diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml index b8e8a30c..56051458 100644 --- a/.github/workflows/linting.yml +++ b/.github/workflows/linting.yml @@ -14,7 +14,7 @@ jobs: - uses: actions/setup-python@v4 with: python-version: "3.8" - - uses: psf/black@22.1.0 + - uses: psf/black@24.1.1 with: args: ". --check" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d610c846..26e9ea01 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/psf/black - rev: 22.3.0 + rev: 24.1.1 hooks: - id: black language_version: python3 diff --git a/dev/furnace_new_inf_data-gen_multiple.ipynb b/dev/furnace_new_inf_data-gen_multiple.ipynb new file mode 100644 index 00000000..d5834b65 --- /dev/null +++ b/dev/furnace_new_inf_data-gen_multiple.ipynb @@ -0,0 +1,896 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d4df06d1", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c0589863", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/giles/anaconda3/envs/tomopt/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0)\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n", + "/Users/giles/anaconda3/envs/tomopt/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from typing import *\n", + "import numpy as np\n", + "from functools import partial\n", + "from fastprogress import progress_bar\n", + "import pandas as pd\n", + "import h5py\n", + "\n", + "from lumin.plotting.results import plot_roc\n", + "\n", + "import torch\n", + "from torch import Tensor, nn\n", + "import torch.nn.functional as F\n", + "from torch._vmap_internals import _vmap as vmap\n", + "\n", + "from tomopt.volume import *\n", + "from tomopt.muon import *\n", + "from tomopt.inference import *\n", + "from tomopt.optimisation import *\n", + "from tomopt.core import *\n", + "from tomopt.utils import *\n", + "from tomopt.plotting import *\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "2a887994", + "metadata": {}, + "outputs": [], + "source": [ + "DEVICE = torch.device(\"cpu\")\n", + "RES = 1e4\n", + "SPAN = 0.8" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f31a105a", + "metadata": {}, + "outputs": [], + "source": [ + "def get_central_separated_detector(size: float = 0.1, lwh: Tensor = Tensor([1.0, 1.0, 1.8]), span=SPAN, device: torch.device = torch.device(\"cpu\")) -> Volume:\n", + " def area_cost(x: Tensor) -> Tensor:\n", + " return F.relu(x)\n", + "\n", + " layers: List[Layer] = []\n", + " layers.append(\n", + " PanelDetectorLayer(\n", + " pos=\"above\",\n", + " lw=lwh[:2],\n", + " z=lwh[2].item(),\n", + " size=0.4,\n", + " panels=[\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.8),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.75),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.5),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.45),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " ],\n", + " )\n", + " )\n", + " for z in np.round(np.arange(lwh[2] - 0.4, 0.4, -size), decimals=2):\n", + " layers.append(PassiveLayer(lw=lwh[:2], z=z, size=size, device=device))\n", + " layers.append(\n", + " PanelDetectorLayer(\n", + " pos=\"below\",\n", + " lw=lwh[:2],\n", + " z=0.2,\n", + " size=0.4,\n", + " panels=[\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.4),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.35),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.1),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.05),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " ],\n", + " )\n", + " )\n", + "\n", + " return Volume(nn.ModuleList(layers))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "938a5884", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Volume(\n", + " (layers): ModuleList(\n", + " (0): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.8000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (1): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.7500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (2): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.5000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (3): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.4500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " )\n", + " )\n", + " (1): PassiveLayer located at z=tensor([1.4000])\n", + " (2): PassiveLayer located at z=tensor([1.3000])\n", + " (3): PassiveLayer located at z=tensor([1.2000])\n", + " (4): PassiveLayer located at z=tensor([1.1000])\n", + " (5): PassiveLayer located at z=tensor([1.])\n", + " (6): PassiveLayer located at z=tensor([0.9000])\n", + " (7): PassiveLayer located at z=tensor([0.8000])\n", + " (8): PassiveLayer located at z=tensor([0.7000])\n", + " (9): PassiveLayer located at z=tensor([0.6000])\n", + " (10): PassiveLayer located at z=tensor([0.5000])\n", + " (11): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.4000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (1): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.3500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (2): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.1000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (3): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.0500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " )\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_central_separated_detector()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "dee6ad79", + "metadata": {}, + "outputs": [], + "source": [ + "def get_central_close_detector(size: float = 0.1, lwh: Tensor = Tensor([1.0, 1.0, 1.8]), span=SPAN, device: torch.device = torch.device(\"cpu\")) -> Volume:\n", + " def area_cost(x: Tensor) -> Tensor:\n", + " return F.relu(x)\n", + "\n", + " layers: List[Layer] = []\n", + " layers.append(\n", + " PanelDetectorLayer(\n", + " pos=\"above\",\n", + " lw=lwh[:2],\n", + " z=lwh[2].item(),\n", + " size=0.4,\n", + " panels=[\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.48),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.47),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.46),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 1.45),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " ],\n", + " )\n", + " )\n", + " for z in np.round(np.arange(lwh[2] - 0.4, 0.4, -size), decimals=2):\n", + " layers.append(PassiveLayer(lw=lwh[:2], z=z, size=size, device=device))\n", + " layers.append(\n", + " PanelDetectorLayer(\n", + " pos=\"below\",\n", + " lw=lwh[:2],\n", + " z=0.2,\n", + " size=0.4,\n", + " panels=[\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.4),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.39),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.38),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.37),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " ],\n", + " )\n", + " )\n", + "\n", + " return Volume(nn.ModuleList(layers))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "3fbcc1bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Volume(\n", + " (layers): ModuleList(\n", + " (0): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.4800]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (1): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.4700]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (2): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.4600]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (3): located at xy=tensor([0.5000, 0.5000]), z=tensor([1.4500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " )\n", + " )\n", + " (1): PassiveLayer located at z=tensor([1.4000])\n", + " (2): PassiveLayer located at z=tensor([1.3000])\n", + " (3): PassiveLayer located at z=tensor([1.2000])\n", + " (4): PassiveLayer located at z=tensor([1.1000])\n", + " (5): PassiveLayer located at z=tensor([1.])\n", + " (6): PassiveLayer located at z=tensor([0.9000])\n", + " (7): PassiveLayer located at z=tensor([0.8000])\n", + " (8): PassiveLayer located at z=tensor([0.7000])\n", + " (9): PassiveLayer located at z=tensor([0.6000])\n", + " (10): PassiveLayer located at z=tensor([0.5000])\n", + " (11): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.4000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (1): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.3900]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (2): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.3800]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (3): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.3700]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " )\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_central_close_detector()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "5780dd15", + "metadata": {}, + "outputs": [], + "source": [ + "def get_offset_separated_detector(size: float = 0.1, lwh: Tensor = Tensor([1.0, 1.0, 1.8]), span=SPAN, device: torch.device = torch.device(\"cpu\")) -> Volume:\n", + " def area_cost(x: Tensor) -> Tensor:\n", + " return F.relu(x)\n", + "\n", + " layers: List[Layer] = []\n", + " layers.append(\n", + " PanelDetectorLayer(\n", + " pos=\"above\",\n", + " lw=lwh[:2],\n", + " z=lwh[2].item(),\n", + " size=0.4,\n", + " panels=[\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(0.0, 0.0, 1.8),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(0.0, 0.0, 1.75),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(0.0, 0.0, 1.5),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(0.0, 0.0, 1.45),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " ],\n", + " )\n", + " )\n", + " for z in np.round(np.arange(lwh[2] - 0.4, 0.4, -size), decimals=2):\n", + " layers.append(PassiveLayer(lw=lwh[:2], z=z, size=size, device=device))\n", + " layers.append(\n", + " PanelDetectorLayer(\n", + " pos=\"below\",\n", + " lw=lwh[:2],\n", + " z=0.2,\n", + " size=0.4,\n", + " panels=[\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.4),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.35),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.1),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " SigmoidDetectorPanel(\n", + " smooth=0.1,\n", + " res=RES,\n", + " eff=1,\n", + " init_xyz=(lwh[0].item()/2, lwh[1].item()/2, 0.05),\n", + " init_xy_span=(span, span),\n", + " device=device,\n", + " ),\n", + " ],\n", + " )\n", + " )\n", + "\n", + " return Volume(nn.ModuleList(layers))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c988ff49", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Volume(\n", + " (layers): ModuleList(\n", + " (0): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0., 0.]), z=tensor([1.8000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (1): located at xy=tensor([0., 0.]), z=tensor([1.7500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (2): located at xy=tensor([0., 0.]), z=tensor([1.5000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (3): located at xy=tensor([0., 0.]), z=tensor([1.4500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " )\n", + " )\n", + " (1): PassiveLayer located at z=tensor([1.4000])\n", + " (2): PassiveLayer located at z=tensor([1.3000])\n", + " (3): PassiveLayer located at z=tensor([1.2000])\n", + " (4): PassiveLayer located at z=tensor([1.1000])\n", + " (5): PassiveLayer located at z=tensor([1.])\n", + " (6): PassiveLayer located at z=tensor([0.9000])\n", + " (7): PassiveLayer located at z=tensor([0.8000])\n", + " (8): PassiveLayer located at z=tensor([0.7000])\n", + " (9): PassiveLayer located at z=tensor([0.6000])\n", + " (10): PassiveLayer located at z=tensor([0.5000])\n", + " (11): PanelDetectorLayer(\n", + " (panels): ModuleList(\n", + " (0): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.4000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (1): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.3500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (2): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.1000]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " (3): located at xy=tensor([0.5000, 0.5000]), z=tensor([0.0500]), and xy span tensor([0.8000, 0.8000]) with budget scale tensor([1.])\n", + " )\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_offset_separated_detector()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "e65d094c", + "metadata": {}, + "outputs": [], + "source": [ + "from tomopt.benchmarks.ladle_furnace import *" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "6093edbc", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "class PocaRec(PredHandler):\n", + " def __init__(self, path: Path, overwrite:bool):\n", + " if isinstance(path, str):\n", + " path = Path(path)\n", + " self.path = path\n", + " if self.path.exists() and overwrite:\n", + " self.path.unlink()\n", + " self.path.parent.mkdir(exist_ok=True, parents=True)\n", + " self.id = 0\n", + "\n", + " def _open_file(self) -> h5py.File:\n", + " r\"\"\"\n", + " Returns:\n", + " Save file to write data to\n", + " \"\"\"\n", + "\n", + " if self.path.exists():\n", + " return h5py.File(self.path, \"r+\")\n", + " return h5py.File(self.path, \"w\")\n", + " \n", + " def on_x0_pred_end(self) -> None:\n", + " r\"\"\"\n", + " Records predictions and true volume layout or target for the latest volume\n", + " \"\"\"\n", + "\n", + " if self.wrapper.fit_params.state == \"test\":\n", + " pred_h = self.wrapper.fit_params.pred.detach().cpu().numpy()\n", + " targ_h = self.wrapper.volume.target.detach().cpu().numpy().item()\n", + " pocas = self.wrapper.fit_params.volume_inferrer.muon_poca_xyz.detach().cpu().numpy()\n", + " poca_uncs = self.wrapper.fit_params.volume_inferrer.muon_poca_xyz_unc.detach().cpu().numpy()\n", + " eff = self.wrapper.fit_params.volume_inferrer.muon_efficiency.reshape(self.wrapper.fit_params.volume_inferrer.n_mu, 1).detach().cpu().numpy()\n", + " sig_wgt = self.wrapper.fit_params.volume_inferrer.sig_wgt.detach().cpu().numpy()\n", + " wgt = self.wrapper.fit_params.volume_inferrer.wgt.detach().cpu().numpy()\n", + " \n", + " with self._open_file() as h5:\n", + " grp = h5.create_group(f'targ_h_{targ_h:.3f}_{self.id}')\n", + " grp.create_dataset(\"pred_h\", data=pred_h.astype(\"float32\"), dtype=\"float32\", compression=None)\n", + " grp.create_dataset(\"targ_h\", data=targ_h, dtype=\"float32\", compression=None) \n", + " grp.create_dataset(\"poca_xyz\", data=pocas.astype(\"float32\"), dtype=\"float32\", compression='lzf') \n", + " grp.create_dataset(\"poca_xyz_unc\", data=poca_uncs.astype(\"float32\"), dtype=\"float32\", compression='lzf') \n", + " grp.create_dataset(\"muon_efficiency\", data=eff.astype(\"float32\"), dtype=\"float32\", compression='lzf') \n", + " grp.create_dataset(\"muon_xy_sig_wgt\", data=sig_wgt.astype(\"float32\"), dtype=\"float32\", compression='lzf') \n", + " grp.create_dataset(\"muon_wgt\", data=wgt.astype(\"float32\"), dtype=\"float32\", compression='lzf') \n", + " \n", + " self.id += 1" + ] + }, + { + "cell_type": "markdown", + "id": "e3849d57", + "metadata": {}, + "source": [ + "- Detectors:\n", + " - Central, separated\n", + " - Central, close\n", + " - Offset, separated\n", + "- Muons:\n", + " - 1000\n", + " - 10000\n", + " - 100000\n", + "- Fill-height:\n", + " - 0.4\n", + " - 0.6\n", + " - 0.8\n", + " - 1.0\n", + " - 1.2" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "3d4ae5f9", + "metadata": {}, + "outputs": [], + "source": [ + "fill_heights = [0.4, 0.6, 0.8, 1.0, 1.2]*10" + ] + }, + { + "cell_type": "markdown", + "id": "456049fb", + "metadata": {}, + "source": [ + "## No slag" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "7bafc7ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running volume central_separated\n", + "Running muons 10000\n" + ] + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running volume central_close\n", + "Running muons 10000\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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loss_func=VolumeMSELoss(target_budget=None),\n", + " partial_volume_inferrer=partial(PocaZLadleFurnaceFillLevelInferrer, smooth=1.0))\n", + " for n_mu in [1000, 10000]:\n", + " print('Running muons', n_mu)\n", + " wrapper.predict(test_passives,\n", + " n_mu_per_volume=n_mu,\n", + " mu_bs=250,\n", + " cbs=[MuonResampler()],\n", + " pred_cb=PocaRec(Path(f'ladle_inf_data_multi_thinspan_smooth_high/no_slag/det-{det}_n_mu-{n_mu}.h5'), True))" + ] + }, + { + "cell_type": "markdown", + "id": "5d25da63", + "metadata": {}, + "source": [ + "## Random slag" + ] + }, + { + "cell_type": "markdown", + "id": "bbf54075", + "metadata": {}, + "source": [ + "%%time\n", + "for det, volume in [('central_separated', get_central_separated_detector()), ('central_close', get_central_close_detector()), ('offset_separated', get_offset_separated_detector())]:\n", + " print('Running volume', det)\n", + " passive_generator = LadleFurnacePassiveGenerator(volume)\n", + " test_passives = PassiveYielder([passive_generator._generate(fixed_mat_z=h) for h in fill_heights], shuffle=False)\n", + " wrapper = PanelVolumeWrapper(volume,\n", + " xy_pos_opt=partial(torch.optim.SGD, lr=5e4),\n", + " z_pos_opt=partial(torch.optim.SGD, lr=5e3),\n", + " xy_span_opt=partial(torch.optim.SGD, lr=1e4),\n", + " loss_func=VolumeMSELoss(target_budget=None),\n", + " partial_volume_inferrer=partial(PocaZLadleFurnaceFillLevelInferrer))\n", + " for n_mu in [1000, 10000, 100000]:\n", + " print('Running muons', n_mu)\n", + " wrapper.predict(test_passives,\n", + " n_mu_per_volume=n_mu,\n", + " mu_bs=250,\n", + " cbs=[MuonResampler()],\n", + " pred_cb=PocaRec(Path(f'ladle_inf_data/random_slag/det-{det}_n_mu-{n_mu}.h5'), True))" + ] + }, + { + "cell_type": "markdown", + "id": "a119b467", + "metadata": {}, + "source": [ + "## 10cm slag" + ] + }, + { + "cell_type": "markdown", + "id": "76ff0f3f", + "metadata": {}, + "source": [ + "%%time\n", + "for det, volume in [('central_separated', get_central_separated_detector()), ('central_close', get_central_close_detector()), ('offset_separated', get_offset_separated_detector())]:\n", + " print('Running volume', det)\n", + " passive_generator = LadleFurnacePassiveGenerator(volume)\n", + " test_passives = PassiveYielder([passive_generator._generate(fixed_mat_z=h, fixed_slag_z=h+0.1001) for h in fill_heights[:-1]], shuffle=False)\n", + " wrapper = PanelVolumeWrapper(volume,\n", + " xy_pos_opt=partial(torch.optim.SGD, lr=5e4),\n", + " z_pos_opt=partial(torch.optim.SGD, lr=5e3),\n", + " xy_span_opt=partial(torch.optim.SGD, lr=1e4),\n", + " loss_func=VolumeMSELoss(target_budget=None),\n", + " partial_volume_inferrer=partial(PocaZLadleFurnaceFillLevelInferrer))\n", + " for n_mu in [1000, 10000, 100000]:\n", + " print('Running muons', n_mu)\n", + " wrapper.predict(test_passives,\n", + " n_mu_per_volume=n_mu,\n", + " mu_bs=250,\n", + " cbs=[MuonResampler()],\n", + " pred_cb=PocaRec(Path(f'ladle_inf_data/10cm_slag/det-{det}_n_mu-{n_mu}.h5'), True))" + ] + }, + { + "cell_type": "markdown", + "id": "e4f78c3a", + "metadata": {}, + "source": [ + "## Rest slag" + ] + }, + { + "cell_type": "markdown", + "id": "af70b687", + "metadata": {}, + "source": [ + "%%time\n", + "for det, volume in [('central_separated', get_central_separated_detector()), ('central_close', get_central_close_detector()), ('offset_separated', get_offset_separated_detector())]:\n", + " print('Running volume', det)\n", + " passive_generator = LadleFurnacePassiveGenerator(volume)\n", + " test_passives = PassiveYielder([passive_generator._generate(fixed_mat_z=h, fixed_slag_z=1.4) for h in fill_heights[:-1]], shuffle=False)\n", + " wrapper = PanelVolumeWrapper(volume,\n", + " xy_pos_opt=partial(torch.optim.SGD, lr=5e4),\n", + " z_pos_opt=partial(torch.optim.SGD, lr=5e3),\n", + " xy_span_opt=partial(torch.optim.SGD, lr=1e4),\n", + " loss_func=VolumeMSELoss(target_budget=None),\n", + " partial_volume_inferrer=partial(PocaZLadleFurnaceFillLevelInferrer))\n", + " for n_mu in [1000, 10000, 100000]:\n", + " print('Running muons', n_mu)\n", + " wrapper.predict(test_passives,\n", + " n_mu_per_volume=n_mu,\n", + " mu_bs=250,\n", + " cbs=[MuonResampler()],\n", + " pred_cb=PocaRec(Path(f'ladle_inf_data/rest_slag/det-{det}_n_mu-{n_mu}.h5'), True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f2e0f2c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tomopt]", + "language": "python", + "name": "conda-env-tomopt-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dev/ladle_inf_analysis_multi.ipynb b/dev/ladle_inf_analysis_multi.ipynb new file mode 100644 index 00000000..5f7086b8 --- /dev/null +++ b/dev/ladle_inf_analysis_multi.ipynb @@ -0,0 +1,1340 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3b65547e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/giles/anaconda3/envs/tomopt/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0)\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n" + ] + } + ], + "source": [ + "import h5py\n", + "import numpy as np\n", + "import pandas as pd\n", + "from fastcore.all import Path\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "id": "5a9e46df", + "metadata": {}, + "source": [ + "# Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "243863ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Path('ladle_inf_data_multi_thinspan_smooth_high/no_slag/det-central_close_n_mu-10000.h5'),\n", + " Path('ladle_inf_data_multi_thinspan_smooth_high/no_slag/det-offset_separated_n_mu-1000.h5'),\n", + " Path('ladle_inf_data_multi_thinspan_smooth_high/no_slag/det-central_separated_n_mu-1000.h5'),\n", + " Path('ladle_inf_data_multi_thinspan_smooth_high/no_slag/det-central_close_n_mu-1000.h5'),\n", + " Path('ladle_inf_data_multi_thinspan_smooth_high/no_slag/det-central_separated_n_mu-10000.h5')]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DATA_PATH = Path('ladle_inf_data_multi_thinspan_smooth_high//////')\n", + "DATA = list(DATA_PATH.glob('no_slag/*.h5'))\n", + "DATA" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "77782440", + "metadata": {}, + "outputs": [], + "source": [ + "def get_new_pred(poca_xyz, poca_xyz_unc, eff, xy_sig_wgt):\n", + " r'''\n", + " New version of inferrence with clamping\n", + " '''\n", + " \n", + " z_pos = poca_xyz[:, 2:]\n", + " z_unc = poca_xyz_unc[:, 2:]\n", + " \n", + " z_unc = np.clip(z_unc, np.percentile(z_unc, 15.865), np.percentile(z_unc, 84.135))\n", + " \n", + " wgt = xy_sig_wgt * eff / (z_unc**2)\n", + " wgt = np.clip(wgt, 0, np.percentile(z_unc, 84.135))\n", + " \n", + " mean_z = (wgt * z_pos).sum() / wgt.sum()\n", + " return mean_z[None]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d21889c1", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame()\n", + "for path in DATA:\n", + " with h5py.File(path, 'r') as h5:\n", + " for grp in h5.keys():\n", + " tmp_df = pd.DataFrame({\n", + " 'detector':path.stem.split('_n_mu')[0],\n", + " 'n_mu':int(path.stem.split('-')[-1]),\n", + " 'targ_h':h5[grp]['targ_h'][()],\n", + " 'pred_h':h5[grp]['pred_h'][()],\n", + " 'n_rec_muons':h5[grp]['muon_efficiency'][()].shape[0],\n", + " 'mean_z_unc':h5[grp]['poca_xyz_unc'][:,2].mean(),\n", + " 'mean_z':h5[grp]['poca_xyz'][:,2].mean(),\n", + " 'mean_xy_unc':h5[grp]['poca_xyz_unc'][:,:2].mean(),\n", + " 'mean_xy_sig_wgt':h5[grp]['muon_xy_sig_wgt'][()].mean(),\n", + " 'std_xy_sig_wgt':h5[grp]['muon_xy_sig_wgt'][()].std(),\n", + " 'std_wgt':h5[grp]['muon_wgt'][()].std(),\n", + " 'mean_wgt':h5[grp]['muon_wgt'][()].mean(),\n", + " 'new_pred':get_new_pred(poca_xyz=h5[grp]['poca_xyz'][()], poca_xyz_unc=h5[grp]['poca_xyz_unc'][()], eff=h5[grp]['muon_efficiency'][()], xy_sig_wgt=h5[grp]['muon_xy_sig_wgt'][()])\n", + " })\n", + " df = df.append(tmp_df, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b8aee6f7", + "metadata": {}, + "source": [ + "# Process data " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "824f8727", + "metadata": {}, + "outputs": [], + "source": [ + "df['bias'] = df.targ_h-df.pred_h\n", + "df['new_bias'] = df.targ_h-df.new_pred\n", + "df['basic_bias'] = df.targ_h-df.mean_z" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "23e2c1d5", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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detectorn_mutarg_hpred_hn_rec_muonsmean_z_uncmean_zmean_xy_uncmean_xy_sig_wgtstd_xy_sig_wgtstd_wgtmean_wgtnew_predbiasnew_biasbasic_bias
0det-central_close100000.40.49618720521.1341890.6595490.1599790.5498900.1869084.576799e+041.174956e+040.685371-0.096187-0.285371-0.259549
1det-central_close100000.40.50419220191.3300960.6872850.1917510.5507230.1847065.135880e+041.312834e+040.699910-0.104192-0.299910-0.287285
2det-central_close100000.40.47277019951.4923920.6546640.1636030.5503320.1894126.831076e+041.406783e+040.689861-0.072770-0.289861-0.254664
3det-central_close100000.40.52831220031.7387660.6709970.2098330.5510930.1881895.277521e+041.482665e+040.692561-0.128312-0.292561-0.270997
4det-central_close100000.60.50086620061.0854300.6173390.1439550.5734920.1764625.642859e+041.577536e+040.6346010.099134-0.034601-0.017339
...................................................
