3419 lines
956 KiB
Plaintext
3419 lines
956 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Get:1 http://archive.ubuntu.com/ubuntu jammy InRelease [270 kB]\n",
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"Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1581 B]\n",
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"Get:3 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n",
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"Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [1192 kB]\n",
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||
"Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n",
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||
"Get:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n",
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||
"Get:7 http://archive.ubuntu.com/ubuntu jammy/restricted amd64 Packages [164 kB]\n",
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||
"Get:8 http://archive.ubuntu.com/ubuntu jammy/universe amd64 Packages [17.5 MB]\n",
|
||
"Get:9 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [45.2 kB]\n",
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||
"Get:10 http://archive.ubuntu.com/ubuntu jammy/multiverse amd64 Packages [266 kB]\n",
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"Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 Packages [1792 kB] \n",
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||
"Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n",
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||
"Get:13 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [3323 kB]\n",
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||
"Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [3446 kB]\n",
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||
"Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1514 kB]\n",
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||
"Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [53.3 kB]\n",
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||
"Get:17 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [33.8 kB]\n",
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||
"Get:18 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [81.4 kB]\n",
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||
"Get:19 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2454 kB]\n",
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"Get:20 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1225 kB]\n",
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||
"Fetched 36.5 MB in 2s (18.2 MB/s) \n",
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||
"Reading package lists... Done\n",
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"Reading package lists... Done\n",
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||
"Building dependency tree... Done\n",
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||
"Reading state information... Done\n",
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||
"The following additional packages will be installed:\n",
|
||
" fontconfig fonts-liberation libann0 libcairo2 libcdt5 libcgraph6 libdatrie1\n",
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||
" libfribidi0 libgraphite2-3 libgts-0.7-5 libgts-bin libgvc6 libgvpr2\n",
|
||
" libharfbuzz0b libice6 liblab-gamut1 libltdl7 libpango-1.0-0\n",
|
||
" libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4 libpixman-1-0 libsm6\n",
|
||
" libthai-data libthai0 libxaw7 libxcb-render0 libxmu6 libxrender1 libxt6\n",
|
||
" x11-common\n",
|
||
"Suggested packages:\n",
|
||
" gsfonts graphviz-doc\n",
|
||
"The following NEW packages will be installed:\n",
|
||
" fontconfig fonts-liberation graphviz libann0 libcairo2 libcdt5 libcgraph6\n",
|
||
" libdatrie1 libfribidi0 libgraphite2-3 libgts-0.7-5 libgts-bin libgvc6\n",
|
||
" libgvpr2 libharfbuzz0b libice6 liblab-gamut1 libltdl7 libpango-1.0-0\n",
|
||
" libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4 libpixman-1-0 libsm6\n",
|
||
" libthai-data libthai0 libxaw7 libxcb-render0 libxmu6 libxrender1 libxt6\n",
|
||
" x11-common\n",
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||
"0 upgraded, 32 newly installed, 0 to remove and 121 not upgraded.\n",
|
||
"Need to get 7298 kB of archives.\n",
|
||
"After this operation, 18.3 MB of additional disk space will be used.\n",
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||
"Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libfribidi0 amd64 1.0.8-2ubuntu3.1 [26.1 kB]\n",
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||
"Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 fontconfig amd64 2.13.1-4.2ubuntu5 [177 kB]\n",
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"Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-liberation all 1:1.07.4-11 [822 kB]\n",
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||
"Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libann0 amd64 1.1.2+doc-7build1 [26.0 kB]\n",
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||
"Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libcdt5 amd64 2.42.2-6ubuntu0.1 [21.1 kB]\n",
|
||
"Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libcgraph6 amd64 2.42.2-6ubuntu0.1 [45.4 kB]\n",
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||
"Get:7 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgts-0.7-5 amd64 0.7.6+darcs121130-5 [164 kB]\n",
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||
"Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpixman-1-0 amd64 0.40.0-1ubuntu0.22.04.1 [264 kB]\n",
|
||
"Get:9 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-render0 amd64 1.14-3ubuntu3 [16.4 kB]\n",
|
||
"Get:10 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxrender1 amd64 1:0.9.10-1build4 [19.7 kB]\n",
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"Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairo2 amd64 1.16.0-5ubuntu2 [628 kB]\n",
|
||
"Get:12 http://archive.ubuntu.com/ubuntu jammy/main amd64 libltdl7 amd64 2.4.6-15build2 [39.6 kB]\n",
|
||
"Get:13 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgraphite2-3 amd64 1.3.14-1build2 [71.3 kB]\n",
|
||
"Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libharfbuzz0b amd64 2.7.4-1ubuntu3.1 [352 kB]\n",
|
||
"Get:15 http://archive.ubuntu.com/ubuntu jammy/main amd64 libthai-data all 0.1.29-1build1 [162 kB]\n",
|
||
"Get:16 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdatrie1 amd64 0.2.13-2 [19.9 kB]\n",
|
||
"Get:17 http://archive.ubuntu.com/ubuntu jammy/main amd64 libthai0 amd64 0.1.29-1build1 [19.2 kB]\n",
|
||
"Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpango-1.0-0 amd64 1.50.6+ds-2ubuntu1 [230 kB]\n",
|
||
"Get:19 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpangoft2-1.0-0 amd64 1.50.6+ds-2ubuntu1 [54.0 kB]\n",
|
||
"Get:20 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpangocairo-1.0-0 amd64 1.50.6+ds-2ubuntu1 [39.8 kB]\n",
|
||
"Get:21 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libpathplan4 amd64 2.42.2-6ubuntu0.1 [23.4 kB]\n",
|
||
"Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libgvc6 amd64 2.42.2-6ubuntu0.1 [724 kB]\n",
|
||
"Get:23 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libgvpr2 amd64 2.42.2-6ubuntu0.1 [192 kB]\n",
|
||
"Get:24 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 liblab-gamut1 amd64 2.42.2-6ubuntu0.1 [1965 kB]\n",
|
||
"Get:25 http://archive.ubuntu.com/ubuntu jammy/main amd64 x11-common all 1:7.7+23ubuntu2 [23.4 kB]\n",
|
||
"Get:26 http://archive.ubuntu.com/ubuntu jammy/main amd64 libice6 amd64 2:1.0.10-1build2 [42.6 kB]\n",
|
||
"Get:27 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsm6 amd64 2:1.2.3-1build2 [16.7 kB]\n",
|
||
"Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxt6 amd64 1:1.2.1-1 [177 kB]\n",
|
||
"Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxmu6 amd64 2:1.1.3-3 [49.6 kB]\n",
|
||
"Get:30 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxaw7 amd64 2:1.0.14-1 [191 kB]\n",
|
||
"Get:31 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 graphviz amd64 2.42.2-6ubuntu0.1 [653 kB]\n",
|
||
"Get:32 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgts-bin amd64 0.7.6+darcs121130-5 [44.3 kB]\n",
|
||
"Fetched 7298 kB in 2s (4771 kB/s) \n",
|
||
"debconf: delaying package configuration, since apt-utils is not installed\n",
|
||
"Selecting previously unselected package libfribidi0:amd64.\n",
|
||
"(Reading database ... 20752 files and directories currently installed.)\n",
|
||
"Preparing to unpack .../00-libfribidi0_1.0.8-2ubuntu3.1_amd64.deb ...\n",
|
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"Unpacking libfribidi0:amd64 (1.0.8-2ubuntu3.1) ...\n",
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"Selecting previously unselected package fontconfig.\n",
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"Preparing to unpack .../01-fontconfig_2.13.1-4.2ubuntu5_amd64.deb ...\n",
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"Unpacking fontconfig (2.13.1-4.2ubuntu5) ...\n",
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"Selecting previously unselected package fonts-liberation.\n",
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"Preparing to unpack .../02-fonts-liberation_1%3a1.07.4-11_all.deb ...\n",
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"Unpacking fonts-liberation (1:1.07.4-11) ...\n",
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"Selecting previously unselected package libann0.\n",
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"Preparing to unpack .../03-libann0_1.1.2+doc-7build1_amd64.deb ...\n",
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"Unpacking libann0 (1.1.2+doc-7build1) ...\n",
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"Selecting previously unselected package libcdt5:amd64.\n",
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"Preparing to unpack .../04-libcdt5_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking libcdt5:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package libcgraph6:amd64.\n",
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"Preparing to unpack .../05-libcgraph6_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking libcgraph6:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package libgts-0.7-5:amd64.\n",
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"Preparing to unpack .../06-libgts-0.7-5_0.7.6+darcs121130-5_amd64.deb ...\n",
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"Unpacking libgts-0.7-5:amd64 (0.7.6+darcs121130-5) ...\n",
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"Selecting previously unselected package libpixman-1-0:amd64.\n",
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"Preparing to unpack .../07-libpixman-1-0_0.40.0-1ubuntu0.22.04.1_amd64.deb ...\n",
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"Unpacking libpixman-1-0:amd64 (0.40.0-1ubuntu0.22.04.1) ...\n",
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"Selecting previously unselected package libxcb-render0:amd64.\n",
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"Preparing to unpack .../08-libxcb-render0_1.14-3ubuntu3_amd64.deb ...\n",
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"Unpacking libxcb-render0:amd64 (1.14-3ubuntu3) ...\n",
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"Selecting previously unselected package libxrender1:amd64.\n",
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"Preparing to unpack .../09-libxrender1_1%3a0.9.10-1build4_amd64.deb ...\n",
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"Unpacking libxrender1:amd64 (1:0.9.10-1build4) ...\n",
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"Selecting previously unselected package libcairo2:amd64.\n",
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"Preparing to unpack .../10-libcairo2_1.16.0-5ubuntu2_amd64.deb ...\n",
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"Unpacking libcairo2:amd64 (1.16.0-5ubuntu2) ...\n",
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"Selecting previously unselected package libltdl7:amd64.\n",
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"Preparing to unpack .../11-libltdl7_2.4.6-15build2_amd64.deb ...\n",
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"Unpacking libltdl7:amd64 (2.4.6-15build2) ...\n",
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"Selecting previously unselected package libgraphite2-3:amd64.\n",
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"Preparing to unpack .../12-libgraphite2-3_1.3.14-1build2_amd64.deb ...\n",
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"Unpacking libgraphite2-3:amd64 (1.3.14-1build2) ...\n",
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"Selecting previously unselected package libharfbuzz0b:amd64.\n",
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"Preparing to unpack .../13-libharfbuzz0b_2.7.4-1ubuntu3.1_amd64.deb ...\n",
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"Unpacking libharfbuzz0b:amd64 (2.7.4-1ubuntu3.1) ...\n",
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"Selecting previously unselected package libthai-data.\n",
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"Preparing to unpack .../14-libthai-data_0.1.29-1build1_all.deb ...\n",
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"Unpacking libthai-data (0.1.29-1build1) ...\n",
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"Selecting previously unselected package libdatrie1:amd64.\n",
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"Preparing to unpack .../15-libdatrie1_0.2.13-2_amd64.deb ...\n",
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"Unpacking libdatrie1:amd64 (0.2.13-2) ...\n",
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"Selecting previously unselected package libthai0:amd64.\n",
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"Preparing to unpack .../16-libthai0_0.1.29-1build1_amd64.deb ...\n",
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"Unpacking libthai0:amd64 (0.1.29-1build1) ...\n",
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"Selecting previously unselected package libpango-1.0-0:amd64.\n",
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"Preparing to unpack .../17-libpango-1.0-0_1.50.6+ds-2ubuntu1_amd64.deb ...\n",
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"Unpacking libpango-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
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"Selecting previously unselected package libpangoft2-1.0-0:amd64.\n",
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"Preparing to unpack .../18-libpangoft2-1.0-0_1.50.6+ds-2ubuntu1_amd64.deb ...\n",
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"Unpacking libpangoft2-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
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"Selecting previously unselected package libpangocairo-1.0-0:amd64.\n",
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"Preparing to unpack .../19-libpangocairo-1.0-0_1.50.6+ds-2ubuntu1_amd64.deb ...\n",
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"Unpacking libpangocairo-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
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"Selecting previously unselected package libpathplan4:amd64.\n",
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"Preparing to unpack .../20-libpathplan4_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking libpathplan4:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package libgvc6.\n",
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"Preparing to unpack .../21-libgvc6_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking libgvc6 (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package libgvpr2:amd64.\n",
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"Preparing to unpack .../22-libgvpr2_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking libgvpr2:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package liblab-gamut1:amd64.\n",
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"Preparing to unpack .../23-liblab-gamut1_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking liblab-gamut1:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package x11-common.\n",
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"Preparing to unpack .../24-x11-common_1%3a7.7+23ubuntu2_all.deb ...\n",
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"Unpacking x11-common (1:7.7+23ubuntu2) ...\n",
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"Selecting previously unselected package libice6:amd64.\n",
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"Preparing to unpack .../25-libice6_2%3a1.0.10-1build2_amd64.deb ...\n",
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"Unpacking libice6:amd64 (2:1.0.10-1build2) ...\n",
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"Selecting previously unselected package libsm6:amd64.\n",
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"Preparing to unpack .../26-libsm6_2%3a1.2.3-1build2_amd64.deb ...\n",
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"Unpacking libsm6:amd64 (2:1.2.3-1build2) ...\n",
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"Selecting previously unselected package libxt6:amd64.\n",
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"Preparing to unpack .../27-libxt6_1%3a1.2.1-1_amd64.deb ...\n",
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"Unpacking libxt6:amd64 (1:1.2.1-1) ...\n",
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"Selecting previously unselected package libxmu6:amd64.\n",
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"Preparing to unpack .../28-libxmu6_2%3a1.1.3-3_amd64.deb ...\n",
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"Unpacking libxmu6:amd64 (2:1.1.3-3) ...\n",
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"Selecting previously unselected package libxaw7:amd64.\n",
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"Preparing to unpack .../29-libxaw7_2%3a1.0.14-1_amd64.deb ...\n",
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"Unpacking libxaw7:amd64 (2:1.0.14-1) ...\n",
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"Selecting previously unselected package graphviz.\n",
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"Preparing to unpack .../30-graphviz_2.42.2-6ubuntu0.1_amd64.deb ...\n",
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"Unpacking graphviz (2.42.2-6ubuntu0.1) ...\n",
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"Selecting previously unselected package libgts-bin.\n",
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"Preparing to unpack .../31-libgts-bin_0.7.6+darcs121130-5_amd64.deb ...\n",
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"Unpacking libgts-bin (0.7.6+darcs121130-5) ...\n",
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"Setting up libgraphite2-3:amd64 (1.3.14-1build2) ...\n",
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"Setting up libpixman-1-0:amd64 (0.40.0-1ubuntu0.22.04.1) ...\n",
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"Setting up fontconfig (2.13.1-4.2ubuntu5) ...\n",
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"Regenerating fonts cache... done.\n",
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"Setting up libxrender1:amd64 (1:0.9.10-1build4) ...\n",
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"Setting up libdatrie1:amd64 (0.2.13-2) ...\n",
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"Setting up libxcb-render0:amd64 (1.14-3ubuntu3) ...\n",
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"Setting up liblab-gamut1:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Setting up x11-common (1:7.7+23ubuntu2) ...\n",
|
||
"invoke-rc.d: could not determine current runlevel\n",
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||
"invoke-rc.d: policy-rc.d denied execution of start.\n",
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||
"Setting up libcairo2:amd64 (1.16.0-5ubuntu2) ...\n",
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"Setting up libgts-0.7-5:amd64 (0.7.6+darcs121130-5) ...\n",
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"Setting up libpathplan4:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Setting up libann0 (1.1.2+doc-7build1) ...\n",
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"Setting up libfribidi0:amd64 (1.0.8-2ubuntu3.1) ...\n",
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"Setting up libltdl7:amd64 (2.4.6-15build2) ...\n",
|
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"Setting up fonts-liberation (1:1.07.4-11) ...\n",
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"Setting up libharfbuzz0b:amd64 (2.7.4-1ubuntu3.1) ...\n",
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"Setting up libthai-data (0.1.29-1build1) ...\n",
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"Setting up libcdt5:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Setting up libcgraph6:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Setting up libgts-bin (0.7.6+darcs121130-5) ...\n",
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"Setting up libice6:amd64 (2:1.0.10-1build2) ...\n",
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"Setting up libthai0:amd64 (0.1.29-1build1) ...\n",
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"Setting up libgvpr2:amd64 (2.42.2-6ubuntu0.1) ...\n",
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"Setting up libsm6:amd64 (2:1.2.3-1build2) ...\n",
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"Setting up libpango-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
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"Setting up libxt6:amd64 (1:1.2.1-1) ...\n",
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"Setting up libpangoft2-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
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"Setting up libpangocairo-1.0-0:amd64 (1.50.6+ds-2ubuntu1) ...\n",
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"Setting up libxmu6:amd64 (2:1.1.3-3) ...\n",
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"Setting up libxaw7:amd64 (2:1.0.14-1) ...\n",
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"Setting up libgvc6 (2.42.2-6ubuntu0.1) ...\n",
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"Setting up graphviz (2.42.2-6ubuntu0.1) ...\n",
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"Processing triggers for libc-bin (2.35-0ubuntu3.3) ...\n",
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"Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting pandas\n",
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" Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/cd/5f/4dba1d39bb9c38d574a9a22548c540177f78ea47b32f99c0ff2ec499fac5/pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n",
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" Obtaining dependency information for pytz>=2020.1 from https://files.pythonhosted.org/packages/11/c3/005fcca25ce078d2cc29fd559379817424e94885510568bc1bc53d7d5846/pytz-2024.2-py2.py3-none-any.whl.metadata\n",
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"Collecting tzdata>=2022.7 (from pandas)\n",
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" Obtaining dependency information for tzdata>=2022.7 from https://files.pythonhosted.org/packages/a6/ab/7e5f53c3b9d14972843a647d8d7a853969a58aecc7559cb3267302c94774/tzdata-2024.2-py2.py3-none-any.whl.metadata\n",
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"Successfully installed pandas-2.2.3 pytz-2024.2 tzdata-2024.2\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting scikit-learn\n",
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" Obtaining dependency information for scikit-learn from https://files.pythonhosted.org/packages/49/21/3723de321531c9745e40f1badafd821e029d346155b6c79704e0b7197552/scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
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"Collecting scipy>=1.6.0 (from scikit-learn)\n",
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" Obtaining dependency information for scipy>=1.6.0 from https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
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"\u001b[?25hCollecting joblib>=1.2.0 (from scikit-learn)\n",
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" Obtaining dependency information for joblib>=1.2.0 from https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl.metadata\n",
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" Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n",
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"Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
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" Obtaining dependency information for threadpoolctl>=3.1.0 from https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl.metadata\n",
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" Downloading threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n",
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"Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n",
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"\u001b[?25hDownloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
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"Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
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"Successfully installed joblib-1.4.2 scikit-learn-1.5.2 scipy-1.14.1 threadpoolctl-3.5.0\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n",
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"Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n",
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"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
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"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting pyarrow\n",
