diff --git "a/Matching Exploration.ipynb" "b/Matching Exploration.ipynb" --- "a/Matching Exploration.ipynb" +++ "b/Matching Exploration.ipynb" @@ -1,5 +1,23 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Todo list:\n", + "- Figure out a way to determine the MIN_DISTANCE dynamically\n", + " - Look that the TARGET_S is covered for at least 80% or so with datapoints? (with a max for when there are no matches)\n", + "- Find the best way to plot (for ourselves) the comparison \n", + "- Make the code for the data cleanup nice\n", + "- Make some more example edits and document the edits\n", + "- Investigate logo removal options?\n", + "- GOAL: Provide clear decision\n", + "\n", + "# Ordered TODO list:\n", + "1. Make some more example edits and document the edits\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -52,92 +70,1312 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Skipping downloading from https://www.dropbox.com/s/rzmicviu1fe740t/Bram%20van%20Ojik%20krijgt%20reprimande.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a already exists.\n", - "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a.index\n", - "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a.index has in total 182 frames\n", + "INFO:root:Skipping downloading from https://www.dropbox.com/s/8c89a9aba0w8gjg/Ploumen.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/75ae859c16eff3f4d876a8aa4a06533c already exists.\n", + "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/75ae859c16eff3f4d876a8aa4a06533c.index\n", + "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/75ae859c16eff3f4d876a8aa4a06533c.index has in total 751 frames\n", "INFO:root:Skipping downloading from https://www.dropbox.com/s/4ognq8lshcujk43/Plenaire_zaal_20200923132426_Omtzigt.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114 already exists.\n", "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index\n", - "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index has in total 7471 frames\n" + "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index has in total 7471 frames\n", + "DEBUG:matplotlib.pyplot:Loaded backend module://matplotlib_inline.backend_inline version unknown.\n", + "DEBUG:matplotlib.pyplot:Loaded backend module://matplotlib_inline.backend_inline version unknown.\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 2.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 2.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansInscriptionalParthian-Regular.ttf', name='Noto Sans Inscriptional Parthian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizTwoSymReg.otf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLisu-Regular.ttf', name='Noto Sans Lisu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Chalkduster.ttf', name='Chalkduster', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralBolIta.otf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AppleGothic.ttf', name='AppleGothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W4.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNabataean-Regular.ttf', name='Noto Sans Nabataean', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiViet-Regular.ttf', name='Noto Sans Tai Viet', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Italic.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Diwan Kufi.ttc', name='Diwan Kufi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sathu.ttf', name='Sathu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Noteworthy.ttc', name='Noteworthy', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniBolIta.otf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansUgaritic-Regular.ttf', name='Noto Sans Ugaritic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBhaiksuki-Regular.ttf', name='Noto Sans Bhaiksuki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldPersian-Regular.ttf', name='Noto Sans Old Persian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansWarangCiti-Regular.ttf', name='Noto Sans Warang Citi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tahoma Bold.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOlChiki-Regular.ttf', name='Noto Sans Ol Chiki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mishafi Gold.ttf', name='Mishafi Gold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/InaiMathi-MN.ttc', name='InaiMathi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PartyLET-plain.ttf', name='Party LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Regular.ttf', name='Roboto', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNewa-Regular.ttf', name='Noto Sans Newa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Kohinoor.ttc', name='Kohinoor Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansRejang-Regular.ttf', name='Noto Sans Rejang', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSiddham-Regular.ttf', name='Noto Sans Siddham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/ChalkboardSE.ttc', name='Chalkboard SE', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72 Smallcaps Book.ttf', name='Bodoni 72 Smallcaps', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTirhuta-Regular.ttf', name='Noto Sans Tirhuta', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansImperialAramaic-Regular.ttf', name='Noto Sans Imperial Aramaic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Pinpoint 6 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi.ttf', name='Gurmukhi MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPhagsPa-Regular.ttf', name='Noto Sans PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLinearB-Regular.ttf', name='Noto Sans Linear B', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Muna.ttc', name='Muna', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/LucidaGrande.ttc', name='Lucida Grande', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPhoenician-Regular.ttf', name='Noto Sans Phoenician', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W2.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/KufiStandardGK.ttc', name='KufiStandardGK', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLepcha-Regular.ttf', name='Noto Sans Lepcha', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSoraSompeng-Regular.ttf', name='Noto Sans Sora Sompeng', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AmericanTypewriter.ttc', name='American Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSerif.ttc', name='PT Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldNorthArabian-Regular.ttf', name='Noto Sans Old North Arabian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Farah.ttc', name='Farah', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Hoefler Text Ornaments.ttf', name='Hoefler Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Symbols.ttf', name='Apple Symbols', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Thonburi.ttc', name='Thonburi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SuperClarendon.ttc', name='Superclarendon', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W9.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sinhala MN.ttc', name='Sinhala MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kefa.ttc', name='Kefa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Courier.ttc', name='Courier', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72.ttc', name='Bodoni 72', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bangla Sangam MN.ttc', name='Bangla Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Rounded Bold.ttf', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Baskerville.ttc', name='Baskerville', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-BoldItalic.ttf', name='Roboto', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSMono.ttf', name='.SF NS Mono', style='normal', variant='normal', weight=295, stretch='normal', size='scalable')) = 10.14975\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W1.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Outline 8 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKaithi-Regular.ttf', name='Noto Sans Kaithi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMasaramGondi-Regular.otf', name='Noto Sans Masaram Gondi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir Next Condensed.ttc', name='Avenir Next Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W5.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizOneSymReg.otf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Shree714.ttc', name='Shree Devanagari 714', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DevanagariMT.ttc', name='Devanagari MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DecoTypeNaskh.ttc', name='DecoType Naskh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mishafi.ttf', name='Mishafi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLydian-Regular.ttf', name='Noto Sans Lydian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AppleMyungjo.ttf', name='AppleMyungjo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMongolian-Regular.ttf', name='Noto Sans Mongolian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpSmBol.otf', name='STIXIntegralsUpSm', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMiao-Regular.ttf', name='Noto Sans Miao', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntDReg.otf', name='STIXIntegralsD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Italic.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansAdlam-Regular.ttf', name='Noto Sans Adlam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMeeteiMayek-Regular.ttf', name='Noto Sans Meetei Mayek', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Telugu MN.ttc', name='Telugu MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralBol.otf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCuneiform-Regular.ttf', name='Noto Sans Cuneiform', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sana.ttc', name='Sana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/AppleSDGothicNeo.ttc', name='Apple SD Gothic Neo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-MediumItalic.ttf', name='Roboto', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Futura.ttc', name='Futura', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBuginese-Regular.ttf', name='Noto Sans Buginese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trattatello.ttf', name='Trattatello', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSundanese-Regular.ttf', name='Noto Sans Sundanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansMyanmar.ttc', name='Noto Sans Myanmar', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Bold Italic.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ明朝 ProN.ttc', name='Hiragino Mincho ProN', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntDBol.otf', name='STIXIntegralsD', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNS.ttf', name='System Font', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansInscriptionalPahlavi-Regular.ttf', name='Noto Sans Inscriptional Pahlavi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPsalterPahlavi-Regular.ttf', name='Noto Sans Psalter Pahlavi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir.ttc', name='Avenir', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansModi-Regular.ttf', name='Noto Sans Modi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMandaic-Regular.ttf', name='Noto Sans Mandaic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kannada MN.ttc', name='Kannada MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Italic.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Geneva.ttf', name='Geneva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorBangla.ttc', name='Kohinoor Bangla', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kannada Sangam MN.ttc', name='Kannada Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Hiragino Sans GB.ttc', name='Hiragino Sans GB', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Black.ttf', name='Arial Black', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Cochin.ttc', name='Cochin', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Marion.ttc', name='Marion', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Bold Italic.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Charter.ttc', name='Charter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Bold.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHanifiRohingya-Regular.ttf', name='Noto Sans Hanifi Rohingya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansAvestan-Regular.ttf', name='Noto Sans Avestan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGlagolitic-Regular.ttf', name='Noto Sans Glagolitic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Rockwell.ttc', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DIN Alternate Bold.ttf', name='DIN Alternate', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntSmReg.otf', name='STIXIntegralsSm', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/STHeiti Medium.ttc', name='Heiti TC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCham-Regular.ttf', name='Noto Sans Cham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKhojki-Regular.ttf', name='Noto Sans Khojki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompactRounded.ttf', name='.SF Compact Rounded', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gujarati Sangam MN.ttc', name='Gujarati Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kokonor.ttf', name='Kokonor', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Myanmar MN.ttc', name='Myanmar MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCoptic-Regular.ttf', name='Noto Sans Coptic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ArialHB.ttc', name='Arial Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiTham-Regular.ttf', name='Noto Sans Tai Tham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Bold.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldHungarian-Regular.ttf', name='Noto Sans Old Hungarian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBuhid-Regular.ttf', name='Noto Sans Buhid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/HelveticaNeue.ttc', name='Helvetica Neue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansArmenian.ttc', name='Noto Sans Armenian', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPahawhHmong-Regular.ttf', name='Noto Sans Pahawh Hmong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompactItalic.ttf', name='.SF Compact', style='italic', variant='normal', weight=1000, stretch='normal', size='scalable')) = 11.62\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Monaco.ttf', name='Monaco', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Herculanum.ttf', name='Herculanum', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Damascus.ttc', name='Damascus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifAhom-Regular.ttf', name='Noto Serif Ahom', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpDBol.otf', name='STIXIntegralsUpD', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Al Tarikh.ttc', name='Al Tarikh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTakri-Regular.ttf', name='Noto Sans Takri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTifinagh-Regular.ttf', name='Noto Sans Tifinagh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/GeezaPro.ttc', name='Geeza Pro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Khmer Sangam MN.ttf', name='Khmer Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMahajani-Regular.ttf', name='Noto Sans Mahajani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCypriot-Regular.ttf', name='Noto Sans Cypriot', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Nadeem.ttc', name='Nadeem', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni Ornaments.ttf', name='Bodoni Ornaments', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifYezidi-Regular.otf', name='Noto Serif Yezidi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLinearA-Regular.ttf', name='Noto Sans Linear A', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Arial Unicode.ttf', name='Arial Unicode MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXVarBol.otf', name='STIXVariants', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Andale Mono.ttf', name='Andale Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NewYork.ttf', name='.New York', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTL.ttf', name='Euclid Flex RTL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPauCinHau-Regular.ttf', name='Noto Sans Pau Cin Hau', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Papyrus.ttc', name='Papyrus', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFArabic.ttf', name='.SF Arabic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSylotiNagri-Regular.ttf', name='Noto Sans Syloti Nagri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Bold.