213det-central_separated100001.20.48625913060.0389500.7966500.0040030.6452880.1482436.026200e+063.051730e+060.7980450.7137410.4019550.403350
214det-central_separated100001.20.48944513770.0760200.7613370.0078330.6329380.1550356.198842e+063.448522e+060.7613370.7105550.4386630.438663
215det-central_separated100001.20.50561014170.0428270.7844560.0040220.6488920.1496627.502611e+063.461192e+060.7844550.6943900.4155450.415545
216det-central_separated100001.20.46815814180.0448140.7974690.0038960.6397780.1518836.233061e+063.237780e+060.7974690.7318420.4025310.402531
217det-central_separated100001.20.50958114080.0525770.7835100.0041050.6419360.1496976.409045e+063.509617e+060.7835100.6904190.4164900.416490
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218 rows × 16 columns

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" + ], + "text/plain": [ + " detector n_mu targ_h pred_h n_rec_muons mean_z_unc \\\n", + "0 det-central_close 10000 0.4 0.496187 2052 1.134189 \n", + "1 det-central_close 10000 0.4 0.504192 2019 1.330096 \n", + "2 det-central_close 10000 0.4 0.472770 1995 1.492392 \n", + "3 det-central_close 10000 0.4 0.528312 2003 1.738766 \n", + "4 det-central_close 10000 0.6 0.500866 2006 1.085430 \n", + ".. ... ... ... ... ... ... \n", + "213 det-central_separated 10000 1.2 0.486259 1306 0.038950 \n", + "214 det-central_separated 10000 1.2 0.489445 1377 0.076020 \n", + "215 det-central_separated 10000 1.2 0.505610 1417 0.042827 \n", + "216 det-central_separated 10000 1.2 0.468158 1418 0.044814 \n", + "217 det-central_separated 10000 1.2 0.509581 1408 0.052577 \n", + "\n", + " mean_z mean_xy_unc mean_xy_sig_wgt std_xy_sig_wgt std_wgt \\\n", + "0 0.659549 0.159979 0.549890 0.186908 4.576799e+04 \n", + "1 0.687285 0.191751 0.550723 0.184706 5.135880e+04 \n", + "2 0.654664 0.163603 0.550332 0.189412 6.831076e+04 \n", + "3 0.670997 0.209833 0.551093 0.188189 5.277521e+04 \n", + "4 0.617339 0.143955 0.573492 0.176462 5.642859e+04 \n", + ".. ... ... ... ... ... \n", + "213 0.796650 0.004003 0.645288 0.148243 6.026200e+06 \n", + "214 0.761337 0.007833 0.632938 0.155035 6.198842e+06 \n", + "215 0.784456 0.004022 0.648892 0.149662 7.502611e+06 \n", + "216 0.797469 0.003896 0.639778 0.151883 6.233061e+06 \n", + "217 0.783510 0.004105 0.641936 0.149697 6.409045e+06 \n", + "\n", + " mean_wgt new_pred bias new_bias basic_bias \n", + "0 1.174956e+04 0.685371 -0.096187 -0.285371 -0.259549 \n", + "1 1.312834e+04 0.699910 -0.104192 -0.299910 -0.287285 \n", + "2 1.406783e+04 0.689861 -0.072770 -0.289861 -0.254664 \n", + "3 1.482665e+04 0.692561 -0.128312 -0.292561 -0.270997 \n", + "4 1.577536e+04 0.634601 0.099134 -0.034601 -0.017339 \n", + ".. ... ... ... ... ... \n", + "213 3.051730e+06 0.798045 0.713741 0.401955 0.403350 \n", + "214 3.448522e+06 0.761337 0.710555 0.438663 0.438663 \n", + "215 3.461192e+06 0.784455 0.694390 0.415545 0.415545 \n", + "216 3.237780e+06 0.797469 0.731842 0.402531 0.402531 \n", + "217 3.509617e+06 0.783510 0.690419 0.416490 0.416490 \n", + "\n", + "[218 rows x 16 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "cb7e18df", + "metadata": {}, + "source": [ + "# Interpret data " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f10745df", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for n_mu in df.n_mu.unique():\n", + " with sns.axes_style({'style':'whitegrid', 'rc':{'patch.edgecolor':'none'}}), sns.color_palette('tab10') as pallete: \n", + " fig = plt.figure(figsize=(12,8))\n", + " for i, det in enumerate(df.detector.unique()):\n", + " sdf = df[(df.detector == det) & (df.n_mu == n_mu)].sort_values('targ_h')\n", + " grps = sdf.groupby('targ_h')\n", + " agg = grps.agg({f:['mean', 'std'] for f in ['pred_h', 'new_pred', 'mean_z']})\n", + " agg.columns = ['_'.join(c).strip() for c in agg.columns.values]\n", + " agg.reset_index(inplace=True)\n", + " plt.errorbar(agg.targ_h, agg.mean_z_mean, yerr=agg.mean_z_std, label=f'Detector: {det}', color=pallete[i], linestyle='--')\n", + " \n", + " plt.xlabel('Target height [m]')\n", + " plt.ylabel('Predicted height [m]')\n", + " plt.legend()\n", + " plt.title(f'N exposed muons {n_mu:.2f}')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2772a378", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for n_mu in df.n_mu.unique():\n", + " with sns.axes_style({'style':'whitegrid', 'rc':{'patch.edgecolor':'none'}}), sns.color_palette('tab10') as pallete: \n", + " fig = plt.figure(figsize=(12,8))\n", + " for i, det in enumerate(df.detector.unique()):\n", + " sdf = df.loc[(df.detector == det) & (df.n_mu == n_mu), ['targ_h', 'pred_h', 'new_pred']].sort_values('targ_h')\n", + " grps = sdf.groupby('targ_h')\n", + " agg = grps.agg({f:['mean', 'std'] for f in ['pred_h', 'new_pred']})\n", + " agg.columns = ['_'.join(c).strip() for c in agg.columns.values]\n", + " agg.reset_index(inplace=True)\n", + " plt.errorbar(agg.targ_h, agg.new_pred_mean, yerr=agg.new_pred_std/np.sqrt(10), label=f'Detector: {det}', color=pallete[i])\n", + " \n", + " plt.xlabel('Target height [m]')\n", + " plt.ylabel('Predicted height [m]')\n", + " plt.legend()\n", + " plt.title(f'N exposed muons {n_mu:.2f}')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "73480a1e", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for n_mu in df.n_mu.unique():\n", + " with sns.axes_style({'style':'whitegrid', 'rc':{'patch.edgecolor':'none'}}), sns.color_palette('tab10') as pallete: \n", + " fig = plt.figure(figsize=(12,8))\n", + " for i, det in enumerate(df.detector.unique()):\n", + " sdf = df[(df.detector == det) & (df.n_mu == n_mu)].sort_values('targ_h')\n", + " grps = sdf.groupby('targ_h')\n", + " agg = grps.agg({f:['mean', 'std'] for f in ['bias', 'new_bias', 'basic_bias']})\n", + " agg.columns = ['_'.join(c).strip() for c in agg.columns.values]\n", + " agg.reset_index(inplace=True)\n", + " plt.errorbar(agg.targ_h, (agg.bias_mean-agg.bias_mean.mean()).abs(), yerr=agg.basic_bias_std, label=f'Detector: {det}', color=pallete[i], linestyle='--')\n", + " \n", + " plt.xlabel('Target height [m]')\n", + " plt.ylabel('Prediction error [m]')\n", + " plt.legend()\n", + " plt.title(f'N exposed muons {n_mu:.2f}')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "096b634c", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for n_mu in df.n_mu.unique():\n", + " with sns.axes_style({'style':'whitegrid', 'rc':{'patch.edgecolor':'none'}}), sns.color_palette('tab10') as pallete: \n", + " fig = plt.figure(figsize=(12,8))\n", + " for i, det in enumerate(df.detector.unique()):\n", + " sdf = df.loc[(df.detector == det) & (df.n_mu == n_mu), ['targ_h', 'bias', 'new_bias']].sort_values('targ_h')\n", + " grps = sdf.groupby('targ_h')\n", + " agg = grps.agg({f:['mean', 'std'] for f in ['bias', 'new_bias']})\n", + " agg.columns = ['_'.join(c).strip() for c in agg.columns.values]\n", + " agg.reset_index(inplace=True)\n", + " plt.errorbar(agg.targ_h, (agg.new_bias_mean-agg.new_bias_mean.mean()).abs(), yerr=agg.new_bias_std, label=f'Detector: {det}', color=pallete[i])\n", + " \n", + " plt.xlabel('Target height [m]')\n", + " plt.ylabel('Absolute Prediction error [m]')\n", + " plt.legend()\n", + " plt.title(f'N exposed muons {n_mu:.2f}')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "630ba471", + "metadata": {}, + "source": [ + "# Correction" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1305fffc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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detectorn_mutarg_hpred_hn_rec_muonsmean_z_uncmean_zmean_xy_uncmean_xy_sig_wgtstd_xy_sig_wgtstd_wgtmean_wgtnew_predbiasnew_biasbasic_bias
0det-central_close100000.40.49618720521.1341890.6595490.1599790.5498900.1869084.576799e+041.174956e+040.685371-0.096187-0.285371-0.259549
1det-central_close100000.40.50419220191.3300960.6872850.1917510.5507230.1847065.135880e+041.312834e+040.699910-0.104192-0.299910-0.287285
2det-central_close100000.40.47277019951.4923920.6546640.1636030.5503320.1894126.831076e+041.406783e+040.689861-0.072770-0.289861-0.254664
3det-central_close100000.40.52831220031.7387660.6709970.2098330.5510930.1881895.277521e+041.482665e+040.692561-0.128312-0.292561-0.270997
4det-central_close100000.60.50086620061.0854300.6173390.1439550.5734920.1764625.642859e+041.577536e+040.6346010.099134-0.034601-0.017339
...................................................
213det-central_separated100001.20.48625913060.0389500.7966500.0040030.6452880.1482436.026200e+063.051730e+060.7980450.7137410.4019550.403350
214det-central_separated100001.20.48944513770.0760200.7613370.0078330.6329380.1550356.198842e+063.448522e+060.7613370.7105550.4386630.438663
215det-central_separated100001.20.50561014170.0428270.7844560.0040220.6488920.1496627.502611e+063.461192e+060.7844550.6943900.4155450.415545
216det-central_separated100001.20.46815814180.0448140.7974690.0038960.6397780.1518836.233061e+063.237780e+060.7974690.7318420.4025310.402531
217det-central_separated100001.20.50958114080.0525770.7835100.0041050.6419360.1496976.409045e+063.509617e+060.7835100.6904190.4164900.416490
\n", + "

218 rows × 16 columns

\n", + "
" + ], + "text/plain": [ + " detector n_mu targ_h pred_h n_rec_muons mean_z_unc \\\n", + "0 det-central_close 10000 0.4 0.496187 2052 1.134189 \n", + "1 det-central_close 10000 0.4 0.504192 2019 1.330096 \n", + "2 det-central_close 10000 0.4 0.472770 1995 1.492392 \n", + "3 det-central_close 10000 0.4 0.528312 2003 1.738766 \n", + "4 det-central_close 10000 0.6 0.500866 2006 1.085430 \n", + ".. ... ... ... ... ... ... \n", + "213 det-central_separated 10000 1.2 0.486259 1306 0.038950 \n", + "214 det-central_separated 10000 1.2 0.489445 1377 0.076020 \n", + "215 det-central_separated 10000 1.2 0.505610 1417 0.042827 \n", + "216 det-central_separated 10000 1.2 0.468158 1418 0.044814 \n", + "217 det-central_separated 10000 1.2 0.509581 1408 0.052577 \n", + "\n", + " mean_z mean_xy_unc mean_xy_sig_wgt std_xy_sig_wgt std_wgt \\\n", + "0 0.659549 0.159979 0.549890 0.186908 4.576799e+04 \n", + "1 0.687285 0.191751 0.550723 0.184706 5.135880e+04 \n", + "2 0.654664 0.163603 0.550332 0.189412 6.831076e+04 \n", + "3 0.670997 0.209833 0.551093 0.188189 5.277521e+04 \n", + "4 0.617339 0.143955 0.573492 0.176462 5.642859e+04 \n", + ".. ... ... ... ... ... \n", + "213 0.796650 0.004003 0.645288 0.148243 6.026200e+06 \n", + "214 0.761337 0.007833 0.632938 0.155035 6.198842e+06 \n", + "215 0.784456 0.004022 0.648892 0.149662 7.502611e+06 \n", + "216 0.797469 0.003896 0.639778 0.151883 6.233061e+06 \n", + "217 0.783510 0.004105 0.641936 0.149697 6.409045e+06 \n", + "\n", + " mean_wgt new_pred bias new_bias basic_bias \n", + "0 1.174956e+04 0.685371 -0.096187 -0.285371 -0.259549 \n", + "1 1.312834e+04 0.699910 -0.104192 -0.299910 -0.287285 \n", + "2 1.406783e+04 0.689861 -0.072770 -0.289861 -0.254664 \n", + "3 1.482665e+04 0.692561 -0.128312 -0.292561 -0.270997 \n", + "4 1.577536e+04 0.634601 0.099134 -0.034601 -0.017339 \n", + ".. ... ... ... ... ... \n", + "213 3.051730e+06 0.798045 0.713741 0.401955 0.403350 \n", + "214 3.448522e+06 0.761337 0.710555 0.438663 0.438663 \n", + "215 3.461192e+06 0.784455 0.694390 0.415545 0.415545 \n", + "216 3.237780e+06 0.797469 0.731842 0.402531 0.402531 \n", + "217 3.509617e+06 0.783510 0.690419 0.416490 0.416490 \n", + "\n", + "[218 rows x 16 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "72e73857", + "metadata": {}, + "outputs": [], + "source": [ + "from torch import Tensor, nn, optim\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "215c07de", + "metadata": {}, + "outputs": [], + "source": [ + "from lumin.nn.losses.advanced_losses import WeightedFractionalMSE" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "b27411d4", + "metadata": {}, + "outputs": [], + "source": [ + "x = Tensor(df.loc[(df.detector == 'det-central_separated') & (df.n_mu == 1000), 'new_pred'].values)[:,None]\n", + "y = Tensor(df.loc[(df.detector == 'det-central_separated') & (df.n_mu == 1000), 'targ_h'].values)[:,None]" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "8b2b956d", + "metadata": {}, + "outputs": [], + "source": [ + "corr_layer = nn.Linear(1,1)\n", + "corr_layer.weight.data[0] = 1.0\n", + "corr_layer.bias.data[0] = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "e7ad5729", + "metadata": {}, + "outputs": [], + "source": [ + "opt = optim.SGD(corr_layer.parameters(), lr=3e-1)\n", + "mse = nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "eaead200", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 0.0718868225812912\n", + "1 0.026785049587488174\n", + "1 0.024950657039880753\n", + "1 0.02463659457862377\n", + "1 0.02437891997396946\n", + "1 0.024126257747411728\n", + "1 0.023876767605543137\n", + "1 0.023630354553461075\n", + "1 0.02338697388768196\n", + "1 0.02314658835530281\n", + "1 0.022909166291356087\n", + "1 0.02267466112971306\n", + "1 0.022443048655986786\n", + "1 0.022214286029338837\n", + "1 0.021988339722156525\n", + "1 0.021765174344182014\n", + "1 0.021544763818383217\n", + "1 0.0213270615786314\n", + "1 0.02111203409731388\n", + "1 0.020899660885334015\n", + "1 0.02068990468978882\n", + "1 0.020482726395130157\n", + "1 0.020278098061680794\n", + "1 0.02007598616182804\n", + "1 0.01987636648118496\n", + "1 0.019679207354784012\n", + "1 0.019484469667077065\n", + "1 0.019292131066322327\n", + "1 0.01910216175019741\n", + "1 0.01891453191637993\n", + "1 0.018729211762547493\n", + "1 0.018546171486377716\n", + "1 0.01836538501083851\n", + "1 0.01818682812154293\n", + "1 0.018010463565587997\n", + "1 0.017836272716522217\n", + "1 0.01766422763466835\n", + "1 0.01749430038034916\n", + "1 0.017326466739177704\n", + "1 0.017160698771476746\n", + "1 0.016996972262859344\n", + "1 0.016835257411003113\n", + "1 0.016675537452101707\n", + "1 0.01651778258383274\n", + "1 0.01636197417974472\n", + "1 0.016208074986934662\n", + "1 0.01605607569217682\n", + "1 0.015905950218439102\n", + "1 0.015757666900753975\n", + "1 0.015611213631927967\n", + "1 0.015466559678316116\n", + "1 0.015323689207434654\n", + "1 0.015182578936219215\n", + "1 0.01504320465028286\n", + "1 0.01490554679185152\n", + "1 0.014769578352570534\n", + "1 0.014635294675827026\n", + "1 0.01450265571475029\n", + "1 0.014371657744050026\n", + "1 0.014242262579500675\n", + "1 0.014114469289779663\n", + "1 0.013988242484629154\n", + "1 0.013863570988178253\n", + "1 0.013740437105298042\n" + ] + } + ], + "source": [ + "for i in range(64):\n", + " y_pred = corr_layer(x)\n", + " loss = mse(y_pred, y)\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + " print(1, loss.item())" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "05c41be5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Parameter containing:\n", + " tensor([[2.3370]], requires_grad=True),\n", + " Parameter containing:\n", + " tensor([-0.5844], requires_grad=True))" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corr_layer.weight, corr_layer.bias" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "6d723efd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(-0.0935)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(x-y).