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" Obtaining dependency information for pyarrow from https://files.pythonhosted.org/packages/5e/b5/9e14e9f7590e0eaa435ecea84dabb137284a4dbba7b3c337b58b65b76d95/pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata\n",
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" Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
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"Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl (40.1 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.1/40.1 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
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"\u001b[?25hInstalling collected packages: pyarrow\n",
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"Successfully installed pyarrow-18.1.0\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting fastparquet\n",
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" Obtaining dependency information for fastparquet from https://files.pythonhosted.org/packages/8d/e8/e1ede861bea68394a755d8be1aa2e2d60a3b9f6b551bfd56aeca74987e2e/fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n",
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"Collecting cramjam>=2.3 (from fastparquet)\n",
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" Obtaining dependency information for cramjam>=2.3 from https://files.pythonhosted.org/packages/79/1d/180f2ca168625073f0df80b16c795926deed91b7e89dbfc045263ba7444b/cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n",
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"Collecting fsspec (from fastparquet)\n",
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" Obtaining dependency information for fsspec from https://files.pythonhosted.org/packages/c6/b2/454d6e7f0158951d8a78c2e1eb4f69ae81beb8dca5fee9809c6c99e9d0d0/fsspec-2024.10.0-py3-none-any.whl.metadata\n",
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" Downloading fsspec-2024.10.0-py3-none-any.whl.metadata (11 kB)\n",
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"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n",
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"Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
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"\u001b[?25hDownloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
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"\u001b[?25hDownloading fsspec-2024.10.0-py3-none-any.whl (179 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.6/179.6 kB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hInstalling collected packages: fsspec, cramjam, fastparquet\n",
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"Successfully installed cramjam-2.9.0 fastparquet-2024.11.0 fsspec-2024.10.0\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting seaborn\n",
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||
" Obtaining dependency information for seaborn from https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl.metadata\n",
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" Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
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"Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n",
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"Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n",
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"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n",
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"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n",
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"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.4.7)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
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"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
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"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
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"Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
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"\u001b[?25hInstalling collected packages: seaborn\n",
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"Successfully installed seaborn-0.13.2\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting tqdm\n",
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" Obtaining dependency information for tqdm from https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl.metadata\n",
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" Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hDownloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hInstalling collected packages: tqdm\n",
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||
"Successfully installed tqdm-4.67.1\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"Collecting pydot\n",
|
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" Obtaining dependency information for pydot from https://files.pythonhosted.org/packages/3e/1b/ef569ac44598b6b24bc0f80d5ac4f811af59d3f0d0d23b0216e014c0ec33/pydot-3.0.3-py3-none-any.whl.metadata\n",
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" Downloading pydot-3.0.3-py3-none-any.whl.metadata (10 kB)\n",
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"Collecting pyparsing>=3.0.9 (from pydot)\n",
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" Obtaining dependency information for pyparsing>=3.0.9 from https://files.pythonhosted.org/packages/be/ec/2eb3cd785efd67806c46c13a17339708ddc346cbb684eade7a6e6f79536a/pyparsing-3.2.0-py3-none-any.whl.metadata\n",
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" Downloading pyparsing-3.2.0-py3-none-any.whl.metadata (5.0 kB)\n",
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"Downloading pydot-3.0.3-py3-none-any.whl (35 kB)\n",
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"Downloading pyparsing-3.2.0-py3-none-any.whl (106 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.9/106.9 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hInstalling collected packages: pyparsing, pydot\n",
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" Attempting uninstall: pyparsing\n",
|
||
" Found existing installation: pyparsing 2.4.7\n",
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" Uninstalling pyparsing-2.4.7:\n",
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" Successfully uninstalled pyparsing-2.4.7\n",
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"Successfully installed pydot-3.0.3 pyparsing-3.2.0\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
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"Collecting tensorflow-io\n",
|
||
" Obtaining dependency information for tensorflow-io from https://files.pythonhosted.org/packages/f0/5e/f47443a14a00816fab54caf74599e2fcb34c05d6059e91f82126f8f4c68d/tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
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"Collecting tensorflow-io-gcs-filesystem==0.37.1 (from tensorflow-io)\n",
|
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" Obtaining dependency information for tensorflow-io-gcs-filesystem==0.37.1 from https://files.pythonhosted.org/packages/66/7f/e36ae148c2f03d61ca1bff24bc13a0fef6d6825c966abef73fc6f880a23b/tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n",
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"Downloading tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.6 MB)\n",
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"\u001b[?25hDownloading tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB)\n",
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"\u001b[?25hInstalling collected packages: tensorflow-io-gcs-filesystem, tensorflow-io\n",
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" Attempting uninstall: tensorflow-io-gcs-filesystem\n",
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" Found existing installation: tensorflow-io-gcs-filesystem 0.34.0\n",
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" Uninstalling tensorflow-io-gcs-filesystem-0.34.0:\n",
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" Successfully uninstalled tensorflow-io-gcs-filesystem-0.34.0\n",
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"Successfully installed tensorflow-io-0.37.1 tensorflow-io-gcs-filesystem-0.37.1\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
|
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"Collecting tensorflow-addons\n",
|
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" Obtaining dependency information for tensorflow-addons from https://files.pythonhosted.org/packages/24/94/80165946ec4986505cbfac29b5ae79544bfe2200d9d7883e1ad7c7342a55/tensorflow_addons-0.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n",
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" Downloading tensorflow_addons-0.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n",
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"Collecting typeguard<3.0.0,>=2.7 (from tensorflow-addons)\n",
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" Obtaining dependency information for typeguard<3.0.0,>=2.7 from https://files.pythonhosted.org/packages/9a/bb/d43e5c75054e53efce310e79d63df0ac3f25e34c926be5dffb7d283fb2a8/typeguard-2.13.3-py3-none-any.whl.metadata\n",
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" Downloading typeguard-2.13.3-py3-none-any.whl.metadata (3.6 kB)\n",
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"Downloading tensorflow_addons-0.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n",
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"\u001b[?25hDownloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
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"Installing collected packages: typeguard, tensorflow-addons\n",
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"Successfully installed tensorflow-addons-0.23.0 typeguard-2.13.3\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
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]
|
||
}
|
||
],
|
||
"source": [
|
||
"!apt-get update\n",
|
||
"!apt-get install graphviz -y\n",
|
||
"\n",
|
||
"!pip install tensorflow\n",
|
||
"!pip install numpy\n",
|
||
"!pip install pandas\n",
|
||
"\n",
|
||
"!pip install keras\n",
|
||
"!pip install scikit-learn\n",
|
||
"!pip install matplotlib\n",
|
||
"!pip install joblib\n",
|
||
"!pip install pyarrow\n",
|
||
"!pip install fastparquet\n",
|
||
"!pip install scipy\n",
|
||
"!pip install seaborn\n",
|
||
"!pip install tqdm\n",
|
||
"!pip install pydot\n",
|
||
"!pip install tensorflow-io\n",
|
||
"!pip install tensorflow-addons"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "a467d3f0dfd9beab",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-06 10:36:10.368632: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
|
||
"2024-12-06 10:36:10.368679: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
|
||
"2024-12-06 10:36:10.368726: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
|
||
"2024-12-06 10:36:10.377750: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
|
||
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Keras version: 2.14.0\n",
|
||
"TensorFlow version: 2.14.0\n",
|
||
"TensorFlow version: 2.14.0\n",
|
||
"CUDA available: True\n",
|
||
"GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
|
||
"1 Physical GPUs, 1 Logical GPUs\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-06 10:36:13.233242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import tensorflow as tf\n",
|
||
"import keras\n",
|
||
"\n",
|
||
"print(f\"Keras version: {keras.__version__}\")\n",
|
||
"print(f\"TensorFlow version: {tf.__version__}\")\n",
|
||
"print(f\"TensorFlow version: {tf.__version__}\")\n",
|
||
"print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n",
|
||
"print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n",
|
||
"\n",
|
||
"# GPU configuration\n",
|
||
"import tensorflow as tf\n",
|
||
"import os\n",
|
||
"\n",
|
||
"# Limita la crescita della memoria GPU\n",
|
||
"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
|
||
"if gpus:\n",
|
||
" try:\n",
|
||
" # Imposta la crescita di memoria dinamica\n",
|
||
" for gpu in gpus:\n",
|
||
" tf.config.experimental.set_memory_growth(gpu, True)\n",
|
||
" \n",
|
||
" # Opzionalmente, limita la memoria GPU massima (uncomment se necessario)\n",
|
||
" # tf.config.experimental.set_virtual_device_configuration(\n",
|
||
" # gpus[0],\n",
|
||
" # [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*4)] # 4GB\n",
|
||
" # )\n",
|
||
" \n",
|
||
" logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
|
||
" print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
|
||
" except RuntimeError as e:\n",
|
||
" print(e)\n",
|
||
" \n",
|
||
"# Imposta le opzioni di logging\n",
|
||
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Riduce i messaggi di log\n",
|
||
" \n",
|
||
"# Configura la modalità mista di precisione\n",
|
||
"tf.keras.mixed_precision.set_global_policy('float32')\n",
|
||
"\n",
|
||
"# Imposta il seed per la riproducibilità\n",
|
||
"##tf.random.set_seed(42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "c0155cde4740b0a3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
|
||
"\n",
|
||
"TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
|
||
"TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
|
||
"Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
|
||
"\n",
|
||
"For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
|
||
"\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import tensorflow_addons as tfa\n",
|
||
"from datetime import datetime\n",
|
||
"import os\n",
|
||
"import joblib\n",
|
||
"import re\n",
|
||
"from typing import List\n",
|
||
"\n",
|
||
"random_state_value = None\n",
|
||
"execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
|
||
"\n",
|
||
"base_project_dir = './'\n",
|
||
"data_dir = '../../sources/'\n",
|
||
"models_project_dir = base_project_dir\n",
|
||
"\n",
|
||
"os.makedirs(base_project_dir, exist_ok=True)\n",
|
||
"os.makedirs(models_project_dir, exist_ok=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "1347fb59-50cc-4aa8-b805-ca9403037af5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def clean_column_name(name: str) -> str:\n",
|
||
" \"\"\"\n",
|
||
" Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" name : str\n",
|
||
" Nome della colonna da pulire\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" str\n",
|
||
" Nome della colonna pulito\n",
|
||
" \"\"\"\n",
|
||
" # Rimuove caratteri speciali\n",
|
||
" name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n",
|
||
" # Converte in snake_case\n",
|
||
" name = name.lower().replace(' ', '_')\n",
|
||
"\n",
|
||
" # Abbreviazioni comuni\n",
|
||
" abbreviations = {\n",
|
||
" 'production': 'prod',\n",
|
||
" 'percentage': 'pct',\n",
|
||
" 'hectare': 'ha',\n",
|
||
" 'tonnes': 't',\n",
|
||
" 'litres': 'l',\n",
|
||
" 'minimum': 'min',\n",
|
||
" 'maximum': 'max',\n",
|
||
" 'average': 'avg'\n",
|
||
" }\n",
|
||
"\n",
|
||
" for full, abbr in abbreviations.items():\n",
|
||
" name = name.replace(full, abbr)\n",
|
||
"\n",
|
||
" return name\n",
|
||
"\n",
|
||
"\n",
|
||
"def clean_column_names(df: pd.DataFrame) -> List[str]:\n",
|
||
" \"\"\"\n",
|
||
" Pulisce tutti i nomi delle colonne in un DataFrame.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" df : pd.DataFrame\n",
|
||
" DataFrame con le colonne da pulire\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" list\n",
|
||
" Lista dei nuovi nomi delle colonne puliti\n",
|
||
" \"\"\"\n",
|
||
" new_columns = []\n",
|
||
"\n",
|
||
" for col in df.columns:\n",
|
||
" # Usa regex per separare le varietà\n",
|
||
" varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n",
|
||
" if varieties:\n",
|
||
" new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n",
|
||
" else:\n",
|
||
" new_columns.append(col)\n",
|
||
"\n",
|
||
" return new_columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "4da1f1bb67343e3e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n",
|
||
" os.makedirs(output_dir, exist_ok=True)\n",
|
||
" filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n",
|
||
" filename = filename.replace(' ', '_').lower()\n",
|
||
" filepath = os.path.join(output_dir, f\"{filename}.png\")\n",
|
||
" plt.savefig(filepath, bbox_inches='tight', dpi=300)\n",
|
||
" print(f\"Plot salvato come: {filepath}\")\n",
|
||
"\n",
|
||
"\n",
|
||
"def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n",
|
||
" if not os.path.exists(mapping_path):\n",
|
||
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n",
|
||
"\n",
|
||
" technique_mapping = joblib.load(mapping_path)\n",
|
||
"\n",
|
||
" # Trova tutte le colonne delle tecniche\n",
|
||
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
||
"\n",
|
||
" # Applica il mapping a tutte le colonne delle tecniche\n",
|
||
" for col in tech_columns:\n",
|
||
" df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n",
|
||
"\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n",
|
||
" if not os.path.exists(mapping_path):\n",
|
||
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
||
"\n",
|
||
" technique_mapping = joblib.load(mapping_path)\n",
|
||
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
||
" reverse_mapping[0] = ''\n",
|
||
"\n",
|
||
" return reverse_mapping.get(technique_value, '')\n",
|
||
"\n",
|
||
"\n",
|
||
"def prepare_comparison_data(simulated_data, olive_varieties):\n",
|
||
" # Pulisci i nomi delle colonne\n",
|
||
" df = simulated_data.copy()\n",
|
||
"\n",
|
||
" df.columns = clean_column_names(df)\n",
|
||
" df = encode_techniques(df)\n",
|
||
"\n",
|
||
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
" comparison_data = []\n",
|
||
"\n",
|
||
" for variety in varieties:\n",
|
||
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
||
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
||
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
||
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
||
"\n",
|
||
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
||
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
||
" variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n",
|
||
"\n",
|
||
" if not variety_data.empty:\n",
|
||
" avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n",
|
||
" avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n",
|
||
" avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n",
|
||
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
||
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
||
"\n",
|
||
" comparison_data.append({\n",
|
||
" 'Variety': variety,\n",
|
||
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
||
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
||
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
||
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
||
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
||
" })\n",
|
||
"\n",
|
||
" return pd.DataFrame(comparison_data)\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_variety_comparison(comparison_data, metric):\n",
|
||
" plt.figure(figsize=(12, 6))\n",
|
||
" bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n",
|
||
" plt.title(f'Comparison of {metric} across Olive Varieties')\n",
|
||
" plt.xlabel('Variety')\n",
|
||
" plt.ylabel(metric)\n",
|
||
" plt.xticks(rotation=45, ha='right')\n",
|
||
"\n",
|
||
" for bar in bars:\n",
|
||
" height = bar.get_height()\n",
|
||
" plt.text(bar.get_x() + bar.get_width() / 2., height,\n",
|
||
" f'{height:.2f}',\n",
|
||
" ha='center', va='bottom')\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_efficiency_vs_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
||
" comparison_data['Oil Efficiency (L/kg)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Oil Efficiency vs Olive Production by Variety')\n",
|
||
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
||
" plt.ylabel('Oil Efficiency (L oil / kg olives)')\n",
|
||
" plt.tight_layout()\n",
|
||
" save_plot(plt, 'efficiency_vs_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_water_efficiency_vs_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
||
" comparison_data['Water Efficiency (L oil/m³ water)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Water Efficiency vs Olive Production by Variety')\n",
|
||
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
||
" plt.ylabel('Water Efficiency (L oil / m³ water)')\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, 'water_efficiency_vs_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def plot_water_need_vs_oil_production(comparison_data):\n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
"\n",
|
||
" plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n",
|
||
" comparison_data['Avg Oil Production (L/ha)'],\n",
|
||
" s=100)\n",
|
||
"\n",
|
||
" for i, row in comparison_data.iterrows():\n",
|
||
" plt.annotate(row['Variety'],\n",
|
||
" (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n",
|
||
" xytext=(5, 5), textcoords='offset points')\n",
|
||
"\n",
|
||
" plt.title('Oil Production vs Water Need by Variety')\n",
|
||
" plt.xlabel('Average Water Need (m³/ha)')\n",
|
||
" plt.ylabel('Average Oil Production (L/ha)')\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" save_plot(plt, 'water_need_vs_oil_production')\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"\n",
|
||
"def analyze_by_technique(simulated_data, olive_varieties):\n",
|
||
" # Pulisci i nomi delle colonne\n",
|
||
" df = simulated_data.copy()\n",
|
||
"\n",
|
||
" df.columns = clean_column_names(df)\n",
|
||
" df = encode_techniques(df)\n",
|
||
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
"\n",
|
||
" technique_data = []\n",
|
||
"\n",
|
||
" for variety in varieties:\n",
|
||
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
||
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
||
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
||
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
||
"\n",
|
||
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
||
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
||
" variety_data = variety_data[variety_data[tech_col] != 0]\n",
|
||
"\n",
|
||
" if not variety_data.empty:\n",
|
||
" for tech in variety_data[tech_col].unique():\n",
|
||
" tech_data = variety_data[variety_data[tech_col] == tech]\n",
|
||
"\n",
|
||
" avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n",
|
||
" avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n",
|
||
" avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n",
|
||
"\n",
|
||
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
||
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
||
"\n",
|
||
" technique_data.append({\n",
|
||
" 'Variety': variety,\n",
|
||
" 'Technique': tech,\n",
|
||
" 'Technique String': decode_single_technique(tech),\n",
|
||
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
||
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
||
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
||
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
||
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
||
" })\n",
|
||
"\n",
|
||
" return pd.DataFrame(technique_data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "9aa4bf176c4affb9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def calculate_real_error(model, test_data, test_targets, scaler_y):\n",
|
||
" # Fare predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
"\n",
|
||
" # Denormalizzare predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
"\n",
|
||
" # Calcolare errore percentuale per ogni target\n",
|
||
" percentage_errors = []\n",
|
||
" absolute_errors = []\n",
|
||
"\n",
|
||
" for i in range(predictions_real.shape[1]):\n",
|
||
" mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n",
|
||
" mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n",
|
||
" percentage_errors.append(mape)\n",
|
||
" absolute_errors.append(mae)\n",
|
||
"\n",
|
||
" # Stampa risultati per ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" print(\"\\nErrori per target:\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"{target}:\")\n",
|
||
" print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n",
|
||
" print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n",
|
||
" print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
"\n",
|
||