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoNastaliq.ttc', name='Noto Nastaliq Urdu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Seravek.ttc', name='Seravek', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBatak-Regular.ttf', name='Noto Sans Batak', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGunjalaGondi-Regular.otf', name='Noto Sans Gunjala Gondi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpBol.otf', name='STIXIntegralsUp', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldItalic-Regular.ttf', name='Noto Sans Old Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSyriac-Regular.ttf', name='Noto Sans Syriac', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansVai-Regular.ttf', name='Noto Sans Vai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NewPeninimMT.ttc', name='New Peninim MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Ayuthaya.ttf', name='Ayuthaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SignPainter.ttc', name='SignPainter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansEgyptianHieroglyphs-Regular.ttf', name='Noto Sans Egyptian Hieroglyphs', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpReg.otf', name='STIXIntegralsUp', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansElbasan-Regular.ttf', name='Noto Sans Elbasan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMultani-Regular.ttf', name='Noto Sans Multani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W0.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Palatino.ttc', name='Palatino', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W8.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Italic.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SukhumvitSet.ttc', name='Sukhumvit Set', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Bold Italic.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Devanagari Sangam MN.ttc', name='Devanagari Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Farisi.ttf', name='Farisi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Bold.ttf', name='Roboto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansWancho-Regular.ttf', name='Noto Sans Wancho', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizThreeSymReg.otf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorGujarati.ttc', name='Kohinoor Gujarati', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTLMedium.ttf', name='Euclid Flex RTL Medium', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Corsiva.ttc', name='Corsiva Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSamaritan-Regular.ttf', name='Noto Sans Samaritan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Luminari.ttf', name='Luminari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/AquaKana.ttc', name='.Aqua Kana', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntSmBol.otf', name='STIXIntegralsSm', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLimbu-Regular.ttf', name='Noto Sans Limbu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72 OS.ttc', name='Bodoni 72 Oldstyle', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Galvji.ttc', name='Galvji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Outline 6 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W6.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHanunoo-Regular.ttf', name='Noto Sans Hanunoo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansDuployan-Regular.ttf', name='Noto Sans Duployan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldPermic-Regular.ttf', name='Noto Sans Old Permic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSans.ttc', name='PT Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMro-Regular.ttf', name='Noto Sans Mro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi MN.ttc', name='Gurmukhi MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneral.otf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Chalkboard.ttc', name='Chalkboard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTagbanwa-Regular.ttf', name='Noto Sans Tagbanwa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tamil MN.ttc', name='Tamil MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Baghdad.ttc', name='Baghdad', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldSouthArabian-Regular.ttf', name='Noto Sans Old South Arabian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AlBayan.ttc', name='Al Bayan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Comic Sans MS.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Optima.ttc', name='Optima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-ThinItalic.ttf', name='Roboto', style='italic', variant='normal', weight=250, stretch='normal', size='scalable')) = 11.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Bold.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bradley Hand Bold.ttf', name='Bradley Hand', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSharada-Regular.ttf', name='Noto Sans Sharada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ丸ゴ ProN W4.ttc', name='Hiragino Maru Gothic Pro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansChakma-Regular.ttf', name='Noto Sans Chakma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Songti.ttc', name='Songti SC', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Comic Sans MS Bold.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOsmanya-Regular.ttf', name='Noto Sans Osmanya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W3.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Bold Italic.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHatran-Regular.ttf', name='Noto Sans Hatran', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Malayalam MN.ttc', name='Malayalam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Unicode.ttf', name='Arial Unicode MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Oriya Sangam MN.ttc', name='Oriya Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSItalic.ttf', name='System Font', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Bold Italic.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Myanmar Sangam MN.ttc', name='Myanmar Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizThreeSymBol.otf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSRounded.ttf', name='.SF NS Rounded', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/MarkerFelt.ttc', name='Marker Felt', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBrahmi-Regular.ttf', name='Noto Sans Brahmi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Bold.ttf', name='Arial Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Medium.ttf', name='Roboto', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Lao Sangam MN.ttf', name='Lao Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Zapfino.ttf', name='Zapfino', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DIN Condensed Bold.ttf', name='DIN Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Black.ttf', name='Roboto', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Athelas.ttc', name='Athelas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Bold Italic.ttf', name='Arial Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Menlo.ttc', name='Menlo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi Sangam MN.ttc', name='Gurmukhi Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansYi-Regular.ttf', name='Noto Sans Yi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorTelugu.ttc', name='Kohinoor Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Krungthep.ttf', name='Krungthep', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNKo-Regular.ttf', name='Noto Sans NKo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Light.ttf', name='Roboto', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bangla MN.ttc', name='Bangla MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPalmyrene-Regular.ttf', name='Noto Sans Palmyrene', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/BigCaslon.ttf', name='Big Caslon', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSMonoItalic.ttf', name='.SF NS Mono', style='italic', variant='normal', weight=295, stretch='normal', size='scalable')) = 11.14975\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMarchen-Regular.ttf', name='Noto Sans Marchen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Apple Chancery.ttf', name='Apple Chancery', style='normal', variant='normal', weight=0, stretch='normal', size='scalable')) = 10.43\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKayahLi-Regular.ttf', name='Noto Sans Kayah Li', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Skia.ttf', name='Skia', style='normal', variant='normal', weight=5, stretch='normal', size='scalable')) = 10.42525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniBol.otf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Italic.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniIta.otf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Diwan Thuluth.ttf', name='Diwan Thuluth', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/GillSans.ttc', name='Gill Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Beirut.ttc', name='Beirut', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCarian-Regular.ttf', name='Noto Sans Carian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldTurkic-Regular.ttf', name='Noto Sans Old Turkic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings 2.ttf', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SnellRoundhand.ttc', name='Snell Roundhand', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifBalinese-Regular.ttf', name='Noto Serif Balinese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Telugu Sangam MN.ttc', name='Telugu Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-LightItalic.ttf', name='Roboto', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/ITFDevanagari.ttc', name='ITF Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFiveSymReg.otf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Times.ttc', name='Times', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXVar.otf', name='STIXVariants', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLycian-Regular.ttf', name='Noto Sans Lycian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansThaana-Regular.ttf', name='Noto Sans Thaana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Al Nile.ttc', name='Al Nile', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Italic.ttf', name='Roboto', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Academy Engraved LET Fonts.ttf', name='Academy Engraved LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow.ttf', name='Arial Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKhudawadi-Regular.ttf', name='Noto Sans Khudawadi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Thin.ttf', name='Roboto', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMendeKikakui-Regular.ttf', name='Noto Sans Mende Kikakui', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Copperplate.ttc', name='Copperplate', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansKannada.ttc', name='Noto Sans Kannada', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizTwoSymBol.otf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUni.otf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSerifCaption.ttc', name='PT Serif Caption', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMeroitic-Regular.ttf', name='Noto Sans Meroitic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tamil Sangam MN.ttc', name='Tamil Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Phosphate.ttc', name='Phosphate', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpDReg.otf', name='STIXIntegralsUpD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Raanana.ttc', name='Raanana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizOneSymBol.otf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir Next.ttc', name='Avenir Next', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Hoefler Text.ttc', name='Hoefler Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTLBold.ttf', name='Euclid Flex RTL Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Brush Script.ttf', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansManichaean-Regular.ttf', name='Noto Sans Manichaean', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Keyboard.ttf', name='.Keyboard', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/PingFang.ttc', name='PingFang HK', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKharoshthi-Regular.ttf', name='Noto Sans Kharoshthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Malayalam Sangam MN.ttc', name='Malayalam Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Microsoft Sans Serif.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSaurashtra-Regular.ttf', name='Noto Sans Saurashtra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Oriya MN.ttc', name='Oriya MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings 3.ttf', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Italic.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompact.ttf', name='.SF Compact', style='normal', variant='normal', weight=1000, stretch='normal', size='scalable')) = 10.62\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTagalog-Regular.ttf', name='Noto Sans Tagalog', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/EuphemiaCAS.ttc', name='Euphemia UCAS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansOriya.ttc', name='Noto Sans Oriya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFourSymBol.otf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Bold.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/STHeiti Light.ttc', name='Heiti TC', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Bold Italic.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PlantagenetCherokee.ttf', name='Plantagenet Cherokee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNewTaiLue-Regular.ttf', name='Noto Sans New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Lao MN.ttc', name='Lao MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-BlackItalic.ttf', name='Roboto', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Silom.ttf', name='Silom', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Waseem.ttc', name='Waseem', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Pinpoint 8 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sinhala Sangam MN.ttc', name='Sinhala Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kailasa.ttc', name='Kailasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W7.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFourSymReg.otf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Iowan Old Style.ttc', name='Iowan Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBassaVah-Regular.ttf', name='Noto Sans Bassa Vah', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSerifMyanmar.ttc', name='Noto Serif Myanmar', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOsage-Regular.ttf', name='Noto Sans Osage', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Didot.ttc', name='Didot', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBamum-Regular.ttf', name='Noto Sans Bamum', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mshtakan.ttc', name='Mshtakan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ZapfDingbats.ttf', name='Zapf Dingbats', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NewYorkItalic.ttf', name='.New York', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Bold.