34, fontweight='bold'); - grid = plt.GridSpec(2, 5,hspace=0,wspace=0,width_ratios=[1, 1, 0.3, 1,1]); - cart_dist = plt.subplot(grid[1, 0:2]); - cart_dist.set_title('Von Mises Distribution (k)',fontsize=20); - h2 = cart_dist.bar((theta1RadFinal1 * 180 / pi), normPower1,color="#dedede"); - cart_dist.set_xlim([-92, 92]); - cart_dist.set_xlabel('Angle (°)',fontsize=18); - cart_dist.set_ylim([0, max(normPower1) + .3]); - cart_dist.set_ylabel('Normalized Intensity',fontsize=18); - - p_act = fitted_func(theta1RadFinal1, kappa) - a = np.vstack([[p_act],[(theta1RadFinal1 * 180 / pi)]]) - a= a.T - b = a[a[:,1].argsort()]#np.sort(a,axis=1) - h3, = cart_dist.plot(b[:,1],b[:,0], linewidth=4,color="#CE7E56"); - - if (max(normPower)<2): - inc = 0.5 - elif (max(normPower)<5): - inc = 1 - elif (max(normPower)<20): - inc = 5 - else: - inc = 10 - - polar_dist = plt.subplot(grid[0:2,3:5],projection='polar'); - polar_dist.plot(Mirtheta1RadFinal1, abs(MirnormPower), 'o'); - polar_dist.plot(th * pi/180, r_line,color="#CE7E56",linewidth=4); - polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); - fig.text(0.75, 0.05, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}); - #polar_dist.text(np.radians(-110),1.95, ' \n Kappa: %s '%(krnd)+' mu: %s \n'%(round((abs(t_final)-180),2))+' R^2: %s '%(round((rValue**2),2))+' sigma: %s \n'%(round(sig,2)),fontsize=20, fontweight='bold', bbox={'facecolor': '#BC5C81', 'alpha': 0.5, 'pad': 0.4}) - orig_fig = plt.subplot(grid[0, 0]); - orig_fig.set_title('Original Image',fontsize=20).set_position([.5, 1.0]); - orig_fig.axis('off'); - fft_fig = plt.subplot(grid[0, 1]); - fft_fig.set_title('Fast Fourier Transform',fontsize=20).set_position([.5, 1.0]); - fft_fig.set_axis_off(); - fft_fig.axis('off'); - #fft_fig.imshow(log(PabsFlip), cmap='gray') - #orig_fig.imshow(images[n],cmap=plt.cm.viridis ) - #plt.tight_layout() - plt.savefig(savepath+'Grouped'+names[n]); - #print(n,'n') - #plt.show() - polar_dist_2 = plt.figure(figsize=(10, 10)); - #print('linebeforeindex') - #print(index,'currentindex') - #Mirtheta1RadFinal1_plot[index]=Mirtheta1RadFinal1; - #MirnormPower_plot[index]=MirnormPower; - #th_plot[index] = th * pi/180 - #print(th_plot[index],'thplotindex') - #r_line_plot[index]=r_line - Mirtheta1RadFinal1_plot.append(Mirtheta1RadFinal1); - MirnormPower_plot.append(MirnormPower); - th_plot.append(th * pi/180) - r_line_plot.append(r_line) - polar_dist.plot(Mirtheta1RadFinal1, MirnormPower); - polar_dist.plot(th * pi/180, r_line,linewidth=4); - polar_dist.set_title('Ellipse Fit (u)',fontsize=20).set_position([.5, 1.08]); - plt.close('all') - #index=index+1 - #print(index,'Index') - if n == (tot_sum_progress-1): - PROGRESSBAR.value = 100 # signal to increment the progress bar -## export of cartesian coordinate data - arr = numpy.array(MirnormPower_plot) - arr2 = numpy.array(Mirtheta1RadFinal1_plot) - arr3 = numpy.array(normPower1_plot) - cv2.imread('src/angles_csv.csv') - angles = np.genfromtxt('src/angles_csv.csv',delimiter=',') - arr4 = angles[:,0] - #arr4 = numpy.array(theta1RadFinal1*180/pi) - a = numpy.transpose(arr3) - b = numpy.transpose(arr4) - b[b> 100] = 0 - b[b<-100] = 0 - export_data=np.hstack(((b[:,None]),a)) - numpy.savetxt(savepath+"data_export.csv", export_data, delimiter=",") - return export_data - From 5de4d5b68a2ef8d0c252acb62df5f1f9b0654f2f Mon Sep 17 00:00:00 2001 From: mprender Date: Sat, 6 Mar 2021 01:42:01 -0500 Subject: [PATCH 142/153] Rename fibers_QQQQQQ_invert (2).py to fibers_QQQQQQ_invert.py --- src/{fibers_QQQQQQ_invert (2).py => fibers_QQQQQQ_invert.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename src/{fibers_QQQQQQ_invert (2).py => fibers_QQQQQQ_invert.py} (100%) diff --git a/src/fibers_QQQQQQ_invert (2).py b/src/fibers_QQQQQQ_invert.py similarity index 100% rename from src/fibers_QQQQQQ_invert (2).py rename to src/fibers_QQQQQQ_invert.py From 41bf6f915bd5d43375428f9ce4d58ef9e8066b39 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 19:40:40 -0500 Subject: [PATCH 143/153] Update environment.yml --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 392f24f..d8dc012 100644 --- a/environment.yml +++ b/environment.yml @@ -23,6 +23,7 @@ dependencies: - pandas - pathlib - pip==19.3.1 + - dateutil ==2.7 - scikit-image - scipy - seaborn From b72a6bd8bdf72d49fefd2a486050a2a086469b27 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:02:04 -0500 Subject: [PATCH 144/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index d8dc012..415ff1b 100644 --- a/environment.yml +++ b/environment.yml @@ -23,7 +23,7 @@ dependencies: - pandas - pathlib - pip==19.3.1 - - dateutil ==2.7 + - python-dateutil - scikit-image - scipy - seaborn From 47a851e87766d3929e01e2444e15219b89c1da2c Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:09:53 -0500 Subject: [PATCH 145/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 415ff1b..70fc9d2 100644 --- a/environment.yml +++ b/environment.yml @@ -23,7 +23,7 @@ dependencies: - pandas - pathlib - pip==19.3.1 - - python-dateutil + - python-dateutil==2.7 - scikit-image - scipy - seaborn From 109b4c0d09463e22cf4c5db695296e97d3bf4512 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:11:22 -0500 Subject: [PATCH 146/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 70fc9d2..b52007e 100644 --- a/environment.yml +++ b/environment.yml @@ -23,7 +23,7 @@ dependencies: - pandas - pathlib - pip==19.3.1 - - python-dateutil==2.7 + - python-dateutil==2.8.2 - scikit-image - scipy - seaborn From 635478ca4235e0c1f080e33b0a67cdaa25495605 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:35:25 -0500 Subject: [PATCH 147/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index b52007e..7db5c27 100644 --- a/environment.yml +++ b/environment.yml @@ -23,7 +23,7 @@ dependencies: - pandas - pathlib - pip==19.3.1 - - python-dateutil==2.8.2 + - python-dateutil==2.2 - scikit-image - scipy - seaborn From 32744840a7f6a804437f46a1c94801e5378733b4 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:40:07 -0500 Subject: [PATCH 148/153] Update environment.yml --- environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 7db5c27..6b1cf89 100644 --- a/environment.yml +++ b/environment.yml @@ -23,7 +23,8 @@ dependencies: - pandas - pathlib - pip==19.3.1 - - python-dateutil==2.2 + - python-dateutil==2.8.2 + - py-dateutil==2.2 - scikit-image - scipy - seaborn From 21d0b94df8f0992d3f248ee9b30e36d88f318821 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:43:58 -0500 Subject: [PATCH 149/153] Update environment.yml --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index 6b1cf89..b52007e 100644 --- a/environment.yml +++ b/environment.yml @@ -24,7 +24,6 @@ dependencies: - pathlib - pip==19.3.1 - python-dateutil==2.8.2 - - py-dateutil==2.2 - scikit-image - scipy - seaborn From b845a0176e79d7fd135a166a0fbf3111cee4bd54 Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 20:48:29 -0500 Subject: [PATCH 150/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index b52007e..a8c3499 100644 --- a/environment.yml +++ b/environment.yml @@ -23,7 +23,7 @@ dependencies: - pandas - pathlib - pip==19.3.1 - - python-dateutil==2.8.2 + - python-dateutil>=2.7 - scikit-image - scipy - seaborn From 19863eaddc322f1abda0308f277a30f5ee9af0da Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 21:28:21 -0500 Subject: [PATCH 151/153] Update environment.yml --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index a8c3499..01e38ac 100644 --- a/environment.yml +++ b/environment.yml @@ -17,7 +17,7 @@ dependencies: - ipython - ipywidgets - jedi - - matplotlib + - matplotlib==3.1.3 - more-itertools - numpy - pandas From d6e12bd0693d2bee2d26f980df12bc5e7798e5df Mon Sep 17 00:00:00 2001 From: mprender Date: Mon, 10 Jan 2022 21:51:10 -0500 Subject: [PATCH 152/153] Update fibers_QQQQQQ_invert.py --- src/fibers_QQQQQQ_invert.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/fibers_QQQQQQ_invert.py b/src/fibers_QQQQQQ_invert.py index 9490ace..1bd36eb 100644 --- a/src/fibers_QQQQQQ_invert.py +++ b/src/fibers_QQQQQQ_invert.py @@ -28,7 +28,8 @@ import skimage.io as io from skimage import data from skimage.filters import sobel -from skimage.morphology import watershed +from skimage.segmentation import watershed +#from skimage.morphology import watershed from skimage import feature from skimage.color import rgb2gray from skimage.feature import corner_harris,corner_peaks From 1aff72c9d736f5dad6dffff143cf7d5922af546c Mon Sep 17 00:00:00 2001 From: mprender Date: Tue, 7 Feb 2023 18:51:03 -0500 Subject: [PATCH 153/153] Update environment.yml --- environment.yml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/environment.yml b/environment.yml index 01e38ac..e352463 100644 --- a/environment.yml +++ b/environment.yml @@ -2,9 +2,6 @@ name: test-environment channels: - conda-forge - defaults -packages: - - package: libgl1-mesa-glx - - package: libgl1-mesa-dev dependencies: - Cycler - decorator @@ -22,7 +19,7 @@ dependencies: - numpy - pandas - pathlib - - pip==19.3.1 + - pip - python-dateutil>=2.7 - scikit-image - scipy

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