" return percentage_errors, absolute_errors"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "b3ba2b96ba678389",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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kiClTpozts+urr74yPj4+plevXmbz5s3m0qVLpl+/fiZr1qyp+rwChFJwCikn3jt27DBr1qwxGzdutG0bMmSIcXFxMcHBwbZgqk2bNraJuzEsHUZq27dvN+nSpTNfffXV796mMHToUJMmTZqHGuYDv9WqVStjsVhM7dq1U223Wq1m5syZplq1asZisZgiRYqY3Llzmz179jioUjizqKgoky5dOuPv72+ee+452618KW+d+fzzz03OnDlNv379THx8PLeiwyblPOnq1asmNjbWFiJs2rTJfPnll2blypW2ZsH37t0zpUuXNkuWLHFEuXiM7NmzxxQoUMA899xztjF1/vx5WzA1adIk27EbNmxgvo3flZCQYFasWGFKlSqV6mEKX3/9tSlZsqR55plnTJEiRUzOnDnN3r17HVwtnI3FmP+/xwVwAv3799eKFSt07949Zc6cWW5ubtqyZYssFotGjBihkSNHqnnz5jpx4oSioqJ08uRJlqAjFWOMLBaLRo8erfDwcC1fvty2LykpKdUS8969e2vmzJk6deqU0qVL54hy4cQSEhJksVjUv39/nT9/XpcuXVLZsmX10Ucfydvb23ac1WrV1q1b5ePjo6xZsypr1qwOrBrO7Pz587p//77q1q0rHx8fbdy4UWnTplV8fLztFtDZs2ercuXKyps3r4OrhbOwWq1ycXnQcWPs2LFavny54uPjlSVLFn377bdKnz69EhMT5ebmpvv37ys2NlatWrXS9evXtX37dm6tQirJ86SUv+/bt0+NGzdWpkyZtHnzZrm7u+vChQuaOnWqJk6cqP79+6tfv3621/x2PgVI0v3797Vhwwb17t1b6dOn16ZNm+Tq6qpTp07pxo0bunPnjgoXLsw8CQ8hlILDpJxkSdKECRM0cuRIrVy5UhUqVNCYMWM0aNAgrV69WrVq1ZIkTZw4UTt37pS7u7u++uorubu788GI39WzZ0/t3btXGzZsSDXOpAd9psqVKycXFxddvXpVmTJlclCVcEa/nbAnbxs2bJjWrFmj5557LlUwdfHiRWXNmvWhcYanW/I4unr1qu2zyt/fX1arVYcOHVLTpk2VLl06hYWFKW3atBo/frzu3Llj62EGSKnfjwYNGqRvvvlGI0eO1DPPPKOBAwfKw8NDq1atUvbs2RUXF6dPPvlEGzZs0N27d7VlyxbmSUgl5dzbarUqPj5eXl5ekqR9+/bpjTfeUObMmW3B1MWLF/XJJ5/oyJEjWrt2rST6tuLX96Xdu3dr9+7dslgsqlSpkgIDA1MFUxkyZNCmTZuYH+EvMULgEFevXpWLi4utqXlSUpIOHjyoUaNGqWLFilq+fLnGjBmjr776SrVq1VJsbKwkqVu3bpo6daqmT58ud3d3JSYmMtGCran5hQsXbH/Oli2bjhw58lBDzri4OH377bf64YcfJIlACqkkT7TCwsLUrVs3DR48WBs2bJDFYlG/fv1Uu3Zt7dy5U/3799etW7c0dOhQNW7cWPfu3XN06XAiyePohx9+UN26dVW1alVVqlRJYWFhcnFxUcmSJTVv3jzdvn1bBQsWVLNmzdS3b1/Vq1fP0aXDSZw+fVrSrwHA2rVrtWrVKi1atEjt27eXu7u7oqKidOPGDb3wwgu6cOGCvLy89NJLL+nVV1/Vtm3bmCchlZSB1Lhx4/TWW2+pYsWKGjt2rPbs2aPSpUtr0aJFunLliqpWraqEhARly5ZNgwYN0tq1awmjIOnXz7fFixerXr16mjFjhubPn69q1aopLCxMnp6eqlGjhj799FPdvn1bpUuXpqk5/prdbxjEU2/YsGHG29vbREZGGmMe9ElISkoyVapUMV999ZVZvXq18fHxMcHBwcaYB72iPvvsM/Ptt9+mOg+9NmDMr+Ng2bJlpkSJEmbGjBm2fRUqVDAlSpQwJ06cMHfu3DFxcXFmwIABJkeOHObs2bMOqhjObsWKFcbLy8u8/PLLply5ciZDhgzmu+++M8YYc/fuXTNy5EgTGBhocuXKZQICAsyOHTscXDGc0YoVK4yPj4/5+OOPzdatW027du1M2rRpU32WXb582XTp0sV06tTJHD582IHVwpk0aNDAfPDBB6m2hYaGmg8//NAYY8yPP/5oMmbMaCZPnmwOHTpkMmbMaEqVKmXOnDmT6jX0/oExD8+X+/fvb/z9/c2gQYNM586dTcGCBc2rr75q1qxZY4wxZu/evaZQoUImX758qXpyMu9Gss2bN5tMmTKZqVOnGmMe9CWzWCzG3d3dLFq0yBjzoF/ismXLTKVKlZhz4y8RSsHutm7daoKCgky+fPlsE6jExEQzYMAAU716dePn52e+/PJL2/HR0dHmlVdeMV988YWjSoaT++GHH4yXl5f5/PPPU/3HLiIiwlSpUsX4+/ubwMBAU7VqVZMpUyYaLOIPXbt2zUyePNk20YqMjDR9+vQxFovFzJ492xjz4IlpO3bsMPPnz3/oP4GAMcb8/PPPplq1auazzz4zxhhz7tw5kzdvXlO0aFHj5uZmQkJCUjWuTm4uDBhjzLZt22wN8KOjo23bL1y4YO7fv29q1qxpBg0aZIwxJiYmxlSuXNm4uLiYevXqGWMID/Cw5PebAwcOmAIFCqR6mNDGjRtNvXr1TP369c358+eN1Wo14eHhpkmTJgSb+F3Dhw83gwcPNsY8aIqfM2dO07ZtW9OhQwfj5uZmVq9ebYx5EEwlP9QD+DOEUrCb77//3vbnnTt3mpdfftnkyZPHnD592hhjzK5du0y2bNlMmTJlzJEjR0xSUpK5ePGiqVOnjqlUqRIfjPhdt2/fNi+//LIZOHDgHx4zffp0M2bMGDNx4kTbeAN+69ChQ8bPz88ULVrUNqEy5sGTrpKDqeQVU8BvJQcBN2/eNPHx8WbUqFHmxo0b5tKlS6Zw4cKmXbt2Jj4+3rz55psmQ4YM5ptvviE8wJ/64osvTMOGDVN9kfLzzz+bXLlymbVr1xpjjLlx44Zp0qSJ2bVrV6qgE+jXr58ZMGBAqm1HjhwxWbJkMZs2bUq1ff369SZ9+vS2cZUS828kf1atXbvWHDlyxERERJht27aZ2NhYU6lSJfPOO+8YYx48Qd1isRiLxWKWL1/uyJLxmKGnFOxizZo1atKkiUaOHClJKl++vEaNGqUCBQqoZs2aOnXqlMqVK6c5c+bo0qVLatGihQoUKKA333xTV69etT29IbkHFZAsLi5OERERKl68uCSlum/d/H9/qbffflv9+vVT165deaIV/pC7u7saNWqk06dP6+bNm5IejKGMGTOqX79+6t+/v1q0aKFFixY5uFI4I4vFovnz56ts2bK6d++e2rZtqwwZMmjixInKmzevxo0bJ3d3d+XIkUMuLi7q16+fYmJiHF02nIj5zbOHcuTIoR07dmjSpEk6ePCgJClnzpzKnDmz+vfvrwULFqhhw4a6ePGiypQpk6pXJ55ut27dUlRUlDZs2KCPP/7Ytj0xMVEWi0UXL160/S5J1atXV86cObVz586HzkVPMlgsFm3ZskX169fXvn37lD9/flWuXFknTpxQfHy83nvvPUlSunTp1KRJEw0cOFD58+d3cNV4nBBKwS6qVaum4OBgffjhhxoxYoSkB8HUyJEjVbBgQb388suKiIjQiy++qLCwMA0ePFidOnVS//79tWPHDpp14g/5+vrK399fu3fvlqRUk/Ldu3dr2rRptmN/O+EHUipUqJD69Omjxo0bq127dgoLC7M1dvX391fPnj01ZMgQFSlSxMGVwhldvHhRs2fPVq9eveTn56eAgABJ0vHjx5UtWzb5+flJevBgj5kzZ+rkyZNKly6dI0uGk0l+v/npp5909+5d1a9fX9OmTVNoaKjGjx+vAwcOSJK++OILeXp6asSIEfL09NT69evl4uIiq9XKPAmSHoQDn3zyiSpUqKBVq1Zp1KhRkqTAwEC98cYb6tixo3bs2CE3NzdJ0i+//CJjjJ599llHlg0n9fPPP2vVqlUaPHiwmjdvbtt+8+ZN7d27V3fv3pUkfffdd7p586YGDBjAXAn/jEPXaeGpcv/+fRMcHGxcXV1tzTqNeXArX61atUzu3LnNqVOnjDEP90Ng6TCM+XVcJCQkmLt379q29+zZ05QsWdLWXDFZ3759TaVKlczNmzftWSYeA8ljKTIy0pw4ccIcPHjQtu/48eOmTZs2JkOGDCY0NDTV8dweg9+ze/du89Zbb5mgoCATHR2d6jNryJAhJm3atGb06NGmVatWJkOGDObEiRMOrBbOJuX7yooVK0xgYKAZN26ciYuLM8YYs3LlSpMzZ07TunVrc/ToUdux586dS/W5CBiTes4cGhpqGjdubPLnz2/rcWeMMU2bNjUeHh6mb9++Zvjw4aZWrVqmRIkSjCM85OjRo6Zy5comd+7c5quvvjLG/DrG7t+/bxo3bmwsFospV66c8fX1Nfv373dkuXhMWYxh6QD+O+b/HxuaLC4uTtOnT1e3bt00bNgwffDBB5KkXbt26YMPPtDp06e1atUqFShQwFElw0klj6VVq1bp22+/1YEDB/Tqq6+qTp06qly5sho0aKAbN26oVKlSCgwM1K5du7R48WJt3bpVgYGBji4fTiR5LC1btkwffPCBYmNj5e3trVq1amn8+PGSHqxu+fjjj7Vq1SrNnDlTtWvXdnDVcGYffvihZs6cqbt37+rkyZPy9fVVQkKC3N3ddefOHQ0cOFAbN25UhgwZNGHCBJUqVcrRJcNJWK1Wubg8uHFh9uzZOnjwoKZOnaoMGTKoV69eat++vby8vLRq1Sq9++67qlGjht59912VK1fud88BJOvVq5cOHDggFxcX7d+/X97e3urcubP69esnSfroo4+0fv16xcfHK3fu3Prmm2/k7u6upKQkVtwhlW7duunbb79VzZo1FRISIl9fX9tc6vr161q1apVu3bql2rVrc9se/jeOTMTwZEv5zV9CQkKq1U8TJ040Li4uD62YKlOmjHnjjTfsWiecX/LYWbZsmfH29jYffPCB+fbbb021atVMnjx5zNmzZ80vv/xiBg8ebKpVq2ZKlChhXn/99VSrX4CUVq1aZXx8fMzkyZPNyZMnzeTJk43FYjGdOnWyHXP8+HHTqFEjkydPHnPnzh2aUuMPxcfHm08//dRky5bNtGzZ0vzyyy/GmNSrfm/cuGHu3LnjqBLh5AYPHmxrgD9r1ixTpUoVU7JkSTN+/Hhz7949Y8yD9y1PT08zcuRIB1cLZzdv3jyTPn16s3PnTnPv3j1z+fJl07p1a1O2bFkzduxY23G3bt1KtbKKlVL4o7lOr169TNGiRc2IESPMrVu37FwVnnSslMJ/IuW3dhMmTND+/ft16tQpNWzYUPXr11fevHkVHBys7t27a+jQobYVU8eOHVOhQoX4xg9atWqVsmfPrsDAQBljdO3aNb355puqX7++evTooXv37ilXrlxq0aKFPv3001Rj5vbt2/Lw8JCHh4cDrwDO6tq1a2rfvr1efPFFvf/++4qKilLlypVVsGBBbdu2TU2bNrX1IouIiJCPj4+yZs3q4KrhLMz/fzt8+fJl20qoHDlyKDExUZ999pkWL16sChUq6KOPPpKvr68SExNtfVuA3zLG6OLFi6pZs6Y++OADtWjRQpJ0584ddejQQbt27dJ7771nWzEVHh6uChUqsJIFf2rs2LGaO3eudu3aZXv/uXDhgjp37qw9e/aoT58+6tmzZ6rXmN/c3YCnT/IY2Llzp8LDw+Xh4aG8efMqKChIktSjRw9t3bpVDRo0ULdu3eTn58dKTTwSjCD8J5LfnPr3769Ro0apfPnyqlGjhqZNm6aOHTvq7t27ateunSZOnKiRI0eqb9++kqQiRYrYmnXi6XX58mV17dpVEyZM0LFjx2SxWOTt7a3bt2+rTp06ioyMVIECBVS/fn2NGzdOLi4uWrNmjSIiIiRJPj4+BFKwMcbYmtxHREQoY8aMeumll1SvXj1duXJFtWrVUlBQkJYvX66ePXtq+vTptv8YFihQgEAKNskT9qVLl6pOnTqqWLGiqlevrpEjR8rNzU29e/dW/fr1tXv3bg0ePFgxMTEEUnhIyu+DLRaL0qZNKxcXF1uz4MTERKVNm1azZs2Si4uLgoODNXXqVN2/f1+VKlXiacT4Q8nz58yZM8tqtdqesme1WpU9e3YNHDhQd+/e1RdffKGQkJBUryWQgsVi0aJFi/Tyyy9rwYIFCg4O1muvvWb7f9qECRNUqVIl/fDDD/r4448VGxtLIIVHglGERy55srVz504tX75cK1asUJcuXVSlShVFRkaqefPm8vb2lqenpzp37qwRI0YoPDw81SSNN7inW5YsWbRw4UIdPnxY48aN0+HDh+Xq6qp79+5p48aNqlWrlurUqaMvv/xSknT27FmFhITo1KlTDq4cziQ2NlbSg0mWxWLR8uXLVbVqVR09elQdO3ZU/vz5tWDBAmXJkkXDhw+Xp6ensmXLprJlyyo8PNw2mQeSWSwWhYaGqmnTpmrTpo2GDx+ubt26afjw4WrXrp1cXV3Vu3dv1atXT2vXrtXIkSN56idSSbka5dq1a5Ikd3d3+fn5KTQ0VJLk5uampKQkubm5qXTp0vLw8NDChQu1detW23lYKQVJD32Jmzx/Ll++vCIjI/X555/r7t27tu0JCQl64YUX1LNnT7Vq1cru9cK5RUREqGvXrhozZoy2bt2qTZs2KSQkRJMmTVL//v0lSRMnTlSxYsUUHh6u+Ph4B1eMJ4YDbhnEE2jo0KFmxYoVqbZt2LDBFClSxBhjzMKFC42vr6/58ssvjTHG3L592yxdutTcuXPHJCUl2e5fpmcLUtq7d68pU6aMadeunbl06ZKZNGmSsVgs5pVXXkl13MCBA03x4sXNuXPnHFQpnE2HDh3M22+/beLj440xxvz888+mSZMmZsqUKamO69y5s6lQoYLt9z59+pjRo0enerojYMyvn0+dO3c2b731Vqp9GzZsMC4uLubjjz82xjx4ItFnn31mIiMj7V0mnFjKXptLliwxNWvWNEeOHDHGGPPTTz8Zb29v07VrV1sfTqvVapo1a2ZWrVplSpcubd58801HlQ4nlHLOPGXKFPP++++bIUOGmJ9//tkY82Du7erqajp27GhWrlxpjhw5YmrXrm06d+5sey1Pt0ZK27dvN4UKFTIXLlxItX3mzJkmTZo0ZuPGjbZt0dHR9i4PTzDWlONfO3z4sNatW6dt27bJy8tLL730kqQH3wb6+/tr7ty56tSpkz7++GN16tRJkrRjxw4tW7ZMxYoVsz2lwXAvO36jdOnSmjZtmtq2bashQ4aoadOm6tWrl8aPH69PPvlEkhQZGanZs2dr8+bNypEjh4MrhjOYN2+eli5dqrVr18rd3V379u1TcHCwLl68qBo1akj6te9d/fr1NWPGDDVo0EAeHh5as2aNwsPDlSZNGgdfBZxF8mfT3bt3lTZtWkVGRipDhgy2fQkJCapWrZpGjBih7777Tq1bt1aWLFn0/vvvO7hyOJOUfVfWr1+vRYsWae/evRo2bJiGDx+uChUqaPbs2WrevLn27dunLFmy6NKlS7px44bmzJmj7du3a8OGDfRvgaTU46l///6aPn26SpYsqStXrmj69OkKDQ1Vo0aNtHz5cvXp00crV66Uq6urMmbMqOXLl8tiscgYw4o7pOLu7q6IiAhFREQoW7Zsts+/GjVqKGvWrIqKirIdmyVLFgdWiicNn2r414oXL65Ro0bJy8tLY8eO1Zo1ayRJ1atX161bt9S8eXN99NFH6ty5syQpLi5O48aNU2xsrPLmzWs7D4EUfk/p0qU1ffp07du3TwsWLFCtWrU0YcIEzZw5U4sWLdIvv/yi7du384h12Jw/f17+/v4qVaqUVq9erdatW2vLli3avXu3IiMjJf16i0PlypU1Y8YM3blzRy4uLtq8ebOKFCniyPLhRJIn5KGhoRoyZIjOnTun119/XRs2bNDu3btlsVjk7u4uScqQIYMsFov8/PwcXDWcUfJ7zvvvv69u3brpmWee0QsvvKDNmzdr8ODBOn78uBo0aKADBw6oVKlS8vPzU8WKFXX48GFJDx4EkzdvXm4HhaRfx9OVK1d09+5drVmzRuvWrdOcOXMUGBio5557TsePH9crr7yitWvXKiwsTPPnz9fOnTvl7u6uxMRE5t1PueT3kmPHjmnLli2KjIxUmTJlVLduXU2ePFn79++3jZFMmTIpffr03K6H/wxP38O/kpCQYJuQz5s3T99++61u376tYcOGqXr16jp27Jhef/11+fv7q2PHjkpMTNT8+fMVHR2tffv2yc3NjW/98Lfs3btXHTt2VKlSpfThhx8qICBAFotFcXFx8vLycnR5cCK7du1Sy5Yt9eyzz2rTpk1au3atEhIS1Lt3b+XNm1dDhgxRuXLlUr3GarUqISFBnp6eDqoazmrx4sVq0aKFBgwYoFdeeUVeXl4aMGCAkpKS9OGHH6ps2bKSpN69e2vPnj1avny5fH19HVw1nNGmTZvUpEkTLVmyRJUqVZIkTZs2TSEhIcqSJYtGjhypIkWKKCkpybaC5cqVK/rkk08UEhKiTZs2qWjRoo68BDiR2bNnq3PnzipatKgWLlxoWy1+6tQpvffeewoPD1d4eLgKFSqU6nUpxxeebkuXLlXLli0VEBCg8+fPa9q0abp3757mzp0rPz8/dezYUblz59bMmTM1Y8YM/fTTT8qdO7ejy8aTyEG3DeIJM3ToUNO4cWMTGBhoXFxcTJUqVUxoaKgxxpiIiAhTs2ZNU7x4cfP888+b1q1b2/q8cC87/om9e/ea8uXLmyZNmpjDhw8bY+hDht/37rvvGovFYipWrGjbNmfOHFOuXDnTsmVLs2fPHtv2lH1egJROnDhh8uTJY4KDg1NtX7p0qalbt67x9/c3r7zyigkKCjJ+fn5m3759jikUj4WwsDDj7+9v+/xK9sUXXxhPT0/TqFEjW48pY4w5f/68+eijj0zBggUZW3jI+vXrTVBQkPHx8bH1kUqeE506dcrUrVvXWCwWc/78eUeWCSeUlJRkrl+/bp5//nnz1VdfmYiICDNixAjj5uZmJk+ebL7++mvTpEkT4+LiYgoXLmzy589v9u7d6+iy8QRjpRT+teDgYPXv318rVqxQ/vz5FR4erkmTJsnFxUWDBw+29XC5evWqvL29lTZtWkkPHnnMo7LxT+3atUt9+vTR3LlzlTVrVkeXAyd07949vfbaa8qbN6+2b9+uwMBAzZ07V5I0Z84cjR8/XsWLF1fnzp1VoUIFB1cLZxYaGqouXbpo7dq1ypUrV6qVvcePH9eePXu0du1aZc+eXS1btlThwoUdXDGckfn/20B37Nih5s2b64svvtCrr75qG09JSUkKDAyUt7e3ihUrptGjRytr1qyyWq2KioqSm5sb/Vuecr93V4ExRrt371bnzp0VExOjbdu2KVOmTLbxduLECU2bNk2jR49mvg1Jv74XxcXFyRijkSNHqnfv3rY+iePHj1ffvn316aefqlmzZoqNjVV8fLz8/f2VOXNmB1ePJxnvUPjXdu7cqXr16qlq1aqSpDfeeEM+Pj7q1auXhgwZIhcXF1WrVk2ZMmWyvcYYwwck/ifly5fX6tWruWUPfyhNmjRasWKFvL29NX36dI0dO1ZvvfWW5syZo7feessWmHt5ealkyZLcsoc/dPv2bd27dy/VtuRbX6Kjo/X888+refPmDqoOzuq3AUJyX5bnnntOBQsW1HvvvaccOXIoMDBQkhQdHa0SJUqoSJEimjVrlo4ePaqsWbPKxcVF2bJlc8g1wHmkHE9LlizRpUuXZLVa9fLLL6t8+fKaOnWqunfvrmrVqmnDhg3KnDmzjDEqVKiQ7aEwfBEM6cF70bJly/Tll1/q/PnzslqtatKkiS2U6tmzpywWi/r27asrV65o4MCBtsUEwH+JRj7415555hldv3491cS9du3aatWqlfbs2aMePXrop59+SvUamivi3yCQwl/x9vaWJDVu3Fj9+vXTvn379NZbb0mSmjZtqjFjxqhv374EUvhTJUuW1LVr1zR16lRJD5oLJ/diWbp0qWbMmEHjV6SSMkBYsGCBhg4dqokTJ2rTpk2SpFWrVilTpkyqV6+ePvroI4WEhKh169a6ffu2hg4dKmOMfvzxR0deApxM8njq27evunTpoo0bN2r69Ol66623NH36dJUpU0Zjx46Vv7+/XnrpJUVHRz80zyaQgiTt3r1brVq1Up48eVShQgWdPn1a06dP188//2w7pkePHvrwww8VHBysuLg4B1aLpwmhFP61wMBAbd++XaGhoameCpMlSxZVrlxZb7zxhsqXL+/ACgE8rXx8fNS4cWP17dtXhw4d0muvvSbpwYrOPHnyOLg6OLs8efJo0qRJ+uSTT9S3b18dPnxYx44dU79+/TRz5kw1a9ZMHh4eji4TTsIYkypA6NGjh/bs2aMlS5aoT58++vbbb2WxWBQeHq6XXnpJK1eu1OjRo+Xh4aEFCxZIkrJmzaqCBQs68jLghObOnau5c+dq+fLlWrBggbp3764jR44offr0kh48SfbTTz9VfHy8evfu7dhi4ZROnz6tFStWaMCAAfryyy81Y8YMff7551q0aJGmTJmSKpjq16+fzpw5I39/fwdWjKcJsTn+tTZt2mjbtm1q0aKFpkyZojJlyiggIECLFy9WjRo1NHDgQFksFp6yB8Ah0qZNq8aNGysuLk4hISG6ePEit8Tgb2vTpo18fX3VsWNHzZ07V15eXnJ1ddX69evpIQWblHOcyZMn6/vvv9eiRYv03HPPKTg4WD179tTQoUN19+5ddezYUdOmTdMvv/wiY4zt1pkhQ4YoMjJSNWvWdOSlwAmdOnVKL774osqVK6cFCxaoR48e+vzzz9WwYUPdvn1bV65cUYUKFbRw4UIVKVLE0eXCycTExKhp06Y6e/as3nnnHdv2zp07y2q1avTo0XJ1dVW7du1sX9glB56APdDoHP9KyklY165dtXTpUiUlJcnX11eurq46dOiQ3NzcbI31AMBR7t69q4SEBKVLl87RpeAxdOnSJf3888+yWCzKkycPjadhk3KOExMTo4EDByp37tzq3bu3li9frlatWum9995TRESENm/erDFjxqhFixa210dERGjIkCHatGmTVq5cqdKlSzvqUuAEfu9L3P79+8vV1VV169bVyy+/rE8++USdOnWSMUYhISG6ceOGunfvLnd3d0m/9r4Dku3bt09NmjRR5syZNWXKFBUvXty2b8qUKerZs6cGDBiggQMHcrsn7I5QCv9ayslYeHi4rl27pjt37ujNN9+Uq6srH4wAAOCJtGHDBl26dEnNmzdXx44dlSFDBr333nu6d++ekpKS9Morr6hLly7q0aOHlixZombNmsnd3V2zZs1SgwYNJElxcXFat26dihQpovz58zv4iuBIKQOp06dPK02aNMqUKZN27dqlKlWqSJLmz5+vN998U5J0584dNWzYUMWLF9dnn33msLrxeDh48KBat26tChUqqHv37ipWrJht3zfffKMXX3xRBQoUcGCFeFoRSuGR+KNb8wikAADAk8YYo9u3b6tRo0aKj4+Xn5+fNm3apC1bttieqjd79mxNnDhRa9euVbp06bR27Vp99dVXqlOnjt5++23mR0gl5Ze8/fv317Jly3T16lUVK1bM1r/u3Xff1fTp0/X8888rJiZGffr00ZUrV7Rz505Wt+Bv2bdvn9q3b68yZcqoZ8+eKlq0qKNLAmh0jt9ntVp/d/sfZZjJgdRvX8eECwAAPGksFot8fX01b948RUdH64cfftDAgQNtgZQkubu769y5c9qyZYvu3r2riRMnKnfu3GrXrp1tJTkgPZg/JwdS8+bN08yZMzVmzBh99tlnqlixonr06KFdu3Zp7NixateunSpVqqRWrVopPj5eP/30k9zc3BhP+FtKly6tadOm6eDBgxoxYoSOHz/u6JIAGp3jYSlXPR0+fFh3795V5syZlTt3blkslj9c/ZTyqTPHjx9Xzpw5bY9lBwAAeNK4uLgoX758ypIli8LCwpQ9e3Y1b95cklS0aFG9+OKLatWqldKnT6+0adNq8eLFslgsMsbwxR1skufPGzduVFhYmPr27avXX39d0oM+Zblz51b//v01d+5cHTlyROfPn5efn59KliwpFxcXJSYmslIKf1vp0qU1adIk9enThz6bcArcvodUUi4dHjRokJYvX65z586pYsWKqlChgkaOHCnp4dvyUr5u4sSJGj16tLZv367cuXPb/RoAAADsKTo6Wu3atdO9e/fUrl07WzB14sQJHT9+XLGxsWrWrJlcXV0JEPC7oqOjVaVKFV25ckX9+vXToEGDbPuuX7+udu3aKUeOHJo4cWKq1/F0a/yv4uLi5OXl5egyAG7fQ2rJwdLIkSM1bdo0ff755zp16pSyZcumSZMmqWvXrpKUatl5ykDqq6++0rBhwzRu3DgCKQAA8FQICAjQpEmT5O3trZkzZ2r69OlKSkrSu+++q0OHDqlFixa2uROBFH5PQECAFi9erMyZM2vx4sXat2+fbZ+/v78yZsyo06dPP/Q6Ain8rwik4Cx4F4Ok1L2ijh49qiVLlui7775TjRo1dODAAX3//feqVauWVq9erR49ekh6EEwlJCSkCqT69u2rqVOnqmnTpo64DAAAAIfIkyePJk6cKF9fX3366acqUKCArly5or59+9qO4ZY9/JnAwEAtXrxYSUlJmjBhgvbv3y9Jio2N1bFjx5Q9e3bHFggA/wFu30OqlU4HDhxQYGCgpk2bpoYNG+rw4cNq2rSpRowYobZt2+q1117T+vXr1bBhQ82ZM8d2jqlTp6pv37765ptv1KhRI0ddCgAAgENFRUVpz549unz5slq3bi03Nzdu2cM/sm/fPrVo0UI3btxQuXLl5OHhocjISO3YsUMeHh6p5u4A8LgjlHrK/fbxszt27NC8efOUJUsWWSwWde7cWW5ubho3bpzc3d3Vp08f7dq1S0WLFtWkSZPk4uKi5cuXq379+lq4cKEaNmzo4CsCAABwHn/0gBjgzxw+fFj16tVT9uzZ9dZbb6lTp06SpISEBLm7uzu4OgB4dLh97ymXHEgdP35c4eHhGjVqlAICAmzbIyMjdeHCBbm7uyspKUk///yzWrZsqcmTJ9vuYX/ttde0YcMGAikAAIDfIJDC/6J48eJavHix4uPjtXfvXp06dUqSCKQAPHFYKQWNHj1aGzdulJeXl2bPni1fX19ZrVZJ0oQJE/Ttt98qa9asiomJ0S+//KIDBw7I1dVVxhgadgIAAAD/kX379qlTp07Kmzevhg4dqsKFCzu6JAB4pFgpBRUpUkTr1q3T1q1bdfbsWUkPnuTh4uKiZs2aqVWrVkqXLp2KFSumffv22Z4eY7FYCKQAAACA/0jp0qU1adIkRUVFKV26dI4uBwAeOVZKPWX+qDFiWFiYatWqpTZt2thu4fsjNOsEAAAA7CcuLk5eXl6OLgMAHjlWSj1FrFarLZC6cuWKzp07Z9tXs2ZNLV26VCEhIRo5cqQuX76c6nXJjDEEUgAAAIAdEUgBeFIRSj0lrFarrTH5hx9+qDp16qh8+fKqXbu2Nm7cqLi4ONWtW1dLly7VlClTNGrUKEVFRUmS7XWSePwsAAAAAAB4JAilngLGGFuwNHToUE2ZMkU9evRQeHi4zpw5o8GDB2vFihWpgqlJkyZp7ty5Dq4cAAAAAAA8qbgP6wl27NgxFSlSxPb7tm3btGzZMs2ePVs1atTQli1bdPHiRRljNHjwYLm6uuqVV17Ra6+9pi1btqhixYoOrB4AAAAAADzJWCn1hPr0009twZPFYpExRhkyZFDXrl1Vo0YNhYWFqWHDhpo8ebIiIiIUFxencePGaf78+YqPj9fzzz8vNzc3JSYmOvpSAAAAAADAE4hQ6glVokQJvfjii3rvvfdswVSBAgVUt25dJSQkaMKECerQoYNatWolY4wKFCigAwcOaNu2bfLw8LCdh6bmAAAAAADgv0Ao9YT5+uuvJUlBQUF69913lT9/fnXr1k2bN2+Wu7u7smTJovj4eF27dk3+/v62XlM5c+bUxo0bNWXKFEeWDwAAAAAAnhIsg3mChIaGqmPHjjpw4IAmTZqkqlWryhij4OBgde/eXRMnTtQLL7wgFxcXubm5aeHChYqJidGWLVt0/fp1lS5dWi4uLkpKSpKrq6ujLwcAAAAAADzBWCn1BClfvrymTp2qhQsXqkuXLpKkatWq6d1331XBggXVrVs3bdy4UWnSpNGiRYvk7e2tbdu2ydfXV7t375aLi4usViuBFAAAAAAA+M9ZjDHG0UXg0YmNjdW8efM0aNAgvfnmm5o8ebIkaePGjQoODtbJkyc1btw41ahRQ3FxcTLGyMvLSxaLRYmJifSQAgAAAAAAdkEC8QQwxshisUiSfH199eabb0qSBg4cKEmaPHmyqlWrJkkKDg5Wnz59NHr0aNWqVSvVOQikAAAAAACAvZBCPOasVqutWbnValViYqLSp0+v1q1bS5IGDBgg6ddgymKxaMSIEZozZ06qUCo51AIAAAAAALAHQqnHWMpA6rPPPtOBAwe0d+9edezYUdWrV1eHDh0kSYMGDZLFYrE1P/fz81PJkiUdWToAAAAAAHjK0VPqCTBgwAB98803GjJkiG7fvq1p06apcOHCmjdvnpKSkrRgwQINHjxYNWvW1HfffWd7XcpQCwAAAAAAwJ5IJB5zO3fu1NKlS7VixQp17dpVVapU0blz59S4cWP5+PgoXbp0atmypQYMGKBffvlFVqvV9loCKQAAAAAA4CikEo85q9UqLy8vVaxYUd9//73q1KmjL774Qq1atdKdO3e0atUqSdI777yjH374QS4uLqmCKQAAAAAAAEcglHqM/F6YdPv2bcXFxWnevHl65513NGbMGHXq1EmStH37ds2ZM0fnzp1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|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_olive_production_kg_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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IOnbsiEqVKmk5UsoLnj59mmumU1RUFI4cOYL4+HgsXboUwOuBe//+/REdHY2xY8diwIAB0NHRQWJiIgwMDGBubq6t8CkfGDduHF6+fIl169YhKyvrrZd1t2/fVoqdDx48GLq6urn+WCQCcq9ccHV1hbe3N6ytrZGcnAwjIyP8/PPPqFmzJvz9/TF69GioVCoYGRnB0tISR44c4Xic/pVvvvkGQUFB2LRpEzp16oTHjx/j+vXrCAgIQNmyZdG6dWtYW1trO0z6yJiYojxl/fr1CAoKQokSJbBy5UoAQGJiInx8fODl5YVBgwZh7ty5AF7XSMheIsPBFUVERKBhw4bKz2FhYRg1ahTKly+P+fPnw9bWFgCQkJCA3r17486dOzhy5AgqVqyorZApH8hOcp49exaXLl1CZmYmmjRpAhsbGyQkJGD+/PmIiIhA9+7d4eLiou1wKQ9asmQJHj58iPnz5yvLppo3b46IiAh8+eWXuZahp6enw9HREbGxsXB0dMSQIUNgZGSkxegpL8r5Mg94PQayt7dH6dKlsWPHDgB/JhjS09Nx6dIl1KtXL9dnZCeviLLlTCjdvHkTCxcuxOjRo1GnTh0cPXoUy5YtQ3R0NHx9fWFra4uEhAQ8evQImZmZqFOnjlJziuNxAv68T50/fx7Xrl2Dvr4+ypUrBzs7OwC5k1Pt2rVjnWBijSnSrpx50YyMDISHh+PIkSOIi4tT2i0tLTF48GCMGjUKmzdvxoQJEwBASUqJCB+CBdzBgwfRtm3bXDt7mJqaQq1WIzAwUBloaTQaWFlZwdfXF5UqVUK9evVw+/ZtLUVN+YFKpcKuXbvQpk0bLF26FIsWLULt2rWxevVqWFlZYdq0aWjQoAECAgIwe/ZsbYdLeZCxsTGcnJygp6eHV69eQU9PDwcOHEC7du0QHR2N3bt3KzU49PX1sWnTJlhYWMDPzw+vXr3ScvSU12g0GiUpdevWLSQnJ0NXVxfdunVDVFQUgoODAfxZdyouLg4LFixAZGRkrs9hUoqy7dmzB8CffWbHjh1KLakyZcoAeL3TrIuLC6ytrdG7d29ERUXBysoKtra2qFevnpJ053icsmXXUWzZsiWWLVuGoUOHYvDgwZg5cyaA1zWnvvzySwwdOhR79+7lRjEECJGWXL9+XV68eCEiIrNnz5ZLly7JrVu3ZPz48WJiYiKrVq3KdX5iYqK4ublJ165dRaPRaCNkysMSEhJERCQ+Pl5pO3/+vNSsWVPq1asnqampIiJK37l37558/fXXEh0d/fGDpXzj4sWLUrx4cfH29pbk5GRJSkqSefPmia6urqxbt05EROLi4sTR0VG+/PJLefLkiZYjprwqLCxMxowZI1evXhURkSdPnkizZs2kSZMmEhAQIFlZWcq5r169krt372orVMqjcvaRmTNnSocOHSQ4OFhERMLDw6V58+bSvXt32b9/v4iI3L59Wzp16iRNmzaVzMxMrcRMedvatWulcePGkpWVpfSRTZs2SYsWLcTc3Fzu3buX6/wTJ05Ily5dpGjRonLz5k1thEx5WM77zMWLF6VYsWLi5eUlaWlpcv36dXF3d5eyZcvKrFmzlPPatWsn5cuXl+TkZC1ETHkJE1P00Wk0Grl48aKoVCpZv369jB49WoyMjOTKlSsiInLz5k0ZP368VKtWTVavXp3r2qdPnyqJBSan6E03btwQlUolP/30k9J2/vx5qVatmjRo0EBJhGb3HQ7U6U1v3leCg4OlRo0akpCQkOvY7NmzxdjYWElsJiQkKMlRKthyJg/S09OVf69bt04qVaokEydOlOvXr4uIyOPHj6Vp06bSpEkT2b9/f65rif6Kq6urlChRQnbv3i2PHj1S2o8cOSIdO3YUCwsLKVOmjFSvXl3s7OyUfsj+RW9KTExUxkKnT59W2v39/cXOzk6aNWsmd+7cyXXNkSNHxNnZmWMoUqxcufKt+8vu3bulevXq8vz5c6XtwYMHMnv2bLGzs1Ne0ogIX8SQiDAxRVq0aNEiMTQ0FGNjYwkPDxeRP/8ovHHjhpKcWrt27VvXMilFf2Xy5MliZGQkP//8s9KWnZxq3LixMnOKKFv2YCrnoOrx48ei0WgkICBA1Gq18tb41atXIiJy//59KV++vPj5+X38gCnPu337tqSkpIiIyJ49e2TOnDkiIrJixQqpW7eujBs3LldyqkWLFlK9enU5dOiQ1mKm/OHYsWNStmxZ+f3330VEJC0tTW7fvi0HDx6Uhw8fSnp6uoSHh4uHh4fs27dPSR5kZGRoM2zKY1xdXZXnmYhISEiIqFQqWbFihdK2a9cucXBwEHt7e4mLi3vn5zA5RVevXpWGDRu+tQIhKChILC0t5cyZM7nao6KixMjISAIDAz9mmJQPsMYUfXRZWVkAgPLlyyMjIwNpaWm4ePEikpKSlLoJ1tbWGD16NNq3b48pU6Zg7969uT4jZ9FPKrjk/2qURUREwNfXFxqNBosXL4azszO+/fZbeHt7AwBq164NX19f3Lx5Ex07dtRmyJQHqdVq3LlzB9OnTwcA+Pn5oX379njy5AlatmyJJk2aYOzYsXj48KFSnFNfXx+Ghoasp0FvSUtLQ+/evdGgQQNs3rwZ3bp1U3ZnHDduHAYOHIiwsDCsWrUKN27cgIWFBXbt2oWyZcuiSpUqWo6e8jqVSgULCwsUKlQI58+fx/Tp0+Hg4IARI0bA3t4eV69exeeff44xY8agY8eO0NHRQVZWFu9VpLh16xZWrVoFBwcHpa5PhQoVMHXqVMyePRuenp4AgO7du2PkyJFQq9UYPHjwO2tyslYZVa5cGUFBQahcuTJ+//13aDQaAK9rBBcpUgS//PILHjx4oJxfpkwZVKtWTTmPSKHtzBgVHG9O8czMzJTMzEyZP3++qNVqWblypSQlJeU65/79+7J06VK+kaG3ZM+a27VrlxQvXlxcXV3l4sWLyvFZs2aJjo5OrplTFy9elJiYmI8eK+VtGo1GFi1aJLVr15ZOnTqJrq6ubNq0STm+YcMGadGihXTq1EliYmLkxo0bMmPGDPnss8/eWuJAJPJ6WcJnn30mBgYGsmbNGhF5PbMl2/Lly6Vu3boyceJEZRk7l1nRm95VuuDMmTNSvnx5sbe3FxMTExk2bJhs27ZNjh8/LtWqVRN/f39thUv5yNmzZ6Vq1arSuHFjZann3bt3ZcaMGWJqaioeHh7KuX5+flKrVi0ZM2aMtsKlfODRo0diY2Mj9erVU55na9asERMTE5k0aZIcO3ZM7t+/Ly4uLmJlZZWrJiyRCJfy0UeSc8B96tQpOXTokISEhChtM2fOFLVaLV5eXkpyatCgQcqAXYTTheltJ0+elMKFC8uaNWveuUxh1qxZYmRk9FYhfaJ3cXR0FJVKJe3atcvVrtFoZOPGjWJvby8qlUpsbGykfPnycvbsWS1FSnldQkKCFC5cWCwsLOTzzz9XlvXlXDqzYsUKKVu2rLi4uEh6ejqXqFMuOcdNjx49kuTkZCWBEBoaKj/99JPs379fKRj88uVLqVu3ruzZs0cb4VI+dPbsWbG2tpbPP/9c6Vvx8fFKcsrT01M59+jRoxyH09/KyMiQgIAAqVOnTq4NF9atWye1a9eWokWLio2NjZQtW1bOnTun5WgpL1KJ/N9aGKKPwNXVFQEBAXj58iVKlCgBXV1dhIWFQaVSYd68eXB3d0e/fv1w/fp1JCQk4MaNG5x+Tm8REahUKixYsADh4eHYt2+fciwrKyvX1PLJkydj48aNiImJQeHChbURLuVxGRkZUKlUcHV1RXx8PO7fvw87Ozt8//33MDY2Vs7TaDQ4fvw4TExMYGVlBSsrKy1GTXldfHw8Xr16hY4dO8LExAQhISEoVKgQ0tPTlSWhW7ZsQZMmTVCxYkUtR0t5iUajgVr9utrGokWLsG/fPqSnp6NkyZLYvHkzihQpgszMTOjq6uLVq1dITk6Go6Mjnjx5gpMnT3J5Fb1T9tgp58+RkZHo2bMnihcvjmPHjkFPTw93797F2rVr4eHhAVdXV7i4uCjXvDnGIsrp1atXOHr0KCZPnowiRYogNDQUOjo6iImJwdOnT5Gamopq1apx/ETvxMQUfTA5B1YAsHz5cri7u2P//v1o2LAhFi5ciGnTpuHgwYNo06YNAMDDwwMRERHQ09PDmjVroKenx4cg/aWJEyfi3LlzOHr0aK6+BryuO1W/fn2o1Wo8evQIxYsX11KUlFe9OUjPbps9ezYOHTqEzz//PFdy6t69e7CysnqrrxFl96VHjx4pzy0LCwtoNBpcvHgRvXv3RuHChREcHIxChQph2bJlSE1NVeqaEWXLeV+aNm0afv75Z7i7u6No0aJwc3ODvr4+Dhw4gNKlSyMtLQ2LFy/G0aNH8eLFC4SFhXHcRO+Uc0yu0WiQnp4OQ0NDAEBkZCS++eYblChRQklO3bt3D4sXL8bly5cRGBgIgPVd6U/Z96kzZ87gzJkzUKlUaNy4MWxtbXMlp8zNzREaGspxE/0r7CX0QTx69AhqtVopdJ6VlYULFy5g/vz5aNSoEfbt24eFCxdizZo1aNOmDZKTkwEAY8eOxdq1a+Ht7Q09PT1kZmZycEUA/ix0fvfuXeXfpUqVwuXLl98qyJmWlobNmzfj119/BQAmpegt2YOq4OBgjB07FtOnT8fRo0ehUqng4uKCdu3aISIiAq6urvjjjz8wa9Ys9OzZEy9fvtR26JTHZPelX3/9FR07dkSLFi3QuHFjBAcHQ61Wo3bt2tixYwdSUlJQpUoV9OnTB87OzujUqZO2Q6c85ObNmwD+/OM/MDAQBw4cgJ+fH4YOHQo9PT0kJCTg6dOnaN68Oe7evQtDQ0N8+eWX+Prrr3HixAmOm+idcialli5dir59+6JRo0ZYtGgRzp49i7p168LPzw8PHz5EixYtkJGRgVKlSmHatGkIDAxkQopyyX7m7d69G506dcKGDRvg6+sLe3t7BAcHw8DAAA4ODliyZAlSUlJQt25dFjqnf+ejLx6kT97s2bPF2NhYYmNjReR1nYSsrCxp1qyZrFmzRg4ePCgmJibi5eUlIq9rR/3444+yefPmXJ/DehuULbsv7N27V2rVqiUbNmxQjjVs2FBq1aol169fl9TUVElLS5OpU6dKmTJl5Pbt21qKmPKDgIAAMTQ0lNatW0v9+vXF3Nxctm7dKiIiL168EHd3d7G1tZVy5cqJpaWlnDp1SssRU14VEBAgJiYm8sMPP8jx48fFyclJChUqlOu59uDBAxk9erSMGDFCLl26pMVoKa/p2rWrzJgxI1dbUFCQzJ07V0REfvvtNylWrJisWrVKLl68KMWKFZM6derIrVu3cl3DGkCU05vjaFdXV7GwsJBp06bJyJEjpUqVKvL111/LoUOHRETk3LlzUrVqValUqVKuup0cj9Objh07JsWLF5e1a9eKyOt6ZSqVSvT09MTPz09EXtdU3Lt3rzRu3JjjcfpXmJii9+748ePStm1bqVSpkjJoyszMlKlTp0rLli3FzMxMfvrpJ+X8xMRE+eqrr2TlypXaCpnygV9//VUMDQ1lxYoVuf6oi46OlmbNmomFhYXY2tpKixYtpHjx4iysSH/r8ePHsmrVKmVQFRsbK1OmTBGVSiVbtmwRkde7qJ06dUp8fX3f+gOQKNudO3fE3t5efvzxRxERiYuLk4oVK0r16tVFV1dXfHx8chWyzi4yTJTtxIkTSmH8xMREpf3u3bvy6tUradWqlUybNk1ERJKSkqRJkyaiVqulU6dOIsLEAf217HtPVFSUWFtb59p4KCQkRDp16iRdunSR+Ph40Wg0Eh4eLr169WKSk/7WnDlzZPr06SLyumB+2bJlZciQITJs2DDR1dWVgwcPisjr5FT25h9E/4SJKXpvfvnlF+XfERER0rp1a6lQoYLcvHlTREROnz4tpUqVknr16snly5clKytL7t27J+3bt5fGjRvzIUh/KSUlRVq3bi1ubm5/eY63t7csXLhQPDw8lD5H9C4XL14UMzMzqV69ujJ4Enm981V2cip75hTRu2QnAp49eybp6ekyf/58efr0qdy/f1+qVasmTk5Okp6eLj169BBzc3P5+eefmTygf7Ry5Urp1q1brhcrd+7ckXLlyklgYKCIiDx9+lR69eolp0+fzpXwJMrm4uIiU6dOzdV2+fJlKVmypISGhuZqP3LkiBQpUkTpXzlxXE7Zsp9fgYGBcvnyZYmOjpYTJ05IcnKyNG7cWL799lsReb3zukqlEpVKJfv27dNmyJQPscYUvReHDh1Cr1694O7uDgBo0KAB5s+fD2tra7Rq1QoxMTGoX78+tm3bhvv376N///6wtrZGjx498OjRI2XXhuyaVEQ5paWlITo6GjVr1gSAXGvV5f/qTQ0ePBguLi4YM2YMd7iiv6Wnp4fu3bvj5s2bePbsGYDX/ahYsWJwcXGBq6sr+vfvDz8/Py1HSnmVSqWCr68v7Ozs8PLlSwwZMgTm5ubw8PBAxYoVsXTpUujp6aFMmTJQq9VwcXFBUlKStsOmPEbe2H+oTJkyOHXqFDw9PXHhwgUAQNmyZVGiRAm4urpi586d6NatG+7du4d69erlquVJBAB//PEHEhIScPToUfzwww9Ke2ZmJlQqFe7du6f8DAAtW7ZE2bJlERER8dZnsVYZZVOpVAgLC0OXLl0QGRmJypUro0mTJrh+/TrS09Mxfvx4AEDhwoXRq1cvuLm5oXLlylqOmvIbJqbovbC3t4eXlxfmzp2LefPmAXidnHJ3d0eVKlXQunVrREdH44svvkBwcDCmT5+OESNGwNXVFadOnWLBTvpbpqamsLCwwJkzZwAg12D8zJkzWL9+vXLumwN9ojdVrVoVU6ZMQc+ePeHk5ITg4GCluKuFhQUmTpyImTNnwsbGRsuRUl517949bNmyBZMmTYKZmRksLS0BANeuXUOpUqVgZmYG4PXGHxs3bsSNGzdQuHBhbYZMeVD2fef333/Hixcv0KVLF6xfvx5BQUFYtmwZoqKiAAArV66EgYEB5s2bBwMDAxw5cgRqtRoajYbjJsqlcOHCWLx4MRo2bIgDBw5g/vz5AABbW1t88803GD58OE6dOgVdXV0AwPPnzyEi+Oyzz7QZNuVxd+7cwYEDBzB9+nT069dPaX/27BnOnTuHFy9eAAC2bt2KZ8+eYerUqRxD0X+n1fla9El59eqVeHl5iY6OjlKwU+T1sr42bdpI+fLlJSYmRkTerofA6cKULbtvZGRkyIsXL5T2iRMnSu3atZWiitmcnZ2lcePG8uzZs48ZJuUT2f0pNjZWrl+/LhcuXFCOXbt2TQYNGiTm5uYSFBSU63wukaG/cubMGenbt6+0bdtWEhMTcz2/Zs6cKYUKFZIFCxaIo6OjmJuby/Xr17UYLeVFOe8vAQEBYmtrK0uXLpW0tDQREdm/f7+ULVtWBg4cKFeuXFHOjYuLy/WMJMop570oKChIevbsKZUrV1bq34mI9O7dW/T19cXZ2VnmzJkjbdq0kVq1arE/0V+6cuWKNGnSRMqXLy9r1qwRkT/72qtXr6Rnz56iUqmkfv36YmpqKufPn9dmuJSPqUQ4vYD+d/J/W4ZmS0tLg7e3N8aOHYvZs2djxowZAIDTp09jxowZuHnzJg4cOABra2tthUx5WHZ/OnDgADZv3oyoqCh8/fXXaN++PZo0aYKuXbvi6dOnqFOnDmxtbXH69Gns3r0bx48fh62trbbDpzwmuz/t3bsXM2bMQHJyMoyNjdGmTRssW7YMwOsZLj/88AMOHDiAjRs3ol27dlqOmvK6uXPnYuPGjXjx4gVu3LgBU1NTZGRkQE9PD6mpqXBzc0NISAjMzc2xfPly1KlTR9shUx6i0WigVr9esLBlyxZcuHABa9euhbm5OSZNmoShQ4fC0NAQBw4cwKhRo+Dg4IBRo0ahfv367/wMojdNmjQJUVFRUKvVOH/+PIyNjTFy5Ei4uLgAAL7//nscOXIE6enpKF++PH7++Wfo6ekhKyuLM/DoncaOHYvNmzejVatW8PHxgampqTLGevLkCQ4cOIA//vgD7dq14xI++t9pMytG+VvON34ZGRm5ZkF5eHiIWq1+a+ZUvXr15JtvvvmocVL+kN1/9u7dK8bGxjJjxgzZvHmz2NvbS4UKFeT27dvy/PlzmT59utjb20utWrWkc+fOuWbAEL3pwIEDYmJiIqtWrZIbN27IqlWrRKVSyYgRI5Rzrl27Jt27d5cKFSpIamoqi1TT30pPT5clS5ZIqVKlZMCAAfL8+XMRyT0T+OnTp5KamqqtECkfmD59ulIYf9OmTdKsWTOpXbu2LFu2TF6+fCkir+9fBgYG4u7uruVoKb/YsWOHFClSRCIiIuTly5fy4MEDGThwoNjZ2cmiRYuU8/74449cM6w4Y4qy/dUYaNKkSVK9enWZN2+e/PHHHx85KioIOGOK/ic539YtX74c58+fR0xMDLp164YuXbqgYsWK8PLywrhx4zBr1ixl5tTVq1dRtWpVvukjAMCBAwdQunRp2NraQkTw+PFj9OjRA126dMGECRPw8uVLlCtXDv3798eSJUty9ZuUlBTo6+tDX19fi9+A8rLHjx9j6NCh+OKLL/Ddd98hISEBTZo0QZUqVXDixAn07t1bqU8WHR0NExMTWFlZaTlqykvk/94IP3jwQJkRVaZMGWRmZuLHH3/E7t270bBhQ3z//fcwNTVFZmamUruF6F1EBPfu3UOrVq0wY8YM9O/fHwCQmpqKYcOG4fTp0xg/frwycyo8PBwNGzbkTBb6VxYtWoTt27fj9OnTyr3o7t27GDlyJM6ePYspU6Zg4sSJua6RN1Y/UMGV3RciIiIQHh4OfX19VKxYEW3btgUATJgwAcePH0fXrl0xduxYmJmZcQYnvTfsRfQ/yb4Bubq6Yv78+WjQoAEcHBywfv16DB8+HC9evICTkxM8PDzg7u4OZ2dnAICNjY1SsJMKtgcPHmDMmDFYvnw5rl69CpVKBWNjY6SkpKB9+/aIjY2FtbU1unTpgqVLl0KtVuPQoUOIjo4GAJiYmDApRbmIiFL8Pjo6GsWKFcOXX36JTp064eHDh2jTpg3atm2Lffv2YeLEifD29lb+KLS2tmZSinLJHqD7+/ujffv2aNSoEVq2bAl3d3fo6upi8uTJ6NKlC86cOYPp06cjKSmJSSl6p5zvgFUqFQoVKgS1Wq0UDM7MzEShQoWwadMmqNVqeHl5Ye3atXj16hUaN27MXYvpH2WPq0uUKAGNRqPsvqfRaFC6dGm4ubnhxYsXWLlyJXx8fHJdy6QUZVOpVPDz80Pr1q2xc+dOeHl5oUOHDsrfccuXL0fjxo3x66+/4ocffkBycjKTUvTesCfRf5Y9wIqIiMC+ffsQEBCA0aNHo1mzZoiNjUW/fv1gbGwMAwMDjBw5EvPmzUN4eHiugRlvYlSyZEns2rULly5dwtKlS3Hp0iXo6Ojg5cuXCAkJQZs2bdC+fXv89NNPAIDbt2/Dx8cHMTExWo6c8prk5GQArwdUKpUK+/btQ4sWLXDlyhUMHz4clStXxs6dO1GyZEnMmTMHBgYGKFWqFOzs7BAeHq4M4IlyUqlUCAoKQu/evTFo0CDMmTMHY8eOxZw5c+Dk5AQdHR1MnjwZnTp1QmBgINzd3bkrKL0l52yUx48fAwD09PRgZmaGoKAgAICuri6ysrKgq6uLunXrQl9fH7t27cLx48eVz+GMKcrpzRe82ePqBg0aIDY2FitWrMCLFy+U9oyMDDRv3hwTJ06Eo6PjR4+X8ofo6GiMGTMGCxcuxPHjxxEaGgofHx94enrC1dUVAODh4YEaNWogPDwc6enpWo6YPilaWD5I+dCsWbMkICAgV9vRo0fFxsZGRER27dolpqam8tNPP4mISEpKivj7+0tqaqpkZWUp65VZu4XedO7cOalXr544OTnJ/fv3xdPTU1QqlXz11Ve5znNzc5OaNWtKXFycliKlvGjYsGEyePBgSU9PFxGRO3fuSK9evWT16tW5zhs5cqQ0bNhQ+XnKlCmyYMGCXDs/EmXLflaNHDlS+vbtm+vY0aNHRa1Wyw8//CAir3cl+vHHHyU2NvZjh0l5XM5anHv27JFWrVrJ5cuXRUTk999/F2NjYxkzZoxSp1Oj0UifPn3kwIEDUrduXenRo4e2Qqc8LOdYevXq1fLdd9/JzJkz5c6dOyLyekyuo6Mjw4cPl/3798vly5elXbt2MnLkSOVa7oZN73Ly5EmpWrWq3L17N1f7xo0bxcjISEJCQpS2xMTEjx0efeI455z+0aVLl3D48GGcOHEChoaG+PLLLwG8fgtoYWGB7du3Y8SIEfjhhx8wYsQIAMCpU6ewd+9e1KhRQ9mdQbiGnd6hbt26WL9+PYYMGYKZM2eid+/emDRpEpYtW4bFixcDAGJjY7FlyxYcO3YMZcqU0XLElFfs2LED/v7+CAwMhJ6eHiIjI+Hl5YV79+7BwcEBwJ/18Lp06YINGzaga9eu0NfXx6FDhxAeHg4jIyMtfwvKS7KfUy9evEChQoUQGxsLc3Nz5VhGRgbs7e0xb948bN26FQMHDkTJkiXx3XffaTlyymty1l05cuQI/Pz8cO7cOcyePRtz5sxBw4YNsWXLFvTr1w+RkZEoWbIk7t+/j6dPn2Lbtm04efIkjh49yvotlEvO/uDq6gpvb2/Url0bDx8+hLe3N4KCgtC9e3fs27cPU6ZMwf79+6Gjo4NixYph3759UKlUEBHOwKN30tPTQ3R0NKKjo1GqVCnlmejg4AArKyskJCQo55YsWVKLkdKniE86+kc1a9bE/PnzYWhoiEWLFuHQoUMAgJYtW+KPP/5Av3798P3332PkyJEAgLS0NCxduhTJycmoWLGi8jlMStFfqVu3Lry9vREZGYmdO3eiTZs2WL58OTZu3Ag/Pz88f/4cJ0+e5LbrlEt8fDwsLCxQp04dHDx4EAMHDkRYWBjOnDmD2NhYAH8ub2jSpAk2bNiA1NRUqNVqHDt2DDY2NtoMn/KY7AF4UFAQZs6cibi4OHTu3BlHjx7FmTNnoFKpoKenBwAwNzeHSqWCmZmZlqOmvCr73vPdd99h7NixKFq0KJo3b45jx45h+vTpuHbtGrp27YqoqCjUqVMHZmZmaNSoES5dugTg9WYxFStW5PJQyiW7Xz18+BAvXrzAoUOHcPjwYWzbtg22trb4/PPPce3aNXz11VcIDAxEcHAwfH19ERERAT09PWRmZnI8TgD+LM1y9epVhIWFITY2FvXq1UPHjh2xatUqnD9/XukrxYsXR5EiRbh0jz4o7spHfysjI0MZiO/YsQObN29GSkoKZs+ejZYtW+Lq1avo3LkzLCwsMHz4cGRmZsLX1xeJiYmIjIyErq4u3/bRv3bu3DkMHz4cderUwdy5c2FpaQmVSoW0tDQYGhpqOzzKY06fPo0BAwbgs88+Q2hoKAIDA5GRkYHJkyejYsWKmDlzJurXr5/rGo1Gg4yMDBgYGGgpasrLdu/ejf79+2Pq1Kn46quvYGhoiKlTpyIrKwtz586FnZ0dAGDy5Mk4e/Ys9u3bB1NTUy1HTXlVaGgoevXqhT179qBx48YAgPXr18PHxwclS5aEu7s7bGxskJWVpcxgefjwIRYvXgwfHx+EhoaievXq2vwKlAdt2bIFI0eORPXq1bFr1y5lJnlMTAzGjx+P8PBwhIeHo2rVqrmuy9nPiADA398fAwYMgKWlJeLj47F+/Xq8fPkS27dvh5mZGYYPH47y5ctj48aN2LBhA37//XeUL19e22HTp0pLSwgpn5k1a5b07NlTbG1tRa1WS7NmzSQoKEhERKKjo6VVq1ZSs2ZNadq0qQwcOFCp98I17PRfnTt3Tho0aCC9evWSS5cuiQhrk9FfGzVqlKhUKmnUqJHStm3bNqlfv74MGDBAzp49q7TnrPdC9Kbr169LhQoVxMvLK1e7v7+/dOzYUSwsLOSrr76Stm3bipmZmURGRmonUMo3goODxcLCQnmWZVu5cqUYGBhI9+7dlZpTIiLx8fHy/fffS5UqVdi/6C8dOXJE2rZtKyYmJkpdqexxUkxMjHTs2FFUKpXEx8drM0zKw7KysuTJkyfStGlTWbNmjURHR8u8efNEV1dXVq1aJevWrZNevXqJWq2WatWqSeXKleXcuXPaDps+cZwxRf/Iy8sLrq6uCAgIQOXKlREeHg5PT0+o1WpMnz5dqeXy6NEjGBsbo1ChQgBeb3/MrbPpf3H69GlMmTIF27dvh5WVlbbDoTzq5cuX6NChAypWrIiTJ0/C1tYW27dvBwBs27YNy5YtQ82aNTFy5Eg0bNhQy9FSXhcUFITRo0cjMDAQ5cqVyzXb99q1azh79iwCAwNRunRpDBgwANWqVdNyxJRXyf8tCz116hT69euHlStX4uuvv1b6VFZWFmxtbWFsbIwaNWpgwYIFsLKygkajQUJCAnR1dVm/hQDgnasORARnzpzByJEjkZSUhBMnTqB48eJKv7t+/TrWr1+PBQsWcBxOuWT3kbS0NIgI3N3dMXnyZKWW4rJly+Ds7IwlS5agT58+SE5ORnp6OiwsLFCiRAktR0+fOt6t6B9FRESgU6dOaNGiBQDgm2++gYmJCSZNmoSZM2dCrVbD3t4exYsXV64RET4M6X/WoEEDHDx4kMv36G8ZGRkhICAAxsbG8Pb2xqJFi9C3b19s27YNffv2VZLnhoaGqF27Npfv0d9KSUnBy5cvc7VlL31JTExE06ZN0a9fPy1FR3nZm8mD7Losn3/+OapUqYLx48ejTJkysLW1BQAkJiaiVq1asLGxwaZNm3DlyhVYWVlBrVajVKlSWvkOlPfk7Fd79uzB/fv3odFo0Lp1azRo0ABr167FuHHjYG9vj6NHj6JEiRIQEVStWlXZPIYviSknlUqFvXv34qeffkJ8fDw0Gg169eqlJKYmTpwIlUoFZ2dnPHz4EG5ubsqEA6IPjYV/6B8VLVoUT548yTVgb9euHRwdHXH27FlMmDABv//+e65rWFiR/n8xKUX/hrGxMQCgZ8+ecHFxQWRkJPr27QsA6N27NxYuXAhnZ2cmpegf1a5dG48fP8batWsBvC4ynF2Pxd/fHxs2bGDhV3pLzuTBzp07MWvWLHh4eCA0NBQAcODAARQvXhydOnXC999/Dx8fHwwcOBApKSmYNWsWRAS//fabNr8C5VHZ/crZ2RmjR49GSEgIvL290bdvX3h7e6NevXpYtGgRLCws8OWXXyIxMfGt8TeTUpTTmTNn4OjoiAoVKqBhw4a4efMmvL29cefOHeWcCRMmYO7cufDy8kJaWpoWo6WChokp+ke2trY4efIkgoKCcu0OU7JkSTRp0gTffPMNGjRooMUIiaigMzExQc+ePeHs7IyLFy+iQ4cOAF7P8KxQoYKWo6P8oEKFCvD09MTixYvh7OyMS5cu4erVq3BxccHGjRvRp08f6OvraztMykNEJFfyYMKECTh79iz27NmDKVOmYPPmzVCpVAgPD8eXX36J/fv3Y8GCBdDX18fOnTsBAFZWVqhSpYo2vwblYdu3b8f27duxb98+7Ny5E+PGjcPly5dRpEgRAK93nF2yZAnS09MxefJk7QZLedrNmzcREBCAqVOn4qeffsKGDRuwYsUK+Pn5YfXq1bmSUy4uLrh16xYsLCy0GDEVNEyj0z8aNGgQTpw4gf79+2P16tWoV68eLC0tsXv3bjg4OMDNzQ0qlYq77xGRVhUqVAg9e/ZEWloafHx8cO/ePS6Lof9k0KBBMDU1xfDhw7F9+3YYGhpCR0cHR44cYU0pyiXnmGfVqlX45Zdf4Ofnh88//xxeXl6YOHEiZs2ahRcvXmD48OFYv349nj9/DhFRls3MnDkTsbGxaNWqlTa/CuVhMTEx+OKLL1C/fn3s3LkTEyZMwIoVK9CtWzekpKTg4cOHaNiwIXbt2gUbGxtth0t5VFJSEnr37o3bt2/j22+/VdpHjhwJjUaDBQsWQEdHB05OTsrLvOzkJ9HHwuLn9LdyDrzGjBkDf39/ZGVlwdTUFDo6Orh48SJ0dXWVYnpERNr24sULZGRkoHDhwtoOhfKp+/fv486dO1CpVKhQoQILUVMuOcc8SUlJcHNzQ/ny5TF58mTs27cPjo6OGD9+PKKjo3Hs2DEsXLgQ/fv3V66Pjo7GzJkzERoaiv3796Nu3bra+iqUh7zrBa+rqyt0dHTQsWNHtG7dGosXL8aIESMgIvDx8cHTp08xbtw46OnpAfizLh7RmyIjI9GrVy+UKFECq1evRs2aNZVjq1evxsSJEzF16lS4ublxCShpBRNT9I9yDsDCw8Px+PFjpKamokePHtDR0eFDkIiIiAqEo0eP4v79++jXrx+GDx8Oc3NzjB8/Hi9fvkRWVha++uorjB49GhMmTMCePXvQp08f6OnpYdOmTejatSsAIC0tDYcPH4aNjQ0qV66s5W9EeUHOpNTNmzdhZGSE4sWL4/Tp02jWrBkAwNfXFz169AAApKamolu3bqhZsyZ+/PFHrcVN+cuFCxcwcOBANGzYEOPGjUONGjWUYz///DO++OILWFtbazFCKsiYmKJ/5a+W6TEpRURERJ86EUFKSgq6d++O9PR0mJmZITQ0FGFhYcpue1u2bIGHhwcCAwNRuHBhBAYGYs2aNWjfvj0GDx7M8RK9U84XwK6urti7dy8ePXqEGjVqKLXtRo0aBW9vbzRt2hRJSUmYMmUKHj58iIiICM5uof8kMjISQ4cORb169TBx4kRUr15d2yERAWDx8wJLo9G8s/2v8pTZSak3r+Mgi4iIiD51KpUKpqam2LFjBxITE/Hrr7/Czc1NSUoBgJ6eHuLi4hAWFoYXL17Aw8MD5cuXh5OTkzLDnCgnjUajJKV27NiBjRs3YuHChfjxxx/RqFEjTJgwAadPn8aiRYvg5OSExo0bw9HREenp6fj999+hq6vLfkX/Sd26dbF+/XpcuHAB8+bNw7Vr17QdEhEAFj8vkHLOfrp06RJevHiBEiVKoHz58lCpVH85Cyrn7jPXrl1D2bJlla3aiYiIiD51arUalSpVQsmSJREcHIzSpUujX79+AIDq1avjiy++gKOjI4oUKYJChQph9+7dUKlUEBG+zKO3ZI+rQ0JCEBwcDGdnZ3Tu3BnA6/pl5cuXh6urK7Zv347Lly8jPj4eZmZmqF27NtRqNTIzMzljiv6zunXrwtPTE1OmTGE9TsozuJSvgMk5XXjatGnYt28f4uLi0KhRIzRs2BDu7u4A3l6il/M6Dw8PLFiwACdPnkT58uU/+ncgIiIi0qbExEQ4OTnh5cuXcHJyUpJT169fx7Vr15CcnIw+ffpAR0eHyQP6W4mJiWjWrBkePnwIFxcXTJs2TTn25MkTODk5oUyZMvDw8Mh1HXfDpv9faWlpMDQ01HYYRAC4lK/AyU4uubu7Y/369VixYgViYmJQqlQpeHp6YsyYMQCQa8p5zqTUmjVrMHv2bCxdupRJKSIiIiqQLC0t4enpCWNjY2zcuBHe3t7IysrCqFGjcPHiRfTv318ZSzEpRX/H0tISu3fvRokSJbB7925ERkYqxywsLFCsWDHcvHnzreuYlKL/X0xKUV7CO1oBkXNi3JUrV7Bnzx5s3boVDg4OiIqKwi+//II2bdrg4MGDmDBhAoDXyamMjIxcSSlnZ2esXbsWvXv31sbXICIiIsoTKlSoAA8PD5iammLJkiWwtrbGw4cP4ezsrJzD5Xv0b9ja2mL37t3IysrC8uXLcf78eQBAcnIyrl69itKlS2s3QCKiD4xL+QqAnDOeoqKiYGtri/Xr16Nbt264dOkSevfujXnz5mHIkCHo0KEDjhw5gm7dumHbtm3KZ6xduxbOzs74+eef0b17d219FSIiIqI8JSEhAWfPnsWDBw8wcOBA6Orqcvke/U8iIyPRv39/PH36FPXr14e+vj5iY2Nx6tQp6Ovr5xrTExF9SpiY+sS9uQXtqVOnsGPHDpQsWRIqlQojR46Erq4uli5dCj09PUyZMgWnT59G9erV4enpCbVajX379qFLly7YtWsXunXrpuVvRERERJR3/dUmMkT/xqVLl9CpUyeULl0affv2xYgRIwAAGRkZ0NPT03J0REQfBpfyfeKyk1LXrl1DeHg45s+fD0tLS6U9NjYWd+/ehZ6eHrKysnDnzh0MGDAAq1atUtaud+jQAUePHmVSioiIiOgfMClF/z9q1qyJ3bt3Iz09HefOnUNMTAwAMClFRJ80zpgqABYsWICQkBAYGhpiy5YtMDU1hUajAQAsX74cmzdvhpWVFZKSkvD8+XNERUVBR0cHIsKinUREREREH1lkZCRGjBiBihUrYtasWahWrZq2QyIi+mA4Y6oAsLGxweHDh3H8+HHcvn0bwOudPNRqNfr06QNHR0cULlwYNWrUQGRkpLKLjEqlYlKKiIiIiOgjq1u3Ljw9PZGQkIDChQtrOxwiog+KM6Y+MX9VFDE4OBht2rTBoEGDlOV8f4UFO4mIiIiItC8tLQ2GhobaDoOI6IPijKlPiEajUZJSDx8+RFxcnHKsVatW8Pf3h4+PD9zd3fHgwYNc12UTESaliIiIiIjyACaliKggYGLqE6HRaJRi5XPnzkX79u3RoEEDtGvXDiEhIUhLS0PHjh3h7++P1atXY/78+UhISAAA5ToA3IKWiIiIiIiIiD4aJqY+ASKiJJdmzZqF1atXY8KECQgPD8etW7cwffp0BAQE5EpOeXp6Yvv27VqOnIiIiIiIiIgKMq7ZyseuXr0KGxsb5ecTJ05g79692LJlCxwcHBAWFoZ79+5BRDB9+nTo6Ojgq6++QocOHRAWFoZGjRppMXoiIiIiIiIiKug4YyqfWrJkiZJ8UqlUEBGYm5tjzJgxcHBwQHBwMLp164ZVq1YhOjoaaWlpWLp0KXx9fZGeno6mTZtCV1cXmZmZ2v4qRERERERERFRAMTGVT9WqVQtffPEFxo8frySnrK2t0bFjR2RkZGD58uUYNmwYHB0dISKwtrZGVFQUTpw4AX19feVzWOiciIiIiIiIiLSFial8Zt26dQCAtm3bYtSoUahcuTLGjh2LY8eOQU9PDyVLlkR6ejoeP34MCwsLpfZU2bJlERISgtWrV2szfCIiIiIiIiIiBafL5CNBQUEYPnw4oqKi4OnpiRYtWkBE4OXlhXHjxsHDwwPNmzeHWq2Grq4udu3ahaSkJISFheHJkyeoW7cu1Go1srKyoKOjo+2vQ0REREREREQFHGdM5SMNGjTA2rVrsWvXLowePRoAYG9vj1GjRqFKlSoYO3YsQkJCYGRkBD8/PxgbG+PEiRMwNTXFmTNnoFarodFomJQiIiIiIiIiojxBJSKi7SDo30tOTsaOHTswbdo09OjRA6tWrQIAhISEwMvLCzdu3MDSpUvh4OCAtLQ0iAgMDQ2hUqmQmZnJmlJERERERERElGcwS5EPiAhUKhUAwNTUFD169AAAuLm5AQBWrVoFe3t7AICXlxemTJmCBQsWoE2bNrk+g0kpIiIiIiIiIspLmKnI4zQajVLAXKPRIDMzE0WKFMHAgQMBAFOnTgXwZ3JKpVJh3rx52LZtW67EVHZii4iIiIiIiIgor2BiKg/LmZT68ccfERUVhXPnzmH48OFo2bIlhg0bBgCYNm0aVCqVUhDdzMwMtWvX1mboRERERERERET/iDWm8oGpU6fi559/xsyZM5GSkoL169ejWrVq2LFjB7KysrBz505Mnz4drVq1wtatW5Xrcia2iIiIiIiIiIjyGmYt8riIiAj4+/sjICAAY8aMQbNmzRAXF4eePXvCxMQEhQsXxoABAzB16lQ8f/4cGo1GuZZJKSIiIiIiIiLKy5i5yOM0Gg0MDQ3RqFEj/PLLL2jfvj1WrlwJR0dHpKam4sCBAwCAb7/9Fr/++ivUanWu5BQRERERERERUV7FxFQe8q6EUkpKCtLS0rBjxw58++23WLhwIUaMGAEAOHnyJLZt24a4uDgYGRlBpVJBRDhTioiIiIiIiIjyBdaYyiNy1oNavXo1ACgJqLZt2+Lw4cPw8PDA6NGjAQBpaWn45ptvYGRkBF9fXyajiIiIiIiIiCjf4a58eUR2YmnKlCnw9fXFwIEDcffuXZQuXRrff/89/vjjDyxbtgyFCxfGs2fPEBAQgPv37+P8+fPK8j0mp4iIiIiIiIgoP+GMqTxky5Yt+O677/Dbb7/Bzs5OaddoNLh27Rrmzp2LqKgolChRAtbW1vjpp5+gp6eHzMxM6Ooyx0hERERERERE+QsTU3mIm5sb7t27h40bNyIrKws6OjpvJZ0ePHgACwsLpY1JKSIiIiIiIiLKr7j2Kw+5d+8eYmNjAQA6OjoQEejq6iItLQ1BQUEAgJIlSyqJqOzjRERERERERET5ERNTWvCu3fcAoG7dunjw4AGOHj2K9PR0qFQqAEBSUhLmzJmD3377Ldf52ceJiIiIiIiIiPIjLuX7yHIWKT99+jQ0Gg10dHRQv359vHr1Ck2bNgUATJ06FU2bNkVKSgomTJiAZ8+e4dixY9DR0dFm+ERERERERERE7w0TUx+RiCiznFxcXLB9+3aoVCo8ePAAffr0waJFi2BqaorOnTvj3r17iImJQfXq1aGnp4fjx49DT09PqT1FRERERERERJTfMTGlBZ6enpgzZw727t0LCwsLxMfHY8CAAWjUqBG2bt0KfX19XLlyBdevX0fJkiXRrFmzdxZCJyIiIiIiIiLKz5iY0oKBAwfCyMgIq1evVmZRnT9/Hl988QXGjh2L+fPnv3UNZ0oRERERERER0aeGxc8/sDfzfhkZGbh37x7S0tKU4+np6ahTpw5mz56NnTt34tmzZ8jKysp1HZNSRERERERERPSpYWLqA9JoNEpNqVu3buHhw4fQ09ODo6Mjdu3aheDgYKjVaujp6QEADAwMUKxYMRQqVIiJKCIiIiIiIiL65DEx9QFl777n5uaGTp06oXr16nB2doaJiQmGDBmC0aNH4+DBg9BoNPjjjz/w66+/olSpUkqiioiIiIiIiIjoU8ZK2h+ARqNRklI7d+7Epk2b4OnpiQsXLuDgwYOIi4vD559/jo4dO6JDhw6oWLEidHR0YGBggNOnT0OlUuXawY+IiIiIiIiI6FPE4ucf0LFjx+Dn54fatWtjyJAhAIB9+/bBw8MD5ubmGDZsGEqUKIHff/8dJiYm6NWrF3ffIyIiIiIiIqICg4mpDyQxMRHNmjXDo0ePMGfOHEyYMEE5FhAQgOXLl8PMzAxTp05Fw4YNlWPcfY+IiIiIiIiICgrWmPpALC0tsXv3blhaWuLAgQO4ePGicqxjx46YNGkSYmJisGfPnlzXMSlFRERERERERAUFZ0x9YFFRURg8eDDq16+P8ePHo0aNGsqxkydPolGjRkxGEREREREREVGBxMTURxAZGYmhQ4fCzs4OEyZMQPXq1XMd5/I9IiIiIiIiIiqImJj6SCIjIzF8+HCUK1cOixYtQoUKFbQdEhERERERERGRVrHG1EdSt25deHp6wtTUFOXKldN2OEREREREREREWscZUx+ZiEClUkGj0UCtZl6QiIiIiIiIiAouJqa0IDs5RURERERERERUkHHKjhYwKUVERERERERExMQUERERERERERFpCRNTRERERERERESkFUxMERERERERERGRVjAxRUREREREREREWsHEFBERERERERERaQUTU0RERET5lEqlgr+/v7bDICIiIvqfMTFFRERE9AF17NgR7dq1e+exsLAwqFQqXLhw4X/67ISEBLRv3/5fnz9o0CB06dLlf/pdRERERB8CE1NEREREH5CTkxMOHz6Mu3fvvnVsw4YNqF+/Pmxtbf/TZ6anpwMALC0tYWBg8F7iJCIiItIGJqaIiIiIPqAOHTqgePHi8PHxydWekpKCnTt3okuXLujTpw9KlSoFY2Nj1KpVC9u3b891rr29PcaMGYMJEyagWLFiaNu2LYC3l/LFx8ejZ8+eKFKkCIoWLYrOnTvj9u3bAIDZs2dj48aN2Lt3L1QqFVQqFUJCQuDg4IAxY8bk+n2PHj2Cvr4+goOD3/v/BxEREVFOTEwRERERfUC6urpwdHSEj48PRERp37lzJ7KystC/f3/Y2dlh//79uHTpEr799lsMGDAAERERuT5n48aN0NfXx4kTJ7B69eq3fk9GRgbatm0LU1NThIWF4cSJEzAxMUG7du2Qnp6OyZMno2fPnmjXrh0SEhKQkJCAJk2aYOjQodi2bRtevXqlfNaWLVtQqlQpODg4fLj/GCIiIiIwMUVERET0wQ0ZMgQ3b95EaGio0rZhwwZ0794d5cqVw+TJk1GnTh1UrFgRY8eORbt27fDLL7/k+gxra2ssWrQIVatWRdWqVd/6Hb6+vtBoNFi/fj1q1aoFGxsbbNiwAXFxcQgJCYGJiQmMjIxgYGAAS0tLWFpaQl9fH926dQMA7N27V/ksHx8fDBo0CCqV6gP9jxARERG9xsQUERER0QdWrVo1NGnSBN7e3gCAmJgYhIWFwcnJCVlZWZg3bx5q1aqFokWLwsTEBIcOHUJcXFyuz7Czs/vb3xEVFYWYmBiYmprCxMQEJiYmKFq0KNLS0nDz5s2/vM7Q0BADBgxQYjt37hwuXbqEQYMG/f99aSIiIqJ/QVfbARAREREVBE5OThg7dixWrVqFDRs2oFKlSmjRogV++OEHrFixAsuXL0etWrVQqFAhTJgwQSlwnq1QoUJ/+/kpKSmws7PD1q1b3zpWvHjxv7126NChqFOnDu7evYsNGzbAwcEB5cqV++9fkoiIiOg/YmKKiIiI6CPo2bMnxo8fj23btmHTpk0YOXIkVCoVTpw4gc6dO6N///4AAI1Ggxs3bqB69er/6fPr1asHX19flChRAmZmZu88R19fH1lZWW+116pVC/Xr18e6deuwbds2eHp6/vcvSERERPQ/4FI+IiIioo/AxMQEvXr1wtSpU5GQkKAslbO2tsbhw4dx8uRJXL16FcOHD8eDBw/+8+f369cPxYoVQ+fOnREWFobY2FiEhIRg3LhxuHv3LgCgfPnyuHDhAq5fv47Hjx8jIyNDuX7o0KFYuHAhRARdu3Z9L9+ZiIiI6J8wMUVERET0kTg5OeHZs2do27YtPvvsMwDA9OnTUa9ePbRt2xb29vawtLREly5d/vNnGxsb49ixYyhbtiy6desGGxsbODk5IS0tTZlBNWzYMFStWhX169dH8eLFceLECeX6Pn36QFdXF3369IGhoeF7+b5ERERE/0QlOfctJiIiIqIC6fbt26hUqRJOnz6NevXqaTscIiIiKiCYmCIiIiIqwDIyMvDkyRNMnjwZsbGxuWZREREREX1oXMpHREREVICdOHECVlZWOH36NFavXq3tcIiIiKiA4YwpIiIiIiIiIiLSCs6YIiIiIiIiIiIirWBiioiIiIiIiIiItIKJKSIiIiIiIiIi0gompoiIiIiIiIiISCuYmCIiIiIiIiIiIq1gYoqIiIiIiIiIiLSCiSkiIiIiIiIiItIKJqaIiIiIiIiIiEgrmJgiIiIiIiIiIiKt+H9QO1u8MBXrZAAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_oil_production_l_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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hhBCPHj0SAMThw4fFy5cvhaqqqggLCxNCvC3aABDTp0+Xtlf2GtDV1RWenp659j9w4EBhamoqnj59qtDeo0cPYWhoKI2f84tilSpVcu0zPzlFKSGEaN68uTAxMZG2zaso1bJlS2FnZyfS09OlNrlcLpycnMR3330ntU2bNk3o6uqKGzduKOzP19dXqKqqiri4OCGEEDt37hQAREBAgBSTlZUlmjRpolRRasWKFXleAznHpqmpqfD1zblmTExMREpKitT+fuHx3ULey5cvReXKlaWvf06/lpaWQn451/iAAQMU8vjhhx9EmTJlFNry+vq4u7vnW4TX0NAQw4YNy7PvQ+Pmda3dvHlTqKioiB9++EHh+1aIt1/LHDmfk+Hh4Qoxo0aNEgDE0aNHpbaXL18KS0tLYWFhIY3ZoUMHUaNGjQ/m+7HPsrwkJSUp9bMhp3Cb83VWpiil7GeKstd3fnI+U5YvX67Q3qhRI1GxYkXpHOZ1ncycOVPIZDKFn1M5n+O+vr654t8vSh09elQAEOvXr1eICw8Pz9Veo0YNhfOT4/2ilFwuF999951wd3dXuIZevXolLC0tRatWraS2T/maExF9zXj7HhFRCZVzy5GyC1vv27cPADBmzBiF9v/9738AkOvWEltbWzg6OkrvGzZsCABo0aIFKleunKs9r7VJfHx8pH/n3CqUmZmJAwcOSO3a2trSv1+8eIHk5GQ0adIkz1sVmjVrptSTvEqVKoVTp07h4cOHefafPXsWjx8/xvDhw6GlpSW1e3h4wMbGJs/bbIYOHarwvkmTJh9dj+XAgQPIzMzEqFGjFNYW8fLygoGBQZ77Udb69etRr149aVF0fX19eHh4KNzC16lTJ6ipqWHTpk1S26VLl3DlyhWFp/WFh4fD0dERtWvXltqMjIyk21s+xsnJCSoqKtJaUcePH4e6ujrq168PPT092NvbS7eo5Pz33Vu6CnINvE8IgW3btqF9+/YQQuDp06fSy93dHcnJybnG8fT0VNinsiZPnoyEhIR815Z6/vw5Dh48iG7duuHly5dSHs+ePYO7uztu3rwp3R66ZcsWNGnSBKVLl1bI2dXVFdnZ2Thy5AiAt9+3ampqGDZsmLQfVVVVjBgxQqmcnz17BgAoXbp0nv0tW7ZUuHUp5/u5c+fOCp8t73+fb9y4EY0bN0azZs3g5OSEbt26KTxN8eDBg8jIyECbNm1y7TOv76Vnz54p3Eb57tcnOTkZT58+RbNmzXDnzh0kJyfnGjPnPH6MMtfazp07IZfL4e/vn2tNoJx1tHJYWlrC3d1doW3fvn1o0KCBwjWup6eHwYMH4969e7hy5QqAt59TDx48wJkzZ/LN92OfZXl5+fIlgI//bMjp/9jtq+9S9jNF2es7P25ubihXrpzCLXx3797FyZMn0bNnT+nr8u7XMy0tDU+fPoWTkxOEELluKweg8H2Uny1btsDQ0BCtWrVSyN3BwQF6eno4dOjQR8d4X2xsLG7evIlevXrh2bNn0phpaWlo2bIljhw5It3W+ClfcyKir9l/e9VXIiLKl4GBAYD/+wXkY+7fvw8VFZVcT3YzMTFBqVKlcP/+fYX2dwtPAGBoaAgAMDMzy7P93bU2gLcLvVapUkWhrWrVqgCgsDbN3r178dtvvyE2NlZhbav3f/kDoPAEtw8JCAiAp6cnzMzM4ODggLZt26Jv375SPjnHWq1atVzb2tjY5FqYW0tLK9e6LKVLl851zO/Lbz8aGhqoUqVKrnOurKSkJOzbtw8+Pj64deuW1O7s7Ixt27bhxo0bqFq1KsqWLYuWLVti8+bNmDZtGgBg06ZNUFNTU3ic+v379xUKkDmUfQpgqVKlUKNGDYXCU506daRfGJ2cnBT6NDQ00KBBA2n7glwD73vy5AmSkpIQHByc71MmcxaRz6HsdfS+pk2bonnz5ggICMhVWAHerrEkhMDEiRMxceLEfHOpWLEibt68iQsXLuS6rt7P+f79+zA1NYWenp5Cf17X7oeIfBYB/9Tv8wEDBmDAgAH57i8sLAz16tVD+fLlP7rPnILZixcvpM+148ePY9KkSYiOjs613lRycrKUz7vHp8z1osy1dvv2baioqChVAM/rWrp//75UxHtX9erVpf6aNWti/PjxOHDgABo0aABra2u4ubmhV69ecHZ2lrb52GdZXnKKTR/72aBs8epdyn6mKHt950dNTQ3du3fH0qVLER8fj4oVK0oFqneL5XFxcfD398fu3btzfR6/X7xUU1NDpUqVPnqMN2/eRHJyMoyNjT8p9/zGBN4WxPOTnJyM0qVLf9LXnIjoa8aiFBFRCWVgYIAKFSrg0qVLBdpOmV/cgLezMQrSnt8vvR9y9OhRfP/992jatCmWLl0KU1NTqKurIyQkJM9FbpWd3dKtWzc0adIEO3bswN9//405c+Zg9uzZ2L59e54zNz4mv2MuLlu2bEFGRgbmzZuHefPm5epfv369tBhxjx490L9/f8TGxqJ27drYvHkzWrZsibJlyxZqTo0bN8ayZcuQlJSE48ePw8nJSepzcnLCqlWr8ObNGxw7dgwODg7SDLWCXgPvy5ld8OOPP+b7C5+9vb3C+0+ZJZVj0qRJcHFxwfLly3M9KTEnl59//jnX7JkcOYU+uVyOVq1aYdy4cXnG5RRwP1eZMmUAvC345PUL+Zf6Pt+3bx/69+//SWPfvn0bLVu2hI2NDebPnw8zMzNoaGhg3759WLBgQZ4LZSclJX30mv7cay0vn3MtVa9eHdevX8fevXsRHh6Obdu2YenSpfD395e+fz/ls8zQ0BCmpqa4cOHCB/d/4cIFVKxYUSoEKkuZz5TCuL5//PFHLFmyBBs2bMDPP/+MDRs2wNbWVprRmZ2djVatWuH58+cYP348bGxsoKuri/j4ePTr1y/XdaKpqanU0/DkcjmMjY0VZp2+K79C28fGBN4uQP/ujNR35RSeC/vnFxFRcWNRioioBGvXrh2Cg4MRHR2d50yXd5mbm0Mul+PmzZvSX+yBt09USkpKgrm5eaHmJpfLcefOHYVfPm7cuAEA0u1C27Ztg5aWFvbv36/wSO6QkJDP3r+pqSmGDx+O4cOH4/Hjx6hbty6mT5+ONm3aSMd6/fp1tGjRQmG769evF9q5eHc/7/6VOzMzE3fv3s3ziWjKWL9+PWrWrCk9se9dy5cvR2hoqPRLbceOHTFkyBDpdpsbN27Az88vV57vzrjKkVdbfho3boygoCAcOHAA58+fx9ixY6U+JycnvH79GmFhYbhz5w46d+4s9RXkGsiroFquXDno6+sjOzv7k89nQTRr1gwuLi6YPXs2/P39Ffpyvsbq6uofzcXKygqpqakfjTM3N0dkZCRSU1MVZktdv35dqXxtbGwAvL31yc7OTqltPtelS5cQFxcHDw+PT9p+z549yMjIwO7duxVmVeV321R8fDwyMzMVPtfyouy1ZmVlBblcjitXruRbQPgQc3PzPL8+165dk/pz6Orqonv37ujevTsyMzPRqVMnTJ8+HX5+flLh9kOfZflp164d/vjjDxw7dizX0w+BtwW6e/fuYciQIQU+PmU+U5S9vj+kYcOGsLKyQmhoKFq1aoXLly9j+vTpUv/Fixdx48YNrFmzBn379pXaIyIiPnmfObkfOHAAzs7OHy06KvtHHisrKwBv/5ikzDn5lK85EdHXimtKERGVYOPGjYOuri4GDRqExMTEXP23b9/GokWLAABt27YFACxcuFAhZv78+QDwyb9Afsi7jwcXQmDJkiVQV1dHy5YtAbydMSGTyZCdnS3F3bt3Dzt37vzkfWZnZ+e6bcPY2BgVKlSQbtepV68ejI2NsWzZMoVbeP766y9cvXq10M6Fq6srNDQ08PvvvyvMMFm5ciWSk5M/aT///vsvjhw5gm7duqFLly65Xv3798etW7dw6tQpAG9vrXN3d8fmzZuxceNGaGhooGPHjgpjuru7Izo6GrGxsVLb8+fP850pkJecX3znz5+PN2/eKMyUsrCwgKmpqfT4+Xd/SS7INaCrq4ukpCSFNlVVVXTu3Bnbtm3Lc9bgkydPlD4GZeWsLfX+7YLGxsbSLKpHjx59MJdu3bohOjoa+/fvzxWXlJSErKwsAG+/b7OyshAUFCT1Z2dnY/HixUrl6uDgAA0NDZw9e1ap+MKwb98+lC9fHvXq1fuk7XNmUr37PZOcnJxvsTomJgYAFK65/MZV5lrr2LEjVFRUMHXq1FyzbZSZKda2bVucPn0a0dHRUltaWhqCg4NhYWEh3RaYs95XDg0NDdja2kIIgTdv3ij1WZafsWPHQltbG0OGDMm1n+fPn2Po0KHQ0dFRKB4rS5nPFGWv74/p3bs3zp8/j0mTJkEmkymsW5bXdSKEkH7mfapu3bohOztbuj3xXVlZWQqfQXl9JuXFwcEBVlZWmDt3LlJTU3P153w2fM7XnIjoa8WZUkREJVjOX5G7d++O6tWro2/fvqhZsyYyMzNx4sQJbNmyBf369QMA1KpVC56enggODkZSUhKaNWuG06dPY82aNejYsSOaN29eqLlpaWkhPDwcnp6eaNiwIf766y+EhYXhl19+kW5/8PDwwPz589G6dWv06tULjx8/RmBgIKytrT9660l+Xr58iUqVKqFLly6oVasW9PT0cODAAZw5c0a61U1dXR2zZ89G//790axZM/Ts2ROJiYlYtGgRLCwsMHr06EI5B+XKlYOfnx+mTJmC1q1b4/vvv8f169exdOlS1K9fHz/++GOBxwwNDYUQAt9//32e/W3btoWamhrWr18vrWvTvXt3/Pjjj1i6dCnc3d1z3XY2btw4rFu3Dq1atcKIESOgq6uLFStWoHLlynj+/LlSswEqV64MMzMzREdHw8LCAhUqVFDod3JywrZt2yCTyRTWzCnINeDg4IADBw5g/vz5qFChAiwtLdGwYUPMmjULhw4dQsOGDeHl5QVbW1s8f/4c586dw4EDB/D8+XNlTq3SmjVrhmbNmuHw4cO5+gIDA9G4cWPY2dnBy8sLVapUQWJiIqKjo/HgwQP8888/AN4WDXbv3o127dqhX79+cHBwQFpaGi5evIitW7fi3r17KFu2LNq3bw9nZ2f4+vri3r17sLW1xfbt2/Nc7DsvWlpacHNzw4EDBzB16tRCPQ/5CQsLQ5s2bZSeRfI+Nzc3aGhooH379hgyZAhSU1Pxxx9/wNjYOM9iX0REBCpXrow6dep8cFxlrzVra2v8+uuvmDZtGpo0aYJOnTpBU1MTZ86cQYUKFTBz5swP7sfX1xcbNmxAmzZtMHLkSBgZGWHNmjW4e/cutm3bJt1C5ubmBhMTEzg7O6N8+fK4evUqlixZAg8PD+jr6yMpKemjn2X5+e6777BmzRr07t0bdnZ2GDhwICwtLXHv3j2sXLkST58+xYYNG6QZPAX1sc8UZa/vj/nxxx8xdepU7Nq1C87OzgqL8tvY2MDKygo///wz4uPjYWBggG3btn10rb+PadasGYYMGYKZM2ciNjYWbm5uUFdXx82bN7FlyxYsWrQIXbp0AfD2MykoKAi//fYbrK2tYWxsnGv2LfB2jcUVK1agTZs2qFGjBvr374+KFSsiPj4ehw4dgoGBAfbs2aPUzy8iov+cIn/eHxERFbkbN24ILy8vYWFhITQ0NIS+vr5wdnYWixcvVng0/Zs3b8SUKVOEpaWlUFdXF2ZmZsLPz08hRoi3jzr38PDItR8AuR5VfffuXQFAzJkzR2rz9PQUurq64vbt28LNzU3o6OiI8uXLi0mTJuV6xPrKlSvFd999JzQ1NYWNjY0ICQmRHh3/sX2/2zdp0iQhhBAZGRli7NixolatWkJfX1/o6uqKWrVqiaVLl+babtOmTaJOnTpCU1NTGBkZid69e4sHDx4oxOQcy/vyyjE/S5YsETY2NkJdXV2UL19eDBs2TLx48SLP8Z48efLBsezs7ETlypU/GOPi4iKMjY3FmzdvhBBCpKSkCG1tbQFArFu3Ls9tzp8/L5o0aSI0NTVFpUqVxMyZM8Xvv/8uAIiEhASljrNnz54CgOjVq1euvvnz5wsAonr16rn6lL0Grl27Jpo2bSodi6enp9SXmJgovL29hZmZmVBXVxcmJiaiZcuWIjg4WIrJeUz7li1blDoeIfK/7nLGAiDOnDmj0Hf79m3Rt29fYWJiItTV1UXFihVFu3btxNatWxXiXr58Kfz8/IS1tbXQ0NAQZcuWFU5OTmLu3LkiMzNTinv27Jno06ePMDAwEIaGhqJPnz7i/PnzAoAICQn56DFs375dyGQyERcX99Fjy+v7+d3j/di5S0pKEmpqamLz5s25+vK7xkNCQgQAcffuXalt9+7dwt7eXmhpaQkLCwsxe/ZssWrVqlxx2dnZwtTUVEyYMOGDeeVQ9loTQohVq1ZJnw+lS5cWzZo1ExEREVJ/fp+TQry9Brp06SJKlSoltLS0RIMGDcTevXsVYpYvXy6aNm0qypQpIzQ1NYWVlZUYO3asSE5OFkIU7LMsPxcuXBA9e/YUpqam0vdFz549xcWLF3PF5vV1aNasmWjWrFmuWGU+U5S9vj+mfv36AkCex33lyhXh6uoq9PT0RNmyZYWXl5f4559/cn1v5Pc5ntNnbm6eqz04OFg4ODgIbW1toa+vL+zs7MS4cePEw4cPpZiEhATh4eEh9PX1BQDpXOV8vxw6dEhhzPPnz4tOnTpJX3Nzc3PRrVs3ERkZKYQonK85EdHXRibEJ6w8S0RE9Bn69euHrVu35nmbAv13jBo1CsuXL0dqaupXt9g7KS87Oxu2trbo1q1bnrckFabNmzejd+/eePr0aa4n5H0JO3fuRK9evXD79m2Ympp+8f0RERFRwXBNKSIiIvqo169fK7x/9uwZ/vzzTzRu3JgFqf84VVVVTJ06FYGBgV+8UFyqVCn8/vvvRVKQAoDZs2fDx8eHBSkiIqKvFGdKERFRkeNMqf+e2rVrw8XFBdWrV0diYiJWrlyJhw8fIjIyEk2bNi3u9IiIiIjoP4gLnRMREdFHtW3bFlu3bkVwcDBkMhnq1q2LlStXsiBFRERERJ+MM6WIiIiIiIiIiKjIcU0pIiIiIiIiIiIqcl9NUWrWrFmQyWQYNWqU1Jaeng5vb2+UKVMGenp66Ny5MxITExW2i4uLg4eHB3R0dGBsbIyxY8ciKytLISYqKgp169aFpqYmrK2tsXr16lz7DwwMhIWFBbS0tNCwYUOcPn36SxwmERERERERERHhK1lT6syZM1i+fDns7e0V2kePHo2wsDBs2bIFhoaG8PHxQadOnXD8+HEAbx9h7OHhARMTE5w4cQKPHj1C3759oa6ujhkzZgAA7t69Cw8PDwwdOhTr169HZGQkBg0aBFNTU7i7uwMANm3ahDFjxmDZsmVo2LAhFi5cCHd3d1y/fh3GxsZKHYNcLsfDhw+hr68PmUxWiGeHiIiIiIiIiOi/QwiBly9fokKFClBR+cB8KFHMXr58Kb777jsREREhmjVrJn766SchhBBJSUlCXV1dbNmyRYq9evWqACCio6OFEELs27dPqKioiISEBCkmKChIGBgYiIyMDCGEEOPGjRM1atRQ2Gf37t2Fu7u79L5BgwbC29tbep+dnS0qVKggZs6cqfRx/PvvvwIAX3zxxRdffPHFF1988cUXX3zxxRdfgPj3338/WEsp9plS3t7e8PDwgKurK3777TepPSYmBm/evIGrq6vUZmNjg8qVKyM6OhqNGjVCdHQ07OzsUL58eSnG3d0dw4YNw+XLl1GnTh1ER0crjJETk3ObYGZmJmJiYuDn5yf1q6iowNXVFdHR0fnmnZGRgYyMDOm9+P/rxf/7778wMDD4tJNBRERERERERPQfl5KSAjMzM+jr638wrliLUhs3bsS5c+dw5syZXH0JCQnQ0NBAqVKlFNrLly+PhIQEKebdglROf07fh2JSUlLw+vVrvHjxAtnZ2XnGXLt2Ld/cZ86ciSlTpuRqNzAwYFGKiIiIiIiIiL55H1veqNgWOv/333/x008/Yf369dDS0iquND6Zn58fkpOTpde///5b3CkREREREREREf1nFFtRKiYmBo8fP0bdunWhpqYGNTU1HD58GL///jvU1NRQvnx5ZGZmIikpSWG7xMREmJiYAABMTExyPY0v5/3HYgwMDKCtrY2yZctCVVU1z5icMfKiqakpzYri7CgiIiIiIiIiooIptqJUy5YtcfHiRcTGxkqvevXqoXfv3tK/1dXVERkZKW1z/fp1xMXFwdHREQDg6OiIixcv4vHjx1JMREQEDAwMYGtrK8W8O0ZOTM4YGhoacHBwUIiRy+WIjIyUYoiIiIiIiIiIqHAV25pS+vr6qFmzpkKbrq4uypQpI7UPHDgQY8aMgZGREQwMDDBixAg4OjqiUaNGAAA3NzfY2tqiT58+CAgIQEJCAiZMmABvb29oamoCAIYOHYolS5Zg3LhxGDBgAA4ePIjNmzcjLCxM2u+YMWPg6emJevXqoUGDBli4cCHS0tLQv3//IjobRERERERERETflmKbKaWMBQsWoF27dujcuTOaNm0KExMTbN++XepXVVXF3r17oaqqCkdHR/z444/o27cvpk6dKsVYWloiLCwMERERqFWrFubNm4cVK1bA3d1diunevTvmzp0Lf39/1K5dG7GxsQgPD8+1+DkRERGRMoKCgmBvby/d4u/o6Ii//vpL6k9ISECfPn1gYmICXV1d1K1bF9u2bZP67927h4EDB8LS0hLa2tqwsrLCpEmTkJmZqbAfIQTmzp2LqlWrQlNTExUrVsT06dOl/mPHjsHZ2RllypSBtrY2bGxssGDBgo/mf+HCBTRp0gRaWlowMzNDQEBArpgtW7bAxsYGWlpasLOzw759+z7lVBEREdE3TCaEEMWdREmQkpICQ0NDJCcnc30pIiKib9yePXugqqqK7777DkIIrFmzBnPmzMH58+dRo0YNuLm5ISkpCUuWLEHZsmURGhqKSZMm4ezZs6hTpw7Cw8OxadMm9OzZE9bW1rh06RK8vLzQp08fzJ07V9rPyJEj8ffffyMgIAB2dnZ4/vw5nj9/jlatWgEAzp8/j2vXrsHe3h66uro4duwYhgwZggULFmDw4MF55p6SkoKqVavC1dUVfn5+uHjxIgYMGICFCxdK25w4cQJNmzbFzJkz0a5dO4SGhmL27Nk4d+5crpnwRERE9O1RtkbColQhYVGKiIiIPsTIyAhz5szBwIEDoaenh6CgIPTp00fqL1OmDGbPno1Bgwbluf2cOXMQFBSEO3fuAACuXr0Ke3t7XLp0CdWqVVM6j06dOkFXVxd//vlnnv1BQUH49ddfkZCQAA0NDQCAr68vdu7ciWvXrgF4O8s8LS0Ne/fulbZr1KgRateujWXLlimdCxEREZVMytZIvurb94iIiIj+67Kzs7Fx40akpaVJD1FxcnLCpk2b8Pz5c8jlcmzcuBHp6elwcXHJd5zk5GQYGRlJ7/fs2YMqVapg7969sLS0hIWFBQYNGoTnz5/nO8b58+dx4sQJNGvWLN+Y6OhoNG3aVCpIAYC7uzuuX7+OFy9eSDGurq4K27m7uyM6OvqD54KIiIjoXSxKEREREX0BFy9ehJ6eHjQ1NTF06FDs2LFDejrw5s2b8ebNG5QpUwaampoYMmQIduzYAWtr6zzHunXrFhYvXowhQ4ZIbXfu3MH9+/exZcsWrF27FqtXr0ZMTAy6dOmSa/tKlSpBU1MT9erVg7e3d76zsYC36129v65mzvuEhIQPxuT0ExERESmj2J6+R0RERFSSVatWDbGxsUhOTsbWrVvh6emJw4cPw9bWFhMnTkRSUhIOHDiAsmXLYufOnejWrRuOHj0KOzs7hXHi4+PRunVrdO3aFV5eXlK7XC5HRkYG1q5di6pVqwIAVq5cCQcHB1y/fl3hlr6jR48iNTUVJ0+ehK+vL6ytrdGzZ8+iORFERERE+WBRioiIiOgL0NDQkGY+OTg44MyZM1i0aBHGjRuHJUuW4NKlS6hRowYAoFatWjh69CgCAwMV1mR6+PAhmjdvDicnJwQHByuMb2pqCjU1NakgBQDVq1cHAMTFxSkUpSwtLQEAdnZ2SExMxOTJk/MtSpmYmCAxMVGhLee9iYnJB2Ny+omIiIiUwdv3iIiIiIpAzsymV69eAQBUVBT/N0xVVRVyuVx6Hx8fDxcXFzg4OCAkJCRXvLOzM7KysnD79m2p7caNGwAAc3Pzj+aRH0dHRxw5cgRv3ryR2iIiIlCtWjWULl1aiomMjFTYLiIiQlozi4iIiEgZLEoRERERFTI/Pz8cOXIE9+7dw8WLF+Hn54eoqCj07t0bNjY2sLa2xpAhQ3D69Gncvn0b8+bNQ0REBDp27Ajg/wpSlStXxty5c/HkyRMkJCQorNnk6uqKunXrYsCAATh//jxiYmIwZMgQtGrVSpo9FRgYiD179uDmzZu4efMmVq5ciblz5+LHH3+UxlmyZAlatmwpve/Vqxc0NDQwcOBAXL58GZs2bcKiRYswZswYKeann35CeHg45s2bh2vXrmHy5Mk4e/YsfHx8vvCZJSIiopKEt+8RERERFbLHjx+jb9++ePToEQwNDWFvb4/9+/ejVatWAIB9+/bB19cX7du3R2pqKqytrbFmzRq0bdsWwNtZR7du3cKtW7dQqVIlhbGFEADezrTas2cPRowYgaZNm0JXVxdt2rTBvHnzpFi5XA4/Pz/cvXsXampqsLKywuzZsxUWTH/69KnCbCtDQ0P8/fff8Pb2hoODA8qWLQt/f38MHjxYinFyckJoaCgmTJiAX375Bd999x127tyJmjVrFv7JJCIiohJLJnL+z4Y+S0pKCgwNDZGcnAwDA4PiToeIiIiIiIiIqFgoWyPh7XtERERERERERFTkWJQiIiIiIiIiIqIixzWliIiI6Jtg4RtW3ClQIbs3y6O4UyAiIqLPwJlSRERERERERERU5FiUIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWORSkiIiIiIiIiIipyLEoREREREREREVGRY1GKiIiIiIiIiIiKHItSRERERERERERU5FiUIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWORSkiIiIiIiIiIipyLEoREREREREREVGRY1GKiIiIiIiIiIiKHItSRERERERERERU5FiUIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWORSkiIiIiIiIiIipyLEoREREREREREVGRY1GKiIiIiIiIiIiKHItSRERERERERERU5FiUIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWuWItSQUFBsLe3h4GBAQwMDODo6Ii//vpL6ndxcYFMJlN4DR06VGGMuLg4eHh4QEdHB8bGxhg7diyysrIUYqKiolC3bl1oamrC2toaq1evzpVLYGAgLCwsoKWlhYYNG+L06dNf5JiJiIiIiIiIiKiYi1KVKlXCrFmzEBMTg7Nnz6JFixbo0KEDLl++LMV4eXnh0aNH0isgIEDqy87OhoeHBzIzM3HixAmsWbMGq1evhr+/vxRz9+5deHh4oHnz5oiNjcWoUaMwaNAg7N+/X4rZtGkTxowZg0mTJuHcuXOoVasW3N3d8fjx46I5EURERERERERE3xiZEEIUdxLvMjIywpw5czBw4EC4uLigdu3aWLhwYZ6xf/31F9q1a4eHDx+ifPnyAIBly5Zh/PjxePLkCTQ0NDB+/HiEhYXh0qVL0nY9evRAUlISwsPDAQANGzZE/fr1sWTJEgCAXC6HmZkZRowYAV9fX6XyTklJgaGhIZKTk2FgYPAZZ4CIiIi+BAvfsOJOgQrZvVkexZ0CERER5UHZGslXs6ZUdnY2Nm7ciLS0NDg6Okrt69evR9myZVGzZk34+fnh1atXUl90dDTs7OykghQAuLu7IyUlRZptFR0dDVdXV4V9ubu7Izo6GgCQmZmJmJgYhRgVFRW4urpKMXnJyMhASkqKwouIiIiIiIiIiJSjVtwJXLx4EY6OjkhPT4eenh527NgBW1tbAECvXr1gbm6OChUq4MKFCxg/fjyuX7+O7du3AwASEhIUClIApPcJCQkfjElJScHr16/x4sULZGdn5xlz7dq1fPOeOXMmpkyZ8nkHT0RERERERET0jSr2olS1atUQGxuL5ORkbN26FZ6enjh8+DBsbW0xePBgKc7Ozg6mpqZo2bIlbt++DSsrq2LMGvDz88OYMWOk9ykpKTAzMyvGjIiIiIiIiIiI/juKvSiloaEBa2trAICDgwPOnDmDRYsWYfny5bliGzZsCAC4desWrKysYGJikuspeYmJiQAAExMT6b85be/GGBgYQFtbG6qqqlBVVc0zJmeMvGhqakJTU7OAR0tERERERERERMBXtKZUDrlcjoyMjDz7YmNjAQCmpqYAAEdHR1y8eFHhKXkREREwMDCQbgF0dHREZGSkwjgRERHSulUaGhpwcHBQiJHL5YiMjFRY24qIiIiIiIiIiApPsc6U8vPzQ5s2bVC5cmW8fPkSoaGhiIqKwv79+3H79m2Ehoaibdu2KFOmDC5cuIDRo0ejadOmsLe3BwC4ubnB1tYWffr0QUBAABISEjBhwgR4e3tLs5iGDh2KJUuWYNy4cRgwYAAOHjyIzZs3Iyzs/57AM2bMGHh6eqJevXpo0KABFi5ciLS0NPTv379YzgsRERERERERUUlXrEWpx48fo2/fvnj06BEMDQ1hb2+P/fv3o1WrVvj3339x4MABqUBkZmaGzp07Y8KECdL2qqqq2Lt3L4YNGwZHR0fo6urC09MTU6dOlWIsLS0RFhaG0aNHY9GiRahUqRJWrFgBd3d3KaZ79+548uQJ/P39kZCQgNq1ayM8PDzX4udERERERERERFQ4ZEIIUdxJlAQpKSkwNDREcnIyDAwMijsdIiIieo+Fb9jHg+g/5d4sj+JOgYiIiPKgbI3kq1tTir4tQUFBsLe3h4GBAQwMDODo6Ii//vpL6h8yZAisrKygra2NcuXKoUOHDrh27ZrCGHFxcfDw8ICOjg6MjY0xduxYZGVlKcRkZGTg119/hbm5OTQ1NWFhYYFVq1blmdPGjRshk8nQsWPHj+YfGBiI6tWrQ1tbG9WqVcPatWtzxSxcuBDVqlWDtrY2zMzMMHr0aKSnpytxdoiIiIiIiIhKrmJ/+h592ypVqoRZs2bhu+++gxACa9asQYcOHXD+/HnUqFEDDg4O6N27NypXroznz59j8uTJcHNzw927d6Gqqors7Gx4eHjAxMQEJ06cwKNHj9C3b1+oq6tjxowZ0n66deuGxMRErFy5EtbW1nj06BHkcnmufO7du4eff/4ZTZo0+WjuQUFB8PPzwx9//IH69evj9OnT8PLyQunSpdG+fXsAQGhoKHx9fbFq1So4OTnhxo0b6NevH2QyGebPn194J5KIiIiIiIjoP4a37xUS3r5XeIyMjDBnzhwMHDgwV9+FCxdQq1Yt3Lp1C1ZWVvjrr7/Qrl07PHz4UFoDbNmyZRg/fjyePHkCDQ0NhIeHo0ePHrhz5w6MjIzy3W92djaaNm2KAQMG4OjRo0hKSsLOnTvzjXdycoKzszPmzJkjtf3vf//DqVOncOzYMQCAj48Prl69qvB0x/djiIioaPD2vZKHt+8RERF9nXj7Hv3nZGdnY+PGjUhLS4Ojo2Ou/rS0NISEhMDS0hJmZmYAgOjoaNjZ2SksSu/u7o6UlBRcvnwZALB7927Uq1cPAQEBqFixIqpWrYqff/4Zr1+/Vhh/6tSpMDY2zrMYlpeMjAxoaWkptGlra+P06dN48+YNgLeFq5iYGJw+fRoAcOfOHezbtw9t27ZV8qwQERERERERlUy8fY+K3cWLF+Ho6Ij09HTo6elhx44dsLW1lfqXLl2KcePGIS0tDdWqVUNERAQ0NDQAAAkJCbmekpjzPiEhAcDbQtCxY8egpaWFHTt24OnTpxg+fDiePXuGkJAQAMCxY8ewcuVKxMbGKp23u7s7VqxYgY4dO6Ju3bqIiYnBihUr8ObNGzx9+hSmpqbo1asXnj59isaNG0MIgaysLAwdOhS//PLL55wyIiIiIiIiov88zpSiYletWjXExsbi1KlTGDZsGDw9PXHlyhWpv3fv3jh//jwOHz6MqlWrolu3bgVaKFwul0Mmk2H9+vVo0KAB2rZti/nz52PNmjV4/fo1Xr58iT59+uCPP/5A2bJllR534sSJaNOmDRo1agR1dXV06NABnp6eAAAVlbffWlFRUZgxYwaWLl2Kc+fOYfv27QgLC8O0adOU3g8REREREdHX4EMPqnr+/DlGjBghPeSpcuXKGDlyJJKTkxXGkMlkuV4bN26U+nPW4H3/VaNGDSkmOzsbEydOhKWlJbS1tWFlZYVp06bhQ6sTbd++Ha1atUK5cuWk3Pfv36/08dGXwZlSVOw0NDRgbW0NAHBwcMCZM2ewaNEiLF++HABgaGgIQ0NDfPfdd2jUqBFKly6NHTt2oGfPnjAxMZFujcuRmJgIADAxMQEAmJqaomLFijA0NJRiqlevDiEEHjx4gLS0NNy7d09anByAtAi6mpoarl+/Disrq1x5a2trY9WqVVi+fDkSExNhamqK4OBg6Ovro1y5cgDeFq769OmDQYMGAQDs7OyQlpaGwYMH49dff5WKV0RERERERF+7Dz2oSgiBhw8fYu7cubC1tcX9+/cxdOhQPHz4EFu3blUYJyQkBK1bt5belypVSvr3okWLMGvWLOl9VlYWatWqha5du0pts2fPRlBQENasWYMaNWrg7Nmz6N+/PwwNDTFy5Mg8cz9y5AhatWqFGTNmoFSpUggJCUH79u1x6tQp1KlT56PH925RjAoPi1L01ZHL5cjIyMizTwgBIYTU7+joiOnTp+Px48cwNjYGAERERMDAwEC6BdDZ2RlbtmxBamoq9PT0AAA3btyAiooKKlWqBJlMhosXLyrsZ8KECXj58iUWLVokrV+VH3V1dVSqVAkAsHHjRrRr104qNr169SpX4UlVVVU6FiIiIiIiov+Kd/+QDwDTp09HUFAQTp48iYEDB2Lbtm1Sn5WVFaZPn44ff/wRWVlZUFP7v/JDqVKlpEkE78uZlJBj586dePHiBfr37y+1nThxAh06dICHx9sHXlhYWGDDhg25Jiy8a+HChQrvZ8yYgV27dmHPnj1SUepDx8ei1JfBaRpUrPz8/HDkyBHcu3cPFy9ehJ+fH6KiotC7d2/cuXMHM2fORExMDOLi4nDixAl07doV2tra0kLhbm5usLW1RZ8+ffDPP/9g//79mDBhAry9vaGpqQkA6NWrF8qUKYP+/fvjypUrOHLkCMaOHYsBAwZAW1sbWlpaqFmzpsKrVKlS0NfXR82aNaX1q/z8/NC3b18p9xs3bmDdunW4efMmTp8+jR49euDSpUuYMWOGFNO+fXsEBQVh48aNuHv3LiIiIjBx4kS0b99eKk4RERERERH913zsQVUApCevvVuQAgBvb2+ULVsWDRo0wKpVqz74B/uVK1fC1dUV5ubmUpuTkxMiIyNx48YNAMA///yDY8eOoU2bNkrnL5fL8fLly3yf0K7M8dHn40wpKlaPHz9G37598ejRIxgaGsLe3h779+9Hq1at8PDhQxw9ehQLFy7EixcvUL58eTRt2hQnTpyQZkWpqqpi7969GDZsGBwdHaGrqwtPT09MnTpV2oeenh4iIiIwYsQI1KtXD2XKlEG3bt3w22+/FSjXR48eIS4uTnqfnZ2NefPm4fr161BXV0fz5s1x4sQJWFhYSDETJkyATCbDhAkTEB8fj3LlyqF9+/aYPn365504IiIiIiKiYvCxB1XlePr0KaZNm4bBgwcrtE+dOhUtWrSAjo4O/v77bwwfPhypqal53nb38OFD/PXXXwgNDVVo9/X1RUpKCmxsbKCqqors7GxMnz4dvXv3Vvo45s6di9TUVHTr1u2Tjo8Kh0zwHqJCkZKSAkNDQ6kSTERERF8XC9+w4k6BCtm9WR7FnQIR0TcnMzMTcXFxSE5OxtatW7FixQocPnxYoXCTkpKCVq1awcjICLt374a6unq+4/n7+yMkJAT//vtvrr6ZM2di3rx5ePjwoXQHC/B22ZSxY8dizpw5qFGjBmJjYzFq1CjMnz9fevjUh4SGhsLLywu7du2Cq6trgY+PPk7ZGgmLUoWERSkiIqKvG4tSJQ+LUkRExc/V1RVWVlbSg6pevnwJd3d36OjoYO/evdDS0vrg9mFhYWjXrh3S09OlJViAt2vwVq1aFe3atcOCBQsUtjEzM4Ovry+8vb2ltt9++w3r1q3DtWvXPri/jRs3YsCAAdiyZYu0JlVBjo+Uo2yNhGtKEREREREREdEnefdBVSkpKXBzc4OGhgZ279790YIUAMTGxqJ06dIKBSkAOHz4MG7duoWBAwfm2ia/B0rlPEU9Pxs2bED//v2xYcMGpQpSwIcfxEWfj2tKUS78S3LJw78kExERERHR5/Lz80ObNm1QuXJlvHz5EqGhoYiKisL+/fulgtSrV6+wbt06pKSkICUlBQBQrlw5qKqqYs+ePUhMTESjRo2gpaWFiIgIzJgxAz///HOufa1cuRINGzZEzZo1c/XlrNNbuXJl1KhRA+fPn8f8+fMxYMAAhVzj4+Oxdu1aAG9v2fP09MSiRYvQsGFDJCQkAAC0tbWlp/196Pjoy2BRioiIiIiIiIg+6kMPqoqKisKpU6cAANbW1grb3b17FxYWFlBXV0dgYCBGjx4NIQSsra0xf/58eHl5KcQnJydj27ZtWLRoUZ55LF68GBMnTsTw4cPx+PFjVKhQAUOGDIG/v78U8/6DqoKDg5GVlQVvb2+F2/48PT2xevXqjx4ffRlcU6qQlKQ1pThTquThTCkiIv58K4n4842IiOjrxDWliIiIiIiIiIjoq8WiFBERERERERERFTmuKUVERERERET0H8Xb00ueb+n2dM6UIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWORSkiIiIiIiIiIipyLEoREREREREREVGRY1GKiIiIiIiIiIiKHItSRERERERERERU5FiUIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWORSkiIiIiIiIiIipyLEoREREREREREVGRY1GKiIiIiIiIiIiKHItSRERERERERERU5FiUIiIiIiIiIiKiIseiFBERERERERERFTkWpYiIiIiIiIiIqMixKEVEREREREREREWORSkiIiIiIiIiIipyLEoREREREREREVGRK9aiVFBQEOzt7WFgYAADAwM4Ojrir7/+kvrT09Ph7e2NMmXKQE9PD507d0ZiYqLCGHFxcfDw8ICOjg6MjY0xduxYZGVlKcRERUWhbt260NTUhLW1NVavXp0rl8DAQFhYWEBLSwsNGzbE6dOnv8gxExERERERERFRMRelKlWqhFmzZiEmJgZnz55FixYt0KFDB1y+fBkAMHr0aOzZswdbtmzB4cOH8fDhQ3Tq1EnaPjs7Gx4eHsjMzMSJEyewZs0arF69Gv7+/lLM3bt34eHhgebNmyM2NhajRo3CoEGDsH//film06ZNGDNmDCZNmoRz586hVq1acHd3x+PHj4vuZBARERERERERfUOKtSjVvn17tG3bFt999x2qVq2K6dOnQ09PDydPnkRycjJWrlyJ+fPno0WLFnBwcEBISAhOnDiBkydPAgD+/vtvXLlyBevWrUPt2rXRpk0bTJs2DYGBgcjMzAQALFu2DJaWlpg3bx6qV68OHx8fdOnSBQsWLJDymD9/Pry8vNC/f3/Y2tpi2bJl0NHRwapVq4rlvBARUfGYOXMm6tevD319fRgbG6Njx464fv26QkxCQgL69OkDExMT6Orqom7duti2bZtCzLlz59CqVSuUKlUKZcqUweDBg5Gamir1P3v2DK1bt0aFChWgqakJMzMz+Pj4ICUlRYrZvn07WrVqhXLlykmzid/9g0p+Nm/ejNq1a0NHRwfm5uaYM2eOQv+xY8fg7OyMMmXKQFtbGzY2Ngo/E4mIiIiIispXs6ZUdnY2Nm7ciLS0NDg6OiImJgZv3ryBq6urFGNjY4PKlSsjOjoaABAdHQ07OzuUL19einF3d0dKSoo02yo6OlphjJyYnDEyMzMRExOjEKOiogJXV1cpJi8ZGRlISUlReBER0X/b4cOH4e3tjZMnTyIiIgJv3ryBm5sb0tLSpJi+ffvi+vXr2L17Ny5evIhOnTqhW7duOH/+PADg4cOHcHV1hbW1NU6dOoXw8HBcvnwZ/fr1k8ZQUVFBhw4dsHv3bty4cQOrV6/GgQMHMHToUCnmyJEjaNWqFfbt24eYmBg0b94c7du3l/aTl7/++gu9e/fG0KFDcenSJSxduhQLFizAkiVLpBhdXV34+PjgyJEjuHr1KiZMmIAJEyYgODi4EM8kEREREdHHqRV3AhcvXoSjoyPS09Ohp6eHHTt2wNbWFrGxsdDQ0ECpUqUU4suXL4+EhAQAb/9a/W5BKqc/p+9DMSkpKXj9+jVevHiB7OzsPGOuXbuWb94zZ87ElClTPumYiYjo6xQeHq7wfvXq1TA2NkZMTAyaNm0KADhx4gSCgoLQoEEDAMCECROwYMECxMTEoE6dOti7dy/U1dURGBgIFZW3f/tZtmwZ7O3tcevWLVhbW6N06dIYNmyYtB9zc3MMHz5cYVbTwoULFXKZMWMGdu3ahT179qBOnTp55v/nn3+iY8eOUnGrSpUq8PPzw+zZs+Ht7Q2ZTIY6deoobG9hYYHt27fj6NGjGDx48CeeOSIiIiKigiv2mVLVqlVDbGwsTp06hWHDhsHT0xNXrlwp7rQ+ys/PD8nJydLr33//Le6UiIiokCUnJwMAjIyMpDYnJyds2rQJz58/h1wux8aNG5Geng4XFxcAb2fSamhoSAUpANDW1gbw9ta5vDx8+BDbt29Hs2bN8s1FLpfj5cuXCrm8LyMjA1paWgpt2traePDgAe7fv5/nNufPn8eJEyc+uG8iIiIioi+h2ItSGhoasLa2hoODA2bOnIlatWph0aJFMDExQWZmJpKSkhTiExMTYWJiAgAwMTHJ9TS+nPcfizEwMIC2tjbKli0LVVXVPGNyxsiLpqam9NTAnBcREZUccrkco0aNgrOzM2rWrCm1b968GW/evEGZMmWgqamJIUOGYMeOHbC2tgYAtGjRAgkJCZgzZw4yMzPx4sUL+Pr6AgAePXqksI+ePXtCR0cHFStWhIGBAVasWJFvPnPnzkVqaiq6deuWb4y7uzu2b9+OyMhIyOVy3LhxA/Pmzctz35UqVYKmpibq1asHb29vDBo0qGAniIiIiIjoMxV7Uep9crkcGRkZcHBwgLq6OiIjI6W+69evIy4uDo6OjgAAR0dHXLx4UeEpeRERETAwMICtra0U8+4YOTE5Y2hoaMDBwUEhRi6XIzIyUoohIqJvj7e3Ny5duoSNGzcqtE+cOBFJSUk4cOAAzp49izFjxqBbt264ePEiAKBGjRpYs2YN5s2bBx0dHZiYmMDS0hLly5dXmD0FAAsWLMC5c+ewa9cu3L59G2PGjMkzl9DQUEyZMgWbN2+GsbFxvjl7eXnBx8cH7dq1g4aGBho1aoQePXoAQK59Hz16FGfPnsWyZcuwcOFCbNiwocDniIiIiIjoc8iEEKK4du7n54c2bdqgcuXKePnyJUJDQzF79mzs378frVq1wrBhw7Bv3z6sXr0aBgYGGDFiBIC363kAbxdHr127NipUqICAgADpiUiDBg3CjBkzAAB3795FzZo14e3tjQEDBuDgwYMYOXIkwsLC4O7uDgDYtGkTPD09sXz5cjRo0AALFy7E5s2bce3atVxrTeUnJSUFhoaGSE5O/s/PmrLwDSvuFKiQ3ZvlUdwpEP2n+Pj4YNeuXThy5AgsLS2l9tu3b8Pa2hqXLl1CjRo1pPachc2XLVumME5iYiJ0dXUhk8lgYGCAjRs3omvXrnnu89ixY2jSpAkePnwIU1NTqX3jxo0YMGAAtmzZAg8P5b6Xs7OzkZCQgHLlyiEyMhJt27bF48ePUa5cuTzjf/vtN/z555+5njRY0vDnW8nDn29ERPz5VhKVhJ9vytZIinWh88ePH6Nv37549OgRDA0NYW9vLxWkgLd/QVZRUUHnzp2RkZEBd3d3LF26VNpeVVUVe/fuxbBhw+Do6AhdXV14enpi6tSpUoylpSXCwsIwevRoLFq0CJUqVcKKFSukghQAdO/eHU+ePIG/vz8SEhJQu3ZthIeHK12QIiKikkEIgREjRmDHjh2IiopSKEgBwKtXrwDknnWkqqoKuVyea7ycnyOrVq2ClpaW9PMtLznbZ2RkSG0bNmzAgAEDsHHjRqULUjn5VKxYURrD0dEx34JUzr7f3S8RERERUVEo1qLUypUrP9ivpaWFwMBABAYG5htjbm6Offv2fXAcFxeXDz5CG3j7V3EfH58PxhARUcnm7e2N0NBQ7Nq1C/r6+tKTXA0NDaGtrQ0bGxtYW1tjyJAhmDt3LsqUKYOdO3ciIiICe/fulcZZsmQJnJycoKenh4iICIwdOxazZs2Snii7b98+JCYmon79+tDT08Ply5cxduxYODs7w8LCAsDbW/Y8PT2xaNEiNGzYUMpFW1sbhoaG0n527Ngh3YL+9OlTbN26FS4uLkhPT0dISAi2bNmCw4cPS7kFBgaicuXKsLGxAQAcOXIEc+fOxciRI7/ouSUiIiIiel+xFqWIiIi+JkFBQQAgPUkvR0hICPr16wd1dXXs27cPvr6+aN++PVJTU2FtbY01a9agbdu2Uvzp06cxadIkpKamwsbGBsuXL0efPn2kfm1tbfzxxx8YPXo0MjIyYGZmhk6dOkkLogNAcHAwsrKy4O3tDW9vb6nd09MTq1evBvC2CHX79m2FXNesWYOff/4ZQgg4OjoiKioKDRo0kPrlcjn8/Pxw9+5dqKmpwcrKCrNnz8aQIUM++/wRERERERVEsa4pVZJwTSn6mpWEe5KJiD4Xf76VPPz5RkTEn28lUUn4+aZsjeSre/oeERERERERERGVfCxKERERERERERFRkeOaUkRE9FXjlPSSqSRMSyciIiKiz8OZUkREREREREREVORYlCIiIiIiIiIioiLHohQRERERERERERU5FqWIiIiIiIiIiKjIsShFRERERERERERFjkUpIiIiIiIiIiIqcixKERERERERERFRkWNRioiIiIiIiIiIihyLUkREREREREREVOTUPmWjuLg43L9/H69evUK5cuVQo0YNaGpqFnZuRERERERERERUQildlLp37x6CgoKwceNGPHjwAEIIqU9DQwNNmjTB4MGD0blzZ6iocAIWERERERERERHlT6nq0ciRI1GrVi3cvXsXv/32G65cuYLk5GRkZmYiISEB+/btQ+PGjeHv7w97e3ucOXPmS+dNRERERERERET/YUrNlNLV1cWdO3dQpkyZXH3GxsZo0aIFWrRogUmTJiE8PBz//vsv6tevX+jJEhERERERERFRyaBUUWrmzJlKD9i6detPToaIiIiIiIiIiL4NXPyJiIiIiOgrNHPmTNSvXx/6+vowNjZGx44dcf369Vxx0dHRaNGiBXR1dWFgYICmTZvi9evXAN6uCztw4EBYWlpCW1sbVlZWmDRpEjIzM6Xt7927B5lMlut18uRJKeaPP/5AkyZNULp0aZQuXRqurq44ffr0B/N/9OgRevXqhapVq0JFRQWjRo3KM27hwoWoVq0atLW1YWZmhtGjRyM9Pf0TzhgREf3XfFJRauvWrejWrRsaNWqEunXrKryIiIiIiOjzHT58GN7e3jh58iQiIiLw5s0buLm5IS0tTYqJjo5G69at4ebmhtOnT+PMmTPw8fGRHjx07do1yOVyLF++HJcvX8aCBQuwbNky/PLLL7n2d+DAATx69Eh6OTg4SH1RUVHo2bMnDh06hOjoaJiZmcHNzQ3x8fH55p+RkYFy5cphwoQJqFWrVp4xoaGh8PX1xaRJk3D16lWsXLkSmzZtyjM/+nSFUeAEgOnTp8PJyQk6OjooVapUnvvKq8C5ceNGqf/YsWNwdnZGmTJloK2tDRsbGyxYsEDpY7l16xb09fXz3H9SUhK8vb1hamoKTU1NVK1aFfv27VN6bCIqeko/fS/H77//jl9//RX9+vXDrl270L9/f9y+fRtnzpyBt7f3l8iRiIiIiOibEx4ervB+9erVMDY2RkxMDJo2bQoAGD16NEaOHAlfX18prlq1atK/W7durbC8RpUqVXD9+nUEBQVh7ty5CuOXKVMGJiYmeeayfv16hfcrVqzAtm3bEBkZib59++a5jYWFBRYtWgQAWLVqVZ4xJ06cgLOzM3r16iVt07NnT5w6dSrPePo0OQXO+vXrIysrC7/88gvc3Nxw5coV6OrqAvi/Aqefnx8WL14MNTU1/PPPPwpPVs/MzETXrl3h6OiIlStX5ru/kJAQhevu3QKSrq4ufHx8YG9vD11dXRw7dgxDhgyBrq4uBg8e/MHjePPmDXr27IkmTZrgxIkTCn2ZmZlo1aoVjI2NsXXrVlSsWBH379/Pt3hGRF+HAhelli5diuDgYPTs2ROrV6/GuHHjUKVKFfj7++P58+dfIkciIiIiom9ecnIyAMDIyAgA8PjxY5w6dQq9e/eGk5MTbt++DRsbG0yfPh2NGzf+4Dg5Y7zr+++/R3p6OqpWrYpx48bh+++/z3eMV69e4c2bN3mOUxBOTk5Yt24dTp8+jQYNGuDOnTvYt28f+vTp81njkqLCKHACwJQpU6TtP6RUqVL5Fjjr1KmDOnXqSO8tLCywfft2HD169KNFqQkTJsDGxgYtW7bMVZRatWoVnj9/jhMnTkBdXV0am4i+bgW+fS8uLg5OTk4AAG1tbbx8+RIA0KdPH2zYsKFwsyMiIiIiIsjlcowaNQrOzs6oWbMmAODOnTsAgMmTJ8PLywvh4eGoW7cuWrZsiZs3b+Y5zq1bt7B48WIMGTJEatPT08O8efOwZcsWhIWFoXHjxujYsSN2796dbz7jx49HhQoV4Orq+lnH1atXL0ydOhWNGzeGuro6rKys4OLiwtv3vrD8CpzGxsZwcnJC+fLl0axZMxw7duyTxvf29kbZsmXRoEEDrFq1CkKIfGPPnz+PEydOoFmzZh8c8+DBg9iyZQsCAwPz7N+9ezccHR3h7e2N8uXLo2bNmpgxYways7M/6RiIqGgUeKaUiYkJnj9/DnNzc1SuXBknT55ErVq1cPfu3Q9+2BARERER0afx9vbGpUuXFIoEcrkcADBkyBD0798fwNtZKJGRkVi1alWuJ2jHx8ejdevW6Nq1K7y8vKT2smXLYsyYMdL7+vXr4+HDh5gzZ06es6VmzZqFjRs3IioqClpaWp91XFFRUZgxYwaWLl2Khg0b4tatW/jpp58wbdo0TJw48bPGprx9rMA5d+5c1K5dG2vXrkXLli1x6dIlfPfdd0qPP3XqVLRo0QI6Ojr4+++/MXz4cKSmpmLkyJEKcZUqVcKTJ0+QlZWFyZMnY9CgQfmO+ezZM/Tr1w/r1q2DgYFBnjF37tzBwYMH0bt3b+zbtw+3bt3C8OHD8ebNG0yaNEnp/ImoaBW4KNWiRQvs3r0bderUQf/+/TF69Ghs3boVZ8+eRadOnb5EjkRERERE3ywfHx/s3bsXR44cQaVKlaR2U1NTAICtra1CfPXq1REXF6fQ9vDhQzRv3hxOTk4IDg7+6D4bNmyIiIiIXO1z587FrFmzcODAAdjb23/K4SiYOHEi+vTpIxUk7OzskJaWhsGDB+PXX39VWM+ICkdhFDg/5N1iYp06dZCWloY5c+bkKkodPXoUqampOHnyJHx9fWFtbY2ePXvmOaaXlxd69eol3WqYF7lcDmNjYwQHB0NVVRUODg6Ij4/HnDlzWJQi+ooVuCgVHBwsfWh5e3ujTJkyOHHiBL7//nuFacBERERERPTphBAYMWIEduzYgaioKFhaWir0W1hYoEKFCrmeonbjxg20adNGeh8fH4/mzZvDwcEBISEhShV6YmNjpaJXjoCAAEyfPh379+9HvXr1PuPI/s+rV69y5aOqqgoAvAvjCyiMAmdBNWzYENOmTUNGRgY0NTWl9pzr2c7ODomJiZg8eXK+RamDBw9i9+7d0uL8QgjI5XKoqakhODgYAwYMgKmpKdTV1aXrJyf/hIQEZGZmQkND47OOg4i+jAIXpVRUVBR+cPTo0QM9evQo1KSIiIiIiL513t7eCA0Nxa5du6Cvr4+EhAQAgKGhIbS1tSGTyTB27FhMmjQJtWrVQu3atbFmzRpcu3YNW7duBfC2IOXi4gJzc3PMnTsXT548kcbPWYh6zZo10NDQkBaf3r59O1atWoUVK1ZIsbNnz4a/vz9CQ0NhYWEh5aKnpwc9PT0AgJ+fH+Lj47F27Vppu9jYWABAamoqnjx5gtjYWGhoaEjFj/bt22P+/PmoU6eOdPvexIkT0b59e4XiAn2ewipwforY2FiULl1aoSD1PrlcjoyMjHz7o6OjFdaG2rVrF2bPno0TJ06gYsWKAABnZ2eEhoZCLpdLv6/euHEDpqamLEgRfcUKXJQCgKSkJJw+fRqPHz+WZk3lyO+RsEREREREpLygoCAAgIuLi0J7SEgI+vXrBwAYNWoU0tPTMXr0aDx//hy1atVCREQErKysAAARERG4desWbt26pTAzBlCciTRt2jTcv38fampqsLGxwaZNm9ClSxeFXDIzMxXaAGDSpEmYPHkyAODRo0e5ZtW8+5S1mJgYhIaGwtzcHPfu3QPw9mlqMpkMEyZMQHx8PMqVK4f27dtj+vTpBTtZ9EGFUeAE3j706vnz54iLi0N2drZUdLS2toaenh727NmDxMRENGrUCFpaWoiIiMCMGTPw888/S2MEBgaicuXKsLGxAQAcOXIEc+fOVbi9b8mSJdixYwciIyMBvJ3x9K6zZ89CRUVFWhMLAIYNG4YlS5bgp59+wogRI3Dz5k3MmDEj122DRPR1kYkCzovds2cPevfujdTUVBgYGEAmk/3fYDIZnj9/XuhJ/hekpKTA0NAQycnJ+S6+919h4RtW3ClQIbs3y6O4UyD6ZPxMKpmK43OJ11LJw59vRMp593e2d71b4ATeLmIfGBgoFTgDAgLQuHFjqb9fv35Ys2ZNrnEOHToEFxcXhIeHw8/PD7du3YIQAtbW1hg2bBi8vLyk2UuLFy/G8uXLcffuXaipqcHKygpeXl4YMmSIFDN58mSsXr1aKl6+b/Xq1Rg1ahSSkpIU2qOjozF69GjExsaiYsWKGDhwIMaPH1/iZ93x51vJUxJ+vilbIylwUapq1apo27YtZsyYAR0dnc9OtKRgUYq+ZiXhQ42+XfxMKplYlKLCwJ9vRET8+VYSlYSfb8rWSAr8OIv4+HiMHDmSBSkiIiIiIiIiIvpkBV5Tyt3dHWfPnkWVKlW+RD5ERAU2c+ZMbN++HdeuXYO2tjacnJwwe/ZsVKtWLVesEAJt27ZFeHg4duzYgY4dO0p9kZGRmDhxIi5evAhdXV14enpi+vTpUFP7v4/KzZs3Y8aMGbhx4wbKlSsHHx8fjB07Ns+8jh8/jmbNmqFmzZrSmgv5EUJg3rx5CA4Oxv3791G2bFkMHz4cv/76qxSzfv16BAQE4ObNmzA0NESbNm0wZ84clClTpmAnjIiIPgtnJZQ8JWFWAhHRf5FSRandu3dL//bw8MDYsWNx5coV2NnZQV1dXSH2+++/L9wMiYg+4vDhw/D29kb9+vWRlZWFX375BW5ubrhy5Qp0dXUVYhcuXJjnugr//PMP2rZti19//RVr165FfHw8hg4diuzsbOnxw3/99Rd69+6NxYsXw83NDVevXoWXlxe0tbXh4+OjMF5SUhL69u2Lli1bIjEx8aPH8NNPP+Hvv//G3LlzYWdnh+fPnyus0Xf8+HH07dsXCxYsQPv27aX8vLy8sH379k85bURERFSMWNwsmVjgJCoYpYpS784kyDF16tRcbTKZTOFRnURERSE8PFzh/erVq2FsbIyYmBg0bdpUao+NjcW8efNw9uxZmJqaKmyzadMm2Nvbw9/fH8Dbp8gEBASgW7dumDRpEvT19fHnn3+iY8eOGDp0KACgSpUq8PPzw+zZs+Ht7a1Q7Bo6dCh69eoFVVVV7Ny584P5X716FUFBQbh06ZI0u+v9RzVHR0fDwsJCeoKMpaUlhgwZgtmzZxfgTBEREREREX09lFpTSi6XK/ViQYqIvgbJyckAACMjI6nt1atX6NWrFwIDA2FiYpJrm4yMDGhpaSm0aWtrIz09HTExMR+MefDgAe7fvy+1hYSE4M6dO5g0aZJS+e7ZswdVqlTB3r17YWlpCQsLCwwaNEhhppSjoyP+/fdf7Nu3D0IIJCYmYuvWrWjbtq1S+yAiIiIiIvraKL3Qed++fbFt2zakpaV9yXyIiD6LXC7HqFGj4OzsjJo1a0rto0ePhpOTEzp06JDndu7u7jhx4gQ2bNiA7OxsxMfHSzNCHz16JMVs374dkZGRkMvluHHjBubNm6cQc/PmTfj6+mLdunUKa1F9yJ07d3D//n1s2bIFa9euxerVqxETE4MuXbpIMc7Ozli/fj26d+8ODQ0NmJiYwNDQEIGBgQU/SURERERERF8BpYtS1tbWmDFjBsqWLYs2bdogKCgI8fHxXzI3IqIC8/b2xqVLl7Bx40apbffu3Th48CAWLlyY73Zubm6YM2cOhg4dCk1NTVStWlWahaSi8vaj0svLCz4+PmjXrh00NDTQqFEj9OjRQ4rJzs5Gr169MGXKFFStWlXpnOVyOTIyMrB27Vo0adIELi4uWLlyJQ4dOoTr168DAK5cuYKffvoJ/v7+iImJQXh4OO7duyfdSkhERERERPRfo3RRKucXoZs3b6J9+/bYuXMnrKys4ODggKlTp370yVJERF+aj48P9u7di0OHDqFSpUpS+8GDB3H79m2UKlUKampq0gymzp07w8XFRYobM2YMkpKSEBcXh6dPn0qzqnKeNiqTyTB79mykpqbi/v37SEhIQIMGDaSYly9f4uzZs/Dx8ZH2M3XqVPzzzz9QU1PDwYMH88zb1NQUampqCoWs6tWrAwDi4uIAvH3CoLOzM8aOHQt7e3u4u7tj6dKlWLVqlTRLi4iIiIiI6L9EuXtL3lGpUiUMHz4cw4cPx8uXL/HXX39h165daNGiBfT19dG+fXsMGzYMNWrU+BL5EhHlIoTAiBEjsGPHDkRFReVaJNzX1xeDBg1SaLOzs5OeZPcumUyGChUqAAA2bNgAMzMz1K1bVyFGVVUVFStWlGIcHR1Rrlw5yOVyXLx4USF26dKlOHjwILZu3ZorrxzOzs7IysrC7du3YWVlBQC4ceMGAMDc3BzA2zWx3r8dUFVVVTp+IiIiIiKi/5oCF6Xepa+vj27duqFbt27Izs5GVFQUdu/ejejoaBaliKjIeHt7IzQ0FLt27YK+vj4SEhIAAIaGhtDW1oaJiUmei5tXrlxZoVA0Z84ctG7dGioqKti+fTtmzZqFzZs3S8Wfp0+fYuvWrXBxcUF6ejpCQkKwZcsWHD58GMDbW/jeXccKAIyNjaGlpaXQvmTJEuzYsQORkZEAAFdXV9StWxcDBgzAwoULIZfL4e3tjVatWkmzp9q3bw8vLy8EBQXB3d0djx49wqhRo9CgQQOpiEZERERERPRfovTte+np6bh16xYyMjKwe/fuXAueq6qqomXLlli0aFGuGQn5mTlzJurXrw99fX0YGxujY8eO0vopOVxcXCCTyRRe76+hEhcXBw8PD+jo6MDY2Bhjx45FVlaWQkxUVBTq1q0LTU1NWFtbY/Xq1bnyCQwMhIWFBbS0tNCwYUOcPn1aqeMgouIVFBSE5ORkuLi4wNTUVHpt2rSpQOP89ddfaNKkCerVq4ewsDDs2rULHTt2VIhZs2YN6tWrB2dnZ1y+fBlRUVHSLXzKevr0KW7fvi29V1FRwZ49e1C2bFk0bdoUHh4eqF69usK6WP369cP8+fOxZMkS1KxZE127dkW1atWwffv2Au2biIiIiIjoa6H0TKl+/fph586dGD9+PCIjI7Fu3Tps3rz5s3Z++PBheHt7o379+sjKysIvv/wCNzc3XLlyBbq6ulKcl5eX9BQsANDR0ZH+nZ2dDQ8PD5iYmODEiRN49OgR+vbtC3V1dcyYMQMAcPfuXXh4eGDo0KFYv349IiMjMWjQIJiamsLd3R0AsGnTJowZMwbLli1Dw4YNsXDhQri7u+P69eswNjb+rOMkoi/rU25fy2ub/NZ8ylG2bFlER0cXaD+TJ0/G5MmTP9pWoUIFbNu27YNjjRgxAiNGjCjQ/omIiIiIiL5WSs+USkpKgo2NDSZMmICjR49K6518jvDwcPTr1w81atRArVq1sHr1asTFxSEmJkYhTkdHR7r9xsTEBAYGBlLf33//jStXrmDdunWoXbs22rRpg2nTpiEwMBCZmZkAgGXLlsHS0hLz5s1D9erV4ePjgy5dumDBggXSOPPnz4eXlxf69+8PW1tbLFu2DDo6Oli1atVnHycRERERERERESlSuiilra2N7t27Q11dHTKZDIaGhoWeTHJyMgDAyMhIoX39+vUoW7YsatasCT8/P7x69Urqi46Ohp2dHcqXLy+1ubu7IyUlBZcvX5ZiXF1dFcZ0d3eXZjxkZmYiJiZGIUZFRQWurq75zorIyMhASkqKwouIiIiIiIiIiJSj9O173bt3R48ePQC8XV/q3UeXFwa5XI5Ro0bB2dlZYUHgXr16wdzcHBUqVMCFCxcwfvx4XL9+XVpHJSEhQaEgBUB6n7PYcX4xKSkpeP36NV68eIHs7Ow8Y65du5ZnvjNnzsSUKVM+76CJSjgL37DiToEK2b1ZHsWdAhERERERlRBKF6VyClIAoKWlhT/++KNQE/H29salS5dw7NgxhfbBgwdL/7azs4OpqSlatmyp8Oj04uDn54cxY8ZI71NSUmBmZlZs+RARERERERER/ZcoXZR6V3p6Oi5cuIDHjx9DLpcr9H3//fcFHs/Hxwd79+7FkSNHUKlSpQ/GNmzYEABw69YtWFlZwcTEJNdT8hITEwFAegS8iYmJ1PZujIGBAbS1taGqqgpVVdU8Y/J6jDwAaGpqQlNTU/mDJCIiIiIiIiIiSYGLUuHh4ejbty+ePn2aq08mkyE7O1vpsYQQGDFiBHbs2IGoqChYWlp+dJvY2FgAgKmpKQDA0dER06dPx+PHj6Wn5EVERMDAwAC2trZSzL59+xTGiYiIgKOjIwBAQ0MDDg4OiIyMlB7/LpfLERkZCR8fH6WPh4iIiIiIiIiIlKP0Quc5RowYga5du+LRo0eQy+UKr4IUpIC3t+ytW7cOoaGh0NfXR0JCAhISEvD69WsAwO3btzFt2jTExMTg3r172L17N/r27YumTZvC3t4eAODm5gZbW1v06dMH//zzD/bv348JEybA29tbmsk0dOhQ3LlzB+PGjcO1a9ewdOlSbN68GaNHj5ZyGTNmDP744w+sWbMGV69exbBhw5CWlob+/fsX9BQREREREREREdFHFHimVGJiIsaMGZNrUfBPERQUBABwcXFRaA8JCUG/fv2goaGBAwcOYOHChUhLS4OZmRk6d+6MCRMmSLGqqqrYu3cvhg0bBkdHR+jq6sLT0xNTp06VYiwtLREWFobRo0dj0aJFqFSpElasWAF3d3cppnv37njy5An8/f2RkJCA2rVrIzw8vFCOk4iIiIiIiIiIFBW4KNWlSxdERUUVyiLjQogP9puZmeHw4cMfHcfc3DzX7Xnvc3Fxwfnz5z8Y4+Pjw9v1iIiIiIiIiIiKQIGLUkuWLEHXrl1x9OhR2NnZQV1dXaF/5MiRhZYcERERERERERGVTAUuSm3YsAF///03tLS0EBUVBZlMJvXJZDIWpYiIiIiIiIiI6KMKXJT69ddfMWXKFPj6+kJFpcDrpBMRERERERERERX86XuZmZno3r07C1JERERERERERPTJClxZ8vT0xKZNm75ELkRERERERERE9I0o8O172dnZCAgIwP79+2Fvb59rofP58+cXWnJERERERERERFQyFbgodfHiRdSpUwcAcOnSJYW+dxc9JyIiIiIiIiIiyk+Bi1KHDh36EnkQEREREREREdE3hKuVExERERERERFRkVOqKDV06FA8ePBAqQE3bdqE9evXf1ZSRERERERERERUsil1+165cuVQo0YNODs7o3379qhXrx4qVKgALS0tvHjxAleuXMGxY8ewceNGVKhQAcHBwV86byIiIiIiIiIi+g9Tqig1bdo0+Pj4YMWKFVi6dCmuXLmi0K+vrw9XV1cEBwejdevWXyRRIiIiIiIiIiIqOZRe6Lx8+fL49ddf8euvv+LFixeIi4vD69evUbZsWVhZWfHJe0REREREREREpLQCP30PAEqXLo3SpUsXdi5ERERERERERPSN4NP3iIiIiIiIiIioyLEoRURERERERERERY5FKSIiIiIiIiIiKnIsShERERERERERUZFjUYqIiIiIiIiIiIqcUk/fq1OnDmQymVIDnjt37rMSIiIiIiIiIiKikk+polTHjh2lf6enp2Pp0qWwtbWFo6MjAODkyZO4fPkyhg8f/kWSJCIiIiIiIiKikkWpotSkSZOkfw8aNAgjR47EtGnTcsX8+++/hZsdERERERERERGVSAVeU2rLli3o27dvrvYff/wR27ZtK5SkiIiIiIiIiIioZCtwUUpbWxvHjx/P1X78+HFoaWkVSlJERERERERERFSyKXX73rtGjRqFYcOG4dy5c2jQoAEA4NSpU1i1ahUmTpxY6AkSEREREREREVHJU+CilK+vL6pUqYJFixZh3bp1AIDq1asjJCQE3bp1K/QEiYiIiIiIiIio5ClwUQoAunXrxgIUERERERERERF9sgKvKQUASUlJWLFiBX755Rc8f/4cAHDu3DnEx8cXanJERERERERERFQyFXim1IULF+Dq6gpDQ0Pcu3cPgwYNgpGREbZv3464uDisXbv2S+RJREREREREREQlSIFnSo0ZMwb9+vXDzZs3FZ6217ZtWxw5cqRQkyMiIiIiIiIiopKpwEWpM2fOYMiQIbnaK1asiISEhEJJioiIiIiIiIiISrYCF6U0NTWRkpKSq/3GjRsoV65coSRFREREREREREQlW4GLUt9//z2mTp2KN2/eAABkMhni4uIwfvx4dO7cudATJCIiIiIiIiKikqfARal58+YhNTUVxsbGeP36NZo1awZra2vo6+tj+vTpXyJHIiIiIiIiIiIqYQr89D1DQ0NERETg2LFjuHDhAlJTU1G3bl24urp+ifyIiIiIiIiIiKgEKnBRKkfjxo1Rr149aGpqQiaTFWZORERERERERERUwhX49j25XI5p06ahYsWK0NPTw927dwEAEydOxMqVKws9QSIiIiIiIiIiKnkKXJT67bffsHr1agQEBEBDQ0Nqr1mzJlasWFGoyRERERERERERUclU4KLU2rVrERwcjN69e0NVVVVqr1WrFq5du1aoyRERERERERERUclU4KJUfHw8rK2tc7XL5XK8efOmUJIiIiIiIiIiIqKSrcBFKVtbWxw9ejRX+9atW1GnTp1CSYqIiIiIiIiIiEq2Ahel/P394ePjg9mzZ0Mul2P79u3w8vLC9OnT4e/vX6CxZs6cifr160NfXx/Gxsbo2LEjrl+/rhCTnp4Ob29vlClTBnp6eujcuTMSExMVYuLi4uDh4QEdHR0YGxtj7NixyMrKUoiJiopC3bp1oampCWtra6xevTpXPoGBgbCwsICWlhYaNmyI06dPF+h4iIiIiIiIiIhIOQUuSnXo0AF79uzBgQMHoKurC39/f1y9ehV79uxBq1atCjTW4cOH4e3tjZMnTyIiIgJv3ryBm5sb0tLSpJjRo0djz5492LJlCw4fPoyHDx+iU6dOUn92djY8PDyQmZmJEydOYM2aNVi9erVCgezu3bvw8PBA8+bNERsbi1GjRmHQoEHYv3+/FLNp0yaMGTMGkyZNwrlz51CrVi24u7vj8ePHBT1FRERERERERET0EWqfslGTJk0QERHx2TsPDw9XeL969WoYGxsjJiYGTZs2RXJyMlauXInQ0FC0aNECABASEoLq1avj5MmTaNSoEf7++29cuXIFBw4cQPny5VG7dm1MmzYN48ePx+TJk6GhoYFly5bB0tIS8+bNAwBUr14dx44dw4IFC+Du7g4AmD9/Pry8vNC/f38AwLJlyxAWFoZVq1bB19f3s4+ViIiIiIiIiIj+T4FnSn1JycnJAAAjIyMAQExMDN68eQNXV1cpxsbGBpUrV0Z0dDQAIDo6GnZ2dihfvrwU4+7ujpSUFFy+fFmKeXeMnJicMTIzMxETE6MQo6KiAldXVynmfRkZGUhJSVF4ERERERERERGRcpSeKWVpaQmZTPbBGJlMhtu3b39SInK5HKNGjYKzszNq1qwJAEhISICGhgZKlSqlEFu+fHkkJCRIMe8WpHL6c/o+FJOSkoLXr1/jxYsXyM7OzjPm2rVreeY7c+ZMTJky5ZOOlYiIiIiIiIjoW6d0UWrUqFH59t27dw/Lly9HRkbGJyfi7e2NS5cu4dixY588RlHy8/PDmDFjpPcpKSkwMzMrxoyIiIiIiIiIiP47lC5K/fTTT7nanj9/jmnTpiEoKAgNGzbE7NmzPykJHx8f7N27F0eOHEGlSpWkdhMTE2RmZiIpKUlhtlRiYiJMTEykmPefkpfzdL53Y95/Yl9iYiIMDAygra0NVVVVqKqq5hmTM8b7NDU1oamp+UnHS0RERERERET0rfukNaVev36N6dOnw8rKCocOHcL27dtx+PBhNGrUqEDjCCHg4+ODHTt24ODBg7C0tFTod3BwgLq6OiIjI6W269evIy4uDo6OjgAAR0dHXLx4UeEpeRERETAwMICtra0U8+4YOTE5Y2hoaMDBwUEhRi6XIzIyUoohIiIiIiIiIqLCU6Cn72VnZ+OPP/7AlClToKWlhd9//x0//vjjR9eayo+3tzdCQ0Oxa9cu6OvrS2tAGRoaQltbG4aGhhg4cCDGjBkDIyMjGBgYYMSIEXB0dJQKYG5ubrC1tUWfPn0QEBCAhIQETJgwAd7e3tJMpqFDh2LJkiUYN24cBgwYgIMHD2Lz5s0ICwuTchkzZgw8PT1Rr149NGjQAAsXLkRaWpr0ND4iIiIiIiIiIio8ShelNm/ejAkTJiApKQm//vorhg0bBg0Njc/aeVBQEADAxcVFoT0kJAT9+vUDACxYsAAqKiro3LkzMjIy4O7ujqVLl0qxqqqq2Lt3L4YNGwZHR0fo6urC09MTU6dOlWIsLS0RFhaG0aNHY9GiRahUqRJWrFgBd3d3KaZ79+548uQJ/P39kZCQgNq1ayM8PDzX4udERERERERERPT5lC5K9ejRA9ra2ujZsyfu378PX1/fPOPmz5+v9M6FEB+N0dLSQmBgIAIDA/ONMTc3x759+z44jouLC86fP//BGB8fH/j4+Hw0JyIiIiIiIiIi+jxKF6WaNm0KmUyG27dv5xvzqbfxERERERERERHRt0XpolRUVNQXTIOIiIiIiIiIiL4ln/T0PSIiIiIiIiIios/BohQRERERERERERU5FqWIiIiIiIiIiKjIsShFRERERERERERFrkBFqaysLEydOhUPHjz4UvkQEREREREREdE3oEBFKTU1NcyZMwdZWVlfKh8iIiIiIiIiIvoGFPj2vRYtWuDw4cNfIhciIiIiIiIiIvpGqBV0gzZt2sDX1xcXL16Eg4MDdHV1Ffq///77QkuOiIiIiIiIiIhKpgIXpYYPHw4AmD9/fq4+mUyG7Ozsz8+KiIiIiIiIiIhKtAIXpeRy+ZfIg4iIiIiIiIiIviEFXlPqXenp6YWVBxERERERERERfUMKXJTKzs7GtGnTULFiRejp6eHOnTsAgIkTJ2LlypWFniAREREREREREZU8BS5KTZ8+HatXr0ZAQAA0NDSk9po1a2LFihWFmhwREREREREREZVMBS5KrV27FsHBwejduzdUVVWl9lq1auHatWuFmhwREREREREREZVMBS5KxcfHw9raOle7XC7HmzdvCiUpIiIiIiIiIiIq2QpclLK1tcXRo0dztW/duhV16tQplKSIiIiIiIiIiKhkUyvoBv7+/vD09ER8fDzkcjm2b9+O69evY+3atdi7d++XyJGIiIiIiIiIiEqYAs+U6tChA/bs2YMDBw5AV1cX/v7+uHr1Kvbs2YNWrVp9iRyJiIiIiIiIiKiEKfBMKQBo0qQJIiIiCjsXIiIiIiIiIiL6RhR4plSVKlXw7NmzXO1JSUmoUqVKoSRFREREREREREQlW4GLUvfu3UN2dnau9oyMDMTHxxdKUkREREREREREVLIpffve7t27pX/v378fhoaG0vvs7GxERkbCwsKiUJMjIiIiIiIiIqKSSemiVMeOHQEAMpkMnp6eCn3q6uqwsLDAvHnzCjU5IiIiIiIiIiIqmZQuSsnlcgCApaUlzpw5g7Jly36xpIiIiIiIiIiIqGQr8NP37t69+yXyICIiIiIiIiKib0iBi1IAkJaWhsOHDyMuLg6ZmZkKfSNHjiyUxIiIiIiIiIiIqOQqcFHq/PnzaNu2LV69eoW0tDQYGRnh6dOn0NHRgbGxMYtSRERERERERET0USoF3WD06NFo3749Xrx4AW1tbZw8eRL379+Hg4MD5s6d+yVyJCIiIiIiIiKiEqbARanY2Fj873//g4qKClRVVZGRkQEzMzMEBAT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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_water_need_m³_ha.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_oil_efficiency_l_kg.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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DNu3PPfeckWRWrVplbfP39zeSrCGEMcZ8/fXXRpKpUKGCzS9B77zzjpFkVq9ebW3LC7+eeOIJa1tubq7p06ePcXNzM0eOHLG2nzp1yqae7Oxs06JFC9OzZ0+bdknGxcXFbN26Nd+2/fsHaW9vb/P4448X+lxkZ2ebmjVrmhYtWpjTp09b27/44gsjyYwaNSrftrz66qs2Y7Rt29YEBQUV+hjGnP8F1s3NzfTq1csmtJs+fbqRZObMmWNty/vl/cLnpjDLly83ksxHH31kjDHm0KFDRpJZs2aNOXHihHF1dTXLly83xpwPUSSZ119/3bp+UZ9zT0/PfOGBMcY89NBDplatWubo0aM27QMHDjTe3t7W8fN+2WvYsGG+xyyMpEJvH3/8sbVfYc9XRESE8fT0zDfuv4OQUaNGGUk2QVKevF/yCgqlbr75ZtOyZUtz5swZm/5dunQxTZo0sbbl/TIeEhJiHc8YY5555hnj6upqjh8/bowx5vjx46Zy5cqmU6dONvvihXUYY0znzp1Np06dbJZ/+umn+d57BRk0aJCpXr16vvbihlL2vH+KGkolJibmC1qDg4ONJDN//nxr2/bt262fBT/88IO1Pe/zKe+1On36tM177u677zYPPfTQRberefPmNsFVu3btTP/+/Y0k89tvvxlj/vecXxgaF7Rvh4aG5vuDQfPmzW2Cjjxjx441np6eZufOnTbtI0aMMK6uriY1NdUY87/Xy8vLyxw+fLjAbZg/f76RdMmwo6Cak5OTjSTz4YcfWtuK8j4p7H1e1P3k77//vuR+uHTpUiPJ/PTTTxfdrn+Li4szkszSpUsL7XPs2DEjydx9993WtkuFUsacf0/++ztg/fr1Ns9hbm6uadKkiQkNDbV5P586dco0aNDA3HLLLZfchv79+xsPDw+boDHvfRATE2Mz5oVK4ru0qJ/1P/30U77nJ8+/Q6nvvvvOSDLz5s2z6ZeQkGDTXtzXHADsxel7AFBMeadGVK5cuUj9v/zyS0lSdHS0Tfuzzz4rSflODwgMDFTnzp2t9/NOfenZs6fq16+fr33Pnj35HnPYsGHW/+edMpCdnW1zmk3eqTqS9PfffysjI0PdunXLd6qdJAUHByswMPASW3p+nqEff/xRBw8eLHD5hg0bdPjwYQ0dOtRmDo0+ffqoadOmBZ4q8e/5eLp161bgNl9o5cqVys7O1tNPP20zt0pUVJS8vLyKPd9Xly5d5OLiYp0r6vvvv1f58uXVoUMHVapUSa1atbKeGpL374XzSdnznP+bMUZLlizRHXfcIWOMjh49ar2FhoYqIyMj3zgRERE2j3kpd955p1asWJHvVpKTdC9ZskStW7fWXXfdlW9ZYaebHTt2TKtWrdK9996rEydOWLf7r7/+UmhoqHbt2pXvtKRHHnnEZrxu3bopJyfHetrPihUrdOLECY0YMSLffC4XrhceHq4ff/xRu3fvtrbNmzdP9erVU3Bw8EW39a+//lLVqlUv2scexXn/XMqXX36pwMDAfKf5VKpUSQMHDrTev/7661WlShU1a9bM5nS8f38O7dixQzfeeKOCg4PVrl07WSyWS16JsVu3bvruu+8knT8F+Oeff9YjjzwiHx8fa/t3331ncyqsZPt+ysjI0NGjRxUcHKw9e/YoIyPjktu+aNEidevWTVWrVrV5P4WEhCgnJ0fffvutTf9+/fqpRo0aBY6V9zrnnVpXmAtrPnfunP766y81btxYVapUsXn/2vM++ff7vKj7Sd4ccElJSflO38qTN3fcF198oXPnzl102y504sQJSRf/nsxbZu9pxQMGDNDGjRtt3pMLFy6Uu7u77rzzTknS5s2btWvXLt1///3666+/rK9tVlaWbr75Zn377beXnAR80KBBOnPmjD799FNrW96pvXmn7kkl/11anM/6oli0aJG8vb11yy232IwZFBSkSpUqafXq1ZKK/5oDgL0IpQCgmLy8vCT974fuS9m3b59cXFzyXdnNz89PVapUyTc3xoXBkyR5e3tLkurVq1dg+79/mXBxcVHDhg1t2vKunnbhPCFffPGFbrjhBnl4eKhatWqqUaOGZs6cWeAvcxdeUe5iJkyYoC1btqhevXrq2LGjXnnlFZsAKW9br7/++nzrNm3aNN9z4eHhke+XwKpVqxb6C9SlHsfNzU0NGzYscD6SoqhSpYqaN29uEzy1bdvW+ktJly5dbJa5ubmpY8eO1vXtec7/7ciRIzp+/LhmzZqlGjVq2NwiIyMl/W8S8jxFfd3y1K1bVyEhIfluF7s6lr12795tEywUxe+//y5jjF5++eV82543x9W/t/3f76O80CBv38n7hfZStQwYMEDu7u6aN2+epPPhxxdffKEHHnigSHM2mX/N5XY57H3/FMXy5cvVp0+ffO1169bNt33e3t6X/Bxq3bq11q1bpzVr1iglJUWLFy9WzZo1L1pDt27ddOjQIf3+++9at26dLBaLOnfubBNWfffdd+ratatNyPz9998rJCREnp6eqlKlimrUqKGRI0dKUpHeU7t27VJCQkK+fSpvDjB73k95r/Ol9onTp09r1KhR1jmsfHx8VKNGDR0/ftymZnveJ/+uq6j7ibu7u8aPH6+vvvpKvr6+6t69uyZMmKC0tDRr/+DgYPXr109jxoyRj4+P7rzzTs2dOzfffIj/lhc4Xex7sijBVUH69+8vFxcXLVy4UNL5537RokXq3bu39ft5165dks4Hdv9+fd99912dPXv2kvtI7969Va1aNWsQJUkff/yxWrdurebNm1vbSvq7tDif9UWxa9cuZWRkqGbNmvnGPXnypHXM4r7mAGAvrr4HAMXk5eWl2rVra8uWLXatV9RJh11dXe1qL84vvd9995369u2r7t276+2331atWrVUvnx5zZ071+YH8DxFPdrm3nvvVbdu3bR06VJ98803mjhxosaPH69PP/20WJfOLmybnenGG29UfHy8jh8/ru+//15dunSxLuvSpYvmzJmjc+fOae3atQoKCrIeqWDvc/5veX/VHzRokCIiIgrs06pVK5v79hwldSXL2/bnnntOoaGhBfb5d+hbUu+XqlWr6vbbb9e8efM0atQoLV68WGfPnr3kVQolqXr16pcMUJ1p79692r59e77JwSXHfA7lyTua8Ntvv9WePXvUrl07eXp6qlu3bnrrrbd08uRJbdq0Sa+//rp1nd27d+vmm29W06ZNNXnyZNWrV09ubm768ssvNWXKlEseBSOd369uueUWPf/88wUuzwvz81zs/ZT3Ovv4+Fz0MZ944gnNnTtXTz/9tDp37ixvb29ZLBYNHDiwSDUX5HLe508//bTuuOMOLVu2TF9//bVefvllxcbGatWqVWrbtq0sFosWL16sH374Qf/973/19ddfa8iQIXrzzTf1ww8/qFKlSgWO26xZM0nSL7/8orCwsAL75E1uX5SjcC9Uu3ZtdevWTZ988olGjhypH374QampqRo/fry1T95zOXHiRLVp06bAcQqrPU/58uV17733avbs2UpPT1dqaqp27dqlCRMmWPuUxndpcT7riyI3N1c1a9a0Buz/lvcHoOK+5gBgL0IpALgMt99+u2bNmqXk5GSbU+0K4u/vr9zcXO3atcv6g7okpaen6/jx4/L39y/R2nJzc7Vnzx6bX6h27twpSdZTdJYsWSIPDw99/fXXNpe7njt37mU/fq1atTR06FANHTpUhw8fVrt27fT666+rd+/e1m3dsWNHvsvP79ixo8Seiwsf58KjxrKzs7V3797LuhrajTfeqJkzZ2rlypXatGmThg8fbl3WpUsXnT59WsuXL9eePXvUr18/6zJ7nvOCAswaNWqocuXKysnJKfGruTlSo0aN7A50817D8uXLl9i2N2rUSJK0ZcuWfIHWv4WHh+vOO+/UTz/9pHnz5qlt27Y2R0oUpmnTppo3b54yMjKsRxRdjpJ+/yxfvlze3t42p5g6Q/369VW/fn1999132rNnj7p16yZJ6t69u6Kjo7Vo0SLl5OSoe/fu1nX++9//6uzZs/r8889tjorLOwXpQoX9QaBRo0Y6efJkiexTe/fulYuLS74g698WL16siIgIvfnmm9a2M2fO2FwVNK82e98neezdTxo1aqRnn31Wzz77rHbt2qU2bdrozTff1H/+8x9rnxtuuEE33HCDXn/9dc2fP18PPPCAFixYoIcffrjAGm688UZVqVJF8+fP14svvlhgmPnhhx9KOv99aq8BAwZo6NCh2rFjhxYuXKiKFSvqjjvusNkm6fwfkS7n9X3ggQcUHx+vhQsXau/evbJYLLrvvvusy0vju9Sez/qi/rFLOv+crFy5Ul27di1SOGbvaw4A9uL0PQC4DM8//7w8PT318MMPKz09Pd/y3bt3a+rUqZKk2267TZIUFxdn02fy5MmSVOCpM5dr+vTp1v8bYzR9+nSVL19eN998s6TzRztYLBbl5ORY+/3xxx9atmxZsR8zJycn3+kKNWvWVO3ata2H/bdv3141a9ZUfHy8zakAX331lX777bcSey5CQkLk5uamt956y+YIjvfee08ZGRmX9Th5v8BPnjxZ586dszlSKiAgQLVq1bL+Jf3CX/btec49PT3z/ZLq6uqqfv36acmSJQX+snrkyJFib5Mj9evXTz///LOWLl2ab1lhR9vUrFlTN910k9555x0dOnQo3/LibHuvXr1UuXJlxcbG6syZMxeto3fv3vLx8dH48eO1Zs2aIh0lJUmdO3eWMUYbN260u76ClPT758svv1SvXr1Urpzz/1bZrVs3rVq1SuvXr7eGUm3atFHlypU1btw4VahQQUFBQdb+eSHHha9VRkZGgWFAQe8n6fyRncnJyfr666/zLTt+/Lj++eefIte/ceNGNW/e/JLho6ura779a9q0aTafC1Lx3id5irqfnDp1Kt++36hRI1WuXNm63t9//53v8fKOPLrY6VwVK1bUc889px07dujFF1/Mt3z58uV6//3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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n",
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/efficiency_vs_production.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/water_efficiency_vs_production.png\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Plot salvato come: .//2024-12-06_10-36_plots/water_need_vs_oil_production.png\n",
|
||
" Variety Technique Technique String \\\n",
|
||
"0 nocellara_delletna 3 tradizionale \n",
|
||
"1 nocellara_delletna 1 intensiva \n",
|
||
"2 nocellara_delletna 2 superintensiva \n",
|
||
"3 leccino 1 intensiva \n",
|
||
"4 leccino 2 superintensiva \n",
|
||
"5 leccino 3 tradizionale \n",
|
||
"6 frantoio 2 superintensiva \n",
|
||
"7 frantoio 3 tradizionale \n",
|
||
"8 frantoio 1 intensiva \n",
|
||
"9 coratina 1 intensiva \n",
|
||
"10 coratina 2 superintensiva \n",
|
||
"11 coratina 3 tradizionale \n",
|
||
"12 taggiasca 3 tradizionale \n",
|
||
"13 taggiasca 2 superintensiva \n",
|
||
"14 taggiasca 1 intensiva \n",
|
||
"15 pendolino 1 intensiva \n",
|
||
"16 pendolino 2 superintensiva \n",
|
||
"17 pendolino 3 tradizionale \n",
|
||
"18 moraiolo 2 superintensiva \n",
|
||
"19 moraiolo 1 intensiva \n",
|
||
"20 moraiolo 3 tradizionale \n",
|
||
"\n",
|
||
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
||
"0 9564.638687 2088.362004 \n",
|
||
"1 13699.079622 2991.183032 \n",
|
||
"2 17826.710664 3892.059753 \n",
|
||
"3 16432.379678 3229.053194 \n",
|
||
"4 20528.499013 4033.942398 \n",
|
||
"5 10937.982122 2149.449585 \n",
|
||
"6 24621.040119 6047.876212 \n",
|
||
"7 13740.739760 3375.103688 \n",
|
||
"8 20550.900635 5047.942655 \n",
|
||
"9 16429.706879 4215.265516 \n",
|
||
"10 19164.700743 4916.649709 \n",
|
||
"11 12318.510310 3160.037128 \n",
|
||
"12 6839.506230 1381.247995 \n",
|
||
"13 16433.741502 3319.210170 \n",
|
||
"14 10968.603159 2215.371493 \n",
|
||
"15 13705.431414 2468.678455 \n",
|
||
"16 19183.689269 3455.879324 \n",
|
||
"17 10960.549241 1974.357984 \n",
|
||
"18 17793.971752 3885.415851 \n",
|
||
"19 13144.222436 2870.020002 \n",
|
||
"20 8765.195655 1913.745255 \n",
|
||
"\n",
|
||
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"0 32997.227891 0.218342 \n",
|
||
"1 33079.012125 0.218349 \n",
|
||
"2 33118.708645 0.218327 \n",