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Khmer MN.ttc', name='Khmer MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralItalic.otf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Savoye LET.ttc', name='Savoye LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Helvetica.ttc', name='Helvetica', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpSmReg.otf', name='STIXIntegralsUpSm', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/MuktaMahee.ttc', name='Mukta Mahee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/GujaratiMT.ttc', name='Gujarati MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTMono.ttc', name='PT Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Italic.ttf', name='Arial Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGothic-Regular.ttf', name='Noto Sans Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiLe-Regular.ttf', name='Noto Sans Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansJavanese-Regular.otf', name='Noto Sans Javanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCaucasianAlbanian-Regular.ttf', name='Noto Sans Caucasian Albanian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to Arial ('/System/Library/Fonts/Supplemental/Arial.ttf') with score of 0.050000.\n", + "WARNING:py.warnings:/usr/local/lib/python3.9/site-packages/seaborn/relational.py:654: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " kws[\"alpha\"] = 1 if self.alpha == \"auto\" else self.alpha\n", + "\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=11.0.\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 2.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 2.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansInscriptionalParthian-Regular.ttf', name='Noto Sans Inscriptional Parthian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizTwoSymReg.otf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLisu-Regular.ttf', name='Noto Sans Lisu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Chalkduster.ttf', name='Chalkduster', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralBolIta.otf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AppleGothic.ttf', name='AppleGothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W4.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNabataean-Regular.ttf', name='Noto Sans Nabataean', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiViet-Regular.ttf', name='Noto Sans Tai Viet', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Italic.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Diwan Kufi.ttc', name='Diwan Kufi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sathu.ttf', name='Sathu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Noteworthy.ttc', name='Noteworthy', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniBolIta.otf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansUgaritic-Regular.ttf', name='Noto Sans Ugaritic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBhaiksuki-Regular.ttf', name='Noto Sans Bhaiksuki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldPersian-Regular.ttf', name='Noto Sans Old Persian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansWarangCiti-Regular.ttf', name='Noto Sans Warang Citi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tahoma Bold.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOlChiki-Regular.ttf', name='Noto Sans Ol Chiki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mishafi Gold.ttf', name='Mishafi Gold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/InaiMathi-MN.ttc', name='InaiMathi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PartyLET-plain.ttf', name='Party LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Regular.ttf', name='Roboto', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNewa-Regular.ttf', name='Noto Sans Newa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Kohinoor.ttc', name='Kohinoor Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansRejang-Regular.ttf', name='Noto Sans Rejang', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSiddham-Regular.ttf', name='Noto Sans Siddham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/ChalkboardSE.ttc', name='Chalkboard SE', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72 Smallcaps Book.ttf', name='Bodoni 72 Smallcaps', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTirhuta-Regular.ttf', name='Noto Sans Tirhuta', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansImperialAramaic-Regular.ttf', name='Noto Sans Imperial Aramaic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Pinpoint 6 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi.ttf', name='Gurmukhi MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPhagsPa-Regular.ttf', name='Noto Sans PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLinearB-Regular.ttf', name='Noto Sans Linear B', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Muna.ttc', name='Muna', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/LucidaGrande.ttc', name='Lucida Grande', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPhoenician-Regular.ttf', name='Noto Sans Phoenician', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W2.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/KufiStandardGK.ttc', name='KufiStandardGK', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLepcha-Regular.ttf', name='Noto Sans Lepcha', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSoraSompeng-Regular.ttf', name='Noto Sans Sora Sompeng', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AmericanTypewriter.ttc', name='American Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSerif.ttc', name='PT Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldNorthArabian-Regular.ttf', name='Noto Sans Old North Arabian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Farah.ttc', name='Farah', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Hoefler Text Ornaments.ttf', name='Hoefler Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Symbols.ttf', name='Apple Symbols', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Thonburi.ttc', name='Thonburi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SuperClarendon.ttc', name='Superclarendon', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W9.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sinhala MN.ttc', name='Sinhala MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kefa.ttc', name='Kefa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Courier.ttc', name='Courier', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72.ttc', name='Bodoni 72', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bangla Sangam MN.ttc', name='Bangla Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Rounded Bold.ttf', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Baskerville.ttc', name='Baskerville', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-BoldItalic.ttf', name='Roboto', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSMono.ttf', name='.SF NS Mono', style='normal', variant='normal', weight=295, stretch='normal', size='scalable')) = 10.14975\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W1.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Outline 8 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKaithi-Regular.ttf', name='Noto Sans Kaithi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMasaramGondi-Regular.otf', name='Noto Sans Masaram Gondi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir Next Condensed.ttc', name='Avenir Next Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W5.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizOneSymReg.otf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Shree714.ttc', name='Shree Devanagari 714', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DevanagariMT.ttc', name='Devanagari MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DecoTypeNaskh.ttc', name='DecoType Naskh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mishafi.ttf', name='Mishafi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLydian-Regular.ttf', name='Noto Sans Lydian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AppleMyungjo.ttf', name='AppleMyungjo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMongolian-Regular.ttf', name='Noto Sans Mongolian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpSmBol.otf', name='STIXIntegralsUpSm', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMiao-Regular.ttf', name='Noto Sans Miao', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntDReg.otf', name='STIXIntegralsD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Italic.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansAdlam-Regular.ttf', name='Noto Sans Adlam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMeeteiMayek-Regular.ttf', name='Noto Sans Meetei Mayek', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Telugu MN.ttc', name='Telugu MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralBol.otf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCuneiform-Regular.ttf', name='Noto Sans Cuneiform', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sana.ttc', name='Sana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/AppleSDGothicNeo.ttc', name='Apple SD Gothic Neo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-MediumItalic.ttf', name='Roboto', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Futura.ttc', name='Futura', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBuginese-Regular.ttf', name='Noto Sans Buginese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trattatello.ttf', name='Trattatello', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSundanese-Regular.ttf', name='Noto Sans Sundanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansMyanmar.ttc', name='Noto Sans Myanmar', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Bold Italic.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ明朝 ProN.ttc', name='Hiragino Mincho ProN', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntDBol.otf', name='STIXIntegralsD', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNS.ttf', name='System Font', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansInscriptionalPahlavi-Regular.ttf', name='Noto Sans Inscriptional Pahlavi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPsalterPahlavi-Regular.ttf', name='Noto Sans Psalter Pahlavi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir.ttc', name='Avenir', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansModi-Regular.ttf', name='Noto Sans Modi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMandaic-Regular.ttf', name='Noto Sans Mandaic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kannada MN.ttc', name='Kannada MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Italic.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Geneva.ttf', name='Geneva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorBangla.ttc', name='Kohinoor Bangla', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kannada Sangam MN.ttc', name='Kannada Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Hiragino Sans GB.ttc', name='Hiragino Sans GB', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Black.ttf', name='Arial Black', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Cochin.ttc', name='Cochin', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Marion.ttc', name='Marion', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Bold Italic.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Charter.ttc', name='Charter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Bold.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHanifiRohingya-Regular.ttf', name='Noto Sans Hanifi Rohingya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansAvestan-Regular.ttf', name='Noto Sans Avestan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGlagolitic-Regular.ttf', name='Noto Sans Glagolitic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Rockwell.ttc', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DIN Alternate Bold.ttf', name='DIN Alternate', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntSmReg.otf', name='STIXIntegralsSm', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/STHeiti Medium.ttc', name='Heiti TC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCham-Regular.ttf', name='Noto Sans Cham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKhojki-Regular.ttf', name='Noto Sans Khojki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompactRounded.ttf', name='.SF Compact Rounded', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gujarati Sangam MN.ttc', name='Gujarati Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kokonor.ttf', name='Kokonor', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Myanmar MN.ttc', name='Myanmar MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCoptic-Regular.ttf', name='Noto Sans Coptic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ArialHB.ttc', name='Arial Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiTham-Regular.ttf', name='Noto Sans Tai Tham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Bold.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldHungarian-Regular.ttf', name='Noto Sans Old Hungarian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBuhid-Regular.ttf', name='Noto Sans Buhid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/HelveticaNeue.ttc', name='Helvetica Neue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansArmenian.ttc', name='Noto Sans Armenian', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPahawhHmong-Regular.ttf', name='Noto Sans Pahawh Hmong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompactItalic.ttf', name='.SF Compact', style='italic', variant='normal', weight=1000, stretch='normal', size='scalable')) = 11.62\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Monaco.ttf', name='Monaco', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Herculanum.ttf', name='Herculanum', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Damascus.ttc', name='Damascus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifAhom-Regular.ttf', name='Noto Serif Ahom', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpDBol.otf', name='STIXIntegralsUpD', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Al Tarikh.ttc', name='Al Tarikh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTakri-Regular.ttf', name='Noto Sans Takri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTifinagh-Regular.ttf', name='Noto Sans Tifinagh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/GeezaPro.ttc', name='Geeza Pro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Khmer Sangam MN.ttf', name='Khmer Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMahajani-Regular.ttf', name='Noto Sans Mahajani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCypriot-Regular.ttf', name='Noto Sans Cypriot', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Nadeem.ttc', name='Nadeem', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni Ornaments.ttf', name='Bodoni Ornaments', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifYezidi-Regular.otf', name='Noto Serif Yezidi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLinearA-Regular.ttf', name='Noto Sans Linear A', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Arial Unicode.ttf', name='Arial Unicode MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXVarBol.otf', name='STIXVariants', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Andale Mono.ttf', name='Andale Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NewYork.ttf', name='.New York', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTL.ttf', name='Euclid Flex RTL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPauCinHau-Regular.ttf', name='Noto Sans Pau Cin Hau', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Papyrus.ttc', name='Papyrus', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFArabic.ttf', name='.SF Arabic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSylotiNagri-Regular.ttf', name='Noto Sans Syloti Nagri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Bold.