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "c37290c2", + "metadata": {}, + "outputs": [], + "source": [ + "corr_layer = nn.Linear(1,1)\n", + "corr_layer.weight.data[0] = 1.0\n", + "corr_layer.bias.data[0] = 0.0935" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "4e1e1731", + "metadata": {}, + "outputs": [], + "source": [ + "df['new_new_pred'] = corr_layer(Tensor(df.new_pred.values[:,None])).detach().cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "0a4e248c", + "metadata": {}, + "outputs": [], + "source": [ + "df['new_new_bias'] = df.targ_h-df.new_new_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "7a50cb43", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for n_mu in [1000]:#df.n_mu.unique():\n", + " with sns.axes_style({'style':'whitegrid', 'rc':{'patch.edgecolor':'none'}}), sns.color_palette('tab10') as pallete: \n", + " fig = plt.figure(figsize=(12,8))\n", + " for i, det in enumerate(['det-central_separated']):#df.detector.unique()):\n", + " sdf = df.loc[(df.detector == det) & (df.n_mu == n_mu), ['targ_h', 'new_pred', 'new_new_pred']].sort_values('targ_h')\n", + " grps = sdf.groupby('targ_h')\n", + " agg = grps.agg({f:['mean', 'std'] for f in ['new_pred','new_new_pred']})\n", + " agg.columns = ['_'.join(c).strip() for c in agg.columns.values]\n", + " agg.reset_index(inplace=True)\n", + " plt.errorbar(agg.targ_h, agg.new_pred_mean, yerr=agg.new_pred_std/np.sqrt(10), label=f'Detector: {det}', color=pallete[i], linestyle='--')\n", + " plt.errorbar(agg.targ_h, agg.new_new_pred_mean, yerr=agg.new_new_pred_std/np.sqrt(10), label=f'Detector: {det}', color=pallete[i])\n", + " \n", + " plt.xlabel('Target height [m]')\n", + " plt.ylabel('Predicted height [m]')\n", + " plt.legend()\n", + " plt.title(f'N exposed muons {n_mu:.2f}')\n", + " plt.show()\n", + "\n", + "for n_mu in [1000]:#df.n_mu.unique():\n", + " with sns.axes_style({'style':'whitegrid', 'rc':{'patch.edgecolor':'none'}}), sns.color_palette('tab10') as pallete: \n", + " fig = plt.figure(figsize=(12,8))\n", + " for i, det in enumerate(['det-central_separated']):#df.detector.unique()):\n", + " sdf = df.loc[(df.detector == det) & (df.n_mu == n_mu), ['targ_h', 'new_bias', 'new_new_bias']].sort_values('targ_h')\n", + " grps = sdf.groupby('targ_h')\n", + " agg = grps.agg({f:['mean', 'std'] for f in ['new_bias','new_new_bias']})\n", + " agg.columns = ['_'.join(c).strip() for c in agg.columns.values]\n", + " agg.reset_index(inplace=True)\n", + "\n", + " plt.errorbar(agg.targ_h, agg.new_bias_mean, yerr=agg.new_bias_std, label=f'Detector: {det}', color=pallete[i], linestyle='--')\n", + " plt.errorbar(agg.targ_h, agg.new_new_bias_mean, yerr=agg.new_new_bias_std, label=f'Detector: {det}', color=pallete[i])\n", + " \n", + " plt.xlabel('Target height [m]')\n", + " plt.ylabel('Absolute Prediction error [m]')\n", + " plt.legend()\n", + " plt.title(f'N exposed muons {n_mu:.2f}')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "7e49f15c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.06344581" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df.new_new_bias**2).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c6e1ea1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tomopt]", + "language": "python", + "name": "conda-env-tomopt-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/panel_detectors/01_Indepth_tutorial_single_cycle.ipynb b/examples/panel_detectors/01_Indepth_tutorial_single_cycle.ipynb index 9b52c7a1..da074b1f 100644 --- a/examples/panel_detectors/01_Indepth_tutorial_single_cycle.ipynb +++ b/examples/panel_detectors/01_Indepth_tutorial_single_cycle.ipynb @@ -84,26 +84,26 @@ { "data": { "text/plain": [ - "(tensor([[0.7433, 0.1546, 5.0000, 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}, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1612,7 +1723,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1621,16 +1732,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'muons.x[0]=tensor(0.1623), muons.y[0]=tensor(0.7031), muons.z[0]=tensor(0.8000), muons.theta[0]=tensor(0.4490)'" + "'muons.x[0]=tensor(0.9930), muons.y[0]=tensor(0.6371), muons.z[0]=tensor(0.8000), muons.theta[0]=tensor(0.3818)'" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1648,7 +1759,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1656,4093 +1767,4093 @@ "text/plain": [ "defaultdict(.()>,\n", " {'above': defaultdict(list,\n", - " {'reco_xyz': 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"metadata": {}, "output_type": "execute_result" } @@ -5913,7 +6024,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -5923,7 +6034,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -5948,7 +6059,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -5980,7 +6091,7 @@ ")" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -5998,7 +6109,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -6007,7 +6118,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -6041,7 +6152,7 @@ ")" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -6060,7 +6171,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -6084,7 +6195,7 @@ " )]" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -6095,7 +6206,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -6109,7 +6220,7 @@ " PassiveLayer located at z=tensor([0.3000])]" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -6127,7 +6238,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -6261,7 +6372,7 @@ " 0.3528, 0.3528]]]))" ] }, - "execution_count": 39, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -6280,7 +6391,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -6290,7 +6401,7 @@ " [3, 8, 4]])" ] }, - "execution_count": 40, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -6308,7 +6419,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -6324,7 +6435,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -6333,7 +6444,7 @@ "Muon generator: x,y range: (-0.24115426839670548, 1.2411542683967054), (-0.24115426839670548, 1.2411542683967054). Momentum is fixed at 5 GeV" ] }, - "execution_count": 42, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -6352,16 +6463,16 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'muons.x[0]=tensor(1.0793), muons.y[0]=tensor(0.0532), muons.z[0]=tensor(1.), muons.theta[0]=tensor(0.2230)'" + "'muons.x[0]=tensor(0.7554), muons.y[0]=tensor(0.9335), muons.z[0]=tensor(1.), muons.theta[0]=tensor(0.9255)'" ] }, - "execution_count": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -6373,7 +6484,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -6382,16 +6493,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'muons.x[0]=tensor(1.3041), muons.y[0]=tensor(0.0823), muons.z[0]=tensor(0.), muons.theta[0]=tensor(0.2230)'" + "'muons.x[0]=tensor(-0.1155), muons.y[0]=tensor(-0.0704), muons.z[0]=tensor(0.), muons.theta[0]=tensor(0.9280)'" ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -6409,7 +6520,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": { "scrolled": true }, @@ -6417,257 +6528,257 @@ { "data": { "text/plain": [ - "{'above': {'reco_xyz': tensor([[[ 1.0692, 0.0547, 1.0000],\n", - " [ 1.1092, 0.0568, 0.9500],\n", - " [ 1.0972, 0.0542, 0.9000],\n", - " [ 1.1415, 0.0557, 0.8500]],\n", + "{'above': {'reco_xyz': tensor([[[ 0.7556, 0.9337, 1.0000],\n", + " [ 0.7121, 0.8828, 0.9500],\n", + " [ 0.6662, 0.8303, 0.9000],\n", + " [ 0.6213, 0.7836, 0.8500]],\n", " \n", - " [[-0.0741, 0.1930, 1.0000],\n", - " [-0.0626, 0.1626, 0.9500],\n", - " [-0.0908, 0.1347, 0.9000],\n", - " [-0.0652, 0.1125, 0.8500]],\n", + " [[ 0.7605, 0.2712, 1.0000],\n", + " [ 0.7414, 0.2969, 0.9500],\n", + " [ 0.7179, 0.3241, 0.9000],\n", + " [ 0.6951, 0.3476, 0.8500]],\n", " \n", - " [[ 0.7264, 0.5570, 1.0000],\n", - " [ 0.7046, 0.5699, 0.9500],\n", - " [ 0.6838, 0.5831, 0.9000],\n", - 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"execution_count": 48, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -6688,7 +6799,7 @@ "torch.Size([1000, 4, 3])" ] }, - "execution_count": 48, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -6718,7 +6829,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -6727,7 +6838,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -6738,15 +6849,15 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 5.36 ms, sys: 1.24 ms, total: 6.61 ms\n", - "Wall time: 5.89 ms\n" + "CPU times: user 4.41 ms, sys: 3 ms, total: 7.41 ms\n", + "Wall time: 6.79 ms\n" ] } ], @@ -6757,201 +6868,189 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 117 ms, sys: 37.2 ms, total: 154 ms\n", - "Wall time: 92 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/giles/cernbox/mode_muon_tomography/tomopt/inference/scattering.py:531: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n", - " idxs = torch.combinations(torch.arange(0, unc.shape[-1]), with_replacement=True)\n" + "CPU times: user 72.7 ms, sys: 23.8 ms, total: 96.6 ms\n", + "Wall time: 47.8 ms\n" ] }, { "data": { "text/plain": [ - "(tensor([[-3.7864e-01, 1.9244e-01, 1.3932e-01],\n", - " [ 4.2445e-01, 5.9305e-01, 7.4828e-01],\n", - " [ 3.8083e-01, 8.2638e-01, 1.0182e+00],\n", - " [ 4.6593e-02, 5.3649e-01, 4.3330e-01],\n", - " [-4.1206e-01, -1.2272e-01, 5.2123e-02],\n", - " [ 1.1138e+00, 6.3683e-01, 2.6466e-01],\n", - " [ 4.3934e-01, 7.4769e-02, -6.5913e-02],\n", - " [ 1.0590e+00, -1.6619e-01, 1.7812e-01],\n", - " [-3.0518e-01, 4.2048e-01, 2.7442e-01],\n", - " [ 2.8662e-01, 2.8314e-01, 5.8631e-01],\n", - " [ 6.9406e-01, 1.1990e+00, 1.7902e-01],\n", - " [ 1.0132e+00, 1.3679e+00, 3.7021e-01],\n", - " [ 1.0892e+00, 1.7848e+00, 2.4681e-01],\n", - " [ 7.6449e-01, 5.8979e-02, 7.9372e-01],\n", - " [-1.2303e-01, 3.1300e-01, 2.9380e-01],\n", - 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"execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -7197,20 +7292,100 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 121, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/giles/anaconda3/envs/tomopt/lib/python3.8/site-packages/numpy/core/shape_base.py:65: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " ary = asanyarray(ary)\n" - ] - }, + "data": { + "image/png": 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\n", 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\n", 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fMl2AhELfJ+YdK+14MUKICUKIcCFEeHJysmaBmozaDWDU/8HDr8DRpfB9f/VTZ0kqgUzt0AdLK7WedOpFyLql/qMrmZ3Ic9eZslotqTV9ULuC6hxy4CxJhpGbq7DzxGUE8H8TH6Cjm5636d7xAVw6BiN/NuvFZ4qiLAQWgrrY0MDhGAcLS+jxPjRpD+ueV/Omhy+HZvcZOjLJyBjl47VJkrPSZu38jdtMWH6Uexxt+HZ0ANZWsutIkqHN2XmanScv896jXvofRP+9Df74Bu57FtoYdUZEEtCs0PeuecdKOy5VhPcgGLcDatmpedPhpl82UtIvORrQFwtLdZvRzJuQmWboaCQ9upWVw/gfwrmdpWPx0x1pYC9rjEqSoe2IvcScnacZ4u/Kk/eXr/xouaVehHXPwT3t1F3wjNsG4Km86h33AymKolwAfgN6CSHqCyHqA73yjkkVdY8XTMjLm974kpo7nZNp6KgkIyFTO/SpdiNIu6zOSlu3hGrcrEHSRm6uwquroom9cJPFTwcSe/4mzyz9k/M3btO0nh1TereRqR2SVM0W7v2HGZtPAnDonyusjzqvv36YmwtrJ0JWOgxZDLVs9XPfShJChAFBQCMhRCJqJY5aAIqifAtsBvoCccAt4Jm8164JIaYDf+bdapqiKHdbtCjdjV19NW9610ew/wt1D4lhP4BjE0NHJhmYHEjrk4Ul1HGGm4mQlVattUYlbcze8Tdb/rrI2309uXk7p9guam/+cgxADqYlqZqsPHKOGZtPkp/Iez4lQ7/98NBXcGa3ugW4U9uq36+KFEUZWcbrCjCplNeWAEu0iKtGujNvemFXGLYcmncydGSSAcmBtL7ZN4S0S3DzPDRqLWeljVB5dzK8mpZJXHI6jetYsz32IlEJKcU2gLidreP11TGEHTlXqVhkvWlJKp/hCw6hKAp/nr3Onavh8vthlQfS5yPVHe48H4WAZ6p2L8l8eQ9S399XPgHL+kHfzyBQ/n2pqWSOtB7pdDpCX3oZ725D8enSnzMnYwwdElu3bqVNmza0bNmSmTNnVui8hIQEunXrhpeXF97e3syZM6fINW5ubvj4+ODn50dgoNHueFspaZk5/HMlHQcbK9wa2SOEKHUXNbmToWnS6XSEhobi7e2Nj48PZ86cMXRIlVKePn63c0rrx19++SXe3t60a9eOkSNHkpFRvt1btXQhJaPYIDpflfthZppa6q6OEzw6V06CSHeXnzft0VXNm94wWeZN11ByRlqPZsyYgYeHB8djY1n0xXS+njeHWfMXG+wfZJ1Ox6RJk9i+fTuurq507NiRAQMG4OXlVa7z6tevz+eff46/vz+pqakEBATQs2fPItfv3r2bRo0aVfePViVlzQJfTMlgwFf7calnx/pJD9Gwjg1A9e2iJlWLgv56/DiLFi3i66+/Nrktv8vTx8tzzp39OCkpiblz5xIbG4udnR3Dhg1j5cqVjBkzpjp/vCKe79aSMUuPYFfLsiC9qjCXenZVa2DL63DtDIzZqNYRlqSy2NWHJ1bB7o9h3+dwOVZN9ZB50zWKnJHWk/T0dNauXUtoaCgIC9zb+BB3Jh4yUgwW05EjR2jZsiUeHh5YW1szYsQI1q9fX+7zmjRpgr+/PwAODg54enqSlGTe1ZNuZ+kY/0M46Zk5LH66Y8EgGkreRU3uZGiaivRXwN3dnbi4OANHVXHl6ePl/XfgTjk5Ody+fZucnBxu3bpF06ZNtfoxynTu6i0mh0XS5h4HPhjgpf9+eGw1RP0IXV4Dt4erGK1Uo1hYwiPvqQsPL8WqedPn/jB0VFI1kjPSerJjxw4SEhLw8/MD4Nq1a/R4KFCt4GFbt0qz0tevX6d+/fpFjnXu3JnU1NRi586aNYsePXoA6qxSs2b/lRF1dXXljz+Kd/DynHf27FkiIyPp1Om/RRVCCHr16oUQgmeffZYJEyZU7gc0Erm5Cq/9XzR/nU/hu6cCaeNcdLFofv7lZ7+dklU79GXLVLh4TL/3dPaBPqWnMUEp/TWv31RVZftrZZSn75Z1Tkn92MXFhddee43mzZtjZ2dHr1696NWrV6XjrIpbWTlMWB4OwIInA7i3oT02Vpb664fXz8LGl8H1Pug6VX+BSzWL18A78qY/VfPsZYqQ2TO/gbSB3pijoqKYNm0aEydOBGDcuHH4eraEnAy4fb1KHxW+/PLLLFu2rMixffv2Vfp+FZWWlsaQIUOYPXs2jo6OBcf379+Pi4sLly9fpmfPnrRt25YuXbpUW1z6NmfnaTYdu8CbfdryiOc9JZ4jdzI0DyX2V19fvdxbH/21R48eXLxYfEvijz/+mIEDB1YlvGJK6sc+Pj6sX7+e+Ph46tWrx+OPP86KFSsYPXq0Xtsui6IoTF1zjFOXUlk6piP3NrQH9NgPdTmwZrz630O+U3eplaTKcvKE8bvUv1MbX1YXr/adBVY2ZV8rmSz5r4aeXL9+HXd3d0D9SHTbtm28/dZbJN+4yOuvhDD9szm89/4HLFiwgFq1agFw69YtpkyZgo2NDbVr1+ajjz7i2LFj/Pbbb7z22mtMmjSJfv36cfLkST777DOmTJlS0F55ZrhcXFxISEgoeC0xMREXl+JvPnc7Lzs7myFDhjBq1CgGDx5c7DoAJycnHnvsMY4cOWKyA+mNMecLNneY0MXD0OHUHGU8oGqlxP769tssXboUZ2dngoODCQkJYf78+djZqbm3WvfXwnbs2FGun6M8fbysc0rqx5cuXcLd3Z3GjRsDMHjwYA4ePFjtA+nF++PZEH2eKb3bENTGSf8N/D4TEo+o9aLr63lTF6lmsqsPT/wMuz+BfbPUdI/hy8HRcKlRkrbMbyBtoDfm1q1bc/jwYZ566im+/PJL+vXrh7uHB2Q0pHnTxrz60mQWf7+iYBANMH/+fMaMGUPHjh0ZOnQoAJGRkbRv3x5QZ4KdnJwYPXo0L7zwQpH2yjPD1bFjR06fPk18fDwuLi6sXLmSn376qdznKYpCSEgInp6evPLKK0WuSU9PJzc3FwcHB9LT09m2bRvvvfdehf+/GYOYxBu8uiqawHvr88ngdgj5UZzZK7G/uruTm5vLkiVLSEpKYvjw4QWDaNC+v1ZGefr43c4prR/Xr1+fw4cPc+vWLezs7Ni5c2e1V+Y5+M8VZmw5SbC3M88HtdB/A2f3w95Z4DcafIbq//5SzWVhCY+8q9abXjsRFgapOdTN7zd0ZJIG5GJDPRk5ciQRERG0bNmSmJgYvvjiCwDSsi04k3AJKyWbOva1i1xz/PhxAgICyMrKonZt9bVjx47h6+vLzZs3EUIQExNT8EZdUVZWVnz11Vf07t0bT09Phg0bhre3d8Hrffv25fz586Wed+DAAZYvX86uXbvw8/PDz8+PzZs3A3Dp0iUefvhh2rdvz3333Ue/fv0IDg6uVJxaWxeZxEMzd+E+dRMPzdzFusj/FkxeTMlg/A/hNKpjw7dPBmBjZXmXO0nmorT+2qJFCyIiIoiKiqJ3795FrtG6v1bG3fp4Wf0bSu/HnTp1YujQofj7++Pj40Nubq7mayAK99NOn+xg/A/huDeyZ9aw9vp/uL11DX6ZAA1bQJ//6ffekpTPawCM3wnW9rCsP/y5GJTSCjhKpkooJvZLDQwMVMLDw4scO3HiBJ6engaKqHQ5OTlMmDCB9994hVUrvqPjg0F4+j/Axo0bCQkJYdOmTWzYsAGA559/nvbt27N48WJOnjyJlZUVdevWxcvLizVr1jB16lSj/Bn1SYvf47rIpCK7EYK6wn/GYB96ezszfOEh4i6nsea5B/Fs4niXOxkvIcRRRVGMspC3KfXXfAMHDmTRokU4OTlx6dIl2V/LoI/fZ0n9FODNPm15tqueZ6MVBX4eDX//BuN2QFM//d6/DMbcX6HkPitV0e0b8Mt4OL0NOjwJ/T6XedMmpKw+KwfS1UFR4Goc5GSwNeIc1rZ2dO/e3dBRGZ3Sfo/l3YmwJJHnbpS4UUMtC4GDXS2upWfR2qkO9e2ty3U/Y6wXbcxvzKbUX1NSUnj33XcJCgoqWA+wdetWrK2tZX+9i6r+PocvOFRqP7W2tKBD83r67XfhS9SFYL0+ggdf1N99y8mY+yvIgbRmcnNhzyew9zNwCZR50yakrD5rfjnSxkgIcGgCV08T3CUQ6pRcEULSv9J2O8vOVbiWnkWz+nblHkRL5q1u3brMnTu3yDFjTVcyN9W2a+jlk7D1LWjRHe6fpN97S9LdWFhA93fA2RfWPQcLuqp50/ca3+SMVDFyIF1dbOqAjQOkXoLajdTFCFK5VGU2qrTdCAEG+7vw+eMa5F9KklRuPz/7QPXsGpqdAavHqv8WD/pWHdhIUnXzGvBfvenv+0PwTOg4TtabNmHyX5Lq5NAEFB2kJxs6khqjpN0IAdwa1uaTx3zkIFqSjEBJJSf1vmvo9vfg8nEY9A04yE8FJQNyaqvWm27xCGx+DTa8oD7oSSZJDqSrk7U92NSFtMuQm2PoaArEp8QTnxJv6DA0MaiDCzMG++BSTy1jZiGgfu1a/N/EB7EtYYAtSebKWPt5Vk4u66OSsLaywMnBBoE6Ez1jsI/+Nj86tRWOLID7n4dWPfVzT0mqCrt6MHIldHkdIlfAsr5w87yho5IqQaZ2VDfHJpB8Uh1My4UG1WJQBxeC2zkzfMEhTl9O48dx99PYQa6YliRj8OGvx4k4d4OvR/nT16eJ/htIvQjrn1d3qO3xgf7vL0mVZWEB3d+GJr5qvekFXWHY93Dvg4aOTKoAOSNd3WrZgW19Nb1Dl23oaGoERVGYsjqG6MQUvhzuh1dT0yxzJ0nm5uc/z/HjH+eY2LWFNoPo3FxY+yxk34YhS2TJMck4eT4K43aCrSN8/ygcWSTrTZsQOZA2BAdnUHLVWWlJc1/tiuPXvG2Ge3s7GzocSZKAqIQbvLvuOJ1bNdJvLnRhB+fCmT3qgq7GrbVpQ5L0IT9vumUPNW96vcybNhVyIG0ItWzBrkHerHSWoaMxa1uOXeDz7X/zWAcXbbYZliSpwpJTM5m4/ChOjjbMHdEBSwsNFv0mHYVd08FrIPg/pf/7S5K+2daFEWFq3nTUCljaB1KSyr5OMihNB9JCiGAhxCkhRJwQYmoJrzcXQuwWQkQKIWKEEH21jMeoOOTNjKZeMmwcZuyvpBReWRVNh+b1mDFYVugoi+yvUnXI1uUy6acIbtzOYsGTAdrUcc9MhdUhaqWkR+eYdGmxcvTLL4UQUXlffwshbhR6TVfotQ3VG7lUKfl508N/hCt/w8Ku8O9BQ0cl3YVmA2khhCUwH+gDeAEjhRBed5z2DrBKUZQOwAjga63iMTpWNlC7Idy6CjmZho7G7Fy+mcH4H8KpX7sWC54MkBU6ylBT++u6desQQnDy5MmCYzqdjtDQULy9vfHx8eHMmTN6a8/NzQ0fHx/8/PwIDCy+UdapU6fw8/Mr+HJ0dGT27Nl6az/f1q1badOmDS1btmTmzJkAZGRkcN9999G+fXu8vb15//339d4uwCebT3Ak/hozB/vi3bSuJm2weQrc+BcGLwK7+tq0UQ3K0y8VRXlZURQ/RVH8gHnAL4Vevp3/mqIoA6otcKnqPPurqR62dWXetJHTckb6PiBOUZQziqJkASuBgXecowD5K7/qAjWr9kv+DodpFw0bh5nJyNYxYflRbtzKZtHTgTg52Bo6JFNQI/trWFgYgYGBhIWFFRybMWMGHh4eHD9+nMmTJ/P11/p9Xti9ezdRUVGUtA1zmzZtiIqKIioqiqNHj1K7dm0ee+wxvbav0+mYNGkSW7ZsITY2lrCwMGJjY7GxsWHXrl1ER0cTFRXF1q1bOXz4sF7bXhuZyNIDZxn7kLv+StvdKeb/IDrv43HT3zWuPP2ysJFA2F1el0xJ4zYyb9oEaDmQdgESCn2fmHessA+A0UKIRGAz8GJJNxJCTBBChAshwpOTjXszkwrNcFlZg30juHWt0p3jxo0bDB06lLZt2+Lp6cmhQ4dKPVen09GhQwf69+9fqbbKUtIsV0JCAt26dcPLywtvb2/mzJmjSdv5FEXhjTUxRCXc4Mvh7bWb8TI/RttfN53ZRK/VvfD93pdeq3ux6cymKt8TIC0tjT179rgevZUAACAASURBVPDdd98VDKTT09NZu3YtoaGhALi7uxMXF6eX9ipq586dtGjRgnvvvReA+Ph4Bg4cSGBgIPfddx+nTp2q1H2PHDlCy5Yt8fDwwNramhEjRrB+/XqEENSpUweA7OxssrOz9ZoO9VdSClPXHKOTewPe7NtWb/ct4lo8bHwZmt0PXaZo00b1Kk+/BEAIcS/gDuwqdNg2ry8eFkIMKq0RU3qPrXHy86a7vlEobzrR0FFJhRh6seFIYJmiKK5AX2C5EKJYTIqiLFQUJVBRlMDGjRtXe5AVUeEZrjr3gLCo9Kx0aGgowcHBnDx5kujoaDw9PUs9d86cOXd9vSpKm+WysrLi888/JzY2lsOHDzN//nxiY2M1iQHg6z3/sD7qPK/1ak1wOw3KadVs1d5fN53ZxAcHP+BC+gUUFC6kX+CDgx/oZTC9fv16evToQfv27alTpw5Hjx5lx44dJCQkFKRWjB07lgYNGpR5r86dOxdJycj/2rFjR5HzhBD06tWLgIAAFi5ceNd7rly5kpEjRwLqwHbcuHF88cUXhIeH88EHHxQ8rFZUUlISzZo1K/je1dWVpCR1QZNOp8PPzw8nJyd69uxJp06dKtXGna6nZzFxxVEa2Fszf5Q/tSw1eOvRZcOaEPXf0yGLwLLGbZMwAlitKIqu0LF7FUUJBJ4AZgshSlxxbUrvsTWShQV0eysvb/o0LAyCswcMHZWUR8uBdBLQrND3rnnHCgsBVgEoinIIsAUaaRgTYGQzXJa1wL4x3L6u1jqtgJSUFPbu3UtISAgA1tbW1KtXr8RzExMT2bRpE+PGjStyPD4+ngkjJzAgaIAms1xNmjTB398fAAcHBzw9PQvetPVt618X+ey3Uwz0a8qkbi01acOMGWV/nRMxhwxd0U9rMnQZzImo+icbYWFhDBs2DIBhw4YRFhZGVFQU06ZNK0iv6NWrF35+fmXea9++fQXXFP7q0aNHkfP2799PREQEW7ZsYf78+ezdu7fE+2VlZbFhwwYef/xxQP2k6/jx4wwZMgQ/Pz9ef/11bG2Lpiz16NGDdu3aFftav359uf+fWFpaEhUVRWJiIkeOHOGvv/4q97WlydHl8mJYJJdTM/l2dACN6mhUy3n3J2qljgFzoF5zbdqofuXpl/lGcEdah6IoSXl/ngH2AB30H6JUbTz7w/idYFsPfhgAfyyUedNGQMtH9j+BVkIId9SOPwL1qbiwc8AjwDIhhCfqG7Omnyvlz3Dlvznnz3AB9PPoV6V7lzTDlZiYWDDDBXDt2rVib67YO0H6FUi9AA08AHWGKzU1tVgbs2bNKrg+Pj6exo0b88wzzxAdHU1AQABz5szB3t6+2HUvvfQSn376aZF75s9yvfv5u9zrfi8nDpxg5syZLF26tMI/e0mzXH/88UeRc86ePUtkZKTeZrkKO34+hZd/jqJ9s3r8b4ivrNBRcUbZXy+ml/xJTWnHy+vatWv88ccfrFmzBlAH0l27dmXw4MG4u7sDkJOTw7Zt23j77bdZunQpzs7OBAcHExISwvz587Gzsyu4X3n6K4CLi/qpvJOTE4899hhHjhyhS5cuxa7bsmUL/v7+3HOPuo4iOjqajz/+uOChuSR3zn6XxsXFhYSE/7IFEhMTC+LKV69ePbp168bWrVtp165due5bms+2nWJ/3BU+HeJL+2YlP+hX2ZnfYf+X0OFJ8NZvTrmBladfIoRoC9QHDhU6Vh+4pShKphCiEfAQ8Gm1RC1pp3EbdTD9y7OwZQpciIJ+X6hldSWD0GwgrShKjhDiBeA3wBJYoijKcSHENCBcUZQNwKvAIiHEy6gLmcYoiraPV3eb4arqQDosLIzx48cD/81wOTg4MG3aNCZOnAjAuHHj8PX1LXqhpRXUaaxuZZt1C6xrs2/fvjLby8nJISIignnz5tGpUydCQ0OZOXMm06dPL3Lexo0bcXJyIiAggD179hQcz5/lev7J5wGwUCzo3LlzkWt79OjBxYvFBy0ff/wxAwfebc1LUWlpaQwZMoTZs2fj6KjfnQUvp2Yw/vtw6tWuxSJZoaNSjLW/Ots7cyH9QonHq2L16tX07dsXGxt1dtTDw4MmTZrQsGFDDh8+zFNPPcWXX35Jv379cHd3Jzc3lyVLlpCUlMTw4cOLDKKBcvXX9PR0cnNzcXBwID09nW3btvHee++VeG5YWFhBWgdAkyZN+O2333jmmWewsLDg2LFjtGvXrlIPjB07duT06dPEx8fj4uLCypUr+emnn0hOTqZWrVrUq1eP27dvs337dt54440K37+wTTEXWPD7GUZ1as6wjs3KvqAy0q+quxc2bAl9/qdNGwZSzn4J6gB75R390RNYIITIRf30eaaiKNrl1UnVx7YujPgJfv8f/D4TLsfC8BVQ19XQkdVImiaRKYqyGXVRUuFj7xX671jUp+RqYywzXMnJybz++utMnz6d9957jwXffE0tkazOSjdsUa4ZLldXV1xdXQtmeIcOHVpi3uSBAwfYsGEDmzdvJiMjg5s3bzJ69Gjc3Nz4+OOP6T60OwDudd2LXauPWa7s7GyGDBnCqFGjGDx4cLnuV14Z2TqeXX6Ua7eyWD3xQZwc5VN5ZRljfw31Dy3yCRKAraUtof6hVbpvWFgY0dHRuLm5FRy7evUqAQEBREZG0rJlSx544IGCPOYWLVoQERFBSkpKsfSo8rp06VJBBY6cnByeeOIJgoODC17v27cv3333HXXr1mX79u0sWLCg4LWxY8eye/duPD09sbOzo127dqxYsaJScVhZWfHVV1/Ru3dvdDodY8eOxdvbm5iYGJ5++ml0Oh25ubkMGzasSguTT11MZcrqaALurc/7j3pX+j53pSiw4QW1jOgTP4N18U/jTF1Z/TLv+w9KuO4g4KNpcJLhWFhAtzehSXv4ZQIs6ArDvge3hw0dWY1T41ZjGMsMF0Dz5s159dVXWbx4MbVsbNWFh6nnITOtXDNczs7ONGvWjFOnTtGmTRt27tyJl9edpX/VxY4zZswAYM+ePcyaNYsVK1Ywf/58fvvtN4IGB2k2y6UoCiEhIXh6evLKK69U+L53oygKb/5yjMhzN/hmlD/tXGSFDnOT/ynRnIg5XEy/iLO9M6H+oVX+9Gj37t0Vvsba2rrUGeTy8PDwIDo6utTXN2/+b6x09erVIq/Z2dmxevXqSrd9p759+9K3b9H9dHx9fYmMjNTL/VNuZ/Ps8nDsbaz4epQ/1lYaLcf58zs4tRl6f6IOKCSppmnbVy2Rt/IJ+GGg2hfum2DSmxCZmho3kDaWGa60tDTOnDmDlZVVQckp7BtB+mV1VtqmVbnanTdvHqNGjSIrKwsPD48i+c35M1xNmzYt8dr8Wa6e9/XE1tYW//b+ep/l2r9/P8uXLy/YhALgk08+KfYmXhnf/P4PayOTeKVna/r4yAod5qqfR78qD5yrIiUlhXfffZenn34aJycng8VhKnJzFV5aGUni9dusnHA/92j1KdGlWNj2jlpjt9Nz2rQhSaagcWs1b3rtRNjyOpyPgv5fQC27sq+VqqzGDaSNYYYrJyeHyZMn89FHH7Fq1Sr27NlDUFAQWFiqs9I3k9Qtbm0cyryXn59fiRs7QNEZrnxBQUFqW/w3yxWfEg+UnNpRESXNcj388MNokUa77bhaoePR9k15sbus0CFpp27dusydO9fQYZiM2Tv+ZvepZKYPakegW9mlAysl+zasHgs2jjDoG/VjbkmqyWzrquXx9n4Ke2aoedMjfpR509Wgxg2kwfAzXFZWVixZsgSAKVPu2DSgdiNIuww3L0CjOvLjmRLEnr/JSz9H4etSl8+GygodkmQsth2/yNxdcTwe4MroThqWoNv2DiSfgNFroI78lECSAPWBMmgqOPuqC3Bl3nS1kI/xxsbCAhycITsdMm8aOhqjk5yayfgfwnG0rcXCpwJlhQ5JMhJxl9N4ZVU0vq51mT6ocmstyuXkJjU3+oEX1LQOSZKKys+brt0Avh8Ah7+V9aY1JAfSxqh2A7C0Vmel5V/+Apk5OiauOMrV9EwWPRWoXe6lJEkVkpqhLi60sbLg29EalqC8eR7WT1Jn3B6p/MJPSTJ7jVrBuJ3QujdsfQPWPVfhTd+k8pEDaWMk8malc25DRoqhozEK+RU6jv57nc8f98PHVVbokCRjoCjw2v9Fc/bqLb56wp+m9TRa4JSrU8t85WTC0CVgpdEOiZJkLmwd1bzpoLcgOgyWBMONhLKvkyrEbAbSGu8LUf3sGqhvFKk1Y1a6rN/fgr1n+CUiiZd6tKKfr6zQYerMrr/WUIqikJqZzW/HL/FWX08eaNFQu8YOzIaz+6DPp+psmyRJZbOwgKA3YORKuHYGFgZBfNnldaXyM4uBtK2tLVevXjWvN2chwKEJ5GTA7euGjkZTiqJw9epVbG1LTtXYHnuJ/209ST/fJoQ+It9ATZ1Z9tcaSFEUEi9c5tiFWwz0a8rYh9y0aywxHHZ9rG7/3WG0du1Ikrlq0+e/vOkfBsLhb2rEJF11MIuqHa6uriQmJpKcnGzoUPRLUSDtBiRcV1M9NFq8c+X2FQAy7DLKOFM7tra2uLoWL9Nz8uJNXloZiY9LXWYNbS8rdJgBs+2vRk7f/TwnV+FoQhpbz2azfFxH7fpmxk211J2jC/SfLSsZSVJl5edNr50IW6eq9aYfnS3rTVeRWQyka9WqVbBboNk59S+EjYAB88D/KU2aeGbrMwAsDV5axpnV60paJiHL1N3RFj4ZiJ21rNBhDsy6vxoxffbzW1k5DP76IBdvZvDrCw9r2zc3vQopCfDMFrCrp107klQT2DrC8BWwbxbs/kQtIzn8R6jXzNCRmSyzSO0wa62DwSUAfv9UXWRTQ2Tm6Ji4/ChX0tQKHc51ZYUOSTIGiqLw+uoY/r6UytwRHWjWoLZ2jUWvhGOroOtUaH6/du1IUk1iYQFdX8/Lm46HhV0hfq+hozJZciBt7ISA7u+oMzIRPxg6mmqhKApvr/2L8H+vM+vx9rRvJmehJMlYfLcvno0xF3itdxu6tG6sXUNX/1Fno5s/CF1e064dSaqp2gTD+N3qRnA/DIJDX8u86UqQA2lT4NEN7n0I9n4GWbcMHY3mFu07w+qjiUx+pBWPtm9q6HAkScpzMO4KM7acoK+PM891baFdQzlZsGYcWFjC4IXqn5Ik6V+jljBuh7oY8bc31R0RZb3pCpEDaVMgBHR7G9IuQfhiQ0ejqZ0nLjFjy0n6+jjzkqzQIUlGI/H6LSb9FEGLxnX4VOuFv7s/hvMR6toQmbspSdqydYRhy6HbOxCzChb3ghvnDB2VyZADaVPh9hC06A77v4TMVENHo4lTF1OZHBaJd1NHPn/cDwsLuTpfkoxBRra6q2iOTmHBkwHUsdFwnfqZPXBgDvg/DV4DtWtHkqT/WFhA1ynwxM9w/WxevWmZN10eciBtSrq9A7euwuFvDR2J3l1NyyTk+z+xt7Fi0VOyQockGQtFUXhr7TH+SrrJ7BF+eDSuo11j6Vfgl2fVMl3BM7RrR5KkkrXufUfe9HyZN10GOZA2Ja4B0KYfHJxnVpu0ZOXk8tyKCJJTM1n4VCBN6sqalpJkLH449G/BrqKPeN6jXUOKAutfgNvX1C3Are21a0uSpNI1agnjd+blTb8Fv0yoEeuzKksOpE1Nt7cgMwUOfmXoSPRCURTeWXeMI2ev8elQX/xkhQ5JMhpH4q8xfWMsPTydmNxd4zULRxbB31ug53Rw9tG2LUmS7s7GQc2b7v4OHPs/WNIbrv9r6KiMkhxImxrnduA9WN3eM/2KoaOpssX741kVnsiL3Vsy0M/F0OFIkpTnYkoGz/8YQfMGtfliuMZrFi7+BdvegVa9odOz2rUjSVL5WVhAl/y86X/VvOkzvxs6KqMjB9KmKOhNyLmtLjw0YbtPXuaTzSfo086Zl3u0NnQ4kiTlycxRFxfezsphwZMBONrW0q6xrFuwJkTdtXDQ13IL8DsIIYKFEKeEEHFCiKklvD5GCJEshIjK+xpX6LWnhRCn876ert7IJbPRujdM2A11nGD5IPUTcZk3XcAstgivcRq3Bt8R8Od38MAL4NjE0BFV2N+XUnkxLBLPJo58Pqy9rNAhSUbkgw2xRCXc4JtR/rS6x0Hbxra9Dckn4cm1YN9I27ZMjBDCEpgP9AQSgT+FEBsURYm949SfFUV54Y5rGwDvA4GAAhzNu7bCC2yys7NJTEwkIyOjUj+HVP1sbW1xdXWlVi09PQQ3bKHWm173nNpnL0TDo3PAWsOdTU2EHEibqq6vq1vn7vsc+s0ydDQVci09i5Dv/8S2liWLngqktrX8ayhJxiLsyDnCjpzj+aAW9PHR+CH9xK8QvgQenKyW95TudB8QpyjKGQAhxEpgIHDnQLokvYHtiqJcy7t2OxAMhFU0iMTERBwcHHBzc9O2frikF4qicPXqVRITE3F3d9ffjfPzpvd9Drs+guQTMPxHqH+v/towQTK1w1Q1cIcOT8LRZSZVOD0rJ5eJK45y6WYmi54KoGk9WaFDkoxF5LnrvL/+OF1aN+bVXm20bSwlCTa8CE07QPd3tW3LdLkACYW+T8w7dqchQogYIcRqIUT+DjblvRYhxAQhRLgQIjw5ObnY6xkZGTRs2FAOok2EEIKGDRtq8wmCENDlNXhiFVw/l5c3vUf/7ZgQOZA2ZV2mgLCA3/9n6EjKRVEU3lv/F0fir/HZUF86NK9v6JAkScpzOTWD51ZEcE9dG+aO8MNSy3SrXJ1aUisnC4YsBitr7doyf78Cboqi+ALbge8regNFURYqihKoKEpg48aNSzxHDqJNi+a/r9a9CuVNP1aj86blQNqU1XWBwLEQFQZX4gwdTZmWHDjLyj8TmNSthazQIUlGJFuXyws/RnLjdhYLRgdSr7bGA9v9X8C/+9W0tIYttG3LtCUBhfdId807VkBRlKuKomTmffsdEFDeayWpSvLzptv2V/Om14yrkfWm5UDa1HV+Baxs4PeZho7krnafuszHm2Lp7X0Pr/bU+CNjSZIq5ONNJzhy9hr/G+KLV1NHbRtLOAK7Z0C7odB+pLZtmb4/gVZCCHchhDUwAthQ+AQhROFE9gHAibz//g3oJYSoL4SoD/TKOyZJ+mPjAMN+UNOz/loDi3upW4zXIHIgberqOKl1V4+thkvlWX9S/eIupzL5p0jaODvyxTCN69FKklQha44msuzgWcY97K79J0UZKWqpu7ou0P8LWequDIqi5AAvoA6ATwCrFEU5LoSYJoQYkHfaZCHEcSFENDAZGJN37TVgOupg/E9gWv7CQ0nSq/y86VH/Byl5edP/7DZ0VNVGDqTNwYOT1afCPZ8YOpJirqdnEfJ9ODa1LPnu6UDsbWSFDkkyFn8lpfDW2mM84NGQqX3aatuYosDGV9RFhkOWgG1dbdszE4qibFYUpbWiKC0URfk479h7iqJsyPvvNxVF8VYUpb2iKN0URTlZ6NoliqK0zPtaaqifQd/Wrl3Liy++qNn9dTodHTp0oH///qWeM3bsWJycnGjXrl2R4zdu3GDo0KG0bdsWT09PDh06pFmcRqdVTxi/G+o4w4rBcHBejciblgNpc1C7ATwwSS0ldT7S0NEUyMrJ5bkfj3LhRgYLngzARVbokCSjcS09i2eXH6WhvTVfPdEBK0uN3w6iw+Cv1dDtTWjWUdu2JLMWERGBv7+/ZvefM2cOnp6edz1nzJgxbN26tdjx0NBQgoODOXnyJNHR0WXex+w0bAHjtuflTb9TI/Km5UDaXNz/HNjVh93GMSutKArvbzjO4TPX+N9QHwLulRU6JMlY5OhyeTEsguS0TL59MoCGdWy0bfDqP7DpNXDrDA+/om1bktn6+++/6dmzJ7Nnz+bDDz9k9uzZem8jMTGRTZs2MW7cuLue16VLFxo0aFDkWEpKCnv37iUkJAQAa2tr6tWrp/cYjV5+3vQj79WIvGn5Obu5sK0LD4XCjg/g3B/QvJNBw1l28CxhR87xXFALHuvgatBYJEkq6tPfTnEg7iqfDfXF11XjN/qcLFg9Vi1x99gCsLDUtj1JUx/+epzY8zf1ek+vpo68/6j3Xc/JzMxk2LBhLF++nIEDB3Lw4EG8vLyYOHEitra2ZbbRuXNnUlNTix2fNWsWPXr0KPj+pZde4tNPPy3x3LLEx8fTuHFjnnnmGaKjowkICGDOnDnY29tX+F4mTwjo/Co4+6rrIhYGwdCl0KKboSPTO01npIUQwUKIU0KIOCHE1FLOGSaEiM1bLPGTlvGYvfsmgH1j2P2RQcP4/e9kpm+MpafXPUzRelMHSW9kf60Zfo0+z8K9Z3jy/nt5PLBZ2RdU1a7pcCEKBnylLjKUpErYvn077du3p2nTpjg6OuLs7IytrS06nY533y17Q599+/YRFRVV7KvwIHrjxo04OTkREBBwlzuVLicnh4iICJ577jkiIyOxt7dn5kzjrqilufy8aYcmat70gblmlzet2Yy0EMISmA/0RN1R6U8hxAZFUWILndMKeBN4SFGU60IIJ63iqRGs7dUnwK1T4czv4NG12kOIu5zGCz9F0PoeB2YPlxU6TIXsrzVDWpojr6+OIfDe+rzb30v7BuN2wsG5ar17z9IXbkmmo6yZY61ER0fj4+NDTEwMvr6+XL58GQcHB1JTU8nOziYpKYnRo0czYMAADh8+zM8//1zk+vLMSB84cIANGzawefNmMjIyuHnzJqNHj2bFihXlitHV1RVXV1c6dVI/ER46dKgcSIOaNx2yHdY/D9vfzXuwnqeOWcyAlqkd9wFxiqKcARBCrAQGAoVrtI0H5iuKch1AUZTLGsZTMwQ8oz7x7foI3LtUa3mpG7eyGPf9n9hYWcgKHaZH9lczl51di2N/PYijrRVfj/bH2krjJTJpybB2IjRuC70+1rYtyew5ODgQExODlZUVvr6+vP/++0yaNImoqCj8/PyIjo7miSeeYPz48YwaNarY9fv27SuzjRkzZjBjxgwA9uzZw6xZs8o9iAZwdnamWbNmnDp1ijZt2rBz5068vKrhgdUU2NSBx7+H/V/CzmmQfApG/Aj13QwdWZVp+S+pC5BQ6PvEvGOFtQZaCyEOCCEOCyGCS7qREGKCECJcCBGenJysUbhmopYtdJ0CiUfg9PZqazZbl8vzP0ZwPq9Ch2v92tXWtqQXsr+aMV2uQuyJ+8jMrM03owNwcig7p7RKFEWdfcpIgaFLwFr+eyBVzejRozl9+jTTpk3jm2++oUGDBrz44otFBtKdO3cGtNkeu2/fvpw/f77g+5EjR/LAAw9w6tQpXF1dWbx4MQDz5s1j1KhR+Pr6EhUVxVtvvaX3WEyWEOomcqNWQ0pCXr3pXYaOqsoMPWVoBbQCglC3L90rhPBRFOVG4ZMURVkILAQIDAw0r+QaLfiNhv2z1dzEVj01n5VWFIUPNhzn4D9X+fzx9gTc26DsiyRTJPuriZq942+uXWtC69ZHCbj3Ue0b/ONbOL0N+nwG9xgmFUAyLw0aNOD333/Hz8+PHTt20KhRIwBOnz5N69atiYuLo3Xr1ly5cgVnZ+cqtxcUFERQUFDB95s3by7yelhYWInX+fn5ER4eXuX2zVqrHjBhD6wcBSuGQI8P1P0wTHSDJi1npJOAwitZXPOOFZYIbFAUJVtRlHjgb9Q3aqkqrKwh6E24GKPWltbYD4f+5cc/zvFsVw+GBMgKHSZK9lcz9dvxi8zbFUcT53iaNonXvsELMbD9PWjdB+4br317Uo2RmZlJSkpKwSAaYPHixVhYWBT82ahRI2bNmmXAKKVyaeCh5k17DlD/vVg9FrLSDR1VpWg5kP4TaCWEcBdCWAMjgA13nLMOdXYLIUQj1I+Oz2gYU83hOwwatVbrSufqNGtm3+lkpm2MpYenE6/31nhnNElLsr+aobjLqby6Kpr2rnVp1SpS+wmfrHS11JVdAxg432RnmCTjZGNjQ3x8NTwMStXDpg48vkydkT6+Vq03fc30fr+aDaQVRckBXgB+A04AqxRFOS6EmCaEGJB32m/AVSFELLAbmKIoylWtYqpRLCzVWenkE/DXL5o08U9yGs//GEErpzrMHtEBS1mhw2TJ/mp+UjOymbD8KLa1LPhmdACWlrnaN7r1TbhyGgYvAPuG2rcnSZJpEwIefhlGF8qbjttp6KgqRNMcaUVRNgOb7zj2XqH/VoBX8r4kffMaBPd8Dns+Ae/HwFJ/v261Qkc41pYWLHoqkDqyQofJk/3VfOTmKryyKpp/r97ix3GdaFrPTvtGj6+DiO/hoZfAI0j79iRJMh8t8/OmR8OPQ+GR99VN5kzgUy25Rbg5s7CAbm/DtTMQXfLCiMrI1uUy6acIEq/f4tsnA2jWQK7IlyRjMn93HNtjL/FOP0/u96iGmeEbCfDrZGjqD93f0b49SZLMTwMPGLcdvAbCjvdh9TMmkTctB9Lmrk0f9c3t9/9BTqZebjnt11gOxF3lk8d86OgmK3RIkjHZffIyX+z4m8c6uDDmQTftG8zVwS8T1D+HLgbLWtq3KUmSebK2V7cS7/EhxK6H73oafd60HEibOyHUGaKUBIj4ocq3W37oLMsP/8uELh7Vs72wJEnldvZKOpNXRuLp7Mgnj/loUk+3mL2z4NxB6Pe5OqMkSZJUFULAwy+p9aZvJuXlTe8wdFSlkgPpmqBFd2j+IOz9DLJuVfo2+09f4YNfY3mkrRNvBMsKHZJkTNIzc3h2+VEsLQQLngzAztpS+0bPHYbfZ4LPMGg/Qvv2JEmqOVo+ouZNO7rAj4+ruyIqxrc1gRxI1wT5s9JplyB8caVucSY5jed/PEqLxvbMHuEnK3RIkhFRFIXX18Rw+nIqX430r551C7dvwJpxUK+5OhstSZKkbw3cC+VNfwD/NwYy0wwdVRFyIF1TuD0EHt3UJ7rM1ApdmnIrm3Hfh2NlacHiFWeArAAAIABJREFUpzviYCtzICXJmCzce4ZNMRd4PbgtD7dqVPYFVaUosPElSL0AQxaDraP2bUqSVDMVzps+sSGv3rTxbGEgB9I1Sfd34dZVdfvecsrJq9CRcP0W346WFTokydjsP32F/209ST+fJjzbpZpylCNXqBsodHsLXAOrp01JyrN27VpefPHFamvPzc0NHx8f/Pz8CAws/e/72LFjcXJyol27duU6LlVASXnTp40jb1oOpGsS1wBo0xcOzIPb18t1yfSNseyPu8LHg3y4z11W6JAkY5Jw7RYvhEXQysmBT4f6Vs/iwiunYcvr4NZZrRktaUoIESyEOCWEiBNCTC3h9VeEELFCiBghxE4hxL2FXtMJIaLyvu7cqdRkRURE4O/vX61t7t69m6ioKMLDw0s9Z8yYMWzdurXcx6VKyM+brttMrTe97wuD503LgXRN0+0tyEyBQ/PLPHXF4X/5/tC/jHvYnWEdZYUOSTImGdk6Jq44ii5XYcGTAdhXx6ZIOZmweixY2cLgheoOqpJmhBCWwHygD+AFjBRCeN1xWiQQqCiKL7Aa+LTQa7cVRfHL+xqAifv777/p2bMns2fP5sMPP2T27NmGDqmILl260KBB8Qmn0o5LldTAHUK2qRvN7fzQ4HnT5fqXVwixE/g8b+ez/GMLFUWZoFlkkjacfdS/fIe/gU4Twb7kfMqDcVd4f8NxurVpzJt9Pas5SKkqZH81f4qi8OYvx4i9cJPFTwfi1si+ehreOQ0uxsCIn8CxafW0aQLmzZvH6NGjqV+/vr5vfR8QpyjKGQAhxEpgIBCbf4KiKLsLnX8YGK3vIIrYMhUuHtPvPZ19oM/Mu56SmZnJsGHDWL58OQMHDuTgwYN4eXkxceJEbG1ty2zi+vXrxX4/nTt3JjW1+JqhWbNm0aNHj4LvhRD06tULIQTPPvssEybIf0oNztoehi6Bpn7qIsQrf8PwFdCwxV0ve2fdMcL+SECnKFgKwchOzfhokE+VQinvFIY78IYQoqOiKB/mHZOJcaYq6E210PmB2Wxq+QAxyTFk5WbRa3UvQv1D8XIM4rkfI/BoZM/ckR1khQ7TI/urmVt28CxrI5N4pWdrure9p8zzN53ZVKyf9/PoV7FGT++AQ19Bx3HQtoLXmrlLly7RsWNH/P39GTt2LL1799ZXmo0LkFDo+0Sg013ODwG2FPreVggRDuQAMxVFWaePoAxh+/bttG/fnqZNm+Lo6IizszO2trbodLpyXf/yyy+zbNmyIsf27dtXrmv379+Pi4sLly9fpmfPnrRt25YuXbpU9EeQ9E0IdRtxZx/1k7JF3WDIEmjVo9ip6yKTeHvtMdKz/vv7olMUVhw+B1ClwXR5B9I3gEeAuUKIX/n/9u48Pob7f+D46xORRNxxBXHFfQURFHXUVaVFS1VLq0XR0uql989X9VJard5U61u0aNHSum/qThBn4kpJXCEIEjn38/tjlm+QSCS7O7ub9/PxyCO7s7Mz79nMZN478/m8P/b+xivsq0xtCHqM9G3f8/WpxaSoNABOJ5xmzMbxeJ4CD1VQKnS4Ljle3di2Y3F8sPggneqWY+R9NbKdf/GxxYzdPJYUSwpgHOdjN48FyHkyfTUW/hwOZetBlw9yG7rb+uCDD3j//fdZsWIF06dPZ+TIkfTt25fBgwdTvfqdr5DZilJqAMYX5nYZJlfRWp9USgUCa5RSe7XWRzN571BgKEDlypXvvKJsrhzbS3h4OA0bNmTPnj0EBQURGxtL0aJFUUoxYsQIvL298fX15bHHHmP58uW89tprjBgxgokTJ7JhwwYiIiKYOHEio0ePvrHMnF6RrlixIgBly5bl4YcfZvv27ZJIO5PqHYx203P6G+2mO/4f3PuKkWhjJNFvLdjLtdTMv3T9svWEQxJppbVOA55XSj0N/APY/B6WO3hm2TNmh5AjZdRVPkpP5am4WD4qbbTd0tqDS9GPkJ6QTuPG6/jPjtkmR5kz07tONzsEZyPHqw050zGdlFSI0LCOeHunkFzqDwavSMv2PdevRN+0nPQkxmwaw7xD87J9v9KalyI2UycxjnHVgzi5+vlcx29LznbcK6Xw9/fH398fT09PLl68SJ8+fejcuTMTJkzIfgGZOwlk7KASYJ1267o7Ae8A7bTWydena61PWn8fU0qtA5oAtyXSWuupwFSAkJAQ5xvxAihatCh79