|
||
"3 25013.303736 0.196506 \n",
|
||
"4 24989.459147 0.196504 \n",
|
||
"5 24981.219100 0.196512 \n",
|
||
"6 28874.473543 0.245639 \n",
|
||
"7 29003.452741 0.245628 \n",
|
||
"8 28921.261327 0.245631 \n",
|
||
"9 38270.638622 0.256564 \n",
|
||
"10 38264.650562 0.256547 \n",
|
||
"11 38253.676395 0.256528 \n",
|
||
"12 26219.134374 0.201951 \n",
|
||
"13 26253.317778 0.201975 \n",
|
||
"14 26284.027794 0.201974 \n",
|
||
"15 26154.359691 0.180124 \n",
|
||
"16 26153.199618 0.180147 \n",
|
||
"17 26152.823801 0.180133 \n",
|
||
"18 32561.911109 0.218356 \n",
|
||
"19 32577.899255 0.218348 \n",
|
||
"20 32594.860153 0.218335 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"0 0.063289 \n",
|
||
"1 0.090425 \n",
|
||
"2 0.117518 \n",
|
||
"3 0.129093 \n",
|
||
"4 0.161426 \n",
|
||
"5 0.086043 \n",
|
||
"6 0.209454 \n",
|
||
"7 0.116369 \n",
|
||
"8 0.174541 \n",
|
||
"9 0.110144 \n",
|
||
"10 0.128491 \n",
|
||
"11 0.082607 \n",
|
||
"12 0.052681 \n",
|
||
"13 0.126430 \n",
|
||
"14 0.084286 \n",
|
||
"15 0.094389 \n",
|
||
"16 0.132140 \n",
|
||
"17 0.075493 \n",
|
||
"18 0.119324 \n",
|
||
"19 0.088097 \n",
|
||
"20 0.058713 \n",
|
||
"Comparison by Variety:\n",
|
||
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
||
"Variety \n",
|
||
"nocellara_delletna 13696.683690 2990.507461 \n",
|
||
"leccino 15971.162702 3138.439782 \n",
|
||
"frantoio 19648.631813 4826.360700 \n",
|
||
"coratina 15974.164423 4098.136472 \n",
|
||
"taggiasca 11412.636779 2305.011278 \n",
|
||
"pendolino 14617.432649 2633.129635 \n",
|
||
"moraiolo 13232.961913 2889.399172 \n",
|
||
"\n",
|
||
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"Variety \n",
|
||
"nocellara_delletna 33064.983905 0.218338 \n",
|
||
"leccino 24994.676451 0.196507 \n",
|
||
"frantoio 28932.932409 0.245633 \n",
|
||
"coratina 38262.995517 0.256548 \n",
|
||
"taggiasca 26252.184893 0.201970 \n",
|
||
"pendolino 26153.461822 0.180136 \n",
|
||
"moraiolo 32578.228327 0.218349 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"Variety \n",
|
||
"nocellara_delletna 0.090443 \n",
|
||
"leccino 0.125564 \n",
|
||
"frantoio 0.166812 \n",
|
||
"coratina 0.107104 \n",
|
||
"taggiasca 0.087803 \n",
|
||
"pendolino 0.100680 \n",
|
||
"moraiolo 0.088691 \n",
|
||
"\n",
|
||
"Best Varieties by Water Efficiency:\n",
|
||
" Variety Avg Olive Production (kg/ha) \\\n",
|
||
"2 frantoio 19648.631813 \n",
|
||
"1 leccino 15971.162702 \n",
|
||
"3 coratina 15974.164423 \n",
|
||
"5 pendolino 14617.432649 \n",
|
||
"0 nocellara_delletna 13696.683690 \n",
|
||
"\n",
|
||
" Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
||
"2 4826.360700 28932.932409 0.245633 \n",
|
||
"1 3138.439782 24994.676451 0.196507 \n",
|
||
"3 4098.136472 38262.995517 0.256548 \n",
|
||
"5 2633.129635 26153.461822 0.180136 \n",
|
||
"0 2990.507461 33064.983905 0.218338 \n",
|
||
"\n",
|
||
" Water Efficiency (L oil/m³ water) \n",
|
||
"2 0.166812 \n",
|
||
"1 0.125564 \n",
|
||
"3 0.107104 \n",
|
||
"5 0.100680 \n",
|
||
"0 0.090443 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n",
|
||
"olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n",
|
||
"# Esecuzione dell'analisi\n",
|
||
"comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"# Genera i grafici\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n",
|
||
"plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n",
|
||
"plot_efficiency_vs_production(comparison_data)\n",
|
||
"plot_water_efficiency_vs_production(comparison_data)\n",
|
||
"plot_water_need_vs_oil_production(comparison_data)\n",
|
||
"\n",
|
||
"# Analisi per tecnica\n",
|
||
"technique_data = analyze_by_technique(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"print(technique_data)\n",
|
||
"\n",
|
||
"# Stampa un sommario statistico\n",
|
||
"print(\"Comparison by Variety:\")\n",
|
||
"print(comparison_data.set_index('Variety'))\n",
|
||
"print(\"\\nBest Varieties by Water Efficiency:\")\n",
|
||
"print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "bbe87b415168368",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def prepare_transformer_data(df, olive_varieties_df):\n",
|
||
" # Crea una copia del DataFrame per evitare modifiche all'originale\n",
|
||
" df = df.copy()\n",
|
||
"\n",
|
||
" # Ordina per zona e anno\n",
|
||
" df = df.sort_values(['zone', 'year'])\n",
|
||
"\n",
|
||
" # Definisci le feature\n",
|
||
" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
||
" static_features = ['ha'] # Feature statiche base\n",
|
||
" target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" # Ottieni le varietà pulite\n",
|
||
" all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n",
|
||
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
||
"\n",
|
||
" # Crea la struttura delle feature per ogni varietà\n",
|
||
" variety_features = [\n",
|
||
" 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n",
|
||
" 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n",
|
||
" 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Prepara dizionari per le nuove colonne\n",
|
||
" new_columns = {}\n",
|
||
"\n",
|
||
" # Prepara le feature per ogni varietà\n",
|
||
" for variety in varieties:\n",
|
||
" # Feature esistenti\n",
|
||
" for feature in variety_features:\n",
|
||
" col_name = f\"{variety}_{feature}\"\n",
|
||
" if col_name in df.columns:\n",
|
||
" if feature != 'tech': # Non includere la colonna tech direttamente\n",
|
||
" static_features.append(col_name)\n",
|
||
"\n",
|
||
" # Feature binarie per le tecniche di coltivazione\n",
|
||
" for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n",
|
||
" col_name = f\"{variety}_{technique}\"\n",
|
||
" new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n",
|
||
" df[f\"{variety}_tech\"].str.lower() == technique\n",
|
||
" ).fillna(False)\n",
|
||
" static_features.append(col_name)\n",
|
||
"\n",
|
||
" # Aggiungi tutte le nuove colonne in una volta sola\n",
|
||
" new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n",
|
||
"\n",
|
||
" # Ordiniamo per zona e anno per mantenere la continuità temporale\n",
|
||
" df_sorted = new_df.sort_values(['zone', 'year'])\n",
|
||
"\n",
|
||
" # Definiamo la dimensione della finestra temporale\n",
|
||
" window_size = 41\n",
|
||
"\n",
|
||
" # Liste per raccogliere i dati\n",
|
||
" temporal_sequences = []\n",
|
||
" static_features_list = []\n",
|
||
" targets_list = []\n",
|
||
"\n",
|
||
" # Iteriamo per ogni zona\n",
|
||
" for zone in df_sorted['zone'].unique():\n",
|
||
" zone_data = df_sorted[df_sorted['zone'] == zone].reset_index(drop=True)\n",
|
||
"\n",
|
||
" if len(zone_data) >= window_size: # Verifichiamo che ci siano abbastanza dati\n",
|
||
" # Creiamo sequenze temporali scorrevoli\n",
|
||
" for i in range(len(zone_data) - window_size + 1):\n",
|
||
" # Sequenza temporale\n",
|
||
" temporal_window = zone_data.iloc[i:i + window_size][temporal_features].values\n",
|
||
" # Verifichiamo che non ci siano valori NaN\n",
|
||
" if not np.isnan(temporal_window).any():\n",
|
||
" temporal_sequences.append(temporal_window)\n",
|
||
"\n",
|
||
" # Feature statiche (prendiamo quelle dell'ultimo timestep della finestra)\n",
|
||
" static_features_list.append(zone_data.iloc[i + window_size - 1][static_features].values)\n",
|
||
"\n",
|
||
" # Target (prendiamo quelli dell'ultimo timestep della finestra)\n",
|
||
" targets_list.append(zone_data.iloc[i + window_size - 1][target_features].values)\n",
|
||
"\n",
|
||
" # Convertiamo in array numpy\n",
|
||
" X_temporal = np.array(temporal_sequences)\n",
|
||
" X_static = np.array(static_features_list)\n",
|
||
" y = np.array(targets_list)\n",
|
||
"\n",
|
||
" print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n",
|
||
"\n",
|
||
" # Split dei dati (usando indici casuali per una migliore distribuzione)\n",
|
||
" indices = np.random.permutation(len(X_temporal))\n",
|
||
"\n",
|
||
" #train_idx = int(len(indices) * 0.7) # 70% training\n",
|
||
" #val_idx = int(len(indices) * 0.85) # 15% validation\n",
|
||
" # Il resto rimane 15% test\n",
|
||
"\n",
|
||
" train_idx = int(len(indices) * 0.65) # 65% training\n",
|
||
" val_idx = int(len(indices) * 0.85) # 20% validation\n",
|
||
" # Il resto rimane 15% test\n",
|
||
"\n",
|
||
" #train_idx = int(len(indices) * 0.60) # 60% training\n",
|
||
" #val_idx = int(len(indices) * 0.85) # 25% validation\n",
|
||
" # Il resto rimane 15% test\n",
|
||
"\n",
|
||
" train_indices = indices[:train_idx]\n",
|
||
" val_indices = indices[train_idx:val_idx]\n",
|
||
" test_indices = indices[val_idx:]\n",
|
||
"\n",
|
||
" # Split dei dati\n",
|
||
" X_temporal_train = X_temporal[train_indices]\n",
|
||
" X_temporal_val = X_temporal[val_indices]\n",
|
||
" X_temporal_test = X_temporal[test_indices]\n",
|
||
"\n",
|
||
" X_static_train = X_static[train_indices]\n",
|
||
" X_static_val = X_static[val_indices]\n",
|
||
" X_static_test = X_static[test_indices]\n",
|
||
"\n",
|
||
" y_train = y[train_indices]\n",
|
||
" y_val = y[val_indices]\n",
|
||
" y_test = y[test_indices]\n",
|
||
"\n",
|
||
" # Standardizzazione\n",
|
||
" scaler_temporal = StandardScaler()\n",
|
||
" scaler_static = StandardScaler()\n",
|
||
" scaler_y = StandardScaler()\n",
|
||
"\n",
|
||
" # Standardizzazione dei dati temporali\n",
|
||
" X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)\n",
|
||
" X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)\n",
|
||
" X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)\n",
|
||
"\n",
|
||
" # Standardizzazione dei dati statici\n",
|
||
" X_static_train = scaler_static.fit_transform(X_static_train)\n",
|
||
" X_static_val = scaler_static.transform(X_static_val)\n",
|
||
" X_static_test = scaler_static.transform(X_static_test)\n",
|
||
"\n",
|
||
" # Standardizzazione dei target\n",
|
||
" y_train = scaler_y.fit_transform(y_train)\n",
|
||
" y_val = scaler_y.transform(y_val)\n",
|
||
" y_test = scaler_y.transform(y_test)\n",
|
||
"\n",
|
||
" print(\"\\nShape dopo lo split e standardizzazione:\")\n",
|
||
" print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n",
|
||
" print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n",
|
||
" print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n",
|
||
"\n",
|
||
" # Prepara i dizionari di input\n",
|
||
" train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n",
|
||
" val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n",
|
||
" test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n",
|
||
"\n",
|
||
" joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n",
|
||
" joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n",
|
||
" joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n",
|
||
"\n",
|
||
" return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "9c4d5f0f3fafdc2d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Dataset completo - Temporal: (3920000, 41, 3), Static: (3920000, 113), Target: (3920000, 5)\n",
|
||
"\n",
|
||
"Shape dopo lo split e standardizzazione:\n",
|
||
"Train - Temporal: (2548000, 41, 3), Static: (2548000, 113), Target: (2548000, 5)\n",
|
||
"Val - Temporal: (784000, 41, 3), Static: (784000, 113), Target: (784000, 5)\n",
|
||
"Test - Temporal: (588000, 41, 3), Static: (588000, 113), Target: (588000, 5)\n",
|
||
"Temporal data shape: (2548000, 41, 3)\n",
|
||
"Static data shape: (2548000, 113)\n",
|
||
"Target shape: (2548000, 5)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n",
|
||
"olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n",
|
||
"\n",
|
||
"(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n",
|
||
"\n",
|
||
"scaler_temporal, scaler_static, scaler_y = scalers\n",
|
||
"\n",
|
||
"print(\"Temporal data shape:\", train_data['temporal'].shape)\n",
|
||
"print(\"Static data shape:\", train_data['static'].shape)\n",
|
||
"print(\"Target shape:\", train_targets.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "604c952c7195f40c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class DataAugmentation(tf.keras.layers.Layer):\n",
|
||
" \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, noise_stddev=0.03, **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.noise_stddev = noise_stddev\n",
|
||
"\n",
|
||
" def call(self, inputs, training=None):\n",
|
||
" if training:\n",
|
||
" return inputs + tf.random.normal(\n",
|
||
" shape=tf.shape(inputs),\n",
|
||
" mean=0.0,\n",
|
||
" stddev=self.noise_stddev\n",
|
||
" )\n",
|
||
" return inputs\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" config = super().get_config()\n",
|
||
" config.update({\"noise_stddev\": self.noise_stddev})\n",
|
||
" return config\n",
|
||
"\n",
|
||
"\n",
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class PositionalEncoding(tf.keras.layers.Layer):\n",
|
||
" \"\"\"Custom layer per l'encoding posizionale\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, d_model, **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.d_model = d_model\n",
|
||
"\n",
|
||
" def build(self, input_shape):\n",
|
||
" _, seq_length, _ = input_shape\n",
|
||
"\n",
|
||
" # Crea la matrice di encoding posizionale\n",
|
||
" position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n",
|
||
" div_term = tf.exp(\n",
|
||
" tf.range(0, self.d_model, 2, dtype=tf.float32) *\n",
|
||
" (-tf.math.log(10000.0) / self.d_model)\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Calcola sin e cos\n",
|
||
" pos_encoding = tf.zeros((1, seq_length, self.d_model))\n",
|
||
" pos_encoding_even = tf.sin(position * div_term)\n",
|
||
" pos_encoding_odd = tf.cos(position * div_term)\n",
|
||
"\n",
|
||
" # Assegna i valori alle posizioni pari e dispari\n",
|
||
" pos_encoding = tf.concat(\n",
|
||
" [tf.expand_dims(pos_encoding_even, -1),\n",
|
||
" tf.expand_dims(pos_encoding_odd, -1)],\n",
|
||
" axis=-1\n",
|
||
" )\n",
|
||
" pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n",
|
||
" pos_encoding = pos_encoding[:, :, :self.d_model]\n",
|
||
"\n",
|
||
" # Salva l'encoding come peso non trainabile\n",
|
||
" self.pos_encoding = self.add_weight(\n",
|
||
" shape=(1, seq_length, self.d_model),\n",
|
||
" initializer=tf.keras.initializers.Constant(pos_encoding),\n",
|
||
" trainable=False,\n",
|
||
" name='positional_encoding'\n",
|
||
" )\n",
|
||
"\n",
|
||
" super().build(input_shape)\n",
|
||
"\n",
|
||
" def call(self, inputs):\n",
|
||
" # Broadcast l'encoding posizionale sul batch\n",
|
||
" batch_size = tf.shape(inputs)[0]\n",
|
||
" pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n",
|
||
" return inputs + pos_encoding_tiled\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" config = super().get_config()\n",
|
||
" config.update({\"d_model\": self.d_model})\n",
|
||
" return config\n",
|
||
"\n",
|
||
"\n",
|
||
"@keras.saving.register_keras_serializable()\n",
|
||
"class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
|
||
" \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n",
|
||
" super().__init__()\n",
|
||
" self.initial_learning_rate = initial_learning_rate\n",
|
||
" self.warmup_steps = warmup_steps\n",
|
||
" self.decay_steps = decay_steps\n",
|
||
"\n",
|
||
" def __call__(self, step):\n",
|
||
" warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n",
|
||
" warmup_lr = self.initial_learning_rate * warmup_pct\n",
|
||
" decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n",
|
||
" decayed_lr = self.initial_learning_rate * decay_factor\n",
|
||
" return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n",
|
||
"\n",
|
||
" def get_config(self):\n",
|
||
" return {\n",
|
||
" 'initial_learning_rate': self.initial_learning_rate,\n",
|
||
" 'warmup_steps': self.warmup_steps,\n",
|
||
" 'decay_steps': self.decay_steps\n",
|
||
" }\n",
|
||
"\n",
|
||
"\n",
|
||
"def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n",
|
||
" d_model=128, num_heads=8, ff_dim=256,\n",
|
||
" num_transformer_blocks=4, mlp_units=None,\n",
|
||
" dropout=0.2):\n",
|
||
" \"\"\"\n",
|
||
" Crea un transformer per la predizione della produzione di olio d'oliva.\n",
|
||
" \"\"\"\n",
|
||
" # Input layers\n",
|
||
" if mlp_units is None:\n",
|
||
" mlp_units = [256, 128, 64]\n",
|
||
"\n",
|
||
" temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n",
|
||
" static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n",
|
||
"\n",
|
||
" # === TEMPORAL PATH ===\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n",
|
||
" x = DataAugmentation()(x)\n",
|
||
"\n",
|
||
" # Temporal projection\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model // 2,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
" x = tf.keras.layers.Dropout(dropout)(x)\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" d_model,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
"\n",
|
||
" # Positional encoding\n",
|
||
" x = PositionalEncoding(d_model)(x)\n",
|
||
"\n",
|
||
" # Transformer blocks\n",
|
||
" skip_connection = x\n",
|
||
" for _ in range(num_transformer_blocks):\n",
|
||
" # Self-attention\n",
|
||
" attention_output = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads,\n",
|
||
" key_dim=d_model // num_heads,\n",
|
||
" value_dim=d_model // num_heads\n",
|
||
" )(x, x)\n",
|
||
" attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n",
|
||
"\n",
|
||
" # Residual connection con pesi addestrabili\n",
|
||
" residual_weights = tf.keras.layers.Dense(d_model, activation='sigmoid')(x)\n",
|
||
" x = tfa.layers.StochasticDepth(survival_probability=0.3)([x, residual_weights * attention_output])\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Feed-forward network\n",
|
||
" ffn = tf.keras.layers.Dense(ff_dim, activation=\"swish\")(x)\n",
|
||
" ffn = tf.keras.layers.Dropout(dropout)(ffn)\n",
|
||
" ffn = tf.keras.layers.Dense(d_model)(ffn)\n",
|
||
" ffn = tf.keras.layers.Dropout(dropout)(ffn)\n",
|
||
"\n",
|
||
" # Second residual connection\n",
|
||
" x = tfa.layers.StochasticDepth()([x, ffn])\n",
|
||
" x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
|
||
"\n",
|
||
" # Add final skip connection\n",
|
||
" x = tfa.layers.StochasticDepth(survival_probability=0.5)([x, skip_connection])\n",
|
||
"\n",
|
||
" # Temporal pooling\n",
|
||
" attention_pooled = tf.keras.layers.MultiHeadAttention(\n",
|
||
" num_heads=num_heads,\n",
|
||
" key_dim=d_model // 4\n",
|
||
" )(x, x)\n",
|
||
" attention_pooled = tf.keras.layers.GlobalAveragePooling1D()(attention_pooled)\n",
|
||
"\n",
|
||
" # Additional pooling operations\n",
|
||
" avg_pooled = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
|
||
" max_pooled = tf.keras.layers.GlobalMaxPooling1D()(x)\n",
|
||
"\n",
|
||
" # Combine pooling results\n",
|
||
" temporal_features = tf.keras.layers.Concatenate()(\n",
|
||
" [attention_pooled, avg_pooled, max_pooled]\n",
|
||
" )\n",
|
||
"\n",
|
||
" # === STATIC PATH ===\n",
|
||
" static_features = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n",
|
||
" for units in [256, 128, 64]:\n",
|
||
" static_features = tf.keras.layers.Dense(\n",
|
||
" units,\n",
|
||
" activation='swish',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(static_features)\n",
|
||
" static_features = tf.keras.layers.Dropout(dropout)(static_features)\n",
|
||
"\n",
|
||
" # === FEATURE FUSION ===\n",
|
||
" combined = tf.keras.layers.Concatenate()([temporal_features, static_features])\n",
|
||
"\n",
|
||
" # === MLP HEAD ===\n",
|
||
" x = combined\n",
|
||
" for units in mlp_units:\n",
|
||
" x = tf.keras.layers.BatchNormalization()(x)\n",
|
||
" x = tf.keras.layers.Dense(\n",
|
||
" units,\n",
|
||
" activation=\"swish\",\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
" x = tf.keras.layers.Dropout(dropout)(x)\n",
|
||
"\n",
|
||
" # Output layer\n",
|
||
" outputs = tf.keras.layers.Dense(\n",
|
||
" num_outputs,\n",
|
||
" activation='linear',\n",
|
||
" kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n",
|
||
" )(x)\n",
|
||
"\n",
|
||
" # Create model\n",
|
||
" model = tf.keras.Model(\n",
|
||
" inputs={'temporal': temporal_input, 'static': static_input},\n",
|
||
" outputs=outputs,\n",
|
||
" name='OilTransformer'\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"\n",
|
||
"def create_transformer_callbacks(target_names, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Crea i callbacks per il training del modello.\n",
|
||
" \n",
|
||
" Parameters:\n",
|
||
" -----------\n",
|
||
" target_names : list\n",
|
||
" Lista dei nomi dei target per il monitoraggio specifico\n",
|
||
" val_data : dict\n",
|
||
" Dati di validazione\n",
|
||
" val_targets : array\n",
|
||
" Target di validazione\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" --------\n",
|
||
" list\n",
|
||
" Lista dei callbacks configurati\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" # Custom Metric per target specifici\n",
|
||
" class TargetSpecificMetric(tf.keras.callbacks.Callback):\n",
|
||
" def __init__(self, validation_data, target_names):\n",
|
||
" super().__init__()\n",
|
||
" self.validation_data = validation_data\n",
|
||
" self.target_names = target_names\n",
|
||
"\n",
|
||
" def on_epoch_end(self, epoch, logs={}):\n",
|
||
" x_val, y_val = self.validation_data\n",
|
||
" y_pred = self.model.predict(x_val, verbose=0)\n",
|
||
"\n",
|
||
" for i, name in enumerate(self.target_names):\n",
|
||
" mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n",
|
||
" logs[f'val_{name}_mae'] = mae\n",
|
||
"\n",
|
||
"\n",
|
||
" callbacks = [\n",
|
||
" # Early Stopping\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=20,\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0005,\n",
|
||
" mode='min'\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Model Checkpoint\n",
|
||
" tf.keras.callbacks.ModelCheckpoint(\n",
|
||
" filepath=f'{execute_name}_best_oil_model.h5',\n",
|
||
" monitor='val_loss',\n",
|
||
" save_best_only=True,\n",
|
||
" mode='min',\n",
|
||
" save_weights_only=True\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Metric per target specifici\n",
|
||
" TargetSpecificMetric(\n",
|
||
" validation_data=(val_data, val_targets),\n",
|
||
" target_names=target_names\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # Reduce LR on Plateau\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.5,\n",
|
||
" patience=10,\n",
|
||
" min_lr=1e-6,\n",
|
||
" verbose=1\n",
|
||
" ),\n",
|
||
"\n",
|
||
" # TensorBoard logging\n",
|
||
" tf.keras.callbacks.TensorBoard(\n",
|
||
" log_dir=f'./logs_{execute_name}',\n",
|
||
" histogram_freq=1,\n",
|
||
" write_graph=True,\n",
|
||
" update_freq='epoch'\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" return callbacks\n",
|
||
"\n",
|
||
"\n",
|
||
"def compile_model(model, learning_rate=1e-3):\n",
|
||
" \"\"\"\n",
|
||
" Compila il modello con le impostazioni standard.\n",
|
||
" \"\"\"\n",
|
||
" lr_schedule = WarmUpLearningRateSchedule(\n",
|
||
" initial_learning_rate=learning_rate,\n",
|
||
" warmup_steps=500,\n",
|
||
" decay_steps=5000\n",
|
||
" )\n",
|
||
"\n",
|
||
" model.compile(\n",
|
||
" optimizer=tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=lr_schedule,\n",
|
||
" weight_decay=0.01\n",
|
||
" ),\n",
|
||
" loss=tf.keras.losses.Huber(),\n",
|
||
" metrics=['mae']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"\n",
|
||
"def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n",
|
||
" \"\"\"\n",
|
||
" Configura e prepara il transformer con dimensioni dinamiche basate sui dati.\n",
|
||
" \"\"\"\n",
|
||
" # Estrai le shape dai dati\n",
|
||
" temporal_shape = (train_data['temporal'].shape[1], train_data['temporal'].shape[2])\n",
|
||
" static_shape = (train_data['static'].shape[1],)\n",
|
||
" num_outputs = train_targets.shape[1]\n",
|
||
"\n",
|
||
" print(f\"Shape rilevate:\")\n",
|
||
" print(f\"- Temporal shape: {temporal_shape}\")\n",
|
||
" print(f\"- Static shape: {static_shape}\")\n",
|
||
" print(f\"- Numero di output: {num_outputs}\")\n",
|
||
"\n",
|
||
" # Target names basati sul numero di output\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
"\n",
|
||
" # Assicurati che il numero di target names corrisponda al numero di output\n",
|
||
" assert len(target_names) == num_outputs, \\\n",
|
||
" f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n",
|
||
"\n",
|
||
" # Crea il modello con le dimensioni rilevate\n",
|
||
" model = create_olive_oil_transformer(\n",
|
||
" temporal_shape=temporal_shape,\n",
|
||
" static_shape=static_shape,\n",
|
||
" num_outputs=num_outputs\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Compila il modello\n",
|
||
" model = compile_model(model)\n",
|
||
"\n",
|
||
" # Crea i callbacks\n",
|
||
" callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n",
|
||
"\n",
|
||
" return model, callbacks, target_names\n",
|
||
"\n",
|
||
"\n",
|
||
"def train_transformer(train_data, train_targets, val_data, val_targets, epochs=150, batch_size=64, save_name='final_model'):\n",
|
||
" \"\"\"\n",
|
||
" Funzione principale per l'addestramento del transformer con ottimizzazioni.\n",
|
||
" \"\"\"\n",
|
||
" # Conversione dei dati in tf.data.Dataset per una gestione più efficiente della memoria\n",
|
||
" train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n",
|
||
" .cache()\\\n",
|
||
" .shuffle(buffer_size=1024)\\\n",
|
||
" .batch(batch_size)\\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n",
|
||
" .cache()\\\n",
|
||
" .batch(batch_size)\\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" # Setup del modello\n",
|
||
" strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n",
|
||
" \n",
|
||
" with strategy.scope():\n",
|
||
" model, callbacks, target_names = setup_transformer_training(\n",
|
||
" train_data, train_targets, val_data, val_targets\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Mostra il summary del modello\n",
|
||
" model.summary()\n",
|
||
" \n",
|
||
" try:\n",
|
||
" keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not create model plot: {e}\")\n",
|
||
"\n",
|
||
" # Training con gestione degli errori\n",
|
||
" try:\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1,\n",
|
||
" workers=4,\n",
|
||