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoNastaliq.ttc', name='Noto Nastaliq Urdu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Seravek.ttc', name='Seravek', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBatak-Regular.ttf', name='Noto Sans Batak', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGunjalaGondi-Regular.otf', name='Noto Sans Gunjala Gondi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpBol.otf', name='STIXIntegralsUp', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldItalic-Regular.ttf', name='Noto Sans Old Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSyriac-Regular.ttf', name='Noto Sans Syriac', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansVai-Regular.ttf', name='Noto Sans Vai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NewPeninimMT.ttc', name='New Peninim MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Ayuthaya.ttf', name='Ayuthaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SignPainter.ttc', name='SignPainter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansEgyptianHieroglyphs-Regular.ttf', name='Noto Sans Egyptian Hieroglyphs', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpReg.otf', name='STIXIntegralsUp', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansElbasan-Regular.ttf', name='Noto Sans Elbasan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMultani-Regular.ttf', name='Noto Sans Multani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W0.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Palatino.ttc', name='Palatino', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W8.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Italic.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SukhumvitSet.ttc', name='Sukhumvit Set', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Bold Italic.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Devanagari Sangam MN.ttc', name='Devanagari Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Farisi.ttf', name='Farisi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Bold.ttf', name='Roboto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansWancho-Regular.ttf', name='Noto Sans Wancho', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizThreeSymReg.otf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorGujarati.ttc', name='Kohinoor Gujarati', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTLMedium.ttf', name='Euclid Flex RTL Medium', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Corsiva.ttc', name='Corsiva Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSamaritan-Regular.ttf', name='Noto Sans Samaritan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Luminari.ttf', name='Luminari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/AquaKana.ttc', name='.Aqua Kana', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntSmBol.otf', name='STIXIntegralsSm', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLimbu-Regular.ttf', name='Noto Sans Limbu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72 OS.ttc', name='Bodoni 72 Oldstyle', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Galvji.ttc', name='Galvji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Outline 6 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W6.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHanunoo-Regular.ttf', name='Noto Sans Hanunoo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansDuployan-Regular.ttf', name='Noto Sans Duployan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldPermic-Regular.ttf', name='Noto Sans Old Permic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSans.ttc', name='PT Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMro-Regular.ttf', name='Noto Sans Mro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi MN.ttc', name='Gurmukhi MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneral.otf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Chalkboard.ttc', name='Chalkboard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTagbanwa-Regular.ttf', name='Noto Sans Tagbanwa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tamil MN.ttc', name='Tamil MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Baghdad.ttc', name='Baghdad', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldSouthArabian-Regular.ttf', name='Noto Sans Old South Arabian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AlBayan.ttc', name='Al Bayan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Comic Sans MS.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Optima.ttc', name='Optima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-ThinItalic.ttf', name='Roboto', style='italic', variant='normal', weight=250, stretch='normal', size='scalable')) = 11.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Bold.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bradley Hand Bold.ttf', name='Bradley Hand', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSharada-Regular.ttf', name='Noto Sans Sharada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ丸ゴ ProN W4.ttc', name='Hiragino Maru Gothic Pro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansChakma-Regular.ttf', name='Noto Sans Chakma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Songti.ttc', name='Songti SC', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Comic Sans MS Bold.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOsmanya-Regular.ttf', name='Noto Sans Osmanya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W3.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Bold Italic.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHatran-Regular.ttf', name='Noto Sans Hatran', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Malayalam MN.ttc', name='Malayalam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Unicode.ttf', name='Arial Unicode MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Oriya Sangam MN.ttc', name='Oriya Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSItalic.ttf', name='System Font', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Bold Italic.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Myanmar Sangam MN.ttc', name='Myanmar Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizThreeSymBol.otf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSRounded.ttf', name='.SF NS Rounded', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/MarkerFelt.ttc', name='Marker Felt', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBrahmi-Regular.ttf', name='Noto Sans Brahmi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Bold.ttf', name='Arial Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Medium.ttf', name='Roboto', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Lao Sangam MN.ttf', name='Lao Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Zapfino.ttf', name='Zapfino', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DIN Condensed Bold.ttf', name='DIN Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Black.ttf', name='Roboto', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Athelas.ttc', name='Athelas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Bold Italic.ttf', name='Arial Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Menlo.ttc', name='Menlo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi Sangam MN.ttc', name='Gurmukhi Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansYi-Regular.ttf', name='Noto Sans Yi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorTelugu.ttc', name='Kohinoor Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Krungthep.ttf', name='Krungthep', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNKo-Regular.ttf', name='Noto Sans NKo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Light.ttf', name='Roboto', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bangla MN.ttc', name='Bangla MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPalmyrene-Regular.ttf', name='Noto Sans Palmyrene', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/BigCaslon.ttf', name='Big Caslon', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSMonoItalic.ttf', name='.SF NS Mono', style='italic', variant='normal', weight=295, stretch='normal', size='scalable')) = 11.14975\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMarchen-Regular.ttf', name='Noto Sans Marchen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Apple Chancery.ttf', name='Apple Chancery', style='normal', variant='normal', weight=0, stretch='normal', size='scalable')) = 10.43\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKayahLi-Regular.ttf', name='Noto Sans Kayah Li', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Skia.ttf', name='Skia', style='normal', variant='normal', weight=5, stretch='normal', size='scalable')) = 10.42525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniBol.otf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Italic.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniIta.otf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Diwan Thuluth.ttf', name='Diwan Thuluth', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/GillSans.ttc', name='Gill Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Beirut.ttc', name='Beirut', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCarian-Regular.ttf', name='Noto Sans Carian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldTurkic-Regular.ttf', name='Noto Sans Old Turkic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings 2.ttf', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SnellRoundhand.ttc', name='Snell Roundhand', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifBalinese-Regular.ttf', name='Noto Serif Balinese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Telugu Sangam MN.ttc', name='Telugu Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-LightItalic.ttf', name='Roboto', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/ITFDevanagari.ttc', name='ITF Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFiveSymReg.otf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Times.ttc', name='Times', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXVar.otf', name='STIXVariants', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLycian-Regular.ttf', name='Noto Sans Lycian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansThaana-Regular.ttf', name='Noto Sans Thaana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Al Nile.ttc', name='Al Nile', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Italic.ttf', name='Roboto', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Academy Engraved LET Fonts.ttf', name='Academy Engraved LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow.ttf', name='Arial Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKhudawadi-Regular.ttf', name='Noto Sans Khudawadi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Thin.ttf', name='Roboto', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMendeKikakui-Regular.ttf', name='Noto Sans Mende Kikakui', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Copperplate.ttc', name='Copperplate', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansKannada.ttc', name='Noto Sans Kannada', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizTwoSymBol.otf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUni.otf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSerifCaption.ttc', name='PT Serif Caption', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMeroitic-Regular.ttf', name='Noto Sans Meroitic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tamil Sangam MN.ttc', name='Tamil Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Phosphate.ttc', name='Phosphate', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpDReg.otf', name='STIXIntegralsUpD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Raanana.ttc', name='Raanana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizOneSymBol.otf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir Next.ttc', name='Avenir Next', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Hoefler Text.ttc', name='Hoefler Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTLBold.ttf', name='Euclid Flex RTL Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Brush Script.ttf', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansManichaean-Regular.ttf', name='Noto Sans Manichaean', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Keyboard.ttf', name='.Keyboard', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/PingFang.ttc', name='PingFang HK', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKharoshthi-Regular.ttf', name='Noto Sans Kharoshthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Malayalam Sangam MN.ttc', name='Malayalam Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Microsoft Sans Serif.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSaurashtra-Regular.ttf', name='Noto Sans Saurashtra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Oriya MN.ttc', name='Oriya MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings 3.ttf', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Italic.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompact.ttf', name='.SF Compact', style='normal', variant='normal', weight=1000, stretch='normal', size='scalable')) = 10.62\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTagalog-Regular.ttf', name='Noto Sans Tagalog', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/EuphemiaCAS.ttc', name='Euphemia UCAS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansOriya.ttc', name='Noto Sans Oriya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFourSymBol.otf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Bold.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/STHeiti Light.ttc', name='Heiti TC', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Bold Italic.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PlantagenetCherokee.ttf', name='Plantagenet Cherokee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNewTaiLue-Regular.ttf', name='Noto Sans New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Lao MN.ttc', name='Lao MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-BlackItalic.ttf', name='Roboto', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Silom.ttf', name='Silom', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Waseem.ttc', name='Waseem', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Pinpoint 8 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sinhala Sangam MN.ttc', name='Sinhala Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kailasa.ttc', name='Kailasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W7.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFourSymReg.otf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Iowan Old Style.ttc', name='Iowan Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBassaVah-Regular.ttf', name='Noto Sans Bassa Vah', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSerifMyanmar.ttc', name='Noto Serif Myanmar', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOsage-Regular.ttf', name='Noto Sans Osage', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Didot.ttc', name='Didot', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBamum-Regular.ttf', name='Noto Sans Bamum', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mshtakan.ttc', name='Mshtakan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ZapfDingbats.ttf', name='Zapf Dingbats', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NewYorkItalic.ttf', name='.New York', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Bold.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Khmer MN.ttc', name='Khmer MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralItalic.otf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Savoye LET.ttc', name='Savoye LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Helvetica.ttc', name='Helvetica', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpSmReg.otf', name='STIXIntegralsUpSm', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/MuktaMahee.ttc', name='Mukta Mahee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/GujaratiMT.ttc', name='Gujarati MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTMono.ttc', name='PT Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Italic.ttf', name='Arial Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGothic-Regular.ttf', name='Noto Sans Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiLe-Regular.ttf', name='Noto Sans Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansJavanese-Regular.otf', name='Noto Sans Javanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCaucasianAlbanian-Regular.ttf', name='Noto Sans Caucasian Albanian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=11.0 to Arial ('/System/Library/Fonts/Supplemental/Arial.ttf') with score of 0.050000.\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 2.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 2.