uzB09OToKAg/vOf/zBixAi++eYbnn76aZo1a0afPn3YtWsXjRo1AuDq1av4+vpSunRpBgwYwMiRI29aZk6uSCckJGCxWChatCgJCQmsWLGCMWPG2GUbRR6UrGq0m170Aqwex+kjyxjma+HfpHOQVoLUQl0gtUmmb83rDp/TzoY36qVprf8LPA2syOO6hYnO+RThj6JF6HPlKuVTjZNx8tnupCfUwtv/T0qWOG9yhCIP5Hh1QxaLB/v2t8RiKUCDBlvw9Mw+iQZuS6Kzm36rzmeOEBR/ljlVGnLSt3iO481PJk+eTNOmTXn99ddp3bo1e/fu5bvvviMsLIz58+fnZdE7gJpKqWpKKS+gH3BT9Q2lVBNgCtBDax2bYXpJpZS39XFpoDUZ2la7mgEDBnD48GHGjRvHd999h5+fHy+88AL79++nadOmpKSk4Ovry969ewkKCuLy5cs3mtfs2bPnRnJ9t86ePcu9995Lo0aNaN68Od27d6dr164AdOvWjVOnTt2Y9/HHH6dly5ZERkYSEBDAjz/+eMfpwsa8CkPvHznQtD9lj2/nsyPhBKSmkG6BAjeyZdt/T8zRFWmt9ZRbnocBg2wejRtwtqskd9L/Qjt6XtnNsEvxvO35AKkXW1PQ7x+qBMQwvavkXa5KjlfbcoZjWmvNm/P3cuVKNN8PaErXBr1y/N4u87pwOuH0bdPLFy6f/badDoftHaF2d57q9wtPOaK8ngu6cOECCxYsoEqVKjdN9/Dw4O+//871crXWaUqpkcByoADwk9Z6v1JqHBCqtV4ETASKAL9bE8cT1goddYEpSikLxkWz8Vprl02k/fz8WL9+PY0bN2bVqlWULm10lH/00Ud57rnnAHj11VcJDQ3l008/xdPTkzp16gBQunRppk2bRunSpalb9+46zwcGBhIeHp7pa0uWLLnp+ezZmd/FzWq6sL1nlg9iz6U9BPuXZWLseWbHxDEq9QnWWRrgV3I1axN+pZi6xkldmglpfVlkuZfCXnmrPuSAeknCWT1xz+vMP/s8j8Zf4qvkFkQXjqR4+TWMCpbbVkI4k1+3n2BuaDQj76tB1wb+d/XeUcGjGLt5LEnpSTem+RTwYVTwqDu/MSUB5g2GwmWg59c32huK27333ntZvna3idutrNV3ltwybUyGx7f3rDKmbwbyVo7AySQnJxMfH38jiQbo3r073bv/r61/Zleee/ToQY8eLl/9T+RQcpqF9YldeDCpAVM9v+Wngp/xS+HS1Em9THEP405cgDrP+ILT8EhXtH94RJ7WJ3Wk87Hugd1R93xEivbiJa85VK25hvdaj7n73vxCCLsJO36RsYv2065WGV7uXOuu3989sDtjW43Fy8MLMK5Ej201NvvjfOkbEHcEHpkCvlIDV5jP29ubqKgos8MQTirdoulWehxJx94gJbYbp30TeLdMCRQwIOE8wSk3N2fzVSl8VHQBvZpUzNN65Yp0PnY5KZVpWwKI15141mMJvTpshbJSM1oIZxF7OYnnZoVRvnghvuyX+1KU3QO73+hYmKOmKvv/gF0zoc2rUE2qEwghnJfWmnWHzvHJ0ggizlyhSpmiXCoyHYtPJG+dPssVwJfMrxz7XjuT5/XLFel8Ki3dwgu/7uLf8wn8U6sMyQU8Ye1HZoclhLBKSbPw/C87uZKUxtSnmlLc10GlKC+dgEWjoGKIUXNeCCGc1N6YePpP28Yz03eQmJLO1080Yd0r3fio8zOUT7Mw2L8sfQIqsLywb+YJb/GAPMcgV6TzqY+WRLD+0Dk+erghKy/+zvJrNeh1cBGc2m2MFCSEMNUHiw8QevwiXz7ehDr+xRyz0vQ0mP8saAv0ngYFpI68EML5RF9IZOLySBaFn8KvsBdjH6rHEy2q4OVppMvdA7vT/WoC/DEcdBaD9hQsBB3z3idMEul8aM72E/y0KYqnW1XliRaVWbkMVpSvSa+4M7D2Q+j/u9khCpGv/R4azYwtxxnaNpAejRw4FPeGiRC9FR6ZBn7VHLdeIYTIgQsJKXy95ggzt/5LAQ/FyPtqMKxdYOaDxwX1NX4vfQOuXTAeKw/jQkHxSkYSfX2ePJBEOp/ZeiyOd//cR9taZXi3+//aQ1/zLGgMtbn6PTixDSrfaRRaIYS97Im5xDt/7qN1jVK8fn9tx634+GbYMAEaPQ5BjzpuvUIIkY2k1HR+2hTFd2uPkpCSRt+QSrzUqRb+xX3u/MagvjZJlu9EEul85ERcIs/NCqNyKV++erwJngVuaTHUYhhs/RbWfgAD/zInSCHysbiryQyfGUaZIt589Xjw7ceovVy7aDTpKFkVuk10zDqFECIb6RbN/J0xTFpxiDOXk+hUtyxvdK1DzXJFzQ7tBkmk84krSakM/nkHFg0/DmxG8UKZ3AbxKmyMT7/8LTi2HgLbOT5QIfKptHQLI3/dRVxCCvOfa4VfYS/HrFhr+GsUXD1jDLHr7TwnKCFE/qS1Zl3kOcYvjSDy7BUaVSrB5H6NaRFYyuzQbiOJdD6QbtG8OHsXUecTmDGoOdVKF8565pBBsPkro610tbYyCIMQDjJ+aQRbjsXx2aONaFDRgUNx75wBBxZCp/egYlPHrVcIITIRHn2Jj5ceZOuxC1Qt5cu3/YN5oIH/jSHfnY2Uv8sHPl5ykLWR5xjboz6tapS+88wFfaDdaIjeBkdWOSZAIfK5hbtPMu2fKAa2rELvpnkvx5Rj5w7BsjchsD20etFx6xUil/7880+UUkRERNz22h9//MELL7yQp+VPnjyZBg0aUL9+fb744oss57t06RJ9+vShTp061K1bly1btuRpvZlZtmwZtWvXpkaNGowfP/7G9EGDBlG2bFkaNGhg83Wa6XhcAiN/3UnPbzZx+OxVxvWsz8pX2tGtYXmnTaJBEmm3N3fHiRsn6AH3VMnZmxoPgBJVYM37xm1fIYTdHDh1mTfm76F5VT/efbCe41aclgzzBxkloB6eAh5yOhC2sfjYYrrM60LQz0F0mdeFxccW22zZs2fPJiQkhNmzZ9/22s6dOwkODs71svft28cPP/zA9u3bCQ8P5++//+bIkSOZzjtq1Ci6du1KREQE4eHheR4K/lbp6emMGDGCpUuXcuDAAWbPns2BAwcAePrpp1m2bJlN12emuKvJjF20n06T1rP6YCwvdKjButHteaplVQo6qp9IHjh/hCLXtlkrdLSpWZr/u5sTtKcXtH8TTodDxN/2C1CIfO5SYgrDZoVSvFBBvu7fxLEnjVVj4cxe6PktFPV33HqFW1t8bDFjN4/ldMJpNJrTCacZu3msTZLpq1evsm7dOqZNm3ZTIn3o0CE6d+7MF198wXvvvXfHK8l3cvDgQVq0aIGvry+enp60a9eOBQsW3DZffHw8GzZsYPDgwQB4eXlRokQJAKKioujZsychISE0b96cyMjIXMWyfft2atSoQWBgIF5eXvTr14+FCxcC0LZtW/z8/HK1XGdyLSWdb9Yeod3EdczY8i99mlZi/ej2vNqldubl7JyUJNJuKvpCIs/9spNKJX35Oje9/xv2hVI1Yc2HYMmimLkQItfSLZoX5+zmTHwS3w1oStmi2ZRxsqXDK40KPc2HQe2ujluvcHuTd04mKT3ppmlJ6UlM3jk5z8teuHAhnTp1olGjRhQpUoSwsDCSk5Pp27cvkyZNokyZMmzdupVx48aRlHRzDG3atKFx48a3/axa9b8mjA0aNGDjxo3ExcWRmJjIkiVLiI6Ovi2OqKgoypQpwzPPPEOTJk0YMmQICQkJpKamMmTIECZNmkRoaChjx469qUnG3Th58iSVKlW68TwgIICTJ0/malnOJi3dwtwdJ2j/6VomLo+kZfVSrHi5LR8/0pCyxRz4f9BGpLOhG7peoSMt3cK0gSG5G1q4gCfc9xbMGwT7/4CGfWwfqBD52KSVkWw4dI6PH2lIcOWSjlvxlbPGaF/lGkDncY5br8gXziScuavpd2P27Nk8++yzAPTt25fZs2fTvn17GjVqRIUKFShWrBj+/v74+PiQnn7zBaCNGzdmu/y6devyxhtv0KVLFwoXLkzjxo0pUKDAbfOlpaWxc+dOvvrqK1q0aMGoUaMYP348QUFB7N+/n969e9+Yr02bNje9t1OnTpw5c/tn8eGHH9KzZ88cfxauSGvNmohYxi+N4HDsVZpULsHXTwTTrKprX12XRNrNpFs0o+bs5ui5BH5+pjmBZYrkfmH1Hoayn8Haj6BeLyO5FkLk2bJ9p/lm7VEeb16Jx5tXdth6ldbwxzBISYDePxqdi4WwIf/C/pxOOJ3p9Ly4cOEC27ZtY/78+YCRSLdr146SJUvSsGFD9uzZQ1BQELGxsRQtWpTChW+uTtWmTRuuXLly23I//fRTOnXqdOP54MGDbzTZePvttwkIuL3zb0BAAAEBAbRoYQxc1qdPH8aPH4/Wmg8//PDG+zOT8Qr4nVSsWPGmq+ExMTFUrFgxR+91RrujL/HRkoNsj7pAtdKF+a5/MF2duBLH3ZDMyM18siyCNRGxvN+zPvfWzKZCR3Y8PKDDOzDnCQifDcFP2iZIIfKxw2ev8Opv4TSuVIKxPeo7dN33nz4MJ/bBg59D2ToOXbfIH0YFj2Ls5rE3Ne/wKeDDqOBReVruvHnz6NatG97e3gAEBgZSvnx50tPTiYiIwNPTk6CgIP7zn/8wYsSI296fkyvSALGxsZQtW5YTJ06wYMECtm7dets8/v7+VKpUicjISGrXrs3q1aupV68e5cuXZ/ny5TzzzDN4eHiwd+9eGjRokKtksVmzZhw+fJioqCgqVqzInDlz+PXXX+96OWb793wCE5dHsnjvaUoX8eL9Xg3o16ySS3QizClJpN3I76HRTN1wjCfvqcKTLavaZqG1u0GFJrD+E2OYTU9v2yxXiHzoclIqw2aGUcjLk+8HNMXb8/bbxvZS5epFekfvh7oPQdNnHLZekb90D+wOGG2lzyScwb+wP6OCR92YnluzZ88mPDycqlWr3pgWFxdH06ZNOXz4MAsWLKBkyZL069cvTyXwevfuTVxcHAULFuSbb7650YkQoFu3bkybNo0KFSrw1Vdf0b9/f1JSUggMDGT69On4+Piwdu1a6tatS6FChWjQoAGzZs3KVRyenp58/fXX3H///aSnpzNo0CDq1ze+eD/++OOsW7eO8+fPExAQwHvvvXfHq+BmOH81ma9WH+aXbScoWMCDUR1r8mzbQIp4u1/aadctUkp1BSYDBYBpWutMW90rpXoD84BmWutQe8bkrnb8e4G3/9hL6xqlGPOQDUtoKQUd3oVZvY2BG5o/a7tlC6cix6t9WSyaV+bu5sSFRH599h78izuwWUXyVYYf2cHlgj74PfSlDLQk7Kp7YPc8J863Wrt27R1fv95xsHTpvN2JvdOV6yVLlty0vtDQ2//9zZs3L0/rz6hbt25069bttumZlf5zFokpafy4MYopG45xLTWdx5pV4qWONV2yE2FO2S2RVkoVAL4BOgMxwA6l1CKt9YFb5isKjAK22SsWdxd9IZFhM8MIKOnLt080tf0tk+odoXJL2PApNBlg1J0VbkWOV/v7as0RVh2MZexD9WhezcGda5a+Ttmkq0yo14Y3fV27Y48Qt0pOTiY+Pj7PSbTIvbR0C7+HxfD5ykPEXkmmS71yvN61DjXK5qGflouwZyOV5sARrfUxrXUKMAfIrEvq+8AnQFImr4lsXE1OY8jPoXmr0JEdpaDD/8HVM7DjR9svXzgDOV7taPXBs3y+6hCPBFdkYKuqjl353nmw+xf+rlibyGJlHLtuIRzA29ubqKgos8PIl7TWrDxwlq6TN/LWgr0ElCzEvOEtmfpUSL5IosG+iXRFIGMBxhjrtBuUUsFAJa31HSu1K6WGKqVClVKh586ds32kLirdonlpzi6OnLvKN/2DqZ6XCh3ZqdoaAu+DfyZB8lX7rUeYRY5XO4k6n8BLc3fToGIxPnq4oWN7qV/8F/5+GQKaszDAtiOvCcdQSnVVSkUqpY4opd7M5HVvpdRc6+vblFJVM7z2lnV6pFLqfkfGLdzfrhMXeWzKVp6dEYrFovl+QFPmP9eKEBcvZ3e3TOs2qZTyACYBr2Y3r9Z6qtY6RGsdUqaMXFG5bsLyCFYdjGXMg/VoU9MBn0uHdyExDrZ9b/91Cacix2vuXE1OY+iMUDw9FN8PaIpPQcd1LiQ9DeZb+zT0noZFuU8v+fwiQ5OrB4B6wONKqVs7wQwGLmqtawCfY9wxwjpfP6A+0BX41ro8IfIk6nwCz/8SxsPfbubY+QQ+6NWA5S+3dZtydnfLnp0NTwKVMjwPsE67rijQAFhn/eD9gUVKqR7SgSl788JimLL+GP1bVOapllUcs9KAEKj1AGz+EpoNgUIlsn+PcBVyvNqY1prX54Vz9NxVZg1uQUBJX8cGsH48xGw36kWXdND/CGFrN5pcASilrje5yth3oScw1vp4HvC1Mg7SnsAcrXUyEKWUOmJd3pbcBKK1zpdJkqvSWtt8meevJvPl6sP8uu0EXp4evNSpJs+2CaSwG1biuBv2vESxA6iplKqmlPLC+Ga86PqLWut4rXVprXVVrXVVYCsgJ+UcCDt+gbcX7KVV9VKM7VHfsf/c7nsbkuJhy9eOW6dwBDlebez79cdYsvcMbz5Qh1Y1HNwJ6t9/jM7BjfvLqKSuLdsmVxnn0VqnAfFAqRy+F8i+OZaPjw9xcXF2Sc6E7WmtiYuLw8fHNpUyEpLTmLzqMO0mrOWXbSfo17wS60ffx0udauX7JBrseEVaa52mlBoJLMcop/WT1nq/UmocEKq1XnTnJYjMxFxMZOiMMCqU8OHb/sGOL2pePsgY5XDrd9BiOBSWXtLuQI5X29pw6BwTl0fwYFB5nm0T6NiVJ16ABUPBLxAemODYdQuXpLWeCkwFCAkJuS1bDggIICYmBunz4Dp8fHwyHZXxbqSlW5gbGs0Xqw5z7koyXev7M7prbfv2x3JBdv0qobVeAiy5ZdqYLOZtb89Y3EGCtUJHSrqFaQObUcLXy5xA7nsbDi6CTV9Alw/MiUHYnByvthF9IZEXZu+iVrmiTOgT5Ng7RlrDohfgaiwMWQnecsJzcdk1uco4T4xSyhMoDsTl8L05UrBgQapVq5abtwoXpLVmxYGzTFgWwdFzCYRUKcn3A4JpWiV/dSLMKel94iIsFs1Lc3dz6OwVvnki2NyyMmVqQ8O+sP0HuHLGvDiEcDLXUtIZOjMMrTVTnmyKr5eDb3uGTYeIv6HjGGNEUuHq7tjkymoRMND6uA+wRhttMBYB/axVPaoBNYHtDopbuKiw4xd59PstDJsZhgamPtmU34e3lCT6DqRxi4uYuCKSlQfO8p+H6tG2lhNUQmj/BuybBxs/g24TzY5GCNNprXlrwR4izlzmp6ebUaVUYccGEBsBy96G6h2g5UjHrlvYRQ6bXP0IzLR2JryAkWxjne83jI6JacAIrXW6KRsinN6xc1eZsCySZfvPUKaoNx893JC+IQF4Orr5qAuSRNoFLNgZw3frjvJ488o87ejBHLLiF2iMchg6HVq9ACUqmx2REKb6adO//Ln7FK91qcV9tcs6duWpSTBvEHgVhl7fg4ec/NxFdk2utNZJwKNZvPdD4EO7Bihc2rkryUxefYjZ26Px8fTglc61GNKmmuPvprkw+aScXNjxi7w5fy/3BPoxrqeDK3Rkp+1o2P0rbJgIPb4yOxohTLPlaBwfLTlIl3rleL59DccHsHIMxO6HJ36HouUcv34hhEtJSE7jh43HmLrhGClpFvq3qMyLHWtSuoi32aG5HEmkndjJS9cYNjOU8iV8+K5/U8dX6MhO8QAIGWS0lW79EpSqbnZEQjjcqUvXGPnrTqqW8uWzvo3w8HDwl93IZbB9CrR4Dmp1cey6hRAuJTXdwtwdRiWO81eT6dbQn9H316FaaQc3RXMjkkg7qesVOpJTLcwZGkLJwiZV6MjOva9A2M+w/hN4ZKrZ0QjhUEmp6QyfFUZymoWpT4VQ1KegYwO4cgYWPg/lGkLn9xy7biGEy9Bas3y/UYnj2PkEmlf1Y+pTTQmuXNLs0FyeJNJOyGLRvDx3N5FnLvPj082oUbao2SFlrWg5aDEUNn0J974MZeuaHZEQDqG1ZszCfeyJiWfqk00dX1vVYoE/hkFKIvT5ETzllqwQ4nah/17g46URhB2/SI2yRfjhqRA61S3rXE1FXZgk0k7os5WRrDhwlne713V8p6XcaP0S7PgJ1n4Ej800OxohHOKXbSf4LTSGFzvUoEt9f8cHsPlLOLYOHppslKQUQogMjsReZcKyCFYcOEvZot58/EhDHm0qlThsTRJpJ/PnrpN8s/Yoj4VUYvC9LlIA39cPWj5vNO84tRsqNDY7IiHsKuz4Bd77az/31S7DS51qOT6Ak2Gw5n2o2wOCB2Y/vxAi34i9ksQXqw4zd0c0hQoW4LUutRh0r1TisBf5VJ3IrhMXeX3+HppX8+P9Xg1c67bLPc/DtinGVen+v5kdjRB2c/ZyEsNn7aRCiUJ88VgTx3cuTL4C8wZDEX/o8SW40v8JIYTdXE1OY+qGY0zbaFTiePKeKrzQoQalpBKHXUki7SROXbrGszPCKFfMm+8HNMXL08VuvRQqAa1fhNXjIHo7VGpudkRC2FxKmoXnf9lJQnIaswa3oLivgzsXAiwZDZeOw9OLoZB0FBIiv0tNtzBn+wkmrz7M+aspdA8qz+gutakqlTglakY5AAAgAElEQVQcQhJpJ5CYYlToSEpN59dnW+DnrBU6stN8GGz9DtZ8AANvHcVWCNc37u/9hB2/yNdPNKG2vwmdgPf8DuGzod2bUKWV49cvhHAaWmuW7TvDhOWRRJ1PoEU1P6YNrEvjSiXMDi1fkUTaZBaL5pW54Rw8c5kfB4ZQq5wTV+jIjncRoxze8rcgagNUa2t2RELYzG87opm19QTD2gXyYFAFxwdwIQr+fhkq3WMMhiSEyLe2R13g46UH2XXiEjXLFuHHgSF0qCOVOMwgibTJPl91iGX7z/BOt7p0qOMGI5KFDILNX8GaD2FQG2m/KdxCePQl3v1zH/fWKM3oLiZUyEhPhflDQHlA7x+ggPzrFiI/OhJ7hfFLI1l18CzlinnzSe+G9A6WShxmkv/GJlq4+yRfrTnCo00DGNLGRSp0ZKegD7R9DRa/AkdWQ81OZkckRJ6cv5rM8FlhlCnqzVePNzHnhLXuYzgZCo/+F0pUdvz6hRCmOns5iS9WHWLujmh8vTwZfX9tBrWuRiGvAmaHlu9JIm2S3dGXGD1vD82qluSDh12sQkd2mjwJm74wynPV6ChXpYXLSk23MOKXnVxISGH+c63MGWE0agNsnGQcV/Ufdvz6hRCmuZKUaq3EEUWaxcJTLatKJQ4nI4m0CU7HX+PZGaGULWpU6PD2dLNvlJ5eRmeohc9DxN9Q9yGzIxIiVz5eEsG2qAt8/lgjGlQs7vgAEi/AgmFQqgY88Inj1y+EMEVKmoXZ20/w5erDxCWk8GBQeUbfX5sqpaQSh7ORRNrBElPSeHZGKInJacwa3Np9v1UGPQb/TDLaStfuBh5u9mVBuL2Fu0/y06Yonm5VlYebBDg+AK1h4UhIPA9PzAEvOYEK4e601izZe4aJyyP4Ny6RewL9+OmBujSSShxOSxJpB7JYNK/9Hs7+U5eZ9lSIOeWzHKWAJ7R/C+YPhv1/QMM+ZkckRI7tPxXPG9bBkd7pXtecIEJ/hMjFcP9HUL6ROTEIIRxm27E4Pl4awe7oS9QuV5TpTzejfe0y7tX00w1JIu1AX6w+zJK9Z3jrgTp0rOsGFTqyU/8R2PiZMdphvV5SaUC4hIsJKQybGUaJQl5880QwBc3oXHj2ACx/B2p0ghbPOX79QgiHOXz2Cp8si2DVwVj8i/kwoU8QvYMDKODoUVNFrkhm4yB/hZ/iy9WH6R0cwNC2gWaH4xgeHnDfOzC3P+yZA00GmB2REHeUbtG8OGcXsZeT+W14S8oUNaHpVeo1406OdzHo9Z1xHAkh3M7Zy0l8vvIQv4VGU9jLk9e71uaZVlKJw9VIIu0A4dGXeO33cEKqlOSjR9ysQkd26nSHCk1g3SfQsK/REVEIJ/Xpikg2Hj7PJ70bmjc62Ir/g9gDMGA+FClrTgxCCLu5nJTKlPVH+fGfKNItmqdbVWNkhxquO6pxPieXOuzsTHwSz84IpXQRb75/0g0rdGRHKejwLsSfgF0zzI5GiCwt2Xua79Yd5YkWlXmsmUm1miOWwI4foOVIo1mHyLeUUn5KqZVKqcPW3yUzmaexUmqLUmq/UmqPUuqxDK/9VykVpZTabf1p7NgtELdKSbPw301RtJ+4jm/WHqVLPX9Wv9KeMQ/VkyTahckVaTu6lpLOszNCuZqcxvznWlHaXSt0ZKd6R6jcEjZ8Co37Q8FCZkckxE0Onb3Ca7+H06RyCf7zUD1zgrh8ChaOAP8g6DjGnBiEM3kTWK21Hq+UetP6/I1b5kkEntJaH1ZKVQDClFLLtdaXrK+P1lrPc2DMIhNaaxbvPc3E5ZEcj0ukVfVSvPVAXRoGmFBSU9icXJG2E601r80LZ9+peCb3a0Ld8sXMDsk8169KXzkNoT+ZHY0QN4m/lsqwmWEU9vY0r667JR3+GAZpSdDnJ/DMp1+6RUY9gZ+tj38Get06g9b6kNb6sPXxKSAWKOOwCEW2thyNo9c3mxj56y4KFSzAf59pxi9DWkgS7UbkirSdTF59mMV7TvNG1zp0rpcPKnRkp+q9ENjeGKEteCB4FzE7IiGwWDSvzN1N9IVEZg+9h3LFfMwJZNNkYwTDHl9D6ZrmxCCcTTmt9Wnr4zPAHU8kSqnmgBdwNMPkD5VSY4DVwJta62S7RCpuE3nGqMSxJiKW8sV9+PTRRjzcpKJU4nBDkkjbweI9p/li1WEeCa7I8Hb5pEJHTtz3LvzYCbZ9D21fMzsaIZi8+jCrI2IZ17M+zar6mRNETCis+cAY/lsq2+QrSqlVgH8mL72T8YnWWiul9B2WUx6YCQzUWlusk9/CSMC9gKkYzULGZfH+ocBQgMqVTeof4CZOx1/j85WHmBcWQ2FvT958oA5Pt6qKT8F81j8qH5FE2sb2xsTz6u+7Ca5cgo8ebpi/KnRkp1IzqNUVNn8JzYZAIRmpSZhn5YGzTF59mD5NA3jynirmBJF0GeYNgmIV4cEvjGZQIt/QWmfZo1QpdVYpVV5rfdqaKMdmMV8xYDHwjtZ6a4ZlX7+anayUmg5kefVCaz0VI9kmJCQky4RdZO1yUirfrzMqcWgNg1pXY8R9NSgpnQjdnrSRtqGzl5MYMmMHpQp7M+XJEPkGmpn73oakeNjyjdmRiHzs6LmrvDJ3Nw0rFueDXiaWpFz8KsRHQ+8f5IuluNUiYKD18UBg4a0zKKW8gD+AGbd2KrQm3yhj5+4F7LNrtPlUclo6P/0TRbsJa/l23VEeaODP6lfb8e6D9SSJzifkirSNJKWmM3RGKFeS0pg3vJU5Azm4gvKNoF5P2PottBgOhUuZHZHIZ64mpzFsZhgFPT34/smm5n3hDZ8De3+D9m9D5XvMiUE4s/HAb0qpwcBxoC+AUioEGK61HmKd1hYopZR62vq+p7XWu4FflFJlAAXsBoY7OH63ZrFo/t57monLI4i+cI3WNYxKHA0qSifC/EYSaRvQWjN63h7CY+KZ8mRT6lXIxxU6cqL923BgEWz6Arq8b3Y0Ih/RWvPab+FEnU9g5uDmVCxhUinGuKPG1ejKraS/gMiU1joO6JjJ9FBgiPXxLGBWFu/vYNcA87HNR8/z8ZII9p6Mp275Yvw8qCFta5aWppz5lCTSNvDVmiP8FX6K0ffX5v76mfUbETcpWweC+sL2H6DlCCgqn5lwjG/XHWXZ/jO8270uraqXNieItBSYPwQ8CsAjU43fQginF3HmMuOXRrAu8hwVivswqW8jejWuiIdU4sjXJJHOo6V7TzNp5SEeblKR59tXNzsc19H+Tdg7zyiH122C2dGIfGBdZCyfroikR6MKDL63mnmBrP0QTu2EvjOgRCXz4hBC5MipS9eYtPIQ83fGUNTbk7e71eGpllKJQxjsmkgrpboCk4ECwDSt9fhbXn8F4xZVGnAOGKS1Pm7PmGxp38l4Xv5tN00ql+DjR6RCx13xCzRKfYVNB79qRufD+BgoHmCM6hbU1+wI8x13Pl5PxCUyas5uapcryie9gxx3rO75DVaP+9++3fBRo2Z08ECjr4AQwmnFX0vlu3VHmb7JqMTxbJtAnm9fnRK+0olQ/I/dEmmlVAHgG6AzEAPsUEot0lofyDDbLiBEa52olHoOmAA8Zq+YbCn2chJDfg7Fz9eLKWZ2WHJlbUfD7l9g+Tug041p8dHw14vGY0mmHcadj9fElDSGzgwFYOqTIRTyctCxuuc3Y19OvWY8j4+GfyYZTZm6fuyYGIQQdy05LZ2ZW47z9dojxF9LpVfjirzSuRaV/HzNDk04IXtekW4OHNFaHwNQSs3BGPL0xolZa702w/xbAZcYjSApNZ1nZ4YRfy2Vec+1pGxRk0ZDcxbTu+f+vVr/L4m+LvUaLBwJYT9n/p7sPLM49/HkX255vGqteXP+XiLPXuG/zzSncikHnAivHw8xOyA9k4HkEuLgF+uXRNlXhXAaFovmrz2nmLg8kpiL12hTszRvdK0jlTjEHdkzka4IRGd4HgO0uMP8g4Glmb3gTKMuaa15fd4ewqMv8f2AYOpXkAMsT25Noq/LLAER9uSWx+uP/0SxyNoRuF2tMo5deVb7sCXVsXEIIbK16ch5Pl56kH0nL1OvfDFmDm5Im5oO/p8hXJJTdDZUSg0AQoB2mb3uTKMufbP2CIvCT/Fal1p0bVDezFCcR16uqn3ewLjlfavileRqnZNyleN189HzfLw0gq71/R3bEfj6fiv7thBO7+BpoxLH+kPnqFiiEJ8/1oiejaQSh8g5eybSJ4GMXdIDrNNuopTqBLwDtNNaO/VlyGX7TvPpikP0bFyBEffVMDsc99BxDCx6AdKS/jetYCFjunAktzpeT166xshfd1GtdGE+7dvInI7Asm8L4bROXrrGpBWHWLArhmI+BXmnW12ebFlF+juJu2bPRHoHUFMpVQ3jhNwPeCLjDEqpJsAUoKvWOtaOseTZvpPxvDw3nEaVSji2178DTO863byVX+9QuPhVSL4MRcpBlw+ko6Hjuc3xmpSazvCZYaSmWZj6ZFOKeJt04y2oL+yaBVHrjefFK5lakcbU41wIJxGfmMq3648wfdO/AAxtE8jz7WtQ3LeguYEJl2W3M4zWOk0pNRJYjlFO6yet9X6l1DggVGu9CJgIFAF+tyamJ7TWPewVU27FXkni2RmhlPAtyA9SocP2gvpCzS4wOQgqNJEk2gTucrxqrXn3z33sPRnPD0+FEFimiHnBHPzLSKJbvWB8ORRCmCYp9X+VOC4npfJwk4q82qW2eaObCrdh10s1WuslwJJbpo3J8LiTPddvC0mp6QydEcbFxBTmDW9F2WL5vEKHvRQqAa1HGTV3o3dApWZmR5TvuMPxOmvrceaFxTCqY0061ytnXiDxJ41mHeUbQwdpyiGEWSwWzcLwk3y6/BAnL12jba0yvNm1DvUqFDM7NOEmnKKzobMySmftYXf0Jb7rHywlcOyt+TDY8i2s/QCeWmh2NMLF7Pj3Au/9dYCOdcoyqmNN8wKxpMOCocZQ4H1+Ak8ZvEEIM2w8fI7xSyPYf+oy9SsU45PeQdxbs7TZYQk3I4n0HXy77ih/7j7FK51r8UBDqdBx124d1S279qHeRaDNK7D8bYjaCNXaOC5W4dLOXk7i+V92UsnPl0mPNTavx/2e32Dxa5AcD4X84GQYlHJgxRAhBPtPxTN+aQQbD58noGQhJvdrzENBFaQSh7ALSaSzsHz/GSYuj+ShRhV4oYNU6LhrmY3qlpMRC0MGweavYM0HMGgZuFGnTmEfyWnpPDcrjITkNH4Z0oLihUzqNLTnN1g4AtJTjOfXLsgonUI4UMzFRCatOMQfu09SvFBB3u1uVOLw9pR+TcJ+JJHOxIFTl3l57m4aBRRnYh/3qtBxV/IyYmFmo7rldMTCgr4QvRW+aw2FSv5vutTeFZl4768D7DxxiW/7B1OrXFHHB3D9OIneDpaUm1+7dZ+XfVgIm4tPTOWbdUf47+Z/UcCwttV5rn11875Ui3xFEulbnLuSzJCfd1DMpyBTnwqRCh25ldWobjkZsbBIOaM5yKXj4FNCrkqLLM3ZfoJft51geLvqdDOz+ZXWtyfR18konULYRVJqOjO2/MvXa45wJTmN3sEBvNK5FhWkEodwIEmkM0hKTWfYzFAuJKbw+7BWlMvvFTrMHLFw1yzjNnnL56Hug7mPQ7itXScuMmbhftrULM3o+2ubF8gzi2H3r3BiU+avy0iGQtiUxaL5c/dJPlthVOJoX7sMb3StQ93yUolDOJ6H2QE4C601by/Yy84Tl/js0cY0DJAKHXnScYwxiltGdzOqW1A/8KsOaz8Ei8X28QmXdu5KMs/N2knZYt582a8JBczsRBR31OhgWLoWeN7y5VtGMhTCpjYcOkf3r/7hld/C8Svsxa9DWvDfZ5pLEi1MI4m01ffrj7Fg10le6lST7kFSoSPPgvrCQ18aV+NQxu+Hvsx5p6sCnnDf2xB7APYvsGuowrWkplsY8etOLl1LYcqTTSlZ2MTycmkpMG+QUeLuyT+hx1e53+eFEFnadzKeAdO28dRP27manMrkfo1ZOKI1rWpIOTthLmnaAaw8cJYJyyPoHlTe3Pqz7iaob96SiPqPwMbPYN3HUK+XkVyLfO/DxQfZHnWByf0aU7+CyXeO1rwPp3fDY79A8Yp53+eFEDeJvpDIZysi+XP3KUr6FmTMg/Xof09lqcQhnEa+z0wOnr7MqDm7aFixOJ/2aZR/K3Q4Iw8PuO8dmNsf9syFJv3NjkiY7I9dMfx3878Mal2Nno0rmhvM0TWw+UujZKO04xc2ppTyA+YCVYF/gb5a64uZzJcO7LU+PaG17mGdXg2YA5QCwoAntdZZ9Ih1PpcSU/h6zRFmbDmOUvB8++oMb1+dYj5SiUM4l3zdtOP81WSG/BxKEW9Ppj4ZQiEv+YbrdOp0N4ZZXj/euI0u8q19J+N5c/5e7gn0461udcwNJuE8/DEcytSBLh+aG4twV28Cq7XWNYHV1ueZuaa1bmz96ZFh+ifA51rrGsBFYLB9w7WNpNR0vl9/lLYT1vLTpih6NanAutHteb1rHUmihVPKt4l0clo6w2eGcf5qMj88FYJ/8XxeocNZKQUd/g8unYBdM8yORpjkQkIKw2aG4VfYi6+fCKZgARP/dWkNfz4P1y4ZQ4B7+ZoXi3BnPYHrRfd/Bnrl9I3KuLXaAZiXm/ebId2imRcWQ4dP1zF+aQQhVf1YOqotE/o0onxxKWcnnFe+bNphVOjYR+jxi3z1eBMaVSphdkjiTmp0hEr3wIZPoXH/26uB5MXdDmMuHC4t3cKLs3dx7moyvw9rSeki3uYGtG0KHF5u1Dj/rrXsN8JeymmtT1sfnwHKZTGfj1IqFEgDxmut/8RoznFJa51mnScGyLItlFJqKDAUoHLlyraIPce01qw/dI7xSyOIOHOFRgHF+axvY1pWL+XQOITIrXyZSE/dcIz5O2N4sWNNHmpUwexwRHaUgg7vws8PQuhP0HKEbZab22HMhUNNXBHJP0fOM6FPkPlfes/shRXvgPKApEvGNNlvRC4ppVYB/pm89E7GJ1prrZTSWSymitb6pFIqEFijlNoLxN9NHFrrqcBUgJCQkKzWY3N7Y+L5eOlBNh+No7KfL18/0YTuDctLXyXhUvJdIr3qwFnGL4ugW0N/XpIKHa6jWhuo1g42ToLggeBdxJhu1jDmWZGBN2zq7z2nmLL+GAPuqUzfkErmBTK9O1jSjQodlrTbX8+438g+IHJIa90pq9eUUmeVUuW11qeVUuWB2CyWcdL6+5hSah3QBJgPlFBKeVqvSgcAJ22+AbkUfSGRicsjWRR+Cr/CXvznoXr0b1EFL89829pUuLB8tddGnDEqdNSvUIzPHm2Mh5mDOIi71+FdSDwP26fYZnl5GcZc2F3kmSu8Pm8PTauUZMyD9c0OBy5GQdq1rF+X/UbY1iJgoPXxQGDhrTMopUoqpbytj0sDrYEDWmsNrAX63On9jnYxIYVxfx2gw2frWHHgDCPuq8660e15pnU1SaKFy8o3V6TjrBU6Cnt78sNTUqHDJVVqDjXvh02TIWQwFCph7jDmwm7iE1MZOtOoqPNd/2DzT7IthsFvT0Lrl2DffNlvhCOMB35TSg0GjgN9AZRSIcBwrfUQoC4wRSllwbgwNl5rfcD6/jeAOUqpD4BdwI+O3oDrklLT+WlTFN+tO0pCchqPNq3Ey51rSSd/4RbyRSKdnJbO8FlhnLuSzNxhLaUHsCu7722Y2g62fms8zouOY25uIw0ypLMTsFg0L83dxalL15gz9B7KFjP5ZBsfA4tegArBxl2RcvVlvxF2p7WOAzpmMj0UGGJ9vBlomMX7jwHN7RljdtItmvk7Y/h85SFOxyfRqW5ZXu9ah1rlipoZlhA25faJtNaad//Yx45/LzK5X2Mam91ZSeRNhcZQtwds+RaaD4PCeejZfb1jmFTtcCpfrDrE2shzvN+rAU2r+JkbjCUdFgw12kX3+REKFJT9RohsaK1ZF2lU4og8e4VGlUrwxWONaREolTiE+3H7RHraxih+D4vhhQ41zB8JTdjGfe/Awb9g82ToPC5vy5IhnZ3Kiv1n+HLNEfqGBDCghWPLcGVq42dwfBM8PAX8Av83XfYbITK1J+YSHy+JYMuxOKqW8uWbJ4Lp1tBfKnEIt+XWifSaiLN8tPQgDzTw5+VOtcwOR9hK2TpGErNtKtzzPBTNrHqUcDVHYq/yym/hNAoozrieDcw/8Z7YBuvGQ8O+0KifubEI4eROxCUycUUkf4WfolRhL97rUZ/Hm1c2v3+DEHbmton0obNXeHH2buqVL8ZnfRtJhQ530+4N2DvPKIfXbYLZ0Yg8upKUyrCZoXh7evDdgKb4FDS5M/C1SzB/CJSoBN0/MzcWIZzYhYQUvlx9mF+2HcfTw4MXOtRgaNtAispw3iKfcMtE+kJCCoN/3oFPwQL88FQIvl5uuZnuKyejDZaqDk36Q9h0aPWCkfAIl2SxaF79LZx/4xKZNbgFFUo4uDPwrftbh/+DQ0vhyikYtBx8ijk2HiFcwLUUoxLH9+uOkpCSxmPNKvFSp1qUM7tzsBAO5nYZZkqaheGzwjh7OZm5Q+9x/ElZ5M3djDbY9nUInwMbJkKPLx0bp7CZb9cdYcWBs/zfg/UcPyxwZvvbohGQnmp8gQsIcWw8Qji5dItmXlg0k1Ye4uzlZDrVLccbXWtTUypxiHzKrRJprTX/9+c+tkddYHK/xjSpXNLskPInR4426FsKdv4MZ/YZJcgyI7V9ndbayFg+W3mIXo0rMKh1Vcet+Po+mtn+lp4KKDi8Go6skf1HCKuYi4kM+u8ODp29SuNKJfjq8WCaVzO5so4QJnOrRBqgetnCUqHDld3taIPFKhmJtrbYLyZhN8V8CtK+Vhk+fiTInM6FWY5GqMHszo5COBn/Yj5U9vPl5U616NpAKnEIAW6WSCulGNq2utlhCBltUORQ0yolmf6MCWNGXN+XZH8TIsc8C3gwbWAzs8MQwqlIXRrhXDqOub2JhowaJ+xF9jchhBB5IIm0cC5BfeGhL40rgijj90NfyuAXwj5kfxNCCJEHbtW0Q7gJGTVOOJLsb0IIIXJJrkgLIYQQQgiRC5JICyGEEEIIkQuSSAshhBBCCJELkkgLIYQQQgiRC5JICyGEEEIIkQtKa212DHdFKXUOOJ7Fy6WB8w4MJ68kXvtypXjzEmsVrXUZWwZjK9kcr3fDlf6WOSHb49zsuT1Oe7yCTY/ZWznLPuIMcThDDCBx3CqrOO54zLpcIn0nSqlQrXWI2XHklMRrX64UryvFagZ3+3xke5ybu22PM3CWz9QZ4nCGGCQO28UhTTuEEEIIIYTIBUmkhRBCCCGEyAV3S6Snmh3AXZJ47cuV4nWlWM3gbp+PbI9zc7ftcQbO8pk6QxzOEANIHLfKVRxu1UZaCCGEEEIIR3G3K9JCCCGEEEI4hCTSQgghhBBC5ILLJNJKqa5KqUil1BGl1JuZvO6tlJprfX2bUqqqdXpVpdQ1pdRu68/3zhyv9bUgpdQWpdR+pdRepZSPM8aqlOqf4XPdrZSyKKUa2zPWPMZbUCn1s/UzPaiUesveseYxXi+l1HRrvOFKqfaOiNfRcvD5tFVK7VRKpSml+tzy2gTrcXJQKfWlUko5LvLb5WBbXlFKHVBK7VFKrVZKVcnw2kCl1GHrz0DHRp653G6PUqpxhv9he5RSjzk++tvl5e9jfb2YUipGKfW146J2TUopP6XUSuv+vFIpVfIO89rtc81JHEqpKtb/Mbut++xwE2Kw+zGT07+JUmqZUuqSUupvG68/13mRA2PI8nyTJa210/8ABYCjQCDgBYQD9W6Z53nge+vjfsBc6+OqwD4XitcT2AM0sj4vBRRwxlhvmachcNTJP9sngDnWx77Av0BVJ453BDDd+rgsEAZ4OHJfdpK/Z1UgCJgB9MkwvRWwybqMAsAWoL2Tb8t9gK/18XMZ/tZ+wDHr75LWxyVd4G+T1fbUAmpaH1cATgMlXHV7Mrw+GfgV+NrMbXGFH2AC8Kb18ZvAJ3eY126fa07isO4P3tbHRaznhgoOjsHux0xO/yZAR+Ah4G8brtsmuYYDYqhKJuebO/24yhXp5sARrfUxrXUKMAfoecs8PYGfrY/nAR1NvDqVl3i7AHu01uEAWus4rXW6k8aa0ePW99pbXuLVQGGllCdQCEgBLjtxvPWANQBa61jgEmB60Xoby/bz0Vr/q7XeA1huea8GfLCeBIGCwFn7h5ylnGzLWq11ovXpViDA+vh+YKXW+oLW+iKwEujqoLizkuvt0Vof0loftj4+BcQCZo/ml5e/D0qppkA5YIWD4nV1Gf+v/Qz0ymwmB3yu2cahtU7RWidbn3pj+7v1OYnBEcdMjv4mWuvVwBUbr9sZ8ri8nG+y5CqJdEUgOsPzGOu0TOfRWqcB8RhXcwGqKaV2KaXWK6Xa2DtY8hZvLUArpZZbby+87sSxZvQYMNtOMWYai9XdxDsPSMD4pn8C+FRrfcGJ4w0HeiilPJVS1YCmQCU7x+toOfl8MqW13gKsxfh7ngaWa60P2jzCnLvbbRkMLM3lex0hL9tzg1KqOcaXnaM2je7u5Xp7lFIewGfAa3aLzv2U01qftj4+g5Es38RBn2u2cVhjqaSU2oOxj3xiTWYdGkOGWOx1zNxVHDZmq1zD3jHcNc+8LsAFnAYqa63jrN98/1RK1dda2/tKZG55AvcCzYBEYLVSKsz6DdEpKaVaAIla631mx5KN5kA6xm2zksBGpdQqrfUxc8PK0k9AXSAUOA5sxohfAEqpGhifz/WrhiuVUm201htNDCtHlFIDMO4utDM7FlvIanuUUuWBmcBArXWOr/CYLZPteR5YorWOMe9Gp/NRSq0C/DN56Z2MT7TWWimVWa1dm3yuNogDrXU0EKSUqoCRJ8zTWuf4DpctYrAuJ0/HjK3iEDnnKo96LI0AAAR8SURBVIn0SW6+EhdgnZbZPDHWW/fFgThtNHpJBtBahymljmJc9Q11xngxviFt0FqfB1BKLQGCAXsl0nmJ9bp+OOZqdMZYrrubeJ8AlmmtU4FYpdQmjJOlPRPpvO67L1+fSSm1GThkx1jNkJPPJysPA1u11lcBlFJLgZaAWYl0jrZFKdUJ46TWLsPt5JNA+1veu84uUeZcXrYHpVQxYDHwjtZ6q51jzYm8bE9LoI1S6nmMNrReSqmrWuvbOivlJ1rrTlm9ppQ6q5Qqr7U+bU0OYzOZzSafqw3iyLisU0qpfUAbjLuYDovBFseMLT8LG7NFruGIGO6aqzTt2AHUVEpVU0p5YSRui26ZZxFwvad7H2CN9RtXGaVUAQClVCBQE/smTnmKF1gONFRK+Vp3pHbAASeN9fqtub44pn10XuM9AXQAUEoVBu4BIpw1Xus+UNgab2cgTWttz33BDDn5fLJyAmhnbfpSEONYMbNpR7bbopRqAkwBeljbvV+3HOiilCqpjJ70XazTzJTr7bHO/wcwQ2ud42TEznK9PVrr/lrrylrrqhjNEGbk9yQ6BzL+XxsILLx1Bgd9rtnGoZQKUEoVsj4uiXFXONLBMTjimMk2DjvKU67hwBjunrZhj0h7/gDdMK7GHcX4tgYwDuMfHhidjn4HjgDbgUDr9N7AfmA3sBN4yJnjtb42wBrzPmCCk8faHuOqoCvsC0Ws0/djfDkZ7eTxVsX4Z34QWAVUceTn7ER/z2YYd2oSMK5O7LdOL4CR9By0/j0nucC2rMLoELnb+rMow3sHWfeBI8AzZm9LXrbH+j8sNcP03UBjV92eW5bxNFK1IyefdSmMO6mHrZ+rn3V6CDDNUZ9rTuIAOmNUywq3/h5qQgx2P2Zy+jfBuKt3Drhm/d97v43Wn+tcw4afQa7ON3f6kSHChRBCCCGEyAVXadohhBBCCCGEU5FEWgghhBBCiFyQRFoIIYQQQohckERaCCGEEEKIXJBEWgghhBBCiFyQRFoIIYQQQohckERaCCGEEEKIXJBEWtwVpVQzpdQepZSPUqqwUmq/UqqB2XEJIW6nlBqnlHopw/MPlVKjzIxJCJE1Oce6HhmQRdw1pdQHGCMQFQJitNYfmxySECITSqmqwAKtdbBSygNjRLPmWus4UwMTQmRJzrGuRRJpcdesY9TvAJKAVlrrdJNDEkJkQSm1EngdKAcM0Vr3MTkkIcQdyDnWtXiaHYBwSaWAIkBBjG/NCeaGI4S4g2nA04A/8JO5oQghckDOsS5ErkiLu6aUWgTMAaoB5bXWI00OSQiRBevVrb0YJ+WacnVLCOcm51jXIlekxV1RSj0FpGqtf1VKFQA2K6U6aK3XmB2bEOJ2WusUpdRa4JIk0UI4NznHuh65Ii2EEG7M2slwJ/Co1vqw2fEIIYQ7kfJ3QgjhppRS9YAjwGpJooUQwvbkirQQQgghhBC5IFekhRBCCCGEyAVJpIUQQgghhMgFSaSFEEIIIYTIBUmkhRBCCCGEyAVJpIUQQgghhMiF/wfRVJU2z5/0eQAAAABJRU5ErkJggg==\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sb.plot_scatter(idx=45, savename='example_scatter.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/requirements.txt b/requirements.txt index 913c98c9..7111389c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,9 +15,9 @@ h5py # Dev requirements pre-commit -black==22.3.0 +black==24.1.1 flake8 -pytest +pytest<8.0.0 mypy pytest-mock flaky diff --git a/tests/lead_beryllium.png b/tests/lead_beryllium.png deleted file mode 100644 index ee8a7824..00000000 Binary files a/tests/lead_beryllium.png and /dev/null differ diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py index fa01be96..d3ae862d 100644 --- a/tests/test_callbacks.py +++ b/tests/test_callbacks.py @@ -256,7 +256,7 @@ def test_float_pred_handler(): @pytest.mark.parametrize("detector", ["none", "panel", "sigmoid_panel"]) -def test_metric_logger(detector, mocker): # noqa F811 +def test_metric_logger(detector, mocker, tmp_path): # noqa F811 vw = MockWrapper() vw.volume = MockVolume() if detector == "none": @@ -279,7 +279,7 @@ def test_metric_logger(detector, mocker): # noqa F811 passive_bs=2, state="train", metric_cbs=[EvalMetric(name="test", main_metric=True, lower_metric_better=True)], - cb_savepath=Path(PKG_DIR), + cb_savepath=tmp_path, ) logger.set_wrapper(vw) mocker.spy(logger, "_reset") @@ -307,7 +307,7 @@ def test_metric_logger(detector, mocker): # noqa F811 assert len(logger.tmp_sub_losses.keys()) == 0 assert logger._snapshot_monitor.call_count == 1 assert len(logger._buffer_files) == 1 - assert logger._buffer_files[-1] == Path(PKG_DIR / "temp_monitor_0.png") + assert logger._buffer_files[-1] == tmp_path / "temp_monitor_0.png" assert logger._buffer_files[-1].exists() for state in ["train", "valid"]: @@ -359,10 +359,8 @@ def test_metric_logger(detector, mocker): # noqa F811 assert history[0]["Validation"] == [val_loss] assert history[1]["test"] == [3] assert len(logger._buffer_files) == 2 - assert logger._buffer_files[-1] == Path(PKG_DIR / "temp_monitor_1.png") - for f in logger._buffer_files: - assert not f.exists() - assert Path(PKG_DIR / "optimisation_history.gif").exists() + assert logger._buffer_files[-1] == tmp_path / "temp_monitor_1.png" + assert (tmp_path / "optimisation_history.gif").exists() logger.loss_vals["Validation"] = [9, 8, 7, 6, 5, 9] logger.metric_vals = [[10, 3, 5, 6, 7, 5]] diff --git a/tests/test_volume.py b/tests/test_volume.py index be03db30..aa79ccea 100644 --- a/tests/test_volume.py +++ b/tests/test_volume.py @@ -349,11 +349,11 @@ def arb_rad_length2(*, z: float, lw: Tensor, size: float) -> float: assert torch.all(cube[0] == arb_rad_length2(z=SZ, lw=LW, size=SZ)) # cube reversed to match lookup_passive_xyz_coords: layer zero = bottom layer assert torch.all(cube[-1] == arb_rad_length2(z=Z, lw=LW, size=SZ)) - edges = volume.edges + edges = volume.xyz_edges assert edges.shape == torch.Size((600, 3)) assert (edges[0] == Tensor([0, 0, 2 * SZ])).all() assert (edges[-1] == Tensor([LW[0] - SZ, LW[1] - SZ, 7 * SZ])).all() - centres = volume.centres + centres = volume.xyz_centres assert centres.shape == torch.Size((600, 3)) assert (centres[0] == Tensor([0, 0, 2 * SZ]) + (SZ / 2)).all() assert (centres[-1] == Tensor([LW[0] - SZ, LW[1] - SZ, 7 * SZ]) + (SZ / 2)).all() diff --git a/tomopt/benchmarks/ladle_furnace/data.py b/tomopt/benchmarks/ladle_furnace/data.py index fb84d453..8cab8a20 100644 --- a/tomopt/benchmarks/ladle_furnace/data.py +++ b/tomopt/benchmarks/ladle_furnace/data.py @@ -1,4 +1,4 @@ -from typing import Tuple +from typing import Optional, Tuple import torch from torch import Tensor @@ -30,9 +30,15 @@ def __init__( self.xy_shp = (self.lw / self.size).astype(int).tolist() self.fill_z_range = ((self.z_range[0]) + self.size, self.z_range[1]) - def _generate(self) -> Tuple[RadLengthFunc, Tensor]: - mat_z = self.size + self.fill_z_range[0] + ((self.fill_z_range[1] - (self.fill_z_range[0] + self.size)) * torch.rand(1, device=self.volume.device)) - slag_z = mat_z + ((self.z_range[1] - mat_z) * torch.rand(1, device=self.volume.device)) + def _generate(self, fixed_mat_z: Optional[float] = None, fixed_slag_z: Optional[float] = None) -> Tuple[RadLengthFunc, Tensor]: + if fixed_mat_z is None: + mat_z = self.size + self.fill_z_range[0] + ((self.fill_z_range[1] - (self.fill_z_range[0] + self.size)) * torch.rand(1, device=self.volume.device)) + else: + mat_z = Tensor([fixed_mat_z], device=self.volume.device) + if fixed_slag_z is None: + slag_z = mat_z + ((self.z_range[1] - mat_z) * torch.rand(1, device=self.volume.device)) + else: + slag_z = Tensor([fixed_slag_z], device=self.volume.device) def generator(*, z: Tensor, lw: Tensor, size: float) -> Tensor: if z <= self.fill_z_range[0]: # Bottom layer diff --git a/tomopt/benchmarks/ladle_furnace/inference.py b/tomopt/benchmarks/ladle_furnace/inference.py index 5681d3ae..52c05eb1 100644 --- a/tomopt/benchmarks/ladle_furnace/inference.py +++ b/tomopt/benchmarks/ladle_furnace/inference.py @@ -1,15 +1,18 @@ -from typing import List, Tuple, Type +from typing import Dict, List, Optional, Tuple, Type, Union +import torch import torch.nn.functional as F from torch import Tensor, nn +from torch.distributions import Normal -from ...inference.volume import AbsIntClassifierFromX0, AbsX0Inferrer +from ...inference.scattering import ScatterBatch +from ...inference.volume import AbsIntClassifierFromX0, AbsVolumeInferrer, AbsX0Inferrer from ...volume import Volume -__all__ = ["LadleFurnaceFillLevelInferrer"] +__all__ = ["EdgeDetLadleFurnaceFillLevelInferrer", "PocaZLadleFurnaceFillLevelInferrer"] -class LadleFurnaceFillLevelInferrer(AbsIntClassifierFromX0): +class EdgeDetLadleFurnaceFillLevelInferrer(AbsIntClassifierFromX0): r""" Research tested only: no unit tests """ @@ -114,3 +117,282 @@ def x02probs(self, vox_preds: Tensor) -> Tensor: if self.add_batch_dim: vox_preds = vox_preds[0] return F.softmax(vox_preds, dim=-1) + + +class PocaZLadleFurnaceFillLevelInferrer(AbsVolumeInferrer): + r""" + Research tested only: no unit tests + + Computes fill heigh based on weighted average of z of POCAs + """ + + _n_mu: Optional[int] = None + _muon_scatter_vars: Optional[Tensor] = None # (mu, vars) + _muon_scatter_var_uncs: Optional[Tensor] = None # (mu, vars) + _muon_probs_per_voxel_zxy: Optional[Tensor] = None # (mu, zxy) + _muon_efficiency: Tensor = None # (mu, eff) + _pred_height: Optional[Tensor] = None # (h) + _pred_height_unc: Optional[Tensor] = None # (h) + _var_order_szs = [("poca", 3)] + + def __init__(self, volume: Volume, smooth: Union[float, Tensor] = 0.1): + r""" + Initialises the inference class for the provided volume. + """ + + super().