" use_multiprocessing=True\n",
|
||
" )\n",
|
||
" except tf.errors.ResourceExhaustedError:\n",
|
||
" print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n",
|
||
" # Riprova con batch size più piccolo\n",
|
||
" batch_size = batch_size // 2\n",
|
||
" train_dataset = train_dataset.unbatch().batch(batch_size)\n",
|
||
" val_dataset = val_dataset.unbatch().batch(batch_size)\n",
|
||
" history = model.fit(\n",
|
||
" train_dataset,\n",
|
||
" validation_data=val_dataset,\n",
|
||
" epochs=epochs,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Salva il modello finale\n",
|
||
" try:\n",
|
||
" save_path = f'{execute_name}_{save_name}.keras'\n",
|
||
" model.save(save_path, save_format='keras')\n",
|
||
" \n",
|
||
" os.makedirs(f'{execute_name}/weights', exist_ok=True)\n",
|
||
" model.save_weights(f'{execute_name}/weights')\n",
|
||
" print(f\"\\nModello salvato in: {save_path}\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Warning: Could not save model: {e}\")\n",
|
||
"\n",
|
||
" return model, history"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "35490e902e494c4a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Shape rilevate:\n",
|
||
"- Temporal shape: (41, 3)\n",
|
||
"- Static shape: (113,)\n",
|
||
"- Numero di output: 5\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-06 11:43:09.026945: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: \"OilTransformer\"\n",
|
||
"__________________________________________________________________________________________________\n",
|
||
" Layer (type) Output Shape Param # Connected to \n",
|
||
"==================================================================================================\n",
|
||
" temporal (InputLayer) [(None, 41, 3)] 0 [] \n",
|
||
" \n",
|
||
" layer_normalization (Layer (None, 41, 3) 6 ['temporal[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" data_augmentation (DataAug (None, 41, 3) 0 ['layer_normalization[0][0]'] \n",
|
||
" mentation) \n",
|
||
" \n",
|
||
" dense (Dense) (None, 41, 64) 256 ['data_augmentation[0][0]'] \n",
|
||
" \n",
|
||
" dropout (Dropout) (None, 41, 64) 0 ['dense[0][0]'] \n",
|
||
" \n",
|
||
" dense_1 (Dense) (None, 41, 128) 8320 ['dropout[0][0]'] \n",
|
||
" \n",
|
||
" positional_encoding (Posit (None, 41, 128) 5248 ['dense_1[0][0]'] \n",
|
||
" ionalEncoding) \n",
|
||
" \n",
|
||
" multi_head_attention (Mult (None, 41, 128) 66048 ['positional_encoding[0][0]', \n",
|
||
" iHeadAttention) 'positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dense_2 (Dense) (None, 41, 128) 16512 ['positional_encoding[0][0]'] \n",
|
||
" \n",
|
||
" dropout_1 (Dropout) (None, 41, 128) 0 ['multi_head_attention[0][0]']\n",
|
||
" \n",
|
||
" tf.math.multiply (TFOpLamb (None, 41, 128) 0 ['dense_2[0][0]', \n",
|
||
" da) 'dropout_1[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth (Stochast (None, 41, 128) 0 ['positional_encoding[0][0]', \n",
|
||
" icDepth) 'tf.math.multiply[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_1 (Lay (None, 41, 128) 256 ['stochastic_depth[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_3 (Dense) (None, 41, 256) 33024 ['layer_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_2 (Dropout) (None, 41, 256) 0 ['dense_3[0][0]'] \n",
|
||
" \n",
|
||
" dense_4 (Dense) (None, 41, 128) 32896 ['dropout_2[0][0]'] \n",
|
||
" \n",
|
||
" dropout_3 (Dropout) (None, 41, 128) 0 ['dense_4[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_1 (Stocha (None, 41, 128) 0 ['layer_normalization_1[0][0]'\n",
|
||
" sticDepth) , 'dropout_3[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_2 (Lay (None, 41, 128) 256 ['stochastic_depth_1[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" multi_head_attention_1 (Mu (None, 41, 128) 66048 ['layer_normalization_2[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_5 (Dense) (None, 41, 128) 16512 ['layer_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_4 (Dropout) (None, 41, 128) 0 ['multi_head_attention_1[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_1 (TFOpLa (None, 41, 128) 0 ['dense_5[0][0]', \n",
|
||
" mbda) 'dropout_4[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_2 (Stocha (None, 41, 128) 0 ['layer_normalization_2[0][0]'\n",
|
||
" sticDepth) , 'tf.math.multiply_1[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_3 (Lay (None, 41, 128) 256 ['stochastic_depth_2[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_6 (Dense) (None, 41, 256) 33024 ['layer_normalization_3[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_5 (Dropout) (None, 41, 256) 0 ['dense_6[0][0]'] \n",
|
||
" \n",
|
||
" dense_7 (Dense) (None, 41, 128) 32896 ['dropout_5[0][0]'] \n",
|
||
" \n",
|
||
" dropout_6 (Dropout) (None, 41, 128) 0 ['dense_7[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_3 (Stocha (None, 41, 128) 0 ['layer_normalization_3[0][0]'\n",
|
||
" sticDepth) , 'dropout_6[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_4 (Lay (None, 41, 128) 256 ['stochastic_depth_3[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" multi_head_attention_2 (Mu (None, 41, 128) 66048 ['layer_normalization_4[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_4[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_8 (Dense) (None, 41, 128) 16512 ['layer_normalization_4[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_7 (Dropout) (None, 41, 128) 0 ['multi_head_attention_2[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_2 (TFOpLa (None, 41, 128) 0 ['dense_8[0][0]', \n",
|
||
" mbda) 'dropout_7[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_4 (Stocha (None, 41, 128) 0 ['layer_normalization_4[0][0]'\n",
|
||
" sticDepth) , 'tf.math.multiply_2[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_5 (Lay (None, 41, 128) 256 ['stochastic_depth_4[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_9 (Dense) (None, 41, 256) 33024 ['layer_normalization_5[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_8 (Dropout) (None, 41, 256) 0 ['dense_9[0][0]'] \n",
|
||
" \n",
|
||
" dense_10 (Dense) (None, 41, 128) 32896 ['dropout_8[0][0]'] \n",
|
||
" \n",
|
||
" dropout_9 (Dropout) (None, 41, 128) 0 ['dense_10[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_5 (Stocha (None, 41, 128) 0 ['layer_normalization_5[0][0]'\n",
|
||
" sticDepth) , 'dropout_9[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_6 (Lay (None, 41, 128) 256 ['stochastic_depth_5[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" multi_head_attention_3 (Mu (None, 41, 128) 66048 ['layer_normalization_6[0][0]'\n",
|
||
" ltiHeadAttention) , 'layer_normalization_6[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" dense_11 (Dense) (None, 41, 128) 16512 ['layer_normalization_6[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_10 (Dropout) (None, 41, 128) 0 ['multi_head_attention_3[0][0]\n",
|
||
" '] \n",
|
||
" \n",
|
||
" tf.math.multiply_3 (TFOpLa (None, 41, 128) 0 ['dense_11[0][0]', \n",
|
||
" mbda) 'dropout_10[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_6 (Stocha (None, 41, 128) 0 ['layer_normalization_6[0][0]'\n",
|
||
" sticDepth) , 'tf.math.multiply_3[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_7 (Lay (None, 41, 128) 256 ['stochastic_depth_6[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dense_12 (Dense) (None, 41, 256) 33024 ['layer_normalization_7[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_11 (Dropout) (None, 41, 256) 0 ['dense_12[0][0]'] \n",
|
||
" \n",
|
||
" dense_13 (Dense) (None, 41, 128) 32896 ['dropout_11[0][0]'] \n",
|
||
" \n",
|
||
" static (InputLayer) [(None, 113)] 0 [] \n",
|
||
" \n",
|
||
" dropout_12 (Dropout) (None, 41, 128) 0 ['dense_13[0][0]'] \n",
|
||
" \n",
|
||
" layer_normalization_9 (Lay (None, 113) 226 ['static[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" stochastic_depth_7 (Stocha (None, 41, 128) 0 ['layer_normalization_7[0][0]'\n",
|
||
" sticDepth) , 'dropout_12[0][0]'] \n",
|
||
" \n",
|
||
" dense_14 (Dense) (None, 256) 29184 ['layer_normalization_9[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" layer_normalization_8 (Lay (None, 41, 128) 256 ['stochastic_depth_7[0][0]'] \n",
|
||
" erNormalization) \n",
|
||
" \n",
|
||
" dropout_13 (Dropout) (None, 256) 0 ['dense_14[0][0]'] \n",
|
||
" \n",
|
||
" stochastic_depth_8 (Stocha (None, 41, 128) 0 ['layer_normalization_8[0][0]'\n",
|
||
" sticDepth) , 'positional_encoding[0][0]']\n",
|
||
" \n",
|
||
" dense_15 (Dense) (None, 128) 32896 ['dropout_13[0][0]'] \n",
|
||
" \n",
|
||
" multi_head_attention_4 (Mu (None, 41, 128) 131968 ['stochastic_depth_8[0][0]', \n",
|
||
" ltiHeadAttention) 'stochastic_depth_8[0][0]'] \n",
|
||
" \n",
|
||
" dropout_14 (Dropout) (None, 128) 0 ['dense_15[0][0]'] \n",
|
||
" \n",
|
||
" global_average_pooling1d ( (None, 128) 0 ['multi_head_attention_4[0][0]\n",
|
||
" GlobalAveragePooling1D) '] \n",
|
||
" \n",
|
||
" global_average_pooling1d_1 (None, 128) 0 ['stochastic_depth_8[0][0]'] \n",
|
||
" (GlobalAveragePooling1D) \n",
|
||
" \n",
|
||
" global_max_pooling1d (Glob (None, 128) 0 ['stochastic_depth_8[0][0]'] \n",
|
||
" alMaxPooling1D) \n",
|
||
" \n",
|
||
" dense_16 (Dense) (None, 64) 8256 ['dropout_14[0][0]'] \n",
|
||
" \n",
|
||
" concatenate (Concatenate) (None, 384) 0 ['global_average_pooling1d[0][\n",
|
||
" 0]', \n",
|
||
" 'global_average_pooling1d_1[0\n",
|
||
" ][0]', \n",
|
||
" 'global_max_pooling1d[0][0]']\n",
|
||
" \n",
|
||
" dropout_15 (Dropout) (None, 64) 0 ['dense_16[0][0]'] \n",
|
||
" \n",
|
||
" concatenate_1 (Concatenate (None, 448) 0 ['concatenate[0][0]', \n",
|
||
" ) 'dropout_15[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization (Batch (None, 448) 1792 ['concatenate_1[0][0]'] \n",
|
||
" Normalization) \n",
|
||
" \n",
|
||
" dense_17 (Dense) (None, 256) 114944 ['batch_normalization[0][0]'] \n",
|
||
" \n",
|
||
" dropout_16 (Dropout) (None, 256) 0 ['dense_17[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_1 (Bat (None, 256) 1024 ['dropout_16[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_18 (Dense) (None, 128) 32896 ['batch_normalization_1[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_17 (Dropout) (None, 128) 0 ['dense_18[0][0]'] \n",
|
||
" \n",
|
||
" batch_normalization_2 (Bat (None, 128) 512 ['dropout_17[0][0]'] \n",
|
||
" chNormalization) \n",
|
||
" \n",
|
||
" dense_19 (Dense) (None, 64) 8256 ['batch_normalization_2[0][0]'\n",
|
||
" ] \n",
|
||
" \n",
|
||
" dropout_18 (Dropout) (None, 64) 0 ['dense_19[0][0]'] \n",
|
||
" \n",
|
||
" dense_20 (Dense) (None, 5) 325 ['dropout_18[0][0]'] \n",
|
||
" \n",
|
||
"==================================================================================================\n",
|
||
"Total params: 972077 (3.71 MB)\n",
|
||
"Trainable params: 965165 (3.68 MB)\n",
|
||
"Non-trainable params: 6912 (27.00 KB)\n",
|
||
"__________________________________________________________________________________________________\n",
|
||
"Epoch 1/150\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-12-06 11:43:25.651745: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7d7e70d1ce40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
|
||
"2024-12-06 11:43:25.651778: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n",
|
||
"2024-12-06 11:43:25.659099: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
|
||
"2024-12-06 11:43:25.722749: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n",
|
||
"2024-12-06 11:43:25.861911: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"9954/9954 [==============================] - 481s 46ms/step - loss: 0.0460 - mae: 0.1872 - val_loss: 0.0145 - val_mae: 0.0865 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 1.0219e-05\n",
|
||
"Epoch 2/150\n",
|
||
"9954/9954 [==============================] - 473s 47ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0603 - lr: 1.0438e-07\n",
|
||
"Epoch 3/150\n",
|
||
"9954/9954 [==============================] - 477s 48ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0861 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0602 - lr: 1.0661e-09\n",
|
||
"Epoch 4/150\n",
|
||
"9954/9954 [==============================] - 508s 51ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0867 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0616 - lr: 1.0889e-11\n",
|
||
"Epoch 5/150\n",
|
||
"9954/9954 [==============================] - 431s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0612 - lr: 1.1122e-13\n",
|
||
"Epoch 6/150\n",
|
||
"9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0598 - lr: 1.1361e-15\n",
|
||
"Epoch 7/150\n",
|
||
"9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0868 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0620 - lr: 1.1604e-17\n",
|
||
"Epoch 8/150\n",
|
||
"9954/9954 [==============================] - 433s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0611 - lr: 1.1852e-19\n",
|
||
"Epoch 9/150\n",
|
||
"9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0608 - lr: 1.2106e-21\n",
|
||
"Epoch 10/150\n",
|
||
"9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0607 - lr: 1.2365e-23\n",
|
||
"Epoch 11/150\n",
|
||
"9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.2630e-25\n",
|
||
"Epoch 12/150\n",
|
||
"9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0866 - val_olive_prod_mae: 0.0968 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0606 - lr: 1.2900e-27\n",
|
||
"Epoch 13/150\n",
|
||
"9954/9954 [==============================] - 425s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0144 - val_mae: 0.0868 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0619 - lr: 1.3177e-29\n",
|
||
"Epoch 14/150\n",
|
||
"9954/9954 [==============================] - 409s 41ms/step - loss: 0.0272 - mae: 0.1504 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0605 - lr: 1.3459e-31\n",
|
||
"Epoch 15/150\n",
|
||
"9954/9954 [==============================] - 439s 44ms/step - loss: 0.0273 - mae: 0.1509 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0926 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0609 - lr: 1.3747e-33\n",
|
||
"Epoch 16/150\n",
|
||
"9954/9954 [==============================] - 421s 42ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.4041e-35\n",
|
||
"Epoch 17/150\n",
|
||
"9954/9954 [==============================] - 429s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0600 - lr: 1.4342e-37\n",
|
||
"Epoch 18/150\n",
|
||
"9954/9954 [==============================] - 414s 41ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0602 - lr: 0.0000e+00\n",
|
||
"Epoch 19/150\n",
|
||
"9954/9954 [==============================] - 441s 44ms/step - loss: 0.0272 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0608 - lr: 0.0000e+00\n",
|
||
"Epoch 20/150\n",
|
||
"9954/9954 [==============================] - 440s 44ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0601 - lr: 0.0000e+00\n",
|
||
"Epoch 21/150\n",
|
||
"9954/9954 [==============================] - 448s 45ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 0.0000e+00\n",
|
||
"\n",
|
||
"Modello salvato in: 2024-12-06_10-36_final_model.keras\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 512)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "3e2fb5a5341dac92",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"24500/24500 [==============================] - 102s 4ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1585.45\n",
|
||
"Errore percentuale medio: 6.91%\n",
|
||
"Precisione: 93.09%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 319.12\n",
|
||
"Errore percentuale medio: 6.61%\n",
|
||
"Precisione: 93.39%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 387.31\n",
|
||
"Errore percentuale medio: 6.74%\n",
|
||
"Precisione: 93.26%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 337.11\n",
|
||
"Errore percentuale medio: 6.46%\n",
|
||
"Precisione: 93.54%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 1775.48\n",
|
||
"Errore percentuale medio: 4.24%\n",
|
||
"Precisione: 95.76%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "4af58aa9bbc156f5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def evaluate_model_performance(model, data, targets, set_name=\"\"):\n",
|
||
" \"\"\"\n",
|
||
" Valuta le performance del modello su un set di dati specifico.\n",
|
||
" \"\"\"\n",
|
||
" predictions = model.predict(data, verbose=0)\n",
|
||
"\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
|
||
" metrics = {}\n",
|
||
"\n",
|
||
" for i, name in enumerate(target_names):\n",
|
||
" mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n",
|
||
" mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n",
|
||
" rmse = np.sqrt(mse)\n",
|
||
" mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n",
|
||
"\n",
|
||
" metrics[f\"{name}_mae\"] = mae\n",
|
||
" metrics[f\"{name}_rmse\"] = rmse\n",
|
||
" metrics[f\"{name}_mape\"] = mape\n",
|
||
"\n",
|
||
" if set_name:\n",
|
||
" print(f\"\\nPerformance sul set {set_name}:\")\n",
|
||
" for metric, value in metrics.items():\n",
|
||
" print(f\"{metric}: {value:.4f}\")\n",
|
||
"\n",
|
||
" return metrics\n",
|
||
"\n",
|
||
"\n",
|
||
"def retrain_model(base_model, train_data, train_targets,\n",
|
||
" val_data, val_targets,\n",
|
||
" test_data, test_targets,\n",
|
||
" epochs=50, batch_size=128):\n",
|
||
" \"\"\"\n",
|
||
" Implementa il retraining del modello con i dati combinati.\n",
|
||
" \"\"\"\n",
|
||
" print(\"Valutazione performance iniziali del modello...\")\n",
|
||
" initial_metrics = {\n",
|
||
" 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n",
|
||
" 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n",
|
||
" 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Combina i dati per il retraining\n",
|
||
" combined_data = {\n",
|
||
" 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n",
|
||
" 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n",
|
||
" }\n",
|
||
" combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n",
|
||
"\n",
|
||
" # Crea una nuova suddivisione per la validazione\n",
|
||
" indices = np.arange(len(combined_targets))\n",
|
||
" np.random.shuffle(indices)\n",
|
||
"\n",
|
||
" split_idx = int(len(indices) * 0.9)\n",
|
||
" train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n",
|
||
"\n",
|
||
" # Prepara i dati per il retraining\n",
|
||
" retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n",
|
||
" retrain_targets = combined_targets[train_idx]\n",
|
||
" retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n",
|
||
" retrain_val_targets = combined_targets[val_idx]\n",
|
||
"\n",
|
||
" # Configura callbacks\n",
|
||
" callbacks = [\n",
|
||
" tf.keras.callbacks.EarlyStopping(\n",
|
||
" monitor='val_loss',\n",
|
||
" patience=10,\n",
|
||
" restore_best_weights=True,\n",
|
||
" min_delta=0.0001\n",
|
||
" ),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(\n",
|
||
" monitor='val_loss',\n",
|
||
" factor=0.2,\n",
|
||
" patience=5,\n",
|
||
" min_lr=1e-6,\n",
|
||
" verbose=1\n",
|
||
" ),\n",
|
||
" tf.keras.callbacks.ModelCheckpoint(\n",
|
||
" filepath=f'{execute_name}_retrained_best_oil_model.h5',\n",
|
||
" monitor='val_loss',\n",
|
||
" save_best_only=True,\n",
|
||
" mode='min',\n",
|
||
" save_weights_only=True\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
" # Imposta learning rate per il fine-tuning\n",
|
||
" optimizer = tf.keras.optimizers.AdamW(\n",
|
||
" learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n",
|
||
" initial_learning_rate=1e-4,\n",
|
||
" decay_steps=1000,\n",
|
||
" decay_rate=0.9\n",
|
||
" ),\n",
|
||
" weight_decay=0.01\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Ricompila il modello con il nuovo optimizer\n",
|
||
" base_model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=tf.keras.losses.Huber(),\n",
|
||
" metrics=['mae']\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nAvvio retraining...\")\n",
|
||
" history = base_model.fit(\n",
|
||
" retrain_data,\n",
|
||
" retrain_targets,\n",
|
||
" validation_data=(retrain_val_data, retrain_val_targets),\n",
|
||
" epochs=epochs,\n",
|
||
" batch_size=batch_size,\n",
|
||
" callbacks=callbacks,\n",
|
||
" verbose=1\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nValutazione performance finali...\")\n",
|
||
" final_metrics = {\n",
|
||
" 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n",
|
||
" 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n",
|
||
" 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Salva il modello finale\n",
|
||
" save_path = f'{execute_name}_retrained_model.keras'\n",
|
||
" os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n",
|
||
" \n",
|
||
" base_model.save_weights(f'{execute_name}_retrained/weights')\n",
|
||
" base_model.save(save_path, save_format='keras')\n",
|
||
" print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n",
|
||
"\n",
|
||
" # Report miglioramenti\n",
|
||
" print(\"\\nMiglioramenti delle performance:\")\n",
|
||
" for dataset in ['train', 'val', 'test']:\n",
|
||
" print(f\"\\nSet {dataset}:\")\n",
|
||
" for metric in initial_metrics[dataset].keys():\n",
|
||
" initial = initial_metrics[dataset][metric]\n",
|
||
" final = final_metrics[dataset][metric]\n",
|
||
" improvement = ((initial - final) / initial) * 100\n",
|
||
" print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n",
|
||
"\n",
|
||
" return base_model, history, final_metrics\n",
|
||
"\n",
|
||
"\n",
|
||
"def start_retraining(model_path, train_data, train_targets,\n",
|
||
" val_data, val_targets,\n",
|
||
" test_data, test_targets,\n",
|
||
" epochs=50, batch_size=128):\n",
|
||
" \"\"\"\n",
|
||
" Avvia il processo di retraining in modo sicuro.\n",
|
||
" \"\"\"\n",
|
||
" try:\n",
|
||
" print(\"Caricamento del modello...\")\n",
|
||
" base_model = tf.keras.models.load_model(model_path, compile=False)\n",
|
||
" print(\"Modello caricato con successo!\")\n",
|
||
"\n",
|
||
" return retrain_model(\n",
|
||
" base_model=base_model,\n",
|
||
" train_data=train_data,\n",
|
||
" train_targets=train_targets,\n",
|
||
" val_data=val_data,\n",
|
||
" val_targets=val_targets,\n",
|
||
" test_data=test_data,\n",
|
||
" test_targets=test_targets,\n",
|
||
" epochs=epochs,\n",
|
||
" batch_size=batch_size\n",
|
||
" )\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Errore durante il retraining: {str(e)}\")\n",
|
||
" raise"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "588c7e49371f4a0c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Caricamento del modello...\n",
|
||
"Modello caricato con successo!\n",
|
||
"Valutazione performance iniziali del modello...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0963\n",
|
||
"olive_prod_rmse: 0.1300\n",
|
||
"olive_prod_mape: 77.2491\n",
|
||
"min_oil_prod_mae: 0.0936\n",
|
||
"min_oil_prod_rmse: 0.1312\n",
|
||
"min_oil_prod_mape: 91.4612\n",
|
||
"max_oil_prod_mae: 0.0936\n",
|
||
"max_oil_prod_rmse: 0.1304\n",
|
||
"max_oil_prod_mape: 88.9396\n",
|
||
"avg_oil_prod_mae: 0.0895\n",
|
||
"avg_oil_prod_rmse: 0.1238\n",
|
||
"avg_oil_prod_mape: 89.5317\n",
|
||
"total_water_need_mae: 0.0598\n",
|
||
"total_water_need_rmse: 0.0808\n",
|
||
"total_water_need_mape: 44.4531\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0964\n",
|
||
"olive_prod_rmse: 0.1301\n",
|
||
"olive_prod_mape: 133.2427\n",
|
||
"min_oil_prod_mae: 0.0935\n",
|
||
"min_oil_prod_rmse: 0.1310\n",
|
||
"min_oil_prod_mape: 120.7693\n",
|
||
"max_oil_prod_mae: 0.0936\n",
|
||
"max_oil_prod_rmse: 0.1304\n",
|
||
"max_oil_prod_mape: 86.2224\n",
|
||
"avg_oil_prod_mae: 0.0894\n",
|
||
"avg_oil_prod_rmse: 0.1237\n",
|
||
"avg_oil_prod_mape: 83.8138\n",
|
||
"total_water_need_mae: 0.0598\n",
|
||
"total_water_need_rmse: 0.0809\n",
|
||
"total_water_need_mape: 53.9347\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0962\n",
|
||
"olive_prod_rmse: 0.1298\n",
|
||
"olive_prod_mape: 77.9806\n",
|
||
"min_oil_prod_mae: 0.0935\n",
|
||
"min_oil_prod_rmse: 0.1312\n",
|
||
"min_oil_prod_mape: 95.5886\n",
|
||
"max_oil_prod_mae: 0.0934\n",
|
||
"max_oil_prod_rmse: 0.1301\n",
|
||
"max_oil_prod_mape: 76.3217\n",
|
||
"avg_oil_prod_mae: 0.0893\n",
|
||
"avg_oil_prod_rmse: 0.1237\n",
|
||
"avg_oil_prod_mape: 111.2211\n",
|
||
"total_water_need_mae: 0.0596\n",
|
||
"total_water_need_rmse: 0.0806\n",
|
||
"total_water_need_mape: 38.1699\n",
|
||
"\n",
|
||
"Avvio retraining...\n",
|
||
"Epoch 1/50\n",
|
||
"27563/27563 [==============================] - 851s 30ms/step - loss: 0.0261 - mae: 0.1520 - val_loss: 0.0118 - val_mae: 0.0804 - lr: 5.4806e-06\n",
|
||
"Epoch 2/50\n",
|
||
"27563/27563 [==============================] - 852s 31ms/step - loss: 0.0245 - mae: 0.1478 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 3.0034e-07\n",
|
||
"Epoch 3/50\n",
|
||
"27563/27563 [==============================] - 836s 30ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0117 - val_mae: 0.0807 - lr: 1.6459e-08\n",
|
||
"Epoch 4/50\n",
|
||
"27563/27563 [==============================] - 863s 31ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 9.0196e-10\n",
|
||
"Epoch 5/50\n",
|
||
"27563/27563 [==============================] - 854s 31ms/step - loss: 0.0243 - mae: 0.1474 - val_loss: 0.0119 - val_mae: 0.0812 - lr: 4.9428e-11\n",
|
||
"Epoch 6/50\n",
|
||
"27563/27563 [==============================] - 869s 32ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.7087e-12\n",
|
||
"Epoch 7/50\n",
|
||
"27563/27563 [==============================] - 867s 31ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0806 - lr: 1.4844e-13\n",
|
||
"Epoch 8/50\n",
|
||
"27563/27563 [==============================] - 899s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 8.1345e-15\n",
|
||
"Epoch 9/50\n",
|
||
"27563/27563 [==============================] - 966s 35ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0804 - lr: 4.4578e-16\n",
|
||
"Epoch 10/50\n",
|
||
"27563/27563 [==============================] - 930s 34ms/step - loss: 0.0244 - mae: 0.1474 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.4429e-17\n",
|
||
"Epoch 11/50\n",
|
||
"27563/27563 [==============================] - 921s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0809 - lr: 1.3387e-18\n",