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/usr/local/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansInscriptionalParthian-Regular.ttf', name='Noto Sans Inscriptional Parthian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizTwoSymReg.otf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLisu-Regular.ttf', name='Noto Sans Lisu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Chalkduster.ttf', name='Chalkduster', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralBolIta.otf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AppleGothic.ttf', name='AppleGothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W4.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNabataean-Regular.ttf', name='Noto Sans Nabataean', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiViet-Regular.ttf', name='Noto Sans Tai Viet', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Italic.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Diwan Kufi.ttc', name='Diwan Kufi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sathu.ttf', name='Sathu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Noteworthy.ttc', name='Noteworthy', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniBolIta.otf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansUgaritic-Regular.ttf', name='Noto Sans Ugaritic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBhaiksuki-Regular.ttf', name='Noto Sans Bhaiksuki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldPersian-Regular.ttf', name='Noto Sans Old Persian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansWarangCiti-Regular.ttf', name='Noto Sans Warang Citi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tahoma Bold.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOlChiki-Regular.ttf', name='Noto Sans Ol Chiki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mishafi Gold.ttf', name='Mishafi Gold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/InaiMathi-MN.ttc', name='InaiMathi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PartyLET-plain.ttf', name='Party LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Regular.ttf', name='Roboto', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNewa-Regular.ttf', name='Noto Sans Newa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Kohinoor.ttc', name='Kohinoor Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansRejang-Regular.ttf', name='Noto Sans Rejang', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSiddham-Regular.ttf', name='Noto Sans Siddham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/ChalkboardSE.ttc', name='Chalkboard SE', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72 Smallcaps Book.ttf', name='Bodoni 72 Smallcaps', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTirhuta-Regular.ttf', name='Noto Sans Tirhuta', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansImperialAramaic-Regular.ttf', name='Noto Sans Imperial Aramaic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Pinpoint 6 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi.ttf', name='Gurmukhi MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPhagsPa-Regular.ttf', name='Noto Sans PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLinearB-Regular.ttf', name='Noto Sans Linear B', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Muna.ttc', name='Muna', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/LucidaGrande.ttc', name='Lucida Grande', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPhoenician-Regular.ttf', name='Noto Sans Phoenician', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W2.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/KufiStandardGK.ttc', name='KufiStandardGK', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLepcha-Regular.ttf', name='Noto Sans Lepcha', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSoraSompeng-Regular.ttf', name='Noto Sans Sora Sompeng', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AmericanTypewriter.ttc', name='American Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSerif.ttc', name='PT Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldNorthArabian-Regular.ttf', name='Noto Sans Old North Arabian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Farah.ttc', name='Farah', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Hoefler Text Ornaments.ttf', name='Hoefler Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Symbols.ttf', name='Apple Symbols', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Thonburi.ttc', name='Thonburi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SuperClarendon.ttc', name='Superclarendon', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W9.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sinhala MN.ttc', name='Sinhala MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kefa.ttc', name='Kefa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Courier.ttc', name='Courier', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72.ttc', name='Bodoni 72', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bangla Sangam MN.ttc', name='Bangla Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Rounded Bold.ttf', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Baskerville.ttc', name='Baskerville', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-BoldItalic.ttf', name='Roboto', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSMono.ttf', name='.SF NS Mono', style='normal', variant='normal', weight=295, stretch='normal', size='scalable')) = 10.14975\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W1.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Outline 8 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKaithi-Regular.ttf', name='Noto Sans Kaithi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMasaramGondi-Regular.otf', name='Noto Sans Masaram Gondi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir Next Condensed.ttc', name='Avenir Next Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W5.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizOneSymReg.otf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Shree714.ttc', name='Shree Devanagari 714', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DevanagariMT.ttc', name='Devanagari MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DecoTypeNaskh.ttc', name='DecoType Naskh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mishafi.ttf', name='Mishafi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLydian-Regular.ttf', name='Noto Sans Lydian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AppleMyungjo.ttf', name='AppleMyungjo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMongolian-Regular.ttf', name='Noto Sans Mongolian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpSmBol.otf', name='STIXIntegralsUpSm', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMiao-Regular.ttf', name='Noto Sans Miao', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntDReg.otf', name='STIXIntegralsD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Italic.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansAdlam-Regular.ttf', name='Noto Sans Adlam', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMeeteiMayek-Regular.ttf', name='Noto Sans Meetei Mayek', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Telugu MN.ttc', name='Telugu MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralBol.otf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCuneiform-Regular.ttf', name='Noto Sans Cuneiform', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sana.ttc', name='Sana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/AppleSDGothicNeo.ttc', name='Apple SD Gothic Neo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-MediumItalic.ttf', name='Roboto', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Futura.ttc', name='Futura', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBuginese-Regular.ttf', name='Noto Sans Buginese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trattatello.ttf', name='Trattatello', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSundanese-Regular.ttf', name='Noto Sans Sundanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansMyanmar.ttc', name='Noto Sans Myanmar', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Bold Italic.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ明朝 ProN.ttc', name='Hiragino Mincho ProN', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntDBol.otf', name='STIXIntegralsD', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNS.ttf', name='System Font', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansInscriptionalPahlavi-Regular.ttf', name='Noto Sans Inscriptional Pahlavi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPsalterPahlavi-Regular.ttf', name='Noto Sans Psalter Pahlavi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir.ttc', name='Avenir', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansModi-Regular.ttf', name='Noto Sans Modi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMandaic-Regular.ttf', name='Noto Sans Mandaic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kannada MN.ttc', name='Kannada MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Italic.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Geneva.ttf', name='Geneva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorBangla.ttc', name='Kohinoor Bangla', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kannada Sangam MN.ttc', name='Kannada Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Hiragino Sans GB.ttc', name='Hiragino Sans GB', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Black.ttf', name='Arial Black', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Cochin.ttc', name='Cochin', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Marion.ttc', name='Marion', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Bold Italic.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Charter.ttc', name='Charter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS Bold.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHanifiRohingya-Regular.ttf', name='Noto Sans Hanifi Rohingya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansAvestan-Regular.ttf', name='Noto Sans Avestan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGlagolitic-Regular.ttf', name='Noto Sans Glagolitic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Rockwell.ttc', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DIN Alternate Bold.ttf', name='DIN Alternate', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntSmReg.otf', name='STIXIntegralsSm', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/STHeiti Medium.ttc', name='Heiti TC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCham-Regular.ttf', name='Noto Sans Cham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKhojki-Regular.ttf', name='Noto Sans Khojki', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompactRounded.ttf', name='.SF Compact Rounded', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gujarati Sangam MN.ttc', name='Gujarati Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Trebuchet MS.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kokonor.ttf', name='Kokonor', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Myanmar MN.ttc', name='Myanmar MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCoptic-Regular.ttf', name='Noto Sans Coptic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ArialHB.ttc', name='Arial Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiTham-Regular.ttf', name='Noto Sans Tai Tham', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Bold.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldHungarian-Regular.ttf', name='Noto Sans Old Hungarian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBuhid-Regular.ttf', name='Noto Sans Buhid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/HelveticaNeue.ttc', name='Helvetica Neue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansArmenian.ttc', name='Noto Sans Armenian', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPahawhHmong-Regular.ttf', name='Noto Sans Pahawh Hmong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompactItalic.ttf', name='.SF Compact', style='italic', variant='normal', weight=1000, stretch='normal', size='scalable')) = 11.62\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Monaco.ttf', name='Monaco', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Herculanum.ttf', name='Herculanum', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Damascus.ttc', name='Damascus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifAhom-Regular.ttf', name='Noto Serif Ahom', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpDBol.otf', name='STIXIntegralsUpD', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Al Tarikh.ttc', name='Al Tarikh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTakri-Regular.ttf', name='Noto Sans Takri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTifinagh-Regular.ttf', name='Noto Sans Tifinagh', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/GeezaPro.ttc', name='Geeza Pro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Khmer Sangam MN.ttf', name='Khmer Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMahajani-Regular.ttf', name='Noto Sans Mahajani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCypriot-Regular.ttf', name='Noto Sans Cypriot', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Nadeem.ttc', name='Nadeem', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni Ornaments.ttf', name='Bodoni Ornaments', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifYezidi-Regular.otf', name='Noto Serif Yezidi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLinearA-Regular.ttf', name='Noto Sans Linear A', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Arial Unicode.ttf', name='Arial Unicode MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXVarBol.otf', name='STIXVariants', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Andale Mono.ttf', name='Andale Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NewYork.ttf', name='.New York', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTL.ttf', name='Euclid Flex RTL', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPauCinHau-Regular.ttf', name='Noto Sans Pau Cin Hau', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Papyrus.ttc', name='Papyrus', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFArabic.ttf', name='.SF Arabic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSylotiNagri-Regular.ttf', name='Noto Sans Syloti Nagri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Bold.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoNastaliq.ttc', name='Noto Nastaliq Urdu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Seravek.ttc', name='Seravek', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBatak-Regular.ttf', name='Noto Sans Batak', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGunjalaGondi-Regular.otf', name='Noto Sans Gunjala Gondi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpBol.otf', name='STIXIntegralsUp', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldItalic-Regular.ttf', name='Noto Sans Old Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSyriac-Regular.ttf', name='Noto Sans Syriac', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansVai-Regular.ttf', name='Noto Sans Vai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NewPeninimMT.ttc', name='New Peninim MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Ayuthaya.ttf', name='Ayuthaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SignPainter.ttc', name='SignPainter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansEgyptianHieroglyphs-Regular.ttf', name='Noto Sans Egyptian Hieroglyphs', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpReg.otf', name='STIXIntegralsUp', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansElbasan-Regular.ttf', name='Noto Sans Elbasan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMultani-Regular.ttf', name='Noto Sans Multani', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W0.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Palatino.ttc', name='Palatino', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W8.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Italic.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SukhumvitSet.ttc', name='Sukhumvit Set', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana Bold Italic.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Devanagari Sangam MN.ttc', name='Devanagari Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Farisi.ttf', name='Farisi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Bold.ttf', name='Roboto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansWancho-Regular.ttf', name='Noto Sans Wancho', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizThreeSymReg.otf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorGujarati.ttc', name='Kohinoor Gujarati', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTLMedium.ttf', name='Euclid Flex RTL Medium', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Corsiva.ttc', name='Corsiva Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSamaritan-Regular.ttf', name='Noto Sans Samaritan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Luminari.ttf', name='Luminari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/AquaKana.ttc', name='.Aqua Kana', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntSmBol.otf', name='STIXIntegralsSm', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLimbu-Regular.ttf', name='Noto Sans Limbu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bodoni 72 OS.ttc', name='Bodoni 72 Oldstyle', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Galvji.ttc', name='Galvji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Outline 6 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W6.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHanunoo-Regular.ttf', name='Noto Sans Hanunoo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansDuployan-Regular.ttf', name='Noto Sans Duployan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldPermic-Regular.ttf', name='Noto Sans Old Permic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSans.ttc', name='PT Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMro-Regular.ttf', name='Noto Sans Mro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi MN.ttc', name='Gurmukhi MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneral.otf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Chalkboard.ttc', name='Chalkboard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTagbanwa-Regular.ttf', name='Noto Sans Tagbanwa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tamil MN.ttc', name='Tamil MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Baghdad.ttc', name='Baghdad', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldSouthArabian-Regular.ttf', name='Noto Sans Old South Arabian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/AlBayan.ttc', name='Al Bayan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Comic Sans MS.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Optima.ttc', name='Optima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-ThinItalic.ttf', name='Roboto', style='italic', variant='normal', weight=250, stretch='normal', size='scalable')) = 11.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Bold.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bradley Hand Bold.ttf', name='Bradley Hand', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSharada-Regular.ttf', name='Noto Sans Sharada', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ丸ゴ ProN W4.ttc', name='Hiragino Maru Gothic Pro', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansChakma-Regular.ttf', name='Noto Sans Chakma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Songti.ttc', name='Songti SC', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Comic Sans MS Bold.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOsmanya-Regular.ttf', name='Noto Sans Osmanya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W3.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Bold Italic.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansHatran-Regular.ttf', name='Noto Sans Hatran', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Malayalam MN.ttc', name='Malayalam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Unicode.ttf', name='Arial Unicode MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Oriya Sangam MN.ttc', name='Oriya Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSItalic.ttf', name='System Font', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Bold Italic.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Myanmar Sangam MN.ttc', name='Myanmar Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizThreeSymBol.otf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSRounded.ttf', name='.SF NS Rounded', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/MarkerFelt.ttc', name='Marker Felt', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBrahmi-Regular.ttf', name='Noto Sans Brahmi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Bold.ttf', name='Arial Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Medium.ttf', name='Roboto', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Lao Sangam MN.ttf', name='Lao Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Zapfino.ttf', name='Zapfino', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/DIN Condensed Bold.ttf', name='DIN Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Black.ttf', name='Roboto', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Athelas.ttc', name='Athelas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Bold Italic.ttf', name='Arial Narrow', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Menlo.ttc', name='Menlo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Gurmukhi Sangam MN.ttc', name='Gurmukhi Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansYi-Regular.ttf', name='Noto Sans Yi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/KohinoorTelugu.ttc', name='Kohinoor Telugu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Krungthep.ttf', name='Krungthep', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNKo-Regular.ttf', name='Noto Sans NKo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Light.ttf', name='Roboto', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Bangla MN.ttc', name='Bangla MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansPalmyrene-Regular.ttf', name='Noto Sans Palmyrene', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/BigCaslon.ttf', name='Big Caslon', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFNSMonoItalic.ttf', name='.SF NS Mono', style='italic', variant='normal', weight=295, stretch='normal', size='scalable')) = 11.14975\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMarchen-Regular.ttf', name='Noto Sans Marchen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Apple Chancery.ttf', name='Apple Chancery', style='normal', variant='normal', weight=0, stretch='normal', size='scalable')) = 10.43\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKayahLi-Regular.ttf', name='Noto Sans Kayah Li', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Skia.ttf', name='Skia', style='normal', variant='normal', weight=5, stretch='normal', size='scalable')) = 10.42525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniBol.otf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Italic.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUniIta.otf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Diwan Thuluth.ttf', name='Diwan Thuluth', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/GillSans.ttc', name='Gill Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Beirut.ttc', name='Beirut', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCarian-Regular.ttf', name='Noto Sans Carian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOldTurkic-Regular.ttf', name='Noto Sans Old Turkic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings 2.ttf', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/SnellRoundhand.ttc', name='Snell Roundhand', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSerifBalinese-Regular.ttf', name='Noto Serif Balinese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Telugu Sangam MN.ttc', name='Telugu Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-LightItalic.ttf', name='Roboto', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/ITFDevanagari.ttc', name='ITF Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFiveSymReg.otf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Times.ttc', name='Times', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXVar.otf', name='STIXVariants', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansLycian-Regular.ttf', name='Noto Sans Lycian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansThaana-Regular.ttf', name='Noto Sans Thaana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Al Nile.ttc', name='Al Nile', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Italic.ttf', name='Roboto', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Academy Engraved LET Fonts.ttf', name='Academy Engraved LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow.ttf', name='Arial Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKhudawadi-Regular.ttf', name='Noto Sans Khudawadi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-Thin.ttf', name='Roboto', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMendeKikakui-Regular.ttf', name='Noto Sans Mende Kikakui', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Copperplate.ttc', name='Copperplate', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansKannada.ttc', name='Noto Sans Kannada', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizTwoSymBol.otf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXNonUni.otf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTSerifCaption.ttc', name='PT Serif Caption', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansMeroitic-Regular.ttf', name='Noto Sans Meroitic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Tamil Sangam MN.ttc', name='Tamil Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Phosphate.ttc', name='Phosphate', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpDReg.otf', name='STIXIntegralsUpD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Raanana.ttc', name='Raanana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizOneSymBol.otf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Avenir Next.ttc', name='Avenir Next', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Hoefler Text.ttc', name='Hoefler Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/EuclidFlexRTLBold.ttf', name='Euclid Flex RTL Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Brush Script.ttf', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansManichaean-Regular.ttf', name='Noto Sans Manichaean', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Keyboard.ttf', name='.Keyboard', style='normal', variant='normal', weight=100, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/PingFang.ttc', name='PingFang HK', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansKharoshthi-Regular.ttf', name='Noto Sans Kharoshthi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Malayalam Sangam MN.ttc', name='Malayalam Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Microsoft Sans Serif.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansSaurashtra-Regular.ttf', name='Noto Sans Saurashtra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Oriya MN.ttc', name='Oriya MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Wingdings 3.ttf', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Georgia Italic.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/SFCompact.ttf', name='.SF Compact', style='normal', variant='normal', weight=1000, stretch='normal', size='scalable')) = 10.62\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTagalog-Regular.ttf', name='Noto Sans Tagalog', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/EuphemiaCAS.ttc', name='Euphemia UCAS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSansOriya.ttc', name='Noto Sans Oriya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFourSymBol.otf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Bold.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/STHeiti Light.ttc', name='Heiti TC', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Times New Roman Bold Italic.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PlantagenetCherokee.ttf', name='Plantagenet Cherokee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansNewTaiLue-Regular.ttf', name='Noto Sans New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Lao MN.ttc', name='Lao MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/Library/Fonts/Roboto-BlackItalic.ttf', name='Roboto', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Silom.ttf', name='Silom', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Waseem.ttc', name='Waseem', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Apple Braille Pinpoint 8 Dot.ttf', name='Apple Braille', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Sinhala Sangam MN.ttc', name='Sinhala Sangam MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Kailasa.ttc', name='Kailasa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ヒラギノ角ゴシック W7.ttc', name='Hiragino Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXSizFourSymReg.otf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Iowan Old Style.ttc', name='Iowan Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBassaVah-Regular.ttf', name='Noto Sans Bassa Vah', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NotoSerifMyanmar.ttc', name='Noto Serif Myanmar', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansOsage-Regular.ttf', name='Noto Sans Osage', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Didot.ttc', name='Didot', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansBamum-Regular.ttf', name='Noto Sans Bamum', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Mshtakan.ttc', name='Mshtakan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/ZapfDingbats.ttf', name='Zapf Dingbats', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/NewYorkItalic.ttf', name='.New York', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Courier New Bold.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Khmer MN.ttc', name='Khmer MN', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXGeneralItalic.otf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Savoye LET.ttc', name='Savoye LET', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Helvetica.ttc', name='Helvetica', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/STIXIntUpSmReg.otf', name='STIXIntegralsUpSm', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/MuktaMahee.ttc', name='Mukta Mahee', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/GujaratiMT.ttc', name='Gujarati MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/PTMono.ttc', name='PT Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/Arial Narrow Italic.ttf', name='Arial Narrow', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansGothic-Regular.ttf', name='Noto Sans Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansTaiLe-Regular.ttf', name='Noto Sans Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansJavanese-Regular.otf', name='Noto Sans Javanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: score(FontEntry(fname='/System/Library/Fonts/Supplemental/NotoSansCaucasianAlbanian-Regular.ttf', name='Noto Sans Caucasian Albanian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + "DEBUG:matplotlib.font_manager:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to Arial ('/System/Library/Fonts/Supplemental/Arial.ttf') with score of 0.050000.\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ "target = video_urls[-1:] # (Plenaire Zaal) Long video\n", - "url = video_urls[1] # (0: Ploumen) (1: Bram) (2: Baudet) Short video which is a (maybe mixed up) subset of the soure video\n", - " \n", "\n", - "# Source\n", - "video_index = index_hashes_for_video(url)\n", - "video_index.make_direct_map()\n", - "# video_index.ntotal # Total number of frames for the video, after changing its FPS to 5 \n", - "hash_vectors = np.array([video_index.reconstruct(i) for i in range(video_index.ntotal)])\n", + "url = video_urls[0] # (0: Ploumen) (1: Bram) (2: Baudet) Short video which is a (maybe mixed up) subset of the soure video\n", + "# url = move_video_to_tempdir(\"videos\", \"removed_part.mp4\") # Set is_file to TRUE if using a file instead of a url\n", "\n", - "# Target\n", - "target_indices = [index_hashes_for_video(x) for x in [target][0]]\n" + "x = compare_videos(url, target[0], MIN_DISTANCE=3)" ] }, { - "cell_type": "code", - "execution_count": 171, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Skipping downloading from https://www.dropbox.com/s/rzmicviu1fe740t/Bram%20van%20Ojik%20krijgt%20reprimande.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a already exists.\n", - "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a.index\n", - "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a.index has in total 182 frames\n", - "INFO:root:Skipping downloading from https://www.dropbox.com/s/4ognq8lshcujk43/Plenaire_zaal_20200923132426_Omtzigt.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114 already exists.\n", - "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index\n", - "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index has in total 7471 frames\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "x = compare_videos(url, target[0], MIN_DISTANCE=10)" + "# From here on out code experimentation" ] }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Skipping downloading from https://www.dropbox.com/s/rzmicviu1fe740t/Bram%20van%20Ojik%20krijgt%20reprimande.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a already exists.\n", - "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a.index\n", - "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/e295c0e13c21aa3e971921627e8c8b1a.index has in total 182 frames\n", - "INFO:root:Skipping downloading from https://www.dropbox.com/s/4ognq8lshcujk43/Plenaire_zaal_20200923132426_Omtzigt.mp4?dl=1 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114 already exists.\n", - "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index\n", - "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/95fc56d68e602bc591942581d1c98114.index has in total 7471 frames\n" + "INFO:root:Skipping copying from videos/Ploumen_CO_5.0s_to_10.0_at_15.0.mp4 because /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/Ploumen_CO_5.0s_to_10.0_at_15.0.mp4 already exists.\n", + "INFO:root:Loading indexed hashes from /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/Ploumen_CO_5.0s_to_10.0_at_15.0.mp4.index\n", + "INFO:root:Index /var/folders/hy/qkxzx5jj0hvcj_l_lpvn81sc0000gp/T/Ploumen_CO_5.0s_to_10.0_at_15.0.mp4.index has in total 751 frames\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'encode'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/ijanssen/videomatch/Matching Exploration.ipynb Cell 5\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m hash_vectors \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([video_index\u001b[39m.\u001b[39mreconstruct(i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(video_index\u001b[39m.\u001b[39mntotal)]) \u001b[39m# Retrieve original indices\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[39m# Target video (long video)\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m target_indices \u001b[39m=\u001b[39m [index_hashes_for_video(x) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [target][\u001b[39m0\u001b[39m]]\n\u001b[1;32m 19\u001b[0m \u001b[39m# The results are returned as a triplet of 1D arrays \u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[39m# lims, D, I, where result for query i is in I[lims[i]:lims[i+1]] \u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[39m# (indices of neighbors), D[lims[i]:lims[i+1]] (distances).\u001b[39;00m\n\u001b[1;32m 22\u001b[0m lims, D, I \u001b[39m=\u001b[39m target_indices[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mrange_search(hash_vectors, MIN_DISTANCE)\n", + "\u001b[1;32m/Users/ijanssen/videomatch/Matching Exploration.ipynb Cell 5\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 14\u001b[0m hash_vectors \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([video_index\u001b[39m.\u001b[39mreconstruct(i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(video_index\u001b[39m.\u001b[39mntotal)]) \u001b[39m# Retrieve original indices\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[39m# Target video (long video)\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m target_indices \u001b[39m=\u001b[39m [index_hashes_for_video(x) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [target][\u001b[39m0\u001b[39m]]\n\u001b[1;32m 19\u001b[0m \u001b[39m# The results are returned as a triplet of 1D arrays \u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[39m# lims, D, I, where result for query i is in I[lims[i]:lims[i+1]] \u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[39m# (indices of neighbors), D[lims[i]:lims[i+1]] (distances).\u001b[39;00m\n\u001b[1;32m 22\u001b[0m lims, D, I \u001b[39m=\u001b[39m target_indices[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mrange_search(hash_vectors, MIN_DISTANCE)\n", + "File \u001b[0;32m~/videomatch/app.py:81\u001b[0m, in \u001b[0;36mindex_hashes_for_video\u001b[0;34m(url, is_file)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mindex_hashes_for_video\u001b[39m(url, is_file \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m 80\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m is_file:\n\u001b[0;32m---> 81\u001b[0m filename \u001b[39m=\u001b[39m download_video_from_url(url)\n\u001b[1;32m 82\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 83\u001b[0m filename \u001b[39m=\u001b[39m url\n", + "File \u001b[0;32m~/videomatch/app.py:41\u001b[0m, in \u001b[0;36mdownload_video_from_url\u001b[0;34m(url)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdownload_video_from_url\u001b[39m(url):\n\u001b[1;32m 40\u001b[0m \u001b[39m\"\"\"Download video from url or return md5 hash as video name\"\"\"\u001b[39;00m\n\u001b[0;32m---> 41\u001b[0m filename \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(video_directory, hashlib\u001b[39m.\u001b[39mmd5(url\u001b[39m.\u001b[39;49mencode())\u001b[39m.\u001b[39mhexdigest())\n\u001b[1;32m 42\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexists(filename):\n\u001b[1;32m 43\u001b[0m \u001b[39mwith\u001b[39;00m (urllib\u001b[39m.\u001b[39mrequest\u001b[39m.\u001b[39murlopen(url)) \u001b[39mas\u001b[39;00m f, \u001b[39mopen\u001b[39m(filename, \u001b[39m'\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m fileout:\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'encode'" ] } ], "source": [ - "MIN_DISTANCE = 10\n", + "MIN_DISTANCE = 4 # Distance always increases by 2: 0, 2, 4 .. \n", "\n", "# Url (short video) \n", - "video_index = index_hashes_for_video(url)\n", + "url = move_video_to_tempdir(\"videos\", \"Ploumen_CO_5.0s_to_10.0_at_15.0.mp4\")\n", + "# url = video_urls[1] # (0: Ploumen) (1: Bram) (2: Baudet) Short video which is a (maybe mixed up) subset of the soure video\n", + "\n", + "if url.endswith('dl=1'):\n", + " IS_FILE = False\n", + "elif url.endswith('.mp4'):\n", + " IS_FILE = True\n", + "\n", + "video_index = index_hashes_for_video(url, is_file = IS_FILE)\n", "video_index.make_direct_map() # Make sure the index is indexable\n", "hash_vectors = np.array([video_index.reconstruct(i) for i in range(video_index.ntotal)]) # Retrieve original indices\n", "\n", @@ -152,14 +1390,23 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 66, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:py.warnings:/usr/local/lib/python3.9/site-packages/seaborn/relational.py:654: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " kws[\"alpha\"] = 1 if self.alpha == \"auto\" else self.alpha\n", + "\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -203,7 +1450,47 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_distances(D, MIN_DISTANCE = 3):\n", + " sns.set_theme()\n", + "\n", + " # Create figure and dataframe to plot with sns\n", + " fig = plt.figure()\n", + " df = pd.DataFrame(D, columns = ['D'])\n", + " df['D'] = df['D'].astype(int)\n", + " \n", + " # Countplot\n", + " g = sns.countplot(data = df, x = 'D')\n", + " # g = sns.barplot(x = np.arange(0, MIN_DISTANCE, step = 2), y = df['D'].value_counts())\n", + " # g = sns.histplot(data=df, x='D', binwidth=1)\n", + " \n", + " # Proportion\n", + " # g = sns.ecdfplot(data=df, x='D', complementary=False)\n", + " # plt.xlim(0, MIN_DISTANCE)\n", + " \n", + " plt.subplots_adjust(bottom=0.25, left=0.20)\n", + " return fig \n", + "\n", + "_ = plot_distances(D, MIN_DISTANCE = MIN_DISTANCE)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -227,119 +1514,159 @@ " \n", " \n", " \n", - " X\n", - " Y\n", - " X_WEIGHT\n", + " TARGET_S\n", + " SOURCE_S\n", " \n", " \n", " \n", " \n", " 0\n", " 0.0\n", - " 614.8\n", - " 0.2\n", + " 487.800000\n", " \n", " \n", " 1\n", - " 0.0\n", - " 630.4\n", - " 0.6\n", + " 0.2\n", + " 488.000000\n", " \n", " \n", " 2\n", - " 0.0\n", - " 631.0\n", - " 0.4\n", + " 1.8\n", + " 489.800000\n", " \n", " \n", " 3\n", - " 0.0\n", - " 631.8\n", - " 0.2\n", + " 3.6\n", + " 491.400000\n", " \n", " \n", " 4\n", - " 0.0\n", - " 632.0\n", - " 0.2\n", + " 4.0\n", + " 492.000000\n", " \n", " \n", " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 1806\n", - " 36.2\n", - " 1256.8\n", - " 0.4\n", + " 424\n", + " 149.2\n", + " 637.200000\n", " \n", " \n", - " 1807\n", - " 36.2\n", - " 1257.0\n", - " 0.4\n", + " 425\n", + " 149.4\n", + " 637.266667\n", " \n", " \n", - " 1808\n", - " 36.2\n", - " 1257.2\n", - " 0.4\n", + " 426\n", + " 149.6\n", + " 637.300000\n", " \n", " \n", - " 1809\n", - " 36.2\n", - " 1257.4\n", - " 0.6\n", + " 427\n", + " 149.8\n", + " 633.500000\n", " \n", " \n", - " 1810\n", - " 36.2\n", - " 1257.6\n", - " 0.8\n", + " 428\n", + " 150.0\n", + " 633.500000\n", " \n", " \n", "\n", - "

1811 rows × 3 columns

\n", + "

429 rows × 2 columns

\n", "" ], "text/plain": [ - " X Y X_WEIGHT\n", - "0 0.0 614.8 0.2\n", - "1 0.0 630.4 0.6\n", - "2 0.0 631.0 0.4\n", - "3 0.0 631.8 0.2\n", - "4 0.0 632.0 0.2\n", - "... ... ... ...\n", - "1806 36.2 1256.8 0.4\n", - "1807 36.2 1257.0 0.4\n", - "1808 36.2 1257.2 0.4\n", - "1809 36.2 1257.4 0.6\n", - "1810 36.2 1257.6 0.8\n", + " TARGET_S SOURCE_S\n", + "0 0.0 487.800000\n", + "1 0.2 488.000000\n", + "2 1.8 489.800000\n", + "3 3.6 491.400000\n", + "4 4.0 492.000000\n", + ".. ... ...\n", + "424 149.2 637.200000\n", + "425 149.4 637.266667\n", + "426 149.6 637.300000\n", + "427 149.8 633.500000\n", + "428 150.0 633.500000\n", "\n", - "[1811 rows x 3 columns]" + "[429 rows x 2 columns]" ] }, - "execution_count": 174, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = [(lims[i+1]-lims[i]) * [i] for i in range(hash_vectors.shape[0])]\n", - "x = [i/FPS for j in x for i in j]\n", - "y = [i/FPS for i in I]\n", "\n", + "target = [(lims[i+1]-lims[i]) * [i] for i in range(hash_vectors.shape[0])]\n", + "target_s = [i/FPS for j in target for i in j]\n", + "source_s = [i/FPS for i in I]\n", + "\n", + "# Make df\n", + "df = pd.DataFrame(zip(target_s, source_s), columns = ['TARGET_S', 'SOURCE_S'])\n", + "df['TARGET_WEIGHT'] = 1 - D/MIN_DISTANCE # Higher value means a better match\n", + "\n", + "# Multiply the weight (which indicates a better match) with the value for Y\n", + "# and aggregate to get a less noisy estimate of Y\n", + "df['SOURCE_WEIGHTED_VALUE'] = df['SOURCE_S'] * df['TARGET_WEIGHT'] \n", + "\n", + "# Group by X so for every second/x there will be 1 value of Y in the end\n", + "grouped_X = df.groupby('TARGET_S').agg({'SOURCE_WEIGHTED_VALUE' : 'sum', 'TARGET_WEIGHT' : 'sum'})\n", + "grouped_X['FINAL_SOURCE_VALUE'] = grouped_X['SOURCE_WEIGHTED_VALUE'] / grouped_X['TARGET_WEIGHT'] \n", "\n", - "df = pd.DataFrame(zip(x, y), columns = ['X', 'Y'])\n", - "df['X_WEIGHT'] = 1 - D/MIN_DISTANCE # Higher value means a better match\n", + "# Remake the dataframe\n", + "df_cleaned = grouped_X.reset_index()\n", + "df_cleaned = df_cleaned.drop(columns=['SOURCE_WEIGHTED_VALUE', 'TARGET_WEIGHT'])\n", + "df_cleaned = df_cleaned.rename({'FINAL_SOURCE_VALUE' : 'SOURCE_S'}, axis='columns')\n", + "df = df_cleaned\n", "df" ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x=df['TARGET_S'], y = df['SOURCE_S'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Try CUSUMDetector from KATS 101 on the 'averaged' data" + ] + }, + { + "cell_type": "code", + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -363,35 +1690,35 @@ " \n", " \n", " \n", - " X\n", - " Y\n", + " TARGET_S\n", + " SOURCE_S\n", " \n", " \n", " \n", " \n", " 0\n", " 0.0\n", - " 632.864711\n", + " 487.800000\n", " \n", " \n", " 1\n", " 0.2\n", - " 628.870286\n", + " 488.000000\n", " \n", " \n", " 2\n", - " 0.4\n", - " 630.206667\n", + " 1.8\n", + " 489.800000\n", " \n", " \n", " 3\n", - " 0.6\n", - " 629.586347\n", + " 3.6\n", + " 491.400000\n", " \n", " \n", " 4\n", - " 0.8\n", - " 632.092322\n", + " 4.0\n", + " 492.000000\n", " \n", " \n", " ...\n", @@ -399,124 +1726,459 @@ " ...\n", " \n", " \n", - " 146\n", - " 35.4\n", - " 1256.155524\n", + " 424\n", + " 149.2\n", + " 637.200000\n", " \n", " \n", - " 147\n", - " 35.6\n", - " 1256.695256\n", + " 425\n", + " 149.4\n", + " 637.266667\n", " \n", " \n", - " 148\n", - " 35.8\n", - " 1256.545523\n", + " 426\n", + " 149.6\n", + " 637.300000\n", " \n", " \n", - " 149\n", - " 36.0\n", - " 1256.582592\n", + " 427\n", + " 149.8\n", + " 633.500000\n", " \n", " \n", - " 150\n", - " 36.2\n", - " 1256.582592\n", + " 428\n", + " 150.0\n", + " 633.500000\n", " \n", " \n", "\n", - "

151 rows × 2 columns

\n", + "

429 rows × 2 columns

\n", "" ], "text/plain": [ - " X Y\n", - "0 0.0 632.864711\n", - "1 0.2 628.870286\n", - "2 0.4 630.206667\n", - "3 0.6 629.586347\n", - "4 0.8 632.092322\n", - ".. ... ...\n", - "146 35.4 1256.155524\n", - "147 35.6 1256.695256\n", - "148 35.8 1256.545523\n", - "149 36.0 1256.582592\n", - "150 36.2 1256.582592\n", + " TARGET_S SOURCE_S\n", + "0 0.0 487.800000\n", + "1 0.2 488.000000\n", + "2 1.8 489.800000\n", + "3 3.6 491.400000\n", + "4 4.0 492.000000\n", + ".. ... ...\n", + "424 149.2 637.200000\n", + "425 149.4 637.266667\n", + "426 149.6 637.300000\n", + "427 149.8 633.500000\n", + "428 150.0 633.500000\n", "\n", - "[151 rows x 2 columns]" + "[429 rows x 2 columns]" ] }, - "execution_count": 175, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Multiply the weight (which indicates a better match) with the value for Y\n", - "# and aggregate to get a less noisy estimate of Y\n", - "df['Y_WEIGHTED_VALUE'] = df['Y'] * df['X_WEIGHT'] \n", - "\n", - "# Group by X so for every second/x there will be 1 value of Y in the end\n", - "grouped_X = df.groupby('X').agg({'Y_WEIGHTED_VALUE' : 'sum', 'X_WEIGHT' : 'sum'})\n", - "grouped_X['FINAL_Y_VALUE'] = grouped_X['Y_WEIGHTED_VALUE'] / grouped_X['X_WEIGHT'] \n", + "# import packages\n", + "from kats.detectors.cusum_detection import CUSUMDetector\n", + "from kats.consts import TimeSeriesData\n", "\n", - "# Remake the dataframe\n", - "df_cleaned = grouped_X.reset_index()\n", - "df_cleaned = df_cleaned.drop(columns=['Y_WEIGHTED_VALUE', 'X_WEIGHT'])\n", - "df_cleaned = df_cleaned.rename({'FINAL_Y_VALUE' : 'Y'}, axis='columns')\n", - "df = df_cleaned\n", "df" ] }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 71, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:root:Detecting increase changepoint.\n", + "DEBUG:root:Detecting decrease changepoint.\n", + "INFO:root:Max iteration reached and no stable changepoint found.\n", + "WARNING:root:No change points detected!\n" + ] + }, { "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['time'] = pd.to_datetime(df[\"TARGET_S\"], unit='s') # Needs a datetime as input\n", + "\n", + "tsd = TimeSeriesData(df.loc[:,['time','SOURCE_S']])\n", + "detector = CUSUMDetector(tsd)\n", + "change_points = detector.detector() # Both directions are allowed\n", + "\n", + "detector.plot(change_points)\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CUSUMChangePoint(start_time: 1970-01-01 00:01:27.200000, end_time: 1970-01-01 00:01:27.200000, confidence: 1.0, direction: increase, index: 242, delta: 74.2245742406136, regression_detected: True, stable_changepoint: True, mu0: 536.2953569156037, mu1: 610.5199311562174, llr: 551.1893035744112, llr_int: inf, p_value: 0.0, p_value_int: nan)]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "change_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Try KATS outlier detection :D" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "" + " time SOURCE_S\n", + "0 1970-01-01 00:00:00.000 487.800000\n", + "1 1970-01-01 00:00:00.200 488.000000\n", + "2 1970-01-01 00:00:01.800 489.800000\n", + "3 1970-01-01 00:00:03.600 491.400000\n", + "4 1970-01-01 00:00:04.000 492.000000\n", + ".. ... ...\n", + "424 1970-01-01 00:02:29.200 637.200000\n", + "425 1970-01-01 00:02:29.400 637.266667\n", + "426 1970-01-01 00:02:29.600 637.300000\n", + "427 1970-01-01 00:02:29.800 633.500000\n", + "428 1970-01-01 00:02:30.000 633.500000\n", + "\n", + "[429 rows x 2 columns]" ] }, - "execution_count": 176, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" + } + ], + "source": [ + "tsd" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Setting frequency to Daily since it cannot be inferred\n", + "ERROR:root:!! Traceback (most recent call last):\n", + "!! File \"/usr/local/lib/python3.9/site-packages/kats/detectors/outlier.py\", line 134, in detector\n", + " outliers_index, output_scores, time_index = self.__clean_ts__(ts)\n", + "!! File \"/usr/local/lib/python3.9/site-packages/kats/detectors/outlier.py\", line 95, in __clean_ts__\n", + " result = seasonal_decompose(\n", + "!! File \"/usr/local/lib/python3.9/site-packages/pandas/util/_decorators.py\", line 207, in wrapper\n", + " return func(*args, **kwargs)\n", + "!! File \"/usr/local/lib/python3.9/site-packages/statsmodels/tsa/seasonal.py\", line 143, in seasonal_decompose\n", + " raise ValueError(\"You must specify a period or x must be a \"\n", + "!! ValueError: You must specify a period or x must be a pandas object with a DatetimeIndex with a freq not set to None\n", + "\n", + "ERROR:root:Outlier Detection Failed\n" + ] + } + ], + "source": [ + "from kats.detectors.outlier import OutlierDetector\n", + "\n", + "outlier_ts = OutlierDetector(tsd, 'additive') # call OutlierDetector\n", + "outlier_ts.detector() # apply OutlierDetector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Missing Values (and how to deal with them)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20000000000000284 0.20000000000004547\n" + ] }, { "data": { - "image/png": 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okSNHeu3LycnRmDFj5HT+LSNlZGTo5MmTOn/+vPLy8lRZWanMzEzP/piYGA0dOlT79u1r0zUAAAAaa3fPTFZWlrKysprdV1hYqOTkZK9t8fHxkqRz586psLBQktSvX78mxzTsa+0aX/nKV9rbZEmS0+n7uc7BwQ6v3+2Cuu1Vt2Tf2qmbuu3C9NrbHWZaUl1drdDQUK9tYWFhkqSamhpVVVVJUrPHXLx4sU3X6AiHI0ixsVEdOrctYmIi/Hbt7oy67ceutVO3vdi1bsnc2n0aZsLDwz0TeRs0BJDIyEiFh4dLkmpraz1/bjgmIiKiTdfoCLfbUnn55Q6d25LgYIdiYiJUXl4ll8vt8+t3V9Rtr7ol+9ZO3dRtF92x9piYiDb3FPk0zCQmJqq4uNhrW8PXCQkJqq+v92y7/vrrvY4ZPHhwm67RUfX1/ntzXC63X6/fXVG3/di1duq2F7vWLZlbu08Hx9LT07V//365XC7Ptj179igpKUl9+vTRkCFD1KtXL2VnZ3v2l5eXKzc3V+np6W26BgAAQGM+DTOTJ09WRUWFnn/+eRUUFGjr1q1av369Zs2aJenKXJmpU6dq+fLl+uyzz5SXl6d58+YpMTFRd955Z5uuAQAA0JhPh5n69OmjtWvXaunSpZo0aZL69u2rBQsWaNKkSZ5j5s6dq/r6ei1atEjV1dVKT0/XunXrFBIS0uZrAAAANAiyLMsKdCP8zeVyq6Sk0ufXdTodio2NUmlppZFjjB1F3faqW7Jv7dRN3XbRHWuPi4tq8wRgM28oBwAA+CvCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADCaz8NMRUWFXnjhBY0dO1ZjxozRM888owsXLnj27969W/fdd59GjhypiRMnaseOHV7n19TUaPHixcrMzFRqaqqefvpplZSU+LqZAACgh/B5mPn+97+v3/72t1q6dKk2btyoqqoqfec731Ftba2OHTumWbNmady4cdq6davuv/9+LViwQLt37/ac/+KLL2rXrl1atWqVNmzYoOPHj2vu3Lm+biYAAOghnL682OHDh7Vr1y7967/+q8aPHy9JeuWVV3Tbbbdpx44d+vzzzzV48GDNmzdPkjRw4EDl5uZq7dq1yszMVFFRkbZv367Vq1crLS1NkrRixQpNnDhRn3/+uVJTU33ZXAAA0AP4NMycPHlSkjxBRJKioqJ0ww03aO/evTpw4IAmTJjgdU5GRoaWLl0qy7K0f/9+z7YGSUlJSkhI0L59+zoVZpxO308PCg52eP1uF9Rtr7ol+9ZO3dRtF6bX7tMwEx8fL0k6d+6cBg4cKElyuVwqLCxUnz59VFhYqMTExCbnVFVVqbS0VEVFRYqNjVVYWFiTYwoLCzvcLocjSLGxUR0+vzUxMRF+u3Z3Rt32Y9faqdte7Fq3ZG7tPg0zw4cP14033qgXXnhBr776qnr37q3XXntNpaWlqqurU3V1tUJDQ73Oafi6trZWVVVVTfZLUlhYmGpqajrcLrfbUnn55Q6ffy3BwQ7FxESovLxKLpfb59fvrqjbXnVL9q2duqnbLrpj7TExEW3uKfJpmAkNDdXPfvYzLViwQOPHj1dISIjuuece3X777XI4HAoLC1Ntba3XOQ1fR0REKDw8vMl+6codThERnUuL9fX+e3NcLrdfr99dUbf92LV26rYXu9YtmVu7T8OMdGVS75YtW1RWVian06levXppypQpysjIUL9+/VRcXOx1fHFxsSIjIxUdHa3ExESVlZWptrbWq4emuLhYCQkJvm4qAADoAXw606eiokJTp05VXl6errvuOvXq1UtnzpxRbm6uvva1ryktLU179+71OmfPnj0aNWqUHA6HRo8eLbfb7ZkILEknTpxQUVGR0tPTfdlUAADQQ/g0zPTq1UuWZWnp0qU6evSovvjiC82ePVsZGRnKzMzUtGnTdPDgQS1fvlzHjh3T22+/rY8//lgzZ86UJCUkJOiuu+7SokWLlJ2drYMHD2r+/PkaM2aMUlJSfNlUAADQQ/j8HqwVK1aod+/eeuCBBzRr1iyNHj1aq1atkiQNGjRIb7zxhn7729/q3nvv1ebNm7Vs2TJlZmZ6zl+yZIkyMzP15JNP6pFHHtGNN96o1157zdfNBAAAPUSQZVlWoBvhby6XWyUllT6/rtPpUGxslEpLK42cMNVR1G2vuiX71k7d1G0X3bH2uLioNt/NZObqOAAAAH9FmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACM5vMwU19fr5/+9Ke6/fbblZqaqoceekh//OMfPfsPHz6sqVOnKiUlRVlZWXr33Xe9zne73Xrttdc0btw4paSk6NFHH9Xp06d93UwAANBD+DzMvPnmm9q8ebOWLFmi7du3KykpSTNnzlRxcbFKS0s1ffp0XX/99dqyZYueeOIJLV++XFu2bPGc/8Ybb2jTpk1asmSJ3n//fbndbs2cOVO1tbW+bioAAOgBfB5mdu7cqbvvvltjx47VDTfcoGeffVaXLl3SH//4R33wwQcKCQnRSy+9pIEDB2ry5Mn67ne/qzVr1kiSamtr9fbbb2vu3Lm67bbbNGTIEP3kJz9RYWGhPv30U183FQAA9AA+DzN9+vTRr3/9a505c0Yul0v/9m//ptDQUA0ZMkQ5OTkaM2aMnE6n5/iMjAydPHlS58+fV15eniorK5WZmenZHxMTo6FDh2rfvn2+bioAAOgBnK0f0j7PP/+8vv/97+uOO+5QcHCwHA6HVq1apeuvv16FhYVKTk72Oj4+Pl6SdO7cORUWFkqS+vXr1+SYhn0d5XT6fq5zcLDD63e7oG571S3Zt3bqpm67ML12n4eZgoICRUdH6/XXX1dCQoI2b96sZ555Ru+9956qq6sVGhrqdXxYWJgkqaamRlVVVZLU7DEXL17scJscjiDFxkZ1+PzWxMRE+O3a3Rl1249da6due7Fr3ZK5tfs0zJw7d05PP/201q9fr7S0NEnS8OHDVVBQoFWrVik8PLzJRN6amhpJUmRkpMLDwyVdmTvT8OeGYyIiOv4X7HZbKi+/3OHzryU42KGYmAiVl1fJ5XL7/PrdFXXbq27JvrVTN3XbRXesPSYmos09RT4NMwcOHFBdXZ2GDx/utX3kyJH63e9+p7/7u79TcXGx176GrxMSElRfX+/Zdv3113sdM3jw4E61rb7ef2+Oy+X26/W7K+q2H7vWTt32Yte6JXNr9+ngWGJioiTpyJEjXtvz8/M1YMAApaena//+/XK5XJ59e/bsUVJSkvr06aMhQ4aoV69eys7O9uwvLy9Xbm6u0tPTfdlUAADQQ/g0zIwYMUKjR4/WwoULtWfPHp08eVIrV67U7t279dhjj2ny5MmqqKjQ888/r4KCAm3dulXr16/XrFmzJF2ZKzN16lQtX75cn332mfLy8jRv3jwlJibqzjvv9GVTAQBAD+HTYSaHw6E333xTK1eu1HPPPaeLFy8qOTlZ69ev18iRIyVJa9eu1dKlSzVp0iT17dtXCxYs0KRJkzzXmDt3rurr67Vo0SJVV1crPT1d69atU0hIiC+bCgAAeoggy7KsQDfC31wut0pKKn1+XafTodjYKJWWVho5xthR1G2vuiX71k7d1G0X3bH2uLioNk8ANvOGcgAAgL8izAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABG82mYyc7O1uDBg5v9dccdd0iSzpw5o1mzZmnUqFEaO3asVq5cKZfL5XWdjRs36o477tCIESP04IMPKjc315fNBAAAPYjTlxdLTU3Vrl27vLb98Y9/1Jw5c/T444+rrq5OjzzyiAYMGKD3339fX375pZ5//nk5HA7NnTtXkrRt2za98sorWrJkiYYOHao1a9Zo+vTp+uijjxQXF+fL5gIAgB7Apz0zoaGh6tu3r+dXVFSUfvzjH2vSpEmaPHmyPvnkE/35z3/WK6+8ouTkZE2YMEHz58/Xhg0bVFtbK0lavXq1pk6dqm9+85u66aab9PLLLysiIkKbN2/2ZVMBAEAP4dc5M6tXr1ZVVZUWLlwoScrJydEtt9yi3r17e47JyMhQRUWFDh8+rAsXLujkyZPKzMz07Hc6nUpLS9O+ffv82VQAAGAonw4zNVZSUqL169fr6aef1nXXXSdJKiwsVGJiotdx8fHxkqRz587J6bzSnH79+jU5Ji8vr1PtcTp9n9uCgx1ev9sFddurbsm+tVM3dduF6bX7Lcxs2rRJ0dHR+va3v+3ZVl1drZiYGK/jwsLCJEk1NTWqqqqSdGW46upjampqOtwWhyNIsbFRHT6/NTExEX67dndG3fZj19qp217sWrdkbu1+CzPbt2/Xvffeq/DwcM+28PBwz9yYBg0hJTIy0nNsc8dERHT8L9jttlRefrnD519LcLBDMTERKi+vksvl9vn1uyvqtlfdkn1rp27qtovuWHtMTESbe4r8Emby8vJ0+vRp3XPPPV7bExMTlZ+f77WtuLhYkpSQkOAZXiouLtbAgQO9jklISOhUm+rr/ffmuFxuv16/u6Ju+7Fr7dRtL3atWzK3dr8MjuXk5KhPnz4aMmSI1/b09HTl5uaqoqLCs23Pnj2KiorSkCFD1KdPHyUlJSk7O9uzv76+Xjk5OUpPT/dHUwEAgOH8EmZyc3M1ePDgJtsnTJigvn376qmnnlJeXp527typFStWaMaMGZ55MjNmzNA777yjbdu2qaCgQD/4wQ9UXV2tKVOm+KOpQLfhdlvKO1WqPbmFyjtVKrfbCnSTAMAIfhlm+stf/uK5g6mxsLAwrV27VosXL9a3vvUt9e7dWw8++KAef/xxzzHf+ta3dOnSJa1cuVJlZWUaNmyY3nnnHRbMQ4+2/0ixNu08qtJLf5voHhsdpgcnDNLowfEBbBkAdH9BlmX1+P/+uVxulZRU+vy6TqdDsbFRKi2tNHKMsaOo27d17z9SrNe3/ema+5+YNCzggYb3nLrtwK51S92z9ri4qDZPADbzhnKgh3C7LW3aebTFY36x8yhDTgDQAsIMEED5p8u8hpaaU3KpRvmny7qmQQBgIMIMEEBllW1bDLKtxwGAHRFmgAC6LirMp8cBgB0RZoAASv7qdYqNbjmoxEWHKfmr13VNgwDAQIQZIIAcjiA9OGFQi8c8MGGQHI6gLmoRAJiHMAME2OjB8Xpi0rAmPTRx0WHd4rZsAOju/PagSQBtN3pwvFIH9VX+6TKVVdbouqgrQ0v0yABA6wgzQDfhcARpyA2xgW4GABiHMAMA6Fbcbkv5p8tUUlGtiso6RUeGqndUqBR0ZZmCiso6RUWEqLLqyr7YaHoy7Y4wAwAImIbg0jC8eqmqTu9/drTVxSSvxrPM7I0wAwA9RFt6NNqy7XJVvRLjoxUiSy631a5zG7a1pbekuQesdlTppRq9vu1PTJq3KcIMAATI1b0SnRkq8WUw8JWWektae8BqR/1i51GlDurLkJPNEGYA2FJbg4TLbenwyRJdKK/2HCfJc25MRMd6KpoLHx0dKvFXMOisa/WWtOUBqx3V8CwzJtPbC2EGgO00FyR6RYQoY2i8vtI7whNICv58UTv3n1HF5TrPceGhwbIsqabO1e7XbQgrkpoNHx0ZKvFnMPCVq3tL2vKA1c7gWWb2Q5gB4FONezza0mvR+K6UjszvaO+8jWv1YlRU1Wnn/rOt1ldd2/4Q06AhrESFt/yttz1DJf4OBr5wdW+Jv8MGzzKzH8IMYHNtCR/BTofqjpWo8C+XFBUe0mxIcLst/ecfTuq/ck6rsro+gBV5azx00116MVr7+2nPUIkpvRCN2+nPsMGzzOyJMAMYprnwUX65tsV5H9eaH9KZSaONQ8L+I8Va/1FetwoxDRoP3USFh3T7XowGbQ0ppvRCNG5nwwNW/fFe8CwzeyLMAD7U3iGWxtsaBxJJHQofUeFOfT2tv+7+h6RWJ5reenO8Pt57usO1NoSEiWO+2qnrdJVf7Dyqyf84MNDNaLO2hhR/BgNfubq3pOEBq76ctBwXHaYHWGfGtggz6LTm1rYwZUXO1sLHtYZT8k6VegUNST4bYmmYT9H4Om0NH5XV9dq+66T+K+eMvvt/h0i69kRTXwWQT/Z1/yAjXRm6uXS5NtDNaJP2DJX4Ixj4WnO9JQ0PWL06aMdFh+nbd9yk6IhQVgBGmxFm0Ckt9RR0dkXOtgYNqflejKuvc/U3xvwzZfps/5lWw0eviBBl3pKgtCHxcn9Zpn/d9oVKGtUbFe5Uvcutmjp3h+q8WnPtaW/4qKyub9NEU1+wLL+/hM/0igrpFr0YvcKdqmjh3117h0quFQwCrbXeEh6wCl8JsiyTvhV1jMvlVklJpc+v63Q6FBsbpdLSStXX++YHmQka6v509wmt+uXBVo9vy22mVweXtgaNa/ViNJ7L0d2+wSNwFjyQqsrquoD3YjwxaZgkNdsr0ZmhEtNWAO5u7Po9XeqetcfFRSk42NGmY+mZQYe43JY2fnKkTce2dptpZwLHtXoxTJrLga4R1+iHa6B6Ma4OK77ulfDVk9e74w82oCWEGcO09D+v1u5oufoa7Rm+aXzc5ap61VnyGmppydW3mTZ+/eKSKm3fdaLTfy/NMWUuh+mCgswYamo8dNN4eOPzo3/RnkNFulRV1+L5YSEOBQUFea0zExXu1ITRf69BX43tUE+Fr8IHYHeEmQ5y/3WJ87oTpQoJsjTw73q3eEtsRybHXn3u+YvVys5t/Ztuc3e0NGhrL0hzwzedUVZZ0+XrkJjwA7Yn+D/p3bsH7FpDNw1BYsgNsfp21qBm/5MQ7HTIpSDPZ1xqeX4WgMBgzkwHXOtW1/931Qz8lsJHc0unN+5duVRVp/c/61w3eHiIQ2NH9PO8RlHpZX34+5Mdvl5n3Ds2qdstpmYHrU00nTjmq8o+XNyhf2eNQ0Jzn4m29lp09QrA7WHX4RbqtlfdUvesvT1zZggz7dRdH+jWnYU6HartJh8O07U3fLRlommbVwB2q9UVgHtar0V3/AbfFajbXnVL3bN2JgD7SXdZCt00gQoypszluFqvcKcseQ/xNQ4fU267qdU7v9oz0bQt8zba8o2O+R8AAoUw0w4mPNANfxPIuRztmRjanhWApaahYWhSnL75taQWe0UIGgB6MsJMO5jyQDe7CwqSvvf/DVP6kHgN/PvebZ7wfHX4aG7OU5/e4aqudamyhUnY0REhevWJr8npbFv36LW0J3wQVgDYGWGmHUx5oFtP0zho5J4q0a92f9ni8ZZ1JVBI3rfgduR29v93xyBPj0efmHBF9grXD1f/ocXXv1RVp4KzFwkXANBFCDPtYMID3XqKXuFOPTAhuclE0/I2PluncS9aZ3otGp/rdDp08ERpu18fAOBfnesHt5mGB7rB/x7+v0OUOSxRQ26I9eoxaWvvmL960eJiwgP6+gCApuiZaafRg+M1+95btPrfDwXsTpnoiBDdetUaNW19llFzvvm1AYqPi9DhE6X6/Z8KWz3uyKky/c/Bc50pQVLzK6q29myatvSOteeJw+019MY+iosOa3H1Y3++PgCgKcJMB6QPSZAUpDe3t229mcbho62r+F4tIjRYD309WXEx4c3O72h8R0tbl2e/Ojj8wy39lDLoK60+/G78yL/X2NT+emvrQa8f6nHRYRpzc3yb7iC6d+wA3f0PSZLat6JqQ+9YS2v9tPeJw+0R7AjSQ/9ncIsP2PTn6wMAmmLRvE74vOC8Nv1Xvi5crPZsu7rXpKXFxRpPSG1tdd62PHm6sc48w6m1xc8a1hy5cKFCuSdKmhzX0iMTOvtU4AbNvYavrn0tjddayT5U2OWvH0jdcUGtrkDd1G0X3bF2VgC+ir/CjNPpUEzvSGUfOKML5dWdXvU0ED+gO6It/+g780yqturqFWevrrsnrnh7Ld3xG11XoG7qtovuWDsrAHehYEeQbh4Q55M3v/FtxKb/gOyKdU8CvbZKoF8fAHAFYaab4QckAADtw63ZAADAaIQZAABgNMIMAAAwml/CzPbt2/WNb3xDw4cP11133aWPPvrIs+/MmTOaNWuWRo0apbFjx2rlypVyuVxe52/cuFF33HGHRowYoQcffFC5ubn+aCYAAOgBfB5m/v3f/13PP/+8HnroIe3YsUN333235s+fr88//1x1dXV65JFHJEnvv/++XnzxRf3iF7/Q66+/7jl/27ZteuWVV/T9739fW7duVf/+/TV9+nSVlJT4uqkAAKAH8OndTJZl6ac//am+853v6KGHHpIkzZ49Wzk5Odq7d6/Onj2rP//5z/rggw/Uu3dvJScn68KFC3rllVf0ve99T6GhoVq9erWmTp2qb37zm5Kkl19+WRMmTNDmzZs1a9YsXzYXAAD0AD4NMydOnNDZs2d1zz33eG1ft26dJOnFF1/ULbfcot69e3v2ZWRkqKKiQocPH1b//v118uRJZWZm/q2BTqfS0tK0b9++ToUZp9P3I2oNi/m0dVGfnoK67VW3ZN/aqZu67cL02n0eZiTp8uXLeuSRR5Sbm6v+/ftr9uzZysrKUmFhoRITE73OiY+/srLtuXPn5HReaU6/fv2aHJOXl9fhdjkcQYqNjerw+a2JiYnw27W7M+q2H7vWTt32Yte6JXNr92mYqaiokCQtXLhQTz75pJ555hl98sknevzxx/XOO++ourpaMTExXueEhYVJkmpqalRVVSVJCg0NbXJMTc21n1LcGrfbUnn55Q6ffy3BwQ7FxESovLxKLlf3WP65K1C3veqW7Fs7dVO3XXTH2mNiIgLzOIOQkBBJ0iOPPKJJkyZJkm6++Wbl5ubqnXfeUXh4uGpra73OaQgpkZGRCg8Pl6Rmj4mI6HhadDiC/Jo2o6LC/Hbt7oy67ceutVO3vdi1bql71d6eR/n4NMwkJCRIkpKTk72233TTTfrNb36jMWPGKD8/32tfcXGx59yG4aXi4mINHDjQ65iGa3dEUFCQgoP993wjU8cYO4u67ceutVO3vdi1bsnc2n3a6ltuuUVRUVE6cOCA1/b8/Hxdf/31Sk9PV25urmc4SpL27NmjqKgoDRkyRH369FFSUpKys7M9++vr65WTk6P09HRfNhUAAPQQPg0z4eHhmjlzpl5//XX953/+p7788ku9+eab+v3vf6/p06drwoQJ6tu3r5566inl5eVp586dWrFihWb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TARGET_SSOURCE_S
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" + " TARGET_S SOURCE_S\n", + "0 0.0 487.800000\n", + "1 0.2 488.000000\n", + "2 0.4 488.225000\n", + "3 0.6 488.450000\n", + "4 0.8 488.675000\n", + ".. ... ...\n", + "746 149.2 637.033333\n", + "747 149.4 637.266667\n", + "748 149.6 637.300000\n", + "749 149.8 633.500000\n", + "750 150.0 633.500000\n", + "\n", + "[751 rows x 2 columns]" ] }, + "execution_count": 78, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "plt.scatter(x=df['X'], y = df['Y'])" + "if 'time' in df.columns:\n", + " df = df.drop(columns='time')\n", + "\n", + "# 1. Identify missing x values (0.0, 0.2, 1.8 as detected, so 0.4 to 1.6 is missing)\n", + "median_x_increase = np.median(df['TARGET_S'][1:] - df['TARGET_S'].shift(1)[1:])\n", + "median_y_increase = np.median(df['SOURCE_S'][1:] - df['SOURCE_S'].shift(1)[1:])\n", + "print(median_x_increase, median_y_increase) # 1/FPS\n", + "rounded_x_inc = np.round(median_x_increase, 3)\n", + "\n", + "# Add NAN to \"missing\" x values\n", + "# base it off hash vecotr, not target s\n", + "step_size = 1/FPS\n", + "x_complete = np.round(np.arange(start=0.0, stop = max(df['TARGET_S'])+step_size, step = step_size), 1)\n", + "# x_complete = np.linspace(start = min(df['TARGET_S']), stop = max(df['TARGET_S']), num=int(max(df['TARGET_S']) * FPS))\n", + "df['TARGET_S'] = np.round(df['TARGET_S'], 1)\n", + "df_complete = pd.DataFrame(x_complete, columns=['TARGET_S'])\n", + "\n", + "# Merge dataframes to get NAN values \n", + "df_merged = df_complete.merge(df, on='TARGET_S', how='left')\n", + "df_merged\n" ] }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "df_merged = df_merged.interpolate(method='linear', limit_direction='both', axis=0)\n", + "df = df_merged" + ] + }, + { + "cell_type": "code", + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "" ] }, - "execution_count": 177, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -524,29 +2186,29 @@ } ], "source": [ - "plt.plot(df['Y'])" + "plt.scatter(x=df['TARGET_S'], y = df['SOURCE_S'])" ] }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "" ] }, - "execution_count": 178, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -554,31 +2216,34 @@ } ], "source": [ - "WINDOW_SIZE = 25\n", - "df['Rolling_Y'] = df['Y'].rolling(WINDOW_SIZE).mean()\n", - "plt.plot(df['Rolling_Y'])" + "df['TIMESHIFT'] = df['SOURCE_S'].shift(1) - df['SOURCE_S']\n", + "sns.lineplot(data = df, x='TARGET_S', y='SOURCE_S')\n", + "# plt.plot(x=df['X'], y = df['Y'])\n", + "# sns.scatterplot()\n", + "# df['Y-1'] \n", + "# sns.scatterplot(df['Y-1'])" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0.5, 0, 'Time of source video in seconds')" + "" ] }, - "execution_count": 179, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -586,21 +2251,31 @@ } ], "source": [ - "df['ROLLSHIFT_Y'] = df['Y'].rolling(WINDOW_SIZE).mean().shift(3) - df['Y'].rolling(WINDOW_SIZE).mean()\n", - "sns.lineplot(data = df, x='X', y='ROLLSHIFT_Y')\n", - "plt.xlabel('Time of source video in seconds')" + "WINDOW_SIZE = 10\n", + "df['ROLL_SOURCE_S'] = df['SOURCE_S'].rolling(WINDOW_SIZE).median()\n", + "sns.lineplot(data = df, x = 'TARGET_S', y = 'ROLL_SOURCE_S')" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "Text(0.5, 0, 'Time of source video in seconds')" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -608,38 +2283,9 @@ } ], "source": [ - "def plot_comparison(lims, D, I, hash_vectors, MIN_DISTANCE = 3):\n", - " sns.set_theme()\n", - "\n", - " x = [(lims[i+1]-lims[i]) * [i] for i in range(hash_vectors.shape[0])]\n", - " x = [i/FPS for j in x for i in j]\n", - " y = [i/FPS for i in I]\n", - " \n", - " # Create figure and dataframe to plot with sns\n", - " fig = plt.figure()\n", - " # plt.tight_layout()\n", - " df = pd.DataFrame(zip(x, y), columns = ['X', 'Y'])\n", - " g = sns.scatterplot(data=df, x='X', y='Y', s=50*(1-D/MIN_DISTANCE), alpha=1-D/MIN_DISTANCE)\n", - "\n", - " # Set x-labels to be more readable\n", - " x_locs, x_labels = plt.xticks() # Get original locations and labels for x ticks\n", - " x_labels = [time.strftime('%H:%M:%S', time.gmtime(x)) for x in x_locs]\n", - " plt.xticks(x_locs, x_labels)\n", - " plt.xticks(rotation=90)\n", - " plt.xlabel('Time in source video (H:M:S)')\n", - " plt.xlim(0, None)\n", - "\n", - " # Set y-labels to be more readable\n", - " y_locs, y_labels = plt.yticks() # Get original locations and labels for x ticks\n", - " y_labels = [time.strftime('%H:%M:%S', time.gmtime(y)) for y in y_locs]\n", - " plt.yticks(y_locs, y_labels)\n", - " plt.ylabel('Time in target video (H:M:S)')\n", - "\n", - " # Adjust padding to fit gradio\n", - " plt.subplots_adjust(bottom=0.25, left=0.20)\n", - " return fig \n", - "\n", - "x = plot_comparison(lims, D, I, hash_vectors, MIN_DISTANCE = 3)" + "df['ROLLSHIFT_SOURCE_S'] = df['SOURCE_S'].rolling(WINDOW_SIZE).median().shift(1) - df['SOURCE_S'].rolling(WINDOW_SIZE).median()\n", + "sns.lineplot(data = df, x='TARGET_S', y='ROLLSHIFT_SOURCE_S')\n", + "plt.xlabel('Time of source video in seconds')" ] }, { @@ -651,11 +2297,8 @@ } ], "metadata": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - }, "kernelspec": { - "display_name": "Python 3.10.6 64-bit", + "display_name": "Python 3.9.13 64-bit", "language": "python", "name": "python3" }, @@ -669,9 +2312,14 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.9.13" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b" + } + } }, "nbformat": 4, "nbformat_minor": 2