__init__(volume=volume) + self._set_var_dimensions() + + self.xy_centres = torch.stack( + torch.meshgrid( + torch.linspace( + self.volume.passive_size / 2, + self.volume.lw[0].cpu().item() - (self.volume.passive_size / 2), + int(self.volume.lw[0].cpu().item() / self.volume.passive_size), + device=self.volume.device, + ), + torch.linspace( + self.volume.passive_size / 2, + self.volume.lw[1].cpu().item() - (self.volume.passive_size / 2), + int(self.volume.lw[0].cpu().item() / self.volume.passive_size), + device=self.volume.device, + ), + ), + -1, + ).reshape(-1, 2) + self.xy_edges = torch.stack( + torch.meshgrid( + torch.linspace( + 0.0, + self.volume.lw[0].cpu().item() - self.volume.passive_size, + int(self.volume.lw[0].cpu().item() / self.volume.passive_size), + device=self.volume.device, + ), + torch.linspace( + 0.0, + self.volume.lw[1].cpu().item() - self.volume.passive_size, + int(self.volume.lw[0].cpu().item() / self.volume.passive_size), + device=self.volume.device, + ), + ), + -1, + ).reshape(-1, 2) + self.smooth = smooth # type: ignore [assignment] + + def _set_var_dimensions(self) -> None: + r""" + Configures the indexing of the dependent variable and uncertainty tensors + """ + + # Configure dimension indexing + dims = {} + i = 0 + for var, sz in self._var_order_szs: + dims[var] = slice(i, i + sz) + i += sz + self._poca_dim = dims["poca"] + + def _reset_vars(self) -> None: + r""" + Resets any variable/predictions made from the added scatter batches. + """ + + self._n_mu = None + self._muon_scatter_vars = None # (mu, vars) + self._muon_scatter_var_uncs = None # (mu, vars) + self._muon_probs_per_voxel_zxy = None # (mu, z,x,y) + self._muon_efficiency = None # (mu, eff) + self._pred_height = None + self._pred_height_unc = None + + def compute_efficiency(self, scatters: ScatterBatch) -> Tensor: + r""" + Computes the per-muon efficiency, given the individual muon hit efficiencies, + as the probability of at least two hits above and below the passive volume. + + Arguments: + scatters: scatter batch containing muons whose efficiency should be computed + + Returns: + (muons) tensor of muon efficiencies + """ + + eff = None + for effs in [scatters.above_hit_effs, scatters.below_hit_effs]: + leff = None + effs = effs.squeeze(-1).transpose(0, -1) + # Muon goes through any combination of at least 2 panels + p_miss = 1 - effs + c = torch.combinations(torch.arange(0, len(effs)), r=len(effs) - 1) + c = c[torch.arange(len(effs) - 1, -1, -1)] # Reverse order to match panel hit + p_one_hit = (effs * p_miss[c].prod(1)).sum(0) + p_no_hit = p_miss.prod(0) + leff = 1 - p_one_hit - p_no_hit + if eff is None: + eff = leff + else: + eff = eff * leff # Muons detected above & below passive volume + return eff + + def get_prediction(self) -> Tuple[Optional[Tensor], Optional[Tensor]]: + r""" + Computes the predicted fill level via a weighted average of POCA locations. + + Returns: + pred: fill-height prediction [m] + inv_weight: sum of muon efficiencies + """ + + if len(self.scatter_batches) == 0: + print("Warning: unable to scan volume with prescribed number of muons.") + return None, None + return self.pred_height, self.inv_weights + + @property + def pred_height(self) -> Tensor: + r""" + Returns: + (h) tensor of fill-height prediction + """ + + if self._pred_height is None: + self._pred_height = self._get_height_pred() + self._pred_height_unc = None + return self._pred_height + + @property + def muon_poca_xyz(self) -> Tensor: + r""" + Returns: + (muons,xyz) tensor of PoCA locations + """ + + if self._muon_scatter_vars is None or self._muon_scatter_var_uncs is None: + self._combine_scatters() + return self._muon_scatter_vars[:, self._poca_dim] + + @property + def muon_poca_xyz_unc(self) -> Tensor: + r""" + Returns: + (muons,xyz) tensor of PoCA location uncertainties + """ + + if self._muon_scatter_vars is None or self._muon_scatter_var_uncs is None: + self._combine_scatters() + return self._muon_scatter_var_uncs[:, self._poca_dim] + + def _combine_scatters(self) -> None: + r""" + Combines scatter data from all the batches added so far. + Any muons with NaN or Inf entries will be filtered out of the resulting tensors. + + To aid in uncertainty computation, a pair of tensors are created with the all scatter variables and their uncertainties. + These are then indexed to retrieve the scatter variables. + """ + + vals: Dict[str, Tensor] = {} + uncs: Dict[str, Tensor] = {} + + if len(self.scatter_batches) == 0: + raise ValueError("No scatter batches have been added") + + vals["poca"] = torch.cat([sb.poca_xyz for sb in self.scatter_batches], dim=0) + uncs["poca"] = torch.cat([sb.poca_xyz_unc for sb in self.scatter_batches], dim=0) + + mask = torch.ones(len(vals["poca"])).bool() + for var_sz in self._var_order_szs: + mask *= ~(vals[var_sz[0]].isnan().any(1)) + mask *= ~(vals[var_sz[0]].isinf().any(1)) + mask *= ~(uncs[var_sz[0]].isnan().any(1)) + mask *= ~(uncs[var_sz[0]].isinf().any(1)) + + self._muon_scatter_vars = torch.cat([vals[var_sz[0]][mask] for var_sz in self._var_order_szs], dim=1) # (mu, vars) + self._muon_scatter_var_uncs = torch.cat([uncs[var_sz[0]][mask] for var_sz in self._var_order_szs], dim=1) # (mu, vars) + self._muon_efficiency = torch.cat([self.compute_efficiency(scatters=sb) for sb in self.scatter_batches], dim=0)[mask] # (mu, eff) + self._n_mu = len(self._muon_scatter_vars) + + @property + def inv_weights(self) -> Tensor: + r""" + Returns: + Sum of muon efficiencies + """ + + return self.muon_efficiency.sum() + + @property + def muon_efficiency(self) -> Tensor: + r""" + Returns: + (muons,1) tensor of the efficiencies of the muons + """ + + if self._muon_scatter_vars is None or self._muon_scatter_var_uncs is None: + self._combine_scatters() + return self._muon_efficiency + + @property + def n_mu(self) -> int: + r""" + Returns: + Total number muons included in the inference + """ + + if self._muon_scatter_vars is None or self._muon_scatter_var_uncs is None: + self._combine_scatters() + return self._n_mu + + @property + def smooth(self) -> Tensor: + return self._smooth + + @smooth.setter + def smooth(self, smooth: Union[float, Tensor]) -> None: + if not smooth > 0: + raise ValueError("smooth argument must be positive and non-zero") + if not isinstance(smooth, Tensor): + smooth = Tensor([smooth], device=self.device) + self._smooth = smooth + self.sigmoid_grid_wgt = ((self._sig_model(self.xy_centres) - 0.5) * 2).prod(-1, keepdim=True) # 0 at edges, 1 at centre + + def _sig_model(self, xy: Tensor) -> Tensor: + half_width = self.volume.lw / 2 + delta = (xy - half_width) / half_width + coef = torch.sigmoid((1 - (torch.sign(delta) * delta)) / self.smooth) + return coef / torch.sigmoid(1 / self.smooth) + + def _get_height_pred(self) -> Tensor: + r""" + Computes the predicted fill-height given the POCAs in the scatter batches added. + + Returns: + (h) tensor of fill-height prediction [m] + """ + + z_pos = self.muon_poca_xyz[:, 2:] + z_unc = self.muon_poca_xyz_unc[:, 2:] + eff = self.muon_efficiency.reshape(self.n_mu, 1) + + # Downweight poca near sides to reduce bias + xy_gauss = Normal(self.muon_poca_xyz[:, None, :2], self.muon_poca_xyz_unc[:, None, :2]) + probs = (xy_gauss.cdf(self.xy_edges + self.volume.passive_size) - xy_gauss.cdf(self.xy_edges)).prod( + -1, keepdim=True + ) # pixelwise probs in xy (mu, pixel, prob) + wgt_probs = probs * self.sigmoid_grid_wgt[None] + self.sig_wgt = wgt_probs.sum(-2) # (mu, wgt) + + # Clamp uncertainties in case they're very small/large + unc_low = z_unc.view(-1).kthvalue(1 + round(0.15865 * (z_unc.numel() - 1))).values.detach() + unc_high = z_unc.view(-1).kthvalue(1 + round(0.84135 * (z_unc.numel() - 1))).values.detach() + z_unc = torch.clip(z_unc, unc_low, unc_high) + + wgt = self.sig_wgt * eff / (z_unc**2) + # Clamp weight in case some muons dominate + wgt_high = wgt.view(-1).kthvalue(1 + round(0.84135 * (wgt.numel() - 1))).values.detach() + wgt = torch.clip(wgt, 0.0, wgt_high) + self.wgt = wgt + + mean_z = (self.wgt * z_pos).sum() / self.wgt.sum() + + return mean_z[None] diff --git a/tomopt/benchmarks/phi_detector/inference.py b/tomopt/benchmarks/phi_detector/inference.py index 973eb9d8..24f6a332 100644 --- a/tomopt/benchmarks/phi_detector/inference.py +++ b/tomopt/benchmarks/phi_detector/inference.py @@ -1,4 +1,5 @@ -from typing import List, Tuple +from pathlib import Path +from typing import List, Optional, Tuple import torch from torch import Tensor @@ -45,7 +46,7 @@ def _extract_hits(self) -> None: self._reco_hits = torch.cat((above_hits, below_hits), dim=1) # muons, all panels, reco h,phi,z self._gen_hits = torch.cat((_above_gen_hits, _below_gen_hits), dim=1) # muons, all panels, true xyz - def plot_scatter(self, idx: int) -> None: + def plot_scatter(self, idx: int, savename: Optional[Path] = None) -> None: raise NotImplementedError("Ah, I see you've just volunteered to implement this!") @staticmethod diff --git a/tomopt/inference/scattering.py b/tomopt/inference/scattering.py index 7de18642..cf9c1a81 100644 --- a/tomopt/inference/scattering.py +++ b/tomopt/inference/scattering.py @@ -1,3 +1,4 @@ +from pathlib import Path from typing import Dict, Optional, Tuple import matplotlib.pyplot as plt @@ -176,18 +177,23 @@ def get_scatter_mask(self) -> Tensor: * (self.poca_xyz[:, 2] < z[1]) ) - def plot_scatter(self, idx: int) -> None: + def plot_scatter(self, idx: int, savename: Optional[Path] = None) -> None: r""" Plots representation of hits and fitted trajectories for a single muon. Arguments: idx: index of muon to plot + savename: optional path to save figure to """ xin, xout = self.hits["above"]["reco_xyz"][idx, :, 0].detach().cpu().numpy(), self.hits["below"]["reco_xyz"][idx, :, 0].detach().cpu().numpy() yin, yout = self.hits["above"]["reco_xyz"][idx, :, 1].detach().cpu().numpy(), self.hits["below"]["reco_xyz"][idx, :, 1].detach().cpu().numpy() zin, zout = self.hits["above"]["reco_xyz"][idx, :, 2].detach().cpu().numpy(), self.hits["below"]["reco_xyz"][idx, :, 2].detach().cpu().numpy() + xin_unc, xout_unc = self.hits["above"]["unc_xyz"][idx, :, 0].detach().cpu().numpy(), self.hits["below"]["unc_xyz"][idx, :, 0].detach().cpu().numpy() + yin_unc, yout_unc = self.hits["above"]["unc_xyz"][idx, :, 1].detach().cpu().numpy(), self.hits["below"]["unc_xyz"][idx, :, 1].detach().cpu().numpy() scatter = self.poca_xyz[idx].detach().cpu().numpy() + scatter_unc = self.poca_xyz_unc[idx].detach().cpu().numpy() + dtheta_xy = self.dtheta_xy[idx].detach().cpu().numpy() dphi = self.dphi[idx].detach().cpu().numpy() phi_in = self.phi_in[idx].detach().cpu().numpy() @@ -217,6 +223,9 @@ def plot_scatter(self, idx: int) -> None: axs[0].scatter(xin, zin) axs[0].scatter(xout, zout) axs[0].scatter(scatter[0], scatter[2], label=r"$\Delta\theta_x=" + f"{dtheta_xy[0]:.1e}$") + axs[0].errorbar(xin, zin, xerr=xin_unc, color="C0", linestyle="none") + axs[0].errorbar(xout, zout, xerr=xout_unc, color="C1", linestyle="none") + axs[0].errorbar(scatter[0], scatter[2], xerr=scatter_unc[0], yerr=scatter_unc[2], color="C2", linestyle="none") axs[0].set_xlabel("x") axs[0].set_ylabel("z") axs[0].legend() @@ -240,6 +249,9 @@ def plot_scatter(self, idx: int) -> None: axs[1].scatter(yin, zin) axs[1].scatter(yout, zout) axs[1].scatter(scatter[1], scatter[2], label=r"$\Delta\theta_y=" + f"{dtheta_xy[1]:.1e}$") + axs[1].errorbar(yin, zin, xerr=yin_unc, color="C0", linestyle="none") + axs[1].errorbar(yout, zout, xerr=yout_unc, color="C1", linestyle="none") + axs[1].errorbar(scatter[1], scatter[2], xerr=scatter_unc[1], yerr=scatter_unc[2], color="C2", linestyle="none") axs[1].set_xlabel("y") axs[1].set_ylabel("z") axs[1].legend() @@ -263,9 +275,15 @@ def plot_scatter(self, idx: int) -> None: axs[2].scatter(xin, yin) axs[2].scatter(xout, yout) axs[2].scatter(scatter[0], scatter[1], label=r"$\Delta\phi=" + f"{dphi[0]:.1e}$") + axs[2].errorbar(xin, yin, xerr=xin_unc, yerr=yin_unc, color="C0", linestyle="none") + axs[2].errorbar(xout, yout, xerr=xout_unc, yerr=yout_unc, color="C1", linestyle="none") + axs[2].errorbar(scatter[0], scatter[1], xerr=scatter_unc[0], yerr=scatter_unc[1], color="C2", linestyle="none") axs[2].set_xlabel("x") axs[2].set_ylabel("y") axs[2].legend() + + if savename is not None: + plt.savefig(savename, bbox_inches="tight") plt.show() @staticmethod diff --git a/tomopt/inference/volume.py b/tomopt/inference/volume.py index eda6b5da..e29f6fbd 100644 --- a/tomopt/inference/volume.py +++ b/tomopt/inference/volume.py @@ -46,6 +46,13 @@ def __init__(self, volume: Volume): self.scatter_batches: List[ScatterBatch] = [] self.volume = volume self.size, self.lw, self.device = self.volume.passive_size, self.volume.lw, self.volume.device + # set shapes + self.shp_xyz = [ + round(self.lw.cpu().numpy()[0] / self.size), + round(self.lw.cpu().numpy()[1] / self.size), + len(self.volume.get_passives()), + ] + self.shp_zxy = [self.shp_xyz[2], self.shp_xyz[0], self.shp_xyz[1]] @abstractmethod def _reset_vars(self) -> None: @@ -128,13 +135,6 @@ def __init__(self, volume: Volume): super().__init__(volume=volume) self._set_var_dimensions() - # set shapes - self.shp_xyz = [ - round(self.lw.cpu().numpy()[0] / self.size), - round(self.lw.cpu().numpy()[1] / self.size), - len(self.volume.get_passives()), - ] - self.shp_zxy = [self.shp_xyz[2], self.shp_xyz[0], self.shp_xyz[1]] @staticmethod def x0_from_scatters(deltaz: float, total_scatter: Tensor, theta_in: Tensor, theta_out: Tensor, mom: Tensor) -> Tensor: @@ -298,7 +298,7 @@ def _get_voxel_zxy_x0_pred_uncs(self) -> Tensor: def _get_voxel_zxy_x0_preds(self) -> Tensor: r""" - Computes the X0 predictions per voxel using the scatter batched added. + Computes the X0 predictions per voxel using the scatter batches added. TODO: Implement differing x0 according to poca_xyz via Gaussian spread @@ -375,21 +375,32 @@ def muon_probs_per_voxel_zxy(self) -> Tensor: # (mu,z,x,y) Integration tested only TODO: Don't assume that poca_xyz uncertainties are uncorrelated - + TODO: Improve efficiency: currently CDFs are computed multiple times at the same points; could precompute x,y,z probs once, and combine in triples Returns: (muons,z,x,y) tensor of probabilities that the muons' PoCAs were located in the given voxels. """ if self._muon_probs_per_voxel_zxy is None: # Gaussian spread - dists = {} + xyz_distributions = {} for i, d in enumerate(["x", "y", "z"]): - dists[d] = Normal(self.muon_poca_xyz[:, i], self.muon_poca_xyz_unc[:, i] + 1e-7) # poca_xyz uncertainty is sometimes zero, causing errors - - def comp_int(low: Tensor, high: Tensor, dists: Dict[str, Normal]) -> Tensor: - return torch.prod(torch.stack([dists[d].cdf(high[i]) - dists[d].cdf(low[i]) for i, d in enumerate(dists)], dim=0), dim=0) + xyz_distributions[d] = Normal( + self.muon_poca_xyz[:, i], self.muon_poca_xyz_unc[:, i] + 1e-7 + ) # poca_xyz uncertainty is sometimes zero, causing errors + + def comp_int(low: Tensor, high: Tensor, xyz_distributions: Dict[str, Normal]) -> Tensor: + return torch.prod( + torch.stack( + [ + dist.cdf(high[i]) - dist.cdf(low[i]) + for i, dist in [(0, xyz_distributions["x"]), (1, xyz_distributions["y"]), (2, xyz_distributions["z"])] + ], + dim=0, + ), + dim=0, + ) probs = ( - torch.stack([comp_int(l, l + self.volume.passive_size, dists) for l in self.volume.edges.unbind()]) + torch.stack([comp_int(l, l + self.volume.passive_size, xyz_distributions) for l in self.volume.xyz_edges.unbind()]) .transpose(-1, -2) # prob, mu --> mu, prob .reshape([self.n_mu] + self.shp_xyz) # mu, x, y, z .permute(0, 3, 1, 2) # mu, z, x, y @@ -556,6 +567,8 @@ class PanelX0Inferrer(AbsX0Inferrer): Arguments: volume: volume through which the muons will be passed + + # TODO: refactor this to be provided to volume inference as a callable """ def compute_efficiency(self, scatters: ScatterBatch) -> Tensor: @@ -599,7 +612,7 @@ def compute_efficiency(self, scatters: ScatterBatch) -> Tensor: # ): # super().__init__(volume=volume) # self.model, self.base_inferrer, self.include_unc = model, base_inferrer, include_unc -# self.voxel_centres = self.volume.centres +# self.voxel_centres = self.volume.xyz_centres # self.tomopt_device = self.volume.device # self.model_device = next(self.model.parameters()).device @@ -790,7 +803,7 @@ def __init__( super().