|
||
"\n",
|
||
"Valutazione performance finali...\n",
|
||
"\n",
|
||
"Performance sul set training:\n",
|
||
"olive_prod_mae: 0.0901\n",
|
||
"olive_prod_rmse: 0.1222\n",
|
||
"olive_prod_mape: 75.7735\n",
|
||
"min_oil_prod_mae: 0.0886\n",
|
||
"min_oil_prod_rmse: 0.1245\n",
|
||
"min_oil_prod_mape: 91.0646\n",
|
||
"max_oil_prod_mae: 0.0888\n",
|
||
"max_oil_prod_rmse: 0.1243\n",
|
||
"max_oil_prod_mape: 89.5375\n",
|
||
"avg_oil_prod_mae: 0.0845\n",
|
||
"avg_oil_prod_rmse: 0.1171\n",
|
||
"avg_oil_prod_mape: 86.3355\n",
|
||
"total_water_need_mae: 0.0495\n",
|
||
"total_water_need_rmse: 0.0678\n",
|
||
"total_water_need_mape: 41.0436\n",
|
||
"\n",
|
||
"Performance sul set validazione:\n",
|
||
"olive_prod_mae: 0.0901\n",
|
||
"olive_prod_rmse: 0.1222\n",
|
||
"olive_prod_mape: 138.3196\n",
|
||
"min_oil_prod_mae: 0.0885\n",
|
||
"min_oil_prod_rmse: 0.1243\n",
|
||
"min_oil_prod_mape: 126.9523\n",
|
||
"max_oil_prod_mae: 0.0888\n",
|
||
"max_oil_prod_rmse: 0.1243\n",
|
||
"max_oil_prod_mape: 82.7593\n",
|
||
"avg_oil_prod_mae: 0.0843\n",
|
||
"avg_oil_prod_rmse: 0.1169\n",
|
||
"avg_oil_prod_mape: 84.3605\n",
|
||
"total_water_need_mae: 0.0495\n",
|
||
"total_water_need_rmse: 0.0679\n",
|
||
"total_water_need_mape: 48.6941\n",
|
||
"\n",
|
||
"Performance sul set test:\n",
|
||
"olive_prod_mae: 0.0899\n",
|
||
"olive_prod_rmse: 0.1219\n",
|
||
"olive_prod_mape: 77.0356\n",
|
||
"min_oil_prod_mae: 0.0886\n",
|
||
"min_oil_prod_rmse: 0.1243\n",
|
||
"min_oil_prod_mape: 96.3498\n",
|
||
"max_oil_prod_mae: 0.0885\n",
|
||
"max_oil_prod_rmse: 0.1238\n",
|
||
"max_oil_prod_mape: 76.4509\n",
|
||
"avg_oil_prod_mae: 0.0843\n",
|
||
"avg_oil_prod_rmse: 0.1167\n",
|
||
"avg_oil_prod_mape: 87.8912\n",
|
||
"total_water_need_mae: 0.0494\n",
|
||
"total_water_need_rmse: 0.0677\n",
|
||
"total_water_need_mape: 30.6997\n",
|
||
"\n",
|
||
"Modello riaddestrato salvato in: 2024-12-06_10-36_retrained_model.keras\n",
|
||
"\n",
|
||
"Miglioramenti delle performance:\n",
|
||
"\n",
|
||
"Set train:\n",
|
||
"olive_prod_mae: 6.48% di miglioramento\n",
|
||
"olive_prod_rmse: 6.00% di miglioramento\n",
|
||
"olive_prod_mape: 1.91% di miglioramento\n",
|
||
"min_oil_prod_mae: 5.29% di miglioramento\n",
|
||
"min_oil_prod_rmse: 5.12% di miglioramento\n",
|
||
"min_oil_prod_mape: 0.43% di miglioramento\n",
|
||
"max_oil_prod_mae: 5.11% di miglioramento\n",
|
||
"max_oil_prod_rmse: 4.70% di miglioramento\n",
|
||
"max_oil_prod_mape: -0.67% di miglioramento\n",
|
||
"avg_oil_prod_mae: 5.58% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 5.45% di miglioramento\n",
|
||
"avg_oil_prod_mape: 3.57% di miglioramento\n",
|
||
"total_water_need_mae: 17.16% di miglioramento\n",
|
||
"total_water_need_rmse: 15.99% di miglioramento\n",
|
||
"total_water_need_mape: 7.67% di miglioramento\n",
|
||
"\n",
|
||
"Set val:\n",
|
||
"olive_prod_mae: 6.51% di miglioramento\n",
|
||
"olive_prod_rmse: 6.04% di miglioramento\n",
|
||
"olive_prod_mape: -3.81% di miglioramento\n",
|
||
"min_oil_prod_mae: 5.33% di miglioramento\n",
|
||
"min_oil_prod_rmse: 5.16% di miglioramento\n",
|
||
"min_oil_prod_mape: -5.12% di miglioramento\n",
|
||
"max_oil_prod_mae: 5.13% di miglioramento\n",
|
||
"max_oil_prod_rmse: 4.70% di miglioramento\n",
|
||
"max_oil_prod_mape: 4.02% di miglioramento\n",
|
||
"avg_oil_prod_mae: 5.62% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 5.48% di miglioramento\n",
|
||
"avg_oil_prod_mape: -0.65% di miglioramento\n",
|
||
"total_water_need_mae: 17.23% di miglioramento\n",
|
||
"total_water_need_rmse: 16.08% di miglioramento\n",
|
||
"total_water_need_mape: 9.72% di miglioramento\n",
|
||
"\n",
|
||
"Set test:\n",
|
||
"olive_prod_mae: 6.52% di miglioramento\n",
|
||
"olive_prod_rmse: 6.09% di miglioramento\n",
|
||
"olive_prod_mape: 1.21% di miglioramento\n",
|
||
"min_oil_prod_mae: 5.32% di miglioramento\n",
|
||
"min_oil_prod_rmse: 5.22% di miglioramento\n",
|
||
"min_oil_prod_mape: -0.80% di miglioramento\n",
|
||
"max_oil_prod_mae: 5.22% di miglioramento\n",
|
||
"max_oil_prod_rmse: 4.83% di miglioramento\n",
|
||
"max_oil_prod_mape: -0.17% di miglioramento\n",
|
||
"avg_oil_prod_mae: 5.64% di miglioramento\n",
|
||
"avg_oil_prod_rmse: 5.59% di miglioramento\n",
|
||
"avg_oil_prod_mape: 20.98% di miglioramento\n",
|
||
"total_water_need_mae: 17.22% di miglioramento\n",
|
||
"total_water_need_rmse: 16.03% di miglioramento\n",
|
||
"total_water_need_mape: 19.57% di miglioramento\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model_path = f'{execute_name}_final_model.keras'\n",
|
||
"\n",
|
||
"retrained_model, retrain_history, final_metrics = start_retraining(\n",
|
||
" model_path=model_path,\n",
|
||
" train_data=train_data,\n",
|
||
" train_targets=train_targets,\n",
|
||
" val_data=val_data,\n",
|
||
" val_targets=val_targets,\n",
|
||
" test_data=test_data,\n",
|
||
" test_targets=test_targets,\n",
|
||
" epochs=50,\n",
|
||
" batch_size=128\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"24500/24500 [==============================] - 137s 6ms/step\n",
|
||
"\n",
|
||
"Errori per target:\n",
|
||
"--------------------------------------------------\n",
|
||
"olive_prod:\n",
|
||
"MAE assoluto: 1482.22\n",
|
||
"Errore percentuale medio: 5.77%\n",
|
||
"Precisione: 94.23%\n",
|
||
"--------------------------------------------------\n",
|
||
"min_oil_prod:\n",
|
||
"MAE assoluto: 302.12\n",
|
||
"Errore percentuale medio: 5.68%\n",
|
||
"Precisione: 94.32%\n",
|
||
"--------------------------------------------------\n",
|
||
"max_oil_prod:\n",
|
||
"MAE assoluto: 367.45\n",
|
||
"Errore percentuale medio: 5.78%\n",
|
||
"Precisione: 94.22%\n",
|
||
"--------------------------------------------------\n",
|
||
"avg_oil_prod:\n",
|
||
"MAE assoluto: 318.15\n",
|
||
"Errore percentuale medio: 5.49%\n",
|
||
"Precisione: 94.51%\n",
|
||
"--------------------------------------------------\n",
|
||
"total_water_need:\n",
|
||
"MAE assoluto: 1469.51\n",
|
||
"Errore percentuale medio: 3.31%\n",
|
||
"Precisione: 96.69%\n",
|
||
"--------------------------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import tensorflow as tf\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from typing import List, Dict, Tuple, Union\n",
|
||
"\n",
|
||
"def analyze_feature_importance(model: tf.keras.Model, \n",
|
||
" test_data: dict, \n",
|
||
" feature_names: List[str]) -> Dict[str, float]:\n",
|
||
" \"\"\"\n",
|
||
" Analizza l'importanza delle feature usando perturbazione.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n",
|
||
" feature_names: Lista dei nomi delle feature\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con l'importanza relativa di ogni feature\n",
|
||
" \"\"\"\n",
|
||
" # Estrai i dati temporali e statici\n",
|
||
" temporal_data = test_data['temporal']\n",
|
||
" static_data = test_data['static']\n",
|
||
" \n",
|
||
" # Ottieni la predizione base\n",
|
||
" base_prediction = model.predict(test_data)\n",
|
||
" feature_importance = {}\n",
|
||
" \n",
|
||
" # Per ogni feature temporale\n",
|
||
" for i, feature in enumerate(feature_names):\n",
|
||
" if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n",
|
||
" # Crea copia perturbata dei dati\n",
|
||
" perturbed_data = {\n",
|
||
" 'temporal': temporal_data.copy(),\n",
|
||
" 'static': static_data.copy()\n",
|
||
" }\n",
|
||
" \n",
|
||
" # Trova l'indice della feature temporale\n",
|
||
" temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n",
|
||
" \n",
|
||
" # Crea rumore per la feature temporale\n",
|
||
" feature_values = temporal_data[..., temp_idx]\n",
|
||
" noise = np.random.normal(0, np.std(feature_values) * 0.1, \n",
|
||
" size=feature_values.shape)\n",
|
||
" \n",
|
||
" # Applica il rumore alla feature temporale\n",
|
||
" perturbed_temporal = perturbed_data['temporal'].copy()\n",
|
||
" perturbed_temporal[..., temp_idx] = feature_values + noise\n",
|
||
" perturbed_data['temporal'] = perturbed_temporal\n",
|
||
" \n",
|
||
" else: # Feature statiche\n",
|
||
" # Crea copia perturbata dei dati\n",
|
||
" perturbed_data = {\n",
|
||
" 'temporal': temporal_data.copy(),\n",
|
||
" 'static': static_data.copy()\n",
|
||
" }\n",
|
||
" \n",
|
||
" # Trova l'indice della feature statica\n",
|
||
" static_idx = ['ha'].index(feature)\n",
|
||
" \n",
|
||
" # Crea rumore per la feature statica\n",
|
||
" feature_values = static_data[..., static_idx]\n",
|
||
" noise = np.random.normal(0, np.std(feature_values) * 0.1, \n",
|
||
" size=feature_values.shape)\n",
|
||
" \n",
|
||
" # Applica il rumore alla feature statica\n",
|
||
" perturbed_static = perturbed_data['static'].copy()\n",
|
||
" perturbed_static[..., static_idx] = feature_values + noise\n",
|
||
" perturbed_data['static'] = perturbed_static\n",
|
||
" \n",
|
||
" # Calcola nuova predizione\n",
|
||
" perturbed_prediction = model.predict(perturbed_data)\n",
|
||
" \n",
|
||
" # Calcola impatto della perturbazione\n",
|
||
" impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n",
|
||
" feature_importance[feature] = float(impact)\n",
|
||
" \n",
|
||
" # Normalizza le importanze\n",
|
||
" total_importance = sum(feature_importance.values())\n",
|
||
" feature_importance = {k: v/total_importance \n",
|
||
" for k, v in feature_importance.items()}\n",
|
||
" \n",
|
||
" return feature_importance\n",
|
||
"\n",
|
||
"class ProbabilityFunctions:\n",
|
||
" @staticmethod\n",
|
||
" def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola statistiche di base usando TensorFlow.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Tensor o array dei dati\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con le statistiche\n",
|
||
" \"\"\"\n",
|
||
" if not isinstance(data, tf.Tensor):\n",
|
||
" data = tf.convert_to_tensor(data, dtype=tf.float32)\n",
|
||
" \n",
|
||
" mean = tf.reduce_mean(data)\n",
|
||
" # Calcola varianza manualmente\n",
|
||
" squared_deviations = tf.square(data - mean)\n",
|
||
" variance = tf.reduce_mean(squared_deviations)\n",
|
||
" std = tf.sqrt(variance)\n",
|
||
" \n",
|
||
" # Ordina il tensor per il calcolo della mediana\n",
|
||
" sorted_data = tf.sort(data)\n",
|
||
" size = tf.size(data)\n",
|
||
" mid_index = size // 2\n",
|
||
" median = sorted_data[mid_index]\n",
|
||
" \n",
|
||
" return {\n",
|
||
" 'mean': mean.numpy(),\n",
|
||
" 'variance': variance.numpy(),\n",
|
||
" 'std': std.numpy(),\n",
|
||
" 'min': tf.reduce_min(data).numpy(),\n",
|
||
" 'max': tf.reduce_max(data).numpy(),\n",
|
||
" 'median': median.numpy()\n",
|
||
" }\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Probability Mass Function (PMF) dei dati.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, bin_edges)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola l'istogramma\n",
|
||
" hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
|
||
" \n",
|
||
" # Calcola i centri dei bin\n",
|
||
" bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
|
||
" \n",
|
||
" # Normalizza per ottenere la PMF\n",
|
||
" pmf = hist / np.sum(hist)\n",
|
||
" \n",
|
||
" return bin_centers, pmf, bin_edges\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Cumulative Mass Function (CMF) dalla PMF.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" pmf: Probability Mass Function\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" array: Cumulative Mass Function\n",
|
||
" \"\"\"\n",
|
||
" return np.cumsum(pmf)\n",
|
||
"\n",
|
||
" def plot_distributions(self, data: np.ndarray, \n",
|
||
" bins: int = 50, \n",
|
||
" title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
|
||
" \"\"\"\n",
|
||
" Calcola e visualizza PMF e CMF delle distribuzioni.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati da analizzare\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" title: Titolo del grafico\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, cmf)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola PMF e CMF\n",
|
||
" bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n",
|
||
" cmf = self.calculate_cmf(pmf)\n",
|
||
" \n",
|
||
" # Crea il plot\n",
|
||
" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
|
||
" \n",
|
||
" # Plot PMF\n",
|
||
" width = np.diff(bin_edges)\n",
|
||
" ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n",
|
||
" ax1.set_title('Probability Mass Function')\n",
|
||
" ax1.set_ylabel('Probability')\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" ax1.legend()\n",
|
||
" \n",
|
||
" # Plot CMF\n",
|
||
" ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n",
|
||
" ax2.set_title('Cumulative Mass Function')\n",
|
||
" ax2.set_xlabel('Value')\n",
|
||
" ax2.set_ylabel('Cumulative Probability')\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" ax2.legend()\n",
|
||
" \n",
|
||
" # Imposta il titolo generale\n",
|
||
" fig.suptitle(title, y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" return bin_centers, pmf, cmf\n",
|
||
"\n",
|
||
"def analyze_model_predictions(model: tf.keras.Model, \n",
|
||
" test_data: np.ndarray,\n",
|
||
" test_targets: np.ndarray,\n",
|
||
" scaler_y) -> None:\n",
|
||
" \"\"\"\n",
|
||
" Analizza le distribuzioni di probabilità delle predizioni del modello.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dati di test\n",
|
||
" test_targets: Target di test\n",
|
||
" scaler_y: Scaler usato per denormalizzare i target\n",
|
||
" \"\"\"\n",
|
||
" # Ottieni le predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
" \n",
|
||
" # Denormalizza predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" # Inizializza la classe per l'analisi delle probabilità\n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" \n",
|
||
" # Analizza ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi per {target}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Calcola errori\n",
|
||
" errors = predictions_real[:, i] - targets_real[:, i]\n",
|
||
" \n",
|
||
" # Calcola statistiche degli errori\n",
|
||
" error_stats = prob.calculate_statistics(errors)\n",
|
||
" print(\"\\nStatistiche degli Errori:\")\n",
|
||
" for key, value in error_stats.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza le distribuzioni degli errori\n",
|
||
" bin_centers, pmf, cmf = prob.plot_distributions(\n",
|
||
" errors, \n",
|
||
" bins=50,\n",
|
||
" title=f\"Distribuzione degli Errori - {target}\"\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Calcola intervalli di confidenza\n",
|
||
" confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n",
|
||
" for level in confidence_levels:\n",
|
||
" lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n",
|
||
" upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n",
|
||
" \n",
|
||
" print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n",
|
||
" print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n",
|
||
"\n",
|
||
"def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n",
|
||
" \"\"\"\n",
|
||
" Esegue un'analisi completa del modello includendo errori,\n",
|
||
" importanza delle feature e distribuzioni.\n",
|
||
" \"\"\"\n",
|
||
" print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n",
|
||
" \n",
|
||
" # 1. Analisi degli errori\n",
|
||
" print(\"\\n1. ANALISI DEGLI ERRORI\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n",
|
||
" \n",
|
||
" # 2. Analisi dell'importanza delle feature\n",
|
||
" print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Definisci i nomi delle feature\n",
|
||
" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
||
" static_features = ['ha']\n",
|
||
" \n",
|
||
" all_features = temporal_features + static_features\n",
|
||
" importance = analyze_feature_importance(retrained_model, test_data, all_features)\n",
|
||
" \n",
|
||
" print(\"\\nImportanza relativa delle feature:\")\n",
|
||
" for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n",
|
||
" print(f\"{feature}: {imp:.4f}\")\n",
|
||
" \n",
|
||
" # 3. Analisi distribuzionale\n",
|
||
" print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" predictions = retrained_model.predict(test_data)\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi distribuzionale per {target}\")\n",
|
||
" \n",
|
||
" # Statistiche\n",
|
||
" stats_pred = prob.calculate_statistics(predictions_real[:, i])\n",
|
||
" stats_true = prob.calculate_statistics(targets_real[:, i])\n",
|
||
" \n",
|
||
" print(\"\\nStatistiche Predizioni:\")\n",
|
||
" for key, value in stats_pred.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" print(\"\\nStatistiche Target Reali:\")\n",
|
||
" for key, value in stats_true.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza distribuzioni\n",
|
||
" prob.plot_distributions(predictions_real[:, i], bins=50,\n",
|
||
" title=f\"Distribuzione Predizioni - {target}\")\n",
|
||
" prob.plot_distributions(targets_real[:, i], bins=50,\n",
|
||
" title=f\"Distribuzione Target Reali - {target}\")\n",
|
||
"\n",
|
||
"def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n",
|
||
" \"\"\"\n",
|
||
" Analizza le distribuzioni di probabilità delle predizioni del modello.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" model: Modello TensorFlow addestrato\n",
|
||
" test_data: Dati di test\n",
|
||
" test_targets: Target di test\n",
|
||
" scaler_y: Scaler usato per denormalizzare i target\n",
|
||
" \"\"\"\n",
|
||
" # Ottieni le predizioni\n",
|
||
" predictions = model.predict(test_data)\n",
|
||
" \n",
|
||
" # Denormalizza predizioni e target\n",
|
||
" predictions_real = scaler_y.inverse_transform(predictions)\n",
|
||
" targets_real = scaler_y.inverse_transform(test_targets)\n",
|
||
" \n",
|
||
" # Inizializza la classe per l'analisi delle probabilità\n",
|
||
" prob = ProbabilityFunctions()\n",
|
||
" \n",
|
||
" # Analizza ogni target\n",
|
||
" target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n",
|
||
" 'avg_oil_prod', 'total_water_need']\n",
|
||
" \n",
|
||
" for i, target in enumerate(target_names):\n",
|
||
" print(f\"\\nAnalisi per {target}\")\n",
|
||
" print(\"-\" * 50)\n",
|
||
" \n",
|
||
" # Calcola errori\n",
|
||
" errors = predictions_real[:, i] - targets_real[:, i]\n",
|
||
" \n",
|
||
" # Calcola statistiche degli errori\n",
|
||
" error_stats = prob.calculate_statistics(errors)\n",
|
||
" print(\"\\nStatistiche degli Errori:\")\n",
|
||
" for key, value in error_stats.items():\n",
|
||
" print(f\"{key}: {value:.3f}\")\n",
|
||
" \n",
|
||
" # Visualizza le distribuzioni degli errori\n",
|
||
" bin_centers, pmf, cmf = prob.plot_distributions(\n",
|
||
" errors, \n",
|
||
" bins=50,\n",
|
||
" title=f\"Distribuzione degli Errori - {target}\"\n",
|
||
" )\n",
|
||
" \n",
|
||
" # Calcola intervalli di confidenza\n",
|
||
" confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n",
|
||
" for level in confidence_levels:\n",
|
||
" lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n",
|
||
" upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n",
|
||
" \n",
|
||
" print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n",
|
||
" print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n",
|
||
"\n",
|
||
"class ProbabilityFunctions:\n",
|
||
" @staticmethod\n",
|
||
" def calculate_statistics(data):\n",
|
||
" \"\"\"\n",
|
||
" Calcola statistiche di base usando TensorFlow.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Tensor dei dati\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" dict: Dizionario con le statistiche\n",
|
||
" \"\"\"\n",
|
||
" if not isinstance(data, tf.Tensor):\n",
|
||
" data = tf.convert_to_tensor(data, dtype=tf.float32)\n",
|
||
" \n",
|
||
" mean = tf.reduce_mean(data)\n",
|
||
" # Calculate variance manually\n",
|
||
" squared_deviations = tf.square(data - mean)\n",
|
||
" variance = tf.reduce_mean(squared_deviations)\n",
|
||
" std = tf.sqrt(variance)\n",
|
||
" \n",
|
||
" # Sort the tensor for median calculation\n",
|
||
" sorted_data = tf.sort(data)\n",
|
||
" size = tf.size(data)\n",
|
||
" mid_index = size // 2\n",
|
||
" median = sorted_data[mid_index]\n",
|
||
" \n",
|
||
" return {\n",
|
||
" 'mean': mean.numpy(),\n",
|
||
" 'variance': variance.numpy(),\n",
|
||
" 'std': std.numpy(),\n",
|
||
" 'min': tf.reduce_min(data).numpy(),\n",
|
||
" 'max': tf.reduce_max(data).numpy(),\n",
|
||
" 'median': median.numpy()\n",
|
||
" }\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_pmf(data, bins=50):\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Probability Mass Function (PMF) dei dati.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, bin_edges)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola l'istogramma\n",
|
||
" hist, bin_edges = np.histogram(data, bins=bins, density=True)\n",
|
||
" \n",
|
||
" # Calcola i centri dei bin\n",
|
||
" bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
|
||
" \n",
|
||
" # Normalizza per ottenere la PMF\n",
|
||
" pmf = hist / np.sum(hist)\n",
|
||
" \n",
|
||
" return bin_centers, pmf, bin_edges\n",
|
||
"\n",
|
||
" @staticmethod\n",
|
||
" def calculate_cmf(pmf):\n",
|
||
" \"\"\"\n",
|
||
" Calcola la Cumulative Mass Function (CMF) dalla PMF.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" pmf: Probability Mass Function\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" array: Cumulative Mass Function\n",
|
||
" \"\"\"\n",
|
||
" return np.cumsum(pmf)\n",
|
||
"\n",
|
||
" def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n",
|
||
" \"\"\"\n",
|
||
" Calcola e visualizza PMF e CMF delle distribuzioni.\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" data: Array di dati da analizzare\n",
|
||
" bins: Numero di bin per l'istogramma\n",
|
||
" title: Titolo del grafico\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (bin_centers, pmf, cmf)\n",
|
||
" \"\"\"\n",
|
||
" # Calcola PMF e CMF\n",
|
||
" bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n",
|
||
" cmf = self.calculate_cmf(pmf)\n",
|
||
" \n",
|
||
" # Crea il plot\n",
|
||
" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
|
||
" \n",
|
||
" # Plot PMF\n",
|
||
" width = np.diff(bin_edges)\n",
|
||
" ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n",
|
||
" ax1.set_title('Probability Mass Function')\n",
|
||
" ax1.set_ylabel('Probability')\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
" ax1.legend()\n",
|
||
" \n",
|
||
" # Plot CMF\n",
|
||
" ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n",
|
||
" ax2.set_title('Cumulative Mass Function')\n",
|
||
" ax2.set_xlabel('Value')\n",
|
||
" ax2.set_ylabel('Cumulative Probability')\n",
|
||
" ax2.grid(True, alpha=0.3)\n",
|
||
" ax2.legend()\n",
|
||
" \n",
|
||
" # Set overall title\n",
|
||
" fig.suptitle(title, y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
" return bin_centers, pmf, cmf"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== ANALISI COMPLETA DEL MODELLO ===\n",
|
||
"\n",
|
||
"1. ANALISI DEGLI ERRORI\n",
|
||
"--------------------------------------------------\n",
|
||
"18375/18375 [==============================] - 78s 4ms/step\n",
|
||
"\n",
|
||
"Analisi per olive_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -71.944\n",
|
||
"variance: 4009595.000\n",
|
||
"std: 2002.397\n",
|
||
"min: -18637.889\n",
|
||
"max: 12871.579\n",
|
||
"median: 48.672\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 68.0%:\n",
|
||
"Range: [-1937.87, 1843.27]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4458.63, 3733.83]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-6979.39, 5624.40]\n",
|
||
"\n",
|
||
"Analisi per min_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -32.785\n",
|
||
"variance: 179026.016\n",
|
||
"std: 423.115\n",
|
||
"min: -4439.664\n",
|
||
"max: 3453.714\n",
|
||
"median: -12.655\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 68.0%:\n",
|
||
"Range: [-414.04, 375.30]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-1045.51, 848.90]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1519.11, 1164.63]\n",
|
||
"\n",
|
||
"Analisi per max_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -34.971\n",
|
||
"variance: 261409.344\n",
|
||
"std: 511.282\n",
|
||
"min: -5732.709\n",
|
||
"max: 4274.197\n",
|
||
"median: -11.391\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 68.0%:\n",
|
||
"Range: [-429.05, 371.50]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-1229.60, 971.92]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1830.02, 1572.33]\n",
|
||
"\n",
|
||
"Analisi per avg_oil_prod\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -33.549\n",
|
||
"variance: 192810.531\n",
|
||
"std: 439.102\n",
|
||
"min: -4876.229\n",
|
||
"max: 3813.953\n",
|
||
"median: -13.710\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 68.0%:\n",
|
||
"Range: [-444.24, 250.98]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-965.65, 772.39]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-1487.06, 1293.80]\n",
|
||
"\n",
|
||
"Analisi per total_water_need\n",
|
||
"--------------------------------------------------\n",
|
||
"\n",
|
||
"Statistiche degli Errori:\n",
|
||
"mean: -216.226\n",
|
||
"variance: 3987062.750\n",
|
||
"std: 1996.763\n",
|
||
"min: -22812.350\n",
|
||
"max: 13374.520\n",
|
||
"median: -119.823\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Intervallo di Confidenza 68.0%:\n",
|
||
"Range: [-2185.83, 1432.85]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 95.0%:\n",
|
||
"Range: [-4357.05, 3604.06]\n",
|
||
"\n",
|
||
"Intervallo di Confidenza 99.0%:\n",
|
||
"Range: [-7252.00, 5051.54]\n",
|
||
"\n",
|
||
"2. IMPORTANZA DELLE FEATURE\n",
|
||
"--------------------------------------------------\n",
|
||
"18375/18375 [==============================] - 79s 4ms/step\n",
|
||
"18375/18375 [==============================] - 80s 4ms/step\n",
|
||
"18375/18375 [==============================] - 99s 5ms/step\n",
|
||
"18375/18375 [==============================] - 96s 5ms/step\n",
|
||
"13976/18375 [=====================>........] - ETA: 21s"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.0rc1"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|