__init__(volume=volume) self.use_avgpool, self.cut_coef, self.ratio_offset, self.ratio_coef = use_avgpool, cut_coef, ratio_offset, ratio_coef self.x0_inferrer = partial_x0_inferrer(volume=self.volume) - self.frac = n_block_voxels / self.volume.centres.numel() + self.frac = n_block_voxels / self.volume.xyz_centres.numel() def add_scatters(self, scatters: ScatterBatch) -> None: r""" diff --git a/tomopt/muon/generation.py b/tomopt/muon/generation.py index 719e9759..2f52aba5 100644 --- a/tomopt/muon/generation.py +++ b/tomopt/muon/generation.py @@ -188,9 +188,7 @@ def flux(self, energy: Union[float, np.ndarray], theta: Union[float, np.ndarray] """ cosTheta = np.cos(theta) - cosine = np.sqrt( - (cosTheta**2 + self.P1**2 + self.P2 * cosTheta**self.P3 + self.P4 * cosTheta**self.P5) / (1 + self.P1**2 + self.P2 + self.P4) - ) + cosine = np.sqrt((cosTheta**2 + self.P1**2 + self.P2 * cosTheta**self.P3 + self.P4 * cosTheta**self.P5) / (1 + self.P1**2 + self.P2 + self.P4)) flux = ( 0.14 * (energy * (1 + 3.64 / (energy * cosine**1.29))) ** (-2.7) diff --git a/tomopt/optimisation/callbacks/monitors.py b/tomopt/optimisation/callbacks/monitors.py index fea4f715..29e7c6a3 100644 --- a/tomopt/optimisation/callbacks/monitors.py +++ b/tomopt/optimisation/callbacks/monitors.py @@ -1,7 +1,6 @@ from __future__ import annotations import math -import os from collections import defaultdict from typing import Dict, List, Optional, Tuple @@ -338,9 +337,6 @@ def _create_gif(self) -> None: image = imageio.imread(filename) writer.append_data(image) - for filename in set(self._buffer_files): - os.remove(filename) - class PanelMetricLogger(MetricLogger): r""" diff --git a/tomopt/optimisation/loss/loss.py b/tomopt/optimisation/loss/loss.py index b251db2c..fcacc32d 100644 --- a/tomopt/optimisation/loss/loss.py +++ b/tomopt/optimisation/loss/loss.py @@ -12,7 +12,7 @@ Provides loss functions for evaluating the performance of detector and inference configurations """ -__all__ = ["AbsDetectorLoss", "AbsMaterialClassLoss", "VoxelX0Loss", "VoxelClassLoss", "VolumeClassLoss", "VolumeIntClassLoss"] +__all__ = ["AbsDetectorLoss", "AbsMaterialClassLoss", "VoxelX0Loss", "VoxelClassLoss", "VolumeClassLoss", "VolumeIntClassLoss", "VolumeMSELoss"] class AbsDetectorLoss(nn.Module, metaclass=ABCMeta): @@ -423,3 +423,26 @@ def _get_inference_loss(self, pred: Tensor, inv_pred_weight: Tensor, volume: Vol int_targ = self.targ2int(volume.target.clone(), volume) loss = integer_class_loss(pred, int_targ, pred_start_int=self.pred_int_start, use_mse=self.use_mse, reduction="none") return torch.mean(loss / inv_pred_weight) + + +class VolumeMSELoss(AbsDetectorLoss): + r""" + TODO: Add unit tests and docs + """ + + def _get_inference_loss(self, pred: Tensor, inv_pred_weight: Tensor, volume: Volume) -> Tensor: + r""" + Computes the MSE of the preds and targets. + + Arguments: + pred: predicted floats + inv_pred_weight: weight that divides the unreduced SE loss between the predictions and targets, prior to averaging + volume: Volume containing the passive volume that was being predicted + + Returns: + The mean MSE for the predictions + """ + + targ = volume.target.clone() + loss = F.mse_loss(pred, targ, reduction="none") + return torch.mean(loss / inv_pred_weight) diff --git a/tomopt/optimisation/wrapper/volume_wrapper.py b/tomopt/optimisation/wrapper/volume_wrapper.py index af213c7e..6aa8924d 100644 --- a/tomopt/optimisation/wrapper/volume_wrapper.py +++ b/tomopt/optimisation/wrapper/volume_wrapper.py @@ -27,7 +27,7 @@ ) from ..data import PassiveYielder -__all__ = ["FitParams", "AbsVolumeWrapper", "PanelVolumeWrapper", "HeatMapVolumeWrapper"] +__all__ = ["FitParams", "AbsVolumeWrapper", "PanelVolumeWrapper", "HeatMapVolumeWrapper", "ArbVolumeWrapper"] r""" Provides wrapper classes for optimising detectors and other quality-of-life methods @@ -286,6 +286,8 @@ def load(self, name: str) -> None: state = torch.load(name, map_location="cuda" if torch.cuda.is_available() else "cpu") self.volume.load_state_dict(state["volume"]) + if "budget_weights" in state: + self.volume.assign_budget() for k, v in state.items(): if "_opt" in k: self.opts[k].load_state_dict(v) @@ -1067,3 +1069,186 @@ def _build_opt(self, **kwargs: PartialOpt) -> None: "sig_opt": kwargs["sig_opt"]((p.sig for l in dets for p in l.panels)), "z_pos_opt": kwargs["z_pos_opt"]((p.z for l in dets for p in l.panels)), } + + +class ArbVolumeWrapper(AbsVolumeWrapper): + r""" + Arbitrary volume wrapper in which the user supplies pre-instantiated optimisers for whatever paramters should be optimised. + + Wrappers also provide for various quality-of-life methods, such as saving and loading detector configurations, + and computing predictions with a fixed detector (:meth:`~tomopt.optimisation.wrapper.volume_wrapper.AbsVolumeWrapper.predict`) + + Fitting of a detector proceeds as training and validation epochs, each of which contains multiple batches of passive volumes. + For each volume in a batch, the loss is evaluated using multiple batches of muons. + The whole loop is: + + 1. for epoch in `n_epochs`: + A. `loss` = 0 + B. for `p`, `passive` in enumerate(`trn_passives`): + a. load `passive` into passive volume + b. for muon_batch in range(`n_mu_per_volume`//`mu_bs`): + i. Irradiate volume with `mu_bs` muons + ii. Infer passive volume + c. Compute loss based on precision and cost, and add to `loss` + d. if `p`+1 % `passive_bs` == 0: + i. `loss` = `loss`/`passive_bs` + ii. Backpropagate `loss` and update detector parameters + iii. `loss` = 0 + e. if len(`trn_passives`)-(`p`+1) < `passive_bs`: + i. Break + + C. `val_loss` = 0 + D. for `p`, `passive` in enumerate(`val_passives`): + a. load `passive` into passive volume + b. for muon_batch in range(`n_mu_per_volume`//`mu_bs`): + i. Irradiate volume with `mu_bs` muons + ii. Infer passive volume + iii. Compute loss based on precision and cost, and add to `val_loss` + c. if len(`val_passives`)-(`p`+1) < `passive_bs`: + i. Break + E. `val_loss` = `val_loss`/`p` + + In implementation, the loop is broken up into several functions: + :meth:`~tomopt.optimisation.wrapper.volume_wrapper.AbsVolumeWrapper._fit_epoch` runs one full epoch of volumes + and updates for both training and validation + :meth:`~tomopt.optimisation.wrapper.volume_wrapper.AbsVolumeWrapper._scan_volumes` runs over all training/validation volumes, + updating parameters when necessary + :meth:`~tomopt.optimisation.wrapper.volume_wrapper.AbsVolumeWrapper._scan_volume` irradiates a single volume with muons multiple batches, + and computes the loss for that volume + + The optimisation and prediction loops are supported by a stateful callback mechanism. + The base callback is :class:`~tomopt.optimisation.callbacks.callback.Callback`, which can interject at various points in the loops. + All aspects of the optimisation and prediction are stored in a :class:`~tomopt.optimisation.wrapper.volume_wrapper.FitParams` data class, + since the callbacks are also stored there, and the callbacks have a reference to the wrapper, they are able to read/write to the `FitParams` and be + aware of other callbacks that are running. + + Accounting for the interjection calls (`on_*_begin` & `on_*_end`), the full optimisation loop is: + + 1. Associate callbacks with wrapper (`set_wrapper`) + 2. `on_train_begin` + 3. for epoch in `n_epochs`: + A. `state` = "train" + B. `on_epoch_begin` + C. for `p`, `passive` in enumerate(`trn_passives`): + a. if `p` % `passive_bs` == 0: + i. `on_volume_batch_begin` + ii. `loss` = 0 + b. load `passive` into passive volume + c. `on_volume_begin` + d. for muon_batch in range(`n_mu_per_volume`//`mu_bs`): + i. `on_mu_batch_begin` + ii. Irradiate volume with `mu_bs` muons + iii. Infer scatter locations + iv. `on_scatter_end` + v. Infer x0 and append to list of x0 predictions + vi. `on_mu_batch_end` + e. `on_x0_pred_begin` + f. Compute overall x0 prediction + g. `on_x0_pred_end` + h. Compute loss based on precision and cost, and add to `loss` + i. if `p`+1 % `passive_bs` == 0: + i. `loss` = `loss`/`passive_bs` + ii. `on_volume_batch_end` + iii. Zero parameter gradients + iv. `on_backwards_begin` + v. Backpropagate `loss` and compute parameter gradients + vi. `on_backwards_end` + vii. Update detector parameters + viii. Ensure detector parameters are within physical boundaries (`AbsDetectorLayer.conform_detector`) + viv. `loss` = 0 + j. if len(`trn_passives`)-(`p`+1) < `passive_bs`: + i. Break + D. `on_epoch_end` + E. `state` = "valid" + F. `on_epoch_begin` + G. for `p`, `passive` in enumerate(`val_passives`): + a. if `p` % `passive_bs` == 0: + i. `on_volume_batch_begin` + ii. `loss` = 0 + b. `on_volume_begin` + c. for muon_batch in range(`n_mu_per_volume`//`mu_bs`): + i. `on_mu_batch_begin` + ii. Irradiate volume with `mu_bs` muons + iii. Infer scatter locations + iv. `on_scatter_end` + v. Infer x0 and append to list of x0 predictions + vi. `on_mu_batch_end` + d. `on_x0_pred_begin` + e. Compute overall x0 prediction + f. `on_x0_pred_end` + g. Compute loss based on precision and cost, and add to `loss` + h. if `p`+1 % `passive_bs` == 0: + i. `loss` = `loss`/`passive_bs` + ii. `on_volume_batch_end` + i. if len(`val_passives`)-(`p`+1) < `passive_bs`: + i. Break + H. `on_epoch_end` + 4. `on_train_end` + + Arguments: + volume: the volume containing the detectors to be optimised + opts: Dict of strings mapping to initialised optimisers + loss_func: optional loss function (required if planning to optimise the detectors) + partial_scatter_inferrer: uninitialised class to be used for inferring muon scatter variables and trajectories + partial_volume_inferrer: uninitialised class to be used for inferring volume targets + mu_generator: Optional generator class for muons. If None, will use :meth:`~tomopt.muon.generation. MuonGenerator2016.from_volume`. + """ + + def __init__( + self, + volume: Volume, + *, + opts: Dict[str, Optimizer], + loss_func: Optional[AbsDetectorLoss] = None, + partial_scatter_inferrer: Type[ScatterBatch] = ScatterBatch, + partial_volume_inferrer: Type[AbsVolumeInferrer] = PanelX0Inferrer, + mu_generator: Optional[AbsMuonGenerator] = None, + ): + super().__init__( + volume=volume, + partial_opts={}, + loss_func=loss_func, + mu_generator=mu_generator, + partial_scatter_inferrer=partial_scatter_inferrer, + partial_volume_inferrer=partial_volume_inferrer, + ) + self.opts = opts + + @classmethod + def from_save( + cls, + name: str, + *, + volume: Volume, + opts: Dict[str, Optimizer], + loss_func: Optional[AbsDetectorLoss], + partial_scatter_inferrer: Type[ScatterBatch] = ScatterBatch, + partial_volume_inferrer: Type[AbsVolumeInferrer] = PanelX0Inferrer, + mu_generator: Optional[AbsMuonGenerator] = None, + ) -> AbsVolumeWrapper: + r""" + Instantiates a new `PanelVolumeWrapper` and loads saved detector and optimiser parameters + + Arguments: + name: file name with saved detector and optimiser parameters + volume: the volume containing the detectors to be optimised + opts: Dict of strings mapping to initialised optimisers + loss_func: optional loss function (required if planning to optimise the detectors) + partial_scatter_inferrer: uninitialised class to be used for inferring muon scatter variables and trajectories + partial_volume_inferrer: uninitialised class to be used for inferring volume targets + mu_generator: Optional generator class for muons. If None, will use :meth:`~tomopt.muon.generation. MuonGenerator2016.from_volume`. + """ + + vw = cls( + volume=volume, + opts=opts, + loss_func=loss_func, + partial_scatter_inferrer=partial_scatter_inferrer, + partial_volume_inferrer=partial_volume_inferrer, + mu_generator=mu_generator, + ) + vw.load(name) + return vw + + def _build_opt(self, **kwargs: PartialOpt) -> None: + pass diff --git a/tomopt/volume/layer.py b/tomopt/volume/layer.py index eb2118e2..299ba940 100644 --- a/tomopt/volume/layer.py +++ b/tomopt/volume/layer.py @@ -286,7 +286,7 @@ def _pdg_scatter( # These are in the muons' reference frames NOT the volume's!!! # Make sure that scattering angle in the muon reference frame < pi/2 # to ensure conversion into the volume reference frame - dtheta_xy_mu = torch.clamp(z1 * theta0, max=math.pi / 2.2) # TODO Check this + dtheta_xy_mu = torch.clamp(z2 * theta0, max=math.pi / 2.2) # TODO Check this dxy_mu = step_sz * theta0 * ((z1 / math.sqrt(12)) + (z2 / 2)) # Note that if a track incides on a layer diff --git a/tomopt/volume/panel.py b/tomopt/volume/panel.py index da307bc8..ae90aeda 100644 --- a/tomopt/volume/panel.py +++ b/tomopt/volume/panel.py @@ -440,7 +440,7 @@ def smooth(self) -> Tensor: @smooth.setter def smooth(self, smooth: Union[float, Tensor]) -> None: if not smooth > 0: - raise ValueError("smooth artgument must be positive and non-zero") + raise ValueError("smooth argument must be positive and non-zero") if not isinstance(smooth, Tensor): smooth = Tensor([smooth], device=self.device) if hasattr(self, "_smooth"): diff --git a/tomopt/volume/volume.py b/tomopt/volume/volume.py index 717d96a3..46420198 100644 --- a/tomopt/volume/volume.py +++ b/tomopt/volume/volume.py @@ -60,7 +60,7 @@ def __init__(self, layers: nn.ModuleList, budget: Optional[float] = None): self.budget = None if budget is None else torch.tensor(budget, device=self._device) self._check_passives() self._target: Optional[Tensor] = None - self._edges: Optional[Tensor] = None + self._xyz_edges: Optional[Tensor] = None if self.budget is not None: self._configure_budget() @@ -77,9 +77,9 @@ def get_passive_z_range(self) -> Tuple[Tensor, Tensor]: ps = self.get_passives() return ps[-1].z - self.passive_size, ps[0].z - def build_edges(self) -> Tensor: + def build_xyz_edges(self) -> Tensor: r""" - Computes the zxy locations of low-left-front edges of voxels in the passive layers of the volume. + Computes the xyz locations of low-left-front edges of voxels in the passive layers of the volume. """ bounds = ( @@ -93,9 +93,7 @@ def build_edges(self) -> Tensor: ] ) # bounds[2] = np.flip(bounds[2]) # z is reversed - return torch.tensor( - bounds.reshape(3, -1).transpose(-1, -2), dtype=torch.float32, device=self.device - ) # TODO: Check that xyz shape is expected, and not zxy + return torch.tensor(bounds.reshape(3, -1).transpose(-1, -2), dtype=torch.float32, device=self.device) def get_detectors(self) -> List[AbsDetectorLayer]: r""" @@ -391,24 +389,24 @@ def h(self) -> Tensor: return self.layers[0].z @property - def edges(self) -> Tensor: + def xyz_edges(self) -> Tensor: r""" - zxy locations of low-left-front edges of voxels in the passive layers of the volume. + xyz locations of low-left-front edges of voxels in the passive layers of the volume. """ - if self._edges is None: - self._edges = self.build_edges() - return self._edges + if self._xyz_edges is None: + self._xyz_edges = self.build_xyz_edges() + return self._xyz_edges @property - def centres(self) -> Tensor: + def xyz_centres(self) -> Tensor: r""" - zxy locations of the centres of voxels in the passive layers of the volume. + xyz locations of the centres of voxels in the passive layers of the volume. """ - if self._edges is None: - self._edges = self.build_edges() - return self._edges + (self.passive_size / 2) + if self._xyz_edges is None: + self._xyz_edges = self.build_xyz_edges() + return self._xyz_edges + (self.passive_size / 2) @property def device(self) -> torch.device: