Repository: chanchanchan97/ICAFusion Branch: main Commit: 1eccff4edac6 Files: 134 Total size: 15.3 MB Directory structure: gitextract_37ncapyb/ ├── .idea/ │ ├── .gitignore │ ├── ICAFusion.iml │ ├── deployment.xml │ ├── inspectionProfiles/ │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── modules.xml │ └── vcs.xml ├── LICENSE ├── README.md ├── confluence.py ├── data/ │ ├── GlobalWheat2020.yaml │ ├── VisDrone.yaml │ ├── argoverse_hd.yaml │ ├── coco.yaml │ ├── coco128.yaml │ ├── hyp.finetune.yaml │ ├── hyp.scratch.yaml │ ├── hyp.scratch_VEDAI.yaml │ ├── multispectral/ │ │ ├── CVC14.yaml │ │ ├── FLIR-align-3class.yaml │ │ ├── FLIR-align.yaml │ │ ├── LLVIP.yaml │ │ ├── VEDAI.yaml │ │ └── kaist.yaml │ ├── scripts/ │ │ ├── get_argoverse_hd.sh │ │ ├── get_coco.sh │ │ └── get_voc.sh │ └── voc.yaml ├── descriptor/ │ ├── CFOG.py │ ├── CFOG_matlab.p │ ├── LSS.py │ ├── denseLSS.m │ └── mexCalcSsdescs1.mexw64 ├── detect_twostream.py ├── evaluation_script/ │ ├── KAIST_annotation.json │ ├── README.md │ ├── __init__.py │ ├── coco.py │ ├── cocoeval.py │ ├── evaluation_script.py │ ├── null │ └── state_of_arts/ │ ├── ARCNN_result.txt │ ├── CIAN_result.txt │ ├── MBNet_result.txt │ ├── MLPD_result.json │ ├── MLPD_result.txt │ └── MSDS-RCNN_result.txt ├── global_var.py ├── gradcam_visual.py ├── hubconf.py ├── models/ │ ├── __init__.py │ ├── common.py │ ├── experimental.py │ ├── export.py │ ├── gradcam.py │ ├── hub/ │ │ ├── anchors.yaml │ │ ├── yolov3-spp.yaml │ │ ├── yolov3-tiny.yaml │ │ ├── yolov3.yaml │ │ ├── yolov5-fpn.yaml │ │ ├── yolov5-p2.yaml │ │ ├── yolov5-p6.yaml │ │ ├── yolov5-p7.yaml │ │ ├── yolov5-panet.yaml │ │ ├── yolov5l6.yaml │ │ ├── yolov5m6.yaml │ │ ├── yolov5s-transformer.yaml │ │ ├── yolov5s6.yaml │ │ └── yolov5x6.yaml │ ├── transformer/ │ │ ├── yolov5_ResNet50_NiNfusion_FLIR.yaml │ │ ├── yolov5_ResNet50_NiNfusion_kaist.yaml │ │ ├── yolov5_ResNet50_Transfusion_FLIR.yaml │ │ ├── yolov5_ResNet50_Transfusion_kaist.yaml │ │ ├── yolov5_VGG16_NiNfusion_FLIR.yaml │ │ ├── yolov5_VGG16_NiNfusion_kaist.yaml │ │ ├── yolov5_VGG16_Transfusion_FLIR.yaml │ │ ├── yolov5_VGG16_Transfusion_kaist.yaml │ │ ├── yolov5l_Add_FLIR.yaml │ │ ├── yolov5l_MobileViT_NiNfusion_FLIR.yaml │ │ ├── yolov5l_NiNfusion_FLIR.yaml │ │ ├── yolov5l_NiNfusion_LLVIP.yaml │ │ ├── yolov5l_NiNfusion_VEDAI.yaml │ │ ├── yolov5l_Transfusion_FLIR.yaml │ │ ├── yolov5l_Transfusion_LLVIP.yaml │ │ ├── yolov5l_Transfusion_VEDAI.yaml │ │ ├── yolov5l_Transfusion_kaist.yaml │ │ ├── yolov5m_Add_kaist.yaml │ │ ├── yolov5m_NiNfusion_FLIR.yaml │ │ ├── yolov5m_NiNfusion_kaist.yaml │ │ ├── yolov5m_Transfusion_FLIR.yaml │ │ ├── yolov5m_Transfusion_SeaDrone.yaml │ │ ├── yolov5m_Transfusion_VEDAI.yaml │ │ ├── yolov5m_Transfusion_kaist.yaml │ │ ├── yolov5m_weightedAdd_kaist.yaml │ │ ├── yolov5n_Add_kaist.yaml │ │ ├── yolov5n_NiNfusion_FLIR.yaml │ │ ├── yolov5n_Transfusion_FLIR.yaml │ │ ├── yolov5n_Transfusion_kaist.yaml │ │ ├── yolov5s_Add_kaist.yaml │ │ ├── yolov5s_Transfusion_FLIR.yaml │ │ └── yolov5s_Transfusion_kaist.yaml │ ├── yolo.py │ ├── yolo_test.py │ ├── yolov5l.yaml │ ├── yolov5m.yaml │ ├── yolov5s.yaml │ └── yolov5x.yaml ├── requirements.txt ├── test.py ├── train.py └── utils/ ├── __init__.py ├── activations.py ├── autoanchor.py ├── aws/ │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── confluence.py ├── datasets.py ├── flask_rest_api/ │ ├── example_request.py │ └── restapi.py ├── general.py ├── google_app_engine/ │ ├── Dockerfile │ ├── additional_requirements.txt │ └── app.yaml ├── google_utils.py ├── gradcam.py ├── loss.py ├── metrics.py ├── plots.py ├── torch_utils.py └── wandb_logging/ ├── __init__.py ├── log_dataset.py └── wandb_utils.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .idea/.gitignore ================================================ # 默认忽略的文件 /shelf/ /workspace.xml # 基于编辑器的 HTTP 客户端请求 /httpRequests/ # Datasource local storage ignored files /dataSources/ /dataSources.local.xml ================================================ FILE: .idea/ICAFusion.iml ================================================ ================================================ FILE: .idea/deployment.xml ================================================ ================================================ FILE: .idea/inspectionProfiles/Project_Default.xml ================================================ ================================================ FILE: .idea/inspectionProfiles/profiles_settings.xml ================================================ ================================================ FILE: .idea/modules.xml ================================================ ================================================ FILE: .idea/vcs.xml ================================================ ================================================ FILE: LICENSE ================================================ GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source. For example, if your program is a web application, its interface could display a "Source" link that leads users to an archive of the code. There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements. You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see . ================================================ FILE: README.md ================================================ ##
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection
### Introduction In this paper, we propose a novel feature fusion framework of dual cross-attention transformers to model global feature interaction and capture complementary information across modalities simultaneously. In addition, we introdece an iterative interaction mechanism into dual cross-attention transformers, which shares parameters among block-wise multimodal transformers to reduce model complexity and computation cost. The proposed method is general and effective to be integrated into different detection frameworks and used with different backbones. Experimental results on KAIST, FLIR, and VEDAI datasets show that the proposed method achieves superior performance and faster inference, making it suitable for various practical scenarios. Paper download in: https://arxiv.org/pdf/2308.07504.pdf ### Overview
Fig 1. Overview of our multispectral object detection framework
Fig 2. Illustration of the proposed DMFF module
### Installation Clone repo and install requirements.txt in a Python>=3.8.0 conda environment, including PyTorch>=1.12. ``` git clone https://github.com/chanchanchan97/ICAFusion.git cd ICAFusion pip install -r requirements.txt ``` ### Datasets - **KAIST** Link：https://pan.baidu.com/s/1UdwQJH-cHVL91pkMW-ij6g Code：ig3y - **FLIR-aligned** Link：https://pan.baidu.com/s/1ljr8qJYdz-60Lj-iVEHBvg Code：uqzs - **VEDAI** Link：https://pan.baidu.com/s/1UKSI0Go0Ddt62tXNIySz9w Code：5ett ### Weights - **KAIST** Link：https://pan.baidu.com/s/18UXctOSgjp6EUcJXIGbWTQ Code：9eku - **FLIR-aligned** We fixed some bugs and now update the results on the FLIR dataset with the model weights as follows: ~~Link：https://pan.baidu.com/s/1VZbsTE4o6bw2XBypPW3zoA Code：xli9~~ Link: https://pan.baidu.com/s/1fEFOEyzVIxNCMjJjuujJUQ?pwd=a81f code: a81f ### Results update - **FLIR** | Methods | person | car | bicycle | mAP@50 | |----------------------|----------|----------|----------|----------| | ~~yolov5_ICAFusion~~ | ~~81.6~~ | ~~89.0~~ | ~~66.9~~ | ~~79.2~~ | | yolov5_ICAFusion | 84.9 | 89.8 | 73.8 | 82.8 | - **M3FD** | People | Car | Bus | Motorcycle | Lamp | Truck | mAP@50 | |--------|------|------|------------|------|-------|--------| | 82.3 | 93.4 | 93.9 | 89.6 | 82.2 | 87.8 | 88.2 | - **DroneVehicle** | Car | Truck | Bus | Van | freight_car | mAP@50 | |------|-------|------|------|-------------|--------| | 98.0 | 84.2 | 97.1 | 71.0 | 73.8 | 84.8 | - **DVTOP** | Car | bicycle | person | mAP@50 | |------|------|-------------|--------| | 92.6 |83.6|84.6 |86.9| - **SeaDroneSee** | Swimmer | Floater | Boat | mAP@50 | |------|------|-------------|--------| |14.7 |69.5|99.5 |61.2| ### Files **Note**: This is the txt files for evaluation. We continuously optimize our codes, which results in the difference in detection performance. However, the codes of module for multimodal feature fusion still remain consistent with the methods proposed in this paper. - **KAIST** Link：https://pan.baidu.com/s/1N7SNEPXKX7KFaO2Th7vq2g Code：zijw ### Our new works - Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection **[[paper]](https://arxiv.org/abs/2507.14643)** **[[code]](https://github.com/61s61min/MS2Fusion)** - IRDFusion: Iterative Relation-Map Difference guided Feature Fusion for Multispectral Object Detection **[[paper]](https://arxiv.org/html/2509.09085v1)** **[[code]](https://github.com/61s61min/IRDFusion.git)** ### Citation If you find our work useful in your research, please consider citing: ``` @article{SHEN2023109913, title={ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection}, author={Shen, Jifeng and Chen, Yifei and Liu, Yue and Zuo, Xin and Fan, Heng and Yang, Wankou}, journal={Pattern Recognition}, pages={109913}, year={2023}, issn={0031-3203}, doi={https://doi.org/10.1016/j.patcog.2023.109913}, author={Jifeng Shen and Yifei Chen and Yue Liu and Xin Zuo and Heng Fan and Wankou Yang}, } ``` ================================================ FILE: confluence.py ================================================ """ Author: Andrew Shepley Contact: asheple2@une.edu.au Source: Confluence Methods a) assign_boxes_to_classes b) normalise_coordinates c) confluence_nms - returns maxima scoring box, removes false positives using confluence - efficient d) confluence - returns most confluent box, removes false positives using confluence - less efficient but better box """ from collections import defaultdict import numpy as np def assign_boxes_to_classes(bounding_boxes, classes, scores): """ Parameters: bounding_boxes: list of bounding boxes (x1,y1,x2,y2) classes: list of class identifiers (int value, e.g. 1 = person) scores: list of class confidence scores (0.0-1.0) Returns: boxes_to_classes: defaultdict(list) containing mapping to bounding boxes and confidence scores to class """ boxes_to_classes = defaultdict(list) for each_box, each_class, each_score in zip(bounding_boxes, classes, scores): if each_score >= 0.05: boxes_to_classes[each_class].append(np.array([each_box[0],each_box[1],each_box[2],each_box[3], each_score])) return boxes_to_classes def normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y): """ Parameters: x1, y1, x2, y2: bounding box coordinates to normalise min_x,max_x,min_y,max_y: minimum and maximum bounding box values (min = 0, max = 1) Returns: Normalised bounding box coordinates (scaled between 0 and 1) """ x1, y1, x2, y2 = (x1-min_x)/(max_x-min_x), (y1-min_y)/(max_y-min_y), (x2-min_x)/(max_x-min_x), (y2-min_y)/(max_y-min_y) return x1, y1, x2, y2 def confluence_nms(bounding_boxes,scores,classes,confluence_thr,gaussian,score_thr=0.05,sigma=0.5): """ Parameters: bounding_boxes: list of bounding boxes (x1,y1,x2,y2) classes: list of class identifiers (int value, e.g. 1 = person) scores: list of class confidence scores (0.0-1.0) confluence_thr: value between 0 and 2, with optimum from 0.5-0.8 gaussian: boolean switch to turn gaussian decaying of suboptimal bounding box confidence scores (setting to False results in suppression of suboptimal boxes) score_thr: class confidence score sigma: used in gaussian decaying. A smaller value causes harsher decaying. Returns: output: A dictionary mapping class identity to final retained boxes (and corresponding confidence scores) """ class_mapping = assign_boxes_to_classes(bounding_boxes, classes, scores) output = {} for each_class in class_mapping: dets = np.array(class_mapping[each_class]) retain = [] while dets.size > 0: max_idx = np.argmax(dets[:, 4], axis=0) dets[[0, max_idx], :] = dets[[max_idx, 0], :] retain.append(dets[0, :]) x1, y1, x2, y2 = dets[0, 0], dets[0, 1], dets[0, 2], dets[0, 3] min_x = np.minimum(x1, dets[1:, 0]) min_y = np.minimum(y1, dets[1:, 1]) max_x = np.maximum(x2, dets[1:, 2]) max_y = np.maximum(y2, dets[1:, 3]) x1, y1, x2, y2 = normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y) xx1, yy1, xx2, yy2 = normalise_coordinates(dets[1:, 0], dets[1:, 1], dets[1:, 2], dets[1:, 3],min_x,max_x,min_y,max_y) md_x1,md_x2,md_y1,md_y2 = abs(x1-xx1),abs(x2-xx2),abs(y1-yy1),abs(y2-yy2) manhattan_distance = (md_x1+md_x2+md_y1+md_y2) weights = np.ones_like(manhattan_distance) if (gaussian == True): gaussian_weights = np.exp(-((1-manhattan_distance) * (1-manhattan_distance)) / sigma) weights[manhattan_distance<=confluence_thr]=gaussian_weights[manhattan_distance<=confluence_thr] else: weights[manhattan_distance<=confluence_thr]=manhattan_distance[manhattan_distance<=confluence_thr] dets[1:, 4] *= weights to_reprocess = np.where(dets[1:, 4] >= score_thr)[0] dets = dets[to_reprocess + 1, :] output[each_class]=retain return output def confluence(bounding_boxes,scores,classes,confluence_thr,gaussian,score_thr=0.05,sigma=0.5): """ Parameters: bounding_boxes: list of bounding boxes (x1,y1,x2,y2) classes: list of class identifiers (int value, e.g. 1 = person) scores: list of class confidence scores (0.0-1.0) confluence_thr: value between 0 and 2, with optimum from 0.5-0.8 gaussian: boolean switch to turn gaussian decaying of suboptimal bounding box confidence scores (setting to False results in suppression of suboptimal boxes) score_thr: class confidence score sigma: used in gaussian decaying. A smaller value causes harsher decaying. Returns: output: A dictionary mapping class identity to final retained boxes (and corresponding confidence scores) """ class_mapping = assign_boxes_to_classes(bounding_boxes, classes, scores) output = {} for each_class in class_mapping: dets = np.array(class_mapping[each_class]) retain = [] while dets.size > 0: confluence_scores,proximities = [],[] while len(confluence_scores)0): confluence_score = np.amax(cconf_scores) else: confluence_score = confidence_score if(all_proximities.size>0): proximity = (sum(all_proximities)/all_proximities.size)*(1-confidence_score) else: proximity = sum(all_proximities)*(1-confidence_score) confluence_scores.append(confluence_score) proximities.append(proximity) conf = np.array(confluence_scores) prox = np.array(proximities) dets_temp = np.concatenate((dets, prox[:, None]), axis=1) dets_temp = np.concatenate((dets_temp, conf[:, None]), axis=1) min_idx = np.argmin(dets_temp[:, 5], axis=0) dets[[0, min_idx], :] = dets[[min_idx, 0], :] dets_temp[[0, min_idx], :] = dets_temp[[min_idx, 0], :] dets[0,4]=dets_temp[0,6] retain.append(dets[0, :]) x1, y1, x2, y2 = dets[0, 0], dets[0, 1], dets[0, 2], dets[0, 3] min_x = np.minimum(x1, dets[1:, 0]) min_y = np.minimum(y1, dets[1:, 1]) max_x = np.maximum(x2, dets[1:, 2]) max_y = np.maximum(y2, dets[1:, 3]) x1, y1, x2, y2 = normalise_coordinates(x1, y1, x2, y2,min_x,max_x,min_y,max_y) xx1, yy1, xx2, yy2 = normalise_coordinates(dets[1:, 0], dets[1:, 1], dets[1:, 2], dets[1:, 3],min_x,max_x,min_y,max_y) md_x1,md_x2,md_y1,md_y2 = abs(x1-xx1),abs(x2-xx2),abs(y1-yy1),abs(y2-yy2) manhattan_distance = (md_x1+md_x2+md_y1+md_y2) weights = np.ones_like(manhattan_distance) if (gaussian == True): gaussian_weights = np.exp(-((1-manhattan_distance) * (1-manhattan_distance)) / sigma) weights[manhattan_distance<=confluence_thr]=gaussian_weights[manhattan_distance<=confluence_thr] else: weights[manhattan_distance<=confluence_thr]=manhattan_distance[manhattan_distance<=confluence_thr] dets[1:, 4] *= weights to_reprocess = np.where(dets[1:, 4] >= score_thr)[0] dets = dets[to_reprocess + 1, :] output[each_class]=retain return output ================================================ FILE: data/GlobalWheat2020.yaml ================================================ # Global Wheat 2020 dataset http://www.global-wheat.com/ # Train command: python train.py --data GlobalWheat2020.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /datasets/GlobalWheat2020 # /yolov5 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: # 3422 images - ../datasets/GlobalWheat2020/images/arvalis_1 - ../datasets/GlobalWheat2020/images/arvalis_2 - ../datasets/GlobalWheat2020/images/arvalis_3 - ../datasets/GlobalWheat2020/images/ethz_1 - ../datasets/GlobalWheat2020/images/rres_1 - ../datasets/GlobalWheat2020/images/inrae_1 - ../datasets/GlobalWheat2020/images/usask_1 val: # 748 images (WARNING: train set contains ethz_1) - ../datasets/GlobalWheat2020/images/ethz_1 test: # 1276 - ../datasets/GlobalWheat2020/images/utokyo_1 - ../datasets/GlobalWheat2020/images/utokyo_2 - ../datasets/GlobalWheat2020/images/nau_1 - ../datasets/GlobalWheat2020/images/uq_1 # number of classes nc: 1 # class names names: [ 'wheat_head' ] # download command/URL (optional) -------------------------------------------------------------------------------------- download: | from utils.general import download, Path # Download dir = Path('../datasets/GlobalWheat2020') # dataset directory urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] download(urls, dir=dir) # Make Directories for p in 'annotations', 'images', 'labels': (dir / p).mkdir(parents=True, exist_ok=True) # Move for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': (dir / p).rename(dir / 'images' / p) # move to /images f = (dir / p).with_suffix('.json') # json file if f.exists(): f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations ================================================ FILE: data/VisDrone.yaml ================================================ # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset # Train command: python train.py --data VisDrone.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /VisDrone # /yolov5 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../VisDrone/VisDrone2019-DET-train/images # 6471 images val: ../VisDrone/VisDrone2019-DET-val/images # 548 images test: ../VisDrone/VisDrone2019-DET-test-dev/images # 1610 images # number of classes nc: 10 # class names names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ] # download command/URL (optional) -------------------------------------------------------------------------------------- download: | from utils.general import download, os, Path def visdrone2yolo(dir): from PIL import Image from tqdm import tqdm def convert_box(size, box): # Convert VisDrone box to YOLO xywh box dw = 1. / size[0] dh = 1. / size[1] return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') for f in pbar: img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size lines = [] with open(f, 'r') as file: # read annotation.txt for row in [x.split(',') for x in file.read().strip().splitlines()]: if row[4] == '0': # VisDrone 'ignored regions' class 0 continue cls = int(row[5]) - 1 box = convert_box(img_size, tuple(map(int, row[:4]))) lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: fl.writelines(lines) # write label.txt # Download dir = Path('../VisDrone') # dataset directory urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] download(urls, dir=dir) # Convert for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels ================================================ FILE: data/argoverse_hd.yaml ================================================ # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ # Train command: python train.py --data argoverse_hd.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /argoverse # /yolov5 # download command/URL (optional) download: bash data/scripts/get_argoverse_hd.sh # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview # number of classes nc: 8 # class names names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] ================================================ FILE: data/coco.yaml ================================================ # COCO 2017 dataset http://cocodataset.org # Train command: python train.py --data coco.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco # /yolov5 # download command/URL (optional) download: bash data/scripts/get_coco.sh # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../coco/train2017.txt # 118287 images val: ../coco/val2017.txt # 5000 images test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 # number of classes nc: 80 # class names names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ] # Print classes # with open('data/coco.yaml') as f: # d = yaml.safe_load(f) # dict # for i, x in enumerate(d['names']): # print(i, x) ================================================ FILE: data/coco128.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # download command/URL (optional) download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../coco128/images/train2017/ # 128 images val: ../coco128/images/train2017/ # 128 images # number of classes nc: 80 # class names names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ] ================================================ FILE: data/hyp.finetune.yaml ================================================ # Hyperparameters for VOC finetuning # python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials # Hyperparameter Evolution Results # Generations: 306 # P R mAP.5 mAP.5:.95 box obj cls # Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146 lr0: 0.02 #(0.0032) lrf: 0.12 momentum: 0.843 weight_decay: 0.00036 warmup_epochs: 2.0 warmup_momentum: 0.5 warmup_bias_lr: 0.05 box: 0.0296 cls: 0.243 cls_pw: 0.631 obj: 0.301 obj_pw: 0.911 iou_t: 0.2 anchor_t: 2.91 # anchors: 3.63 fl_gamma: 0.0 hsv_h: 0.0138 hsv_s: 0.664 hsv_v: 0.464 degrees: 0.373 translate: 0.245 scale: 0.898 shear: 0.602 perspective: 0.0 flipud: 0.00856 fliplr: 0.5 mosaic: 1.0 mixup: 0.243 ================================================ FILE: data/hyp.scratch.yaml ================================================ # Hyperparameters for COCO training from scratch # python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials lr0: 0.01 # initial learning rate ( original SGD=1E-2, Adam=1E-3) lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) momentum: 0.937 # SGD momentum/Adam beta1 weight_decay: 0.0005 # optimizer weight decay 5e-4 warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr box: 0.05 # box loss gain cls: 0.5 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 1.0 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold # anchors: 3 # anchors per output layer (0 to ignore) fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0.015 # image HSV-Hue augmentation (fraction) hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) hsv_v: 0.4 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0.1 # image translation (+/- fraction) scale: 0.5 # image scale (+/- gain) shear: 0.0 # image shear (+/- deg) perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 1.0 # image mosaic (probability) mixup: 0.0 # image mixup (probability) ================================================ FILE: data/hyp.scratch_VEDAI.yaml ================================================ # Hyperparameters for COCO training from scratch # python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials lr0: 0.01 # initial learning rate ( original SGD=1E-2, Adam=1E-3) lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) momentum: 0.937 # SGD momentum/Adam beta1 weight_decay: 0.0005 # optimizer weight decay 5e-4 warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_momentum: 0.8 # warmup initial momentum warmup_bias_lr: 0.1 # warmup initial bias lr box: 0.05 # box loss gain cls: 0.5 # cls loss gain cls_pw: 1.0 # cls BCELoss positive_weight obj: 1.0 # obj loss gain (scale with pixels) obj_pw: 1.0 # obj BCELoss positive_weight iou_t: 0.20 # IoU training threshold anchor_t: 4.0 # anchor-multiple threshold # anchors: 3 # anchors per output layer (0 to ignore) fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) hsv_h: 0.015 # image HSV-Hue augmentation (fraction) hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) hsv_v: 0.4 # image HSV-Value augmentation (fraction) degrees: 0.0 # image rotation (+/- deg) translate: 0.1 # image translation (+/- fraction) scale: 0.5 # image scale (+/- gain) shear: 0.0 # image shear (+/- deg) perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 0.0 # image mosaic (probability) mixup: 0.0 # image mixup (probability) ================================================ FILE: data/multispectral/CVC14.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # download command/URL (optional) download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] path: /home/shen/Chenyf/CVC14 train_rgb: /home/shen/Chenyf/CVC14/visible/train # 128 images val_rgb: /home/shen/Chenyf/CVC14/visible/test # 128 images train_ir: /home/shen/Chenyf/CVC14/infrared/train # 128 images val_ir: /home/shen/Chenyf/CVC14/infrared/test # 128 images # number of classes nc: 1 # class names names: ['person'] ================================================ FILE: data/multispectral/FLIR-align-3class.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # download command/URL (optional) download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] path: /home/shen/Chenyf/FLIR-align-3class train_rgb: /home/shen/Chenyf/FLIR-align-3class/visible/train # 128 images val_rgb: /home/shen/Chenyf/FLIR-align-3class/visible/test # 128 images train_ir: /home/shen/Chenyf/FLIR-align-3class/infrared/train # 128 images val_ir: /home/shen/Chenyf/FLIR-align-3class/infrared/test # 128 images # number of classes nc: 3 # class names names: ['person', 'car', 'bicycle'] ================================================ FILE: data/multispectral/FLIR-align.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # download command/URL (optional) download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] path: /home/shen/Chenyf/FLIR-align train_rgb: /home/shen/Chenyf/FLIR-align/visible/train # 128 images val_rgb: /home/shen/Chenyf/FLIR-align/visible/test # 128 images train_ir: /home/shen/Chenyf/FLIR-align/infrared/train # 128 images val_ir: /home/shen/Chenyf/FLIR-align/infrared/test # 128 images # number of classes nc: 1 # class names names: ['person'] ================================================ FILE: data/multispectral/LLVIP.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] path: /home/shen/Chenyf/LLVIP train_rgb: /home/shen/Chenyf/LLVIP/visible/train/ # 128 images val_rgb: /home/shen/Chenyf/LLVIP/visible/test/ # 128 images train_ir: /home/shen/Chenyf/LLVIP/infrared/train/ # 128 images val_ir: /home/shen/Chenyf/LLVIP/infrared/test/ # 128 images # number of classes nc: 1 # class names names: ['person'] ================================================ FILE: data/multispectral/VEDAI.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # download command/URL (optional) download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] path: /home/shen/Chenyf/VEDAI/fold01 train_rgb: /home/shen/Chenyf/VEDAI/fold01/visible/train # 128 images val_rgb: /home/shen/Chenyf/VEDAI/fold01/visible/test # 128 images train_ir: /home/shen/Chenyf/VEDAI/fold01/infrared/train # 128 images val_ir: /home/shen/Chenyf/VEDAI/fold01/infrared/test # 128 images # number of classes nc: 9 # class names names: ['car', 'truck', 'pickup', 'tractor', 'camper', 'ship', 'van', 'plane', 'other'] ================================================ FILE: data/multispectral/kaist.yaml ================================================ # COCO 2017 dataset http://cocodataset.org - first 128 training images # Train command: python train.py --data coco128.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /coco128 # /yolov5 # download command/URL (optional) download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] path: /home/shen/Chenyf/kaist train_rgb: /home/shen/Chenyf/kaist/visible/train/ # 128 images val_rgb: /home/shen/Chenyf/kaist/visible/test/ # 128 images train_ir: /home/shen/Chenyf/kaist/infrared/train/ # 128 images val_ir: /home/shen/Chenyf/kaist/infrared/test/ # 128 images # number of classes nc: 1 # class names names: ['person'] ================================================ FILE: data/scripts/get_argoverse_hd.sh ================================================ #!/bin/bash # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ # Download command: bash data/scripts/get_argoverse_hd.sh # Train command: python train.py --data argoverse_hd.yaml # Default dataset location is next to YOLOv5: # /parent_folder # /argoverse # /yolov5 # Download/unzip images d='../argoverse/' # unzip directory mkdir $d url=https://argoverse-hd.s3.us-east-2.amazonaws.com/ f=Argoverse-HD-Full.zip curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background wait # finish background tasks cd ../argoverse/Argoverse-1.1/ ln -s tracking images cd ../Argoverse-HD/annotations/ python3 - "$@" <train.txt cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val python3 - "$@" <= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0) p, s, im0_, frame = path, '', im0s_.copy(), getattr(dataset2, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # img.jpg txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh # print(gn) mem = '%.4gM' % (torch.cuda.memory_reserved() / 1E6 if torch.cuda.is_available() else 0) print('GPU Memory:', mem) # # ---------------------------------------------------------------------------- # # 画GT，替换det # # # # --------------------------------------------------------------------------- # annoPath = "/home/fqy/proj/paper/test_result/gt/" # annoName = (path_.split("/")[-1]).split(".")[0] + ".txt" # annoPath += annoName # # print(annoPath) # gt = np.loadtxt(annoPath) # gt = gt.reshape((-1, 5)) # ones = np.ones((gt.shape[0], 1)) # gt = np.hstack((gt, ones)) # gt[:, [0,1,2,3,4,5]] = gt[:, [1,2,3,4,5,0]] # gt = torch.from_numpy(gt).to(device) # # print(gt[:, :4]) # gt[:, :4] = xywh2xyxy(gt[:, :4]) * 640 # # det = gt # print(det) if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img or opt.save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}') plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness) plot_one_box(xyxy, im0_, label=label, color=colors(c, True), line_thickness=opt.line_thickness) if opt.save_crop: save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # Print time (inference + NMS) print(f'{s}Done. ({t2 - t1:.6f}s, {1/(t2 - t1):.6f}Hz)') # add all the fps img_num += 1 fps_sum += 1/(t2 - t1) # Stream results if view_img: cv2.imshow(str(p), img0) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == 'image': save_path_rgb = save_path.split('.')[0] + '_rgb.' + save_path.split('.')[1] save_path_ir = save_path.split('.')[0] + '_ir.' + save_path.split('.')[1] print(save_path_rgb) cv2.imwrite(save_path_rgb, im0) cv2.imwrite(save_path_ir, im0_) else: # 'video' or 'stream' if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer if vid_cap: # video fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path += '.mp4' vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f"Results saved to {save_dir}{s}") print(f'Done. ({time.time() - t0:.3f}s)') print(f'Average Speed: {fps_sum/img_num:.6f}Hz') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='/home/ubuntu/Chenyf/multispectral-object-detection/runs/train/exp6/weights/best.pt', help='model.pt path(s)') parser.add_argument('--source1', type=str, default='/home/shen/Chenyf/FLIR-align-3class/visible/test/', help='source') # file/folder, 0 for webcam parser.add_argument('--source2', type=str, default='/home/shen/Chenyf/FLIR-align-3class/infrared/test/', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.1, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', default=False, action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') parser.add_argument('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--update', action='store_true', help='update all models') parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--line-thickness', default=2, type=int, help='bounding box thickness (pixels)') parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') parser.add_argument('--hide-conf', default=True, action='store_true', help='hide confidences') opt = parser.parse_args() print(opt) check_requirements(exclude=('pycocotools', 'thop')) with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: detect(opt=opt) strip_optimizer(opt.weights) else: print("helloxxxxxxxxxxxxxxxxxxxx") detect(opt=opt) ================================================ FILE: evaluation_script/KAIST_annotation.json ================================================ { "info": { "dataset": "KAIST Multispectral Pedestrian Benchmark", "url": "https://soonminhwang.github.io/rgbt-ped-detection/", "related_project_url": "http://multispectral.kaist.ac.kr", "publish": "CVPR 2015" }, "info_improved": { "sanitized_annotation": { "publish": "BMVC 2018", "url": "https://li-chengyang.github.io/home/MSDS-RCNN/", "target": "files in train-all-02.txt (set00-set05)" }, "improved_annotation": { "url": "https://github.com/denny1108/multispectral-pedestrian-py-faster-rcnn", "publish": "BMVC 2016", "target": "files in test-all-20.txt (set06-set11)" } }, "images": [ { "id": 0, "im_name": "set06/V000/I00019", "height": 512, "width": 640 }, { "id": 1, "im_name": "set06/V000/I00039", "height": 512, "width": 640 }, { "id": 2, "im_name": "set06/V000/I00059", "height": 512, "width": 640 }, { "id": 3, "im_name": "set06/V000/I00079", "height": 512, "width": 640 }, { "id": 4, "im_name": "set06/V000/I00099", "height": 512, "width": 640 }, { "id": 5, "im_name": "set06/V000/I00119", "height": 512, "width": 640 }, { "id": 6, "im_name": "set06/V000/I00139", "height": 512, "width": 640 }, { "id": 7, "im_name": "set06/V000/I00159", "height": 512, "width": 640 }, { "id": 8, "im_name": "set06/V000/I00179", "height": 512, "width": 640 }, { "id": 9, "im_name": "set06/V000/I00199", "height": 512, "width": 640 }, { "id": 10, "im_name": "set06/V000/I00219", "height": 512, "width": 640 }, { "id": 11, "im_name": "set06/V000/I00239", "height": 512, "width": 640 }, { "id": 12, "im_name": "set06/V000/I00259", "height": 512, "width": 640 }, { "id": 13, "im_name": "set06/V000/I00279", "height": 512, "width": 640 }, { "id": 14, "im_name": "set06/V000/I00299", "height": 512, "width": 640 }, { "id": 15, "im_name": "set06/V000/I00319", "height": 512, "width": 640 }, { "id": 16, "im_name": "set06/V000/I00339", "height": 512, "width": 640 }, { "id": 17, "im_name": "set06/V000/I00359", "height": 512, "width": 640 }, { "id": 18, "im_name": "set06/V000/I00379", "height": 512, "width": 640 }, { "id": 19, "im_name": "set06/V000/I00399", "height": 512, "width": 640 }, { "id": 20, "im_name": "set06/V000/I00419", "height": 512, "width": 640 }, { "id": 21, "im_name": "set06/V000/I00439", "height": 512, "width": 640 }, { "id": 22, "im_name": "set06/V000/I00459", "height": 512, "width": 640 }, { "id": 23, "im_name": "set06/V000/I00479", "height": 512, "width": 640 }, { "id": 24, "im_name": "set06/V000/I00499", "height": 512, "width": 640 }, { "id": 25, "im_name": "set06/V000/I00519", "height": 512, "width": 640 }, { "id": 26, "im_name": "set06/V000/I00539", "height": 512, "width": 640 }, { "id": 27, "im_name": "set06/V000/I00559", "height": 512, "width": 640 }, { "id": 28, "im_name": "set06/V000/I00579", "height": 512, "width": 640 }, { "id": 29, "im_name": "set06/V000/I00599", "height": 512, "width": 640 }, { "id": 30, "im_name": "set06/V000/I00619", "height": 512, "width": 640 }, { "id": 31, "im_name": "set06/V000/I00639", "height": 512, "width": 640 }, { "id": 32, "im_name": "set06/V000/I00659", "height": 512, "width": 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2249, "category_id": 1, "bbox": [ 71, 203, 25, 45 ], "height": 45, "occlusion": 0, "ignore": 0 }, { "id": 4253, "image_id": 2250, "category_id": 1, "bbox": [ 16, 196, 29, 57 ], "height": 57, "occlusion": 0, "ignore": 0 } ], "categories": [ { "id": 0, "name": "__ignore__" }, { "id": 1, "name": "person" }, { "id": 2, "name": "cyclist" }, { "id": 3, "name": "people" }, { "id": 4, "name": "person?" } ] } ================================================ FILE: evaluation_script/README.md ================================================ ================================================ FILE: evaluation_script/__init__.py ================================================ ================================================ FILE: evaluation_script/coco.py ================================================ __author__ = 'tylin' __version__ = '2.0' # Interface for accessing the Microsoft COCO dataset. # Microsoft COCO is a large image dataset designed for object detection, # segmentation, and caption generation. pycocotools is a Python API that # assists in loading, parsing and visualizing the annotations in COCO. # Please visit http://mscoco.org/ for more information on COCO, including # for the data, paper, and tutorials. The exact format of the annotations # is also described on the COCO website. For example usage of the pycocotools # please see pycocotools_demo.ipynb. In addition to this API, please download both # the COCO images and annotations in order to run the demo. # An alternative to using the API is to load the annotations directly # into Python dictionary # Using the API provides additional utility functions. Note that this API # supports both *instance* and *caption* annotations. In the case of # captions not all functions are defined (e.g. categories are undefined). # The following API functions are defined: # COCO - COCO api class that loads COCO annotation file and prepare data structures. # decodeMask - Decode binary mask M encoded via run-length encoding. # encodeMask - Encode binary mask M using run-length encoding. # getAnnIds - Get ann ids that satisfy given filter conditions. # getCatIds - Get cat ids that satisfy given filter conditions. # getImgIds - Get img ids that satisfy given filter conditions. # loadAnns - Load anns with the specified ids. # loadCats - Load cats with the specified ids. # loadImgs - Load imgs with the specified ids. # annToMask - Convert segmentation in an annotation to binary mask. # showAnns - Display the specified annotations. # loadRes - Load algorithm results and create API for accessing them. # download - Download COCO images from mscoco.org server. # Throughout the API "ann"=annotation, "cat"=category, and "img"=image. # Help on each functions can be accessed by: "help COCO>function". # See also COCO>decodeMask, # COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds, # COCO>getImgIds, COCO>loadAnns, COCO>loadCats, # COCO>loadImgs, COCO>annToMask, COCO>showAnns # Microsoft COCO Toolbox. version 2.0 # Data, paper, and tutorials available at: http://mscoco.org/ # Code written by Piotr Dollar and Tsung-Yi Lin, 2014. # Licensed under the Simplified BSD License [see bsd.txt] import json import time import matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon import numpy as np import copy import itertools # from . import mask as maskUtils import os from collections import defaultdict import sys PYTHON_VERSION = sys.version_info[0] if PYTHON_VERSION == 2: from urllib import urlretrieve elif PYTHON_VERSION == 3: from urllib.request import urlretrieve def _isArrayLike(obj): return hasattr(obj, '__iter__') and hasattr(obj, '__len__') class COCO: def __init__(self, annotation_file=None): """ Constructor of Microsoft COCO helper class for reading and visualizing annotations. :param annotation_file (str): location of annotation file :param image_folder (str): location to the folder that hosts images. :return: """ # load dataset self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict() self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) if not annotation_file == None: #print('loading annotations into memory...') tic = time.time() dataset = json.load(open(annotation_file, 'r')) assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) #print('Done (t={:0.2f}s)'.format(time.time()- tic)) self.dataset = dataset self.createIndex() def createIndex(self): # create index #print('creating index...') anns, cats, imgs = {}, {}, {} imgToAnns,catToImgs = defaultdict(list), defaultdict(list) if 'annotations' in self.dataset: for ann in self.dataset['annotations']: imgToAnns[ann['image_id']].append(ann) anns[ann['id']] = ann if 'images' in self.dataset: for img in self.dataset['images']: imgs[img['id']] = img if 'categories' in self.dataset: for cat in self.dataset['categories']: cats[cat['id']] = cat if 'annotations' in self.dataset and 'categories' in self.dataset: for ann in self.dataset['annotations']: catToImgs[ann['category_id']].append(ann['image_id']) #print('index created!') # create class members self.anns = anns self.imgToAnns = imgToAnns self.catToImgs = catToImgs self.imgs = imgs self.cats = cats def info(self): """ Print information about the annotation file. :return: """ for key, value in self.dataset['info'].items(): print('{}: {}'.format(key, value)) def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): """ Get ann ids that satisfy given filter conditions. default skips that filter :param imgIds (int array) : get anns for given imgs catIds (int array) : get anns for given cats areaRng (float array) : get anns for given area range (e.g. [0 inf]) iscrowd (boolean) : get anns for given crowd label (False or True) :return: ids (int array) : integer array of ann ids """ imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(imgIds) == len(catIds) == len(areaRng) == 0: anns = self.dataset['annotations'] else: if not len(imgIds) == 0: lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns] anns = list(itertools.chain.from_iterable(lists)) else: anns = self.dataset['annotations'] anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] if not iscrowd == None: ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] else: ids = [ann['id'] for ann in anns] return ids def getCatIds(self, catNms=[], supNms=[], catIds=[]): """ filtering parameters. default skips that filter. :param catNms (str array) : get cats for given cat names :param supNms (str array) : get cats for given supercategory names :param catIds (int array) : get cats for given cat ids :return: ids (int array) : integer array of cat ids """ catNms = catNms if _isArrayLike(catNms) else [catNms] supNms = supNms if _isArrayLike(supNms) else [supNms] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(catNms) == len(supNms) == len(catIds) == 0: cats = self.dataset['categories'] else: cats = self.dataset['categories'] cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] ids = [cat['id'] for cat in cats] return ids def getImgIds(self, imgIds=[], catIds=[]): ''' Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids ''' imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] catIds = catIds if _isArrayLike(catIds) else [catIds] if len(imgIds) == len(catIds) == 0: ids = self.imgs.keys() else: ids = set(imgIds) for i, catId in enumerate(catIds): if i == 0 and len(ids) == 0: ids = set(self.catToImgs[catId]) else: ids &= set(self.catToImgs[catId]) return list(ids) def loadAnns(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying anns :return: anns (object array) : loaded ann objects """ if _isArrayLike(ids): return [self.anns[id] for id in ids] elif type(ids) == int: return [self.anns[ids]] def loadCats(self, ids=[]): """ Load cats with the specified ids. :param ids (int array) : integer ids specifying cats :return: cats (object array) : loaded cat objects """ if _isArrayLike(ids): return [self.cats[id] for id in ids] elif type(ids) == int: return [self.cats[ids]] def loadImgs(self, ids=[]): """ Load anns with the specified ids. :param ids (int array) : integer ids specifying img :return: imgs (object array) : loaded img objects """ if _isArrayLike(ids): return [self.imgs[id] for id in ids] elif type(ids) == int: return [self.imgs[ids]] def showAnns(self, anns, draw_bbox=False): """ Display the specified annotations. :param anns (array of object): annotations to display :return: None """ if len(anns) == 0: return 0 if 'segmentation' in anns[0] or 'keypoints' in anns[0]: datasetType = 'instances' elif 'caption' in anns[0]: datasetType = 'captions' else: raise Exception('datasetType not supported') if datasetType == 'instances': ax = plt.gca() ax.set_autoscale_on(False) polygons = [] color = [] for ann in anns: c = (np.random.random((1, 3))*0.6+0.4).tolist()[0] if 'segmentation' in ann: if type(ann['segmentation']) == list: # polygon for seg in ann['segmentation']: poly = np.array(seg).reshape((int(len(seg)/2), 2)) polygons.append(Polygon(poly)) color.append(c) else: # mask t = self.imgs[ann['image_id']] if type(ann['segmentation']['counts']) == list: rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width']) else: rle = [ann['segmentation']] m = maskUtils.decode(rle) img = np.ones( (m.shape[0], m.shape[1], 3) ) if ann['iscrowd'] == 1: color_mask = np.array([2.0,166.0,101.0])/255 if ann['iscrowd'] == 0: color_mask = np.random.random((1, 3)).tolist()[0] for i in range(3): img[:,:,i] = color_mask[i] ax.imshow(np.dstack( (img, m*0.5) )) if 'keypoints' in ann and type(ann['keypoints']) == list: # turn skeleton into zero-based index sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1 kp = np.array(ann['keypoints']) x = kp[0::3] y = kp[1::3] v = kp[2::3] for sk in sks: if np.all(v[sk]>0): plt.plot(x[sk],y[sk], linewidth=3, color=c) plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2) plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2) if draw_bbox: [bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox'] poly = [[bbox_x, bbox_y], [bbox_x, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y+bbox_h], [bbox_x+bbox_w, bbox_y]] np_poly = np.array(poly).reshape((4,2)) polygons.append(Polygon(np_poly)) color.append(c) p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) ax.add_collection(p) p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) ax.add_collection(p) elif datasetType == 'captions': for ann in anns: print(ann['caption']) def loadRes(self, resFile): """ Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object """ res = COCO() res.dataset['images'] = [img for img in self.dataset['images']] #print('Loading and preparing results...') tic = time.time() if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode): anns = json.load(open(resFile)) elif type(resFile) == np.ndarray: anns = self.loadNumpyAnnotations(resFile) else: anns = resFile assert type(anns) == list, 'results in not an array of objects' annsImgIds = [ann['image_id'] for ann in anns] assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ 'Results do not correspond to current coco set' if 'caption' in anns[0]: imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] for id, ann in enumerate(anns): ann['id'] = id+1 elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): bb = ann['bbox'] x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] if not 'segmentation' in ann: ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] ann['area'] = bb[2]*bb[3] ann['id'] = id+1 ann['iscrowd'] = 0 elif 'segmentation' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): # now only support compressed RLE format as segmentation results ann['area'] = maskUtils.area(ann['segmentation']) if not 'bbox' in ann: ann['bbox'] = maskUtils.toBbox(ann['segmentation']) ann['id'] = id+1 ann['iscrowd'] = 0 elif 'keypoints' in anns[0]: res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) for id, ann in enumerate(anns): s = ann['keypoints'] x = s[0::3] y = s[1::3] x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y) ann['area'] = (x1-x0)*(y1-y0) ann['id'] = id + 1 ann['bbox'] = [x0, y0, x1-x0, y1-y0] #print('DONE (t={:0.2f}s)'.format(time.time()- tic)) res.dataset['annotations'] = anns res.createIndex() return res def download(self, tarDir = None, imgIds = [] ): ''' Download COCO images from mscoco.org server. :param tarDir (str): COCO results directory name imgIds (list): images to be downloaded :return: ''' if tarDir is None: print('Please specify target directory') return -1 if len(imgIds) == 0: imgs = self.imgs.values() else: imgs = self.loadImgs(imgIds) N = len(imgs) if not os.path.exists(tarDir): os.makedirs(tarDir) for i, img in enumerate(imgs): tic = time.time() fname = os.path.join(tarDir, img['file_name']) if not os.path.exists(fname): urlretrieve(img['coco_url'], fname) print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) def loadNumpyAnnotations(self, data): """ Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} :param data (numpy.ndarray) :return: annotations (python nested list) """ print('Converting ndarray to lists...') assert(type(data) == np.ndarray) print(data.shape) assert(data.shape[1] == 7) N = data.shape[0] ann = [] for i in range(N): if i % 1000000 == 0: print('{}/{}'.format(i,N)) ann += [{ 'image_id' : int(data[i, 0]), 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], 'score' : data[i, 5], 'category_id': int(data[i, 6]), }] return ann def annToRLE(self, ann): """ Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array) """ t = self.imgs[ann['image_id']] h, w = t['height'], t['width'] segm = ann['segmentation'] if type(segm) == list: # polygon -- a single object might consist of multiple parts # we merge all parts into one mask rle code rles = maskUtils.frPyObjects(segm, h, w) rle = maskUtils.merge(rles) elif type(segm['counts']) == list: # uncompressed RLE rle = maskUtils.frPyObjects(segm, h, w) else: # rle rle = ann['segmentation'] return rle def annToMask(self, ann): """ Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. :return: binary mask (numpy 2D array) """ rle = self.annToRLE(ann) m = maskUtils.decode(rle) return m ================================================ FILE: evaluation_script/cocoeval.py ================================================ __author__ = 'tsungyi' import numpy as np import datetime import time from collections import defaultdict # from . import mask as maskUtils import copy class COCOeval: # Interface for evaluating detection on the Microsoft COCO dataset. # # The usage for CocoEval is as follows: # cocoGt=..., cocoDt=... # load dataset and results # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object # E.params.recThrs = ...; # set parameters as desired # E.evaluate(); # run per image evaluation # E.accumulate(); # accumulate per image results # E.summarize(); # display summary metrics of results # For example usage see evalDemo.m and http://mscoco.org/. # # The evaluation parameters are as follows (defaults in brackets): # imgIds - [all] N img ids to use for evaluation # catIds - [all] K cat ids to use for evaluation # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation # recThrs - [0:.01:1] R=101 recall thresholds for evaluation # areaRng - [...] A=4 object area ranges for evaluation # maxDets - [1 10 100] M=3 thresholds on max detections per image # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' # iouType replaced the now DEPRECATED useSegm parameter. # useCats - [1] if true use category labels for evaluation # Note: if useCats=0 category labels are ignored as in proposal scoring. # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. # # evaluate(): evaluates detections on every image and every category and # concats the results into the "evalImgs" with fields: # dtIds - [1xD] id for each of the D detections (dt) # gtIds - [1xG] id for each of the G ground truths (gt) # dtMatches - [TxD] matching gt id at each IoU or 0 # gtMatches - [TxG] matching dt id at each IoU or 0 # dtScores - [1xD] confidence of each dt # gtIgnore - [1xG] ignore flag for each gt # dtIgnore - [TxD] ignore flag for each dt at each IoU # # accumulate(): accumulates the per-image, per-category evaluation # results in "evalImgs" into the dictionary "eval" with fields: # params - parameters used for evaluation # date - date evaluation was performed # counts - [T,R,K,A,M] parameter dimensions (see above) # precision - [TxRxKxAxM] precision for every evaluation setting # recall - [TxKxAxM] max recall for every evaluation setting # Note: precision and recall==-1 for settings with no gt objects. # # See also coco, mask, pycocoDemo, pycocoEvalDemo # # Microsoft COCO Toolbox. version 2.0 # Data, paper, and tutorials available at: http://mscoco.org/ # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. # Licensed under the Simplified BSD License [see coco/license.txt] def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'): ''' Initialize CocoEval using coco APIs for gt and dt :param cocoGt: coco object with ground truth annotations :param cocoDt: coco object with detection results :return: None ''' if not iouType: print('iouType not specified. use default iouType segm') self.cocoGt = cocoGt # ground truth COCO API self.cocoDt = cocoDt # detections COCO API self.evalImgs = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements self.eval = {} # accumulated evaluation results self._gts = defaultdict(list) # gt for evaluation self._dts = defaultdict(list) # dt for evaluation self.params = Params(iouType=iouType) # parameters self._paramsEval = {} # parameters for evaluation self.stats = [] # result summarization self.ious = {} # ious between all gts and dts if not cocoGt is None: self.params.imgIds = sorted(cocoGt.getImgIds()) self.params.catIds = sorted(cocoGt.getCatIds()) def _prepare(self): ''' Prepare ._gts and ._dts for evaluation based on params :return: None ''' def _toMask(anns, coco): # modify ann['segmentation'] by reference for ann in anns: rle = coco.annToRLE(ann) ann['segmentation'] = rle p = self.params if p.useCats: gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) else: gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) # convert ground truth to mask if iouType == 'segm' if p.iouType == 'segm': _toMask(gts, self.cocoGt) _toMask(dts, self.cocoDt) # set ignore flag for gt in gts: gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 gt['ignore'] = 'iscrowd' in gt and gt['iscrowd'] if p.iouType == 'keypoints': gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore'] self._gts = defaultdict(list) # gt for evaluation self._dts = defaultdict(list) # dt for evaluation for gt in gts: self._gts[gt['image_id'], gt['category_id']].append(gt) for dt in dts: self._dts[dt['image_id'], dt['category_id']].append(dt) self.evalImgs = defaultdict(list) # per-image per-category evaluation results self.eval = {} # accumulated evaluation results def evaluate(self): ''' Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None ''' tic = time.time() print('Running per image evaluation...') p = self.params # add backward compatibility if useSegm is specified in params if not p.useSegm is None: p.iouType = 'segm' if p.useSegm == 1 else 'bbox' print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) print('Evaluate annotation type *{}*'.format(p.iouType)) p.imgIds = list(np.unique(p.imgIds)) if p.useCats: p.catIds = list(np.unique(p.catIds)) p.maxDets = sorted(p.maxDets) self.params=p self._prepare() # loop through images, area range, max detection number catIds = p.catIds if p.useCats else [-1] if p.iouType == 'segm' or p.iouType == 'bbox': computeIoU = self.computeIoU elif p.iouType == 'keypoints': computeIoU = self.computeOks self.ious = {(imgId, catId): computeIoU(imgId, catId) \ for imgId in p.imgIds for catId in catIds} evaluateImg = self.evaluateImg maxDet = p.maxDets[-1] self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet) for catId in catIds for areaRng in p.areaRng for imgId in p.imgIds ] self._paramsEval = copy.deepcopy(self.params) toc = time.time() print('DONE (t={:0.2f}s).'.format(toc-tic)) def computeIoU(self, imgId, catId): p = self.params if p.useCats: gt = self._gts[imgId,catId] dt = self._dts[imgId,catId] else: gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] if len(gt) == 0 and len(dt) ==0: return [] inds = np.argsort([-d['score'] for d in dt], kind='mergesort') dt = [dt[i] for i in inds] if len(dt) > p.maxDets[-1]: dt=dt[0:p.maxDets[-1]] if p.iouType == 'segm': g = [g['segmentation'] for g in gt] d = [d['segmentation'] for d in dt] elif p.iouType == 'bbox': g = [g['bbox'] for g in gt] d = [d['bbox'] for d in dt] else: raise Exception('unknown iouType for iou computation') # compute iou between each dt and gt region iscrowd = [int(o['iscrowd']) for o in gt] ious = maskUtils.iou(d,g,iscrowd) return ious def computeOks(self, imgId, catId): p = self.params # dimention here should be Nxm gts = self._gts[imgId, catId] dts = self._dts[imgId, catId] inds = np.argsort([-d['score'] for d in dts], kind='mergesort') dts = [dts[i] for i in inds] if len(dts) > p.maxDets[-1]: dts = dts[0:p.maxDets[-1]] # if len(gts) == 0 and len(dts) == 0: if len(gts) == 0 or len(dts) == 0: return [] ious = np.zeros((len(dts), len(gts))) sigmas = p.kpt_oks_sigmas vars = (sigmas * 2)**2 k = len(sigmas) # compute oks between each detection and ground truth object for j, gt in enumerate(gts): # create bounds for ignore regions(double the gt bbox) g = np.array(gt['keypoints']) xg = g[0::3]; yg = g[1::3]; vg = g[2::3] k1 = np.count_nonzero(vg > 0) bb = gt['bbox'] x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2 y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2 for i, dt in enumerate(dts): d = np.array(dt['keypoints']) xd = d[0::3]; yd = d[1::3] if k1>0: # measure the per-keypoint distance if keypoints visible dx = xd - xg dy = yd - yg else: # measure minimum distance to keypoints in (x0,y0) & (x1,y1) z = np.zeros((k)) dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0) dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0) e = (dx**2 + dy**2) / vars / (gt['area']+np.spacing(1)) / 2 if k1 > 0: e=e[vg > 0] ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] return ious def evaluateImg(self, imgId, catId, aRng, maxDet): ''' perform evaluation for single category and image :return: dict (single image results) ''' p = self.params if p.useCats: gt = self._gts[imgId,catId] dt = self._dts[imgId,catId] else: gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] if len(gt) == 0 and len(dt) ==0: return None for g in gt: if g['ignore'] or (g['area']aRng[1]): g['_ignore'] = 1 else: g['_ignore'] = 0 # sort dt highest score first, sort gt ignore last gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') gt = [gt[i] for i in gtind] dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') dt = [dt[i] for i in dtind[0:maxDet]] iscrowd = [int(o['iscrowd']) for o in gt] # load computed ious ious = self.ious[imgId, catId][:, gtind] if len(self.ious[imgId, catId]) > 0 else self.ious[imgId, catId] T = len(p.iouThrs) G = len(gt) D = len(dt) gtm = np.zeros((T,G)) dtm = np.zeros((T,D)) gtIg = np.array([g['_ignore'] for g in gt]) dtIg = np.zeros((T,D)) if not len(ious)==0: for tind, t in enumerate(p.iouThrs): for dind, d in enumerate(dt): # information about best match so far (m=-1 -> unmatched) iou = min([t,1-1e-10]) m = -1 for gind, g in enumerate(gt): # if this gt already matched, and not a crowd, continue if gtm[tind,gind]>0 and not iscrowd[gind]: continue # if dt matched to reg gt, and on ignore gt, stop if m>-1 and gtIg[m]==0 and gtIg[gind]==1: break # continue to next gt unless better match made if ious[dind,gind] < iou: continue # if match successful and best so far, store appropriately iou=ious[dind,gind] m=gind # if match made store id of match for both dt and gt if m ==-1: continue dtIg[tind,dind] = gtIg[m] dtm[tind,dind] = gt[m]['id'] gtm[tind,m] = d['id'] # set unmatched detections outside of area range to ignore a = np.array([d['area']aRng[1] for d in dt]).reshape((1, len(dt))) dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) # store results for given image and category return { 'image_id': imgId, 'category_id': catId, 'aRng': aRng, 'maxDet': maxDet, 'dtIds': [d['id'] for d in dt], 'gtIds': [g['id'] for g in gt], 'dtMatches': dtm, 'gtMatches': gtm, 'dtScores': [d['score'] for d in dt], 'gtIgnore': gtIg, 'dtIgnore': dtIg, } def accumulate(self, p = None): ''' Accumulate per image evaluation results and store the result in self.eval :param p: input params for evaluation :return: None ''' print('Accumulating evaluation results...') tic = time.time() if not self.evalImgs: print('Please run evaluate() first') # allows input customized parameters if p is None: p = self.params p.catIds = p.catIds if p.useCats == 1 else [-1] T = len(p.iouThrs) R = len(p.recThrs) K = len(p.catIds) if p.useCats else 1 A = len(p.areaRng) M = len(p.maxDets) precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories recall = -np.ones((T,K,A,M)) scores = -np.ones((T,R,K,A,M)) # create dictionary for future indexing _pe = self._paramsEval catIds = _pe.catIds if _pe.useCats else [-1] setK = set(catIds) setA = set(map(tuple, _pe.areaRng)) setM = set(_pe.maxDets) setI = set(_pe.imgIds) # get inds to evaluate k_list = [n for n, k in enumerate(p.catIds) if k in setK] m_list = [m for n, m in enumerate(p.maxDets) if m in setM] a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] i_list = [n for n, i in enumerate(p.imgIds) if i in setI] I0 = len(_pe.imgIds) A0 = len(_pe.areaRng) # retrieve E at each category, area range, and max number of detections for k, k0 in enumerate(k_list): Nk = k0*A0*I0 for a, a0 in enumerate(a_list): Na = a0*I0 for m, maxDet in enumerate(m_list): E = [self.evalImgs[Nk + Na + i] for i in i_list] E = [e for e in E if not e is None] if len(E) == 0: continue dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) # different sorting method generates slightly different results. # mergesort is used to be consistent as Matlab implementation. inds = np.argsort(-dtScores, kind='mergesort') dtScoresSorted = dtScores[inds] dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds] dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds] gtIg = np.concatenate([e['gtIgnore'] for e in E]) npig = np.count_nonzero(gtIg==0 ) if npig == 0: continue tps = np.logical_and( dtm, np.logical_not(dtIg) ) fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): tp = np.array(tp) fp = np.array(fp) nd = len(tp) rc = tp / npig pr = tp / (fp+tp+np.spacing(1)) q = np.zeros((R,)) ss = np.zeros((R,)) if nd: recall[t,k,a,m] = rc[-1] else: recall[t,k,a,m] = 0 # numpy is slow without cython optimization for accessing elements # use python array gets significant speed improvement pr = pr.tolist(); q = q.tolist() for i in range(nd-1, 0, -1): if pr[i] > pr[i-1]: pr[i-1] = pr[i] inds = np.searchsorted(rc, p.recThrs, side='left') try: for ri, pi in enumerate(inds): q[ri] = pr[pi] ss[ri] = dtScoresSorted[pi] except: pass precision[t,:,k,a,m] = np.array(q) scores[t,:,k,a,m] = np.array(ss) self.eval = { 'params': p, 'counts': [T, R, K, A, M], 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'precision': precision, 'recall': recall, 'scores': scores, } toc = time.time() print('DONE (t={:0.2f}s).'.format( toc-tic)) def summarize(self): ''' Compute and display summary metrics for evaluation results. Note this functin can *only* be applied on the default parameter setting ''' def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): p = self.params iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' titleStr = 'Average Precision' if ap == 1 else 'Average Recall' typeStr = '(AP)' if ap==1 else '(AR)' iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ if iouThr is None else '{:0.2f}'.format(iouThr) aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] if ap == 1: # dimension of precision: [TxRxKxAxM] s = self.eval['precision'] # IoU if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:,:,:,aind,mind] else: # dimension of recall: [TxKxAxM] s = self.eval['recall'] if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:,:,aind,mind] if len(s[s>-1])==0: mean_s = -1 else: mean_s = np.mean(s[s>-1]) print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) return mean_s def _summarizeDets(): stats = np.zeros((12,)) stats[0] = _summarize(1) stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) return stats def _summarizeKps(): stats = np.zeros((10,)) stats[0] = _summarize(1, maxDets=20) stats[1] = _summarize(1, maxDets=20, iouThr=.5) stats[2] = _summarize(1, maxDets=20, iouThr=.75) stats[3] = _summarize(1, maxDets=20, areaRng='medium') stats[4] = _summarize(1, maxDets=20, areaRng='large') stats[5] = _summarize(0, maxDets=20) stats[6] = _summarize(0, maxDets=20, iouThr=.5) stats[7] = _summarize(0, maxDets=20, iouThr=.75) stats[8] = _summarize(0, maxDets=20, areaRng='medium') stats[9] = _summarize(0, maxDets=20, areaRng='large') return stats if not self.eval: raise Exception('Please run accumulate() first') iouType = self.params.iouType if iouType == 'segm' or iouType == 'bbox': summarize = _summarizeDets elif iouType == 'keypoints': summarize = _summarizeKps self.stats = summarize() def __str__(self): self.summarize() class Params: ''' Params for coco evaluation api ''' def setDetParams(self): self.imgIds = [] self.catIds = [] # np.arange causes trouble. the data point on arange is slightly larger than the true value self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True) self.maxDets = [1, 10, 100] self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] self.areaRngLbl = ['all', 'small', 'medium', 'large'] self.useCats = 1 def setKpParams(self): self.imgIds = [] self.catIds = [] # np.arange causes trouble. the data point on arange is slightly larger than the true value self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True) self.maxDets = [20] self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] self.areaRngLbl = ['all', 'medium', 'large'] self.useCats = 1 self.kpt_oks_sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 def __init__(self, iouType='segm'): if iouType == 'segm' or iouType == 'bbox': self.setDetParams() elif iouType == 'keypoints': self.setKpParams() else: raise Exception('iouType not supported') self.iouType = iouType # useSegm is deprecated self.useSegm = None ================================================ FILE: evaluation_script/evaluation_script.py ================================================ """Evaluate performance on multispectral pedestrian detection benchmark This script evalutes multispectral detection performance. We adopt [cocoapi](https://github.com/cocodataset/cocoapi) and apply minor modification for KAISTPed benchmark. """ from collections import defaultdict import argparse import copy import datetime import json import matplotlib import numpy as np import os import pdb import sys import tempfile import traceback # matplotlib.use('Agg') # from matplotlib.patches import Polygon import matplotlib.pyplot as plt from evaluation_script.coco import COCO from evaluation_script.cocoeval import COCOeval, Params font = {'size': 22} matplotlib.rc('font', **font) class KAISTPedEval(COCOeval): def __init__(self, kaistGt=None, kaistDt=None, iouType='segm', method='unknown'): ''' Initialize CocoEval using coco APIs for gt and dt :param cocoGt: coco object with ground truth annotations :param cocoDt: coco object with detection results :return: None ''' super().__init__(kaistGt, kaistDt, iouType) self.params = KAISTParams(iouType=iouType) # parameters self.method = method def _prepare(self, id_setup): ''' Prepare ._gts and ._dts for evaluation based on params :return: None ''' p = self.params if p.useCats: gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) else: gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) # set ignore flag for gt in gts: gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 gbox = gt['bbox'] gt['ignore'] = 1 \ if gt['height'] < self.params.HtRng[id_setup][0] \ or gt['height'] > self.params.HtRng[id_setup][1] \ or gt['occlusion'] not in self.params.OccRng[id_setup] \ or gbox[0] < self.params.bndRng[0] \ or gbox[1] < self.params.bndRng[1] \ or gbox[0] + gbox[2] > self.params.bndRng[2] \ or gbox[1] + gbox[3] > self.params.bndRng[3] \ else gt['ignore'] self._gts = defaultdict(list) # gt for evaluation self._dts = defaultdict(list) # dt for evaluation for gt in gts: self._gts[gt['image_id'], gt['category_id']].append(gt) for dt in dts: self._dts[dt['image_id'], dt['category_id']].append(dt) self.evalImgs = defaultdict(list) # per-image per-category evaluation results self.eval = {} # accumulated evaluation results def evaluate(self, id_setup): ''' Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None ''' p = self.params # add backward compatibility if useSegm is specified in params if p.useSegm is not None: p.iouType = 'segm' if p.useSegm == 1 else 'bbox' #print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) # print('Evaluate annotation type *{}*'.format(p.iouType)) p.imgIds = list(np.unique(p.imgIds)) if p.useCats: p.catIds = list(np.unique(p.catIds)) p.maxDets = sorted(p.maxDets) self.params = p self._prepare(id_setup) # loop through images, area range, max detection number catIds = p.catIds if p.useCats else [-1] computeIoU = self.computeIoU self.ious = {(imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds} evaluateImg = self.evaluateImg maxDet = p.maxDets[-1] HtRng = self.params.HtRng[id_setup] OccRng = self.params.OccRng[id_setup] self.evalImgs = [evaluateImg(imgId, catId, HtRng, OccRng, maxDet) for catId in catIds for imgId in p.imgIds] self._paramsEval = copy.deepcopy(self.params) def computeIoU(self, imgId, catId): p = self.params if p.useCats: gt = self._gts[imgId, catId] dt = self._dts[imgId, catId] else: gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] if len(gt) == 0 and len(dt) == 0: return [] inds = np.argsort([-d['score'] for d in dt], kind='mergesort') dt = [dt[i] for i in inds] if len(dt) > p.maxDets[-1]: dt = dt[0:p.maxDets[-1]] if p.iouType == 'segm': g = [g['segmentation'] for g in gt] d = [d['segmentation'] for d in dt] elif p.iouType == 'bbox': g = [g['bbox'] for g in gt] d = [d['bbox'] for d in dt] else: raise Exception('unknown iouType for iou computation') # compute iou between each dt and gt region iscrowd = [int(o['ignore']) for o in gt] ious = self.iou(d, g, iscrowd) return ious def iou(self, dts, gts, pyiscrowd): dts = np.asarray(dts) gts = np.asarray(gts) pyiscrowd = np.asarray(pyiscrowd) ious = np.zeros((len(dts), len(gts))) for j, gt in enumerate(gts): gx1 = gt[0] gy1 = gt[1] gx2 = gt[0] + gt[2] gy2 = gt[1] + gt[3] garea = gt[2] * gt[3] for i, dt in enumerate(dts): dx1 = dt[0] dy1 = dt[1] dx2 = dt[0] + dt[2] dy2 = dt[1] + dt[3] darea = dt[2] * dt[3] unionw = min(dx2, gx2) - max(dx1, gx1) if unionw <= 0: continue unionh = min(dy2, gy2) - max(dy1, gy1) if unionh <= 0: continue t = unionw * unionh if pyiscrowd[j]: unionarea = darea else: unionarea = darea + garea - t ious[i, j] = float(t) / unionarea return ious def evaluateImg(self, imgId, catId, hRng, oRng, maxDet): ''' perform evaluation for single category and image :return: dict (single image results) ''' try: p = self.params if p.useCats: gt = self._gts[imgId, catId] dt = self._dts[imgId, catId] else: gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]] dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]] if len(gt) == 0 and len(dt) == 0: return None for g in gt: if g['ignore']: g['_ignore'] = 1 else: g['_ignore'] = 0 # sort dt highest score first, sort gt ignore last gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') gt = [gt[i] for i in gtind] dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') dt = [dt[i] for i in dtind[0:maxDet]] if len(dt) == 0: return None # load computed ious ious = self.ious[imgId, catId][dtind, :] if len(self.ious[imgId, catId]) > 0 else self.ious[imgId, catId] ious = ious[:, gtind] T = len(p.iouThrs) G = len(gt) D = len(dt) gtm = np.zeros((T, G)) dtm = np.zeros((T, D)) gtIg = np.array([g['_ignore'] for g in gt]) dtIg = np.zeros((T, D)) if not len(ious) == 0: for tind, t in enumerate(p.iouThrs): for dind, d in enumerate(dt): # information about best match so far (m=-1 -> unmatched) iou = min([t, 1 - 1e-10]) bstOa = iou bstg = -2 bstm = -2 for gind, g in enumerate(gt): m = gtm[tind, gind] # if this gt already matched, and not a crowd, continue if m > 0: continue # if dt matched to reg gt, and on ignore gt, stop if bstm != -2 and gtIg[gind] == 1: break # continue to next gt unless better match made if ious[dind, gind] < bstOa: continue # if match successful and best so far, store appropriately bstOa = ious[dind, gind] bstg = gind if gtIg[gind] == 0: bstm = 1 else: bstm = -1 # if match made store id of match for both dt and gt if bstg == -2: continue dtIg[tind, dind] = gtIg[bstg] dtm[tind, dind] = gt[bstg]['id'] if bstm == 1: gtm[tind, bstg] = d['id'] except Exception: ex_type, ex_value, ex_traceback = sys.exc_info() # Extract unformatter stack traces as tuples trace_back = traceback.extract_tb(ex_traceback) # Format stacktrace stack_trace = list() for trace in trace_back: stack_trace.append("File : %s , Line : %d, Func.Name : %s, Message : %s" % (trace[0], trace[1], trace[2], trace[3])) sys.stderr.write("[Error] Exception type : %s \n" % ex_type.__name__) sys.stderr.write("[Error] Exception message : %s \n" % ex_value) for trace in stack_trace: sys.stderr.write("[Error] (Stack trace) %s\n" % trace) pdb.set_trace() # store results for given image and category return { 'image_id': imgId, 'category_id': catId, 'hRng': hRng, 'oRng': oRng, 'maxDet': maxDet, 'dtIds': [d['id'] for d in dt], 'gtIds': [g['id'] for g in gt], 'dtMatches': dtm, 'gtMatches': gtm, 'dtScores': [d['score'] for d in dt], 'gtIgnore': gtIg, 'dtIgnore': dtIg, } def accumulate(self, p=None): ''' Accumulate per image evaluation results and store the result in self.eval :param p: input params for evaluation :return: None ''' if not self.evalImgs: pass #print('Please run evaluate() first') # allows input customized parameters if p is None: p = self.params p.catIds = p.catIds if p.useCats == 1 else [-1] T = len(p.iouThrs) R = len(p.fppiThrs) K = len(p.catIds) if p.useCats else 1 M = len(p.maxDets) ys = -np.ones((T, R, K, M)) # -1 for the precision of absent categories xx_graph = [] yy_graph = [] # create dictionary for future indexing _pe = self._paramsEval catIds = [1] # _pe.catIds if _pe.useCats else [-1] setK = set(catIds) setM = set(_pe.maxDets) setI = set(_pe.imgIds) # get inds to evaluate k_list = [n for n, k in enumerate(p.catIds) if k in setK] m_list = [m for n, m in enumerate(p.maxDets) if m in setM] i_list = [n for n, i in enumerate(p.imgIds) if i in setI] I0 = len(_pe.imgIds) # retrieve E at each category, area range, and max number of detections for k, k0 in enumerate(k_list): Nk = k0 * I0 for m, maxDet in enumerate(m_list): E = [self.evalImgs[Nk + i] for i in i_list] E = [e for e in E if e is not None] if len(E) == 0: continue dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) # different sorting method generates slightly different results. # mergesort is used to be consistent as Matlab implementation. inds = np.argsort(-dtScores, kind='mergesort') dtm = np.concatenate([e['dtMatches'][:, 0:maxDet] for e in E], axis=1)[:, inds] dtIg = np.concatenate([e['dtIgnore'][:, 0:maxDet] for e in E], axis=1)[:, inds] gtIg = np.concatenate([e['gtIgnore'] for e in E]) npig = np.count_nonzero(gtIg == 0) if npig == 0: continue tps = np.logical_and(dtm, np.logical_not(dtIg)) fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg)) inds = np.where(dtIg == 0)[1] tps = tps[:, inds] fps = fps[:, inds] tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float64) fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float64) for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): tp = np.array(tp) fppi = np.array(fp) / I0 nd = len(tp) recall = tp / npig q = np.zeros((R,)) xx_graph.append(fppi) yy_graph.append(1 - recall) # numpy is slow without cython optimization for accessing elements # use python array gets significant speed improvement recall = recall.tolist() q = q.tolist() for i in range(nd - 1, 0, -1): if recall[i] < recall[i - 1]: recall[i - 1] = recall[i] inds = np.searchsorted(fppi, p.fppiThrs, side='right') - 1 try: for ri, pi in enumerate(inds): q[ri] = recall[pi] except Exception: pass ys[t, :, k, m] = np.array(q) self.eval = { 'params': p, 'counts': [T, R, K, M], 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'TP': ys, 'xx': xx_graph, 'yy': yy_graph } @staticmethod def draw_figure(ax, eval_results, methods, colors): """Draw figure""" assert len(eval_results) == len(methods) == len(colors) for eval_result, method, color in zip(eval_results, methods, colors): mrs = 1 - eval_result['TP'] mean_s = np.log(mrs[mrs < 2]) mean_s = np.mean(mean_s) mean_s = float(np.exp(mean_s) * 100) xx = eval_result['xx'] yy = eval_result['yy'] ax.plot(xx[0], yy[0], color=color, linewidth=3, label=f'{mean_s:.2f}%, {method}') ax.set_yscale('log') ax.set_xscale('log') ax.legend() yt = [1, 5] + list(range(10, 60, 10)) + [64, 80] yticklabels = ['.{:02d}'.format(num) for num in yt] yt += [100] yt = [yy / 100.0 for yy in yt] yticklabels += [1] ax.set_yticks(yt) ax.set_yticklabels(yticklabels) ax.grid(which='major', axis='both') ax.set_ylim(0.01, 1) ax.set_xlim(2e-4, 50) ax.set_ylabel('miss rate') ax.set_xlabel('false positives per image') def summarize(self, id_setup, res_file=None): ''' Compute and display summary metrics for evaluation results. Note this functin can *only* be applied on the default parameter setting ''' def _summarize(iouThr=None, maxDets=100): OCC_TO_TEXT = ['none', 'partial_occ', 'heavy_occ'] p = self.params iStr = ' {:<18} {} @ {:<18} [ IoU={:<9} | height={:>6s} | visibility={:>6s} ] = {:0.2f}%' titleStr = 'Average Miss Rate' typeStr = '(MR)' setupStr = p.SetupLbl[id_setup] iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ if iouThr is None else '{:0.2f}'.format(iouThr) heightStr = '[{:0.0f}:{:0.0f}]'.format(p.HtRng[id_setup][0], p.HtRng[id_setup][1]) occlStr = '[' + '+'.join(['{:s}'.format(OCC_TO_TEXT[occ]) for occ in p.OccRng[id_setup]]) + ']' mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] # dimension of precision: [TxRxKxAxM] s = self.eval['TP'] # IoU if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] mrs = 1 - s[:, :, :, mind] if len(mrs[mrs < 2]) == 0: mean_s = -1 else: mean_s = np.log(mrs[mrs < 2] + 1e-5) mean_s = np.mean(mean_s) mean_s = np.exp(mean_s) if res_file: res_file.write(iStr.format(titleStr, typeStr, setupStr, iouStr, heightStr, occlStr, mean_s * 100)) res_file.write('\n') return mean_s if not self.eval: raise Exception('Please run accumulate() first') return _summarize(iouThr=.5, maxDets=1000) class KAISTParams(Params): """Params for KAISTPed evaluation api""" def setDetParams(self): super().setDetParams() # Override variables for KAISTPed benchmark self.iouThrs = np.array([0.5]) self.maxDets = [1000] # KAISTPed specific settings self.fppiThrs = np.array([0.0100, 0.0178, 0.0316, 0.0562, 0.1000, 0.1778, 0.3162, 0.5623, 1.0000]) #self.HtRng = [[55, 1e5 ** 2], [50, 75], [50, 1e5 ** 2], [20, 1e5 ** 2]] self.HtRng = [[55, 1e5 ** 2], [115, 1e5 ** 2], [45, 115], [1, 45], [1, 1e5 ** 2], [1, 1e5 ** 2], [1, 1e5 ** 2]] # jifengshen #self.OccRng = [[0, 1], [0, 1], [2], [0, 1, 2]] self.OccRng = [[0, 1], [0], [0], [0], [0], [1], [2]] #self.SetupLbl = ['Reasonable', 'Reasonable_small', 'Reasonable_occ=heavy', 'All'] self.SetupLbl = ['Reasonable', 'scale=near', 'scale=medium', 'scale=far', 'occ=none', 'occ=partial', 'occ=heavy', 'All'] self.bndRng = [5, 5, 635, 507] # discard bbs outside this pixel range class KAIST(COCO): def txt2json(self, txt): """ Convert txt file to coco json format Arguments: `txt`: Path to annotation file that txt """ predict_result = [] f = open(txt, 'r') #print(f) lines = f.readlines() for line in lines: json_format = {} pred_info = [float(ll) for ll in line.split(',')] json_format["image_id"] = pred_info[0] - 1 # image id json_format["category_id"] = 1 # pedestrian json_format["bbox"] = [pred_info[1], pred_info[2], pred_info[3], pred_info[4]] # bbox json_format["score"] = pred_info[5] predict_result.append(json_format) return predict_result def loadRes(self, resFile): """ Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object """ # If resFile is a text file, convert it to json if type(resFile) == str and resFile.endswith('.txt'): anns = self.txt2json(resFile) _resFile = next(tempfile._get_candidate_names()) with open(_resFile, 'w') as f: json.dump(anns, f, indent=4) res = super().loadRes(_resFile) os.remove(_resFile) elif type(resFile) == str and resFile.endswith('.json'): res = super().loadRes(resFile) else: raise Exception('[Error] Exception extension : %s \n' % resFile.split('.')[-1]) return res def evaluate(test_annotation_file: str, user_submission_file: str, phase_codename: str = 'Multispectral', plot=False): """Evaluates the submission for a particular challenge phase and returns score Parameters ---------- test_annotations_file: str Path to test_annotation_file on the server user_submission_file: str Path to file submitted by the user phase_codename: str Phase to which submission is made Returns ------- Dict Evaluated/Accumulated KAISTPedEval objects for All/Day/Night """ kaistGt = KAIST(test_annotation_file) kaistDt = kaistGt.loadRes(user_submission_file) imgIds = sorted(kaistGt.getImgIds()) method = os.path.basename(user_submission_file).split('_')[0] kaistEval = KAISTPedEval(kaistGt, kaistDt, 'bbox', method) kaistEval.params.catIds = [1] eval_result = { 'all': copy.deepcopy(kaistEval), 'day': copy.deepcopy(kaistEval), 'night': copy.deepcopy(kaistEval), 'near': copy.deepcopy(kaistEval), 'medium': copy.deepcopy(kaistEval), 'far': copy.deepcopy(kaistEval), 'none': copy.deepcopy(kaistEval), 'partial': copy.deepcopy(kaistEval), 'heavy': copy.deepcopy(kaistEval), } eval_result['all'].params.imgIds = imgIds eval_result['all'].evaluate(0) eval_result['all'].accumulate() MR_all = eval_result['all'].summarize(0) eval_result['day'].params.imgIds = imgIds[:1455] eval_result['day'].evaluate(0) eval_result['day'].accumulate() MR_day = eval_result['day'].summarize(0) eval_result['night'].params.imgIds = imgIds[1455:] eval_result['night'].evaluate(0) eval_result['night'].accumulate() MR_night = eval_result['night'].summarize(0) eval_result['near'].params.imgIds = imgIds eval_result['near'].evaluate(1) eval_result['near'].accumulate() MR_near = eval_result['near'].summarize(1) eval_result['medium'].params.imgIds = imgIds eval_result['medium'].evaluate(2) eval_result['medium'].accumulate() MR_medium = eval_result['medium'].summarize(2) eval_result['far'].params.imgIds = imgIds eval_result['far'].evaluate(3) eval_result['far'].accumulate() MR_far = eval_result['far'].summarize(3) eval_result['none'].params.imgIds = imgIds eval_result['none'].evaluate(4) eval_result['none'].accumulate() MR_none = eval_result['none'].summarize(4) eval_result['partial'].params.imgIds = imgIds eval_result['partial'].evaluate(5) eval_result['partial'].accumulate() MR_partial = eval_result['partial'].summarize(5) eval_result['heavy'].params.imgIds = imgIds eval_result['heavy'].evaluate(6) eval_result['heavy'].accumulate() MR_heavy = eval_result['heavy'].summarize(6) recall_all = 1 - eval_result['all'].eval['yy'][0][-1] if plot: msg = f'\n########## Method: {method} ##########\n' \ + f'MR_all: {MR_all * 100:.2f}\n' \ + f'MR_day: {MR_day * 100:.2f}\n' \ + f'MR_night: {MR_night * 100:.2f}\n' \ + f'MR_near: {MR_near * 100:.2f}\n' \ + f'MR_medium: {MR_medium * 100:.2f}\n' \ + f'MR_far: {MR_far * 100:.2f}\n' \ + f'MR_none: {MR_none * 100:.2f}\n' \ + f'MR_partial: {MR_partial * 100:.2f}\n' \ + f'MR_heavy: {MR_heavy * 100:.2f}\n' \ + f'recall_all: {recall_all * 100:.2f}\n' \ + '######################################\n\n' print(msg) return eval_result def draw_all(eval_results, filename='figure.jpg'): """Draw all results in a single figure as Miss rate versus false positive per-image (FPPI) curve Parameters ---------- eval_results: List of Dict Aggregated evaluation results from evaluate function. Dictionary contains KAISTPedEval objects for All/Day/Night filename: str Filename of figure """ fig, axes = plt.subplots(1, 3, figsize=(45, 10)) methods = [res['all'].method for res in eval_results] colors = [plt.cm.get_cmap('Paired')(ii)[:3] for ii in range(len(eval_results))] eval_results_all = [res['all'].eval for res in eval_results] KAISTPedEval.draw_figure(axes[0], eval_results_all, methods, colors) axes[0].set_title('All') eval_results_day = [res['day'].eval for res in eval_results] KAISTPedEval.draw_figure(axes[1], eval_results_day, methods, colors) axes[1].set_title('Day') eval_results_night = [res['night'].eval for res in eval_results] KAISTPedEval.draw_figure(axes[2], eval_results_night, methods, colors) axes[2].set_title('Night') filename += '' if filename.endswith('.jpg') or filename.endswith('.png') else '.jpg' plt.savefig(filename) if __name__ == "__main__": parser = argparse.ArgumentParser(description='eval models') parser.add_argument('--annFile', type=str, default='D:/Project/MLPD-Multi-Label-Pedestrian-Detection-main/evaluation_script/KAIST_annotation.json', help='Please put the path of the annotation file. Only support json format.') parser.add_argument('--rstFiles', type=str, nargs='+', default=['evaluation_script/MLPD_result.json'], help='Please put the path of the result file. Only support json, txt format.') parser.add_argument('--evalFig', type=str, default='KASIT_BENCHMARK.jpg', help='Please put the output path of the Miss rate versus false positive per-image (FPPI) curve') args = parser.parse_args() args.rstFiles = 'D:/Project/MLPD-Multi-Label-Pedestrian-Detection-main/evaluation_script/yolov5_CMAFF_result.txt' phase = "Multispectral" results = [evaluate(args.annFile, args.rstFiles, phase)] # Sort results by MR_all results = sorted(results, key=lambda x: x['all'].summarize(0), reverse=True) draw_all(results, filename=args.evalFig) ================================================ FILE: evaluation_script/null ================================================ ================================================ FILE: evaluation_script/state_of_arts/ARCNN_result.txt ================================================ 1,1.8509424,385.5025,34.075916,92.43564,0.004068177 1,617.82874,213.01526,21.317932,74.87131,0.00073474366 1,621.2999,85.17759,17.84674,53.534065,0.00040503542 1,617.1093,384.41315,22.037354,81.61331,0.0002240716 1,60.253998,208.5963,36.627075,80.82201,0.00020134078 1,611.279,127.677635,27.867676,126.48026,0.0001580608 1,4.3060613,263.98697,74.91318,217.2779,0.00012929543 1,55.3457,156.11827,35.050594,80.91371,0.00012378176 1,601.17126,179.9815,37.975403,183.31726,0.00012363264 1,2.405735,99.232414,36.497448,78.14384,7.584808e-05 1,1.8206682,131.69342,19.870426,136.00238,5.625618e-05 1,601.0312,318.29498,38.11548,165.80301,5.036889e-05 1,1.2138758,139.20737,13.966359,54.069077,4.2034644e-05 1,608.683,439.76437,30.463684,64.892365,4.003858e-05 1,1.417395,296.6046,20.748873,159.52863,2.3794037e-05 1,1.4988167,81.02112,18.539545,71.45775,1.4620369e-05 1,2.5578873,162.69936,34.624157,201.31407,1.3334804e-05 1,482.89615,161.00914,75.09836,163.00887,1.2871361e-05 1,1.5276961,219.98215,17.861868,75.33618,1.2131533e-05 1,284.2464,229.91508,75.55762,199.36015,1.1388308e-05 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1078,493.02365,220.54414,57.944916,146.70125,99.96159 1078,564.77826,214.67319,25.882446,53.406433,84.11165 1078,523.9563,211.43161,23.205566,50.93753,72.26571 1078,600.52246,282.5691,36.113464,94.575806,0.9061924 1078,575.4318,209.67581,31.666443,73.23468,0.4639057 1078,502.0896,219.79271,31.864502,74.670425,0.039377652 1078,439.76303,242.25703,25.212341,62.060715,0.022419395 1078,441.5726,195.03673,24.898346,52.750153,0.010777852 1078,403.08014,183.62993,30.879822,87.48241,0.009242104 1078,509.40173,117.81222,23.554993,54.025566,0.008616047 1078,526.88696,206.38348,38.707214,109.86151,0.0069913752 1078,567.71265,207.39604,69.34894,209.43282,0.0054134843 1078,477.4336,164.72722,142.4278,213.30644,0.004458335 1078,614.22,108.87645,24.926697,103.85155,0.0023799536 1078,611.7294,166.20894,27.417297,158.5937,0.00035662306 1078,1.2203035,203.0379,19.323193,140.35071,8.936073e-05 1078,610.73676,366.0708,28.409912,137.94894,7.8924895e-05 1078,587.05634,49.728336,51.641296,271.03006,7.729421e-05 1078,4.25626,237.55371,57.796658,233.74744,5.798916e-05 1078,615.7737,30.139395,23.372986,90.94131,2.9099345e-05 1078,1.1349715,393.0617,17.154799,103.02731,8.740042e-06 1078,62.020504,5.413734,45.47392,64.14081,8.319344e-06 1078,0.7468799,278.08167,20.047813,148.24539,7.900881e-06 1078,3.7447314,395.28616,56.121506,91.8277,5.10643e-06 1078,1.4344157,96.75535,19.15272,144.87177,4.066203e-06 1078,86.30592,8.785782,43.023636,56.872528,2.8307664e-07 1078,1.4980356,10.63907,37.90369,84.0153,7.914388e-08 1078,319.29205,1.8612728,43.02411,55.678314,1.9896331e-09 1079,546.96313,92.72578,31.962097,77.14744,0.49430043 1079,538.0905,265.63312,27.83368,53.867126,0.100219585 1079,497.88693,209.20816,21.887115,47.432953,0.08826967 1079,482.86282,210.13544,26.281036,60.43213,0.014847856 1079,604.40436,208.538,32.221436,58.670776,0.014319499 1079,509.45737,206.97011,23.56839,52.69313,0.013796737 1079,464.38428,244.60992,32.49463,87.621216,0.0023363763 1079,588.7487,212.68158,22.74823,49.83139,0.0011978719 1079,551.29346,115.41166,45.209473,128.12457,0.001014248 1079,586.9367,201.88991,39.825623,99.17525,0.00077186886 1079,616.66364,244.94907,22.483032,89.69058,0.00037039278 1079,603.9553,89.70529,34.890625,194.49152,0.00023153258 1079,600.4715,227.74602,38.67517,204.84593,0.000108014225 1079,490.21356,141.94034,24.419983,49.49115,9.2209564e-05 1079,1.2975578,125.96555,21.733452,118.0324,8.006647e-05 1079,617.5702,82.542,21.576477,74.08016,7.465095e-05 1079,608.98804,439.2429,30.15863,67.15555,6.1508894e-05 1079,4.539546,396.12427,54.274307,98.4581,2.4087236e-05 1079,584.82983,387.2276,54.316833,108.74423,1.993306e-05 1079,0.752959,268.1139,18.78173,78.51895,1.5320318e-05 1079,4.0651903,200.17424,57.410107,250.03764,1.144487e-05 1079,1.3918352,375.47095,18.977314,121.3963,9.086316e-06 1079,608.45074,6.5285354,30.695923,132.12616,5.9088925e-06 1079,47.399166,0.6648926,48.545483,70.24241,5.6209424e-06 1079,2.3469532,36.836994,57.66032,244.52821,9.541847e-07 1079,1.3810612,5.8891377,37.28968,53.178715,6.0710687e-07 1079,0.7556641,10.251566,12.004549,84.74941,2.3988278e-07 1079,88.42548,6.8728614,41.076546,59.895565,1.3494236e-08 1079,115.68311,5.2226434,42.514717,61.433323,2.9999017e-09 1079,143.33466,4.581992,42.289185,56.76675,2.5180827e-10 1079,344.96387,4.7493896,44.21472,53.769855,5.694882e-11 1080,616.92633,316.53204,22.033447,79.16135,5.808583 1080,541.8694,209.03192,27.66394,58.48593,0.14583367 1080,475.18173,227.90677,30.18692,76.17828,0.14162186 1080,493.3295,210.96013,32.650116,86.88783,0.011098243 1080,610.0528,239.35103,28.882263,55.639694,0.010253091 1080,425.74194,175.92938,28.934845,66.5943,0.0075164074 1080,604.2848,242.31485,34.30768,194.93921,0.0060317963 1080,614.949,49.142273,24.197693,89.7563,0.0011423575 1080,73.685135,217.70491,22.373985,47.92427,0.0010464753 1080,604.6563,111.395004,34.46753,89.52815,0.0009537448 1080,590.14954,111.955734,47.174255,241.7415,0.0007655308 1080,620.8416,394.87265,18.305054,59.597473,0.0005444645 1080,476.9573,202.67456,79.80905,165.07037,0.00049129647 1080,478.95126,115.192894,42.355377,166.69116,0.00044257523 1080,399.29553,219.95227,28.17575,59.979156,0.00024489156 1080,573.2602,350.91907,65.52423,137.49533,9.3299954e-05 1080,609.0045,435.71176,30.14215,70.91107,7.952388e-05 1080,1.2105001,224.89719,16.123884,124.93625,3.603893e-05 1080,457.15646,100.78844,35.700623,103.5181,3.3413693e-05 1080,613.8189,0.0,25.327759,46.469257,2.2849443e-05 1080,1.7587826,153.56183,18.768063,132.10266,1.6905069e-05 1080,4.2600293,214.79118,55.86505,232.41725,1.4437334e-05 1080,0.8927792,287.798,19.723145,141.94101,8.353848e-06 1080,1.1041366,387.1148,18.950619,111.81369,6.1291294e-06 1080,1.6682903,93.66634,18.313618,78.35229,5.0854355e-06 1080,6.039219,360.36493,76.77283,127.72284,2.2589222e-06 1080,1.8315423,48.653576,30.163174,188.75539,1.3673824e-06 1080,3.3352978,430.13248,52.342556,67.486176,1.0569921e-06 1080,41.25436,3.2431397,43.497143,50.424816,5.6135997e-07 1080,0.80304855,9.790723,12.465972,87.767845,1.8811419e-07 1080,15.987044,3.2679265,44.22474,47.900578,1.7930752e-07 1080,3.9685092,13.571667,81.15698,168.49208,1.0285399e-07 1080,63.001213,8.071372,41.663475,55.44036,4.6082224e-08 1080,89.07893,7.414232,43.16844,56.70275,1.6881973e-08 1080,115.876045,3.7086296,41.596672,59.964092,1.4797857e-09 1080,148.82196,3.661976,43.201096,57.83311,1.8139067e-11 1081,582.497,211.28746,25.73944,78.251205,94.60229 1081,532.7539,197.62747,36.305847,85.95877,0.06844381 1081,437.13574,203.76013,25.303894,48.269897,0.02197253 1081,561.5996,163.27748,44.326843,139.64082,0.01640559 1081,2.6299968,148.13855,37.79156,111.64398,0.0026283143 1081,470.29065,166.93668,35.36133,96.070984,0.0017022375 1081,623.0535,142.26834,16.09314,51.23932,0.001540371 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1312,540.4775,224.87915,31.774353,67.41876,99.93935 1312,138.81909,218.57787,33.354233,79.75856,99.93438 1312,519.23486,218.70795,23.85144,56.713135,99.91623 1312,53.46283,220.30896,47.935814,127.25708,99.89685 1312,598.0271,214.53271,34.15039,73.21091,99.89677 1312,354.77704,218.68774,25.413757,53.196564,99.8442 1312,269.2933,217.43964,24.784302,67.510376,99.84195 1312,458.21057,217.43802,41.149292,128.8599,99.813805 1312,221.31676,215.57234,30.499344,76.20851,99.80406 1312,107.21266,217.45216,25.454895,62.620438,99.52264 1312,503.15558,216.01575,22.967346,57.818726,99.14376 1312,438.6821,213.58232,37.55777,129.81624,92.55327 1312,252.54395,221.01157,22.22284,62.91815,69.323235 1312,18.400175,210.11287,33.544785,64.66728,52.805084 1312,77.25501,210.82474,31.644081,74.09454,49.969006 1312,585.6808,214.42511,26.269653,59.295166,36.873512 1312,46.30011,211.24031,22.359688,53.469986,25.10742 1312,607.73584,9.055947,31.327698,50.269073,1.533077e-05 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1321,513.6271,280.84503,50.44586,149.63342,0.00027786332 1321,455.42258,169.4979,85.340485,187.36172,0.00019146796 1321,8.72058,152.17076,129.37885,240.01277,0.0001379282 1321,149.42462,173.7721,71.9884,164.27881,4.087705e-05 1321,1.4670687,226.26797,32.937622,209.34177,3.2041284e-05 1321,359.01907,151.09291,131.39978,230.84706,2.6893405e-05 1321,586.2799,394.59006,52.08844,98.22226,2.1616135e-05 1321,607.2556,7.739019,31.891052,52.49413,1.22887795e-05 1321,1.5740097,365.9007,32.9605,125.1066,2.0477298e-06 1321,0.8194141,440.30948,22.288704,63.438354,2.0474408e-06 1321,1.7872965,7.9653225,35.401806,85.350395,5.023207e-10 1321,403.0794,0.0,45.57364,63.076836,9.201588e-12 1322,67.65599,218.21599,30.997063,63.010483,99.936646 1322,551.34674,213.71924,26.992188,60.439484,99.93489 1322,45.900585,217.40132,27.882122,63.70961,99.91361 1322,155.64587,219.78957,30.457031,54.391342,99.61413 1322,127.757126,219.17618,22.86206,50.241974,99.50917 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1808,433.5568,196.93643,34.112427,94.1095,99.897964 1808,403.50763,200.03246,36.771454,86.97774,99.64484 1808,617.6484,201.15016,20.2005,54.98314,61.17153 1808,385.9703,199.40894,21.249847,53.405624,21.214664 1808,306.70752,240.60661,55.082275,153.28975,0.07180927 1808,366.86337,197.23274,23.158783,54.8293,0.069814615 1808,443.6625,249.90376,20.109955,45.2686,0.030252773 1808,483.9437,213.38336,33.568146,72.685974,0.012332305 1808,22.107576,209.7231,36.88459,75.00978,0.008452732 1808,605.2415,183.6351,33.90515,169.92273,0.007562205 1808,317.93127,254.61923,26.228851,58.646667,0.0044112224 1808,620.03094,68.873085,19.115723,57.74666,0.0025026829 1808,398.81628,179.22113,93.89221,161.9559,0.001205925 1808,616.04803,289.66843,23.098633,88.21542,0.0010622898 1808,466.73526,193.35182,75.261444,141.02296,0.0004752876 1808,554.846,175.14745,61.899048,97.23912,0.00039225793 1808,585.7749,6.929798,53.371765,111.1196,0.00023045445 1808,134.13602,200.79704,40.972275,125.44133,0.00021743194 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1811,465.59357,193.6273,53.15454,135.29283,99.84016 1811,543.9381,194.95721,51.691467,154.06473,98.4977 1811,452.97064,191.99475,24.786041,72.91083,66.96739 1811,428.09955,195.03256,29.864166,74.3573,0.7843872 1811,107.77872,214.26875,28.046509,56.304886,0.1426787 1811,310.94705,254.78319,46.356964,137.04274,0.101288 1811,494.2017,262.02957,24.69748,59.348236,0.0127503 1811,384.49615,189.59082,31.797607,69.48184,0.011545667 1811,554.7848,226.07927,28.30597,70.354996,0.007022862 1811,102.61715,201.72354,41.259117,132.95386,0.0029424517 1811,436.83005,166.58537,124.140656,218.16875,0.00036015507 1811,610.2745,220.70537,28.872192,132.23552,0.00027097814 1811,501.6086,147.76285,128.14645,240.608,0.00020622065 1811,609.6979,84.92306,29.448792,131.99557,0.00013956682 1811,612.1497,317.52762,26.996948,137.15479,0.00010723131 1811,1.1226147,183.04306,20.215649,119.99634,9.53697e-05 1811,584.7232,5.9845247,54.423462,119.77605,2.8976609e-05 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1812,623.12384,245.24823,16.022827,46.490448,0.026989877 1812,403.4956,189.67345,32.783722,76.882126,0.02060968 1812,522.711,237.3702,24.664246,63.51143,0.015176202 1812,308.73654,248.78,55.920135,157.06046,0.0071403147 1812,507.33914,213.48755,27.936981,72.91745,0.006561158 1812,624.7907,290.49268,14.355957,46.420563,0.0039907806 1812,603.9028,234.75865,26.653442,67.74455,0.0039025268 1812,105.171394,216.36055,25.654213,55.92366,0.0018760406 1812,470.9344,176.43935,121.78644,225.1418,0.0008341614 1812,101.24065,244.7159,44.83316,143.55879,0.0007414624 1812,601.5127,313.48026,37.633972,180.28094,6.2202664e-05 1812,608.88055,83.81049,30.266113,157.94864,5.3076958e-05 1812,1.1643058,196.4806,18.875603,121.688,2.3915336e-05 1812,609.2683,437.29825,29.878357,66.54025,2.3062144e-05 1812,2.7638853,18.067814,54.40304,115.534515,1.2025996e-05 1812,1.0977498,360.35974,18.669933,126.2301,8.471495e-06 1812,586.62634,16.640287,52.520325,102.5147,7.918563e-06 1812,3.5860906,170.19986,56.15994,203.52667,6.89782e-06 1812,1.2273145,107.22456,18.472015,132.74237,6.49791e-06 1812,3.4423275,315.51202,55.88301,157.88861,6.024263e-06 1812,1.324069,9.338438,29.783184,58.067238,6.619659e-08 1812,212.5782,4.340015,43.178528,46.912403,4.6042516e-08 1812,251.31776,2.923789,45.392258,54.871212,2.0943618e-08 1812,501.57117,3.5999057,47.766785,51.843716,8.3913e-09 1812,461.58344,4.4771748,47.302094,52.482033,1.7905852e-09 1813,548.60516,199.66547,60.285095,185.05475,99.77306 1813,544.0445,208.2382,36.2865,107.6389,0.2359946 1813,412.54004,189.29643,31.35199,63.974716,0.2038683 1813,497.49033,219.26587,40.42697,95.70261,0.08333518 1813,572.4337,253.91795,25.0,60.19084,0.040307287 1813,312.23477,254.95152,45.572174,146.47182,0.035269435 1813,319.3736,253.69402,27.535675,64.55208,0.0010506532 1813,88.28402,203.9236,44.903465,138.10762,0.0005438532 1813,570.60034,283.04727,28.00061,74.374176,0.0003910806 1813,613.1103,246.87595,26.036377,120.535675,0.00036683385 1813,479.63898,82.10036,71.756226,224.61427,0.00028902967 1813,610.6338,78.3632,28.512878,142.25461,0.00010951983 1813,617.1885,47.702366,21.95819,79.751816,0.00010309866 1813,1.4796274,202.18213,18.543165,138.38785,5.446611e-05 1813,603.7516,321.9271,35.371216,160.70187,4.726767e-05 1813,1.0892415,329.22507,18.023275,134.75,3.9636474e-05 1813,609.6373,436.6194,29.509338,68.065735,2.4821196e-05 1813,585.9466,17.204287,52.539978,98.98809,2.376034e-05 1813,3.339865,148.43042,58.1252,214.82632,1.174141e-05 1813,3.694292,322.14734,57.627274,158.73495,9.460302e-06 1813,1.0335644,70.73298,17.7883,142.63849,7.5133207e-06 1813,1.9661369,132.7237,28.879942,153.53375,3.323147e-06 1813,1.9838884,414.40268,34.457066,82.57742,1.1641781e-06 1813,1.7828875,11.06432,35.64592,79.05992,2.8114334e-07 1813,319.17065,4.788815,44.555695,52.031036,3.692401e-08 1813,381.82953,1.3376839,45.409393,50.762005,8.80505e-09 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1828,509.6182,1.6793555,46.85013,45.248913,5.948644e-09 1828,442.58298,0.39695314,46.829315,52.3972,3.2584688e-09 1828,374.13596,4.2967854,45.31732,49.62977,2.294684e-09 1829,0.07422038,362.57016,44.10147,120.72653,2.8604376 1829,280.8742,210.94183,23.145172,45.469727,0.10646051 1829,570.60333,203.43735,25.796875,50.406906,0.051023968 1829,497.17267,213.80115,42.882324,103.89203,0.038543697 1829,385.57437,199.44409,33.234528,54.3656,0.022939164 1829,0.3125993,388.63812,17.335638,59.39688,0.018625967 1829,607.9783,311.2099,31.168396,154.73132,0.008545302 1829,618.5756,337.85474,20.571045,65.6857,0.0072516995 1829,587.8465,181.52563,51.30017,240.81293,0.0015092415 1829,72.649734,216.34177,27.459732,53.26883,0.0012378069 1829,189.98933,275.4902,34.768585,85.7587,0.0012098232 1829,1.1710441,218.53691,13.6294365,53.426346,0.0007377987 1829,2.1491544,194.9002,28.660194,96.252106,0.00043348063 1829,2.9139552,255.22018,88.419495,230.16138,0.00041771404 1829,168.88075,233.6712,71.10521,178.0198,0.00037396836 1829,610.6712,127.98457,28.475464,137.73808,0.00020435257 1829,615.7016,233.6011,23.445068,80.4429,0.00016520912 1829,608.50256,434.6412,30.644104,69.350464,0.00016127128 1829,1.0557853,166.9801,18.62904,80.29118,7.888644e-05 1829,310.0434,226.08662,72.740326,191.46864,5.3895666e-05 1829,616.57056,38.70853,22.57611,84.666214,4.149393e-05 1829,605.23724,4.1833854,33.909424,56.31726,2.0714258e-05 1829,0.84472495,122.71615,18.65789,74.92329,1.7147098e-05 1829,3.9979022,146.42368,56.018486,231.0055,1.6253043e-05 1829,0.80809736,300.93283,19.142195,84.62204,1.2848843e-05 1829,600.4479,18.564772,38.69879,195.34541,5.995536e-06 1829,1.1246338,241.61813,31.412666,197.4784,5.2412433e-06 1829,3.736657,434.82147,46.20606,75.407196,3.318194e-06 1829,1.5704867,54.20013,33.869423,193.01003,4.4974416e-07 1829,2.0630567,15.210228,58.242165,117.693245,2.3788027e-07 1829,376.1516,2.6413054,42.71643,53.692726,1.741406e-10 1830,38.82277,319.07187,71.98937,168.04095,0.3449895 1830,295.47678,210.85646,26.928894,56.502518,0.13231938 1830,63.13246,369.25742,35.88056,82.30109,0.056530662 1830,538.7738,212.63187,36.977295,85.42636,0.048230946 1830,298.5983,212.74693,43.090057,117.02348,0.032860402 1830,577.8773,206.89746,28.911194,54.411682,0.008750066 1830,53.12531,280.98834,36.669514,89.691986,0.008650814 1830,560.13306,193.16534,32.481445,70.94586,0.0036827303 1830,180.38147,220.11516,58.818954,142.54439,0.0012482502 1830,617.41364,222.59396,21.733032,83.404175,0.00044455618 1830,1.3999748,214.20294,18.28709,77.727936,0.0002607901 1830,4.789183,317.93622,57.22618,173.17477,0.00018127926 1830,612.0716,136.15248,27.075073,121.47383,0.00012890957 1830,612.65015,328.27493,26.496521,136.32468,0.00011774359 1830,617.4387,64.01861,21.707947,81.16175,7.7477525e-05 1830,259.63098,160.39438,97.92581,199.75546,7.1988026e-05 1830,609.2626,439.16617,29.884094,66.714264,5.72574e-05 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1831,548.7283,202.499,41.48401,107.404724,0.022000477 1831,81.16303,209.9746,33.63188,59.309433,0.0056409487 1831,561.0261,221.13521,20.623291,46.035934,0.0031640197 1831,62.80558,227.05672,47.043106,137.30525,0.002280389 1831,491.54245,177.64592,22.56137,45.28154,0.0017004564 1831,1.3675318,209.61555,18.125801,82.94026,0.0012817129 1831,134.05086,319.5434,50.18329,141.8613,0.001225037 1831,2.2205648,227.59938,41.111088,137.96611,0.0011162611 1831,616.5195,224.25917,22.627197,81.45137,0.0009407556 1831,1.2589136,258.0012,19.652702,87.13107,0.0006621308 1831,201.41975,225.45259,39.36447,99.23622,0.00048066553 1831,92.70945,252.19302,116.57676,221.20538,0.0002436755 1831,609.0783,440.83823,30.06836,65.628815,0.00016533678 1831,587.3692,184.3148,50.78003,211.74828,0.00013811182 1831,617.37317,71.90392,21.773499,78.83196,0.00012054603 1831,612.9993,322.2995,26.147339,137.2962,0.00011972651 1831,274.32782,193.47235,71.586395,147.97986,0.00011318282 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1860,609.9812,440.50015,29.165466,64.07672,2.5227902e-05 1860,2.9948015,211.65175,57.47822,224.4861,2.2639564e-05 1860,587.32825,147.73949,51.81842,213.66887,1.8759838e-05 1860,0.561978,419.53946,13.613256,46.729767,1.3168417e-05 1860,89.21077,272.57812,80.80246,179.67981,1.14387485e-05 1860,0.94497156,328.50012,18.606695,127.03964,8.93796e-06 1860,5.842871,353.7321,77.87316,129.33606,3.8431567e-06 1860,0.931298,443.1218,21.720318,60.09555,2.8333716e-06 1860,1.0140991,58.80357,18.67129,72.03047,1.3633174e-06 1860,2.712108,429.60202,53.119442,69.21939,4.95961e-07 1860,1.687837,21.166466,35.5793,163.49942,1.1141338e-07 1860,1.0443034,7.1322575,22.223814,55.391106,9.133343e-08 1860,370.06186,1.6236036,41.364777,46.484417,2.9538844e-08 1860,1.7929102,5.791029,54.42435,75.34541,1.9566333e-08 1861,443.19388,206.35696,23.186768,46.85565,0.0059480467 1861,424.93796,203.2579,39.858826,150.418,0.00092530536 1861,1.2594662,157.56984,17.89526,71.97652,0.00035695278 1861,609.3893,84.332245,29.757385,143.27606,0.0003544118 1861,1.455083,201.59818,20.422207,106.84393,0.0003454839 1861,616.3187,58.644768,22.827942,76.00649,0.00027727545 1861,54.695667,202.95114,25.555164,53.796387,0.0002421438 1861,583.47046,11.856189,54.839478,105.9127,0.00023898426 1861,617.62463,209.51913,21.522034,82.23215,0.00020148582 1861,621.2239,130.45355,17.92279,57.24591,0.0001794647 1861,618.159,380.7594,20.98767,81.64078,0.00015747102 1861,135.00703,247.51646,26.56131,60.10338,0.00010956945 1861,618.1388,272.64664,21.007874,78.736145,0.00010169182 1861,617.8429,320.15985,21.303772,80.62637,0.000100054545 1861,606.17926,4.9260354,32.967407,55.24787,8.5239095e-05 1861,1.6572087,224.06349,31.447596,170.45793,4.4812605e-05 1861,3.4177198,146.43044,57.21207,193.50151,4.3660286e-05 1861,586.61835,145.53935,52.52832,196.56892,3.7303973e-05 1861,138.0724,213.30965,41.696365,115.264206,3.60718e-05 1861,0.96390873,284.59283,18.395327,77.73596,2.7823458e-05 1861,609.96735,440.27734,29.179321,64.16199,2.57402e-05 1861,113.75488,190.93636,40.49904,142.7152,2.3466466e-05 1861,400.3909,176.54642,106.31595,218.95483,2.12018e-05 1861,0.58316284,426.23663,13.44477,45.674255,1.3382527e-05 1861,4.308581,268.3588,78.26663,200.62247,9.567144e-06 1861,588.1263,328.20505,51.020386,158.26193,8.7907665e-06 1861,1.1925302,101.03879,18.446943,77.42203,8.655634e-06 1861,0.95522624,328.04596,18.999142,127.379364,7.217996e-06 1861,0.89383465,442.60834,21.750187,60.45044,2.6759085e-06 1861,2.1003745,66.07985,35.36431,185.70323,8.597548e-07 1861,2.980446,393.32315,56.749016,98.82065,7.775998e-07 1861,1.1125122,18.235264,21.431013,106.065186,1.9662004e-07 1861,2.7442155,12.950339,82.47894,158.01225,8.3493475e-08 1861,1.6691537,5.113034,54.38198,75.67789,6.046405e-08 1861,377.17264,1.1857015,41.304504,49.203598,3.853032e-08 1862,0.8721761,162.98769,13.33439,50.114914,0.00042919637 1862,610.0646,90.13628,29.082092,139.85483,0.00033278237 1862,618.1636,174.43925,20.983093,72.2612,0.0003236592 1862,1.5441586,179.38867,19.280516,130.81271,0.00030245652 1862,617.7416,219.16505,21.40509,84.67268,0.00026063446 1862,618.2459,395.34567,20.900757,78.588776,0.00017234347 1862,618.23645,277.1514,20.910217,80.27347,0.00015323066 1862,430.465,197.00725,40.63736,141.70808,0.00013878148 1862,608.5596,10.172729,30.587097,108.74583,0.00012242228 1862,617.9904,340.52267,21.15625,80.98999,0.000113148075 1862,135.25175,249.07376,26.453537,60.397552,0.00010465903 1862,0.70973474,209.35771,13.929948,55.28621,6.5671855e-05 1862,130.20912,201.7657,41.358505,141.19344,4.8082045e-05 1862,1.2521664,256.10062,18.777802,132.31973,3.493073e-05 1862,4.033742,152.8993,55.761585,208.3698,3.3020857e-05 1862,93.61155,303.99194,34.166862,74.49826,3.0732273e-05 1862,601.90125,258.62906,37.245422,195.3981,2.6885862e-05 1862,1.0453638,115.58118,18.240395,77.13431,2.6047144e-05 1862,609.9717,440.80237,29.174988,63.327637,2.4483359e-05 1862,602.8076,160.88339,36.33905,176.6698,2.2463466e-05 1862,49.54103,189.43874,33.346657,74.321884,2.0552588e-05 1862,405.95193,174.93083,109.31906,214.601,1.5886144e-05 1862,0.5635799,412.7956,13.6325245,46.764587,1.1399761e-05 1862,4.214033,268.83606,78.42039,200.2044,1.0527592e-05 1862,569.88153,17.387995,69.26514,237.57912,8.795027e-06 1862,0.8269719,373.0552,18.098967,70.0358,6.72373e-06 1862,573.0789,359.0976,66.06775,130.54529,6.052249e-06 1862,1.2360108,328.53406,33.082676,156.45776,3.6675715e-06 1862,0.93796146,443.1947,21.72357,59.976135,2.4736876e-06 1862,1.000555,59.866825,18.64293,71.512054,1.8518796e-06 1862,1.8956624,74.33147,35.154446,187.95702,8.650992e-07 1862,0.82020104,13.709251,13.813556,80.54472,5.7946903e-07 1862,2.7181365,429.66922,52.97722,68.96268,3.9087348e-07 1862,2.9327996,15.9278,82.64055,151.57173,7.5860605e-08 1862,370.1947,1.679139,41.68524,46.712814,3.5065934e-08 1862,1.6676286,5.958776,54.106754,75.72513,2.987769e-08 1863,442.37756,205.1574,22.671509,48.974976,0.004453078 1863,621.0922,53.64686,18.054443,50.669838,0.00087297783 1863,620.83344,138.97977,18.313232,58.916153,0.00074574916 1863,1.1322396,186.1869,18.198101,75.716354,0.0005999974 1863,609.81104,75.27631,29.335632,141.90524,0.000587561 1863,1.2020923,232.65146,18.03181,76.08856,0.00037017875 1863,618.13275,211.2493,21.013916,80.28598,0.00026467373 1863,617.952,340.49127,21.194641,81.73865,0.00017476603 1863,1.8790056,186.7795,32.415016,167.63918,0.00016776806 1863,618.3076,401.74167,20.83905,78.99484,0.0001669746 1863,617.6021,172.06046,21.544556,73.679214,0.0001605816 1863,135.23509,248.44118,26.934708,61.418655,0.00011713277 1863,618.18744,271.60968,20.959229,80.446014,0.000114243725 1863,414.42026,163.20815,69.13159,178.46312,0.00011205126 1863,584.2507,13.185332,54.2771,105.06166,9.519314e-05 1863,1.1173365,130.48302,18.105116,76.159805,7.754006e-05 1863,135.05263,194.22092,42.319122,144.77292,6.196891e-05 1863,587.0185,137.99823,52.128174,199.66473,5.721406e-05 1863,607.31244,5.0348763,31.834229,56.757942,5.4415854e-05 1863,113.78768,183.71252,40.601936,150.87872,3.0545736e-05 1863,602.6732,306.2709,36.47345,177.6861,2.9437417e-05 1863,5.244678,154.31848,78.17359,226.88876,2.240912e-05 1863,0.84554774,284.7417,18.451998,78.05887,1.93778e-05 1863,0.8569121,99.36134,13.686687,46.813705,1.7449813e-05 1863,0.620586,426.1883,13.446338,45.6875,1.5568019e-05 1863,48.22796,206.14977,23.736122,49.550217,1.3573814e-05 1863,0.9881413,328.67548,19.11177,127.00125,9.350441e-06 1863,96.815674,176.31836,111.27542,220.37857,9.079542e-06 1863,3.0275424,88.615395,47.722153,180.53561,8.674707e-06 1863,2.716019,321.07178,57.112263,156.6376,8.304868e-06 1863,575.1766,425.00247,63.970093,73.65427,3.520496e-06 1863,1.0519824,392.37112,28.52656,102.79242,2.9269445e-06 1863,1.2848747,50.040127,21.155647,132.22636,4.6471675e-07 1863,7.797015,430.5117,75.14132,68.5177,3.5354998e-07 1863,1.0414356,10.715541,18.829988,73.60243,7.1354215e-08 1863,2.920573,16.673204,82.69533,149.99228,6.023901e-08 1863,369.92337,1.5018295,41.875153,47.326115,2.467333e-08 1863,397.94302,0.8860254,43.325775,51.774704,1.4952606e-08 1863,24.48598,0.0,42.836235,49.368935,3.6104836e-10 1864,610.11926,87.976746,29.027405,135.7752,0.00045845486 1864,621.2548,128.93907,17.891846,57.357635,0.00041887566 1864,1.3533252,203.98978,19.339981,129.3389,0.00039878103 1864,0.79041344,213.81802,13.421525,49.561554,0.0003485158 1864,615.64777,59.431637,23.498901,79.257706,0.00034217277 1864,618.323,169.15744,20.82367,71.318665,0.00027412322 1864,618.116,223.97894,21.03064,80.52228,0.00019812271 1864,0.94255006,241.75146,13.257233,50.02603,0.00018345007 1864,92.25882,307.52567,34.67376,75.99295,0.00016428591 1864,618.19727,395.17126,20.949402,79.035095,0.00016348055 1864,584.0794,13.636384,54.515198,106.50577,0.00012139778 1864,617.88324,341.172,21.263428,80.15451,0.00011208888 1864,417.0458,188.37605,72.33847,170.6378,8.5493426e-05 1864,142.35005,211.94807,25.130173,55.186478,8.3661806e-05 1864,1.0593295,151.78224,18.111311,72.961975,5.590672e-05 1864,603.10364,170.66301,36.04303,177.25871,4.100308e-05 1864,135.29013,249.34172,25.650497,56.963486,3.8668706e-05 1864,1.9664747,124.4852,32.38242,170.80838,2.8880217e-05 1864,3.4273388,214.48625,57.361786,218.7209,2.749254e-05 1864,610.01965,439.66934,29.127014,64.5867,2.7448217e-05 1864,1.0421883,106.703896,18.346874,76.783455,2.3207585e-05 1864,0.9137704,285.79156,18.38939,77.23352,1.8711311e-05 1864,135.36911,201.85127,42.027267,140.87141,1.8616865e-05 1864,0.61629194,426.0189,13.43591,46.05847,1.4003358e-05 1864,587.18243,234.9422,51.964233,238.86041,1.3021084e-05 1864,0.7842033,385.28976,18.132843,70.88614,7.6609085e-06 1864,0.93727946,335.12656,18.197813,67.1821,4.9848963e-06 1864,6.0820055,354.2219,78.07402,128.89056,4.2508887e-06 1864,0.90271974,442.734,21.597471,60.49173,3.084684e-06 1864,1.0222982,59.73721,18.76287,71.25969,2.914438e-06 1864,4.005726,43.763622,80.73276,220.82957,3.4478282e-07 1864,0.9578573,11.197657,18.909435,74.36707,9.365141e-08 1864,2.0903678,14.16071,58.884087,117.24387,5.7313184e-08 1864,370.02908,1.7696599,41.433105,46.90878,2.8608522e-08 1864,1.2993832,2.615026,44.546394,48.95511,9.633306e-09 1865,91.47674,316.7004,28.957962,68.97943,0.0027476384 1865,620.5434,54.236053,18.603271,52.473793,0.0009438739 1865,0.907181,214.35954,13.351521,49.13527,0.0006867312 1865,1.3423536,204.85107,19.267693,128.0629,0.00036659252 1865,608.96027,63.10898,30.186401,146.57362,0.00029951092 1865,612.85565,184.74284,26.291016,121.949234,0.00022748693 1865,621.25525,137.10394,17.891418,55.543945,0.00021545881 1865,0.9904265,242.09613,13.13859,49.496338,0.00018109714 1865,618.1402,380.65933,21.00647,81.127716,0.0001394169 1865,584.3341,13.641113,54.079163,105.850815,0.00012720242 1865,618.2489,271.4635,20.897766,80.415405,0.00011365951 1865,617.8519,320.08322,21.294739,80.8483,9.826708e-05 1865,416.41013,187.37029,73.66446,171.2006,9.7022814e-05 1865,135.0799,249.78732,25.799652,59.078827,9.576032e-05 1865,1.0532227,157.33127,18.20455,73.623535,5.011791e-05 1865,135.77046,195.5536,42.879272,140.9192,4.80566e-05 1865,3.6398976,214.96512,56.857513,218.83969,3.3709242e-05 1865,1.1976327,115.00057,18.295849,76.81019,3.0258263e-05 1865,2.1167579,124.95016,32.608475,170.50204,2.7719798e-05 1865,603.05774,221.12064,36.08893,195.0163,2.6473583e-05 1865,609.9873,440.6588,29.159363,63.349,2.3845783e-05 1865,113.37737,189.37253,40.763145,145.67767,1.6356851e-05 1865,0.55811197,419.62808,13.578019,46.17926,1.5402124e-05 1865,0.9251913,286.5711,18.331268,76.18527,1.5307685e-05 1865,588.20087,328.24658,50.9458,157.59744,7.66911e-06 1865,0.9881901,321.27002,18.961163,127.881134,7.662004e-06 1865,5.95861,354.44614,77.95965,128.38715,3.5293506e-06 1865,0.8974553,442.991,21.599712,60.20166,2.7934589e-06 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1869,616.8069,254.2644,22.339783,81.357635,0.000240127 1869,618.13696,386.64572,21.009705,81.081635,0.00016619013 1869,1.0796126,164.97365,18.204351,71.32347,0.00010757507 1869,617.85925,311.50455,21.287415,82.20126,9.576872e-05 1869,414.88068,169.81534,68.5419,178.22223,9.393717e-05 1869,584.1365,15.538376,54.513245,106.3703,6.278614e-05 1869,0.8745301,142.3335,13.619619,48.867508,4.796471e-05 1869,135.21265,249.57977,25.834457,58.00775,4.141617e-05 1869,587.3954,226.77037,51.75128,239.71312,3.6738595e-05 1869,3.8384213,216.10217,56.786873,217.83884,3.0315772e-05 1869,1.9819678,123.83732,32.90316,171.28995,2.8043714e-05 1869,135.54659,197.03531,42.04123,144.28278,2.6004207e-05 1869,608.1339,5.955822,31.012756,52.742687,2.5168927e-05 1869,610.0521,439.6595,29.094543,64.161896,2.345474e-05 1869,0.54666626,419.49268,13.575805,46.17395,1.9624364e-05 1869,0.9470825,285.72385,18.342493,76.41458,1.8489296e-05 1869,1.1396916,101.40027,18.336643,75.26845,1.1428542e-05 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1870,609.1339,79.77876,30.012756,148.27458,0.0005314748 1870,471.2351,283.517,31.521301,64.13498,0.0004912984 1870,1.1008537,253.6749,12.872548,49.52765,0.00024938604 1870,54.590813,208.5879,23.127563,46.38919,0.00022409043 1870,618.4894,254.78514,20.657288,74.46962,0.00018495499 1870,617.89124,323.306,21.255432,75.97009,0.00014937505 1870,618.0312,381.20496,21.115479,80.81302,0.00014261504 1870,563.5308,190.37587,69.23999,171.08366,0.00014012805 1870,584.6476,13.793507,53.818237,105.60733,0.00011079718 1870,1.0144922,152.05309,18.404808,74.51921,8.287413e-05 1870,508.6185,187.7306,80.77988,153.59233,4.9981303e-05 1870,4.3062043,161.31305,56.61588,202.85696,4.9212842e-05 1870,131.7509,234.49138,33.673782,84.55653,3.339579e-05 1870,0.91268396,134.4481,13.637526,50.002487,3.2260017e-05 1870,609.97845,440.8187,29.168213,62.878754,2.5803907e-05 1870,601.20874,253.37927,37.937927,190.34424,2.3999462e-05 1870,1.0112289,285.0548,18.262405,76.890564,2.2412922e-05 1870,113.08752,179.08678,81.04209,182.32625,1.7871393e-05 1870,0.6092615,413.20895,13.606543,45.822144,1.3472292e-05 1870,1.2835336,281.82156,30.553532,169.11652,1.335886e-05 1870,4.578102,272.035,77.72298,196.54636,1.2341277e-05 1870,572.8452,354.58182,66.30145,131.33981,8.422452e-06 1870,1.0359343,86.8611,18.388748,72.47833,7.511658e-06 1870,0.8555534,373.77216,18.054377,69.681854,5.9256186e-06 1870,0.9261548,443.0728,21.636257,59.950012,2.9130822e-06 1870,3.1207488,393.91602,56.63919,98.50177,1.0843927e-06 1870,2.038392,79.73248,35.37992,185.95935,7.784293e-07 1870,0.78275675,13.362986,13.1835375,84.77878,3.0620438e-07 1870,376.2266,1.5589991,42.20572,49.255554,6.478166e-08 1870,3.1804948,16.830242,82.4101,150.57533,5.16713e-08 1870,1.4886117,2.4818099,32.765015,46.053017,1.604768e-08 1871,621.9449,173.1824,17.201782,48.3844,0.0026414788 1871,609.4841,68.60644,29.662598,153.68295,0.001273734 1871,620.47424,135.86351,18.672424,60.497925,0.0008280751 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1879,93.17132,304.5662,33.534477,76.30469,0.00074614683 1879,618.1188,376.13092,21.027893,75.82242,0.0006882561 1879,619.5627,133.4195,19.583984,62.531174,0.0006511311 1879,147.39207,235.79321,37.777252,106.95209,0.0006073022 1879,621.7331,64.057014,17.413574,47.70691,0.00058362936 1879,1.1807259,191.14378,17.87633,69.83516,0.00042616963 1879,616.12384,284.52032,23.022827,85.60339,0.00027115393 1879,117.4105,191.28433,56.212837,146.47949,0.00027022176 1879,0.9238208,154.02722,13.500457,49.51219,0.00014602116 1879,0.7925737,239.47833,13.431388,51.26239,0.000106660365 1879,0.9343221,225.33453,18.024094,134.13235,9.917486e-05 1879,583.17224,58.00193,55.665527,275.2351,6.90026e-05 1879,608.99316,11.42198,30.153503,107.948166,5.6145233e-05 1879,2.1931787,123.12623,32.800644,169.10593,4.5612167e-05 1879,609.4211,438.38995,29.725586,65.966736,3.1287585e-05 1879,0.9183777,113.80828,13.643216,48.487694,2.830445e-05 1879,3.4895296,215.14891,57.434067,217.21938,2.6008027e-05 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1880,614.6155,418.06522,24.531189,83.86197,2.7030916e-05 1880,78.04644,194.56882,41.60608,144.85126,2.555943e-05 1880,587.0207,226.30324,52.125977,245.9771,2.5407442e-05 1880,1.0569775,92.81909,18.371828,78.29608,1.4653101e-05 1880,0.72328043,399.53912,18.125912,70.606995,1.1528463e-05 1880,6.2069955,116.95198,77.78069,249.60316,7.936253e-06 1880,2.8554819,299.37268,56.97899,176.41193,3.7208251e-06 1880,0.9408252,442.9749,21.828747,59.782806,2.7293781e-06 1880,2.1109018,65.75421,35.307552,177.57803,1.368147e-06 1880,2.6929915,429.03543,53.122322,69.14261,5.078435e-07 1880,1.1862736,19.592377,21.72419,105.45234,2.1070483e-07 1880,3.0558758,16.237696,82.61957,152.37329,1.456548e-07 1880,368.41913,1.8313005,44.144318,52.208473,5.3861605e-08 1880,12.447717,0.0,43.40097,51.66214,5.103187e-10 1881,552.8534,204.80885,24.788574,63.42906,99.11377 1881,7.4017067,196.51508,33.01554,86.878235,0.05794662 1881,29.9392,231.83095,24.539476,52.767014,0.026456203 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1881,536.23376,171.38057,72.12921,149.88176,6.238332e-05 1881,606.7086,5.519144,32.43805,55.91806,5.5768265e-05 1881,603.39246,218.05179,35.75421,198.3374,4.6587782e-05 1881,203.04909,200.84355,70.96092,175.3748,2.3863575e-05 1881,609.95306,440.61636,29.193604,63.044952,2.2793161e-05 1881,1.620534,254.61703,27.77518,158.75897,2.1109116e-05 1881,588.8668,332.2842,50.279846,154.45444,8.45403e-06 1881,0.7053174,392.52576,18.212523,71.83005,6.3978405e-06 1881,0.79125816,350.22382,18.42466,74.17236,4.500035e-06 1881,3.2320313,294.8898,55.88259,183.3035,3.4057966e-06 1881,1.021993,444.13422,21.687778,58.803467,1.9822912e-06 1881,1.1362948,59.55922,18.789688,74.93599,1.7360701e-06 1881,2.9247575,429.96252,53.114902,68.83322,3.6621873e-07 1881,0.86286783,12.544327,13.307621,85.22371,1.874784e-07 1881,1.6037859,21.665905,36.524837,192.72453,1.16534615e-07 1881,369.00388,2.004235,43.97119,50.44598,5.869214e-08 1881,6.419082,1.2613136,44.472645,51.85133,7.8569545e-10 1882,542.9032,209.37772,24.815002,60.704926,98.8367 1882,526.55115,209.04538,21.079773,53.442932,0.015926171 1882,1.9070175,195.15517,28.48373,72.51137,0.011884757 1882,213.02104,213.70833,31.599487,66.89894,0.006633873 1882,1.2571106,181.13464,13.538126,50.994034,0.004049729 1882,620.7944,80.40871,18.352295,51.23407,0.0030662522 1882,608.30994,72.419464,30.836731,143.65315,0.001690277 1882,617.0146,195.12428,22.13208,77.03102,0.0004096425 1882,589.6813,106.38021,49.207336,216.08545,0.0002576056 1882,584.03864,15.938721,54.549377,106.05956,0.00018276519 1882,618.0951,395.4027,21.051575,78.1449,0.00016635092 1882,618.07513,259.24698,21.071533,77.15164,0.00016313401 1882,1.4140991,124.35874,21.183184,112.34345,0.0001408048 1882,617.8685,341.6173,21.278137,79.94937,0.00011286334 1882,0.9586304,142.1575,13.329084,49.746994,9.5798496e-05 1882,521.6985,174.90652,68.2334,151.57,7.8486395e-05 1882,3.396242,114.456406,56.0641,203.0418,6.7466666e-05 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1883,535.1125,212.50334,21.05365,54.45146,98.65976 1883,545.404,217.89784,26.704163,59.80049,31.303125 1883,520.8297,212.59836,21.322205,51.71759,0.02159416 1883,40.379875,189.37029,26.454498,52.807556,0.0095079765 1883,1.1253512,182.94334,13.18874,51.17682,0.0022988655 1883,620.33014,66.62603,18.816528,51.047096,0.002055176 1883,347.83398,206.72969,25.684845,48.887222,0.00092725584 1883,1.5663958,202.6653,18.338448,75.55733,0.0008571012 1883,611.54956,183.15508,27.597107,118.5894,0.00058723724 1883,608.49866,66.90244,30.64801,154.3194,0.00039467745 1883,618.1127,395.04248,21.033997,78.68988,0.00018888965 1883,584.5643,14.730993,54.1416,105.594986,0.00013814277 1883,617.9471,341.92474,21.199585,79.51901,0.00010929789 1883,1.7155982,196.82996,33.08514,166.92181,7.251645e-05 1883,0.5356706,298.46878,14.103015,50.394318,6.54675e-05 1883,1.0757895,128.8954,18.429638,80.3795,5.6161833e-05 1883,522.4484,178.30789,72.555664,149.83209,4.37378e-05 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1909,587.04663,187.88464,52.100037,235.71033,6.0583592e-05 1909,1.2075049,412.49557,18.805529,89.53891,5.2923304e-05 1909,3.7645054,90.48046,56.259666,246.1098,4.27808e-05 1909,1.1720223,132.65604,20.218893,143.04568,4.1265444e-05 1909,1.7879868,410.79327,61.574554,86.19336,3.6817368e-05 1909,585.85406,396.4116,53.292603,95.22937,2.5488256e-05 1909,0.82169026,49.162106,20.97963,125.00444,5.9781314e-07 1909,1.3399056,6.9480925,35.252655,79.5614,1.263833e-07 1909,0.6182727,4.372448,9.482841,87.705444,5.256856e-08 1909,0.7637346,1.0329509,20.491627,45.317345,2.2613381e-08 1909,399.64728,2.8897526,43.2359,47.761055,6.3961716e-09 1910,579.9049,194.9936,56.304382,188.4981,0.026517745 1910,328.4234,217.3472,21.217957,46.30905,0.011893633 1910,252.69975,188.78995,21.725632,51.296173,0.010698841 1910,379.45197,220.54243,30.546326,72.19313,0.006884515 1910,39.64414,262.7873,77.64656,196.55429,0.006600668 1910,621.944,159.0128,17.202698,48.619568,0.0037949136 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1910,605.05457,1.0987468,34.092102,65.191536,0.00010119873 1910,598.2816,236.49683,27.273071,75.21225,9.2773014e-05 1910,1.272338,350.41486,32.77209,140.39297,8.200296e-05 1910,580.58044,0.515306,58.566223,137.16545,7.8626945e-05 1910,0.59853274,131.69029,19.066616,86.13599,3.674136e-05 1910,369.16,425.31876,73.83157,78.807495,2.0198591e-05 1910,322.64423,424.64276,73.09259,75.73865,1.986641e-05 1910,0.58194256,286.98343,18.844803,81.99176,1.9182165e-05 1910,263.9863,426.10617,77.48758,74.59488,1.4424975e-05 1910,5.3400164,377.24976,86.010254,115.2876,1.2837071e-05 1910,194.71608,423.7585,72.25572,76.08426,1.1906616e-05 1910,1.8277556,80.73317,34.203255,205.77406,1.040403e-05 1910,121.596146,422.43564,76.16521,77.27777,9.971165e-06 1910,77.71954,419.52167,76.69798,80.48053,4.414992e-06 1910,0.96818525,20.755085,17.418602,140.50899,7.195025e-07 1910,2.6123374,15.55058,57.790028,119.37048,4.10858e-07 1910,0.5673055,9.675202,8.4493685,83.75833,2.9435932e-07 1910,1.2727019,8.048155,29.082666,56.37081,7.292911e-08 1910,435.20206,1.3972038,43.901123,51.496502,1.1571851e-09 1911,562.99396,326.55063,46.744507,102.50018,0.106254876 1911,610.9056,163.18855,28.241089,116.05287,0.04084348 1911,369.28043,224.02591,23.558167,55.26837,0.019948702 1911,622.20166,211.26277,16.945007,52.609818,0.017497843 1911,9.6396,338.41736,77.56693,148.88129,0.016284367 1911,113.10928,233.2417,22.836288,51.241486,0.01553675 1911,34.271233,214.81648,33.91465,68.09157,0.011962115 1911,439.61646,246.77849,34.47336,80.0479,0.0053318893 1911,1.2442286,231.01389,13.243026,51.67102,0.002265342 1911,335.8943,204.48108,40.290833,113.33545,0.0020118773 1911,620.9077,292.84177,18.238953,59.227264,0.0019217414 1911,579.2599,324.18784,57.112244,157.88239,0.0018732264 1911,1.1577588,331.40387,18.94609,89.34955,0.0008561584 1911,327.90845,209.30077,24.003265,50.122635,0.0008324061 1911,585.5432,148.02202,51.97998,227.69826,0.00075114955 1911,1.7099594,274.5035,35.857674,200.64572,0.000665392 1911,615.6362,388.6738,23.510437,81.38397,0.0006015427 1911,1.3674895,196.67294,20.880314,129.83447,0.00040988403 1911,427.3486,223.16193,77.78708,174.87106,0.00032177658 1911,610.5177,240.70993,28.628967,157.93991,0.00029687618 1911,3.4294484,148.81705,60.963596,285.19113,0.00019816996 1911,5.42291,419.93185,54.194077,73.758514,0.0001657166 1911,615.46075,40.16939,23.685913,87.09094,0.00012066231 1911,608.7218,433.87418,30.424866,70.16287,0.00012045065 1911,0.6835995,142.83467,19.335098,94.566864,5.521335e-05 1911,593.8995,6.8488708,45.192017,82.18251,4.6264882e-05 1911,624.40894,365.11435,14.737732,46.49182,3.5647066e-05 1911,1.0707967,419.37952,16.733719,79.46744,1.8306437e-05 1911,581.96216,10.499127,57.18451,212.24864,1.0868364e-05 1911,1.6240772,61.529274,32.616,210.11108,6.2092367e-06 1911,446.44122,3.786883,45.485657,51.377666,6.8791337e-07 1911,1.5836687,9.350726,35.77313,76.3213,4.7042946e-08 1911,421.1333,1.8810335,46.601807,49.772194,4.3470124e-08 1912,331.91934,219.59326,34.237915,63.66577,0.91365093 1912,94.78212,234.79938,32.904846,76.52786,0.034485564 1912,413.28424,216.60385,24.375153,56.568176,0.026830602 1912,440.9355,262.1526,30.224579,71.1116,0.021587655 1912,620.71716,191.63678,18.429504,53.890884,0.0074016787 1912,390.2538,208.67128,36.11499,92.49132,0.0042749136 1912,351.18237,208.39247,24.008606,50.011642,0.003385538 1912,492.5487,223.92055,21.63794,47.417465,0.0026345628 1912,166.71877,227.47542,32.5336,69.31618,0.0024763397 1912,359.52768,216.67444,39.777985,95.09042,0.0018146218 1912,610.6522,108.20226,28.494446,144.8122,0.0018104422 1912,621.16644,99.65882,17.980225,52.25344,0.00062005065 1912,0.79925984,219.35464,13.537493,51.155273,0.0005829246 1912,617.1939,288.27853,21.952759,82.1134,0.00046417757 1912,610.8599,185.41911,28.286743,151.6273,0.00029029598 1912,346.4802,190.2829,85.68817,156.66675,0.0002651479 1912,460.96164,214.59763,72.54031,164.92087,0.0002448019 1912,69.98492,166.05688,76.738625,168.36395,0.00017971525 1912,419.52576,196.19887,74.88748,158.99037,0.00014729095 1912,479.74628,231.70241,21.25531,45.334793,0.0001386869 1912,1.1110368,103.45982,18.687927,76.21541,0.00011495433 1912,617.561,333.95535,21.585693,84.54761,0.00010241221 1912,1.0988729,193.85483,19.750381,125.103424,8.926397e-05 1912,582.7686,0.0,56.37805,266.50333,8.1402985e-05 1912,612.36444,376.78604,26.782227,121.02042,7.5891636e-05 1912,585.29144,239.71298,53.75531,236.4191,3.8131006e-05 1912,609.6247,20.206818,29.521973,143.92226,3.2628774e-05 1912,1.8647348,87.76573,33.760284,194.1414,2.1976231e-05 1912,0.43677002,348.97903,18.518475,76.57703,1.845723e-05 1912,0.47028646,388.6317,27.39813,113.56961,1.6690408e-05 1912,3.0770688,154.63083,57.845615,207.28189,1.4997572e-05 1912,0.89938235,261.0698,32.004913,188.95969,1.1241675e-05 1912,1.934922,13.989245,58.645576,121.38576,1.1505317e-06 1912,4.437608,396.2372,84.987114,99.059845,4.937232e-07 1912,1.2231348,6.597383,30.125124,58.97416,1.9798255e-08 1913,86.59516,223.40834,30.05989,55.413742,12.228452 1913,476.681,269.74463,37.54709,91.56015,0.15468185 1913,336.76834,218.86157,32.979156,74.30759,0.043387048 1913,160.18959,223.73605,32.78859,70.57355,0.018713195 1913,77.355255,204.62297,60.226852,118.52495,0.0034930792 1913,91.65581,247.67445,26.734589,66.551346,0.003058063 1913,490.6429,277.55557,21.795563,45.059143,0.0017212688 1913,513.2668,223.45364,32.142273,72.308685,0.0010446436 1913,621.25433,214.9183,17.892334,56.329773,0.00088022754 1913,471.1479,222.43855,74.85913,158.90428,0.0008472345 1913,442.33496,216.52953,27.964478,56.985184,0.0006705405 1913,621.5718,119.80455,17.57489,52.803925,0.0005957525 1913,611.67523,138.43315,27.471436,142.3191,0.0004716456 1913,615.5711,52.302917,23.575562,89.34236,0.00030942302 1913,1.5847706,190.4393,27.64374,138.40784,0.00021276978 1913,621.8064,248.09831,17.340271,53.18419,0.00020276595 1913,609.9603,441.25632,29.18634,63.358337,9.0989386e-05 1913,611.85785,237.17029,27.288818,136.52884,6.09137e-05 1913,583.45447,22.974005,55.6922,216.38297,6.0778613e-05 1913,1.1615877,90.55801,18.87471,81.714714,5.6465855e-05 1913,612.76074,324.75906,26.385925,139.23605,5.1464707e-05 1913,1.101272,162.37349,18.881868,93.962234,3.3143613e-05 1913,2.5881543,190.22272,58.25246,223.95908,2.69356e-05 1913,392.27713,102.39803,119.95041,244.88031,2.4659108e-05 1913,0.5987232,271.03613,20.15341,137.63614,2.0470508e-05 1913,585.5839,396.51157,53.562744,98.83954,1.570653e-05 1913,597.16974,9.061143,41.97693,81.092064,1.3783258e-05 1913,0.6356372,361.87497,19.720203,132.2366,5.445874e-06 1913,44.077965,40.34022,85.98235,243.6317,1.051655e-06 1913,3.116592,398.6701,56.500263,95.22861,9.330628e-07 1913,1.946167,26.380781,59.668118,260.54333,5.1829653e-07 1913,395.64713,0.32490396,45.745117,51.38509,1.1348866e-07 1913,1.4575146,8.826934,37.419167,80.04902,6.8691655e-08 1913,531.30023,0.0,45.77716,46.97324,2.0987445e-08 1914,595.7598,271.3871,41.924316,156.0776,0.40111393 1914,353.55338,199.74164,33.957306,72.57123,0.39174208 1914,609.0809,251.07378,30.065796,85.86339,0.121487506 1914,161.61732,240.04001,25.017258,57.042603,0.10469756 1914,479.32672,300.58023,27.136353,61.40097,0.09045272 1914,76.84996,224.37227,26.209885,55.393356,0.059825946 1914,613.1328,320.77872,21.823853,50.57968,0.03893954 1914,469.93808,233.21527,27.243011,52.554413,0.02498192 1914,583.0313,193.50887,54.94336,160.80339,0.020908335 1914,466.59836,232.02177,51.563812,134.22762,0.013225797 1914,622.49176,299.39365,16.654907,54.81662,0.004087403 1914,623.36426,338.3391,15.78241,49.950073,0.0026945134 1914,622.45605,234.64636,16.690613,52.399902,0.002683776 1914,1.3893352,206.40013,18.015036,75.99165,0.0023473643 1914,614.6726,374.01398,24.47406,86.78635,0.0023361268 1914,619.9371,188.76385,19.209595,65.739044,0.0011855683 1914,580.28046,230.62813,24.611877,52.942947,0.0009543389 1914,22.756075,203.46289,38.715538,61.308716,0.00074994843 1914,451.6863,252.9268,36.17572,83.70891,0.00062002166 1914,441.3498,172.46936,30.552612,62.793198,0.00054545247 1914,56.3662,144.17781,76.09297,182.66518,0.00046514274 1914,610.1494,95.04896,28.997253,180.01035,0.0002335297 1914,615.98944,56.200462,23.157227,84.13853,0.00014088539 1914,1.3484359,195.6313,33.737114,167.28557,9.002011e-05 1914,608.8426,434.6169,30.304077,68.85483,8.438892e-05 1914,423.8448,101.573456,79.4422,211.77618,5.2842453e-05 1914,1.7158464,100.844864,29.624607,166.3,3.120427e-05 1914,0.47253582,301.2427,21.084799,106.01074,9.887382e-06 1914,0.64664066,391.4802,19.341354,107.880066,4.555634e-06 1914,595.10547,0.95435876,44.0412,87.099785,3.4688653e-06 1914,2.754406,314.77115,57.80203,165.67282,8.26427e-07 1914,2.733369,427.20807,53.47196,72.22839,3.6721195e-07 1914,1.0508912,28.240847,18.559929,146.93648,1.07038375e-07 1914,385.05536,0.0,48.934692,151.6312,7.610829e-08 1914,1.6034408,6.3361263,37.858643,85.4423,6.782484e-08 1914,0.98965496,0.67943686,20.883272,45.245857,1.544704e-08 1914,393.18683,0.15909669,45.169067,54.84713,1.22152555e-08 1915,62.5159,224.22818,29.29242,74.651825,0.68683594 1915,491.3973,204.2292,43.09549,118.27751,0.47963434 1915,379.543,199.63263,25.121307,52.62436,0.104329154 1915,146.02708,218.20045,33.694748,74.142166,0.04415868 1915,403.1666,211.0575,38.334778,93.90042,0.02598956 1915,519.4926,209.3978,37.822388,116.12851,0.018442072 1915,566.7564,199.46428,34.796326,75.879105,0.017603202 1915,588.7556,223.42574,47.858276,220.06386,0.01503538 1915,472.66205,206.92676,37.46936,71.65585,0.014597416 1915,420.94705,214.50026,24.285095,50.04738,0.007272786 1915,620.01697,392.23477,19.1297,52.169098,0.0042314064 1915,526.4093,180.81267,71.87366,181.84123,0.0042152074 1915,459.74484,177.1817,105.56711,186.75049,0.0024883226 1915,76.0422,220.23006,38.84433,87.919754,0.0023570864 1915,609.59656,157.6765,29.55011,142.2671,0.0022075002 1915,621.75995,254.91957,17.386719,60.09114,0.0019962175 1915,621.0257,129.78052,18.120972,53.51758,0.00092302455 1915,612.54016,326.21628,26.606506,105.51929,0.0008905454 1915,0.9176823,215.84898,17.95997,68.48564,0.00064258854 1915,556.6708,159.5141,28.554932,58.640076,0.000607202 1915,609.96136,76.66279,29.185303,144.00623,0.00058520446 1915,397.476,198.5577,73.91055,182.88109,0.00032691553 1915,583.628,379.4274,55.216064,110.82251,0.00030431367 1915,0.8251839,90.77387,20.52804,151.44724,0.00016460929 1915,582.717,44.53547,56.429688,257.80096,9.238173e-05 1915,3.3000848,116.27651,58.838345,215.62854,8.753311e-05 1915,0.4825391,258.36838,18.202957,76.43704,8.0330334e-05 1915,43.784374,200.71565,84.75824,150.76817,8.001425e-05 1915,606.6139,0.91871095,32.532776,63.079075,4.5372504e-05 1915,1.6911117,209.14471,32.86148,165.56656,3.7746868e-05 1915,615.9187,36.96097,23.227966,91.67685,1.6406282e-05 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1916,540.4352,282.2712,29.611267,62.62848,0.0020488237 1916,620.91895,84.4794,18.227722,54.774277,0.00149569 1916,517.70166,208.94597,24.30371,50.811142,0.0014937884 1916,620.98145,209.48466,18.165222,59.11595,0.0014029563 1916,621.1104,261.04422,18.036255,58.404266,0.0011557041 1916,515.0688,267.83875,49.66266,139.98209,0.0011129968 1916,500.25485,217.27162,110.84958,212.68233,0.0008844567 1916,617.502,377.2498,21.644653,76.76471,0.0008500729 1916,1.192605,104.423134,18.587181,79.76273,0.00057813735 1916,610.12854,67.02897,29.018127,159.92584,0.00042290558 1916,621.316,133.98785,17.830688,53.419006,0.00040938015 1916,33.254868,164.46637,80.37328,170.20197,0.00036684712 1916,581.9677,15.496892,57.178955,103.53527,0.00029915926 1916,584.91895,286.3009,53.59784,189.97708,0.00023673786 1916,3.3039453,92.25464,60.881733,223.72159,0.00021455683 1916,59.87671,231.70694,33.262177,80.25552,0.00016731955 1916,0.7098682,268.99692,18.307455,71.3562,0.00015513421 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1918,621.6663,186.56331,17.480347,49.00087,0.023005685 1918,1.5160352,208.51863,25.579424,88.22545,0.012830161 1918,449.58856,173.60669,38.043213,92.17093,0.012110654 1918,608.63275,199.48193,30.513916,126.160645,0.012079169 1918,471.07745,258.39722,24.158386,48.770905,0.008343053 1918,1.0310689,248.42699,11.737069,45.390305,0.0074351183 1918,620.57416,373.1704,18.57251,59.459747,0.0058422713 1918,624.04425,239.18413,15.102417,47.210556,0.00520721 1918,619.71014,46.082794,19.436523,56.441208,0.004925561 1918,431.15356,188.38716,39.79297,113.45567,0.0045603537 1918,621.9243,268.41583,17.222351,55.290527,0.0036682263 1918,2.5488298,162.69432,57.884,196.42232,0.0016651644 1918,571.2198,204.32712,65.11157,173.46066,0.0015989143 1918,5.2765512,259.861,24.57972,57.41873,0.0014205391 1918,449.84424,222.0654,23.93396,57.259674,0.0010509539 1918,0.65606123,147.58347,19.009352,84.40842,0.0006119276 1918,609.34076,67.0885,29.805908,166.91885,0.00039600272 1918,600.90674,0.0,38.23993,69.966576,0.00032643514 1918,596.62354,277.2886,41.9588,198.74527,0.00024888615 1918,1.2378451,253.69818,19.696619,148.3873,0.00010931458 1918,0.9247363,95.51723,18.296974,79.69174,6.9205846e-05 1918,609.6367,439.49908,29.509949,64.95175,3.926327e-05 1918,0.6355656,367.40018,19.22632,129.26477,2.5641904e-05 1918,2.9241164,331.61676,57.758553,153.82907,4.8142792e-06 1918,1.1265903,49.604076,36.22546,227.17482,2.2292093e-06 1918,1.131167,14.521944,21.280046,106.91422,1.3735901e-07 1918,522.12195,1.4341471,44.760986,52.062645,1.0099392e-08 1918,433.10803,0.6029395,45.865265,56.54064,5.9306665e-12 1919,508.4321,194.25476,43.927887,88.84805,10.722353 1919,504.65573,188.17133,24.143524,61.826157,0.88815826 1919,59.76581,226.2906,29.064423,67.71959,0.84968525 1919,68.54146,218.30205,42.70616,111.44707,0.46774694 1919,608.5109,233.22105,30.635742,164.8893,0.04424887 1919,569.8456,221.27985,27.730347,65.16116,0.021247244 1919,518.4349,191.15253,22.44226,50.332092,0.012423568 1919,570.46313,217.54892,65.68274,246.80057,0.010520481 1919,620.9913,332.06097,18.155396,60.910492,0.008784645 1919,532.59985,224.72906,27.060242,56.31958,0.007435025 1919,621.8143,280.75714,17.332397,53.262177,0.005352715 1919,495.6797,178.91219,89.361694,151.43346,0.0027940194 1919,70.1377,209.5128,27.369156,56.340256,0.0023625414 1919,552.5595,180.78966,35.26361,84.40614,0.0021716028 1919,530.8197,177.71841,36.238464,73.967575,0.0017443071 1919,394.64807,223.43997,22.494385,48.756897,0.001699944 1919,272.78323,190.16289,21.667725,49.765717,0.0016926063 1919,0.84744465,102.39556,19.197445,77.36242,0.0012170579 1919,1.2427311,164.95407,28.031534,157.20401,0.0010421411 1919,457.54153,225.78647,23.301239,45.249237,0.0010043973 1919,1.2393075,255.51901,18.594841,81.00653,0.00090416404 1919,621.9873,199.64365,17.159363,49.05246,0.00083073904 1919,0.5643433,147.17772,19.250309,85.88286,0.00078859075 1919,611.03143,348.40768,28.115234,153.00296,0.00034970505 1919,610.54846,127.55752,28.598206,142.32874,0.0002811124 1919,616.3618,69.17812,22.784851,85.742775,0.0001576058 1919,569.9332,416.97516,68.12897,80.52792,0.0001253527 1919,3.0375602,194.21672,57.017754,208.53572,9.291345e-05 1919,0.6045565,302.1124,18.668617,79.0387,5.4588207e-05 1919,1.2296582,290.35574,33.437748,188.77359,2.5308194e-05 1919,582.7653,32.72483,56.381348,294.50955,2.1609003e-05 1919,0.54968184,365.76627,18.498056,74.52609,1.713595e-05 1919,0.703763,421.92487,16.733364,79.92807,1.4032394e-05 1919,595.3193,1.7054265,43.827393,86.03555,1.14123795e-05 1919,2.957417,398.89612,56.274536,95.093414,2.3430277e-06 1919,1.6004883,31.40336,62.66358,238.44319,1.0915311e-06 1919,0.5849768,7.8786755,10.333353,92.61293,2.8521999e-08 1919,1.1807797,4.9100847,37.42586,91.24697,1.8190396e-09 1920,47.18451,230.95529,40.692764,93.846344,0.02362885 1920,75.46879,230.2636,35.797493,74.256454,0.0180335 1920,422.41257,219.61485,22.037537,45.011154,0.016694559 1920,622.91766,180.5913,16.229004,52.927017,0.008943945 1920,268.1776,207.78458,22.541016,46.514496,0.008063931 1920,621.7827,209.43846,17.363953,58.32054,0.006575488 1920,609.48914,205.21858,29.657532,156.85893,0.0061969003 1920,0.34540528,148.94206,13.782031,47.571854,0.001895065 1920,543.4072,203.65015,36.13855,90.89926,0.0009952838 1920,18.5608,211.07568,87.619095,152.73041,0.0008852297 1920,0.95852786,187.28754,20.136086,134.8714,0.0008344805 1920,572.4498,185.86278,66.06897,234.11964,0.0007203869 1920,0.9825252,270.75418,18.059654,77.84088,0.00066626957 1920,612.0476,121.53672,27.09906,116.35386,0.0006070011 1920,521.8314,139.25925,74.54443,184.75894,0.000604953 1920,617.8673,396.7761,21.279358,76.86334,0.00044747276 1920,602.45715,290.35745,36.689514,165.62454,0.0003880201 1920,1.3527849,261.39212,33.30344,178.41705,0.00029218214 1920,0.54491293,326.829,14.005282,48.126556,0.00021578041 1920,623.3575,297.95218,15.789185,54.411804,0.00021365329 1920,605.1872,0.0,33.959473,68.826584,0.00020940739 1920,615.80725,38.65568,23.339417,94.366455,0.00013046383 1920,2.69771,91.07175,58.110924,239.1814,9.890509e-05 1920,609.4338,439.74268,29.71289,64.786865,4.4267174e-05 1920,349.77264,90.01599,37.03705,72.929535,4.382206e-05 1920,0.53382814,361.87262,19.395178,135.16873,2.392857e-05 1920,582.3935,0.0,56.753174,270.68204,1.105182e-05 1920,1.1498893,70.458244,19.132326,132.00061,8.221479e-06 1920,4.6210904,342.3387,78.999596,145.91052,3.589192e-06 1920,2.859904,428.93372,53.24394,70.15079,1.1722195e-06 1920,2.0152996,8.375997,60.70879,138.77338,2.1539968e-08 1920,0.6556926,6.0494566,10.358135,94.66106,1.2373516e-08 1920,1.1723063,1.8012403,30.364498,68.11573,1.6880096e-09 1920,455.41855,0.0,44.789185,51.655052,3.987867e-10 1921,475.92532,210.8946,22.423859,45.53392,1.2346259 1921,39.775574,253.32344,26.825577,60.311295,0.023237355 1921,539.4856,196.14613,34.21637,74.16493,0.014099652 1921,621.0661,204.6945,18.080566,51.416397,0.003429785 1921,613.1732,58.771328,25.97345,87.966324,0.0030620205 1921,623.86505,33.6423,15.281616,46.64014,0.0029116631 1921,610.9235,176.86958,28.223145,129.73824,0.0011040575 1921,0.81115156,95.518,18.43099,75.76653,0.0007598313 1921,391.0594,54.17345,28.910675,70.90604,0.00040466935 1921,617.2849,149.9916,21.861755,76.18576,0.00029292624 1921,1.3501189,215.78267,18.28718,72.83717,0.0002086857 1921,3.0819206,107.6209,59.113426,233.52802,0.00019707043 1921,617.12994,391.4546,22.016724,83.22348,0.00019542465 1921,617.3531,258.35822,21.79358,89.17157,0.00017438545 1921,1.179834,264.42056,18.291065,71.07532,0.00015925703 1921,1.8952849,213.7131,33.518303,163.08853,0.0001476576 1921,0.6062183,135.06584,18.516537,82.73271,0.00014033534 1921,586.7525,209.64401,52.37207,266.16568,0.00012326462 1921,606.51337,0.0,32.6333,65.14523,0.00010798333 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1922,570.1967,190.99075,32.316345,74.12335,0.44150758 1922,51.16875,207.41658,42.62273,113.087296,0.2503993 1922,406.44836,205.45149,33.301758,72.31615,0.063917436 1922,619.26575,186.81044,19.88092,62.98256,0.023209944 1922,426.51483,217.3672,24.808502,55.020157,0.00799685 1922,392.79263,212.69566,27.058655,61.720596,0.0069992077 1922,273.75684,197.24191,21.067535,49.49872,0.0042712423 1922,592.04095,195.58392,44.873474,135.30954,0.0027696472 1922,0.54269695,87.36284,20.637829,95.06831,0.0020868669 1922,621.3847,240.8576,17.761963,54.968597,0.001789043 1922,621.0322,114.81279,18.11444,54.34346,0.0006766824 1922,15.765899,185.1141,109.66205,197.77945,0.0005769124 1922,60.88319,207.71251,25.1087,50.142807,0.0005669022 1922,0.8544491,264.66507,17.982805,77.176025,0.00048580059 1922,1.4108797,186.0374,18.018473,80.77666,0.00038166877 1922,616.61237,321.88705,22.534302,86.424774,0.00030326756 1922,1.5379866,90.42816,34.061596,219.71466,0.000299944 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1929,168.88922,134.4812,71.67967,184.46182,7.6686565e-06 1929,586.8162,66.04516,52.304077,257.79724,7.591916e-06 1929,595.8768,5.250843,43.269897,79.9082,6.4070236e-06 1929,1.7432846,413.79077,34.993435,84.381744,2.840682e-06 1929,1.1175652,19.033216,29.24046,122.28169,3.056932e-07 1929,398.69794,54.656715,76.088104,206.89154,1.02089004e-07 1929,0.49996012,8.108613,10.447312,86.39122,2.3511012e-08 1929,288.52747,3.6266456,40.54425,50.058567,2.2632571e-09 1930,420.34656,223.47168,34.39966,78.51761,0.029006239 1930,140.76196,227.52092,36.363647,77.405106,0.020424148 1930,1.616958,315.04575,31.951374,162.58365,0.013419575 1930,0.93013185,366.9624,17.490812,74.3624,0.0067964275 1930,621.05347,291.7402,18.0932,59.095703,0.005679526 1930,586.79486,324.3256,50.925903,166.77759,0.0041166795 1930,3.9465497,209.36134,72.15253,269.1557,0.0040154033 1930,624.79663,398.69806,14.350037,47.56726,0.003639924 1930,514.2568,211.2114,32.66046,67.37039,0.0033532456 1930,623.57806,432.1699,15.5686035,46.549225,0.002756538 1930,609.68,266.45505,29.466675,160.35852,0.0010384723 1930,610.9417,194.05734,28.204956,132.95685,0.0009159097 1930,513.1697,328.76114,44.1911,134.30365,0.0008273836 1930,405.03168,195.97334,66.752045,164.73125,0.00081999344 1930,609.8096,411.69907,29.337097,99.4476,0.00078099413 1930,619.4358,114.75781,19.710876,58.69667,0.0007429311 1930,529.1802,234.16516,24.835876,52.888275,0.0007078764 1930,1.2280307,246.76053,18.6996,80.79608,0.0006280558 1930,620.88824,230.03401,18.258423,58.536667,0.0006104808 1930,2.33958,210.4241,32.117657,84.45422,0.00048556764 1930,621.27344,341.06845,17.87323,57.66678,0.00046126946 1930,620.6101,81.55553,18.53656,53.425735,0.00043490503 1930,615.6804,359.62888,23.466248,101.903076,0.00034172277 1930,42.752296,251.04712,84.0066,191.17981,0.0003024796 1930,609.06213,73.22797,30.084534,144.15208,0.00026584952 1930,575.3529,135.73773,62.814148,295.74942,0.0002369945 1930,492.68668,278.56372,114.43979,195.47473,0.00022243455 1930,0.6522673,297.10812,14.875801,62.21277,0.00014086027 1930,1.0586165,168.43118,18.491663,76.29442,0.00010157473 1930,1.0687647,406.54944,18.001648,93.14218,5.4660097e-05 1930,0.86969405,86.65977,19.44553,82.51597,3.702208e-05 1930,595.34106,3.955804,43.805603,86.31385,2.6242773e-05 1930,2.7158465,420.36453,71.86951,75.99063,1.00952075e-05 1930,2.4321063,34.074337,82.386955,319.4952,2.4690632e-06 1930,2.0963461,16.213959,57.32302,115.6891,1.9141983e-07 1930,0.5221366,8.018727,10.159769,87.31144,3.064163e-08 1930,1.1261052,7.4762306,29.282328,56.9007,4.5997632e-09 1931,395.38422,237.22542,36.216583,85.57167,0.026119929 1931,8.772765,233.17941,31.796051,78.278076,0.014974213 1931,587.57025,311.0004,49.90149,175.01535,0.012951481 1931,521.35876,213.70168,27.685852,53.679672,0.010047169 1931,132.3444,217.53459,34.108276,75.63423,0.008468869 1931,1.0984286,239.11888,13.626464,55.3658,0.005618652 1931,6.2338414,230.79118,76.22967,250.2058,0.004513609 1931,611.52997,267.81888,27.6167,105.46585,0.0043527866 1931,624.44714,398.10986,14.699524,47.611176,0.0030808314 1931,492.9084,205.38214,31.860413,72.80466,0.0030235613 1931,623.3206,438.007,15.82605,46.82486,0.002668303 1931,612.4639,395.46234,26.68274,100.393036,0.0015035871 1931,67.11149,219.8224,22.92102,46.18306,0.0013037107 1931,621.20416,217.55435,17.942505,58.097443,0.0011402395 1931,1.9970688,234.19478,31.730757,204.13524,0.00084689434 1931,618.91046,126.83289,20.236206,58.387688,0.0008250797 1931,620.54706,339.70706,18.59961,60.773956,0.0007955704 1931,611.7453,168.32066,27.401367,138.59923,0.00075218466 1931,621.11786,89.29887,18.028809,51.57891,0.00052504754 1931,1.2846184,175.97087,20.11602,139.4746,0.0005007996 1931,1.188217,334.11407,19.570452,135.7786,0.00047726117 1931,579.90186,273.07892,34.724792,65.54077,0.00044341933 1931,386.92523,176.80461,66.653046,176.2806,0.0004185171 1931,609.161,79.98388,29.985657,139.10828,0.0004170127 1931,587.8627,143.90839,50.377808,260.3168,0.0003465352 1931,0.83174074,262.7466,18.769705,95.45221,0.00025380368 1931,47.84716,233.08449,84.406715,205.33272,0.00023420325 1931,500.86273,297.1683,76.303406,174.28842,0.00015115601 1931,2.4511979,89.304665,48.89752,215.909,9.570024e-05 1931,0.67936933,138.47644,18.952913,78.27396,9.327532e-05 1931,122.61514,182.68314,74.35019,157.78894,8.960966e-05 1931,3.1655307,374.60483,57.027737,113.02164,7.445813e-05 1931,0.80475587,415.44287,17.267256,84.16324,5.1723175e-05 1931,0.0,309.8481,11.675891,48.652283,1.869376e-05 1931,584.5514,0.0,54.595276,135.39442,8.16793e-06 1931,390.34842,60.892353,77.83942,217.30653,9.860483e-07 1931,1.2442937,18.574518,30.789547,143.7568,1.8601163e-07 1931,0.56957763,7.6003714,10.296591,83.94897,4.4616062e-08 1931,1.1937972,6.9369597,29.476294,58.30561,1.6276843e-08 1932,383.48752,236.65079,33.9693,75.42685,20.802221 1932,398.50177,233.76616,22.922424,45.210648,0.08826584 1932,587.70703,309.7917,50.176575,176.98056,0.008129389 1932,611.73175,248.23495,26.498413,54.543488,0.007093588 1932,609.6,276.1939,29.546692,76.285,0.0044576502 1932,624.2535,411.23932,14.893188,47.65985,0.0033994496 1932,623.2449,438.2272,15.901794,46.381042,0.003387437 1932,492.7031,202.15118,31.932465,69.681335,0.0029718648 1932,65.26623,228.12584,58.251015,173.15147,0.0024387343 1932,514.0742,204.9842,32.084473,70.35451,0.002225399 1932,103.40451,230.39746,77.653275,161.24796,0.001783818 1932,621.3181,210.65723,17.828552,58.673615,0.0014969626 1932,613.4552,389.23737,25.691467,99.62753,0.0014229172 1932,620.8639,70.82621,18.282776,54.36818,0.00090671575 1932,600.20624,169.62541,38.29242,213.08128,0.0008170334 1932,620.0966,344.35544,19.050049,65.134766,0.0006580767 1932,374.73685,194.4089,69.08728,155.50696,0.0006146061 1932,611.3665,126.36461,27.780151,140.459,0.00025818922 1932,0.69272053,137.48695,19.33478,83.440216,0.00025177575 1932,0.59346354,232.50383,13.803663,53.4261,0.00021554063 1932,1.2238363,154.54935,29.025885,149.02853,0.0001317028 1932,607.60925,0.0,31.537415,119.29646,9.097854e-05 1932,1.201788,251.45366,20.044481,103.91261,8.643093e-05 1932,615.366,91.157814,23.78064,90.41213,6.258724e-05 1932,203.85063,157.40854,68.18138,181.68631,5.5534798e-05 1932,4.1807113,132.97122,57.174995,225.71146,4.8016154e-05 1932,0.6867497,403.28992,18.19684,70.782684,1.9684192e-05 1932,1.7572755,285.3996,29.999533,165.3801,1.5359086e-05 1932,582.5834,0.0,56.563293,242.5824,8.711898e-06 1932,5.0698795,343.44592,79.51278,139.85028,5.2324335e-06 1932,0.754362,441.1692,22.054335,62.295776,2.451008e-06 1932,2.2540772,423.46027,53.40897,74.98239,5.014504e-07 1932,1.1001676,16.233614,31.247587,150.13408,2.0732898e-07 1932,0.5090389,7.947969,9.608721,85.328064,3.8254548e-08 1932,0.9955729,6.733656,29.569656,58.929245,1.561926e-08 1933,373.0191,227.52399,39.662903,90.111694,99.74165 1933,393.24905,218.8668,24.763245,52.0645,0.02769563 1933,140.57602,255.85292,28.914398,79.718796,0.027076684 1933,619.11926,289.7905,20.027405,67.496155,0.010251937 1933,518.3522,214.65254,25.590454,52.33934,0.009155238 1933,587.5791,313.1163,50.354248,174.55283,0.007953061 1933,127.76852,208.60344,58.99169,143.2668,0.00727133 1933,620.1813,238.04839,18.965393,61.31276,0.0040077986 1933,500.06818,204.75221,30.552002,72.57765,0.002793838 1933,618.27014,405.00323,20.876526,70.90262,0.0014191607 1933,620.6956,79.69845,18.45105,52.444557,0.0012217188 1933,599.52185,267.85883,39.624817,125.44583,0.0010234943 1933,0.91315514,252.17946,13.34468,47.370102,0.000957296 1933,624.62537,380.24634,14.521301,46.13794,0.000859998 1933,605.5152,434.71783,33.63147,76.42883,0.0005412589 1933,609.30725,75.98266,29.839417,147.28345,0.00028087926 1933,349.0328,199.29626,85.779236,163.27399,0.00025058808 1933,1.0040251,204.6199,17.88447,79.698425,0.00024947216 1933,615.49677,335.14755,23.649902,106.92819,0.00018742183 1933,508.45135,321.0693,74.25635,159.94293,0.00017176884 1933,2.202632,137.87471,32.281914,180.90276,6.657118e-05 1933,0.8373194,138.9513,18.805725,77.31424,6.316468e-05 1933,95.47916,177.86247,126.85788,243.75368,5.34046e-05 1933,1.1069767,234.69743,18.972105,129.16093,4.3303175e-05 1933,584.18616,5.7348375,54.96051,126.498985,2.7636494e-05 1933,0.6729159,365.12906,19.717794,130.49872,1.1898489e-05 1933,2.6616359,278.20264,56.300983,194.77795,5.6621e-06 1933,2.6089258,429.02792,53.680626,70.278534,5.8711646e-07 1933,1.8130567,22.11935,63.183556,245.66498,8.2525474e-08 1933,1.1159343,11.678197,28.89822,124.80894,2.975678e-08 1933,0.5180302,5.804896,9.416051,87.64432,2.3007681e-08 1933,294.41812,2.5673811,40.95871,52.085667,2.5954547e-10 1934,374.9066,225.81691,35.259064,88.95503,99.40686 1934,615.72107,131.01799,23.425598,63.05107,0.017846772 1934,0.37082845,367.1857,13.499965,46.59897,0.012959858 1934,389.19244,209.61865,23.03363,48.27484,0.0059580836 1934,178.45499,224.56761,38.873184,107.51689,0.0041469955 1934,619.9549,65.74165,19.191772,57.916725,0.0028051846 1934,0.04640625,340.33502,39.50304,163.02985,0.0025188595 1934,622.22577,297.14688,16.920898,53.016296,0.0024398577 1934,394.7927,229.88243,34.93695,74.09267,0.0023399352 1934,358.0961,224.04207,31.37439,80.7484,0.0023215532 1934,525.21893,216.31169,34.28723,68.74815,0.001650131 1934,499.4274,205.48128,31.734955,73.21904,0.0016122474 1934,622.0656,226.37418,17.081055,51.182922,0.000755169 1934,610.4223,298.35782,28.724365,145.13043,0.00074687443 1934,600.3496,66.45231,38.79706,155.85623,0.00065690547 1934,57.745003,217.51344,39.56246,111.11046,0.0005534121 1934,344.78677,211.85785,25.074005,56.31717,0.00047237208 1934,2.64188,202.27925,37.160282,103.376785,0.00042796246 1934,611.9227,151.75629,27.223938,144.40411,0.0004069566 1934,617.6523,245.35724,21.494385,76.651794,0.00036094693 1934,621.3464,346.46295,17.800293,58.907227,0.00034268483 1934,3.5394304,203.58195,74.70563,267.88214,0.0002563667 1934,1.4125521,183.61526,18.743307,81.57886,0.00023898586 1934,348.08063,200.31784,87.65509,162.00626,0.00021651549 1934,569.7939,354.3951,67.65161,131.69885,0.00018428931 1934,587.33984,194.17625,50.566162,234.1974,0.00017252601 1934,0.5481592,442.9483,30.269655,63.87494,0.0001600117 1934,1.0379883,241.16656,30.917498,189.53107,0.00013099641 1934,0.8933016,139.41731,19.036865,74.307236,8.29661e-05 1934,1.0082479,268.9265,18.485018,80.3627,6.7212684e-05 1934,1.546237,86.95806,29.688435,174.54651,5.042363e-05 1934,583.10547,4.8765235,56.0412,129.79932,3.926001e-05 1934,0.62589437,314.66492,14.510529,58.71826,2.1754116e-05 1934,35.501995,171.85313,112.76146,228.06587,1.03967295e-05 1934,600.75555,411.13556,38.391113,85.38721,1.0391021e-05 1934,0.8000488,13.346797,18.81143,126.28292,1.570647e-07 1934,305.24255,3.7950897,43.14795,48.959038,1.549128e-10 1935,375.99564,227.99243,35.907043,83.29794,99.65725 1935,615.87897,367.99323,23.2677,91.999695,0.028721642 1935,395.90057,231.60094,34.094696,70.811356,0.025559522 1935,1.6283937,269.2158,33.5595,193.27173,0.016960697 1935,503.91714,207.94029,27.287506,56.343033,0.008697109 1935,6.7326612,243.53313,37.23919,81.34001,0.0040355804 1935,0.7484977,259.4854,14.396652,54.55789,0.0033359549 1935,325.71896,212.88768,23.00241,47.984604,0.002255366 1935,598.52716,297.38297,40.237488,191.24454,0.0017121136 1935,4.2104006,182.51802,75.33917,274.65875,0.0015400654 1935,358.0486,208.90784,78.01953,132.5904,0.0014822767 1935,539.646,222.29745,33.636536,66.3186,0.0014563255 1935,0.9326359,222.08177,21.481508,145.34636,0.001369382 1935,612.7361,181.29832,26.410583,107.34801,0.0012779002 1935,621.1484,290.60898,17.998291,60.10388,0.00094550644 1935,58.20482,268.56955,64.00088,145.22818,0.0005312873 1935,0.60734296,200.99428,13.909314,53.92021,0.0004969815 1935,608.4346,432.8247,30.712097,73.81995,0.0003672724 1935,611.124,90.70567,28.022644,145.10033,0.00030142046 1935,620.137,113.31139,19.009644,59.501274,0.00024444633 1935,194.01602,215.37088,36.481796,70.262695,0.00024258714 1935,1.2110832,395.08447,19.251541,86.364746,0.00022912907 1935,610.5406,236.65367,28.60608,154.5944,0.00019817316 1935,0.99826336,135.56245,29.213474,157.72343,0.00014065864 1935,616.37067,56.753445,22.776001,86.040985,0.000113578775 1935,2.236867,381.74496,54.609673,106.64197,0.00010827104 1935,606.0326,0.53935874,33.114075,66.236725,8.818624e-05 1935,0.6217513,72.068436,19.326212,152.82219,4.3931857e-05 1935,1.0859734,451.19922,28.091484,53.55365,2.379694e-05 1935,581.3551,0.0,57.791565,258.01584,1.8864755e-05 1935,0.57186484,8.5842285,8.42064,87.30595,1.18903344e-07 1935,1.759362,11.757729,63.596123,222.00903,8.989035e-08 1935,1.722155,10.608822,35.31896,75.32187,5.4336773e-08 1935,497.0966,4.4998374,43.858917,49.094513,9.072704e-10 1936,393.29987,211.98311,43.999146,103.9189,99.727936 1936,132.82095,225.00906,27.514648,60.84796,0.3079662 1936,415.40634,211.0747,34.836365,74.209015,0.04530257 1936,104.013275,255.0269,34.78827,99.893906,0.044122368 1936,623.8535,367.77808,15.293152,45.5571,0.00786251 1936,101.8681,223.3317,72.3499,163.24588,0.007026252 1936,358.72495,220.59021,26.087341,64.255615,0.0053619924 1936,375.19635,206.03,37.53293,117.89047,0.004251257 1936,310.93704,250.79012,28.167328,53.016098,0.0030478793 1936,619.2115,49.596508,19.93518,56.793674,0.0026362317 1936,611.3646,366.4635,27.782043,128.30603,0.0022922785 1936,619.8386,116.78792,19.308044,56.409027,0.0018327226 1936,620.3682,88.84479,18.778442,52.803207,0.0012792659 1936,609.5231,78.05144,29.623596,143.28706,0.0010813691 1936,617.17377,203.92088,21.9729,77.232315,0.0009609509 1936,618.27386,258.6331,20.872803,74.86203,0.000919177 1936,591.0013,4.2817807,45.224,46.41553,0.0006344242 1936,1.015044,197.82297,18.063883,69.75708,0.00025251252 1936,601.9591,237.968,36.732666,197.5247,0.00020802366 1936,345.33786,143.65872,149.65945,245.91452,0.00015832014 1936,0.54164714,130.71347,19.211267,82.21318,0.000111859605 1936,207.7893,189.17892,41.100693,96.83618,8.711472e-05 1936,1.9376303,96.186195,33.714676,184.73822,6.701414e-05 1936,0.7623226,252.87883,18.477509,135.88597,3.928409e-05 1936,583.0037,0.0,56.142944,286.40427,2.4844974e-05 1936,0.80213624,342.27393,18.983597,128.46594,1.1035179e-05 1936,3.7626026,169.93913,56.338406,216.60713,9.691224e-06 1936,5.9437337,349.83755,78.80917,132.52411,5.3631006e-06 1936,0.6109888,420.20782,17.075987,81.587555,5.3051294e-06 1936,0.8044426,39.38233,19.437891,138.77136,8.463448e-07 1936,2.4304786,425.99747,53.773373,73.18787,4.3966537e-07 1936,2.1405323,13.165886,57.179287,120.17289,3.341297e-07 1936,387.9861,7.538921,42.232513,48.69239,1.4818886e-07 1936,302.60034,2.2802637,45.04953,51.43572,8.490154e-08 1936,0.49775472,6.660495,10.400751,89.791756,8.207214e-08 1936,362.18924,7.9682293,40.753937,46.008987,3.3501145e-08 1936,0.96839035,6.3340983,29.734905,60.108574,2.2949303e-08 1937,419.3629,215.66458,55.055573,119.59628,99.88972 1937,148.12144,221.3097,44.76819,96.29672,0.08852345 1937,449.47412,213.22638,34.89093,75.070465,0.073923565 1937,619.84406,218.0656,19.302612,58.65117,0.05720837 1937,404.37936,211.32297,33.88376,93.07614,0.046135597 1937,312.17078,249.31186,30.133026,56.01129,0.03843798 1937,615.98785,345.32913,23.158813,91.10062,0.03828014 1937,582.09924,307.49088,55.539185,152.47205,0.036735833 1937,609.5734,248.26451,28.103455,54.865067,0.006370874 1937,607.4876,221.44087,31.659058,166.73967,0.005105865 1937,375.7775,223.86896,41.698517,120.058044,0.0027360208 1937,618.7257,299.31552,20.42096,73.48685,0.0015305601 1937,351.5331,199.19376,127.189514,192.58432,0.0005217154 1937,621.0614,94.45261,18.085266,53.457535,0.00035327423 1937,611.5468,130.71786,27.599854,143.75742,0.00031490685 1937,1.0419873,230.83441,19.295612,131.64618,0.0001180564 1937,127.0268,186.03246,96.46876,193.12431,0.000110252025 1937,3.743737,156.77727,57.89648,217.20256,0.00010862764 1937,0.44979492,332.46603,19.943491,134.35742,0.00010010313 1937,595.15405,400.8374,43.992615,110.309265,6.270291e-05 1937,1.72521,161.24026,28.1772,141.46033,5.0263134e-05 1937,606.16876,1.4993718,32.977905,61.604946,4.0180952e-05 1937,0.5715129,419.78708,17.05497,82.378815,1.50199285e-05 1937,615.9788,38.282665,23.167847,86.170944,1.1460837e-05 1937,0.97409916,68.30051,19.010818,150.43025,1.039209e-05 1937,2.6379101,285.1426,57.820724,191.87354,8.860659e-06 1937,583.3059,6.883086,55.84076,285.36496,3.8994954e-06 1937,2.5774755,428.45694,53.47685,70.509,1.2378225e-06 1937,2.1830697,14.830716,56.5949,121.91722,1.0718097e-07 1937,0.74277264,6.3455114,20.22388,58.69513,1.4856746e-08 1938,476.8749,220.69865,59.790863,142.01866,99.802086 1938,415.85428,226.04808,37.16739,89.810715,0.36925548 1938,507.22058,221.78162,36.09729,90.19705,0.19965626 1938,470.73804,231.4286,25.088837,71.018234,0.054879405 1938,327.1864,209.81177,21.41098,49.684967,0.052269194 1938,182.35785,224.6679,31.218765,59.446457,0.040933076 1938,413.1283,201.3235,26.534576,51.201218,0.011672094 1938,621.74927,209.96307,17.3974,45.471252,0.011596389 1938,455.635,187.68828,116.796936,252.68819,0.008198583 1938,445.45938,193.6579,60.318085,143.03107,0.0054555032 1938,315.19562,247.34798,28.19217,56.992813,0.0036164518 1938,295.91428,252.75266,22.037018,48.160492,0.003475518 1938,395.53345,199.9762,74.1127,153.5661,0.0027460274 1938,610.561,189.83131,28.585693,116.49553,0.0012415545 1938,611.44556,101.32864,27.70111,140.88235,0.00094728445 1938,620.4551,120.25375,18.69159,55.503983,0.00075261237 1938,611.9128,379.48325,27.233887,114.79822,0.00048070675 1938,616.74,56.668232,22.406677,85.9554,0.0002826487 1938,160.80525,188.92735,68.85011,142.87556,0.00022520103 1938,586.16974,172.98987,52.128418,217.96994,0.0001831169 1938,1.0246794,253.77977,18.988533,129.80046,0.00013821367 1938,490.89325,253.35982,28.83844,83.3698,0.00010493877 1938,2.912772,148.63857,59.346836,214.9178,9.0923466e-05 1938,0.8070231,130.95253,18.632936,77.349976,5.5256645e-05 1938,0.43247235,318.7872,20.142189,145.27426,5.23431e-05 1938,1.6642855,184.76738,26.991034,146.9694,4.831647e-05 1938,1.3932716,87.897865,29.492327,169.22977,3.0892184e-05 1938,583.62134,32.13332,55.52533,248.2643,1.5740885e-05 1938,0.64701986,420.94476,16.96411,81.349945,1.4273408e-05 1938,599.2641,6.091481,39.88257,61.72202,1.1829799e-05 1938,2.58931,333.87674,58.094727,153.11197,5.3760255e-06 1938,1.0979606,17.723398,29.714624,127.72463,2.495723e-07 1938,0.5337455,6.576615,9.515514,88.17987,4.2993683e-08 1938,420.5057,0.0,44.34091,52.678192,2.1577047e-09 1939,319.25183,242.69342,27.106384,55.85965,0.21949199 1939,599.2545,265.58618,39.15143,116.750885,0.15752383 1939,433.43414,196.75917,33.530457,51.722504,0.10485072 1939,624.33636,269.68253,14.810303,46.058594,0.030982025 1939,622.8648,326.15988,16.28186,50.530243,0.0070296284 1939,525.8409,311.47003,67.003784,139.87003,0.0068380074 1939,619.1615,369.64752,19.985168,66.24609,0.006006517 1939,610.745,101.31903,28.401672,54.41249,0.0047336323 1939,620.8706,231.35347,18.276062,58.80803,0.0035992896 1939,603.5797,319.41132,35.566956,165.45004,0.002769761 1939,426.76797,202.60515,79.13724,187.08308,0.001576367 1939,610.0176,181.46548,29.12909,156.7685,0.001493794 1939,622.3321,196.47421,16.814575,47.30934,0.0013088331 1939,512.92474,260.05643,44.235107,166.25348,0.0009910706 1939,573.8923,123.062996,63.942993,277.9446,0.000758011 1939,1.3045061,177.9406,18.39462,78.696335,0.0005863631 1939,621.3021,156.52795,17.844543,54.75699,0.00046251257 1939,2.5028272,210.13316,35.33558,106.60686,0.00022124324 1939,601.8118,72.33156,37.33484,166.11978,0.00016267633 1939,3.6884897,122.0755,57.025616,217.97275,0.00014568871 1939,0.5947103,133.5449,19.071466,81.19551,0.00010921453 1939,0.5511564,309.3951,18.535276,75.68149,4.546433e-05 1939,583.5134,4.323151,55.63324,126.81868,4.2887787e-05 1939,571.8816,423.61456,67.032776,73.5365,3.491022e-05 1939,0.6120866,384.58618,18.359,74.388824,2.7674312e-05 1939,3.664318,244.47327,56.56761,211.82684,2.6040789e-05 1939,368.8835,426.33975,73.37546,70.87094,1.7254742e-05 1939,1.0574666,269.01743,18.383593,74.56818,1.4783457e-05 1939,276.92715,424.31488,72.1922,72.80545,8.83821e-06 1939,0.8023633,441.51437,22.014704,62.28427,5.1365946e-06 1939,3.026286,396.48685,56.381886,97.06067,2.5209856e-06 1939,1.3464502,52.15025,32.04588,186.81303,2.1014066e-06 1939,0.8197087,18.790236,19.19558,131.22137,1.9259245e-07 1939,1.8640105,15.481944,59.640823,115.06408,1.801594e-07 1939,0.5605017,8.698734,8.960864,83.60162,8.925991e-08 1939,1.1250927,7.41634,29.827745,55.712013,1.0601671e-08 1940,430.435,208.9012,38.569855,85.598404,0.079044536 1940,583.2716,238.40819,32.430298,65.98009,0.025454028 1940,571.26184,159.54778,64.75824,171.8087,0.0046948716 1940,607.7681,242.87318,31.10846,68.33238,0.003298101 1940,619.7892,121.16964,19.357483,58.720055,0.0031198342 1940,621.12714,224.4974,18.019531,50.994812,0.0028616504 1940,462.73553,234.53386,80.893005,200.84789,0.001901089 1940,608.7567,69.29251,30.389954,141.59474,0.0013846677 1940,518.6906,243.80173,35.28003,91.25037,0.0013442505 1940,551.8417,246.05313,22.479065,47.748413,0.0012968187 1940,620.1908,73.53965,18.955872,56.29528,0.0012211901 1940,585.21533,268.22208,52.525696,218.57544,0.0006483694 1940,610.1681,358.38873,28.978577,142.8841,0.00046444774 1940,390.71417,219.68214,39.58304,123.79973,0.00037424383 1940,2.279717,86.04658,49.77985,198.83984,0.00028676732 1940,1.0341073,88.819275,19.249216,81.38101,0.00028172327 1940,0.7276294,232.3025,18.135555,76.075485,0.0001967891 1940,617.1809,281.15787,21.96576,89.16452,0.00015673015 1940,413.9629,159.20607,104.64398,230.17815,0.00014530685 1940,0.82158774,173.00508,18.17812,78.756226,0.00012243945 1940,584.583,9.121374,54.56366,119.37257,5.0324474e-05 1940,620.4284,28.308304,18.718262,58.292404,4.0115672e-05 1940,3.4511883,217.92703,57.656185,216.83734,3.7657486e-05 1940,0.8520549,282.9737,18.307312,73.546814,3.1574204e-05 1940,0.70471114,323.84744,19.729826,134.82986,2.966162e-05 1940,323.65018,424.76242,73.94058,76.543,2.3698289e-05 1940,0.695249,420.57425,16.912832,81.7164,1.0538562e-05 1940,2.909738,396.4005,56.228714,96.61038,2.2370846e-06 1940,1.8227441,16.573029,59.0877,123.69507,3.0784057e-07 1940,0.543138,7.568594,9.06756,88.13003,5.5407096e-08 1940,1.0734701,6.8430796,30.118141,57.232468,7.72776e-09 1941,373.10242,230.77008,33.583252,82.38754,0.12458753 1941,609.97296,209.12141,28.669617,50.205368,0.055797078 1941,614.7336,369.63092,24.413086,65.30905,0.04628651 1941,394.91415,248.08769,21.580719,53.189255,0.017702645 1941,453.26047,183.43723,37.477936,70.76729,0.0120245 1941,571.3536,360.7901,66.26416,123.54236,0.005909116 1941,600.89545,141.00468,38.22223,146.54549,0.0023004182 1941,244.85368,218.74089,23.084976,52.733963,0.001877988 1941,571.62244,156.46632,66.48157,275.39868,0.0010407586 1941,620.838,156.71104,18.308655,54.306747,0.0009217596 1941,585.31067,242.6329,25.885986,60.08716,0.00070953625 1941,406.1351,230.97287,39.457214,100.00192,0.0006221528 1941,620.8584,114.941666,18.288269,55.794357,0.00055372046 1941,1.595171,102.98628,28.628963,152.22768,0.00048079845 1941,356.36142,204.77722,80.477356,161.37418,0.00042255648 1941,449.29102,237.97229,80.65369,199.62573,0.00029599442 1941,608.84686,59.00468,30.299805,160.8461,0.0002777791 1941,0.6587769,144.74252,14.463385,52.974167,0.00027395936 1941,0.9558887,94.084724,18.829224,77.13064,0.00027219005 1941,0.8619792,214.52414,17.915035,72.88046,0.00024356443 1941,617.2075,253.25821,21.939148,86.49121,0.0002328099 1941,620.6789,318.17062,18.467773,62.23117,0.00022342078 1941,608.008,442.49527,31.138672,68.127075,6.4780295e-05 1941,3.0071502,132.97086,58.12535,205.65561,5.901695e-05 1941,0.5381958,397.86377,18.489769,74.305084,5.7588277e-05 1941,0.6045817,258.13168,18.302475,74.46567,3.6799396e-05 1941,594.6737,4.2060843,44.47296,85.52926,3.6612746e-05 1941,0.59433025,304.5697,19.696012,137.51593,1.961366e-05 1941,1.3916488,222.15135,32.056465,174.39043,1.7059541e-05 1941,311.96976,392.58487,116.49097,96.82971,1.11082045e-05 1941,2.593864,302.11932,57.85296,177.33054,8.76388e-06 1941,0.75879395,440.89386,22.06479,62.258545,8.426453e-06 1941,296.79315,423.51218,71.82501,72.00424,5.607942e-06 1941,1.3586279,18.61877,29.699734,122.60736,2.3379643e-07 1941,0.5411015,8.744551,9.145769,87.25956,9.7565746e-08 1941,454.95047,0.0,43.300262,50.757343,2.7048494e-10 1942,434.43546,270.64835,53.446075,113.77771,0.063752316 1942,379.44318,256.0398,30.862762,63.55609,0.020290047 1942,480.881,194.25581,39.87863,77.50337,0.010506465 1942,415.17844,237.13725,24.046265,55.143417,0.0052941525 1942,400.5767,243.18388,24.329315,56.61348,0.004703574 1942,619.47437,77.75742,19.672302,55.35543,0.0039997953 1942,413.57086,204.03935,108.4917,221.14153,0.0023966166 1942,621.63165,237.45212,17.515015,51.049225,0.0022834223 1942,608.5428,80.36299,30.603882,118.52371,0.0016917962 1942,620.80664,203.84666,18.340027,56.641403,0.0014533135 1942,572.9037,86.81305,65.652954,272.671,0.0011504961 1942,457.43164,297.683,21.746033,47.60846,0.000998736 1942,362.87335,219.08261,64.22931,124.82272,0.0009048852 1942,610.2141,185.14058,28.932556,129.38387,0.000855618 1942,621.0367,135.02397,18.109985,54.147263,0.00067566417 1942,611.211,326.75574,27.935669,140.27875,0.00046375103 1942,1.8253435,109.471985,28.1156,145.64612,0.00039720663 1942,1.0659709,92.375854,18.5325,73.35118,0.00023628269 1942,0.9462492,220.41805,18.150204,73.194565,0.00023131166 1942,617.2038,267.17493,21.942871,85.360016,0.00017536608 1942,0.8728312,187.47127,13.338477,50.42827,0.00017336203 1942,583.8843,387.9572,54.223877,103.17978,0.00011578204 1942,403.12268,56.025875,39.60196,79.34259,9.464684e-05 1942,0.6226298,267.78986,13.757214,47.614777,6.852075e-05 1942,0.6585181,398.03604,18.313028,73.44318,5.4545482e-05 1942,2.8779492,141.72136,58.497944,213.07585,4.987793e-05 1942,608.5791,4.921019,30.567566,109.39777,4.7460366e-05 1942,0.5852726,261.9362,19.672762,141.5257,3.9312123e-05 1942,0.54968345,372.01547,13.845011,49.22357,3.885701e-05 1942,530.7314,287.0861,81.31976,175.80313,2.6024401e-05 1942,1.3646908,308.787,33.635963,172.3298,1.4272986e-05 1942,284.93768,433.1083,81.37833,67.587494,8.955432e-06 1942,0.8892586,440.99963,21.733492,62.108307,8.541945e-06 1942,2.088851,41.510677,62.12468,194.51479,2.055964e-06 1942,0.49768475,7.323174,8.810164,94.959305,3.9295433e-08 1942,1.4261361,6.3633757,36.722687,89.55278,1.0120296e-08 1943,531.22876,208.62715,25.56549,54.448807,1.5413108 1943,460.43677,284.56244,55.521667,123.6254,0.029698595 1943,405.77982,217.38371,24.941223,51.730453,0.026963081 1943,607.263,250.61626,31.622559,71.70616,0.015118547 1943,443.63245,243.23631,42.00049,141.49321,0.014267245 1943,509.78558,182.00717,37.479797,82.498474,0.012367328 1943,577.5045,195.72209,25.371704,50.172363,0.01107188 1943,440.29865,248.99709,27.376465,66.25282,0.010245664 1943,465.93735,310.01193,22.184082,46.55371,0.006883215 1943,621.83374,282.79962,17.312927,49.067505,0.005143368 1943,477.76917,310.41574,22.593048,50.829315,0.0020061794 1943,420.12943,211.23979,113.16983,222.36562,0.0015282907 1943,0.68150634,127.40956,19.789837,85.23851,0.0013749738 1943,497.70703,121.78834,71.71576,178.93542,0.0010995175 1943,588.6855,268.57626,49.550964,205.62082,0.0007811461 1943,616.72906,382.4294,22.417603,84.786835,0.00056097883 1943,610.4385,141.36012,28.70819,152.09087,0.00025747958 1943,1.2760571,196.736,17.989145,69.37338,0.00024247424 1943,617.1086,300.41602,22.038086,89.31433,0.0002346101 1943,1.4996501,134.69885,34.59677,178.24774,0.00016243232 1943,616.68,76.48615,22.466675,85.3532,0.00015964222 1943,418.7051,49.80231,29.428802,63.27959,0.0001525675 1943,609.0435,436.6513,30.10315,66.95947,9.590783e-05 1943,0.83122075,234.7001,19.84013,139.60727,8.279892e-05 1943,614.7127,13.974401,24.43396,88.38035,2.8128301e-05 1943,0.7591675,322.0674,20.78236,104.775055,1.9969173e-05 1943,3.0210807,187.36653,57.22094,227.80327,1.7360213e-05 1943,583.91785,22.564949,55.22882,284.29007,8.781482e-06 1943,0.6375977,421.24182,17.034075,80.62219,6.470565e-06 1943,2.7004037,35.65578,81.0316,282.52505,3.931969e-06 1943,1.1613656,353.8579,32.16571,137.75189,3.6136184e-06 1943,0.95898926,15.015749,31.065485,171.22108,6.0744514e-07 1943,1.6932976,5.673167,30.161028,60.473034,4.0485702e-08 1944,565.32935,170.692,35.65149,81.88202,0.5946349 1944,600.0184,189.15268,36.61029,78.35217,0.11379431 1944,504.93323,227.69714,25.44934,55.661957,0.06649477 1944,529.6414,239.54758,23.495728,49.727936,0.01547728 1944,624.423,192.34842,14.723694,46.899704,0.015215468 1944,558.7374,108.084885,72.22662,172.36049,0.00545152 1944,587.60986,197.61803,19.509644,45.562103,0.002629618 1944,0.9009798,132.6833,19.189045,85.37361,0.0024465288 1944,619.721,155.1439,19.42566,59.398026,0.0024120894 1944,585.54987,315.71783,51.970215,173.84323,0.002135393 1944,621.58716,313.28705,17.55951,55.998077,0.0017162005 1944,620.94086,114.6011,18.20581,56.14509,0.0010680342 1944,609.7531,82.37817,29.393555,163.04773,0.0008504864 1944,0.5974056,204.34473,14.102027,54.427673,0.0008252448 1944,621.2968,55.08896,17.849854,52.819252,0.00079311297 1944,405.99426,180.92026,44.05899,106.08807,0.00077511603 1944,615.40106,228.03537,23.745605,104.05673,0.00054916093 1944,437.93173,242.9848,19.837616,45.093323,0.0003691212 1944,1.6143311,125.86062,34.573223,182.41934,0.00015085761 1944,0.6460775,221.45584,20.133228,143.32379,9.636882e-05 1944,0.7578182,385.91528,18.337309,72.26431,6.73825e-05 1944,608.37573,440.37762,30.770935,67.85458,4.690032e-05 1944,4.5932293,120.5009,78.00271,258.56586,4.4148073e-05 1944,1.076665,309.22565,28.602222,144.93466,2.5507126e-05 1944,390.58173,147.15802,98.88672,204.37515,1.8805802e-05 1944,595.11194,1.9544271,44.03473,89.372925,1.6439863e-05 1944,3.000923,395.5486,55.902607,96.127686,4.688874e-06 1944,0.8689673,439.72797,22.103853,64.17172,4.395809e-06 1944,1.1945133,11.814186,17.542112,139.53302,1.9689472e-07 1944,1.7337272,7.738054,58.10716,131.91856,3.0897215e-08 1945,427.20688,184.55144,38.718353,73.853195,0.049525145 1945,0.115224615,196.96416,15.162476,58.79799,0.01361952 1945,556.69415,264.47705,26.164917,59.259796,0.009254998 1945,620.70233,218.10219,18.444336,55.28215,0.003101321 1945,621.2431,87.04161,17.903564,53.423065,0.0018771554 1945,620.8394,176.47885,18.307251,56.790756,0.0018247318 1945,0.59148437,210.125,20.18406,144.48022,0.0014376758 1945,610.2719,177.59341,28.874756,128.3045,0.0013871962 1945,587.37573,234.24435,34.251648,65.85013,0.00082813995 1945,1.0201042,134.36148,28.930758,146.85916,0.00079090067 1945,618.05133,263.60257,21.095337,77.2005,0.00067273097 1945,1.0261434,84.838295,18.422619,74.469124,0.00056554686 1945,609.9639,63.41875,29.18274,145.95482,0.00048729382 1945,408.35355,154.62038,80.205536,161.96944,0.00015319692 1945,516.0386,186.49196,83.312195,173.41914,0.0001499577 1945,2.5325212,173.99979,58.028744,232.62823,9.790302e-05 1945,612.5712,383.73962,26.57544,115.406494,9.2677954e-05 1945,583.4143,50.824154,55.73236,268.67642,8.326938e-05 1945,607.90814,0.12766276,31.238525,121.84726,6.200343e-05 1945,0.5027368,389.66907,18.566917,75.909485,5.8249505e-05 1945,0.652487,292.56787,19.861149,140.58475,5.3934127e-05 1945,588.1009,250.09172,51.045776,223.37325,3.1540618e-05 1945,0.78915775,442.69122,22.17668,61.27707,7.749837e-06 1945,2.6400065,397.0671,56.094414,97.17328,2.4536087e-06 1945,1.9026693,34.86831,62.38277,208.44388,7.9886e-07 1945,1.4699463,23.614044,29.373772,121.74536,1.9884705e-07 1945,0.9204956,8.771689,11.639618,86.89696,1.8400618e-08 1945,1.5029705,3.4676645,33.45427,48.590054,2.122932e-09 1946,298.7915,237.6569,24.769531,53.68132,0.27128533 1946,404.98276,194.93034,31.995941,65.299576,0.12015678 1946,460.36453,194.92609,41.42148,87.80075,0.0042468 1946,621.1081,353.59082,18.038574,52.252136,0.0020918483 1946,209.60777,208.63582,23.120651,45.601456,0.0010884523 1946,1.4635612,186.92474,17.654055,61.555634,0.00046074364 1946,612.37506,338.09433,26.771606,126.68765,0.0003832883 1946,612.20715,159.25131,26.939514,107.628235,0.0003315387 1946,392.26108,123.369064,57.2446,152.89381,0.0003149798 1946,616.21155,59.602867,22.93512,85.69427,0.0003025148 1946,1.9289372,116.2706,28.509161,138.66939,0.00014158335 1946,617.0215,233.01996,22.125183,87.16934,0.00013051361 1946,585.0906,245.90347,53.15564,223.66968,0.00012637212 1946,0.6337427,382.58743,18.323334,73.750336,0.00012630597 1946,0.8775342,94.03975,18.006832,75.40773,8.163482e-05 1946,0.67180663,222.18074,20.007162,147.01115,6.524574e-05 1946,3.3671665,160.45107,57.354446,223.88972,5.6730612e-05 1946,0.6280005,315.44403,18.46172,81.18024,5.5130004e-05 1946,585.86584,72.227646,53.280823,256.30743,4.66309e-05 1946,613.7037,414.82513,25.442993,87.734406,4.5141296e-05 1946,457.48047,162.21194,86.74585,184.69894,3.625227e-05 1946,585.2289,3.3982227,53.917786,123.19215,1.926356e-05 1946,1.2991341,309.02448,34.137474,169.1854,1.4574665e-05 1946,0.7164331,440.0583,22.173939,64.0061,5.665752e-06 1946,3.162422,424.00244,53.10482,74.66144,2.619321e-06 1946,0.46125653,7.124642,10.298035,92.96884,2.6940603e-08 1946,407.9816,1.4503516,44.088257,48.321095,2.5008637e-08 1946,1.2554297,20.305286,29.238255,122.8911,2.3236039e-08 1947,523.567,207.10384,26.007324,57.962204,92.7819 1947,537.8393,210.68315,24.202637,49.40059,18.658506 1947,424.8205,206.05656,22.870026,45.873474,1.1188893 1947,440.53226,191.19162,24.179047,55.21231,0.105158985 1947,510.35938,182.12608,55.610718,102.37294,0.04967322 1947,296.2969,200.61127,22.683716,51.162277,0.02476091 1947,418.7762,154.57971,44.73053,112.06021,0.023571495 1947,618.0437,212.00906,21.102966,65.3407,0.021898871 1947,503.47256,222.65466,22.43814,50.955536,0.005013124 1947,608.1991,314.7617,30.94757,143.64957,0.002586812 1947,1.5440153,184.11243,17.876038,70.46875,0.0013020346 1947,0.7769206,84.68792,14.197939,58.757042,0.0008285695 1947,610.0791,112.66956,29.067566,166.24634,0.00069063436 1947,0.61497396,120.137344,20.130686,99.97568,0.00044930552 1947,587.89886,149.8897,51.032593,257.0871,0.00043556787 1947,616.1344,271.52142,23.012268,94.45117,0.00020736686 1947,1.351071,150.20468,33.775486,178.5018,0.00011656644 1947,608.8785,437.40662,30.268188,66.53708,0.00011502209 1947,583.6176,384.91687,55.461975,109.006256,0.00010609347 1947,0.40141684,236.34839,18.616209,79.62109,8.7743974e-05 1947,615.8079,46.733395,23.338745,88.76776,5.5512293e-05 1947,406.92334,141.29033,100.55499,184.67094,5.130877e-05 1947,476.4556,129.02281,136.46127,229.0732,3.5383528e-05 1947,0.55370283,336.1119,19.346441,135.71246,2.957513e-05 1947,0.54116213,308.57068,18.406199,76.44476,2.520116e-05 1947,594.7521,3.5305405,44.394592,85.73809,9.673089e-06 1947,2.9116554,226.0799,56.752983,227.51825,8.944285e-06 1947,0.6216577,420.94727,17.305315,81.42114,7.314581e-06 1947,2.5268734,394.15622,56.5732,99.22009,1.8202746e-06 1947,0.90172285,18.12189,21.705465,153.79109,1.2978037e-06 1947,1.779642,22.135963,60.56209,226.23483,1.0381982e-06 1948,460.63214,181.8333,41.757965,82.29634,0.03195379 1948,620.10754,189.52092,19.039124,57.821304,0.0032408019 1948,621.71924,152.64886,17.42743,50.853714,0.001240338 1948,610.41296,110.29529,28.733704,146.79645,0.0011942538 1948,612.3896,230.57318,26.75708,150.81796,0.0008864529 1948,434.16992,153.99449,90.80792,161.15105,0.00085508934 1948,524.9983,247.29665,23.091125,47.68184,0.0006587122 1948,611.4361,355.61407,27.710571,141.63312,0.000435105 1948,1.3291528,184.10873,17.220743,62.697372,0.00041031273 1948,384.32397,189.91728,80.65625,161.09901,0.00025417042 1948,621.2435,46.841656,17.903137,56.684193,0.00019828262 1948,616.04065,77.17232,23.106018,83.355835,0.000121289704 1948,0.68654215,345.3901,18.428976,74.03778,7.6239405e-05 1948,1.653755,127.21672,29.011505,137.37007,5.8972393e-05 1948,3.7858303,219.1035,57.498817,230.2213,5.3092695e-05 1948,581.2755,0.0,57.871155,282.83234,4.970679e-05 1948,0.4877352,234.1241,20.003098,144.06645,3.349799e-05 1948,0.6716439,389.95816,18.965979,108.37799,3.0163044e-05 1948,608.1755,0.26555339,30.971191,126.20808,2.4549201e-05 1948,1.0784839,91.88292,18.884102,80.14734,1.9830331e-05 1948,5.2087893,356.8709,79.45559,128.72592,4.6725186e-06 1948,3.369733,428.25317,52.49542,71.16257,1.3668584e-06 1948,2.1768782,31.553907,62.523262,224.93547,5.558029e-07 1948,0.85751224,6.7893915,13.089403,75.77443,9.593174e-08 1948,433.96057,0.0,85.79535,169.35278,7.92659e-09 1948,1.5286475,0.18504883,39.13867,52.88362,3.1684613e-09 1949,521.63806,203.65555,27.059937,62.209045,99.20996 1949,501.21432,206.73978,24.54544,52.090668,98.96395 1949,459.20303,209.58165,23.142181,48.45662,1.5159249 1949,538.42487,209.34972,27.636902,62.286057,0.38759947 1949,506.90085,170.39032,57.151703,112.474396,0.14535548 1949,484.1698,211.0635,21.491882,46.101288,0.0020590427 1949,621.2189,196.23149,17.927795,56.270935,0.0013862097 1949,453.06403,100.777725,46.995728,168.44432,0.0010043638 1949,610.7963,179.0511,28.350342,125.975174,0.0006449953 1949,0.86030114,239.18896,18.346798,75.48917,0.000436895 1949,617.28375,364.57324,21.862915,84.848236,0.00035341713 1949,469.42,102.16633,141.32663,247.46832,0.00018668403 1949,616.9095,82.78985,22.237183,79.664665,0.00016144884 1949,0.80648196,98.17836,13.716236,49.229263,0.00012113942 1949,1.3379338,155.08658,19.163967,79.81354,0.00010875244 1949,611.5096,245.2421,27.637085,157.06561,9.335132e-05 1949,1.5347428,158.12466,34.022835,166.1022,8.34488e-05 1949,609.8413,442.6159,29.305359,63.353424,7.6446246e-05 1949,588.0914,292.5521,50.87085,187.42548,6.1419334e-05 1949,584.48615,48.73284,54.449158,244.34967,4.401955e-05 1949,595.0537,252.94884,22.598877,48.672073,3.2102453e-05 1949,615.91437,29.954754,23.2323,80.14166,2.7358525e-05 1949,0.6174292,340.2923,19.729355,132.74939,2.1258937e-05 1949,459.91522,132.69579,30.050049,72.05934,1.8206152e-05 1949,1.0661564,85.01035,21.876009,115.810646,1.778777e-05 1949,0.66424155,419.70184,17.004152,82.824615,9.420904e-06 1949,4.42888,99.96055,85.943214,224.17688,7.0519213e-06 1949,2.7110987,290.73053,57.771526,186.04413,4.427515e-06 1949,2.546188,427.6318,53.37374,71.711426,1.0681104e-06 1949,1.3196354,10.379616,62.26332,127.96569,2.6883523e-08 1949,386.80835,0.50588703,43.581085,52.235794,3.0329776e-09 1949,1.4311581,2.315817,19.890339,61.29852,1.7962601e-09 1950,578.215,205.36436,26.857544,65.05812,99.74686 1950,604.0813,200.01646,29.360535,68.555466,99.69337 1950,621.8363,194.84915,17.310364,59.043854,4.987449 1950,517.9377,307.9418,52.537354,156.73257,0.1206654 1950,584.8847,168.3559,51.61548,247.89609,0.08967013 1950,564.436,209.89816,26.203613,63.06137,0.057791132 1950,572.9392,355.5575,64.08008,121.53726,0.021609256 1950,477.24344,211.86827,22.108032,47.391586,0.009028505 1950,137.71233,136.04926,32.529068,54.68483,0.008713621 1950,595.9502,202.82019,19.482178,45.59294,0.0067561953 1950,484.92773,186.73373,37.295166,74.35535,0.0063922526 1950,616.88477,351.1925,22.261902,75.84073,0.002660287 1950,526.71075,231.74399,23.222961,48.07959,0.0026370264 1950,615.26447,219.7927,23.882202,92.171234,0.0013073438 1950,1.4019629,196.62408,17.62397,66.284454,0.0005304894 1950,606.6896,431.4604,32.457092,76.79822,0.000395806 1950,475.07492,136.9964,98.20126,273.6035,9.4786126e-05 1950,620.7026,35.492382,18.444092,56.44502,9.325131e-05 1950,615.40875,123.29896,23.737915,115.69886,5.0932787e-05 1950,0.61281335,322.18542,19.676142,136.8663,3.3746168e-05 1950,0.55211264,230.95834,20.1087,137.84598,2.7742013e-05 1950,1.7274854,138.06895,36.295406,160.52849,2.603706e-05 1950,0.9518384,141.80806,18.463928,84.108246,2.5534775e-05 1950,0.6688013,420.26285,16.810047,81.671844,1.1246264e-05 1950,596.11017,3.8724773,43.0365,82.40809,1.0333545e-05 1950,3.4137354,202.51988,56.9578,223.78468,9.144595e-06 1950,110.791565,85.57623,86.91135,183.827,8.538979e-06 1950,609.97455,26.299772,29.17212,167.43372,5.2944956e-06 1950,2.6921713,397.96222,56.39755,95.35852,2.2280105e-06 1950,1.3127939,51.881447,18.772444,135.11041,1.2051738e-07 1950,1.3897754,6.2216797,58.6593,130.24236,5.800625e-08 1950,0.9217005,6.9322333,10.706477,90.50139,2.1415596e-08 1950,1.2828109,0.0,31.568897,74.691345,1.0496535e-09 1951,538.6841,207.66246,30.780334,57.33461,99.321594 1951,494.04706,217.87006,22.944214,49.50717,0.06194031 1951,621.67236,244.78488,17.474304,48.159668,0.04548825 1951,356.3498,200.19594,31.441711,73.67976,0.01248475 1951,594.466,203.12735,42.730896,93.03607,0.007981764 1951,572.2666,134.6703,65.66693,238.42146,0.003277489 1951,621.03046,206.88957,18.11621,54.540848,0.0023880405 1951,608.72296,365.3781,30.423706,142.66492,0.0016113436 1951,104.92888,128.93541,32.30725,57.166855,0.0013927516 1951,1.3921102,195.72751,17.887785,74.06442,0.00091065257 1951,608.41754,115.63539,30.729126,154.34969,0.0009013712 1951,620.81244,73.83819,18.334229,54.131645,0.00068069465 1951,611.2721,246.75652,27.874573,149.2741,0.00059544574 1951,520.71405,164.14647,84.66583,156.05559,0.00043192366 1951,1.1991976,149.18462,18.468744,81.58281,0.00019628945 1951,1.5892888,159.55888,33.985645,165.06078,5.6590874e-05 1951,0.66749185,227.61438,19.98345,140.74469,3.7999554e-05 1951,608.0509,1.7541081,31.095764,118.68582,1.9108e-05 1951,0.6709969,320.67792,19.805979,134.60449,1.6748576e-05 1951,2.8177605,233.30214,57.65647,228.05095,1.4577944e-05 1951,0.9821574,108.20694,18.356995,73.51247,1.235845e-05 1951,0.7342709,421.36703,16.82416,80.483826,8.1624485e-06 1951,3.0420296,397.2931,56.379665,95.88025,1.595735e-06 1951,1.733418,9.187064,57.94518,126.619576,2.0594794e-07 1951,1.2911085,44.318596,30.282433,166.61761,1.154074e-07 1951,1.6143001,1.1129884,30.882233,47.019978,1.538494e-08 1952,489.84692,210.36037,24.034912,45.71318,0.049799517 1952,620.6053,230.63667,18.541382,57.053574,0.008272419 1952,610.5606,167.36899,28.58606,142.40524,0.001517387 1952,616.6354,254.8968,22.511292,89.9818,0.0009206491 1952,616.7882,385.9945,22.35846,87.86154,0.0007668212 1952,511.66714,226.7774,23.815277,45.809723,0.00066687865 1952,621.20465,162.3115,17.942017,54.526062,0.00059113326 1952,66.55079,117.60134,36.215996,60.467354,0.0003693508 1952,569.5758,158.4987,69.300415,245.21996,0.00032448344 1952,601.75745,273.57745,37.38922,204.02283,0.00027504185 1952,1.3700961,180.20418,18.404984,71.63095,0.00014976732 1952,610.8872,76.28696,28.25946,119.677246,0.00014132516 1952,3.109253,90.96348,47.415665,174.4213,5.3798714e-05 1952,0.67905354,405.7394,18.434126,70.66153,4.812223e-05 1952,609.355,436.5857,29.791687,67.78183,3.774662e-05 1952,1.2151693,128.64928,19.22232,83.75217,3.5521305e-05 1952,317.06512,141.4893,83.89978,169.76164,3.3710883e-05 1952,0.6419621,372.67255,13.67268,48.187683,1.6482121e-05 1952,0.71953124,308.84027,19.899696,134.85275,1.5735077e-05 1952,615.4646,27.712523,23.682068,82.54348,1.1224876e-05 1952,0.602229,233.44025,20.175968,137.99994,1.090098e-05 1952,3.1780112,202.90378,57.03889,234.58801,5.691009e-06 1952,2.6017203,397.07864,56.623623,95.97565,2.3636212e-06 1952,1.9602491,10.749258,55.436886,124.43635,3.3609325e-07 1952,0.9328532,4.4463463,37.46316,47.100037,7.0311515e-08 1952,450.2985,0.38742188,43.85022,53.40763,1.4599852e-08 1952,0.69065595,3.172972,10.738204,96.94161,1.36614275e-08 1953,523.67334,200.9017,26.608643,55.63739,0.085032456 1953,607.5162,211.07616,31.630493,58.828506,0.06591725 1953,622.8675,196.88937,16.279175,48.201828,0.017586125 1953,487.15997,200.2452,33.18506,60.260117,0.016805159 1953,622.6996,254.72339,16.447083,46.421997,0.015483805 1953,65.450096,224.09985,24.687233,54.59259,0.012043137 1953,585.6668,211.31458,38.039734,77.881134,0.006911016 1953,619.35645,360.91125,19.790222,64.85724,0.0046165553 1953,597.1354,192.5761,41.861145,187.95583,0.0044059884 1953,510.32382,215.23215,26.299347,52.621857,0.0018917966 1953,537.2077,211.7287,25.094482,58.263794,0.0014877772 1953,615.4783,290.2699,23.668396,106.18991,0.0005130053 1953,603.83655,341.39984,35.31012,148.11392,0.00033776154 1953,616.7453,144.16414,22.401367,81.92961,0.00021076939 1953,1.0250546,226.64168,17.991426,69.56584,8.2755505e-05 1953,609.2075,439.052,29.939148,65.953735,5.643361e-05 1953,616.05426,63.106422,23.092407,80.697365,3.8547714e-05 1953,327.49945,148.17894,86.23953,173.5157,3.261891e-05 1953,3.4616618,119.75506,59.80968,211.44452,3.0832733e-05 1953,1.7932065,141.73872,19.471693,134.24278,2.364871e-05 1953,615.3873,17.720781,23.759338,82.51594,1.441381e-05 1953,0.59354657,398.65143,18.267036,71.64755,1.2161688e-05 1953,2.0488443,214.74612,32.938656,166.78873,1.1421438e-05 1953,0.541722,358.18765,18.441704,73.16226,7.645712e-06 1953,581.3534,12.85155,57.793274,264.28122,7.2404996e-06 1953,0.6537988,309.8887,18.36962,72.66312,6.921251e-06 1953,0.7714396,441.40762,21.916302,61.80829,2.3995244e-06 1953,1.0351067,344.33942,32.90616,144.15533,2.0681089e-06 1953,1.6722087,8.879682,32.22622,167.75102,6.531021e-07 1953,582.64044,0.5889112,46.17102,53.83698,7.1730284e-08 1953,0.95056075,5.1222754,20.864355,59.43119,7.166793e-08 1953,488.8976,2.0764258,44.554596,51.636635,4.0450177e-09 1953,463.38928,1.1794401,45.62375,51.357628,7.214899e-10 1954,5.3256674,234.73526,23.459179,54.255096,0.15001734 1954,609.40784,218.70276,29.73883,80.47781,0.12144626 1954,10.582224,206.74573,40.349922,81.50882,0.057467148 1954,436.7413,164.05554,34.770752,77.03558,0.04638892 1954,623.80676,260.6115,15.339905,46.530884,0.020237492 1954,619.52344,189.71617,19.62323,68.38463,0.00421174 1954,620.93835,137.2079,18.208313,55.164673,0.0012948588 1954,422.96838,193.87144,25.108917,52.393738,0.0012260537 1954,586.93317,112.39253,52.2135,208.59087,0.00081363623 1954,362.7033,154.35936,91.168945,155.10704,0.00026958986 1954,1.6739079,200.9547,34.32712,171.56004,0.00024874663 1954,1.2125098,148.99298,20.354338,141.3422,0.0001910415 1954,617.96265,357.86148,21.184021,80.58453,0.00013189197 1954,4.620641,182.62968,76.71037,265.01825,9.316632e-05 1954,616.1562,81.55771,22.990479,88.81894,8.2138205e-05 1954,0.6195785,259.88632,18.520979,86.830414,8.163303e-05 1954,613.8854,418.60843,25.261292,85.68039,8.137574e-05 1954,613.0671,279.8976,26.07959,127.37427,5.0571914e-05 1954,607.3626,1.8303288,31.784058,60.697426,4.0789193e-05 1954,588.47296,237.63719,50.673706,241.60783,2.683627e-05 1954,0.48682863,322.2276,13.885757,48.055817,2.451724e-05 1954,0.63816327,399.47653,18.293163,71.77985,2.2771956e-05 1954,1.2522551,118.187126,19.076359,77.680046,2.1552692e-05 1954,584.14294,7.072715,55.003723,118.23197,1.7448516e-05 1954,0.5582642,294.66403,28.761536,154.74149,8.826653e-06 1954,2.8145263,72.93798,58.55515,228.85031,7.1725976e-06 1954,0.7759945,441.56418,21.951267,62.112488,4.112401e-06 1954,2.9137452,398.30518,56.52813,95.5202,1.1229903e-06 1954,1.2537924,15.663959,31.845642,157.48149,1.3527229e-07 1954,0.66920656,6.7474937,9.750557,88.908615,5.4765547e-08 1954,1.2787501,7.0959444,29.390574,57.216793,3.5022754e-08 1955,194.40292,205.55914,25.646194,50.949768,0.041617278 1955,570.75183,216.78375,25.060852,52.62079,0.039343324 1955,503.44406,155.33966,26.777924,53.924667,0.03889285 1955,577.5047,164.09485,59.47052,123.43048,0.026217379 1955,597.0349,174.65385,28.195862,61.832947,0.018975595 1955,585.0339,208.83495,29.752014,59.18547,0.018947788 1955,611.4761,156.40987,26.933105,59.51297,0.011323395 1955,621.63885,260.77945,17.507812,56.62152,0.008361189 1955,612.94214,222.24889,26.046265,57.843643,0.0076266006 1955,323.1813,176.02446,35.633392,77.648254,0.005780386 1955,621.19415,194.06816,17.952515,58.58177,0.0036022123 1955,620.93945,131.83237,18.207214,55.2502,0.0020288168 1955,358.7571,222.755,23.207428,45.180725,0.0008655452 1955,307.2853,138.28719,74.14276,158.83202,0.00029418882 1955,0.9888184,166.34764,18.280333,75.68179,0.0002441109 1955,612.50055,383.9152,26.646118,114.80267,0.00010953978 1955,608.5113,31.03086,30.635376,168.94241,0.000102934304 1955,618.1689,327.7845,20.977783,80.24338,8.133726e-05 1955,586.38086,217.33308,52.76581,257.95477,7.132243e-05 1955,0.98011315,189.51108,27.713345,140.38275,6.79553e-05 1955,1.7739853,88.655685,29.565033,148.6826,4.633849e-05 1955,0.52680624,263.6394,14.162023,52.01996,4.1345647e-05 1955,593.1606,5.659593,45.986084,85.576996,3.4073502e-05 1955,0.55359536,268.8958,20.021046,138.91043,2.3581642e-05 1955,0.58980715,366.33615,20.917046,104.60205,1.3421331e-05 1955,2.1986833,189.84064,56.604767,222.57031,5.3529425e-06 1955,0.8311743,443.59985,22.042913,60.695312,5.022681e-06 1955,2.9551482,338.30392,57.713463,149.4636,1.812075e-06 1955,2.022129,9.065671,58.23855,139.7686,1.7685046e-07 1955,1.1410775,17.613894,18.739412,138.46327,1.4133107e-07 1955,1.2513232,6.436937,29.302732,58.35623,2.3684658e-08 1956,556.50555,221.52432,31.445068,55.029602,98.31579 1956,622.1573,225.22755,16.98938,50.11943,0.009366414 1956,543.2899,197.39284,64.28021,101.9789,0.0074005774 1956,617.1949,241.18105,21.951782,82.59218,0.0044421568 1956,618.433,73.042656,20.713684,65.66151,0.0024511057 1956,619.90955,140.32147,19.237122,56.2854,0.0017571632 1956,588.2431,79.70248,49.485962,225.89746,0.0012719885 1956,603.17285,0.0,35.973816,163.60266,0.00055334036 1956,340.62268,185.42085,33.38086,63.713898,0.00036601126 1956,592.5058,170.10278,35.88568,68.19302,0.00024051688 1956,1.687185,187.94316,18.162321,71.39073,0.00023949853 1956,618.15594,408.8471,20.990723,77.68228,0.00020758825 1956,603.4453,225.73595,35.55591,193.42165,0.00018385833 1956,0.8316203,239.07536,13.621492,48.61052,5.0934348e-05 1956,505.51294,141.67467,127.487,228.80327,4.3898184e-05 1956,1.6181128,199.3004,28.224056,134.91875,3.6064303e-05 1956,2.8877735,95.15356,56.796097,222.04477,3.5717785e-05 1956,370.65033,150.93196,81.585266,165.13737,2.9445224e-05 1956,572.83405,424.06842,66.31262,75.318054,2.2199556e-05 1956,0.74371666,363.0448,18.903467,129.5683,1.3164977e-05 1956,1.397775,84.61265,20.070028,149.76932,1.0075318e-05 1956,0.8392367,276.85895,19.438921,135.22574,9.281818e-06 1956,2.976981,248.30069,56.941353,222.21823,4.9778746e-06 1956,2.2790072,16.155788,56.337376,128.71062,3.1016284e-06 1956,2.4196143,426.61978,53.867428,72.188354,1.1571341e-06 1956,584.17725,0.9626839,45.795532,55.90172,4.6525068e-07 1956,560.80164,7.0567384,45.807922,147.77147,5.5677766e-08 1956,0.61558145,6.074154,9.666422,97.25894,5.3102063e-08 1956,1.1365951,6.3384314,29.50866,61.26934,9.8470165e-09 1956,532.08704,1.9917139,43.792603,48.06697,3.4266423e-09 1957,410.19177,201.42613,25.823029,49.994293,81.44641 1957,603.00885,221.30777,35.427612,73.89395,23.875198 1957,623.3278,253.16122,15.818848,46.60092,0.011948698 1957,619.11285,194.73923,20.033813,71.84119,0.0022456478 1957,520.1873,189.33273,41.91211,91.57898,0.0018633133 1957,392.25903,167.26407,59.02957,101.44214,0.0012503425 1957,589.23926,180.35077,49.90741,193.05014,0.0007227802 1957,360.11258,193.43912,25.262268,49.80977,0.00051010353 1957,1.3593075,169.69183,21.436056,122.46918,0.00043785898 1957,221.2318,196.3377,27.162735,55.303757,0.00034446007 1957,617.1545,82.588684,21.992188,74.75354,0.00032828233 1957,3.639157,174.15448,56.20112,232.17453,4.0847666e-05 1957,612.90234,305.57187,26.244324,141.74747,3.7901475e-05 1957,609.8369,440.2393,29.309753,64.12271,2.5582596e-05 1957,363.34924,106.546776,137.24948,227.33444,2.2676253e-05 1957,478.9893,117.51404,142.65262,221.77512,1.9207167e-05 1957,0.62215006,259.62808,18.299742,75.44702,1.4936836e-05 1957,0.86035156,324.53015,18.709188,133.54596,8.652679e-06 1957,595.78064,7.3098993,43.366028,80.073296,5.41348e-06 1957,585.43036,395.04007,53.71631,98.64102,5.2213873e-06 1957,1.3599317,64.6816,20.502327,160.80774,4.5668744e-06 1957,0.8131804,420.66333,16.540926,80.49188,4.0297145e-06 1957,601.8674,39.51371,37.279297,183.1384,3.6438576e-06 1957,2.9792953,395.98633,56.34041,96.55823,9.2008224e-07 1957,2.316932,22.501017,61.758423,248.23952,9.1236683e-07 1957,0.77802,6.419818,9.93483,91.822334,2.3200695e-08 1957,1.8671192,6.356406,36.253723,87.955505,7.2356294e-09 1958,453.87357,227.19957,24.56009,51.267563,98.785 1958,438.63065,224.01474,21.314209,50.720093,0.30557722 1958,609.67487,204.13196,29.471802,78.987885,0.07219665 1958,231.08928,215.3622,31.92598,56.248917,0.037172392 1958,468.0851,227.10321,26.425934,51.66693,0.03459281 1958,548.01135,188.77852,35.835693,74.09041,0.015438223 1958,622.6872,194.429,16.459473,50.476395,0.009660169 1958,621.3404,139.48372,17.806274,49.122955,0.006742783 1958,400.87445,228.49487,26.23236,50.033478,0.0044339844 1958,620.3188,242.54997,18.82788,57.253952,0.0035755374 1958,384.65356,138.62273,40.85263,138.22856,0.00092207437 1958,611.2232,86.43387,27.923462,143.45145,0.0008723255 1958,438.9842,198.45694,61.977234,99.1651,0.00069030706 1958,586.6068,149.76178,52.539856,189.32666,0.00045276992 1958,0.7872567,166.04001,14.084433,55.533676,0.00021538427 1958,616.5259,64.33966,22.620789,79.6299,0.00012988842 1958,1.1902491,199.2791,17.896755,73.95628,6.644717e-05 1958,612.79663,365.40604,26.350037,130.17014,6.541895e-05 1958,406.12018,146.26248,152.52911,219.45236,4.1042436e-05 1958,0.6655294,240.59511,13.605596,47.864304,3.490409e-05 1958,612.41046,259.15594,26.736206,158.28531,3.291292e-05 1958,2.5744386,102.03295,46.84718,193.52985,2.1237984e-05 1958,1.418165,212.48534,28.491451,139.78737,1.9295037e-05 1958,0.59767497,334.30585,19.806622,132.74265,1.17104455e-05 1958,1.1928207,88.73118,20.047577,147.23038,1.0509657e-05 1958,3.0274007,220.0403,58.16484,217.7491,1.0217831e-05 1958,0.5624113,297.29263,18.432777,71.48215,7.681525e-06 1958,0.6965365,423.16086,16.897257,79.37659,6.119107e-06 1958,1.8970981,14.696823,57.439785,114.60599,1.7284315e-06 1958,586.80096,10.615066,52.345703,124.23428,1.665523e-06 1958,2.7674692,397.65237,56.36674,96.043884,9.907463e-07 1958,0.76423097,7.0865726,9.994868,90.3033,4.2537962e-08 1958,1.3397461,6.3982553,30.173859,59.42733,1.7858964e-08 1959,545.14624,209.57077,37.348755,77.48309,99.63778 1959,572.27277,208.0414,31.84021,61.288864,95.022 1959,397.47495,213.29527,23.729736,48.02162,2.0451453 1959,246.99394,201.95692,36.38835,76.05788,0.10404786 1959,573.6672,250.97276,28.233582,62.50911,0.036071036 1959,438.72034,127.05776,40.693848,141.15378,0.0050700675 1959,484.99326,157.09949,20.463318,46.96953,0.0046862205 1959,12.902869,214.45726,34.039246,68.676895,0.002729214 1959,530.01056,187.69199,86.25275,140.5333,0.0017283399 1959,438.29855,190.34064,23.595734,56.69406,0.0017197 1959,450.42392,140.15445,21.729279,52.736786,0.0016087182 1959,622.1755,169.99023,16.971191,51.633774,0.0014561825 1959,459.0471,101.32172,45.232086,160.08539,0.0011882009 1959,612.22845,189.66704,26.918213,125.496445,0.000446794 1959,1.1744344,187.58055,29.136002,137.83382,0.00034346778 1959,609.8888,97.55713,29.257874,145.61606,0.00015872938 1959,612.1307,257.76434,27.015991,132.61862,9.00244e-05 1959,0.058815107,265.03,19.77777,80.76074,8.753436e-05 1959,612.6924,367.08963,26.454285,128.712,6.180519e-05 1959,2.9165463,196.1998,56.31179,211.11057,5.2589683e-05 1959,587.0671,225.60191,52.07959,234.65633,3.7201517e-05 1959,595.98645,5.4670963,43.160217,77.968285,3.32137e-05 1959,432.82755,74.13861,107.066376,231.86792,2.6313293e-05 1959,0.35716227,318.5601,20.129108,137.84659,2.0879907e-05 1959,0.68402916,421.52,16.614256,80.587585,4.699457e-06 1959,1.0145744,66.53627,20.734268,149.16245,3.4301393e-06 1959,117.18405,2.5674007,42.46454,50.854553,1.7164173e-06 1959,2.514751,327.19055,57.855354,155.00629,1.464217e-06 1959,2.208112,12.962439,57.058674,108.11315,1.1582999e-06 1959,387.73627,3.5123014,42.24478,50.274235,6.1755026e-07 1959,1.698711,4.3155503,30.916824,61.512527,9.780386e-08 1959,141.30898,4.9562535,46.005356,52.862953,7.314643e-08 1959,42.082436,0.0,50.459038,46.147015,5.4022724e-08 1959,195.1427,1.4711556,45.779648,48.570145,2.4609644e-08 1959,507.43262,5.045962,46.821594,49.953842,3.0829266e-09 1960,377.92197,81.93555,35.54251,53.78247,0.032028206 1960,603.55316,111.22977,35.593506,181.29974,0.024834467 1960,607.9435,105.97429,31.203186,64.20836,0.02208246 1960,493.95258,142.61938,45.228577,121.03546,0.006652892 1960,616.02747,134.67563,23.119202,71.1373,0.006387371 1960,620.6716,232.30022,18.475098,56.079697,0.0047859177 1960,517.22455,165.37224,22.567627,49.208527,0.001941933 1960,534.77295,108.12995,43.467712,162.23459,0.001580024 1960,618.75024,184.25954,20.396423,66.98633,0.0010880455 1960,552.5713,76.26963,81.54932,202.25916,0.0003881863 1960,255.76569,146.00154,31.38089,67.53722,0.00021451937 1960,477.61133,109.370094,115.6723,206.9404,0.00018292665 1960,598.7828,15.9258795,40.36389,179.82585,0.00017635121 1960,612.8262,348.03174,26.320496,132.8523,8.38611e-05 1960,611.22217,222.83725,27.9245,149.55533,7.406508e-05 1960,3.5148683,23.541235,54.829845,101.76163,6.3168416e-05 1960,1.1913794,110.179245,13.68368,50.466812,4.5972094e-05 1960,609.5672,438.28214,29.579468,64.93121,4.416151e-05 1960,607.5838,4.1235514,31.562866,50.583164,3.9838385e-05 1960,1.1748551,209.26721,17.703838,69.3226,3.4693956e-05 1960,2.122776,66.913246,18.711508,76.42338,2.1634944e-05 1960,4.2194743,50.002384,58.82477,220.87238,1.6876975e-05 1960,339.0209,140.961,97.68146,209.69415,1.5513036e-05 1960,0.65862226,244.17409,20.061357,131.42351,1.4526042e-05 1960,1.6749561,161.7357,34.008865,167.36263,1.3786163e-05 1960,0.5998836,327.23178,19.808414,132.88968,1.0135996e-05 1960,1.4272884,123.59865,20.765701,114.96864,7.93913e-06 1960,0.68022627,422.1773,16.973936,79.881775,5.449345e-06 1960,2.675936,247.03865,57.7529,222.347,3.4598788e-06 1960,1.9249471,12.929043,17.714487,80.39043,1.8638391e-06 1960,2.819891,396.769,56.413467,96.90427,1.0315897e-06 1960,2.3847916,8.759998,43.93513,45.019768,4.0103902e-07 1961,441.76538,205.10818,35.635254,64.787964,0.0006565297 1961,11.011973,186.58041,40.81636,98.56662,0.00026796223 1961,611.01276,152.92284,28.133911,105.91127,0.00022948245 1961,618.1997,372.08258,20.94696,83.05701,0.00022297975 1961,0.5608366,267.59442,14.003019,54.395294,0.00019373506 1961,1.0621794,223.72792,19.719765,133.36592,0.00019075714 1961,265.97195,149.24611,25.314606,53.251297,0.00015869744 1961,429.29413,204.91333,25.26532,52.892365,0.00010524617 1961,617.227,82.8844,21.919678,83.995285,0.000101768856 1961,617.1269,213.6813,22.019775,83.65683,9.3724906e-05 1961,617.7558,280.72723,21.39087,79.01764,9.302019e-05 1961,0.39121664,317.92902,18.5204,75.87921,4.2416475e-05 1961,616.082,21.932598,23.064697,75.5591,3.7517686e-05 1961,610.0097,437.5459,29.136963,66.569336,2.9228173e-05 1961,1.5556064,173.64084,18.586943,69.82643,2.7107382e-05 1961,586.4988,122.33376,52.64789,216.9176,2.6705706e-05 1961,602.63104,271.65454,36.515625,191.09875,1.874378e-05 1961,2.8570216,231.5734,56.74548,232.17746,1.58826e-05 1961,0.6246574,399.01407,18.216051,71.22058,1.03593575e-05 1961,1.8102368,67.982605,18.570337,68.14299,9.141055e-06 1961,586.10223,391.38315,53.044434,101.89188,6.674842e-06 1961,0.46043703,371.78976,13.809078,46.66806,6.638024e-06 1961,584.0439,20.447794,55.102783,197.03325,5.347126e-06 1961,4.23639,57.645195,57.716087,246.18329,2.7532835e-06 1961,0.8428996,442.58313,21.860447,60.756683,2.6118828e-06 1961,1.2538322,102.69852,18.614567,83.745834,1.8926777e-06 1961,2.6693587,20.095434,56.575516,106.96032,5.804601e-07 1961,2.8587565,395.1172,56.3251,98.2522,5.781464e-07 1961,0.9476705,11.382614,11.475284,77.410515,5.1680665e-08 1961,327.57596,4.150705,43.674408,45.656376,9.599337e-09 1961,402.98428,3.1510174,44.874054,45.443573,3.593694e-09 1961,447.26813,1.319209,44.58313,52.227974,3.0088467e-09 1961,353.53235,1.3100994,45.461334,45.38881,1.3474502e-09 1962,420.5563,196.94066,25.170685,49.85797,0.17166759 1962,504.88818,208.3797,21.809448,49.81366,0.018224543 1962,567.4053,105.55162,68.27496,200.54594,0.009223518 1962,401.14148,201.07358,23.814331,46.993332,0.008275544 1962,622.01215,205.35336,17.134521,54.944,0.006521223 1962,606.28784,120.88016,32.858826,87.4144,0.0058214716 1962,610.6121,203.65977,28.534546,123.74254,0.0033743018 1962,621.2517,270.59338,17.894958,60.003357,0.0031425727 1962,443.77505,180.49274,47.26651,109.40286,0.0021444845 1962,416.9475,168.92232,43.90329,100.24339,0.0020195204 1962,620.97186,80.74576,18.174805,56.808304,0.00064213644 1962,1.103422,212.24554,13.38666,51.98587,0.00037859817 1962,617.78015,295.2193,21.366516,81.38992,0.00020277503 1962,1.3052304,180.93483,20.55652,125.780045,0.00015100329 1962,586.4743,199.4699,52.14038,226.0771,0.00011094542 1962,279.64737,130.28152,34.746796,86.81261,0.00010871895 1962,612.7163,362.7383,26.430359,130.83826,6.345498e-05 1962,583.9257,14.926491,55.220947,117.8722,4.5992005e-05 1962,2.6491294,158.2395,57.504623,188.26437,4.3833174e-05 1962,0.5177962,256.7176,18.480509,80.800415,4.1153475e-05 1962,0.71904784,101.76737,14.150637,55.46296,3.9888302e-05 1962,402.4513,142.1101,109.8526,187.65335,3.979715e-05 1962,1.9496729,80.1136,32.92479,201.02812,2.98001e-05 1962,0.8419149,124.41175,19.071533,87.37606,2.3288867e-05 1962,0.40511638,315.0805,19.81238,135.798,1.1125846e-05 1962,0.7323674,422.80072,16.884697,79.092316,3.4437878e-06 1962,2.7555697,333.35178,57.544434,150.20245,8.3229264e-07 1962,328.67273,3.2920997,84.95639,76.38634,4.920686e-07 1962,1.4477938,21.208029,19.972454,116.46552,2.217119e-07 1962,381.68658,2.499253,46.239258,46.40601,6.434364e-08 1962,307.62503,1.6418865,43.070923,54.33657,1.2529785e-08 1962,428.62402,4.572178,45.859375,47.93711,8.088757e-09 1962,1.0370866,0.56720054,20.796389,45.17325,5.590749e-09 1963,503.31757,192.18515,33.259705,78.12537,0.32656112 1963,432.28516,206.64427,26.575256,55.28984,0.08456031 1963,490.7158,209.36047,26.09201,59.066986,0.02447447 1963,622.64514,184.48657,16.501526,46.86528,0.0032135376 1963,416.94226,212.51764,25.484253,55.431732,0.0018749781 1963,280.5632,122.95401,36.87274,92.73471,0.0006787267 1963,363.7781,184.29613,41.501556,75.594986,0.00067659136 1963,611.137,185.43004,28.009644,124.25334,0.0006428736 1963,461.41052,86.94832,73.939514,197.7632,0.0004081374 1963,490.18445,103.49397,37.208496,100.80794,0.00022436713 1963,1.2215406,208.18842,18.094742,65.764435,0.00016615241 1963,610.8669,73.407364,28.279785,141.75043,0.00015606105 1963,618.1296,315.3538,21.01709,77.66916,0.00011715108 1963,570.6124,122.67478,68.53424,233.60559,6.886763e-05 1963,0.77577555,226.24571,20.1189,132.75307,6.4053376e-05 1963,612.77344,369.15533,26.37323,127.20581,4.5057815e-05 1963,1.3165438,104.53777,19.879305,141.31448,4.0122366e-05 1963,602.1517,247.08568,36.994995,199.11406,2.8556766e-05 1963,0.581049,252.02406,14.08844,49.652573,2.2824483e-05 1963,337.84702,149.27414,85.97891,149.83746,2.245627e-05 1963,595.903,10.918047,43.243652,77.96618,1.1030761e-05 1963,1.8017546,152.148,35.919765,162.05467,9.853344e-06 1963,0.5601587,298.66437,19.964252,131.57611,9.439213e-06 1963,2.9951546,247.64365,56.82942,216.3623,6.1530122e-06 1963,0.6787809,380.88492,19.428162,116.09863,5.303248e-06 1963,267.56668,64.45509,81.99008,194.22636,4.597376e-06 1963,2.3850033,18.564493,57.08548,106.082115,1.5639279e-06 1963,2.972754,395.82056,56.274994,97.55539,4.8639515e-07 1963,1.4547274,25.943249,19.294785,116.252304,4.1693266e-07 1963,508.69412,3.9851823,46.15695,56.912098,1.15285516e-07 1963,482.72662,1.1356739,46.11084,62.476463,4.5455963e-08 1963,297.69937,3.5758626,42.679474,48.406086,2.4663874e-08 1963,260.5944,5.207751,43.1304,45.958565,1.931346e-08 1963,1.0586377,2.789712,32.158386,49.7682,3.1880598e-09 1963,553.41425,2.1853826,43.948975,50.36935,3.0822372e-10 1964,514.10144,213.16797,23.314636,46.157867,0.64311886 1964,471.94907,206.11826,35.169495,80.04428,0.023580214 1964,519.7611,191.55988,22.14502,48.28006,0.01934591 1964,621.36066,175.53096,17.78601,55.09938,0.014555581 1964,507.60294,159.77481,45.894684,117.394226,0.013619104 1964,460.1799,121.96159,71.04477,173.64766,0.0050832257 1964,621.47186,73.94829,17.674805,56.368866,0.0050355485 1964,610.98834,179.50087,28.158325,125.25023,0.0028368807 1964,383.60394,187.41148,47.00067,87.42381,0.0016525984 1964,429.48987,215.49051,24.990356,53.645874,0.000835801 1964,1.339935,181.13914,21.519424,124.102646,0.00056506164 1964,611.6163,84.16918,27.530396,151.8628,0.0004810021 1964,588.45404,194.92232,50.692627,222.01967,0.00041114035 1964,489.22382,118.03197,112.433044,197.2693,0.00037763108 1964,606.3288,3.9815104,32.81787,125.93925,0.00027142093 1964,612.31757,321.2259,26.829102,139.56812,0.00022506704 1964,3.157386,124.49808,57.224464,207.76303,0.00021851831 1964,289.5546,129.9327,42.082,104.88469,0.00016595983 1964,0.9866675,125.84477,19.636623,101.29521,0.00013885947 1964,349.58865,106.786865,145.95688,219.32422,4.7525496e-05 1964,583.4322,0.0,55.431335,268.2633,3.957151e-05 1964,0.46240643,264.10324,20.172161,139.91061,3.1903037e-05 1964,609.7223,438.73553,29.424377,66.44855,3.063115e-05 1964,585.80457,385.96103,53.342102,109.043945,8.511435e-06 1964,0.61305094,372.69778,19.472168,124.32495,7.605816e-06 1964,2.5819614,297.20755,57.72175,180.28867,1.7400216e-06 1964,1.5385051,21.154545,19.08502,121.66167,1.3966062e-06 1964,297.45105,0.0,45.479675,70.106995,8.1513053e-07 1964,271.5935,0.0,45.71347,68.68016,6.105418e-07 1964,284.91394,0.0,65.36383,184.6352,3.4314985e-07 1964,1.6850114,19.278393,36.19383,230.45624,3.148878e-07 1964,384.92197,0.0,86.058105,223.17195,2.191318e-07 1964,387.7592,1.1664844,43.629364,58.222656,1.7420332e-07 1964,486.8494,2.291452,48.158417,67.25613,1.6831119e-07 1964,583.7642,0.27740562,45.373108,54.229935,7.7462246e-08 1964,0.9109937,10.220387,9.921912,81.75843,7.442215e-08 1964,420.06134,3.2962923,47.92514,59.928905,1.08624425e-08 1964,447.60648,1.5752084,47.01053,63.989872,4.5846544e-09 1965,622.0538,215.36339,17.092896,55.245773,0.026609678 1965,621.1988,296.20767,17.947876,60.5867,0.02266156 1965,611.2264,220.25117,27.920288,140.41072,0.00655666 1965,611.34674,149.24487,27.799927,132.16644,0.005340845 1965,443.38956,205.61397,34.027374,77.694565,0.0043876027 1965,621.0416,109.312515,18.105042,51.16484,0.0031224594 1965,569.88245,159.50615,65.40234,177.31093,0.0019556747 1965,421.3237,118.53187,68.1398,175.28363,0.001193376 1965,621.0066,157.11111,18.140076,58.118835,0.0008596609 1965,564.8702,208.64569,27.62677,59.749573,0.0006018175 1965,610.15704,73.716774,28.989624,138.7286,0.0004642334 1965,0.7549707,112.15265,14.649084,58.562332,0.000291 1965,620.0196,40.188427,19.127075,59.404324,0.0002603314 1965,612.4434,297.1815,26.703247,147.22385,0.00020594746 1965,1.1611181,204.77367,18.020388,66.23256,0.00019895658 1965,1.2648755,69.27686,20.946404,81.24981,0.00018937881 1965,364.75037,174.5576,58.251312,108.90663,0.00016522488 1965,605.2856,0.3463965,33.861084,67.71343,0.00011168554 1965,1.7780844,83.40498,35.587162,186.72221,8.0143014e-05 1965,1.2247599,153.53873,18.590899,81.270004,3.216043e-05 1965,0.6039901,239.93787,19.72575,134.745,3.0881463e-05 1965,610.01575,437.4352,29.13092,66.58261,2.6194417e-05 1965,334.12662,123.03897,143.55048,218.64578,1.5515521e-05 1965,0.57563806,331.01895,19.858095,134.17758,1.3575212e-05 1965,3.224673,179.80898,56.627872,203.15947,1.2244945e-05 1965,573.1178,348.22583,66.02887,136.33511,1.02358945e-05 1965,0.6997722,421.51724,16.9625,80.98001,5.0612343e-06 1965,388.26538,0.0,122.559845,232.55719,1.8324668e-06 1965,2.8431463,326.73105,57.675728,156.38809,1.1615609e-06 1965,1.8262419,13.195222,57.84694,125.173645,1.1052638e-06 1965,2.6895769,428.5706,53.49236,70.79114,4.9387575e-07 1965,435.12582,0.0,45.09372,163.2355,1.55223e-07 1965,316.95328,2.6373243,44.900208,58.575214,1.3638186e-07 1965,460.93246,0.13666342,46.829987,61.186558,1.9687171e-08 1965,486.26245,0.0,47.568054,57.286922,1.42332475e-08 1965,503.46454,0.0,90.52106,94.32987,9.36185e-09 1965,578.63696,0.80416346,45.363586,50.591663,3.4514631e-09 1965,407.7907,0.0,47.737274,65.55559,1.9410649e-09 1965,1.7492871,2.559398,30.818634,68.66967,1.3830923e-09 1966,456.7048,220.20628,33.59012,71.71193,99.88673 1966,564.92786,216.17258,42.443542,96.355316,99.4069 1966,369.0103,207.51062,24.637085,51.838226,0.64774954 1966,622.74506,234.9199,16.401611,50.976456,0.060162432 1966,444.63443,66.26468,37.18338,66.71811,0.0023920042 1966,611.72723,182.50243,27.419434,119.83601,0.0023667673 1966,1.1918726,190.3821,13.245507,47.92668,0.0016266769 1966,585.32965,9.173255,52.03479,112.73379,0.0015504649 1966,298.72412,169.20702,26.107483,45.490555,0.0011175054 1966,439.102,193.64735,72.05533,141.63353,0.00032428856 1966,617.8581,400.44693,21.288574,79.181305,0.00028286144 1966,539.5981,183.12949,90.076904,171.30487,0.00017702693 1966,584.94476,48.86526,53.376953,266.84583,0.00016749771 1966,1.2746875,91.114784,19.831528,141.36252,0.0001664993 1966,586.8963,219.61902,52.2229,248.16641,0.00015241843 1966,1.0314063,179.92784,21.139822,122.49794,0.00010972682 1966,610.26556,58.123093,28.881104,144.54465,8.166815e-05 1966,296.43335,98.36942,44.104095,124.33225,2.5362266e-05 1966,3.6957715,119.9986,58.184864,197.9946,2.479172e-05 1966,0.61126953,347.0623,19.74863,132.15396,1.453387e-05 1966,0.5009676,281.63507,20.806963,103.62366,6.9409894e-06 1966,0.8468335,442.63434,22.091217,61.306366,4.4223493e-06 1966,2.9502246,281.86288,57.60574,192.43326,1.5257193e-06 1966,1.7555306,11.365658,59.073025,120.40965,7.2252936e-07 1966,2.724782,428.56372,53.500057,70.940094,6.6080815e-07 1966,1.1317782,19.823015,18.846624,136.54918,3.3994218e-07 1966,0.7445557,4.6324806,10.324231,93.03496,3.6734207e-08 1966,313.6127,3.2962127,44.077606,51.83953,1.4326123e-08 1966,1.1230828,4.0340886,31.403013,67.61597,7.7276265e-09 1966,568.46436,5.967702,44.996704,47.15602,6.4477144e-09 1966,530.7734,7.696003,44.459656,48.17323,4.547623e-09 1967,407.66138,205.32986,24.362549,59.30542,1.6433796 1967,510.52823,205.19846,34.3649,76.91092,0.08869716 1967,423.45203,211.91849,20.476898,46.70822,0.028270602 1967,6.288742,195.66698,22.181929,47.961914,0.008845723 1967,1.3127637,199.90225,13.103815,48.10208,0.004455551 1967,445.58667,217.4415,33.719025,60.230133,0.004209777 1967,434.2275,216.67151,24.648865,48.94928,0.003521694 1967,621.8799,183.5739,17.266785,52.82689,0.0013705252 1967,583.53186,6.280977,54.436157,124.3145,0.00052608614 1967,497.36658,144.16837,65.693054,167.8316,0.00048199896 1967,617.806,216.04774,21.340637,81.36476,0.00035781134 1967,1.5441496,140.27434,20.329418,131.18764,0.0002768231 1967,609.48303,58.927868,29.663635,150.08357,0.00024238316 1967,612.0154,241.5511,27.131287,137.78621,0.00014307113 1967,617.7536,327.16083,21.393066,81.01547,0.0001201841 1967,453.53262,116.32274,77.77261,181.05006,5.777289e-05 1967,1.0999455,65.23955,18.52165,78.38214,5.4133652e-05 1967,612.8103,369.57294,26.336365,123.40823,4.592954e-05 1967,0.54260176,227.67703,19.526333,138.43533,3.888081e-05 1967,3.4231691,123.23791,58.854294,195.38544,3.856264e-05 1967,585.98846,128.39072,53.147156,228.4259,3.5745896e-05 1967,0.4434953,323.00137,19.960167,134.46396,1.42656245e-05 1967,312.38058,22.724194,40.39676,114.061905,6.1955134e-06 1967,0.67504394,421.62854,16.898556,80.21326,4.2498996e-06 1967,2.7937028,265.94882,57.510155,203.37698,1.2623399e-06 1967,2.7020493,429.112,53.41522,69.94238,3.8056834e-07 1967,277.6166,0.0,122.632324,228.0075,3.6654438e-07 1967,350.73572,2.3019857,48.57321,62.12195,3.5124052e-07 1967,378.97964,2.9567416,47.38611,58.194893,1.7882695e-07 1967,0.901047,12.868396,11.571989,76.27921,1.4597659e-07 1967,1.7618978,18.106876,57.284485,108.30855,1.2831578e-07 1967,304.5795,3.2290723,45.903107,55.306103,8.210658e-08 1967,273.45673,0.0,44.54358,48.402645,2.3432745e-09 1968,491.87048,147.20143,46.880737,146.39677,0.28463885 1968,447.77814,202.23615,30.64801,57.05258,0.014978127 1968,0.9316838,129.30722,18.654575,83.57434,0.010364774 1968,479.95215,177.8453,36.79706,88.46146,0.006946769 1968,0.864882,191.58585,13.39411,53.12523,0.0018577453 1968,492.61453,224.73326,21.910614,48.563187,0.001290379 1968,611.87506,177.03252,27.271606,101.880875,0.0011831602 1968,502.87653,159.11932,27.987183,69.59906,0.0008568192 1968,575.5758,6.6127605,62.341064,80.04688,0.00030424463 1968,0.73489827,81.96523,18.816353,75.19872,0.00021294864 1968,1.4347006,128.19414,33.895527,175.04207,0.00016906371 1968,0.4886255,211.41188,18.540443,78.05611,8.3469255e-05 1968,612.70135,314.73306,26.445312,139.40668,5.7599045e-05 1968,586.1527,62.53392,52.70929,240.84004,4.526437e-05 1968,602.21106,204.59009,36.935608,193.31699,3.8774946e-05 1968,610.76526,38.793346,28.381409,160.20927,3.0674353e-05 1968,610.0656,435.62054,29.081055,67.795105,2.9099456e-05 1968,0.5235905,337.5739,19.843777,134.59833,1.3443287e-05 1968,0.5729907,267.92697,18.27579,74.18805,9.049839e-06 1968,585.87823,392.95648,53.268433,98.82614,7.4427753e-06 1968,3.2713053,194.45642,56.755955,227.77191,4.458761e-06 1968,0.6660221,420.5838,16.80829,81.3587,4.228642e-06 1968,360.5504,3.940194,44.699036,54.478058,1.4830007e-06 1968,5.130889,353.914,79.591354,132.75778,6.799443e-07 1968,2.7477002,427.8251,53.55633,71.27432,3.5871236e-07 1968,1.9058383,10.573041,59.372185,127.80118,3.1396365e-07 1968,331.94617,2.1552866,47.746857,58.801918,1.7438549e-07 1968,477.18787,2.978926,44.078735,50.38821,1.3283919e-07 1968,0.57853925,8.415534,9.395281,86.66052,4.2921418e-08 1968,519.03046,2.5982275,43.792725,49.194958,7.869287e-09 1968,551.37756,2.1280096,64.84204,49.31141,4.7282835e-09 1968,1.5486817,1.9093294,33.156944,46.83574,4.2859876e-09 1969,477.54642,205.13458,24.83078,49.25534,0.18737859 1969,565.2164,168.63419,42.973022,102.19983,0.08286305 1969,577.41797,188.40154,24.163269,46.780457,0.008933682 1969,622.2272,159.4998,16.919495,49.292114,0.0021232455 1969,617.25433,186.61745,21.892334,77.6638,0.0011741108 1969,1.0071021,156.70755,18.197048,79.923386,0.0006584346 1969,610.93384,105.231705,28.21283,132.65298,0.0002644073 1969,537.40326,125.15341,99.0567,182.32071,0.0001811462 1969,375.3149,32.793594,31.49884,66.62942,0.00015980308 1969,1.0240853,74.04476,13.662046,50.653656,0.00015625004 1969,400.25687,44.64714,27.14566,63.90895,0.00015278764 1969,567.69366,220.295,23.400818,51.51448,0.00013495698 1969,617.7063,249.64964,21.440369,80.69072,0.00012957132 1969,1.885363,89.92008,33.317696,183.37164,0.0001028154 1969,616.3459,59.19052,22.800781,78.66943,8.293676e-05 1969,0.7880892,102.91568,18.610846,83.23682,5.965798e-05 1969,601.7615,195.78465,37.385193,180.37675,4.178317e-05 1969,612.72076,328.71606,26.425903,139.06088,2.9262703e-05 1969,0.50018066,200.07793,19.98093,141.60796,2.3363922e-05 1969,609.7836,439.90002,29.363037,65.066284,2.2995382e-05 1969,585.71686,15.022764,53.149353,105.38916,1.1546178e-05 1969,0.5569141,283.9031,19.985624,133.35333,7.734421e-06 1969,0.68160075,362.6351,19.510769,130.69647,5.6257363e-06 1969,2.8294613,196.47078,57.620464,229.24495,5.2088735e-06 1969,585.442,390.67416,53.70465,103.4733,4.7506555e-06 1969,3.042793,343.23984,57.720722,142.401,5.759386e-07 1969,2.3293426,17.337666,57.541363,109.73416,4.9769227e-07 1969,1.4578475,23.431087,19.446291,121.91852,4.686529e-07 1969,420.46182,1.7050749,44.77594,55.89718,1.1023998e-07 1969,1.0619051,9.935332,10.495384,84.599686,4.658996e-08 1969,388.9635,1.0851009,44.536835,54.283077,8.289869e-09 1969,1.76493,3.4779754,30.479847,69.86419,2.2495197e-09 1970,354.9837,203.73883,23.287231,45.21573,0.12412965 1970,621.6195,169.60477,17.52716,55.91461,0.08208698 1970,526.32654,212.35995,33.821106,67.5217,0.034897223 1970,396.30084,209.45003,28.476074,48.19011,0.0143465875 1970,610.9692,106.487595,28.17749,137.26233,0.0059571685 1970,357.18744,181.99863,44.563477,74.88461,0.0032930847 1970,621.65466,203.77968,17.492004,55.310074,0.003289331 1970,428.2947,190.57559,22.1539,47.223145,0.0024815218 1970,0.8457471,147.05858,13.437082,49.86554,0.0014484761 1970,621.2019,100.189064,17.944763,53.84458,0.0010042882 1970,611.4701,183.4376,27.676575,142.6114,0.0004972721 1970,0.8877458,164.2514,18.056736,75.53923,0.0004908333 1970,1.0277597,69.08138,18.516024,79.40974,0.00021684532 1970,574.93,99.43464,63.8136,220.03784,0.00018586616 1970,1.5910319,82.479935,28.03252,144.42984,0.00018245833 1970,617.78986,337.18854,21.356812,82.159454,0.00012979766 1970,612.757,365.2159,26.389648,130.14056,7.9852645e-05 1970,0.61298585,209.10506,18.572937,76.65636,5.6796976e-05 1970,611.38245,14.689271,27.764221,120.67665,4.4331176e-05 1970,1.3035214,163.85286,31.814749,177.47667,3.4845292e-05 1970,300.0505,145.76402,33.957977,67.90143,3.158113e-05 1970,0.5446509,346.03983,19.645935,130.31232,2.0069176e-05 1970,0.68780273,272.36624,19.276281,133.19244,1.4404698e-05 1970,587.7046,253.1844,51.442078,219.69029,1.2850804e-05 1970,0.84418136,442.1985,21.940817,61.462677,3.0193605e-06 1970,2.9355047,302.21774,57.037712,172.09457,1.9428605e-06 1970,2.5344906,426.9315,53.48197,72.383514,4.5883857e-07 1970,585.73413,1.1012012,44.717346,45.88792,4.1714028e-07 1970,1.7544206,19.260328,58.373486,108.22745,1.4812726e-07 1970,0.70399415,13.760944,9.212471,80.20741,3.5643016e-08 1970,515.5982,1.6936198,47.714905,56.477856,1.6227125e-08 1971,367.33322,195.75777,25.950226,47.4917,1.5008544 1971,73.39562,202.5564,23.156845,45.816574,1.1753991 1971,447.0598,180.96912,31.533997,66.927216,0.921805 1971,607.14685,179.34258,31.874268,75.34244,0.01155481 1971,47.059746,183.4023,46.234673,76.308,0.0052439813 1971,413.97098,191.60097,42.79544,70.16109,0.0019232761 1971,620.26416,217.83159,18.882507,60.689377,0.0012425187 1971,586.9671,116.78524,51.828613,205.4278,0.00070408854 1971,1.0898576,183.05135,17.68281,67.912796,0.00041610101 1971,620.87695,121.05623,18.269714,56.820114,0.00026616154 1971,0.8139055,162.42401,13.706127,51.921494,0.00022092529 1971,616.97687,249.64578,22.1698,86.581055,0.00018175531 1971,308.42584,137.2349,32.70276,66.65468,0.0001737891 1971,609.726,68.365204,29.420654,159.54543,0.00013446971 1971,610.92426,6.981322,28.222412,85.98545,0.00011250462 1971,612.9356,366.97195,26.21106,130.50458,4.2248368e-05 1971,0.34886232,227.38394,18.45153,78.99786,3.9893057e-05 1971,612.3023,281.08868,26.84436,148.732,2.6910235e-05 1971,1.482885,174.20541,32.11393,162.4899,2.4836305e-05 1971,1.4361459,100.82683,28.16103,146.68861,2.043996e-05 1971,28.399132,135.69704,91.81996,166.56963,1.8188417e-05 1971,0.7951221,112.62415,13.854494,50.549477,1.122676e-05 1971,0.48323977,308.83636,19.872261,134.38568,9.85842e-06 1971,393.44394,141.36174,105.07446,204.04036,9.231394e-06 1971,0.7257568,85.47459,13.858366,48.676308,7.224139e-06 1971,584.9887,7.103208,45.554077,48.401314,7.084141e-06 1971,567.48737,11.7795315,71.6593,160.37175,5.6360145e-06 1971,0.68869877,422.3322,16.823757,80.00958,3.6451431e-06 1971,3.4447103,266.6401,56.932194,203.71518,1.0139008e-06 1971,2.683641,430.01337,52.97564,69.355774,3.878176e-07 1971,1.1760164,21.66584,20.548954,134.15442,3.8275294e-07 1971,1.3411459,6.278571,23.212719,55.70234,1.0780774e-07 1971,2.6821225,33.070724,59.811462,222.81467,9.865785e-08 1971,7.707515,1.3557016,45.397636,45.041782,9.979904e-09 1971,433.75372,5.2962046,44.355713,48.658806,5.636468e-09 1972,409.98517,178.62349,37.929993,80.78313,0.31098685 1972,606.95197,217.33176,30.997375,77.36902,0.18537748 1972,524.8281,219.57678,35.732178,64.68213,0.12635961 1972,28.248549,209.01266,28.452337,47.326477,0.017692672 1972,602.19165,220.4323,20.345154,47.668594,0.0074899136 1972,619.5625,106.20785,19.584167,56.20614,0.0015605625 1972,624.0102,258.56613,15.136475,52.591522,0.0005518352 1972,416.27838,218.84595,21.957977,48.871674,0.00046911245 1972,609.831,68.6668,29.315674,130.84702,0.0003848949 1972,588.3447,204.08635,50.313232,201.0403,0.00037478455 1972,7.2216425,177.50154,46.15972,88.09355,0.00036551035 1972,1.87945,58.216343,25.862392,70.69818,0.0003377957 1972,391.59406,148.48038,73.53647,145.02048,0.00030143425 1972,312.92752,144.52194,33.7453,64.849396,0.00011527445 1972,1.2032511,207.67735,17.48863,74.014694,8.5529304e-05 1972,616.08545,33.631298,23.061218,78.98538,5.878636e-05 1972,612.96576,300.4862,26.180908,147.98297,3.4614903e-05 1972,610.08905,439.16104,29.057617,65.71976,2.7443713e-05 1972,0.65380615,235.05246,19.121376,134.1513,2.3346061e-05 1972,567.3031,31.211088,71.84357,132.12329,2.0716867e-05 1972,5.6593003,129.41524,77.94731,183.39882,1.5317544e-05 1972,0.7304175,115.81353,13.812516,49.168022,1.52317e-05 1972,0.5062345,335.97156,19.625376,133.71237,1.5020673e-05 1972,1.8407114,92.20452,29.314837,170.79236,1.4610499e-05 1972,1.1208268,152.46198,18.611736,79.52254,6.5659206e-06 1972,586.0924,394.3829,53.05426,99.64728,4.8739466e-06 1972,0.6991073,421.78183,16.808435,80.092926,4.7588173e-06 1972,4.2766,201.92854,55.781395,233.70467,4.6894465e-06 1972,2.3554964,15.272354,58.749355,109.616554,1.9356034e-06 1972,4.8861656,352.11633,79.497894,133.7258,7.348769e-07 1972,364.94336,78.12052,139.72186,266.27612,5.936431e-07 1972,511.47528,27.447403,84.11505,205.78453,5.5807186e-07 1972,2.6799593,428.3613,53.06548,70.580475,4.7087318e-07 1972,422.46838,2.6842334,43.476624,53.314587,2.4627502e-07 1972,523.93555,5.318828,44.421326,56.131004,5.848819e-08 1972,419.1223,0.0,91.246704,179.75972,3.642943e-08 1972,1.7654965,3.7646484,30.496302,66.28706,3.1638354e-09 1972,387.97388,0.0,48.39624,46.853138,2.425089e-09 1973,422.488,208.59206,23.046387,46.085266,89.048164 1973,534.09546,222.74724,19.579895,46.0103,0.017322836 1973,547.60486,227.68181,22.180298,49.260056,0.0067526796 1973,528.4531,168.14848,44.605835,140.88002,0.0027078416 1973,513.88727,222.53128,35.18335,77.340485,0.0014952905 1973,401.15775,170.04118,62.89096,103.819565,0.00054011727 1973,621.9011,134.88022,17.245544,51.02585,0.0005137299 1973,0.817771,155.85083,13.898079,48.76129,0.00050122483 1973,2.5120687,71.1748,34.971035,71.35792,0.0003594362 1973,617.7721,243.45058,21.374573,81.583206,0.0002756545 1973,0.6053304,196.57155,18.103022,72.25743,0.00027093219 1973,504.20007,232.40396,24.451233,52.95819,0.00025728028 1973,1.135586,101.273476,20.400007,135.35019,0.00024185993 1973,0.6242041,109.564766,14.331329,53.39631,0.00019205974 1973,612.48254,167.75858,26.664124,126.912964,0.00015713694 1973,310.91953,154.79245,24.70459,47.223923,0.0001289102 1973,610.5624,69.68911,28.58429,149.1485,0.00011655844 1973,612.6788,361.9371,26.467896,132.2781,9.1742564e-05 1973,499.21466,136.19604,111.42499,211.2106,7.6131146e-05 1973,0.472015,225.1087,19.571774,140.39926,6.1437255e-05 1973,586.2695,106.05356,52.41217,232.20032,5.6461602e-05 1973,612.3077,270.50934,26.83899,145.30215,4.683127e-05 1973,0.36678955,341.23157,18.450537,74.0296,4.5849156e-05 1973,584.14636,11.600371,55.000305,128.18538,2.7328799e-05 1973,3.3356626,146.06386,57.26848,210.2566,2.3847831e-05 1973,0.47962728,382.19992,18.458178,74.00708,1.00356165e-05 1973,381.6002,128.68497,138.72098,208.30139,9.46337e-06 1973,1.0621485,295.8124,32.98572,170.8139,4.9183795e-06 1973,59.550217,56.598824,83.73479,188.39963,4.1239705e-06 1973,0.6887508,420.98395,16.988194,81.46118,4.083862e-06 1973,4.9133854,352.13318,79.124405,133.66797,1.2952318e-06 1973,2.5850716,38.537964,81.594864,222.05396,1.1213573e-06 1973,2.7093458,427.80502,53.395206,71.51196,4.2558673e-07 1973,1.0419642,6.682936,12.976716,80.09356,2.864304e-07 1973,322.84396,425.97525,72.41922,76.88873,2.6608572e-07 1973,1.6243018,2.3350523,38.039978,49.79975,2.6630733e-09 1974,415.22687,204.59705,20.494537,45.624573,99.08165 1974,449.8212,208.74825,23.67334,49.934097,72.13136 1974,445.51038,166.70592,29.891815,73.34758,4.93703 1974,604.93616,186.08258,34.21051,73.244965,0.004214146 1974,427.5831,203.61678,21.817902,47.046722,0.0037982077 1974,473.02148,178.40656,39.277466,81.43964,0.0016011602 1974,402.33804,179.25198,41.328278,96.43762,0.0013460619 1974,624.11346,174.16214,15.033203,47.482574,0.00081034296 1974,0.925118,166.41783,19.485863,136.08769,0.0004365964 1974,0.8290145,145.67058,14.0601425,52.618027,0.0004304142 1974,610.40314,75.48932,28.74353,156.20049,0.00036351432 1974,0.6762948,98.56241,14.123068,52.92224,0.0002618655 1974,293.66757,145.98029,32.98639,62.61473,0.00025946143 1974,1.0344479,186.08186,13.221989,48.67392,0.00025220352 1974,618.24384,395.53226,20.902832,78.40912,0.00022535099 1974,1.1999178,89.42436,20.060413,135.07962,0.00019214676 1974,617.79584,315.1079,21.35083,78.79993,0.000153328 1974,621.5976,95.89249,17.549072,52.73172,0.00014667098 1974,585.16675,121.95776,53.97992,201.48032,0.00013176825 1974,0.596569,247.20097,13.918659,49.91426,0.0001286194 1974,617.7708,241.58339,21.375854,83.54036,0.00010636721 1974,425.92105,161.63225,77.918915,151.54405,5.807317e-05 1974,0.54599446,266.19272,18.464045,76.07547,3.481566e-05 1974,609.7824,440.20605,29.364258,64.22302,2.6892581e-05 1974,3.0788574,169.49959,57.358154,218.94646,1.98222e-05 1974,602.50836,278.84412,36.638306,191.06589,1.8528553e-05 1974,0.5594499,386.00037,18.357208,72.50995,1.6902039e-05 1974,0.4274748,308.21225,18.532887,73.89551,1.4579071e-05 1974,614.9168,28.769691,24.229858,94.49505,6.289443e-06 1974,0.79550296,441.8043,22.11632,62.125824,4.539224e-06 1974,2.0107112,36.934006,62.31627,225.62996,4.1882586e-06 1974,1.1269922,330.36307,32.994488,158.07544,3.6357626e-06 1974,1.0915804,20.164186,30.743603,134.63991,1.2685616e-07 1974,0.6056763,8.179935,10.512572,84.974304,3.2889602e-08 1975,439.79297,215.09033,25.374908,61.444427,99.848564 1975,500.88116,221.58928,28.591187,63.312485,98.1644 1975,547.99603,216.90553,22.731506,52.71028,62.050396 1975,613.30383,226.66862,25.842834,61.270798,0.026203588 1975,423.45892,219.53258,22.184631,51.37651,0.0060851797 1975,424.66943,187.60919,57.05197,104.51865,0.0010945374 1975,1.2247299,166.94089,18.594004,79.966354,0.0006694287 1975,528.738,167.75616,60.289795,122.89096,0.00064147165 1975,391.64026,173.85599,35.085938,75.639435,0.00053356105 1975,0.9794739,218.70961,13.479929,48.72139,0.00036974478 1975,602.653,145.35664,36.493652,175.58897,0.00033730577 1975,621.45514,78.84706,17.691528,54.46434,0.0002345949 1975,482.87943,173.52687,70.30588,152.44945,0.00017567106 1975,1.2517546,162.23987,34.569927,170.78088,8.80858e-05 1975,610.46686,55.557873,28.67981,143.86362,7.93585e-05 1975,612.9289,331.7676,26.217773,137.2272,5.375312e-05 1975,0.48896077,241.31108,18.602402,77.843796,3.4077595e-05 1975,609.96484,441.28833,29.181824,63.21631,3.0974254e-05 1975,1.2108212,74.459984,18.397186,72.92481,2.8775634e-05 1975,387.7135,137.27422,143.81378,225.24492,2.8003838e-05 1975,492.79315,129.47064,142.07477,222.12872,2.7785041e-05 1975,0.6384408,348.54285,19.756609,130.81723,1.2321297e-05 1975,0.7377344,256.67413,28.473963,150.79727,9.896967e-06 1975,597.10065,6.5385256,42.04602,87.527466,7.3283354e-06 1975,585.71436,394.52203,53.432312,100.58502,6.2473696e-06 1975,0.8425383,442.47226,21.993034,61.161285,3.755054e-06 1975,2.5366278,235.61958,57.665554,235.7971,3.6319434e-06 1975,2.7606657,395.1308,56.30388,99.02353,9.0861914e-07 1975,1.4364926,48.539043,29.61259,155.77255,4.3280687e-07 1975,2.100039,11.799408,58.35845,118.95187,4.5375227e-08 1975,0.68445194,8.899329,11.327238,84.465385,2.7536537e-08 1975,301.33112,4.548506,42.75284,46.779617,1.7240767e-08 1976,500.15048,216.5731,37.30716,84.79433,99.82819 1976,519.8859,225.0588,26.497864,60.33142,0.0069918423 1976,609.55774,87.16191,29.588928,133.89037,0.0028788454 1976,620.3445,112.8005,18.802185,59.665398,0.002082366 1976,1.1050725,122.0732,18.6545,79.99963,0.001163455 1976,620.9105,74.21434,18.236145,61.18936,0.0007754814 1976,447.41943,205.37932,24.884064,52.80742,0.00075239275 1976,621.4264,151.36353,17.720276,56.890564,0.0006038106 1976,482.38116,184.30565,75.95068,146.39821,0.0005975084 1976,429.9496,224.7302,24.717499,57.44745,0.0004399317 1976,617.8087,181.00725,21.337952,77.8221,0.00039772558 1976,258.35178,168.68153,25.348328,46.26866,0.00025751177 1976,618.31616,387.38092,20.830505,80.43921,0.00023152884 1976,586.37897,108.83133,52.577454,242.37457,0.00020020823 1976,617.937,327.99408,21.209656,80.90848,0.00015129347 1976,617.4267,238.60709,21.71997,87.09564,0.00012960444 1976,567.7668,29.015236,70.748474,141.87518,8.277429e-05 1976,1.7447526,149.67343,35.15446,166.24847,7.163027e-05 1976,1.1168857,219.2238,18.07815,74.23427,5.960975e-05 1976,601.5672,236.8688,37.579468,199.65,3.6641497e-05 1976,606.80383,6.5507617,32.342834,56.042286,2.8766417e-05 1976,609.90155,439.20673,29.245117,65.4436,2.3461766e-05 1976,0.57922775,262.68942,19.453007,135.53516,1.4305132e-05 1976,0.41186523,356.7038,18.448162,72.7337,1.1360192e-05 1976,585.9921,392.94504,53.15454,102.002014,5.7745356e-06 1976,0.67838705,403.92438,20.523117,96.39111,3.823518e-06 1976,1.0085092,301.73572,33.163963,167.51096,2.616964e-06 1976,5.05556,353.5331,79.73516,133.07285,6.3084474e-07 1976,1.4627475,36.43162,36.036694,182.87364,5.431867e-07 1976,1.1618848,13.465059,38.05053,75.52282,5.1865623e-08 1976,570.0445,5.796745,45.62738,51.24392,4.2150532e-08 1976,11.194989,4.4125943,45.402298,49.033207,1.3441371e-08 1976,0.7590617,0.77500325,21.291508,46.537453,2.424178e-09 1976,533.1344,1.5438917,44.897095,53.48909,2.1445168e-10 1977,450.38892,180.78688,33.929443,75.66875,0.037833788 1977,388.0119,192.24193,24.740509,46.526016,0.03703378 1977,615.15173,136.28163,23.994934,90.77942,0.0011711373 1977,599.24677,139.69896,39.188538,188.25594,0.00083090866 1977,615.91046,248.0563,23.236206,89.53412,0.00026567717 1977,616.923,190.39311,22.223694,91.706894,0.00025565748 1977,244.34982,149.8034,25.073029,46.844757,0.0002296151 1977,606.702,7.4526043,32.44464,116.002556,0.00021608973 1977,618.236,368.26318,20.910645,80.530365,0.00012724866 1977,1.4086792,117.55872,18.118355,74.39037,4.236764e-05 1977,609.9696,438.9456,29.177063,66.046875,3.5499022e-05 1977,602.7403,305.1992,36.406372,180.95557,3.1722033e-05 1977,0.5794804,193.07137,13.769231,48.992447,1.8504432e-05 1977,0.76968104,153.2106,20.174074,133.30063,1.5242773e-05 1977,427.57516,125.95856,75.19229,162.18481,1.3628811e-05 1977,0.7218156,331.80396,17.871674,73.56244,1.31532925e-05 1977,0.7572233,378.6819,18.67244,116.69931,1.2135908e-05 1977,318.66925,66.79801,26.810425,56.919167,9.6280655e-06 1977,328.34158,130.52173,87.0029,162.78317,7.688032e-06 1977,1.6935971,74.72707,18.247074,71.78788,7.369939e-06 1977,0.5983659,276.6709,18.29149,74.24292,7.358814e-06 1977,587.01575,5.069346,46.292114,49.04954,4.2485053e-06 1977,1.2232707,292.4571,31.98912,166.57135,2.530925e-06 1977,3.4761295,111.12912,57.578156,214.03845,1.5680828e-06 1977,3.075524,394.01553,55.56231,96.48819,1.1412951e-06 1977,402.0814,4.2208724,43.423767,55.487568,1.8790082e-07 1977,360.68936,1.9401953,44.92148,55.650444,1.00938905e-07 1977,2.1561654,24.326706,31.899944,137.73569,5.4427534e-08 1977,544.7067,5.471494,47.197327,55.873676,3.0160134e-08 1977,0.9560958,10.557028,10.60354,79.0341,1.7606919e-08 1977,484.19388,3.4958172,46.915802,51.04093,3.1819183e-09 1977,1.6606007,5.5353746,30.819323,59.289692,2.0428268e-09 1977,516.9438,2.3934865,46.80127,57.148964,5.4788485e-10 1978,576.2442,110.84863,61.312622,181.33865,0.004082884 1978,242.76292,160.93364,25.252457,47.532913,0.002517692 1978,1.5875146,104.086525,21.520664,129.2327,0.0019635123 1978,0.95212483,130.23868,14.084019,52.80069,0.0016646944 1978,1.0433321,193.18951,13.233097,46.516174,0.0015420642 1978,442.49585,195.88235,32.52603,72.21188,0.0011097991 1978,332.11734,169.07863,43.599792,79.82442,0.0010259638 1978,620.9406,234.7853,18.206055,53.060684,0.00045677644 1978,614.1024,184.62883,24.785828,89.800156,0.00039501788 1978,614.6721,136.78116,24.474548,87.28856,0.00036998183 1978,617.4641,276.51648,21.682556,83.32068,0.000116255374 1978,0.8775895,173.33745,20.059927,130.75395,8.6248954e-05 1978,617.0777,57.25055,22.06897,79.574844,7.596535e-05 1978,312.00684,79.487686,77.3454,189.79808,6.8176385e-05 1978,429.85153,90.65282,44.05072,160.07797,6.328696e-05 1978,612.7355,306.03537,26.411194,140.01694,5.354413e-05 1978,314.83835,50.60849,19.848114,46.276268,5.1183848e-05 1978,330.50076,42.276455,29.250671,71.094986,4.418851e-05 1978,604.6269,5.1918783,34.519775,56.450237,4.4113604e-05 1978,620.4728,25.578236,18.67389,58.343014,2.639971e-05 1978,599.70544,185.47789,39.432556,208.60918,2.6147345e-05 1978,609.6765,436.58432,29.470154,68.11511,2.3039021e-05 1978,306.94617,47.564793,37.380493,97.112274,2.147768e-05 1978,3.5348244,133.11954,56.421673,204.8675,1.7704806e-05 1978,384.6509,28.659105,29.021393,68.27288,1.3187532e-05 1978,0.73698246,368.87,17.738224,128.8349,1.2780454e-05 1978,0.6517367,263.98828,19.703094,135.47104,1.17288755e-05 1978,583.6644,9.21543,55.452576,114.6224,7.4486543e-06 1978,572.9942,363.19345,66.152466,123.7262,6.996963e-06 1978,345.5055,33.080727,27.011383,66.89948,2.288747e-06 1978,2.8479004,393.30475,55.361893,98.93582,1.6421395e-06 1978,1.7830795,27.581297,35.06164,152.45387,3.2542113e-07 1978,286.7155,2.5771713,44.964172,57.916046,1.6801481e-07 1978,282.64148,1.1035677,84.464386,202.70432,5.35925e-08 1978,0.870518,9.841768,10.400697,81.1365,4.3352212e-08 1978,359.10248,5.2489567,45.72119,51.141716,2.0945935e-08 1978,313.6574,3.2015023,46.228638,49.993004,1.6279204e-08 1978,448.08502,3.1278322,43.886566,49.82651,1.5535592e-08 1978,574.37885,1.191198,46.714478,47.111233,8.288763e-09 1979,391.37592,217.95369,22.841919,49.829422,1.9592483 1979,493.26935,46.51887,30.558777,72.84236,0.0077417754 1979,240.4432,217.30537,30.460815,52.590714,0.005729168 1979,351.73923,51.4248,28.71457,55.438206,0.0044163205 1979,452.1299,209.6131,32.0141,68.43771,0.0029310922 1979,358.49518,197.7131,43.045807,68.134186,0.0010506172 1979,339.00958,69.06465,27.055573,56.77279,0.0006733613 1979,583.0066,0.0,55.974915,140.37755,0.00042836223 1979,379.05933,123.670395,81.183716,169.88914,0.00018714965 1979,347.0431,67.26201,39.466553,87.18141,0.00016427619 1979,610.82263,159.40654,28.324036,124.68663,0.0001594423 1979,617.54913,244.50214,21.597534,79.19229,0.0001583168 1979,596.893,3.3410661,42.253662,53.12294,0.0001492338 1979,0.66826093,208.19339,17.955984,70.12244,9.219409e-05 1979,1.3662248,129.09839,18.798868,83.002625,6.0074985e-05 1979,612.1878,333.9771,26.958862,137.47696,5.4190263e-05 1979,609.39233,436.17938,29.754333,68.51715,3.9992938e-05 1979,2.406364,91.90862,34.9417,167.66235,3.2447984e-05 1979,588.0492,183.33186,51.097473,218.06456,3.1543834e-05 1979,0.5007959,326.58786,18.659739,133.84558,1.7853916e-05 1979,0.8880192,212.34727,28.225412,140.74516,1.0738917e-05 1979,3.572998,143.01161,56.62221,208.25072,9.476754e-06 1979,0.46023276,417.08002,17.031717,84.74899,9.416376e-06 1979,477.5958,15.872175,39.45761,81.95985,5.5955798e-06 1979,2.8884897,317.68976,57.33109,160.14212,1.4369981e-06 1979,570.27673,84.751945,27.237427,58.510033,1.3680321e-06 1979,442.7569,3.4098308,45.131226,129.03528,4.8221705e-07 1979,2.1159692,55.60978,21.658587,107.51969,2.658235e-07 1979,346.44873,4.609961,48.95819,51.908527,1.7557565e-07 1979,2.5297136,15.097826,58.48081,118.83551,8.503021e-08 1979,467.13043,2.7736232,65.821045,49.153473,5.3744614e-08 1979,430.4757,2.3740268,46.587738,53.34997,3.035213e-08 1979,321.63483,3.9195182,44.248688,46.677868,2.7692018e-08 1979,0.9620162,11.032901,11.005424,80.96039,1.7081558e-08 1979,510.9057,2.9109392,45.140625,48.511044,5.665192e-09 1979,1.42007,2.5257862,31.149319,46.639217,2.6208762e-09 1980,502.9192,199.50662,42.843506,90.74063,5.186289 1980,438.0433,214.34964,25.258728,55.455353,0.71047455 1980,482.22397,209.05624,33.357086,81.72208,0.17189032 1980,353.1041,186.4086,23.654846,51.807922,0.023547728 1980,335.86816,176.55833,24.93695,56.282013,0.015259379 1980,338.28738,143.37578,46.258392,96.393295,0.011866818 1980,390.10333,182.41885,39.985107,70.57378,0.0043097436 1980,468.06778,193.81602,27.598755,62.062027,0.0025084654 1980,471.70346,125.407776,103.88852,206.16843,0.0017671959 1980,413.1264,47.68897,28.50064,59.473545,0.0013595773 1980,370.67938,100.02388,28.063751,60.206055,0.0012089409 1980,465.67737,102.16335,45.32547,179.08109,0.0007557857 1980,610.3291,129.15738,28.621826,131.2261,0.0003698083 1980,0.70542485,193.54391,17.960403,70.03726,0.00027538722 1980,617.86316,216.18845,21.283508,80.50589,0.00013615369 1980,435.90106,43.98777,37.64749,90.08039,0.00012695132 1980,498.85028,85.69956,45.99237,131.80063,9.380378e-05 1980,612.97437,347.44394,26.172302,138.5676,6.3715845e-05 1980,374.95987,118.77938,82.835236,178.41101,4.181211e-05 1980,1.4463412,101.24448,19.31423,131.71317,4.0427796e-05 1980,609.69556,438.23465,29.45111,66.387695,3.9762854e-05 1980,1.2985644,160.47087,32.48052,167.15712,1.8970333e-05 1980,603.11444,210.93207,36.032227,190.11386,1.8062094e-05 1980,608.1994,0.0,30.947266,166.59808,1.6379581e-05 1980,0.4325513,255.84691,20.21473,134.70457,8.445745e-06 1980,0.68693036,391.67368,18.711533,106.96924,5.5428927e-06 1980,488.19067,0.0,52.15033,130.44618,4.7900617e-06 1980,516.2147,15.891566,40.171753,89.11199,2.2450267e-06 1980,2.8915431,220.49078,57.471413,222.8273,2.077467e-06 1980,3.0462518,71.85573,58.63957,217.56657,6.1013554e-07 1980,2.9587224,395.41296,56.38182,97.98581,5.7600886e-07 1980,1.8802075,23.422781,20.007645,142.26003,3.355827e-07 1980,406.71063,1.8780566,45.32547,50.988575,2.2529743e-07 1980,2.6687272,13.829928,59.419567,123.433304,9.400397e-08 1980,478.096,0.16007161,47.99121,56.041855,9.291162e-08 1980,435.86127,0.39034668,47.061462,51.329254,6.518622e-08 1980,375.79837,0.63988936,43.048676,49.126534,5.4763255e-08 1980,1.27698,8.765362,13.580901,77.04811,1.6026757e-08 1980,2.0805647,2.987518,31.444483,46.801838,1.6460474e-09 1980,516.61615,0.0,49.066956,48.634277,7.7741613e-10 1981,431.78104,203.61691,24.095642,49.956894,99.1255 1981,396.9492,70.19177,37.299377,74.44237,0.19388756 1981,610.84015,233.23102,27.422974,53.67691,0.102962196 1981,386.2795,201.78114,22.953766,46.954147,0.08650701 1981,352.87265,172.20613,31.707794,72.382996,0.012747868 1981,544.62665,243.46472,24.273071,50.49991,0.0047559896 1981,364.7632,144.22217,41.61084,107.63075,0.0021956756 1981,0.7086963,130.82935,19.550348,89.30055,0.0018831854 1981,377.63306,182.55328,18.748016,45.317993,0.0017630884 1981,617.37885,165.20714,21.767822,89.03136,0.001224077 1981,412.70114,162.40459,61.605377,112.85234,0.0007706759 1981,599.1717,170.89923,39.54895,159.96307,0.00054873014 1981,0.6059111,208.09364,13.6805315,49.645187,0.00044291295 1981,617.1366,262.4478,22.01007,90.340515,0.00026916497 1981,280.00415,154.60406,24.940552,45.50096,0.00026038056 1981,0.9821159,155.22638,28.436537,135.90558,0.00022345659 1981,3.7611508,104.14832,57.70947,202.79712,5.7912457e-05 1981,612.3544,354.7189,26.792297,135.31992,5.7672474e-05 1981,616.53613,111.489235,22.610535,82.65325,5.4483564e-05 1981,616.3315,26.726273,22.815186,92.38791,2.0547259e-05 1981,381.75272,103.30935,135.35544,238.11081,1.8278177e-05 1981,0.43334392,236.27849,18.43772,77.68187,1.793675e-05 1981,0.5629248,326.34653,19.854008,131.70651,9.619328e-06 1981,0.48099366,289.0678,18.359325,72.56427,9.207198e-06 1981,0.6847168,421.7632,16.87366,80.44769,3.8024925e-06 1981,598.6616,0.0,40.485046,200.71405,2.674551e-06 1981,2.7702832,266.3075,57.558327,207.48883,1.3909544e-06 1981,2.7107763,429.3235,53.33313,70.05292,3.6831605e-07 1981,1.5074016,20.438366,20.452415,144.59013,3.3879388e-07 1981,2.0881803,11.164206,59.806286,127.62922,3.188446e-07 1981,320.3999,2.9518816,44.117584,45.997543,9.558152e-10 1982,509.16428,214.40541,25.859283,49.45015,99.728264 1982,463.21143,212.0819,22.43515,50.758133,0.30480945 1982,473.29047,48.344322,31.49112,64.203285,0.28779304 1982,485.46127,50.489315,41.089752,89.57556,0.009710096 1982,414.03314,194.85936,31.462128,66.700455,0.005169039 1982,621.7239,189.72362,17.42279,49.378784,0.0033218472 1982,617.09985,56.868282,22.046814,77.271515,0.0016120876 1982,395.77554,200.36478,22.427032,47.18271,0.0010067804 1982,611.3297,144.271,27.816956,129.82217,0.00027637993 1982,621.6623,240.27983,17.484375,54.254227,0.00024332808 1982,617.8791,315.16592,21.267578,78.618835,0.0001111567 1982,1.4072257,102.86736,18.804379,137.54846,0.00010232318 1982,493.8283,187.1825,62.586487,98.1124,9.518489e-05 1982,585.39764,13.798695,53.339844,102.29666,7.132998e-05 1982,612.5971,365.62863,26.54956,130.41003,4.46396e-05 1982,0.67772055,219.42175,18.200981,72.77426,2.8327835e-05 1982,604.57556,223.1024,34.571106,163.69185,2.3132237e-05 1982,4.0500813,101.934,56.41356,215.66684,1.4914232e-05 1982,0.50467205,303.79294,18.33123,72.08197,9.3650415e-06 1982,0.65573406,391.0426,18.896378,107.71527,5.851849e-06 1982,1.1466146,207.55656,33.78423,175.85982,5.636047e-06 1982,458.03552,115.37013,146.3227,246.61113,4.1923427e-06 1982,446.8419,23.48166,88.88956,165.9052,3.7622876e-06 1982,2.9484751,327.76822,57.61988,154.27493,8.226935e-07 1982,2.766932,16.689213,59.152527,120.77582,5.1837554e-07 1982,416.11264,39.501484,47.232178,175.84084,1.9794342e-07 1982,1.1381185,10.772465,14.376884,89.600876,5.0906028e-08 1982,1.0039324,3.2021143,41.79334,51.598377,7.984266e-09 1982,388.82953,5.446447,42.326355,47.70378,3.1759275e-09 1983,609.233,222.18782,29.913696,71.256454,0.20769078 1983,504.3683,196.036,32.29651,72.95265,0.14067098 1983,527.8146,206.0002,35.1109,71.613846,0.048258953 1983,430.61578,197.95673,23.003387,46.526062,0.011041643 1983,621.6029,191.5738,17.543762,57.37581,0.0067942115 1983,622.1649,253.34625,16.98175,57.23532,0.0038325808 1983,530.41876,49.690643,30.286743,72.06057,0.0037968606 1983,586.07874,113.466225,52.740417,209.7991,0.0020983275 1983,340.99432,151.03398,25.100128,48.662033,0.001132139 1983,469.0275,192.57799,23.604584,46.92476,0.0009035124 1983,420.85196,59.13612,29.304657,66.217636,0.00069057045 1983,542.38214,66.89416,38.057312,72.640114,0.0005845936 1983,1.3263363,75.06483,19.917337,148.88391,0.00030520975 1983,616.1326,58.28589,23.014038,72.09331,0.00025522214 1983,510.68054,174.4871,24.515686,54.300613,0.00023234946 1983,602.54706,220.9218,36.59961,219.74864,0.00019511813 1983,0.77703047,173.42604,18.421331,77.44301,0.00015748618 1983,621.27246,122.62792,17.874207,52.18914,0.00015273986 1983,391.80673,62.416767,39.948822,83.70271,0.00013292616 1983,617.7399,386.0975,21.406738,81.02985,9.910649e-05 1983,605.3114,0.2625228,33.835266,63.77006,7.197147e-05 1983,0.45070395,222.49435,18.252607,77.654755,4.692972e-05 1983,583.8538,5.9521875,54.96338,119.98529,2.5510044e-05 1983,609.64526,438.8643,29.501404,65.618286,2.4090867e-05 1983,493.11966,92.7722,44.353363,156.15541,1.4397089e-05 1983,0.6167196,339.22192,19.31425,130.80716,1.31042125e-05 1983,1.2506039,154.58086,33.628742,175.08092,1.0777289e-05 1983,0.49818605,275.0543,18.364624,74.07922,9.057748e-06 1983,572.3639,355.44528,66.782776,130.66281,6.9724365e-06 1983,475.59662,74.80034,25.958862,58.87137,5.2611545e-06 1983,0.66315347,420.9595,17.097599,81.253174,3.2961163e-06 1983,2.6504347,13.097923,58.2623,117.427895,2.3504804e-06 1983,3.0160792,241.82797,57.240063,220.14093,1.2237073e-06 1983,501.06622,6.3888674,79.2923,178.17856,7.99273e-07 1983,2.6429396,391.9284,56.255383,101.082245,7.430729e-07 1983,2.8701792,56.940205,58.852676,219.06636,6.6626285e-07 1983,1.117883,7.930384,14.491013,87.51817,1.8656878e-07 1983,541.66187,4.348825,47.601624,60.027122,1.1503336e-07 1983,490.98788,2.5178843,45.484344,53.226467,1.0273635e-07 1983,413.511,4.683314,42.112976,49.888947,7.494535e-08 1983,1.5319824,4.592497,44.105633,54.53965,5.274573e-08 1983,382.74164,2.9713118,43.523163,49.993877,2.0404682e-08 1983,25.047665,3.1856446,47.793736,57.354954,1.7569539e-09 1983,51.735386,1.954056,45.27992,53.941303,2.5498653e-10 1984,482.2031,202.56271,25.235718,53.559906,2.3552866 1984,533.65485,192.1443,35.64612,82.29808,0.14326225 1984,526.45734,108.73926,59.39258,170.10013,0.013844484 1984,553.4431,149.70067,20.689636,45.663055,0.004972758 1984,560.19275,154.11469,44.4209,114.82022,0.0034625437 1984,552.9371,230.7752,19.506714,45.651154,0.0015541927 1984,540.50134,135.5119,31.025024,90.65277,0.0008133501 1984,610.22266,146.69688,28.924011,132.00664,0.00054980343 1984,1.0367953,188.29337,18.128513,75.875275,0.00035377426 1984,592.8224,0.0,45.08362,92.96137,0.00017105872 1984,493.33023,42.80255,140.42776,268.33148,0.00016599229 1984,1.0010108,115.334404,13.725822,51.343758,0.00014988906 1984,1.0253687,113.537506,21.144234,123.19394,0.00014770296 1984,0.74840254,219.92227,19.812357,134.7771,0.00010890635 1984,611.6829,237.89293,27.463745,148.08635,9.412655e-05 1984,1.9311882,59.27913,18.903671,80.09755,9.3143404e-05 1984,617.6821,345.95294,21.464539,82.67044,7.809129e-05 1984,610.94354,48.564316,28.203125,157.1941,4.2347405e-05 1984,3.5115187,104.82657,57.138508,223.11029,3.3089607e-05 1984,378.1056,105.695885,141.20972,226.43167,2.6143107e-05 1984,609.92645,440.28006,29.220215,64.47366,2.3271949e-05 1984,0.44269776,333.91473,19.921951,136.95413,1.7025961e-05 1984,585.4341,391.4606,53.712585,102.73685,7.668788e-06 1984,2.3704526,12.341999,59.396618,120.782074,5.9405115e-06 1984,2.8874936,236.4986,56.759174,228.38684,4.4968606e-06 1984,0.7151408,421.57205,16.739664,80.528625,3.7918824e-06 1984,550.19806,0.0,88.94861,179.51282,2.2027255e-06 1984,1.2483215,7.5764747,12.442854,90.71631,1.1089486e-06 1984,2.7972608,391.2833,56.367363,101.76672,7.6323045e-07 1984,1.4991227,3.1310418,42.3036,59.84305,2.6216926e-08 1984,435.63937,1.6113656,44.687317,53.989796,1.3432554e-08 1984,570.98535,0.6334912,45.57306,51.193943,4.057721e-09 1984,39.049305,1.3849187,42.182888,52.1942,1.3843234e-11 1985,580.4154,166.84889,40.77533,112.94304,0.53303975 1985,488.6263,213.67278,26.04477,46.251144,0.1893561 1985,434.64706,193.35745,40.110962,68.36429,0.008798822 1985,621.7765,216.19493,17.370178,54.541397,0.006126801 1985,622.3608,178.52888,16.785889,49.62883,0.0053958055 1985,609.538,135.96146,29.608643,127.78995,0.0038132402 1985,620.21454,122.35249,18.932129,52.6474,0.0029762636 1985,545.423,80.47431,79.56433,201.54904,0.0014044837 1985,478.67725,162.58081,42.507202,111.565125,0.0009841773 1985,445.2957,51.18732,26.781311,59.59461,0.0009004329 1985,610.8152,202.5374,28.331482,135.2252,0.00088534696 1985,587.8737,231.2736,22.791626,53.019424,0.0005146675 1985,612.6796,6.070726,26.467041,89.84515,0.00033974965 1985,1.0741862,213.5022,18.280836,73.02423,0.00012618316 1985,1.3145622,100.06036,19.874668,144.13998,9.596931e-05 1985,617.7945,334.06146,21.352173,80.01367,9.260524e-05 1985,0.8406421,109.75761,13.957598,52.426903,7.488926e-05 1985,612.5905,365.38104,26.556152,130.66882,4.268332e-05 1985,601.4314,245.56194,37.71527,215.16563,4.0444338e-05 1985,1.7447461,60.62345,18.995394,77.087105,3.1877124e-05 1985,417.88263,154.66913,84.98926,152.81995,2.4401823e-05 1985,0.4415088,251.51776,19.366283,139.33517,2.3489796e-05 1985,1.5068116,175.87842,32.782375,163.29712,2.0605028e-05 1985,580.64954,0.0,58.49713,215.10365,1.3469581e-05 1985,0.5408594,360.25168,19.90186,133.60147,9.879746e-06 1985,2.7313917,36.30969,60.423786,239.44028,3.4460743e-06 1985,2.7397804,244.48116,57.31535,222.26115,3.2639473e-06 1985,2.9190202,395.0009,56.609444,97.91095,9.048143e-07 1985,1.985516,8.640069,39.366314,83.789246,4.5293626e-07 1985,566.0059,22.454985,38.384827,82.752335,1.04130336e-07 1985,490.52252,0.0,48.97821,144.9809,6.095118e-08 1985,523.52264,18.630775,39.67169,72.907364,3.799922e-08 1985,479.2407,1.3586166,42.676117,52.65483,1.4170025e-08 1985,1.1620224,0.0,21.603285,47.897617,7.723472e-09 1985,345.19876,2.2989535,62.868286,47.34076,4.8280286e-09 1985,502.09912,0.0,46.52649,52.809307,1.386653e-09 1986,489.02356,205.82022,23.620178,48.232758,99.41327 1986,607.8115,209.76251,30.82074,74.48105,4.998673 1986,620.65936,184.34326,18.487305,62.928925,0.0030775715 1986,599.78845,92.581604,39.358215,207.66388,0.002071982 1986,473.96002,171.80045,59.867737,102.20001,0.0014006543 1986,0.7263241,199.50467,13.7135,52.406723,0.0012843388 1986,621.3927,121.40431,17.753967,52.474518,0.00038857115 1986,0.7230599,189.64105,19.858065,126.79465,0.00014989049 1986,542.7618,25.899057,27.769653,82.27431,0.00011174818 1986,608.5784,16.12545,30.568237,113.52677,0.00010952474 1986,0.8074886,150.38286,18.564802,79.140015,8.633463e-05 1986,617.7634,321.44226,21.38324,78.47763,6.117754e-05 1986,604.4914,219.80498,34.655273,185.31154,5.914858e-05 1986,612.72516,366.0059,26.421509,130.07913,4.4067474e-05 1986,1.6354574,116.64172,33.86649,171.67883,3.694427e-05 1986,1.0898365,81.95925,19.366194,83.05804,2.8231767e-05 1986,1.2606169,221.20459,33.022835,177.14801,1.5075882e-05 1986,0.58170414,351.36697,20.89581,102.90982,7.808088e-06 1986,437.32928,113.59273,139.66199,237.7476,5.47443e-06 1986,0.6834725,421.6097,17.643648,81.00958,3.3837555e-06 1986,516.1008,12.739734,82.13855,135.99696,1.6370359e-06 1986,1.4797999,9.7966795,59.4455,121.61325,6.199956e-07 1986,2.8409457,395.01694,56.56674,99.11047,5.1930584e-07 1986,586.0827,3.4360206,46.500427,45.80967,2.4546372e-07 1986,1.2017261,6.106436,14.135303,95.703926,9.988555e-08 1986,563.1151,5.8740706,46.414368,51.878296,5.581312e-08 1986,522.7882,3.47882,65.44208,49.628178,2.4564436e-08 1986,417.8999,4.729287,43.11252,47.05312,9.028155e-09 1986,441.5507,3.3119142,45.387085,48.695366,3.7540886e-09 1986,23.965033,0.0,46.04361,59.94599,3.7094924e-11 1987,566.02905,194.559,35.036377,76.20082,99.74654 1987,442.54443,188.26743,33.615204,61.213852,0.7469936 1987,607.6306,208.78056,31.48523,81.14488,0.030749261 1987,603.7438,68.68926,35.402893,72.66927,0.009205997 1987,589.46356,193.23857,33.276306,74.30232,0.004130301 1987,620.6078,184.7404,18.53888,62.21588,0.0033615604 1987,619.7893,52.498867,19.35736,56.46578,0.0024579267 1987,318.18903,147.97357,26.371307,56.57701,0.0018807121 1987,583.9768,100.7551,55.16986,222.36188,0.001685408 1987,620.97327,120.918205,18.1734,55.36695,0.0013877943 1987,617.7127,16.674324,21.43396,62.484756,0.0012589861 1987,0.98161215,190.19612,17.568077,67.75981,0.0002489308 1987,583.389,9.638099,55.75769,113.270836,0.00014557538 1987,617.8373,341.46158,21.309387,78.94348,8.8115456e-05 1987,0.53251225,211.00655,19.668533,135.48189,6.899096e-05 1987,0.865944,127.965996,18.201403,78.934975,5.0779243e-05 1987,601.57,235.43288,37.57666,208.59306,3.7317353e-05 1987,612.6243,371.57416,26.522339,123.03558,3.299734e-05 1987,438.1113,102.99747,137.59042,231.09744,2.0183146e-05 1987,1.6213917,108.18023,33.378036,166.82224,1.6041062e-05 1987,0.4710555,322.77374,19.827845,134.05008,1.3719667e-05 1987,1.0406828,83.91166,18.610489,78.51714,1.3534286e-05 1987,0.6847534,422.0957,17.138857,80.099884,3.9335896e-06 1987,3.0115252,232.69756,57.073036,221.08775,2.056168e-06 1987,2.8895345,395.38083,56.44703,98.24008,5.52834e-07 1987,527.0282,0.0,49.759705,61.32322,2.992955e-07 1987,1.1769247,38.56134,19.489628,79.92133,9.1842836e-08 1987,581.8436,0.0,43.19989,54.56978,9.1597556e-08 1987,497.6534,0.07564453,48.313507,59.938625,4.0072837e-08 1987,1.5733919,11.834616,60.01305,117.79273,2.9395745e-08 1987,1.5181128,6.6997724,21.267132,56.683537,1.04173195e-08 1988,487.29834,189.71466,31.440918,73.07703,0.018173805 1988,621.5353,189.36629,17.61139,51.022934,0.0071196794 1988,509.4074,192.24995,25.58966,57.891724,0.004621276 1988,0.8789274,159.30608,18.417732,78.18205,0.0025559862 1988,524.2614,199.40433,22.993347,50.358383,0.0018965292 1988,610.9584,153.25392,28.188293,122.62889,0.0014409578 1988,610.85254,73.55307,28.294128,113.27309,0.00043715345 1988,0.53914714,213.7599,14.047944,54.12349,0.00031466698 1988,510.10687,230.0293,24.591919,48.197083,0.0002615236 1988,584.3902,97.26189,54.612976,222.98439,0.00024672569 1988,498.32352,131.8767,36.93802,86.4211,0.00021032727 1988,609.83984,5.7670836,29.306824,107.410385,0.00019405034 1988,1.5156022,131.23584,35.307972,171.3693,0.00014578419 1988,0.9249748,105.362526,19.188148,78.81992,0.0001299101 1988,617.792,254.00519,21.354675,83.70621,0.000118511 1988,302.49353,170.61432,27.065552,50.434204,0.000109051965 1988,0.64355797,204.90288,19.726007,141.34569,0.00010798683 1988,617.86084,320.02567,21.285828,79.536224,8.1995844e-05 1988,613.07886,371.20245,26.06781,126.070465,4.813609e-05 1988,0.38375488,328.72293,13.864025,47.89322,3.0278556e-05 1988,587.4034,213.52911,51.743286,256.22064,2.1184904e-05 1988,0.4748348,329.30817,19.937016,135.6806,1.6957772e-05 1988,2.8280356,181.2206,57.021145,222.7228,1.5680564e-05 1988,0.5038676,275.57187,13.696947,49.862396,1.29388045e-05 1988,0.43413004,385.1879,13.83954,47.10562,9.523123e-06 1988,0.7038851,422.15433,17.236538,80.15283,3.505032e-06 1988,2.7568686,332.96747,57.664303,151.07217,1.0644962e-06 1988,544.0649,9.803425,47.800354,113.29144,8.4928786e-07 1988,1.8744923,14.116485,57.15719,119.64281,3.559344e-07 1988,420.78525,3.3611574,42.33554,49.440895,1.7188242e-07 1988,470.55127,1.2309098,42.801636,50.099396,3.0600514e-08 1988,583.9157,0.0,44.918213,49.321667,1.2318837e-08 1988,381.34796,1.6667514,43.72879,49.08952,1.0809347e-08 1988,0.927653,5.5792904,12.749577,78.29023,2.4760864e-09 1988,557.06274,0.0,45.615295,56.158836,1.622939e-11 1989,459.13422,201.86479,34.21408,56.248917,86.49632 1989,551.0001,199.94977,25.538208,53.758698,0.016376799 1989,482.92023,200.16899,22.512817,47.645523,0.01153287 1989,601.2404,166.10495,32.930237,95.171814,0.010177889 1989,560.2729,182.70764,33.190002,80.45468,0.008999537 1989,537.0011,189.33502,26.69519,59.061554,0.008553455 1989,311.98776,219.8879,21.714783,49.397873,0.0071086353 1989,621.207,201.54437,17.939697,61.3367,0.004174779 1989,255.16884,197.28857,30.102036,60.679962,0.0026319462 1989,620.1929,105.6362,18.953796,58.54033,0.0011746247 1989,614.2916,152.25598,24.074707,55.533325,0.0011479645 1989,617.6315,264.92645,21.515198,84.64331,0.0008371811 1989,570.9036,131.36006,64.32977,172.28502,0.000615816 1989,0.90656006,154.09074,18.707613,82.5497,0.00037123158 1989,0.6955933,92.02757,19.277344,84.74806,0.00024310267 1989,388.6178,98.138,44.274323,144.78519,0.0002110969 1989,618.1431,386.21188,21.00354,81.66089,0.00018399673 1989,615.04584,47.447735,24.10083,87.66028,0.00012469241 1989,1.4380844,100.40426,34.82316,167.26349,0.000117742034 1989,438.26917,158.78906,78.53613,147.7493,9.152793e-05 1989,617.568,328.41962,21.578674,80.71228,7.19504e-05 1989,585.75104,179.49738,26.127136,65.32518,6.372982e-05 1989,0.42640787,204.25504,18.758049,80.93436,5.99057e-05 1989,602.2435,219.49756,36.903137,188.53787,5.730793e-05 1989,609.6968,438.91147,29.44989,65.71292,5.3345757e-05 1989,588.3333,325.3086,50.813354,158.52985,1.2387926e-05 1989,0.7519694,399.1933,18.05837,71.09024,1.0076164e-05 1989,0.3722583,245.61475,20.327559,139.83447,9.421406e-06 1989,0.525328,338.64508,18.354063,72.16492,6.093717e-06 1989,596.3368,7.9573474,42.809875,81.90529,4.42084e-06 1989,0.9642424,441.611,21.664215,61.154755,2.8313998e-06 1989,3.0031397,211.77034,57.218296,228.4958,2.2242614e-06 1989,1.0614258,334.48956,32.819584,151.57187,1.9279616e-06 1989,1.521049,47.468052,18.33236,74.4218,7.1359426e-07 1989,2.8893116,428.6815,53.15466,69.491486,4.703741e-07 1989,2.7898731,17.745913,80.93951,155.53957,2.398169e-07 1989,366.13446,4.375389,64.34674,47.157997,1.03890684e-07 1989,332.90927,2.7782114,44.07831,50.902573,3.091095e-08 1989,2.0140738,7.8413577,36.567173,88.508575,2.0459982e-08 1990,454.36166,208.12694,32.06143,64.55556,93.00529 1990,518.9261,211.78809,24.835876,47.41809,0.11653324 1990,446.8489,212.12283,18.978607,47.532684,0.09560812 1990,573.6765,245.06157,36.745117,71.37697,0.0951527 1990,563.81616,231.07433,27.717834,70.40819,0.026346428 1990,253.27173,221.77734,29.470581,60.630463,0.020840403 1990,474.62323,207.88077,22.832916,51.262512,0.011126623 1990,394.52322,192.30092,24.743927,55.615128,0.0026163456 1990,622.0068,199.1803,17.139893,53.911453,0.0018756147 1990,366.1247,46.02072,29.032288,67.50397,0.00046664808 1990,617.3781,235.85045,21.768555,78.10191,0.00046586726 1990,611.8493,152.23056,27.297363,129.20938,0.0003810803 1990,617.8944,92.04405,21.252258,74.65747,0.00020315095 1990,583.8206,166.46146,55.134583,187.32904,0.0001749156 1990,1.2862257,181.9191,17.726065,62.92955,0.00013377835 1990,440.05505,165.95871,75.68518,152.5989,0.0001233053 1990,617.96747,361.08508,21.1792,81.08716,0.0001038723 1990,614.73444,11.96058,24.412231,82.07695,6.873149e-05 1990,0.69462407,202.91454,19.395279,128.6375,5.0901326e-05 1990,612.87946,289.55386,26.267212,112.79654,4.487958e-05 1990,1.5958464,91.579926,20.16325,135.13748,3.8275357e-05 1990,613.9687,416.82208,25.177979,87.04022,3.766905e-05 1990,1.4266276,97.84704,13.414721,48.880043,3.2019412e-05 1990,1.6744857,138.83778,33.74547,155.86879,1.5945827e-05 1990,0.8841447,138.42899,13.839199,50.441956,1.1925924e-05 1990,0.62120444,326.70627,18.847021,130.98813,9.252723e-06 1990,571.99286,360.9949,67.15381,128.9111,7.9204665e-06 1990,599.4147,13.247826,39.731995,215.59732,5.0123544e-06 1990,589.6611,5.6030746,46.477417,50.38646,4.8054626e-06 1990,3.2754655,207.67648,56.694637,210.37952,3.7966877e-06 1990,0.69421226,420.90167,16.745008,81.44672,3.412481e-06 1990,2.9721942,342.51413,57.71345,140.85504,7.7522594e-07 1990,2.3335042,37.74788,60.75101,224.43596,3.2155504e-07 1990,2.4528565,26.591251,32.006393,130.39914,2.2993478e-07 1990,509.3022,2.0286818,45.340515,58.817177,2.6465521e-08 1990,1.0975114,10.923535,11.413197,81.37515,2.5039709e-08 1990,565.98096,3.6734002,43.354797,53.973274,1.739363e-08 1990,538.7036,1.275804,44.913452,56.950943,5.697147e-09 1990,375.1138,5.082515,43.295807,50.202454,4.251482e-09 1991,482.60632,212.34445,26.428894,58.566345,34.40803 1991,571.19543,222.85237,27.171875,48.776352,7.303095 1991,505.50543,220.5259,31.653748,85.732956,0.19883041 1991,621.7354,180.61453,17.411255,47.811874,0.0018209801 1991,620.2162,216.63106,18.930481,49.089706,0.0013020084 1991,190.48528,224.58247,30.837067,70.76213,0.0009893151 1991,610.66113,152.52159,28.485535,115.39775,0.0006076799 1991,217.60738,231.77847,22.914246,53.26587,0.0005479656 1991,0.7533309,144.48375,18.191116,79.28548,0.00023511496 1991,585.33124,92.177376,53.32202,214.48865,0.00019172046 1991,410.42734,178.15897,33.912354,76.610825,0.00017895931 1991,617.84656,257.1665,21.30011,82.00589,0.00014732649 1991,468.94174,183.21513,75.17258,164.29509,0.00014380831 1991,617.6777,352.595,21.468994,81.94769,0.00013042166 1991,618.37286,400.33276,20.773804,80.31885,0.0001236251 1991,329.19293,153.59842,32.53842,62.908646,7.459914e-05 1991,602.69635,220.55261,36.450317,200.09973,6.44904e-05 1991,463.44913,72.93442,38.94113,99.44849,5.6591954e-05 1991,609.6204,45.100758,29.526245,149.56691,4.782877e-05 1991,0.5388371,204.3522,18.3534,75.66135,3.005542e-05 1991,605.7428,4.635661,33.247864,55.974503,2.6088861e-05 1991,261.84317,151.05055,71.723755,164.51634,2.5792e-05 1991,1.502845,141.53389,32.759533,173.36238,2.3883336e-05 1991,0.5897925,318.54675,19.333832,131.57507,1.1385388e-05 1991,0.49766195,246.27324,18.340248,75.100784,1.0797906e-05 1991,457.80414,42.27759,79.42609,238.71317,8.34434e-06 1991,1.7912916,74.77649,20.781746,94.57893,6.124478e-06 1991,573.02527,359.9134,66.1214,128.36069,5.404576e-06 1991,0.7281714,422.29068,16.81012,79.809906,3.4426976e-06 1991,3.1964927,210.95201,57.104187,216.17946,1.9264621e-06 1991,4.86833,353.85162,79.713554,132.31052,7.5107386e-07 1991,4.378555,28.64534,78.21163,135.40433,5.57675e-07 1991,2.7005599,429.30573,53.106564,69.75906,3.9957914e-07 1991,4.3961067,99.53366,80.478546,232.50363,2.9316809e-07 1991,421.33563,4.65521,44.04239,51.265144,2.680847e-07 1991,449.3012,5.613597,45.81375,52.84313,9.824023e-08 1991,477.20117,5.526841,45.826477,46.249905,2.6258466e-08 1991,524.44586,4.729325,46.275696,51.01477,2.1722576e-08 1991,2.0697672,12.856136,37.845306,76.99896,1.6904657e-08 1991,572.9503,0.8781315,45.05774,50.045307,5.8634715e-09 1992,526.3369,205.7531,36.489624,80.2563,76.064156 1992,507.23672,209.55896,33.66507,68.32651,0.016792681 1992,125.89468,214.49706,32.78164,71.27367,0.0071974183 1992,621.4027,141.1176,17.743958,53.82016,0.00426017 1992,619.3112,309.8405,19.83545,73.42279,0.0031502573 1992,213.69702,149.02945,39.612183,159.5633,0.0015441594 1992,336.48035,143.41302,34.245117,74.75058,0.0011777116 1992,450.35345,143.74004,35.815735,99.94664,0.0010331045 1992,426.75992,93.28951,41.838806,109.03956,0.0008871312 1992,610.98816,203.46642,28.158508,167.65572,0.0008395934 1992,376.727,39.743294,28.227722,67.279305,0.0007779213 1992,1.2254077,119.15801,17.746191,75.044945,0.0004774865 1992,610.2812,73.79988,28.865479,141.09622,0.00041011476 1992,620.23114,49.266373,18.915527,57.1363,0.0003781933 1992,467.3043,156.76334,25.821228,57.33255,0.0003522025 1992,0.5428036,179.77637,17.982374,72.7209,0.00022224477 1992,602.725,0.44477215,36.42169,69.68092,0.00016060396 1992,382.4014,89.56881,44.02942,147.68066,0.00014249365 1992,616.9589,186.33798,22.187744,80.92673,0.000121652294 1992,493.9377,164.14279,99.16269,182.01785,7.584751e-05 1992,609.0122,438.22375,30.13446,66.370636,4.3111813e-05 1992,588.3448,278.63028,50.80188,189.61905,4.0908482e-05 1992,1.6167921,111.39266,32.65597,170.18869,3.551128e-05 1992,0.46007407,217.84862,19.903723,136.42451,3.0153438e-05 1992,0.6111068,317.06836,19.324772,132.76862,1.270769e-05 1992,3.8112695,155.33022,55.997612,210.99165,9.149253e-06 1992,401.84763,38.927788,82.251434,225.30975,7.5305657e-06 1992,0.69045573,400.1401,20.39468,98.81183,7.2444363e-06 1992,353.89734,24.559607,39.38144,92.7192,3.870825e-06 1992,390.2829,12.21696,45.42035,156.12198,8.8405335e-07 1992,5.085534,355.3053,79.28233,130.51544,7.537129e-07 1992,1.494362,17.932585,19.08901,128.94768,1.7970365e-07 1992,517.46704,0.0,44.59424,54.791473,9.598976e-08 1992,545.3499,0.0,45.658875,57.698425,7.569584e-08 1992,0.8732015,8.82696,10.134914,83.296455,2.9642916e-08 1992,572.71954,0.0,48.04059,49.463642,2.0358538e-08 1992,395.14728,1.7602003,45.39502,53.85917,1.651577e-08 1992,358.19455,0.9563981,47.67227,53.26645,1.5884394e-08 1992,490.06027,0.0,45.392853,49.640717,6.8564128e-09 1992,421.1025,1.4326384,43.955933,48.385395,9.974628e-10 1993,594.00586,226.34673,37.961792,66.74681,69.83868 1993,591.6836,115.85071,44.49884,196.5871,0.36941686 1993,605.66034,95.32335,33.217896,77.801895,0.14939614 1993,620.87463,234.87485,18.272034,56.9953,0.14305823 1993,71.68848,221.97018,34.04093,73.40433,0.10104453 1993,557.99884,54.652386,49.937195,142.45888,0.05472101 1993,173.61964,184.98807,43.19049,138.08542,0.01167155 1993,618.7932,140.87192,20.353455,73.90233,0.0027811765 1993,531.4653,0.0,107.6814,272.76965,0.0020221025 1993,368.53073,196.30275,24.105438,53.17285,0.0017865674 1993,425.8032,186.21617,32.117493,67.6611,0.0015541646 1993,618.4745,185.27988,20.67218,65.50104,0.001327655 1993,301.0662,110.61433,43.997284,130.36795,0.001226125 1993,440.3618,97.830055,44.253265,134.92679,0.00072802266 1993,392.31097,37.462143,42.575043,114.443405,0.0005712177 1993,600.8212,214.08597,38.15039,176.3964,0.00038521658 1993,1.3465023,120.339745,18.072552,74.58959,0.00027559846 1993,581.7979,11.617533,57.348755,121.8308,0.0001453081 1993,0.93844974,151.64415,19.812983,127.818405,9.0920345e-05 1993,368.8511,143.02617,35.246185,86.22618,7.122313e-05 1993,604.4431,0.045113932,34.703552,66.14787,4.4482324e-05 1993,610.04614,436.93423,29.100525,68.07739,2.4282868e-05 1993,3.190542,132.39151,59.150074,204.75589,2.1185673e-05 1993,604.4096,332.72552,34.73706,153.35126,2.0819938e-05 1993,0.6565682,227.655,19.972597,132.5806,2.0101088e-05 1993,0.49460775,317.02335,19.665386,133.62936,1.0332402e-05 1993,305.53882,107.08836,27.153687,63.11583,4.509934e-06 1993,0.7187858,422.08627,16.96145,79.87686,3.791463e-06 1993,379.48495,0.0,88.48468,206.89888,3.0028432e-06 1993,2.8858578,242.16397,57.357082,219.09045,2.8927122e-06 1993,366.86487,2.9585238,46.915802,55.148365,1.9402312e-06 1993,2.9223228,395.614,56.218044,97.52911,5.723216e-07 1993,413.60898,0.0,48.18457,59.218452,5.045492e-07 1993,2.0002003,65.234245,36.418076,188.4815,2.9970624e-07 1993,439.7205,0.05822754,45.153473,53.16191,8.429339e-08 1993,511.36215,0.0,47.105377,51.62524,5.720637e-08 1993,476.86523,2.5305274,42.973694,49.630188,2.0801968e-08 1993,1.817308,6.6526694,37.300446,82.566315,7.4115984e-09 1994,585.1063,136.37843,32.410522,72.70401,0.64208186 1994,191.5065,217.98448,26.323181,58.047318,0.4267711 1994,570.58936,58.78032,50.753418,174.07828,0.14116639 1994,45.80508,214.30406,36.883656,81.57356,0.07199235 1994,421.42947,117.550156,47.13141,122.43532,0.066223904 1994,335.89526,78.55245,31.672882,57.63114,0.05352675 1994,618.40265,159.12514,20.744019,56.193344,0.032501727 1994,615.8067,68.40574,23.339966,68.42036,0.019825453 1994,333.5959,177.86063,25.577026,48.720627,0.0066540213 1994,609.33514,163.61148,29.811523,133.1582,0.0064115864 1994,604.66907,69.51276,34.4776,158.07104,0.0038669324 1994,302.42273,80.21333,26.562134,58.86342,0.0030971798 1994,0.9131144,147.23665,19.384792,86.42789,0.0016168387 1994,575.2373,141.42142,22.516357,63.94017,0.0011655205 1994,3.7258725,86.02961,55.896133,222.28561,0.0008620673 1994,519.10785,7.3414717,116.45453,311.73364,0.00082783034 1994,1.5262085,100.112404,19.82228,88.133125,0.0007015095 1994,581.49164,2.943939,57.65503,134.93272,0.00045273837 1994,0.7186776,189.6892,19.527723,133.85887,0.0002887696 1994,612.6085,268.2359,26.538147,134.47137,0.00020606632 1994,583.95105,173.35995,54.046265,239.13217,7.841951e-05 1994,489.44522,0.0,47.040924,95.76025,6.698538e-05 1994,610.1572,436.6365,28.98944,68.47034,2.9370398e-05 1994,606.01416,0.0,33.132507,69.12428,2.6294281e-05 1994,0.56925786,287.5675,19.937956,134.96732,1.9789411e-05 1994,602.89954,339.1836,36.239807,147.37424,1.9524827e-05 1994,398.9444,50.039207,104.180145,232.69632,1.5848676e-05 1994,2.6516619,251.86238,58.066845,216.62877,1.1511577e-05 1994,0.64046633,421.0473,17.064901,81.16223,4.0717755e-06 1994,1.0257275,341.2895,32.50997,147.02286,2.9379967e-06 1994,477.9739,0.0,101.31656,172.65215,2.250244e-06 1994,349.60718,1.8442091,45.979736,61.01737,1.7795126e-06 1994,517.8864,0.0,45.659058,80.32323,1.3154931e-06 1994,461.11752,0.9209538,43.472015,82.84978,8.8699215e-07 1994,317.59357,2.931683,47.997833,59.595127,1.0491061e-07 1994,402.45093,0.92157227,44.55493,59.48107,7.969526e-08 1994,436.40543,0.0,47.250214,50.82739,1.57956e-08 1994,236.34436,2.1732602,46.78647,47.544125,1.3376256e-08 1994,1.9699594,5.886706,37.415623,87.19855,6.9179293e-09 1994,293.68277,1.1463476,45.548584,47.64195,2.5409332e-09 1995,204.17018,226.84157,24.647202,61.362656,1.0187073 1995,570.4405,71.52315,64.14514,202.55092,0.3506571 1995,602.22986,89.320526,36.91681,115.55171,0.07737357 1995,46.130478,220.53888,36.96425,80.35596,0.06746076 1995,581.8803,109.45651,31.53064,92.10971,0.021904716 1995,347.91882,169.56566,34.68985,72.25903,0.008306219 1995,326.87323,59.19125,29.606567,59.8521,0.005113672 1995,84.126015,218.78456,32.580276,77.48918,0.004438126 1995,1.3308513,159.80219,18.782051,80.092926,0.0032741583 1995,621.1528,86.92435,17.993896,62.07686,0.0026014477 1995,323.33224,71.141945,48.958527,158.26227,0.0020058618 1995,624.5421,192.78836,14.604553,46.249817,0.0020025056 1995,188.31303,190.17789,57.90721,121.03076,0.001966336 1995,428.50375,208.46759,23.272675,53.29071,0.0019587693 1995,617.1917,144.227,21.954956,76.041885,0.0012966197 1995,359.5425,89.01073,25.89563,58.12491,0.0011834649 1995,624.6475,222.13556,14.4991455,47.63864,0.0008423981 1995,618.08466,247.28514,21.062012,72.23842,0.00073738437 1995,5.6400557,87.05952,70.68011,197.82608,0.00068489334 1995,606.53754,180.25949,32.60913,101.097046,0.0005061069 1995,1.8238379,136.03055,33.892303,170.69147,0.00043442255 1995,326.87607,118.37984,29.33017,67.84787,0.0002784894 1995,449.2309,48.406185,37.96225,83.18808,0.0001670501 1995,0.8569206,127.54711,13.652193,49.93081,0.0001478718 1995,0.7354313,223.5936,17.738726,74.14517,0.0001302813 1995,612.20953,271.6691,26.937134,139.75558,0.00011178741 1995,1.2451746,56.26913,13.43044,49.48287,0.0001017768 1995,580.1922,0.078541666,58.954468,144.83206,4.7189707e-05 1995,3.1726742,182.44708,57.59246,217.45767,4.2179767e-05 1995,609.5102,436.1991,29.636475,68.75018,3.0713578e-05 1995,1.2148495,76.19254,18.60146,79.5523,3.003296e-05 1995,0.56662846,272.90906,20.098938,134.74576,1.859316e-05 1995,0.5883586,365.6356,19.657263,129.90573,1.0239976e-05 1995,585.6002,385.15527,53.546448,106.454895,7.856311e-06 1995,394.76328,25.117998,38.89633,71.21008,3.5157448e-06 1995,2.9239764,322.8448,57.59615,158.88177,1.3871181e-06 1995,1.4709929,8.117077,37.737564,188.89018,4.7013998e-07 1995,1.2696095,5.5839095,21.817865,62.67089,1.7599776e-07 1995,497.24127,0.0,50.62323,58.71377,2.9277445e-09 1995,339.92157,6.217479,43.97223,47.101696,4.4582163e-10 1995,526.5182,0.0,49.500183,56.911057,2.5571111e-11 1996,45.08037,222.6905,37.924587,82.38643,1.602611 1996,215.73207,244.02118,22.96965,45.12506,0.111003906 1996,527.15466,166.91089,34.92578,83.01489,0.02529185 1996,608.62964,237.34485,30.517029,182.92258,0.013728104 1996,82.798225,217.61133,33.342117,78.49838,0.0024196412 1996,613.37445,9.050381,25.772217,91.294205,0.0016959024 1996,618.366,337.04062,20.78064,78.35632,0.0009246014 1996,622.00323,182.50714,17.143433,51.539536,0.00082262524 1996,385.07394,196.16008,31.679718,57.232193,0.0004602619 1996,1.1346709,159.96991,13.446016,48.390472,0.0003596114 1996,355.90833,72.83118,39.568085,101.95476,0.00029314868 1996,611.8446,153.86931,27.302063,121.53606,0.000275553 1996,512.2098,134.3401,71.16022,180.01479,0.00025008956 1996,584.5335,52.298008,53.947754,271.18518,0.00016152307 1996,386.6731,75.22881,36.753204,91.16602,0.00015274336 1996,608.79895,42.799812,30.347717,150.16263,0.00013429478 1996,1.4500188,157.20972,19.923216,127.64444,0.00012519571 1996,3.8489585,110.05258,58.307594,203.29382,7.08156e-05 1996,608.9001,431.65222,30.246582,73.59512,6.175022e-05 1996,1.2153027,128.39938,13.678982,48.332764,5.658375e-05 1996,561.236,12.506485,77.628174,147.31187,5.4736312e-05 1996,555.40515,27.420353,29.934387,74.835464,4.8124344e-05 1996,341.2875,47.56825,81.62979,196.1275,4.3746728e-05 1996,573.65546,347.139,65.49121,137.065,4.0846924e-05 1996,0.77927494,266.30075,17.929066,73.07379,3.6824604e-05 1996,1.8879207,82.56342,21.069489,135.3732,2.7669512e-05 1996,270.81653,53.396236,43.355743,116.312546,2.0613676e-05 1996,435.50674,43.951645,26.398956,59.547966,1.6983731e-05 1996,1.5545427,213.46964,33.757893,170.9636,1.5053527e-05 1996,0.5650708,289.52985,19.971918,132.45282,1.0045442e-05 1996,0.66575116,367.93787,19.53316,127.57761,7.758594e-06 1996,2.877033,275.38925,57.555805,196.87982,4.807168e-06 1996,575.0134,3.3249643,49.523132,60.289074,3.4156978e-06 1996,518.24194,25.776703,36.019775,71.46787,6.8474543e-07 1996,4.159805,27.625801,79.94695,140.11536,5.2287e-07 1996,1.4522274,51.64741,18.786663,71.81459,5.103584e-07 1996,550.10565,5.123566,48.214417,53.41919,3.1930227e-07 1996,1.4337802,11.641592,27.50468,77.219955,7.841512e-08 1996,420.69827,4.899419,45.24237,46.77594,3.644041e-08 1996,449.2477,3.5527914,43.243958,45.733185,9.71477e-09 1996,328.73413,6.8135076,44.216034,47.850784,9.174439e-09 1996,477.8556,3.0691733,44.61975,47.73413,8.239426e-09 1996,393.686,5.7691245,46.874847,46.643005,8.130676e-09 1996,353.73624,7.2701564,45.64209,51.441803,1.5467196e-09 1997,39.7365,217.4949,40.327045,86.7435,0.20678669 1997,580.9342,198.20477,25.806824,47.16484,0.019579098 1997,211.3845,209.76205,38.87378,88.03265,0.014419908 1997,83.78972,220.94008,33.719948,80.437485,0.002056764 1997,621.74036,179.02104,17.406311,51.236237,0.0017664073 1997,1.0650871,172.39972,13.746645,52.241074,0.0016066236 1997,380.99704,69.33379,36.090515,92.55924,0.0013175947 1997,585.77435,192.26463,52.65222,241.93794,0.0010340745 1997,1.5740439,155.57169,20.393408,125.705444,0.0007884794 1997,4.107085,124.87456,56.4755,192.39568,0.0006065324 1997,389.7167,170.73878,33.679596,62.502808,0.00048923877 1997,617.46356,207.28503,21.683105,83.05209,0.00048082002 1997,621.40906,138.86082,17.73761,49.618027,0.0004470641 1997,444.63257,214.7816,33.606598,69.8098,0.00033956667 1997,611.0975,135.39685,28.049194,119.670105,0.00033394308 1997,442.37726,50.26739,36.648132,95.87932,0.00030842426 1997,620.3373,325.66858,18.809387,58.20642,0.00030085616 1997,65.97983,253.52751,23.753601,53.5504,0.00017276326 1997,611.49915,342.62747,27.647522,110.55124,0.00015217325 1997,1.3844604,118.24828,18.50832,75.335014,0.0001517074 1997,1.0949081,238.33969,17.75008,71.07062,0.00011899708 1997,613.00244,408.58197,26.144226,95.95007,5.9509555e-05 1997,1.4961361,194.54553,33.835304,178.40292,5.9063463e-05 1997,1.8258497,77.37128,31.377516,159.99756,5.175115e-05 1997,608.90704,9.071498,30.239624,120.44558,3.8799284e-05 1997,338.63315,13.505027,39.357605,76.98714,1.128634e-05 1997,584.42676,63.73077,54.553284,226.56372,1.0656567e-05 1997,0.59822595,304.48248,19.556583,132.52377,1.0563395e-05 1997,0.692465,392.47986,18.550243,105.991974,7.006337e-06 1997,369.07193,26.670963,22.560516,52.635685,5.400959e-06 1997,461.72308,3.7097983,47.037872,129.87135,3.7096995e-06 1997,361.78708,3.8994858,46.373993,144.28542,3.031166e-06 1997,2.9842432,333.23352,57.44173,148.4584,1.0042239e-06 1997,1.531989,17.928314,21.525007,128.32066,1.7811075e-07 1997,395.80365,17.906277,38.438995,77.65874,9.494675e-08 1997,418.24997,0.0,84.91879,222.52266,9.076833e-08 1997,351.59665,2.408024,47.53586,54.32753,6.178927e-08 1997,306.43393,0.93619955,45.460358,51.00315,2.7182988e-08 1997,467.95398,0.46404785,49.529114,50.427692,5.411572e-09 1997,239.36667,2.0356104,43.109985,46.340168,3.9816825e-09 1997,264.10364,0.6735889,47.413574,54.108986,3.2588914e-09 1997,441.54916,2.9753125,46.478027,48.979942,1.8895223e-09 1997,7.628368,0.38354167,45.032024,46.55996,1.030939e-09 1997,383.60028,0.0,46.923218,46.51745,6.039438e-10 1997,414.39627,3.3370492,46.254456,49.94512,5.742934e-10 1998,409.8279,191.92223,31.338806,57.86801,2.3488028 1998,24.288248,224.71167,42.186165,78.34845,0.37384504 1998,221.8024,205.11607,24.69902,60.75865,0.06940465 1998,530.4344,119.62266,41.774292,139.0426,0.028617203 1998,196.45541,192.0255,59.58902,121.87215,0.0014974124 1998,1.4296305,209.0717,18.72924,81.28325,0.0010120971 1998,2.345848,82.112,34.83513,205.47775,0.0006945888 1998,523.7918,164.00832,30.69934,73.25856,0.000560697 1998,513.8103,104.069595,115.54755,211.4087,0.00032765212 1998,617.4251,164.05861,21.721558,79.01767,0.00022714073 1998,618.06256,339.05798,21.084106,82.227936,0.00021648081 1998,618.1517,244.60667,20.994995,80.6364,0.00021127674 1998,617.2165,114.79603,21.930176,74.330376,0.00012330154 1998,1.4380974,127.12603,19.943134,91.82668,7.890187e-05 1998,583.0725,8.688529,56.074158,109.65625,7.7109995e-05 1998,600.3925,72.53874,38.75415,180.88199,5.0683833e-05 1998,585.64166,139.81032,52.823486,233.28429,4.8624235e-05 1998,1.55563,202.94424,34.10239,174.74023,4.6298912e-05 1998,609.2878,435.59067,29.858887,69.82712,3.9533188e-05 1998,21.01825,208.9616,84.20378,163.78828,3.7158326e-05 1998,168.12318,142.55449,137.0467,219.69563,3.0200808e-05 1998,601.5639,288.8379,37.582764,180.12036,1.7336983e-05 1998,406.14362,54.024452,26.985626,60.081017,1.5695556e-05 1998,0.6741821,312.22708,19.890882,133.86081,1.0402303e-05 1998,449.39172,3.1483984,40.71933,85.73323,5.503252e-06 1998,1.7860107,16.814526,31.415318,131.10779,4.7004432e-06 1998,0.72437584,422.19287,16.788267,80.037506,3.9794104e-06 1998,482.1749,0.0,47.528595,151.41501,3.267797e-06 1998,2.8914096,313.38223,57.48721,165.63419,1.4574936e-06 1998,2.7132015,429.41144,52.92782,69.71869,4.08863e-07 1998,1.4022291,5.6617093,23.375174,65.2646,1.3689397e-07 1998,365.84735,0.0,123.11636,263.5075,1.0225426e-07 1998,493.3831,2.522383,50.654327,53.42382,3.9735514e-08 1998,400.19138,8.666575,47.307434,185.79524,3.4915836e-08 1998,416.95844,1.7780404,44.389282,49.459827,1.297525e-08 1998,532.21173,4.64639,44.80249,55.865993,2.1266686e-09 1998,557.19385,3.5932505,45.656555,47.360607,1.8298444e-09 1999,205.32697,241.40271,23.17366,46.425537,4.254146 1999,1.0161548,223.528,32.34629,80.08745,0.31982502 1999,483.167,167.22144,36.81671,77.0632,0.05934312 1999,197.16692,201.95518,37.076767,108.86238,0.015463799 1999,470.63333,187.33746,27.84021,56.53363,0.015183598 1999,211.55598,207.65642,20.775406,48.350174,0.008514003 1999,426.86685,185.16208,25.148926,59.137985,0.0032391606 1999,411.63745,155.89787,32.54541,70.01445,0.0013688245 1999,459.4562,133.26862,74.26987,160.02692,0.00085298007 1999,0.82533246,257.9465,11.946428,46.525665,0.0006021567 1999,220.61008,197.99847,23.814987,45.499527,0.0006007086 1999,610.6896,76.17934,28.457092,135.85396,0.00030332597 1999,617.0529,196.60092,22.09375,87.02911,0.00019594195 1999,1.5755949,91.80974,17.713997,66.46936,0.00013420708 1999,618.12714,338.86182,21.019531,82.65579,0.00012711996 1999,0.9084278,252.69604,29.867855,173.88037,0.000113140515 1999,3.7678256,210.28372,77.87409,170.43774,0.000102859325 1999,612.90045,248.82431,26.246216,137.10696,6.984561e-05 1999,583.116,12.473533,56.03064,106.2358,6.6043795e-05 1999,11.158503,253.08011,35.816025,83.954926,5.781257e-05 1999,405.91644,136.1125,76.079834,157.9734,4.725956e-05 1999,609.0422,436.2243,30.104492,69.00156,3.0856732e-05 1999,586.9372,270.51385,52.066895,203.42386,2.8505448e-05 1999,0.9378231,295.07034,18.489902,80.583405,2.5883517e-05 1999,585.99097,130.04573,53.1557,239.82533,2.4491066e-05 1999,172.50677,180.2235,100.761505,201.10057,1.9194058e-05 1999,345.45297,71.86378,39.69809,94.51708,1.7552558e-05 1999,0.6832723,338.95007,19.990765,134.17117,1.4915513e-05 1999,0.94175947,139.28,19.378664,147.64426,1.4588641e-05 1999,0.7268937,421.7006,17.233006,80.52402,4.336847e-06 1999,3.485363,329.78693,57.34077,153.38583,2.1917372e-06 1999,383.83704,76.40132,37.18991,91.01514,1.6327388e-06 1999,1.9378972,19.075659,30.642975,123.66828,1.0154955e-06 1999,1.0498288,9.832061,14.162718,86.43674,6.5741636e-07 1999,1.7647494,0.0,60.373573,263.68808,3.3654487e-07 1999,467.75867,0.0,48.481262,166.17514,3.0018222e-07 1999,367.16705,4.0709863,44.634155,51.888474,7.320576e-08 1999,0.84465337,1.4152117,43.319,59.43474,2.2050772e-08 1999,355.24393,0.0,84.96326,222.82918,9.5792725e-09 1999,317.85004,1.1179346,64.22412,46.96952,2.068248e-09 1999,263.81015,4.3611183,44.664124,46.435497,7.489669e-10 2000,189.82385,244.95802,22.668411,51.22725,35.103085 2000,415.19006,213.39879,23.209625,55.563553,0.98797154 2000,193.74646,211.88942,26.007187,58.989822,0.76973295 2000,518.86017,140.67575,58.46057,132.80396,0.015506883 2000,149.84149,247.30806,30.05046,79.851364,0.007048525 2000,201.15118,197.32188,24.151031,45.08409,0.006760385 2000,435.98022,38.49015,23.577759,50.52494,0.004787364 2000,313.6452,89.67103,38.23642,80.97085,0.0042522955 2000,427.78815,207.33272,23.44812,49.39,0.002234143 2000,172.94873,198.32454,54.843964,119.1808,0.0019773801 2000,460.5785,188.7745,25.365631,54.85466,0.0016195427 2000,583.2243,138.46124,54.83429,270.6402,0.00092222734 2000,621.72107,182.9941,17.425598,50.01024,0.000680326 2000,485.70804,120.44934,43.298065,104.08507,0.00044403673 2000,610.20026,85.96754,28.946411,129.97394,0.0004252699 2000,617.3543,203.51694,21.792358,84.605194,0.0002800469 2000,610.328,294.17465,28.818665,141.22214,0.00027876577 2000,567.2375,338.02124,71.90918,145.89832,0.00010671348 2000,2.919349,221.2588,58.090458,214.67299,8.935085e-05 2000,461.646,91.03025,85.10913,185.0228,7.2137285e-05 2000,1.2720768,269.53464,20.755846,134.13193,6.933486e-05 2000,612.8064,413.67505,26.340271,91.34366,6.463608e-05 2000,0.86174643,352.29156,18.622086,79.76672,5.995182e-05 2000,1.3209465,214.36345,19.0078,83.12755,4.0305644e-05 2000,608.0688,3.0806966,31.07788,58.75711,3.6536396e-05 2000,140.56834,152.28276,137.5738,224.80663,1.970166e-05 2000,360.18076,125.09183,27.278564,62.500458,1.079424e-05 2000,1.4401807,90.70465,18.937748,127.3537,1.0790946e-05 2000,599.80554,5.543262,39.341125,156.67114,1.0066252e-05 2000,1.9325993,118.742645,34.12631,193.79776,8.334749e-06 2000,4.3688216,349.53015,78.478714,137.16412,4.308386e-06 2000,0.7155762,419.90396,17.114405,82.25656,4.1105027e-06 2000,385.1016,42.893883,40.282288,105.45722,3.4479679e-06 2000,2.7634554,426.94528,53.171646,72.191925,3.7781348e-07 2000,1.4240121,15.642507,30.069183,120.45335,2.6477247e-07 2000,434.89078,8.708158,47.429077,103.442894,2.4191317e-07 2000,1.6871746,17.112787,61.160923,244.09549,3.9250867e-08 2000,456.14532,3.3184245,43.60199,57.053375,3.2697514e-08 2000,0.91626835,7.294955,13.134978,90.20426,2.9768135e-08 2000,483.10553,0.87622726,45.470215,60.6097,2.1726926e-08 2000,372.30753,0.0,160.89175,251.38855,1.3550048e-08 2000,416.44617,3.4958317,43.745514,46.91277,9.336315e-09 2000,508.86667,2.6196778,83.60025,80.05702,4.079823e-09 2000,0.59541994,0.27079752,36.06877,55.574966,3.2103993e-09 2000,307.443,5.4092236,43.53949,45.198616,6.6668643e-10 2001,186.35089,200.83588,24.78334,52.651474,3.3564708 2001,524.09485,186.35739,39.8042,98.82211,0.92626464 2001,162.13046,220.59105,36.35199,105.94237,0.2472637 2001,532.16925,244.89748,21.611633,47.5298,0.16027199 2001,508.82758,177.71474,34.872375,84.524124,0.016380766 2001,37.949844,310.532,37.966515,87.371826,0.012895793 2001,621.3311,188.08253,17.815552,54.415726,0.003952056 2001,493.82455,138.18721,74.813385,180.4663,0.0029944244 2001,608.2346,266.33398,30.912048,178.99994,0.0019575853 2001,610.197,133.13321,28.949646,127.3082,0.00089298794 2001,617.56555,215.21655,21.581116,83.92639,0.0004910253 2001,587.7916,139.17978,51.35504,251.1607,0.00034469142 2001,607.8947,434.20206,31.251953,71.24075,0.00025687378 2001,319.34454,58.49059,38.65619,88.74866,0.0002438966 2001,152.5765,158.36589,88.31651,170.53462,0.00022067598 2001,402.4538,57.535927,24.216705,51.422615,0.00020971076 2001,22.377068,263.81714,73.968575,184.01584,0.00013129222 2001,584.8439,375.99808,54.302795,120.43213,0.00012879493 2001,1.9619036,268.29413,33.229492,177.77124,5.3133124e-05 2001,1.1890146,321.47708,18.73021,74.25589,3.849797e-05 2001,607.7284,4.6459017,31.418274,58.253006,3.6201727e-05 2001,3.874489,171.80193,54.053825,215.98375,3.1500156e-05 2001,583.2847,10.746621,55.86194,104.12482,2.5377267e-05 2001,610.99585,43.82163,28.150818,149.81648,2.165059e-05 2001,1.2602865,222.26978,19.396362,83.90915,1.7874323e-05 2001,1.0494125,386.39468,19.439804,112.84589,1.7200426e-05 2001,2.644751,127.204636,29.14331,142.26656,1.0520767e-05 2001,3.849318,420.8669,64.829994,77.390594,2.8812533e-06 2001,1.5770085,48.53743,20.396692,128.73874,2.580999e-07 2001,2.2283432,18.024456,58.170017,105.70735,6.106696e-08 2001,0.85561854,7.174261,13.935017,77.76674,3.0725847e-08 2001,306.18597,3.8524463,44.9653,48.89186,2.4322968e-09 2001,452.35083,0.24743979,63.42572,47.24115,2.0185882e-09 2001,332.61096,4.0625634,45.36917,50.663536,7.5951895e-10 2001,430.64185,2.8696926,43.323273,49.77446,5.4221694e-10 2001,511.4826,2.1282065,43.15796,51.505936,3.8716813e-10 2001,359.75293,6.946574,44.40448,48.836605,1.932512e-10 2001,558.8863,2.4650848,43.48065,50.38004,1.06402505e-10 2002,592.42535,167.52834,35.336975,90.60408,11.2461 2002,580.93427,175.62268,23.14325,53.312485,0.11903129 2002,440.37256,198.62903,35.51297,80.072754,0.030361878 2002,611.8189,149.78949,26.617126,97.751816,0.02828942 2002,623.1383,141.17441,16.008362,46.469147,0.009432779 2002,0.72789145,158.26904,21.650778,115.20123,0.0032682754 2002,423.28745,110.497345,58.389496,148.29993,0.0029066524 2002,461.27512,102.31986,24.678009,58.205086,0.0024971091 2002,0.6705322,149.1304,15.087434,58.33983,0.002255579 2002,439.6708,77.77782,39.943054,97.13339,0.000904033 2002,560.46405,129.26321,78.344604,180.2445,0.0007113473 2002,617.5908,232.97949,21.555847,84.19861,0.00064243167 2002,0.58809817,232.83069,18.11779,74.026825,0.00052389124 2002,108.880066,185.24786,69.29697,177.94543,0.0005227409 2002,597.98145,35.502586,40.36328,220.36972,0.00036744797 2002,2.903265,141.72174,59.675735,235.07056,0.00033923556 2002,0.9543775,254.7449,31.653238,188.04901,0.00030353345 2002,0.39499024,278.8339,18.613829,81.51346,0.00029489104 2002,114.13281,258.28137,37.48915,94.53348,0.00014538143 2002,130.10278,109.38801,75.51022,186.10025,9.9299585e-05 2002,607.0875,2.407002,32.059143,63.157284,7.5201155e-05 2002,612.5727,261.3221,26.573975,146.92813,3.740786e-05 2002,609.15326,437.4551,29.993408,67.92819,3.3182263e-05 2002,0.76655763,385.19077,18.429771,110.00711,1.6018193e-05 2002,449.37213,7.5791993,46.061005,138.65659,9.760902e-06 2002,570.7002,354.74353,68.44647,134.76367,6.099066e-06 2002,4.5121584,340.71683,77.6711,139.91501,3.4182135e-06 2002,0.9245744,38.47446,20.785128,145.28148,8.960623e-07 2002,318.02768,0.0,49.648346,137.78123,6.353803e-07 2002,2.7669044,423.41943,59.169037,74.175385,5.356208e-07 2002,474.1634,0.17415366,49.26886,164.56804,4.8133677e-07 2002,497.4265,1.5629655,44.1308,50.825123,1.6215436e-07 2002,0.694399,10.613695,11.831977,81.036156,1.1072826e-07 2002,304.19995,1.5004883,48.968628,56.8435,5.5713567e-08 2002,358.93958,0.0,48.4292,51.249905,2.067058e-08 2002,2.1770346,14.691433,59.193733,109.24196,1.9584702e-08 2002,436.72595,1.6207845,44.505096,56.02742,1.5775697e-08 2002,407.92706,1.132666,45.83493,59.258286,1.4667033e-08 2003,616.02496,60.99271,23.121704,67.85928,0.024813853 2003,621.9274,153.27138,17.219238,49.48297,0.01428783 2003,556.7681,169.96452,43.705994,117.35658,0.01029469 2003,435.14932,215.71599,24.392578,55.630875,0.008255587 2003,563.34863,159.2565,27.510925,61.42238,0.007799142 2003,0.0,290.2194,46.043255,174.15063,0.0061498284 2003,45.145046,190.96985,25.01514,53.7912,0.0057270387 2003,621.5951,257.14496,17.551575,58.112946,0.0050470666 2003,612.1368,157.0471,27.009888,110.58443,0.0021645357 2003,579.70386,122.602425,33.149963,86.21841,0.0016073072 2003,1.1204541,295.80554,21.725609,86.8472,0.0015431368 2003,585.3197,144.98077,52.504395,202.09845,0.0014342852 2003,576.5641,229.07545,26.534607,61.10234,0.0011234841 2003,139.24472,176.14647,40.70146,105.951706,0.00072409655 2003,4.091364,142.35045,56.548073,275.89142,0.00058338896 2003,581.8756,3.0204363,57.271057,127.85279,0.0004257317 2003,564.11694,73.4587,61.52893,181.65405,0.00040220734 2003,600.61633,54.807007,38.530334,178.38269,0.00026720637 2003,544.9772,113.440056,43.567688,124.750374,0.00024962972 2003,617.13885,21.418734,22.007812,71.840614,0.00015093102 2003,0.78240967,189.85838,18.17393,77.51381,0.00012904228 2003,0.84168947,119.18125,19.00545,88.19578,0.00012148929 2003,612.73035,282.4326,26.41632,145.18729,9.6055206e-05 2003,0.47172284,359.4214,21.491,142.44046,6.692906e-05 2003,1.3803092,167.13919,29.080238,166.3216,5.5052828e-05 2003,612.50494,365.7024,26.641724,130.23563,3.0750038e-05 2003,286.89572,150.80373,104.10593,202.05257,4.9547716e-06 2003,1.0597111,19.476074,20.724081,153.0435,1.8592411e-06 2003,382.10648,0.0,47.67807,131.18167,5.898733e-07 2003,556.16364,420.81265,77.84375,76.658295,5.5591414e-07 2003,1.8314258,8.524368,59.4502,132.48941,2.2766812e-07 2003,368.76996,0.27648765,46.58838,57.386375,7.283903e-08 2003,314.1762,7.5109196,43.79306,49.524956,5.9138927e-08 2003,530.97235,0.0,49.836548,156.7274,9.298656e-09 2003,399.95346,2.6612906,44.644196,49.45457,9.259702e-09 2003,475.4755,0.72127604,47.656464,174.04053,7.169086e-09 2003,512.26,1.8362142,43.231262,49.8484,5.6068745e-09 2003,441.5308,0.0,44.567047,45.604095,2.3406388e-09 2003,556.7203,3.5619533,44.365173,51.067707,6.941982e-10 2004,603.5606,176.24623,34.626892,81.64513,0.41570905 2004,566.83777,196.98378,28.849365,65.06465,0.088200025 2004,194.93256,231.87875,25.292236,60.65912,0.022337291 2004,619.0798,209.36113,20.066895,66.129684,0.020331295 2004,64.41945,210.8062,31.75093,72.547195,0.01661406 2004,596.2028,181.6364,19.912415,47.989883,0.013266543 2004,578.9044,196.57253,34.758972,89.559006,0.0095603075 2004,623.978,163.75142,15.16864,45.371445,0.0068886657 2004,573.2127,133.9023,63.358032,177.06163,0.0042959815 2004,46.030678,155.02682,45.9588,141.9383,0.0026332892 2004,303.451,189.17758,45.82135,88.86783,0.0005630542 2004,4.1481333,119.78716,57.523106,214.67267,0.00040892905 2004,1.7556152,191.62326,21.396627,104.52881,0.00018125921 2004,0.951416,130.22835,18.551289,77.66203,0.00016221621 2004,599.73663,216.29924,39.410034,214.99794,0.00012416411 2004,1.6061182,91.71198,30.560432,164.17438,0.00010330328 2004,612.77997,339.63458,26.3667,140.23178,4.7943304e-05 2004,1.6725049,54.4719,22.03194,112.27157,3.176363e-05 2004,609.36237,437.35303,29.784302,68.59262,2.8687296e-05 2004,1.792728,229.14381,32.563423,187.5854,2.4155877e-05 2004,608.216,2.6979687,30.930664,59.66227,2.293021e-05 2004,608.9198,38.234818,30.226868,159.82487,2.1533999e-05 2004,19.845219,56.46207,90.586945,231.02637,2.112829e-05 2004,300.14984,153.70193,89.29938,197.40833,1.2287478e-05 2004,0.4456006,316.57654,20.367733,138.70184,1.1580208e-05 2004,0.77331465,422.75308,17.67862,80.19812,3.2883925e-06 2004,1.7828223,15.427429,59.77589,119.17622,1.1266268e-06 2004,2.9974675,399.11984,55.830914,96.147156,8.281502e-07 2004,1.145472,7.0764356,15.663849,77.31813,4.997231e-07 2004,348.50906,4.178561,46.674408,153.71642,1.1911898e-07 2004,357.84726,0.0,47.707794,51.386833,9.643117e-10 2004,515.59186,0.0,46.076782,56.189,6.6023735e-11 2005,258.5682,208.736,35.19113,60.80728,0.16726586 2005,580.0888,218.56625,26.228577,57.346344,0.068676546 2005,98.7536,251.99149,48.37419,144.60806,0.049950585 2005,620.74756,241.28479,18.399109,57.57492,0.023936624 2005,129.69127,241.9519,45.584015,123.79843,0.012667976 2005,594.688,214.32625,20.171509,46.748795,0.010903895 2005,610.6111,225.5174,28.535583,156.19553,0.0061540357 2005,574.245,157.56926,62.57135,168.57335,0.0027453569 2005,609.89746,166.90291,29.249207,114.58501,0.0018283376 2005,315.09766,198.30264,46.060577,86.35846,0.0007064292 2005,56.59989,219.9519,118.83404,218.10822,0.0004413735 2005,1.0625293,191.89035,18.27099,80.543976,0.0003150345 2005,611.2309,88.669426,27.915771,135.9487,0.00019922372 2005,611.2008,309.80945,27.945862,159.12317,0.00019669383 2005,18.917036,86.28738,25.389353,54.296196,0.00019495423 2005,4.2523847,161.70163,56.070015,235.68701,0.00012277492 2005,609.5413,7.302995,29.605347,143.81993,7.123753e-05 2005,4.2295117,307.00073,55.87416,166.32568,6.0956474e-05 2005,317.9165,164.5055,86.85446,152.02515,5.0720002e-05 2005,608.3903,435.76547,30.756348,70.17047,4.0567375e-05 2005,2.409813,95.798645,36.403927,190.21304,2.690002e-05 2005,1.3121802,361.48227,19.528185,129.02899,1.9988378e-05 2005,1.3951905,269.48016,17.387583,136.42401,1.1945389e-05 2005,2.8827996,17.806355,58.551125,117.92345,5.2853716e-07 2005,2.6138427,424.56763,53.18297,74.25931,4.2666937e-07 2005,1.0260881,9.893193,12.408743,86.69519,7.7667295e-08 2005,420.79166,2.3726985,46.203033,55.386234,3.1464452e-08 2005,0.981447,3.230316,41.581985,54.78371,4.330393e-09 2005,393.664,4.8277183,45.9433,55.245747,3.4308734e-09 2006,321.5048,216.14894,25.246277,52.95314,99.8218 2006,352.2005,214.5593,24.180603,50.516663,99.75439 2006,613.47,220.29573,24.333069,46.84282,0.102818504 2006,476.3755,195.30023,40.707764,104.87073,0.08190407 2006,415.516,201.54562,21.832703,53.46103,0.03053396 2006,244.34001,226.49628,26.768387,60.479492,0.025512071 2006,21.407764,333.38776,31.028828,63.53711,0.022702085 2006,12.910734,298.93326,44.66548,151.84512,0.013242113 2006,258.2638,221.14412,32.3356,63.339798,0.008154498 2006,235.5379,213.38121,25.896667,51.012527,0.008014782 2006,444.3022,192.23795,76.56445,191.31482,0.006129741 2006,595.7074,216.98885,34.275818,80.688095,0.0051603373 2006,611.24744,125.01064,27.899231,135.5679,0.0013736972 2006,1.0560441,310.28964,23.994947,174.45422,0.0012874044 2006,3.133877,71.11287,33.484272,74.642136,0.0010843205 2006,587.6749,149.76689,50.438965,189.67455,0.00073740404 2006,620.1928,238.70436,18.953857,58.939774,0.00063898065 2006,611.99524,231.74472,27.151428,143.1146,0.0005233609 2006,1.0284017,281.4094,125.204544,195.999,0.00050767406 2006,304.2213,196.96877,64.706726,91.88982,0.00037987417 2006,0.61631024,151.44968,18.295631,81.27405,0.00030886312 2006,78.637115,285.92603,80.5919,165.49435,0.00022984056 2006,2.0898275,198.36594,35.30079,212.35501,0.00016532198 2006,1.7149073,102.03045,18.630928,76.04388,0.00014902999 2006,1.7012142,104.19722,29.187143,162.1522,0.00014447115 2006,608.66016,5.078724,30.486511,153.99191,4.289384e-05 2006,604.11316,340.0482,35.03351,150.94324,3.763738e-05 2006,609.2578,437.62344,29.888855,69.34302,3.111803e-05 2006,341.18457,167.29,85.83475,160.36281,2.3431132e-05 2006,0.3992741,410.3955,18.507301,100.59451,1.2467797e-05 2006,0.9617033,8.155033,12.949319,75.97911,6.502119e-08 2006,1.226971,2.2810645,31.446514,49.20785,1.0833827e-08 2006,475.88116,3.7750456,43.207153,46.341274,2.6169142e-10 2006,449.7957,2.2493978,44.093445,61.36972,2.653012e-11 2007,303.1047,203.8008,32.14084,69.18651,99.93125 2007,347.05994,204.84634,25.658783,57.375885,99.694855 2007,207.54324,229.81248,29.492798,66.25346,1.48775 2007,235.69662,203.83311,25.474731,50.848114,0.13702726 2007,293.6231,199.85983,25.546082,52.913406,0.076970614 2007,612.4944,221.00906,24.025024,50.486572,0.031102978 2007,323.07733,206.11136,24.18512,53.30771,0.013087123 2007,4.9805374,239.63594,85.37563,203.14668,0.00648536 2007,331.59854,185.22636,56.50357,96.19878,0.0012611287 2007,610.93634,128.00125,28.158386,133.28934,0.0010421331 2007,608.43634,0.0,30.710327,95.641235,0.00090617716 2007,1.395485,270.72037,32.7798,181.63214,0.00021762507 2007,0.68,358.20868,19.646582,80.542175,0.0001942922 2007,582.76196,20.14237,55.10681,237.70505,0.00013756589 2007,612.2773,244.03906,26.869385,118.2146,0.00012666326 2007,1.3572721,144.21648,18.442307,132.88487,9.72086e-05 2007,287.15176,172.58812,71.48822,143.92632,8.712884e-05 2007,609.2452,439.4295,29.90149,63.66873,4.0023995e-05 2007,4.008607,86.46484,58.970886,266.56064,3.9908584e-05 2007,588.1489,184.21555,50.99774,227.79208,3.840904e-05 2007,2.3998177,68.92403,29.370443,158.98035,3.8386337e-05 2007,294.8906,127.438324,141.29257,218.86029,2.1497268e-05 2007,611.9281,339.034,27.218567,139.92413,2.1162528e-05 2007,481.56058,221.14693,83.09067,234.27162,1.3042907e-05 2007,3.1244857,377.9571,57.397263,110.83359,8.643492e-06 2007,0.55973065,417.75513,17.030611,83.7052,4.8454813e-06 2007,2.6778076,18.947638,58.736946,120.08604,4.1171174e-06 2007,572.38855,415.1781,65.14972,81.621765,3.1219088e-06 2007,1.1829215,10.867986,13.331582,83.72449,2.1950157e-07 2007,558.51685,1.0513769,48.376648,46.43992,1.14695915e-08 2007,1.4875196,1.492365,39.4797,50.77449,5.1612767e-09 2007,223.88432,4.1749463,42.863174,47.850174,1.0987477e-10 2008,226.44974,202.22418,36.32733,82.68237,99.90177 2008,283.92557,203.70631,32.553528,73.70653,98.97909 2008,42.304707,188.75186,36.539776,58.94153,88.50718 2008,542.47705,220.9592,24.184875,47.52591,0.0012677913 2008,611.0024,168.89412,28.144287,113.58531,0.00073806895 2008,0.9145272,201.24007,18.137558,73.538895,0.00047610965 2008,160.17365,200.69814,23.773254,46.073273,0.00020448765 2008,1.1323714,112.85683,19.369268,132.05482,0.00013628919 2008,264.06235,175.24542,73.83264,143.79782,0.0001333777 2008,587.5943,200.85283,50.331787,229.32454,0.00012241598 2008,611.13885,371.67886,28.007812,120.644684,0.000120579956 2008,195.45335,168.40843,94.73311,166.7199,8.85754e-05 2008,3.1700847,90.1079,59.06308,218.90881,5.9050166e-05 2008,611.7891,19.27336,27.357544,128.07692,2.2798118e-05 2008,1.4159994,202.95201,32.84845,179.88762,2.2620356e-05 2008,1.2536523,4.946146,82.55621,179.30539,1.0064793e-05 2008,0.84620935,331.02182,19.322266,125.68536,9.7112825e-06 2008,586.39435,81.881485,52.466187,221.5131,8.072094e-06 2008,0.8035718,11.430424,21.527353,154.86253,7.634696e-06 2008,0.4488851,1.2637599,46.122097,64.1679,7.3247306e-06 2008,0.8494808,439.85184,21.812592,62.442535,2.8483105e-06 2008,3.2718816,260.08505,57.323334,206.80267,2.1662024e-06 2008,70.687744,2.429206,42.65145,48.66363,2.105674e-07 2008,95.46497,0.49541342,47.839592,63.650093,1.0774643e-08 2008,134.61755,3.7867043,43.84024,49.27208,1.0708538e-10 2009,92.7448,200.74843,55.1882,127.492905,99.85838 2009,184.23875,217.0538,41.087738,85.21747,51.549328 2009,263.05142,186.79347,34.917297,69.59056,0.26633823 2009,599.0348,232.44727,23.529602,49.184937,0.19821368 2009,205.98483,234.08618,25.004913,47.67517,0.18711604 2009,187.74509,255.41586,23.104065,56.103577,0.03543975 2009,612.4316,236.23677,26.047913,50.308548,0.03306318 2009,600.94336,189.15817,37.473145,137.8132,0.0021331988 2009,621.5376,130.5532,17.60907,52.65442,0.0013038972 2009,616.2553,145.7068,22.891357,89.37335,0.0001377443 2009,586.7754,233.92293,51.736267,232.0529,0.0001375491 2009,616.8877,78.6281,22.258972,83.599045,0.00013603584 2009,1.5335042,115.25123,29.51594,153.51276,7.416835e-05 2009,612.25037,365.58328,26.896301,129.93704,7.2501585e-05 2009,615.6584,325.43817,23.488281,84.201935,5.0244773e-05 2009,57.52056,126.4124,136.61188,275.44366,4.76354e-05 2009,586.16406,45.849754,52.35199,235.05551,4.70238e-05 2009,0.9490186,243.67764,18.847921,78.7695,3.068483e-05 2009,0.76847905,343.08505,19.529686,127.15683,8.71464e-06 2009,1.0049332,271.42648,27.958967,146.88486,6.2081235e-06 2009,587.13666,22.03035,52.01001,94.10727,3.400941e-06 2009,0.7343482,420.01782,16.66173,81.05536,3.0870208e-06 2009,2.4382243,174.98917,57.038456,231.31241,2.3410532e-06 2009,1.3715552,44.402195,20.493568,134.09984,2.0608204e-06 2009,5.611533,358.94415,78.96027,125.180664,7.2952525e-07 2009,1.5872803,11.657298,59.98668,127.44245,6.904214e-07 2009,0.75127894,6.7614813,12.9121475,92.30117,2.3752245e-08 2009,1.0802035,2.1014438,32.39107,50.44095,5.056605e-09 2009,552.5219,4.381525,45.18164,45.43142,4.283405e-09 2010,0.0,205.76193,45.812958,137.75928,90.158936 2010,70.3342,203.16306,35.224426,68.133514,29.319927 2010,374.52536,229.3861,28.902527,47.19899,9.72206 2010,415.4068,234.79001,23.919617,45.071716,1.567782 2010,13.319408,234.12334,27.572975,53.18657,0.052253418 2010,612.2599,143.57729,26.328857,56.065796,0.027121847 2010,0.46601972,242.42105,14.567044,54.339752,0.012986427 2010,8.509152,267.90637,25.844826,61.000916,0.008087849 2010,621.1805,206.14117,17.966187,51.82837,0.0052084806 2010,0.6519975,278.02356,14.624075,54.028687,0.0033105602 2010,623.80634,171.81241,15.340332,46.345627,0.0020961554 2010,0.71727055,183.28896,20.920044,98.94325,0.0012831315 2010,600.9025,92.90418,38.24414,166.46481,0.0007821942 2010,322.0869,196.20418,34.49298,52.11731,0.0005525219 2010,612.3268,374.17737,26.819885,119.28128,0.00037328602 2010,0.0,168.87355,91.08214,235.21236,0.00027249262 2010,611.3836,181.58762,27.763062,142.77597,0.00025952773 2010,0.69827396,298.41718,19.948467,95.206665,0.00014795418 2010,1.5765593,83.94055,29.7795,166.5755,9.281744e-05 2010,587.3727,183.74623,51.773987,268.91687,9.2289265e-05 2010,40.805763,158.80472,103.88758,198.68832,3.2608183e-05 2010,616.2389,55.09888,22.907776,92.88118,3.2196887e-05 2010,611.94556,263.9911,27.20111,149.7442,2.2083968e-05 2010,0.59473634,362.83054,19.557928,134.57837,4.4514445e-06 2010,587.0812,19.717884,52.06549,107.414276,4.3491896e-06 2010,2.3524952,332.65982,57.487144,152.5404,1.3780256e-06 2010,1.4392074,29.22459,20.572466,136.40457,6.7256514e-07 2010,2.1375132,20.141752,56.529057,110.26051,4.2003526e-07 2010,0.96102583,15.285736,13.456043,73.986176,5.078335e-08 2011,297.19016,233.73097,25.839844,48.442215,0.53479975 2011,618.49274,386.1981,20.65393,71.47394,0.0028125132 2011,622.8131,221.66331,16.333557,47.039536,0.0022870924 2011,602.6656,257.15878,36.48108,209.32935,0.0017090503 2011,511.827,260.22504,21.213898,46.14679,0.0005890039 2011,616.00287,237.84404,22.822937,74.239395,0.00056691247 2011,1.1104321,155.59409,18.024794,73.592606,0.00053240923 2011,172.74934,208.20222,31.258484,68.98329,0.00044997406 2011,0.7658504,197.42072,18.254955,73.1579,0.000361605 2011,192.61745,210.18959,31.234299,64.61742,0.00034165886 2011,587.44977,389.60132,51.6969,99.77301,0.00023416529 2011,616.9923,307.1521,22.154358,89.08768,0.00013981154 2011,612.3025,82.01745,26.844177,103.91328,0.00010842272 2011,0.9663143,103.357925,14.121248,52.69185,9.820244e-05 2011,2.1910563,97.45916,34.53976,174.87619,7.328796e-05 2011,0.7354232,411.3795,13.588912,51.80246,4.1139036e-05 2011,604.15656,116.88701,34.990112,160.58522,2.4180079e-05 2011,0.7884782,361.62823,19.869577,134.27408,2.0669031e-05 2011,63.53379,63.692783,36.383453,81.18892,2.031911e-05 2011,1.1442856,188.4368,33.483643,178.24313,2.005934e-05 2011,0.40035564,241.65477,18.599192,79.98616,1.9847283e-05 2011,610.7584,10.29573,28.388245,115.24744,1.949725e-05 2011,617.3805,150.07404,21.766174,75.08737,1.8802612e-05 2011,315.9245,178.00603,83.56137,166.10988,1.4931195e-05 2011,0.55521566,332.3074,18.134075,70.56564,1.0299818e-05 2011,5.6388154,146.5538,77.96791,271.9098,1.0146596e-05 2011,0.5834912,290.4918,18.285345,72.664795,6.417909e-06 2011,1.2141536,455.15872,28.122742,50.285156,6.2223016e-06 2011,2.687422,396.9681,56.068657,97.84512,4.3611676e-06 2011,579.382,1.1680453,46.184753,47.364464,1.7091061e-06 2011,1.379047,22.370775,19.649204,141.45999,3.0612964e-07 2011,2.8412013,17.515224,55.993526,115.064514,2.4211397e-07 2011,484.3287,7.002477,43.639984,46.659393,4.2879684e-08 2011,0.8390271,10.082846,10.707903,82.004135,3.016842e-08 2011,545.475,6.9062,48.58838,51.343918,2.4631058e-08 2011,518.93744,8.293346,43.756836,47.478172,1.7764112e-08 2011,1.483488,8.339977,29.779873,56.351192,8.2340845e-09 2011,95.66455,2.0893636,40.881622,47.62308,4.4736337e-10 2011,156.31148,0.5880518,42.281143,47.820217,1.9170089e-10 2012,203.4309,225.48419,37.579666,56.568634,0.97557455 2012,155.84549,220.7696,34.41835,56.382004,0.15147287 2012,0.68395346,163.49812,14.894049,59.81311,0.055513065 2012,2.1820085,154.2275,42.36704,94.40637,0.008883018 2012,90.11564,212.26828,41.823265,67.8703,0.008574647 2012,623.17773,233.66983,15.968933,52.28055,0.004537225 2012,0.90387535,197.03433,13.641895,55.400833,0.0026915804 2012,38.231308,176.29456,25.219643,53.770737,0.0019333147 2012,611.68896,202.67702,27.457703,112.946335,0.00025849868 2012,0.4671574,223.18326,18.359995,80.53128,0.00013113981 2012,618.0314,382.08014,21.115295,79.075195,0.000114791204 2012,604.9418,2.0608335,34.204895,62.028294,0.000104508625 2012,588.2315,245.45241,50.72406,225.06718,0.00010286452 2012,617.35645,92.1368,21.790222,77.36404,8.0109996e-05 2012,617.6327,304.72812,21.513977,76.217926,7.7980105e-05 2012,617.51196,156.24731,21.634705,81.28285,3.4986344e-05 2012,609.9127,441.16272,29.233948,63.199493,2.3380286e-05 2012,0.9993433,63.80431,21.286322,167.91359,2.3279274e-05 2012,616.1672,25.608831,22.979492,89.15186,1.8383309e-05 2012,0.55377686,397.22565,18.32482,72.34412,1.5294174e-05 2012,3.9555535,128.07718,79.12639,249.62173,1.3530065e-05 2012,409.01212,261.0243,30.568298,59.491608,1.2000288e-05 2012,0.6073096,323.50784,13.592934,47.06201,1.078428e-05 2012,0.6375944,282.55508,18.256466,72.75046,9.2414e-06 2012,601.82446,71.356064,37.322205,200.1291,7.291583e-06 2012,179.22292,160.59595,101.781204,214.16245,6.094519e-06 2012,0.9865007,216.90501,33.668037,197.61559,5.217246e-06 2012,0.7915894,441.79672,21.896412,61.6886,2.6870734e-06 2012,1.1306796,343.03516,32.91904,144.97427,2.2490299e-06 2012,482.13257,151.12468,83.05646,161.32268,2.0136845e-06 2012,2.583558,427.5386,52.96083,71.07361,4.466223e-07 2012,568.9121,11.62987,70.23456,140.71149,3.0879443e-07 2012,1.8944108,11.8546295,61.374954,120.422775,1.04510775e-07 2012,1.3589469,13.553932,22.213972,96.1237,7.2835e-08 2012,348.43512,6.596169,42.835266,45.388233,3.3372956e-08 2012,442.85132,8.899112,45.743958,45.145206,2.4543176e-08 2012,190.24825,3.2766292,43.850113,46.920265,1.6139337e-08 2012,476.88235,6.8233056,43.803192,49.98659,1.2775987e-08 2012,1.092238,2.9944565,36.218563,49.507626,9.260374e-09 2012,511.6241,4.9733934,41.329193,48.698822,1.5911486e-10 2013,620.1882,79.672424,18.958496,57.21054,0.00071762834 2013,617.8505,227.00552,21.296143,72.352875,0.0006294571 2013,1.2082675,192.41095,17.897388,73.883026,0.0005390027 2013,620.70215,193.88849,18.444519,51.894592,0.00028244418 2013,622.0433,298.9251,17.103394,50.794678,0.00021287838 2013,0.45269206,240.25076,13.989027,53.511993,0.00012461893 2013,618.19226,382.44296,20.954407,78.848145,0.00012173864 2013,617.9106,320.99054,21.236084,80.05191,0.000105958796 2013,610.5894,110.907135,28.557251,152.31287,8.5743435e-05 2013,0.8575277,85.24297,18.400368,74.338875,4.5661345e-05 2013,587.63257,244.58844,51.5141,228.77652,3.26824e-05 2013,2.9883854,86.582375,47.822514,188.6878,2.7639286e-05 2013,0.87921065,212.39807,28.058979,143.53549,2.6924607e-05 2013,0.8450326,126.468834,18.409946,75.306465,2.6391757e-05 2013,585.5763,21.12823,52.777893,104.49231,2.5435418e-05 2013,609.90326,441.5633,29.243408,62.538635,2.1553311e-05 2013,0.54203856,396.94183,18.301855,71.92798,1.4646184e-05 2013,607.87384,4.0680504,31.272827,56.779198,1.4414978e-05 2013,0.523186,378.2938,13.543326,46.3201,9.656314e-06 2013,574.39435,70.20802,64.0495,247.93413,8.3205805e-06 2013,0.6548462,332.02005,18.113537,70.85449,7.405194e-06 2013,5.4104495,209.87358,78.448074,252.93315,2.8289382e-06 2013,0.7132666,441.26205,22.03887,62.244934,2.6091743e-06 2013,1.0558448,346.56522,32.25555,140.41275,2.1763362e-06 2013,1.3294694,26.079096,29.94253,120.14466,5.172536e-07 2013,2.408475,426.8518,53.377613,71.88986,4.170862e-07 2013,255.00826,426.7822,73.524826,70.1051,4.0662323e-07 2013,174.82692,429.83844,71.21501,67.927246,3.587096e-07 2013,134.72752,428.14026,73.56012,71.3457,2.948977e-07 2013,475.69424,5.896483,45.713226,50.11581,2.0345891e-07 2013,503.91354,7.8570933,44.831207,48.082966,4.3144833e-08 2013,531.2657,7.030978,44.90802,50.429607,2.6160182e-08 2013,577.70245,4.5386996,47.025818,46.627045,1.582141e-08 2014,326.35226,223.48473,27.897278,55.953476,0.005137074 2014,621.49225,143.92274,17.654419,60.245544,0.001996368 2014,610.5151,77.836494,28.631592,142.78345,0.0009704126 2014,617.51086,179.73628,21.635803,77.02528,0.0008972477 2014,89.237686,187.14275,42.25351,75.32817,0.00026621486 2014,586.102,12.603665,52.2218,103.71824,0.00023155335 2014,617.8727,241.74326,21.273987,76.37314,0.00018574743 2014,298.4081,257.3241,30.092377,56.563385,0.00015641285 2014,618.2869,401.82538,20.859741,78.54953,0.0001494946 2014,618.10834,355.52823,21.03833,79.608185,0.00012424644 2014,602.97577,168.05855,36.1709,174.56245,0.00010047681 2014,0.55136925,199.7661,13.916931,53.300262,9.704743e-05 2014,617.96454,302.28204,21.182129,77.55905,9.3196366e-05 2014,600.2998,4.040663,38.846863,45.54847,8.62822e-05 2014,0.4660901,245.39154,13.817032,49.48526,3.4151843e-05 2014,0.79468346,159.5599,20.387138,129.35977,3.212892e-05 2014,602.53937,295.86362,36.6073,184.0242,2.4047025e-05 2014,0.5887752,242.23941,19.96313,134.14319,1.5881978e-05 2014,0.57610273,404.2625,18.242739,71.70166,1.22597385e-05 2014,0.7490967,126.81712,18.29042,74.4097,1.15996945e-05 2014,0.77521324,85.43674,18.338873,73.12433,9.897306e-06 2014,0.5900704,351.78055,13.668787,46.017487,7.5976814e-06 2014,332.4783,160.26804,79.49808,167.00406,5.0654353e-06 2014,1.0478915,302.6929,28.149805,137.6283,4.4385233e-06 2014,72.778015,144.75185,82.37253,166.4102,4.2899524e-06 2014,575.14197,425.13928,64.0047,73.65808,3.3014771e-06 2014,2.4426563,147.4138,48.331783,170.09193,2.7913443e-06 2014,1.5442164,55.987392,34.04605,174.73975,1.3823376e-06 2014,3.1011622,234.63217,57.550404,220.93369,1.2840202e-06 2014,2.7274237,391.90268,56.22042,99.82904,7.385161e-07 2014,320.30386,8.097227,43.517303,45.5698,3.5587874e-07 2014,0.79954064,14.040515,15.362288,72.51833,1.9446169e-07 2014,2.361123,23.354445,55.843864,93.16569,1.9058155e-07 2014,455.94138,3.3372607,43.03186,49.86306,6.624519e-09 2014,410.17133,5.163913,42.917267,50.86121,3.24073e-09 2015,581.7432,221.49704,34.59619,66.6658,99.738594 2015,191.99187,210.11565,28.757355,55.828964,0.009417409 2015,604.3623,228.17305,23.44928,50.865097,0.0036835484 2015,582.5163,189.95529,53.30365,183.63275,0.00079330656 2015,182.88414,230.17589,26.863861,57.83104,0.000507209 2015,617.68616,243.1844,21.46051,82.52727,0.0004676014 2015,618.1672,400.22922,20.979492,79.7771,0.000160092 2015,617.0178,320.14203,22.128845,78.83081,0.00013845114 2015,612.0328,149.37498,27.113892,139.22725,0.00013645306 2015,617.5386,53.864883,21.608093,83.48186,0.000110660745 2015,0.48471153,245.54443,13.827579,49.58777,8.4691266e-05 2015,202.36798,235.87447,25.54596,51.318222,7.6208446e-05 2015,0.8593498,202.7214,18.038464,70.744446,7.564841e-05 2015,0.8566911,114.982506,20.360783,136.39966,5.187013e-05 2015,0.6840918,180.87076,13.771651,50.176987,2.8758954e-05 2015,0.732522,154.404,13.933767,48.627777,2.7910577e-05 2015,0.5692554,253.14519,19.347773,136.16124,2.3310775e-05 2015,0.57488525,350.0337,20.105988,101.737,1.4397392e-05 2015,0.7144674,127.851555,13.92721,47.732826,1.4329273e-05 2015,588.08966,326.7131,51.057007,156.31601,1.2827894e-05 2015,0.7501864,71.00094,18.630383,77.217,1.2327327e-05 2015,586.96246,11.066091,52.184204,104.207985,1.0287841e-05 2015,3.075223,131.20816,58.043503,212.2894,6.9248963e-06 2015,0.55877197,418.03683,17.013287,83.872345,5.17547e-06 2015,3.3173828,261.1529,56.31143,204.96353,3.1593056e-06 2015,1.955376,23.93977,56.63185,99.50807,1.1260854e-06 2015,2.9584506,33.591778,82.3703,212.21877,5.9224294e-07 2015,2.247399,425.03937,53.12057,73.85928,5.6341844e-07 2015,0.8404932,14.02516,15.765817,79.65036,2.6931266e-07 2015,211.34,4.574092,44.17137,55.198368,3.4166634e-09 2016,501.6913,205.67839,37.24295,80.21486,99.49864 2016,414.022,215.0358,24.56546,51.591797,0.08432098 2016,447.66434,212.77795,23.978027,50.644897,0.006948983 2016,621.7687,196.66075,17.37799,55.200897,0.0015856078 2016,617.15674,147.77187,21.98993,81.51814,0.00033580718 2016,407.84027,152.47995,76.19437,156.15875,0.00029846732 2016,617.7194,234.17636,21.427246,84.13049,0.0002287147 2016,617.18835,338.21405,21.958313,84.03351,0.00016255784 2016,608.6432,2.9296322,30.503479,105.13692,0.00015091375 2016,487.0521,182.7344,72.29477,141.52542,7.332355e-05 2016,0.8126038,120.09507,13.5976305,45.95838,7.120085e-05 2016,0.8535173,153.30247,18.437572,77.97656,6.6038505e-05 2016,0.9435425,198.57439,18.347971,77.58931,5.787535e-05 2016,601.31635,229.04787,37.830322,209.28725,4.177871e-05 2016,601.4225,110.33709,37.724182,176.81406,3.7029848e-05 2016,0.7742676,228.9399,19.838884,135.05513,2.7534052e-05 2016,609.7251,438.42126,29.42157,64.750305,2.5998503e-05 2016,1.5761068,104.33393,33.585472,183.92532,2.0125255e-05 2016,0.5602816,84.74025,13.928752,51.31537,1.8613066e-05 2016,0.577885,317.8246,19.589956,131.63528,1.822321e-05 2016,585.81384,389.88602,53.332825,103.06128,8.951846e-06 2016,0.62880456,403.14465,20.629572,96.93967,4.3755153e-06 2016,3.463164,157.94876,57.000286,211.50153,4.110675e-06 2016,582.1307,4.1882095,57.01599,195.83066,3.0830254e-06 2016,0.6715528,36.671227,18.926954,71.048584,9.021571e-07 2016,4.949642,350.79504,79.77713,135.15704,8.609659e-07 2016,1.2865154,0.2906299,41.93781,46.26425,2.501237e-07 2016,2.0177069,13.168601,57.00664,111.09649,5.6880182e-08 2016,354.1554,2.8137078,43.592407,48.383804,6.5729284e-09 2016,306.24725,2.5675032,61.269775,45.321342,6.0608354e-09 2016,114.38725,0.0,45.184708,63.270782,8.134643e-10 2017,495.8284,200.0974,37.542084,89.87959,99.78363 2017,621.266,179.49919,17.880676,57.503525,0.0063021216 2017,381.79596,229.53456,25.578888,54.547684,0.005521518 2017,517.7272,204.8077,30.934387,75.637375,0.0024971517 2017,376.57913,185.61992,37.18573,76.01387,0.0012002732 2017,611.54395,102.54758,27.602722,142.65451,0.0007098259 2017,621.5633,220.69128,17.583374,55.387024,0.00069401815 2017,621.77136,115.2868,17.375305,51.54029,0.00043189485 2017,617.81396,282.87366,21.332703,86.21942,0.00016671255 2017,617.394,384.85672,21.752686,82.666565,0.0001521586 2017,472.16656,164.60428,90.315186,173.4667,9.715744e-05 2017,0.68753177,225.65503,18.209293,72.61963,9.5137155e-05 2017,0.740494,92.94614,19.761667,145.71931,8.1048594e-05 2017,587.1959,175.28842,51.950745,227.47906,3.980903e-05 2017,609.6472,438.56888,29.49945,65.5939,2.5929618e-05 2017,601.0008,319.11453,38.145874,165.94531,1.4193254e-05 2017,1.2808204,152.63568,34.160854,172.89255,1.2343348e-05 2017,0.5085059,331.3804,19.93415,132.58884,1.2040364e-05 2017,0.5485287,268.12018,20.524113,102.97278,1.1687518e-05 2017,610.13403,8.775814,29.012634,119.50879,8.634099e-06 2017,0.69075525,422.49997,16.943161,79.70352,3.5551934e-06 2017,3.0057993,224.09438,57.200916,216.88448,1.8346454e-06 2017,2.6140153,42.871777,59.310596,228.56584,1.2199878e-06 2017,5.033519,354.2087,79.69211,132.65005,6.29675e-07 2017,2.6830063,429.0981,53.053898,69.94644,3.69539e-07 2017,2.172435,27.748465,32.302097,132.04015,3.5664658e-07 2017,1.0876473,13.8297,15.20507,79.45833,6.725006e-08 2017,2.5395916,9.077829,54.35563,75.046974,3.4074173e-08 2017,22.550365,1.7757715,67.81308,56.846066,9.722828e-10 2018,580.002,194.98396,44.248047,95.156815,8.307435 2018,588.3319,195.58582,23.67157,45.71315,0.37438285 2018,622.0475,200.12894,17.099182,65.11682,0.12608917 2018,431.38437,233.94412,25.75174,49.29184,0.032837912 2018,610.14056,196.71425,29.006104,148.94933,0.008297955 2018,571.0883,159.59459,64.59863,244.23186,0.0029071267 2018,418.55743,202.50655,44.16696,100.741165,0.0010905949 2018,621.2563,298.85968,17.89038,59.053284,0.0005212499 2018,616.1529,327.28464,22.993774,87.13611,0.00043890244 2018,621.42786,52.521694,17.718811,51.30257,0.00024859817 2018,2.62347,17.348509,62.437237,105.98656,0.00015957402 2018,617.4469,404.83957,21.699768,80.42566,0.00015506943 2018,0.55917805,117.10521,13.75709,47.54094,0.00011739893 2018,483.40704,239.5025,25.335327,58.589325,0.00011182175 2018,1.4702612,55.723557,18.583992,77.45729,0.000101314516 2018,602.17816,89.51745,36.968506,183.4328,8.715652e-05 2018,616.9072,84.37788,22.23944,79.62148,8.113103e-05 2018,0.7263501,221.55148,18.345882,75.525635,7.960918e-05 2018,0.7159424,128.92506,20.444397,140.00099,6.529877e-05 2018,472.2523,173.90842,74.83566,166.61557,6.4045555e-05 2018,0.67175865,182.32701,13.940769,50.986176,4.3603366e-05 2018,600.6725,264.08255,38.474182,205.92474,3.890692e-05 2018,0.429681,145.09595,13.961483,48.388596,2.5720177e-05 2018,387.5023,140.86835,138.4094,229.16336,1.277831e-05 2018,601.3902,11.717195,37.75647,115.01768,1.1942337e-05 2018,0.6229785,392.1942,18.251406,71.15216,9.738233e-06 2018,3.0046241,72.77272,58.264088,239.39095,7.985633e-06 2018,1.1751953,192.96901,33.041996,166.84517,7.805915e-06 2018,0.62969404,276.0342,18.18427,71.29718,6.2832796e-06 2018,0.48129314,352.19617,18.396627,71.35104,5.7563907e-06 2018,0.8482194,442.6888,21.878788,60.90744,2.4386368e-06 2018,1.1263127,306.9898,32.7237,168.19324,2.137795e-06 2018,427.9337,36.90621,27.579193,60.27987,6.945158e-07 2018,5.1811037,358.1712,79.3662,128.64053,6.048484e-07 2018,2.6890886,429.26007,53.05878,69.72858,3.8668603e-07 2018,2.6574707,7.5677376,42.245186,54.54122,1.7925792e-07 2018,586.43274,2.5860646,42.929443,47.485424,1.4586303e-07 2018,47.03323,3.1143994,43.23048,56.382847,4.711851e-10 2018,415.35928,2.8471143,45.10797,48.59633,4.294823e-10 2018,389.47055,5.3469157,44.126404,51.227345,1.9414192e-10 2019,600.9501,6.2613025,36.486755,98.02238,0.02216836 2019,621.73895,55.017548,17.407715,58.96865,0.016870985 2019,580.2395,232.9212,43.498535,114.74402,0.016821943 2019,579.10095,61.04197,57.68805,276.92352,0.011132872 2019,621.56415,192.05145,17.58252,49.908478,0.0055029886 2019,622.4566,252.12057,16.690063,47.211334,0.004728101 2019,618.93866,148.61203,20.208008,56.594223,0.00405149 2019,621.1964,21.826757,17.950256,57.732826,0.0014724918 2019,611.2681,208.02768,27.87854,130.35242,0.0010033768 2019,197.2201,218.63191,22.57463,45.296127,0.0006774469 2019,609.12384,124.09339,30.022827,143.46011,0.00047602522 2019,617.4426,333.35614,21.70404,80.89011,0.00042777718 2019,624.44055,103.5561,14.706116,46.260475,0.00029276815 2019,596.0423,248.4111,42.768005,185.98657,0.00012007664 2019,528.6295,23.795235,39.691467,121.54493,0.00010118165 2019,1.1612183,91.269035,17.84962,65.9036,0.00010063235 2019,552.0585,220.81706,25.57251,50.40297,8.367975e-05 2019,0.8106112,125.42003,18.261742,73.5417,7.0169954e-05 2019,612.17676,366.5302,26.96991,132.11032,6.3860265e-05 2019,1.2066357,219.99841,18.119318,71.66235,5.583777e-05 2019,1.0668677,168.6382,18.078455,77.12811,3.3973174e-05 2019,1.9310751,150.71179,33.013683,167.25504,2.4139621e-05 2019,0.8134359,260.87805,19.464329,133.84314,1.4276144e-05 2019,412.93378,52.728645,29.555817,60.949966,1.364961e-05 2019,506.2376,0.0,130.39746,233.87411,9.805873e-06 2019,0.60067874,384.83716,18.323233,71.91931,8.638778e-06 2019,0.5600814,345.65897,18.34038,71.059906,7.2480993e-06 2019,5.7173147,39.150143,78.47274,268.75186,6.4322962e-06 2019,498.54776,8.390247,48.017487,140.62973,5.3798417e-06 2019,3.2091408,278.92422,56.60533,193.21506,4.2774845e-06 2019,2.4439552,17.164675,64.26331,113.825745,4.2100114e-06 2019,0.6910905,422.1532,16.925346,79.776184,3.550856e-06 2019,1.8320931,52.08509,29.543196,135.55533,8.859625e-07 2019,420.93488,0.465319,48.1528,134.47571,6.673694e-07 2019,2.6581185,428.63815,52.827785,70.216125,3.9289608e-07 2019,578.96405,1.0005046,47.900024,58.09951,2.682054e-07 2019,1.1375216,12.251185,15.232023,81.08417,1.9041768e-07 2019,440.0538,2.0470247,45.780273,49.822826,4.3506617e-08 2019,551.40753,0.90599775,45.72644,51.711124,3.451623e-08 2019,525.71313,2.3110516,44.414185,55.434296,2.3245612e-08 2019,38.55047,2.553916,44.39547,57.14743,1.3364512e-10 2020,547.0148,64.43173,37.397156,65.87431,0.010873235 2020,612.5904,53.699158,25.699707,81.0168,0.004398457 2020,582.53455,10.409903,56.61212,116.637115,0.0006164529 2020,0.7599854,166.15971,19.149435,84.13284,0.00061129354 2020,618.0398,237.65201,21.106873,80.09964,0.00055293733 2020,0.8838591,66.78316,12.976999,46.650856,0.0004354919 2020,617.40796,400.20187,21.738708,81.12607,0.00024400618 2020,0.5705607,218.62569,13.990924,52.532394,0.00018001888 2020,617.7201,297.79947,21.426575,83.00748,0.00016566225 2020,609.31323,77.805565,29.833435,161.61902,0.00015846877 2020,1.2665105,114.65203,32.840763,174.86774,0.00015203241 2020,0.84749025,98.40057,18.054789,72.8374,0.00015046938 2020,574.19336,100.6028,64.95331,279.15125,0.00014538171 2020,587.5857,315.946,51.560974,166.53406,3.4905424e-05 2020,0.5121452,244.91959,18.388529,75.23541,1.153251e-05 2020,5.2807946,100.79291,78.49821,256.49387,1.0995785e-05 2020,0.58820397,319.02563,19.764454,132.1838,9.5758705e-06 2020,0.49099123,288.49722,18.34591,73.04947,8.118384e-06 2020,0.48136395,399.2647,13.758646,46.90161,6.7456313e-06 2020,0.9751978,215.89314,32.982727,183.94453,3.9136376e-06 2020,0.71541834,422.20465,16.84425,79.925354,3.0472236e-06 2020,2.4642286,20.117773,61.718784,107.11387,2.7068936e-06 2020,2.9875603,330.167,57.67748,152.41656,6.9418866e-07 2020,1.4487126,22.95827,21.784843,91.40547,5.6259375e-07 2020,2.7650423,429.079,52.999954,69.93643,3.392685e-07 2020,449.21808,18.942747,49.240204,119.74751,1.9341707e-07 2020,417.4268,6.9136267,132.14719,216.83058,1.0304618e-08 2020,1.8325456,4.8283076,40.99654,59.75676,6.5394903e-09 2020,86.73335,0.64526695,44.936157,57.3847,9.3003016e-10 2020,422.46378,0.28359538,43.61029,48.013775,1.8676564e-11 2020,557.68317,1.89618,43.982056,47.963444,1.5486782e-11 2020,530.6968,1.8329346,46.205566,48.109447,2.3803353e-12 2021,98.67105,218.18939,36.37265,68.23895,0.030429691 2021,606.4948,37.520817,31.901001,71.503006,0.014356905 2021,0.534587,158.64896,11.972543,45.66992,0.014324471 2021,440.28235,210.32957,25.920532,50.142715,0.009413961 2021,0.92771405,149.14076,27.089684,86.86427,0.0026372024 2021,3.839655,92.05782,46.59816,193.81723,0.0016394394 2021,620.5699,99.668175,18.576782,55.57728,0.0010342165 2021,31.041609,219.61873,34.726494,65.2213,0.0006132081 2021,610.83453,94.291405,28.312134,142.63727,0.0004405257 2021,0.7289372,84.58576,13.521037,49.355797,0.0004385426 2021,0.6611865,97.44599,18.764252,88.67449,0.00035345284 2021,617.53937,177.28922,21.6073,82.674805,0.000248266 2021,617.66016,384.44177,21.486511,84.787506,0.00023739508 2021,617.4979,229.6718,21.648743,80.84944,0.00022779565 2021,592.3017,9.460808,46.84497,166.3689,0.000205586 2021,617.8559,314.5597,21.290771,81.79584,0.00012674482 2021,1.1727661,219.35376,17.524475,65.44403,0.0001017211 2021,0.91623294,186.03575,13.965844,58.602463,8.59545e-05 2021,0.41899985,259.2632,13.48977,48.343567,5.9869886e-05 2021,609.5301,438.3136,29.616577,66.29901,2.8349672e-05 2021,601.93665,239.31511,37.210022,201.46782,2.706884e-05 2021,572.77313,351.48328,66.373535,133.98975,1.5350623e-05 2021,1.4463501,200.89923,32.60515,174.34802,1.2648722e-05 2021,0.669738,406.2024,18.162611,71.03375,8.626823e-06 2021,0.5903084,320.22006,19.713032,131.42334,8.146646e-06 2021,3.0056837,267.9995,57.12697,204.28696,2.2173274e-06 2021,4.235036,16.515762,80.77227,147.06917,1.5672216e-06 2021,1.8626107,27.267807,28.666004,132.10718,9.814177e-07 2021,1.0513464,16.946238,13.582027,78.02752,5.981097e-07 2021,2.7613363,430.11588,53.00668,69.3631,3.633182e-07 2021,583.5604,1.9887859,46.36255,48.9711,7.582155e-08 2021,510.9716,1.2421582,44.47751,47.04345,1.3839486e-11 2022,525.2602,191.83769,28.212463,56.15143,0.006759321 2022,546.38586,246.76572,36.532715,77.430084,0.003768105 2022,620.4069,189.07367,18.739746,63.58673,0.0014185559 2022,606.57983,7.7298245,32.566833,117.354996,0.0008026954 2022,520.86334,165.72838,44.097473,128.74773,0.0005186664 2022,1.3546606,79.70471,19.517271,136.20724,0.0002955245 2022,617.3533,231.66487,21.793396,86.44182,0.00022837271 2022,618.1281,398.68234,21.018555,82.21927,0.00016692078 2022,611.2548,105.63143,27.891846,155.17368,0.00015231583 2022,0.6630363,168.89363,18.316559,74.05234,6.1030114e-05 2022,601.3341,184.80147,37.81256,185.25334,5.2603526e-05 2022,612.6726,287.9625,26.47406,141.12732,4.976945e-05 2022,0.7934766,112.36383,13.711966,48.406906,4.5491393e-05 2022,495.4525,129.70175,110.15674,224.5906,2.8240816e-05 2022,0.8170589,180.3829,28.437239,147.03424,1.3489198e-05 2022,1.6320662,44.904915,18.782602,75.62743,1.2242973e-05 2022,0.60385257,391.94528,18.265934,71.70935,9.354831e-06 2022,0.5632792,276.37994,13.733618,47.91275,9.123375e-06 2022,3.1483953,62.75961,58.641098,232.13983,8.803703e-06 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2204,580.9036,214.09232,45.01233,76.41589,77.34352 2204,516.9486,219.04636,29.029602,59.11328,0.1036223 2204,441.5958,203.91725,26.101715,52.954483,0.007517254 2204,3.3544989,189.71172,51.399857,97.01311,0.005201324 2204,159.66388,220.60973,38.322678,77.97334,0.0050090724 2204,584.8701,58.537617,26.881897,57.309963,0.0027772435 2204,272.03506,237.37956,33.302795,64.544235,0.0019209463 2204,582.44147,17.2761,54.415527,109.04008,0.0010814475 2204,596.1505,202.18037,41.989014,167.52095,0.0006972163 2204,617.36456,231.65753,21.782104,77.51752,0.0003661886 2204,582.19147,43.73422,53.534607,266.98593,0.00027959057 2204,479.44934,319.60245,71.428406,147.90985,0.000187097 2204,1.9792114,142.08817,20.60715,135.46228,0.00017625051 2204,1.1571937,218.59438,13.221157,54.317062,0.0001021093 2204,140.91328,187.33878,71.63779,161.29993,7.471762e-05 2204,621.4268,140.55058,17.719849,54.57834,7.4201525e-05 2204,612.1121,364.9918,27.034546,130.16074,7.108002e-05 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2206,246.99219,238.12375,33.09915,75.95578,0.04805633 2206,507.31802,138.08113,29.88205,66.26524,0.0070142685 2206,443.57687,200.74715,22.780731,45.39406,0.005408881 2206,258.53595,217.79266,36.657684,70.369934,0.005054831 2206,579.14484,74.149666,59.078552,177.58939,0.004403225 2206,585.66956,213.9771,27.988892,60.192245,0.0029528409 2206,615.94305,156.96927,23.203613,82.296265,0.00070494454 2206,619.765,74.72286,19.381653,65.94062,0.0002616885 2206,616.491,230.98438,22.65564,91.85434,0.00021437538 2206,621.0409,118.345604,18.105774,58.43505,0.00021214534 2206,427.10727,269.58774,24.706604,57.345337,0.0002005545 2206,617.6637,332.08194,21.482971,80.88553,0.00017738112 2206,404.57425,245.65012,40.66159,81.5361,0.00015870894 2206,585.2352,173.04802,53.418335,246.42241,0.000139627 2206,617.96826,395.16226,21.178406,78.88666,0.00013649197 2206,1.2418457,202.0365,17.918516,72.7406,6.0413506e-05 2206,0.45954347,283.27628,14.498354,52.261993,5.555678e-05 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1320,241.30997,214.16487,24.890228,54.488235,78.958 1320,209.17255,217.92464,25.980942,60.50273,75.167 1320,21.553646,217.59927,32.052483,66.69495,60.734 1320,84.596954,223.62447,26.003517,57.56334,56.599 1320,392.63928,220.65907,27.985077,56.377853,56.124 1320,7.2593727,211.5401,34.07658,89.83923,52.182 1320,221.46225,213.83536,26.312805,63.138702,44.843 1320,601.6216,237.07333,22.253662,52.640564,43.235 1320,64.92186,227.28099,21.405212,48.345505,32.41 1320,471.50424,220.85883,20.648346,45.970093,32.154 1320,607.5274,227.79855,24.123108,68.125305,24.221 1320,231.12306,217.28207,27.806717,58.926605,23.866 1320,49.50791,229.8411,21.737625,44.59543,23.803 1320,37.167953,222.70952,28.48626,56.32213,22.466 1320,618.83496,228.52971,19.90448,56.326004,22.022 1320,-1.4424992,222.23955,28.67488,82.00154,20.979 1320,95.09476,225.64001,27.341957,60.08383,18.128 1320,488.17032,215.54799,26.918365,68.41478,15.072 1321,59.32049,216.01614,28.965725,63.67563,88.082 1321,86.37625,212.50122,28.706596,65.83838,87.505 1321,183.29242,220.17816,29.77536,57.697174,83.661 1321,406.55286,223.1635,21.781494,49.91057,74.747 1321,525.93243,220.8385,24.735962,52.36786,72.519 1321,165.00229,215.33536,28.641891,58.113617,65.091 1321,479.05115,221.62364,21.916534,49.078506,62.795 1321,505.95056,220.27515,19.809692,50.50714,61.846 1321,464.96637,217.61482,23.779053,50.997635,43.069 1321,4.3725133,217.51762,30.626274,60.802902,30.15 1321,605.5148,221.49779,28.912598,75.68504,22.496 1321,21.32647,217.56752,29.949677,56.574112,18.894 1321,39.54862,225.30048,23.797844,50.59436,16.588 1321,488.00784,223.86333,21.91806,47.739548,15.094 1321,599.33185,218.2759,26.066162,59.859726,14.535 1321,596.1718,215.86061,19.906921,47.15544,14.134 1321,47.549854,217.88083,29.824375,63.231964,12.113 1322,69.829094,213.66457,29.982826,66.1953,92.477 1322,150.63922,224.15285,27.18863,48.118637,64.985 1322,553.48035,221.00594,23.849915,49.55153,61.3 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1805,471.2958,188.28992,28.41272,80.15686,6.896 1805,626.7808,489.0412,20.315247,44.67047,6.55 1805,-6.117205,489.19174,18.598991,42.319366,6.243 1805,-11.4747925,477.7655,29.24709,64.20978,5.81 1805,621.52924,200.74187,26.958618,72.58507,5.652 1805,622.61847,-33.980297,26.970093,74.88838,5.624 1805,613.06573,454.24582,39.710144,107.789154,5.599 1806,378.5769,208.20056,28.884308,66.10559,88.705 1806,412.34613,207.2156,29.428406,76.54202,83.955 1806,603.0123,193.19592,30.50586,82.53357,74.397 1806,573.9034,191.65565,27.591309,64.80611,51.733 1806,514.0083,199.83536,24.566345,49.09395,47.083 1806,592.1163,191.32135,29.017395,74.21991,20.513 1806,493.91577,207.6337,25.081665,42.441177,9.986 1806,614.83167,196.38284,22.42456,61.493896,8.774 1806,370.55457,200.85889,27.733337,65.32266,7.192 1806,548.0623,190.54681,30.553955,81.46088,6.977 1806,525.5795,196.40903,29.043762,69.63382,6.395 1806,626.6811,489.17416,20.531921,44.75638,5.873 1806,620.3141,199.37329,27.717285,77.5437,5.872 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1807,617.7427,184.37279,18.944275,53.228104,7.569 1807,433.42166,232.97038,20.221863,54.143448,7.09 1807,502.2367,196.54674,34.883728,91.356094,6.416 1807,510.25134,206.62245,24.144592,48.1019,5.813 1807,627.0035,-19.12905,17.39801,48.843235,5.756 1807,626.38617,488.86353,20.725647,44.91797,5.751 1807,369.54633,199.47719,23.906616,54.476105,5.739 1807,-11.920943,476.53732,29.968353,64.865326,5.728 1807,454.6908,200.08325,34.36554,92.23071,5.682 1807,613.055,453.89008,39.882385,108.28265,5.659 1807,-5.8825274,196.95407,19.379013,48.7838,5.574 1807,-11.342615,-32.545937,29.687073,72.180565,5.557 1807,-6.0925655,488.77982,18.89884,43.005035,5.451 1807,2.0225334,191.56425,25.671957,66.04549,5.449 1807,622.52374,181.31964,24.515198,76.1167,5.437 1807,44.085655,224.24524,26.494942,44.919617,5.364 1807,-6.069467,-18.679537,17.911652,43.32453,5.326 1807,227.19727,194.72256,20.297623,45.553864,5.324 1807,-17.892859,-66.58819,56.140686,151.63077,5.284 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1932,536.2436,309.6902,143.36816,341.06042,4.922 1932,-22.121891,400.799,79.694984,176.64728,4.848 1932,-72.38978,266.00186,199.99796,461.73544,4.829 1932,626.1376,487.03317,21.63147,45.93631,4.822 1932,581.5825,265.00085,39.143494,107.1087,4.665 1932,583.1196,409.6176,71.771484,179.13495,4.627 1932,494.01587,227.95691,237.4751,514.3789,4.547 1932,275.21085,312.41962,31.873749,60.51303,4.507 1932,498.99023,233.42642,31.17035,57.973267,4.483 1932,-4.1948314,367.79395,15.929554,35.69107,4.456 1932,-37.037197,-125.51294,116.98956,272.7116,4.398 1932,-70.370964,-189.50543,210.95541,456.21616,4.388 1932,-6.7817106,222.40662,19.41695,42.241272,4.384 1932,131.25734,279.6154,33.071,87.504425,4.237 1932,514.8313,229.33688,39.697998,82.72873,4.185 1932,599.77014,264.53094,35.49243,111.8353,4.171 1932,79.633934,220.09213,32.98062,63.012817,4.148 1932,507.15198,341.91937,34.308655,57.628357,4.112 1932,387.93134,242.21587,30.22174,70.3965,4.104 1932,40.46507,273.93265,32.71139,67.88547,4.096 1932,619.44696,-4.428437,18.032593,40.521442,4.06 1932,443.7896,220.69702,29.996399,68.685,3.982 1932,394.7441,-14.365166,59.23282,113.34193,3.931 1932,566.2307,254.98059,40.230713,102.79242,3.806 1932,398.80704,241.81413,23.932678,49.21373,3.779 1932,-1.0201395,33.076096,34.578823,85.75888,3.752 1932,332.1624,306.0139,31.170471,53.219757,3.751 1932,548.5546,241.3935,40.50122,96.35254,3.749 1932,-10.830393,-3.1987305,41.09836,97.336876,3.733 1932,486.3628,-35.441246,28.301697,75.65297,3.64 1932,3.0707269,-15.93186,16.282885,38.441578,3.58 1932,482.5279,-18.828524,18.421661,42.729557,3.575 1932,613.229,207.80142,36.058594,108.98505,3.562 1932,484.59766,230.23546,38.988037,81.03609,3.525 1932,-12.482575,219.12006,29.983776,66.329254,3.518 1932,364.63434,285.03574,30.238586,66.46503,3.495 1932,598.2975,-21.37713,39.92389,91.369255,3.454 1932,614.0641,147.47598,34.72992,102.80145,3.446 1932,-9.799996,132.45415,39.460487,108.7896,3.427 1932,496.97263,342.04428,24.025665,43.32196,3.423 1932,-3.8349974,381.489,15.382074,34.34082,3.39 1932,538.44556,277.39948,60.01538,128.95032,3.381 1932,573.0869,312.1264,54.9209,177.6222,3.374 1932,2.1351247,491.56848,19.434404,39.845093,3.372 1932,524.76355,214.21051,50.78534,120.0307,3.366 1932,1.9522572,-40.304676,42.749786,99.68448,3.362 1932,515.19836,349.00543,36.170593,66.30673,3.317 1932,1.1568689,-16.917576,51.3842,126.70592,3.312 1932,598.712,218.30545,37.67511,99.58292,3.302 1932,589.29675,233.2013,58.2453,210.62598,3.288 1933,373.30353,228.93396,39.120544,91.349304,96.502 1933,32.782246,249.8848,24.504341,38.68347,6.483 1933,-11.469852,-32.369118,29.800884,71.90047,6.394 1933,-12.370031,477.1036,30.632153,63.779755,5.901 1933,-6.2283792,489.15427,18.823269,42.770233,5.823 1933,-6.1481667,-18.268595,18.046217,42.844837,5.698 1933,615.8555,281.77466,23.549255,76.96518,5.657 1933,610.6096,-48.392128,42.012573,100.94467,5.5 1933,627.3808,-17.450287,16.745483,46.159534,5.488 1933,40.083473,246.25311,25.215607,37.09442,5.351 1933,612.9635,455.78888,39.096497,105.13031,5.336 1933,395.76257,233.63641,27.08963,57.58783,5.141 1933,-73.058945,265.60992,201.04169,463.01447,5.086 1933,532.6539,-182.18251,148.97461,423.05203,5.056 1933,-17.692768,-61.71708,56.41584,143.90706,4.948 1933,586.2854,-98.69478,74.19037,231.48666,4.923 1933,536.39197,310.57217,143.35297,340.57126,4.838 1933,21.38947,235.88606,31.30608,61.455704,4.831 1933,-22.017603,401.5365,79.88004,175.67377,4.815 1933,583.13495,410.2576,71.783325,178.39438,4.643 1933,51.2601,238.27924,32.80052,52.449524,4.58 1933,56.2511,252.90941,22.93032,33.381516,4.565 1933,60.391205,209.3128,53.86296,112.3147,4.553 1933,390.99442,225.24097,24.029419,52.999207,4.542 1933,364.384,221.85782,34.73056,81.6474,4.536 1933,494.42947,229.33472,237.14926,512.44403,4.503 1933,626.4236,488.27057,21.228088,45.12195,4.454 1933,400.01282,228.35042,40.178772,95.8607,4.407 1933,-70.417564,-188.2932,210.70233,454.4386,4.374 1933,-37.340508,-125.04372,117.13235,272.8586,4.266 1933,-3.9022768,29.424423,29.39095,89.6075,4.139 1933,380.0154,221.9537,29.304901,64.428314,4.136 1933,613.4456,208.11899,35.78949,105.81839,4.105 1933,310.30594,289.94504,29.898682,54.595,4.072 1933,28.387669,219.1785,54.18762,110.152985,4.063 1933,620.23596,-3.665391,17.377075,39.281506,4.051 1933,339.87613,238.14536,40.01297,83.81726,4.051 1933,83.716415,217.35693,41.162437,77.510376,4.043 1933,49.207493,238.59076,23.10735,35.110474,4.042 1933,-4.3541417,247.4677,17.643904,44.929184,4.031 1933,498.78726,223.94397,30.960297,67.02185,4.012 1933,-5.4656405,226.3712,17.697819,43.305313,3.944 1933,599.2821,268.54657,35.73474,110.12186,3.915 1933,-10.908786,-2.4760666,41.259384,96.51585,3.858 1933,95.040665,208.14012,51.26999,120.14488,3.833 1933,57.382378,231.75252,22.719482,33.268967,3.761 1933,514.83923,228.91489,40.296387,84.75986,3.757 1933,598.6437,216.01613,37.460815,98.650925,3.736 1933,-0.23860216,227.36331,25.065727,71.18298,3.736 1933,614.0165,146.63918,35.118164,103.63867,3.729 1933,582.26904,269.25778,39.028442,102.54443,3.724 1933,43.021523,199.47646,55.860733,104.67311,3.72 1933,380.41895,261.83228,29.89325,71.21051,3.656 1934,373.82065,235.07632,36.875885,83.858826,94.892 1934,-0.2577591,361.33075,28.615454,101.667786,7.478 1934,626.77484,487.4543,20.115112,46.316437,7.359 1934,-5.8412004,488.13657,18.220432,42.50186,6.584 1934,-11.275664,-32.552864,29.64095,71.311485,6.32 1934,-11.7055,476.96115,29.446009,62.9946,6.045 1934,188.17737,224.65144,31.77069,63.923233,5.912 1934,-5.8945236,-17.999294,17.70067,41.916428,5.781 1934,-9.636404,458.51904,38.93595,97.531006,5.701 1934,612.7642,457.70477,38.84088,103.31622,5.545 1934,610.81354,-48.231804,41.820007,101.274895,5.508 1934,627.48425,-17.35257,16.601074,46.370583,5.389 1934,401.44107,236.62518,39.704254,95.84589,5.248 1934,-72.365204,267.17783,200.68935,460.33292,5.221 1934,8.434819,255.5734,25.989529,58.18631,5.215 1934,515.1965,229.26625,40.895447,83.85472,5.186 1934,532.5068,-182.30342,149.12805,423.30334,5.105 1934,387.54114,233.55115,26.899841,62.17688,5.089 1934,507.24796,241.76697,32.88907,59.444458,5.067 1934,276.40247,267.98367,30.766968,47.990326,5.028 1934,-18.49041,423.69522,55.78154,152.74667,5.018 1934,-17.51661,-61.545033,56.30302,144.29877,5.005 1934,-37.47054,365.68494,108.371475,272.03387,4.957 1934,586.1341,-98.69212,74.32214,232.2112,4.95 1934,-10.756396,343.82126,47.4159,143.5209,4.842 1934,171.75856,231.5299,32.36403,84.145996,4.775 1934,536.54974,310.7179,143.45526,340.3111,4.771 1934,583.1407,411.35837,71.88043,177.68082,4.737 1934,304.52936,274.96762,21.726288,39.027588,4.664 1934,288.44568,275.08826,22.631927,38.741882,4.629 1934,339.27432,249.63943,34.06668,67.54056,4.62 1934,-5.814391,360.89136,18.422514,47.358,4.557 1934,366.3098,223.80255,33.49652,79.794556,4.547 1934,253.41423,268.059,29.590714,57.65979,4.49 1934,494.46866,230.00394,236.74045,512.14075,4.484 1934,-5.357214,259.62372,18.0055,38.276917,4.418 1934,-37.07189,-125.90024,117.09975,273.2823,4.31 1934,121.028984,241.7178,21.721474,30.942688,4.291 1934,-70.59256,-188.46472,210.94772,455.0605,4.263 1934,397.79874,230.35614,25.741241,50.07788,4.236 1934,484.02115,228.78479,40.563446,85.99487,4.215 1934,495.90045,209.17996,35.11737,81.63913,4.167 1934,-9.474894,355.36465,29.797194,81.582794,4.147 1934,148.64725,215.89064,40.44217,78.14421,4.085 1934,99.480194,225.43733,41.039047,79.02983,4.067 1934,167.9921,238.81876,26.136017,46.36902,3.973 1934,618.6419,490.08688,20.025818,42.39572,3.968 1934,132.19789,215.16312,39.814194,77.3609,3.953 1934,91.51489,223.49748,32.590836,60.72612,3.912 1934,194.63365,258.6797,30.29332,75.98547,3.886 1934,4.36483,267.36554,19.486898,37.02304,3.885 1934,12.278845,229.63437,52.88862,111.70386,3.883 1934,-10.503135,-3.5328102,40.75424,98.25694,3.87 1934,231.73157,259.32584,29.331635,69.92682,3.839 1935,375.35693,231.66286,33.23639,88.33226,89.073 1935,4.4029007,229.31004,32.587208,74.90358,7.357 1935,357.73636,235.15434,33.615997,74.30089,6.844 1935,85.15908,236.20096,32.010826,64.20148,5.911 1935,-6.071279,487.7752,18.59946,43.648804,5.788 1935,626.4106,487.98654,21.161194,45.311005,5.767 1935,-11.2008,-32.856457,29.448854,73.13838,5.702 1935,613.0187,455.10373,39.688232,106.59311,5.549 1935,-5.9653034,-18.57154,17.946796,43.60595,5.479 1935,-12.287608,476.7226,30.442986,63.223694,5.461 1935,627.35864,-17.098236,16.857605,44.8688,5.348 1935,532.75214,-182.28767,148.75067,424.22095,5.333 1935,610.81964,-48.39093,41.96649,100.21593,5.202 1935,-72.87071,264.66846,201.2916,464.77258,5.162 1935,116.473755,224.09323,39.256897,80.03128,5.067 1935,347.2718,232.4107,32.170105,68.36487,5.049 1935,386.5939,244.98898,30.90683,79.459595,4.985 1935,536.30914,309.8042,143.78644,341.2351,4.967 1935,-17.277473,-62.420288,56.090397,144.39061,4.966 1935,533.6367,239.61891,34.949585,63.198837,4.947 1935,180.7766,222.37454,31.513214,54.736267,4.904 1935,-22.329006,400.04434,79.939804,177.21158,4.881 1935,500.06152,231.44539,40.51422,82.27855,4.804 1935,36.556908,220.37627,38.704063,81.64412,4.787 1935,403.3098,236.246,37.490967,97.80679,4.78 1935,586.35223,-99.02453,74.13458,233.15012,4.71 1935,583.0513,410.04843,71.798035,178.30618,4.677 1935,15.890231,231.94418,34.546707,69.675415,4.605 1935,12.046888,220.80075,54.97441,134.14749,4.576 1935,494.3765,229.1123,236.79965,512.1786,4.538 1935,68.48342,222.99945,41.474274,80.12793,4.33 1935,613.5009,207.26102,35.643738,107.56381,4.32 1935,-37.268173,-124.90871,117.05318,274.0427,4.318 1935,599.1538,211.4483,36.948914,102.48645,4.311 1935,-70.08111,-188.6784,210.54987,454.92807,4.279 1935,615.021,280.14713,25.355835,67.245544,4.255 1935,99.94117,229.85785,42.108833,87.35022,4.196 1935,131.09206,227.90326,39.89415,86.102295,4.154 1935,-9.854903,234.33244,29.842888,69.04335,4.111 1935,516.0679,232.39934,40.72986,77.47786,4.085 1935,321.86172,253.17575,31.475739,66.19086,4.061 1935,613.99084,148.80414,34.86261,100.06465,4.046 1935,539.84064,-16.801498,31.309753,65.98072,4.009 1935,-5.0950737,181.70282,17.775627,44.503662,3.963 1935,-12.661621,209.4895,46.94422,149.146,3.92 1935,41.71207,237.92313,57.14831,129.93524,3.917 1935,187.5959,227.71628,33.719025,70.01129,3.895 1935,1.0400355,239.16005,24.249725,53.673782,3.892 1935,618.358,489.64288,20.745422,42.556274,3.838 1935,-10.0434675,-6.0401382,40.30276,99.7387,3.83 1935,174.21715,231.31299,32.621964,66.9422,3.816 1935,52.28422,220.24934,39.41437,81.140335,3.812 1935,598.0946,270.20474,37.88336,100.75403,3.796 1935,77.69943,208.76906,53.124313,112.08067,3.79 1936,394.0259,213.1102,42.861664,101.38403,91.034 1936,375.96927,214.91885,41.613464,100.98132,21.034 1936,136.97287,229.04552,31.005676,67.44316,7.154 1936,359.27502,213.31699,39.33087,85.30313,6.538 1936,99.621544,227.35786,39.865746,96.119156,6.358 1936,-11.509535,-32.301613,29.756016,72.48771,6.207 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1936,404.7691,253.16241,29.718384,73.065674,4.753 1936,436.45837,220.19925,38.24228,102.04846,4.6 1936,115.31101,230.99127,31.08953,66.72662,4.556 1936,494.05365,228.76709,237.92383,511.60413,4.541 1936,211.6259,219.58105,31.802444,67.608154,4.489 1936,537.55707,228.19171,31.482727,70.2561,4.456 1936,-37.190033,-124.85672,116.9443,272.48767,4.432 1936,-69.88231,-187.63333,210.09135,453.68878,4.257 1936,580.61,227.55228,38.63861,94.70334,4.217 1936,586.77875,-15.579073,33.089783,62.1577,4.201 1936,546.79144,238.80835,40.100952,92.92853,4.19 1936,114.80911,236.33575,41.038376,100.35034,4.077 1936,131.92735,215.6413,46.239548,116.39557,3.992 1936,412.56897,217.55986,25.854767,60.698868,3.987 1936,148.38339,220.87746,34.87796,85.073105,3.982 1936,538.9414,-16.654305,17.609253,38.6015,3.974 1936,129.72299,225.53188,26.068665,51.980515,3.922 1936,533.1586,-19.000101,30.042053,62.277473,3.901 1936,237.22287,241.58,28.733673,69.23186,3.9 1936,140.55867,248.97742,29.528702,79.815796,3.856 1936,-10.050571,-3.92416,40.432106,96.96608,3.855 1936,223.46964,215.98296,21.687088,40.060074,3.823 1936,64.71061,225.29254,24.111572,45.890564,3.807 1936,165.7557,216.50354,36.773438,80.9953,3.792 1936,83.838196,237.91249,41.548836,99.830734,3.78 1936,618.15405,489.62885,21.294128,42.676697,3.77 1936,266.59894,256.51624,18.791595,35.065796,3.765 1936,385.03812,211.39432,26.228699,55.991455,3.761 1937,425.812,219.33076,45.819183,107.22084,86.258 1937,133.95648,228.29588,35.575775,69.65614,6.739 1937,123.45387,220.75311,34.176483,64.44266,6.576 1937,-11.325748,-32.21872,29.41761,71.85369,6.214 1937,145.11896,235.92236,35.032562,80.23053,6.052 1937,-6.1981277,488.3506,18.594656,42.952454,6.037 1937,-6.116116,-18.389694,18.071688,43.300907,5.922 1937,165.61081,235.60641,29.740082,64.884094,5.863 1937,233.60196,216.3071,19.521225,36.69287,5.605 1937,-12.6502075,477.2618,31.238749,63.261017,5.571 1937,237.30055,229.35762,27.2397,70.02754,5.529 1937,413.26562,230.17416,31.549103,90.57312,5.523 1937,623.18384,-33.710373,26.07605,75.84321,5.382 1937,627.4523,-17.775206,16.688477,46.125458,5.364 1937,459.27625,226.73175,33.968018,75.1557,5.227 1937,-17.467464,-62.007645,56.44695,143.54976,5.179 1937,-72.70622,265.98807,201.0026,462.4747,5.129 1937,536.6258,308.68213,143.53424,343.1184,5.101 1937,613.09686,454.6657,39.133484,107.095215,5.09 1937,532.573,-183.64497,149.58209,424.68692,5.025 1937,467.09842,221.71027,40.031464,100.356445,4.915 1937,586.2437,-99.59524,74.364136,232.70529,4.907 1937,159.63069,231.52766,24.258438,49.480667,4.833 1937,-22.057545,401.5035,79.96875,175.8818,4.831 1937,297.02765,255.5704,20.730896,36.668793,4.823 1937,181.82495,219.64969,36.100525,83.1637,4.813 1937,582.8958,410.32703,71.831665,178.06238,4.779 1937,494.16382,227.49536,237.44696,513.2779,4.709 1937,262.93906,243.79959,26.72696,59.11725,4.692 1937,290.1754,255.46152,19.457428,35.593384,4.672 1937,602.9763,-57.580425,53.71936,136.25938,4.658 1937,283.37018,255.36414,18.53125,35.245544,4.571 1937,626.62604,488.31073,20.764404,44.67334,4.57 1937,324.4057,252.34535,30.605133,70.8145,4.557 1937,594.03107,-46.62758,44.960022,97.790695,4.447 1937,437.76245,213.88055,53.7706,129.16528,4.416 1937,-37.346935,-124.14093,117.164185,272.14417,4.361 1937,229.62515,218.38568,28.76706,65.98956,4.325 1937,422.29498,224.90433,33.287476,76.946625,4.292 1937,-70.48244,-188.65385,210.881,454.92078,4.281 1937,176.26813,239.88457,22.724197,46.2968,4.25 1937,-10.2059765,-4.6483574,40.654617,97.66872,4.212 1937,-4.77775,211.27242,16.765726,40.733475,4.195 1937,119.17451,221.87315,25.78196,40.516434,4.158 1937,304.7391,252.57558,21.841217,39.002396,4.127 1937,95.37218,201.52422,51.399597,127.267075,4.116 1937,615.6222,337.99496,26.276611,82.61615,3.967 1937,227.69273,212.85136,18.37941,32.797012,3.883 1937,83.65282,217.09427,39.935608,88.11517,3.854 1937,402.11035,225.0758,54.61029,134.20001,3.836 1937,387.0677,235.41821,41.70868,98.4895,3.833 1937,275.20578,253.38,18.269653,38.371765,3.832 1937,-4.29235,28.480251,30.474972,78.97595,3.826 1937,2.7161777,-16.800077,16.879099,39.7502,3.826 1937,80.22543,226.3098,22.371292,36.501236,3.823 1937,454.11578,216.02707,26.677338,59.08658,3.814 1938,479.13943,221.2669,59.22946,124.09552,98.325 1938,502.75702,220.28087,50.698547,110.51979,7.225 1938,-11.450515,-32.84035,29.820396,72.994064,6.265 1938,246.45886,223.97711,26.893127,74.82489,6.176 1938,627.1483,-18.011312,17.131836,46.404873,5.959 1938,-6.127865,488.34015,18.66056,42.700256,5.87 1938,-12.385387,476.45416,30.989532,64.236206,5.797 1938,166.5052,210.38626,33.43254,68.906494,5.628 1938,613.1599,453.6858,39.751404,107.61047,5.621 1938,622.6744,-33.65578,26.54657,75.42534,5.614 1938,535.32056,310.37943,144.64154,341.03146,5.47 1938,-6.157612,-18.339989,17.97119,43.34425,5.386 1938,626.5159,488.231,21.082703,44.433197,5.374 1938,582.86755,411.08563,72.410645,176.34406,5.333 1938,-72.46094,265.97232,201.317,462.66855,5.282 1938,451.86346,213.71027,44.715546,87.19556,5.268 1938,468.0458,219.52214,49.16678,95.995804,5.148 1938,532.7134,-182.45937,149.61365,426.13187,5.108 1938,-17.326857,-62.800495,56.31627,145.12415,5.034 1938,296.51288,260.74857,22.552551,39.44583,5.034 1938,275.29196,245.83238,18.489197,38.5851,4.979 1938,-21.902489,402.0504,79.8279,175.63855,4.954 1938,290.72055,252.9451,20.570007,39.04883,4.946 1938,519.51776,211.01743,57.103577,128.62863,4.897 1938,586.3525,-99.43421,73.78247,232.10477,4.84 1938,494.80942,231.39801,236.56314,509.5852,4.837 1938,603.06805,-57.04442,53.369263,136.03159,4.665 1938,403.49948,254.84917,21.044006,49.6837,4.465 1938,436.414,-31.187916,29.503906,75.98013,4.438 1938,429.40158,215.82896,55.5455,123.34651,4.429 1938,142.76077,216.26317,24.864365,36.99724,4.427 1938,249.02234,221.33252,19.300385,41.314148,4.415 1938,180.54024,222.05035,31.683975,70.42944,4.386 1938,-37.13692,-125.271324,117.08499,273.44598,4.379 1938,270.29626,239.99919,27.74942,57.18416,4.345 1938,-70.67465,-188.42627,210.98071,454.59534,4.186 1938,422.62817,-32.723637,28.734314,75.39521,4.152 1938,593.96674,-44.98528,45.099182,96.51319,4.132 1938,0.15995026,264.2901,23.17553,51.368713,4.097 1938,440.93445,-18.3017,20.008087,45.10646,4.06 1938,131.6224,215.73174,31.856354,53.337753,4.052 1938,449.15042,-17.19188,19.569427,43.146408,3.954 1938,459.5204,-17.47456,32.617126,64.84217,3.949 1938,49.15844,249.19424,32.472572,63.31595,3.941 1938,488.23578,261.291,36.921753,89.90622,3.933 1938,280.9212,246.35107,27.43396,55.11908,3.902 1938,243.90326,220.69432,17.929962,32.29869,3.886 1938,2.2437298,489.64136,19.391207,40.745117,3.82 1938,457.7495,-19.273638,18.89325,43.771187,3.81 1938,283.72217,247.99232,18.589783,35.479507,3.803 1938,619.45593,-17.637781,17.03125,42.276043,3.799 1938,-9.974888,-5.818516,40.725975,99.99611,3.774 1938,116.43355,217.42558,30.045883,54.193375,3.767 1938,147.00366,210.92775,33.057297,61.523087,3.743 1939,-5.9051723,488.68295,18.449589,42.173187,6.748 1939,-12.05309,478.1075,30.50055,62.54312,6.377 1939,616.4768,268.40756,24.424011,61.24234,6.247 1939,627.21893,-16.898632,16.826782,45.60443,5.989 1939,77.43895,251.28264,28.98027,60.459976,5.856 1939,-12.608583,-39.84295,41.41853,100.88252,5.727 1939,-5.9206095,-18.383759,17.76532,43.183712,5.725 1939,612.95734,454.04834,39.83795,108.0282,5.401 1939,250.4589,212.45198,19.424103,32.500183,5.34 1939,622.71124,-33.362778,26.572754,76.509605,5.258 1939,-72.38543,266.04773,200.5433,462.6018,5.232 1939,586.0083,-97.43376,74.24469,229.71721,5.158 1939,535.7052,310.0845,144.09344,340.5128,5.13 1939,-17.657331,436.8416,55.32894,140.73999,5.017 1939,532.4114,-181.98839,149.73828,423.63983,5.005 1939,582.6681,409.73636,72.06647,178.8234,4.956 1939,493.24692,228.37646,237.62173,514.5526,4.891 1939,602.7965,-55.85765,53.41809,136.26767,4.771 1939,-20.201698,-85.62528,71.52979,193.68578,4.748 1939,329.70587,230.9463,31.543518,60.37706,4.704 1939,-40.59234,366.83987,111.12354,275.2336,4.665 1939,534.6437,288.38763,60.1214,138.41925,4.537 1939,613.3877,241.52429,27.153137,69.499084,4.507 1939,599.08356,242.6803,36.68524,105.10852,4.476 1939,133.74101,252.58844,31.02356,64.459595,4.394 1939,626.54895,488.05927,20.917786,45.570557,4.386 1939,-37.04372,-125.605446,116.80853,272.96906,4.376 1939,1.2678635,235.60075,28.486187,66.910355,4.331 1939,485.80133,-33.14037,28.405212,73.598595,4.331 1939,573.64484,249.37843,54.481384,140.55956,4.315 1939,-70.32004,-187.2125,210.8,453.39877,4.194 1939,86.70938,235.19386,37.11821,87.24135,4.167 1939,476.94034,-15.045706,30.953613,65.097755,4.126 1939,-4.65053,218.75305,17.918892,38.81784,4.122 1939,298.0733,243.51607,19.489563,39.532944,4.096 1939,101.50425,230.07646,36.080498,81.95924,4.085 1939,562.0773,314.8515,42.359436,108.83484,4.081 1939,612.2827,250.72011,35.863342,111.984695,4.076 1939,612.39703,329.004,38.67749,115.37274,4.066 1939,590.2396,274.1193,51.59082,159.69263,3.992 1939,569.46576,323.01675,59.112976,172.93088,3.979 1939,563.8549,258.2765,43.06189,98.10593,3.975 1939,497.5183,329.48376,35.154114,73.86853,3.965 1939,468.42438,306.57968,30.43866,67.82358,3.942 1939,253.16519,214.26347,28.72638,70.83626,3.929 1939,516.9117,311.95502,30.744385,66.663574,3.881 1939,485.0479,318.86563,34.601868,70.96896,3.873 1939,2.185876,489.88235,19.814903,40.51474,3.862 1939,493.3022,-9.666203,30.964539,65.789856,3.811 1939,243.41498,209.8794,19.131256,33.698242,3.803 1939,505.23575,293.93457,61.70468,129.36514,3.803 1939,593.901,-44.08502,45.435913,96.32086,3.778 1939,555.5248,-2.811821,18.421814,35.550453,3.776 1939,517.54297,-32.902725,29.120117,71.94721,3.733 1939,497.2087,-17.113682,18.981964,41.741463,3.727 1939,541.1585,268.62958,93.66266,244.59174,3.727 1939,489.35886,-17.742474,19.085815,42.61642,3.715 1939,301.83902,237.8469,27.289642,55.45636,3.693 1939,58.727844,250.24931,34.254883,59.18889,3.669 1939,283.9462,241.04015,18.079468,35.235397,3.665 1939,246.03946,211.52765,27.668793,56.501587,3.661 1939,583.1648,242.15141,39.057434,88.8741,3.64 1939,446.49402,218.85553,52.043213,127.15045,3.635 1939,482.0055,-16.589457,18.591888,41.828312,3.627 1939,613.06995,195.91985,35.25537,106.5464,3.604 1939,279.66977,237.59949,26.880768,55.852478,3.59 1939,132.54378,214.22194,38.69272,86.05452,3.571 1939,306.47272,263.85178,19.785034,38.549347,3.56 1939,530.9118,323.68503,42.954712,114.58551,3.543 1939,2.9279404,-16.142607,16.679665,39.37833,3.539 1939,619.7607,-16.05194,16.481262,42.100945,3.525 1939,551.39233,267.0429,61.585205,139.54962,3.506 1939,546.46185,259.26315,43.848083,97.47052,3.503 1939,462.25635,231.40543,52.045227,130.94232,3.481 1940,165.89285,234.54121,28.378983,62.284042,6.543 1940,-12.296189,476.03754,31.200802,64.576965,6.164 1940,-6.0959234,487.89413,18.603195,43.573578,6.147 1940,-11.492124,-32.40928,29.66563,72.112564,6.118 1940,19.565868,229.93239,31.144028,68.571365,6.014 1940,-5.960163,-18.28827,17.826124,42.67203,6.011 1940,4.2090025,228.02954,30.359768,75.356445,5.945 1940,613.381,454.71274,38.994568,106.656586,5.512 1940,-72.718475,265.7422,201.06851,463.20398,5.322 1940,-17.423351,-62.188698,55.919918,144.52307,5.059 1940,586.1001,-98.8685,74.5647,231.88449,5.026 1940,627.3962,-17.641737,16.714844,45.842922,5.01 1940,-21.724863,402.59613,79.456436,175.445,4.964 1940,532.21893,-181.55627,149.79883,421.79004,4.879 1940,626.6271,488.8955,20.64624,44.741394,4.873 1940,583.25995,410.69116,71.54834,177.66156,4.862 1940,623.1694,-33.76131,26.03009,75.62793,4.848 1940,536.4532,311.2904,142.92096,341.04126,4.713 1940,298.68686,241.23558,19.523834,34.978622,4.68 1940,125.416,245.87161,31.455444,60.821045,4.583 1940,493.46313,227.14331,237.55066,515.02246,4.504 1940,331.4306,232.47458,30.036102,63.012848,4.466 1940,603.1669,-57.1537,53.65381,138.9551,4.427 1940,525.5163,274.7141,28.835388,71.29437,4.398 1940,249.00499,211.13333,21.060287,34.383057,4.394 1940,436.6423,200.24336,31.535614,71.190506,4.276 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1940,53.80531,213.1337,37.402332,82.660736,3.554 1940,150.41388,219.03717,37.003174,78.27362,3.536 1940,472.8985,284.8716,22.543823,52.78601,3.531 1940,475.05283,227.44386,52.135834,141.01768,3.528 1940,388.29016,231.0599,39.75476,91.79675,3.476 1940,312.79758,238.85841,21.947021,45.942276,3.473 1940,221.50998,236.00545,29.994507,54.663498,3.458 1940,1.747694,-40.01858,43.477646,101.0473,3.411 1940,481.87354,-17.979877,19.019562,41.713608,3.364 1940,445.71588,201.9154,54.34024,143.617,3.336 1940,-8.755803,255.84949,37.997654,108.147766,3.327 1940,506.21783,-20.414324,18.483643,44.186787,3.311 1940,304.85257,241.69368,20.657837,42.68965,3.284 1940,618.44244,491.73526,20.437805,40.825043,3.277 1940,460.6427,204.86345,32.067566,62.76967,3.275 1940,486.06415,-35.663963,28.793518,75.25506,3.27 1940,473.99905,209.64851,22.485016,49.2343,3.262 1940,2.9555714,-16.274675,16.6058,38.880527,3.261 1940,613.468,361.74826,38.28949,116.771484,3.26 1940,14.439823,203.47884,51.30261,118.04045,3.233 1941,-5.921218,487.83072,18.633183,43.852753,6.887 1941,372.03983,227.37328,38.158722,93.66353,6.385 1941,626.19434,487.1301,20.933838,47.43869,6.367 1941,361.39767,233.16547,36.76773,74.9046,6.191 1941,-12.217493,476.028,31.128853,64.18732,5.847 1941,-5.9980497,-19.116861,17.889168,44.078167,5.788 1941,-12.575504,-40.092674,41.39499,101.325096,5.741 1941,613.04474,455.77997,39.575256,105.9986,5.618 1941,350.63354,238.82059,32.960052,68.99841,5.508 1941,-72.575645,265.80084,200.88525,462.5782,5.253 1941,622.84094,-33.92121,26.254578,77.11717,5.116 1941,627.21405,-18.120409,16.931335,47.228653,5.099 1941,585.8896,-98.777374,74.57153,231.57825,5.096 1941,-21.771599,402.42642,79.583466,175.56741,4.97 1941,491.36096,-181.4904,234.05164,426.22342,4.849 1941,477.56158,203.16125,29.559204,63.134796,4.772 1941,583.4069,410.1682,71.2984,177.88416,4.753 1941,154.00443,238.63489,33.373413,57.880737,4.735 1941,-20.134642,-86.507,71.35348,196.12122,4.7 1941,602.84143,-56.920322,53.50592,135.49199,4.688 1941,325.82016,230.58061,35.885498,79.68663,4.575 1941,536.3715,311.09918,142.7232,341.34528,4.522 1941,-6.4019995,217.38156,18.086006,40.712128,4.444 1941,4.3266478,213.10973,30.08812,71.04631,4.396 1941,-37.445473,-125.43465,117.432465,275.6126,4.331 1941,493.26318,226.90204,237.6134,515.72687,4.324 1941,-70.50153,-188.2709,210.54614,454.59567,4.31 1941,612.97943,359.8275,37.404602,119.26068,4.154 1941,310.27,231.55981,35.13025,80.18555,4.132 1941,-14.520044,179.55191,48.19928,142.07797,4.12 1941,179.5442,239.80157,32.691498,60.227356,4.094 1941,593.86096,-46.250633,45.09192,97.126945,4.049 1941,492.3386,-16.869293,33.20529,63.844067,4.025 1941,140.76692,242.4599,31.151642,59.726257,3.974 1941,293.0554,238.69662,38.239624,93.89435,3.929 1941,387.3683,235.32898,40.071655,98.20709,3.841 1941,618.45844,488.8684,20.748657,44.299377,3.802 1941,-5.543255,127.81145,17.81689,45.039032,3.8 1941,254.12636,220.1074,26.386307,60.80223,3.791 1941,99.49101,235.01598,40.795578,96.85741,3.768 1941,2.2207685,489.15015,19.170029,41.53546,3.75 1941,67.29158,233.85516,42.184624,98.68201,3.722 1941,404.3694,236.77934,37.67383,102.676285,3.719 1941,521.1393,-20.21471,20.241882,42.85801,3.711 1941,410.21805,237.99258,22.8768,40.302185,3.686 1941,501.64737,-33.811443,29.89267,72.57834,3.675 1941,165.52066,218.73238,36.450912,78.165665,3.672 1941,516.9033,-34.758083,29.412842,69.62808,3.663 1941,298.47934,246.8937,20.207886,38.0495,3.632 1941,306.21133,243.13622,19.608368,39.05107,3.631 1941,619.58624,-18.120024,16.921692,44.76767,3.614 1941,485.7701,-34.30078,29.450592,73.02734,3.61 1941,474.62796,217.5351,21.133331,46.992126,3.577 1941,4.550885,229.97585,39.231792,108.822495,3.574 1941,512.8762,-18.924194,20.63031,42.573227,3.555 1941,117.45799,218.89146,37.649216,75.72853,3.529 1941,571.3352,-4.390106,19.13739,37.05554,3.514 1941,1.9099939,227.47812,21.215715,50.97403,3.513 1941,555.7142,-5.528246,19.131348,36.529537,3.496 1941,248.69601,223.41652,21.568024,41.60811,3.487 1941,505.0787,-19.224968,19.743439,42.48712,3.486 1941,137.10068,241.0906,22.744324,35.390076,3.449 1941,234.25244,230.20523,21.727844,40.981476,3.437 1941,352.6608,230.2255,23.459198,50.5466,3.435 1941,26.144138,236.30385,35.07745,61.55925,3.431 1941,243.44196,265.69235,18.151245,35.806213,3.424 1941,313.46887,243.37157,21.643585,41.592026,3.417 1942,386.8792,232.66656,38.998352,100.06128,7.987 1942,-12.34893,477.42285,30.768593,63.31244,6.667 1942,-6.0226774,488.30646,18.626217,43.14862,6.632 1942,-11.582336,-32.414417,29.87931,71.63314,6.588 1942,-5.9964867,-18.844742,18.047428,43.270382,6.054 1942,-72.6382,265.42947,200.89218,463.09872,5.352 1942,108.29103,225.24161,32.892273,57.574066,5.328 1942,627.40955,-17.30999,16.705566,45.68718,5.281 1942,-17.179508,-62.86978,56.123333,145.70613,5.178 1942,422.3088,233.57217,39.24115,108.39838,5.156 1942,306.35498,247.48004,20.190369,34.88159,5.126 1942,613.1122,453.86868,39.455566,108.39316,5.092 1942,585.8625,-98.50331,74.778076,230.68523,5.043 1942,-21.937151,402.0511,79.759926,175.87189,4.981 1942,623.01953,-33.827423,26.409729,76.29431,4.979 1942,583.09454,409.5954,71.629944,178.48724,4.959 1942,90.394226,225.67223,34.27337,58.986862,4.902 1942,626.5076,487.9314,20.741882,45.484863,4.874 1942,536.3637,310.77957,143.01276,341.49948,4.862 1942,491.16547,-181.04256,234.1734,425.65094,4.832 1942,132.61961,223.80685,39.46228,88.01544,4.798 1942,165.5062,217.4982,37.144867,74.975494,4.742 1942,396.16815,221.68417,50.07617,123.081726,4.645 1942,603.2195,-57.331665,53.393738,136.25674,4.643 1942,299.2107,247.83267,19.66397,33.727325,4.611 1942,493.07437,227.36478,238.20908,513.55457,4.553 1942,476.61023,-11.599827,33.45645,60.83603,4.497 1942,66.51679,223.41104,34.79194,67.12579,4.49 1942,508.66785,318.95514,30.704834,76.63098,4.489 1942,481.9599,-15.655245,18.556152,39.950516,4.456 1942,442.4733,219.49619,52.621277,146.5289,4.433 1942,-37.50328,-125.59664,117.64076,274.99554,4.413 1942,149.66663,218.19789,38.239685,74.934494,4.403 1942,116.78193,224.34604,40.717293,89.04602,4.378 1942,238.15443,233.9727,28.59816,57.897354,4.223 1942,313.7408,248.22395,20.55896,34.679733,4.22 1942,364.46082,243.91103,33.172363,63.733627,4.22 1942,-70.164764,-187.59875,210.35567,453.59125,4.213 1942,50.3136,222.93164,34.90851,67.6781,4.154 1942,247.0151,221.68039,34.190887,79.286804,4.124 1942,375.5936,230.4086,37.658905,83.12378,4.093 1942,571.5413,328.4667,55.15674,168.79184,4.07 1942,483.53497,199.10793,31.623657,63.16472,4.062 1942,2.764691,-16.976067,17.023544,39.97954,4.017 1942,136.3689,233.40395,25.044891,37.7191,4.0 1942,-14.068933,180.36826,48.323418,141.38849,3.99 1942,181.49731,215.43227,36.89241,76.80327,3.964 1942,5.191579,211.5194,37.893326,97.24245,3.951 1942,169.47488,241.04543,23.662186,43.07985,3.948 1942,594.07544,-46.054455,45.012024,97.16726,3.946 1942,198.4758,214.03604,35.732346,77.653595,3.832 1942,474.1641,-16.640965,17.875732,40.021477,3.828 1942,266.19714,233.71393,22.206207,42.654602,3.755 1942,2.0708756,490.00122,19.432814,40.847717,3.705 1942,230.42075,220.09908,29.296082,58.090927,3.686 1942,120.59613,242.04001,22.614334,40.574097,3.684 1942,98.71426,231.137,42.894997,98.06641,3.671 1942,-10.161505,-5.194416,40.443047,101.88956,3.636 1942,499.97147,196.06615,37.933624,96.451614,3.62 1942,489.312,-17.176231,18.845276,41.993378,3.612 1942,380.27072,227.25525,54.724823,161.68573,3.605 1942,12.507253,173.00063,53.482594,114.87703,3.603 1943,-5.86664,488.7898,18.302717,42.66394,6.368 1943,626.81757,488.78644,20.460327,44.24536,6.208 1943,-13.046041,-39.719658,41.796383,101.59086,6.04 1943,123.57649,227.54141,32.214615,62.035583,5.985 1943,610.5983,-48.153152,42.096497,99.86448,5.945 1943,-11.89302,477.8083,30.190655,63.06079,5.91 1943,436.5195,284.89993,31.873688,60.160675,5.685 1943,-6.1024904,-18.795086,18.023605,43.594257,5.643 1943,108.93714,225.55263,31.231422,59.95723,5.582 1943,-72.82768,265.36322,201.0565,463.0755,5.316 1943,627.291,-18.726809,17.195557,47.38401,5.113 1943,571.28436,183.31128,29.526733,66.13974,5.063 1943,613.10254,454.8628,39.496033,107.39142,5.025 1943,586.4126,-98.78879,74.1228,229.84305,4.964 1943,583.2464,410.31314,71.66559,177.62497,4.928 1943,491.44354,-181.88048,233.95453,425.74387,4.922 1943,536.3449,310.51917,143.1148,341.58533,4.916 1943,-21.84514,402.0897,79.58711,175.913,4.911 1943,-5.0817566,205.41595,18.350817,38.568176,4.868 1943,-20.252949,-84.83481,71.73973,194.17058,4.78 1943,314.1887,246.35233,20.61789,36.28131,4.719 1943,493.30774,226.94037,237.47931,513.99304,4.687 1943,5.0991545,208.25,36.561222,86.618835,4.66 1943,-14.149401,185.65454,48.755455,136.70184,4.655 1943,92.35408,221.28267,32.178757,63.27043,4.547 1943,1.1311662,213.40977,21.692602,39.835144,4.545 1943,-37.521057,-124.4221,118.2087,272.1608,4.541 1943,231.0193,211.6007,35.20793,80.32121,4.413 1943,429.3631,243.6696,53.33377,118.271286,4.412 1943,458.96185,258.54953,57.154724,144.44626,4.387 1943,382.20367,229.51263,28.758698,55.67383,4.386 1943,316.07236,235.51202,29.409332,55.05139,4.355 1943,397.5971,224.9825,28.626312,64.100876,4.328 1943,-70.4558,-188.39392,210.83893,455.01022,4.317 1943,19.258152,212.81561,39.921158,77.916016,4.297 1943,419.58615,257.46158,41.27002,84.46661,4.205 1943,65.95181,212.76651,34.327118,69.45615,4.204 1943,557.1676,189.50471,33.854553,68.72218,4.178 1943,187.5058,234.45325,31.85234,61.3006,4.061 1943,214.48494,210.96413,36.2713,79.740005,4.045 1943,404.62585,254.86252,39.172363,84.89606,4.031 1943,583.39166,191.41245,22.47284,47.406128,3.913 1943,250.50577,214.67828,17.919678,27.21936,3.851 1943,132.9733,203.12,40.099335,76.76062,3.815 1943,51.825176,221.63242,34.335773,66.75668,3.81 1943,523.35657,185.81113,31.822998,72.30936,3.803 1943,563.2517,192.73834,41.29181,100.44867,3.798 1943,618.8648,491.11948,19.917603,40.47525,3.769 1943,399.05542,276.81293,28.112274,65.15015,3.744 1943,197.67905,203.71082,37.59131,75.897644,3.743 1943,35.747334,201.34589,40.715923,74.34924,3.742 1943,202.22716,245.97156,21.665741,40.88611,3.724 1943,305.48108,240.7576,21.125854,36.128418,3.721 1943,149.15862,212.8193,39.264572,78.66794,3.715 1943,474.35846,-18.946457,18.1763,42.244953,3.711 1943,106.09079,231.94687,22.709229,44.186157,3.705 1943,234.96642,251.73184,18.233475,34.38971,3.691 1943,497.0012,294.31036,42.296356,110.65042,3.678 1943,2.8032672,490.25378,18.871748,40.70874,3.677 1943,-9.75186,202.48341,28.147165,61.01686,3.674 1943,151.92995,226.49413,25.021072,38.38713,3.668 1943,-0.058060884,227.14825,25.787212,74.58087,3.665 1943,163.60873,222.683,40.937332,92.232956,3.66 1943,11.280268,-11.346798,60.23899,118.32225,3.65 1943,243.97057,237.15706,29.765518,60.13768,3.635 1943,377.61896,231.3063,23.605682,37.673065,3.617 1943,450.4292,252.50482,41.795563,95.23712,3.614 1943,469.72876,-34.37337,28.642212,72.118706,3.61 1943,242.36938,251.76677,19.879883,36.911087,3.599 1944,506.18445,249.21931,22.030579,42.262222,12.179 1944,399.89557,233.3435,25.60492,49.991333,7.574 1944,-6.1152163,488.9733,18.88567,42.135468,6.839 1944,440.68558,243.1821,23.861053,45.722595,6.421 1944,419.0821,209.80942,18.831543,39.239258,6.178 1944,626.44885,487.24435,20.799683,46.54312,6.159 1944,524.99243,243.90099,27.80896,52.9814,6.017 1944,-12.179994,477.40622,31.226788,62.776825,5.981 1944,627.33594,-16.853474,16.814392,45.104675,5.886 1944,-11.550288,-33.16912,29.780697,72.84801,5.801 1944,-6.0165596,-18.738289,18.09684,43.729725,5.67 1944,612.7732,455.1309,40.1062,106.8638,5.632 1944,380.20862,234.29498,33.277374,60.95868,5.59 1944,622.928,-33.754044,26.332947,75.306274,5.424 1944,-72.820885,265.44238,201.02335,462.82587,5.284 1944,514.06635,254.53748,21.585754,41.472412,5.141 1944,-16.874039,-62.158363,56.33158,144.87935,5.14 1944,586.1354,-98.56162,74.214355,231.26303,5.03 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1954,13.89194,208.49641,51.65951,112.329025,4.207 1954,4.9320707,243.39447,38.36794,115.1781,4.206 1954,-70.50865,-188.31529,211.0794,454.87256,4.197 1954,422.92053,-34.849052,28.65689,71.22338,4.184 1954,293.56335,223.49147,28.816406,55.948715,4.076 1954,323.94717,227.17279,29.63086,63.732483,4.07 1954,379.22104,188.26434,51.377472,102.40344,3.979 1954,310.20123,227.8217,28.38736,58.984573,3.954 1954,1.8387842,220.53665,23.071186,52.80191,3.953 1954,385.50623,207.33296,19.121002,32.086777,3.91 1954,440.79077,-18.829788,20.32196,43.327534,3.906 1954,-11.510208,206.99171,30.642813,66.40004,3.894 1954,618.4215,489.9671,20.575623,41.943054,3.873 1954,433.1441,-19.424175,20.393066,44.244324,3.85 1954,340.71017,202.95966,39.052856,73.09973,3.841 1954,413.03342,173.38217,52.182037,116.978485,3.828 1954,460.55084,-33.37618,29.875732,73.824234,3.802 1954,425.66595,-20.159843,20.042236,44.29631,3.782 1954,-5.5494976,167.669,18.385391,46.077682,3.757 1954,305.49725,233.6851,22.487549,44.38762,3.735 1954,35.826836,213.4127,38.15883,80.0237,3.695 1954,2.191925,490.52814,19.691393,40.485107,3.674 1954,517.1151,-3.1512089,29.848022,63.93708,3.67 1954,-10.762458,-4.293442,41.22331,99.38604,3.605 1955,-5.9210243,488.95712,18.476593,42.29062,6.845 1955,361.21323,233.64517,21.71463,37.02307,6.532 1955,449.38068,214.40889,19.505096,38.54796,5.784 1955,-11.254874,-32.575,29.504604,72.88403,5.734 1955,-11.891089,477.93417,29.972023,62.880035,5.637 1955,289.29456,272.17612,20.29303,42.53128,5.588 1955,-6.0154223,-18.631668,18.006193,43.69849,5.547 1955,622.9881,-34.03113,26.331665,76.917046,5.491 1955,627.3863,-17.266624,16.84906,46.058895,5.479 1955,-17.14774,-63.135246,55.84453,147.31842,5.321 1955,325.5856,219.23718,29.429382,59.457703,5.199 1955,-72.982666,265.14288,201.15178,462.83368,5.175 1955,358.74402,214.82468,35.306213,73.624115,5.153 1955,490.65335,-182.4426,234.2344,429.28787,5.129 1955,369.4472,235.4395,21.812439,36.990677,5.102 1955,613.40045,454.8369,38.971313,107.12036,5.024 1955,583.08704,411.6407,71.57184,176.6258,5.009 1955,373.49542,213.77493,35.84903,72.976105,5.006 1955,-21.84699,402.24774,79.62345,175.51947,4.938 1955,585.4507,136.76868,54.024902,156.6253,4.892 1955,536.0081,309.77402,144.39197,342.58536,4.843 1955,586.3022,-98.86761,73.88098,232.97113,4.77 1955,-6.965967,242.86421,21.008873,46.839096,4.753 1955,444.39603,213.2004,17.656952,31.680695,4.704 1955,626.4106,487.74164,21.281921,45.845337,4.673 1955,493.60645,227.07162,237.32184,514.36584,4.583 1955,603.1908,-56.80716,53.296265,136.38686,4.582 1955,-36.538845,-127.10811,116.60768,276.68396,4.453 1955,-5.9044065,212.14449,18.063282,40.168884,4.417 1955,354.146,230.41238,21.761963,35.2399,4.405 1955,342.38477,213.80066,36.096497,75.32794,4.362 1955,-70.602905,-188.84827,211.56433,455.2029,4.294 1955,-6.515157,227.10588,19.927765,43.14421,4.181 1955,377.1905,230.98782,22.638184,37.786224,4.18 1955,610.5315,146.60674,38.095093,121.4518,4.158 1955,548.164,208.5647,41.902893,80.23999,4.129 1955,-9.86642,7.3560104,39.96131,104.493004,3.997 1955,312.43793,195.8773,20.36026,42.330093,3.926 1955,293.05396,207.39272,38.464905,79.475296,3.916 1955,594.36206,-45.803726,44.556274,97.868195,3.902 1955,561.5392,147.69199,66.68359,180.76547,3.878 1955,389.95795,211.46727,35.91623,74.51515,3.873 1955,382.7525,215.60071,23.099365,44.772766,3.862 1955,539.964,216.39073,33.69165,63.366013,3.85 1955,619.8197,-16.9633,16.502441,42.590324,3.848 1955,260.38687,205.34946,38.88446,78.39084,3.804 1955,-17.052992,156.72406,52.56598,140.0816,3.792 1955,310.66312,209.96606,38.083588,78.2005,3.744 1955,426.22174,210.94238,18.575684,39.19751,3.724 1955,-5.4841323,194.86292,17.683681,38.24234,3.693 1955,406.31564,200.06291,36.97107,76.252426,3.687 1955,5.9737253,164.7134,27.85136,67.876495,3.676 1955,516.28955,-15.173443,31.526367,66.68725,3.673 1955,-1.9062448,185.65337,28.55362,70.24602,3.581 1955,-8.742552,212.22913,38.40018,88.91235,3.573 1955,228.53833,204.70108,38.531982,78.417725,3.563 1955,283.4386,244.4283,30.365479,64.20381,3.55 1955,-12.137968,197.6432,30.3891,67.93858,3.534 1955,336.64334,232.64746,22.35138,39.886475,3.524 1955,0.5506635,243.29184,22.905659,53.256653,3.513 1955,563.4353,183.47697,42.87683,91.655716,3.507 1955,16.77874,201.3716,43.123596,79.56813,3.493 1955,346.04007,232.41658,21.240509,37.97917,3.487 1955,322.57507,230.81361,22.378662,42.46431,3.469 1955,573.5488,-21.839603,53.057312,131.71593,3.449 1955,429.37973,212.38652,30.070648,61.34813,3.448 1955,541.56366,-19.143864,55.8667,127.79146,3.443 1955,199.92424,230.34816,21.884628,35.99666,3.429 1955,618.0433,489.64957,21.271667,43.163177,3.427 1955,562.5474,10.64624,32.51996,73.16202,3.422 1955,-5.0159845,176.59392,17.53143,42.531555,3.417 1955,3.966341,233.67355,38.615707,106.22458,3.417 1955,564.1189,228.53806,40.97583,88.84235,3.399 1955,390.85272,32.260914,31.486023,74.78528,3.389 1955,-8.364357,67.85984,38.455544,107.109886,3.362 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1969,-36.27193,-126.707855,116.1268,273.6932,4.783 1969,347.5882,202.01881,19.573944,39.49832,4.755 1969,536.50305,310.75787,143.29742,342.38422,4.655 1969,478.43735,196.76508,25.940979,60.54785,4.515 1969,493.65704,226.96747,237.52167,515.56146,4.393 1969,-70.69039,-189.51189,211.14026,456.53772,4.336 1969,536.8975,181.50531,19.74408,40.223038,4.322 1969,440.66998,-3.4258919,33.318237,62.544548,4.197 1969,-9.282857,-19.626759,38.48579,100.086075,4.054 1969,258.25076,204.22835,19.64032,31.570435,4.011 1969,155.12033,215.3808,18.496399,28.936752,3.996 1969,569.1229,185.60901,32.28827,80.778625,3.974 1969,90.406784,216.60683,19.536613,38.423325,3.954 1969,402.32275,203.03664,21.295105,38.460358,3.903 1969,464.03506,187.64874,29.803772,67.34221,3.88 1969,323.12854,200.2931,18.305481,31.805511,3.879 1969,487.67206,203.76744,21.189636,46.77646,3.834 1969,2.500186,49.850742,20.902409,53.49377,3.832 1969,425.60406,-15.079031,18.759155,39.983524,3.796 1969,360.72937,209.0859,19.25351,35.93994,3.79 1969,341.14102,202.82501,17.995514,36.70308,3.757 1969,81.78728,216.70439,19.765427,39.042694,3.748 1969,-5.258936,185.37875,18.820238,43.549652,3.719 1969,339.31882,189.78384,18.239319,31.133102,3.707 1969,4.343978,209.64688,29.301544,67.86502,3.695 1969,432.88086,-15.7519045,19.443634,41.630886,3.672 1969,440.73163,-18.311193,19.653046,43.100857,3.65 1969,533.163,-31.667767,29.104065,63.13124,3.558 1969,427.79663,-13.158836,33.41913,63.456623,3.55 1969,539.1234,-23.644577,59.069824,132.92882,3.529 1969,527.84204,-41.42005,47.09088,91.29067,3.516 1969,1.4332914,-38.83822,43.13889,99.986595,3.498 1969,266.02612,208.97707,19.266815,30.180283,3.472 1969,571.9445,174.79613,52.56714,143.709,3.468 1969,274.0567,208.08136,19.620026,29.695633,3.451 1969,75.78305,210.62903,30.326996,58.389557,3.429 1969,560.2111,-42.453686,46.950012,92.085,3.425 1969,105.80748,209.80818,19.421944,38.40866,3.404 1969,189.64409,220.60443,15.396286,22.067795,3.401 1969,531.17175,187.91017,19.32544,38.86302,3.4 1969,619.5889,-19.406942,17.682068,44.114258,3.394 1969,197.19241,220.39853,15.395294,22.78624,3.386 1969,449.58194,-18.8204,19.205292,42.124615,3.382 1969,573.44495,163.01744,37.241943,80.123535,3.381 1969,244.12485,218.54642,16.896698,27.57611,3.379 1969,-5.3678946,134.58212,17.891344,44.009247,3.371 1969,252.86592,219.26514,16.956741,26.777863,3.369 1969,422.23666,-33.91709,29.236694,74.73961,3.361 1969,122.486145,211.1896,18.969955,35.14966,3.347 1969,444.2772,-32.752438,30.118805,72.18756,3.313 1969,156.3327,-1.358923,17.18982,32.69156,3.298 1969,490.44122,-31.588354,58.74713,124.14936,3.297 1969,513.5807,-17.847496,19.336609,39.09323,3.295 1969,402.4203,183.81139,19.873474,40.89662,3.283 1969,-5.3602943,151.87418,17.904856,44.585754,3.282 1969,205.26944,219.94446,14.205597,23.05658,3.277 1969,172.1125,211.84924,16.710297,26.371063,3.262 1969,171.9982,-0.4439335,16.812225,31.825758,3.254 1969,83.74442,202.55861,30.49385,56.42418,3.246 1969,458.71533,-32.516266,58.067444,123.13879,3.237 1969,2.3975575,-16.592304,17.595634,41.15812,3.212 1969,529.68005,-16.889078,19.339172,38.55164,3.206 1969,418.23584,-14.07482,18.062744,39.059193,3.193 1969,558.2948,174.05159,30.84552,78.705795,3.185 1969,236.01128,220.09807,16.070358,24.779068,3.178 1969,606.38214,-20.207642,29.31903,66.42432,3.17 1969,531.5724,-135.90172,115.84857,270.25613,3.165 1969,380.7729,41.974316,31.651978,75.64548,3.151 1970,530.4087,206.63773,31.501404,70.54416,7.912 1970,383.47156,213.28432,23.80194,47.874466,7.658 1970,389.11362,202.39043,28.368256,56.97966,7.109 1970,-11.261274,-32.142242,29.988546,76.57157,6.9 1970,71.10777,213.05486,25.149689,44.876175,6.5 1970,370.09113,210.2714,21.04654,40.239807,6.147 1970,420.8524,194.15799,29.851715,60.15738,6.111 1970,-6.263112,-16.38713,17.96238,44.101444,5.763 1970,610.5632,-47.9209,41.45819,100.823814,5.709 1970,-9.192026,463.90894,37.85981,92.61127,5.598 1970,-6.1451845,488.60577,18.73534,43.30188,5.595 1970,626.47614,487.4746,20.815247,46.102295,5.57 1970,-20.299356,-86.03274,71.94624,194.28563,5.146 1970,612.9666,454.62582,39.352356,107.34897,5.057 1970,-72.870224,265.35504,200.61145,462.1225,5.054 1970,583.21387,411.794,71.46393,176.41473,5.041 1970,-21.814293,401.91248,79.4769,175.706,4.895 1970,586.3537,-99.0222,74.28851,230.08554,4.871 1970,589.5439,177.69214,33.032654,62.24791,4.854 1970,627.136,-20.301632,18.092041,46.803642,4.839 1970,532.7334,-182.49876,148.64203,423.57755,4.82 1970,536.2485,310.58093,144.0011,342.74756,4.794 1970,-36.521282,-126.74851,116.28462,273.94528,4.7 1970,493.79877,226.66754,237.08624,515.2018,4.507 1970,73.328995,229.27724,22.540054,43.069168,4.355 1970,-70.64318,-188.9804,211.24905,455.61163,4.354 1970,405.28488,197.64581,29.62497,57.767685,4.335 1970,598.1634,175.39754,36.67395,99.13603,4.281 1970,259.4543,233.19754,19.106628,30.41034,4.247 1970,429.81897,204.3914,28.302338,58.86989,4.233 1970,306.23346,176.50851,17.784485,38.64363,4.218 1970,350.20404,203.23792,26.390625,50.22586,4.216 1970,-9.322101,-18.741652,38.648308,98.7934,4.2 1970,121.70745,218.66586,21.611015,37.84198,4.103 1970,612.8137,186.51794,36.63501,108.413025,4.062 1970,379.32672,222.9146,18.678528,38.080246,4.056 1970,2.0294971,-15.45304,17.096432,43.01465,4.043 1970,518.2689,198.19423,31.810059,70.20604,3.97 1970,252.38914,235.05115,19.092606,29.535034,3.945 1970,432.32742,193.38315,21.55835,43.49707,3.927 1970,617.7233,179.0387,23.905212,62.555466,3.911 1970,580.9384,188.29901,40.657166,97.007385,3.871 1970,465.14923,197.97675,22.509003,45.582,3.838 1970,603.12964,-27.181545,52.532837,134.74556,3.789 1970,436.15936,188.43391,29.619385,60.305054,3.681 1970,605.0021,-35.11346,29.32135,74.56664,3.614 1970,60.33525,218.86432,33.981537,58.81122,3.613 1970,511.34338,189.65756,28.557678,62.617966,3.59 1970,541.43286,161.33597,53.99182,110.56186,3.543 1970,618.0796,-20.725105,19.338257,45.940273,3.516 1970,425.82153,215.72496,20.628296,40.943314,3.497 1970,8.838444,-89.32956,73.87596,205.00168,3.497 1970,577.34045,189.79276,23.348267,43.34781,3.484 1970,446.0409,204.01859,27.709076,57.438843,3.424 1970,532.98816,184.24075,38.94806,73.928955,3.413 1970,172.52135,222.4022,16.440292,24.027405,3.411 1970,589.9624,-35.328716,28.604614,73.96125,3.383 1970,564.0551,165.77368,40.06427,79.32298,3.38 1970,362.60992,213.12239,20.501373,38.871475,3.369 1970,561.78784,188.8332,23.171692,43.690018,3.35 1970,451.16394,211.77827,41.602966,83.83296,3.344 1970,485.1187,181.71674,39.34778,78.70361,3.325 1970,1.7682886,-37.565056,43.06602,98.33005,3.313 1970,573.89655,-24.345684,53.938904,127.420715,3.31 1970,-4.897371,263.32162,16.583342,39.476593,3.284 1970,589.1985,-57.083466,53.447083,133.76157,3.255 1971,369.39548,203.93355,23.76419,44.1071,17.644 1971,451.28036,179.08144,28.695526,74.278946,12.795 1971,384.36774,205.5099,21.822876,40.089508,6.748 1971,424.273,207.69922,21.098541,46.42659,6.283 1971,-11.232384,-32.53256,29.782291,74.361206,5.876 1971,-6.225424,-18.685919,18.230019,45.192463,5.606 1971,-8.965671,464.18115,37.90402,92.78955,5.581 1971,627.3609,-17.614555,16.715149,47.625065,5.526 1971,611.41345,-49.942245,41.211548,105.80195,5.249 1971,-6.226577,489.44415,18.821386,42.241943,5.179 1971,461.78265,183.86908,21.487427,50.995697,5.08 1971,-72.585205,266.12366,200.39574,461.1455,5.058 1971,-16.889015,-63.00735,55.81916,143.52336,5.04 1971,583.0151,412.21747,71.70148,175.97693,5.027 1971,626.5904,488.27032,20.869995,45.749207,4.938 1971,393.05508,205.40126,21.823914,38.73477,4.937 1971,-21.684992,402.76205,79.22238,174.9639,4.891 1971,585.8039,-102.14687,75.64398,237.40717,4.828 1971,489.83832,-181.39104,235.4798,425.958,4.796 1971,612.92413,455.5685,39.21942,106.355804,4.788 1971,536.3287,310.43143,143.51532,342.38632,4.739 1971,613.3326,205.84552,27.195984,85.84564,4.615 1971,-36.69727,-128.26773,116.36217,277.16046,4.545 1971,414.2881,-3.1346645,28.213135,63.296173,4.472 1971,493.5951,226.69516,237.55511,515.2809,4.464 1971,-70.747765,-189.323,211.3117,455.49112,4.368 1971,427.97836,197.99597,29.072693,59.34436,4.274 1971,-1.0599029,188.09967,27.328318,64.763336,4.171 1971,316.02136,180.91165,15.8498535,30.279419,4.087 1971,72.76456,197.5766,21.88536,41.084778,4.08 1971,438.8818,189.71288,29.871826,69.02272,4.061 1971,58.68347,196.03947,20.62854,41.775925,3.998 1971,373.92725,206.80606,26.8461,57.455444,3.918 1971,402.42264,216.15039,19.12558,39.332245,3.914 1971,45.877476,195.49756,29.532528,56.38568,3.791 1971,-8.766066,-6.2611427,39.03086,101.0452,3.695 1971,297.36978,205.32912,20.19217,34.490967,3.678 1971,407.14047,197.00739,27.814026,58.02893,3.672 1971,283.11765,202.70491,19.023682,33.65573,3.613 1971,426.63727,-14.012751,16.609467,38.612816,3.595 1971,2.539109,192.73776,39.212204,97.70364,3.585 1971,1.7404366,-39.191853,43.00256,97.63747,3.561 1971,619.90283,-3.9253922,17.869446,40.66806,3.532 1971,404.16,-16.445282,30.930573,71.06063,3.514 1971,419.08035,-13.830132,16.715607,39.526325,3.483 1971,289.26266,205.11519,19.83841,35.239807,3.435 1971,425.67737,1.9063225,22.11322,48.265068,3.419 1971,43.774967,198.66644,20.989674,41.35028,3.415 1971,450.51804,-16.480927,17.808136,40.90045,3.404 1971,276.12863,201.84921,18.217896,35.65654,3.403 1971,428.18713,-13.134636,30.785461,65.655975,3.372 1971,117.33311,234.17949,14.385963,24.274887,3.324 1971,232.461,192.18082,20.511658,41.2939,3.29 1971,2.5203223,-17.657999,17.417444,43.934208,3.283 1971,9.499739,-92.07114,73.84126,208.84421,3.272 1971,554.3792,-17.065033,17.627869,42.236782,3.243 1971,442.3933,-16.48831,17.395142,40.55551,3.239 1971,618.7836,246.0664,19.377625,52.692566,3.22 1971,539.9532,137.12726,55.94684,117.5078,3.218 1971,410.5451,-15.681116,17.138153,42.30192,3.209 1971,548.6517,-15.939991,32.154724,65.16034,3.196 1971,589.27026,-58.922466,53.491272,145.31578,3.188 1971,124.6579,226.0791,14.9423065,25.736557,3.168 1971,-5.959966,213.73026,17.922245,41.921417,3.152 1972,18.554289,209.83755,21.048986,43.45224,8.292 1972,598.0267,196.01982,36.37097,94.547134,6.623 1972,590.0929,217.16364,31.50708,70.56964,6.478 1972,-11.494801,-33.107918,31.144266,73.65808,6.209 1972,403.74084,220.53867,19.317993,38.16922,6.192 1972,627.1412,-18.364355,17.378479,46.512447,5.901 1972,528.04816,221.88498,31.106873,67.13425,5.782 1972,-5.8382487,-17.643547,18.354832,43.45265,5.639 1972,-11.904473,477.34384,30.273655,63.517914,5.622 1972,-6.2433367,489.5974,18.84691,41.942383,5.589 1972,622.709,-34.041275,26.841309,76.309,5.504 1972,-17.307343,-62.608524,56.004654,143.17593,5.297 1972,613.0309,454.2914,39.425415,107.85843,5.168 1972,490.85648,-182.52696,234.30038,427.17447,5.095 1972,626.4033,487.70053,21.032837,46.343475,5.068 1972,427.6162,9.841164,36.28241,66.987686,5.007 1972,-72.978546,266.09485,200.80624,461.45392,4.984 1972,32.756126,209.50845,21.997044,36.386093,4.982 1972,579.097,194.92424,46.051086,119.66536,4.953 1972,532.667,199.48558,39.313843,81.321915,4.925 1972,582.98114,411.84277,71.767395,176.39844,4.92 1972,535.99176,309.79907,144.29724,342.92468,4.891 1972,-21.700577,402.86853,79.31288,174.78381,4.861 1972,586.5424,-101.6134,74.79974,234.46886,4.854 1972,548.771,200.86299,39.27191,79.70653,4.832 1972,602.9099,-57.465836,53.482666,137.73933,4.765 1972,566.52893,200.59517,38.376343,82.65379,4.747 1972,-36.64334,-127.3474,116.30888,274.8945,4.675 1972,9.025768,200.3978,26.673534,66.72861,4.592 1972,612.73303,183.53224,35.281982,102.39165,4.592 1972,493.85727,227.41043,237.40604,514.52295,4.465 1972,416.87747,218.29216,21.764648,43.276382,4.36 1972,-70.85229,-189.2504,211.20535,455.63177,4.347 1972,387.9685,226.20563,17.509277,29.291763,4.267 1972,607.5236,216.4732,23.709534,61.492615,4.252 1972,594.5177,-46.900776,44.576843,99.54593,4.136 1972,-8.450214,-4.929516,39.06767,99.78357,4.003 1972,-5.3987656,205.67474,18.184078,46.046402,4.002 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1973,626.31616,487.59598,21.20874,46.373444,4.953 1973,-22.015009,402.0253,79.61763,175.7991,4.924 1973,536.4249,310.80954,143.91992,341.02115,4.875 1973,-36.611526,-126.69372,116.125374,274.55652,4.872 1973,582.98425,411.89438,71.8313,176.31497,4.734 1973,585.871,-100.04881,75.14575,234.65535,4.669 1973,490.00647,-178.6745,235.59094,423.3877,4.656 1973,6.3168135,51.446617,28.950909,73.344635,4.637 1973,408.70114,215.58505,19.969055,41.795715,4.563 1973,494.34204,227.5599,236.76526,513.2556,4.499 1973,452.7359,210.63486,37.617188,85.29489,4.456 1973,619.25195,-19.491999,17.883972,45.923595,4.422 1973,-70.70991,-190.10365,211.15079,456.70514,4.418 1973,0.66298366,28.638165,28.62406,77.41208,4.392 1973,420.36566,221.9157,19.766602,41.380234,4.363 1973,594.406,-44.290405,44.099365,97.95979,4.219 1973,432.37787,221.48506,21.230194,40.59349,3.968 1973,219.20045,216.81508,20.373337,34.275482,3.93 1973,436.86795,209.26309,37.052185,83.6452,3.925 1973,571.0925,185.91235,53.474182,133.2732,3.894 1973,484.83167,219.18042,40.39337,83.080505,3.888 1973,456.53638,213.16064,20.778198,47.619446,3.781 1973,561.1469,201.23671,33.513306,67.80473,3.702 1973,483.97778,163.6977,17.849884,41.191757,3.636 1973,-9.919238,83.70647,39.406338,106.23299,3.63 1973,211.19275,215.6086,20.529175,35.11142,3.629 1973,537.4265,192.92451,56.30713,123.21785,3.615 1973,422.18518,209.34045,34.14319,70.68558,3.608 1973,468.52826,213.87076,38.977783,85.82739,3.593 1973,538.52856,219.59323,35.312805,81.36023,3.588 1973,9.220402,-89.79276,73.66286,205.15205,3.585 1973,2.5652008,-16.927334,17.393944,41.60243,3.539 1973,507.568,229.55492,58.67755,116.08482,3.509 1973,494.17334,192.38574,53.625183,116.27887,3.508 1973,466.5489,212.94781,19.91449,44.54303,3.505 1973,516.2529,205.86168,42.556152,88.64116,3.46 1973,425.7535,203.51176,18.605469,36.1969,3.321 1973,494.73883,209.60501,27.719604,59.46396,3.246 1973,194.23767,213.83348,21.121521,36.01056,3.243 1973,203.01215,215.27599,19.843857,36.0988,3.219 1973,546.49524,172.09639,41.098877,98.362656,3.214 1973,475.89313,226.49623,57.63153,122.00426,3.21 1973,597.5109,206.16682,39.5343,101.62596,3.205 1973,499.87912,181.98517,29.894989,76.098816,3.203 1973,19.451355,57.39176,31.55619,68.19403,3.185 1973,618.0203,489.10968,21.165222,44.565857,3.171 1973,611.70276,-6.380295,39.01245,117.269966,3.166 1973,610.8126,-16.935694,17.59552,42.66797,3.154 1973,406.3291,206.44823,36.30609,66.31984,3.149 1974,414.7229,208.39958,22.36435,44.216324,71.507 1974,451.79565,209.63159,22.1622,52.347046,58.203 1974,482.91547,203.33595,19.277863,46.713837,25.527 1974,-11.114206,-31.411568,29.670427,71.9902,6.475 1974,-5.6715417,-16.7646,17.512112,41.675056,6.47 1974,622.2845,-33.47257,27.009277,75.95044,6.253 1974,-6.292673,489.16193,18.926083,42.544983,6.001 1974,421.78058,212.29099,22.939636,48.43582,5.909 1974,432.63885,212.59727,22.996857,50.08171,5.831 1974,626.6668,-19.944279,17.751892,49.48278,5.633 1974,407.8223,206.3299,34.382477,65.91052,5.585 1974,441.7008,211.85982,26.093689,60.898056,5.551 1974,-12.264127,477.04407,30.559914,63.69568,5.453 1974,626.5437,488.06384,20.83667,45.530884,5.259 1974,612.9807,454.4178,39.5166,107.91608,5.136 1974,394.1687,202.72578,20.631653,43.890778,5.116 1974,-72.84171,265.89136,200.70274,461.21143,5.098 1974,602.86786,-57.550392,53.44934,131.82826,5.076 1974,-17.001646,-61.956657,56.103485,142.72401,5.074 1974,593.9094,-45.820908,44.878784,93.897545,5.008 1974,462.65118,210.5915,22.192596,54.90117,5.004 1974,-21.970818,402.05527,79.71404,175.71695,4.942 1974,583.0401,411.32773,71.64197,176.85226,4.932 1974,473.04184,207.08682,25.73996,61.38858,4.929 1974,535.94293,310.14386,144.50067,342.6153,4.855 1974,493.88135,226.42337,236.8772,514.90735,4.744 1974,-36.762684,-127.410164,116.39879,277.3662,4.705 1974,487.55038,207.8369,21.759949,51.264633,4.701 1974,490.87952,-180.87845,234.57886,422.86478,4.654 1974,411.76892,217.19308,20.038483,49.55475,4.606 1974,457.46887,222.80075,20.527008,52.0076,4.494 1974,402.86374,211.84756,21.160889,47.995056,4.48 1974,586.6217,-102.32788,74.82446,235.76585,4.379 1974,-8.776431,-5.694557,38.90013,99.48733,4.333 1974,2.7636003,-14.544901,16.219284,38.198853,4.255 1974,-70.410065,-190.1466,211.33664,456.4095,4.189 1974,-5.794071,182.06445,18.744482,45.024002,4.112 1974,218.41673,215.52005,20.166306,34.00441,4.041 1974,476.6294,200.64047,19.50293,40.76683,3.993 1974,617.81354,-10.919277,20.47467,48.910175,3.928 1974,209.8313,215.19516,21.363434,34.849,3.911 1974,115.333595,181.91435,40.53118,78.69371,3.879 1974,-5.17857,139.98523,17.748877,41.21454,3.867 1974,598.1147,172.96796,37.31555,105.75134,3.616 1974,3.7264872,171.62044,31.709099,83.50325,3.599 1974,618.21747,490.13058,20.873535,42.594208,3.575 1974,613.04956,172.9533,36.685425,108.634964,3.458 1974,445.5402,204.37732,19.698425,41.683212,3.44 1974,226.30698,215.17986,19.670944,35.012253,3.422 1974,201.84709,210.03917,21.75444,35.562088,3.408 1974,274.2976,207.16702,18.756714,35.32019,3.399 1974,586.8256,-2.9830875,18.934082,39.898994,3.361 1975,503.65463,225.49115,28.896759,63.69922,84.117 1975,442.14362,221.73892,23.462616,55.367706,81.037 1975,546.8973,219.5757,24.225891,49.07463,48.307 1975,453.42834,228.65895,23.850342,48.380142,10.828 1975,488.53174,223.9428,28.516846,68.21971,8.37 1975,556.4692,220.48886,25.178406,50.14435,6.098 1975,-5.940424,-17.111954,17.5899,42.960022,5.909 1975,-6.1338067,489.30566,18.453669,42.20233,5.71 1975,610.1122,-48.037437,42.430786,98.36992,5.583 1975,-11.273843,-31.877565,29.773129,73.53521,5.512 1975,-12.475889,476.98468,30.677546,63.66974,5.491 1975,536.0702,309.4763,144.7185,341.66336,5.125 1975,-72.76902,265.9237,200.84674,461.07068,5.121 1975,613.08435,454.29736,39.4115,108.03601,5.112 1975,582.9716,411.4426,71.775635,176.65115,5.006 1975,-16.862988,-62.95676,55.45138,144.45154,4.887 1975,627.2916,-18.453592,17.217285,46.350616,4.887 1975,393.05356,211.55388,22.957123,44.76396,4.877 1975,-21.789307,402.7019,79.453445,175.13855,4.871 1975,479.74628,209.83395,28.564941,66.512665,4.796 1975,495.04584,227.64407,236.06592,513.60046,4.789 1975,626.5892,487.9506,20.77832,45.70993,4.706 1975,533.10803,-183.72156,148.08026,426.92087,4.587 1975,536.19385,216.18512,34.613037,75.66736,4.502 1975,432.72345,227.34169,27.099274,67.229904,4.443 1975,-36.681686,-128.14531,116.45796,280.08548,4.407 1975,-8.543033,-6.3958626,38.6619,101.57327,4.398 1975,540.22406,226.57388,20.385803,47.276276,4.307 1975,586.93414,-101.594345,74.40094,234.17818,4.287 1975,451.31207,212.24821,37.301575,70.1942,4.28 1975,516.4121,236.70343,24.435608,51.029907,4.248 1975,469.3709,228.69643,37.82437,73.55693,4.228 1975,-70.34117,-189.064,211.36696,455.58536,4.177 1975,423.67212,222.88963,28.803131,60.239334,4.16 1975,0.68983436,182.66516,25.572954,71.61224,4.16 1975,343.25806,204.25754,26.56308,67.70798,4.131 1975,499.45633,218.52606,21.467377,53.44156,4.08 1975,462.36066,231.35553,21.793365,42.513885,4.005 1975,-14.88652,148.98961,48.580772,143.06496,3.987 1975,373.38928,203.64534,29.280457,56.78856,3.939 1975,2.8315525,-15.546862,16.640532,40.014854,3.826 1975,554.2964,-16.49398,17.269226,39.95474,3.795 1976,502.32007,222.96016,35.631348,83.09895,95.76 1976,469.0652,220.14307,37.379395,78.85535,7.29 1976,489.08368,232.0122,29.656006,71.46405,6.515 1976,-11.566542,-32.614567,30.850449,74.13918,6.505 1976,627.3151,-18.04533,17.152649,46.944473,5.983 1976,-6.08701,489.93515,18.58842,41.154816,5.679 1976,623.03937,-33.695023,26.252747,76.54858,5.579 1976,-5.953847,-16.577686,17.918507,42.244938,5.544 1976,-16.794977,-62.8678,55.427643,145.79857,5.508 1976,-8.968841,464.34988,37.867496,92.74808,5.414 1976,612.93555,454.7592,39.497986,107.1185,5.228 1976,472.07684,254.38567,19.686188,38.92064,5.166 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1976,534.1856,-18.816935,30.597595,67.962234,3.5 1976,244.67264,215.1322,37.40869,95.372314,3.495 1976,515.71924,224.05258,25.489624,55.73047,3.408 1976,-6.023589,214.72537,17.559196,42.199158,3.38 1977,335.33447,190.14839,22.027893,41.607025,6.586 1977,-6.4923944,489.0793,19.063616,43.787445,6.49 1977,-11.957593,476.84357,29.791485,64.87372,6.182 1977,-11.41403,-33.0969,30.77442,74.6979,6.088 1977,458.02118,194.76564,29.33078,65.98375,6.034 1977,627.3222,-17.121958,16.731262,44.717484,5.775 1977,622.9878,-33.32921,26.14801,74.203094,5.486 1977,354.3968,195.58392,21.450928,37.834473,5.475 1977,-5.871353,-17.353209,18.25635,43.58349,5.435 1977,626.5791,488.31,20.87915,45.71228,5.316 1977,-16.909405,-62.157288,55.8089,143.32318,5.235 1977,613.3902,454.71985,38.690735,107.46057,5.226 1977,490.59738,-182.00648,234.36954,426.9812,5.018 1977,583.2405,411.9656,71.473145,176.19589,4.855 1977,-72.595215,267.8922,199.78247,459.2433,4.826 1977,-21.970974,402.5289,79.42848,174.87503,4.81 1977,250.04153,158.67242,17.096558,39.53537,4.746 1977,-36.69397,-127.73261,116.30107,277.76624,4.643 1977,536.70514,310.81158,142.82214,342.09692,4.641 1977,603.2628,-56.36024,53.266968,136.8812,4.593 1977,585.9083,-103.15353,75.76062,238.39447,4.55 1977,502.06335,179.5742,37.44098,84.04909,4.387 1977,493.4273,226.97934,237.77014,515.93286,4.305 1977,-71.02355,-189.84496,212.1944,456.6789,4.302 1977,619.8554,-17.210901,16.6568,41.569626,4.276 1977,-8.459591,-5.659401,38.85063,100.26719,4.221 1977,531.7663,180.26224,18.039001,40.896973,4.194 1977,595.60535,-15.783713,16.700989,40.80736,4.114 1977,594.52313,-46.25179,44.383667,98.71454,4.038 1977,524.6334,179.9002,28.209656,66.224106,3.999 1977,331.53506,177.90738,21.451233,45.15912,3.977 1977,370.21106,-16.855179,18.642395,41.33834,3.863 1977,538.02673,-17.292547,17.223022,42.522907,3.857 1977,157.07362,189.87653,29.723587,55.441483,3.84 1977,385.4605,-18.10132,19.401825,41.880726,3.692 1977,444.83398,-34.356354,30.153229,67.17184,3.682 1977,377.52222,-18.632736,19.025452,42.82441,3.654 1977,2.0532048,489.12424,19.791006,43.651337,3.639 1977,-4.9397187,168.96768,16.737185,42.088287,3.627 1977,546.78894,-16.53204,17.240051,42.1577,3.615 1977,357.41388,188.11058,28.030273,51.220596,3.597 1977,345.60764,196.01106,21.241913,36.57811,3.592 1977,373.998,-33.964203,29.317932,76.63414,3.585 1977,545.8245,220.17091,31.548584,71.931015,3.529 1977,162.3311,210.30396,44.38887,96.75824,3.521 1977,404.59915,-33.275326,30.461945,72.97556,3.498 1977,516.57104,-19.631674,32.19586,65.290794,3.479 1977,524.16437,174.27394,17.11084,35.36441,3.468 1977,615.7934,126.12758,24.207092,68.93451,3.463 1977,2.1354933,-16.325417,17.859375,42.35981,3.443 1977,558.4801,231.18507,23.003784,42.748093,3.438 1977,195.76903,194.8715,40.819946,100.02992,3.434 1977,1.8048978,-38.49669,42.684475,98.80489,3.428 1977,508.2093,-33.978275,29.859192,71.98133,3.428 1977,570.6513,-16.876581,16.957092,40.517605,3.407 1977,532.64746,179.83525,40.01947,85.28996,3.398 1977,425.30573,-19.039211,19.519592,41.370087,3.391 1977,511.31076,181.60951,21.572052,54.119965,3.388 1977,449.87515,-19.74646,19.381744,42.01068,3.381 1977,603.28906,-16.844894,16.116821,40.747646,3.378 1977,494.05273,-34.162937,29.304932,75.06836,3.37 1977,140.44801,194.47679,31.829178,58.100113,3.361 1977,442.05685,-19.498095,19.241486,41.74793,3.359 1977,154.01537,201.37097,21.78743,34.8956,3.352 1977,537.3386,179.2603,19.010437,35.71727,3.314 1977,-9.232664,35.29014,38.579857,107.61193,3.31 1977,146.14479,211.68439,19.547531,31.097824,3.299 1977,489.7617,-18.27632,18.939789,41.216152,3.282 1977,420.4923,-31.59585,30.373444,69.31734,3.28 1977,180.49963,187.87389,38.84955,88.56444,3.273 1977,575.0105,-36.03376,27.60205,74.10519,3.264 1977,152.44304,233.7219,60.20282,112.39517,3.26 1977,587.53,-15.95006,16.384277,39.751198,3.257 1977,362.65405,-16.770065,17.564758,40.330883,3.249 1977,522.8422,-19.385458,18.118164,43.1852,3.241 1977,389.00558,-32.898575,30.112396,73.81483,3.237 1977,140.39194,218.78702,18.060455,29.716125,3.235 1977,382.4863,189.59299,27.918823,56.554718,3.229 1977,497.74988,-16.757494,19.65564,40.90895,3.221 1977,542.05865,175.2204,50.143555,120.477905,3.21 1977,605.7915,132.80019,49.150635,128.9055,3.21 1977,505.28653,-17.709473,19.728058,41.93184,3.185 1977,510.6122,166.26588,50.664703,117.31038,3.173 1977,533.99445,-33.855003,27.84552,73.858986,3.156 1977,471.2754,202.23207,22.758728,48.00226,3.149 1978,344.35394,194.63145,21.870605,46.339554,7.865 1978,335.88934,150.6258,34.18283,84.64278,6.904 1978,-11.353322,-32.244522,29.79282,75.785545,6.474 1978,-5.7458687,-16.301203,17.51035,43.152245,6.288 1978,-6.228424,489.328,18.724653,42.943115,6.043 1978,626.91815,-19.454956,17.892029,49.040775,5.981 1978,-9.014167,464.16345,37.632828,92.66681,5.904 1978,622.5999,-33.77724,27.12793,77.67306,5.862 1978,613.1887,454.33325,39.06952,106.88849,5.521 1978,626.706,488.6176,20.657654,45.26935,5.416 1978,490.3053,-182.28946,234.47144,429.38477,5.24 1978,585.6949,-100.44056,75.0293,235.18939,5.065 1978,-19.745712,-87.6072,71.33806,198.03871,4.92 1978,-72.72869,267.1048,199.96518,459.9698,4.909 1978,-22.056297,401.7066,79.54715,175.76166,4.879 1978,583.00385,411.74582,71.64142,176.70633,4.764 1978,602.8035,-57.46293,53.143738,139.34822,4.758 1978,536.6013,310.05334,142.98962,342.8379,4.741 1978,-36.653973,-127.51874,116.721085,279.2634,4.722 1978,-8.504963,-5.8875275,38.972935,100.504456,4.695 1978,362.3565,198.9483,22.405273,47.91066,4.546 1978,439.54495,211.20322,27.82431,63.458984,4.472 1978,-70.68251,-190.15848,211.86377,456.94882,4.437 1978,493.5071,226.61652,237.92038,515.92725,4.341 1978,320.99484,193.47449,22.178925,46.504288,4.21 1978,2.9038596,-14.080664,16.107477,40.27431,4.156 1978,619.083,-7.967867,20.016602,48.624985,4.115 1978,597.6192,199.3808,32.32538,76.31427,3.987 1978,244.29568,171.66098,19.084106,41.292023,3.974 1978,556.1644,187.30948,29.792236,57.81836,3.972 1978,441.9679,-14.265865,33.48645,68.92744,3.9 1978,350.6241,-33.397995,28.806122,73.943275,3.881 1978,611.28296,197.01282,28.142944,81.73337,3.857 1978,594.5586,-46.978626,44.366943,100.45723,3.85 1978,154.31883,205.8979,20.980988,33.33841,3.799 1978,269.5373,-36.61108,28.218567,66.459625,3.796 1978,162.44519,206.0631,21.341248,34.380722,3.759 1978,404.55795,-6.0056267,32.236725,67.21425,3.745 1978,421.10104,-7.1339073,32.40573,70.18301,3.74 1978,365.66553,185.83484,31.632477,61.63205,3.692 1978,572.32983,-24.435509,54.876343,152.31221,3.691 1978,2.2357779,489.82434,19.596508,42.328186,3.666 1978,458.4299,-36.90403,59.16684,131.67448,3.637 1978,253.04826,-36.373466,29.068405,65.89757,3.585 1978,425.63138,-32.508003,59.93109,125.02547,3.566 1978,317.57587,-33.926796,28.999115,76.07132,3.564 1978,452.54218,-34.121468,30.467194,76.71027,3.547 1978,410.67154,11.644306,34.46698,74.02365,3.536 1978,218.93341,200.74619,19.6913,33.874863,3.506 1978,146.99345,207.88643,20.426102,30.08017,3.486 1978,618.7276,490.78345,20.300476,42.010498,3.479 1978,-7.996417,85.19147,38.136623,103.446686,3.469 1978,353.73682,-16.812103,18.550903,40.860985,3.461 1978,582.4317,180.3238,39.628357,80.937454,3.453 1978,394.81064,-33.418465,60.526733,121.84696,3.451 1978,436.36676,6.0539436,35.06668,75.52351,3.451 1978,427.14093,34.100018,32.940186,72.86749,3.436 1978,532.1244,-136.56389,115.55908,279.79904,3.435 1978,321.69147,-15.844086,18.860016,43.387062,3.434 1978,563.97534,181.555,42.076233,76.33356,3.42 1978,1.7176843,-39.110397,42.956444,100.265945,3.386 1978,361.34277,-35.12699,60.519897,125.947,3.35 1978,372.74402,10.054691,32.43152,77.9211,3.349 1978,417.5974,-8.066589,22.413574,48.16746,3.338 1978,286.40042,-36.078846,27.898468,70.29028,3.335 1978,425.85617,-18.68333,18.415924,46.30275,3.32 1978,430.3402,-34.025955,29.34552,81.41638,3.309 1978,-9.294452,36.424458,38.59514,108.30789,3.303 1978,567.09235,195.3278,23.249329,41.798706,3.295 1978,540.7841,162.80376,56.38684,109.230606,3.287 1978,-15.449538,147.0101,48.998817,142.11746,3.273 1978,2.4365807,184.18222,19.818779,42.247437,3.241 1978,347.0382,213.8007,18.354248,36.83516,3.234 1978,333.2345,-33.62358,29.455566,73.441345,3.217 1978,490.34116,-40.144146,59.21158,137.4699,3.199 1978,451.13745,214.79024,28.55658,62.247604,3.194 1979,368.27884,212.2152,22.256897,40.693054,7.538 1979,-11.594273,-32.068546,30.530527,75.76361,7.01 1979,395.8294,216.04929,29.313965,65.47116,6.849 1979,-6.502462,-16.752155,18.16287,43.35425,5.887 1979,249.79749,224.11612,23.474396,43.61435,5.869 1979,-11.776363,476.0833,29.084133,64.95654,5.817 1979,612.9665,454.8242,39.404175,106.960175,5.782 1979,585.34924,-96.14549,74.33948,229.94888,5.541 1979,460.45892,215.2243,27.85318,67.02411,5.363 1979,610.5222,-47.87184,41.988464,101.32351,5.314 1979,-6.1883516,489.522,18.527472,42.391754,5.145 1979,-16.57709,-62.243942,55.937645,144.51805,5.081 1979,531.80884,-181.10245,149.74951,419.86142,5.045 1979,-21.91576,402.58435,79.35571,174.97888,4.979 1979,-72.48205,266.78467,199.8837,460.20203,4.968 1979,426.47675,229.05191,19.34613,39.21707,4.943 1979,536.65784,309.96954,142.7633,342.4809,4.841 1979,627.1423,-18.040508,17.131897,46.911453,4.841 1979,372.01697,-5.495117,30.1391,75.08519,4.775 1979,582.9315,411.4418,71.804565,176.89572,4.704 1979,596.1731,193.0091,28.30658,63.90567,4.644 1979,-36.585423,-127.30654,116.69154,277.57816,4.637 1979,626.1998,487.94464,21.612732,46.11676,4.477 1979,438.5992,219.22626,28.37741,63.55014,4.421 1979,6.736648,83.03496,29.76733,83.8336,4.405 1979,-70.57889,-189.27974,211.44183,456.2069,4.395 1979,493.8814,226.06668,237.60968,515.8862,4.334 1979,329.60983,209.23459,20.35611,41.09842,4.319 1979,461.47476,-34.336582,29.778717,79.06093,4.301 1979,589.0741,-18.763855,52.18628,164.15729,4.198 1979,377.58038,-18.714718,19.208282,44.91805,4.127 1979,379.94373,-14.070147,32.33029,70.263306,4.114 1979,-8.4710455,-4.892315,39.201965,99.845276,4.092 1979,-5.1469145,97.95068,19.055897,51.36525,4.013 1979,530.193,1.6148262,33.95465,81.76726,3.969 1979,525.1588,-16.367815,32.25354,66.56754,3.968 1979,311.2789,197.56381,25.46994,55.620773,3.964 1979,365.54858,-33.986496,29.079071,78.17968,3.933 1979,466.91864,-7.6889534,33.48059,73.93327,3.845 1979,1.9769287,-16.056786,17.3192,42.22209,3.779 1979,369.86813,-17.899158,19.082397,44.474316,3.758 1979,518.18195,-33.90396,29.316895,74.83,3.748 1979,438.15933,-34.973103,29.348541,74.566795,3.717 1979,385.6314,-18.135544,19.380188,43.767635,3.703 1979,187.34871,220.56204,20.03418,31.606293,3.702 1979,522.3559,-17.645,17.96283,41.566563,3.676 1979,492.5601,-33.073166,30.574127,77.14245,3.62 1979,389.73962,0.87319183,30.30429,80.73921,3.592 1979,572.6354,3.06884,55.210144,166.92407,3.59 1979,180.0044,223.71526,17.851608,29.628052,3.555 1979,419.21524,230.33841,18.45169,37.733246,3.545 1979,349.54797,-34.58773,29.173584,77.989525,3.541 1979,548.91986,-36.053524,29.126953,72.68032,3.541 1979,389.07068,-33.163155,29.697296,75.00743,3.54 1979,393.57907,-18.042196,19.27417,43.943707,3.535 1979,2.1026325,96.93295,24.242584,86.69885,3.534 1979,378.82434,10.108406,30.93274,81.70061,3.507 1979,613.9591,3.5739174,25.831299,84.86832,3.506 1979,476.58777,-31.030468,30.248016,73.68271,3.505 1979,583.17175,198.58276,29.703552,61.25775,3.484 1979,1.8610573,-38.49858,43.212013,100.441635,3.469 1979,-5.806589,179.90671,19.518393,50.197693,3.464 1979,285.16614,198.78317,27.60318,66.59445,3.453 1979,425.6766,-37.220383,61.143616,129.48082,3.448 1979,529.687,-17.879124,18.988892,42.437057,3.44 1979,-4.8080096,68.11678,18.017267,48.2389,3.436 1979,356.63998,-7.4150887,32.186584,69.85378,3.431 1979,-4.084716,-17.25026,30.992706,74.26658,3.418 1979,401.622,-17.989975,18.944885,44.012024,3.411 1979,266.46335,173.16116,17.962646,41.30725,3.403 1979,598.1349,160.31427,37.493652,88.39032,3.402 1979,571.1951,206.79901,20.26947,29.717438,3.395 1979,473.58292,-17.784355,19.780243,44.47265,3.385 1979,548.6819,77.88406,37.782166,79.1308,3.368 1979,539.29065,-19.570566,33.311157,66.61341,3.355 1979,505.03586,-3.492796,21.864777,42.654068,3.332 1979,619.3126,-5.967766,18.737976,44.38941,3.33 1979,171.065,218.09976,20.26065,31.76326,3.325 1980,336.50723,179.1141,24.069275,49.29039,22.387 1980,400.63867,198.12263,20.27774,41.754776,19.788 1980,355.11478,185.91837,21.681915,55.843185,12.86 1980,483.47876,212.09888,31.01892,82.03815,9.384 1980,510.7053,198.38007,41.023956,110.7771,7.479 1980,622.9308,-32.64664,26.643799,73.85427,6.532 1980,627.3373,-17.22445,16.905518,45.530563,6.506 1980,-11.308615,-33.378708,29.768938,73.32443,6.246 1980,-6.065719,-18.420542,18.336535,44.472946,6.018 1980,612.89716,455.62613,39.435303,105.709015,5.75 1980,317.5622,181.03104,29.964508,61.423477,5.657 1980,-12.281823,477.1049,30.678211,63.713226,5.622 1980,-6.358583,489.07352,18.978846,42.754425,5.608 1980,385.52948,199.65785,23.362427,49.64978,5.335 1980,490.00153,-181.0519,234.9607,425.3621,5.289 1980,626.4147,488.008,21.039307,45.76587,5.123 1980,-16.779545,-63.424725,55.56081,144.66846,4.993 1980,-72.30951,266.95093,199.93927,459.59814,4.981 1980,501.1702,220.9864,33.072296,78.44588,4.929 1980,-21.6959,403.03598,79.24679,175.02725,4.862 1980,342.0396,178.62155,29.700775,65.299194,4.802 1980,536.5303,310.19482,142.99377,342.91077,4.777 1980,-36.65393,-128.72858,116.79216,281.15112,4.756 1980,603.26843,-57.873314,53.513245,135.02971,4.755 1980,582.9148,411.82803,71.80316,176.51407,4.726 1980,471.7251,218.31497,30.619995,68.268005,4.679 1980,586.2104,-97.74957,73.97711,230.99141,4.65 1980,493.45892,209.12901,25.911865,64.467545,4.618 1980,497.71335,-10.431454,45.762177,82.35649,4.517 1980,1.0956025,83.27994,25.260878,82.4763,4.512 1980,1.9898808,-18.543787,18.163956,45.329285,4.504 1980,444.26175,-1.5940742,33.156647,73.19003,4.457 1980,452.7571,-33.41716,30.70517,77.868225,4.413 1980,493.75958,227.4072,237.77307,515.3247,4.406 1980,458.0609,-2.0733337,32.59961,74.76079,4.344 1980,594.10583,-45.91357,45.08539,95.77405,4.311 1980,194.19916,195.87843,22.540497,40.349808,4.31 1980,-5.0146365,105.43868,18.091133,48.442543,4.269 1980,-70.48183,-189.68474,211.7386,456.60004,4.262 1980,613.87274,147.43533,34.591675,99.59956,4.138 1980,620.04694,-3.2696657,17.932861,38.101063,4.095 1980,477.52826,175.7815,38.197266,80.040436,4.059 1980,527.2334,-38.62781,48.08612,90.75738,4.047 1980,490.7202,-1.3336926,35.408264,59.520615,4.045 1980,382.1726,-34.228333,29.571045,73.64527,4.039 1980,-9.693676,92.29126,26.897272,72.800415,3.941 1980,421.0762,-31.464611,30.226044,72.779015,3.901 1980,-9.550972,-7.114254,39.515423,102.92778,3.89 1980,397.79968,-33.550808,29.4516,73.95303,3.887 1980,599.0331,151.55823,35.17627,92.07866,3.885 1981,432.79578,202.5986,22.70111,51.492905,53.305 1981,361.06735,200.57707,21.768616,43.60022,8.973 1981,384.25238,176.327,25.75708,73.04802,7.616 1981,-5.8254185,-17.67848,17.441364,43.245396,6.377 1981,-11.362972,-32.815163,29.937054,72.70319,6.056 1981,357.45288,167.29428,27.225739,69.26323,5.872 1981,623.2313,-32.901592,26.776001,74.99642,5.851 1981,364.13916,188.25484,28.104675,65.20625,5.564 1981,-6.062689,489.63647,18.50668,41.560303,5.455 1981,626.9198,-19.048985,17.514282,48.13353,5.301 1981,-16.960295,-64.30495,55.655457,145.36504,5.283 1981,-8.814097,463.81772,37.656162,93.053375,5.282 1981,626.3598,488.1374,21.21521,45.24121,5.257 1981,613.03687,456.1634,39.133606,105.816895,5.232 1981,-72.63053,266.34863,200.558,460.3233,5.066 1981,585.3326,-96.80918,75.346985,230.09918,4.901 1981,-21.823505,402.24432,79.481094,175.55426,4.883 1981,583.0211,411.72174,71.71088,176.55902,4.767 1981,536.7455,310.45465,142.74536,341.46344,4.758 1981,-36.419804,-129.17213,116.71286,280.1371,4.692 1981,603.14996,-57.42769,52.8963,135.75774,4.59 1981,489.73492,-178.43695,235.2627,423.96533,4.589 1981,493.8886,226.13,237.6159,515.871,4.284 1981,547.7514,238.67102,33.738953,55.91455,4.231 1981,337.61752,201.01706,21.491974,42.441452,4.218 1981,-70.47429,-189.29117,211.70744,456.00177,4.202 1981,315.48724,211.96803,17.831268,34.116898,4.192 1981,-5.78963,228.75745,17.789156,42.471527,4.169 1981,208.4835,203.08977,23.702225,42.417435,4.086 1981,594.29,-46.18194,44.82953,97.18299,4.063 1981,2.8247933,-16.356787,16.769373,42.163883,4.053 1981,623.03375,217.52696,28.095032,73.13191,4.007 1981,342.41022,195.8714,27.72165,56.84694,4.0 1981,376.82703,161.4344,27.76587,72.36954,3.975 1981,569.0409,38.741596,35.86609,67.73045,3.885 1981,-1.0455012,141.88318,52.239418,135.0758,3.778 1981,-9.964247,-6.9205284,40.136086,104.2316,3.762 1981,330.29834,200.04407,20.146637,40.97513,3.75 1981,423.80212,195.94669,35.708984,74.71323,3.707 1981,446.98505,202.99489,22.945038,44.264984,3.622 1981,320.73114,201.01837,20.14743,39.450134,3.591 1981,1.9493628,-40.627956,42.800488,99.451195,3.584 1981,243.54369,228.86281,18.700333,33.36412,3.583 1981,30.107374,83.141556,30.999613,62.2538,3.562 1981,-8.899485,182.04362,38.189846,99.524185,3.558 1981,605.10187,213.9202,28.958252,68.04941,3.53 1981,124.03863,221.18466,16.6419,32.82457,3.512 1981,1.3167214,122.379585,20.971699,48.151848,3.51 1981,260.17737,204.7024,28.106995,62.28116,3.481 1981,314.06696,196.41763,20.01706,38.98416,3.476 1981,201.73471,209.36574,23.045563,40.128143,3.454 1981,190.49823,200.22931,30.696716,55.978363,3.442 1981,18.100845,97.25958,41.071564,96.26239,3.421 1981,185.37799,204.40518,22.150696,36.3199,3.413 1981,427.98975,196.44653,21.095093,45.47731,3.412 1981,75.346596,221.18855,17.463951,31.577911,3.4 1981,618.50586,490.9296,20.60315,42.370026,3.388 1981,354.46674,209.10287,21.651093,43.35289,3.348 1981,4.8569098,97.43213,37.88182,102.73636,3.348 1981,-4.831857,117.96273,17.923315,43.44407,3.334 1981,108.64056,222.02591,16.09449,29.481247,3.319 1981,-13.7506275,56.4168,48.962357,141.3732,3.3 1982,508.6893,212.51306,27.838898,58.526123,72.309 1982,421.96777,214.24419,28.0914,63.781693,8.12 1982,-11.1594925,-33.180622,29.6638,75.56679,6.309 1982,-6.103488,-17.627985,18.663769,43.99426,5.829 1982,627.0156,-18.196293,17.325317,46.296112,5.734 1982,-12.353369,477.10373,30.78113,63.716217,5.693 1982,-6.390936,488.90808,19.01308,42.69104,5.434 1982,626.57715,488.01645,20.74121,46.119232,5.335 1982,-16.978962,-64.49292,55.55394,145.23692,5.123 1982,622.9289,-33.592415,26.36316,75.55828,5.036 1982,612.88965,454.87592,39.59442,107.38684,5.036 1982,583.0097,411.56946,71.80591,176.75592,5.026 1982,-72.46073,266.16858,200.31717,460.58984,5.016 1982,-21.828249,402.4003,79.47819,175.42368,4.838 1982,536.1437,310.48627,143.92865,341.3081,4.802 1982,585.5363,-97.13941,75.37488,230.61676,4.798 1982,602.92426,-57.30614,53.467896,138.88588,4.719 1982,493.6926,226.87183,237.6232,514.70984,4.581 1982,490.74988,-179.1195,234.04272,424.30554,4.575 1982,481.29147,49.352013,37.830597,83.43351,4.554 1982,-36.706863,-129.35129,117.25963,279.956,4.553 1982,499.1251,207.122,27.309235,64.40132,4.451 1982,1.5855342,-18.15462,18.378166,44.053898,4.209 1982,-70.56705,-188.85623,211.56647,454.9774,4.185 1982,-10.182208,-7.53553,40.05177,105.63277,3.858 1982,300.39984,216.4941,28.069641,58.283127,3.849 1982,346.87283,231.4243,19.704742,33.748734,3.838 1982,425.89575,237.19353,20.220154,43.229233,3.831 1982,355.5747,228.476,18.64087,33.07686,3.818 1982,-0.9318614,23.329296,26.11012,95.45885,3.783 1982,2.100408,-28.612904,50.35835,148.6554,3.763 1982,417.48123,172.29568,34.315033,83.9238,3.756 1982,361.07162,217.94287,19.817596,35.04988,3.643 1982,47.15274,88.62967,28.33538,67.1994,3.577 1982,379.7214,219.36241,18.032043,35.28865,3.558 1982,42.704506,114.28622,21.15403,42.03556,3.506 1982,2.151326,142.68803,20.537823,39.748215,3.47 1982,615.5988,-20.848827,26.582336,72.149536,3.461 1982,-4.754391,135.97098,16.907616,39.175674,3.449 1982,468.27203,49.500603,34.180054,80.72202,3.424 1982,234.98795,221.49544,20.40596,33.71797,3.422 1982,4.951459,-35.622307,30.060665,78.777794,3.411 1982,618.37177,489.98303,20.929688,43.046143,3.379 1982,26.323185,94.189896,30.871075,58.37761,3.377 1982,-0.72408795,113.507805,24.698942,62.85743,3.291 1982,2.1304727,490.31616,19.837202,41.92401,3.29 1982,555.9888,270.7103,21.05951,40.463623,3.275 1982,615.9268,67.910934,23.565308,54.027298,3.258 1982,619.5089,-18.119678,16.789368,42.83945,3.245 1982,463.4677,233.34239,21.605835,45.621933,3.214 1982,219.20041,218.88538,19.01567,35.324295,3.156 1982,526.1702,216.41158,24.216492,47.496994,3.108 1982,-5.6171374,244.4577,17.401524,42.329163,3.102 1982,242.97493,218.4684,21.355026,32.982025,3.098 1982,297.26282,217.7458,21.664154,36.299942,3.097 1982,394.27094,215.53621,19.305115,40.78215,3.088 1982,226.7976,221.5972,19.838776,33.956757,3.062 1982,494.00653,43.346436,38.96869,74.3228,3.053 1982,18.104885,101.574005,39.524277,83.15527,3.044 1982,546.64307,413.85455,83.55237,166.39252,3.042 1982,-9.359992,198.29056,38.7105,101.699585,3.038 1982,349.70038,209.43979,30.160614,50.98822,3.017 1982,61.24609,102.56247,29.510273,67.39282,3.012 1982,515.57587,54.570137,31.400635,71.81137,3.001 1982,163.28127,-15.11306,15.648468,36.362152,2.997 1982,80.17122,-40.53678,46.612083,92.57324,2.989 1983,615.0999,207.85555,24.893616,79.576096,10.231 1983,514.3031,206.12315,32.489746,63.531906,7.853 1983,394.8952,204.92455,19.17569,40.43892,7.808 1983,623.1102,-33.73512,26.634277,73.25571,7.151 1983,-10.72345,-33.936348,29.766953,77.94429,6.719 1983,-5.63021,-18.64569,18.058498,45.984158,6.698 1983,627.2422,-19.809364,17.690002,46.842762,6.079 1983,508.97495,202.01186,27.024689,53.045013,5.897 1983,-12.173972,476.96085,30.599915,63.60385,5.655 1983,387.45386,207.99687,20.585052,39.351135,5.613 1983,-6.1940184,488.85406,18.949333,42.515564,5.502 1983,-17.614233,-63.480118,56.262794,146.44263,5.259 1983,603.25397,-56.89988,53.175232,135.07037,5.198 1983,490.4031,-180.53609,234.30222,428.88766,5.185 1983,612.88464,455.11963,39.489502,106.566284,5.125 1983,626.6555,487.36703,20.666199,46.680573,5.103 1983,531.25604,217.9716,32.557922,59.931503,5.046 1983,585.8473,-97.1148,74.15088,230.88684,5.027 1983,-72.50581,266.7068,200.1462,459.7223,4.941 1983,-21.779293,402.5976,79.404526,175.11536,4.896 1983,582.84595,412.04666,71.76599,176.21164,4.865 1983,-36.183,-129.83961,116.887634,278.09866,4.85 1983,536.19806,310.45587,143.4375,341.81616,4.814 1983,594.4123,-47.44858,44.23706,100.84265,4.562 1983,459.45728,-16.334625,17.593628,37.970356,4.541 1983,474.54114,-16.983004,17.689453,39.31742,4.533 1983,260.81305,204.68297,17.189697,32.406113,4.497 1983,-71.48835,-189.89294,212.33066,456.33752,4.457 1983,252.41103,215.49202,17.854477,33.958786,4.403 1983,493.5455,226.29178,238.34482,516.3092,4.394 1983,451.25854,-15.192218,17.162659,36.94588,4.346 1983,2.8728735,-17.027546,17.347689,45.83837,4.338 1983,467.33853,-15.480076,17.956512,37.68464,4.337 1983,605.8341,-34.60303,27.878723,70.81799,4.281 1983,5.6406546,-12.181583,27.28666,80.50629,4.277 1983,598.1195,209.50235,39.43872,107.46231,4.044 1983,520.5639,227.90791,24.145569,51.472733,3.916 1983,243.78812,212.28056,18.057281,36.475204,3.911 1983,516.8433,192.8591,25.551575,45.967148,3.82 1983,443.04785,-15.530901,16.827026,36.53792,3.818 1983,402.7735,210.99374,18.054138,42.448166,3.809 1983,482.4115,-16.958652,18.049774,39.573826,3.761 1983,267.2486,209.80211,18.745178,35.519592,3.754 1983,618.90875,-20.488827,18.68988,44.580933,3.683 1983,525.60046,207.50328,26.807251,48.389847,3.68 1983,496.30347,196.3869,28.316772,64.76779,3.586 1983,21.921478,-6.1265507,29.373528,72.73351,3.581 1983,0.2824211,-34.456963,52.052372,143.64297,3.551 1983,490.19315,-18.553026,18.55606,41.75231,3.543 1983,585.4436,99.95695,61.560852,213.87161,3.54 1983,-10.435085,55.324104,28.860312,72.834435,3.491 1983,-5.3718624,168.7002,18.421003,43.17366,3.453 1983,-5.8878403,69.68921,19.41907,44.836937,3.452 1983,-5.268511,-2.4565525,18.605078,51.75316,3.425 1983,536.6813,238.46178,22.187073,44.855423,3.405 1983,-10.100077,-5.0442505,40.47158,103.72521,3.404 1983,9.733249,-18.56615,18.07721,47.990917,3.4 1983,478.35803,-34.693462,28.45343,70.33444,3.392 1983,621.3275,52.925827,27.454163,81.68683,3.39 1983,523.69257,-15.608068,31.943909,73.566185,3.362 1983,4.513999,73.512054,38.660255,106.04906,3.352 1983,500.5226,-17.481245,32.042572,69.39918,3.328 1983,2.0639598,490.00906,19.988138,41.82376,3.297 1983,6.053876,62.828014,27.399132,69.56654,3.286 1983,252.52502,204.92307,16.748749,30.909561,3.277 1984,424.69135,207.3495,20.340515,44.03337,13.745 1984,483.46957,192.95956,28.085419,63.73752,9.659 1984,-5.929599,-18.591326,18.781218,46.028687,6.91 1984,-10.827775,-34.01442,29.970459,78.88985,6.898 1984,401.2877,203.71748,22.75711,46.6857,6.579 1984,269.26398,216.18275,15.186096,30.014969,6.29 1984,409.6811,207.59105,21.664185,45.586823,6.102 1984,610.2468,-45.393127,42.098022,95.72897,6.074 1984,-9.065603,464.08243,37.81881,92.90524,5.502 1984,-6.1194444,489.20773,18.664162,42.386566,5.496 1984,627.0973,-18.427092,17.315063,45.39619,5.424 1984,-17.47874,-63.7818,56.548206,143.74205,5.342 1984,606.80347,-33.909554,27.552246,72.75942,5.306 1984,551.45514,6.7083893,35.44989,84.00274,5.235 1984,586.062,-94.11331,73.25934,225.00299,5.21 1984,-72.82345,265.93228,200.6788,461.19992,5.067 1984,626.43445,487.38626,20.882446,46.57608,5.061 1984,613.0376,454.9732,39.207336,107.01758,5.052 1984,583.09625,412.18182,71.48682,175.94879,4.959 1984,275.8454,213.8322,17.054749,31.542831,4.95 1984,490.4436,-180.75183,233.7826,426.56918,4.927 1984,-21.845673,402.12122,79.4159,175.46613,4.858 1984,261.25888,215.6214,14.911713,29.580765,4.827 1984,-36.575752,-128.8021,117.02442,276.8473,4.811 1984,618.8589,-4.8985586,19.788391,40.088413,4.75 1984,536.4143,310.6878,143.47607,341.9848,4.702 1984,595.9221,-15.519004,32.720825,62.38351,4.63 1984,-71.11078,-189.79607,212.00568,456.6317,4.521 1984,252.82166,215.33017,14.703949,29.981873,4.471 1984,-9.588451,-5.9958954,40.175415,100.44053,4.456 1984,236.59935,214.78816,15.402176,33.26425,4.438 1984,543.91547,195.05565,36.585938,79.5146,4.399 1984,585.25525,-15.798224,18.294556,40.479595,4.382 1984,493.5644,226.80084,237.6358,515.5259,4.379 1984,589.17145,-32.883762,28.73053,71.8472,4.334 1984,539.04443,144.01236,33.961,104.19136,4.287 1984,563.2523,-4.063389,32.290894,67.53014,4.212 1984,470.5992,-32.392887,28.282928,69.68668,4.207 1984,482.34094,-15.656038,18.154602,39.594135,4.18 1984,433.1281,212.19998,19.109497,42.301453,4.065 1984,578.78973,-11.709728,43.054077,82.375946,4.046 1984,244.91234,214.20909,14.870132,31.44011,4.044 1984,284.09982,211.83995,15.7752075,29.38063,4.041 1984,116.63174,209.43306,31.309486,60.168045,4.029 1984,-5.452751,69.192505,18.500078,46.069412,4.029 1984,228.22357,213.26247,16.509384,35.15074,3.995 1984,5.913533,77.39464,29.57005,72.54283,3.903 1984,465.8725,-2.690631,20.917633,36.30735,3.889 1984,531.8728,187.75266,34.03882,87.30588,3.887 1984,474.51492,-16.382404,18.082886,38.613266,3.848 1984,2.312224,-23.543472,50.47341,135.0871,3.847 1984,594.50964,-2.985777,19.750916,38.50792,3.842 1984,-5.632633,34.680588,19.404514,51.248947,3.828 1984,219.57275,212.70503,17.393097,36.673508,3.8 1984,458.82358,6.189087,34.586823,75.199936,3.788 1984,489.93137,-16.62031,18.626251,42.542397,3.771 1984,465.97076,-13.938324,42.98987,84.07127,3.73 1984,548.0201,-15.467085,32.07263,69.87057,3.712 1984,5.137018,-9.728401,29.62985,78.01775,3.711 1984,587.04596,-32.541653,53.795654,128.12079,3.666 1984,-10.257822,34.78714,29.061049,77.40416,3.637 1984,457.6322,-2.7642593,21.21521,36.10936,3.63 1984,497.82242,-15.541666,18.946442,42.98481,3.629 1984,565.33057,-32.16555,29.67102,73.422905,3.623 1984,-5.5634236,99.4818,18.128635,44.809708,3.598 1984,533.6676,-32.924564,29.255127,75.474205,3.589 1984,458.69775,-13.027222,33.69153,61.52791,3.572 1985,-5.350208,-18.067692,17.595142,44.15699,6.333 1985,610.27606,-45.868866,42.278748,95.65859,5.824 1985,-12.012601,477.35983,30.342113,63.51071,5.768 1985,-6.197915,489.2212,18.93671,42.625366,5.627 1985,-10.562297,-32.934223,29.418278,75.752144,5.406 1985,443.79706,214.43034,20.754639,43.949417,5.32 1985,-73.12343,265.31616,201.27007,462.1911,5.214 1985,-17.283384,-64.41778,56.216385,144.08708,5.199 1985,585.579,-95.18858,74.56262,226.16003,5.113 1985,583.0175,411.88797,71.5932,176.27377,4.986 1985,612.7993,454.48917,39.611633,107.56436,4.948 1985,251.84323,215.51146,15.884766,33.017258,4.907 1985,-21.902723,402.03375,79.58276,175.62506,4.889 1985,410.87552,208.34831,20.587341,45.997177,4.883 1985,-5.445376,200.79388,18.01379,44.660217,4.852 1985,626.9298,-18.687101,17.83551,46.69081,4.816 1985,533.7637,-1.8410454,38.09796,82.59241,4.788 1985,536.2782,310.40985,143.57275,341.83618,4.765 1985,489.99942,-179.21692,234.44363,424.7504,4.712 1985,380.15283,208.22273,31.30072,63.562424,4.71 1985,-37.09282,-128.5164,117.10778,277.35886,4.544 1985,626.50494,487.5571,20.972351,46.61508,4.539 1985,493.49768,226.05591,237.71619,515.5213,4.474 1985,4.369912,218.88077,38.342587,106.10242,4.458 1985,501.04425,87.85872,31.32904,73.444016,4.449 1985,259.75177,214.99648,16.684479,33.170425,4.442 1985,-71.122215,-190.28244,212.09412,457.1506,4.427 1985,588.9092,185.84164,29.569458,82.37393,4.26 1985,2.3905897,-17.072765,17.37339,43.67202,4.167 1985,492.86188,95.01053,29.29065,80.63742,4.116 1985,564.39514,-2.8329868,32.098755,66.76537,4.05 1985,267.7245,217.69359,17.015533,31.032852,3.941 1985,-9.848873,-6.902855,40.43316,100.79492,3.881 1985,579.6461,157.47073,37.378174,71.40004,3.877 1985,243.88335,210.82503,18.01793,34.88623,3.863 1985,1.2734586,207.08878,19.905598,47.24881,3.845 1985,59.394623,221.86899,32.160286,61.460236,3.84 1985,19.606892,70.269226,31.72202,71.35797,3.81 1985,-15.053864,152.33011,49.19912,138.78561,3.769 1985,5.0707316,213.12387,32.04916,64.446014,3.726 1985,1.8140221,-40.26049,43.611645,97.45339,3.687 1985,576.7457,148.13943,49.665466,137.40762,3.686 1985,52.23452,233.46176,31.677086,65.121735,3.635 1985,600.06055,10.215435,35.256042,70.95758,3.627 1985,-5.244658,51.777,19.260561,49.69628,3.608 1985,-9.205614,115.63689,38.995243,108.50583,3.592 1985,0.9253478,231.47145,25.25524,75.44914,3.551 1985,1.3226616,70.4193,25.74691,74.04242,3.544 1985,571.59296,-16.189713,55.741882,121.66807,3.543 1985,11.321184,173.99892,55.506653,120.850266,3.54 1985,418.73184,212.85576,17.932556,43.626358,3.484 1985,-10.120801,196.18999,27.478718,69.226,3.475 1985,546.13275,-14.573627,35.01892,65.69212,3.474 1985,35.464424,210.43474,42.351257,92.47542,3.454 1985,115.79902,210.68765,32.718178,61.500214,3.44 1985,396.0567,213.67653,30.12384,67.47771,3.434 1985,605.78687,-34.33946,28.650879,74.82338,3.43 1985,612.5207,169.64868,35.959045,112.276306,3.421 1985,513.1443,-18.842457,19.931946,46.757305,3.402 1985,537.43286,-17.019142,18.90924,41.28299,3.396 1985,9.686755,-17.563414,18.444344,43.15477,3.387 1985,516.20984,-33.37727,30.868347,82.35436,3.375 1985,601.919,-23.159504,53.862854,130.43524,3.326 1985,275.16464,214.2642,19.121185,34.530792,3.325 1985,354.13092,220.24832,32.66153,58.43518,3.319 1986,488.48993,207.55177,21.785065,45.326996,73.019 1986,503.81586,207.8468,23.705566,45.325104,12.087 1986,613.07117,215.00111,26.199707,76.34941,12.031 1986,432.24908,192.43195,23.146545,54.581863,8.627 1986,622.0785,-32.889774,27.539368,77.96361,6.904 1986,626.7842,-19.457878,18.07605,49.80429,6.365 1986,-10.495127,-33.54955,29.592018,76.672844,6.16 1986,-6.1082244,488.87143,18.802172,42.63138,5.856 1986,-11.880202,477.5228,30.103813,63.701508,5.819 1986,-5.586962,-18.669863,17.994534,45.503147,5.801 1986,602.4247,-55.89952,53.53357,134.37776,5.483 1986,526.8474,21.247066,34.526917,82.25708,5.275 1986,585.3962,-97.840805,74.925354,231.29445,5.215 1986,-72.71168,266.23456,200.65237,460.59613,5.106 1986,-17.9622,-63.975075,56.6398,144.14944,5.095 1986,583.20996,412.19537,71.47156,175.97406,5.017 1986,613.07404,455.008,39.257385,106.74762,4.958 1986,495.56427,212.65216,21.102295,49.181946,4.922 1986,594.3628,-43.939285,45.179504,95.769005,4.891 1986,-21.936493,401.92383,79.59194,175.67236,4.875 1986,484.92612,195.07202,38.714386,68.72867,4.854 1986,626.4379,487.59296,21.04834,46.199463,4.811 1986,-4.2481613,182.4196,17.650246,45.58806,4.805 1986,536.21533,310.28986,143.76703,342.21307,4.788 1986,0.46738267,52.14927,27.199755,81.248314,4.743 1986,490.71893,-179.36887,234.23578,425.7828,4.74 1986,251.56494,207.72925,17.272156,34.741806,4.668 1986,-36.880383,-129.30542,117.43919,279.93256,4.594 1986,493.68,227.31943,237.58862,514.8711,4.488 1986,-71.16024,-189.68732,211.96542,456.7221,4.44 1986,-10.530521,-5.781185,40.830055,103.14229,4.277 1986,266.9904,213.9321,18.632202,36.097473,4.271 1986,258.86835,214.02898,18.141876,34.541275,4.254 1986,464.6156,196.74034,24.057953,56.12648,4.146 1986,613.21265,-21.17905,29.173523,78.91008,4.127 1986,361.23273,235.68463,22.3667,31.42215,4.097 1986,553.3517,-19.468845,20.089966,45.87063,4.053 1986,541.65967,-33.976933,28.503296,75.78245,4.044 1986,544.97095,-18.739704,20.278137,44.233467,4.02 1986,-5.4612103,64.2064,20.306795,51.801804,4.015 1986,244.30899,214.15576,15.615448,32.514694,4.009 1986,353.61407,233.94441,21.39737,30.735916,3.998 1986,618.8276,-19.775032,18.46344,50.344814,3.928 1986,475.1643,200.92435,19.51178,46.383728,3.893 1986,-4.719347,19.24568,18.305845,49.28913,3.883 1986,3.4464586,-19.767204,31.137518,78.06157,3.841 1986,570.2468,-32.439262,60.31311,127.08768,3.832 1986,237.07132,216.43048,14.770905,29.966461,3.826 1986,527.04724,-33.4234,27.80951,76.36912,3.8 1986,2.5543551,-16.747139,17.33614,44.308826,3.785 1986,355.27713,228.15201,33.266296,54.40912,3.74 1986,615.039,5.9559402,24.429504,83.10301,3.712 1986,598.5482,212.90768,38.356323,101.35071,3.686 1986,275.72415,221.62532,18.50293,38.65782,3.684 1986,420.7542,196.55458,31.062988,68.36757,3.679 1986,563.9336,-6.8744106,30.765076,72.82622,3.661 1986,585.05927,-6.7252827,24.22583,58.28459,3.655 1986,252.75816,222.17067,16.375626,32.34723,3.584 1986,558.3761,-34.317005,28.231506,78.38307,3.575 1986,510.81622,224.04492,21.951965,40.292114,3.557 1986,2.037952,-23.921886,51.118748,145.8036,3.54 1986,529.7378,-17.678883,18.60138,40.159847,3.526 1987,567.7724,195.04704,32.77173,79.32649,93.255 1987,481.4532,201.75336,19.296051,44.764236,9.141 1987,600.5947,197.26193,35.847717,101.175415,8.52 1987,622.6481,-33.418983,27.52002,73.83681,7.186 1987,-5.348872,-17.769173,17.714947,44.810757,6.691 1987,-10.804312,-33.791016,29.790573,76.92406,5.888 1987,555.3916,170.32326,55.60547,109.39253,5.83 1987,457.91995,202.41722,22.60373,50.171432,5.652 1987,602.79376,-56.22256,53.531372,131.05525,5.612 1987,-9.021302,464.1497,37.754272,92.64249,5.609 1987,-6.2546053,489.02597,18.891132,42.470734,5.357 1987,626.91736,-19.003143,17.965698,46.86241,5.348 1987,-17.970243,-63.680298,56.226105,142.68756,5.252 1987,277.2306,218.0316,15.97699,27.20546,5.195 1987,586.3414,-98.36094,74.0672,230.19125,5.181 1987,580.11743,197.19041,25.490356,57.43193,5.162 1987,613.1304,455.4041,39.055603,106.460754,5.102 1987,-72.94096,266.21362,200.7333,460.854,5.066 1987,583.3619,411.91458,71.32123,176.21036,5.041 1987,593.4668,-44.949993,44.87549,94.58284,5.011 1987,615.2394,217.21709,25.214966,81.74272,5.006 1987,551.3883,195.01837,32.625732,71.059235,4.993 1987,285.5923,218.51793,16.09021,26.124283,4.957 1987,626.3556,487.9092,20.930664,46.213104,4.88 1987,-21.92584,401.80307,79.45555,175.64029,4.859 1987,489.9812,-180.70605,235.09436,425.8058,4.834 1987,474.50714,199.26772,20.639893,41.065735,4.779 1987,-36.775517,-128.4282,116.65349,277.42023,4.735 1987,536.242,310.12094,143.73358,343.00784,4.728 1987,612.94434,-20.450527,29.96466,68.862076,4.723 1987,2.9780297,-16.892385,16.984526,44.38924,4.657 1987,533.64307,195.96866,38.50946,77.44803,4.638 1987,292.7339,216.333,17.194824,28.867096,4.427 1987,493.6403,227.47406,237.6511,514.8128,4.425 1987,-70.798645,-190.00131,211.55914,457.2538,4.358 1987,-5.633582,187.13544,17.889008,39.508682,4.348 1987,3.8235664,192.11852,39.207672,97.9597,4.322 1987,331.7734,217.5829,17.863922,30.383957,4.251 1987,468.4131,183.23564,37.77246,78.29256,4.175 1987,299.4949,216.71896,17.880981,29.324036,4.164 1987,618.2457,-20.106573,18.95819,45.67403,4.006 1987,573.37103,-22.24762,54.408997,125.32243,3.868 1987,-9.662471,-6.041424,39.848766,99.54344,3.824 1987,515.3057,199.30319,30.842896,61.549683,3.821 1987,596.17847,-3.9703484,42.197327,105.228,3.769 1987,269.47516,218.42636,14.634369,26.821243,3.719 1987,494.01767,-34.182953,28.628693,73.60189,3.715 1987,10.234298,-18.721634,17.619453,46.55294,3.71 1987,-5.564314,171.30235,17.729935,39.04489,3.69 1987,-9.512414,199.10608,38.763058,99.092834,3.68 1987,613.22595,229.04878,37.192627,114.49608,3.674 1987,346.92035,232.82744,18.724792,28.220718,3.663 1987,29.27435,65.247696,32.74526,63.88559,3.641 1987,236.15787,215.37326,15.88974,30.189453,3.62 1987,43.01422,62.49866,33.052612,66.5289,3.575 1987,1.6406679,195.74428,21.126713,41.106476,3.553 1987,203.38783,206.63235,17.293839,32.95871,3.551 1987,338.9909,230.52791,19.721527,29.589218,3.538 1987,355.26962,234.68698,18.460815,29.643677,3.524 1988,-11.382954,-32.621677,30.484571,72.355194,6.925 1988,562.86774,-0.2427063,31.8656,62.82321,6.632 1988,-6.2924247,-17.93242,18.572544,43.270752,6.587 1988,-6.110151,489.7413,18.609016,42.13968,5.958 1988,339.77594,246.02837,19.657684,33.82991,5.843 1988,602.45654,-54.285748,53.219482,131.38426,5.693 1988,623.2545,-33.440014,25.963501,74.221756,5.687 1988,-8.884228,464.847,37.626312,91.393005,5.614 1988,613.1997,455.64362,39.100586,106.51111,5.573 1988,-17.594498,-62.700447,55.99691,143.7309,5.564 1988,585.6256,-95.485306,73.82507,226.68251,5.36 1988,627.0709,-16.707716,16.704468,44.43023,5.156 1988,508.7222,125.14688,28.842072,71.391205,5.122 1988,-72.60484,266.17252,200.42766,460.99277,5.068 1988,583.2853,411.68292,71.329895,176.30273,4.914 1988,-21.799038,402.4273,79.31137,175.15863,4.866 1988,490.21613,-179.39542,235.10516,425.0298,4.76 1988,595.02344,-0.15907097,18.75647,41.3079,4.745 1988,593.9979,-41.6735,44.157898,94.700294,4.726 1988,536.5093,310.39154,143.07825,341.78046,4.704 1988,2.8102887,195.7461,30.988771,68.857086,4.658 1988,580.11206,-10.745625,33.011047,62.6317,4.633 1988,626.369,487.56073,20.99054,46.651978,4.628 1988,587.04175,-2.353857,20.084473,42.25892,4.617 1988,226.323,210.73947,20.448883,35.360107,4.485 1988,235.49472,215.07014,17.41957,33.20453,4.477 1988,-37.04093,-129.23671,116.75312,277.94818,4.468 1988,493.9798,227.5625,236.98218,514.6708,4.432 1988,577.17993,-3.4820862,21.937622,44.843452,4.41 1988,-70.664444,-189.50189,211.49934,456.55594,4.31 1988,548.87854,-4.957367,34.700684,68.38366,4.219 1988,-9.565373,-5.0566063,39.820175,101.77258,4.141 1988,615.14465,-18.576418,26.25824,68.05883,4.055 1988,1.3759208,-21.912899,50.347027,139.72095,4.047 1988,572.3286,-33.873886,30.549866,77.759705,4.005 1988,550.45715,-34.222885,29.530212,78.069466,3.97 1988,253.61908,226.85455,15.333038,28.54602,3.951 1988,595.5303,-11.931009,29.34845,62.911278,3.947 1988,442.17706,-17.249805,18.207642,39.744144,3.83 1988,456.60986,149.46948,20.8349,51.670578,3.821 1988,569.2666,-16.966528,19.17456,42.93598,3.809 1988,261.85315,228.42998,15.161987,27.17949,3.759 1988,572.47144,-13.663155,52.806152,111.70289,3.743 1988,376.90903,254.01434,23.06314,35.988525,3.719 1988,489.44733,194.01503,22.232666,51.023224,3.684 1988,450.4623,-16.316462,18.08075,39.12669,3.682 1988,-1.1901867,219.41963,28.338133,72.87779,3.674 1988,332.3148,246.6031,18.568268,31.82283,3.671 1988,453.16226,-33.24112,30.254547,72.02341,3.638 1988,308.49115,232.55133,18.026459,34.228333,3.624 1988,425.60135,-4.235359,22.117584,41.079647,3.622 1988,243.82156,216.28299,16.777985,31.905258,3.609 1988,-6.1280007,212.15921,19.172483,46.45926,3.592 1988,218.8068,210.3121,20.424713,33.81122,3.584 1988,602.13605,-0.39300156,17.664246,39.268745,3.544 1988,437.92355,-33.55084,29.274384,71.02031,3.542 1988,597.1364,-4.3677483,39.614746,98.60257,3.533 1988,341.4096,234.38719,27.931396,49.717728,3.528 1988,619.5364,-15.969832,16.559265,40.84211,3.527 1988,210.55759,214.8881,20.024307,31.615738,3.495 1988,378.3815,240.16565,34.891937,51.8786,3.494 1988,433.71826,-16.210928,18.21399,39.067154,3.49 1988,2.3906589,-17.861916,17.66383,42.13972,3.485 1988,1.9473219,-39.494946,42.872448,98.522156,3.484 1988,546.2593,-17.053827,18.593079,39.839413,3.462 1988,3.6104548,204.6947,39.923904,102.46698,3.454 1988,-10.94561,196.81001,30.59453,73.17207,3.412 1988,425.88376,-16.941797,18.17807,40.15869,3.385 1988,2.233287,491.2446,19.454578,41.189484,3.375 1988,553.0512,-17.647995,20.06836,41.902077,3.359 1988,19.30073,204.63644,31.189459,61.95694,3.357 1988,352.8838,244.63373,22.082947,39.370117,3.311 1988,90.355385,167.53839,59.0776,115.23752,3.285 1988,-8.303915,32.592876,38.819305,107.820404,3.278 1988,469.09314,-33.08203,30.225098,71.61879,3.257 1988,92.08268,203.75641,31.910286,54.35147,3.246 1988,473.65787,-3.186016,21.498383,38.24738,3.214 1988,494.98553,121.14979,27.677307,74.05166,3.208 1988,590.75507,4.8504066,26.576538,62.80891,3.192 1988,530.77893,-132.50847,115.43335,265.28473,3.189 1989,462.9442,201.66211,27.674438,59.76831,33.831 1989,-6.036527,-18.128157,18.040749,43.858295,6.337 1989,-11.115703,-33.131187,29.538555,73.25068,6.283 1989,316.55728,235.44986,30.22284,61.16574,6.203 1989,-6.0752172,489.43558,18.490799,41.947968,5.787 1989,627.2027,-18.000599,17.144592,45.552414,5.721 1989,-9.046073,464.00452,37.761517,93.06561,5.58 1989,613.3761,454.67758,38.90393,108.139496,5.435 1989,490.40677,-182.5297,234.8104,429.40256,5.409 1989,622.84753,-33.643887,26.555603,75.0074,5.393 1989,314.21893,245.34943,21.476013,40.73639,5.346 1989,-16.726301,-63.656593,54.96112,144.99863,5.087 1989,-72.56237,266.11102,200.30174,461.06158,5.082 1989,626.5069,487.9631,21.041626,45.57193,5.061 1989,538.58813,178.09729,33.186646,68.84532,4.974 1989,-21.906551,402.01898,79.490456,175.59814,4.889 1989,583.1887,412.0342,71.549194,176.20029,4.88 1989,586.1645,-101.0957,74.54297,236.54628,4.73 1989,536.4092,310.12482,143.2063,342.20367,4.719 1989,524.6038,180.63847,33.818665,66.705444,4.704 1989,602.815,-57.363075,53.67743,139.57869,4.7 1989,1.093502,187.76251,30.019228,59.992294,4.64 1989,-37.2723,-130.55095,117.295105,283.62936,4.506 1989,550.3907,168.48395,31.201782,73.90527,4.485 1989,493.64243,227.0043,237.61789,515.8385,4.388 1989,-1.737119,202.46675,28.666712,69.76396,4.275 1989,331.33447,240.4764,31.23404,61.99205,4.231 1989,423.54617,185.83731,25.144928,73.469604,4.218 1989,-70.43155,-189.15967,211.81993,456.04745,4.214 1989,574.6253,174.03876,53.08673,140.51074,4.199 1989,201.36469,198.01094,22.831467,38.177094,4.147 1989,2.8470755,-17.042248,17.021679,42.377663,4.137 1989,322.09457,253.31071,20.316498,37.52382,4.086 1989,328.67355,247.75804,21.784546,42.659286,4.04 1989,306.68027,245.01149,21.218262,37.670456,4.002 1989,477.40686,205.00558,25.29889,54.50174,3.951 1989,611.8016,182.54239,35.38971,114.72635,3.95 1989,357.6906,-35.431187,28.182465,69.7678,3.93 1989,188.16043,190.95496,31.091644,52.715637,3.923 1989,598.57965,177.57846,37.224854,110.677,3.881 1989,-9.984901,-7.5628967,40.014183,105.39523,3.86 1989,619.9391,-5.3623486,18.476807,39.927727,3.847 1989,345.8925,251.55162,20.345276,35.78099,3.781 1989,618.21313,490.42902,21.078125,42.447144,3.773 1989,-4.5842047,172.462,17.273926,39.099686,3.771 1989,470.40012,198.12616,25.415863,45.438217,3.763 1989,594.39276,-47.485073,44.626343,102.57089,3.734 1989,-4.8835564,137.67154,17.77724,43.012817,3.657 1989,459.75403,199.44916,20.327576,41.129364,3.645 1989,-5.672082,199.88026,19.65873,44.184677,3.559 1989,-4.349994,157.84789,16.905643,38.3246,3.541 1989,205.80814,194.16162,28.315186,54.258026,3.521 1989,451.1911,206.00041,40.79535,84.78755,3.496 1989,-9.767827,174.85793,30.40889,67.29463,3.481 1989,583.96295,160.37381,37.94458,106.21344,3.436 1989,1.35836,178.68506,21.560913,43.658813,3.399 1989,-15.415184,150.76224,49.199482,139.13425,3.362 1989,341.838,-35.714878,28.40332,68.20363,3.362 1989,5.666791,-35.282703,28.36839,77.49348,3.353 1989,-5.306599,90.07243,17.369646,41.08011,3.346 1989,339.1495,263.99802,17.125183,34.409515,3.34 1989,184.00969,197.98212,24.871384,37.56317,3.335 1989,337.7616,249.15656,21.009094,39.877686,3.306 1990,454.50598,208.70016,28.860779,65.20615,37.176 1990,464.78085,207.92178,31.048584,69.53256,7.854 1990,-11.884656,-33.022675,31.166637,74.862434,6.354 1990,240.56763,242.58188,23.165894,43.88611,6.351 1990,-5.981953,489.82684,18.433344,42.00409,5.922 1990,208.92273,224.15056,28.112717,57.402573,5.904 1990,622.60144,-33.62278,27.312744,74.65206,5.884 1990,626.7683,488.02805,20.077759,45.075714,5.655 1990,-8.898324,464.2564,37.698997,92.659,5.639 1990,-6.244576,-17.58123,18.30755,43.544395,5.618 1990,627.0279,-19.397661,17.75885,48.321552,5.528 1990,-17.082584,-62.409184,55.892212,142.94975,5.29 1990,273.76035,253.96849,23.972198,41.96974,5.256 1990,486.22073,-35.13199,29.460297,76.46988,5.181 1990,-72.63284,266.20398,200.27408,461.36023,5.083 1990,583.36523,411.74164,71.31775,176.38538,5.063 1990,613.169,455.77893,38.801514,106.184326,5.062 1990,489.85638,-181.11395,234.9726,426.04123,5.048 1990,602.5626,-58.590588,53.824707,138.37679,4.947 1990,-21.78064,402.51953,79.39239,175.12903,4.889 1990,256.40884,241.61133,21.554688,41.671204,4.871 1990,585.91266,-98.19743,74.50049,229.65498,4.861 1990,515.76154,-17.520336,32.145203,74.741,4.754 1990,594.02734,-45.83108,45.009766,98.73653,4.754 1990,536.59314,311.13568,143.08447,341.99274,4.737 1990,-37.148693,-128.10663,116.571106,280.13608,4.579 1990,229.59877,230.8064,29.748642,59.118652,4.552 1990,520.84894,206.65402,23.664795,45.05638,4.4 1990,493.78772,227.12613,237.31836,515.297,4.389 1990,499.3694,-19.035156,35.039307,71.0,4.362 1990,-70.310135,-189.85913,211.58879,456.67847,4.332 1990,532.7416,189.24496,31.071045,63.261093,4.287 1990,539.5959,-21.003712,31.611572,74.63869,4.266 1990,565.63074,194.47888,30.21106,92.072296,4.256 1990,177.48412,203.323,22.761642,33.109238,4.198 1990,-15.9880085,152.18018,49.834877,135.12897,4.178 1990,395.11377,170.04048,28.137909,65.58275,4.071 1990,525.12683,-34.743652,29.687744,79.7034,4.066 1990,513.62805,-18.570974,20.07129,46.723427,3.988 1990,372.05948,170.48265,28.966888,71.28096,3.976 1990,446.38446,189.17494,28.897156,81.25923,3.951 1990,248.88052,250.57266,21.19986,38.172913,3.924 1990,1.8370324,-16.73508,17.726112,42.746803,3.915 1990,505.34454,-20.092552,20.490479,46.18443,3.914 1990,-8.876599,-5.913685,38.757694,101.88939,3.91 1990,489.02982,-20.662128,20.272797,44.859634,3.826 1990,448.36798,188.53786,47.354095,104.321335,3.782 1990,349.36,165.97003,33.551147,77.32938,3.78 1990,481.99783,-18.590183,19.404816,41.17109,3.764 1990,496.8647,-20.72102,20.557861,45.366116,3.722 1990,484.2843,209.77878,39.323425,85.55737,3.701 1990,618.2484,-8.656733,20.176758,44.52968,3.618 1990,470.52658,-34.042995,29.129333,71.99582,3.607 1990,618.6144,490.2156,20.011658,41.668365,3.59 1990,223.89006,238.74062,24.132431,44.01143,3.543 1990,393.1909,174.63313,21.123077,43.85608,3.541 1990,-8.774186,119.46627,38.19975,101.956,3.504 1990,1.7213178,-39.618904,42.894817,99.27084,3.488 1990,549.33734,174.99323,31.218445,78.875916,3.475 1990,-10.888072,62.541805,30.182701,75.4487,3.463 1990,3.3133006,199.15422,38.61873,87.9203,3.452 1990,537.5302,-20.146967,18.917175,48.82508,3.441 1991,485.2718,216.25624,30.08612,64.311325,25.822 1991,627.10974,-17.696507,17.11914,46.460617,7.461 1991,-11.14102,-31.162703,29.674438,72.52937,6.63 1991,-5.812707,-16.682858,17.489609,41.74036,6.508 1991,181.6308,225.64078,34.179565,67.75351,6.442 1991,623.17004,-33.715008,26.158508,76.24863,6.291 1991,-11.98492,477.90442,30.279804,63.373108,6.153 1991,-6.1760974,489.63818,18.721434,41.775208,5.825 1991,499.13867,210.3033,30.493225,69.52391,5.392 1991,603.0129,-55.780746,53.227295,131.94016,5.334 1991,-16.921139,-62.314625,55.76491,144.59503,5.223 1991,613.073,454.81262,39.34131,106.7724,5.028 1991,626.4566,487.7037,20.793152,46.07901,5.01 1991,586.156,-98.90982,74.30853,232.22717,5.0 1991,-72.72396,266.2572,200.43323,460.91724,4.985 1991,490.65985,-180.08342,234.64581,426.41614,4.974 1991,536.46674,310.25537,143.3139,341.9297,4.899 1991,582.94824,411.49844,71.84247,177.05673,4.839 1991,-21.794033,402.49387,79.44052,175.29031,4.818 1991,593.52167,-44.0504,44.8974,94.374374,4.608 1991,339.8199,159.0423,29.41336,79.07312,4.584 1991,475.3616,217.90999,22.501709,54.32878,4.507 1991,493.7163,226.81113,237.34784,514.9729,4.476 1991,-36.698303,-128.33435,116.931366,279.27164,4.451 1991,189.71225,221.06046,24.317963,44.7995,4.376 1991,470.9674,208.76585,37.569885,87.11696,4.222 1991,619.7434,-16.681955,16.803467,42.65382,4.187 1991,403.44873,205.92801,33.673615,63.593597,4.134 1991,-70.198105,-189.19554,211.51405,454.52606,4.126 1991,616.0298,-18.399632,26.553345,69.51568,4.109 1991,362.89752,169.81424,29.598785,76.80827,4.102 1991,-9.164027,-4.831291,38.748642,99.4835,4.078 1991,2.9361587,-14.4963455,16.249973,38.482716,4.027 1991,170.8101,245.95723,39.64897,53.629227,4.012 1991,477.8949,207.27084,51.017883,120.914215,3.952 1991,164.93332,216.1996,31.775131,57.12419,3.84 1991,297.94867,187.75266,21.81604,46.867523,3.751 1991,397.77734,163.6471,29.706726,77.086685,3.694 1991,524.8412,-20.176235,32.526367,70.49561,3.621 1991,403.5226,-17.105564,17.165833,40.31606,3.621 1991,350.95135,167.89954,29.881653,78.1362,3.612 1991,2.0541096,490.98904,19.558504,40.769073,3.606 1991,492.84436,-34.856174,29.614868,76.38633,3.605 1991,211.39734,251.72057,33.10942,62.77864,3.573 1991,579.3562,-35.04742,30.685486,72.60755,3.565 1991,584.9026,-20.210213,18.785278,43.09466,3.559 1991,410.6848,-16.6929,17.292114,41.84693,3.473 1991,454.7769,201.61717,38.77539,73.9601,3.398 1991,395.4068,-17.460293,16.479431,40.564037,3.386 1991,586.00916,-20.034632,35.05847,68.158035,3.368 1991,576.6328,-18.147228,19.702515,42.095303,3.354 1991,191.5251,239.39001,36.024643,66.60254,3.34 1991,521.6889,-7.5758924,21.209473,45.152287,3.336 1991,506.34113,197.1989,32.609253,72.27696,3.311 1991,421.3858,-33.401066,28.00055,72.013054,3.274 1991,569.1377,-17.162134,35.558655,64.67994,3.262 1991,518.13403,-34.40307,28.944702,75.11927,3.251 1991,538.29614,-19.150204,17.72937,44.13027,3.233 1991,137.60678,218.7829,20.175217,28.78537,3.228 1991,495.94196,-40.59308,47.189636,92.08244,3.211 1991,473.7711,-18.308176,18.750702,44.254345,3.192 1992,529.0319,208.69307,35.79364,71.13463,39.291 1992,121.36481,217.3429,35.789673,68.25839,7.577 1992,209.26933,255.81277,36.78528,71.16193,7.254 1992,346.06262,136.0495,31.406586,86.79039,6.851 1992,627.1683,-19.235949,17.302673,49.51721,6.55 1992,-5.684599,-16.453417,17.448784,42.281094,6.135 1992,622.68115,-33.624184,26.544373,78.069664,6.036 1992,-6.123331,490.23026,18.445992,40.890656,5.955 1992,-11.220469,-31.737581,29.774796,74.24288,5.951 1992,613.1661,455.20404,38.94519,106.763306,5.768 1992,-11.681904,478.6918,29.774359,62.767303,5.717 1992,377.26382,160.30292,24.775116,59.503265,5.656 1992,492.5008,-2.489338,29.312134,59.321064,5.574 1992,602.8758,-56.26505,53.201782,135.00238,5.231 1992,483.79395,-9.764429,30.012634,57.52948,5.23 1992,626.40045,487.23645,20.998352,46.343506,5.165 1992,490.78156,-180.8826,233.97565,427.04538,5.151 1992,503.05283,203.21687,39.990936,76.77162,5.121 1992,283.0403,171.83289,18.707,42.410233,5.076 1992,586.05115,-97.347305,74.14844,228.30595,5.061 1992,536.7758,310.39612,143.24774,341.44318,5.023 1992,-72.44765,266.5504,200.38428,460.97644,4.991 1992,-16.96509,-62.10617,55.748764,144.15373,4.889 1992,523.3574,212.71616,24.12793,57.332886,4.814 1992,-21.657255,403.3263,79.18807,174.32562,4.79 1992,582.83704,411.43842,71.83655,177.20874,4.713 1992,446.52686,145.05896,51.999146,127.39639,4.636 1992,594.3657,-44.732697,44.47235,98.28955,4.551 1992,482.36243,210.5072,43.220276,84.24115,4.517 1992,494.04077,228.58984,237.30566,513.496,4.48 1992,541.55096,201.7224,25.36438,48.662094,4.446 1992,-36.84592,-128.24283,117.02147,279.4311,4.43 1992,515.8683,212.97722,43.654175,93.59459,4.394 1992,-8.797106,-5.47641,39.17804,100.02413,4.312 1992,267.2744,172.21371,19.052155,42.240326,4.288 1992,461.6057,-11.246225,30.156677,56.01803,4.274 1992,469.97626,-31.712408,29.216095,71.12378,4.217 1992,-70.61537,-188.63118,212.02248,454.49866,4.109 1992,2.7402587,-14.392653,16.405252,39.436516,3.996 1992,483.3278,184.07916,41.375397,76.266174,3.98 1992,619.33374,-19.194042,17.800964,46.7437,3.895 1992,542.5458,202.66049,42.18158,90.87332,3.802 1992,572.2805,-21.711727,54.540527,124.78116,3.794 1992,504.22488,219.59555,22.68283,46.127686,3.762 1992,569.5642,-16.273586,18.477295,41.197937,3.747 1992,500.057,-15.407761,33.681763,65.95173,3.745 1992,515.0697,-15.9064865,33.84027,68.84556,3.676 1992,489.03674,-16.766651,19.243835,42.381924,3.651 1992,531.69745,-4.363323,33.339233,69.7946,3.65 1992,444.4617,-13.506763,31.087921,64.107834,3.634 1992,457.96024,176.31476,56.66794,115.6095,3.619 1992,528.8576,175.94392,44.4494,83.23192,3.618 1993,596.13904,227.18292,31.357971,66.578,11.253 1993,622.42883,-33.87604,26.921265,78.53564,7.009 1993,587.4686,105.68986,51.950928,180.65106,6.587 1993,-11.284299,-31.128117,29.72919,71.94915,6.494 1993,626.7424,-19.472933,17.823975,49.92065,6.423 1993,-6.03581,-17.482483,17.823229,42.284718,6.151 1993,532.1581,-180.02324,149.68567,420.76767,5.719 1993,-8.763938,464.62628,37.55562,91.79779,5.567 1993,67.02069,220.86028,36.071877,73.296524,5.471 1993,612.96954,455.18726,39.220886,107.09412,5.436 1993,83.21513,252.28265,33.60376,60.961548,5.428 1993,-6.0706925,489.41293,18.597206,42.15512,5.396 1993,609.20056,207.39627,31.165161,84.66626,5.305 1993,626.4658,487.98798,21.120544,46.272522,5.116 1993,-72.545044,266.47888,200.48145,460.9662,4.961 1993,-21.816048,402.60645,79.211174,174.96838,4.836 1993,537.0833,310.66565,142.24176,340.2744,4.816 1993,602.6754,-57.334743,53.325012,134.97173,4.805 1993,582.8696,412.03265,71.95984,176.52899,4.757 1993,-16.940744,-62.482464,55.698006,144.80138,4.752 1993,586.3616,-97.796555,73.50995,236.76646,4.592 1993,-36.747948,-127.775444,116.82138,278.40018,4.554 1993,594.3322,-44.91736,45.064392,95.63936,4.525 1993,618.3944,-8.704134,21.089905,48.73603,4.504 1993,-70.93647,-188.8471,211.97476,455.05304,4.276 1993,493.89142,227.23581,238.10986,515.1315,4.229 1993,-6.1721663,198.50595,18.926628,43.20288,4.048 1993,190.71762,251.41728,35.062393,79.646194,4.044 1993,-9.019245,-5.419613,39.354576,100.160355,4.012 1993,511.19632,-3.5874119,24.294342,42.433266,4.006 1993,577.6842,182.5365,53.384216,146.36346,3.811 1993,349.48856,-4.7008305,29.586823,65.33999,3.785 1993,97.49081,250.75552,36.770836,57.098633,3.766 1993,609.8561,138.66693,37.54431,136.84528,3.763 1993,440.1843,-17.249702,20.095825,42.392506,3.761 1993,-6.109984,227.50606,17.752682,42.034836,3.746 1993,380.5169,72.51053,34.95636,88.18387,3.738 1993,424.85202,135.95505,36.108124,92.42734,3.731 1993,505.9941,-17.616905,19.732758,42.82975,3.685 1993,549.7078,-10.382019,88.696045,309.72845,3.645 1993,611.6828,230.73727,37.59027,109.983765,3.616 1993,494.6104,-35.666195,28.665833,75.622345,3.611 1993,412.57907,127.16796,53.490295,151.60031,3.608 1993,2.745388,-15.393917,16.664333,39.020435,3.608 1993,607.88794,238.72447,23.57837,60.056534,3.606 1993,583.0767,-5.330841,65.42059,240.50093,3.6 1993,432.01843,-39.55741,46.813354,91.365265,3.58 1993,370.26898,-14.973256,18.581726,40.833054,3.57 1993,4.603889,199.65178,38.005135,87.370316,3.543 1993,490.03363,-17.898186,18.254028,40.80656,3.521 1993,498.40234,-17.622086,19.163635,41.036896,3.517 1993,193.40076,224.67264,34.994095,77.03903,3.51 1993,393.19913,1.077961,33.99289,69.76523,3.504 1993,428.66425,-31.055122,30.131226,71.66227,3.499 1993,464.04004,-39.669037,47.398224,90.264885,3.487 1993,386.9909,159.17004,42.024445,110.631256,3.467 1993,-4.4176955,114.27822,17.979357,46.409615,3.445 1993,362.21332,-16.555407,18.39627,42.023098,3.445 1993,71.41887,256.4396,26.870857,45.225525,3.401 1993,-5.5243835,176.73357,18.240961,42.537766,3.389 1993,361.65472,90.81525,57.571716,150.03333,3.351 1993,498.89954,-15.465881,42.454773,79.74181,3.34 1994,415.40613,72.03474,58.464966,185.02454,10.575 1994,543.7942,-16.718979,90.86371,311.20135,9.933 1994,191.80342,233.22539,26.139175,68.26445,6.995 1994,622.4657,-33.377068,26.996277,76.64332,6.976 1994,532.0085,-178.65974,150.3504,421.425,6.663 1994,626.6964,-19.396467,17.748901,49.161858,6.524 1994,-11.732645,-33.023457,31.06727,73.53638,6.328 1994,-6.1258993,-17.465107,18.289286,43.47993,6.077 1994,428.3038,149.40988,34.035095,95.87923,5.846 1994,626.66736,487.55722,20.641846,45.814117,5.603 1994,-8.855014,464.1219,37.72818,93.211975,5.595 1994,-6.0702186,489.61328,18.565273,41.577454,5.468 1994,-17.221066,-62.73618,55.79253,142.74748,5.372 1994,613.1988,454.18164,38.995544,107.89191,5.343 1994,582.8207,-54.814316,68.35474,216.0554,5.319 1994,-72.4977,266.48676,200.74606,460.95026,5.069 1994,341.31503,83.012085,29.947052,77.32822,5.03 1994,611.3645,115.687294,38.66327,124.28658,5.002 1994,602.5867,-56.135258,53.749878,137.99579,4.995 1994,536.68616,310.1268,142.60797,341.09708,4.943 1994,481.93433,22.70552,34.011414,93.55707,4.857 1994,538.7068,-33.75694,32.169617,74.86685,4.787 1994,-21.828323,402.46164,79.434975,175.1434,4.785 1994,582.9687,411.09494,71.89044,177.41177,4.749 1994,420.63812,-31.546806,30.01065,77.09292,4.728 1994,181.55371,241.28542,28.304855,68.39012,4.694 1994,41.52502,212.99097,39.0127,78.006775,4.553 1994,-37.07776,-128.17606,116.63125,277.52213,4.548 1994,570.8526,146.08131,57.374207,159.79619,4.521 1994,50.46421,239.64932,34.022476,61.165344,4.5 1994,618.729,-10.278403,20.52887,51.244648,4.484 1994,493.97113,227.15472,237.89465,515.3562,4.345 1994,425.09875,-17.942652,20.075073,45.896366,4.284 1994,-70.80317,-189.13051,211.75192,456.30066,4.276 1994,389.3476,-33.232025,28.752014,75.24274,4.269 1994,76.15712,257.0391,32.908134,56.26999,4.242 1994,173.53253,232.34084,27.556625,67.56102,4.233 1994,315.69348,200.98853,18.255768,30.004684,4.177 1994,594.287,-44.37809,44.673706,99.80626,4.157 1994,-8.965492,-6.0306396,39.108925,100.80831,4.071 1994,1.9755846,-17.012054,18.00472,42.443184,4.004 1994,553.2064,-21.966663,20.406006,45.65291,3.963 1994,408.7718,13.333214,36.563293,85.69956,3.941 1994,594.63947,160.91736,40.780334,119.38428,3.894 1994,557.1451,-35.29946,29.262146,69.35126,3.856 1994,299.77362,198.62863,16.667938,35.74234,3.847 1994,299.32837,77.4397,30.883118,56.114365,3.843 1994,424.37964,-6.279999,34.012238,68.011765,3.781 1994,187.26173,236.11523,20.860977,47.442993,3.76 1994,528.29987,-17.492535,36.586365,72.80028,3.727 1994,484.2744,-19.535536,31.982239,78.2336,3.679 1994,312.52505,89.86171,22.536926,39.202194,3.651 1994,489.94165,182.167,19.668701,45.158188,3.603 1994,148.19577,256.8279,32.72708,70.27789,3.589 1994,539.70105,151.07628,60.596436,148.37459,3.581 1994,409.71317,-18.240635,19.541962,46.128998,3.58 1994,487.7914,-62.342957,246.02237,459.1114,3.564 1994,57.14337,208.2313,34.382355,79.11063,3.552 1994,307.091,200.04959,18.237946,35.27539,3.548 1994,60.341244,253.24658,32.362232,59.197205,3.522 1994,467.562,271.5921,17.560791,28.437714,3.511 1995,553.9939,-19.442535,87.937195,323.9855,15.06 1995,204.34467,246.92538,30.447495,71.07898,9.946 1995,164.91528,246.08003,31.923798,74.291245,7.034 1995,190.65335,250.7492,27.975113,62.86554,6.998 1995,621.3949,-33.83436,28.183838,77.699715,6.718 1995,626.4911,-19.730812,18.514282,48.50802,6.253 1995,-11.224246,-33.210854,29.79279,75.43909,6.168 1995,57.12552,247.37675,36.58914,63.771133,6.136 1995,532.6544,-178.44058,149.64832,423.2543,6.135 1995,612.2129,-21.170597,29.962036,81.597496,5.83 1995,-6.233785,489.02158,19.045313,42.713226,5.773 1995,-12.185063,477.2835,30.54572,63.77069,5.771 1995,585.9652,56.81231,52.328552,216.04327,5.712 1995,612.9613,454.49597,39.44684,107.72974,5.6 1995,-5.886708,-18.13929,17.97505,44.43248,5.497 1995,181.25092,241.89828,28.211288,65.528656,5.424 1995,42.51998,248.78108,35.901154,60.353287,5.417 1995,603.05566,-56.544952,52.858032,136.43567,5.336 1995,201.4497,247.97319,22.598923,51.203873,5.146 1995,626.3396,487.75476,20.951355,45.601135,5.134 1995,-72.75256,266.08954,201.2253,461.58844,5.079 1995,-17.261286,-63.391197,55.842903,144.69211,5.037 1995,537.0221,310.0157,142.20337,341.1184,4.864 1995,-21.871944,402.14047,79.55545,175.26236,4.787 1995,583.0577,411.4467,71.81329,176.91104,4.677 1995,575.1269,133.38536,54.79004,176.25032,4.635 1995,593.9756,-44.876965,45.70044,97.30684,4.617 1995,67.32858,260.38876,34.42119,61.901672,4.519 1995,-36.979378,-128.4094,117.10777,277.68024,4.426 1995,518.19165,-36.64209,28.885498,82.89362,4.362 1995,195.48248,259.69937,32.57367,71.31192,4.327 1995,586.7342,-98.29583,73.02869,238.81512,4.3 1995,-70.96583,-188.78688,211.41089,455.65283,4.26 1995,523.317,-9.270092,35.89508,71.61436,4.254 1995,493.69336,226.54376,238.15686,515.8715,4.249 1995,324.335,205.45259,15.61618,28.81659,4.076 1995,45.318535,218.33548,37.223885,74.8064,3.929 1995,308.23013,217.31905,16.71811,27.640518,3.919 1995,122.51022,268.91846,34.690186,61.148407,3.889 1995,331.14154,57.089172,29.922546,65.38957,3.794 1995,-9.616552,-6.0135117,39.798477,102.239296,3.767 1995,617.6894,-20.714996,19.707703,50.317383,3.714 1995,176.05002,256.62167,22.373383,50.559418,3.655 1995,82.07689,258.8972,35.757957,59.393066,3.654 1995,2.6370091,221.3456,17.458683,40.21791,3.645 1995,542.9398,-1.67836,36.023743,67.83374,3.621 1995,338.68057,206.05188,16.005249,27.844376,3.602 1995,317.07892,215.37344,16.235962,28.80484,3.584 1995,154.53403,262.30457,34.83545,71.059784,3.535 1996,622.60254,-32.546936,27.800781,73.826584,6.745 1996,532.3958,190.7083,33.069458,80.29988,6.552 1996,216.36581,244.08693,31.985336,61.236435,6.065 1996,44.025784,222.508,34.750553,77.18408,5.924 1996,626.70667,-20.17577,18.492554,48.650146,5.859 1996,-11.052635,-33.478943,29.867226,75.84583,5.858 1996,602.59717,-55.81388,53.150208,135.15704,5.849 1996,189.13953,242.27464,30.477417,65.15666,5.827 1996,-5.8735094,-17.598343,17.705948,44.239464,5.778 1996,-12.449892,477.68936,30.970112,62.8873,5.742 1996,585.417,-95.87872,74.52106,228.3258,5.715 1996,339.9308,208.58192,18.132233,32.767853,5.68 1996,613.06995,454.02618,39.123962,108.16565,5.644 1996,-6.295514,489.42252,18.990164,42.240326,5.385 1996,626.38464,487.25766,20.900513,45.85605,5.234 1996,-17.163965,-63.762764,55.88443,145.86972,5.157 1996,-72.79032,265.7821,201.26282,461.43982,5.035 1996,490.0774,-180.74423,234.63513,426.6565,4.928 1996,205.59523,250.32228,32.890106,66.68355,4.862 1996,-21.841297,402.47473,79.51143,175.10962,4.774 1996,583.0862,411.2712,71.61481,177.03433,4.731 1996,593.9358,-44.200836,44.56482,98.241974,4.696 1996,536.7434,310.9367,142.57452,341.2364,4.645 1996,165.31784,247.83165,32.929245,67.4973,4.552 1996,59.30976,246.20801,32.537468,61.41638,4.498 1996,364.2528,25.930656,30.44757,69.66017,4.454 1996,581.9075,-33.85586,28.91034,74.11767,4.402 1996,77.65117,263.96448,31.80754,57.241302,4.362 1996,-36.975094,-129.0873,117.09155,278.6996,4.342 1996,538.85895,-15.71641,17.056335,41.389503,4.329 1996,-71.09905,-188.64304,211.58728,455.04752,4.242 1996,493.4121,226.50317,238.08563,515.5708,4.231 1996,585.7525,-19.406797,18.904602,43.77055,4.182 1996,578.3858,-17.569365,17.946838,41.926098,3.921 1996,152.90663,235.47658,58.881577,122.38228,3.893 1996,89.34909,269.7191,33.636826,54.086487,3.873 1996,593.8727,-18.441961,19.507751,42.786633,3.753 1996,-9.335499,-5.7674828,39.45221,102.49217,3.707 1996,179.57092,225.59482,32.278214,72.42584,3.693 1996,56.300243,258.50226,25.295704,42.26758,3.685 1996,546.60626,186.23105,32.263916,74.739136,3.596 1996,394.66418,-15.47039,16.885986,41.22984,3.587 1996,491.24817,-33.0355,59.033936,126.47086,3.555 1996,523.10516,-31.649649,59.230713,126.34028,3.554 1996,540.8216,31.63834,31.805481,80.18228,3.518 1996,3.3153415,25.848694,31.770771,87.53372,3.505 1996,526.4364,171.66193,51.529785,116.77493,3.492 1996,618.0922,-20.942142,19.636414,47.27394,3.492 1996,531.3801,-133.26164,114.76965,267.67627,3.474 1996,546.5782,-14.742683,16.953003,40.87597,3.466 1996,574.8955,-16.904171,54.362427,138.67107,3.408 1996,530.6177,-14.693346,17.060059,40.97776,3.393 1996,1.7708802,490.69156,20.234032,41.69809,3.346 1996,499.53412,192.27191,41.457947,95.761536,3.279 1996,410.9275,-13.753811,16.569305,38.496403,3.273 1997,-5.778023,-17.12822,18.209679,43.132874,6.883 1997,-10.81532,-31.835844,29.658619,73.542915,6.128 1997,-12.146093,476.79358,30.698177,64.63965,5.92 1997,612.9817,453.4912,39.33789,108.88501,5.889 1997,627.08356,-18.87238,17.362183,47.6015,5.882 1997,626.46204,487.6424,20.745544,45.59088,5.773 1997,-6.2754784,488.8311,19.118654,43.21237,5.522 1997,419.05734,-4.1593723,31.140564,67.846146,5.15 1997,622.6141,-33.86773,26.669067,76.19555,5.125 1997,59.822838,254.32901,31.661926,58.461243,5.108 1997,-17.005516,-63.385338,55.595512,144.84715,5.076 1997,-72.71888,266.08145,201.26035,460.83115,4.987 1997,509.76605,-4.105356,35.176514,62.048645,4.918 1997,403.0551,0.9188347,32.278473,84.45096,4.843 1997,603.1923,-57.803463,53.229126,137.83127,4.835 1997,50.529247,223.45595,33.347248,76.09926,4.826 1997,585.9092,-95.94821,74.64349,228.0093,4.822 1997,582.91754,411.5807,71.77899,176.82117,4.77 1997,-21.769712,403.22458,79.51028,174.29117,4.758 1997,536.6841,311.0283,142.5274,340.66605,4.695 1997,449.2744,-1.972971,34.280945,63.258354,4.686 1997,489.77612,-179.68861,234.65198,423.15973,4.672 1997,-36.969788,-129.26639,117.43831,279.54886,4.527 1997,-71.18335,-188.78014,211.93536,455.29407,4.245 1997,388.16974,-7.468685,34.92694,80.9193,4.228 1997,493.501,226.55005,238.12485,515.73004,4.208 1997,492.38162,-2.7105503,35.269135,62.894867,4.166 1997,472.98068,-3.053833,33.8078,70.49286,4.15 1997,445.11542,-9.44326,57.355774,107.826866,4.105 1997,89.93483,276.50507,23.135399,36.69577,4.054 1997,-9.7722645,-7.7205276,39.82083,103.254555,4.002 1997,356.00928,-13.669716,34.029236,64.383095,3.962 1997,594.6035,-47.94142,44.870483,101.01245,3.958 1997,494.60513,-36.12603,49.345917,85.25352,3.944 1997,168.55125,267.75104,35.47888,75.818665,3.934 1997,432.85364,193.13075,35.135498,69.31856,3.88 1997,300.54788,-11.518751,31.721252,62.717407,3.866 1997,412.7882,-32.46711,29.826416,76.005035,3.855 1997,1.6880989,-15.588191,18.14533,41.58711,3.791 1997,173.60312,290.54037,39.94644,69.98053,3.769 1997,411.61557,-11.346073,55.036316,110.408554,3.738 1997,214.63702,248.0781,28.100983,64.40521,3.731 1997,371.44397,-6.6693497,31.892334,72.32659,3.652 1997,-6.2588816,153.48004,19.902952,44.81578,3.587 1997,388.98376,-33.631424,29.940796,77.76029,3.585 1997,437.05804,-12.074766,31.731598,66.64222,3.578 1997,76.87526,261.42197,31.618484,55.085358,3.561 1997,463.10425,-34.176167,48.84137,85.73975,3.559 1997,432.55588,-1.7101746,45.170135,85.58401,3.507 1997,72.57738,265.32776,24.689072,41.098114,3.507 1997,315.92645,-9.929852,32.822266,63.07422,3.466 1997,1.9410264,489.81995,20.01414,42.368958,3.462 1997,43.802452,254.72098,32.79609,57.378174,3.459 1997,-10.347719,52.00245,29.84073,77.8691,3.428 1997,4.14099,47.85761,31.861109,91.13495,3.421 1997,1.9693375,-40.341557,42.678047,99.95756,3.419 1997,364.1637,-30.10936,58.29547,121.0787,3.405 1997,619.32764,-18.734545,17.083557,45.28982,3.399 1997,365.65338,-33.19791,29.207397,75.07528,3.384 1997,55.716408,259.49426,25.54673,42.27133,3.377 1997,184.38809,267.09125,22.172592,46.63614,3.364 1998,-5.9238997,-18.155344,18.135658,44.019184,6.717 1998,-11.047292,-32.757225,29.68343,75.00854,6.491 1998,626.9259,-18.771334,17.567566,47.16491,6.25 1998,622.67706,-32.96915,26.79065,74.37045,6.197 1998,-12.329983,478.69412,30.783598,62.121735,5.979 1998,613.0524,453.90836,39.008423,107.92197,5.671 1998,-6.411165,489.57642,18.977884,41.76581,5.322 1998,214.37477,240.71268,28.122635,64.54205,5.219 1998,-17.174015,-64.21711,55.669895,146.37178,5.038 1998,-72.57261,266.443,200.71503,461.00928,5.015 1998,490.76794,-180.60344,234.42493,426.58942,5.009 1998,626.4365,487.59015,20.68457,45.876038,4.982 1998,603.35455,-57.091206,52.945496,138.13599,4.955 1998,-21.811676,402.58078,79.38223,174.96243,4.826 1998,42.118816,259.57498,35.54984,57.627747,4.77 1998,196.42091,235.17564,30.262283,68.490616,4.759 1998,536.7882,310.91608,142.45587,340.77032,4.735 1998,583.0412,411.68167,71.70636,176.75943,4.734 1998,585.66113,-96.00113,74.69983,228.1963,4.731 1998,-37.084232,-129.54259,117.545975,280.29425,4.444 1998,2.6317234,-17.988373,17.724886,43.993923,4.3 1998,493.96173,227.45047,237.43933,515.0027,4.29 1998,307.08408,200.63234,16.318054,25.491959,4.182 1998,362.25397,209.62494,18.848389,33.823914,4.166 1998,4.5683136,179.253,31.508125,96.946915,4.15 1998,-70.98619,-188.63211,211.51857,455.055,4.138 1998,-10.31032,20.845676,28.791468,81.21991,4.049 1998,356.3954,209.10655,17.76117,31.235016,3.95 1998,408.03937,36.565773,34.12329,85.19934,3.936 1998,416.53796,198.09615,21.37323,49.55696,3.932 1998,-5.8722644,250.43483,18.134241,40.408005,3.907 1998,28.659962,86.201096,30.704426,62.718094,3.9 1998,300.35373,202.90482,18.62503,33.5876,3.788 1998,594.4392,-46.167732,44.95996,100.75686,3.761 1998,480.57465,-5.7517242,54.78363,134.60403,3.731 1998,182.73634,232.12572,28.758987,72.13646,3.63 1998,316.72202,213.62273,16.763214,35.910095,3.623 1998,396.87762,198.4319,29.558563,60.64885,3.588 1998,363.601,199.25226,29.061646,55.684906,3.575 1998,-5.1913347,27.862322,18.656166,50.862976,3.566 1998,496.9189,-12.378962,32.98044,71.441216,3.56 1998,31.472395,221.22214,36.40365,76.13153,3.5 1998,509.2258,89.00465,58.483612,208.44293,3.497 1998,1.8895674,490.99014,19.706352,40.93045,3.491 1998,-5.485607,121.60664,17.50948,40.28479,3.468 1998,542.4904,-33.392685,27.843933,76.28799,3.437 1998,0.5762291,30.471386,27.678165,95.750595,3.435 1998,324.02316,215.33237,17.033295,34.06862,3.376 1998,619.24585,-19.217793,17.701294,44.99554,3.359 1998,521.2728,166.55739,34.784424,101.00444,3.354 1998,299.6323,215.43924,18.052185,34.48651,3.342 1998,332.6886,217.76834,16.745972,30.6996,3.341 1998,257.26736,215.36275,21.547699,38.70427,3.328 1998,154.31964,238.08968,56.14621,115.14717,3.306 1998,339.9464,217.44888,17.536469,30.505554,3.286 1998,2.2762704,-25.680382,49.582306,151.23747,3.277 1998,58.07484,266.1144,34.30753,57.964996,3.277 1998,5.1931167,-35.29159,29.492575,79.12899,3.262 1998,347.82504,217.76735,17.694672,31.192123,3.254 1998,10.247859,-19.749723,18.332191,46.441856,3.238 1998,352.01187,207.59169,26.226013,50.40303,3.237 1998,315.7155,201.5289,15.861603,24.848495,3.213 1998,563.68335,-7.9696636,31.218262,67.35434,3.199 1998,306.6355,205.67421,18.953644,39.167862,3.193 1998,40.638603,204.36403,32.980354,63.205215,3.187 1998,53.821583,269.22244,27.252476,40.852386,3.181 1998,-9.314647,50.128178,30.426361,81.18446,3.18 1998,83.60925,261.8226,34.01042,59.305298,3.164 1998,421.21844,184.49466,28.496948,67.89366,3.161 1998,437.37155,-35.446724,30.490356,79.11587,3.148 1998,203.97809,281.72858,31.49176,70.50229,3.133 1998,-11.512975,233.2321,29.169685,73.599625,3.121 1998,203.4127,139.18713,19.260468,51.472794,3.106 1998,208.27394,250.94624,23.963455,56.481827,3.105 1998,-10.676665,143.99734,29.533924,74.961395,3.103 1998,-6.1669865,313.0561,17.58303,40.674805,3.081 1998,597.1483,200.59654,38.59424,112.183426,3.08 1998,39.36068,269.99304,26.612389,39.44098,3.077 1998,597.06903,460.7693,38.701843,99.548645,3.073 1999,315.25424,215.70682,19.271484,36.69867,7.442 1999,3.3268845,262.2493,34.522736,63.84912,6.416 1999,389.18393,194.22858,28.777832,68.22626,6.216 1999,-11.298266,-33.372715,30.386272,75.82922,6.181 1999,-5.894294,-18.84586,18.02216,45.37517,6.142 1999,626.3783,488.3429,20.934448,45.266174,5.929 1999,627.20514,-17.224113,16.884277,45.303333,5.787 1999,613.01306,454.01846,39.62378,107.93399,5.701 1999,314.23083,207.69786,19.536896,28.21614,5.68 1999,-11.869435,476.71567,30.271877,64.37857,5.656 1999,-6.0787745,489.1129,18.757404,42.572998,5.47 1999,-2.170496,239.72581,30.039738,77.78096,5.196 1999,2.4638617,210.38297,31.965986,69.286835,5.132 1999,-72.80191,265.95038,201.21112,461.96942,5.112 1999,622.9339,-33.948914,26.228638,75.376816,5.103 1999,299.8392,209.97858,16.908813,26.133698,5.089 1999,-17.19952,-63.232754,55.79407,144.46199,5.005 1999,603.2634,-57.248177,53.127197,140.51715,4.91 1999,-21.932465,402.35263,79.74939,175.18582,4.886 1999,425.43427,176.99026,29.630402,83.10947,4.808 1999,490.1499,-179.22495,235.14441,425.86807,4.795 1999,536.772,311.36124,142.43481,339.77634,4.794 1999,583.0338,410.78217,71.79346,177.73694,4.769 1999,156.04607,256.7212,31.31047,66.76807,4.721 1999,585.7764,-97.852005,75.29932,231.88408,4.714 1999,196.03004,248.47784,32.753998,67.65857,4.615 1999,-36.576977,-128.47191,117.10443,276.02893,4.579 1999,207.68324,229.14113,32.558487,66.29683,4.46 1999,493.67706,226.59708,237.86273,514.84094,4.343 1999,-6.404803,263.9258,19.864973,44.308105,4.314 1999,179.00394,248.6919,33.48468,66.03851,4.308 1999,492.2491,188.76329,31.82779,83.9875,4.304 1999,4.5036087,50.59643,31.179646,80.11569,4.282 1999,-9.344725,-6.050171,39.468388,101.19431,4.259 1999,161.47137,238.34395,44.419357,70.97569,4.259 1999,17.206913,263.3012,36.0297,62.273895,4.247 1999,-70.97367,-189.01376,211.59503,456.13382,4.162 1999,-6.3426948,218.18855,20.210354,44.586197,4.151 1999,382.50302,200.54343,22.986206,57.353058,4.006 1999,349.73553,-35.8701,29.862213,80.150185,3.974 1999,307.43887,210.0967,17.321259,28.4059,3.929 1999,-13.198528,265.73398,31.6054,65.99277,3.929 1999,339.2248,214.16042,19.111816,35.957092,3.927 1999,618.18677,489.53033,21.051453,43.02942,3.881 1999,411.29907,137.28636,52.233643,143.07137,3.795 1999,316.06152,203.2626,28.435822,52.98404,3.775 1999,405.39703,152.73853,39.803772,104.18506,3.711 1999,50.836365,276.737,34.0457,57.78604,3.71 1999,505.61353,189.43774,30.878662,70.93234,3.644 1999,1.6682887,-39.000454,43.280613,99.35943,3.636 1999,0.589422,65.155785,24.11128,73.75144,3.617 1999,9.493565,219.87526,34.544975,82.64635,3.588 1999,1.0912061,276.74783,22.24309,42.614807,3.572 1999,30.161171,48.844406,28.308868,85.89049,3.562 1999,84.11144,282.1678,32.11055,59.140625,3.498 1999,-10.239911,190.71251,30.814316,67.870285,3.483 1999,492.75684,-34.6439,29.665405,70.124596,3.478 1999,619.81024,-16.616758,16.19751,41.64733,3.474 1999,34.579662,259.12408,34.897846,67.77396,3.458 1999,144.59702,234.63522,45.03125,74.84152,3.453 1999,369.1179,205.73705,27.745392,59.264847,3.449 2000,147.8325,252.02145,30.702744,77.752686,7.231 2000,185.2085,244.75557,31.910934,67.15956,7.047 2000,-12.125626,476.79068,30.660162,64.28909,6.39 2000,-5.831503,-17.097916,18.20966,43.007942,6.357 2000,300.22162,212.05254,14.674103,22.531876,6.227 2000,626.84033,490.25494,20.719238,43.150146,6.133 2000,-6.195956,489.02496,18.890526,42.43994,5.882 2000,613.31964,454.0413,39.43817,108.36368,5.689 2000,-11.164814,-32.322193,29.860065,73.97425,5.465 2000,618.75604,492.2989,20.146057,39.807373,5.433 2000,627.2196,-18.154713,17.237305,46.592766,5.402 2000,611.1647,-48.474487,41.64624,101.80581,5.117 2000,121.427345,250.35614,32.789833,84.05292,5.069 2000,-72.50221,266.84216,200.49664,460.54578,5.016 2000,-19.862797,-87.226364,71.60301,197.45471,4.971 2000,-21.946064,401.8113,79.74632,175.69907,4.875 2000,531.63477,-179.17746,150.48962,418.90515,4.869 2000,583.11383,410.39923,71.71924,177.96411,4.84 2000,585.8743,-96.74577,74.58795,229.46083,4.819 2000,162.39775,250.85721,34.498383,68.45825,4.767 2000,536.5641,310.94354,142.60394,341.1988,4.747 2000,-36.215706,-128.48335,116.65452,278.59354,4.601 2000,299.4346,215.74365,17.979828,32.79666,4.517 2000,539.1378,-18.699162,32.46228,80.37697,4.506 2000,493.42493,226.30566,238.22357,515.6546,4.345 2000,314.59604,217.5151,19.077423,33.050323,4.275 2000,316.6256,209.93835,15.1345825,25.12648,4.219 2000,-70.585396,-189.49477,211.49033,456.31348,4.21 2000,432.54492,-9.758072,45.929565,84.86146,4.196 2000,2.1278338,-15.886305,17.731133,40.92488,4.113 2000,563.10474,-21.629656,32.14691,72.11131,4.052 2000,307.0376,216.19421,18.43988,33.548294,4.019 2000,306.5984,211.53331,14.99353,24.123306,3.875 2000,-9.228192,-18.215782,38.54543,98.99349,3.87 2000,1.8655288,489.90555,19.94136,41.38626,3.804 2000,492.7809,-9.4686775,29.427582,76.91266,3.785 2000,522.2226,185.53896,32.0036,85.63835,3.691 2000,537.23395,207.49611,35.138367,89.58714,3.647 2000,294.22977,206.926,12.3272705,20.002045,3.566 2000,429.36646,14.865154,36.048035,87.383316,3.541 2000,9.286281,-17.341202,18.089779,41.885326,3.523 2000,525.8054,-18.050657,32.17865,77.37439,3.496 2000,-7.0397377,62.532574,37.40629,110.78125,3.483 2000,597.7046,461.1089,38.730774,100.415344,3.454 2000,440.7522,-26.84211,61.809418,123.10908,3.442 2000,11.011694,-13.245743,57.203148,160.61684,3.433 2000,198.24503,241.97227,22.955063,43.32759,3.425 2000,500.3786,186.18478,41.977356,102.54343,3.392 2000,282.8639,215.71368,18.696808,32.660538,3.326 2000,130.49084,230.56146,31.918518,74.55554,3.326 2000,505.36496,-19.821354,19.332489,45.306942,3.324 2000,17.958206,-17.121857,17.590164,40.61456,3.294 2000,511.89478,165.74414,53.8584,150.72827,3.278 2000,26.044132,-17.721424,17.753685,40.270077,3.261 2000,346.18536,-18.491276,17.97522,46.65286,3.193 2000,284.81946,209.82327,14.795593,22.903824,3.187 2000,325.9173,-20.582167,30.520996,72.08578,3.182 2000,619.44574,-18.225147,17.06482,43.713196,3.173 2000,338.73996,-18.949612,18.149109,44.666725,3.165 2000,325.45288,203.59422,30.280792,51.45912,3.146 2000,-12.301778,282.12222,30.72836,69.22653,3.139 2001,306.0326,214.08954,17.769348,33.821304,10.135 2001,314.63077,217.35829,17.964722,32.567184,7.633 2001,80.216484,266.7927,56.789467,126.32297,7.256 2001,379.39395,219.04662,20.128296,41.172684,6.999 2001,-6.418504,-17.73621,18.348927,43.096584,6.29 2001,-11.904436,-32.441696,30.12841,73.731094,6.254 2001,613.4254,453.9478,39.45691,108.164734,6.145 2001,626.56946,489.40887,21.03357,44.64435,5.992 2001,-6.067025,490.04608,18.592564,41.692688,5.863 2001,-11.847239,479.25177,29.989206,61.98938,5.85 2001,355.126,217.30165,20.013031,36.69478,5.543 2001,627.035,-18.977997,17.581116,47.30706,5.449 2001,623.01,-34.154804,26.439392,75.75627,5.358 2001,-72.54777,267.36877,200.56189,461.45782,5.243 2001,101.6914,275.23837,36.95774,84.44272,5.222 2001,515.0013,160.27353,39.996826,116.47595,5.077 2001,490.87262,-181.00421,234.64325,427.14484,5.023 2001,-16.772497,-63.23027,55.47467,145.85846,4.994 2001,162.96877,252.16597,32.66118,75.401535,4.975 2001,603.41956,-56.56986,52.748352,137.25642,4.971 2001,1.7766702,-17.392227,17.95846,42.920887,4.957 2001,583.01953,410.9036,71.75922,177.57114,4.906 2001,322.89917,217.97488,18.421814,33.349915,4.9 2001,300.9718,210.6333,16.706787,24.728882,4.891 2001,53.69881,64.38762,29.760845,75.39691,4.826 2001,362.6459,217.49023,19.682098,39.803406,4.708 2001,536.6743,310.74622,142.35205,341.31018,4.692 2001,-21.59563,401.29218,78.95269,175.99084,4.677 2001,585.84186,-96.64451,74.55829,229.26093,4.638 2001,618.73755,491.82993,20.276245,40.898773,4.637 2001,330.77942,220.46147,18.358154,30.215317,4.617 2001,486.58307,168.98225,39.090576,104.0529,4.556 2001,302.78027,207.74004,25.767517,55.95912,4.477 2001,-36.55046,-129.35133,116.69248,280.7266,4.453 2001,348.0146,219.55727,18.729858,32.20746,4.433 2001,301.38644,218.69069,16.654694,34.902634,4.426 2001,493.65118,227.04044,237.87628,515.12256,4.284 2001,-70.423676,-189.82416,211.71841,457.2076,4.263 2001,8.423767,232.09344,77.81381,208.00696,4.218 2001,338.54935,219.50694,19.751251,31.224075,4.198 2001,385.68842,225.7947,18.612854,40.06508,4.187 2001,532.4494,-20.864157,30.02887,69.698395,4.166 2001,370.95013,216.64746,19.566833,37.498306,4.145 2001,36.092342,245.07545,65.96268,150.09386,4.119 2001,283.81442,213.71837,18.283234,34.452347,4.118 2001,307.78262,205.55363,14.920074,23.897919,4.072 2001,76.564545,81.6998,30.478775,77.30939,4.023 2001,190.14001,190.40457,29.787415,64.40607,4.021 2001,324.07922,210.94617,16.207947,24.99553,3.942 2001,541.704,-35.102077,28.203857,74.46184,3.928 2001,90.61039,96.70245,20.529762,49.878113,3.927 2001,594.80493,-46.515865,44.132874,101.484116,3.918 2001,-6.780181,50.097435,37.016655,107.63505,3.898 2001,500.79193,3.3934174,33.019592,87.90097,3.891 2001,2.4552572,491.73407,19.116894,39.63684,3.85 2001,-7.9804325,3.7517052,37.86389,112.430756,3.738 2001,597.28296,460.9619,39.192932,100.30469,3.689 2001,213.8328,197.00311,35.811005,84.63562,3.662 2001,9.767731,-18.856829,18.245422,42.350197,3.658 2001,510.6573,-17.987392,29.871521,72.49326,3.63 2001,5.3825855,189.62799,28.77746,79.426636,3.63 2001,103.042496,254.89935,58.76747,111.89508,3.609 2001,-9.771988,111.3769,30.829758,76.553246,3.606 2001,571.0122,-16.879766,32.367065,75.789665,3.591 2001,67.78039,268.07892,46.347054,113.93994,3.585 2001,364.9272,115.396095,33.04883,78.10061,3.574 2001,-11.66421,343.36804,29.919205,71.85828,3.558 2001,73.707344,83.362915,21.965378,55.781708,3.548 2001,501.0755,176.25766,41.387756,106.57596,3.525 2001,580.7613,-34.22895,29.129395,80.31245,3.454 2001,477.21725,-4.6689606,36.952423,87.890076,3.427 2001,309.68158,215.70825,28.732697,59.80493,3.419 2001,488.42346,-6.4821587,21.962463,45.921795,3.418 2002,109.893074,262.37735,44.357033,89.382416,25.88 2002,596.08105,164.50159,34.322083,76.34543,10.551 2002,306.45087,212.32794,18.48471,33.117355,7.936 2002,323.04376,212.28307,18.46994,32.405167,7.305 2002,298.92203,212.91928,19.051392,33.28746,7.041 2002,314.62094,213.94957,18.0495,33.011078,7.028 2002,-6.0590954,-16.86744,17.668518,41.803944,6.649 2002,-11.55938,-32.343983,29.93043,71.510635,6.591 2002,612.9768,453.70108,39.73181,107.95053,5.988 2002,338.63406,214.86655,20.143402,35.154205,5.822 2002,573.76825,169.74606,31.284424,75.389404,5.789 2002,330.58844,214.34644,18.694702,34.626297,5.782 2002,-11.779554,476.8183,30.333374,64.05383,5.668 2002,602.779,-57.37204,53.071594,140.46326,5.606 2002,-6.1884284,489.12906,19.110905,43.315155,5.565 2002,627.1868,-18.293247,17.24176,46.831818,5.508 2002,626.59705,487.86636,20.971619,45.76999,5.5 2002,487.57733,-13.065916,36.81671,61.523277,5.416 2002,-17.24608,-63.422256,55.60164,145.76549,5.384 2002,369.54523,215.58298,23.085938,38.753372,5.363 2002,58.959312,77.2961,33.556664,74.219696,5.259 2002,396.16882,-33.01962,30.514526,77.20879,5.183 2002,-72.266266,267.50714,200.51724,462.11298,5.108 2002,302.40536,208.76231,26.834778,53.365158,5.052 2002,490.20142,-181.54901,234.84412,427.31293,5.005 2002,582.9725,411.16254,71.7746,177.06903,4.936 2002,622.903,-33.874844,26.381165,75.82825,4.933 2002,478.54193,-4.6616974,48.98645,86.716034,4.799 2002,499.4361,-33.69307,31.674377,71.75944,4.772 2002,536.50476,310.56427,142.76428,341.7724,4.764 2002,274.12625,212.00394,21.384491,36.796616,4.747 2002,532.55774,53.128143,37.360962,78.59335,4.677 2002,290.55032,213.55191,20.161407,33.28984,4.674 2002,-40.27245,364.48102,110.90788,276.52618,4.664 2002,470.09363,199.724,30.73053,61.23947,4.653 2002,585.78125,-97.3934,74.50714,232.83629,4.579 2002,116.58644,228.97104,58.39853,122.70798,4.54 2002,-36.564705,-128.80176,116.54396,278.95285,4.532 2002,618.6652,489.33633,20.429138,42.726532,4.499 2002,443.00385,-7.407734,33.188538,71.29928,4.312 2002,-17.798063,432.81842,55.504356,143.7063,4.299 2002,493.35852,226.45541,238.28076,515.475,4.299 2002,-70.67289,-189.10237,211.19348,456.13635,4.27 2002,282.218,213.9307,20.350952,33.772247,4.258 2002,454.6182,207.13303,23.628937,50.329315,4.236 2002,2.80441,-16.262642,16.510582,41.170048,4.234 2002,384.9751,213.60303,22.860565,44.967834,4.093 2002,373.95663,211.95645,28.404816,53.12619,4.084 2002,582.6255,169.00517,38.084595,98.46037,4.083 2002,180.17288,186.7239,39.50937,80.537384,4.077 2002,418.94904,-16.86734,33.842834,72.163475,4.069 2002,128.50061,254.98024,34.718475,82.27171,4.03 2002,-2.0126152,332.345,26.934444,91.91812,3.996 2002,505.4026,-5.3558216,34.301514,60.107857,3.964 2002,468.48737,-34.750492,31.125702,79.775505,3.963 2002,317.81638,210.8857,27.646545,54.67723,3.937 2002,610.34143,152.77483,29.170044,77.88078,3.883 2002,358.72467,213.70253,29.402802,51.542313,3.878 2002,307.9053,227.67294,18.166138,34.585052,3.832 2003,312.8617,208.64455,19.548523,35.679245,14.3 2003,331.45462,210.13074,19.579132,33.524323,10.716 2003,538.329,-22.623657,91.28381,309.7707,6.986 2003,-5.999065,-18.660015,18.194498,43.86031,6.616 2003,-6.2686434,489.12546,18.781166,43.445404,6.42 2003,-11.443987,-32.49352,30.111433,71.11013,6.053 2003,626.7938,-19.13501,17.427124,48.36983,6.044 2003,622.76385,-33.21573,26.417786,76.34881,6.012 2003,-12.083116,477.64334,29.928097,63.998993,5.999 2003,281.3299,213.22592,21.312744,37.84114,5.913 2003,490.58972,-184.47546,234.09277,430.35696,5.907 2003,626.60815,488.68655,21.054077,44.471893,5.828 2003,322.64145,211.34691,17.624329,33.940628,5.682 2003,613.2717,454.05713,39.359985,107.87402,5.63 2003,346.73672,216.72328,18.009369,32.954605,5.522 2003,603.32263,-56.6249,52.604126,139.52437,5.26 2003,-16.496563,-64.44829,54.899864,147.31447,5.158 2003,338.6339,213.81674,17.154297,33.799316,5.116 2003,-72.61511,267.51703,200.70221,461.03308,5.112 2003,536.5235,310.1145,143.1297,341.16772,4.943 2003,-40.667324,361.7411,111.8775,278.364,4.932 2003,582.86255,411.5983,71.94617,176.7211,4.932 2003,394.60327,-5.9340286,31.60431,72.23756,4.884 2003,0.8825171,294.31274,35.9406,89.17584,4.838 2003,130.36046,187.66296,42.56259,76.88556,4.735 2003,493.89136,227.49545,237.67554,513.9802,4.46 2003,-4.141058,195.84,17.091309,45.380875,4.398 2003,-5.056182,118.98032,18.432693,45.010277,4.386 2003,-18.065922,433.72333,55.471558,142.12964,4.376 2003,307.16376,206.43396,19.079681,31.990234,4.345 2003,-37.11455,-128.91753,116.628265,283.5808,4.327 2003,-70.480934,-188.58305,211.34903,455.09552,4.316 2003,585.9213,-97.82623,73.81018,234.5509,4.295 2003,530.8134,159.40999,40.064392,114.19383,4.288 2003,98.76377,196.32773,34.701027,60.351166,4.28 2003,199.27994,224.48022,28.00795,55.535065,4.23 2003,524.8226,-14.83201,32.32141,70.02258,4.106 2003,398.29785,215.84299,26.824585,56.438446,4.076 2003,618.86053,490.68027,20.168274,41.273285,4.06 2003,-0.38224697,101.29535,25.88969,70.47168,4.048 2003,-7.996954,3.7939758,38.14671,111.918335,3.993 2003,382.5752,-34.619488,28.934082,80.48018,3.989 2003,504.27127,5.881298,34.827972,75.22983,3.931 2003,-0.7596433,320.61853,32.194183,102.212036,3.924 2003,116.77969,193.64015,36.58165,62.412827,3.901 2003,385.56012,216.58174,21.08078,42.05742,3.847 2003,598.1701,125.62048,38.85956,116.279205,3.844 2003,308.66278,217.46143,16.5531,31.996582,3.798 2003,436.21796,-2.3690338,31.702911,68.7142,3.787 2003,-13.019485,302.2546,51.88492,150.23691,3.772 2003,565.9411,23.63041,58.200073,195.68903,3.769 2003,596.50397,233.27626,39.84131,103.92949,3.767 2003,594.5385,-43.913406,44.485596,98.64477,3.767 2003,555.4395,146.20209,53.90503,158.73666,3.751 2003,2.622106,-17.473444,17.346622,41.989315,3.75 2003,-5.7222843,89.72987,18.511293,42.839203,3.725 2003,-4.9552827,215.18768,16.258217,39.03572,3.714 2003,619.1427,-19.147339,17.497375,45.860912,3.705 2003,409.62338,-16.58043,18.817596,44.798447,3.676 2003,145.98083,198.21164,39.92276,74.75607,3.675 2003,229.94347,211.06334,34.793777,73.82863,3.646 2003,421.66833,206.67085,38.455933,84.02394,3.617 2003,5.1237154,-35.85892,30.019985,77.34464,3.607 2003,525.16797,183.2732,32.89209,70.686905,3.606 2003,515.31305,-2.3467426,31.365784,67.65733,3.557 2003,414.41693,215.2942,26.980011,58.595535,3.53 2003,437.2791,207.224,39.04416,82.476135,3.506 2003,-10.175734,310.72586,33.857758,77.273926,3.505 2003,-0.28228164,160.44965,26.071692,75.3434,3.498 2004,319.32492,217.55638,19.22992,39.04271,35.796 2004,194.182,239.7117,21.697586,57.617584,12.318 2004,340.83252,219.0676,17.715027,33.55374,10.799 2004,62.231834,220.05823,36.752342,68.8031,10.746 2004,354.818,217.2554,17.825195,32.19368,9.576 2004,197.82306,245.46912,28.008972,71.38989,9.306 2004,380.67145,219.42851,16.4693,27.541946,7.757 2004,209.23723,240.29112,21.07373,48.365555,7.704 2004,347.27112,216.60358,18.379395,32.530136,6.88 2004,627.2442,-18.00176,17.048279,46.806335,6.408 2004,565.3371,202.53754,34.62561,79.509796,6.308 2004,613.30634,454.27615,39.0719,107.55301,6.13 2004,280.20578,218.29816,20.916382,39.28119,6.121 2004,-11.696766,478.63043,29.982574,62.86963,5.973 2004,-6.356101,489.82025,19.23162,42.657715,5.936 2004,-11.029905,-32.81281,29.75793,74.25574,5.706 2004,-5.963843,-18.605791,18.198456,45.181393,5.677 2004,626.6674,488.46005,20.68457,45.315216,5.568 2004,363.30286,220.06999,16.185791,28.636612,5.522 2004,623.10724,-33.340115,26.170044,75.69798,5.418 2004,198.34637,215.20943,34.596664,79.66734,5.38 2004,27.420055,63.38353,33.740177,88.80582,5.372 2004,-72.399216,266.33405,200.54572,462.48682,5.138 2004,329.3969,220.82527,18.789886,34.73645,5.132 2004,-17.04479,-63.84244,55.619614,145.34172,5.103 2004,569.7008,129.40002,57.471313,160.64453,4.984 2004,536.43933,310.31494,143.36047,341.51123,4.943 2004,373.0252,220.01183,16.282043,28.426254,4.89 2004,582.86346,410.91068,71.912415,177.70871,4.86 2004,131.86035,202.08249,41.134644,78.898834,4.811 2004,603.1079,-57.58332,52.85559,138.4712,4.769 2004,213.2997,230.29945,27.844696,60.141647,4.719 2004,-21.773666,402.74283,79.16295,174.57126,4.691 2004,469.12704,206.82175,38.317352,79.26987,4.685 2004,490.85114,-181.82585,234.59045,425.46246,4.671 2004,53.156696,244.90906,30.572514,66.6994,4.647 2004,493.77374,226.99817,237.63873,513.9522,4.535 2004,69.50748,235.00066,37.217995,70.60939,4.477 2004,527.17114,-1.5498924,36.322144,69.69925,4.474 2004,80.26947,199.09671,42.205902,84.77902,4.464 2004,183.91801,216.86867,35.180206,79.016464,4.362 2004,586.07117,-100.631584,75.35205,234.02393,4.349 2004,-70.3594,-188.22604,210.73575,455.6987,4.339 2004,156.73465,237.29959,31.200546,59.56485,4.33 2004,484.70618,205.00601,37.859436,80.561554,4.323 2004,387.0835,220.83534,16.536743,27.875198,4.323 2004,100.0538,199.4968,41.245148,80.77609,4.314 2004,-36.783463,-127.502365,116.60901,278.6105,4.296 2004,323.48083,230.51285,16.68219,33.681366,4.054 2004,594.41034,-46.712685,45.08496,99.131584,4.009 2004,115.627365,197.64792,41.02646,80.045685,4.005 2004,148.19727,200.26291,40.064056,82.060394,3.971 2004,537.5224,-18.927559,18.825867,44.11425,3.964 2004,-6.2017584,216.86197,17.99057,40.47354,3.934 2004,-3.8272228,104.60425,16.816767,41.292908,3.884 2005,350.54477,217.93312,21.973846,40.617447,27.766 2005,324.47302,221.24707,20.29486,37.178284,22.789 2005,338.01935,220.20786,19.71643,36.069977,8.362 2005,258.71356,221.60722,18.991272,32.436584,8.324 2005,376.54114,82.40235,33.478363,74.135155,6.842 2005,613.351,453.92233,39.57776,107.96579,6.022 2005,585.9258,196.28424,32.269897,74.272095,5.939 2005,-6.2575316,489.72403,19.228512,42.71811,5.706 2005,-8.835892,465.1998,37.624706,91.4874,5.67 2005,627.1612,-17.947956,17.458801,44.960693,5.55 2005,626.74005,488.36633,20.7453,45.27246,5.541 2005,610.673,-47.0974,42.0047,99.343956,5.512 2005,538.25745,226.00793,21.693665,39.53894,5.392 2005,-12.773362,-39.230667,41.80534,100.472626,5.388 2005,481.83008,219.13994,20.698364,32.50432,5.282 2005,545.5,217.19072,22.535217,43.439102,5.265 2005,-6.101527,-16.851357,18.555988,42.703735,5.252 2005,455.33878,90.332146,28.195831,62.217354,5.225 2005,253.53607,219.05467,17.48758,28.114944,5.224 2005,496.09668,221.0114,24.038086,37.72084,5.167 2005,514.79724,219.07913,41.348083,94.91864,5.11 2005,265.21176,219.0302,18.342987,31.262497,5.098 2005,146.17616,257.99738,32.63362,90.816925,5.063 2005,585.92303,-98.9355,74.88458,231.14635,5.009 2005,-72.85495,267.03687,200.91821,461.015,5.003 2005,583.06555,410.97913,71.728516,177.27026,4.955 2005,19.846334,73.94833,30.326315,76.44998,4.828 2005,490.8128,-180.91716,234.47498,425.5263,4.822 2005,67.030594,224.10397,32.686844,64.21747,4.8 2005,536.31665,310.56406,143.77856,341.08438,4.77 2005,-19.90282,-86.709404,71.59888,197.10768,4.737 2005,-17.834866,436.22498,55.694355,139.79022,4.708 2005,528.04083,192.93869,49.195007,111.77316,4.683 2005,500.69525,214.2323,38.13849,84.075134,4.612 2005,-40.569893,365.0681,111.08796,276.11652,4.588 2005,-1.1744165,199.46156,29.5219,74.91197,4.586 2005,381.4652,233.42032,15.750549,28.006592,4.563 2005,467.17075,94.04023,28.886139,62.07435,4.561 2005,361.46268,225.54489,18.459625,33.278595,4.534 2005,197.93306,250.28964,28.87352,66.5058,4.485 2005,445.0342,228.9834,17.757568,32.4877,4.458 2005,494.0768,227.23184,237.17471,514.10376,4.455 2005,-36.92946,-127.19145,117.0909,277.34805,4.427 2005,171.4224,257.13916,32.082214,75.07382,4.374 2005,315.2544,227.17575,19.436584,32.348175,4.339 2005,-70.74565,-189.38004,210.77887,456.14676,4.291 2005,474.09186,219.45242,19.818512,33.622345,4.233 2005,485.53513,212.16072,36.397064,74.72594,4.216 2005,573.66077,206.31793,31.482178,69.57791,4.198 2005,548.44824,209.15752,39.142517,91.31398,4.197 2005,330.78217,232.69824,17.72586,29.82425,4.179 2005,618.9818,491.02325,20.07727,41.88806,4.128 2005,418.52246,205.39941,28.010681,52.49362,4.08 2005,539.2059,-15.463146,33.49469,66.785934,4.056 2005,577.35187,170.9037,47.94055,123.35008,4.039 2005,50.80295,215.5369,33.193363,69.7175,3.987 2005,271.93256,227.59663,23.754547,42.376694,3.984 2005,396.56207,63.9222,33.974884,71.00975,3.976 2005,561.00696,42.659843,33.4328,88.94538,3.936 2005,264.92078,228.1799,21.12973,37.523712,3.933 2005,306.99216,222.05695,19.435486,33.386536,3.932 2005,467.93488,223.28682,18.743347,32.735977,3.902 2005,529.3944,227.09372,22.02356,39.07486,3.872 2005,-8.4969635,-5.237976,39.097965,100.64056,3.839 2005,179.73305,241.38055,29.717728,76.87598,3.822 2005,131.17125,259.32318,33.347565,89.06314,3.807 2005,533.4015,-33.72651,29.071411,75.963776,3.789 2006,322.92627,217.89008,23.874817,54.765533,79.698 2006,354.98798,219.73875,22.681335,44.45793,71.021 2006,2.1683133,60.741753,29.11836,88.99003,15.586 2006,366.9022,216.63754,21.267853,41.57614,13.468 2006,148.30284,277.99692,32.221115,75.95358,6.744 2006,335.23474,220.71,20.698944,44.267838,6.537 2006,170.23094,274.24957,33.014877,71.39136,6.419 2006,272.23627,225.52629,21.402191,33.265244,6.342 2006,-6.3444686,-17.418318,17.91706,42.64538,6.253 2006,-6.176033,488.85425,19.321695,43.53784,6.177 2006,-11.594685,-32.24263,30.038986,72.30927,6.124 2006,-11.656158,477.22556,30.077257,63.68161,5.966 2006,613.2897,453.17255,39.29242,109.216,5.877 2006,268.13977,229.63445,29.466553,55.800125,5.818 2006,626.99023,-18.497917,17.494568,47.208294,5.424 2006,626.66406,487.97433,20.663208,46.050568,5.192 2006,-16.432491,-63.20102,54.836723,146.54935,5.165 2006,622.4214,-33.26203,26.86261,76.04559,5.036 2006,-72.85092,265.8985,200.69522,461.93256,5.03 2006,544.218,211.8857,25.550537,39.847733,4.973 2006,603.5616,-56.310295,52.2984,138.94576,4.946 2006,253.00879,233.83812,32.55188,58.685074,4.943 2006,583.19543,410.8475,71.59656,177.14407,4.919 2006,449.78198,220.25781,22.486084,41.244934,4.913 2006,30.18425,86.633064,30.52414,75.66802,4.899 2006,2.9341388,298.12323,31.30777,73.94745,4.897 2006,258.1332,227.047,19.380585,30.91632,4.893 2006,536.6031,310.40628,143.58759,341.37778,4.841 2006,275.9762,237.36832,32.07025,68.23483,4.668 2006,-21.987783,400.6881,79.56431,177.08191,4.662 2006,171.18272,228.31412,17.965439,29.74112,4.627 2006,374.05847,213.61325,21.753937,39.09311,4.625 2006,383.36255,70.398544,36.73752,73.87381,4.625 2006,241.55153,230.1462,19.945297,31.999802,4.605 2006,426.78717,244.3018,19.078247,35.007706,4.605 2006,491.14484,-180.85754,233.72333,425.90918,4.592 2006,19.634966,79.3741,31.738096,74.04117,4.571 2006,147.61667,247.03058,42.19426,90.05475,4.515 2006,127.83533,223.48805,21.689514,39.71547,4.473 2006,43.20617,89.908424,30.001808,73.3561,4.473 2006,493.84006,227.09775,237.29239,514.5642,4.457 2006,585.25134,-99.90878,76.05365,232.04567,4.456 2006,-36.628124,-129.43748,117.026306,280.18134,4.39 2006,234.6352,233.57411,19.56755,28.396194,4.382 2006,132.13727,276.06946,32.913864,80.119446,4.361 2006,152.66435,220.10106,21.723602,36.542343,4.323 2006,266.31323,229.88586,20.791138,34.852417,4.29 2006,-70.46713,-188.62228,211.0027,454.93652,4.266 2006,474.14465,218.42633,32.16391,64.10788,4.134 2006,1.8675905,-16.560547,17.661047,41.56654,4.118 2006,237.51,228.20006,29.507706,52.651382,4.111 2006,227.47887,232.56013,19.563248,28.466171,4.111 2006,160.47748,290.6543,23.491302,59.525787,4.082 2007,305.18427,203.74533,30.04889,68.95322,92.259 2007,346.19507,207.45267,27.872803,59.93402,70.48 2007,353.63602,200.34993,31.880585,60.02806,6.774 2007,484.80646,216.83618,32.59668,56.56024,6.166 2007,626.5049,487.5112,21.134888,45.6445,6.079 2007,616.5075,225.00914,22.313965,45.744125,6.025 2007,613.19977,452.94553,39.375793,109.2576,5.898 2007,-11.425177,-32.661472,30.547056,74.28544,5.797 2007,378.33527,219.29428,23.035553,37.637207,5.745 2007,-6.1064615,-16.906693,18.484375,43.226776,5.697 2007,478.80814,56.01962,35.563873,79.64555,5.6 2007,622.76624,-33.67928,26.48706,73.7684,5.582 2007,-6.3099813,489.0413,18.871193,42.626373,5.474 2007,384.23395,226.79065,23.586609,34.57312,5.467 2007,-12.071743,477.352,30.260311,64.102356,5.445 2007,627.0092,-17.547342,17.462158,45.16409,5.327 2007,335.09338,202.72401,31.568054,56.6707,5.247 2007,367.3167,208.12611,23.1875,44.085495,5.179 2007,-73.38974,264.8178,201.70409,463.49683,5.131 2007,-16.221981,-63.2491,54.864025,145.83273,5.119 2007,497.5855,212.45625,32.137756,52.92375,5.053 2007,59.455406,289.87616,37.775963,71.65137,5.033 2007,400.78824,234.112,23.636505,35.188477,4.954 2007,551.6545,210.40526,23.192993,41.131577,4.953 2007,535.9158,309.59174,144.96777,343.15088,4.942 2007,583.1332,411.28036,71.52478,176.52524,4.932 2007,242.00763,211.52863,22.396942,44.353973,4.914 2007,603.3941,-54.86284,52.556824,132.40356,4.883 2007,202.34865,226.50679,35.3096,61.194138,4.79 2007,-40.996216,362.32312,111.93408,278.21967,4.781 2007,569.61383,214.3114,20.823486,34.50238,4.759 2007,229.34796,215.62915,30.428955,59.063324,4.746 2007,218.86172,224.10365,32.30916,59.46254,4.699 2007,493.7232,227.60294,237.26349,513.35803,4.689 2007,377.26285,56.596127,36.214935,81.95178,4.645 2007,469.42633,211.49893,31.71997,52.285583,4.64 2007,586.0366,-98.84003,74.60571,232.68983,4.608 2007,319.35858,200.93701,23.015533,40.466187,4.581 2007,-37.2139,-129.37238,116.83894,280.33868,4.485 2007,-18.59146,433.42896,56.21264,142.8722,4.481 2007,490.98026,-180.95877,234.16489,425.92056,4.382 2007,323.36285,201.00414,32.489197,54.5121,4.37 2007,-1.6668952,320.37244,27.909885,80.14194,4.368 2007,510.2716,215.65814,24.793152,43.293304,4.349 2007,613.53564,-20.17901,28.4375,71.6634,4.291 2007,-70.400795,-188.28357,210.81796,454.41727,4.246 2007,618.64746,-3.870634,19.353699,40.24492,4.224 2007,458.85822,246.64238,19.76529,30.21135,4.213 2007,618.6578,489.86328,20.734192,41.62195,4.093 2008,283.05173,206.68375,32.495667,73.57709,87.067 2008,228.36792,205.24654,35.13797,81.182175,82.547 2008,2.7693236,205.7561,21.049236,38.264084,8.796 2008,434.05292,33.474213,36.10498,87.525986,8.715 2008,418.99173,211.23372,34.891754,60.080765,8.342 2008,428.83102,195.75195,31.374664,59.52205,8.163 2008,415.6079,225.17924,25.680786,44.21141,8.104 2008,534.39996,228.50618,25.385742,38.729843,8.05 2008,24.615536,207.60945,22.287243,36.67917,7.602 2008,48.443825,209.88817,23.28659,37.216263,7.317 2008,-6.057155,-19.236237,18.496769,44.098915,6.362 2008,-11.321244,-32.764374,29.624428,69.93416,6.355 2008,401.45813,226.21767,34.92926,57.67943,6.326 2008,627.21747,-17.891489,17.095947,45.72726,6.313 2008,440.9314,196.49498,34.19461,61.53386,5.85 2008,531.0305,221.63083,40.49646,64.38339,5.565 2008,383.9356,220.68964,36.818665,58.377136,5.549 2008,8.494631,199.81073,22.15299,40.06865,5.538 2008,603.18695,-56.367813,52.75482,135.38121,5.415 2008,183.63687,224.93996,23.873337,37.442307,5.402 2008,362.20697,218.01015,38.30179,53.436142,5.382 2008,506.8977,221.5899,20.643494,40.86148,5.339 2008,-8.76402,464.41064,37.576,93.11157,5.31 2008,623.1469,-34.09146,26.103577,75.15453,5.303 2008,-6.142559,489.47842,18.731232,42.13089,5.288 2008,-73.252,264.849,201.15602,463.7544,5.218 2008,33.207817,209.26186,23.54634,36.906174,5.163 2008,613.23425,453.3091,39.55133,109.433014,5.115 2008,521.9267,230.57973,21.335083,37.645493,5.054 2008,3.906802,203.28508,39.10431,77.988144,5.028 2008,583.10004,411.5406,71.65039,176.57587,4.982 2008,-16.890215,-63.46252,55.975677,146.4165,4.944 2008,263.68054,209.62408,38.719025,80.21826,4.89 2008,536.271,310.1029,144.48718,342.21082,4.826 2008,-21.989986,401.32794,79.64217,176.07788,4.779 2008,-15.172382,155.55823,48.78258,129.79205,4.767 2008,0.29405117,174.6014,25.65409,64.42906,4.752 2008,594.0054,229.05492,21.809814,36.976425,4.741 2008,512.37317,229.59254,21.857117,37.387802,4.727 2009,176.30914,221.89476,53.76555,85.28908,89.158 2009,91.234085,204.8256,56.499054,117.53267,76.963 2009,192.26315,211.90729,34.90744,70.56,8.264 2009,521.36554,228.16397,23.008179,38.905884,7.851 2009,627.02747,-17.834703,17.338379,47.038296,7.448 2009,252.30566,204.46622,36.83249,63.477936,7.124 2009,622.8242,-34.499756,26.615723,77.398186,7.048 2009,-5.9754953,-16.921343,17.414738,41.83515,6.709 2009,153.54214,217.69734,40.54956,55.100693,6.158 2009,182.48956,251.37462,32.565582,59.659744,6.149 2009,-11.335604,-31.612694,29.581375,72.15853,6.022 2009,553.8142,228.22504,22.039612,35.716583,5.915 2009,375.40356,19.504532,38.837708,82.920685,5.905 2009,-8.74937,463.7872,37.488956,93.77194,5.706 2009,99.235855,236.40945,36.260178,81.97397,5.575 2009,-73.7577,264.27615,202.26471,464.9017,5.436 2009,227.24768,217.83961,35.57025,56.050095,5.397 2009,211.1466,219.91463,33.970856,62.327744,5.389 2009,145.7588,226.63052,49.62619,84.476074,5.368 2009,613.12006,455.01245,39.303345,107.147766,5.323 2009,-6.0512066,489.01752,18.713112,42.497314,5.269 2009,626.35406,487.65033,21.205627,46.112183,5.242 2009,242.26657,216.95648,35.613007,64.81433,5.237 2009,265.37778,185.57245,35.054016,66.24684,5.052 2009,298.94397,202.13345,19.352081,42.12088,4.975 2009,583.1876,412.06976,71.44299,175.8125,4.965 2009,-16.503712,-61.469486,55.3881,144.29933,4.9 2009,386.0863,250.41379,34.603363,48.05652,4.884 2009,11.651127,-9.616646,56.195026,119.26951,4.813 2009,585.7372,-97.24274,74.83716,228.97351,4.799 2009,536.4662,309.9136,143.72559,341.27536,4.785 2009,602.8576,-56.412003,52.90503,131.72833,4.776 2009,-22.18473,400.73355,80.06059,176.21664,4.757 2009,101.12588,204.16022,38.07363,74.91699,4.744 2009,535.3591,232.63112,25.116516,37.551132,4.743 2009,56.274956,130.1906,22.9926,45.305893,4.655 2009,614.7744,-19.906174,27.323914,70.2812,4.601 2009,118.61626,209.22507,62.64389,118.356384,4.533 2009,-36.676113,-128.25534,117.21117,278.16742,4.526 2010,74.361916,208.32642,35.9494,74.804016,12.001 2010,371.2993,217.5835,30.580414,58.97943,9.518 2010,-2.345339,280.0291,28.234554,76.03812,7.983 2010,-7.094489,238.45007,38.704098,109.33063,6.939 2010,59.489403,211.98596,61.76838,99.17291,6.915 2010,-5.5003643,-17.664097,17.199736,42.135323,6.317 2010,-6.193465,490.31097,18.798326,41.072937,6.183 2010,-11.105908,-32.42524,29.570118,71.68326,6.077 2010,622.5471,-33.222385,26.99939,75.09372,5.947 2010,283.0638,-0.13613129,42.512695,78.58052,5.809 2010,377.32574,231.54285,32.680206,60.297607,5.774 2010,-11.704573,479.2939,29.658356,62.18979,5.74 2010,626.953,-18.52114,17.612366,47.40997,5.626 2010,84.02936,208.35368,39.492508,58.592697,5.54 2010,-0.11725664,206.62906,46.574158,129.2663,5.455 2010,355.50427,225.77708,35.85605,61.127487,5.446 2010,-74.40889,264.1703,202.97655,464.07013,5.263 2010,603.2485,-57.655304,53.899048,132.20366,5.238 2010,626.4576,487.70224,20.768066,46.266754,5.235 2010,-16.47021,-63.344833,54.985954,147.33273,5.202 2010,613.0252,455.20502,39.266357,106.944824,5.121 2010,593.82117,-46.116535,44.99121,93.89694,5.105 2010,583.09106,411.9283,71.618286,176.47507,5.027 2010,536.3519,310.51443,143.6615,340.7734,4.898 2010,585.96155,-97.55405,74.70178,227.38039,4.884 2010,-6.141077,169.47401,19.326588,43.463272,4.778 2010,-17.723125,437.33838,55.62375,139.07178,4.741 2010,523.22,257.16818,35.083252,52.094116,4.674 2010,531.80164,-180.87093,149.6955,420.9449,4.65 2010,-40.697113,365.32,111.46667,276.81793,4.505 2010,493.95416,227.55994,237.08862,513.53186,4.493 2010,-37.549328,-128.63843,117.61235,276.04556,4.476 2010,292.54092,-30.386747,29.919434,71.71398,4.464 2010,3.5033643,-15.836644,15.633003,38.95667,4.438 2010,122.381714,3.5024452,35.785065,84.77103,4.371 2010,119.22783,-27.823051,64.85887,118.64321,4.363 2010,463.86032,247.80898,36.629486,51.96248,4.34 2010,451.66028,256.51016,34.06244,51.806488,4.268 2010,-71.09508,-188.98453,212.11781,455.58575,4.246 2010,104.02088,220.66446,38.758385,56.97638,4.195 2010,472.86438,259.8088,35.10196,59.127594,4.184 2010,295.7333,-2.6757927,22.44992,41.073803,4.179 2010,8.042871,189.38043,59.087532,116.523315,4.167 2010,416.3667,244.76003,23.906189,41.404068,4.125 2010,296.6531,-16.17108,19.075195,40.495144,4.122 2010,111.15727,-37.616444,49.237457,89.329636,4.097 2010,472.72797,232.11572,34.151276,56.77173,4.004 2010,275.5472,-13.61257,36.156097,65.0176,3.971 2010,132.60057,-32.638466,30.319077,71.71785,3.971 2010,437.3105,247.99261,32.474976,52.92462,3.965 2011,337.03094,241.97725,25.691528,45.841476,16.928 2011,193.65259,209.70068,33.03058,71.20569,8.305 2011,-11.142205,-31.241676,29.538948,71.924805,6.788 2011,-6.242565,490.0421,18.771284,41.53882,6.54 2011,-5.9096837,-17.385666,17.605242,42.46429,6.469 2011,178.80083,209.44196,35.069885,68.35956,6.334 2011,-11.659824,478.78226,29.98801,61.632782,6.068 2011,320.80225,248.69856,23.09851,39.4218,5.829 2011,326.70593,234.30437,39.022675,74.07393,5.684 2011,627.32764,-19.107906,17.616333,47.94633,5.603 2011,626.586,487.66394,20.522034,45.950623,5.504 2011,622.9687,-33.823906,26.932129,76.172554,5.395 2011,-17.482466,-62.41912,56.4253,143.99365,5.316 2011,613.0498,454.52075,39.18805,107.70886,5.29 2011,371.06534,237.55992,34.284973,52.122635,5.1 2011,539.058,260.77386,33.87268,50.94577,5.048 2011,-73.41283,265.04227,201.60007,462.7363,5.042 2011,585.6331,-99.07172,75.3916,231.54549,4.944 2011,582.97076,412.0693,71.67456,176.35654,4.917 2011,536.4987,309.9759,143.54675,340.57043,4.908 2011,163.26923,211.22328,34.002594,65.08864,4.837 2011,-22.166153,401.1598,79.777016,175.64606,4.811 2011,490.74072,-180.01915,234.72803,425.26547,4.801 2011,211.36855,217.20062,31.406189,61.463867,4.797 2011,404.15283,235.08313,33.548218,52.822815,4.733 2011,602.8265,-57.487457,53.28015,142.04933,4.708 2011,-2.560426,23.416103,37.32158,81.10121,4.695 2011,226.87798,71.30252,32.34236,86.565094,4.68 2011,-37.05237,-126.48696,117.27084,272.51627,4.66 2011,242.6219,284.61813,35.81212,55.845306,4.534 2011,493.89386,228.32617,237.17328,513.08984,4.504 2011,337.74292,239.4436,41.437225,82.218506,4.474 2011,350.6795,248.7463,24.630371,41.288208,4.415 2011,566.1582,264.1301,25.897217,44.15924,4.383 2011,-70.62509,-190.59624,211.26923,458.60223,4.383 2011,296.58588,234.57927,31.243378,53.601456,4.249 2011,549.9993,259.71234,38.108032,55.07599,4.233 2011,182.62285,172.24495,60.37207,114.20946,4.105 2011,144.80829,-13.693628,46.26744,84.16346,4.091 2011,144.95818,188.99246,47.1474,79.355865,4.091 2011,306.42017,239.60843,32.44165,53.663177,4.047 2011,248.47276,228.07887,23.370132,44.638504,4.033 2011,614.5231,257.31815,23.090332,42.048126,4.032 2011,522.8928,254.47159,33.39679,55.835022,3.936 2011,99.57816,265.98447,38.28681,65.364624,3.934 2011,1.9888067,-40.273445,42.8818,99.3027,3.876 2011,355.1349,234.4614,33.76355,55.38031,3.816 2011,156.24982,182.99475,60.358994,110.772675,3.813 2011,595.0777,-48.17084,44.00305,103.428024,3.806 2011,-5.554519,474.32114,16.234646,38.228973,3.8 2011,2.78405,-15.472065,16.456722,38.88304,3.75 2011,401.23846,243.55066,24.620178,37.600647,3.749 2011,230.02684,215.77296,31.988693,63.142044,3.724 2011,477.84045,-35.874653,27.851105,67.654526,3.718 2011,376.49414,251.68704,23.576996,33.623962,3.713 2011,52.29167,176.60478,30.599472,63.479553,3.695 2011,509.4696,-34.545322,28.850647,71.45804,3.658 2011,506.927,-19.462532,18.254639,42.170204,3.657 2012,606.3837,245.88011,23.950195,44.129227,8.509 2012,-11.373343,-31.539091,30.083023,73.79651,6.566 2012,506.70532,214.3032,19.086243,35.439026,6.552 2012,-5.9671397,-15.959052,18.541277,42.637066,6.361 2012,627.3577,-17.349686,17.41455,45.872253,5.813 2012,584.3339,247.48305,23.23944,35.971725,5.594 2012,626.4669,487.50296,20.73944,46.522125,5.556 2012,-8.914701,464.5119,37.753242,92.14789,5.441 2012,582.66876,237.51585,36.859924,69.87331,5.393 2012,-6.1064625,489.26886,18.561537,42.18329,5.329 2012,-17.78598,-62.67434,56.6077,141.55453,5.253 2012,622.71094,-34.150238,26.899658,75.71502,5.169 2012,-73.30737,265.06723,201.66269,462.59122,5.143 2012,160.09152,227.38432,33.095398,51.364456,5.127 2012,1.6114372,-15.28829,17.925005,41.728165,5.109 2012,583.1435,412.40466,71.49725,175.70245,5.075 2012,585.5986,-99.12564,75.112854,231.38506,5.052 2012,615.7515,238.44167,21.139587,42.393845,5.013 2012,490.89178,-180.97964,233.97186,428.94193,5.006 2012,536.4832,309.47174,143.66913,341.0445,4.934 2012,612.9149,455.18808,39.34247,106.38309,4.92 2012,4.8211536,175.88155,31.755522,81.39897,4.859 2012,-6.3036923,204.03172,18.880081,45.716843,4.843 2012,-21.855244,402.3768,79.61461,175.56296,4.836 2012,596.97723,231.51784,35.420105,79.53642,4.8 2012,603.2879,-55.607445,53.063416,133.76189,4.752 2012,-36.90738,-127.183556,117.269485,273.80548,4.685 2012,577.8498,252.80704,22.814087,35.243286,4.678 2012,195.77583,228.87003,39.8002,75.94324,4.587 2012,493.7409,228.40378,237.57916,513.59717,4.58 2012,364.449,2.1014824,29.728577,57.28202,4.514 2012,-9.480306,-5.9272156,40.133366,97.98425,4.477 2012,76.73091,223.07213,34.92296,59.622818,4.408 2012,394.6495,269.87585,35.57321,64.79419,4.398 2012,188.81396,217.74269,33.62593,56.077866,4.392 2012,-70.99176,-189.03546,211.35162,457.24802,4.374 2012,309.3805,273.8465,40.68628,57.938293,4.242 2012,593.14716,248.78546,22.77649,36.949036,4.236 2012,147.7602,225.00443,42.213425,78.649994,4.213 2012,619.19507,-2.0262184,18.520508,37.901024,4.208 2012,614.0624,-18.817066,28.621033,69.88594,4.135 2012,89.705215,232.86478,36.572327,58.03485,4.089 2012,202.12608,231.4602,26.001343,44.37198,4.018 2012,-3.9989233,-15.695061,30.56522,66.414955,3.915 2012,594.08466,-45.057568,45.031006,95.35863,3.9 2012,613.1191,183.18036,36.523315,98.637085,3.856 2012,148.97011,208.4456,34.057617,63.22966,3.846 2012,283.69778,265.1605,37.251007,63.803802,3.816 2012,293.6494,-33.41273,29.17987,70.101456,3.804 2012,567.35876,262.11786,23.628845,31.111603,3.797 2012,295.66534,283.44937,37.862457,58.45227,3.767 2012,298.09872,-6.351942,20.743958,38.56356,3.733 2012,2.4812865,-20.433086,50.817436,129.09778,3.728 2012,547.6644,246.46532,34.661865,53.747635,3.697 2012,-5.674877,229.66864,18.095398,43.67151,3.696 2012,250.67781,226.55759,33.700577,62.673218,3.662 2012,379.08066,-14.715521,16.297607,36.66775,3.659 2012,173.24557,230.0988,26.040283,41.915665,3.653 2012,210.82172,228.65796,29.609177,59.05603,3.653 2012,218.04033,-17.694904,18.408783,40.87439,3.635 2012,356.42883,127.0116,30.222137,66.647095,3.626 2012,354.9803,-1.1236553,19.380005,35.706207,3.573 2012,237.68881,-37.81722,29.068512,75.121544,3.553 2012,378.7816,-1.2303505,18.777863,35.481956,3.547 2013,-10.994835,-32.94751,29.363068,72.07912,7.095 2013,-5.448353,-19.22691,17.64603,44.61407,6.727 2013,545.3764,239.48637,20.944641,37.285904,6.686 2013,622.4616,-34.073654,26.859985,79.36756,6.241 2013,626.51086,-20.036926,17.789734,51.225708,6.019 2013,-6.2100067,489.38037,19.092768,41.89441,5.811 2013,553.60266,242.11598,19.009521,33.68538,5.786 2013,-12.459089,477.82495,30.84633,62.436035,5.566 2013,-18.058477,-64.29473,56.45111,143.5425,5.432 2013,626.6358,488.1472,20.52069,45.66812,5.38 2013,490.9523,-181.54088,233.90231,428.44073,5.065 2013,583.0751,412.0149,71.56818,176.08179,5.065 2013,-73.458206,265.63367,201.61725,461.8556,5.021 2013,613.0292,454.1034,39.296875,107.772766,4.969 2013,563.85846,240.49765,31.659363,57.83423,4.964 2013,-72.115814,-189.86234,214.0234,457.3041,4.953 2013,536.5334,309.5525,143.74744,341.4956,4.88 2013,-35.954178,-131.04425,116.75098,278.31973,4.821 2013,586.01416,-98.64838,74.667114,230.42784,4.791 2013,-21.793877,402.04483,79.512726,175.60794,4.774 2013,602.6411,-56.146023,53.576782,134.31166,4.726 2013,3.5430849,227.25375,30.55982,71.02167,4.54 2013,3.1680584,-17.908499,16.69709,43.668854,4.526 2013,493.62033,227.91483,237.4725,513.55396,4.492 2013,561.69855,245.9317,21.645569,38.863647,4.462 2013,533.53125,235.0818,30.026245,49.54033,4.254 2013,313.6407,276.06104,36.14218,60.233917,4.236 2013,547.54895,240.13785,30.725647,53.93103,4.23 2013,24.44946,225.54332,23.387913,40.43303,4.222 2013,594.25885,-46.22847,44.658997,97.933075,4.159 2013,76.08385,222.94092,33.28656,58.789734,4.124 2013,10.325367,-20.421194,17.773216,44.19653,4.062 2013,1.794396,232.112,19.144138,42.348938,4.049 2013,476.5443,0.6547928,16.626587,30.888336,4.026 2013,81.369484,230.86928,22.670288,38.154953,3.905 2013,282.14032,264.9927,37.082825,64.96512,3.835 2013,179.30383,-3.3757992,18.913757,36.383087,3.788 2013,171.19305,-3.781742,19.24405,36.992218,3.78 2013,1.8480444,-40.6655,43.121227,97.94792,3.747 2013,248.88976,268.53818,37.315414,58.98819,3.735 2013,475.94144,-14.17973,15.388031,34.927227,3.729 2013,468.64917,0.90449524,16.468903,29.682348,3.703 2013,418.73642,245.16273,34.0961,60.67552,3.667 2013,523.3952,1.2476959,18.185242,33.237465,3.643 2013,618.9117,-20.582697,17.847656,49.988194,3.641 2013,531.4366,0.26239777,18.483032,34.39227,3.605 2013,468.2121,-13.611298,15.201141,33.9632,3.587 2013,21.832245,-33.251358,29.287788,71.03938,3.578 2013,57.191513,233.43256,20.543617,37.147217,3.564 2013,459.75223,-14.04134,15.543793,34.522655,3.547 2013,-10.779094,-6.8595734,40.67599,101.0369,3.505 2013,507.74695,1.7831402,17.559937,32.390343,3.489 2013,483.54324,-13.263651,15.644745,34.744476,3.458 2013,427.50565,-14.522451,15.866913,34.08243,3.45 2013,580.2833,222.81952,39.567993,77.03168,3.421 2013,45.76004,224.5746,30.571152,59.74669,3.385 2013,327.60016,276.15485,37.42502,59.592712,3.38 2013,570.3354,241.32124,20.229065,34.800064,3.364 2013,166.2605,-33.075928,28.080551,70.35771,3.347 2013,515.35583,1.0517559,17.492554,33.450104,3.33 2013,443.24826,-14.415863,16.264526,34.716595,3.32 2013,-0.77994704,-22.54796,26.349333,70.99963,3.302 2013,267.18616,-16.140686,16.35379,37.878975,3.299 2013,1.8707979,490.8806,20.343967,40.97003,3.299 2013,60.40168,215.29697,31.024849,56.181366,3.288 2013,187.30956,-3.2831736,18.439713,36.40031,3.273 2013,293.97467,282.03418,26.962372,40.23291,3.263 2013,73.66899,239.11624,22.618507,36.53125,3.243 2013,573.6574,-35.4756,28.285828,76.11808,3.229 2013,577.88916,-17.621847,17.73413,42.750473,3.215 2013,484.1489,0.39065552,16.583557,32.828316,3.204 2013,538.87463,-15.037975,16.64148,38.112812,3.202 2013,613.158,237.64706,36.84131,107.4032,3.194 2013,11.118229,-35.338394,58.553333,131.90652,3.187 2013,596.8318,222.7923,36.199707,92.190125,3.174 2013,163.0736,-16.355183,17.223495,39.133247,3.163 2013,-4.755507,68.587234,17.495562,46.552803,3.162 2014,-5.8820925,-17.987762,17.782932,42.446487,6.528 2014,627.3119,-16.983063,16.788513,45.231285,6.12 2014,-11.395199,-31.60202,29.860157,71.56061,6.073 2014,85.52449,209.90073,32.586838,58.15732,6.027 2014,230.64697,269.9472,24.82837,34.318512,5.777 2014,239.22441,276.13422,23.772568,34.94232,5.414 2014,-8.939794,464.32623,37.680805,91.87964,5.27 2014,-6.216786,488.90454,18.876865,42.389526,5.183 2014,100.6805,208.7825,29.238998,56.278778,5.144 2014,623.13513,-33.737194,25.99121,75.816734,5.08 2014,583.003,411.81348,71.696045,176.41748,5.039 2014,-73.06851,265.75458,201.06346,461.68915,4.997 2014,602.8889,-55.97588,53.36389,139.38069,4.997 2014,612.7904,455.29114,39.419678,106.48645,4.937 2014,-20.080055,-88.72963,72.02298,198.7302,4.886 2014,105.101685,208.43558,20.269226,34.77809,4.838 2014,536.4425,310.3982,143.48706,341.33484,4.799 2014,-21.87241,401.84497,79.351425,175.59918,4.79 2014,-71.760254,-188.91356,213.66058,456.23743,4.718 2014,626.24066,487.16266,21.07721,47.105408,4.715 2014,218.47223,249.91362,35.94084,57.261032,4.642 2014,-35.973316,-132.0026,116.779785,279.68073,4.61 2014,291.7666,263.6579,32.606506,58.432343,4.554 2014,493.8057,226.87378,237.17294,515.2111,4.551 2014,490.5057,-180.73729,234.35696,423.89566,4.478 2014,3.0360484,-16.259407,16.76103,39.251537,4.452 2014,258.6508,282.11505,18.118256,31.0531,4.45 2014,248.27599,269.49866,23.407944,36.64798,4.347 2014,585.90436,-100.86102,75.584656,235.29305,4.307 2014,204.24768,-14.238176,14.907974,34.108826,4.054 2014,619.1062,201.76346,20.489807,42.83734,4.05 2014,275.3771,-12.088236,15.6423645,35.42362,3.836 2014,306.97614,-11.215031,15.60611,34.010822,3.829 2014,195.92215,-14.064601,15.505844,34.03966,3.825 2014,239.66829,260.3689,22.62004,36.657898,3.807 2014,253.13516,255.83006,33.026764,63.640457,3.779 2014,-5.652802,247.67514,18.236526,39.493866,3.763 2014,275.9864,0.43239594,17.348938,33.430435,3.759 2014,440.567,-18.419167,19.779114,41.77004,3.712 2014,450.5315,220.54388,31.232605,58.831482,3.681 2014,315.79858,2.206503,16.704163,29.703247,3.666 2014,267.49606,289.871,17.44693,28.170227,3.665 2014,283.15625,-11.781069,15.573669,35.17482,3.656 2014,187.48312,-15.094564,15.601471,34.87186,3.646 2014,204.6012,-0.501359,15.623474,29.689812,3.629 2014,-9.917129,-20.757927,38.582558,100.09627,3.628 2014,298.93607,-11.728332,15.835327,34.02098,3.626 2014,275.32208,293.35846,18.682373,31.162537,3.626 2014,282.73172,285.92096,19.931183,34.693085,3.601 2014,283.3898,1.1627254,17.857727,32.11786,3.597 2014,10.164971,-16.688,17.345383,39.665985,3.593 2014,268.9691,0.5872555,16.48117,31.469393,3.592 2014,315.3995,-12.887668,15.279358,35.05385,3.572 2014,92.03392,-13.497711,15.103043,34.82254,3.568 2014,351.81592,246.62367,33.952972,58.154465,3.565 2014,233.20946,286.23303,19.929504,29.233276,3.555 2014,267.60776,-13.775463,15.473877,35.50198,3.551 2014,212.23865,-13.05542,14.625732,32.79531,3.514 2014,339.87454,252.462,34.634796,58.89087,3.492 2014,447.40106,229.508,24.23523,39.13376,3.472 2014,76.0173,2.8749266,16.52658,28.232159,3.462 2014,204.74312,243.71732,35.365997,62.42285,3.455 2014,233.37564,249.07533,34.52423,60.317123,3.427 2014,259.88153,295.6359,16.77533,27.129578,3.356 2014,221.04416,-0.67147064,15.358719,28.83059,3.353 2014,506.1344,221.52557,20.363525,26.12471,3.339 2014,324.28027,1.3160591,17.081879,31.949944,3.338 2014,250.85495,288.71704,17.965515,27.835419,3.318 2014,84.26243,-12.540691,15.100288,33.602627,3.317 2014,299.4895,1.4632597,17.766235,30.391907,3.3 2014,75.88277,-13.9202,15.223022,34.39463,3.294 2014,188.51921,-0.72161293,16.434723,30.174078,3.29 2014,212.80695,-0.183321,15.215637,29.619833,3.289 2014,-5.2552996,-1.1395035,17.919312,43.98483,3.286 2014,619.86426,-16.022923,16.231567,41.833286,3.275 2014,108.161804,-15.569945,15.498291,37.593365,3.268 2014,436.5692,-33.35137,29.99588,71.02857,3.266 2014,545.0095,216.44167,22.451843,34.554718,3.259 2014,2.66813,247.3151,19.566635,36.30542,3.257 2014,211.92404,267.03812,33.765533,49.00949,3.253 2014,455.65625,228.02524,22.916748,33.37503,3.25 2014,497.28116,220.56154,20.126556,25.328964,3.249 2014,115.46283,-16.186668,16.21457,38.06158,3.245 2014,196.44453,-0.91751194,15.819229,30.411655,3.23 2014,307.308,2.2502966,17.158295,29.945084,3.228 2014,5.4862537,-20.035599,28.993994,68.965546,3.214 2014,92.27843,197.63264,31.983803,54.910233,3.211 2014,158.32048,-34.44163,27.930267,69.13951,3.21 2014,448.80252,-18.26974,19.92517,41.422592,3.208 2014,377.53757,-18.411734,18.454071,42.65812,3.202 2014,96.04461,216.8689,45.47587,76.33649,3.201 2014,432.5407,-16.277592,20.300629,40.191685,3.19 2014,331.06656,-14.944301,16.273163,37.132698,3.19 2014,220.23302,-14.162104,14.480408,33.39345,3.19 2014,436.52502,202.86563,39.88452,75.057556,3.171 2014,2.0318632,489.975,19.82045,41.520477,3.155 2014,323.58282,-13.220315,15.809082,35.17713,3.15 2014,98.40858,200.02634,20.525154,34.530304,3.141 2014,179.58487,-15.166869,15.536438,34.66955,3.134 2014,613.0653,128.48346,36.227417,107.63229,3.129 2015,584.9953,222.5954,28.927979,69.62842,91.369 2015,596.8383,225.3988,23.025818,56.279633,7.807 2015,-5.424408,-18.438435,17.630814,42.947254,7.385 2015,613.4477,224.1564,34.833008,96.40228,6.177 2015,-11.141783,-32.63472,29.475489,71.03914,5.974 2015,-6.218806,489.29926,18.785711,42.049927,5.908 2015,249.53001,261.16452,21.203049,35.681488,5.823 2015,-12.064028,478.6187,30.358429,63.028137,5.807 2015,627.12897,-17.439356,16.985718,45.679707,5.802 2015,361.97144,236.01501,22.792114,30.618317,5.405 2015,592.78064,222.99489,36.954956,89.21355,5.242 2015,610.5168,-47.330544,42.19226,98.19833,5.158 2015,575.7476,213.51787,47.91046,122.40376,4.956 2015,582.91724,412.197,71.59564,175.94937,4.953 2015,-72.82002,265.86725,200.65524,461.23273,4.939 2015,626.5308,488.0769,20.865723,45.945007,4.938 2015,612.815,454.55704,39.575134,107.410736,4.925 2015,536.1884,310.402,143.93805,341.87143,4.865 2015,-20.297733,-88.03079,72.0504,197.71416,4.85 2015,-21.7897,402.18106,79.46315,175.457,4.823 2015,-71.79379,-189.5581,213.04689,456.79074,4.674 2015,-36.24538,-131.20467,117.32881,278.76526,4.602 2015,493.86,226.92554,237.22546,515.2244,4.544 2015,585.9531,-99.120384,75.83942,232.40677,4.514 2015,532.5748,-183.54886,148.74237,424.10413,4.308 2015,312.7751,231.20047,21.86438,28.64746,4.212 2015,257.60898,237.96918,20.793121,30.710815,4.183 2015,264.83246,232.2469,19.959717,30.086868,4.085 2015,3.387872,-17.582842,16.48893,40.10514,4.032 2015,91.88294,2.945921,16.911888,27.143398,3.968 2015,75.664536,1.9671144,17.093018,27.66853,3.895 2015,2.068634,490.64944,19.525166,40.90628,3.797 2015,59.19066,-13.523937,16.557434,34.81739,3.791 2015,-9.788103,-21.068027,38.929596,100.762924,3.773 2015,59.920288,-0.59054756,18.077065,31.331375,3.771 2015,569.13965,218.88936,31.095703,64.34538,3.763 2015,-4.0120564,220.71086,15.678693,37.357956,3.747 2015,607.85486,224.05545,22.57965,51.486786,3.718 2015,83.775116,2.8269157,16.947464,27.657646,3.713 2015,91.38767,-12.193665,15.681999,33.244926,3.698 2015,612.8326,188.54636,35.45111,95.58206,3.694 2015,75.06365,-12.146117,15.706917,33.21988,3.655 2015,83.36663,-11.007628,15.502251,32.461224,3.639 2015,-5.021028,205.06433,16.525078,38.068893,3.613 2015,98.53454,6.8582325,20.17315,32.424973,3.611 2015,182.39098,-33.668255,28.378235,70.82874,3.589 2015,263.36932,242.57353,21.608154,33.251633,3.583 2015,67.27662,-11.918868,16.007217,33.34787,3.563 2015,10.56088,-18.346815,17.267242,40.719265,3.548 2015,-4.783399,247.5989,15.873159,37.51477,3.529 2015,354.70062,229.40695,22.371704,29.868347,3.517 2015,232.59758,-18.165918,20.299057,42.337757,3.495 2015,345.53357,226.74355,20.545685,28.125336,3.43 2015,619.5871,-17.130169,16.529358,42.568405,3.419 2015,67.85668,0.7090063,17.42836,30.018414,3.409 2015,504.07874,229.60146,23.484375,36.759018,3.381 2015,106.73797,-12.308811,16.32492,33.74478,3.379 2015,321.20074,230.91418,21.236267,29.35968,3.328 2015,257.56757,228.69713,18.929565,27.560959,3.321 2015,297.62195,233.86667,21.508514,27.764923,3.32 2015,11.32394,-33.04464,57.52515,134.25455,3.285 2015,245.43031,250.37715,30.513077,61.273056,3.285 2015,198.1987,-32.359898,29.531616,71.743774,3.276 2015,99.51218,-11.500367,15.835152,32.93159,3.266 2015,2.0376992,-40.196205,42.973545,100.625435,3.239 2015,307.45212,222.7654,40.63217,64.64354,3.216 2015,50.940247,-13.53241,16.781898,34.28163,3.216 2015,617.6847,231.80186,20.522095,53.29048,3.211 2015,93.56747,-11.6326275,30.031776,55.790443,3.205 2015,55.30613,-32.005177,27.679161,66.35899,3.188 2015,329.5019,231.89078,20.71991,29.207489,3.165 2015,70.380325,-31.956657,27.911926,66.885765,3.163 2016,507.2965,212.4599,31.14795,76.598755,92.866 2016,-5.1979184,-16.360859,17.15133,40.39518,6.713 2016,-11.033774,-31.742165,28.990124,69.27703,6.685 2016,-6.291232,489.0438,18.984554,42.467926,5.989 2016,-12.357473,477.43515,30.964022,63.207245,5.799 2016,410.14493,229.98746,22.083282,39.91867,5.627 2016,602.4634,-57.121857,53.29962,138.83826,5.576 2016,-17.56821,-63.062374,55.986507,141.7773,5.472 2016,612.92914,454.67438,39.490845,107.29205,5.413 2016,499.0948,218.18689,25.381165,62.44171,5.242 2016,-72.641464,265.5238,200.78949,462.26538,5.164 2016,234.52074,232.95322,19.832626,30.481476,5.155 2016,626.9809,-17.61647,17.205017,45.90567,5.139 2016,626.6114,487.80618,20.720398,45.855743,5.044 2016,536.2829,310.03607,143.69202,341.85895,5.008 2016,585.7166,-96.68316,74.63403,230.33511,4.988 2016,492.201,191.47546,56.00763,100.66501,4.918 2016,582.80023,411.85297,71.8714,176.60773,4.9 2016,-21.631123,402.72974,79.33299,175.22913,4.849 2016,3.610919,-14.74747,15.9557295,36.622036,4.746 2016,494.51263,229.01141,237.06018,512.8641,4.576 2016,491.5879,-178.6498,234.27814,423.87265,4.55 2016,-37.17624,-131.02681,117.24945,279.25247,4.385 2016,622.4098,-33.48558,26.838562,75.454796,4.341 2016,-71.02593,-188.27176,212.30469,453.90256,4.29 2016,470.2818,210.09906,36.6297,77.48605,4.163 2016,614.3037,34.860718,26.02124,72.61784,4.078 2016,235.26207,223.08076,19.56636,27.990906,3.895 2016,517.4205,210.26727,33.508423,73.72424,3.854 2016,424.77362,233.94392,21.715149,39.906876,3.795 2016,3.1887555,22.463966,19.300714,43.391953,3.746 2016,529.62366,220.4188,40.35852,89.4176,3.742 2016,406.46008,235.1035,29.502472,61.36354,3.718 2016,331.2526,206.41568,31.204926,52.596405,3.693 2016,129.77428,242.7304,21.442688,33.162796,3.665 2016,80.98221,243.19788,22.228745,33.265198,3.657 2016,122.79984,244.0268,19.573952,31.548828,3.592 2016,444.28714,244.71202,18.58017,38.118637,3.565 2016,618.2935,489.8075,21.05597,43.23059,3.56 2016,416.02817,236.0341,22.269196,40.56877,3.544 2016,622.4703,21.79573,26.574402,69.16103,3.533 2016,433.54306,234.61986,20.263428,40.86348,3.53 2016,-5.220204,23.352942,17.357256,42.50727,3.499 2016,1.9995749,490.26495,20.05095,41.367126,3.466 2016,229.3785,229.43056,18.257019,26.498383,3.465 2016,19.660421,-15.482962,16.034155,37.382423,3.462 2016,241.01332,225.111,20.018326,31.339386,3.368 2016,27.321835,-17.319014,16.438473,39.54668,3.336 2016,221.01285,228.13342,16.386444,24.391968,3.33 2016,100.16029,230.6901,39.487442,77.20877,3.322 2016,11.292536,-16.246998,16.163013,37.633312,3.317 2016,494.17706,216.54276,54.308594,142.56836,3.28 2016,115.64757,230.40764,40.815964,78.91765,3.262 2016,-4.502798,5.228716,15.679916,35.554863,3.26 2016,619.5902,-3.3299103,18.261047,40.318825,3.257 2016,2.1362948,-42.39396,42.794857,98.291885,3.256 2016,83.16731,-14.905407,16.559097,35.297924,3.256 2016,237.32458,215.77632,16.680542,25.265015,3.245 2016,328.08295,217.15947,24.285278,36.472168,3.235 2016,615.30383,-19.058878,26.536255,72.455826,3.208 2017,494.61737,204.50394,40.12451,89.377716,95.663 2017,-5.3056,-15.843383,17.397633,40.907917,8.523 2017,623.1688,-33.834915,26.663574,75.74213,7.832 2017,472.46606,202.36436,38.46353,90.0807,7.783 2017,627.3263,-18.348618,17.246704,47.397575,6.631 2017,-10.962586,-32.065605,29.637398,73.49739,6.172 2017,2.9597287,-13.233733,16.897154,38.251713,5.553 2017,-9.050797,464.12106,37.797287,92.72653,5.389 2017,613.07526,455.08264,39.24292,106.74048,5.337 2017,-6.199953,488.81934,18.804234,42.487915,5.146 2017,626.6752,488.30582,20.664185,45.806732,5.144 2017,585.8686,-96.657326,73.98126,228.36453,5.104 2017,-72.656105,265.77902,200.87097,461.7803,5.09 2017,484.35352,197.14325,36.245483,78.21628,5.07 2017,536.3523,310.49283,143.33276,341.91647,4.989 2017,-17.63454,-65.317406,55.57821,147.17126,4.984 2017,-21.754726,402.45807,79.42165,175.26501,4.855 2017,582.91846,411.4908,71.93292,177.01382,4.813 2017,491.0242,-178.27379,234.27274,423.65555,4.741 2017,602.8542,-56.35035,53.38263,131.96925,4.645 2017,494.51343,229.26385,237.03735,512.791,4.528 2017,-36.80031,-130.91934,117.378426,280.7412,4.412 2017,619.12085,-18.41943,17.888306,44.4647,4.223 2017,-70.88992,-188.54366,211.8296,454.77618,4.219 2017,593.6808,-43.806057,44.85608,93.01719,4.17 2017,508.98776,202.01202,36.00894,78.67084,4.164 2017,10.229303,-15.643433,17.475813,38.986313,4.025 2017,388.22803,219.72055,32.563904,68.55557,3.954 2017,615.79016,-21.621628,26.151367,71.70699,3.935 2017,148.85405,225.25703,31.310684,50.154922,3.929 2017,154.85696,236.00198,19.744308,29.173126,3.886 2017,-0.38883805,-15.866901,26.948505,69.30716,3.77 2017,488.83838,169.33624,60.289062,114.3374,3.757 2017,178.12448,230.07689,20.686554,32.252945,3.725 2017,-4.9385567,0.67874527,17.77232,44.07977,3.617 2017,507.57034,229.84813,29.76596,69.12001,3.604 2017,530.7742,217.91751,41.558228,94.02194,3.521 2017,520.13995,193.228,33.97632,71.433014,3.521 2017,290.5887,224.19034,18.802094,28.417297,3.43 2017,1.2849474,-42.736412,44.375126,103.11492,3.382 2017,586.10236,0.8606663,20.651245,46.220512,3.339 2017,384.21338,224.3024,23.921661,47.280884,3.336 2017,391.43768,246.28929,30.413086,72.24678,3.313 2017,402.38022,239.77962,31.432526,67.06163,3.31 2017,588.52734,-15.532043,29.931274,67.79337,3.281 2017,202.94064,218.9284,18.506027,29.843597,3.271 2017,163.07104,212.75793,40.185547,69.91345,3.241 2017,497.04614,233.7926,22.958435,61.85193,3.237 2017,400.886,226.33334,24.914948,53.474945,3.231 2017,2.7130008,230.72296,18.299189,36.20923,3.223 2017,179.56146,206.18207,39.749084,67.745056,3.197 2017,169.9785,231.28062,20.161118,32.028366,3.113 2017,618.7912,491.03925,20.497131,42.643066,3.11 2017,597.32776,462.13757,38.65741,97.291504,3.091 2018,-5.6319804,-17.29438,17.628231,42.699738,7.536 2018,576.55414,178.33856,53.98169,163.14673,7.49 2018,627.1449,-17.536171,16.982788,45.783173,6.282 2018,428.67853,217.62856,32.292786,74.02568,6.147 2018,-10.792425,-32.333702,29.400427,72.525185,6.002 2018,623.20416,-34.23171,26.023804,76.061104,5.63 2018,-6.162733,489.09943,18.618366,42.736145,5.495 2018,-9.08722,464.17032,37.905594,92.40622,5.494 2018,613.05707,454.2661,39.627747,107.99707,5.29 2018,-72.37712,266.6776,200.26491,460.8451,5.033 2018,602.64374,-56.049164,53.414307,130.92949,5.017 2018,626.52295,487.91098,20.639648,46.30484,4.985 2018,582.92224,410.79294,71.89758,177.32736,4.904 2018,586.0821,-96.03233,74.02435,227.58957,4.9 2018,536.5001,309.95612,142.89368,342.6541,4.861 2018,-21.65825,403.00095,79.23381,174.91458,4.86 2018,-17.414059,-64.52302,55.769997,146.47488,4.856 2018,489.66772,-179.46146,235.10974,424.36255,4.706 2018,-36.727192,-130.10358,117.34311,279.21634,4.589 2018,493.1845,225.80487,238.47675,515.78534,4.369 2018,305.79932,215.77138,20.86905,36.59572,4.339 2018,-70.98594,-188.67218,211.8291,454.3976,4.292 2018,453.0011,214.63614,31.492035,75.37283,4.274 2018,-4.990701,42.115677,17.139673,43.779236,4.251 2018,593.7615,-43.633728,44.950806,93.20565,4.227 2018,-4.7477603,61.086044,16.329687,36.25316,4.192 2018,593.7813,177.9101,42.674805,106.13446,3.982 2018,615.3311,-19.998175,26.927185,70.65241,3.815 2018,128.9904,235.97258,21.353989,35.22719,3.765 2018,619.29724,-3.8044987,18.805054,39.97023,3.742 2018,590.0366,304.23468,31.775024,62.428436,3.74 2018,611.1964,163.73999,39.140625,108.31879,3.738 2018,-5.14373,76.20505,16.793354,36.97007,3.676 2018,2.9183526,-14.680914,16.831537,39.110252,3.608 2018,610.02936,206.84592,37.502014,126.74011,3.572 2018,307.04187,203.75089,30.034271,53.627808,3.571 2018,616.4295,223.23096,22.299377,56.812866,3.515 2018,587.12054,185.94673,33.82782,61.70157,3.499 2018,-5.3413525,0.18424034,17.714506,43.13466,3.49 2018,573.3696,-3.2941284,30.046143,62.6848,3.447 2018,595.1985,243.20462,41.14453,112.15393,3.442 2018,313.64142,231.18704,18.751617,32.886658,3.434 2018,565.57245,190.96153,45.83661,112.022964,3.401 2018,290.71255,222.69234,19.465973,32.085724,3.399 2018,425.858,0.60637474,22.752228,49.33674,3.397 2018,307.54163,229.91937,18.168396,31.42212,3.345 2018,409.94684,-17.86993,18.665894,43.727215,3.343 2018,2.4024158,490.3986,19.060856,41.103424,3.288 2018,300.94012,232.0007,16.72818,28.253815,3.278 2018,417.85764,-15.388715,18.219696,41.099983,3.265 2018,411.1104,-14.938183,32.599457,67.620895,3.249 2018,602.2725,225.59279,32.28949,76.39049,3.234 2018,-5.011734,88.12639,17.343372,42.106506,3.209 2018,587.74976,197.74706,59.493774,205.84297,3.206 2018,611.95654,266.73465,38.737793,110.03586,3.205 2018,450.95566,-2.668478,31.47226,63.334435,3.2 2018,-0.69272995,12.84161,28.09971,74.7939,3.198 2018,9.418254,-94.40414,75.17575,212.27603,3.178 2018,145.48886,235.83167,22.381378,37.399445,3.178 2018,612.7276,-2.1397705,37.953796,105.57475,3.177 2018,555.3543,305.80637,29.963867,63.45761,3.158 2018,398.6203,-34.59201,27.439331,77.55111,3.155 2018,1.2580848,-41.273872,44.553226,102.3493,3.146 2018,435.0894,-14.948755,33.080353,66.9625,3.082 2018,146.99918,218.52512,39.679443,69.36972,3.08 2018,585.1208,-1.6662254,22.77832,50.604515,3.04 2018,2.68803,52.3236,17.98598,39.86274,3.038 2018,426.61893,-15.77746,17.283203,39.77772,3.034 2018,477.48804,211.34978,31.285828,73.976944,3.007 2018,546.72186,412.3446,83.29706,168.04102,3.006 2018,537.821,-0.8411007,22.065186,47.68995,2.985 2018,586.2339,-15.677075,17.154907,37.79629,2.984 2019,-5.2706347,-17.470158,17.672436,42.749363,8.576 2019,622.2008,-34.068718,27.780273,75.85862,6.988 2019,-10.424107,-31.96273,29.139715,72.031265,5.882 2019,626.82666,-18.870453,18.034302,47.16994,5.853 2019,602.9706,-56.282303,53.76062,132.63304,5.83 2019,-5.8927054,489.89783,18.41738,41.250732,5.753 2019,-11.528501,478.3412,29.541859,63.448853,5.503 2019,3.4886944,-14.994406,16.696632,39.194138,5.46 2019,593.81696,-43.96856,45.246094,93.9853,5.424 2019,490.2685,-182.19754,233.95624,428.9743,5.32 2019,613.0914,453.46732,39.337463,108.50363,5.257 2019,-72.36145,266.2173,200.94704,461.52454,5.108 2019,-16.946077,-64.77778,55.431534,146.37482,4.98 2019,-21.721716,402.5467,79.44405,175.22943,4.924 2019,582.9382,410.61548,71.81415,177.41058,4.901 2019,536.8075,310.55896,142.80566,341.78156,4.841 2019,-36.497387,-129.84544,116.91002,278.44235,4.735 2019,586.1272,-95.96293,73.507935,228.30093,4.721 2019,510.60974,5.3044777,40.619446,88.77409,4.641 2019,-5.3106794,-0.7639656,17.962383,43.866493,4.551 2019,626.23114,487.16132,21.267029,46.72766,4.538 2019,618.12494,-9.431178,21.472168,46.84289,4.446 2019,-71.31076,-188.78816,212.34633,455.16138,4.39 2019,493.45215,226.67297,237.94287,515.29767,4.351 2019,0.010644197,-19.05672,25.672634,67.90272,4.249 2019,496.69815,-13.745504,47.18033,82.87828,4.218 2019,572.08636,208.84267,54.537415,131.9315,4.112 2019,612.9464,4.570423,28.56897,79.46472,4.052 2019,-5.24951,107.023155,17.944092,39.59046,4.003 2019,529.0876,-13.7687435,46.02722,88.30614,3.985 2019,571.8878,-15.355827,56.272522,159.92542,3.984 2019,538.35675,194.86961,59.51587,134.83388,3.936 2019,235.7277,218.7727,16.980392,26.938507,3.918 2019,305.6473,226.62871,21.50238,36.658768,3.905 2019,-5.650387,89.5764,18.267786,40.14102,3.893 2019,187.38739,232.97688,18.317535,27.558182,3.876 2019,2.6898365,289.89822,18.902256,33.102905,3.705 2019,9.967637,-5.9552803,20.23822,40.62928,3.691 2019,453.90436,-35.43382,29.319336,81.58387,3.653 2019,331.58185,216.52353,20.595856,38.92688,3.643 2019,11.434598,-25.646866,58.93137,130.50809,3.641 2019,-6.2109175,266.82578,18.541214,40.479126,3.638 2019,-9.232029,-6.4277,40.498093,99.93779,3.612 2019,196.31578,217.31436,38.483185,69.68579,3.572 2019,463.16705,-40.243835,48.973633,88.923134,3.519 2019,155.30074,227.9021,17.66632,26.023834,3.504 2019,533.4468,-33.969475,29.407104,79.69165,3.49 2019,562.455,227.87166,46.352234,99.55757,3.488 2019,122.66318,219.45422,20.947678,35.17653,3.409 2019,2.353704,491.49176,19.160177,39.71985,3.386 2019,422.73346,-35.142723,28.291077,73.55657,3.377 2019,51.88922,249.2127,40.512985,72.91797,3.346 2019,536.41565,-30.505959,62.21216,130.57333,3.342 2019,522.7294,-12.111824,33.24591,70.319534,3.331 2019,604.39825,5.8053894,51.489563,155.92773,3.33 2019,-1.4742529,200.55263,27.404844,72.22887,3.33 2019,546.83636,200.08978,36.597046,64.170105,3.321 2019,0.5427635,263.49283,26.638363,63.373383,3.308 2019,565.909,-34.59762,29.293274,73.77923,3.3 2019,236.73836,232.79643,16.64534,27.623306,3.296 2019,35.205498,58.25208,31.013756,71.05521,3.25 2019,1.6038346,-41.622097,43.93043,102.53553,3.244 2019,12.460468,243.26227,70.56984,186.36682,3.235 2019,-5.81563,24.368704,17.717588,43.549126,3.23 2019,307.56186,217.43207,31.135925,60.47284,3.227 2019,474.3778,-33.296856,63.128296,121.813416,3.227 2019,-6.001109,71.937675,18.679117,42.20452,3.216 2019,6.545929,-34.277367,27.34829,73.42266,3.198 2019,441.81128,-38.070084,62.32544,123.477776,3.182 2019,-5.9260654,57.212257,18.333414,40.575974,3.174 2019,530.3683,-130.83644,115.7395,270.49124,3.158 2019,171.56114,234.51643,17.360962,24.315598,3.156 2019,562.45337,-16.265915,43.746582,87.99725,3.143 2019,481.13385,-19.155802,20.179626,45.186172,3.132 2019,537.376,-19.0966,19.702576,43.891125,3.11 2019,243.67575,225.67255,17.361267,28.106995,3.105 2020,-5.8624797,-18.062237,18.463774,44.190514,9.85 2020,-10.88345,-31.62183,28.932749,72.605576,7.015 2020,2.4787288,-16.401472,17.74112,41.57207,6.243 2020,-17.584698,-65.64289,56.056057,146.12265,5.8 2020,627.11945,-17.9209,17.055603,46.07762,5.644 2020,-8.913476,464.06628,37.617855,93.17578,5.577 2020,613.19745,453.45758,39.548523,109.513,5.501 2020,610.686,-47.459393,42.163757,99.25445,5.311 2020,585.2807,-94.65976,74.5827,227.87117,5.279 2020,-6.2681913,489.32056,18.97099,41.903076,5.274 2020,-72.58464,266.09186,200.6085,461.22565,5.12 2020,626.63824,487.59262,20.61444,45.68918,5.11 2020,-4.9956985,0.9533348,18.027725,43.795044,5.047 2020,-21.7384,402.54895,79.363266,175.14832,4.855 2020,148.63493,219.64673,30.047592,48.917603,4.841 2020,583.0842,410.82416,71.65021,177.18744,4.83 2020,-36.78808,-129.69266,117.45436,277.54996,4.767 2020,489.59518,-179.23843,234.9932,425.6019,4.764 2020,536.6396,310.64865,142.84747,341.69522,4.714 2020,145.7788,226.89781,22.54683,33.961258,4.663 2020,9.877752,-17.435535,18.134958,41.58127,4.333 2020,493.48483,226.18927,237.90768,515.97473,4.33 2020,-5.6879473,39.763695,18.211365,42.824577,4.254 2020,10.358243,-37.3221,58.935886,132.4483,4.234 2020,-70.75021,-188.54999,211.8532,454.17834,4.156 2020,162.14969,206.14148,41.55333,70.8363,4.143 2020,547.46716,-2.2147026,31.735046,69.07471,4.113 2020,612.376,52.30004,26.736694,84.722786,4.087 2020,122.94686,216.11455,18.189552,31.528183,4.069 2020,315.74506,202.84163,31.54712,59.557846,3.988 2020,484.61646,-2.9665031,30.765259,78.92311,3.871 2020,-6.078413,56.618927,18.374315,39.611603,3.838 2020,-9.383698,-6.1265182,40.733303,96.824135,3.822 2020,43.405067,-15.408314,34.092094,66.73954,3.814 2020,-14.613633,17.826218,48.6839,148.00627,3.739 2020,236.52524,193.9535,30.411346,51.939392,3.687 2020,-5.9559445,72.45892,18.259321,39.730896,3.62 2020,-0.6819725,-16.539097,28.02167,68.27125,3.616 2020,160.19951,224.34544,23.718826,37.912674,3.614 2020,0.9880829,-42.46921,44.55445,101.32402,3.605 2020,388.12515,233.46072,18.529327,34.333374,3.599 2020,380.73267,235.88225,17.91568,30.736526,3.592 2020,38.0981,52.622055,28.954933,64.434586,3.575 2020,371.83307,234.45279,16.463379,28.069824,3.555 2020,347.6326,186.2771,54.02719,103.68732,3.469 2020,18.49752,-16.532612,18.189247,39.551384,3.439 2020,615.14417,-20.648481,26.652344,69.25092,3.432 2020,27.095734,-14.814619,32.76824,65.58745,3.422 2020,131.70052,207.12357,43.45146,66.11594,3.413 2020,530.51965,-130.81528,116.20978,265.54712,3.39 2020,177.54518,211.85608,40.608932,73.83856,3.384 2020,458.93616,-26.927025,59.129944,121.60436,3.384 2020,526.47394,-33.205574,28.32489,83.3271,3.365 2020,619.2854,86.84827,19.132812,59.221695,3.362 2020,461.0645,-1.5966587,31.769684,65.68909,3.355 2020,251.5256,215.79057,18.323212,29.780884,3.351 2020,587.8632,-16.386734,31.707947,64.28834,3.351 2020,406.90518,225.14935,30.939209,59.20276,3.348 2020,622.0489,67.63608,25.636108,80.33057,3.34 2020,45.378166,66.67584,32.19055,60.738297,3.336 2020,42.020424,-32.26705,60.75379,123.38327,3.324 2020,443.77182,1.6969318,31.090698,61.856842,3.323 2020,115.3051,-33.11603,31.078522,73.91514,3.302 2020,115.71022,214.719,17.583786,32.36992,3.289 2020,-4.668486,199.9637,17.914307,44.568573,3.275 2020,618.34314,489.53607,20.972412,43.241272,3.274 2020,115.4669,219.7334,41.86351,70.13953,3.274 2020,12.1421795,16.742073,55.91588,129.96689,3.267 2020,469.69223,6.397434,32.8685,77.98709,3.266 2020,572.4519,-5.2898617,31.046326,66.3627,3.23 2020,1.996938,490.8673,20.00725,41.053406,3.226 2020,35.66782,0.4982834,32.14372,65.76311,3.213 2020,348.54578,240.02988,30.593658,62.286224,3.205 2020,120.993576,-18.846779,19.006592,42.73004,3.199 2020,339.5896,210.26898,42.850647,69.686646,3.18 2021,-5.2123923,-16.006697,17.444527,41.488186,8.189 2021,-10.732719,-31.678957,30.024208,72.200966,6.627 2021,578.2192,2.184555,36.14209,90.965294,6.287 2021,610.3628,-45.072254,42.01703,95.78677,5.935 2021,-17.89232,-63.967484,56.204185,145.12674,5.917 2021,627.00977,-17.803406,17.540771,46.897263,5.853 2021,-12.256198,476.6969,30.68087,64.13977,5.619 2021,104.04573,243.15709,23.921638,38.74324,5.496 2021,584.9909,-93.18396,74.720276,226.40005,5.485 2021,-6.1894083,489.0087,18.80045,42.825104,5.385 2021,-72.885635,266.0691,201.19334,461.93872,5.129 2021,613.2884,453.60223,39.393494,108.77637,5.106 2021,583.23694,410.6878,71.62561,177.35913,4.85 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41,288.3460,196.4610,23.7920,38.0250,0.01094847 42,53.5349,209.4040,35.2622,60.9060,0.54708719 42,276.2360,197.2010,22.4180,35.4360,0.03159145 42,595.2550,214.7950,21.3870,43.8190,0.02490900 43,1.0000,225.6980,24.9627,63.2960,0.08206443 43,272.5490,203.6320,23.3030,38.3890,0.01982207 43,402.5860,214.1180,21.2340,35.9390,0.01778301 44,527.9310,96.9374,39.9860,98.1416,0.06362305 45,446.3770,203.7340,23.8070,51.3090,0.46725100 45,441.7690,3.9412,34.9340,66.7284,0.01021910 46,496.6310,214.5540,23.0240,55.6280,0.67689884 46,476.5810,218.0750,24.0250,56.6870,0.33368665 47,538.3500,223.9780,29.9550,63.4650,0.16708493 48,449.1860,209.9900,21.6880,41.4990,0.01012077 49,130.9780,211.7600,25.3020,45.1770,0.01208815 50,527.1050,213.3100,41.5090,88.1630,0.02922045 51,491.8650,161.7380,33.3080,74.8730,0.01834146 52,305.4390,189.4620,22.4430,36.8490,0.01701054 53,489.8370,204.9310,23.7440,52.9170,0.03064976 53,331.7490,207.1640,20.5110,33.8580,0.01828074 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67,342.0450,201.3650,126.7320,56.4350,0.01204061 67,379.9720,209.6030,49.1690,49.5650,0.01118022 68,563.1080,184.6280,30.6930,73.6750,0.45759404 68,365.4030,214.5300,23.1630,49.1230,0.15855126 68,313.0270,212.7560,21.9600,45.4780,0.12117098 68,345.2470,214.0500,25.8490,58.7470,0.09225549 68,46.0990,218.3700,25.4737,41.9710,0.02320273 68,473.6390,135.0400,32.7570,80.1630,0.01998453 68,411.4490,203.0430,21.6930,47.3900,0.01700537 69,335.9990,208.0580,29.7240,62.1680,0.50855482 69,392.1920,210.0740,26.1530,59.6460,0.50074089 69,364.8070,210.9640,29.7470,64.7650,0.02109291 69,306.8260,214.8780,22.1140,43.6650,0.01850391 69,16.2875,210.4650,152.8095,49.6470,0.01200399 69,257.5150,212.3160,22.3640,43.8840,0.01095290 70,391.8260,213.1940,23.3150,54.1960,0.54576606 70,438.4840,206.7230,28.1570,64.1300,0.49844196 70,415.7000,211.8520,24.3170,58.9820,0.35836518 70,121.1570,206.7130,21.2040,41.8440,0.03224440 70,382.7440,214.8350,22.6250,51.5510,0.01692510 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139,298.6950,183.4920,36.6580,65.1600,0.01248796 142,356.6050,178.3970,23.8340,47.3360,0.03214772 143,400.6250,161.6630,29.1850,63.7550,0.12827154 145,1.0000,202.2840,43.8794,79.0990,0.02151532 146,29.1594,160.9290,41.4609,98.0560,0.04637406 147,1.1955,178.8670,40.9334,98.3620,0.01441438 148,1.1275,107.3290,34.7441,89.8490,0.01198610 149,199.1620,82.3290,31.1970,64.5630,0.02900705 149,29.7288,86.2091,39.5496,95.6149,0.01016519 151,197.2520,217.3470,30.3950,56.4740,0.03169651 152,483.2330,232.6820,22.2680,38.7700,0.01037097 153,408.9190,240.9330,26.8260,49.4250,0.54859161 153,608.4880,205.7040,31.2670,104.3400,0.01589836 154,307.7690,248.5590,30.7400,66.1040,0.55502546 155,163.9680,242.5440,41.1030,79.4140,0.69970447 155,420.3460,205.2380,35.1850,89.4420,0.01769664 159,1.0000,173.7820,24.0288,87.1390,0.02387945 159,39.2157,192.9540,36.0484,72.0520,0.01220929 167,63.4870,209.6620,30.3932,67.5920,0.03787166 168,22.2506,191.3590,30.5028,66.5740,0.01247614 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303,498.0900,218.0810,20.5440,46.2610,0.06891440 303,348.8900,230.0420,21.1210,42.7680,0.05482493 303,422.4450,233.1900,19.4180,38.0890,0.05469012 303,481.7520,213.4080,22.3400,53.0980,0.03801607 303,141.7810,311.0230,27.1650,56.6050,0.01895046 304,555.3130,186.9420,21.9650,45.2350,0.29468879 304,438.4190,191.5040,19.1210,37.3420,0.08306265 304,456.1890,188.8950,19.7420,40.3840,0.07524545 304,355.5190,189.6710,22.2900,42.7170,0.05936208 304,73.6031,189.7010,21.7043,39.6230,0.05095717 304,194.4380,187.9770,19.3450,32.1190,0.01348966 304,105.6510,288.4580,26.9870,57.5980,0.01194578 304,496.4360,176.1810,22.1230,49.9610,0.01066231 305,555.9790,201.3840,23.8410,53.4290,0.52885753 305,475.0550,210.9380,19.4840,41.4010,0.19787802 305,457.5700,215.3350,20.1700,38.6170,0.18730977 305,363.4700,207.0530,23.6280,47.5990,0.05684876 305,70.6088,213.1170,22.3465,42.6250,0.01983442 305,196.3960,208.7750,18.0130,33.1760,0.01168660 305,465.8290,215.7680,21.9140,40.1650,0.01113174 305,1.0000,152.2090,21.0716,47.1390,0.01099140 306,485.6110,211.4960,21.2520,49.1560,0.34766877 306,467.3630,218.0890,20.5070,42.8190,0.31422508 306,549.2800,213.5340,21.5480,43.3340,0.08655769 306,361.9550,207.7710,25.0670,51.3260,0.08243065 306,50.2749,213.4040,21.8472,42.7810,0.07325015 306,113.1990,216.7020,23.0890,42.5920,0.04751596 306,474.8230,210.5850,23.6990,50.5540,0.03753104 307,496.6930,212.2700,20.9660,46.9530,0.40600881 307,478.2360,214.1260,21.0780,47.3890,0.39514145 307,534.7330,184.9280,26.4450,71.9650,0.19056535 307,354.2930,203.7880,25.0320,48.2020,0.02959649 307,82.6227,207.2870,23.6863,42.6440,0.01925760 307,520.9210,179.9440,27.2850,75.7120,0.01612324 308,508.7580,228.8830,23.2100,51.9180,0.52376658 308,534.1130,225.4060,21.0440,51.3930,0.34515491 308,550.4120,205.3630,25.8020,67.2680,0.23311017 308,525.1160,228.8510,21.2220,48.8870,0.04546871 308,539.5510,219.2770,27.9630,56.5030,0.03946082 308,358.6670,217.7110,24.6360,47.6350,0.03206527 308,558.2580,231.7670,22.3450,45.6940,0.01900602 308,56.6491,216.0480,24.8373,47.5320,0.01475553 308,468.3040,224.2900,20.9710,36.1710,0.01186190 308,524.1630,220.7930,62.2680,62.8200,0.01023741 309,614.0050,221.8310,24.1690,68.8410,0.63321060 309,581.6880,224.6800,28.9750,67.0740,0.63270855 309,604.1640,224.3440,24.7390,64.5670,0.03937112 309,154.8410,194.2450,20.8230,44.6430,0.03523909 309,498.2710,220.6850,23.0140,44.9420,0.01489207 309,612.1390,182.4210,25.2950,80.3790,0.01434809 310,153.2200,181.5900,23.5420,55.4110,0.01129742 311,140.7220,182.0820,27.0700,60.8580,0.02504242 311,418.0690,227.6760,20.9780,40.9250,0.01043713 312,425.4220,213.0960,24.2410,47.3230,0.17581367 312,373.1220,201.7530,21.9850,43.8990,0.02143467 313,451.4480,204.6140,26.2730,58.0470,0.21686789 313,554.9840,211.3890,39.9230,96.5870,0.02319843 313,69.1830,152.0460,31.0949,70.4060,0.01676229 313,484.0040,20.4526,23.0040,46.3343,0.01163772 314,481.2160,220.6970,24.0040,52.5380,0.71299279 314,386.6670,212.2000,19.7770,40.9100,0.10698050 314,544.0530,205.4950,26.7060,63.0560,0.06636152 314,413.3290,70.0018,32.7800,69.1402,0.03173504 314,470.6810,219.7720,22.2160,48.8860,0.01451079 314,474.4130,212.3410,40.9370,73.1010,0.01179713 315,525.5450,207.8880,30.8820,68.9600,0.85647035 315,383.3900,200.1360,21.7930,46.4170,0.29521427 315,336.8150,43.6093,24.2890,47.1619,0.04277698 315,511.9030,194.0160,56.0310,97.2450,0.01710199 316,564.8770,142.9200,34.7770,84.7380,0.12725793 316,399.2080,184.6450,27.8680,62.7500,0.06955598 316,395.5760,206.1380,21.4940,39.7210,0.01223773 316,8.8590,93.3265,34.1347,85.8135,0.01223701 317,432.3390,210.7150,20.2590,45.3330,0.32611653 317,419.4480,208.9370,20.3070,47.0770,0.25999060 317,592.3980,185.3250,31.1880,80.6540,0.07471579 317,616.8730,182.2780,22.2870,64.3690,0.02554612 317,455.5790,198.8400,22.8380,48.5540,0.01603830 317,389.9500,202.4110,22.9340,45.8970,0.01186338 318,458.0110,203.4490,21.3890,49.4660,0.32842422 318,441.9950,202.7350,20.8710,46.4510,0.05948393 318,375.2940,205.5200,23.1710,47.7440,0.04475512 318,202.4100,188.3720,21.7890,43.1500,0.02114996 318,464.4070,194.5090,25.9560,54.9690,0.01188976 318,419.8750,196.3340,20.8390,45.4860,0.01068530 319,475.7220,208.5540,26.7130,59.0490,0.24564879 319,495.4170,209.0840,22.3490,48.9710,0.15662085 319,42.6636,212.1480,21.6816,40.6630,0.05388993 319,191.7730,192.2990,21.7650,44.2280,0.02598630 319,482.8160,185.8180,35.2770,87.5780,0.02439160 319,388.1010,204.4560,19.2970,38.1500,0.01025792 320,521.1720,209.9570,28.5420,69.7760,0.79777408 320,541.6730,209.2380,28.7180,76.3560,0.56047195 320,127.4650,192.3190,24.6440,51.0080,0.10516451 320,476.5030,182.9030,28.0410,74.3160,0.04343310 320,182.3590,190.7340,24.1570,46.5800,0.03965347 320,2.7933,209.4480,21.7580,49.7300,0.03595045 320,338.5190,207.3390,19.8170,38.0930,0.01002681 321,579.7270,194.0080,31.9470,81.4260,0.83733451 321,610.8060,191.2520,27.4160,80.2830,0.61612910 321,338.4380,177.8230,21.5890,41.9010,0.03944905 321,465.8030,175.7980,18.9200,37.3580,0.03490716 321,404.8460,177.5360,19.4470,35.5200,0.01306518 321,369.5710,180.6020,20.8720,36.5090,0.01240124 321,447.1500,230.1580,22.5190,38.9030,0.01052579 322,471.5060,210.0430,22.2520,44.4690,0.35473606 322,418.0940,208.0070,19.7490,39.0360,0.11983070 322,386.1670,208.4850,20.3710,40.3990,0.04638955 322,495.0960,186.7770,25.4450,55.6310,0.03528558 322,425.8920,205.1240,20.1560,39.6600,0.03237153 322,96.6719,201.3610,21.6151,41.2330,0.02103882 322,504.2440,198.8390,21.9360,45.8640,0.02064598 322,293.1230,203.7050,20.6310,34.3390,0.01654180 322,510.9910,206.7420,22.7660,41.3750,0.01158578 322,479.8010,198.4320,23.3280,50.7090,0.01022476 323,484.8460,209.6810,22.6080,46.6220,0.57088900 323,435.2250,208.7020,19.4440,39.3860,0.21627209 323,395.7280,209.9740,21.6110,43.9860,0.14093325 323,83.6992,200.7610,24.3408,53.2720,0.08419943 323,537.8530,202.2250,21.9570,43.2500,0.03436431 323,333.3560,208.0690,171.8010,47.2930,0.01801656 323,360.6440,223.5910,21.8440,39.0690,0.01156304 323,561.5580,182.7820,23.8450,49.1680,0.01140963 323,323.8230,217.5810,23.0660,39.0050,0.01028451 324,513.8880,215.8600,23.9940,51.3500,0.68126452 324,417.6920,207.4620,22.3270,55.0930,0.44416884 324,461.5620,213.1570,19.8020,39.4290,0.25254527 324,592.9150,203.5990,21.8530,47.9620,0.05453654 324,469.5180,207.7800,21.0790,43.9570,0.03791982 324,384.2150,217.9640,22.3900,46.0310,0.02751727 324,355.3100,199.7290,21.0330,46.4910,0.01411999 324,107.5420,212.2320,20.7480,39.1680,0.01192629 324,526.6370,217.0360,21.2140,46.9410,0.01171356 325,569.2950,214.4810,26.9150,60.2270,0.75991035 325,501.3280,216.9300,19.5490,43.3920,0.18912822 325,515.1110,211.4180,18.9860,44.5420,0.15609878 325,452.7340,212.8920,21.5740,43.3820,0.13329202 325,48.0353,197.7880,27.0504,53.2020,0.05680538 325,84.5660,211.5210,20.3020,40.9770,0.04049086 326,546.9880,218.9240,22.1180,49.2890,0.49416217 326,487.8730,212.1890,22.4800,46.7230,0.26824644 326,562.0550,215.6050,19.3510,48.4220,0.06274977 326,447.4290,207.7000,22.5460,43.8710,0.02821495 326,552.0940,205.3540,23.1990,54.4040,0.01709179 326,48.2813,202.7930,23.6113,51.3240,0.01517980 326,184.7130,202.4100,20.7560,37.8470,0.01234251 326,613.0270,210.2270,20.6880,43.2470,0.01073925 327,518.0200,213.0230,25.4030,54.9470,0.71195245 327,592.8020,220.0740,21.7130,53.0780,0.47941515 327,608.4090,217.9070,22.2470,54.7510,0.40564254 327,451.5400,207.2010,20.3290,41.9720,0.03699484 327,509.6730,212.3290,22.7670,51.9060,0.03025947 327,582.7210,214.0910,24.2890,60.4100,0.02481230 327,158.1570,200.2470,23.8190,48.2710,0.01471665 327,401.9440,202.8840,22.8390,44.1190,0.01382917 327,597.6270,227.8120,30.6070,63.3760,0.01049271 328,558.8180,210.4050,28.7410,68.3120,0.85455614 328,619.4120,196.9560,20.0520,51.4460,0.08586601 328,456.6800,199.9000,26.5590,61.6200,0.08236940 328,111.9550,199.8300,23.8730,45.1050,0.02116624 328,419.3150,209.0170,20.3670,39.9580,0.01794916 328,387.2470,198.2010,21.5040,46.3990,0.01086839 329,546.6550,219.1160,22.2060,46.8400,0.16970605 329,478.7190,208.3520,22.4930,45.0300,0.09363066 329,71.2224,195.7120,25.8432,53.9700,0.05808714 329,434.8400,206.9870,21.9980,51.4380,0.03641423 329,591.7650,201.6390,20.7680,45.3800,0.03367887 329,57.0386,194.8880,26.7544,52.6640,0.02573208 329,401.5980,198.6280,21.1940,49.3610,0.01924671 329,330.0100,200.7850,20.8160,40.6030,0.01750176 329,385.1090,196.9170,22.0130,49.3330,0.01670307 329,459.2360,177.3780,23.7190,58.1390,0.01393281 329,576.2920,112.8710,28.5210,60.9490,0.01252295 329,507.7390,138.3990,32.0190,78.2960,0.01237648 330,465.3390,209.2480,21.5620,45.0420,0.28353745 330,525.2280,208.7180,24.3350,54.8720,0.18654352 330,368.9150,199.6940,21.5500,45.7300,0.05861372 330,486.7460,162.1760,29.1860,82.4590,0.03792787 330,25.5037,195.8130,26.0661,50.8620,0.02177090 330,386.9850,202.3670,19.5640,39.7070,0.01861870 330,357.1430,198.3000,20.4640,42.3540,0.01760824 330,329.5270,199.2280,21.2900,40.7520,0.01685096 331,615.7500,207.4450,24.2500,66.7330,0.51333392 331,535.3570,208.7170,26.7790,59.6160,0.47096610 331,446.7710,203.0340,23.1640,51.6550,0.25228161 331,412.7600,206.7940,20.8220,45.2760,0.22961879 331,382.5320,192.6610,25.8660,59.7830,0.12279405 331,339.7540,200.0870,20.7990,42.9510,0.04462218 331,392.1240,206.1840,22.9990,49.2670,0.03371809 331,363.5760,208.7090,20.3320,36.8930,0.01661949 331,370.3570,202.0000,22.3960,48.1740,0.01090820 331,526.9860,177.3270,28.2920,66.3370,0.01073334 331,401.8500,206.0440,24.6860,49.7480,0.01068095 332,447.7470,202.2320,24.7570,63.6720,0.58888841 332,503.9250,208.4090,22.4840,52.2830,0.56362766 332,522.8380,208.8110,21.5860,50.8760,0.42451671 332,422.7820,168.9540,30.2160,94.3360,0.27760687 332,513.3410,208.4580,22.7930,54.0320,0.13485919 332,360.6500,199.1320,20.5220,41.5970,0.10037665 332,381.5260,208.8070,20.2000,35.7550,0.06783930 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429,155.4680,204.7810,21.0760,39.2890,0.02055750 430,466.6940,203.6790,23.1610,50.7680,0.43210489 430,445.1440,199.5180,24.0140,56.5710,0.37217274 430,147.4700,200.0930,21.8830,39.4580,0.10085753 430,399.0230,192.2830,21.3780,39.9780,0.04770098 430,366.3000,162.3110,23.5830,49.0140,0.04726642 430,295.2750,208.0830,21.7210,39.3410,0.02780500 430,4.5511,193.5270,39.8025,95.0140,0.02352081 430,1.4762,182.6540,80.2468,88.9740,0.01091179 431,428.8240,205.5310,28.1560,66.7450,0.64739037 431,113.7850,207.2410,25.8990,46.5810,0.49584851 431,368.7710,196.4690,23.3750,44.3370,0.12028419 431,439.9320,207.7620,26.8930,61.0360,0.04658974 431,345.3400,169.0720,20.8990,41.5690,0.03330895 431,261.7930,210.9240,21.1290,36.9190,0.02137325 431,303.0400,203.8080,23.5410,42.0070,0.01300116 432,390.8760,204.8430,37.1520,82.4650,0.79232675 432,95.8864,204.7560,26.0526,51.2580,0.11041372 432,319.5510,185.0460,23.5420,51.5970,0.02501635 433,361.1430,222.3720,31.9970,74.9090,0.79450923 433,284.7590,207.1140,21.4350,43.3210,0.40324208 433,305.4090,178.7420,23.7680,47.3870,0.01516476 433,364.4990,183.9730,32.6530,73.3640,0.01273873 434,327.0730,211.7300,40.0460,92.6480,0.85036016 434,252.6620,202.9520,24.1160,48.0330,0.55445731 434,153.7810,207.6210,24.4550,49.5530,0.31030095 434,303.8730,174.4210,23.1300,44.8670,0.02918278 434,369.6000,182.7040,23.0260,44.4310,0.01475168 434,304.8650,202.2510,78.3210,84.5120,0.01311252 435,289.1860,224.1290,37.1420,90.4250,0.79795814 435,221.5810,208.6160,23.5680,46.5020,0.36419499 435,196.5900,210.9790,21.9460,43.7630,0.22863342 435,303.1800,175.8230,24.0740,46.1370,0.02478543 435,303.6710,228.6860,29.2640,69.2950,0.01961107 435,212.4150,209.8460,21.3110,43.6270,0.01328472 436,246.3550,225.2070,38.4610,86.5080,0.80542105 436,190.8050,207.0830,22.5970,44.4840,0.33511645 436,235.2460,211.0520,24.1960,57.8210,0.09669616 436,302.0660,177.0360,22.4900,44.6300,0.02050313 436,218.4990,212.5090,66.2450,74.7290,0.01190870 436,439.3450,197.7220,26.5350,54.3370,0.01054253 436,35.2690,174.1610,29.6879,57.0290,0.01014164 437,186.8030,228.1020,34.2130,86.1810,0.71154886 437,273.9500,218.6980,24.5900,43.5320,0.49312246 437,199.8250,228.0390,35.7220,89.2010,0.36513859 437,158.3010,203.7970,24.1960,47.9570,0.33520147 437,303.2860,176.0430,23.1900,46.0500,0.01614999 438,123.6210,224.0640,36.7940,92.7190,0.67726529 438,125.8220,203.7930,27.7680,68.0680,0.13310370 438,138.3660,229.3770,30.8020,72.0510,0.04233908 438,149.6800,225.2800,26.2770,56.8880,0.02375189 438,370.4320,186.1510,22.1490,41.0150,0.01574279 438,114.1510,206.4640,89.5040,88.2310,0.01223363 438,311.0040,216.9290,23.3740,39.7570,0.01052682 439,91.4525,222.3600,39.9185,94.8230,0.80027944 439,64.6191,225.2340,37.9789,91.0960,0.67859536 439,339.3450,218.2620,25.7230,52.3420,0.35235962 439,301.9760,180.5390,21.9850,43.2060,0.01887321 439,371.1370,188.9780,21.5470,39.2520,0.01483823 440,369.0730,225.0370,25.7900,52.1530,0.65900522 440,15.9623,218.8740,34.5006,103.9500,0.60529757 440,49.3034,220.2230,49.3526,103.7060,0.44397849 440,2.0375,216.3400,33.0474,105.1690,0.27286869 440,367.2900,196.7870,26.3520,58.5760,0.03260706 440,46.8243,200.3590,30.3399,62.2630,0.02647600 440,294.5500,178.0880,22.1400,43.2410,0.01787031 440,363.8120,185.0520,24.1070,52.1820,0.01193599 440,40.2432,226.1040,38.3334,85.8230,0.01165084 440,205.6990,185.3650,21.4980,43.6740,0.01149868 441,184.6700,228.6290,50.3230,110.0400,0.74759096 441,387.9840,221.4850,27.3960,51.4210,0.54923302 441,172.2370,239.5700,41.8810,100.7730,0.14335680 441,193.5470,231.3050,35.8960,60.7010,0.06607908 441,179.5080,291.5850,31.6310,65.6310,0.02584154 441,345.5970,184.5980,20.5040,39.6910,0.01745567 441,380.2510,209.5110,48.5060,69.7660,0.01427575 441,274.5070,172.9600,22.3450,43.4550,0.01240615 442,401.1790,221.5550,27.4500,58.2220,0.68433690 442,351.8000,235.2050,56.4150,118.1180,0.52894610 442,323.7430,182.4350,20.2980,39.9350,0.02141344 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1308,512.5710,210.5700,19.9100,46.9430,0.08904614 1308,491.5790,216.1640,18.3770,41.3780,0.08625636 1308,96.0418,215.7450,24.6222,49.6900,0.06950244 1308,586.7520,218.1040,19.6830,53.3490,0.06687581 1308,284.4070,211.2260,31.3620,82.8850,0.06665907 1308,43.4605,205.8250,23.4211,46.0180,0.06117992 1308,557.3950,223.1980,22.0700,55.0620,0.06023816 1308,224.9090,222.8740,32.5720,85.9860,0.03760916 1308,200.7680,219.2350,23.7420,71.8300,0.03704676 1308,96.5152,212.9140,109.4648,106.4800,0.03291123 1308,163.0700,218.5390,26.0020,64.3550,0.03012132 1308,422.1330,208.7190,139.2590,57.0960,0.02812319 1308,76.1730,205.1120,20.4982,42.4430,0.02470230 1308,152.4330,218.7820,25.9510,60.7140,0.02125500 1308,441.1570,213.3480,21.4360,40.6430,0.01949716 1308,617.4670,216.2880,22.1660,61.3790,0.01743428 1308,461.2990,213.1490,19.6140,42.9050,0.01739937 1308,574.8530,218.1550,23.5290,53.8770,0.01568973 1308,170.9000,210.3850,123.0560,78.0070,0.01269005 1308,306.7300,216.2550,26.7230,53.8890,0.01261497 1308,3.5729,165.6830,34.8035,83.4950,0.01185931 1308,1.9973,189.8570,123.3517,65.6730,0.01099215 1308,598.2880,232.5480,31.7580,73.6590,0.01097848 1308,323.6330,220.1800,37.4000,85.7770,0.01027124 1309,373.1910,218.5320,32.0220,85.4090,0.90008014 1309,283.3930,215.1340,45.4850,122.2670,0.83781058 1309,219.6020,214.2100,54.5510,154.4280,0.83349121 1309,339.6210,214.8820,32.0960,86.4210,0.78974921 1309,553.3080,223.3390,25.4140,60.1510,0.78786373 1309,581.8090,222.9750,23.8070,57.1660,0.64016360 1309,406.4840,219.0740,23.8660,51.9660,0.61552924 1309,153.6820,218.0040,29.4220,77.1940,0.58122975 1309,473.9010,213.5690,21.4510,53.5750,0.48394334 1309,135.0300,216.2780,26.6330,66.3430,0.41358608 1309,325.1530,217.0410,29.2760,81.8840,0.41049880 1309,198.2120,220.4610,23.3780,57.8830,0.34320596 1309,522.5830,213.1860,20.8990,45.4480,0.25823438 1309,504.7410,213.3200,20.3980,46.1780,0.21563257 1309,607.3680,222.0340,23.6270,57.1620,0.20835501 1309,166.0720,218.4830,26.9750,72.5880,0.15779155 1309,72.1184,214.2300,25.3426,52.1220,0.09642785 1309,388.8580,218.2800,29.4540,67.1770,0.08536770 1309,33.7715,204.8360,22.8878,46.1640,0.05756164 1309,357.0170,216.3000,32.3490,75.5060,0.04396283 1309,462.8220,213.8920,19.0910,46.2850,0.04326770 1309,60.7648,210.4240,26.0723,50.8070,0.03754070 1309,573.5420,224.4810,21.6820,54.7910,0.03455986 1309,239.3830,211.6620,55.0840,136.2110,0.03321321 1309,232.2050,289.4540,35.4670,79.1750,0.03320278 1309,5.1393,197.2350,122.3667,60.6580,0.02692084 1309,189.3090,219.3640,22.0470,56.8850,0.02658993 1309,463.4910,214.3290,51.7130,47.7110,0.02520740 1309,207.4160,217.9390,23.7160,57.0850,0.02472029 1309,494.6170,214.1380,17.8380,44.6680,0.02291602 1309,144.6610,210.2180,28.2160,73.6990,0.02218097 1309,488.5100,212.6130,44.4680,50.3630,0.02125468 1309,441.1740,211.6520,21.0270,42.6390,0.01995618 1309,217.7800,217.5380,23.3870,56.6400,0.01810309 1309,177.3180,218.3860,23.1570,60.6210,0.01726774 1309,589.1840,214.1770,25.9100,60.9470,0.01533260 1309,443.1160,213.1860,115.8080,52.0920,0.01456804 1309,281.7900,256.4330,29.4640,78.1530,0.01418715 1309,103.3810,217.5110,23.7200,47.6010,0.01377277 1309,213.1780,223.3130,31.3230,93.5640,0.01365375 1309,7.6662,201.4830,309.8128,80.8790,0.01255094 1309,313.4670,212.9290,26.6180,74.8280,0.01236190 1309,191.6230,215.9850,37.7830,91.1470,0.01205368 1309,249.5320,214.7470,24.6400,49.5600,0.01143851 1309,288.6050,208.5000,351.3950,75.5020,0.01104510 1309,404.6380,223.0520,192.3290,56.2900,0.01047680 1310,424.9600,218.9810,34.2700,83.8980,0.87794656 1310,251.3900,218.1620,33.4720,80.2230,0.87556207 1310,12.3055,207.0560,45.4445,104.2630,0.74642813 1310,311.3560,228.5590,50.6290,141.1400,0.68732929 1310,107.0530,221.7300,25.0170,57.6500,0.67312968 1310,531.8890,213.3100,23.3380,50.5970,0.63598186 1310,127.8800,218.5080,28.6860,73.1640,0.60817659 1310,341.9280,215.8330,42.9680,117.3700,0.56977743 1310,482.0160,217.5980,21.2700,53.1370,0.46462184 1310,162.9470,220.9720,24.1150,61.7400,0.44289160 1310,578.3910,228.1150,36.6600,91.9300,0.43614569 1310,226.4770,216.0230,22.5230,49.3420,0.35585839 1310,186.6470,222.5830,27.3680,67.6620,0.34458774 1310,564.4760,225.9300,22.8010,55.6040,0.31371772 1310,82.1629,211.2170,20.9991,50.7430,0.22476307 1310,65.4389,211.8900,20.3470,46.6840,0.15894245 1310,30.0847,210.8930,30.1075,75.7260,0.15164445 1310,602.1550,226.4000,25.3490,62.5700,0.11050691 1310,294.6270,214.0210,37.6960,98.4960,0.08863271 1310,571.1130,221.6270,28.9910,76.2470,0.07449356 1310,140.7440,218.1010,25.2960,67.5750,0.07434426 1310,468.8890,213.2790,20.2790,49.3590,0.04022508 1310,489.1520,212.6160,21.9510,50.0660,0.03219857 1310,178.1240,226.8300,24.1390,57.7180,0.02822101 1310,326.6440,274.7410,33.5210,84.8690,0.02628123 1310,498.6680,214.6360,19.3140,45.5520,0.02625574 1310,292.0870,213.3690,28.3770,58.9850,0.02336374 1310,194.2530,241.1800,26.4740,55.5880,0.02180261 1310,437.8150,214.2000,27.7880,61.2410,0.01717755 1310,50.0449,212.1240,23.6601,52.8520,0.01669689 1310,359.5520,237.3140,29.2520,87.2370,0.01527686 1310,477.7980,224.5360,148.8160,72.5560,0.01498949 1310,462.1630,209.4710,19.3410,45.8990,0.01328241 1310,152.7140,227.0240,25.6930,58.3660,0.01307798 1310,242.9520,215.3610,29.8160,64.0350,0.01229745 1310,551.7050,221.6180,77.4460,103.3250,0.01224454 1310,344.7770,214.5330,30.1920,61.1040,0.01147459 1310,287.4430,229.9090,28.7050,72.2230,0.01043905 1310,469.5840,214.4600,61.1120,57.4330,0.01024017 1311,213.5760,218.3780,32.6060,79.1430,0.85526121 1311,455.5760,218.8030,28.9390,75.0500,0.82014197 1311,477.4830,223.3600,40.3620,111.4190,0.79863006 1311,256.8840,213.7780,30.3600,78.3950,0.77427012 1311,82.7536,212.8450,35.9574,93.0470,0.73989850 1311,283.6840,215.3150,29.9310,75.9700,0.68369776 1311,390.6560,222.5730,47.0050,135.0080,0.67185205 1311,585.5830,222.4500,28.5190,68.1070,0.62131917 1311,554.3900,216.2980,23.7560,56.6030,0.61546475 1311,192.6940,215.6630,23.1260,50.4260,0.54483819 1311,130.7360,214.5200,24.9530,66.6020,0.46982950 1311,376.8340,223.2040,41.0360,122.0660,0.36469245 1311,158.6610,241.1780,24.7000,47.7940,0.31779572 1311,270.5190,213.8190,27.0720,77.0650,0.29863825 1311,109.1150,220.5370,25.4490,69.0870,0.23409359 1311,32.1287,210.1570,22.6323,51.1950,0.21567617 1311,613.6980,224.5750,26.2490,73.5860,0.11645410 1311,41.9577,211.1720,20.8533,47.9490,0.10938028 1311,51.0304,209.6470,20.9228,47.4130,0.07025932 1311,467.7090,216.9490,31.0340,89.0840,0.05601204 1311,310.7630,218.3310,24.9950,48.9290,0.05585821 1311,564.4510,220.3080,21.6520,54.7620,0.04726399 1311,120.4180,219.8270,26.1630,66.4310,0.03619306 1311,475.7610,261.7980,29.5860,70.7710,0.03484926 1311,404.6170,267.8810,31.9820,84.8790,0.03248462 1311,197.6840,211.1020,29.0570,71.8680,0.02809670 1311,97.2786,211.9450,27.1954,75.4680,0.02725456 1311,1.0000,237.2650,24.7421,97.1570,0.02615133 1311,22.4255,209.1700,23.6323,50.9770,0.02462564 1311,295.1480,209.6090,27.3640,72.1190,0.02220214 1311,230.9940,210.4570,37.6930,78.7350,0.01975953 1311,405.0320,227.6770,31.9830,85.0180,0.01514566 1311,385.1400,221.7880,31.8350,66.1580,0.01474738 1311,63.5686,210.3660,21.4370,46.3400,0.01328512 1311,398.7310,209.0480,61.6540,106.3910,0.01233329 1311,50.1983,204.7500,124.3837,69.6030,0.01164965 1312,542.5910,226.1270,26.9300,64.3520,0.84807736 1312,182.7660,217.6850,32.1720,78.7830,0.83133239 1312,138.8170,214.4210,34.8100,85.2030,0.82894045 1312,393.4280,223.8430,40.1850,106.0400,0.80223656 1312,225.0050,215.6390,28.8930,77.7230,0.78892827 1312,458.3810,219.5040,41.8370,130.6220,0.73881859 1312,601.5880,219.9180,29.2540,68.8650,0.70252299 1312,51.5509,218.4720,48.9183,124.8250,0.67659450 1312,519.8860,219.8190,22.0930,55.1260,0.64812410 1312,267.0380,218.6170,28.0590,66.5210,0.61163312 1312,434.6680,221.0130,39.6400,125.4740,0.57471800 1312,105.9410,217.4350,25.3680,62.4190,0.55485147 1312,501.6390,218.5950,19.6310,52.6620,0.47214574 1312,245.5050,216.0040,25.6400,71.5120,0.26713112 1312,356.9960,221.1470,22.5290,44.7920,0.19398153 1312,167.1100,214.6170,22.2260,52.8290,0.16729428 1312,257.1000,212.9540,26.8760,76.4090,0.15752585 1312,81.0568,211.4660,25.5882,65.4190,0.12766516 1312,465.4090,270.4280,31.2020,74.0730,0.11401523 1312,24.6240,219.1390,25.8845,49.6720,0.09408726 1312,154.8170,215.6060,30.2850,70.3110,0.07869490 1312,53.5444,228.6870,32.3878,80.3570,0.07581665 1312,508.1060,216.2850,26.8910,57.4950,0.07448243 1312,569.4000,222.8750,20.4090,48.4110,0.05484038 1312,578.5200,219.3250,22.5400,51.9520,0.04816292 1312,132.3710,221.0110,26.8730,69.6260,0.03976008 1312,367.5000,219.5010,19.9360,41.5540,0.03633242 1312,118.7790,218.9630,27.2880,65.2000,0.03566566 1312,68.6539,218.9320,32.6761,81.9620,0.03566397 1312,443.4310,272.9790,28.1570,74.0550,0.03525125 1312,3.4883,210.3930,19.0052,41.0910,0.02521806 1312,177.9780,217.1290,24.5050,56.8980,0.02494830 1312,470.5850,216.0080,27.3310,69.5610,0.02458580 1312,441.6670,216.7180,86.6410,105.1900,0.02351913 1312,198.3250,212.0790,37.3800,79.7870,0.02174949 1312,90.5784,209.7000,27.0706,71.0370,0.02008040 1312,527.1140,226.0870,23.6420,56.9150,0.02002380 1312,48.4820,208.0460,28.5583,74.5090,0.01772231 1312,547.1030,242.6310,30.4850,63.1440,0.01533018 1312,590.7160,206.7120,29.2190,72.1780,0.01468997 1312,406.1060,265.0530,29.8020,62.1600,0.01420704 1312,67.9019,263.0660,34.6251,75.7290,0.01351882 1312,490.1440,213.9860,22.7530,51.0780,0.01294037 1312,559.5940,227.0230,24.2050,53.1480,0.01241055 1312,482.0980,215.6850,21.5280,55.0880,0.01120015 1312,37.3468,209.3960,128.1112,82.1310,0.01103103 1312,410.5610,233.0530,39.7300,100.7000,0.01004981 1313,320.7880,218.4960,39.5060,105.1500,0.84738994 1313,146.3200,222.8960,46.8810,126.0350,0.77485603 1313,192.2740,215.8300,30.3090,78.5260,0.65005022 1313,47.1588,211.5770,30.4183,74.2700,0.58287930 1313,244.6180,218.3370,23.4420,59.9110,0.48052821 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1329,77.5049,203.2580,23.3839,50.1080,0.25142521 1329,475.6410,218.2100,19.3400,44.8170,0.20779252 1329,614.7030,229.1760,22.7140,55.8260,0.19489755 1329,16.1906,215.1950,24.9841,54.5090,0.13295712 1329,43.4267,208.7300,20.2869,47.6650,0.09841897 1329,34.0089,210.4010,20.2086,51.5780,0.07303832 1329,458.0550,212.6770,75.1810,53.4670,0.05694983 1329,6.1233,208.2680,23.9619,56.3080,0.03405416 1329,143.6940,217.2910,24.3660,50.2040,0.02958005 1329,1.0000,204.2270,117.5190,58.5590,0.02167525 1329,467.9800,213.2790,20.3130,44.2650,0.02112999 1329,6.1816,203.8560,305.4574,73.9540,0.01531478 1329,408.6720,211.6370,173.2180,55.4860,0.01491091 1329,40.8963,206.2380,156.2267,51.1620,0.01466452 1329,605.6760,215.1650,34.3130,87.7000,0.01153287 1329,54.9277,206.9270,20.6440,44.1260,0.01020477 1330,484.5330,223.2910,19.7950,45.8510,0.44801277 1330,616.4170,227.5060,23.4760,62.1420,0.43469954 1330,505.8620,215.8040,20.4000,50.2870,0.30585146 1330,42.1256,210.5430,22.7220,50.8040,0.27883232 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1334,394.8560,167.7700,30.4920,73.5450,0.02634505 1334,439.7130,210.7300,30.8410,63.7860,0.02445661 1334,534.1610,213.9220,21.0200,51.7920,0.02245452 1334,556.6580,79.2530,29.7870,61.6820,0.02162225 1334,3.7422,193.1530,123.5508,60.0920,0.01698753 1334,512.2080,83.9888,31.1850,66.0502,0.01249441 1335,558.3090,79.4979,29.8880,66.2711,0.20850323 1335,452.4820,207.0460,20.1560,45.3730,0.14061750 1335,422.1750,201.7790,23.5900,59.8810,0.12649432 1335,619.1740,214.6770,20.8260,68.0560,0.08704128 1335,480.8910,221.7640,40.5060,84.4120,0.08594329 1335,468.8290,207.5370,19.5900,44.8560,0.07967411 1335,460.0080,211.9490,63.7020,64.6350,0.06601164 1335,488.4910,207.9950,22.8280,54.1030,0.05002619 1335,135.5790,216.3740,29.0900,64.5360,0.03186205 1335,561.7710,248.3900,24.8430,48.5660,0.01930133 1335,450.3190,205.5930,53.2580,48.1490,0.01321920 1335,610.7840,45.7790,26.7140,72.5350,0.01320493 1335,3.4205,194.7810,27.7632,51.7800,0.01169570 1335,410.5690,204.2200,131.2010,55.0980,0.01142576 1336,546.3280,209.2000,26.2460,65.5540,0.77048099 1336,525.1850,209.2740,20.4750,51.3650,0.30310881 1336,539.3730,207.1210,21.4660,56.9070,0.24511817 1336,591.5940,221.2810,22.6310,52.0640,0.06087513 1336,574.7980,241.3010,48.3100,108.0740,0.04916628 1336,584.1860,225.1740,34.0900,80.1320,0.02377592 1336,508.1560,207.9220,22.4700,50.5000,0.02077595 1336,455.6390,198.2210,24.1670,53.7290,0.01903473 1337,605.7510,204.8810,28.5610,76.9000,0.59523076 1337,510.4720,200.5230,22.8030,53.1290,0.15591575 1337,45.7180,196.9150,33.2733,74.2120,0.12228026 1337,479.2130,202.7890,21.9590,49.3780,0.04592780 1337,530.3860,200.9270,25.5230,56.8040,0.04038274 1337,515.9220,203.2990,32.3730,68.7730,0.02357380 1337,598.9530,266.3130,34.5240,82.1940,0.02269331 1337,449.1980,185.5030,24.1680,51.3580,0.01811115 1337,363.2170,217.3550,21.2810,40.1730,0.01794482 1337,420.8400,224.4690,26.5300,58.2800,0.01622580 1338,595.1940,198.5930,27.8850,69.6200,0.55361772 1338,607.4230,197.4060,24.9110,75.0660,0.25752619 1338,51.9438,171.9820,36.3091,87.3000,0.23831439 1338,3.6180,174.6990,33.1135,83.2780,0.11929784 1338,519.9780,199.0360,25.7390,61.4510,0.09747840 1338,497.0530,201.4310,22.1750,54.9120,0.04714727 1338,382.1520,223.8500,23.7490,43.8630,0.02971645 1338,500.7010,196.7780,32.9500,73.9120,0.01526361 1338,2.4469,181.5810,86.7829,77.9940,0.01284451 1338,424.9020,200.9800,23.3150,45.3300,0.01124408 1338,275.0040,212.6430,20.6750,36.0310,0.01085590 1338,467.5510,193.3820,151.6330,65.6600,0.01083625 1339,576.1700,198.9900,31.2960,64.9930,0.39290076 1339,537.1170,201.9830,23.8700,51.9210,0.14477043 1339,503.7710,177.8120,24.3640,50.4560,0.03858735 1339,342.8840,209.1140,20.1820,37.7630,0.03455260 1339,274.5880,208.6240,22.2250,45.1840,0.02941137 1339,324.4870,211.2880,21.5400,39.9910,0.01600675 1339,160.7130,209.5550,21.4120,39.4420,0.01445326 1339,448.3520,201.5560,23.9660,45.9350,0.01374998 1339,417.5490,204.1270,22.3930,50.8580,0.01349493 1340,268.6780,217.2550,23.4010,44.6740,0.08597960 1340,587.3420,201.6940,25.7200,58.8370,0.07263612 1340,500.5600,204.4830,22.6600,44.7660,0.04847173 1340,444.6310,213.7970,21.7760,47.0010,0.02791973 1340,136.6230,209.4760,25.0050,48.1210,0.01972160 1340,449.5520,221.6370,24.6820,51.1650,0.01221737 1340,36.8758,157.9610,29.2631,69.1700,0.01019355 1341,479.5560,210.3520,23.9860,58.0300,0.41456425 1341,438.0390,204.8830,23.9590,56.5720,0.11919244 1341,257.3430,215.6350,24.8580,49.4340,0.08951166 1341,496.4760,220.7790,21.4060,48.5820,0.03762788 1341,579.6320,166.3520,24.9670,54.1340,0.03521847 1341,470.3650,210.1140,22.2420,54.6090,0.03343127 1341,459.1460,212.3840,21.4930,47.5680,0.01180409 1341,525.0110,277.5320,36.8620,93.3250,0.01038286 1342,506.8580,210.2800,26.4700,65.6250,0.64949399 1342,535.8780,228.2170,23.3450,50.8790,0.20769247 1342,252.1050,211.5950,25.2860,62.8580,0.15851101 1342,115.3690,205.6080,22.8990,46.7880,0.08112431 1342,475.1240,221.7850,20.8280,46.6420,0.04286465 1342,335.4140,207.8200,22.4090,45.7810,0.04181989 1342,519.1220,217.8220,22.0850,57.8050,0.03264340 1342,490.2780,216.0980,21.7850,52.6380,0.02672177 1342,495.9210,205.8800,26.7960,65.5610,0.01737107 1342,374.6070,214.0500,25.1960,48.9660,0.01285714 1343,542.2850,212.6100,32.0510,80.3230,0.81419754 1343,575.1500,219.6660,27.3530,70.3720,0.46543500 1343,96.4001,214.6380,24.6669,53.8630,0.21767090 1343,81.9605,212.4680,24.8075,57.8510,0.18736182 1343,119.5510,211.3330,23.3350,50.8970,0.14950362 1343,608.6410,217.8640,26.3820,80.1310,0.09320565 1343,600.6730,232.1830,23.8440,63.2070,0.05068269 1343,249.2210,227.6490,24.7240,53.1440,0.04938748 1343,491.0810,224.2980,25.5390,53.7820,0.03489765 1343,344.0230,211.5480,21.4910,42.2710,0.02672395 1343,38.0155,218.8970,29.2626,64.2210,0.02313800 1343,556.7900,217.6230,25.6300,71.4710,0.02217062 1343,82.3404,210.8030,59.6376,57.8370,0.01778300 1343,382.0940,220.6670,27.2510,51.8980,0.01579320 1343,534.6140,212.7520,25.6780,68.5410,0.01470845 1343,614.3050,197.3550,25.6950,72.4430,0.01293143 1343,519.3250,211.5570,23.1440,58.3610,0.01082994 1343,416.8990,226.6960,29.0730,52.5620,0.01051715 1344,594.0950,216.2490,28.6110,86.8690,0.74122655 1344,579.1740,215.4520,28.1060,84.5110,0.67578059 1344,112.1670,213.0010,24.9670,59.2120,0.47870356 1344,247.9160,225.2830,27.0080,57.4000,0.30377892 1344,607.0960,214.6570,24.7090,85.9980,0.23625098 1344,544.6020,205.8350,28.1700,75.8400,0.16796729 1344,129.8100,215.7810,24.2070,54.5050,0.16686092 1344,617.5600,217.9470,21.9280,77.1530,0.08414605 1344,139.9450,219.3990,24.8990,48.5030,0.01903616 1344,390.0890,218.6100,28.1580,52.6730,0.01453443 1344,559.3520,208.1230,26.8370,70.2710,0.01071299 1344,10.7602,222.8230,30.8958,62.6980,0.01064028 1344,506.7810,206.7080,21.1620,48.7540,0.01048413 1345,136.0850,210.4030,27.4600,64.6270,0.70764226 1345,157.6910,209.6040,27.3970,64.1940,0.53681237 1345,580.4520,200.7190,28.9650,79.4250,0.37435499 1345,617.7540,237.6950,22.2460,71.1710,0.27574286 1345,57.1932,207.2910,22.0407,42.4670,0.07917213 1345,546.6770,224.1950,25.5600,57.5500,0.02413833 1345,572.5420,203.4720,25.1050,68.0500,0.02170277 1345,53.4533,182.2990,30.8755,63.2770,0.01545694 1345,513.9500,206.1890,22.6560,43.2760,0.01322514 1345,246.2340,233.0860,26.6830,47.8870,0.01103148 1345,208.5630,218.1180,21.7150,35.9700,0.01086729 1346,394.7080,207.0870,29.6230,71.6060,0.72858167 1346,162.4570,213.6040,29.9520,67.9810,0.68444645 1346,37.3603,218.9360,29.9085,61.8280,0.58520520 1346,184.9000,213.6920,29.7900,67.8220,0.47772062 1346,614.3260,209.7750,22.9530,64.8270,0.21589471 1346,348.0600,219.1250,28.0350,56.4130,0.06082535 1346,22.8906,218.2230,29.5804,64.2560,0.04592204 1346,580.7890,227.1070,26.7020,59.7110,0.02977232 1346,510.9710,198.8710,57.3650,54.8690,0.02547073 1346,8.4818,216.7710,30.6616,75.4410,0.02475963 1346,602.6770,207.4670,26.0800,62.6570,0.02216701 1346,357.3520,209.3710,30.6230,62.6250,0.01527842 1346,476.9710,232.0570,27.8790,47.3020,0.01495023 1346,482.3340,202.0160,124.3930,54.5090,0.01321436 1346,527.3800,206.2260,23.6850,35.9240,0.01313260 1346,553.9920,205.1750,23.9200,44.4170,0.01033487 1347,186.7280,217.0320,29.7270,74.8590,0.84699625 1347,57.5864,220.1920,32.8047,68.8590,0.83632267 1347,379.6810,212.7470,29.0590,66.1730,0.70323366 1347,211.9230,219.1170,28.6660,71.5430,0.62042457 1347,25.1624,219.3010,29.6708,63.1620,0.55416435 1347,85.7731,182.4020,27.2989,62.5780,0.06522411 1347,534.3440,210.8420,25.2670,50.1150,0.06330366 1347,577.1570,208.2840,22.5720,44.5260,0.04432409 1347,544.4830,212.1980,25.3990,47.5230,0.03425700 1347,43.2551,217.6450,31.9691,66.4660,0.02422721 1347,36.7790,216.1330,25.1901,55.8780,0.01826831 1347,97.6934,208.4920,20.7576,37.6730,0.01546725 1347,365.8720,210.9250,29.1420,65.8590,0.01377315 1347,518.8400,206.3980,83.9360,55.4100,0.01202461 1347,357.8740,221.2570,27.2540,62.1360,0.01192448 1348,83.4750,220.0690,32.6770,71.8330,0.86978805 1348,199.5260,217.6490,28.8640,72.8640,0.85480195 1348,366.1410,210.4150,29.1150,76.2160,0.63706917 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1400,538.7130,209.2190,48.2090,66.5190,0.01113121 1401,417.3360,221.3860,22.0850,49.1940,0.72334808 1401,588.2110,217.1150,28.1180,64.1910,0.70692676 1401,92.5704,210.1930,29.3816,62.1890,0.46899226 1401,376.4640,200.6310,22.4180,49.0720,0.09352507 1401,505.9060,220.1760,21.5190,41.2110,0.04434191 1401,470.0670,220.5280,23.6850,43.2830,0.03820033 1401,490.5990,212.8400,49.3990,52.8540,0.02263871 1401,407.1480,220.8310,19.9970,40.9350,0.01618461 1402,24.5895,214.1490,37.5746,88.9910,0.76709783 1402,414.3820,222.7630,23.0370,50.8240,0.76023728 1402,65.3668,226.4640,25.4811,55.4050,0.43416327 1402,362.7110,202.1210,23.2220,49.0950,0.07714778 1402,518.4110,209.9690,33.1730,57.7600,0.02554620 1402,56.7417,212.2500,43.0365,78.7310,0.01953746 1402,41.1594,215.8460,31.5241,77.5430,0.01844960 1402,505.1060,216.5420,23.4140,51.4160,0.01568263 1402,619.8030,210.5500,20.1970,76.6220,0.01367045 1402,584.6790,183.7380,35.1560,88.0070,0.01164709 1403,445.5360,221.2390,26.5770,64.1900,0.85340989 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1410,550.2530,201.3910,23.7660,57.9310,0.09410612 1410,538.8690,205.7720,43.2860,75.0180,0.04718172 1410,513.5010,219.9420,27.8200,60.7030,0.03340771 1410,558.6390,214.3690,30.9850,76.3650,0.02649913 1411,607.9510,200.8210,25.4930,66.3520,0.66408515 1411,580.3780,206.7330,32.7210,96.5160,0.47835007 1411,568.7920,203.6750,29.4440,93.0830,0.37091511 1411,499.5750,213.4830,28.3530,65.8150,0.14501403 1411,541.2550,213.9500,29.5860,74.3390,0.10974910 1411,595.9950,204.0040,30.8260,90.6360,0.10742433 1411,521.1570,206.4160,33.4160,74.7280,0.07474475 1411,532.9920,201.6110,21.1550,52.2110,0.07196540 1411,512.3890,213.4410,21.7350,53.8660,0.04055630 1411,557.7070,203.1370,28.7220,87.1820,0.03838959 1411,492.2680,210.5470,77.1220,73.3420,0.02460695 1411,523.8490,205.8490,20.5000,50.1310,0.02201633 1411,589.2920,199.3790,25.4560,65.0520,0.01393710 1411,575.4150,196.9250,25.3830,58.3340,0.01292792 1411,507.3450,218.5030,33.1370,75.5490,0.01231529 1411,554.0410,201.9040,71.1180,82.7610,0.01220731 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1708,542.7070,131.1380,23.2700,37.8580,0.01810741 1709,378.6610,213.8460,22.2140,40.1810,0.07529704 1709,162.6780,380.3090,29.1770,50.0720,0.04643583 1709,255.4820,372.9710,27.0420,44.1750,0.01259645 1710,381.7450,215.9240,21.9140,38.2200,0.21278104 1710,287.0120,335.7030,24.7480,49.0170,0.02914555 1710,293.4100,224.5860,33.4040,91.6280,0.01144239 1710,260.1020,334.3310,29.2800,45.2410,0.01030285 1711,385.2450,217.3500,21.2450,40.0090,0.21972418 1711,292.8510,314.9940,23.5930,45.0460,0.03156561 1711,75.5194,221.9360,65.6656,54.2230,0.01264106 1711,270.5930,311.5690,27.2160,41.9450,0.01053494 1712,270.8640,297.2290,35.6770,78.5820,0.10573080 1712,310.0280,233.6940,23.7250,43.7150,0.10262831 1712,390.2210,216.8020,23.9570,39.8980,0.09377399 1712,301.4410,299.7640,26.6080,49.1460,0.03153782 1712,91.7426,218.2540,29.3674,55.6870,0.02297298 1712,306.2650,242.7240,31.0950,61.3970,0.01420681 1712,293.2090,263.8730,43.4730,88.8340,0.01326776 1712,559.4210,133.3890,22.3830,36.6950,0.01067956 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1757,482.2220,260.5190,20.0620,36.7610,0.06957279 1757,99.0203,281.1010,33.1507,72.5260,0.03511402 1757,389.8850,240.4630,20.2800,36.9940,0.01746454 1757,203.4860,252.9880,32.6440,67.1310,0.01351196 1757,330.0910,389.4590,25.5770,41.8760,0.01228978 1758,111.1000,278.8960,30.8590,63.8150,0.08516358 1758,229.6580,411.2930,30.6010,54.2470,0.06018963 1758,485.3240,260.8090,19.6910,34.5390,0.04161249 1758,334.0260,402.0470,25.3080,45.7650,0.02410696 1758,67.3222,199.1320,24.6929,39.4760,0.02290650 1758,207.7330,247.2110,24.0310,49.7760,0.01956889 1758,83.0504,212.3980,66.1346,62.4200,0.01503655 1758,343.3400,210.6840,57.4400,106.3350,0.01256760 1758,243.1130,404.2280,27.5870,52.9670,0.01018426 1759,235.9390,407.1370,29.6980,57.4190,0.05562536 1759,119.0000,278.2450,29.1120,59.0550,0.04992125 1759,485.6860,262.6390,20.0700,34.7840,0.04919535 1759,63.2944,203.1430,33.7486,68.1410,0.02825522 1759,335.6220,404.7340,24.8560,43.0720,0.02030651 1759,536.5620,266.0600,21.4910,36.3670,0.01458355 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1776,81.7498,299.4120,39.7902,76.0370,0.12341277 1776,244.9410,382.6790,33.1830,57.6530,0.07599649 1776,413.0760,206.1200,20.8450,36.6190,0.05589653 1776,440.9390,207.9290,19.9930,38.0140,0.04809615 1776,428.4880,295.9980,25.1440,49.0410,0.02239388 1776,349.2110,196.4610,128.5460,53.7660,0.01959020 1776,495.8080,316.9280,29.7740,54.7130,0.01584188 1776,11.3973,223.2730,210.7387,54.3100,0.01452270 1776,385.6630,210.6060,19.1050,34.9000,0.01323811 1777,441.4220,209.0420,19.0380,35.2900,0.08572334 1777,411.5810,208.5780,19.5560,36.9890,0.07583700 1777,255.6630,373.6350,28.4540,52.8950,0.04439521 1777,341.7340,202.5570,147.2750,53.3470,0.02034896 1777,421.5570,299.7250,28.7850,60.7060,0.01786890 1777,379.0980,213.5730,19.1130,31.1470,0.01658767 1777,78.3869,302.6020,37.6461,80.1980,0.01437484 1777,373.9040,207.4110,74.1100,45.1190,0.01235565 1777,490.7150,271.3620,28.6520,47.1950,0.01163206 1778,437.9260,206.7170,19.5040,34.3510,0.09561012 1778,407.3830,202.4220,20.1650,39.8530,0.06610157 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1798,292.4210,199.4600,36.8730,87.8910,0.05711731 1798,330.3700,197.2380,140.7260,54.2280,0.03909893 1798,393.1500,201.9640,24.4530,44.8980,0.03668639 1798,487.2490,198.9610,19.7810,42.8770,0.02590602 1798,127.5340,269.1690,38.9760,91.1890,0.02584091 1798,510.5570,200.1790,22.5260,52.1180,0.02571076 1798,370.2160,205.8270,22.3180,48.7950,0.02387196 1798,377.1110,188.1580,188.9340,66.7340,0.01652656 1798,464.0270,201.1080,126.9370,55.7800,0.01240808 1799,545.9980,200.1170,24.5390,57.3000,0.62516570 1799,352.9360,199.8710,23.3700,49.5330,0.29317141 1799,516.7270,198.1000,24.4730,57.2050,0.22173457 1799,446.2130,199.6240,18.6590,34.7920,0.08457758 1799,457.7750,198.6960,19.0380,36.8400,0.04292260 1799,338.9090,193.6640,131.1060,55.3900,0.03788295 1799,527.3780,199.6690,23.5000,55.0970,0.02435406 1799,64.6350,308.3800,32.1636,71.6150,0.02122935 1799,375.2040,195.6800,33.5520,57.7640,0.02120535 1799,375.8150,183.7940,191.5870,66.3000,0.01806105 1799,531.3620,191.2210,47.4690,73.4120,0.01709277 1799,507.8930,196.9620,23.1330,48.8920,0.01565302 1799,358.6140,198.2470,35.4800,56.4770,0.01366676 1799,399.4830,194.8480,23.7670,43.7410,0.01271880 1799,468.4360,182.0600,132.0640,71.8400,0.01048257 1799,384.2620,194.5180,46.8840,52.2420,0.01027475 1799,441.1640,193.3690,35.5130,49.2230,0.01025588 1800,556.1290,208.9190,23.4360,53.6810,0.35159227 1800,359.9160,206.7240,23.8670,49.3140,0.26941127 1800,545.1540,207.2430,22.5590,54.2390,0.22340274 1800,505.5220,196.8240,23.1920,53.8390,0.18273386 1800,469.2620,205.6150,19.3170,37.1480,0.12838274 1800,454.6580,204.2930,19.1210,35.5420,0.12108776 1800,124.0970,285.3030,34.5680,80.5920,0.06434493 1800,342.2740,199.7660,134.5760,55.8390,0.02881414 1800,534.5440,204.1960,24.0440,55.5910,0.02237223 1800,373.6340,186.8070,190.0420,61.8920,0.02206901 1800,538.3010,197.6110,48.2730,63.0800,0.01484712 1800,406.8040,199.9070,29.8970,52.4770,0.01025081 1801,460.8830,211.0630,21.5480,40.7430,0.25796762 1801,362.7700,216.3540,23.4700,47.6720,0.25595966 1801,481.8470,209.0880,20.8770,45.0860,0.24491975 1801,555.3560,213.0600,27.0000,61.9510,0.22455144 1801,518.3250,206.6540,19.4230,38.6400,0.15056682 1801,527.9550,208.7410,60.6120,57.9080,0.06265990 1801,533.8500,208.2830,19.5230,41.0900,0.04367823 1801,130.2380,293.2530,34.2980,81.3590,0.02696557 1801,548.2450,210.8360,23.1400,52.7390,0.02536189 1801,567.1750,214.7600,23.2760,53.9070,0.02334825 1801,462.0080,205.0700,154.1750,51.1400,0.02108354 1801,75.1021,321.9490,35.3749,72.5990,0.01805469 1801,425.5330,208.5400,19.8050,37.5710,0.01655143 1801,537.3980,206.6110,26.0680,55.7510,0.01370636 1801,363.9570,207.9740,143.5500,56.3560,0.01112736 1802,564.7570,214.3520,25.1670,59.8790,0.35978335 1802,497.5070,209.3200,20.3860,44.8660,0.32937914 1802,574.6960,213.3190,26.2960,65.6210,0.27291664 1802,365.4140,215.0110,23.7740,48.0530,0.24187818 1802,469.1560,208.5270,24.9200,48.6280,0.18884180 1802,128.2200,295.8270,32.8430,78.3470,0.11705370 1802,529.8510,207.2950,20.8860,40.7000,0.10883474 1802,539.9880,206.9140,19.6820,41.6260,0.05549723 1802,518.9030,205.4780,86.2460,52.8760,0.03259417 1802,557.6780,210.7460,21.9830,48.5080,0.02554544 1802,582.8670,227.0700,24.5360,59.9870,0.01928558 1802,378.3940,206.6760,219.4230,60.4730,0.01733308 1802,396.4530,212.0720,29.7580,61.1860,0.01209861 1802,422.8560,209.0940,24.7640,50.8620,0.01011740 1803,571.4070,202.9830,28.4070,64.4990,0.61095959 1803,607.4310,203.6190,29.0540,87.8370,0.51605278 1803,363.1780,202.2890,24.6900,54.9380,0.42677337 1803,505.0670,199.3310,22.4600,47.0800,0.40449831 1803,537.7680,193.8030,22.1150,48.2600,0.26404005 1803,474.0450,198.9300,23.6120,51.1530,0.24725522 1803,409.6930,199.6730,27.0380,66.9240,0.18468753 1803,102.8840,290.6800,36.0680,92.8840,0.16665930 1803,564.4180,201.3950,23.8000,55.1490,0.07840215 1803,547.0870,193.7960,20.6380,45.9660,0.07110605 1803,583.3010,201.8080,28.7870,70.2690,0.03477277 1803,421.7480,197.6920,26.6250,64.7410,0.03035152 1803,553.8410,197.4690,22.4010,47.4450,0.03019189 1803,433.1400,198.2490,23.4960,56.7050,0.02932011 1803,548.7050,198.9210,68.4310,69.4030,0.01381922 1803,195.4380,216.8640,29.1390,64.1360,0.01189296 1803,396.2580,199.2370,64.5360,72.5920,0.01122025 1803,109.0150,199.6200,148.7190,53.5160,0.01089172 1804,364.5840,207.0700,25.0680,58.0460,0.71302271 1804,577.4830,206.1640,28.0080,67.8640,0.43594283 1804,548.2320,196.2180,21.5550,50.9190,0.41085482 1804,519.7850,201.7090,22.1920,49.6570,0.31813425 1804,480.6030,203.4280,21.3360,47.6750,0.26410070 1804,569.1720,205.5150,25.0390,57.9610,0.16182448 1804,600.4940,203.6410,24.2640,55.1740,0.11178627 1804,564.9970,197.0570,20.3810,49.2170,0.07748687 1804,70.2010,306.8300,39.2770,103.9160,0.07652457 1804,407.3340,206.4820,27.1260,64.4620,0.04163857 1804,470.2720,209.2510,20.5250,40.1330,0.02353280 1804,548.6990,202.2270,74.6140,67.8530,0.01961466 1804,551.8000,200.0070,37.8780,59.0570,0.01561341 1804,433.6820,202.6310,21.1840,37.6520,0.01543865 1804,589.8740,201.5840,28.0940,68.7650,0.01457889 1804,454.7180,189.4510,168.1680,62.1970,0.01127475 1804,401.9620,200.8850,124.1930,58.7530,0.01090893 1804,12.5372,215.1760,213.2178,57.6940,0.01019879 1805,367.9850,206.2450,27.5290,63.8300,0.77419537 1805,583.9650,200.2140,29.1390,71.0260,0.65249193 1805,541.2470,195.5030,23.9450,54.5220,0.59366220 1805,565.0930,193.5530,22.1950,53.4480,0.31056932 1805,551.2220,191.4950,26.1640,58.0510,0.13487050 1805,489.2610,199.7600,24.1620,53.4380,0.11543804 1805,402.3790,211.1610,28.3740,70.9030,0.10605077 1805,606.2220,201.4640,26.2360,67.9000,0.10214076 1805,442.0660,197.2710,21.2180,47.7970,0.05552414 1805,575.3690,192.7430,28.8390,65.0990,0.05456968 1805,348.1710,204.1270,19.1700,37.4200,0.01109270 1806,411.5330,203.4560,32.0580,77.7410,0.81790262 1806,378.3990,204.0270,29.3050,70.5200,0.74129707 1806,601.0860,192.1820,31.8160,81.6260,0.66561335 1806,576.9030,194.6320,22.0790,53.1140,0.37467074 1806,514.9500,200.1030,21.7540,46.5580,0.33644336 1806,594.9240,194.6790,24.5310,66.6930,0.16730744 1806,390.0450,202.2440,36.5930,79.6510,0.05455717 1806,492.6480,210.5850,21.2160,36.6150,0.05311488 1806,583.8460,195.6400,25.5570,60.7870,0.02480030 1806,413.7170,237.1460,23.7670,44.4110,0.01939053 1806,1.1130,328.3120,36.1100,108.1430,0.01554545 1806,560.8150,178.7940,79.1850,86.7140,0.01181003 1806,507.6940,195.5860,42.2110,59.2610,0.01140847 1806,392.5120,191.8830,64.3980,99.6590,0.01099791 1806,53.3180,199.0010,193.8970,60.7230,0.01041000 1806,225.3220,198.2490,23.8710,49.8520,0.01034836 1807,422.2330,202.6080,30.8010,78.1240,0.80695367 1807,388.7510,198.2210,31.6850,77.9950,0.77397442 1807,540.6810,192.5610,25.8280,58.1050,0.27617660 1807,507.4770,208.5100,24.0920,39.9150,0.06299094 1807,401.9260,201.6280,32.1140,72.5710,0.05821882 1807,610.4740,185.2950,26.0420,71.2660,0.04599541 1807,384.7920,187.1500,59.9330,102.3940,0.02028269 1807,382.3530,197.5090,24.6950,59.9190,0.01951045 1807,39.3313,215.6910,34.5908,65.4170,0.01290721 1807,53.7968,197.8150,203.0902,57.0130,0.01196644 1807,434.1210,197.5240,33.4800,79.8820,0.01128608 1808,434.7410,203.4060,33.2240,86.8890,0.78293329 1808,404.0310,200.9540,33.8120,86.5840,0.68586934 1808,570.4260,196.0000,26.0440,56.9640,0.56603765 1808,618.2570,201.7440,21.4890,60.3940,0.33821931 1808,418.0470,202.3460,32.8880,81.0350,0.10369134 1808,524.2560,212.4330,22.1980,39.9410,0.02073573 1808,10.5670,213.7660,210.6740,58.7820,0.01677934 1808,398.2190,202.3040,26.5110,64.5260,0.01484484 1808,254.3060,439.6770,29.2150,57.5990,0.01030552 1808,386.0140,200.6900,21.4220,50.4870,0.01018082 1809,463.3070,202.6280,39.6550,106.9600,0.78023422 1809,424.1420,206.0080,38.6960,95.5600,0.76749694 1809,589.9440,197.3590,28.0390,62.4030,0.21557638 1809,539.2340,257.9110,25.6050,58.2540,0.04386406 1809,395.0570,204.7360,24.4870,56.6050,0.02350823 1809,50.5946,211.9470,221.2254,54.9350,0.01626910 1809,418.6690,208.9540,26.9170,74.4350,0.01623330 1809,466.5440,241.7570,27.7240,63.4110,0.01499698 1809,314.1730,261.2170,42.4990,95.5500,0.01042197 1810,498.5850,200.2980,44.6480,114.9390,0.82879990 1810,446.0160,202.5470,40.8210,110.6150,0.81587672 1810,616.7710,196.9970,22.5390,59.3150,0.43258914 1810,407.9740,199.8760,28.7350,73.1170,0.12232168 1810,440.1100,213.0250,32.1010,79.5530,0.11052688 1810,435.0940,207.3090,23.2550,62.4070,0.07893608 1810,377.6080,193.5290,23.7170,49.5460,0.02614058 1810,306.2140,255.0200,48.2610,101.3750,0.02366767 1810,113.4610,208.9120,30.8670,49.4700,0.02309824 1810,504.7450,245.2260,30.7530,73.3120,0.01901767 1810,613.7390,205.8810,26.2610,104.8380,0.01411218 1811,470.1550,199.0310,51.2950,129.3340,0.83998740 1811,546.2920,198.9110,48.7400,129.7050,0.66223907 1811,453.8260,191.8890,28.3260,84.0310,0.07253398 1811,462.8450,198.8490,34.1700,88.3880,0.04886872 1811,550.8280,246.7230,33.4650,89.9490,0.02329142 1811,425.7750,195.4680,30.1280,79.0970,0.02044185 1811,517.2050,185.6710,68.3860,125.4900,0.01629574 1811,102.8910,210.5170,33.0980,58.8640,0.01464294 1811,501.8490,190.3700,51.4890,118.8420,0.01243699 1811,481.5140,254.3380,27.7670,70.0990,0.01052757 1811,555.3160,196.2950,33.5310,82.8200,0.01048915 1812,497.8040,199.8170,56.3010,147.3940,0.85848302 1812,593.7410,197.1370,45.7770,162.8790,0.66186094 1812,497.4120,201.6610,35.6910,96.8590,0.08347859 1812,445.7660,202.5080,26.2160,68.3900,0.05227596 1812,522.7040,210.9990,30.8810,80.9830,0.04106413 1812,18.8027,216.3860,198.0003,64.6070,0.02694486 1812,383.7450,196.8950,21.2080,41.9120,0.02017989 1812,68.0814,311.0720,37.6236,88.5200,0.01411505 1812,598.6780,272.5980,32.5190,87.3350,0.01056018 1812,473.8670,196.2210,33.9710,92.4910,0.01026948 1813,550.1480,208.5240,61.9600,164.7560,0.59097147 1813,540.4930,198.6060,49.9220,127.6930,0.08498780 1813,500.3160,225.1770,36.1120,92.8140,0.06192894 1813,62.3627,221.2290,208.8353,55.0560,0.03647126 1813,569.8210,259.1940,38.2360,113.0100,0.02409621 1813,118.3420,212.9580,70.7510,49.6250,0.02300019 1813,568.6410,212.3300,36.9170,87.2790,0.01453572 1813,568.9000,184.3910,61.5520,147.8550,0.01423953 1813,434.6660,2.0843,31.7910,71.2447,0.01041423 1814,544.4590,259.2620,32.0820,79.3270,0.03649145 1814,6.5698,216.7940,209.5622,61.6260,0.02727634 1814,390.0730,199.5540,22.8160,45.1860,0.01428003 1814,3.1124,229.2610,357.8816,60.2830,0.01415717 1814,540.1000,212.8880,34.3570,105.8170,0.01117144 1814,500.6230,239.5910,43.4720,104.6300,0.01090760 1814,46.3477,245.6860,30.6542,53.2380,0.01013879 1814,105.9330,214.6180,70.4200,51.8690,0.01009975 1815,576.5410,297.4710,34.1840,77.7770,0.06183903 1815,390.1960,192.1520,22.8850,51.7990,0.04868807 1815,168.6290,216.3420,27.7530,55.8890,0.03949546 1815,47.2850,225.7200,226.5680,54.7930,0.03308656 1815,612.6470,182.9290,27.3530,130.0820,0.02230035 1815,172.6760,236.7640,27.3340,48.4160,0.01271825 1816,430.1040,201.0750,19.9990,35.3310,0.04218526 1816,6.5559,230.0190,227.5721,54.8900,0.03238293 1816,409.1380,216.3530,26.9780,58.0860,0.01125137 1817,397.4320,199.7950,20.4150,42.0680,0.09652378 1817,433.8560,200.4570,22.9600,46.1620,0.06574871 1817,104.1300,292.2880,25.9360,50.5700,0.03425042 1817,74.7277,218.8470,178.5373,69.2860,0.01651707 1817,131.2990,267.2300,41.7000,87.9850,0.01331095 1818,402.4190,204.3150,20.3580,40.9360,0.24882248 1818,109.0680,252.1170,33.4760,72.2430,0.03404788 1818,444.7860,217.6090,31.1320,73.6710,0.02423424 1818,489.9480,205.8770,20.0440,35.3140,0.02319168 1818,241.3220,210.7650,24.3430,46.6670,0.01304968 1818,97.0807,222.6730,146.3993,79.9300,0.01073909 1819,85.8132,260.6550,40.4238,93.0380,0.15607850 1819,407.7530,204.2520,24.6220,55.5040,0.12167588 1819,451.9360,202.4970,19.7870,36.3260,0.05196290 1819,477.4760,223.2970,30.7390,75.0000,0.03847137 1819,69.3791,238.5480,77.1719,85.9890,0.02102434 1819,58.5948,236.4710,30.1445,49.9590,0.01936412 1819,500.2300,207.7190,18.8910,37.2940,0.01436653 1819,37.5545,220.6410,142.2155,74.8160,0.01334829 1819,403.5250,200.3990,115.2750,58.2150,0.01246203 1820,461.9000,198.8620,21.5350,41.4570,0.15415557 1820,422.4980,199.5120,25.6330,59.4600,0.15389454 1820,22.9004,239.2300,39.6720,63.9000,0.08080730 1820,44.1381,301.5180,37.6327,86.6240,0.03511251 1821,434.0760,203.7440,20.9760,47.7870,0.36294469 1821,473.1320,200.4550,22.1960,41.5940,0.18339889 1821,523.7210,205.9570,19.0320,32.8460,0.03094028 1821,65.9062,214.4270,210.1278,53.6740,0.01404730 1821,441.2120,202.1780,31.4220,54.9820,0.01160784 1822,441.4180,206.7160,22.4730,49.3630,0.43727735 1822,531.8020,208.1280,19.2840,33.8360,0.06132969 1822,457.0020,202.3350,20.1950,45.9780,0.03097367 1822,55.1276,219.4810,206.3704,60.9790,0.02994800 1822,5.5678,232.1110,367.8312,59.4430,0.01769218 1823,448.4450,207.1260,23.6250,51.6860,0.61990553 1823,481.7020,201.2400,21.5860,45.4080,0.22177249 1823,1.0000,227.7680,236.0130,56.3820,0.05561297 1823,538.5720,203.7960,20.1110,38.9230,0.02422820 1823,350.4460,318.8230,26.9770,55.2680,0.01823147 1823,122.0660,222.2430,184.4050,66.2990,0.01519412 1823,93.0465,212.2340,166.8345,51.3700,0.01479416 1823,133.7460,213.8200,35.0590,66.3890,0.01218890 1823,385.8820,205.3390,20.5940,36.2790,0.01122124 1824,452.7210,204.3870,26.1460,56.5440,0.75877672 1824,490.1410,199.7750,21.8420,51.6020,0.56547326 1824,505.5620,198.2660,21.3680,54.6000,0.46496817 1824,100.7592,288.6550,28.8538,57.3820,0.06284955 1824,541.7140,204.1260,20.6680,39.4970,0.05855719 1824,165.0260,238.7450,28.2560,48.4190,0.02933505 1824,211.6020,243.7590,24.5940,49.1330,0.02737821 1824,1.0000,220.2110,217.3460,54.2790,0.02703724 1824,102.5510,205.9720,26.0090,54.6650,0.02288841 1824,14.1793,205.5830,137.3167,53.8990,0.01746747 1824,369.8910,202.9880,21.1790,39.8800,0.01105982 1824,59.4686,202.3630,57.4824,45.4060,0.01040715 1824,430.4270,187.5270,122.2450,71.5300,0.01024668 1825,470.7180,206.7940,26.7650,63.9630,0.71862364 1825,546.3190,195.8300,27.8900,67.6980,0.71475732 1825,507.7980,200.6610,23.5560,55.2430,0.70013642 1825,58.0874,206.3200,36.3015,84.8700,0.14004676 1825,1.0000,222.0930,203.9550,60.2270,0.03823262 1825,362.3750,204.6670,26.0250,44.6800,0.03578217 1825,481.5620,201.7670,28.6460,64.6370,0.02704466 1825,19.0481,315.7940,36.6556,82.6890,0.01888879 1825,8.1160,209.7970,64.2707,50.8110,0.01839794 1825,145.9700,243.4520,31.2060,56.5360,0.01167420 1825,493.5470,200.0360,29.7970,61.7610,0.01140739 1826,511.2220,199.5250,31.7800,80.9160,0.69094837 1826,553.7140,195.5480,25.8880,61.7400,0.63703907 1826,63.1016,206.6580,43.7704,95.7650,0.11375666 1826,29.1157,203.6230,33.2974,68.1350,0.07904723 1826,380.6420,198.4050,27.4640,55.4330,0.05386557 1826,209.2330,217.4660,48.3120,112.6670,0.02594813 1826,584.7840,206.1230,22.6500,38.1910,0.02396309 1826,1.0000,328.6730,29.0443,101.3360,0.02345356 1826,21.4518,210.5620,125.6702,84.3350,0.02068410 1826,172.9010,243.5670,30.5120,57.2540,0.01099855 1826,9.8500,203.0190,74.1442,73.1110,0.01027037 1827,591.2770,197.8650,31.3870,74.6150,0.83060819 1827,544.3310,205.5560,36.6030,89.0780,0.78613973 1827,21.2481,213.7360,45.5062,115.3440,0.48974448 1827,384.9620,205.5840,22.0210,40.2670,0.07094011 1827,8.3693,211.6990,39.7108,100.7910,0.02912041 1827,221.0080,244.1510,28.6760,61.5300,0.02676290 1827,1.0000,347.7980,28.6888,112.4330,0.02262793 1827,2.3020,208.3280,30.7264,77.2010,0.02185161 1827,35.7863,219.3190,28.9316,72.8900,0.01517182 1827,579.4940,180.9160,52.6170,113.8000,0.01469219 1828,584.7250,206.1650,38.6240,101.5740,0.83665949 1828,1.0000,216.1520,35.5705,129.5360,0.31549898 1828,542.2500,208.2260,26.3290,44.9290,0.09202225 1828,190.2190,257.8700,34.9310,82.3140,0.05992319 1828,390.5280,208.2860,21.5660,34.9030,0.04415822 1828,601.1570,218.6460,29.6960,85.3660,0.02700979 1828,181.0020,235.8020,90.9340,87.6800,0.01932125 1828,1.0000,368.2130,26.5334,110.8970,0.01848149 1828,2.1897,224.0640,24.4404,77.0320,0.01821435 1828,133.4370,250.9380,24.9030,54.4680,0.01382902 1828,542.1480,198.0490,42.4270,74.8400,0.01010557 1829,391.7200,203.0260,23.0510,38.4750,0.04320810 1829,1.0000,381.4380,43.6045,130.5620,0.03977730 1829,77.2639,282.1930,27.4961,58.7120,0.02950359 1830,395.1470,203.0710,22.3730,35.1400,0.08077343 1830,56.0028,281.9670,32.0602,72.4440,0.05756306 1830,390.4110,195.5670,35.7630,53.5590,0.01101158 1830,94.6251,261.2730,30.9949,74.2070,0.01012327 1831,395.0530,206.3440,22.0920,39.8000,0.09321380 1831,280.5630,217.1190,29.8960,60.1340,0.02297240 1831,203.2010,259.9010,33.8200,65.2760,0.01975431 1831,570.0220,210.2200,24.9220,43.7280,0.01481705 1831,138.1450,351.6220,46.4770,90.0580,0.01213531 1831,209.6890,221.0940,126.4290,58.9980,0.01072020 1832,236.3170,331.1140,35.9020,81.7820,0.24819008 1832,404.2350,211.0770,23.0230,39.1910,0.13952444 1832,583.5450,219.7350,44.4210,115.3270,0.01063116 1833,421.1700,212.8190,21.6790,37.6810,0.13616966 1833,1.2671,331.3390,31.6627,88.0690,0.01331673 1833,297.2960,308.1120,31.1110,78.5260,0.01280069 1833,526.8150,263.8710,26.8860,48.6390,0.01088719 1834,425.4050,211.0680,21.4510,39.8840,0.12657925 1835,424.6760,212.1820,23.4560,42.6450,0.15655126 1836,434.8970,214.9040,22.3560,35.5060,0.14930899 1837,452.0530,221.6140,22.7600,39.4580,0.16763715 1837,273.5390,234.3830,25.5240,51.4400,0.11711918 1837,448.2230,215.6660,39.2290,54.0960,0.01031953 1838,468.8100,227.8360,22.3860,40.1840,0.13387807 1838,601.3870,243.6920,23.6130,33.7670,0.01457846 1839,480.3200,223.9050,21.8340,44.6730,0.28082216 1839,472.5430,225.5450,20.8030,39.6400,0.01224577 1839,472.8010,217.4260,42.9340,63.9670,0.01059698 1840,499.3040,215.7760,24.3190,48.9600,0.24766533 1840,509.8520,219.2710,21.7840,44.8120,0.01744661 1841,536.1350,202.3870,25.4850,56.1330,0.39011732 1842,585.2280,208.8650,31.3500,70.9440,0.57697898 1842,578.6920,193.8830,52.9550,107.0300,0.01950344 1843,449.4570,221.4340,22.3900,40.0200,0.03262199 1843,553.8560,202.8570,19.3950,29.0020,0.01578143 1844,577.0970,195.4540,19.9010,33.4030,0.07065227 1844,488.2110,214.8740,23.8460,50.0640,0.04959786 1844,447.1210,196.3180,26.1480,58.5170,0.03157455 1844,548.3840,197.9380,19.7900,32.0400,0.01155383 1845,485.8980,199.9950,27.0520,63.7380,0.09895456 1845,547.2990,231.2240,26.5450,56.4390,0.04718385 1845,548.3000,199.7120,33.6670,80.4980,0.02078521 1847,613.1190,222.7310,26.2030,78.8900,0.01352595 1849,547.3550,349.1060,32.7620,83.5090,0.03871145 1849,482.6800,287.5500,34.3420,84.8680,0.01841228 1849,189.3870,136.5860,30.7740,61.9850,0.01298120 1849,106.0340,208.2940,67.1310,50.7310,0.01186007 1850,428.2660,258.3810,23.0680,55.5290,0.02285467 1850,440.8080,242.7810,34.3140,70.4410,0.01026869 1853,67.0675,216.0270,72.7155,49.1380,0.01503986 1854,279.4540,365.5730,27.3430,42.5320,0.01113116 1855,34.7121,336.6670,44.5061,98.6090,0.04539005 1855,280.0030,365.8060,26.8840,41.9810,0.01440643 1855,35.1485,254.6120,27.3064,42.2960,0.01223790 1856,113.7540,289.2470,31.7590,67.0180,0.05291257 1856,279.5850,365.7840,27.5210,42.7160,0.01037626 1857,133.2120,292.1860,26.4530,58.3940,0.01913169 1857,280.5600,366.0840,26.2930,40.3350,0.01140361 1858,140.5980,287.7060,25.2910,57.3300,0.02583935 1858,280.1750,365.9760,26.6300,41.8310,0.01465367 1859,139.6300,288.3400,25.9000,59.4730,0.02571052 1859,280.0640,365.4060,26.3760,41.3210,0.01187351 1860,139.6730,286.6230,26.2290,60.4260,0.02077595 1860,279.6400,365.9770,27.1660,42.3320,0.01522837 1861,139.5130,287.0900,26.0260,60.6180,0.02810078 1861,280.2400,365.7820,26.4600,41.8710,0.01242147 1861,130.7820,232.9280,35.8570,77.5390,0.01222978 1862,140.1630,288.1920,24.9540,58.0750,0.03161959 1862,280.3940,365.9590,26.5030,42.6300,0.01338122 1863,139.7710,287.0540,25.6500,59.6130,0.02759018 1864,280.5500,366.3490,26.5900,40.8920,0.01494916 1864,139.8050,286.3200,25.8390,60.2180,0.01418146 1865,139.7050,287.5650,25.4430,59.2400,0.02428988 1865,280.9510,366.0090,25.4150,40.0920,0.01319121 1866,139.6570,287.1850,25.8210,60.1720,0.02078785 1867,139.3950,286.7590,26.1140,61.2510,0.01990434 1867,279.5100,365.4210,27.0740,42.9380,0.01256087 1868,139.6250,287.5790,26.0470,59.1320,0.02656292 1868,280.0640,365.2820,26.6300,42.2060,0.01176931 1869,139.8470,287.1540,25.1790,58.4710,0.02764246 1870,139.3890,287.5240,25.8600,60.2010,0.04499237 1870,568.6690,306.7410,24.6590,41.7880,0.01687706 1870,576.3270,268.4330,28.9310,63.4280,0.01227498 1871,449.6240,244.5940,23.1120,46.6220,0.03776743 1871,138.4910,286.0150,27.5430,59.9750,0.03464074 1871,280.3800,365.2300,26.2160,41.8430,0.01420514 1873,139.5420,287.4020,26.2330,60.1660,0.02390584 1873,279.4310,364.6890,26.6640,42.7700,0.01041733 1874,485.6030,225.3160,37.5180,77.1710,0.26477998 1874,139.0420,285.8430,26.2830,58.4370,0.01645563 1875,139.0040,288.1470,26.3710,59.8270,0.05721879 1875,280.3450,365.4460,26.4790,42.2490,0.01131408 1876,613.6120,208.9720,26.2030,70.3970,0.62340283 1876,139.6120,286.5030,25.8470,60.8880,0.01358803 1877,440.8950,279.1620,31.4060,79.3220,0.13638005 1877,139.5000,287.4000,25.9750,59.3670,0.03659361 1877,512.9910,297.0890,26.6290,51.2090,0.02233388 1877,279.6450,365.5080,26.9520,42.2620,0.01338593 1878,585.4030,205.8120,28.8910,68.4560,0.60326487 1878,139.7600,287.2880,25.7420,59.3420,0.02093668 1878,576.8320,194.1580,56.6800,105.3450,0.01508855 1878,93.6679,318.3930,29.4531,63.2530,0.01086699 1878,110.1750,221.5740,74.1940,85.7090,0.01044070 1879,570.6040,205.8930,29.0270,67.9720,0.60651678 1879,496.3120,298.5690,34.1550,78.9310,0.04027073 1879,133.4110,220.9350,43.5520,107.8710,0.02219599 1879,139.4820,285.2660,27.2700,61.6990,0.01735797 1879,539.9780,318.4750,22.4570,35.0170,0.01725912 1880,560.2840,206.7370,28.4750,67.1610,0.60158378 1880,104.5190,315.5260,29.3430,63.1500,0.05691501 1880,142.4010,252.3640,30.6140,67.2740,0.01870195 1880,572.2810,209.0230,23.3640,57.8260,0.01628020 1880,143.8990,279.0000,26.7940,62.4830,0.01147060 1881,551.1930,206.2210,28.9110,64.3740,0.67114347 1881,160.6820,263.9490,30.4060,68.3740,0.01119872 1881,565.0110,209.1920,23.2700,55.2450,0.01069937 1882,543.7350,207.2000,25.4080,57.4310,0.59744596 1882,189.6730,247.6930,26.7510,56.9180,0.09246431 1882,357.5930,198.4850,25.8380,49.7230,0.01040518 1883,534.6620,208.9120,28.3520,61.4810,0.44950390 1883,212.1660,244.2940,24.5520,45.1840,0.03028110 1883,49.8263,247.6320,25.9641,51.8530,0.01228852 1884,534.7140,216.2300,26.3100,59.0090,0.42546412 1884,528.4010,210.6460,46.8790,79.5440,0.01349198 1884,225.5670,226.6180,28.4030,51.4670,0.01347222 1885,540.1290,208.6070,27.6990,61.8230,0.55178285 1885,90.2067,245.0330,24.7133,42.0120,0.01894909 1886,583.4330,208.8850,26.0610,61.6680,0.47221628 1886,418.2640,205.3400,26.7760,53.1770,0.04954209 1886,518.1040,317.3210,40.9350,97.0040,0.02356274 1886,80.8055,297.2380,37.3635,89.4470,0.01770191 1886,115.3220,240.3390,22.3700,41.6350,0.01627865 1887,422.3010,275.8100,34.4200,95.2070,0.28516555 1887,481.3280,319.5280,35.6410,78.0360,0.04122062 1887,410.7830,247.7170,59.7020,102.7530,0.01059408 1888,356.0110,276.2180,30.6470,75.5420,0.16979316 1888,129.6280,250.3150,24.4060,38.9950,0.01791402 1889,309.8740,233.3510,27.9660,69.5720,0.06258621 1889,312.8120,225.6740,42.0080,98.9560,0.03149286 1889,67.4387,215.0080,75.9243,48.8760,0.01524729 1889,449.2510,154.2210,23.8510,50.8490,0.01347719 1889,18.5178,227.7300,204.2422,59.1570,0.01124569 1890,278.7560,240.6640,27.1620,50.2190,0.02005162 1891,373.7470,258.6490,31.8300,85.2460,0.03193343 1891,486.1370,224.0160,19.9280,38.2660,0.02787386 1892,336.6910,224.5240,35.9640,91.2090,0.07048921 1892,348.9070,252.0490,31.9520,93.5250,0.03726174 1893,309.7150,234.7730,26.3110,54.7750,0.04982291 1893,207.1030,214.6370,31.8340,78.1770,0.02438402 1893,367.4810,200.2910,22.5760,40.8210,0.01758705 1893,534.9050,226.3030,22.2330,36.4890,0.01224738 1894,432.7950,205.9460,20.5270,37.6700,0.03853219 1894,570.1310,232.4760,23.2870,39.8600,0.01267891 1895,148.2240,236.5430,30.3800,65.4630,0.17678155 1895,232.3780,234.3740,31.0770,73.2950,0.04032601 1895,232.7240,216.7870,26.1710,60.2040,0.02625188 1895,31.4535,238.9150,31.5809,65.8740,0.01056491 1896,388.7680,208.7330,18.7920,32.0960,0.03556654 1896,135.4140,236.9740,26.9300,61.1960,0.02706891 1896,307.2910,238.5060,25.0290,40.3410,0.01765322 1896,191.3840,229.1390,204.7910,55.2650,0.01303689 1896,519.0710,128.6990,25.2950,52.0870,0.01171241 1896,98.8980,240.4810,25.9300,43.0230,0.01016586 1897,255.9740,202.9520,23.8070,46.9540,0.02834690 1897,236.1280,225.1830,135.7430,55.2270,0.02690880 1897,443.4490,208.5420,20.7790,37.6940,0.01953416 1897,395.3420,205.6990,18.5440,30.4000,0.01744201 1897,68.8102,227.1220,232.4498,55.5840,0.01521631 1897,289.5100,233.3910,44.6960,48.1810,0.01487619 1898,520.8110,214.4600,20.1990,36.4240,0.03802097 1898,460.5530,207.7750,19.8340,33.1570,0.02537059 1898,194.5320,216.5750,22.5010,45.7520,0.01279030 1898,555.0290,112.1640,24.7200,56.1470,0.01130458 1898,297.0720,232.7570,38.2510,56.0630,0.01104500 1899,476.2830,210.9590,20.5750,35.7740,0.03360528 1899,540.5290,215.8390,23.4510,34.3940,0.02262416 1899,212.0690,240.3900,22.4900,44.1910,0.01229604 1899,148.4320,281.6680,32.5390,75.4440,0.01130877 1900,496.5770,217.6710,20.1800,35.2810,0.08326488 1900,570.4550,218.2520,20.5870,37.0340,0.06072159 1900,1.4007,225.0380,204.3393,62.5080,0.01388575 1900,600.9650,106.0990,27.4300,62.6280,0.01042842 1901,602.9090,220.5260,21.8010,43.5460,0.15329635 1901,516.6230,217.2200,21.0920,33.3350,0.02493306 1902,544.0130,220.9720,22.4820,37.3280,0.04681870 1902,42.7653,232.5270,250.1627,55.5980,0.02486248 1902,535.9490,214.5500,36.6330,56.8880,0.01138232 1903,187.0050,220.5260,30.4150,64.0960,0.02925096 1903,564.6650,218.1160,23.9670,40.8020,0.02639090 1903,8.0613,229.0960,221.2757,60.7420,0.01947466 1903,9.9791,213.6630,54.4769,64.8870,0.01214561 1904,109.8300,227.7320,27.8080,47.2770,0.02502248 1904,184.8760,219.5010,28.2310,57.7310,0.01494123 1904,587.0270,224.6230,24.9270,46.6980,0.01155894 1904,73.9356,221.8680,194.5084,58.4000,0.01086767 1906,189.9640,210.3600,28.8470,66.4550,0.02757978 1906,66.1542,221.9910,198.9998,58.2740,0.01400118 1907,72.7720,226.4920,32.3310,60.0270,0.06614628 1908,191.6040,216.7830,28.9060,61.4950,0.02617772 1908,124.5920,231.1730,30.1500,67.8640,0.01156129 1909,197.0150,213.6370,27.3590,62.1970,0.02556049 1910,192.5740,229.6930,22.7780,47.6150,0.04056287 1911,1.0000,226.3560,228.9180,54.3120,0.01738333 1911,109.3150,241.7000,25.5940,52.1160,0.01605595 1911,566.2190,331.1050,36.1520,90.0460,0.01153207 1911,176.9740,220.5350,26.4140,52.8760,0.01074544 1912,442.7210,274.2510,26.8860,53.8780,0.07941933 1912,482.9760,304.6110,25.8300,53.5890,0.01387118 1913,482.4190,276.6720,33.8230,81.3620,0.12016390 1913,90.3887,241.3490,24.8813,52.0210,0.01662882 1913,432.1870,273.0070,22.5150,41.6960,0.01247128 1914,451.2040,281.5520,23.6570,48.6400,0.07336859 1914,81.8388,230.3440,28.3822,68.9820,0.02982938 1914,481.1250,306.4300,24.3600,50.0630,0.02592299 1914,418.6590,260.7660,20.6470,38.3270,0.02233209 1914,598.0860,320.8690,38.7290,109.3750,0.02096141 1914,364.0540,241.8650,20.8350,37.3300,0.01344372 1914,146.7610,110.2910,41.5370,85.2230,0.01307687 1914,360.1030,198.4390,34.7580,55.9340,0.01051578 1915,68.1852,234.6460,29.3752,64.7480,0.06373284 1915,370.0000,202.1670,38.5450,57.7800,0.02821916 1915,404.0060,262.3170,20.9210,37.3180,0.01666725 1915,471.0390,209.7040,35.7270,56.0220,0.01635300 1915,596.9930,350.9450,34.6380,76.0280,0.01320546 1915,143.3190,225.4530,32.5910,58.8000,0.01179480 1915,270.8790,211.5130,25.9770,46.7380,0.01178800 1915,476.0380,222.4320,22.1170,38.3420,0.01148793 1915,534.0850,284.3030,37.9220,100.5730,0.01078119 1915,167.1760,240.9620,25.9190,59.9550,0.01007906 1916,158.6280,244.3920,22.3880,50.7170,0.02065656 1916,390.4930,202.6970,35.1440,58.4210,0.01653101 1916,49.8231,238.7640,26.5303,58.7550,0.01288553 1917,137.2250,241.6620,26.4340,57.6320,0.10779415 1917,19.1141,228.5610,35.7088,78.5510,0.01959966 1917,417.1500,198.8140,37.8530,64.3360,0.01529777 1918,464.4580,200.1700,37.1490,83.5960,0.11389767 1918,119.2330,247.7750,25.4960,55.4650,0.07736619 1918,442.7390,216.2850,36.0810,81.1810,0.01070659 1919,98.8173,239.1590,24.9507,54.4210,0.20783092 1919,515.9470,204.8290,36.8040,71.5710,0.01424723 1919,58.8866,210.4890,32.0844,63.9850,0.01226960 1920,79.9364,248.7310,26.2126,53.0610,0.12732753 1920,267.6630,211.0690,29.0110,51.5900,0.01235423 1920,49.8802,265.4290,24.7125,48.2260,0.01233142 1921,72.3880,242.5370,25.1054,52.8570,0.12087303 1921,273.5760,206.6510,25.0990,41.9990,0.01537689 1922,76.3674,239.9150,24.8336,56.0390,0.10947724 1922,42.1147,214.6940,28.3013,47.9990,0.01310189 1922,482.4560,222.2980,26.8520,48.9420,0.01172752 1922,271.9790,197.6230,26.1510,43.6400,0.01024517 1923,84.2371,227.0440,25.8419,55.7520,0.06756721 1923,561.5140,209.6020,33.9980,74.3240,0.04367869 1924,383.8200,204.1570,22.4430,41.9260,0.01353563 1925,230.0560,194.9680,25.9490,60.9100,0.02589242 1925,603.6000,302.1190,32.5890,75.1000,0.01756585 1925,376.8230,204.1280,21.2600,33.4970,0.01437146 1926,157.5860,223.6400,32.5770,71.8340,0.01744888 1926,102.1870,214.3490,68.0680,51.8840,0.01303938 1927,136.5930,225.9840,34.3880,51.2960,0.01075794 1928,72.7436,216.9370,68.3234,52.9210,0.01444044 1932,387.9900,243.0750,28.9870,68.1490,0.26185870 1932,388.1210,208.3320,29.8690,72.8990,0.02830630 1933,376.2570,228.8240,37.0600,88.2100,0.81432581 1933,17.8130,222.9400,178.8600,67.6170,0.01790117 1933,385.1410,210.5840,26.1390,53.5740,0.01728553 1933,49.9397,256.7050,26.8584,37.5890,0.01363846 1934,376.7100,228.9790,35.3280,86.5330,0.80738515 1934,522.5650,201.8910,35.8080,98.2040,0.01526795 1934,493.5440,210.5290,32.5900,77.6900,0.01496733 1934,65.9909,225.7710,199.5691,60.6940,0.01460626 1935,377.0200,225.2710,36.3970,87.3530,0.79714888 1935,65.6252,224.6220,201.7088,59.3660,0.02969341 1935,134.9050,218.8420,64.9380,52.0950,0.02038773 1936,397.4640,210.1280,40.1150,100.4420,0.75821114 1936,211.8710,215.7910,33.2920,67.7500,0.01611454 1936,165.1280,213.5480,71.3850,49.0460,0.01106886 1936,80.8454,221.8670,189.9076,61.3600,0.01098135 1937,430.2240,214.3690,43.5550,117.1960,0.78977662 1937,453.8550,213.9740,57.3920,124.7820,0.01758097 1937,410.1870,231.8420,36.9420,85.5850,0.01107055 1938,478.1230,218.2860,55.9070,139.0110,0.75093693 1938,293.0100,245.5190,37.3530,56.7600,0.01896805 1938,416.6600,208.4590,23.7830,38.5090,0.01848630 1938,484.9360,219.4990,36.9890,89.9710,0.01618976 1938,498.9960,214.3990,64.7420,148.8500,0.01471534 1938,116.2200,223.2690,67.8520,51.7450,0.01385727 1939,152.1500,210.5390,71.7370,47.6660,0.02119723 1939,435.1530,206.0800,25.7350,42.8430,0.01979636 1939,311.2080,242.6800,35.7740,66.9750,0.01192000 1940,442.0530,205.4860,22.3400,35.7700,0.08425599 1940,306.9290,240.8880,36.5890,61.6880,0.05807978 1941,456.5950,210.1020,25.0230,40.8970,0.02750236 1942,480.7280,204.6640,40.0850,59.2410,0.03618914 1942,483.2150,207.3940,24.0380,41.8620,0.01677271 1943,519.1360,201.5820,24.8060,44.4260,0.02692076 1943,517.3440,194.6640,40.9910,66.0820,0.01612713 1943,403.9000,212.7820,23.4020,37.3350,0.01373322 1944,412.7000,206.3470,22.0280,38.3370,0.01986265 1944,359.1170,201.6060,93.5210,53.4300,0.01618072 1944,384.7930,204.3550,23.1450,36.8000,0.01271301 1944,402.4560,202.5450,40.4630,52.1050,0.01021543 1945,435.5850,197.2510,21.9250,43.1190,0.12677228 1945,445.2450,198.0880,22.4420,43.5770,0.03656900 1945,398.0930,175.4170,26.9390,58.2320,0.01886021 1945,379.6050,193.5600,109.6020,61.8750,0.01861211 1945,423.5730,193.7160,41.6650,55.1490,0.01777890 1946,474.1000,214.3620,21.6120,46.1430,0.16785331 1946,406.9490,169.7610,36.4130,85.0620,0.03771639 1946,482.2300,213.5280,25.3620,54.7560,0.02813244 1946,465.1310,217.3950,22.0580,45.2730,0.01836923 1946,395.6710,207.2190,20.7320,38.4380,0.01771156 1946,400.6610,196.0830,25.3980,51.4420,0.01087156 1947,523.5510,208.5060,27.3760,58.9520,0.26869226 1947,296.7260,199.2170,22.2040,45.1100,0.05846702 1947,424.6210,200.7980,35.2900,52.7670,0.04906236 1947,500.1090,199.0870,71.0570,93.2590,0.01996792 1948,455.0070,212.1830,21.8630,42.7520,0.07491275 1948,471.4010,210.1860,24.8710,45.5710,0.07236046 1948,427.8670,206.9050,84.4260,55.9090,0.04156959 1948,431.7150,209.8250,23.0170,41.0450,0.02560567 1949,499.3720,205.9400,27.2880,58.2250,0.40019667 1949,521.7720,202.3170,33.3630,75.3580,0.33955154 1949,510.1870,204.2390,28.3010,62.8670,0.06002730 1949,456.3060,183.3360,32.5210,71.7660,0.03067368 1949,503.8060,191.2350,66.3090,96.3750,0.01721411 1950,579.4910,204.4960,30.5580,71.1140,0.74935842 1950,606.4500,199.5580,30.4970,74.7730,0.47353071 1950,495.8810,207.7700,23.3450,43.9010,0.12403744 1951,538.2230,204.5330,32.1930,63.0350,0.23427123 1951,336.4140,198.6030,20.1220,37.2130,0.01872352 1951,359.9520,198.2730,24.4790,43.4990,0.01289582 1952,340.3700,210.7370,19.2230,32.8150,0.06305720 1952,208.8290,222.0790,22.2600,39.9370,0.01006413 1953,356.7880,216.1900,21.2230,34.3820,0.15128569 1954,387.2260,217.0850,19.1860,36.3770,0.21596451 1954,202.8770,220.7920,21.0180,37.6680,0.01811697 1955,196.7340,213.9710,20.7260,37.2550,0.01259237 1955,423.5410,211.0080,21.0830,37.0900,0.01159612 1955,444.6410,213.9810,21.1140,33.2490,0.01100050 1956,558.8520,220.9840,27.0950,57.9330,0.59689307 1956,393.1330,216.5100,20.8270,34.3600,0.08806659 1956,334.2380,216.6690,19.9620,31.2090,0.03401624 1957,411.2160,201.2070,23.2990,47.5540,0.21685301 1957,528.2210,213.5560,23.4150,42.9140,0.17394193 1957,369.7560,195.8180,22.4530,43.6630,0.02470521 1958,454.7440,226.9850,23.2420,50.1280,0.48071641 1958,440.2110,223.7460,22.9630,51.1570,0.03938514 1959,547.9460,212.2810,31.8590,71.9120,0.79142427 1959,574.5750,206.0730,29.2350,65.7330,0.38290510 1959,399.1840,214.1850,23.3480,48.9890,0.02555948 1959,336.5370,201.5470,23.4380,43.9480,0.01505452 1960,369.7380,208.5460,24.6710,49.9280,0.03209142 1961,329.7820,192.7930,21.8400,41.1370,0.06387813 1962,416.6100,178.6590,27.6010,59.9220,0.02400503 1963,366.5570,215.2870,19.7320,34.5640,0.13187967 1963,497.0940,207.3350,33.4310,75.2850,0.01118271 1963,278.1020,203.5120,24.3100,39.5590,0.01025546 1964,395.6110,216.4030,21.4740,42.0770,0.20405112 1964,354.0100,217.1080,19.7930,35.3890,0.08519839 1964,569.0390,195.3670,24.6400,50.4010,0.01786282 1964,281.7150,209.7350,23.7850,35.4030,0.01006843 1965,384.1730,215.4520,22.3820,45.2460,0.34751594 1965,440.7560,191.7050,33.4450,80.1170,0.04859272 1966,459.5330,222.9250,29.2250,67.3250,0.79111826 1966,569.4340,222.9480,37.3600,92.8590,0.74472725 1966,366.7900,205.3100,24.9500,54.3690,0.01095614 1967,406.0780,199.5370,25.1340,58.0770,0.05213862 1968,459.1380,201.2580,27.3390,58.5210,0.03063125 1968,338.4600,198.8180,24.0150,45.2970,0.02142963 1968,378.1550,1.0000,37.6660,76.3051,0.01154309 1970,368.8940,188.3430,22.4460,53.9790,0.02533644 1970,303.7180,165.4390,25.8220,62.3830,0.01681294 1970,357.5620,62.5140,29.9270,67.8630,0.01209972 1971,419.3050,210.2260,22.2620,41.0950,0.03180511 1971,64.2610,203.2700,24.9354,35.4300,0.02082670 1971,374.9510,202.5500,43.0340,45.8490,0.02026753 1971,368.9860,201.8260,22.9520,43.7940,0.01484699 1971,379.4340,208.7880,21.6890,37.8150,0.01443882 1972,419.6600,208.4920,28.6890,62.2330,0.07425775 1972,21.6710,211.7170,22.9279,35.1370,0.03945705 1972,602.8170,208.7930,33.6990,95.1200,0.01480195 1972,33.7420,211.4570,24.5686,33.7070,0.01082495 1973,404.6450,214.5150,17.6300,33.8540,0.12527838 1973,427.9150,215.5810,19.0380,37.6870,0.12272997 1973,447.9770,209.5180,21.8000,38.3210,0.07391927 1973,341.9840,221.3840,18.0840,32.3510,0.03124889 1973,372.1270,207.0460,134.0830,52.9260,0.01067854 1974,416.2290,208.2640,20.3920,41.7680,0.37031224 1974,452.4290,208.8890,22.1700,46.7750,0.32996106 1974,480.8460,200.7160,22.1480,43.6700,0.27902588 1975,500.5960,219.8250,29.2640,64.5120,0.78531444 1975,440.7460,221.6290,24.2070,52.9790,0.64790058 1975,547.4570,216.2010,24.3130,49.9960,0.46407607 1976,502.6190,220.0100,33.6460,81.8830,0.81040847 1976,261.3880,162.6960,29.3940,77.2340,0.04256420 1976,489.9640,241.3100,22.5690,56.1820,0.02432467 1976,504.8570,216.2010,24.2140,52.5950,0.01015564 1977,504.3290,182.1370,26.9170,64.7480,0.02437576 1977,331.0970,176.6010,25.2850,54.4240,0.01642311 1977,566.5290,36.2042,34.3880,86.8558,0.01409624 1978,334.4250,154.4860,33.3970,84.9080,0.06503539 1978,340.2160,189.2110,24.8960,55.7500,0.01786000 1978,373.9270,182.9640,27.3650,61.6960,0.01591515 1978,443.0270,196.7210,30.0300,69.2600,0.01109438 1979,365.5490,213.4180,22.6220,37.6070,0.04782798 1979,598.2740,197.6090,20.8500,33.3920,0.01090784 1980,399.7260,198.4600,21.5280,40.3660,0.15909566 1980,504.3150,199.0440,38.4040,102.4460,0.10428935 1980,335.7450,169.9800,25.4720,53.9160,0.01921936 1981,433.0290,205.1850,22.6280,44.6520,0.29586363 1981,604.8510,191.4430,33.6620,93.6710,0.05633057 1981,420.8120,196.7620,43.1960,67.6830,0.01208788 1981,357.9320,194.3170,24.5120,49.0400,0.01130294 1982,510.8570,214.6190,23.3810,50.5400,0.55216730 1982,414.8930,187.4340,31.8670,78.0440,0.08812978 1982,358.9470,203.4390,22.7770,44.3400,0.01162335 1982,458.0850,217.5670,29.6220,60.3740,0.01030845 1983,507.0760,203.3440,30.7000,71.2810,0.01552995 1983,525.1090,210.8990,34.9050,72.0170,0.01298432 1983,396.6520,204.3270,19.1670,34.9340,0.01019625 1984,424.3510,210.6920,20.2200,38.4930,0.16026326 1984,480.7260,193.0650,26.6290,61.5140,0.05858865 1985,440.5780,196.4140,25.8170,58.0180,0.16308023 1985,489.7040,211.3410,24.7880,51.4150,0.01241938 1986,489.6500,207.4390,21.3230,46.1010,0.29507864 1986,467.8290,209.3380,19.5530,33.7880,0.01162051 1986,523.9210,25.6881,39.5730,93.5529,0.01139357 1987,568.1540,200.2700,31.9420,69.4730,0.78990889 1987,494.9290,46.1099,27.6660,60.3761,0.08200888 1987,479.9120,206.1340,21.0100,36.3730,0.06165273 1987,517.1670,21.2476,33.9660,76.1840,0.01868387 1987,379.6010,68.0238,31.7140,71.8232,0.01638402 1988,308.7010,233.2820,18.7590,34.0880,0.06550770 1988,553.8920,1.0000,36.8250,68.6647,0.01876094 1988,482.1870,143.9380,37.7980,99.8890,0.01161556 1989,460.6470,203.6530,29.9990,56.8480,0.19408992 1989,265.4560,227.5400,20.0990,35.7760,0.02381190 1990,459.5700,208.2770,28.4960,64.6470,0.31878847 1990,213.1970,231.4710,20.2180,36.1100,0.02339578 1990,521.0790,205.0520,26.1350,47.1870,0.01041150 1991,481.2310,214.4370,28.6080,69.8500,0.21265814 1991,354.4510,57.8518,36.0880,71.7292,0.01907260 1991,572.3790,221.5060,29.7560,50.9830,0.01738760 1992,528.9790,203.3680,34.7720,77.9330,0.53506637 1992,371.0290,1.0000,39.0240,72.0882,0.01417279 1993,596.7730,231.3460,28.9930,63.0380,0.11787137 1993,70.8209,218.9560,31.8961,59.7220,0.02045900 1993,410.1570,43.7130,68.2620,204.8620,0.01169845 1993,611.9890,115.9990,28.0110,97.2420,0.01051973 1994,342.6850,78.1314,32.3480,63.9896,0.01762153 1994,399.1240,31.8707,30.7630,73.0153,0.01575922 1994,563.1370,199.1180,34.9130,89.1530,0.01153127 1995,208.9640,243.8350,23.0110,43.3700,0.08183587 1995,520.8680,104.0830,25.8990,55.5720,0.01390256 1996,217.7760,250.1330,21.7210,42.7830,0.05368403 1996,275.2800,58.5701,38.0090,75.1499,0.02102497 1996,525.7360,170.7990,32.7200,84.6310,0.01004996 1997,221.2320,249.5910,20.4210,39.1200,0.03838133 1997,367.4810,13.8140,30.0690,64.0018,0.03316483 1997,45.8801,217.6160,30.5311,61.9650,0.01784322 1998,219.9840,246.6100,21.3810,41.3530,0.10329676 1998,228.2690,195.3770,22.0450,37.1840,0.03174359 1998,35.4860,219.8780,29.0201,62.4330,0.02068635 1998,519.6680,214.3770,30.7350,73.2270,0.01322848 1999,208.8080,249.0200,20.4960,39.8400,0.09208941 1999,216.5770,201.2600,25.0400,38.8140,0.02076990 1999,384.8340,204.9740,21.1160,41.1320,0.01647182 1999,307.2880,208.9120,21.3420,30.3120,0.01500718 1999,411.4040,143.0540,34.6050,88.3620,0.01292173 1999,299.5010,205.9580,20.6040,31.6430,0.01188892 2000,191.6500,253.7980,22.0690,42.8190,0.11877861 2000,429.6950,22.2352,34.3290,77.0554,0.07495008 2000,202.1130,198.8780,23.3480,44.5310,0.04341464 2000,298.3010,209.1800,20.1970,31.7350,0.02712669 2000,414.4810,198.4640,25.3920,57.3580,0.01546234 2000,305.7490,210.9590,22.0800,30.9290,0.01485758 2000,150.1380,252.1350,30.1540,71.9630,0.01467196 2001,169.7130,259.3520,27.2730,64.6180,0.07326842 2001,97.3492,272.0490,35.4018,92.9010,0.06965626 2001,304.0390,209.3850,17.8350,31.3510,0.06712277 2001,199.2930,201.2090,23.4850,47.3050,0.03622575 2001,378.1550,220.2500,20.2530,38.0420,0.02165953 2001,185.9430,199.3110,24.2270,48.9930,0.02096957 2001,315.3790,211.4670,19.5580,30.1740,0.01870295 2002,431.9420,64.0981,33.8730,87.5689,0.08412011 2002,301.1970,209.0660,20.2700,30.0090,0.07075575 2002,115.8000,269.5230,35.2220,88.2280,0.05466005 2002,325.4000,210.8670,19.1010,28.8620,0.04454418 2002,313.5700,209.1660,20.6180,32.6150,0.02090791 2002,355.6570,61.0941,31.1300,68.3079,0.01949872 2002,294.5180,204.8550,46.8200,41.2200,0.01422364 2002,245.0800,199.3220,154.4150,49.6810,0.01252972 2002,418.8250,67.4717,31.8600,85.8393,0.01073763 2003,313.1570,208.1790,18.7000,34.2180,0.16383073 2003,332.7460,209.1480,18.1870,30.8240,0.08320574 2003,323.9770,208.9210,18.4170,32.5240,0.03909328 2003,573.4180,35.4994,35.5910,93.0836,0.02696390 2003,263.4300,202.1540,161.6310,50.3690,0.01513138 2003,284.2620,207.3770,19.4390,34.4000,0.01492308 2003,332.0010,58.7700,35.9000,87.1510,0.01452994 2004,318.5180,212.6250,20.3170,38.9460,0.26885355 2004,190.3920,233.1820,29.6020,63.1430,0.21610019 2004,343.4950,216.7850,18.0610,32.2690,0.06202058 2004,294.6540,210.0640,146.0390,51.1500,0.02948688 2004,329.6500,214.0160,19.4030,37.2100,0.02342662 2004,276.3830,216.7820,19.5120,33.6280,0.01965366 2004,324.7920,214.1010,81.4960,38.9980,0.01396182 2004,355.5080,216.7190,18.2630,32.0110,0.01370972 2004,35.0121,210.4080,494.3589,59.4400,0.01106564 2004,379.6820,217.8640,20.5340,31.0850,0.01087971 2004,136.0130,204.7850,195.8200,59.3120,0.01041664 2004,311.9430,210.2130,44.9570,46.8720,0.01030473 2005,327.0330,216.7770,18.7880,41.3290,0.10581482 2005,352.2510,215.9140,20.5410,36.3460,0.07969334 2005,310.0790,215.1010,61.6980,44.2050,0.05117242 2005,339.7420,218.3640,20.1680,39.0260,0.04204845 2005,269.9700,213.7730,144.8450,48.4100,0.02769607 2005,257.3160,218.1080,22.1740,33.9810,0.01873757 2005,480.6000,220.3330,24.5800,40.5790,0.01585999 2005,318.8950,218.9370,20.3290,36.3960,0.01082679 2006,323.5020,216.3950,22.7890,51.9870,0.56308448 2006,356.4320,217.6760,21.7180,44.7000,0.43842641 2006,276.0800,227.1820,21.3770,36.4770,0.06920225 2006,369.2920,216.0740,21.2570,41.3610,0.06460241 2006,183.4390,218.7700,210.3980,55.3990,0.02797617 2006,1.0000,220.8730,472.5350,61.1990,0.02437781 2006,220.6330,219.7310,100.4010,49.1840,0.01669707 2006,67.0462,216.6650,225.6428,57.6930,0.01658966 2006,600.8820,66.5053,28.9140,67.1127,0.01613416 2006,331.5890,213.5440,27.8900,54.8480,0.01075221 2006,334.7510,214.6280,66.2220,53.8770,0.01013648 2007,306.8260,207.1590,27.7770,61.7150,0.74913341 2007,349.0880,207.6650,24.4190,55.6960,0.64440840 2007,549.5950,202.0310,21.5310,45.3880,0.12062351 2007,229.9990,207.4530,25.6030,42.7680,0.01475434 2008,230.0130,203.1920,34.2630,81.9370,0.76406616 2008,284.3760,201.6790,32.3620,75.6720,0.73258090 2008,49.1075,193.0350,29.2903,54.6840,0.12783004 2008,442.3800,196.3090,24.9310,55.5030,0.12736328 2008,278.1710,212.6840,26.3000,59.0790,0.02224419 2008,287.5250,237.6760,22.9330,42.0360,0.01562295 2009,97.0041,200.2760,48.1249,117.3240,0.77085173 2009,181.4720,218.4640,42.6680,94.7230,0.33315673 2009,260.7500,185.1370,33.6550,75.8500,0.16619359 2009,523.3890,226.8700,21.3820,32.3780,0.02778123 2009,188.2710,253.2420,28.4820,57.6910,0.02088875 2010,1.0681,198.8800,39.4055,154.3730,0.30558363 2010,375.0620,210.8670,29.5690,60.6410,0.13133949 2010,74.7894,203.8500,33.8436,69.1500,0.10747410 2010,416.0900,238.0400,25.2840,41.2680,0.02874607 2010,1.0000,263.0290,29.4088,88.1140,0.01948590 2011,338.5820,231.7990,26.3930,46.0960,0.19139484 2011,300.1030,238.9220,26.6380,41.3550,0.05853632 2011,180.0400,189.9190,39.1840,83.8570,0.01124360 2012,204.6750,197.7150,32.7510,70.0820,0.14932095 2012,157.4580,225.8760,29.2390,45.3700,0.13236462 2012,507.2060,211.1500,21.8390,37.5140,0.01852520 2015,587.6010,223.0590,28.5140,62.8100,0.77959424 2016,507.1180,214.8680,31.4390,70.9720,0.82016975 2016,409.5670,206.1520,25.8350,60.1970,0.03903409 2017,499.4510,205.0510,34.5600,83.4630,0.82837003 2017,380.5610,213.5260,27.9410,59.3960,0.01952975 2018,306.2450,212.5340,18.8770,32.8210,0.06256276 2018,584.2130,195.9950,40.7520,89.3620,0.01148268 2019,611.0110,1.5764,28.9890,106.8116,0.01931419 2021,95.7572,232.7930,25.8538,49.3870,0.01570595 2021,111.2230,228.6300,22.8770,46.8190,0.01219624 2022,484.9950,224.1350,21.9300,38.2290,0.02097530 2023,376.5290,210.1570,19.5300,39.3750,0.09447978 2024,375.6020,210.1190,20.9590,37.8980,0.17792366 2025,386.1940,211.3710,22.0120,40.3470,0.12153223 2025,137.9650,200.6890,21.4320,34.5990,0.01845894 2026,405.5830,211.7110,22.0880,43.8310,0.39670309 2026,356.0670,209.3030,18.9270,33.4140,0.10806976 2026,5.2577,199.1870,26.6019,50.4580,0.02264147 2027,424.8270,208.1650,23.4070,48.7800,0.34312701 2027,507.2180,213.7880,23.1180,50.9960,0.20496541 2027,369.9090,207.9560,20.6110,37.6540,0.17762154 2027,131.6570,208.9340,20.8040,32.2430,0.08645626 2028,547.3360,212.9570,28.6930,64.9180,0.60653335 2028,443.2360,211.7840,28.1430,58.4430,0.50346422 2028,380.3010,211.1480,21.5900,38.7770,0.30164796 2028,106.3790,206.9640,21.6680,38.0110,0.12904064 2028,503.9640,225.6950,26.3280,52.2960,0.04180515 2029,490.6260,213.2250,29.5350,66.7100,0.76669955 2029,400.7890,210.2020,21.3380,41.2500,0.29346794 2029,82.8242,206.9660,22.5798,38.7250,0.14970835 2030,576.9330,211.9440,38.2880,89.0250,0.82925272 2030,435.3910,208.5610,23.3220,50.5720,0.59472561 2030,55.0021,206.8600,21.3456,37.8370,0.05103464 2031,488.0120,207.3940,30.3290,72.1480,0.79357672 2031,2.2970,204.1780,22.4278,44.1970,0.12937558 2033,380.6150,213.2860,28.6230,58.5960,0.05293946 2033,88.6774,288.5260,35.2536,86.8800,0.02162372 2033,269.9900,197.7220,32.6190,82.4540,0.01140251 2034,72.2271,306.0890,39.7079,82.3760,0.01808343 2035,152.6840,116.6920,23.8910,36.4590,0.02141705 2036,128.8300,95.6027,26.9030,45.5473,0.01260384 2037,102.0680,219.5060,68.4930,56.9780,0.01368004 2038,121.5330,213.5390,71.8740,49.2240,0.08919553 2039,138.3510,217.7830,59.3010,46.7560,0.01129459 2040,76.0740,210.2930,25.5450,45.1400,0.03611219 2040,43.7704,216.8680,24.9033,46.0340,0.01834370 2041,3.6032,212.1700,29.0424,64.2140,0.01538604 2041,205.4990,237.2590,23.5450,45.0270,0.01265088 2041,38.4356,203.7920,23.5343,40.8050,0.01041400 2043,224.0030,233.2310,24.2090,41.2380,0.02786144 2044,154.3900,197.6350,24.1210,44.1080,0.01161254 2045,156.5960,208.7140,21.6060,33.4640,0.01467114 2047,148.0650,209.0600,21.4690,34.7340,0.02295839 2048,99.7809,204.9610,22.1331,42.0240,0.16478384 2049,27.4350,203.8530,25.9694,49.4410,0.27352321 2052,359.1510,219.4520,37.1540,87.6990,0.03377265 2055,214.5450,211.8260,24.9470,49.4040,0.03134963 2056,120.7680,216.0870,38.2430,84.3250,0.09283975 2056,433.7030,263.8340,25.1470,71.9150,0.08016219 2056,194.0320,199.0920,24.7960,54.8060,0.01647899 2056,355.7800,199.8040,18.4150,32.4300,0.01497644 2056,233.5460,199.4820,22.9500,30.8160,0.01234588 2056,115.1200,196.6080,119.5020,65.1620,0.01044550 2057,509.5940,292.1250,34.5440,80.0580,0.19665414 2057,435.6030,266.5510,27.2580,68.4150,0.10234845 2057,196.4230,205.2620,19.4530,36.7970,0.02389618 2057,585.7140,214.7640,20.9960,41.9790,0.01060519 2058,507.2640,286.8660,35.2580,84.6390,0.34831625 2058,192.7710,206.7560,19.3550,34.3700,0.05534780 2059,529.9020,224.4760,20.4270,34.7290,0.08556759 2059,173.4580,212.9620,19.2820,32.5700,0.02480215 2059,379.2050,212.5360,19.1280,33.8450,0.02225456 2059,50.4380,245.5400,25.0742,46.5440,0.01042857 2060,139.0670,210.6080,20.8280,42.1850,0.11241830 2060,600.2690,229.9910,20.5050,39.6070,0.08573652 2060,76.7812,214.9870,27.1668,57.7670,0.03518861 2060,384.0750,212.4150,21.3430,34.2700,0.02660050 2060,305.3920,213.1720,23.9980,42.8600,0.01044123 2061,94.7809,204.5330,23.2531,48.0310,0.28604516 2061,1.1299,205.6560,22.2623,51.5730,0.10771284 2061,10.8303,204.2790,25.4759,56.1650,0.09941077 2061,378.2510,210.4970,21.7880,35.3630,0.01596099 2062,37.5249,193.5330,30.5207,63.9120,0.40759683 2063,16.9787,192.6360,25.1868,50.1980,0.04323202 2063,436.5450,209.5200,19.7720,36.5760,0.04108328 2063,523.7270,191.2880,33.0570,78.6710,0.01341720 2063,282.5440,136.0360,23.7740,48.8530,0.01254358 2063,411.7850,212.4130,20.0050,36.1960,0.01244279 2064,430.8690,218.5300,18.5570,33.2700,0.02173841 2064,137.2390,211.2040,22.2550,42.5000,0.01503101 2064,270.7320,218.4270,23.8140,43.9020,0.01354862 2065,492.2210,210.7010,21.8640,47.3710,0.28335130 2065,308.4430,221.2620,21.3700,36.6380,0.02443361 2065,38.6368,211.9020,23.9661,42.0710,0.02250493 2065,278.9930,129.2280,25.3190,54.6900,0.02164791 2065,261.4940,218.0060,26.8600,49.5850,0.01981926 2065,99.2725,155.3390,24.0475,45.2440,0.01500163 2065,106.2760,209.4510,20.8740,41.9570,0.01237360 2065,77.8741,214.0800,20.9316,36.6210,0.01219964 2066,529.1890,211.0100,24.5540,52.5940,0.62093276 2066,78.7092,201.5620,27.6118,51.8720,0.03839952 2066,31.6861,187.4800,104.5669,69.6320,0.01815958 2066,42.9810,206.5230,26.6690,55.3520,0.01498731 2066,243.9580,215.1780,26.0820,41.2260,0.01427476 2066,322.1500,210.7890,21.6570,39.4310,0.01358915 2066,75.4993,148.2530,31.5197,63.1990,0.01172769 2067,574.1080,210.9840,27.0650,62.7980,0.72770077 2067,496.5360,225.7370,20.0270,36.4510,0.06622484 2067,235.2880,216.2270,23.0330,38.8090,0.05501515 2067,259.3060,213.1230,25.0800,52.2970,0.04414928 2067,27.2749,198.9060,29.9891,59.5470,0.04081791 2067,380.9320,220.4150,22.6200,37.4610,0.01604807 2067,337.1670,217.9680,21.8800,37.5600,0.01497037 2068,618.4640,209.4970,21.5360,69.2410,0.12006035 2068,223.2470,216.1880,21.8670,36.5830,0.09643278 2068,245.6350,213.3920,22.6600,47.5870,0.06530687 2068,357.6890,217.6010,20.7470,38.0170,0.06522622 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1225,375.3146,198.1183,20.9824,39.0126,0.20731679 1226,276.3395,193.7835,22.4607,45.7850,0.53707081 1226,379.7348,193.1174,19.8891,37.6660,0.25540507 1226,321.5372,189.2081,17.9045,40.1021,0.20665908 1226,538.3193,197.6496,44.8582,125.9099,0.18230335 1227,296.1220,189.6975,22.3444,46.3636,0.82363814 1227,338.1225,185.9888,19.3364,44.1534,0.55069602 1227,476.4679,186.9141,20.6581,41.8636,0.20088226 1228,348.8657,186.0585,23.0414,46.2267,0.98160899 1228,306.4976,191.3951,22.7708,43.2850,0.89739281 1228,496.1900,184.6505,22.3566,48.2448,0.22067454 1228,417.7537,195.9503,23.5220,39.7204,0.16420566 1229,354.2195,186.6680,21.9965,45.1806,0.96979022 1229,312.4654,190.1378,21.8637,47.1572,0.96779382 1229,424.8944,193.8732,19.1544,41.9795,0.37372315 1230,354.2891,189.8750,22.9660,44.3171,0.98279262 1230,313.3670,194.5969,21.7084,46.8757,0.92097771 1230,423.2688,192.1752,21.2173,45.6358,0.61200953 1230,505.4879,193.9871,20.5200,41.1105,0.48152754 1231,353.4505,190.6236,24.5084,45.1463,0.97914475 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1234,210.8740,211.9382,19.7876,42.2819,0.43270791 1234,241.0227,206.6122,24.0302,45.5160,0.23997511 1234,446.6297,202.7777,19.9716,42.8911,0.16171780 1234,203.0395,212.9149,19.9472,42.6904,0.12699820 1234,231.8002,208.4931,22.5435,45.9904,0.12569000 1235,247.0807,207.1222,24.2166,56.0432,0.99474323 1235,128.4632,209.3923,23.9128,56.7326,0.99448413 1235,154.7996,210.4072,24.2379,57.4480,0.91526282 1235,278.1514,215.9345,22.4002,49.9680,0.83668399 1235,434.4246,206.7357,19.8895,48.6655,0.35021639 1235,139.1229,207.5318,31.8304,58.2724,0.19843253 1235,264.8390,212.4926,20.4490,51.2629,0.17934158 1236,457.5129,209.1207,23.0618,53.2924,0.99811912 1236,80.5788,207.5694,32.3838,67.2432,0.96339947 1236,344.7380,205.8458,25.6507,62.1668,0.77161491 1236,65.2749,206.0379,35.2874,67.3219,0.76144910 1236,92.3886,208.9241,32.0037,58.7724,0.13941890 1236,356.2634,211.3767,25.1248,54.3392,0.12616992 1236,326.5031,216.5071,37.0338,78.9647,0.11689081 1237,230.6143,209.9532,26.8869,58.0064,0.99609303 1237,250.5455,215.0065,23.0100,51.7793,0.93082511 1237,213.6925,212.4624,24.6849,56.7858,0.38897470 1238,264.9413,219.0897,27.5251,73.7750,0.99872530 1242,614.8844,206.0500,19.2757,52.3028,0.63885224 1243,605.1222,208.8719,26.4050,60.2280,0.96299708 1243,590.3463,221.1586,41.6920,108.0879,0.19068569 1244,543.6457,217.5815,21.8063,49.1873,0.72752690 1244,512.9739,220.2941,23.5042,54.9079,0.50719845 1245,445.1630,213.0075,28.8689,88.7921,0.34964752 1246,283.0249,210.7381,17.1278,35.3303,0.12608965 1247,142.3053,210.5500,20.3446,43.9878,0.66495079 1247,277.8167,208.6716,19.8302,38.6704,0.16200176 1248,274.1122,211.0985,21.3918,43.3383,0.97802132 1249,263.5987,209.0630,22.8945,50.8674,0.99940753 1249,583.9078,221.7271,26.4493,61.5319,0.99863768 1249,25.2138,218.1184,28.2154,64.8148,0.88126802 1249,518.2263,210.6150,21.1042,43.6013,0.73118961 1249,544.5491,205.4534,19.0937,43.9262,0.49777824 1249,489.2857,207.7430,21.9102,45.5009,0.16762400 1249,528.2200,208.3250,20.8724,43.4557,0.14535108 1249,498.5168,208.7591,20.5530,45.6461,0.11056471 1250,216.3656,212.3045,26.5450,64.5601,0.99982584 1250,590.5148,207.6972,20.1829,54.6946,0.95836329 1250,479.1384,205.8168,22.8504,54.6368,0.54059303 1250,505.5828,201.9955,21.3532,53.3262,0.37849081 1250,487.1203,208.0072,25.6055,55.3427,0.11769083 1251,129.4635,212.4595,33.9636,83.9502,0.99802691 1251,579.1985,212.8462,22.5516,59.4431,0.98977935 1251,534.2805,202.2982,20.5721,51.6479,0.97986424 1251,596.3998,207.5718,24.2545,58.0963,0.97337854 1251,545.6893,204.5497,20.9260,52.9635,0.82677525 1251,607.2660,209.2145,24.0039,53.2636,0.33838856 1252,506.9961,224.5753,23.5889,48.5759,0.18070924 1252,546.5895,213.7951,20.1544,55.8077,0.10577533 1253,503.6281,209.5890,22.4245,54.6909,0.54005158 1253,489.9319,210.0336,22.2034,46.1281,0.21709064 1253,518.5682,209.8206,19.3487,53.7905,0.17201564 1254,553.5136,207.8535,21.1478,49.3189,0.87065750 1254,580.0774,210.2435,28.9299,61.2744,0.60786003 1254,587.8323,205.6380,41.7348,90.2670,0.10520200 1255,419.6400,186.6306,33.6091,115.1900,0.19441319 1255,439.7571,207.9484,20.6219,56.0344,0.12141865 1255,552.8478,212.7890,19.0324,35.5090,0.10575654 1256,421.6156,172.2757,28.2448,78.3512,0.43988812 1257,584.9065,211.6623,25.0902,58.6592,0.31415799 1257,497.4121,200.1237,56.2480,167.0674,0.13763177 1258,496.3382,206.1490,21.5902,46.0716,0.95496845 1259,563.8561,211.9702,18.8107,53.2077,0.43557188 1259,514.8707,210.9700,24.5575,69.1045,0.35089666 1260,478.0140,204.4639,17.4245,36.6739,0.18200845 1260,585.4701,196.3459,39.8112,62.1593,0.16429739 1261,595.2544,206.3694,35.7758,83.5561,0.99886292 1261,504.7265,207.0607,22.2698,44.6657,0.53427887 1262,432.5175,212.7141,19.8023,52.2106,0.39198703 1262,485.6081,209.6565,23.0938,49.2684,0.32169113 1262,139.8974,205.0977,20.0870,41.9284,0.28958064 1262,440.6834,210.0453,20.7831,49.4641,0.16825524 1262,430.9539,207.5331,34.0360,82.3925,0.15901726 1262,469.6833,196.9652,85.9713,71.0355,0.11800030 1263,467.8485,208.4978,20.3252,56.4100,0.96796554 1263,530.5705,210.0210,21.2621,54.9468,0.94723666 1263,481.3989,210.6909,20.6149,53.1602,0.90098011 1263,546.7850,215.7984,20.7491,50.2374,0.55941319 1263,493.6091,208.3179,62.0176,64.1711,0.22104928 1263,512.9190,211.6260,21.6334,53.2924,0.20708302 1263,569.6064,164.5984,49.5360,106.2125,0.19344294 1263,562.8911,213.3883,20.7695,48.4713,0.17001025 1263,518.6739,211.0611,61.1436,58.3772,0.12546809 1264,504.9630,207.0597,25.0131,59.2582,0.99409962 1264,581.5199,212.1237,21.8615,59.6243,0.98939353 1264,528.1093,213.1670,25.0597,57.6067,0.89366138 1264,109.7859,207.7879,22.5027,45.9771,0.76738942 1264,600.5951,215.6680,19.6507,50.4781,0.39386022 1264,609.7635,212.1591,20.9158,55.2686,0.38408303 1264,564.3675,220.7888,20.3728,47.9963,0.31165886 1264,499.7006,211.8869,98.2139,59.6244,0.15207879 1264,350.2173,211.3763,79.5536,45.8372,0.11919519 1264,536.1198,218.3751,90.9925,58.8636,0.10366951 1265,94.7299,204.5857,22.1662,48.7024,0.98240221 1265,590.3847,218.0985,22.8558,61.0209,0.95037484 1265,454.2300,208.2834,21.6712,53.3616,0.67016947 1265,554.1114,207.2580,31.9034,70.8802,0.56952745 1265,438.7890,213.9328,21.7809,43.2549,0.43628836 1265,575.0218,216.9851,23.8596,58.5829,0.42869014 1265,502.0218,211.8749,21.3505,44.1203,0.24268910 1265,83.0415,204.0104,19.4283,44.0657,0.19794574 1265,422.1542,212.0358,26.2353,43.2455,0.17617510 1265,432.9307,211.9135,125.6147,45.8325,0.15167454 1265,512.7435,201.7045,85.3283,62.3347,0.14286779 1265,427.5395,197.0584,57.7020,79.6826,0.10910273 1265,383.3989,217.3403,29.2681,36.1232,0.10881627 1265,596.0696,230.5246,27.9507,60.2770,0.10191512 1265,518.7992,209.2175,21.3274,44.2113,0.10167810 1266,79.1457,205.5367,23.9850,50.6502,0.94615293 1266,449.9364,217.4580,18.8621,44.0075,0.82281256 1266,468.0790,212.5822,19.3181,47.4179,0.80664217 1266,515.5384,214.7164,21.9651,42.5760,0.73768395 1266,611.7592,202.8981,26.8667,71.8191,0.60352719 1266,558.5280,218.5284,25.1229,52.3189,0.47666866 1266,458.3858,214.4408,19.0780,47.7616,0.44287878 1266,573.5289,219.6224,24.9830,45.5301,0.42630708 1266,392.8427,218.8167,23.3730,41.4435,0.25444615 1266,500.2679,212.8471,31.0737,45.7766,0.23281470 1266,481.6736,204.4717,65.6482,55.2665,0.16700077 1266,481.9387,211.0406,18.0185,46.5928,0.12606594 1266,432.0973,217.7962,19.8059,40.2173,0.10731666 1267,468.8166,212.9746,18.9463,48.5122,0.95115566 1267,521.1762,215.8214,21.4888,45.0494,0.91309041 1267,605.0326,222.4624,23.5407,51.4350,0.68930626 1267,396.0546,219.2896,21.3195,43.5113,0.57320732 1267,448.1474,219.4274,21.2548,42.2225,0.50396961 1267,458.2909,215.5323,18.8264,47.2242,0.43009657 1267,586.1425,225.5603,31.1540,57.4613,0.36425981 1267,481.0535,214.1545,18.0518,48.8151,0.34597135 1267,507.5845,211.5549,18.3727,47.1781,0.28987926 1267,498.9412,210.5425,17.8685,47.6213,0.25747362 1267,489.9505,210.5529,17.0645,49.8393,0.15185091 1267,432.5679,217.3771,22.0752,45.0569,0.12478662 1268,473.1863,216.6663,20.1209,52.4099,0.98915994 1268,520.3168,216.4801,20.1424,45.8355,0.92105436 1268,450.4979,219.1025,22.5385,45.6639,0.86211717 1268,481.1197,217.2505,20.0085,53.1523,0.70953584 1268,396.2688,220.5528,22.9281,43.9566,0.64498055 1268,506.9416,213.2890,17.7448,47.6582,0.52641213 1268,488.3432,213.8016,18.0067,47.6162,0.32202929 1268,595.6913,224.2370,34.4382,74.6837,0.19727075 1268,498.5897,213.5340,19.6118,48.9630,0.14587663 1268,435.3476,221.2684,22.0557,41.5811,0.10759659 1269,516.9064,217.9316,20.1333,48.8740,0.98406732 1269,475.7796,217.6910,18.3927,51.1625,0.96012920 1269,449.3401,220.9540,22.2454,44.7061,0.66598314 1269,467.1989,216.1820,20.5918,50.4767,0.62872791 1269,506.4556,217.6367,19.6435,49.1829,0.43867838 1269,597.3597,222.0369,31.3768,64.6752,0.31318405 1269,433.6875,224.1279,20.2541,39.2727,0.18544304 1269,396.8427,222.8147,19.4469,44.8854,0.17255217 1269,486.7689,215.9089,19.5872,52.0912,0.12532268 1269,32.2601,209.3198,25.3883,52.6647,0.11187708 1270,512.3470,218.1495,21.7277,48.2805,0.98575062 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1309,404.9684,221.7800,21.1814,49.6729,0.98795128 1309,150.4882,218.4179,31.6177,74.5123,0.98430759 1309,197.2795,217.0901,23.9643,67.3209,0.98202974 1309,608.3864,215.1403,23.0511,54.7804,0.91287148 1309,471.5613,217.9794,20.0166,43.0591,0.84343231 1309,73.2933,218.4469,20.0796,46.4580,0.83243740 1309,504.7621,213.6385,20.2428,44.3854,0.78727221 1309,519.5790,212.5558,21.0641,48.8223,0.69716692 1309,63.3562,215.2420,22.5962,51.0613,0.65919614 1309,323.8046,217.1700,24.3246,71.7006,0.63475990 1309,568.7780,224.4308,20.8881,52.3448,0.54185426 1309,592.5987,219.7482,23.0718,51.8325,0.49294341 1309,166.6023,218.8743,29.3906,78.0949,0.26844767 1309,22.2959,202.0070,70.1824,56.8244,0.20641208 1309,480.1657,216.0345,19.1597,43.0367,0.16541237 1309,249.8184,222.4338,23.4269,43.2080,0.16525024 1309,24.3781,204.3309,33.4225,46.7075,0.12928325 1309,489.8480,214.2653,17.3536,42.0280,0.10738574 1309,48.5226,210.8611,23.8310,49.7208,0.10601526 1309,344.7542,212.6831,31.1682,81.1204,0.10326534 1309,490.3580,211.6979,65.3763,57.7544,0.10239521 1310,251.6545,217.8665,32.1212,78.2042,0.99948895 1310,427.3267,213.4962,31.8670,83.8750,0.99928117 1310,16.0707,214.5961,39.5177,94.2916,0.99688804 1310,298.7798,215.1642,54.9509,148.1888,0.99587685 1310,562.1754,223.3870,23.1319,59.3094,0.99097037 1310,126.1707,218.2950,27.9848,74.5195,0.98895752 1310,106.2596,221.2382,24.1138,53.2258,0.98587888 1310,344.1763,215.4612,39.0271,118.7286,0.97517085 1310,192.9025,238.4055,22.4206,55.7070,0.96856326 1310,530.4850,214.7271,23.2423,46.5711,0.94519103 1310,482.8902,220.5640,19.6596,48.3429,0.94472945 1310,160.9642,219.8436,27.3945,66.3038,0.93335056 1310,579.9299,225.0593,30.2067,77.6908,0.68428171 1310,174.7968,218.2601,25.8072,55.6971,0.60768080 1310,600.1536,225.8724,22.3923,55.9165,0.54856253 1310,322.3975,218.6836,50.5051,147.1214,0.52482337 1310,226.5704,217.0536,20.9825,43.6429,0.48527932 1310,476.0327,217.3124,18.8882,45.7492,0.47410467 1310,286.7905,220.0067,30.1244,85.6715,0.38553578 1310,66.6136,212.9568,18.0176,41.1323,0.34609330 1310,142.3027,224.3284,25.3541,62.5286,0.26476055 1310,499.5840,213.2797,17.3660,44.9696,0.24746808 1310,38.6618,214.9019,26.8325,71.0934,0.23418136 1310,81.5690,211.8101,19.1587,48.4632,0.17193697 1310,74.2251,212.6501,19.0475,45.5153,0.16059873 1310,465.9034,215.1001,18.2144,45.3801,0.10912575 1311,584.8523,226.7673,25.3402,62.9194,0.99958801 1311,211.4410,217.0554,34.5971,79.4563,0.99931121 1311,381.5940,224.2439,56.1679,122.7936,0.99927694 1311,455.2417,218.4392,28.9056,73.1640,0.99911332 1311,255.7734,215.1219,29.1478,75.7051,0.99596941 1311,480.6608,222.7361,44.6890,113.9989,0.99245113 1311,84.5039,215.1198,34.0302,92.3262,0.99123716 1311,157.6381,237.4112,25.4237,51.4929,0.99036944 1311,129.2977,220.0927,25.1627,58.5112,0.98427463 1311,554.4790,214.9792,19.3448,54.7567,0.97924340 1311,309.9547,211.6751,26.5596,64.3057,0.97233921 1311,286.3046,214.3703,25.2373,71.0098,0.95847982 1311,192.6582,214.0344,22.3870,51.9986,0.87002397 1311,109.4340,225.2661,26.3100,58.8667,0.83657753 1311,567.8268,219.3752,20.0870,55.5913,0.65073919 1311,33.4028,216.4700,20.4308,44.0694,0.53480947 1311,270.7880,215.8635,26.8243,71.1411,0.44306207 1311,118.2619,217.7591,26.3757,61.1274,0.40172136 1311,41.6106,214.1340,19.0156,44.8826,0.27994850 1311,20.3694,213.5372,21.7204,48.6663,0.22594599 1311,495.6999,220.2095,25.3939,64.1729,0.21166795 1311,379.1165,219.2914,35.7452,96.7417,0.16324955 1311,477.8294,223.3301,27.1648,75.4464,0.15606502 1311,97.9373,222.1216,31.6177,77.1558,0.14403442 1312,222.8265,216.0887,29.4373,76.7196,0.99941492 1312,183.3060,217.8155,30.3560,77.7607,0.99921072 1312,137.3005,217.8395,35.6124,82.5571,0.99888873 1312,521.3844,214.5289,23.5172,59.3017,0.99846071 1312,393.5558,222.1347,41.0865,114.4552,0.99833453 1312,543.5887,226.9447,24.4673,65.1897,0.99818361 1312,52.1889,221.0264,46.4031,123.5168,0.99805307 1312,449.8013,220.1854,49.4890,128.3497,0.99605197 1312,602.8044,223.8857,23.4332,56.1508,0.99062777 1312,268.1843,215.6940,27.5321,65.0567,0.98716092 1312,106.2449,217.7291,23.8580,61.9215,0.97840303 1312,503.3528,219.9524,19.6284,51.6621,0.95229781 1312,358.1867,219.4932,20.9539,47.7113,0.91286528 1312,234.2872,214.3405,28.1282,73.1455,0.68536609 1312,254.9766,222.2158,24.9490,57.1934,0.65145320 1312,20.5026,215.6115,26.9318,58.4432,0.60660154 1312,245.7375,216.1755,25.2859,66.6877,0.58666587 1312,578.7160,217.5249,18.3552,51.2050,0.47386187 1312,435.3893,220.0777,32.9572,121.0515,0.41420272 1312,167.3562,214.0730,23.4821,52.6405,0.37537587 1312,79.1492,214.8857,26.2118,65.3510,0.29380107 1312,568.2216,219.4200,19.8767,49.8994,0.27039093 1312,6.5143,208.2948,53.1139,54.5648,0.23916531 1312,67.5296,213.2539,42.1713,102.0925,0.16238707 1312,447.0291,280.0427,23.2955,57.9085,0.10630962 1312,466.2359,216.3898,30.2437,89.0178,0.10053919 1313,319.3188,218.6980,40.0347,105.5964,0.99846876 1313,147.2488,223.0289,41.5549,118.0748,0.99749678 1313,241.5492,216.3344,22.9086,64.7346,0.99128747 1313,282.4310,214.8069,27.8390,57.1786,0.98771775 1313,85.8239,221.1211,21.7819,56.9815,0.96211112 1313,43.6188,214.6297,28.1723,66.6478,0.95760643 1313,194.7706,217.1559,28.6924,77.3682,0.94076037 1313,233.6742,220.3804,20.6424,59.2609,0.91799390 1313,501.7405,221.4655,43.0906,116.6280,0.84814978 1313,408.2442,219.4948,20.4150,50.3251,0.81810498 1313,490.9982,229.5376,34.8329,99.9415,0.81793761 1313,475.3418,219.2051,33.3289,103.0071,0.63407147 1313,96.6092,228.4435,22.5476,52.3637,0.62385285 1313,561.7655,225.9910,20.8914,49.9317,0.60148072 1313,63.1994,216.8549,23.2916,60.1456,0.51832676 1313,473.1783,217.1611,22.9305,65.5650,0.49338990 1313,205.8317,224.9175,24.0763,62.6280,0.48874211 1313,215.8980,224.3972,20.1620,56.8752,0.35528600 1313,74.3909,209.0071,24.8035,64.4995,0.32531735 1313,224.6628,224.0883,22.1865,58.2989,0.31948984 1313,29.2065,213.4184,24.7865,56.9041,0.30796069 1313,587.4202,230.6300,27.2254,71.3263,0.26063195 1313,554.7292,227.5713,19.9849,43.0119,0.20590121 1313,497.4540,270.9747,22.4781,65.4709,0.14937147 1313,549.1571,217.4604,66.1278,67.3992,0.13522780 1313,189.9468,214.7894,22.9682,62.0337,0.12912825 1313,457.4855,212.9999,17.4892,36.8579,0.12839796 1313,137.1302,217.4456,20.9194,49.2170,0.12783191 1313,574.7157,226.1702,20.7798,49.9515,0.12214057 1313,495.9225,219.2908,23.9626,68.0035,0.12042513 1314,316.6142,216.2931,25.8200,55.9505,0.99931455 1314,123.6900,220.3101,29.4598,66.3419,0.99912006 1314,163.7971,213.9645,31.0171,72.0010,0.99827015 1314,527.8358,216.8218,36.6853,105.7842,0.99705148 1314,271.7830,220.5980,34.2226,94.8769,0.99407136 1314,447.7348,224.2017,24.6949,65.2216,0.99284852 1314,208.1221,218.8003,21.8019,57.9872,0.97787559 1314,556.9815,218.0620,38.9172,107.1505,0.96902788 1314,0.1563,221.2875,50.4249,157.9838,0.95273274 1314,245.0458,222.5302,36.8399,116.6508,0.94991410 1314,216.6878,220.4186,22.2974,57.9635,0.84879142 1314,490.0347,241.7387,40.4557,120.6946,0.83802956 1314,462.4784,219.6725,24.9005,69.6797,0.81312156 1314,37.7324,213.3080,25.4530,60.7056,0.69383937 1314,21.4736,206.8550,36.2818,80.0922,0.65312636 1314,230.0349,218.3567,25.3978,65.7766,0.62865651 1314,177.0328,215.6393,28.4506,67.9064,0.56702983 1314,492.7589,218.2581,29.4019,97.0460,0.53691465 1314,69.2998,220.9643,26.3811,58.2905,0.48723832 1314,239.4495,216.7139,27.6153,80.7925,0.46822932 1314,188.5192,221.0461,25.1299,56.1249,0.38352510 1314,481.0499,210.6098,19.0090,49.4130,0.37598625 1314,508.1057,223.0108,28.3580,88.0015,0.37507123 1314,50.0042,206.4821,50.8520,79.6633,0.27440557 1314,472.4961,213.8586,20.6628,51.7714,0.26040930 1314,617.7932,224.4452,21.7272,53.0540,0.19420125 1314,153.3351,219.9488,26.8621,63.2771,0.18077579 1314,485.9898,216.4175,24.1393,61.1099,0.16969511 1314,518.7904,221.9334,29.9947,91.7972,0.15440616 1314,53.7402,214.6015,26.3116,61.1827,0.10928126 1314,260.1991,229.2570,32.7888,96.5208,0.10825650 1315,260.1346,216.4884,26.7764,71.1780,0.99875522 1315,145.0242,236.0273,71.9138,131.0893,0.99805200 1315,223.2600,220.8301,30.8275,80.4165,0.99737448 1315,100.1317,221.0508,24.8105,65.0612,0.99524343 1315,397.0380,227.4570,52.0706,134.6908,0.99510723 1315,200.8927,220.7849,31.9049,86.6273,0.99090469 1315,567.0659,226.4133,28.0427,88.4241,0.98969567 1315,339.6193,223.1312,43.6936,113.3072,0.98357427 1315,132.2497,219.3788,26.7970,65.3810,0.97703385 1315,542.9742,220.9168,29.8887,81.7604,0.97521675 1315,494.9232,217.3225,19.8627,55.3112,0.96526080 1315,46.2285,212.8340,26.4289,72.4142,0.95648205 1315,472.8860,213.9519,18.7938,49.4207,0.92890191 1315,529.9921,218.0027,18.6050,57.1855,0.92170793 1315,12.2707,213.6449,33.8291,65.8485,0.92003524 1315,514.6804,219.1013,16.5572,50.9495,0.88634276 1315,82.8133,211.4771,21.5408,53.5673,0.83730876 1315,26.7869,218.5447,32.4201,60.2535,0.70680499 1315,152.7211,215.9261,21.6907,62.4787,0.64636934 1315,74.4627,208.7849,19.9979,52.0441,0.47968084 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1329,596.1335,222.1837,28.1792,62.5939,0.99613011 1329,506.2708,214.0386,21.2995,44.8761,0.84370035 1329,22.3632,213.7906,27.4995,51.4860,0.61253440 1329,488.0028,214.7445,19.6469,43.4218,0.53117746 1329,3.4706,205.2602,51.3776,66.0382,0.44127393 1329,474.7549,217.9163,18.4628,44.6976,0.39781481 1329,10.8152,214.9174,24.8630,52.4937,0.31994718 1329,41.1840,215.7339,20.9251,43.8492,0.23926048 1329,613.9547,224.0003,20.3712,61.6697,0.22448751 1330,484.4592,221.9072,18.2103,46.0710,0.80721724 1330,499.1339,217.2194,19.1198,50.0576,0.65500128 1330,42.0970,213.1400,23.6014,45.7879,0.58682704 1330,30.4635,209.5146,27.0691,51.7703,0.33785725 1330,596.6161,220.3613,22.0629,52.1311,0.26479048 1330,615.9628,226.4314,21.3515,60.0577,0.21671431 1330,9.2190,215.5638,25.3167,47.4827,0.16081551 1330,146.4857,228.1385,20.7266,40.7095,0.14333093 1330,108.9192,222.7908,23.2644,45.4674,0.10564870 1331,526.8888,220.8169,20.2420,49.1357,0.94866848 1331,511.3907,220.6760,21.2072,49.1518,0.62097818 1331,499.7899,219.8969,21.1493,40.7067,0.24804822 1331,24.1620,199.2602,65.8484,69.3724,0.23988602 1331,69.4709,220.9756,20.4739,50.7963,0.22667027 1331,543.0630,81.6671,35.9354,61.7446,0.15554678 1331,59.4300,219.6756,20.8202,43.5742,0.11480683 1332,567.4211,225.1944,23.1207,56.3709,0.99103904 1332,555.5528,226.9451,20.9069,54.8679,0.93302822 1332,521.4566,221.6034,21.6811,44.8557,0.89636207 1332,24.4815,213.0295,24.3265,63.7310,0.87084210 1332,14.0932,215.2247,24.7695,62.6096,0.72401601 1332,46.9859,209.7868,28.5329,67.8143,0.55177593 1332,426.2398,196.8033,22.8023,57.5853,0.22541064 1332,38.2564,214.5862,25.2214,52.5464,0.11975287 1333,583.3624,230.3682,23.3083,64.5475,0.99772137 1333,604.2567,221.3094,26.4226,71.4597,0.99649310 1333,531.4809,219.4365,19.3283,49.3978,0.98373717 1333,543.8731,217.9123,19.0194,51.2016,0.92962205 1333,515.1921,220.5017,18.2863,48.2531,0.77266729 1333,522.9013,218.8802,19.0242,49.2553,0.41008943 1333,592.4138,225.3406,24.5737,68.9818,0.19246951 1334,566.1499,209.7465,24.2236,62.3839,0.99578482 1334,544.6191,212.3161,21.1303,53.7358,0.97056860 1334,436.8024,209.8626,30.7961,71.5704,0.29144427 1334,535.4211,217.5168,20.1117,48.4915,0.22518785 1334,408.6722,196.9530,61.2770,71.7394,0.17236823 1335,563.9426,90.8621,19.7814,46.3312,0.27948511 1335,487.5341,209.0638,19.6454,45.6030,0.26638463 1335,461.7360,203.3037,60.9344,73.0298,0.25986820 1335,466.9989,210.0379,20.2425,38.7003,0.24749994 1335,479.5364,228.0765,37.4421,75.6398,0.20024735 1335,425.7936,210.2031,70.9175,47.3098,0.19700137 1335,453.2105,208.1260,27.9118,41.2390,0.12821376 1335,474.9021,205.6968,22.6303,47.9678,0.10786506 1336,547.6839,208.5755,22.7943,65.9761,0.99701518 1336,521.1806,211.9200,20.4339,48.2068,0.75821757 1336,528.6769,208.9174,21.3870,52.7756,0.74043238 1336,589.6626,217.9433,21.5672,53.3827,0.29909289 1336,507.9260,211.4441,17.8030,43.8182,0.21614400 1336,536.7580,208.6866,21.6859,56.5916,0.19290137 1337,603.2848,204.9304,28.7298,77.1384,0.98930478 1337,510.4773,204.4208,20.7041,50.8161,0.87688351 1337,48.5409,206.1900,25.5208,65.4025,0.33564612 1337,476.3644,197.4548,88.1287,59.7921,0.14671814 1337,527.0998,204.6376,23.0281,56.7601,0.13094386 1338,595.8308,201.8029,28.1570,62.0128,0.99228597 1338,12.2174,196.1756,26.4128,55.7418,0.26660585 1338,522.0697,204.8331,19.6998,48.4471,0.22300325 1338,605.9146,195.1951,27.6936,77.1093,0.19447276 1339,578.6245,201.5583,28.6385,58.6174,0.55323178 1339,274.3874,206.0036,19.6593,44.5529,0.33555648 1339,536.3200,202.4729,24.0729,43.3005,0.24256158 1339,546.1450,201.7946,66.9940,64.7884,0.10765296 1340,587.2547,200.2457,24.5737,56.3487,0.57716829 1340,269.6261,211.9489,21.2956,48.1478,0.49672344 1340,579.0551,136.3081,28.1453,51.7713,0.42380488 1340,451.4471,225.0906,25.6178,35.9367,0.10951333 1341,480.4269,209.7466,18.9162,55.3922,0.86754537 1341,522.2876,242.5965,31.8517,72.9083,0.39274365 1341,438.1493,208.4963,18.2918,52.1240,0.25632733 1341,489.2033,216.9429,19.5933,51.7513,0.25106448 1341,511.9772,222.1007,19.9651,45.7702,0.11854268 1341,504.0519,224.1627,18.3703,46.5128,0.11777352 1342,508.0590,205.9551,24.2259,71.0290,0.98800927 1342,534.2770,225.9880,24.5919,52.6317,0.74551320 1342,587.5654,250.2130,37.2137,78.8296,0.43906146 1342,118.1645,207.9001,21.8785,48.1825,0.34609082 1342,253.2393,217.6630,22.0897,56.7351,0.14982004 1343,541.7107,214.2589,30.0786,78.0224,0.99909991 1343,94.7693,216.4292,25.2683,54.4990,0.92706287 1343,576.8334,222.8276,22.1661,66.9097,0.67459649 1343,607.2234,229.2904,24.7710,63.9158,0.60112458 1343,79.9201,214.7078,29.5483,56.5969,0.34943599 1343,249.9366,217.3112,25.4748,70.5506,0.23155662 1343,614.2133,208.1451,25.3254,81.9055,0.18976520 1343,120.9320,209.1840,21.0511,50.2028,0.14755285 1343,594.5532,229.8203,21.6788,61.4442,0.13169995 1343,346.9172,211.2179,18.8855,38.6787,0.12021677 1343,555.6413,219.7242,26.7268,67.7162,0.10582134 1344,111.6193,211.4116,23.9415,59.5818,0.99551344 1344,595.3247,214.5019,29.0707,86.0680,0.99242556 1344,575.4582,216.3906,26.2685,81.4423,0.97326386 1344,135.1529,219.7166,23.6700,49.0974,0.82263339 1344,248.6789,223.9140,23.3667,59.9406,0.58612454 1344,126.8779,213.6432,24.0466,60.1577,0.48597479 1344,546.3844,204.0836,19.6959,59.5976,0.45711344 1344,612.3582,220.0113,24.9478,73.3553,0.20451446 1344,541.9051,225.0833,22.2724,55.5696,0.12475844 1345,157.5969,212.3954,26.9574,62.2035,0.99687505 1345,136.0977,213.4280,24.7652,67.0138,0.99672878 1345,584.8648,203.4056,19.4379,64.5511,0.78327286 1345,58.3416,208.6335,20.8671,38.8020,0.28956234 1345,250.1250,219.9901,20.9601,59.8216,0.24009782 1345,573.9939,213.3132,17.1322,54.3887,0.16531847 1345,618.3547,248.4231,20.3757,62.2861,0.14525288 1346,394.4589,208.9162,28.9943,64.7610,0.99813026 1346,160.3724,213.6865,29.0022,69.1679,0.99578321 1346,37.9526,218.8531,30.5406,55.3285,0.99529529 1346,187.5705,215.7690,25.5576,62.5139,0.92538720 1346,351.7512,218.4182,23.7660,60.7932,0.51092803 1346,611.1757,207.0649,20.8649,64.9768,0.41933692 1346,173.7750,217.2354,27.4986,63.3248,0.32328269 1346,20.8308,217.4368,33.1752,57.9325,0.14824943 1347,379.9732,212.1807,27.0574,65.6268,0.99978304 1347,184.1796,214.5682,32.5264,75.5039,0.99931777 1347,58.8047,218.3937,30.9852,64.3728,0.99857342 1347,210.9200,220.0786,30.0866,72.2469,0.98535144 1347,25.7884,221.8659,32.2256,53.7053,0.92911673 1347,349.7264,217.0146,30.8341,63.6010,0.25751901 1347,45.5158,225.9463,33.1635,52.2408,0.16916892 1347,537.6543,213.4994,19.2983,37.2713,0.13280539 1347,90.3690,204.8851,21.6859,43.0469,0.10403290 1348,198.2769,217.4269,31.5985,71.0769,0.99972296 1348,82.5499,217.6823,34.5848,72.8772,0.99971747 1348,365.8458,213.3380,29.4384,71.5038,0.99911124 1348,224.8397,221.9109,32.8144,80.2339,0.99806952 1348,46.8584,218.7203,26.6826,70.1599,0.99545765 1348,553.2639,215.0311,19.9693,47.9210,0.49323753 1348,562.2172,213.4481,21.7621,42.7905,0.41230381 1348,32.2332,217.5038,27.5888,56.5724,0.32667685 1348,531.0647,212.1651,18.3984,45.5414,0.27069867 1348,535.3307,205.3050,66.9501,48.6097,0.17749274 1348,4.3001,221.8037,28.0923,71.9592,0.17069431 1348,538.5532,212.5737,17.9133,47.2145,0.15494931 1348,25.5449,215.2621,23.1942,48.8870,0.14122178 1349,67.0516,224.9259,30.5130,63.5070,0.99649018 1349,107.9250,221.8982,34.5240,72.6208,0.99629295 1349,8.6910,220.9866,34.4409,77.3423,0.97289991 1349,346.0619,214.3714,28.3365,63.4988,0.81627989 1349,226.3440,218.7066,31.9089,80.8010,0.68846601 1349,582.8527,209.1236,19.7197,43.6907,0.23663285 1349,96.9053,224.6567,30.7652,63.5879,0.22284666 1349,554.9954,212.4828,18.2114,38.9139,0.17804796 1349,545.4736,214.3416,18.7397,35.7271,0.14198031 1349,524.9686,216.0176,31.5955,35.3063,0.12347550 1349,562.1420,212.8394,17.3489,43.3721,0.11069118 1350,98.1817,215.3882,35.3915,81.9584,0.99935973 1350,154.2301,216.5469,36.1843,89.5435,0.99838734 1350,386.7035,225.6538,34.1492,88.4222,0.99778813 1350,32.6229,218.7455,37.4478,86.7129,0.99077928 1350,9.7128,210.7981,44.0841,107.9713,0.91832143 1350,549.2726,209.4276,52.6009,47.2694,0.35671180 1350,327.2689,213.7476,24.6663,65.5545,0.13140127 1350,584.8617,213.7726,21.4191,42.3163,0.11471636 1350,380.0903,226.1017,24.8544,69.3527,0.10717056 1350,509.0368,216.3652,107.5748,43.7778,0.10378227 1351,147.0342,224.3912,34.2318,73.4417,0.99953276 1351,204.3058,224.4012,35.8556,83.8555,0.99710953 1351,52.7514,219.7362,45.5100,92.9229,0.99629730 1351,456.1252,227.6603,37.1136,89.3840,0.99616796 1351,272.5197,211.0416,41.4233,90.4556,0.88574553 1351,599.0182,212.7275,24.4733,46.5699,0.65798700 1351,34.2060,221.6132,49.0354,105.8640,0.63617826 1351,471.4641,221.3176,30.8725,81.4888,0.48548943 1351,193.0915,221.1894,28.7976,81.8842,0.26333988 1351,560.3884,207.7507,67.4810,51.9833,0.15863407 1352,210.0164,211.9679,41.5557,102.1654,0.99830830 1352,98.5625,215.9023,36.4813,96.7685,0.99745214 1352,118.0350,216.2717,45.1866,98.3504,0.99543345 1352,200.8997,216.2762,32.1362,85.3624,0.45957130 1352,565.3045,211.3096,61.6006,45.4765,0.44040847 1352,604.4052,209.6198,24.4156,52.3831,0.37715131 1352,262.4807,217.0707,29.8464,80.0931,0.19657072 1352,374.8042,221.8389,37.7661,93.6680,0.17326909 1352,539.3220,221.0978,36.7261,46.8057,0.17077161 1352,544.9338,216.8055,58.3311,42.3057,0.16333124 1352,499.3283,213.1689,120.5527,50.0767,0.13446198 1352,583.4708,216.0387,24.3655,46.9436,0.12210434 1352,351.7909,214.1756,21.1835,53.9468,0.10902404 1353,408.5964,216.1401,32.6389,83.0767,0.99946600 1353,144.4112,213.2105,45.5372,114.6316,0.99757314 1353,174.6869,215.0497,37.1594,105.7252,0.95485914 1353,197.4869,217.7661,42.2139,104.9160,0.84571493 1353,264.6690,222.6825,27.0755,57.7357,0.49002001 1353,608.0533,211.3494,23.3074,58.8830,0.22361629 1353,580.4193,209.3369,45.6851,51.4315,0.20860347 1353,584.7164,210.8324,21.9208,41.3425,0.12977979 1353,189.6449,222.3162,30.8307,88.9660,0.12864771 1353,566.8649,211.6024,25.8932,36.9681,0.10315565 1354,440.4243,220.2637,33.5684,71.1557,0.99959713 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1791,230.2548,272.1712,21.0371,48.6611,0.25383341 1791,392.4087,203.9531,19.4369,33.3826,0.14628571 1791,284.7179,250.0194,38.7326,91.3079,0.10652081 1792,475.4880,200.8237,17.9688,46.5901,0.28249639 1792,238.0176,246.7495,42.6134,127.5162,0.27193177 1793,490.1777,204.5907,20.1114,44.2358,0.26884130 1793,499.5107,204.8060,17.9867,44.6956,0.13565169 1793,404.0420,202.0397,23.3388,33.0470,0.11325079 1794,512.8055,200.0090,21.8153,51.3559,0.86520499 1794,456.7910,200.2673,21.7166,45.1477,0.17117330 1794,420.5302,197.5808,19.7841,36.8629,0.10913193 1795,520.8998,206.1446,22.7920,51.8691,0.50945431 1795,511.7911,208.9483,21.7467,48.6787,0.46810976 1795,340.9483,206.9866,22.0297,45.2043,0.20162350 1795,462.9575,200.6155,23.1543,49.3037,0.19115241 1795,387.4509,208.5584,16.2817,32.4280,0.13838457 1796,525.7949,204.1999,20.2095,55.2135,0.76588947 1796,514.9031,208.7385,18.7766,52.6178,0.51936775 1796,336.9698,205.8757,21.7255,40.8185,0.27388066 1796,469.2636,204.7697,19.0573,50.6401,0.25042391 1796,250.7898,204.1015,34.2965,66.3682,0.10322580 1797,523.6833,207.4064,20.5795,52.1851,0.95631140 1797,539.5455,202.3024,21.9516,56.6897,0.79845703 1797,338.1158,209.8646,22.0705,45.0437,0.36376292 1797,482.0452,199.5415,22.8934,50.7408,0.31237516 1797,383.2803,195.2816,23.5301,44.2238,0.16805726 1798,542.8225,203.9984,25.7429,57.4880,0.99439788 1798,498.6523,198.0931,24.7262,60.2363,0.99373448 1798,533.5798,206.5306,21.7780,54.1502,0.66138661 1798,347.3170,207.4626,21.6171,47.7035,0.48641247 1798,443.3430,203.2740,15.8202,30.3303,0.12198463 1799,549.1506,202.5042,26.7854,56.9516,0.97403467 1799,354.4230,200.1358,21.8748,50.7271,0.89023316 1799,517.6400,198.7007,23.1960,54.9593,0.77172369 1799,540.5836,202.3467,22.6294,55.1491,0.43576658 1799,456.4208,195.0999,19.3436,42.6827,0.30436501 1799,443.7260,195.4894,23.0329,41.2625,0.16107307 1800,361.0103,204.5029,23.2949,50.0117,0.87644875 1800,559.1357,207.1061,20.6152,58.1351,0.75959998 1800,538.2868,209.2514,20.4994,50.6708,0.42894793 1800,454.1047,202.1914,24.5785,38.9836,0.14139375 1800,507.0305,200.9852,20.8495,41.7283,0.13463186 1801,459.9350,211.0395,24.8476,42.0436,0.66994512 1801,480.7763,210.5353,20.7087,46.0095,0.61776257 1801,515.6414,203.6434,35.8333,45.2388,0.56471372 1801,560.1667,218.2813,24.3674,62.5233,0.37846327 1801,363.8044,213.1151,22.1429,51.5685,0.31841481 1801,469.8581,206.7688,25.5473,45.1840,0.23757997 1801,518.6815,208.3299,55.0301,52.6336,0.20702991 1802,565.1709,212.5627,23.7720,62.9114,0.99005347 1802,467.4861,212.9845,26.2956,42.6629,0.95489717 1802,575.5191,211.0078,24.3111,68.7766,0.93078852 1802,528.2924,207.1111,25.9150,39.1326,0.72124231 1802,497.2343,208.6037,18.6642,48.9839,0.61136675 1802,479.5202,212.4459,22.2409,45.0388,0.35665667 1802,543.1705,206.0312,23.8178,45.4949,0.26384783 1802,366.2921,217.2433,25.1735,49.0013,0.20980318 1802,583.5537,225.2911,24.4347,63.4246,0.10607043 1803,573.6731,198.9348,26.6203,66.8866,0.99137115 1803,364.8040,207.1136,25.2814,53.8015,0.95073402 1803,470.8778,201.6925,25.1271,45.2985,0.94283658 1803,605.5006,212.2430,27.4232,67.6007,0.88742971 1803,535.9851,196.1731,25.1015,42.5671,0.80263531 1803,505.4628,201.8245,21.1418,45.4462,0.69365197 1803,548.3281,194.2644,24.3838,45.6017,0.49130428 1803,415.9011,206.5594,23.1997,59.6842,0.33853209 1803,432.1750,200.0966,22.2594,56.1138,0.24190590 1803,563.2611,198.4979,25.1280,62.6515,0.13330591 1803,404.2707,212.6695,19.8017,58.0871,0.11318873 1804,364.9467,207.4665,25.3440,61.1158,0.99898106 1804,479.8212,207.5927,22.7052,45.4461,0.97431517 1804,595.0794,202.5714,30.0557,65.7807,0.97153324 1804,547.0936,197.6835,22.1017,47.7630,0.92680204 1804,518.8438,202.5450,19.8651,43.2321,0.86995786 1804,580.8829,205.6377,26.7598,63.6681,0.86878753 1804,570.5736,205.3663,26.2088,66.7075,0.38373142 1804,558.0809,200.1440,23.6068,46.0547,0.31539723 1805,367.3197,205.9186,25.7613,67.8443,0.99882221 1805,539.6650,196.2469,21.9997,54.1644,0.99588788 1805,569.0740,190.2544,24.2202,59.6600,0.96115869 1805,409.3477,213.9401,22.3306,72.3925,0.63963729 1805,584.6643,196.6864,27.0530,74.0499,0.60568869 1805,604.5580,200.1988,26.1392,66.4953,0.50166291 1805,493.1151,202.9941,24.1115,49.5872,0.34456339 1805,578.8858,190.4367,24.7510,58.7551,0.12017444 1805,551.2205,196.8917,22.7179,51.4206,0.11280331 1806,413.0067,202.8784,28.9316,79.5289,0.99718881 1806,576.1622,193.1408,22.8492,52.5885,0.98836643 1806,377.4356,200.5833,28.6978,80.1420,0.98474646 1806,517.7129,199.0806,19.4989,46.7323,0.95995045 1806,603.9284,192.6691,28.6256,85.1508,0.82331133 1806,591.2122,198.4017,25.4100,56.4707,0.40941447 1806,505.7674,203.8714,24.1517,44.5162,0.17705742 1806,495.0276,205.9890,25.1140,43.3045,0.14468135 1807,421.3047,198.2233,30.2699,89.3237,0.99734133 1807,388.4380,201.6682,30.0523,79.2208,0.99446297 1807,542.3545,197.7433,21.2407,48.7902,0.77690947 1807,608.7959,186.9014,22.3346,58.6249,0.36705473 1807,399.8318,196.7310,32.6537,84.9366,0.11441240 1808,569.1802,198.4152,23.5860,57.0742,0.99902040 1808,432.6819,197.2768,34.5256,92.1058,0.99187255 1808,404.8131,202.7774,35.3500,85.6001,0.93919694 1808,418.0660,196.9036,35.3306,89.0330,0.29182696 1809,425.8869,208.6788,33.0633,92.7951,0.99585283 1809,462.9537,195.8175,37.6138,118.0275,0.97466147 1809,593.9772,205.8449,20.1325,53.2045,0.37991428 1810,495.1606,195.5465,46.7986,123.2135,0.97739363 1810,442.1326,200.9266,42.9967,108.2010,0.96012986 1810,433.4595,209.9367,31.5369,77.4859,0.11234770 1811,468.4193,194.6304,44.3470,127.1254,0.97174573 1811,541.8111,192.6550,49.9007,148.8941,0.61192852 1811,455.4976,192.6724,28.6616,77.7586,0.46196353 1811,465.6036,199.4222,30.5874,89.0589,0.10965440 1812,496.4073,195.4493,56.5945,159.3469,0.97246313 1812,594.1314,192.8981,39.2046,155.0497,0.32713354 1812,103.4371,221.9533,24.3840,43.9015,0.20086116 1813,540.6556,200.6006,63.7543,189.6868,0.98474789 1813,561.2793,247.4848,43.8725,119.3323,0.25375855 1813,535.6999,206.3947,41.0938,130.7557,0.14847495 1815,569.6585,274.9331,40.7912,109.9454,0.25182980 1816,152.8685,207.9091,38.6409,95.2025,0.36552835 1816,390.9365,193.2991,20.0697,41.1596,0.10280591 1817,396.2218,198.5679,22.7706,41.3745,0.61440539 1817,434.0594,199.0426,19.8575,40.9214,0.12776716 1817,50.7473,264.3669,52.0771,107.2488,0.10693326 1818,401.3676,203.7832,21.6342,39.2199,0.82303214 1820,459.6201,198.4987,21.3633,41.9988,0.78276896 1820,422.6429,205.1733,22.5037,45.7957,0.27807796 1820,54.4388,189.2043,64.6320,46.0982,0.10323273 1821,474.6329,199.5419,19.5357,43.4457,0.93285292 1821,434.7865,206.9723,19.7789,43.9258,0.89127481 1821,164.8808,207.5988,45.3323,103.5007,0.18821004 1821,450.9533,199.6060,20.9940,46.8752,0.12284300 1822,441.0177,203.9519,21.7009,52.2089,0.99601400 1822,455.9955,201.8927,22.3981,50.6966,0.15422684 1823,447.9050,203.0975,24.9850,58.3873,0.99903369 1823,483.3485,202.8045,21.5697,47.3673,0.72000992 1824,453.9436,198.6004,25.9570,65.2127,0.99850690 1824,488.5390,200.5359,21.7545,49.2004,0.98676383 1824,507.2466,197.3138,19.3272,48.2160,0.97860527 1824,227.2695,262.9615,39.9331,114.3845,0.13417271 1825,504.8629,199.7057,23.4874,56.1117,0.99896371 1825,542.7888,198.7918,29.4646,69.7915,0.98840129 1825,472.0653,206.2864,24.8047,67.2232,0.98009765 1825,52.5257,202.4016,41.1214,102.5762,0.40626118 1825,215.6339,271.3245,51.7348,123.4802,0.12181823 1826,553.1375,193.5682,24.0612,68.7258,0.99944085 1826,512.3401,201.2474,29.3584,80.8726,0.99391103 1826,67.1147,206.9588,35.2427,97.8996,0.85594881 1826,31.8532,206.0973,24.8670,64.1682,0.40567553 1826,53.2283,203.8971,35.5925,97.9308,0.28966272 1826,41.9443,205.9435,30.2062,72.8993,0.20762643 1826,220.1880,256.5629,44.6000,89.2060,0.13497758 1826,581.7915,199.0901,31.2533,50.3764,0.12295675 1826,383.6938,201.1637,20.6756,40.5373,0.10357258 1827,590.3917,196.5447,30.4431,80.5465,0.99869835 1827,541.8623,202.7385,35.6223,91.9290,0.99291646 1827,20.8789,216.6105,42.7804,110.9098,0.91472495 1827,211.2440,245.0857,43.8623,110.6693,0.23553567 1827,386.9293,205.5277,19.1575,38.6324,0.16239643 1828,583.6922,206.6325,42.5964,97.3014,0.99856633 1828,193.4407,252.8989,39.8446,96.6191,0.18956275 1829,493.4423,210.6340,43.5288,137.4175,0.17218773 1830,578.6872,197.7168,32.1542,56.5714,0.16918613 1830,179.3755,276.7123,51.4588,87.5744,0.10394782 1831,370.8971,166.0725,28.2667,86.3048,0.21542676 1831,287.2764,225.5804,21.9233,52.5881,0.11653464 1832,529.1041,195.6805,34.0002,67.0197,0.10462254 1833,404.1171,168.0141,29.1219,80.2665,0.13723695 1833,424.1971,204.8847,21.8967,44.3916,0.13530722 1834,426.7984,204.6299,20.1996,43.5328,0.14342716 1835,459.0798,219.0026,30.2818,61.5164,0.21900173 1835,425.8589,208.9855,25.1928,43.2334,0.11183173 1836,428.0082,159.1146,27.4156,94.2973,0.21888240 1839,479.4046,222.9303,20.9516,43.7911,0.61808920 1840,503.8234,218.0140,20.5384,47.2555,0.72806656 1841,535.9430,205.5112,23.6340,56.1573,0.94709259 1842,585.3550,215.9371,24.7861,61.9789,0.98094898 1842,594.8512,218.6302,28.3949,58.9410,0.65388012 1871,447.0515,233.0237,27.3368,52.8864,0.12447232 1874,485.1105,228.4042,41.5876,89.4855,0.78076637 1878,585.3077,206.6177,28.7688,69.9807,0.95310622 1879,572.0675,211.2652,29.2453,60.9874,0.91706258 1879,113.9692,213.7737,35.9157,84.6689,0.17024021 1880,561.0709,207.4327,24.2910,65.6461,0.98067141 1881,552.1475,208.1854,24.6380,65.0019,0.98588037 1882,543.2240,210.4270,23.5460,54.7116,0.99324548 1883,536.0205,211.4380,22.8611,53.6408,0.94861889 1884,535.6191,220.5777,22.5243,49.1978,0.83283025 1884,524.5137,212.0587,40.6007,76.0492,0.21285684 1885,542.7736,208.2239,24.2830,58.4218,0.99604177 1886,582.9695,214.6056,25.5936,57.4399,0.98981738 1886,588.3451,206.6218,34.5453,72.8947,0.10169414 1888,416.6352,194.1222,40.6906,96.4986,0.11873982 1891,481.2419,211.8337,23.3218,45.7017,0.11699732 1891,448.9881,208.1281,33.7352,34.7090,0.11265948 1892,323.0733,223.2547,36.8913,96.3192,0.17160745 1893,286.0323,221.7325,29.5721,74.8741,0.15680653 1893,197.9415,201.8969,50.6313,131.4728,0.10643499 1895,217.2981,216.0100,27.1170,60.3347,0.37011263 1895,145.4756,221.1261,32.6093,69.9609,0.18103707 1895,229.1429,220.2181,28.1435,67.9257,0.17258121 1895,156.0341,213.7788,34.5992,67.5196,0.11298282 1898,521.2555,212.7311,17.0866,38.0815,0.15804096 1900,570.3174,217.9151,21.6004,34.6115,0.13132912 1901,600.8361,215.9734,26.0282,49.1362,0.68204767 1904,185.1320,223.7426,20.7953,47.4613,0.19363685 1906,187.5402,212.8981,22.1507,57.2609,0.48595512 1906,126.9421,213.6757,28.9706,52.8313,0.14739315 1907,75.5665,225.3613,27.4093,60.7644,0.32903197 1907,184.1691,220.7029,24.0129,53.5524,0.11104849 1908,192.0666,219.3033,22.4816,52.6774,0.70958054 1909,194.1306,212.9263,21.2032,55.4986,0.50499552 1910,174.3229,214.8322,24.1088,52.4759,0.15821974 1911,165.1833,209.8709,33.5654,69.3244,0.38529623 1912,178.8089,228.5159,21.1246,56.5491,0.22806364 1912,169.7862,231.0376,22.3172,60.3718,0.13348435 1912,398.5796,210.5486,22.2832,64.1477,0.10423425 1913,166.9710,228.1456,25.7511,59.9076,0.36933720 1913,150.7815,225.1085,30.3342,64.6116,0.10823904 1915,149.9608,234.5677,27.7371,65.6173,0.16565832 1915,563.4090,212.4217,38.0988,99.1635,0.16534033 1915,141.9637,216.1396,27.9775,71.6659,0.14222139 1915,575.1952,219.9046,24.7189,61.2440,0.12241825 1917,107.5172,234.8794,27.5675,65.7601,0.27323243 1917,426.0653,217.0667,52.5764,140.7180,0.13250953 1917,208.6446,202.4429,21.8214,50.0493,0.12277805 1917,111.9423,231.5455,37.3964,89.9935,0.11406132 1917,19.2423,203.8500,38.0603,96.8892,0.10919275 1918,551.5623,215.6333,42.2598,88.5890,0.34419242 1918,533.4961,180.9632,45.5907,105.8696,0.10235408 1919,81.5657,233.0215,37.3418,93.4256,0.10042644 1919,61.4147,233.9615,24.6855,71.8917,0.10026827 1922,140.6783,198.7029,25.4865,47.5785,0.10987800 1931,391.8329,241.8988,33.9613,84.4635,0.13927615 1932,383.6838,230.7576,33.0549,72.8097,0.39833647 1933,376.4088,229.5239,35.8912,88.4469,0.79252422 1934,375.4620,231.6958,36.1452,82.2465,0.52413225 1934,158.2048,222.8076,45.2636,102.0468,0.29309919 1935,377.6256,230.3262,33.6421,81.6290,0.98455966 1935,173.5225,211.0427,40.6491,72.0624,0.17720540 1935,498.1884,185.7074,40.9176,94.5751,0.16995138 1936,398.6926,213.6664,36.3868,93.1244,0.90390760 1936,359.2547,198.9307,37.3645,130.3690,0.10614675 1937,429.0187,218.7411,43.1321,110.2677,0.85818893 1938,477.8613,219.2494,58.5896,131.0251,0.98188412 1938,404.9807,246.2786,22.9329,59.3886,0.12776352 1938,173.5926,201.6030,44.8604,111.1403,0.11786207 1939,67.7692,209.1337,31.0779,73.1446,0.23330382 1939,326.1879,254.5797,28.6211,70.2227,0.10487493 1939,465.2772,189.6591,32.0642,76.1013,0.10173410 1940,457.9664,246.1346,49.7361,106.3983,0.12912056 1941,455.0988,204.5723,26.1744,50.1875,0.15703773 1941,519.0041,176.6619,28.6944,83.0806,0.15636390 1942,487.0432,208.2902,26.0163,53.6243,0.18395734 1943,545.1617,206.4922,35.8822,75.5110,0.23670925 1943,441.1715,225.8787,42.5289,110.3044,0.14474863 1943,441.2321,274.3264,23.2129,63.3422,0.10073848 1944,417.6760,207.1218,20.9207,37.3271,0.15577731 1944,531.8816,323.5307,66.1252,199.9179,0.10370806 1945,436.5707,199.4144,19.3608,40.9344,0.22871462 1945,234.7215,221.7561,28.2556,68.3851,0.16494603 1946,479.5104,213.5608,23.2548,48.6059,0.81216586 1946,467.1448,215.7539,21.5088,44.4723,0.37235743 1947,523.1419,210.4960,26.1166,57.1937,0.74264896 1947,431.4619,204.7623,25.3639,39.4123,0.27125508 1947,531.7844,205.6922,26.3155,50.9013,0.11085333 1948,453.7677,208.9674,24.9908,42.8562,0.33099034 1948,464.6866,211.2188,23.9808,40.2332,0.27462828 1948,441.1478,199.3800,59.4228,58.5070,0.19117019 1948,441.0173,213.1540,29.3531,42.2414,0.17244405 1948,477.6406,211.7915,22.9502,39.5478,0.14904955 1949,499.4622,208.6768,26.5101,48.6203,0.96951890 1949,524.2701,208.1497,25.3714,52.5953,0.75782365 1949,510.7066,204.5147,26.2730,55.0822,0.29455656 1950,607.2904,205.2357,24.5956,64.1194,0.98760009 1950,579.8180,205.7671,27.8966,66.8704,0.95192349 1950,495.5263,204.8455,23.0535,46.0161,0.40790826 1951,541.1257,205.5501,26.8696,47.7469,0.94592029 1951,473.0876,215.3666,53.7653,51.0847,0.11719014 1956,558.1047,221.1151,26.2421,58.5402,0.98126245 1957,530.3410,213.5030,19.2577,42.8793,0.40400493 1957,412.1528,203.3979,22.0457,46.6155,0.36387277 1958,455.3117,228.5175,22.8090,46.2992,0.93769306 1959,545.3008,208.0879,34.9627,74.2999,0.91184223 1959,575.3151,210.2319,25.4114,55.8107,0.82185572 1959,563.7514,207.2745,29.9577,70.7999,0.24303657 1959,576.8185,207.0845,36.7373,89.0068,0.16389087 1963,365.1433,211.5085,21.4535,40.6991,0.10754794 1964,569.3864,194.0732,28.0321,69.6544,0.16798356 1964,397.1871,211.1613,25.4105,43.3910,0.15998806 1965,384.8971,214.8992,21.3935,45.2912,0.94945967 1966,461.3547,224.8617,27.2549,61.5336,0.99920464 1966,568.8576,221.1380,36.9070,92.7254,0.68591356 1966,87.3732,218.3181,25.3421,51.3323,0.16592917 1970,66.7190,213.1875,25.6192,48.1129,0.15592401 1970,217.0083,208.5096,21.8346,36.6833,0.12018154 1971,46.9000,204.8637,43.4030,45.7038,0.17955810 1971,452.8699,199.2249,21.9657,60.1854,0.12189094 1972,235.2981,204.2205,21.7920,39.4000,0.11949932 1973,228.5479,193.5121,25.8935,45.7275,0.25013345 1973,401.2447,207.5407,17.9974,41.9301,0.14693367 1974,415.6546,207.5372,19.3243,43.3970,0.79164863 1974,481.1806,202.4177,20.0341,41.9112,0.53887159 1974,453.2765,206.6392,19.6609,48.3597,0.35612833 1974,114.5825,189.8965,36.7983,65.2498,0.11753754 1975,442.2768,223.0336,20.2839,48.2365,0.99785632 1975,503.1715,220.8754,25.7071,63.1866,0.99719989 1975,547.0811,220.2429,21.9147,45.9781,0.80732822 1976,504.0987,222.0517,31.7673,80.1444,0.99108428 1979,369.0999,210.5818,18.0942,37.1813,0.18510100 1979,45.3777,206.8098,22.9741,50.5415,0.15096445 1980,401.0140,198.4486,18.7142,37.7612,0.29575616 1980,335.1719,167.4538,22.3604,62.5905,0.24535908 1980,367.8555,182.9478,25.5460,54.8344,0.13002580 1980,439.7671,210.4748,24.5741,53.2492,0.12038291 1981,432.7179,202.2729,20.9191,42.7680,0.58778954 1981,105.5108,200.3560,19.0110,37.8179,0.10323928 1982,511.8488,216.9395,20.0090,43.7979,0.93762034 1982,462.2782,222.4172,24.0646,55.7741,0.25748038 1982,248.3819,199.6971,24.0681,40.8289,0.19353981 1982,418.0890,152.9063,38.4161,107.4227,0.18995219 1986,489.6358,209.4442,19.3152,44.1012,0.59106225 1986,38.6566,210.5162,26.6974,47.5406,0.12133829 1987,568.0714,203.8478,28.6002,64.3048,0.94811034 1987,478.0722,201.1927,21.4764,44.5756,0.14013389 1988,502.8848,166.3213,36.3067,114.4241,0.12322973 1989,462.1984,203.9279,26.1679,51.1525,0.93238121 1990,462.4467,210.2241,22.9126,57.0297,0.79305100 1990,323.2215,193.0156,28.9409,65.3612,0.20612571 1991,483.6459,220.0328,23.1419,58.7189,0.43326932 1992,529.3941,207.8027,30.1070,80.4030,0.75679243 1993,592.4411,225.4257,33.2682,76.6739,0.46113700 1994,413.7016,31.9853,43.4145,127.8616,0.17529395 1999,296.1127,193.6973,26.2530,41.4466,0.10638148 2002,587.6460,152.1861,41.4324,116.8598,0.16524230 2004,319.9706,210.4708,19.0390,42.3925,0.33166283 2004,355.5371,215.9275,18.1686,35.9756,0.10513557 2005,354.1082,213.2666,19.5461,42.8479,0.44357589 2005,323.0308,211.4868,20.5714,48.7036,0.25360379 2005,334.4425,216.7289,28.5779,40.6792,0.14087389 2006,321.7234,213.2398,22.1803,52.8006,0.97525334 2006,355.1896,216.5014,22.2652,50.3810,0.95594019 2006,366.7842,216.5787,22.0486,44.0223,0.26816243 2007,346.9705,206.5817,24.3362,60.2404,0.99656379 2007,304.7196,205.1944,25.4626,63.5904,0.98979259 2008,282.9444,208.5776,35.3311,68.2423,0.99745739 2008,229.9808,206.1199,31.8919,73.7523,0.97796547 2008,48.2656,194.4557,30.5059,52.0156,0.65900481 2009,94.8344,203.8369,48.7503,118.1320,0.96355474 2009,182.2375,205.0814,46.4912,102.3556,0.84464514 2010,76.0683,209.0093,31.8927,76.0771,0.55356562 2011,337.6276,238.4842,25.7910,44.1710,0.21714562 2011,299.0088,239.4733,25.2575,51.3439,0.16588077 2015,585.7864,227.6503,25.1625,59.4022,0.97261941 2015,182.5812,216.5511,38.4409,80.8212,0.17359458 2016,506.5041,217.0197,32.1465,68.9570,0.62459910 2017,498.5701,203.4596,34.7584,82.4967,0.69460458 2024,411.5409,204.0618,23.5728,55.3009,0.16283043 2024,374.7007,210.4156,21.5972,40.7649,0.14815146 2024,285.1950,184.1638,24.5839,55.2446,0.10568795 2026,405.5136,211.7792,20.4892,43.0822,0.64881027 2026,366.2869,172.6587,29.2029,68.1403,0.29815638 2026,7.3299,200.7855,24.8336,53.8902,0.17329651 2026,32.6495,209.1937,26.9594,47.5648,0.10046560 2027,425.9319,207.2690,22.1447,44.7761,0.96337968 2027,506.1962,207.5657,22.6549,52.5411,0.17274965 2028,446.8393,213.5982,23.1993,55.8097,0.99443823 2028,547.8352,210.4081,24.7301,63.4267,0.92107403 2028,381.0539,208.3193,19.3458,42.4404,0.61635470 2028,550.3831,87.0833,42.0184,110.3409,0.15306132 2028,69.6104,200.1629,26.3314,57.2551,0.14977778 2028,107.4557,209.4502,21.5159,35.2181,0.14268494 2029,492.4442,215.2833,24.5139,61.9751,0.97061038 2029,401.3678,205.5564,19.1865,45.1977,0.96303773 2029,415.3973,190.5398,23.7966,52.6686,0.21360576 2030,432.9461,210.1793,21.4886,51.4646,0.98891437 2030,578.1707,213.7287,33.8988,85.9591,0.97800183 2030,443.0642,187.4984,28.3579,68.5827,0.24306354 2030,55.8511,205.7823,21.4931,44.3221,0.19355708 2031,490.3084,208.3319,27.3817,69.7722,0.98344433 2032,306.9982,204.6271,21.3144,54.0844,0.13286628 2035,525.4589,110.3266,78.9870,238.8368,0.29885668 2035,540.5065,43.8606,44.0466,121.5269,0.12317408 2035,91.7620,205.5102,31.6722,42.2457,0.11837262 2035,537.6626,74.8572,58.4921,175.1411,0.10437977 2037,472.6262,220.1239,29.8330,77.6290,0.18400265 2039,47.9711,206.1835,26.4208,52.5958,0.15711252 2041,309.6292,198.3011,27.3403,56.7850,0.12852240 2048,99.5711,209.4903,20.2254,36.9018,0.20122787 2048,7.3574,194.7634,32.2056,73.3162,0.16059971 2049,28.6362,208.0363,22.9276,46.8679,0.48886928 2053,351.8292,204.4994,22.4889,56.6276,0.24886723 2053,591.3861,32.4457,36.9406,95.4800,0.20871541 2054,16.7805,193.4469,33.8385,71.4073,0.28499413 2055,417.8575,168.2198,45.9648,135.4431,0.10065424 2055,93.6452,206.4730,26.5308,44.9652,0.10031983 2056,437.6688,267.6758,21.0373,66.2430,0.20182292 2056,185.7711,186.0776,44.4086,98.9228,0.19187771 2057,437.7858,273.4472,19.7805,54.4374,0.30595359 2058,513.2393,291.3295,25.7869,67.7770,0.38735607 2061,92.7193,207.9033,22.3408,44.1998,0.35630187 2061,413.6600,150.4725,41.4059,107.5173,0.16743277 2061,11.0065,207.6124,24.0869,51.5571,0.10466337 2062,40.1032,197.1335,26.5392,56.2213,0.48791677 2062,479.2524,149.3200,38.1541,113.6910,0.31611720 2062,27.3550,200.9818,25.4321,61.4243,0.11516102 2063,436.1010,208.8999,19.6353,36.2256,0.18016560 2063,22.0121,191.8612,23.1935,55.0506,0.13480635 2064,133.6772,205.0029,25.3010,53.2702,0.16449749 2065,492.4130,211.3306,20.2995,45.7634,0.44603449 2066,528.7686,210.0078,22.3669,52.7531,0.99848735 2066,242.4759,213.2116,32.0376,32.8592,0.20180136 2066,496.1794,118.2983,41.5366,126.1525,0.12057404 2067,575.8488,209.3823,23.1350,65.7902,0.96143264 2067,24.6204,199.5786,24.0239,48.5432,0.29320544 2067,233.4442,210.8363,26.1553,38.8140,0.18309185 2068,221.7025,211.9365,28.1410,43.7311,0.36638206 2068,12.9276,36.4449,49.5801,97.5357,0.31919470 2068,242.4697,211.1962,19.7734,42.1537,0.16167516 2068,23.5400,47.9091,49.4651,129.7405,0.11910184 2069,214.7797,216.4191,35.2736,39.3026,0.73841929 2069,560.9739,48.3146,38.7135,120.0850,0.32920846 2069,456.5732,220.8704,19.0491,46.4380,0.19581874 2069,420.8256,228.6434,27.0380,36.0743,0.14826307 2069,365.7283,181.6994,38.7900,69.6327,0.13590737 2069,204.7831,209.1321,56.3428,57.9073,0.12047328 2069,381.4063,219.7257,19.6423,40.7841,0.10944337 2070,203.5839,215.6475,29.2876,41.9603,0.96561819 2070,482.1386,219.9113,21.1205,54.3082,0.68841136 2070,469.8546,223.4434,21.8395,51.9376,0.28246668 2070,410.3941,228.4634,16.5376,33.5498,0.13747722 2071,182.4804,217.7124,24.5856,43.2928,0.89687800 2071,496.1810,223.5812,19.8980,54.4162,0.87409669 2071,485.5234,223.6099,20.0971,56.8190,0.75932300 2071,206.6406,214.4922,25.4173,41.8357,0.72355020 2071,435.2607,227.4026,19.6059,37.3624,0.39528218 2071,476.5787,205.8934,36.2226,77.1953,0.13742995 2071,183.8227,205.9944,47.1194,58.8322,0.12649210 2072,162.1951,215.0920,25.4061,47.9987,0.98590088 2072,502.5045,222.9258,21.7973,60.8020,0.97749484 2072,205.7606,215.1183,28.4436,43.8461,0.89655209 2072,464.3543,224.5414,20.1145,41.8580,0.77104366 2072,521.1572,222.3628,22.5204,56.8634,0.34184107 2072,493.1353,223.3593,19.8866,55.8147,0.18955800 2072,484.4573,225.2908,16.5337,47.0633,0.11605506 2073,142.1710,215.1060,26.6835,44.4858,0.88753146 2073,527.8275,228.2640,22.2218,58.0863,0.78467107 2073,205.9352,215.9258,33.6877,37.1378,0.71814865 2073,491.5136,223.2965,21.4279,44.4133,0.66682160 2073,501.5009,226.0454,22.5852,44.7823,0.47312295 2073,289.6974,217.9240,19.0823,38.0538,0.20367879 2073,507.8458,221.2332,49.8950,62.1577,0.11112127 2074,208.3639,215.4988,31.2415,37.9198,0.76723164 2074,120.6172,213.8851,19.1391,44.7834,0.73140872 2074,572.9406,213.0728,24.7598,68.9193,0.64922893 2074,266.7931,217.6895,19.3784,44.6881,0.64139086 2074,135.3500,216.7324,16.5431,43.8017,0.41586217 2074,263.8441,175.7954,29.7690,71.5807,0.13701831 2074,201.8821,205.7189,91.0020,60.1222,0.12243845 2074,516.8526,227.3214,18.1676,39.1867,0.10837822 2075,424.8007,227.6196,26.9539,74.9387,0.99897772 2075,233.5559,221.1632,25.8411,64.6833,0.99855816 2075,214.2546,220.8462,27.0003,68.8376,0.99710083 2075,40.4373,229.5968,62.6348,150.2267,0.99411464 2075,405.6340,225.2777,25.8967,76.2997,0.91253805 2075,18.3602,233.6692,58.0145,153.4603,0.43296933 2075,498.9778,125.3889,37.6118,106.7916,0.26294130 2075,366.1707,222.0922,18.2533,28.9368,0.11841213 2076,455.6068,232.9513,31.3055,79.3928,0.99847460 2076,232.4177,218.2129,27.9389,70.0473,0.99359876 2076,216.0552,220.0212,27.8180,69.4565,0.99231446 2076,21.4493,221.0148,69.7796,191.6405,0.99075258 2076,431.2796,228.6470,28.8805,91.3922,0.96210384 2076,203.9209,215.9348,29.8485,68.5949,0.15950096 2076,381.0078,214.9814,62.7322,42.8952,0.11364701 2077,221.8194,209.7015,32.0612,84.3304,0.99950099 2077,477.9889,232.7433,38.0058,111.5135,0.99884725 2077,196.6414,218.9704,30.8018,73.5137,0.99656129 2077,449.3799,231.6756,40.2457,115.0064,0.93263960 2077,427.7849,219.7245,20.0008,50.2264,0.54256558 2077,535.8036,213.4024,26.1030,55.9919,0.36816725 2077,392.1136,217.8872,31.4170,35.6755,0.18655263 2077,372.7593,213.1659,69.7047,43.9590,0.12126161 2078,184.3353,217.4128,33.9889,87.4908,0.99946475 2078,221.3438,215.3435,35.1035,88.8838,0.99905092 2078,535.5313,245.4501,46.2886,139.1578,0.99135476 2078,494.2453,230.7576,47.6286,164.1239,0.87138313 2078,266.8181,215.4057,17.9347,43.6496,0.65789413 2078,425.2681,223.1213,20.3427,54.6631,0.53352159 2078,454.4453,230.6402,21.0719,50.1775,0.21526684 2078,540.8756,196.1780,43.6933,97.5372,0.20983404 2078,373.8285,216.3765,77.9260,48.6270,0.16423073 2078,409.5097,219.8212,18.0645,47.2026,0.15675895 2079,214.7254,215.1882,38.1592,98.1692,0.99927545 2079,176.7331,218.2332,36.4288,94.6159,0.99876451 2079,428.9074,223.1396,24.5321,64.9642,0.88386953 2079,268.1546,217.0271,18.1791,47.5311,0.41858035 2079,606.5426,216.2596,28.3469,74.1969,0.37344703 2079,259.3230,218.1736,17.3092,45.8907,0.37141159 2079,409.7702,221.4297,17.9597,46.6402,0.20355052 2079,591.9571,212.3565,29.8411,69.5073,0.18719247 2079,353.7561,221.9530,26.4061,45.9852,0.12459655 2079,464.8979,231.9019,21.3066,50.5314,0.11138044 2079,367.9304,225.0777,23.2085,42.8053,0.10433700 2080,128.4650,220.0836,40.9001,104.7448,0.99961734 2080,169.4175,209.4103,45.4095,122.4331,0.99873292 2080,422.8310,221.8041,26.9904,71.2936,0.91872168 2080,243.6905,212.6325,19.6317,51.1766,0.68557620 2080,232.9308,213.4990,19.6064,53.2211,0.63978672 2080,458.1106,231.2266,26.6741,68.6710,0.44510585 2080,401.4895,222.6069,19.4278,43.9713,0.39611021 2080,389.2018,223.5919,19.5580,45.2007,0.29755217 2080,14.3202,261.1344,52.5226,74.6958,0.27745101 2080,583.0549,195.3260,26.9099,62.4310,0.19386035 2080,563.4641,268.4977,15.8867,33.2888,0.18129593 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1950,631.3431,210.7296,8.8036,60.6979,0.00000000 1950,629.4816,478.6521,10.6651,33.4946,0.00000000 1951,541.4718,205.5797,29.5892,60.8663,0.00022948 1951,595.6464,237.3385,28.5694,66.6266,0.00000000 1951,559.0096,202.9269,29.6772,60.2832,0.00000000 1951,534.4805,180.2499,52.0601,125.1822,0.00000000 1951,358.1297,198.5903,25.2906,55.5882,0.00000000 1951,634.4756,229.3042,5.6711,76.1712,0.00000000 1951,631.5455,484.0450,8.6012,28.1017,0.00000000 1952,338.2242,208.2366,22.3507,55.6964,0.00000000 1952,629.5993,479.2658,10.5474,32.8809,0.00000000 1953,356.9685,207.0791,22.5902,54.9798,0.00000274 1953,629.3434,477.8786,10.8033,34.2681,0.00000000 1954,384.1606,211.5891,23.4374,54.0323,0.00000011 1954,23.4217,208.2375,34.0649,76.0927,0.00000000 1954,397.4608,207.1356,24.6785,53.0553,0.00000000 1954,629.3303,478.1688,10.8164,33.9779,0.00000000 1955,378.3740,223.7588,23.1520,53.0266,0.00002599 1955,358.0360,225.2416,21.9229,48.3005,0.00001337 1955,333.9879,225.9593,21.1358,46.3365,0.00000000 1955,346.3932,212.9401,20.1090,51.8230,0.00000000 1955,629.1075,477.6502,11.0392,34.4965,0.00000000 1956,560.6611,221.6501,26.6863,57.9430,0.07984048 1956,551.7879,210.4732,22.2982,54.7837,0.00000000 1956,629.4681,478.3529,10.6786,33.7938,0.00000000 1956,525.9941,129.8908,97.2827,214.7520,0.00000000 1957,410.4752,204.3835,24.4758,50.4431,0.33815002 1957,529.4108,210.8499,21.4913,52.1016,0.30709189 1957,422.4403,210.1407,25.3681,57.2751,0.00000000 1957,540.1562,220.5880,22.4839,52.7377,0.00000000 1957,637.1740,261.8406,2.9727,62.8095,0.00000000 1957,629.8166,246.7991,10.3301,82.2726,0.00000000 1957,283.1643,280.7370,86.6839,231.4097,0.00000000 1957,629.5234,478.6218,10.6233,33.5249,0.00000000 1958,454.4878,228.5925,23.6764,55.9279,0.99465287 1958,442.4449,223.7589,23.0329,50.0088,0.00000003 1958,469.1210,230.3661,26.2008,54.8111,0.00000000 1958,441.1896,196.3015,50.9738,105.2650,0.00000000 1958,408.7257,138.5836,100.2944,229.3797,0.00000000 1958,629.4683,478.7280,10.6784,33.4187,0.00000000 1959,548.5076,209.6739,34.1020,81.5903,0.99999976 1959,575.6150,206.7113,30.7478,71.5941,0.99952662 1959,591.2875,203.8177,29.9595,61.4652,0.00000000 1959,397.3580,204.9545,25.4204,59.5397,0.00000000 1959,530.4433,127.4740,89.6686,204.9336,0.00000000 1959,635.6152,486.8819,4.5315,25.2648,0.00000000 1960,368.3457,202.7249,26.2399,58.4164,0.00000000 1960,629.6434,480.0994,10.5033,32.0473,0.00000000 1961,635.7147,488.1747,4.4320,23.9720,0.00000000 1961,628.4432,457.9022,11.7035,54.2445,0.00000000 1962,635.3404,487.9829,4.8063,24.1638,0.00000000 1962,628.2956,458.3322,11.8511,53.8145,0.00000000 1963,364.8915,200.8919,26.7985,54.0926,0.00000000 1963,500.3785,209.7186,30.0213,70.5103,0.00000000 1963,431.6876,213.2001,28.1438,64.8803,0.00000000 1963,635.3698,487.6829,4.7769,24.4638,0.00000000 1963,628.7039,459.0273,11.4428,53.1194,0.00000000 1964,397.4280,212.4641,22.6152,52.5340,0.00003291 1964,567.7443,197.9994,26.5870,55.3339,0.00000280 1964,521.0666,219.9160,22.1005,57.3522,0.00000000 1964,353.0787,206.1548,21.4983,53.9575,0.00000000 1964,555.3322,210.8273,27.4462,70.9494,0.00000000 1964,533.4359,212.7161,28.1470,63.8336,0.00000000 1964,635.6284,488.5823,4.5183,23.5644,0.00000000 1965,383.9392,214.3716,21.8704,51.8927,0.94542015 1965,631.8395,487.7430,8.3072,24.4037,0.00000000 1966,458.9314,224.4210,30.5811,71.2310,1.00000000 1966,571.7654,226.8981,35.6808,93.2701,0.95778483 1966,476.5648,229.0591,25.6332,55.2229,0.00000000 1966,545.3530,185.5843,90.2757,206.7912,0.00000000 1966,631.9270,487.4445,8.2197,24.7022,0.00000000 1967,408.4091,199.4676,28.2812,63.8556,0.00003210 1967,480.8165,104.5749,87.0349,270.0936,0.00000000 1967,302.0832,1.0000,84.8546,219.2107,0.00000000 1967,635.5782,487.8469,4.5685,24.2998,0.00000000 1968,628.2189,1.0000,11.9278,48.7471,0.00000000 1968,635.7588,488.3716,4.3879,23.7751,0.00000000 1968,628.8738,459.9059,11.2729,52.2408,0.00000000 1969,479.5713,202.3709,24.9793,59.1281,0.00000000 1969,558.0173,165.9076,41.9352,117.2314,0.00000000 1969,552.7537,100.4473,81.6290,249.2436,0.00000000 1969,631.8306,487.3495,8.3161,24.7972,0.00000000 1970,635.0967,185.6190,5.0500,78.5716,0.00000000 1970,635.4353,487.9905,4.7114,24.1562,0.00000000 1970,628.3250,458.6360,11.8217,53.5107,0.00000000 1971,369.4952,190.1038,28.1122,56.4480,0.00000000 1971,391.7036,195.6088,24.3306,53.6431,0.00000000 1971,419.3454,203.1001,24.2433,53.8643,0.00000000 1971,635.2424,487.8374,4.9043,24.3093,0.00000000 1971,628.3483,457.9994,11.7984,54.1473,0.00000000 1972,635.4542,487.9573,4.6925,24.1894,0.00000000 1972,628.4907,458.7747,11.6560,53.3720,0.00000000 1973,423.8591,209.2292,19.9971,48.9934,0.00488346 1973,446.9410,208.7994,22.1710,52.9823,0.00042997 1973,405.4420,202.4301,23.1120,51.3649,0.00000000 1973,433.1764,217.7619,20.8234,52.4952,0.00000000 1973,512.8799,162.0473,89.5725,264.7838,0.00000000 1973,635.3669,488.0489,4.7798,24.0978,0.00000000 1973,628.3087,457.7358,11.8380,54.4109,0.00000000 1974,451.5653,211.0406,22.0657,53.3909,0.02470816 1974,414.3430,207.2878,23.0635,48.6793,0.00811997 1974,481.7863,207.5674,21.1487,50.2251,0.00248681 1974,466.9667,208.6022,21.9402,50.9324,0.00000000 1974,433.5845,200.9292,22.4763,50.1378,0.00000000 1974,632.0331,486.8607,8.1136,25.2860,0.00000000 1975,440.1296,218.7668,25.6739,58.9084,0.99999946 1975,501.1498,220.7280,27.0027,64.9288,0.99998361 1975,548.1795,215.9953,24.2037,55.8488,0.99977332 1976,503.1003,221.9786,36.4501,86.2161,0.99999100 1976,468.3810,191.0737,93.4186,193.8868,0.00000000 1976,631.7067,487.0548,8.4400,25.0919,0.00000000 1977,635.8989,488.0434,4.2478,24.1033,0.00000000 1977,628.6954,458.8954,11.4513,53.2513,0.00000000 1978,595.6786,132.0874,44.4681,263.2655,0.00000000 1978,626.7741,165.8445,13.3726,130.3695,0.00000000 1978,635.3573,487.8449,4.7894,24.3018,0.00000000 1978,628.4539,458.8877,11.6928,53.2590,0.00000000 1979,388.8154,204.6573,34.7898,77.5771,0.00000000 1979,370.5366,210.5558,24.6202,59.5058,0.00000000 1979,599.4283,191.3061,22.5091,54.9157,0.00000000 1979,636.2045,488.5907,3.9422,23.5560,0.00000000 1980,503.0587,184.4854,46.7620,129.8066,0.00000000 1980,396.6276,194.6048,24.5785,55.4593,0.00000000 1980,506.1241,1.0000,46.8330,120.2900,0.00000000 1980,470.8927,181.0441,55.7295,143.2987,0.00000000 1980,408.1938,2.3538,62.5370,151.1019,0.00000000 1980,437.0261,31.1673,83.7522,278.0561,0.00000000 1980,515.8492,1.0000,87.2433,221.2517,0.00000000 1980,636.1466,488.4811,4.0001,23.6656,0.00000000 1981,431.9144,202.1276,24.7011,57.3932,0.21325909 1981,384.6323,196.2554,24.9467,57.6918,0.00000000 1981,363.9702,202.8436,24.2815,55.6296,0.00000000 1981,611.5255,241.5474,24.4500,55.4543,0.00000000 1981,628.7064,230.6080,11.4403,78.5089,0.00000000 1981,637.0292,237.5965,3.1175,62.8343,0.00000000 1981,391.7175,129.3033,98.6829,221.9063,0.00000000 1981,635.7564,487.8405,4.3903,24.3062,0.00000000 1981,628.2025,455.5448,11.9442,56.6019,0.00000000 1982,509.5342,213.4143,23.5419,53.7412,0.99530554 1982,417.1443,201.7507,29.7619,70.0783,0.00000005 1982,497.8166,206.2635,21.5951,54.0505,0.00000000 1982,501.6253,179.9288,47.3850,105.4847,0.00000000 1982,632.1941,487.4785,7.9526,24.6682,0.00000000 1983,532.7506,217.1231,28.5014,64.5714,0.00000000 1983,504.6059,198.1181,31.3660,76.4132,0.00000000 1983,600.7219,125.9840,39.4248,264.0536,0.00000000 1983,627.3640,208.4161,12.7827,91.8682,0.00000000 1983,638.9086,212.9572,1.2381,66.5526,0.00000000 1983,634.1935,176.1486,5.9532,84.5284,0.00000000 1983,635.4117,487.7277,4.7350,24.4190,0.00000000 1983,628.7492,458.8634,11.3975,53.2833,0.00000000 1984,484.8659,203.4009,23.3759,56.8496,0.00001856 1984,425.7838,203.5247,23.9713,50.6231,0.00000049 1984,413.4088,199.7711,23.4176,51.9913,0.00000000 1984,528.2336,100.9579,46.9643,139.0111,0.00000000 1984,503.2263,68.7545,83.0652,268.0479,0.00000000 1984,635.5139,488.1133,4.6328,24.0334,0.00000000 1984,628.5143,458.7264,11.6324,53.4203,0.00000000 1985,443.8026,205.2490,25.3514,54.7765,0.00000043 1985,589.7637,214.2571,24.1363,72.8003,0.00000000 1985,355.4030,201.7068,27.6885,62.6151,0.00000000 1985,574.9068,164.9088,50.3137,121.4011,0.00000000 1985,430.0614,167.5034,54.8885,111.5129,0.00000000 1985,635.5474,487.9102,4.5993,24.2365,0.00000000 1985,628.8712,458.5363,11.2755,53.6104,0.00000000 1986,490.9339,205.0844,23.6338,54.7729,0.98934633 1986,503.6994,202.7692,28.8553,58.2426,0.00000002 1986,477.0695,202.1592,23.2287,51.2494,0.00000000 1986,490.0357,168.0591,55.4113,111.9459,0.00000000 1986,602.3876,129.0755,37.7591,264.0168,0.00000000 1986,636.2115,219.6696,3.9352,69.0165,0.00000000 1986,625.9529,175.9088,14.1938,121.9071,0.00000000 1986,635.4794,487.7034,4.6673,24.4433,0.00000000 1986,628.8593,458.0800,11.2874,54.0667,0.00000000 1987,566.8496,200.9202,33.8298,69.2434,0.99923056 1987,475.2709,201.6639,23.7115,52.4594,0.00000060 1987,542.3879,110.1982,94.7992,209.3035,0.00000000 1987,633.4398,210.7664,6.7069,84.1456,0.00000000 1987,635.5543,488.2474,4.5924,23.8993,0.00000000 1987,628.7952,459.4441,11.3515,52.7026,0.00000000 1988,635.4126,487.8340,4.7341,24.3127,0.00000000 1988,628.5762,458.4449,11.5705,53.7018,0.00000000 1989,465.4463,207.6337,26.2685,56.7053,0.99903107 1989,481.3427,211.9571,27.5945,54.4671,0.00000000 1989,579.6715,168.1196,28.1468,65.6350,0.00000000 1989,458.1793,160.6158,49.7165,105.0237,0.00000000 1989,634.5678,188.0700,5.5789,80.7821,0.00000000 1989,635.5835,487.4435,4.5632,24.7032,0.00000000 1989,628.8658,457.0295,11.2809,55.1172,0.00000000 1990,460.1512,210.7124,27.3430,60.3235,0.66596943 1990,448.9651,205.7137,23.6765,58.8157,0.00000000 1990,475.7022,209.0252,26.3028,58.5649,0.00000000 1990,423.8579,117.8475,96.7347,216.7165,0.00000000 1990,635.6691,488.8623,4.4776,23.2844,0.00000000 1990,628.4240,458.2541,11.7227,53.8926,0.00000000 1991,489.0704,215.1792,25.5450,61.4756,0.00000082 1991,187.5631,218.9153,20.7149,57.4689,0.00000001 1991,478.8945,175.6493,49.7527,112.3155,0.00000000 1991,635.4327,487.8802,4.7140,24.2665,0.00000000 1991,628.3564,458.0344,11.7903,54.1123,0.00000000 1992,528.5652,206.9623,35.6123,86.1152,0.38334486 1992,125.7239,208.0715,26.3088,59.1814,0.00000000 1992,515.2598,203.0359,30.8082,65.7156,0.00000000 1992,318.1530,1.0000,89.3886,236.6915,0.00000000 1992,496.5133,190.2426,88.3281,227.8317,0.00000000 1992,408.2147,123.5653,44.5150,142.6188,0.00000000 1992,390.2869,53.6143,86.5017,272.5051,0.00000000 1992,635.8983,487.9715,4.2484,24.1752,0.00000000 1993,73.8394,220.5092,32.5221,68.2754,0.00000009 1993,598.9149,228.2638,34.8811,79.3291,0.00000000 1993,580.2502,143.4659,59.8965,181.5358,0.00000000 1993,391.2709,1.0000,76.1078,199.7880,0.00000000 1993,535.4501,55.3387,87.9909,273.0780,0.00000000 1993,621.4474,165.9297,18.6993,144.7082,0.00000000 1993,631.4182,140.5917,8.7285,108.4162,0.00000000 1993,364.6579,104.2405,89.4242,265.9899,0.00000000 1993,633.1567,217.6854,6.9900,73.8448,0.00000000 1993,41.7150,106.3823,82.4277,259.6988,0.00000000 1993,635.2440,487.7859,4.9027,24.3608,0.00000000 1994,46.9223,219.0640,31.8333,69.4085,0.00000002 1994,424.3510,46.9927,67.2557,188.4372,0.00000000 1994,598.1259,94.2991,42.0208,175.3716,0.00000000 1994,494.9848,1.0000,82.7237,210.4345,0.00000000 1994,628.2814,130.0639,11.8653,111.2152,0.00000000 1994,516.5336,71.3949,88.6013,275.5739,0.00000000 1994,569.5192,97.8009,50.2350,158.0161,0.00000000 1994,635.5684,487.7469,4.5783,24.3998,0.00000000 1995,47.3273,225.0659,32.8176,71.2873,0.00000002 1995,596.4403,104.5062,43.7064,162.3571,0.00000000 1995,29.2620,175.4034,42.2278,113.7070,0.00000000 1995,622.0803,154.1119,18.0664,135.4855,0.00000000 1995,623.5836,71.9440,16.5631,139.5728,0.00000000 1995,464.5932,1.0000,85.3475,255.6234,0.00000000 1995,556.6266,138.9995,83.5201,274.4255,0.00000000 1995,630.6394,211.1229,9.5073,106.3379,0.00000000 1995,635.6201,488.8488,4.5266,23.2979,0.00000000 1995,628.2969,456.6399,11.8498,55.5068,0.00000000 1996,212.6932,248.4209,27.8093,52.9752,0.00000000 1996,635.7563,488.4275,4.3904,23.7192,0.00000000 1996,628.5056,457.7657,11.6411,54.3810,0.00000000 1997,635.7214,488.3861,4.4253,23.7606,0.00000000 1997,628.4705,457.9365,11.6762,54.2102,0.00000000 1998,513.2404,122.5809,58.9408,168.3083,0.00000000 1998,478.5783,1.0000,82.2705,204.0943,0.00000000 1998,635.6450,488.3786,4.5017,23.7681,0.00000000 1998,628.3353,457.8116,11.8114,54.3351,0.00000000 1999,380.0165,205.5790,22.9076,58.7789,0.00000000 1999,635.6813,488.0212,4.4654,24.1255,0.00000000 1999,628.4099,458.6033,11.7368,53.5434,0.00000000 2000,511.9077,140.5928,57.1893,162.0475,0.00000000 2000,635.5396,487.8863,4.6071,24.2604,0.00000000 2000,628.5261,459.6033,11.6206,52.5434,0.00000000 2001,377.5234,219.3175,21.3208,51.7828,0.00000006 2001,190.6756,182.8354,30.6383,61.6188,0.00000000 2001,517.5356,188.2649,43.8677,115.3182,0.00000000 2001,162.6849,200.6672,44.2613,122.8203,0.00000000 2001,10.8434,165.9862,90.5257,284.5225,0.00000000 2001,140.2609,156.0410,87.2257,250.2883,0.00000000 2001,505.7166,111.9023,82.7868,266.7922,0.00000000 2001,635.6052,488.3086,4.5415,23.8381,0.00000000 2001,628.6334,459.6009,11.5133,52.5458,0.00000000 2002,121.2764,269.0255,27.3609,60.1261,0.00000020 2002,111.6720,240.1297,51.1795,114.3138,0.00000000 2002,384.7862,210.8006,23.4375,51.9225,0.00000000 2002,595.5156,90.3094,44.6311,276.9276,0.00000000 2002,635.5551,488.0203,4.5916,24.1264,0.00000000 2002,628.6786,459.1691,11.4681,52.9776,0.00000000 2003,311.0821,199.5451,21.6761,47.9530,0.00037632 2003,331.5202,204.1028,22.7189,50.1141,0.00000000 2003,343.0732,203.5729,23.3917,60.7369,0.00000000 2003,140.8879,172.1385,38.4498,88.4614,0.00000000 2003,567.8705,16.8510,72.2762,261.4256,0.00000000 2003,533.6707,98.7700,88.6862,274.9908,0.00000000 2003,1.0000,359.1268,59.1446,153.0199,0.00000000 2003,630.5400,69.0962,9.6067,101.2132,0.00000000 2003,114.7983,61.3698,79.0581,264.3402,0.00000000 2003,635.4718,487.8364,4.6749,24.3103,0.00000000 2003,628.6721,459.2289,11.4746,52.9178,0.00000000 2004,318.9483,208.9334,22.6225,50.4984,0.68243301 2004,338.5945,207.3459,21.9562,45.5450,0.00000018 2004,351.7288,206.2869,21.2256,46.9395,0.00000000 2004,42.6166,170.1933,56.0407,143.3723,0.00000000 2004,202.7359,248.3539,24.7529,55.6088,0.00000000 2004,633.7974,211.2365,6.3493,80.7298,0.00000000 2004,564.1184,120.7795,76.0283,262.3536,0.00000000 2004,635.3971,487.6483,4.7496,24.4984,0.00000000 2004,628.2944,458.1991,11.8523,53.9476,0.00000000 2005,350.9742,208.0833,23.3092,48.7839,0.00007364 2005,324.5375,212.9975,22.6301,47.8390,0.00000178 2005,580.2736,214.1325,35.4636,79.0482,0.00000000 2005,115.1932,206.5763,68.2088,176.0300,0.00000000 2005,336.8713,211.4777,23.4092,47.1938,0.00000000 2005,68.5866,192.5538,81.2844,263.3511,0.00000000 2005,635.2764,487.5275,4.8703,24.6192,0.00000000 2005,627.8310,457.5378,12.3157,54.6089,0.00000000 2006,323.5755,214.5378,24.1241,57.6936,0.99999321 2006,357.4139,211.6719,23.6116,52.1047,0.00099052 2006,344.5042,208.5432,21.4774,52.0423,0.00000000 2006,313.6651,207.5980,21.1768,56.3873,0.00000000 2006,311.1789,185.8432,49.3431,98.3983,0.00000000 2006,9.0094,221.6782,82.2479,279.8853,0.00000000 2006,281.2489,127.8507,99.2816,210.7807,0.00000000 2006,635.3561,487.7361,4.7906,24.4106,0.00000000 2006,628.2103,459.0608,11.9364,53.0859,0.00000000 2007,305.9954,204.7959,28.2154,67.2774,1.00000000 2007,347.0750,210.2438,25.5567,55.7588,0.99997497 2007,547.0591,192.8454,23.4397,55.4762,0.00000000 2007,331.8053,207.2988,24.9585,60.2244,0.00000000 2007,1.0000,185.4238,77.7330,275.1239,0.00000000 2007,635.3293,487.3154,4.8174,24.8313,0.00000000 2008,226.8600,203.8600,36.1373,87.0051,1.00000000 2008,284.9570,208.7002,31.8892,72.0616,0.99999881 2008,247.1076,200.9363,30.5977,73.2268,0.00000002 2008,438.3412,194.8333,30.5950,61.4596,0.00000000 2008,534.7831,210.6262,23.3959,56.5060,0.00000000 2008,255.9946,115.7023,89.9123,207.6491,0.00000000 2008,631.8900,488.1141,8.2567,24.0326,0.00000000 2009,93.9874,193.5524,51.1707,134.8895,0.99999410 2009,184.4878,219.0412,41.5422,92.6656,0.88000715 2009,158.0740,186.4167,91.8684,204.2577,0.00000000 2009,635.1854,487.3709,4.9613,24.7758,0.00000000 2010,67.5032,200.9591,37.6833,90.8357,0.00000013 2010,1.0000,194.9921,52.3632,171.0382,0.00000000 2010,89.5098,206.9505,28.3554,68.1299,0.00000000 2010,1.0000,1.0000,57.7933,247.4639,0.00000000 2010,631.5515,487.3870,8.5952,24.7597,0.00000000 2011,635.2374,487.5980,4.9093,24.5487,0.00000000 2011,628.0698,458.0645,12.0769,54.0822,0.00000000 2012,634.3882,232.0885,5.7585,74.9815,0.00000000 2012,635.4573,488.2053,4.6894,23.9414,0.00000000 2012,628.6089,459.0276,11.5378,53.1191,0.00000000 2013,629.9437,1.0000,10.2030,52.2864,0.00000000 2013,631.8094,487.3923,8.3373,24.7544,0.00000000 2014,635.4971,487.8525,4.6496,24.2942,0.00000000 2014,629.0307,458.6651,11.1160,53.4816,0.00000000 2015,585.9810,223.7411,30.2120,64.8779,0.99989355 2015,602.6832,228.0910,30.9527,64.8951,0.00000000 2015,558.8460,113.7695,81.3007,215.3329,0.00000000 2015,635.3879,212.6171,4.7588,73.1704,0.00000000 2015,629.3687,229.3716,10.7780,85.2897,0.00000000 2015,632.1053,487.7880,8.0414,24.3587,0.00000000 2016,506.5463,214.0939,30.9320,73.8118,0.99999088 2016,523.6254,220.2283,25.7284,56.0132,0.00000000 2016,474.0669,167.4047,91.9542,206.7197,0.00000000 2016,635.6106,487.3279,4.5361,24.8188,0.00000000 2016,628.8936,457.1965,11.2531,54.9502,0.00000000 2017,499.5768,206.0676,34.9717,83.9150,0.99993223 2017,490.0395,207.7545,25.3518,64.4464,0.00000002 2017,519.7424,208.4858,34.6540,76.1702,0.00000000 2017,474.1692,121.8481,85.7374,221.1199,0.00000000 2017,635.8986,487.9547,4.2481,24.1920,0.00000000 2017,628.6196,457.2331,11.5271,54.9136,0.00000000 2018,588.3516,187.2130,39.0842,110.9273,0.00000000 2018,559.8765,161.3565,80.2702,247.1346,0.00000000 2018,634.5030,214.3746,5.6437,78.7201,0.00000000 2018,635.4072,488.1274,4.7395,24.0193,0.00000000 2018,628.5226,459.5144,11.6241,52.6323,0.00000000 2019,535.9291,1.0000,67.8182,157.7040,0.00000000 2019,626.3322,1.0000,13.8145,118.0363,0.00000000 2019,601.8217,1.0000,38.3250,218.6600,0.00000000 2019,635.3475,487.9059,4.7992,24.2408,0.00000000 2019,628.2361,458.0478,11.9106,54.0989,0.00000000 2020,631.9704,487.8513,8.1763,24.2954,0.00000000 2021,96.3294,226.6559,30.4351,53.7081,0.00000000 2021,635.4330,487.6263,4.7137,24.5204,0.00000000 2021,628.6488,457.6374,11.4979,54.5093,0.00000000 2022,550.5110,255.2012,41.2411,82.5968,0.00000000 2022,635.5151,487.8163,4.6316,24.3304,0.00000000 2022,628.6228,458.8907,11.5239,53.2560,0.00000000 2023,635.3669,488.1486,4.7798,23.9981,0.00000000 2023,628.3926,458.5688,11.7541,53.5779,0.00000000 2024,373.9687,196.1278,28.1608,56.9426,0.00000000 2024,635.4384,488.2535,4.7083,23.8932,0.00000000 2024,628.3851,458.6936,11.7616,53.4531,0.00000000 2025,384.6414,203.1540,24.7976,53.0856,0.00000263 2025,634.8007,30.4920,5.3460,79.8117,0.00000000 2025,635.3958,488.2554,4.7509,23.8913,0.00000000 2025,628.4748,458.5742,11.6719,53.5725,0.00000000 2026,404.2089,207.6570,25.9071,53.8827,0.65808934 2026,10.7575,197.2366,23.1601,53.5554,0.00000000 2026,396.1324,180.1269,50.1060,100.5358,0.00000000 2026,363.6630,118.7234,100.9150,225.5788,0.00000000 2026,635.5305,488.3344,4.6162,23.8123,0.00000000 2026,628.5961,459.1780,11.5506,52.9687,0.00000000 2027,424.6877,207.5113,24.6791,53.6869,0.61367297 2027,502.9667,207.7642,28.8769,59.5079,0.00815768 2027,369.8589,203.2931,23.1279,53.8981,0.00000303 2027,436.5165,219.1837,23.4663,52.4284,0.00000000 2027,415.3939,174.4167,49.5155,103.3442,0.00000000 2027,475.3788,100.6751,91.4747,223.1718,0.00000000 2027,386.4239,120.4226,100.5053,223.2861,0.00000000 2027,635.3705,487.8333,4.7762,24.3134,0.00000000 2027,628.5485,458.1514,11.5982,53.9953,0.00000000 2028,443.1295,211.9542,29.4110,58.5570,0.99885219 2028,378.8395,204.4260,24.2022,52.4114,0.99828923 2028,546.6841,210.9325,28.6627,64.0074,0.98550493 2028,529.5923,208.1251,30.9131,71.2520,0.00000025 2028,561.7128,215.4268,31.1104,63.5285,0.00000000 2028,629.6454,480.1018,10.5013,32.0449,0.00000000 2029,489.5347,211.6503,32.1249,70.4302,0.99997801 2029,399.2675,204.3573,25.3479,54.3363,0.96068555 2029,414.2846,205.8515,25.0225,56.0488,0.00000000 2029,390.5979,173.0734,49.1903,102.6163,0.00000000 2029,462.1317,116.4747,93.8038,209.7380,0.00000000 2029,359.5816,125.5039,99.8639,227.4226,0.00000000 2029,635.4181,488.1549,4.7286,23.9918,0.00000000 2030,434.1326,206.2644,24.9720,58.2229,1.00000000 2030,575.4767,212.8120,39.5824,90.6037,0.99995136 2030,422.4887,199.0756,20.5762,52.6919,0.00000006 2031,487.3675,205.2482,33.2313,75.2715,1.00000000 2031,476.3831,205.8282,24.6101,61.9388,0.00000019 2031,7.5623,205.0464,26.4239,52.2460,0.00000000 2031,507.0582,212.9276,31.4409,66.4823,0.00000000 2031,1.0000,196.0392,15.5455,55.8215,0.00000000 2031,455.7919,143.4418,89.5856,209.4485,0.00000000 2031,635.4023,487.9697,4.7444,24.1770,0.00000000 2031,628.6469,458.4152,11.4998,53.7315,0.00000000 2032,276.8275,213.1911,23.5224,53.5702,0.00000000 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2248,9.5354,196.1332,65.2447,169.3261,0.00000000 2248,637.2643,382.3488,2.8824,66.7495,0.00000000 2248,633.5155,400.2084,6.6312,79.8240,0.00000000 2248,632.2471,352.6037,7.8996,84.2825,0.00000000 2248,594.4415,311.2731,45.7052,200.8736,0.00000000 2248,632.9293,1.0000,7.2174,39.8032,0.00000000 2248,635.0496,486.4420,5.0971,25.7047,0.00000000 2248,627.4830,456.1350,12.6637,56.0117,0.00000000 2249,464.7914,298.8157,27.8775,63.0454,0.00000001 2249,628.5337,1.0000,11.6130,49.1671,0.00000000 2249,631.3853,486.5346,8.7614,25.6121,0.00000000 2250,69.2508,197.6626,23.1530,52.5162,0.00000050 2250,631.3450,486.2343,8.8017,25.9124,0.00000000 2251,16.7299,195.2545,23.5616,55.9852,0.00079114 2251,631.7059,487.3580,8.4408,24.7887,0.00000000 2252,631.9257,486.6549,8.2210,25.4918,0.00000000 ================================================ FILE: global_var.py ================================================ # @Time : 2021/6/22 下午4:46 # @Author : Richard FANG # @File : global_var.py.py # @Software: PyCharm def _init(): # 初始化 global _global_dict _global_dict = {} def set_value(key, value): # 定义一个全局变量 _global_dict[key] = value def get_value(key): # 获得一个全局变量，不存在则提示读取对应变量失败 try: return _global_dict[key] except: print('读取'+key+'失败\r\n') ================================================ FILE: gradcam_visual.py ================================================ import os import time import argparse import numpy as np from models.gradcam import YOLOV5GradCAM from models.yolo_v5_object_detector import YOLOV5TorchObjectDetector import cv2 from deep_utils import Box, split_extension target = ['model_30_cv1_act', 'model_30_cv2_act', 'model_30_cv3_act', \ 'model_33_cv1_act', 'model_33_cv2_act', 'model_33_cv3_act', \ 'model_36_cv1_act', 'model_36_cv2_act', 'model_36_cv3_act'] # Arguments parser = argparse.ArgumentParser() parser.add_argument('--model-path', type=str, default="/home/shen/Chenyf/exp_save/multispectral-object-detection/5m_NiNfusion/weights/best.pt", help='Path to the model') parser.add_argument('--source1', type=str, default='/home/shen/Chenyf/kaist/visible/test', help='source') # file/folder, 0 for webcam parser.add_argument('--source2', type=str, default='/home/shen/Chenyf/kaist/infrared/test', help='source') # file/folder, 0 for webcam parser.add_argument('--output-dir', type=str, default='/home/shen/Chenyf/kaist/Grad_CAM_visual/outputs_nin_head', help='output dir') parser.add_argument('--img-size', type=int, default=640, help="input image size") parser.add_argument('--target-layer', type=str, default=target, help='The layer hierarchical address to which gradcam will applied,' ' the names should be separated by underline') parser.add_argument('--method', type=str, default='gradcam', help='gradcam or gradcampp') parser.add_argument('--device', type=str, default='cpu', help='cuda or cpu') parser.add_argument('--names', type=str, default='person', help='The name of the classes. The default is set to None and is set to coco classes. Provide your custom names as follow: object1,object2,object3') #'person, car, bicycle' args = parser.parse_args() def get_res_img2(heat, mask, res_img): mask = mask.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy().astype(np.uint8) heatmap = cv2.applyColorMap(mask, cv2.COLORMAP_JET) n_heatmat = (heatmap / 255).astype(np.float32) heat.append(n_heatmat) return res_img, heat def get_res_img(bbox, mask, res_img): mask = mask.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy().astype(np.uint8) heatmap = cv2.applyColorMap(mask, cv2.COLORMAP_JET) #n_heatmat = (Box.fill_outer_box(heatmap, bbox, value=0) / 255).astype(np.float32) n_heatmat = (heatmap / 255).astype(np.float32) res_img = cv2.addWeighted(res_img, 0.7, n_heatmat, 0.3, 0) res_img = (res_img / res_img.max()) return res_img, n_heatmat def put_text_box(bbox, cls_name, res_img): x1, y1, x2, y2 = bbox # this is a bug in cv2. It does not put box on a converted image from torch unless it's buffered and read again! #cv2.imwrite('temp.jpg', (res_img * 255).astype(np.uint8)) #res_img = cv2.imread('temp.jpg') res_img = Box.put_box(res_img, bbox) #res_img = Box.put_text(res_img, cls_name, (x1 - 3, y1)) # res_img = Box.put_text(res_img, str(round((float(cls_name)+0.40), 2)), (x1-3, y1)) return res_img def concat_images(images): w, h = images[0].shape[:2] width = w height = h * len(images) base_img = np.zeros((width, height, 3), dtype=np.uint8) for i, img in enumerate(images): base_img[:, h * i:h * (i + 1), ...] = img return base_img def main(img_vis_path, img_ir_path): device = args.device input_size = (args.img_size, args.img_size) img_vis, img_ir = cv2.imread(img_vis_path), cv2.imread(img_ir_path) print('[INFO] Loading the model') # load model model = YOLOV5TorchObjectDetector(args.model_path, device, img_size=input_size, names=None if args.names is None else args.names.strip().split(","), confidence=0.3) # preprocess the images torch_img_vis, torch_img_ir = model.preprocessing(img_vis[..., ::-1], img_ir[..., ::-1]) result = torch_img_vis.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy() result = result[..., ::-1] # convert to bgr images = [] if args.method == 'gradcam': for layer in args.target_layer: saliency_method = YOLOV5GradCAM(model=model, layer_name=layer, img_size=input_size) tic = time.time() masks, logits, [boxes, _, class_names, confs] = saliency_method(torch_img_vis, torch_img_ir) print("total time:", round(time.time() - tic, 4)) res_img = result.copy() res_img = res_img / 255 heat = [] for i, mask in enumerate(masks): bbox = boxes[0][i] mask = mask.squeeze(0).mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).detach().cpu().numpy().astype(np.uint8) heatmap = cv2.applyColorMap(mask, cv2.COLORMAP_JET) n_heatmat = (heatmap / 255).astype(np.float32) heat.append(n_heatmat) #res_img, heat_map = get_res_img(bbox, mask, res_img) #res_img = put_text_box(bbox, cls_name, res_img) # plot the bboxes #images.append(res_img) if(len(heat) != 0): heat_all = heat[0] for h in heat[1:]: heat_all += h heat_avg = heat_all / len(heat) res_img = cv2.addWeighted(res_img, 0.3, heat_avg, 0.7, 0) res_img = (res_img / res_img.max()) cv2.imwrite('temp.jpg', (res_img * 255).astype(np.uint8)) heat_map = cv2.imread('temp.jpg') # for i, mask in enumerate(masks): # bbox, cls_name, conf = boxes[0][i], class_names[0][i], str(confs[0][i]) # heat_map = put_text_box(bbox, conf, heat_avg) # plot the bboxes final_image = heat_map images.append(final_image) # save the images suffix = '-res-' + layer img_name = split_extension(os.path.split(img_vis_path)[-1], suffix=suffix) output_path = f'{args.output_dir}/{img_name}' os.makedirs(args.output_dir, exist_ok=True) print(f'[INFO] Saving the final image at {output_path}') cv2.imwrite(output_path, final_image) img_name = split_extension(os.path.split(img_vis_path)[-1], suffix='_avg') output_path = f'{args.output_dir}/{img_name}' img_all = images[0].astype(np.uint16) for img in images[1:]: img_all += img img_avg = img_all / len(images) cv2.imwrite(output_path, img_avg.astype(np.uint8)) if __name__ == '__main__': if os.path.isdir(args.source1): img_vis_list = os.listdir(args.source1) img_vis_list.sort() for item in img_vis_list[1127:]: img_vis_path = os.path.join(args.source1 ,item) if args.source1 == '/home/shen/Chenyf/FLIR-align-3class/visible/test': new_item = item[:-4] + '.jpeg' img_ir_path = os.path.join(args.source2, new_item) else: img_ir_path = os.path.join(args.source2, item) main(img_vis_path, img_ir_path) print(item) else: main(img_vis_path, img_ir_path) ================================================ FILE: hubconf.py ================================================ """YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/ Usage: import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s') """ from pathlib import Path import torch from models.yolo import Model from utils.general import check_requirements, set_logging from utils.google_utils import attempt_download from utils.torch_utils import select_device dependencies = ['torch', 'yaml'] check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop')) def create(name, pretrained, channels, classes, autoshape, verbose): """Creates a specified YOLOv5 model Arguments: name (str): name of model, i.e. 'yolov5s' pretrained (bool): load pretrained weights into the model channels (int): number of input channels classes (int): number of model classes Returns: pytorch model """ try: set_logging(verbose=verbose) cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path model = Model(cfg, channels, classes) if pretrained: fname = f'{name}.pt' # checkpoint filename attempt_download(fname) # download if not found locally ckpt = torch.load(fname, map_location=torch.device('cpu')) # load msd = model.state_dict() # model state_dict csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter model.load_state_dict(csd, strict=False) # load if len(ckpt['model'].names) == classes: model.names = ckpt['model'].names # set class names attribute if autoshape: model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available return model.to(device) except Exception as e: help_url = 'https://github.com/ultralytics/yolov5/issues/36' s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url raise Exception(s) from e def custom(path_or_model='path/to/model.pt', autoshape=True, verbose=True): """YOLOv5-custom model https://github.com/ultralytics/yolov5 Arguments (3 options): path_or_model (str): 'path/to/model.pt' path_or_model (dict): torch.load('path/to/model.pt') path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] Returns: pytorch model """ set_logging(verbose=verbose) model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint if isinstance(model, dict): model = model['ema' if model.get('ema') else 'model'] # load model hub_model = Model(model.yaml).to(next(model.parameters()).device) # create hub_model.load_state_dict(model.float().state_dict()) # load state_dict hub_model.names = model.names # class names if autoshape: hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available return hub_model.to(device) def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-small model https://github.com/ultralytics/yolov5 return create('yolov5s', pretrained, channels, classes, autoshape, verbose) def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-medium model https://github.com/ultralytics/yolov5 return create('yolov5m', pretrained, channels, classes, autoshape, verbose) def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-large model https://github.com/ultralytics/yolov5 return create('yolov5l', pretrained, channels, classes, autoshape, verbose) def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 return create('yolov5x', pretrained, channels, classes, autoshape, verbose) def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 return create('yolov5s6', pretrained, channels, classes, autoshape, verbose) def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 return create('yolov5m6', pretrained, channels, classes, autoshape, verbose) def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 return create('yolov5l6', pretrained, channels, classes, autoshape, verbose) def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 return create('yolov5x6', pretrained, channels, classes, autoshape, verbose) if __name__ == '__main__': model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained # model = custom(path_or_model='path/to/model.pt') # custom # Verify inference import cv2 import numpy as np from PIL import Image imgs = ['data/images/zidane.jpg', # filename 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg', # URI cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV Image.open('data/images/bus.jpg'), # PIL np.zeros((320, 640, 3))] # numpy results = model(imgs) # batched inference results.print() results.save() ================================================ FILE: models/__init__.py ================================================ ================================================ FILE: models/common.py ================================================ # YOLOv5 common modules import math from copy import copy from pathlib import Path import warnings import cv2 import numpy as np import pandas as pd import requests import torch import torch.nn as nn from torch import einsum from PIL import Image from torch.cuda import amp import torch.nn.functional as F from torch.autograd import Function from torch.nn.modules.utils import _triple, _pair, _single from einops import rearrange, repeat from einops.layers.torch import Rearrange from utils.datasets import letterbox from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box from utils.plots import colors, plot_one_box from utils.torch_utils import time_synchronized from timm.models.layers import DropPath from torch.nn import init, Sequential import math import matplotlib.pyplot as plt from torchvision import transforms from torchvision.utils import save_image import numpy as np def autopad(k, p=None): # kernel, padding # Pad to 'same' if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad return p def DWConv(c1, c2, k=1, s=1, act=True): # Depthwise convolution return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) class Conv(nn.Module): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Conv, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) def fuseforward(self, x): return self.act(self.conv(x)) class TransformerLayer(nn.Module): # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) def __init__(self, c, num_heads): super().__init__() self.q = nn.Linear(c, c, bias=False) self.k = nn.Linear(c, c, bias=False) self.v = nn.Linear(c, c, bias=False) self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) self.fc1 = nn.Linear(c, c, bias=False) self.fc2 = nn.Linear(c, c, bias=False) def forward(self, x): x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x x = self.fc2(self.fc1(x)) + x return x class TransformerBlock(nn.Module): # Vision Transformer https://arxiv.org/abs/2010.11929 def __init__(self, c1, c2, num_heads, num_layers): super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2) self.linear = nn.Linear(c2, c2) # learnable position embedding self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) self.c2 = c2 def forward(self, x): if self.conv is not None: x = self.conv(x) b, _, w, h = x.shape p = x.flatten(2) p = p.unsqueeze(0) p = p.transpose(0, 3) p = p.squeeze(3) e = self.linear(p) x = p + e x = self.tr(x) x = x.unsqueeze(3) x = x.transpose(0, 3) x = x.reshape(b, self.c2, w, h) return x class VGGblock(nn.Module): def __init__(self, num_convs, c1, c2): super(VGGblock, self).__init__() self.blk = [] for num in range(num_convs): if num == 0: self.blk.append(nn.Sequential(nn.Conv2d(in_channels=c1, out_channels=c2, kernel_size=3, padding=1), nn.ReLU(), )) else: self.blk.append(nn.Sequential(nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=3, padding=1), nn.ReLU(), )) self.blk.append(nn.MaxPool2d(kernel_size=2, stride=2)) self.vggblock = nn.Sequential(*self.blk) def forward(self, x): out = self.vggblock(x) return out class ResNetblock(nn.Module): expansion = 4 def __init__(self, c1, c2, stride=1): super(ResNetblock, self).__init__() self.conv1 = nn.Conv2d(in_channels=c1, out_channels=c2, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(c2) self.conv2 = nn.Conv2d(in_channels=c2, out_channels=c2, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(c2) self.conv3 = nn.Conv2d(in_channels=c2, out_channels=self.expansion*c2, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion*c2) self.shortcut = nn.Sequential() if stride != 1 or c1 != self.expansion*c2: self.shortcut = nn.Sequential(nn.Conv2d(in_channels=c1, out_channels=self.expansion*c2, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion*c2), ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) out += self.shortcut(x) out = F.relu(out) return out class ResNetlayer(nn.Module): expansion = 4 def __init__(self, c1, c2, stride=1, is_first=False, num_blocks=1): super(ResNetlayer, self).__init__() self.blk = [] self.is_first = is_first if self.is_first: self.layer = nn.Sequential(nn.Conv2d(in_channels=c1, out_channels=c2, kernel_size=7, stride=2, padding=3, bias=False), nn.BatchNorm2d(c2), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) else: self.blk.append(ResNetblock(c1, c2, stride)) for i in range(num_blocks - 1): self.blk.append(ResNetblock(self.expansion*c2, c2, 1)) self.layer = nn.Sequential(*self.blk) def forward(self, x): out = self.layer(x) return out class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super(Bottleneck, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 1, g=g) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class BottleneckCSP(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(BottleneckCSP, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) self.cv4 = Conv(2 * c_, c2, 1, 1) self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) self.act = nn.LeakyReLU(0.1, inplace=True) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) class C3(nn.Module): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(C3, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) class C3TR(C3): # C3 module with TransformerBlock() def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = TransformerBlock(c_, c_, 4, n) class SPP(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13)): super(SPP, self).__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) def forward(self, x): x = self.cv1(x) return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class SPPF(nn.Module): # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) class Focus(nn.Module): # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Focus, self).__init__() # print("c1 * 4, c2, k", c1 * 4, c2, k) self.conv = Conv(c1 * 4, c2, k, s, p, g, act) # self.contract = Contract(gain=2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) # print("Focus inputs shape", x.shape) # print() return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) # return self.conv(self.contract(x)) class Contract(nn.Module): # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' s = self.gain x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) class Expand(nn.Module): # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' s = self.gain x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80) x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160) class Concat(nn.Module): # Concatenate a list of tensors along dimension def __init__(self, dimension=1): super(Concat, self).__init__() self.d = dimension def forward(self, x): # print(x.shape) return torch.cat(x, self.d) class Add(nn.Module): # Add a list of tensors and averge def __init__(self, weight=0.5): super().__init__() self.w = weight def forward(self, x): return x[0] * self.w + x[1] * (1 - self.w) class Add2(nn.Module): # x + transformer[0] or x + transformer[1] def __init__(self, c1, index): super().__init__() self.index = index def forward(self, x): if self.index == 0: return torch.add(x[0], x[1][0]) elif self.index == 1: return torch.add(x[0], x[1][1]) # return torch.add(x[0], x[1]) class NiNfusion(nn.Module): def __init__(self, c1, c2, k=1, s=1, p=None, g=1): super(NiNfusion, self).__init__() self.concat = Concat(dimension=1) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.act = nn.SiLU() def forward(self, x): y = self.concat(x) y = self.act(self.conv(y)) return y class DMAF(nn.Module): def __init__(self, c2): super(DMAF, self).__init__() def forward(self, x): x1 = x[0] x2 = x[1] subtract_vis = x1 - x2 avgpool_vis = nn.AvgPool2d(kernel_size=(subtract_vis.size(2), subtract_vis.size(3))) weight_vis = torch.tanh(avgpool_vis(subtract_vis)) subtract_ir = x2 - x1 avgpool_ir = nn.AvgPool2d(kernel_size=(subtract_ir.size(2), subtract_ir.size(3))) weight_ir = torch.tanh(avgpool_ir(subtract_ir)) x1_weight = subtract_vis * weight_ir x2_weight = subtract_ir * weight_vis return x1_weight, x2_weight class NMS(nn.Module): # Non-Maximum Suppression (NMS) module conf = 0.25 # confidence threshold iou = 0.45 # IoU threshold classes = None # (optional list) filter by class def __init__(self): super(NMS, self).__init__() def forward(self, x): return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) class autoShape(nn.Module): # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS conf = 0.25 # NMS confidence threshold iou = 0.45 # NMS IoU threshold classes = None # (optional list) filter by class def __init__(self, model): super(autoShape, self).__init__() self.model = model.eval() def autoshape(self): print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() return self @torch.no_grad() def forward(self, imgs, size=640, augment=False, profile=False): # Inference from various sources. For height=640, width=1280, RGB images example inputs are: # filename: imgs = 'data/images/zidane.jpg' # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) # numpy: = np.zeros((640,1280,3)) # HWC # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images t = [time_synchronized()] p = next(self.model.parameters()) # for device and type if isinstance(imgs, torch.Tensor): # torch with amp.autocast(enabled=p.device.type != 'cpu'): return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference # Pre-process n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images shape0, shape1, files = [], [], [] # image and inference shapes, filenames for i, im in enumerate(imgs): f = f'image{i}' # filename if isinstance(im, str): # filename or uri im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im elif isinstance(im, Image.Image): # PIL Image im, f = np.asarray(im), getattr(im, 'filename', f) or f files.append(Path(f).with_suffix('.jpg').name) if im.shape[0] < 5: # image in CHW im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input s = im.shape[:2] # HWC shape0.append(s) # image shape g = (size / max(s)) # gain shape1.append([y * g for y in s]) imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad x = np.stack(x, 0) if n > 1 else x[0][None] # stack x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 t.append(time_synchronized()) with amp.autocast(enabled=p.device.type != 'cpu'): # Inference y = self.model(x, augment, profile)[0] # forward t.append(time_synchronized()) # Post-process y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS for i in range(n): scale_coords(shape1, y[i][:, :4], shape0[i]) t.append(time_synchronized()) return Detections(imgs, y, files, t, self.names, x.shape) class Detections: # detections class for YOLOv5 inference results def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super(Detections, self).__init__() d = pred[0].device # device gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations self.imgs = imgs # list of images as numpy arrays self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) self.names = names # class names self.files = files # image filenames self.xyxy = pred # xyxy pixels self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized self.n = len(self.pred) # number of images (batch size) self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) self.s = shape # inference BCHW shape def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' if pred is not None: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string if show or save or render or crop: for *box, conf, cls in pred: # xyxy, confidence, class label = f'{self.names[int(cls)]} {conf:.2f}' if crop: save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i]) else: # all others plot_one_box(box, im, label=label, color=colors(cls)) im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np if pprint: print(str.rstrip(', ')) if show: im.show(self.files[i]) # show if save: f = self.files[i] im.save(save_dir / f) # save print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') if render: self.imgs[i] = np.asarray(im) def print(self): self.display(pprint=True) # print results print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) def show(self): self.display(show=True) # show results def save(self, save_dir='runs/hub/exp'): save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir self.display(save=True, save_dir=save_dir) # save results def crop(self, save_dir='runs/hub/exp'): save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir self.display(crop=True, save_dir=save_dir) # crop results print(f'Saved results to {save_dir}\n') def render(self): self.display(render=True) # render results return self.imgs def pandas(self): # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) new = copy(self) # return copy ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) return new def tolist(self): # return a list of Detections objects, i.e. 'for result in results.tolist():' x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] for d in x: for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: setattr(d, k, getattr(d, k)[0]) # pop out of list return x def __len__(self): return self.n class Classify(nn.Module): # Classification head, i.e. x(b,c1,20,20) to x(b,c2) def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups super(Classify, self).__init__() self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) self.flat = nn.Flatten() def forward(self, x): z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list return self.flat(self.conv(z)) # flatten to x(b,c2) class LearnableCoefficient(nn.Module): def __init__(self): super(LearnableCoefficient, self).__init__() self.bias = nn.Parameter(torch.FloatTensor([1.0]), requires_grad=True) def forward(self, x): out = x * self.bias return out class LearnableWeights(nn.Module): def __init__(self): super(LearnableWeights, self).__init__() self.w1 = nn.Parameter(torch.tensor([0.5]), requires_grad=True) self.w2 = nn.Parameter(torch.tensor([0.5]), requires_grad=True) def forward(self, x1, x2): out = x1 * self.w1 + x2 * self.w2 return out class CrossAttention(nn.Module): def __init__(self, d_model, d_k, d_v, h, attn_pdrop=.1, resid_pdrop=.1): ''' :param d_model: Output dimensionality of the model :param d_k: Dimensionality of queries and keys :param d_v: Dimensionality of values :param h: Number of heads ''' super(CrossAttention, self).__init__() assert d_k % h == 0 self.d_model = d_model self.d_k = d_model // h self.d_v = d_model // h self.h = h # key, query, value projections for all heads self.que_proj_vis = nn.Linear(d_model, h * self.d_k) # query projection self.key_proj_vis = nn.Linear(d_model, h * self.d_k) # key projection self.val_proj_vis = nn.Linear(d_model, h * self.d_v) # value projection self.que_proj_ir = nn.Linear(d_model, h * self.d_k) # query projection self.key_proj_ir = nn.Linear(d_model, h * self.d_k) # key projection self.val_proj_ir = nn.Linear(d_model, h * self.d_v) # value projection self.out_proj_vis = nn.Linear(h * self.d_v, d_model) # output projection self.out_proj_ir = nn.Linear(h * self.d_v, d_model) # output projection # regularization self.attn_drop = nn.Dropout(attn_pdrop) self.resid_drop = nn.Dropout(resid_pdrop) # layer norm self.LN1 = nn.LayerNorm(d_model) self.LN2 = nn.LayerNorm(d_model) self.init_weights() def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) def forward(self, x, attention_mask=None, attention_weights=None): ''' Computes Self-Attention Args: x (tensor): input (token) dim:(b_s, nx, c), b_s means batch size nx means length, for CNN, equals H*W, i.e. the length of feature maps c means channel, i.e. the channel of feature maps attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking. attention_weights: Multiplicative weights for attention values (b_s, h, nq, nk). Return: output (tensor): dim:(b_s, nx, c) ''' rgb_fea_flat = x[0] ir_fea_flat = x[1] b_s, nq = rgb_fea_flat.shape[:2] nk = rgb_fea_flat.shape[1] # Self-Attention rgb_fea_flat = self.LN1(rgb_fea_flat) q_vis = self.que_proj_vis(rgb_fea_flat).contiguous().view(b_s, nq, self.h, self.d_k).permute(0, 2, 1, 3) # (b_s, h, nq, d_k) k_vis = self.key_proj_vis(rgb_fea_flat).contiguous().view(b_s, nk, self.h, self.d_k).permute(0, 2, 3, 1) # (b_s, h, d_k, nk) K^T v_vis = self.val_proj_vis(rgb_fea_flat).contiguous().view(b_s, nk, self.h, self.d_v).permute(0, 2, 1, 3) # (b_s, h, nk, d_v) ir_fea_flat = self.LN2(ir_fea_flat) q_ir = self.que_proj_ir(ir_fea_flat).contiguous().view(b_s, nq, self.h, self.d_k).permute(0, 2, 1, 3) # (b_s, h, nq, d_k) k_ir = self.key_proj_ir(ir_fea_flat).contiguous().view(b_s, nk, self.h, self.d_k).permute(0, 2, 3, 1) # (b_s, h, d_k, nk) K^T v_ir = self.val_proj_ir(ir_fea_flat).contiguous().view(b_s, nk, self.h, self.d_v).permute(0, 2, 1, 3) # (b_s, h, nk, d_v) att_vis = torch.matmul(q_ir, k_vis) / np.sqrt(self.d_k) att_ir = torch.matmul(q_vis, k_ir) / np.sqrt(self.d_k) # att_vis = torch.matmul(k_vis, q_ir) / np.sqrt(self.d_k) # att_ir = torch.matmul(k_ir, q_vis) / np.sqrt(self.d_k) # get attention matrix att_vis = torch.softmax(att_vis, -1) att_vis = self.attn_drop(att_vis) att_ir = torch.softmax(att_ir, -1) att_ir = self.attn_drop(att_ir) # output out_vis = torch.matmul(att_vis, v_vis).permute(0, 2, 1, 3).contiguous().view(b_s, nq, self.h * self.d_v) # (b_s, nq, h*d_v) out_vis = self.resid_drop(self.out_proj_vis(out_vis)) # (b_s, nq, d_model) out_ir = torch.matmul(att_ir, v_ir).permute(0, 2, 1, 3).contiguous().view(b_s, nq, self.h * self.d_v) # (b_s, nq, h*d_v) out_ir = self.resid_drop(self.out_proj_ir(out_ir)) # (b_s, nq, d_model) return [out_vis, out_ir] class CrossTransformerBlock(nn.Module): def __init__(self, d_model, d_k, d_v, h, block_exp, attn_pdrop, resid_pdrop, loops_num=1): """ :param d_model: Output dimensionality of the model :param d_k: Dimensionality of queries and keys :param d_v: Dimensionality of values :param h: Number of heads :param block_exp: Expansion factor for MLP (feed foreword network) """ super(CrossTransformerBlock, self).__init__() self.loops = loops_num self.ln_input = nn.LayerNorm(d_model) self.ln_output = nn.LayerNorm(d_model) self.crossatt = CrossAttention(d_model, d_k, d_v, h, attn_pdrop, resid_pdrop) self.mlp_vis = nn.Sequential(nn.Linear(d_model, block_exp * d_model), # nn.SiLU(), # changed from GELU nn.GELU(), # changed from GELU nn.Linear(block_exp * d_model, d_model), nn.Dropout(resid_pdrop), ) self.mlp_ir = nn.Sequential(nn.Linear(d_model, block_exp * d_model), # nn.SiLU(), # changed from GELU nn.GELU(), # changed from GELU nn.Linear(block_exp * d_model, d_model), nn.Dropout(resid_pdrop), ) self.mlp = nn.Sequential(nn.Linear(d_model, block_exp * d_model), # nn.SiLU(), # changed from GELU nn.GELU(), # changed from GELU nn.Linear(block_exp * d_model, d_model), nn.Dropout(resid_pdrop), ) # Layer norm self.LN1 = nn.LayerNorm(d_model) self.LN2 = nn.LayerNorm(d_model) # Learnable Coefficient self.coefficient1 = LearnableCoefficient() self.coefficient2 = LearnableCoefficient() self.coefficient3 = LearnableCoefficient() self.coefficient4 = LearnableCoefficient() self.coefficient5 = LearnableCoefficient() self.coefficient6 = LearnableCoefficient() self.coefficient7 = LearnableCoefficient() self.coefficient8 = LearnableCoefficient() def forward(self, x): rgb_fea_flat = x[0] ir_fea_flat = x[1] assert rgb_fea_flat.shape[0] == ir_fea_flat.shape[0] bs, nx, c = rgb_fea_flat.size() h = w = int(math.sqrt(nx)) for loop in range(self.loops): # with Learnable Coefficient rgb_fea_out, ir_fea_out = self.crossatt([rgb_fea_flat, ir_fea_flat]) rgb_att_out = self.coefficient1(rgb_fea_flat) + self.coefficient2(rgb_fea_out) ir_att_out = self.coefficient3(ir_fea_flat) + self.coefficient4(ir_fea_out) rgb_fea_flat = self.coefficient5(rgb_att_out) + self.coefficient6(self.mlp_vis(self.LN2(rgb_att_out))) ir_fea_flat = self.coefficient7(ir_att_out) + self.coefficient8(self.mlp_ir(self.LN2(ir_att_out))) # without Learnable Coefficient # rgb_fea_out, ir_fea_out = self.crossatt([rgb_fea_flat, ir_fea_flat]) # rgb_att_out = rgb_fea_flat + rgb_fea_out # ir_att_out = ir_fea_flat + ir_fea_out # rgb_fea_flat = rgb_att_out + self.mlp_vis(self.LN2(rgb_att_out)) # ir_fea_flat = ir_att_out + self.mlp_ir(self.LN2(ir_att_out)) return [rgb_fea_flat, ir_fea_flat] class TransformerFusionBlock(nn.Module): def __init__(self, d_model, vert_anchors=16, horz_anchors=16, h=8, block_exp=4, n_layer=1, embd_pdrop=0.1, attn_pdrop=0.1, resid_pdrop=0.1): super(TransformerFusionBlock, self).__init__() self.n_embd = d_model self.vert_anchors = vert_anchors self.horz_anchors = horz_anchors d_k = d_model d_v = d_model # positional embedding parameter (learnable), rgb_fea + ir_fea self.pos_emb_vis = nn.Parameter(torch.zeros(1, vert_anchors * horz_anchors, self.n_embd)) self.pos_emb_ir = nn.Parameter(torch.zeros(1, vert_anchors * horz_anchors, self.n_embd)) # downsampling # self.avgpool = nn.AdaptiveAvgPool2d((self.vert_anchors, self.horz_anchors)) # self.maxpool = nn.AdaptiveMaxPool2d((self.vert_anchors, self.horz_anchors)) self.avgpool = AdaptivePool2d(self.vert_anchors, self.horz_anchors, 'avg') self.maxpool = AdaptivePool2d(self.vert_anchors, self.horz_anchors, 'max') # LearnableCoefficient self.vis_coefficient = LearnableWeights() self.ir_coefficient = LearnableWeights() # init weights self.apply(self._init_weights) # cross transformer self.crosstransformer = nn.Sequential(*[CrossTransformerBlock(d_model, d_k, d_v, h, block_exp, attn_pdrop, resid_pdrop) for layer in range(n_layer)]) # Concat self.concat = Concat(dimension=1) # conv1x1 self.conv1x1_out = Conv(c1=d_model * 2, c2=d_model, k=1, s=1, p=0, g=1, act=True) @staticmethod def _init_weights(module): if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=0.02) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def forward(self, x): rgb_fea = x[0] ir_fea = x[1] assert rgb_fea.shape[0] == ir_fea.shape[0] bs, c, h, w = rgb_fea.shape # ------------------------- cross-modal feature fusion -----------------------# #new_rgb_fea = (self.avgpool(rgb_fea) + self.maxpool(rgb_fea)) / 2 new_rgb_fea = self.vis_coefficient(self.avgpool(rgb_fea), self.maxpool(rgb_fea)) new_c, new_h, new_w = new_rgb_fea.shape[1], new_rgb_fea.shape[2], new_rgb_fea.shape[3] rgb_fea_flat = new_rgb_fea.contiguous().view(bs, new_c, -1).permute(0, 2, 1) + self.pos_emb_vis #new_ir_fea = (self.avgpool(ir_fea) + self.maxpool(ir_fea)) / 2 new_ir_fea = self.ir_coefficient(self.avgpool(ir_fea), self.maxpool(ir_fea)) ir_fea_flat = new_ir_fea.contiguous().view(bs, new_c, -1).permute(0, 2, 1) + self.pos_emb_ir rgb_fea_flat, ir_fea_flat = self.crosstransformer([rgb_fea_flat, ir_fea_flat]) rgb_fea_CFE = rgb_fea_flat.contiguous().view(bs, new_h, new_w, new_c).permute(0, 3, 1, 2) if self.training == True: rgb_fea_CFE = F.interpolate(rgb_fea_CFE, size=([h, w]), mode='nearest') else: rgb_fea_CFE = F.interpolate(rgb_fea_CFE, size=([h, w]), mode='bilinear') new_rgb_fea = rgb_fea_CFE + rgb_fea ir_fea_CFE = ir_fea_flat.contiguous().view(bs, new_h, new_w, new_c).permute(0, 3, 1, 2) if self.training == True: ir_fea_CFE = F.interpolate(ir_fea_CFE, size=([h, w]), mode='nearest') else: ir_fea_CFE = F.interpolate(ir_fea_CFE, size=([h, w]), mode='bilinear') new_ir_fea = ir_fea_CFE + ir_fea new_fea = self.concat([new_rgb_fea, new_ir_fea]) new_fea = self.conv1x1_out(new_fea) # ------------------------- feature visulization -----------------------# # save_dir = '/home/shen/Chenyf/FLIR-align-3class/feature_save/' # fea_rgb = torch.mean(rgb_fea, dim=1) # fea_rgb_CFE = torch.mean(rgb_fea_CFE, dim=1) # fea_rgb_new = torch.mean(new_rgb_fea, dim=1) # fea_ir = torch.mean(ir_fea, dim=1) # fea_ir_CFE = torch.mean(ir_fea_CFE, dim=1) # fea_ir_new = torch.mean(new_ir_fea, dim=1) # fea_new = torch.mean(new_fea, dim=1) # block = [fea_rgb, fea_rgb_CFE, fea_rgb_new, fea_ir, fea_ir_CFE, fea_ir_new, fea_new] # black_name = ['fea_rgb', 'fea_rgb After CFE', 'fea_rgb skip', 'fea_ir', 'fea_ir After CFE', 'fea_ir skip', 'fea_ir NiNfusion'] # plt.figure() # for i in range(len(block)): # feature = transforms.ToPILImage()(block[i].squeeze()) # ax = plt.subplot(3, 3, i + 1) # ax.set_xticks([]) # ax.set_yticks([]) # ax.set_title(black_name[i], fontsize=8) # plt.imshow(feature) # plt.savefig(save_dir + 'fea_{}x{}.png'.format(h, w), dpi=300) # -----------------------------------------------------------------------------# return new_fea class AdaptivePool2d(nn.Module): def __init__(self, output_h, output_w, pool_type='avg'): super(AdaptivePool2d, self).__init__() self.output_h = output_h self.output_w = output_w self.pool_type = pool_type def forward(self, x): bs, c, input_h, input_w = x.shape if (input_h > self.output_h) or (input_w > self.output_w): self.stride_h = input_h // self.output_h self.stride_w = input_w // self.output_w self.kernel_size = (input_h - (self.output_h - 1) * self.stride_h, input_w - (self.output_w - 1) * self.stride_w) if self.pool_type == 'avg': y = nn.AvgPool2d(kernel_size=self.kernel_size, stride=(self.stride_h, self.stride_w), padding=0)(x) else: y = nn.MaxPool2d(kernel_size=self.kernel_size, stride=(self.stride_h, self.stride_w), padding=0)(x) else: y = x return y class SE_Block(nn.Module): def __init__(self, inchannel, ratio=16): super(SE_Block, self).__init__() self.gap = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Sequential( nn.Linear(inchannel, inchannel // ratio, bias=False), # 从 c -> c/r nn.ReLU(), nn.Linear(inchannel // ratio, inchannel, bias=False), # 从 c/r -> c nn.Sigmoid() ) def forward(self, x): b, c, h, w = x.size() y = self.gap(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y.expand_as(x) # 通道注意力模块 class Channel_Attention(nn.Module): def __init__(self, in_channels, reduction_ratio=16, pool_types=['avg', 'max']): ''' :param in_channels: 输入通道数 :param reduction_ratio: 输出通道数量的缩放系数 :param pool_types: 池化类型 ''' super(Channel_Attention, self).__init__() self.pool_types = pool_types self.in_channels = in_channels self.shared_mlp = nn.Sequential(nn.Flatten(), nn.Linear(in_features=in_channels, out_features=in_channels//reduction_ratio), nn.ReLU(), nn.Linear(in_features=in_channels//reduction_ratio, out_features=in_channels) ) def forward(self, x): channel_attentions = [] for pool_types in self.pool_types: if pool_types == 'avg': pool_init = nn.AvgPool2d(kernel_size=(x.size(2), x.size(3))) avg_pool = pool_init(x) channel_attentions.append(self.shared_mlp(avg_pool)) elif pool_types == 'max': pool_init = nn.MaxPool2d(kernel_size=(x.size(2), x.size(3))) max_pool = pool_init(x) channel_attentions.append(self.shared_mlp(max_pool)) pooling_sums = torch.stack(channel_attentions, dim=0).sum(dim=0) output = nn.Sigmoid()(pooling_sums).unsqueeze(2).unsqueeze(3).expand_as(x) return x * output # 空间注意力模块 class Spatial_Attention(nn.Module): def __init__(self, kernel_size=7): super(Spatial_Attention, self).__init__() self.spatial_attention = nn.Sequential(nn.Conv2d(in_channels=2, out_channels=1, kernel_size=kernel_size, stride=1, dilation=1, padding=(kernel_size-1)//2, bias=False), nn.BatchNorm2d(num_features=1, eps=1e-5, momentum=0.01, affine=True) ) def forward(self, x): x_compress = torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1) # 在通道维度上分别计算平均值和最大值，并在通道维度上进行拼接 x_output = self.spatial_attention(x_compress) # 使用7x7卷积核进行卷积 scaled = nn.Sigmoid()(x_output) return x * scaled # 将输入F'和通道注意力模块的输出Ms相乘，得到F'' class CBAM(nn.Module): def __init__(self, in_channels, reduction_ratio=16, pool_types=['avg', 'max'], spatial=True): super(CBAM, self).__init__() self.spatial = spatial self.channel_attention = Channel_Attention(in_channels=in_channels, reduction_ratio=reduction_ratio, pool_types=pool_types) if self.spatial: self.spatial_attention = Spatial_Attention(kernel_size=7) def forward(self, x): x_out = self.channel_attention(x) if self.spatial: x_out = self.spatial_attention(x_out) return x_out ================================================ FILE: models/experimental.py ================================================ # YOLOv5 experimental modules import numpy as np import torch import torch.nn as nn from models.common import Conv, DWConv from utils.google_utils import attempt_download class CrossConv(nn.Module): # Cross Convolution Downsample def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): # ch_in, ch_out, kernel, stride, groups, expansion, shortcut super(CrossConv, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, (1, k), (1, s)) self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class Sum(nn.Module): # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 def __init__(self, n, weight=False): # n: number of inputs super(Sum, self).__init__() self.weight = weight # apply weights boolean self.iter = range(n - 1) # iter object if weight: self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights def forward(self, x): y = x[0] # no weight if self.weight: w = torch.sigmoid(self.w) * 2 for i in self.iter: y = y + x[i + 1] * w[i] else: for i in self.iter: y = y + x[i + 1] return y class GhostConv(nn.Module): # Ghost Convolution https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups super(GhostConv, self).__init__() c_ = c2 // 2 # hidden channels self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): y = self.cv1(x) return torch.cat([y, self.cv2(y)], 1) class GhostBottleneck(nn.Module): # Ghost Bottleneck https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride super(GhostBottleneck, self).__init__() c_ = c2 // 2 self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw GhostConv(c_, c2, 1, 1, act=False)) # pw-linear self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() def forward(self, x): return self.conv(x) + self.shortcut(x) class MixConv2d(nn.Module): # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): super(MixConv2d, self).__init__() groups = len(k) if equal_ch: # equal c_ per group i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices c_ = [(i == g).sum() for g in range(groups)] # intermediate channels else: # equal weight.numel() per group b = [c2] + [0] * groups a = np.eye(groups + 1, groups, k=-1) a -= np.roll(a, 1, axis=1) a *= np.array(k) ** 2 a[0] = 1 c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) self.bn = nn.BatchNorm2d(c2) self.act = nn.LeakyReLU(0.1, inplace=True) def forward(self, x): return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) class Ensemble(nn.ModuleList): # Ensemble of models def __init__(self): super(Ensemble, self).__init__() def forward(self, x, augment=False): y = [] for module in self: y.append(module(x, augment)[0]) # y = torch.stack(y).max(0)[0] # max ensemble # y = torch.stack(y).mean(0) # mean ensemble y = torch.cat(y, 1) # nms ensemble return y, None # inference, train output def attempt_load(weights, map_location=None): # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: attempt_download(w) ckpt = torch.load(w, map_location=map_location) # load model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model # Compatibility updates for m in model.modules(): if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: m.inplace = True # pytorch 1.7.0 compatibility elif type(m) is Conv: m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if len(model) == 1: return model[-1] # return model else: print('Ensemble created with %s\n' % weights) for k in ['names', 'stride']: setattr(model, k, getattr(model[-1], k)) return model # return ensemble ================================================ FILE: models/export.py ================================================ """Exports a YOLOv5 *.pt model to ONNX and TorchScript formats Usage: $ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1 """ import argparse import sys import time from pathlib import Path sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories import torch import torch.nn as nn from torch.utils.mobile_optimizer import optimize_for_mobile import models from models.experimental import attempt_load from utils.activations import Hardswish, SiLU from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging from utils.torch_utils import select_device if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) set_logging() t = time.time() # Load PyTorch model device = select_device(opt.device) model = attempt_load(opt.weights, map_location=device) # load FP32 model labels = model.names # Checks gs = int(max(model.stride)) # grid size (max stride) opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples # Input img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection # Update model for k, m in model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if isinstance(m, models.common.Conv): # assign export-friendly activations if isinstance(m.act, nn.Hardswish): m.act = Hardswish() elif isinstance(m.act, nn.SiLU): m.act = SiLU() # elif isinstance(m, models.yolo.Detect): # m.forward = m.forward_export # assign forward (optional) model.model[-1].export = not opt.grid # set Detect() layer grid export for _ in range(2): y = model(img) # dry runs print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)") # TorchScript export ----------------------------------------------------------------------------------------------- prefix = colorstr('TorchScript:') try: print(f'\n{prefix} starting export with torch {torch.__version__}...') f = opt.weights.replace('.pt', '.torchscript.pt') # filename ts = torch.jit.trace(model, img, strict=False) ts = optimize_for_mobile(ts) # https://pytorch.org/tutorials/recipes/script_optimized.html ts.save(f) print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: print(f'{prefix} export failure: {e}') # ONNX export ------------------------------------------------------------------------------------------------------ prefix = colorstr('ONNX:') try: import onnx print(f'{prefix} starting export with onnx {onnx.__version__}...') f = opt.weights.replace('.pt', '.onnx') # filename torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None) # Checks model_onnx = onnx.load(f) # load onnx model onnx.checker.check_model(model_onnx) # check onnx model # print(onnx.helper.printable_graph(model_onnx.graph)) # print # Simplify if opt.simplify: try: check_requirements(['onnx-simplifier']) import onnxsim print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') model_onnx, check = onnxsim.simplify(model_onnx, dynamic_input_shape=opt.dynamic, input_shapes={'images': list(img.shape)} if opt.dynamic else None) assert check, 'assert check failed' onnx.save(model_onnx, f) except Exception as e: print(f'{prefix} simplifier failure: {e}') print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: print(f'{prefix} export failure: {e}') # CoreML export ---------------------------------------------------------------------------------------------------- prefix = colorstr('CoreML:') try: import coremltools as ct print(f'{prefix} starting export with coremltools {ct.__version__}...') # convert model from torchscript and apply pixel scaling as per detect.py model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) f = opt.weights.replace('.pt', '.mlmodel') # filename model.save(f) print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: print(f'{prefix} export failure: {e}') # Finish print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.') ================================================ FILE: models/gradcam.py ================================================ import time import torch import torch.nn.functional as F def find_yolo_layer(model, layer_name): """Find yolov5 layer to calculate GradCAM and GradCAM++ Args: model: yolov5 model. layer_name (str): the name of layer with its hierarchical information. Return: target_layer: found layer """ hierarchy = layer_name.split('_') target_layer = model.model._modules[hierarchy[0]] for h in hierarchy[1:]: target_layer = target_layer._modules[h] return target_layer class YOLOV5GradCAM: def __init__(self, model, layer_name, img_size=(640, 640)): self.model = model self.gradients = dict() self.activations = dict() def backward_hook(module, grad_input, grad_output): self.gradients['value'] = grad_output[0] return None def forward_hook(module, input, output): self.activations['value'] = output return None target_layer = find_yolo_layer(self.model, layer_name) target_layer.register_forward_hook(forward_hook) target_layer.register_backward_hook(backward_hook) device = 'cuda' if next(self.model.model.parameters()).is_cuda else 'cpu' self.model(torch.zeros(1, 3, *img_size, device=device), torch.zeros(1, 3, *img_size, device=device)) print('[INFO] saliency_map size :', self.activations['value'].shape[2:]) def forward(self, img_vis, img_ir, class_idx=True): """ Args: input_img: input image with shape of (1, 3, H, W) Return: mask: saliency map of the same spatial dimension with input logit: model output preds: The object predictions """ saliency_maps = [] b, c, h, w = img_vis.size() tic = time.time() preds, logits = self.model(img_vis, img_ir) print("[INFO] model-forward took: ", round(time.time() - tic, 4), 'seconds') for logit, cls, cls_name, conf in zip(logits[0], preds[1][0], preds[2][0], preds[3][0]): if class_idx: score = logit[cls] else: score = logit.max() self.model.zero_grad() tic = time.time() score.backward(retain_graph=True) print(f"[INFO] {cls_name}, model-backward took: ", round(time.time() - tic, 4), 'seconds') gradients = self.gradients['value'] activations = self.activations['value'] b, k, u, v = gradients.size() alpha = gradients.view(b, k, -1).mean(2) weights = alpha.view(b, k, 1, 1) saliency_map = (weights * activations).sum(1, keepdim=True) saliency_map = F.relu(saliency_map) saliency_map = F.interpolate(input=saliency_map, size=(h, w), mode='bilinear', align_corners=False) saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max() saliency_map = (saliency_map - saliency_map_min).div(saliency_map_max - saliency_map_min).data saliency_maps.append(saliency_map) return saliency_maps, logits, preds def __call__(self, img_vis, img_ir): return self.forward(img_vis, img_ir) ================================================ FILE: models/hub/anchors.yaml ================================================ # Default YOLOv5 anchors for COCO data # P5 ------------------------------------------------------------------------------------------------------------------- # P5-640: anchors_p5_640: - [ 10,13, 16,30, 33,23 ] # P3/8 - [ 30,61, 62,45, 59,119 ] # P4/16 - [ 116,90, 156,198, 373,326 ] # P5/32 # P6 ------------------------------------------------------------------------------------------------------------------- # P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 anchors_p6_640: - [ 9,11, 21,19, 17,41 ] # P3/8 - [ 43,32, 39,70, 86,64 ] # P4/16 - [ 65,131, 134,130, 120,265 ] # P5/32 - [ 282,180, 247,354, 512,387 ] # P6/64 # P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 anchors_p6_1280: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 anchors_p6_1920: - [ 28,41, 67,59, 57,141 ] # P3/8 - [ 144,103, 129,227, 270,205 ] # P4/16 - [ 209,452, 455,396, 358,812 ] # P5/32 - [ 653,922, 1109,570, 1387,1187 ] # P6/64 # P7 ------------------------------------------------------------------------------------------------------------------- # P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 anchors_p7_640: - [ 11,11, 13,30, 29,20 ] # P3/8 - [ 30,46, 61,38, 39,92 ] # P4/16 - [ 78,80, 146,66, 79,163 ] # P5/32 - [ 149,150, 321,143, 157,303 ] # P6/64 - [ 257,402, 359,290, 524,372 ] # P7/128 # P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 anchors_p7_1280: - [ 19,22, 54,36, 32,77 ] # P3/8 - [ 70,83, 138,71, 75,173 ] # P4/16 - [ 165,159, 148,334, 375,151 ] # P5/32 - [ 334,317, 251,626, 499,474 ] # P6/64 - [ 750,326, 534,814, 1079,818 ] # P7/128 # P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 anchors_p7_1920: - [ 29,34, 81,55, 47,115 ] # P3/8 - [ 105,124, 207,107, 113,259 ] # P4/16 - [ 247,238, 222,500, 563,227 ] # P5/32 - [ 501,476, 376,939, 749,711 ] # P6/64 - [ 1126,489, 801,1222, 1618,1227 ] # P7/128 ================================================ FILE: models/hub/yolov3-spp.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [32, 3, 1]], # 0 [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 [-1, 1, Bottleneck, [64]], [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 [-1, 2, Bottleneck, [128]], [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 [-1, 8, Bottleneck, [256]], [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 [-1, 8, Bottleneck, [512]], [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3-SPP head head: [[-1, 1, Bottleneck, [1024, False]], [-1, 1, SPP, [512, [5, 9, 13]]], [-1, 1, Conv, [1024, 3, 1]], [-1, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) [-2, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Bottleneck, [512, False]], [-1, 1, Bottleneck, [512, False]], [-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) [-2, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P3 [-1, 1, Bottleneck, [256, False]], [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov3-tiny.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [10,14, 23,27, 37,58] # P4/16 - [81,82, 135,169, 344,319] # P5/32 # YOLOv3-tiny backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [16, 3, 1]], # 0 [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 [-1, 1, Conv, [32, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 [-1, 1, Conv, [64, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 [-1, 1, Conv, [128, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 [-1, 1, Conv, [256, 3, 1]], [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 [-1, 1, Conv, [512, 3, 1]], [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 ] # YOLOv3-tiny head head: [[-1, 1, Conv, [1024, 3, 1]], [-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) [-2, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) ] ================================================ FILE: models/hub/yolov3.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [32, 3, 1]], # 0 [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 [-1, 1, Bottleneck, [64]], [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 [-1, 2, Bottleneck, [128]], [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 [-1, 8, Bottleneck, [256]], [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 [-1, 8, Bottleneck, [512]], [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3 head head: [[-1, 1, Bottleneck, [1024, False]], [-1, 1, Conv, [512, [1, 1]]], [-1, 1, Conv, [1024, 3, 1]], [-1, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) [-2, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 8], 1, Concat, [1]], # cat backbone P4 [-1, 1, Bottleneck, [512, False]], [-1, 1, Bottleneck, [512, False]], [-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) [-2, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P3 [-1, 1, Bottleneck, [256, False]], [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5-fpn.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, Bottleneck, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, BottleneckCSP, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, BottleneckCSP, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 6, BottleneckCSP, [1024]], # 9 ] # YOLOv5 FPN head head: [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 1, Conv, [512, 1, 1]], [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 1, Conv, [256, 1, 1]], [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5-p2.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: 3 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 9 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 13 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) [ -1, 1, Conv, [ 128, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2 [ -1, 1, C3, [ 128, False ] ], # 21 (P2/4-xsmall) [ -1, 1, Conv, [ 128, 3, 2 ] ], [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P3 [ -1, 3, C3, [ 256, False ] ], # 24 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 27 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 1024, False ] ], # 30 (P5/32-large) [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5-p6.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: 3 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 11 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 15 [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 19 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) [ -1, 1, Conv, [ 768, 3, 2 ] ], [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 [ -1, 3, C3, [ 1024, False ] ], # 32 (P5/64-xlarge) [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5-p7.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: 3 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 3, C3, [ 1024 ] ], [ -1, 1, Conv, [ 1280, 3, 2 ] ], # 11-P7/128 [ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ], [ -1, 3, C3, [ 1280, False ] ], # 13 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 1024, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat backbone P6 [ -1, 3, C3, [ 1024, False ] ], # 17 [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 21 [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 25 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 29 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 26 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 32 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 22 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 768, False ] ], # 35 (P5/32-large) [ -1, 1, Conv, [ 768, 3, 2 ] ], [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P6 [ -1, 3, C3, [ 1024, False ] ], # 38 (P6/64-xlarge) [ -1, 1, Conv, [ 1024, 3, 2 ] ], [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P7 [ -1, 3, C3, [ 1280, False ] ], # 41 (P7/128-xxlarge) [ [ 29, 32, 35, 38, 41 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6, P7) ] ================================================ FILE: models/hub/yolov5-panet.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, BottleneckCSP, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, BottleneckCSP, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, BottleneckCSP, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, BottleneckCSP, [1024, False]], # 9 ] # YOLOv5 PANet head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5l6.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 11 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 15 [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 19 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) [ -1, 1, Conv, [ 768, 3, 2 ] ], [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5m6.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 11 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 15 [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 19 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) [ -1, 1, Conv, [ 768, 3, 2 ] ], [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5s-transformer.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/hub/yolov5s6.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 11 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 15 [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 19 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) [ -1, 1, Conv, [ 768, 3, 2 ] ], [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/hub/yolov5x6.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple # anchors anchors: - [ 19,27, 44,40, 38,94 ] # P3/8 - [ 96,68, 86,152, 180,137 ] # P4/16 - [ 140,301, 303,264, 238,542 ] # P5/32 - [ 436,615, 739,380, 925,792 ] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 [ -1, 3, C3, [ 128 ] ], [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 [ -1, 9, C3, [ 256 ] ], [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 [ -1, 9, C3, [ 512 ] ], [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 [ -1, 3, C3, [ 768 ] ], [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], [ -1, 3, C3, [ 1024, False ] ], # 11 ] # YOLOv5 head head: [ [ -1, 1, Conv, [ 768, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 [ -1, 3, C3, [ 768, False ] ], # 15 [ -1, 1, Conv, [ 512, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 [ -1, 3, C3, [ 512, False ] ], # 19 [ -1, 1, Conv, [ 256, 1, 1 ] ], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) [ -1, 1, Conv, [ 256, 3, 2 ] ], [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 2 ] ], [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) [ -1, 1, Conv, [ 768, 3, 2 ] ], [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) ] ================================================ FILE: models/transformer/yolov5_ResNet50_NiNfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, ResNetlayer, [3, 64, 1, True, 1]], # 0 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 1 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 2 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 3 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 4 [-4, 1, ResNetlayer, [3, 64, 1, True, 1]], # 5 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 6 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 7 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 8 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 9 ######### Fusion Block ############# [[2,7], 1, NiNfusion, [1, 1]], # 10 two stream fuse [[3,8], 1, NiNfusion, [1, 1]], # 11 two stream fuse [[4,9], 1, NiNfusion, [1, 1]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [1024, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [1024, False]], # 16 [-1, 1, Conv, [512, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [512, False]], # 20 (P3/8-small) [-1, 1, Conv, [512, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [1024, False]], # 23 (P4/16-medium) [-1, 1, Conv, [1024, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [2048, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_ResNet50_NiNfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, ResNetlayer, [3, 64, 1, True, 1]], # 0 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 1 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 2 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 3 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 4 [-4, 1, ResNetlayer, [3, 64, 1, True, 1]], # 5 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 6 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 7 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 8 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 9 ######### Fusion Block ############# [[2,7], 1, NiNfusion, [1, 1]], # 10 two stream fuse [[3,8], 1, NiNfusion, [1, 1]], # 11 two stream fuse [[4,9], 1, NiNfusion, [1, 1]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [1024, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [1024, False]], # 16 [-1, 1, Conv, [512, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [512, False]], # 20 (P3/8-small) [-1, 1, Conv, [512, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [1024, False]], # 23 (P4/16-medium) [-1, 1, Conv, [1024, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [2048, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_ResNet50_Transfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, ResNetlayer, [3, 64, 1, True, 1]], # 0 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 1 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 2 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 3 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 4 [-4, 1, ResNetlayer, [3, 64, 1, True, 1]], # 5 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 6 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 7 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 8 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 9 ######### Fusion Block ############# [[2,7], 1, TransformerFusionBlock, [512, 20, 20]], # 10 two stream fuse [[3,8], 1, TransformerFusionBlock, [1024, 16, 16]], # 11 two stream fuse [[4,9], 1, TransformerFusionBlock, [2048, 10, 10]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [1024, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [1024, False]], # 16 [-1, 1, Conv, [512, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [512, False]], # 20 (P3/8-small) [-1, 1, Conv, [512, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [1024, False]], # 23 (P4/16-medium) [-1, 1, Conv, [1024, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [2048, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_ResNet50_Transfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, ResNetlayer, [3, 64, 1, True, 1]], # 0 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 1 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 2 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 3 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 4 [-4, 1, ResNetlayer, [3, 64, 1, True, 1]], # 5 [-1, 1, ResNetlayer, [64, 64, 1, False, 3]], # 6 [-1, 1, ResNetlayer, [256, 128, 2, False, 4]], # 7 [-1, 1, ResNetlayer, [512, 256, 2, False, 6]], # 8 [-1, 1, ResNetlayer, [1024, 512, 2, False, 3]], # 9 ######### Fusion Block ############# [[2,7], 1, TransformerFusionBlock, [512, 20, 20]], # 10 two stream fuse [[3,8], 1, TransformerFusionBlock, [1024, 16, 16]], # 11 two stream fuse [[4,9], 1, TransformerFusionBlock, [2048, 10, 10]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [1024, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [1024, False]], # 16 [-1, 1, Conv, [512, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [512, False]], # 20 (P3/8-small) [-1, 1, Conv, [512, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [1024, False]], # 23 (P4/16-medium) [-1, 1, Conv, [1024, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [2048, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_VGG16_NiNfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, VGGblock, [2, 3, 64]], # 0 [-1, 1, VGGblock, [2, 64, 128]], # 1 [-1, 1, VGGblock, [3, 128, 256]], # 2 [-1, 1, VGGblock, [3, 256, 512]], # 3 [-1, 1, VGGblock, [3, 512, 512]], # 4 [-4, 1, VGGblock, [2, 3, 64]], # 5 [-1, 1, VGGblock, [2, 64, 128]], # 6 [-1, 1, VGGblock, [3, 128, 256]], # 7 [-1, 1, VGGblock, [3, 256, 512]], # 8 [-1, 1, VGGblock, [3, 512, 512]], # 9 ######### Fusion Block ############# [[2,7], 1, NiNfusion, [1, 1]], # 10 two stream fuse [[3,8], 1, NiNfusion, [1, 1]], # 11 two stream fuse [[4,9], 1, NiNfusion, [1, 1]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [512, False]], # 16 [-1, 1, Conv, [256, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [256, False]], # 20 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [512, False]], # 23 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [512, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_VGG16_NiNfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, VGGblock, [2, 3, 64]], # 0 [-1, 1, VGGblock, [2, 64, 128]], # 1 [-1, 1, VGGblock, [3, 128, 256]], # 2 [-1, 1, VGGblock, [3, 256, 512]], # 3 [-1, 1, VGGblock, [3, 512, 512]], # 4 [-4, 1, VGGblock, [2, 3, 64]], # 5 [-1, 1, VGGblock, [2, 64, 128]], # 6 [-1, 1, VGGblock, [3, 128, 256]], # 7 [-1, 1, VGGblock, [3, 256, 512]], # 8 [-1, 1, VGGblock, [3, 512, 512]], # 9 ######### Fusion Block ############# [[2,7], 1, NiNfusion, [1, 1]], # 10 two stream fuse [[3,8], 1, NiNfusion, [1, 1]], # 11 two stream fuse [[4,9], 1, NiNfusion, [1, 1]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [512, False]], # 16 [-1, 1, Conv, [256, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [256, False]], # 20 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [512, False]], # 23 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [512, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_VGG16_Transfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, VGGblock, [2, 3, 64]], # 0 [-1, 1, VGGblock, [2, 64, 128]], # 1 [-1, 1, VGGblock, [3, 128, 256]], # 2 [-1, 1, VGGblock, [3, 256, 512]], # 3 [-1, 1, VGGblock, [3, 512, 512]], # 4 [-4, 1, VGGblock, [2, 3, 64]], # 5 [-1, 1, VGGblock, [2, 64, 128]], # 6 [-1, 1, VGGblock, [3, 128, 256]], # 7 [-1, 1, VGGblock, [3, 256, 512]], # 8 [-1, 1, VGGblock, [3, 512, 512]], # 9 ######### Fusion Block ############# [[2,7], 1, TransformerFusionBlock, [256, 20, 20]], # 10 two stream fuse [[3,8], 1, TransformerFusionBlock, [512, 16, 16]], # 11 two stream fuse [[4,9], 1, TransformerFusionBlock, [512, 10, 10]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [512, False]], # 16 [-1, 1, Conv, [256, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [256, False]], # 20 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [512, False]], # 23 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [512, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5_VGG16_Transfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, VGGblock, [2, 3, 64]], # 0 [-1, 1, VGGblock, [2, 64, 128]], # 1 [-1, 1, VGGblock, [3, 128, 256]], # 2 [-1, 1, VGGblock, [3, 256, 512]], # 3 [-1, 1, VGGblock, [3, 512, 512]], # 4 [-4, 1, VGGblock, [2, 3, 64]], # 5 [-1, 1, VGGblock, [2, 64, 128]], # 6 [-1, 1, VGGblock, [3, 128, 256]], # 7 [-1, 1, VGGblock, [3, 256, 512]], # 8 [-1, 1, VGGblock, [3, 512, 512]], # 9 ######### Fusion Block ############# [[2,7], 1, TransformerFusionBlock, [256, 20, 20]], # 10 two stream fuse [[3,8], 1, TransformerFusionBlock, [512, 16, 16]], # 11 two stream fuse [[4,9], 1, TransformerFusionBlock, [512, 10, 10]], # 12 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 13 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 14 [[-1,11], 1, Concat, [1]], # 15 cat backbone P4 [-1, 3, C3, [512, False]], # 16 [-1, 1, Conv, [256, 1, 1]], # 17 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 18 [[-1,10], 1, Concat, [1]], # 19 cat backbone P3 [-1, 3, C3, [256, False]], # 20 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 21 [[-1,17], 1, Concat, [1]], # 22 cat head P4 [-1, 3, C3, [512, False]], # 23 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 24 [[-1,13], 1, Concat, [1]], # 25 cat head P5 [-1, 3, C3, [512, False]], # 26 (P5/32-large) [[20, 23, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5l_Add_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, Add, [1]], # 20 two stream fuse [[6,16], 1, Add, [1]], # 21 two stream fuse [[9,19], 1, Add, [1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_MobileViT_NiNfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Focus, [32, 3]], # 0-P1/2 320 x 320 [-1, 1, MV2Block, [32, 1, 2]], # 1-P2/4 [-1, 1, MV2Block, [48, 2, 2]], # 160 x 160 [-1, 2, MV2Block, [48, 1, 2]], [-1, 1, MV2Block, [64, 2, 2]], # 80 x 80 [-1, 1, MobileViTBlock, [96, 2, 3, 2, 192]], # 5 in_channel, dim, depth, kernel_size, patch_size, mlp_dim [-1, 1, MV2Block, [80, 2, 2]], # 40 x 40 [-1, 1, MobileViTBlock, [120, 2, 3, 4, 480]], # 7 [-1, 1, MV2Block, [96, 2, 2]], # 20 x 20 [-1, 1, MobileViTBlock, [144, 2, 3, 3, 576]], # 9 [-4, 1, Focus, [32, 3]], # 0-P1/2 320 x 320 [-1, 1, MV2Block, [32, 1, 2]], # 1-P2/4 [-1, 1, MV2Block, [48, 2, 2]], # 160 x 160 [-1, 2, MV2Block, [48, 1, 2]], [-1, 1, MV2Block, [64, 2, 2]], # 80 x 80 [-1, 1, MobileViTBlock, [96, 2, 3, 2, 192]], # 15 in_channel, dim, depth, kernel_size, patch_size, mlp_dim [-1, 1, MV2Block, [80, 2, 2]], # 40 x 40 [-1, 1, MobileViTBlock, [120, 2, 3, 4, 480]], # 17 [-1, 1, MV2Block, [96, 2, 2]], # 20 x 20 [-1, 1, MobileViTBlock, [144, 2, 3, 3, 576]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_NiNfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_NiNfusion_LLVIP.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_NiNfusion_VEDAI.yaml ================================================ # parameters nc: 9 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_Transfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_Transfusion_LLVIP.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_Transfusion_VEDAI.yaml ================================================ # parameters nc: 9 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5l_Transfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5m_Add_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, Add, [1]], # 20 two stream fuse [[6,16], 1, Add, [1]], # 21 two stream fuse [[9,19], 1, Add, [1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5m_NiNfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5m_NiNfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5m_Transfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5m_Transfusion_SeaDrone.yaml ================================================ # parameters nc: 7 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5m_Transfusion_VEDAI.yaml ================================================ # parameters nc: 9 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5m_Transfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5m_weightedAdd_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, weightedAdd, [512]], # 20 two stream fuse [[6,16], 1, weightedAdd, [256]], # 21 two stream fuse [[9,19], 1, weightedAdd, [128]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5n_Add_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, Add, [1]], # 20 two stream fuse [[6,16], 1, Add, [1]], # 21 two stream fuse [[9,19], 1, Add, [1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5n_NiNfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, NiNfusion, [1, 1]], # 20 two stream fuse [[6,16], 1, NiNfusion, [1, 1]], # 21 two stream fuse [[9,19], 1, NiNfusion, [1, 1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5n_Transfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5n_Transfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.25 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5s_Add_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, Add, [1]], # 20 two stream fuse [[6,16], 1, Add, [1]], # 21 two stream fuse [[9,19], 1, Add, [1]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/transformer/yolov5s_Transfusion_FLIR.yaml ================================================ # parameters nc: 3 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/transformer/yolov5s_Transfusion_kaist.yaml ================================================ # parameters nc: 1 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # Two Stream [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 [-4, 1, Conv, [64, 6, 2, 2]], # 10-P1/2 [-1, 1, Conv, [128, 3, 2]], # 11-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 13-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 15-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 17-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 19 ######### Fusion Block ############# [[4,14], 1, TransformerFusionBlock, [256, 20, 20]], # 20 two stream fuse [[6,16], 1, TransformerFusionBlock, [512, 16, 16]], # 21 two stream fuse [[9,19], 1, TransformerFusionBlock, [1024, 10, 10]], # 22 two stream fuse ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], # 23 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 24 [[-1,21], 1, Concat, [1]], # 25 cat backbone P4 [-1, 3, C3, [512, False]], # 26 [-1, 1, Conv, [256, 1, 1]], # 27 [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 28 [[-1,20], 1, Concat, [1]], # 29 cat backbone P3 [-1, 3, C3, [256, False]], # 30 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], # 31 [[-1,27], 1, Concat, [1]], # 32 cat head P4 [-1, 3, C3, [512, False]], # 33 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], # 34 [[-1,23], 1, Concat, [1]], # 35 cat head P5 [-1, 3, C3, [1024, False]], # 36 (P5/32-large) [[30, 33, 36], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] #[10,13, 16,30, 33,23] # P3/8 #[30,61, 62,45, 59,119] # P4/16 #[116,90, 156,198, 373,326] ================================================ FILE: models/yolo.py ================================================ # YOLOv5 YOLO-specific modules import argparse import logging import sys from copy import deepcopy from pathlib import Path sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories logger = logging.getLogger(__name__) from models.common import * from models.experimental import * from utils.autoanchor import check_anchor_order from utils.general import make_divisible, check_file, set_logging from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ select_device, copy_attr try: import thop # for FLOPS computation except ImportError: thop = None class Detect(nn.Module): stride = None # strides computed during build export = False # onnx export def __init__(self, nc=80, anchors=(), ch=()): # detection layer super(Detect, self).__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.zeros(1)] * self.nl # init grid a = torch.tensor(anchors).float().view(self.nl, -1, 2) self.register_buffer('anchors', a) # shape(nl,na,2) self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv def forward(self, x): # x = x.copy() # for profiling z = [] # inference output self.training |= self.export for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i] = self._make_grid(nx, ny).to(x[i].device) y = x[i].sigmoid() y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z.append(y.view(bs, -1, self.no)) return x if self.training else (torch.cat(z, 1), x) @staticmethod def _make_grid(nx=20, ny=20): yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() class Model(nn.Module): def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.safe_load(f) # model dict print("YAML") print(self.yaml) # Define model ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels if nc and nc != self.yaml['nc']: logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value if anchors: logger.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors'] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names # logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model[-1] # Detect() # print("Detect") # print(m) if isinstance(m, Detect): s = 256 # 2x min stride m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward # print("m.stride", m.stride) m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once # logger.info('Strides: %s' % m.stride.tolist()) # Init weights, biases initialize_weights(self) self.info() logger.info('') def forward(self, x, augment=False, profile=False): if augment: img_size = x.shape[-2:] # height, width s = [1, 0.83, 0.67] # scales f = [None, 3, None] # flips (2-ud, 3-lr) y = [] # outputs for si, fi in zip(s, f): xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) yi = self.forward_once(xi)[0] # forward # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi[..., :4] /= si # de-scale if fi == 2: yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud elif fi == 3: yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr y.append(yi) return torch.cat(y, 1), None # augmented inference, train else: return self.forward_once(x, profile) # single-scale inference, train def forward_once(self, x, profile=False): y, dt = [], [] # outputs for m in self.model: if m.f != -1: # if not from previous layer x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers if profile: o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS t = time_synchronized() for _ in range(10): _ = m(x) dt.append((time_synchronized() - t) * 100) if m == self.model[0]: logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s} {'module'}") logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') x = m(x) # run y.append(x if m.i in self.save else None) # save output if profile: logger.info('%.1fms total' % sum(dt)) return x def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # https://arxiv.org/abs/1708.02002 section 3.3 # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. m = self.model[-1] # Detect() module for mi, s in zip(m.m, m.stride): # from b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) def _print_biases(self): m = self.model[-1] # Detect() module for mi in m.m: # from b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) logger.info( ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) # def _print_weights(self): # for m in self.model.modules(): # if type(m) is Bottleneck: # logger.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers logger.info('Fusing layers... ') for m in self.model.modules(): if type(m) is Conv and hasattr(m, 'bn'): m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv delattr(m, 'bn') # remove batchnorm m.forward = m.fuseforward # update forward self.info() return self def nms(self, mode=True): # add or remove NMS module present = type(self.model[-1]) is NMS # last layer is NMS if mode and not present: logger.info('Adding NMS... ') m = NMS() # module m.f = -1 # from m.i = self.model[-1].i + 1 # index self.model.add_module(name='%s' % m.i, module=m) # add self.eval() elif not mode and present: logger.info('Removing NMS... ') self.model = self.model[:-1] # remove return self def autoshape(self): # add autoShape module logger.info('Adding autoShape... ') m = autoShape(self) # wrap model copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes return m def info(self, verbose=False, img_size=640): # print model information model_info(self, verbose, img_size) def parse_model(d, ch): # model_dict, input_channels(3) logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR]: c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] if m in [BottleneckCSP, C3, C3TR]: args.insert(2, n) # number of repeats n = 1 elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Add: # print("ch[f]", f, ch[f[0]]) c2 = ch[f[0]] args = [c2] elif m is Add2: c2 = ch[f[0]] args = [c2, args[1]] # elif m is CMAFF: # c2 = ch[f[0]] # args = [c2] elif m is GPT: c2 = ch[f[0]] args = [c2] elif m is Concat: c2 = sum([ch[x] for x in f]) elif m is Detect: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is Contract: c2 = ch[f] * args[0] ** 2 elif m is Expand: c2 = ch[f] // args[0] ** 2 else: c2 = ch[f] m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type np = sum([x.numel() for x in m_.parameters()]) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: ch = [] ch.append(c2) return nn.Sequential(*layers), sorted(save) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') opt = parser.parse_args() opt.cfg = check_file(opt.cfg) # check file set_logging() device = select_device(opt.device) # Create model model = Model(opt.cfg).to(device) input_rgb = torch.Tensor(8, 3, 640, 640).to(device) output = model(input_rgb) # print(model) # model.train() # torch.save(model, "yolov5s.pth") # Profile # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 320, 320).to(device) # y = model(img, profile=True) # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898) # from torch.utils.tensorboard import SummaryWriter # tb_writer = SummaryWriter('.') # logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/") # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard ================================================ FILE: models/yolo_test.py ================================================ # YOLOv5 YOLO-specific modules import argparse import logging import sys from copy import deepcopy from pathlib import Path sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories logger = logging.getLogger(__name__) from models.common import * from models.experimental import * from utils.autoanchor import check_anchor_order from utils.general import make_divisible, check_file, set_logging from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ select_device, copy_attr #from mmcv.ops import DeformConv2dPack as DCN try: import thop # for FLOPS computation except ImportError: thop = None class Detect(nn.Module): stride = None # strides computed during build export = False # onnx export def __init__(self, nc=80, anchors=(), ch=()): # detection layer super(Detect, self).__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.zeros(1)] * self.nl # init grid a = torch.tensor(anchors).float().view(self.nl, -1, 2) self.register_buffer('anchors', a) # shape(nl,na,2) self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv #self.m = nn.ModuleList(DCN(x, self.no * self.na, kernel_size=(3, 3), stride=1, padding=1, dilation=1, deform_groups=1) for x in ch) # output DCN conv3x3 def forward(self, x): # x = x.copy() # for profiling z = [] # inference output logits_ = [] self.training |= self.export for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i] = self._make_grid(nx, ny).to(x[i].device ) logits = x[i][..., 5:] y = x[i].sigmoid() y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z.append(y.view(bs, -1, self.no)) logits_.append(logits.view(bs, -1, self.no - 5)) return x if self.training else (torch.cat(z, 1), torch.cat(logits_, 1), x) @staticmethod def _make_grid(nx=20, ny=20): yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() class Model(nn.Module): def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes super(Model, self).__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.safe_load(f) # model dict # Define model ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels if nc and nc != self.yaml['nc']: logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value if anchors: logger.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors'] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist self.names = [str(i) for i in range(self.yaml['nc'])] # default names # Build strides, anchors m = self.model[-1] # Detect() # print(m) if isinstance(m, Detect): s = 256 # 2x min stride # m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s), torch.zeros(1, ch, s, s))]) # forward m.stride = torch.Tensor([8.0, 16.0, 32.0]) m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride #self._initialize_biases() # only run once # Init weights, biases initialize_weights(self) self.info() logger.info('') def forward(self, x, x2, augment=False, profile=False): if augment: img_size = x.shape[-2:] # height, width s = [1, 0.83, 0.67] # scales f = [None, 3, None] # flips (2-ud, 3-lr) y = [] # outputs for si, fi in zip(s, f): xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) yi = self.forward_once(xi)[0] # forward # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi[..., :4] /= si # de-scale if fi == 2: yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud elif fi == 3: yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr y.append(yi) return torch.cat(y, 1), None # augmented inference, train else: return self.forward_once(x, x2, profile) # single-scale inference, train def forward_once(self, x, x2, profile=False): y, dt = [], [] # outputs i = 0 for m in self.model: if m.f != -1: # if not from previous layer if m.f != -4: x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers if profile: o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS t = time_synchronized() for _ in range(10): _ = m(x) dt.append((time_synchronized() - t) * 100) if m == self.model[0]: logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s} {'module'}") logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') if m.f == -4: x = m(x2) else: x = m(x) # run y.append(x if m.i in self.save else None) # save output i += 1 if profile: logger.info('%.1fms total' % sum(dt)) return x def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency # https://arxiv.org/abs/1708.02002 section 3.3 # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. m = self.model[-1] # Detect() module for mi, s in zip(m.m, m.stride): # from b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) def _print_biases(self): m = self.model[-1] # Detect() module for mi in m.m: # from b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) logger.info( ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers logger.info('Fusing layers... ') for m in self.model.modules(): if type(m) is Conv and hasattr(m, 'bn'): m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv delattr(m, 'bn') # remove batchnorm m.forward = m.fuseforward # update forward self.info() return self def nms(self, mode=True): # add or remove NMS module present = type(self.model[-1]) is NMS # last layer is NMS if mode and not present: logger.info('Adding NMS... ') m = NMS() # module m.f = -1 # from m.i = self.model[-1].i + 1 # index self.model.add_module(name='%s' % m.i, module=m) # add self.eval() elif not mode and present: logger.info('Removing NMS... ') self.model = self.model[:-1] # remove return self def autoshape(self): # add autoShape module logger.info('Adding autoShape... ') m = autoShape(self) # wrap model copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes return m def info(self, verbose=False, img_size=640): # print model information model_info(self, verbose, img_size) def parse_model(d, ch): # model_dict, input_channels(3) logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR]: if m is Focus: c1, c2 = 3, args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] elif m is Conv and args[0] == 64: # new c1, c2 = 3, args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] else: c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, 8) args = [c1, c2, *args[1:]] if m in [BottleneckCSP, C3, C3TR]: args.insert(2, n) # number of repeats n = 1 elif m is ResNetlayer: if args[3] == True: c2 = args[1] else: c2 = args[1]*4 elif m is VGGblock: c2 = args[2] elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: c2 = sum([ch[x] for x in f]) elif m in [Add, DMAF]: c2 = ch[f[0]] args = [c2] elif m is Add2: c2 = ch[f[0]] args = [c2, args[1]] elif m is Detect: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is Contract: c2 = ch[f] * args[0] ** 2 elif m is Expand: c2 = ch[f] // args[0] ** 2 elif m is NiNfusion: c1 = sum([ch[x] for x in f]) c2 = c1 // 2 args = [c1, c2, *args] elif m is TransformerFusionBlock: c2 = ch[f[0]] args = [c2, *args[1:]] else: c2 = ch[f] m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type np = sum([x.numel() for x in m_.parameters()]) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: ch = [] ch.append(c2) return nn.Sequential(*layers), sorted(save) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='/home/fqy/proj/paper/YOLOFusion/models/transformer/yolov5s_fusion_transformer(x3)_vedai.yaml', help='model.yaml') parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') opt = parser.parse_args() opt.cfg = check_file(opt.cfg) # check file set_logging() device = select_device(opt.device) print(device) model = Model(opt.cfg).to(device) input_rgb = torch.Tensor(8, 3, 640, 640).to(device) input_ir = torch.Tensor(8, 3, 640, 640).to(device) output = model(input_rgb, input_ir) ================================================ FILE: models/yolov5l.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024, False]], # 9 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5m.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024, False]], # 9 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5s.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024, False]], # 9 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: models/yolov5x.yaml ================================================ # parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] [[-1, 1, Focus, [64, 3]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024, False]], # 9 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] ================================================ FILE: requirements.txt ================================================ # pip install -r requirements.txt # base ---------------------------------------- matplotlib>=3.2.2 numpy>=1.18.5 opencv-python>=4.1.2 Pillow PyYAML>=5.3.1 scipy>=1.4.1 torch>=1.7.0 torchvision>=0.8.1 tqdm>=4.41.0 # logging ------------------------------------- tensorboard>=2.4.1 # wandb # plotting ------------------------------------ seaborn>=0.11.0 pandas # export -------------------------------------- # coremltools>=4.1 # onnx>=1.8.1 # scikit-learn==0.19.2 # for coreml quantization # extras -------------------------------------- thop # FLOPS computation pycocotools>=2.0 # COCO mAP ================================================ FILE: test.py ================================================ import argparse import json import os from pathlib import Path from threading import Thread import numpy as np import torch import yaml from tqdm import tqdm from models.experimental import attempt_load from utils.datasets import create_dataloader_rgb_ir from utils.general import logger, coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \ box_iou, non_max_suppression, scale_coords, xyxy2xywh, xyxy2xywh2, xywh2xyxy, set_logging, increment_path, colorstr from utils.metrics import ap_per_class, ConfusionMatrix from utils.plots import plot_images, output_to_target, plot_study_txt from utils.torch_utils import select_device, time_synchronized from evaluation_script.evaluation_script import evaluate from utils.confluence import confluence_process def test(data, weights=None, batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.5, # for NMS save_json=False, single_cls=False, augment=False, verbose=False, model=None, dataloader=None, save_dir=Path(''), # for saving images save_txt=False, # for auto-labelling save_hybrid=False, # for hybrid auto-labelling save_conf=True, # save auto-label confidences plots=False, wandb_logger=None, compute_loss=None, half_precision=True, is_coco=False, opt=None, labels_list=None): # Initialize/load model and set device training = model is not None if training: # called by train.py device = next(model.parameters()).device # get model device (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir if save_txt: labels_dir = increment_path(Path(save_dir) / 'labels' / 'pred', exist_ok=False, mkdir=True) else: # called directly set_logging() device = select_device(opt.device, batch_size=batch_size) # Directories save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir labels_dir = save_dir / 'labels' # Load model model = attempt_load(weights, map_location=device) # load FP32 model gs = max(int(model.stride.max()), 32) # grid size (max stride) imgsz = check_img_size(imgsz, s=gs) # check img_size # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 # if device.type != 'cpu' and torch.cuda.device_count() > 1: # model = nn.DataParallel(model) # Half half = device.type != 'cpu' and half_precision # half precision only supported on CUDA if half: model.half() # Configure model.eval() if isinstance(data, str): is_coco = data.endswith('coco.yaml') with open(data) as f: data = yaml.safe_load(f) check_dataset(data) # check nc = 1 if single_cls else int(data['nc']) # number of classes iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() # Logging log_imgs = 0 if wandb_logger and wandb_logger.wandb: log_imgs = min(wandb_logger.log_imgs, 100) # Dataloader if not training: # if device.type != 'cpu': # model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once print(opt.task) task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images val_path_rgb = data['val_rgb'] val_path_ir = data['val_ir'] dataloader = create_dataloader_rgb_ir(val_path_rgb, val_path_ir, imgsz, batch_size, gs, opt, pad=0.5, rect=True, prefix=colorstr(f'{task}: '))[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} coco91class = coco80_to_coco91_class() if nc == 1: s = ('%20s' + '%12s' * 10) % ('Class', 'Images', 'Labels', 'TP', 'FP', 'FN', 'F1', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') # 设置进度条的显示信息 else: s = ('%20s' + '%12s' * 7) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.75', 'mAP@.5:.95') p, r, f1, mp, mr, map50, map75, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0, 0., 0. tp, fp, fn = 0, 0, 0 loss = torch.zeros(4, device=device) jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): img = img.to(device, non_blocking=True) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 targets = targets.to(device) nb, _, height, width = img.shape # batch size, channels, height, width img_rgb = img[:, :3, :, :] img_ir = img[:, 3:, :, :] with torch.no_grad(): # Run model t = time_synchronized() out, _, train_out = model(img_rgb, img_ir, augment=augment) # inference and training outputs t0 += time_synchronized() - t # Compute loss if compute_loss: loss += compute_loss([x.float() for x in train_out], targets)[1][:4] # box, obj, cls # Run NMS targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling t = time_synchronized() out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls) # out = confluence_process(out, 0.1, 0.5) t1 += time_synchronized() - t # Statistics per image for si, pred in enumerate(out): labels = targets[targets[:, 0] == si, 1:] nl = len(labels) tcls = labels[:, 0].tolist() if nl else [] # target class path = Path(paths[si]) seen += 1 if len(pred) == 0: if nl: stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) continue # Predictions if single_cls: pred[:, 5] = 0 predn = pred.clone() scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred # Append to text file if save_txt: i = labels_list.index(str(path.stem) + '.txt') gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh2(torch.tensor(xyxy).view(1, 4))).view(-1).tolist() # normalized xywh line = (i+1, *xywh, conf) if save_conf else (i+1, *xywh) # label format with open(labels_dir / (path.stem + '.txt'), 'a') as f: f.write(('%g,' * len(line)).rstrip(",") % line + '\n') # W&B logging - Media Panel Plots if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": int(cls), "box_caption": "%s %.3f" % (names[cls], conf), "scores": {"class_score": conf}, "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space #wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name)) #wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None # Append to pycocotools JSON dictionary if save_json: # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(pred.tolist(), box.tolist()): jdict.append({'image_id': image_id, 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]), 'bbox': [round(x, 3) for x in b], 'score': round(p[4], 5)}) # Assign all predictions as incorrect correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) if nl: detected = [] # target indices tcls_tensor = labels[:, 0] # target boxes tbox = xywh2xyxy(labels[:, 1:5]) scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels if plots: confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) # Per target class for cls in torch.unique(tcls_tensor): ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices # Search for detections if pi.shape[0]: # Prediction to target ious ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices # Append detections detected_set = set() for j in (ious > iouv[0]).nonzero(as_tuple=False): d = ti[i[j]] # detected target if d.item() not in detected_set: detected_set.add(d.item()) detected.append(d) correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn if len(detected) == nl: # all targets already located in image break # Append statistics (correct, conf, pcls, tcls) stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # mem = '%.4gM' % (torch.cuda.memory_reserved() / 1E6 if torch.cuda.is_available() else 0) # print(mem) # file_name, extension = os.path.splitext(path.name) # os.rename('/home/shen/Chenyf/FLIR-align-3class/feature_save/fea_20x20.png', '/home/shen/Chenyf/FLIR-align-3class/feature_save/'+file_name+'_20x20'+'.png') # os.rename('/home/shen/Chenyf/FLIR-align-3class/feature_save/fea_40x40.png', '/home/shen/Chenyf/FLIR-align-3class/feature_save/'+file_name+'_40x40'+'.png') # os.rename('/home/shen/Chenyf/FLIR-align-3class/feature_save/fea_80x80.png', '/home/shen/Chenyf/FLIR-align-3class/feature_save/'+file_name+'_80x80'+'.png') # Plot images if plots and batch_i < 3: f1 = save_dir / f'test_batch{batch_i}_labels.jpg' # labels Thread(target=plot_images, args=(img_rgb, targets, paths, f1, names), daemon=True).start() f2 = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions Thread(target=plot_images, args=(img_rgb, output_to_target(out), paths, f2, names), daemon=True).start() # 保存所有预测框结果，后续用于MR的计算 if save_txt: temp = [] files = os.listdir(labels_dir) files.sort() for index, file in enumerate(files): with open(labels_dir / file, 'r') as f: # 打开源文件 for line in f: temp.append(line) with open(labels_dir / 'result.txt', 'a') as ff: for ii in temp: ff.write(ii) # 计算MR指标 # annFile = './evaluation_script/KAIST_annotation.json' # rstFiles = './' + str(labels_dir) + '/result.txt' # phase = "Multispectral" # MR = evaluate(annFile, rstFiles, phase) # MR_all = MR['all'].summarize(0) # MR_day = MR['day'].summarize(0) # MR_night = MR['night'].summarize(0) # MR_near = MR['near'].summarize(1) # MR_medium = MR['medium'].summarize(2) # MR_far = MR['far'].summarize(3) # MR_none = MR['none'].summarize(4) # MR_partial = MR['partial'].summarize(5) # MR_heavy = MR['heavy'].summarize(6) # recall_all = 1 - MR['all'].eval['yy'][0][-1] MR_all = 0.0 MR_day = 0.0 MR_night = 0.0 MR_near = 0.0 MR_medium = 0.0 MR_far = 0.0 MR_none = 0.0 MR_partial = 0.0 MR_heavy = 0.0 recall_all = 0.0 MRresult = [MR_all, MR_day, MR_night, MR_near, MR_medium, MR_far, MR_none, MR_partial, MR_heavy, recall_all] # Compute statistics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy a = len(stats) b = stats[0].any() if len(stats) and stats[0].any(): tp, fp, fn, p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) ap50, ap75, ap = ap[:, 0], ap[:, 5], ap.mean(1) # AP@0.5, AP@0.5:0.95 mp, mr, map50, map75, map = p.mean(), r.mean(), ap50.mean(), ap75.mean(), ap.mean() nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class else: nt = torch.zeros(1) # Print results if nc > 1: pf = '%20s' + '%12i' * 2 + '%12.3g' * 5 # print format logger.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map75, map)) else: pf = '%20s' + '%12i' * 2 + '%12.4g' * 8 # print format logger.info(pf % ('all', seen, nt.sum(), tp, fp, fn, f1, mp, mr, map50, map)) logger.info(('%20s' + '%11s' * 9) % ('MR-all', 'MR-day', 'MR-night', 'MR-near', 'MR-medium', 'MR-far', 'MR-none', 'MR-partial', 'MR-heavy', 'Recall-all')) logger.info(('%20.2f' + '%11.2f' * 9) % (MR_all * 100, MR_day * 100, MR_night * 100, MR_near * 100, MR_medium * 100, MR_far * 100, MR_none * 100, MR_partial * 100, MR_heavy * 100, recall_all * 100)) # Print results per class if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): for i, c in enumerate(ap_class): logger.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap75[i], ap[i])) # Print speeds t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple if not training: print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) if wandb_logger and wandb_logger.wandb: val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))] wandb_logger.log({"Validation": val_batches}) if wandb_images: wandb_logger.log({"Bounding Box Debugger/Images": wandb_images}) # Save JSON if save_json and len(jdict): w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights anno_json = '../coco/annotations/instances_val2017.json' # annotations json pred_json = str(save_dir / f"{w}_predictions.json") # predictions json print('\nEvaluating pycocotools mAP... saving %s...' % pred_json) with open(pred_json, 'w') as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval anno = COCO(anno_json) # init annotations api pred = anno.loadRes(pred_json) # init predictions api eval = COCOeval(anno, pred, 'bbox') if is_coco: eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate eval.evaluate() eval.accumulate() eval.summarize() map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) except Exception as e: print(f'pycocotools unable to run: {e}') # Return results model.float() # for training if not training: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f"Results saved to {save_dir}{s}") maps = np.zeros(nc) + map for i, c in enumerate(ap_class): maps[c] = ap[i] if not isinstance(tp, int): return (tp[0], fp[0], fn[0], f1[0], mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, MRresult, t else: return (tp, fp, fn, f1, mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, MRresult, t if __name__ == '__main__': parser = argparse.ArgumentParser(prog='test.py') parser.add_argument('--weights', nargs='+', type=str, default='/home/shen/Chenyf/exp_save/multispectral-object-detection/5l_FLIR_3class_transformerx2_avgpool+maxpool/weights/best.pt', help='model.pt path(s)') parser.add_argument('--data', type=str, default='./data/multispectral/FLIR-align-3class.yaml', help='*.data path') parser.add_argument('--batch-size', type=int, default=1, help='size of each image batch') parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--task', default='val', help='train, val, test, speed or study') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') parser.add_argument('--augment', default=False, action='store_true', help='augmented inference') parser.add_argument('--verbose', action='store_true', help='report mAP by class') parser.add_argument('--save-txt', default=True, action='store_true', help='save results to *.txt') parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') parser.add_argument('--save-conf', default=True, action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--project', default='runs/test', help='save to project/name') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') opt = parser.parse_args() opt.save_json |= opt.data.endswith('coco.yaml') opt.data = check_file(opt.data) # check file print(opt) print(opt.data) check_requirements() p = "/home/shen/Chenyf/FLIR-align-3class/labels/test" labels_list = os.listdir(p) labels_list.sort() if opt.data in ['./data/multispectral/FLIR-align-3class.yaml', './data/multispectral/FLIR-ADAS.yaml', './data/multispectral/VEDAI.yaml']: opt.verbose = True if opt.task in ('train', 'val', 'test'): # run normally test(opt.data, opt.weights, opt.batch_size, opt.img_size, opt.conf_thres, opt.iou_thres, opt.save_json, opt.single_cls, opt.augment, opt.verbose, save_txt=opt.save_txt | opt.save_hybrid, save_hybrid=opt.save_hybrid, save_conf=opt.save_conf, opt=opt, labels_list=labels_list ) elif opt.task == 'speed': # speed benchmarks for w in opt.weights: test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, opt=opt) elif opt.task == 'study': # run over a range of settings and save/plot # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) for w in opt.weights: f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to y = [] # y axis for i in x: # img-size print(f'\nRunning {f} point {i}...') r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, plots=False, opt=opt) y.append(r + t) # results and times np.savetxt(f, y, fmt='%10.4g') # save os.system('zip -r study.zip study_*.txt') plot_study_txt(x=x) # plot ================================================ FILE: train.py ================================================ import argparse import logging import math import os import random import time from copy import deepcopy from pathlib import Path from threading import Thread import numpy as np import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.optim.lr_scheduler as lr_scheduler import torch.utils.data import yaml from torch.cuda import amp from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm import test # import test.py to get mAP after each epoch from models.experimental import attempt_load from models.yolo_test import Model from utils.autoanchor import check_anchors from utils.datasets import create_dataloader_rgb_ir from utils.general import logger, labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ check_requirements, print_mutation, set_logging, one_cycle, colorstr from utils.google_utils import attempt_download from utils.loss import ComputeLoss from utils.plots import plot_images, plot_labels, plot_results, plot_evolution from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume from utils.datasets import RandomSampler import global_var def train_rgb_ir(hyp, opt, device, tb_writer=None): os.environ["WANDB_MODE"] = "offline" logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) save_dir, epochs, batch_size, total_batch_size, weights, rank = \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank # Directories wdir = save_dir / 'weights' wdir.mkdir(parents=True, exist_ok=True) # make dir last = wdir / 'last.pt' best = wdir / 'best.pt' results_file = save_dir / 'results.txt' # Save run settings with open(save_dir / 'hyp.yaml', 'w') as f: yaml.safe_dump(hyp, f, sort_keys=False) with open(save_dir / 'opt.yaml', 'w') as f: yaml.safe_dump(vars(opt), f, sort_keys=False) # Configure plots = not opt.evolve # create plots cuda = device.type != 'cpu' init_seeds(seed=1 + rank, deterministic=True) with open(opt.data) as f: data_dict = yaml.safe_load(f) # data dict is_coco = opt.data.endswith('coco.yaml') # Logging- Doing this before checking the dataset. Might update data_dict loggers = {'wandb': None} # loggers dict if rank in [-1, 0]: opt.hyp = hyp # add hyperparameters run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict) loggers['wandb'] = wandb_logger.wandb data_dict = wandb_logger.data_dict if wandb_logger.wandb: weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check # Model pretrained = weights.endswith('.pt') #pretrained = False if pretrained: with torch_distributed_zero_first(rank): attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location=device) # load checkpoint model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys state_dict = ckpt['model'].float().state_dict() # to FP32 state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect new_state_dict = state_dict for key in list(state_dict.keys()): new_state_dict[key[:6] + str(int(key[6])+10) + key[7:]] = state_dict[key] model.load_state_dict(new_state_dict, strict=False) # load logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report else: model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create with torch_distributed_zero_first(rank): check_dataset(data_dict) # check train_path_rgb = data_dict['train_rgb'] test_path_rgb = data_dict['val_rgb'] train_path_ir = data_dict['train_ir'] test_path_ir = data_dict['val_ir'] labels_path = data_dict['path'] + '/labels/test' labels_list = os.listdir(labels_path) labels_list.sort() # Freeze freeze = [] # parameter names to freeze (full or partial) for k, v in model.named_parameters(): v.requires_grad = True # train all layers if any(x in k for x in freeze): print('freezing %s' % k) v.requires_grad = False # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay logger.info(f"Scaled weight_decay = {hyp['weight_decay']}") pg0, pg1, pg2 = [], [], [] # optimizer parameter groups for k, v in model.named_modules(): if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): pg2.append(v.bias) # biases if isinstance(v, nn.BatchNorm2d): pg0.append(v.weight) # no decay elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): pg1.append(v.weight) # apply decay if opt.adam: optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum else: optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg2}) # add pg2 (biases) logger.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups " f"{len(pg0)} weight, {len(pg1)} weight (no decay), {len(pg2)} bias") del pg0, pg1, pg2 if opt.linear_lr: lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear else: lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # EMA ema = ModelEMA(model) if rank in [-1, 0] else None # Resume start_epoch, best_fitness = 0, 0.0 if pretrained: # Optimizer if ckpt['optimizer'] is not None: optimizer.load_state_dict(ckpt['optimizer']) best_fitness = ckpt['best_fitness'] # EMA if ema and ckpt.get('ema'): ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) ema.updates = ckpt['updates'] # Results if ckpt.get('training_results') is not None: results_file.write_text(ckpt['training_results']) # write results.txt # Epochs start_epoch = ckpt['epoch'] + 1 if opt.resume: assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) if epochs < start_epoch: logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % (weights, ckpt['epoch'], epochs)) epochs += ckpt['epoch'] # finetune additional epochs del ckpt, state_dict # Image sizes gs = max(int(model.stride.max()), 32) # grid size (max stride) nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj']) # print("nl", nl) imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples # DP mode if cuda and rank == -1 and torch.cuda.device_count() > 1: model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and rank != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) logger.info('Using SyncBatchNorm()') # Trainloader dataloader, dataset = create_dataloader_rgb_ir(train_path_rgb, train_path_ir, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, world_size=opt.world_size, workers=opt.workers, image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: ')) mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class nb = len(dataloader) # number of batches assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) # Process 0 if rank in [-1, 0]: testloader, testdata = create_dataloader_rgb_ir(test_path_rgb, test_path_ir,imgsz_test, 1, gs, opt, hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5, prefix=colorstr('val: ')) if not opt.resume: labels = np.concatenate(dataset.labels, 0) c = torch.tensor(labels[:, 0]) # classes # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency # model._initialize_biases(cf.to(device)) if plots: plot_labels(labels, names, save_dir, loggers) if tb_writer: tb_writer.add_histogram('classes', c, 0) # Anchors if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) model.half().float() # pre-reduce anchor precision # DDP mode if cuda and rank != -1: model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, # nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698 find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules())) # Model parameters hyp['box'] *= 3. / nl # scale to layers hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers hyp['label_smoothing'] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights model.names = names # Start training t0 = time.time() nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training maps = np.zeros(nc) # mAP per class MRresult = 0.0 results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) scheduler.last_epoch = start_epoch - 1 # do not move scaler = amp.GradScaler(enabled=cuda) compute_loss = ComputeLoss(model) # init loss class logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n' f'Using {dataloader.num_workers} dataloader workers\n' f'Logging results to {save_dir}\n' f'Starting training for {epochs} epochs...') for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ model.train() # Update image weights (optional) if opt.image_weights: # Generate indices if rank in [-1, 0]: cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx # Broadcast if DDP if rank != -1: indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() dist.broadcast(indices, 0) if rank != 0: dataset.indices = indices.cpu().numpy() # Update mosaic border # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) # dataset.mosaic_border = [b - imgsz, -b] # height, width borders mloss = torch.zeros(4, device=device) # mean losses if rank != -1: dataloader.sampler.set_epoch(epoch) pbar = enumerate(dataloader) logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'rank', 'labels', 'img_size')) if rank in [-1, 0]: pbar = tqdm(pbar, total=nb) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 imgs_rgb = imgs[:, :3, :, :] imgs_ir = imgs[:, 3:, :, :] # FQY my code 训练数据可视化 flage_visual = global_var.get_value('flag_visual_training_dataset') if flage_visual: from torchvision import transforms unloader = transforms.ToPILImage() for num in range(batch_size): image = imgs[num, :3, :, :].cpu().clone() # clone the tensor image = image.squeeze(0) # remove the fake batch dimension image = unloader(image) image.save('example_%s_%s_%s_color.jpg'%(str(epoch), str(i), str(num))) image = imgs[num, 3:, :, :].cpu().clone() # clone the tensor image = image.squeeze(0) # remove the fake batch dimension image = unloader(image) image.save('example_%s_%s_%s_ir.jpg'%(str(epoch), str(i), str(num))) # Warmup if ni <= nw: xi = [0, nw] # x interp # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) # Multi-scale if opt.multi_scale: sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) # Forward with amp.autocast(enabled=cuda): # pred = model(imgs) # forward pred = model(imgs_rgb, imgs_ir) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if rank != -1: loss *= opt.world_size # gradient averaged between devices in DDP mode if opt.quad: loss *= 4. # Backward scaler.scale(loss).backward() # Optimize if ni % accumulate == 0: scaler.step(optimizer) # optimizer.step scaler.update() optimizer.zero_grad() if ema: ema.update(model) # Print if rank in [-1, 0]: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) s = ('%10s' * 2 + '%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) pbar.set_description(s) if ni < 3: f1 = save_dir / f'train_batch{ni}_vis.jpg' f2 = save_dir / f'train_batch{ni}_inf.jpg' Thread(target=plot_images, args=(imgs_rgb, targets, paths, f1), daemon=True).start() Thread(target=plot_images, args=(imgs_ir, targets, paths, f2), daemon=True).start() # end batch ------------------------------------------------------------------------------------------------ # end epoch ---------------------------------------------------------------------------------------------------- # Scheduler lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard scheduler.step() # DDP process 0 or single-GPU if rank in [-1, 0]: # mAP ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) final_epoch = epoch + 1 == epochs if not opt.notest or final_epoch: # Calculate mAP wandb_logger.current_epoch = epoch + 1 results, maps, MRresult, times = test.test(data_dict, batch_size=1, imgsz=imgsz_test, model=ema.ema, single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, save_txt=True, save_conf=True, verbose=nc < 50 and final_epoch, plots=plots and final_epoch, wandb_logger=wandb_logger, compute_loss=compute_loss, is_coco=is_coco, labels_list=labels_list, ) # log keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', 'train/rank_loss', # train loss 'TP', 'FP', 'FN', 'F1', 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', # metrics 'val/box_loss', 'val/obj_loss', 'val/cls_loss', 'val/rank_loss', # val loss 'x/lr0', 'x/lr1', 'x/lr2', # learning rate 'MR_all', 'MR_day', 'MR_night', 'MR_near', 'MR_medium', 'MR_far', 'MR_none', 'MR_partial', 'MR_heavy', 'Recall_all' # MR ] vals = list(mloss) + list(results) + lr + MRresult dicts = {k: v for k, v in zip(keys, vals)} # dict file = save_dir / 'results.csv' n = len(dicts) + 1 # number of cols s = '' if file.exists() else (('%s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # add header with open(file, 'a') as f: f.write(s + ('%g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] if fi > best_fitness: best_fitness = fi #wandb_logger.end_epoch(best_result=best_fitness == fi) # fi = MRresult[0] # if fi < best_fitness: # best_fitness = fi # Save model if (not opt.nosave) or (final_epoch and not opt.evolve): # if save ckpt = {'epoch': epoch, 'best_fitness': best_fitness, 'model': deepcopy(model.module if is_parallel(model) else model).half(), 'ema': deepcopy(ema.ema).half(), 'updates': ema.updates, 'optimizer': optimizer.state_dict(), 'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None} # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if wandb_logger.wandb: if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1: wandb_logger.log_model( last.parent, opt, epoch, fi, best_model=best_fitness == fi) del ckpt # end epoch ---------------------------------------------------------------------------------------------------- # end training t1 = time.time() t = t1 - t0 if rank in [-1, 0]: # Plots if plots: plot_results(file=save_dir / 'results.csv') # save as results.png if wandb_logger.wandb: files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files if (save_dir / f).exists()]}) # Test best.pt logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) for m in (last, best) if best.exists() else (last): # speed, mAP tests results, _, MRresult, _ = test.test(opt.data, batch_size=1, imgsz=imgsz_test, conf_thres=0.001, iou_thres=0.5, model=attempt_load(m, device).half(), single_cls=opt.single_cls, dataloader=testloader, save_dir=save_dir, save_txt=True, save_conf=True, save_json=False, plots=False, is_coco=is_coco, labels_list=labels_list, verbose=nc > 1, ) # Strip optimizers final = best if best.exists() else last # final model for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if opt.bucket: os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload if wandb_logger.wandb and not opt.evolve: # Log the stripped model wandb_logger.wandb.log_artifact(str(final), type='model', name='run_' + wandb_logger.wandb_run.id + '_model', aliases=['last', 'best', 'stripped']) wandb_logger.finish_run() else: dist.destroy_process_group() torch.cuda.empty_cache() return results if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='yolov5l.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='./models/transformer/yolov5l_Transfusion_FLIR.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default='./data/multispectral/FLIR-align-3class.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=60) parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') parser.add_argument('--workers', type=int, default=0, help='maximum number of dataloader workers') parser.add_argument('--project', default='runs/train', help='save to project/name') parser.add_argument('--entity', default=None, help='W&B entity') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--linear-lr', action='store_true', help='linear LR') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') opt = parser.parse_args() #opt.rect = False # FQY Flag for visualizing the paired training imgs global_var._init() global_var.set_value('flag_visual_training_dataset', False) # Set DDP variables opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 set_logging(opt.global_rank) if opt.global_rank in [-1, 0]: # check_git_status() check_requirements() # Resume wandb_run = check_wandb_resume(opt) if opt.resume and not wandb_run: # resume an interrupted run ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' apriori = opt.global_rank, opt.local_rank with open(Path(ckpt).parent.parent / 'opt.yaml') as f: opt = argparse.Namespace(**yaml.safe_load(f)) # replace opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = \ '', ckpt, True, opt.total_batch_size, *apriori # reinstate logger.info('Resuming training from %s' % ckpt) else: # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) opt.name = 'evolve' if opt.evolve else opt.name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)) # DDP mode opt.total_batch_size = opt.batch_size device = select_device(opt.device, batch_size=opt.batch_size) if opt.local_rank != -1: assert torch.cuda.device_count() > opt.local_rank torch.cuda.set_device(opt.local_rank) device = torch.device('cuda', opt.local_rank) dist.init_process_group(backend='nccl', init_method='env://') # distributed backend assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' opt.batch_size = opt.total_batch_size // opt.world_size # Hyperparameters with open(opt.hyp) as f: hyp = yaml.safe_load(f) # load hyps # Train logger.info(opt) if not opt.evolve: tb_writer = None # init loggers if opt.global_rank in [-1, 0]: prefix = colorstr('tensorboard: ') logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/") tb_writer = SummaryWriter(opt.save_dir) # Tensorboard train_rgb_ir(hyp, opt, device, tb_writer) # Evolve hyperparameters (optional) else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr 'box': (1, 0.02, 0.2), # box loss gain 'cls': (1, 0.2, 4.0), # cls loss gain 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight 'iou_t': (0, 0.1, 0.7), # IoU training threshold 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) 'scale': (1, 0.0, 0.9), # image scale (+/- gain) 'shear': (1, 0.0, 10.0), # image shear (+/- deg) 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) 'mosaic': (1, 0.0, 1.0), # image mixup (probability) 'mixup': (1, 0.0, 1.0)} # image mixup (probability) assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' opt.notest, opt.nosave = True, True # only test/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here if opt.bucket: os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists for _ in range(300): # generations to evolve if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate # Select parent(s) parent = 'single' # parent selection method: 'single' or 'weighted' x = np.loadtxt('evolve.txt', ndmin=2) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations w = fitness(x) - fitness(x).min() # weights if parent == 'single' or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection elif parent == 'weighted': x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate mp, s = 0.8, 0.2 # mutation probability, sigma npr = np.random npr.seed(int(time.time())) g = np.array([x[0] for x in meta.values()]) # gains 0-1 ng = len(meta) v = np.ones(ng) while all(v == 1): # mutate until a change occurs (prevent duplicates) v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) hyp[k] = float(x[i + 7] * v[i]) # mutate # Constrain to limits for k, v in meta.items(): hyp[k] = max(hyp[k], v[1]) # lower limit hyp[k] = min(hyp[k], v[2]) # upper limit hyp[k] = round(hyp[k], 5) # significant digits # Train mutation results = train(hyp.copy(), opt, device) # Write mutation results print_mutation(hyp.copy(), results, yaml_file, opt.bucket) # Plot results plot_evolution(yaml_file) print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') ================================================ FILE: utils/__init__.py ================================================ ================================================ FILE: utils/activations.py ================================================ # Activation functions import torch import torch.nn as nn import torch.nn.functional as F # SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- class SiLU(nn.Module): # export-friendly version of nn.SiLU() @staticmethod def forward(x): return x * torch.sigmoid(x) class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() @staticmethod def forward(x): # return x * F.hardsigmoid(x) # for torchscript and CoreML return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX # Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- class Mish(nn.Module): @staticmethod def forward(x): return x * F.softplus(x).tanh() class MemoryEfficientMish(nn.Module): class F(torch.autograd.Function): @staticmethod def forward(ctx, x): ctx.save_for_backward(x) return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) @staticmethod def backward(ctx, grad_output): x = ctx.saved_tensors[0] sx = torch.sigmoid(x) fx = F.softplus(x).tanh() return grad_output * (fx + x * sx * (1 - fx * fx)) def forward(self, x): return self.F.apply(x) # FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- class FReLU(nn.Module): def __init__(self, c1, k=3): # ch_in, kernel super().__init__() self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) self.bn = nn.BatchNorm2d(c1) def forward(self, x): return torch.max(x, self.bn(self.conv(x))) # ACON https://arxiv.org/pdf/2009.04759.pdf ---------------------------------------------------------------------------- class AconC(nn.Module): r""" ACON activation (activate or not). AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter according to "Activate or Not: Learning Customized Activation" . """ def __init__(self, c1): super().__init__() self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) def forward(self, x): dpx = (self.p1 - self.p2) * x return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x class MetaAconC(nn.Module): r""" ACON activation (activate or not). MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network according to "Activate or Not: Learning Customized Activation" . """ def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r super().__init__() c2 = max(r, c1 // r) self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) # self.bn1 = nn.BatchNorm2d(c2) # self.bn2 = nn.BatchNorm2d(c1) def forward(self, x): y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed dpx = (self.p1 - self.p2) * x return dpx * torch.sigmoid(beta * dpx) + self.p2 * x ================================================ FILE: utils/autoanchor.py ================================================ # Auto-anchor utils import numpy as np import torch import yaml from scipy.cluster.vq import kmeans from tqdm import tqdm from utils.general import colorstr def check_anchor_order(m): # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary a = m.anchor_grid.prod(-1).view(-1) # anchor area da = a[-1] - a[0] # delta a ds = m.stride[-1] - m.stride[0] # delta s if da.sign() != ds.sign(): # same order print('Reversing anchor order') m.anchors[:] = m.anchors.flip(0) m.anchor_grid[:] = m.anchor_grid.flip(0) def check_anchors(dataset, model, thr=4.0, imgsz=640): # Check anchor fit to data, recompute if necessary prefix = colorstr('autoanchor: ') print(f'\n{prefix}Analyzing anchors... ', end='') m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh def metric(k): # compute metric r = wh[:, None] / k[None] x = torch.min(r, 1. / r).min(2)[0] # ratio metric best = x.max(1)[0] # best_x aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold bpr = (best > 1. / thr).float().mean() # best possible recall return bpr, aat anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors bpr, aat = metric(anchors) print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') if bpr < 0.98: # threshold to recompute print('. Attempting to improve anchors, please wait...') na = m.anchor_grid.numel() // 2 # number of anchors try: anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) except Exception as e: print(f'{prefix}ERROR: {e}') new_bpr = metric(anchors)[0] if new_bpr > bpr: # replace anchors anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss check_anchor_order(m) print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') else: print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') print('') # newline def check_anchors_rgb_ir(dataset, model, thr=4.0, imgsz=640): # Check anchor fit to data, recompute if necessary prefix = colorstr('autoanchor: ') print(f'\n{prefix}Analyzing anchors... ', end='') # m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() m = list(model.model.children())[-1] print(m) shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh def metric(k): # compute metric r = wh[:, None] / k[None] x = torch.min(r, 1. / r).min(2)[0] # ratio metric best = x.max(1)[0] # best_x aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold bpr = (best > 1. / thr).float().mean() # best possible recall return bpr, aat anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors bpr, aat = metric(anchors) print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') if bpr < 0.98: # threshold to recompute print('. Attempting to improve anchors, please wait...') na = m.anchor_grid.numel() // 2 # number of anchors try: anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) except Exception as e: print(f'{prefix}ERROR: {e}') new_bpr = metric(anchors)[0] if new_bpr > bpr: # replace anchors anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss check_anchor_order(m) print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') else: print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') print('') # newline def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): """ Creates kmeans-evolved anchors from training dataset Arguments: path: path to dataset *.yaml, or a loaded dataset n: number of anchors img_size: image size used for training thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 gen: generations to evolve anchors using genetic algorithm verbose: print all results Return: k: kmeans evolved anchors Usage: from utils.autoanchor import *; _ = kmean_anchors() """ thr = 1. / thr prefix = colorstr('autoanchor: ') def metric(k, wh): # compute metrics r = wh[:, None] / k[None] x = torch.min(r, 1. / r).min(2)[0] # ratio metric # x = wh_iou(wh, torch.tensor(k)) # iou metric return x, x.max(1)[0] # x, best_x def anchor_fitness(k): # mutation fitness _, best = metric(torch.tensor(k, dtype=torch.float32), wh) return (best * (best > thr).float()).mean() # fitness def print_results(k): k = k[np.argsort(k.prod(1))] # sort small to large x, best = metric(k, wh0) bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') for i, x in enumerate(k): print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg return k if isinstance(path, str): # *.yaml file with open(path) as f: data_dict = yaml.safe_load(f) # model dict from utils.datasets import LoadImagesAndLabels dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) else: dataset = path # dataset # Get label wh shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh # Filter i = (wh0 < 3.0).any(1).sum() if i: print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 # Kmeans calculation print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') s = wh.std(0) # sigmas for whitening k, dist = kmeans(wh / s, n, iter=30) # points, mean distance assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') k *= s wh = torch.tensor(wh, dtype=torch.float32) # filtered wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered k = print_results(k) # Plot # k, d = [None] * 20, [None] * 20 # for i in tqdm(range(1, 21)): # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) # ax = ax.ravel() # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh # ax[0].hist(wh[wh[:, 0]<100, 0],400) # ax[1].hist(wh[wh[:, 1]<100, 1],400) # fig.savefig('wh.png', dpi=200) # Evolve npr = np.random f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar for _ in pbar: v = np.ones(sh) while (v == 1).all(): # mutate until a change occurs (prevent duplicates) v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) kg = (k.copy() * v).clip(min=2.0) fg = anchor_fitness(kg) if fg > f: f, k = fg, kg.copy() pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' if verbose: print_results(k) return print_results(k) ================================================ FILE: utils/aws/__init__.py ================================================ ================================================ FILE: utils/aws/mime.sh ================================================ # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ # This script will run on every instance restart, not only on first start # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- Content-Type: multipart/mixed; boundary="//" MIME-Version: 1.0 --// Content-Type: text/cloud-config; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="cloud-config.txt" #cloud-config cloud_final_modules: - [scripts-user, always] --// Content-Type: text/x-shellscript; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="userdata.txt" #!/bin/bash # --- paste contents of userdata.sh here --- --// ================================================ FILE: utils/aws/resume.py ================================================ # Resume all interrupted trainings in yolov5/ dir including DDP trainings # Usage: $ python utils/aws/resume.py import os import sys from pathlib import Path import torch import yaml sys.path.append('./') # to run '$ python *.py' files in subdirectories port = 0 # --master_port path = Path('').resolve() for last in path.rglob('*/**/last.pt'): ckpt = torch.load(last) if ckpt['optimizer'] is None: continue # Load opt.yaml with open(last.parent.parent / 'opt.yaml') as f: opt = yaml.safe_load(f) # Get device count d = opt['device'].split(',') # devices nd = len(d) # number of devices ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel if ddp: # multi-GPU port += 1 cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' else: # single-GPU cmd = f'python train.py --resume {last}' cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread print(cmd) os.system(cmd) ================================================ FILE: utils/aws/userdata.sh ================================================ #!/bin/bash # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html # This script will run only once on first instance start (for a re-start script see mime.sh) # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir # Use >300 GB SSD cd home/ubuntu if [ ! -d yolov5 ]; then echo "Running first-time script." # install dependencies, download COCO, pull Docker git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 cd yolov5 bash data/scripts/get_coco.sh && echo "Data done." & sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & wait && echo "All tasks done." # finish background tasks else echo "Running re-start script." # resume interrupted runs i=0 list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' while IFS= read -r id; do ((i++)) echo "restarting container $i: $id" sudo docker start $id # sudo docker exec -it $id python train.py --resume # single-GPU sudo docker exec -d $id python utils/aws/resume.py # multi-scenario done <<<"$list" fi ================================================ FILE: utils/confluence.py ================================================ import cv2 import numpy as np import torch import random def xywh2xyxy(x): # Transform box coordinates from [x, y, w, h] to [x1, y1, x2, y2] (where xy1=top-left, xy2=bottom-right) y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y def scale_coords_x(img1_shape, coords, img0_shape): scale_x = img1_shape[1] / img0_shape[1] scale_y = img1_shape[0] / img0_shape[0] coords[:, [0, 2]] /= scale_x coords[:, [1, 3]] /= scale_y clip_coords(coords, img0_shape) return coords def clip_coords(boxes, img_shape): # Clip bounding xyxy bounding boxes to image shape (height, width) boxes[:, 0] = np.clip(boxes[:, 0], 0, img_shape[1]) # x1 boxes[:, 1] = np.clip(boxes[:, 1], 0, img_shape[0]) # y1 boxes[:, 2] = np.clip(boxes[:, 2], 0, img_shape[1]) # x2 boxes[:, 3] = np.clip(boxes[:, 3], 0, img_shape[0]) # y2 def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1) # filled cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) # work for YOLOV3 or YOLOV4 def confluence_process(prediction, conf_thres=0.1, p_thres=0.6): """Performs Confluence on inference results the prediction: (bs, anchors*grid*grid, xywh + confidence + classes) , type: torch.tensor Returns: detections with shape: nx6 (x1, y1, x2, y2, conf, cls) """ if prediction.dtype is torch.float16: prediction = prediction.float() # to FP32 nc = prediction[0].shape[1] - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates # Settings max_det = 300 # maximum number of detections per image time_limit = 10.0 # seconds to quit after redundant = True # require redundant detections multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) #t = time.time() output = [None] * prediction.shape[0] for xi, x in enumerate(prediction): # image index, image inference # Apply constraints x = x[xc[xi]] # confidence # If none remain process next image if not x.shape[0]: continue # Compute conf x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box (center x, center y, width, height) to (x1, y1, x2, y2) box = xywh2xyxy(x[:, :4]) # Detections matrix nx6 (xyxy, conf, cls) if multi_label: i, j = (x[:, 5:] > conf_thres).nonzero().t() x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) else: # best class only conf, j = x[:, 5:].max(1, keepdim=True) x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] # If none remain process next image n = x.shape[0] # number of boxes if not n: continue # confluence dets = x.cpu().numpy() i = confluence(dets, nc, p_thres) output[xi] = x[i] #if (time.time() - t) > time_limit: # break # time limit exceeded return output def confluence(prediction, class_num, p_thres=0.6): """Performs Confluence on inference results the prediction: (n, xyxy + confidence + classID), type: numpy.array Returns: the index of the predicetion. """ index = np.arange(0, len(prediction), 1).reshape(-1,1) infos = np.concatenate((prediction, index), 1) keep = [] for c in range(class_num): pcs = infos[infos[:, 5] == c] while (len(pcs)): n = len(pcs) xs = pcs[:, [0, 2]] ys = pcs[:, [1, 3]] ps = [] # 遍历 pcs，计算每一个box 和其余 box 的 p 值，然后聚类成簇，再根据 wp 挑出 best confluence_min = 10000 best = None for i, pc in enumerate(pcs): if (n == 1): best = 0 break index_other = [j for j in range(n) if j!= i] x_t = xs[i] x_t = np.tile(x_t, (n-1, 1)) x_other = xs[index_other] x_all = np.concatenate((x_t, x_other), 1) y_t = ys[i] y_t = np.tile(y_t, (n-1, 1)) y_other = ys[index_other] y_all = np.concatenate((y_t, y_other), 1) # normalization xmin = x_all.min(1).reshape(-1, 1) xmax = x_all.max(1).reshape(-1, 1) ymin = y_all.min(1).reshape(-1, 1) ymax = y_all.max(1).reshape(-1, 1) x_all = (x_all - xmin)/(xmax - xmin) y_all = (y_all - ymin)/(ymax - ymin) # Manhattan Distance p = abs(x_all[:,0] - x_all[:,2]) + abs(x_all[:,1] - x_all[:,3]) + \ abs(y_all[:,0] - y_all[:,2]) + abs(y_all[:,1] - y_all[:,3]) ps.append(p) # wp wp = p / pc[4] wp = wp[p < 2] if (len(wp) == 0): value = 0 else: value = wp.min() # select the bbox which has the smallest wp as the best bbox if (value < confluence_min): confluence_min = value best = i keep.append(int(pcs[best][6])) if (len(ps) > 0): p = ps[best] index_ = np.where(p < p_thres)[0] index_ = [i if i < best else i +1 for i in index_] else: index_ = [] # delect the bboxes whose Manhattan Distance is below the predefined MD index_eff = [j for j in range(n) if (j != best and j not in index_)] pcs = pcs[index_eff] keep = np.unique(keep) return keep # test the confluence def test(): colors = [[0, 0, 255], [0, 255, 0], [255, 0, 0]] img = cv2.imread('./data/test.png') img = cv2.resize(img, (1080, 720)) prediction = np.loadtxt('./data/prediction.txt') nc = 3 wp_thres = 0.6 i = confluence(prediction, nc, wp_thres) print(i) print(np.shape((prediction))) output = prediction[i] if output is not None and len(output): # Rescale boxes from img_size to im0 size output[:, :4] = scale_coords_x([416, 416], output[:, :4], img.shape).round() # Write results for *xyxy, conf, cls in output: label = '%s %.2f' % (str(cls), conf) plot_one_box(xyxy, img, label=label, color=colors[int(cls)]) cv2.imshow("xx", img) cv2.waitKey(-1) if __name__ == '__main__': test() ================================================ FILE: utils/datasets.py ================================================ # Dataset utils and dataloaders import glob import logging import math import os import random import shutil import time from itertools import repeat from multiprocessing.pool import ThreadPool from pathlib import Path from threading import Thread import cv2 import numpy as np import torch import torch.nn.functional as F from PIL import Image, ExifTags from torch.utils.data import Dataset from tqdm import tqdm from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \ resample_segments, clean_str from utils.torch_utils import torch_distributed_zero_first import global_var # Parameters help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes logger = logging.getLogger(__name__) # FQY 构建随机采样的sampler，为了之后双模态输入 class RandomSampler(torch.utils.data.sampler.RandomSampler): def __init__(self, data_source, replacement=False, num_samples=None): self.data_source = data_source self.replacement = replacement self._num_samples = num_samples if not isinstance(self.replacement, bool): raise ValueError("replacement should be a boolean value, but got " "replacement={}".format(self.replacement)) if self._num_samples is not None and not replacement: raise ValueError("With replacement=False, num_samples should not be specified, " "since a random permute will be performed.") if not isinstance(self.num_samples, int) or self.num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(self.num_samples)) @property def num_samples(self): # dataset size might change at runtime if self._num_samples is None: return len(self.data_source) return self._num_samples def __iter__(self): n = len(self.data_source) if self.replacement: return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64).tolist()) # print("-------------------------") s = global_var.get_value('s') return iter(s) def __len__(self): return self.num_samples # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): if ExifTags.TAGS[orientation] == 'Orientation': break def get_hash(files): # Returns a single hash value of a list of files return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) def exif_size(img): # Returns exif-corrected PIL size s = img.size # (width, height) try: rotation = dict(img._getexif().items())[orientation] if rotation == 6: # rotation 270 s = (s[1], s[0]) elif rotation == 8: # rotation 90 s = (s[1], s[0]) except: pass return s def create_dataloader_rgb_ir(path1, path2, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix='', sampler=None): # Make sure only the first process in DDP process the dataset first, and the following others can use the cache with torch_distributed_zero_first(rank): dataset = LoadMultiModalImagesAndLabels(path1, path2, imgsz, batch_size, augment=augment, # augment images hyp=hyp, # augmentation hyperparameters rect=rect, # rectangular training cache_images=cache, single_cls=opt.single_cls, stride=int(stride), pad=pad, image_weights=image_weights, prefix=prefix) batch_size = min(batch_size, len(dataset)) nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader # global_var.set_value('s', torch.randperm(len(dataset)).tolist()) # sampler = RandomSampler(dataset) # sampler = torch.utils.data.sampler.RandomSampler(dataset) # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() dataloader = loader(dataset, batch_size=batch_size, num_workers=nw, sampler=sampler, pin_memory=True, collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn) return dataloader, dataset class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): """ Dataloader that reuses workers Uses same syntax as vanilla DataLoader """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) self.iterator = super().__iter__() def __len__(self): return len(self.batch_sampler.sampler) def __iter__(self): for i in range(len(self)): yield next(self.iterator) class _RepeatSampler(object): """ Sampler that repeats forever Args: sampler (Sampler) """ def __init__(self, sampler): self.sampler = sampler def __iter__(self): while True: yield from iter(self.sampler) class LoadImages: # for inference def __init__(self, path, img_size=640, stride=32): p = str(Path(path).absolute()) # os-agnostic absolute path if '*' in p: files = sorted(glob.glob(p, recursive=True)) # glob elif os.path.isdir(p): files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir elif os.path.isfile(p): files = [p] # files else: raise Exception(f'ERROR: {p} does not exist') images = [x for x in files if x.split('.')[-1].lower() in img_formats] videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] ni, nv = len(images), len(videos) self.img_size = img_size self.stride = stride self.files = images + videos self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv self.mode = 'image' if any(videos): self.new_video(videos[0]) # new video else: self.cap = None assert self.nf > 0, f'No images or videos found in {p}. ' \ f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}' def __iter__(self): self.count = 0 return self def __next__(self): if self.count == self.nf: raise StopIteration path = self.files[self.count] if self.video_flag[self.count]: # Read video self.mode = 'video' ret_val, img0 = self.cap.read() if not ret_val: self.count += 1 self.cap.release() if self.count == self.nf: # last video raise StopIteration else: path = self.files[self.count] self.new_video(path) ret_val, img0 = self.cap.read() self.frame += 1 print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='') else: # Read image self.count += 1 img0 = cv2.imread(path) # BGR assert img0 is not None, 'Image Not Found ' + path print(f'image {self.count}/{self.nf} {path}: ', end='') # Padded resize img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) return path, img, img0, self.cap def new_video(self, path): self.frame = 0 self.cap = cv2.VideoCapture(path) self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) def __len__(self): return self.nf # number of files class LoadWebcam: # for inference def __init__(self, pipe='0', img_size=640, stride=32): self.img_size = img_size self.stride = stride if pipe.isnumeric(): pipe = eval(pipe) # local camera # pipe = 'rtsp://192.168.1.64/1' # IP camera # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera self.pipe = pipe self.cap = cv2.VideoCapture(pipe) # video capture object self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size def __iter__(self): self.count = -1 return self def __next__(self): self.count += 1 if cv2.waitKey(1) == ord('q'): # q to quit self.cap.release() cv2.destroyAllWindows() raise StopIteration # Read frame if self.pipe == 0: # local camera ret_val, img0 = self.cap.read() img0 = cv2.flip(img0, 1) # flip left-right else: # IP camera n = 0 while True: n += 1 self.cap.grab() if n % 30 == 0: # skip frames ret_val, img0 = self.cap.retrieve() if ret_val: break # Print assert ret_val, f'Camera Error {self.pipe}' img_path = 'webcam.jpg' print(f'webcam {self.count}: ', end='') # Padded resize img = letterbox(img0, self.img_size, stride=self.stride)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) return img_path, img, img0, None def __len__(self): return 0 class LoadStreams: # multiple IP or RTSP cameras def __init__(self, sources='streams.txt', img_size=640, stride=32): self.mode = 'stream' self.img_size = img_size self.stride = stride if os.path.isfile(sources): with open(sources, 'r') as f: sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] else: sources = [sources] n = len(sources) self.imgs = [None] * n self.sources = [clean_str(x) for x in sources] # clean source names for later for i, s in enumerate(sources): # index, source # Start thread to read frames from video stream print(f'{i + 1}/{n}: {s}... ', end='') if 'youtube.com/' in s or 'youtu.be/' in s: # if source is YouTube video check_requirements(('pafy', 'youtube_dl')) import pafy s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam cap = cv2.VideoCapture(s) assert cap.isOpened(), f'Failed to open {s}' w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) self.fps = cap.get(cv2.CAP_PROP_FPS) % 100 _, self.imgs[i] = cap.read() # guarantee first frame thread = Thread(target=self.update, args=([i, cap]), daemon=True) print(f' success ({w}x{h} at {self.fps:.2f} FPS).') thread.start() print('') # newline # check for common shapes s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal if not self.rect: print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') def update(self, index, cap): # Read next stream frame in a daemon thread n = 0 while cap.isOpened(): n += 1 # _, self.imgs[index] = cap.read() cap.grab() if n == 4: # read every 4th frame success, im = cap.retrieve() self.imgs[index] = im if success else self.imgs[index] * 0 n = 0 time.sleep(1 / self.fps) # wait time def __iter__(self): self.count = -1 return self def __next__(self): self.count += 1 img0 = self.imgs.copy() if cv2.waitKey(1) == ord('q'): # q to quit cv2.destroyAllWindows() raise StopIteration # Letterbox img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0] # Stack img = np.stack(img, 0) # Convert img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 img = np.ascontiguousarray(img) return self.sources, img, img0, None def __len__(self): return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years def img2label_paths(img_paths): # Define label paths as a function of image paths sb, t = 'labels', [] # /images/, /labels/ for x in img_paths: if 'visible' in x.split('/'): sa = 'visible' elif 'infrared' in x.split('/'): sa = 'infrared' t.append('txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1))) return t class LoadImagesAndLabels(Dataset): # for training/testing def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''): self.img_size = img_size self.augment = augment self.hyp = hyp self.image_weights = image_weights self.rect = False if image_weights else rect self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride self.path = path try: f = [] # image files for p in path if isinstance(path, list) else [path]: p = Path(p) # os-agnostic if p.is_dir(): # dir f += glob.glob(str(p / '**' / '*.*'), recursive=True) # f = list(p.rglob('**/*.*')) # pathlib elif p.is_file(): # file with open(p, 'r') as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) else: raise Exception(f'{prefix}{p} does not exist') self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib assert self.img_files, f'{prefix}No images found' except Exception as e: raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') # Check cache self.label_files = img2label_paths(self.img_files) # labels # print(self.label_files) cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels if cache_path.is_file(): cache, exists = torch.load(cache_path), True # load if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed cache, exists = self.cache_labels(cache_path, prefix), False # re-cache else: cache, exists = self.cache_labels(cache_path, prefix), False # cache # Display cache nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total if exists: d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' # Read cache cache.pop('hash') # remove hash cache.pop('version') # remove version labels, shapes, self.segments = zip(*cache.values()) self.labels = list(labels) self.shapes = np.array(shapes, dtype=np.float64) self.img_files = list(cache.keys()) # update self.label_files = img2label_paths(cache.keys()) # update if single_cls: for x in self.labels: x[:, 0] = 0 n = len(shapes) # number of images bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index nb = bi[-1] + 1 # number of batches self.batch = bi # batch index of image self.n = n self.indices = range(n) # Rectangular Training if self.rect: # Sort by aspect ratio s = self.shapes # wh ar = s[:, 1] / s[:, 0] # aspect ratio irect = ar.argsort() self.img_files = [self.img_files[i] for i in irect] self.label_files = [self.label_files[i] for i in irect] self.labels = [self.labels[i] for i in irect] self.shapes = s[irect] # wh ar = ar[irect] # Set training image shapes shapes = [[1, 1]] * nb for i in range(nb): ari = ar[bi == i] mini, maxi = ari.min(), ari.max() if maxi < 1: shapes[i] = [maxi, 1] elif mini > 1: shapes[i] = [1, 1 / mini] self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) self.imgs = [None] * n if cache_images: gb = 0 # Gigabytes of cached images self.img_hw0, self.img_hw = [None] * n, [None] * n results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) # 8 threads pbar = tqdm(enumerate(results), total=n) for i, x in pbar: self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i) gb += self.imgs[i].nbytes pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' pbar.close() def cache_labels(self, path=Path('./labels.cache'), prefix=''): # Cache dataset labels, check images and read shapes x = {} # dict nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) for i, (im_file, lb_file) in enumerate(pbar): try: # verify images im = Image.open(im_file) im.verify() # PIL verify shape = exif_size(im) # image size segments = [] # instance segments assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' assert im.format.lower() in img_formats, f'invalid image format {im.format}' # verify labels if os.path.isfile(lb_file): nf += 1 # label found with open(lb_file, 'r') as f: l = [x.split() for x in f.read().strip().splitlines()] if any([len(x) > 8 for x in l]): # is segment classes = np.array([x[0] for x in l], dtype=np.float32) segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) l = np.array(l, dtype=np.float32) if len(l): assert l.shape[1] == 5, 'labels require 5 columns each' assert (l >= 0).all(), 'negative labels' assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' else: ne += 1 # label empty l = np.zeros((0, 5), dtype=np.float32) else: nm += 1 # label missing l = np.zeros((0, 5), dtype=np.float32) x[im_file] = [l, shape, segments] except Exception as e: nc += 1 print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" pbar.close() if nf == 0: print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') x['hash'] = get_hash(self.label_files + self.img_files) x['results'] = nf, nm, ne, nc, i + 1 x['version'] = 0.1 # cache version torch.save(x, path) # save for next time logging.info(f'{prefix}New cache created: {path}') return x def __len__(self): return len(self.img_files) # def __iter__(self): # self.count = -1 # print('ran dataset iter') # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) # return self def __getitem__(self, index): index = self.indices[index] # linear, shuffled, or image_weights hyp = self.hyp mosaic = self.mosaic and random.random() < hyp['mosaic'] if mosaic: # Load mosaic img, labels = load_mosaic(self, index) shapes = None if random.random() < hyp['mixup']: img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1)) r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 img = (img * r + img2 * (1 - r)).astype(np.uint8) labels = np.concatenate((labels, labels2), 0) else: # Load image img, (h0, w0), (h, w) = load_image(self, index) # Letterbox shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling labels = self.labels[index].copy() if labels.size: # normalized xywh to pixel xyxy format labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: # Augment imagespace if not mosaic: img, labels = random_perspective(img, labels, degrees=hyp['degrees'], translate=hyp['translate'], scale=hyp['scale'], shear=hyp['shear'], perspective=hyp['perspective']) # Augment colorspace augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) # Apply cutouts # if random.random() < 0.9: # labels = cutout(img, labels) nL = len(labels) # number of labels if nL: labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 random.seed(index) if self.augment: # flip up-down if random.random() < hyp['flipud']: img = np.flipud(img) if nL: labels[:, 2] = 1 - labels[:, 2] # flip left-right if random.random() < hyp['fliplr']: img = np.fliplr(img) if nL: labels[:, 1] = 1 - labels[:, 1] labels_out = torch.zeros((nL, 6)) if nL: labels_out[:, 1:] = torch.from_numpy(labels) # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) return torch.from_numpy(img), labels_out, self.img_files[index], shapes @staticmethod def collate_fn(batch): img, label, path, shapes = zip(*batch) # transposed for i, l in enumerate(label): l[:, 0] = i # add target image index for build_targets() return torch.stack(img, 0), torch.cat(label, 0), path, shapes @staticmethod def collate_fn4(batch): img, label, path, shapes = zip(*batch) # transposed n = len(shapes) // 4 img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] print("n = len(shapes) // 4", n) print("Image Shape", img.shape) ho = torch.tensor([[0., 0, 0, 1, 0, 0]]) wo = torch.tensor([[0., 0, 1, 0, 0, 0]]) s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW i *= 4 if random.random() < 0.5: im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[ 0].type(img[i].type()) l = label[i] else: im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s img4.append(im) label4.append(l) for i, l in enumerate(label4): l[:, 0] = i # add target image index for build_targets() return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 class LoadMultiModalImagesAndLabels(Dataset): # for training/testing """ FQY 载入多模态数据 （RGB 和 IR） """ def __init__(self, path_rgb, path_ir, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''): self.img_size = img_size self.augment = augment self.hyp = hyp self.image_weights = image_weights self.rect = False if image_weights else rect self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride self.path_rgb = path_rgb self.path_ir = path_ir try: f_rgb = [] # image files f_ir = [] # ----------------------------- rgb ----------------------------- for p_rgb in path_rgb if isinstance(path_rgb, list) else [path_rgb]: p_rgb = Path(p_rgb) # os-agnostic if p_rgb.is_dir(): # dir f_rgb += glob.glob(str(p_rgb / '**' / '*.*'), recursive=True) # f = list(p.rglob('**/*.*')) # pathlib elif p_rgb.is_file(): # file with open(p_rgb, 'r') as t: t = t.read().strip().splitlines() parent = str(p_rgb.parent) + os.sep f_rgb += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) else: raise Exception(f'{prefix}{path_rgb} does not exist') # ----------------------------- ir ----------------------------- for p_ir in path_ir if isinstance(path_ir, list) else [path_ir]: p_ir = Path(p_ir) # os-agnostic if p_ir.is_dir(): # dir f_ir += glob.glob(str(p_ir / '**' / '*.*'), recursive=True) # f = list(p.rglob('**/*.*')) # pathlib elif p_ir.is_file(): # file with open(p_ir, 'r') as t: t = t.read().strip().splitlines() parent = str(p_ir.parent) + os.sep f_ir += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) else: raise Exception(f'{prefix}{p_ir} does not exist') self.img_files_rgb = sorted([x.replace('/', os.sep) for x in f_rgb if x.split('.')[-1].lower() in img_formats]) self.img_files_ir = sorted([x.replace('/', os.sep) for x in f_ir if x.split('.')[-1].lower() in img_formats]) # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib assert (self.img_files_rgb, self.img_files_ir), (f'{prefix}No images found', f'{prefix}No images found') except Exception as e: raise Exception(f'{prefix}Error loading data from {path_rgb,path_ir}: {e}\nSee {help_url}') # Check cache # Check rgb cache self.label_files_rgb = img2label_paths(self.img_files_rgb) # labels # print(self.label_files) cache_rgb_path = (p_rgb if p_rgb.is_file() else Path(self.label_files_rgb[0]).parent).with_suffix('.cache') # cached labels if cache_rgb_path.is_file(): cache_rgb, exists_rgb = torch.load(cache_rgb_path), True # load if cache_rgb['hash'] != get_hash(self.label_files_rgb + self.img_files_rgb) or 'version' not in cache_rgb: # changed cache_rgb, exists_rgb = self.cache_labels(self.img_files_rgb,self.label_files_rgb, cache_rgb_path, prefix), False # re-cache else: cache_rgb, exists_rgb = self.cache_labels(self.img_files_rgb,self.label_files_rgb, cache_rgb_path, prefix), False # cache # Check ir cache self.label_files_ir = img2label_paths(self.img_files_ir) # labels # print(self.label_files) cache_ir_path = (p_ir if p_ir.is_file() else Path(self.label_files_ir[0]).parent).with_suffix('.cache') # cached labels if cache_ir_path.is_file(): cache_ir, exists_ir = torch.load(cache_ir_path), True # load if cache_ir['hash'] != get_hash(self.label_files_ir + self.img_files_ir) or 'version' not in cache_ir: # changed cache_ir, exists_ir = self.cache_labels(self.img_files_ir, self.label_files_ir, cache_ir_path, prefix), False # re-cache else: cache_ir, exists_ir = self.cache_labels(self.img_files_ir, self.label_files_ir, cache_ir_path, prefix), False # cache # Display cache nf_rgb, nm_rgb, ne_rgb, nc_rgb, n_rgb = cache_rgb.pop('results') # found, missing, empty, corrupted, total nf_ir, nm_ir, ne_ir, nc_ir, n_ir = cache_ir.pop('results') # found, missing, empty, corrupted, total if exists_rgb: d = f"Scanning RGB '{cache_rgb_path}' images and labels... {nf_rgb} found, {nm_rgb} missing, {ne_rgb} empty, {nc_rgb} corrupted" tqdm(None, desc=prefix + d, total=n_rgb, initial=n_rgb) # display cache results if exists_ir: d = f"Scanning IR '{cache_rgb_path}' images and labels... {nf_ir} found, {nm_ir} missing, {ne_ir} empty, {nc_ir} corrupted" tqdm(None, desc=prefix + d, total=n_ir, initial=n_ir) # display cache results assert nf_rgb > 0 or not augment, f'{prefix}No labels in {cache_rgb_path}. Can not train without labels. See {help_url}' # Read cache # Read RGB cache cache_rgb.pop('hash') # remove hash cache_rgb.pop('version') # remove version labels_rgb, shapes_rgb, self.segments_rgb = zip(*cache_rgb.values()) self.labels_rgb = list(labels_rgb) self.shapes_rgb = np.array(shapes_rgb, dtype=np.float64) self.img_files_rgb = list(cache_rgb.keys()) # update self.label_files_rgb = img2label_paths(cache_rgb.keys()) # update if single_cls: for x in self.labels_rgb: x[:, 0] = 0 n_rgb = len(shapes_rgb) # number of images bi_rgb = np.floor(np.arange(n_rgb) / batch_size).astype(np.int) # batch index nb_rgb = bi_rgb[-1] + 1 # number of batches self.batch_rgb = bi_rgb # batch index of image self.n_rgb = n_rgb self.indices_rgb = range(n_rgb) # Read IR cache cache_ir.pop('hash') # remove hash cache_ir.pop('version') # remove version labels_ir, shapes_ir, self.segments_ir = zip(*cache_ir.values()) self.labels_ir = list(labels_ir) self.shapes_ir = np.array(shapes_ir, dtype=np.float64) self.img_files_ir = list(cache_ir.keys()) # update self.label_files_ir = img2label_paths(cache_ir.keys()) # update if single_cls: for x in self.labels_ir: x[:, 0] = 0 n_ir = len(shapes_ir) # number of images bi_ir = np.floor(np.arange(n_ir) / batch_size).astype(np.int) # batch index nb_ir = bi_ir[-1] + 1 # number of batches self.batch_ir = bi_ir # batch index of image self.n_ir = n_ir self.indices_ir = range(n_ir) # Rectangular Training if self.rect: # RGB # Sort by aspect ratio s_rgb = self.shapes_rgb # wh ar_rgb = s_rgb[:, 1] / s_rgb[:, 0] # aspect ratio irect_rgb = ar_rgb.argsort() self.img_files_rgb = [self.img_files_rgb[i] for i in irect_rgb] self.label_files_rgb = [self.label_files_rgb[i] for i in irect_rgb] self.labels_rgb = [self.labels_rgb[i] for i in irect_rgb] self.shapes_rgb = s_rgb[irect_rgb] # wh ar_rgb = ar_rgb[irect_rgb] # Set training image shapes shapes_rgb = [[1, 1]] * nb_rgb for i in range(nb_rgb): ari_rgb = ar_rgb[bi_rgb == i] mini, maxi = ari_rgb.min(), ari_rgb.max() if maxi < 1: shapes_rgb[i] = [maxi, 1] elif mini > 1: shapes_rgb[i] = [1, 1 / mini] self.batch_shapes_rgb = np.ceil(np.array(shapes_rgb) * img_size / stride + pad).astype(np.int) * stride # IR # Sort by aspect ratio s_ir = self.shapes_ir # wh ar_ir = s_ir[:, 1] / s_ir[:, 0] # aspect ratio irect_ir = ar_ir.argsort() self.img_files_ir = [self.img_files_ir[i] for i in irect_ir] self.label_files_ir = [self.label_files_ir[i] for i in irect_ir] self.labels_ir = [self.labels_ir[i] for i in irect_ir] self.shapes_ir = s_ir[irect_ir] # wh ar_ir = ar_ir[irect_ir] # Set training image shapes shapes_ir = [[1, 1]] * nb_ir for i in range(nb_ir): ari_ir = ar_ir[bi_ir == i] mini, maxi = ari_ir.min(), ari_ir.max() if maxi < 1: shapes_ir[i] = [maxi, 1] elif mini > 1: shapes_ir[i] = [1, 1 / mini] self.batch_shapes_ir = np.ceil(np.array(shapes_ir) * img_size / stride + pad).astype(np.int) * stride # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) self.imgs_rgb = [None] * n_rgb self.imgs_ir = [None] * n_ir self.labels = self.labels_rgb self.shapes = self.shapes_rgb self.indices = self.indices_rgb def cache_labels(self, imgfiles, labelfiles, path=Path('./labels.cache'), prefix=''): # Cache dataset labels, check images and read shapes img_files = imgfiles label_files = labelfiles x = {} # dict nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate pbar = tqdm(zip(img_files, label_files), desc='Scanning images', total=len(img_files)) for i, (im_file, lb_file) in enumerate(pbar): try: # verify images im = Image.open(im_file) im.verify() # PIL verify shape = exif_size(im) # image size segments = [] # instance segments assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' assert im.format.lower() in img_formats, f'invalid image format {im.format}' # verify labels if os.path.isfile(lb_file): nf += 1 # label found with open(lb_file, 'r') as f: l = [x.split() for x in f.read().strip().splitlines()] if any([len(x) > 8 for x in l]): # is segment classes = np.array([x[0] for x in l], dtype=np.float32) segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) l = np.array(l, dtype=np.float32) if len(l): assert l.shape[1] == 5, 'labels require 5 columns each' assert (l >= 0).all(), 'negative labels' assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' else: ne += 1 # label empty l = np.zeros((0, 5), dtype=np.float32) else: nm += 1 # label missing l = np.zeros((0, 5), dtype=np.float32) x[im_file] = [l, shape, segments] except Exception as e: nc += 1 print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" pbar.close() if nf == 0: print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') x['hash'] = get_hash(label_files + img_files) x['results'] = nf, nm, ne, nc, i + 1 x['version'] = 0.1 # cache version torch.save(x, path) # save for next time logging.info(f'{prefix}New cache created: {path}') return x def __len__(self): return len(self.img_files_rgb) # def __iter__(self): # self.count = -1 # print('ran dataset iter') # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) # return self def __getitem__(self, index): # index = self.indices[index] # linear, shuffled, or image_weights index_rgb = self.indices_rgb[index] # linear, shuffled, or image_weights index_ir = self.indices_ir[index] # linear, shuffled, or image_weights hyp = self.hyp mosaic = self.mosaic and random.random() < hyp['mosaic'] if mosaic: # Load mosaic # img, labels = load_mosaic(self, index) img_rgb, labels_rgb, img_ir, labels_ir = load_mosaic_RGB_IR(self, index_rgb, index_ir) shapes = None else: # Load image img_rgb, img_ir, (h0, w0), (h, w) = load_image_rgb_ir(self, index) # Letterbox shape = self.batch_shapes_rgb[self.batch_rgb[index]] if self.rect else self.img_size # final letterboxed shape img_rgb, ratio, pad = letterbox(img_rgb, shape, auto=False, scaleup=self.augment) img_ir, ratio, pad = letterbox(img_ir, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling labels = self.labels_rgb[index].copy() if labels.size: # normalized xywh to pixel xyxy format labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) labels_rgb = labels labels_ir = labels if self.augment: # Augment colorspace augment_hsv(img_rgb, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) augment_hsv(img_ir, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) nL = len(labels_rgb) # number of labels if nL: labels_rgb[:, 1:5] = xyxy2xywh(labels_rgb[:, 1:5]) # convert xyxy to xywh labels_rgb[:, [2, 4]] /= img_rgb.shape[0] # normalized height 0-1 labels_rgb[:, [1, 3]] /= img_rgb.shape[1] # normalized width 0-1 if self.augment: # flip up-down if random.random() < hyp['flipud']: img_rgb = np.flipud(img_rgb) img_ir = np.flipud(img_ir) if nL: labels_rgb[:, 2] = 1 - labels_rgb[:, 2] # flip left-right if random.random() < hyp['fliplr']: img_rgb = np.fliplr(img_rgb) img_ir = np.fliplr(img_ir) if nL: labels_rgb[:, 1] = 1 - labels_rgb[:, 1] labels_out = torch.zeros((nL, 6)) if nL: labels_out[:, 1:] = torch.from_numpy(labels_rgb) # Convert # img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 # img = np.ascontiguousarray(img) img_rgb = img_rgb[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img_rgb = np.ascontiguousarray(img_rgb) img_ir = img_ir[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img_ir = np.ascontiguousarray(img_ir) #img_ir = shift_augment(self, img_ir) img_all = np.concatenate((img_rgb, img_ir), axis=0) return torch.from_numpy(img_all), labels_out, self.img_files_rgb[index], shapes @staticmethod def collate_fn(batch): img, label, path, shapes = zip(*batch) # transposed for i, l in enumerate(label): l[:, 0] = i # add target image index for build_targets() return torch.stack(img, 0), torch.cat(label, 0), path, shapes @staticmethod def collate_fn4(batch): img, label, path, shapes = zip(*batch) # transposed n = len(shapes) // 4 img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] ho = torch.tensor([[0., 0, 0, 1, 0, 0]]) wo = torch.tensor([[0., 0, 1, 0, 0, 0]]) s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW i *= 4 if random.random() < 0.5: im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[ 0].type(img[i].type()) l = label[i] else: im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s img4.append(im) label4.append(l) for i, l in enumerate(label4): l[:, 0] = i # add target image index for build_targets() return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 # Ancillary functions -------------------------------------------------------------------------------------------------- def shift_augment(self, img): direction = [[-1, 1], [1, 1], [1, -1], [-1, -1]] # left-up, right-up, right-down, left-down shift_X, shift_Y = random.randint(0, 10), random.randint(0, 10) shift_direction = direction[random.randint(0, 3)] c, h, w = img.shape shift_img = np.full((c, h, w), 114, dtype=np.uint8) if shift_direction == [-1, 1]: # left-up shift shift_img[:, :h - shift_Y, :w - shift_X] = img[:, shift_Y:, shift_X:] elif shift_direction == [1, 1]: # right-up shift shift_img[:, :h - shift_Y, shift_X:] = img[:, shift_Y:, :w - shift_X] elif shift_direction == [1, -1]: # right-down shift shift_img[:, shift_Y:, shift_X:] = img[:, :h - shift_Y, :w - shift_X] else: shift_img[:, :h - shift_Y, shift_X:] = img[:, shift_Y:, :w - shift_X] return shift_img def load_image(self, index): # loads 1 image from dataset, returns img, original hw, resized hw img = self.imgs[index] if img is None: # not cached path = self.img_files[index] img = cv2.imread(path) # BGR assert img is not None, 'Image Not Found ' + path h0, w0 = img.shape[:2] # orig hw r = self.img_size / max(h0, w0) # ratio if r != 1: # if sizes are not equal img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR) return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized else: return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized def load_image_rgb_ir(self, index): # loads 1 image from dataset, returns img, original hw, resized hw img_rgb = self.imgs_rgb[index] img_ir = self.imgs_ir[index] # img_rgb = None # img_ir = None if (img_rgb is None) and (img_ir is None): # not cached path_rgb = self.img_files_rgb[index] path_ir = self.img_files_ir[index] img_rgb = cv2.imread(path_rgb) # BGR img_ir = cv2.imread(path_ir) # BGR assert img_rgb is not None, 'Image RGB Not Found ' + path_rgb assert img_ir is not None, 'Image IR Not Found ' + path_ir h0, w0 = img_rgb.shape[:2] # orig hw r = self.img_size / max(h0, w0) # ratio if r != 1: # if sizes are not equal img_rgb = cv2.resize(img_rgb, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR) img_ir = cv2.resize(img_ir, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR) return img_rgb, img_ir, (h0, w0), img_rgb.shape[:2] # img, hw_original, hw_resized else: return self.imgs_rgb[index], self.imgs_ir[index], self.img_hw0_rgb[index], self.img_hw_rgb[index] # img, hw_original, hw_resized def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) dtype = img.dtype # uint8 x = np.arange(0, 256, dtype=np.int16) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed def hist_equalize(img, clahe=True, bgr=False): # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def load_mosaic(self, index): # loads images in a 4-mosaic labels4, segments4 = [], [] s = self.img_size yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) # place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padw, padh) for x in segments] labels4.append(labels) segments4.extend(segments) # Concat/clip labels labels4 = np.concatenate(labels4, 0) for x in (labels4[:, 1:], *segments4): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # img4, labels4 = replicate(img4, labels4) # replicate # Augment img4, labels4 = random_perspective(img4, labels4, segments4, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], shear=self.hyp['shear'], perspective=self.hyp['perspective'], border=self.mosaic_border) # border to remove # print(labels4) return img4, labels4 def load_mosaic_RGB_IR(self, index1, index2): # loads images in a 4-mosaic index_rgb = index1 index_ir = index2 labels4_rgb, segments4_rgb = [], [] labels4_ir, segments4_ir = [], [] s = self.img_size # print("image size ", s) yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y assert index_rgb == index_ir, 'INDEX RGB 不等于 INDEX IR' indices = [index_rgb] + random.choices(self.indices_rgb, k=3) # 3 additional image indices for i, index in enumerate(indices): # Load image # img, _, (h, w) = load_image(self, index) img_rgb, img_ir, _, (h, w) = load_image_rgb_ir(self, index) # place img in img4 if i == 0: # top left img4_rgb = np.full((s * 2, s * 2, img_rgb.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles img4_ir = np.full((s * 2, s * 2, img_ir.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) # img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] # padw = x1a - x1b # padh = y1a - y1b img4_rgb[y1a:y2a, x1a:x2a] = img_rgb[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] img4_ir[y1a:y2a, x1a:x2a] = img_ir[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b labels_rgb, segments_rgb = self.labels_rgb[index].copy(), self.segments_rgb[index].copy() labels_ir, segments_ir = self.labels_ir[index].copy(), self.segments_ir[index].copy() if labels_rgb.size: labels_rgb[:, 1:] = xywhn2xyxy(labels_rgb[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format labels_ir[:, 1:] = xywhn2xyxy(labels_ir[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format segments_rgb = [xyn2xy(x, w, h, padw, padh) for x in segments_rgb] segments_ir = [xyn2xy(x, w, h, padw, padh) for x in segments_ir] labels4_rgb.append(labels_rgb) segments4_rgb.extend(segments_rgb) labels4_ir.append(labels_ir) segments4_ir.extend(segments_ir) # # Concat/clip labels # labels4 = np.concatenate(labels4, 0) # for x in (labels4[:, 1:], *segments4): # np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # # img4, labels4 = replicate(img4, labels4) # replicate labels4_rgb = np.concatenate(labels4_rgb, 0) labels4_ir = np.concatenate(labels4_ir, 0) for x in (labels4_rgb[:, 1:], *segments4_rgb): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() for x in (labels4_ir[:, 1:], *segments4_ir): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # img4, labels4 = replicate(img4, labels4) # replicate img4_rgb, img4_ir, labels4_rgb, labels4_ir = random_perspective_rgb_ir(img4_rgb, img4_ir, labels4_rgb, labels4_ir, segments4_rgb, segments4_ir, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], shear=self.hyp['shear'], perspective=self.hyp['perspective'], border=self.mosaic_border ) # border to remove # print(labels_rgb) # print(labels4_ir) # assert labels4_rgb == labels4_ir, 'LABEL4 RGB 不等于 LABEL4 IR' # print(" labels4_rgb == labels4_ir ", labels4_rgb == labels4_ir) labels4_ir = labels4_rgb # cv2.imwrite("rgb_%s.jpg" % str(index1), img4_rgb) # cv2.imwrite("ir_%s.jpg" % str(index2), img4_ir) return img4_rgb, labels4_rgb, img4_ir, labels4_ir def load_mosaic9(self, index): # loads images in a 9-mosaic labels9, segments9 = [], [] s = self.img_size indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) # place img in img9 if i == 0: # center img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles h0, w0 = h, w c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates elif i == 1: # top c = s, s - h, s + w, s elif i == 2: # top right c = s + wp, s - h, s + wp + w, s elif i == 3: # right c = s + w0, s, s + w0 + w, s + h elif i == 4: # bottom right c = s + w0, s + hp, s + w0 + w, s + hp + h elif i == 5: # bottom c = s + w0 - w, s + h0, s + w0, s + h0 + h elif i == 6: # bottom left c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h elif i == 7: # left c = s - w, s + h0 - h, s, s + h0 elif i == 8: # top left c = s - w, s + h0 - hp - h, s, s + h0 - hp padx, pady = c[:2] x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() if labels.size: labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format segments = [xyn2xy(x, w, h, padx, pady) for x in segments] labels9.append(labels) segments9.extend(segments) # Image img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] hp, wp = h, w # height, width previous # Offset yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] # Concat/clip labels labels9 = np.concatenate(labels9, 0) labels9[:, [1, 3]] -= xc labels9[:, [2, 4]] -= yc c = np.array([xc, yc]) # centers segments9 = [x - c for x in segments9] for x in (labels9[:, 1:], *segments9): np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() # img9, labels9 = replicate(img9, labels9) # replicate # Augment img9, labels9 = random_perspective(img9, labels9, segments9, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], shear=self.hyp['shear'], perspective=self.hyp['perspective'], border=self.mosaic_border) # border to remove return img9, labels9 def replicate(img, labels): # Replicate labels h, w = img.shape[:2] boxes = labels[:, 1:].astype(int) x1, y1, x2, y2 = boxes.T s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices x1b, y1b, x2b, y2b = boxes[i] bh, bw = y2b - y1b, x2b - x1b yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) return img, labels def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): # Resize and pad image while meeting stride-multiple constraints shape = img.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) if not scaleup: # only scale down, do not scale up (for better test mAP) r = min(r, 1.0) # Compute padding ratio = r, r # width, height ratios new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding dw /= 2 # divide padding into 2 sides dh /= 2 if shape[::-1] != new_unpad: # resize img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border # if auto: # minimum rectangle # dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding # elif scaleFill: # stretch # dw, dh = 0.0, 0.0 # new_unpad = (new_shape[1], new_shape[0]) # ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios # # dw /= 2 # divide padding into 2 sides # dh /= 2 # # if shape[::-1] != new_unpad: # resize # img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) # top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) # left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) # img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border return img, ratio, (dw, dh) def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] height = img.shape[0] + border[0] * 2 # shape(h,w,c) width = img.shape[1] + border[1] * 2 # Center C = np.eye(3) C[0, 2] = -img.shape[1] / 2 # x translation (pixels) C[1, 2] = -img.shape[0] / 2 # y translation (pixels) # Perspective P = np.eye(3) P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) # Rotation and Scale R = np.eye(3) a = random.uniform(-degrees, degrees) # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations s = random.uniform(1 - scale, 1 + scale) # s = 2 ** random.uniform(-scale, scale) R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # Shear S = np.eye(3) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) # Translation T = np.eye(3) T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed if perspective: img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) else: # affine img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) # Visualize # import matplotlib.pyplot as plt # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() # ax[0].imshow(img[:, :, ::-1]) # base # ax[1].imshow(img2[:, :, ::-1]) # warped # Transform label coordinates n = len(targets) if n: use_segments = any(x.any() for x in segments) new = np.zeros((n, 4)) if use_segments: # warp segments segments = resample_segments(segments) # upsample for i, segment in enumerate(segments): xy = np.ones((len(segment), 3)) xy[:, :2] = segment xy = xy @ M.T # transform xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine # clip new[i] = segment2box(xy, width, height) else: # warp boxes xy = np.ones((n * 4, 3)) xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 xy = xy @ M.T # transform xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # clip new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) # filter candidates i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) targets = targets[i] targets[:, 1:5] = new[i] return img, targets def random_perspective_rgb_ir(img_rgb, img_ir, targets_rgb=(),targets_ir=(), segments_rgb=(), segments_ir=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] img = img_rgb targets = targets_rgb segments = segments_rgb height = img.shape[0] + border[0] * 2 # shape(h,w,c) width = img.shape[1] + border[1] * 2 # Center C = np.eye(3) C[0, 2] = -img.shape[1] / 2 # x translation (pixels) C[1, 2] = -img.shape[0] / 2 # y translation (pixels) # Perspective P = np.eye(3) P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) # Rotation and Scale R = np.eye(3) a = random.uniform(-degrees, degrees) # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations s = random.uniform(1 - scale, 1 + scale) # s = 2 ** random.uniform(-scale, scale) R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # Shear S = np.eye(3) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) # Translation T = np.eye(3) T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed if perspective: # img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) img_rgb = cv2.warpPerspective(img_rgb, M, dsize=(width, height), borderValue=(114, 114, 114)) img_ir = cv2.warpPerspective(img_ir, M, dsize=(width, height), borderValue=(114, 114, 114)) else: # affine # img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) img_rgb = cv2.warpAffine(img_rgb, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) img_ir = cv2.warpAffine(img_ir, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) # Visualize # import matplotlib.pyplot as plt # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() # ax[0].imshow(img_rgb[:, :, ::-1]) # base # ax[1].imshow(img_ir[:, :, ::-1]) # warped # Transform label coordinates n = len(targets) if n: use_segments = any(x.any() for x in segments) new = np.zeros((n, 4)) if use_segments: # warp segments segments = resample_segments(segments) # upsample for i, segment in enumerate(segments): xy = np.ones((len(segment), 3)) xy[:, :2] = segment xy = xy @ M.T # transform xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine # clip new[i] = segment2box(xy, width, height) else: # warp boxes xy = np.ones((n * 4, 3)) xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 xy = xy @ M.T # transform xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # clip new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) # filter candidates i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) targets = targets[i] targets[:, 1:5] = new[i] return img_rgb, img_ir, targets, targets def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio w1, h1 = box1[2] - box1[0], box1[3] - box1[1] w2, h2 = box2[2] - box2[0], box2[3] - box2[1] ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates def cutout(image, labels): # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 h, w = image.shape[:2] def bbox_ioa(box1, box2): # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 box2 = box2.transpose() # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] # Intersection area inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) # box2 area box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 # Intersection over box2 area return inter_area / box2_area # create random masks scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction for s in scales: mask_h = random.randint(1, int(h * s)) mask_w = random.randint(1, int(w * s)) # box xmin = max(0, random.randint(0, w) - mask_w // 2) ymin = max(0, random.randint(0, h) - mask_h // 2) xmax = min(w, xmin + mask_w) ymax = min(h, ymin + mask_h) # apply random color mask image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] # return unobscured labels if len(labels) and s > 0.03: box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area labels = labels[ioa < 0.60] # remove >60% obscured labels return labels def create_folder(path='./new'): # Create folder if os.path.exists(path): shutil.rmtree(path) # delete output folder os.makedirs(path) # make new output folder def flatten_recursive(path='../coco128'): # Flatten a recursive directory by bringing all files to top level new_path = Path(path + '_flat') create_folder(new_path) for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)): shutil.copyfile(file, new_path / Path(file).name) def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_boxes('../coco128') # Convert detection dataset into classification dataset, with one directory per class path = Path(path) # images dir shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing files = list(path.rglob('*.*')) n = len(files) # number of files for im_file in tqdm(files, total=n): if im_file.suffix[1:] in img_formats: # image im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB h, w = im.shape[:2] # labels lb_file = Path(img2label_paths([str(im_file)])[0]) if Path(lb_file).exists(): with open(lb_file, 'r') as f: lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels for j, x in enumerate(lb): c = int(x[0]) # class f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename if not f.parent.is_dir(): f.parent.mkdir(parents=True) b = x[1:] * [w, h, w, h] # box # b[2:] = b[2:].max() # rectangle to square b[2:] = b[2:] * 1.2 + 3 # pad b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image b[[1, 3]] = np.clip(b[[1, 3]], 0, h) assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False): """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files Usage: from utils.datasets import *; autosplit('../coco128') Arguments path: Path to images directory weights: Train, val, test weights (list) annotated_only: Only use images with an annotated txt file """ path = Path(path) # images dir files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only n = len(files) # number of files indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) for i, img in tqdm(zip(indices, files), total=n): if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label with open(path / txt[i], 'a') as f: f.write(str(img) + '\n') # add image to txt file ================================================ FILE: utils/flask_rest_api/example_request.py ================================================ """Perform test request""" import pprint import requests DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" TEST_IMAGE = "zidane.jpg" image_data = open(TEST_IMAGE, "rb").read() response = requests.post(DETECTION_URL, files={"image": image_data}).json() pprint.pprint(response) ================================================ FILE: utils/flask_rest_api/restapi.py ================================================ """ Run a rest API exposing the yolov5s object detection model """ import argparse import io import torch from PIL import Image from flask import Flask, request app = Flask(__name__) DETECTION_URL = "/v1/object-detection/yolov5s" @app.route(DETECTION_URL, methods=["POST"]) def predict(): if not request.method == "POST": return if request.files.get("image"): image_file = request.files["image"] image_bytes = image_file.read() img = Image.open(io.BytesIO(image_bytes)) results = model(img, size=640) # reduce size=320 for faster inference return results.pandas().xyxy[0].to_json(orient="records") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") parser.add_argument("--port", default=5000, type=int, help="port number") args = parser.parse_args() model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat ================================================ FILE: utils/general.py ================================================ # YOLOv5 general utils import glob import logging import math import os import platform import random import re import subprocess import time from itertools import repeat from multiprocessing.pool import ThreadPool from pathlib import Path import cv2 import numpy as np import pandas as pd import pkg_resources as pkg import torch import torchvision import yaml from utils.google_utils import gsutil_getsize from utils.metrics import fitness from utils.torch_utils import init_torch_seeds import torch.backends.cudnn as cudnn # Settings torch.set_printoptions(linewidth=320, precision=5, profile='long') np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads logger = logging.getLogger(__name__) def set_logging(rank=-1, verbose=True): logging.basicConfig( format="%(message)s", level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN) def init_seeds(seed=0, deterministic=False): # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213 torch.use_deterministic_algorithms(True) torch.backends.cudnn.deterministic = True os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' os.environ['PYTHONHASHSEED'] = str(seed) def get_latest_run(search_dir='.'): # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) return max(last_list, key=os.path.getctime) if last_list else '' def isdocker(): # Is environment a Docker container return Path('/workspace').exists() # or Path('/.dockerenv').exists() def emojis(str=''): # Return platform-dependent emoji-safe version of string return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str def file_size(file): # Return file size in MB return Path(file).stat().st_size / 1e6 def check_online(): # Check internet connectivity import socket try: socket.create_connection(("1.1.1.1", 443), 5) # check host accesability return True except OSError: return False def check_git_status(): # Recommend 'git pull' if code is out of date print(colorstr('github: '), end='') try: assert Path('.git').exists(), 'skipping check (not a git repository)' assert not isdocker(), 'skipping check (Docker image)' assert check_online(), 'skipping check (offline)' cmd = 'git fetch && git config --get remote.origin.url' url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind if n > 0: s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ f"Use 'git pull' to update or 'git clone {url}' to download latest." else: s = f'up to date with {url} ✅' print(emojis(s)) # emoji-safe except Exception as e: print(e) def check_requirements(requirements='requirements.txt', exclude=()): # Check installed dependencies meet requirements (pass *.txt file or list of packages) import pkg_resources as pkg prefix = colorstr('red', 'bold', 'requirements:') if isinstance(requirements, (str, Path)): # requirements.txt file file = Path(requirements) if not file.exists(): print(f"{prefix} {file.resolve()} not found, check failed.") return requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] else: # list or tuple of packages requirements = [x for x in requirements if x not in exclude] n = 0 # number of packages updates for r in requirements: try: pkg.require(r) except Exception as e: # DistributionNotFound or VersionConflict if requirements not met n += 1 print(f"{prefix} {r} not found and is required by YOLOv5, attempting auto-update...") print(subprocess.check_output(f"pip install '{r}'", shell=True).decode()) if n: # if packages updated source = file.resolve() if 'file' in locals() else requirements s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" print(emojis(s)) # emoji-safe def check_img_size(img_size, s=32): # Verify img_size is a multiple of stride s new_size = make_divisible(img_size, int(s)) # ceil gs-multiple if new_size != img_size: print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) return new_size def check_imshow(): # Check if environment supports image displays try: assert not isdocker(), 'cv2.imshow() is disabled in Docker environments' cv2.imshow('test', np.zeros((1, 1, 3))) cv2.waitKey(1) cv2.destroyAllWindows() cv2.waitKey(1) return True except Exception as e: print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') return False def check_file(file): # Search for file if not found if Path(file).is_file() or file == '': return file else: files = glob.glob('./**/' + file, recursive=True) # find file assert len(files), f'File Not Found: {file}' # assert file was found assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file def check_dataset(dict): # Download dataset if not found locally val, s = dict.get('val'), dict.get('download') if val and len(val): val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path if not all(x.exists() for x in val): print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) if s and len(s): # download script if s.startswith('http') and s.endswith('.zip'): # URL f = Path(s).name # filename print(f'Downloading {s} ...') torch.hub.download_url_to_file(s, f) r = os.system(f'unzip -q {f} -d ../ && rm {f}') # unzip elif s.startswith('bash '): # bash script print(f'Running {s} ...') r = os.system(s) else: # python script r = exec(s) # return None print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure')) # print result else: raise Exception('Dataset not found.') def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): # Check version vs. required version current, minimum = (pkg.parse_version(x) for x in (current, minimum)) result = (current == minimum) if pinned else (current >= minimum) # bool s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string if hard: assert result, emojis(s) # assert min requirements met if verbose and not result: LOGGER.warning(s) return result def download(url, dir='.', multi_thread=False): # Multi-threaded file download and unzip function def download_one(url, dir): # Download 1 file f = dir / Path(url).name # filename if not f.exists(): print(f'Downloading {url} to {f}...') torch.hub.download_url_to_file(url, f, progress=True) # download if f.suffix in ('.zip', '.gz'): print(f'Unzipping {f}...') if f.suffix == '.zip': os.system(f'unzip -qo {f} -d {dir} && rm {f}') # unzip -quiet -overwrite elif f.suffix == '.gz': os.system(f'tar xfz {f} --directory {f.parent} && rm {f}') # unzip dir = Path(dir) dir.mkdir(parents=True, exist_ok=True) # make directory if multi_thread: ThreadPool(8).imap(lambda x: download_one(*x), zip(url, repeat(dir))) # 8 threads else: for u in tuple(url) if isinstance(url, str) else url: download_one(u, dir) def make_divisible(x, divisor): # Returns x evenly divisible by divisor return math.ceil(x / divisor) * divisor def clean_str(s): # Cleans a string by replacing special characters with underscore _ return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) def one_cycle(y1=0.0, y2=1.0, steps=100): # lambda function for sinusoidal ramp from y1 to y2 return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 def colorstr(*input): # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string colors = {'black': '\033[30m', # basic colors 'red': '\033[31m', 'green': '\033[32m', 'yellow': '\033[33m', 'blue': '\033[34m', 'magenta': '\033[35m', 'cyan': '\033[36m', 'white': '\033[37m', 'bright_black': '\033[90m', # bright colors 'bright_red': '\033[91m', 'bright_green': '\033[92m', 'bright_yellow': '\033[93m', 'bright_blue': '\033[94m', 'bright_magenta': '\033[95m', 'bright_cyan': '\033[96m', 'bright_white': '\033[97m', 'end': '\033[0m', # misc 'bold': '\033[1m', 'underline': '\033[4m'} return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] def labels_to_class_weights(labels, nc=80): # Get class weights (inverse frequency) from training labels if labels[0] is None: # no labels loaded return torch.Tensor() labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO classes = labels[:, 0].astype(np.int) # labels = [class xywh] weights = np.bincount(classes, minlength=nc) # occurrences per class # Prepend gridpoint count (for uCE training) # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start weights[weights == 0] = 1 # replace empty bins with 1 weights = 1 / weights # number of targets per class weights /= weights.sum() # normalize return torch.from_numpy(weights) def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): # Produces image weights based on class_weights and image contents class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels]) image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample return image_weights def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] return x def xyxy2xywh2(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x1, y1, w, h] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = x[:, 0] # x top-left y[:, 1] = x[:, 1] # y top-left y[:, 2] = x[:, 2] - x[:, 0] # width y[:, 3] = x[:, 3] - x[:, 1] # height return y def xyxy2xywh(x): # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center y[:, 2] = x[:, 2] - x[:, 0] # width y[:, 3] = x[:, 3] - x[:, 1] # height return y def xywh2xyxy(x): # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y return y def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y return y def xyn2xy(x, w=640, h=640, padw=0, padh=0): # Convert normalized segments into pixel segments, shape (n,2) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = w * x[:, 0] + padw # top left x y[:, 1] = h * x[:, 1] + padh # top left y return y def segment2box(segment, width=640, height=640): # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) x, y = segment.T # segment xy inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) x, y, = x[inside], y[inside] return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy def segments2boxes(segments): # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) boxes = [] for s in segments: x, y = s.T # segment xy boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy return xyxy2xywh(np.array(boxes)) # cls, xywh def resample_segments(segments, n=1000): # Up-sample an (n,2) segment for i, s in enumerate(segments): x = np.linspace(0, len(s) - 1, n) xp = np.arange(len(s)) segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy return segments def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): # Rescale coords (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding else: gain = ratio_pad[0][0] pad = ratio_pad[1] coords[:, [0, 2]] -= pad[0] # x padding coords[:, [1, 3]] -= pad[1] # y padding coords[:, :4] /= gain clip_coords(coords, img0_shape) return coords def clip_coords(boxes, img_shape): # Clip bounding xyxy bounding boxes to image shape (height, width) boxes[:, 0].clamp_(0, img_shape[1]) # x1 boxes[:, 1].clamp_(0, img_shape[0]) # y1 boxes[:, 2].clamp_(0, img_shape[1]) # x2 boxes[:, 3].clamp_(0, img_shape[0]) # y2 def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T # Get the coordinates of bounding boxes if x1y1x2y2: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] else: # transform from xywh to xyxy b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 # Intersection area inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) # Union Area w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps union = w1 * h1 + w2 * h2 - inter + eps iou = inter / union if GIoU or DIoU or CIoU: cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared if DIoU: return iou - rho2 / c2 # DIoU elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) with torch.no_grad(): alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU else: # GIoU https://arxiv.org/pdf/1902.09630.pdf c_area = cw * ch + eps # convex area return iou - (c_area - union) / c_area # GIoU else: return iou # IoU def box_iou(box1, box2): # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py """ Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box1 (Tensor[N, 4]) box2 (Tensor[M, 4]) Returns: iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 """ def box_area(box): # box = 4xn return (box[2] - box[0]) * (box[3] - box[1]) area1 = box_area(box1.T) area2 = box_area(box2.T) # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) def wh_iou(wh1, wh2): # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 wh1 = wh1[:, None] # [N,1,2] wh2 = wh2[None] # [1,M,2] inter = torch.min(wh1, wh2).prod(2) # [N,M] return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) def python_nms(dets, scores, iou_thresh): x1 = dets[:, 0] y1 = dets[:, 1] x2 = dets[:, 2] y2 = dets[:, 3] areas = (y2 - y1 + 1) * (x2 - x1 + 1) keep = [] index = scores.argsort()[::-1] while index.size > 0: i = index[0] # every time the first is the biggst, and add it directly keep.append(i) x11 = torch.max(x1[i], x1[index[1:]]) # calculate the points of overlap y11 = torch.max(y1[i], y1[index[1:]]) x22 = torch.min(x2[i], x2[index[1:]]) y22 = torch.min(y2[i], y2[index[1:]]) w = np.maximum(0, x22 - x11 + 1) # the weights of overlap h = np.maximum(0, y22 - y11 + 1) # the height of overlap overlaps = w * h ious = overlaps / (areas[i] + areas[index[1:]] - overlaps) idx = torch.where(ious <= iou_thresh)[0] index = index[idx + 1] # because index start from 1 return keep def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=()): """Runs Non-Maximum Suppression (NMS) on inference results Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ nc = prediction.shape[2] - 5 # number of classes xc = prediction[..., 4] > conf_thres # candidates # Settings min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height max_det = 300 # maximum number of detections per image max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 10.0 # seconds to quit after redundant = True # require redundant detections multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) merge = False # use merge-NMS t = time.time() output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] for xi, x in enumerate(prediction): # image index, image inference # Apply constraints # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height x = x[xc[xi]] # confidence # Cat apriori labels if autolabelling if labels and len(labels[xi]): l = labels[xi] v = torch.zeros((len(l), nc + 5), device=x.device) v[:, :4] = l[:, 1:5] # box v[:, 4] = 1.0 # conf v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls x = torch.cat((x, v), 0) # If none remain process next image if not x.shape[0]: continue # Compute conf #x[:, 5:] = 1 x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box (center x, center y, width, height) to (x1, y1, x2, y2) box = xywh2xyxy(x[:, :4]) # Detections matrix nx6 (xyxy, conf, cls) if multi_label: i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) else: # best class only conf, j = x[:, 5:].max(1, keepdim=True) x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] # Filter by class if classes is not None: x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] # Apply finite constraint # if not torch.isfinite(x).all(): # x = x[torch.isfinite(x).all(1)] # Check shape n = x.shape[0] # number of boxes if not n: # no boxes continue elif n > max_nms: # excess boxes x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence # Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS if i.shape[0] > max_det: # limit detections i = i[:max_det] if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix weights = iou * scores[None] # box weights x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes if redundant: i = i[iou.sum(1) > 1] # require redundancy output[xi] = x[i] if (time.time() - t) > time_limit: print(f'WARNING: NMS time limit {time_limit}s exceeded') break # time limit exceeded return output def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() # Strip optimizer from 'f' to finalize training, optionally save as 's' x = torch.load(f, map_location=torch.device('cpu')) if x.get('ema'): x['model'] = x['ema'] # replace model with ema for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys x[k] = None x['epoch'] = -1 x['model'].half() # to FP16 for p in x['model'].parameters(): p.requires_grad = False torch.save(x, s or f) mb = os.path.getsize(s or f) / 1E6 # filesize print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): # Print mutation results to evolve.txt (for use with train.py --evolve) a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) if bucket: url = 'gs://%s/evolve.txt' % bucket if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local with open('evolve.txt', 'a') as f: # append result f.write(c + b + '\n') x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows x = x[np.argsort(-fitness(x))] # sort np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness # Save yaml for i, k in enumerate(hyp.keys()): hyp[k] = float(x[0, i + 7]) with open(yaml_file, 'w') as f: results = tuple(x[0, :7]) c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') yaml.safe_dump(hyp, f, sort_keys=False) if bucket: os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload def apply_classifier(x, model, img, im0): # Apply a second stage classifier to yolo outputs im0 = [im0] if isinstance(im0, np.ndarray) else im0 for i, d in enumerate(x): # per image if d is not None and len(d): d = d.clone() # Reshape and pad cutouts b = xyxy2xywh(d[:, :4]) # boxes b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad d[:, :4] = xywh2xyxy(b).long() # Rescale boxes from img_size to im0 size scale_coords(img.shape[2:], d[:, :4], im0[i].shape) # Classes pred_cls1 = d[:, 5].long() ims = [] for j, a in enumerate(d): # per item cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] im = cv2.resize(cutout, (224, 224)) # BGR # cv2.imwrite('test%i.jpg' % j, cutout) im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 im /= 255.0 # 0 - 255 to 0.0 - 1.0 ims.append(im) pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections return x def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False): # Save an image crop as {file} with crop size multiplied by {gain} and padded by {pad} pixels xyxy = torch.tensor(xyxy).view(-1, 4) b = xyxy2xywh(xyxy) # boxes if square: b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad xyxy = xywh2xyxy(b).long() clip_coords(xyxy, im.shape) crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2])] cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop if BGR else crop[..., ::-1]) def increment_path(path, exist_ok=False, sep='', mkdir=False): # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. path = Path(path) # os-agnostic if path.exists() and not exist_ok: suffix = path.suffix path = path.with_suffix('') dirs = glob.glob(f"{path}{sep}*") # similar paths matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] i = [int(m.groups()[0]) for m in matches if m] # indices n = max(i) + 1 if i else 2 # increment number path = Path(f"{path}{sep}{n}{suffix}") # update path dir = path if path.suffix == '' else path.parent # directory if not dir.exists() and mkdir: dir.mkdir(parents=True, exist_ok=True) # make directory return path ================================================ FILE: utils/google_app_engine/Dockerfile ================================================ FROM gcr.io/google-appengine/python # Create a virtualenv for dependencies. This isolates these packages from # system-level packages. # Use -p python3 or -p python3.7 to select python version. Default is version 2. RUN virtualenv /env -p python3 # Setting these environment variables are the same as running # source /env/bin/activate. ENV VIRTUAL_ENV /env ENV PATH /env/bin:$PATH RUN apt-get update && apt-get install -y python-opencv # Copy the application's requirements.txt and run pip to install all # dependencies into the virtualenv. ADD requirements.txt /app/requirements.txt RUN pip install -r /app/requirements.txt # Add the application source code. ADD . /app # Run a WSGI server to serve the application. gunicorn must be declared as # a dependency in requirements.txt. CMD gunicorn -b :$PORT main:app ================================================ FILE: utils/google_app_engine/additional_requirements.txt ================================================ # add these requirements in your app on top of the existing ones pip==18.1 Flask==1.0.2 gunicorn==19.9.0 ================================================ FILE: utils/google_app_engine/app.yaml ================================================ runtime: custom env: flex service: yolov5app liveness_check: initial_delay_sec: 600 manual_scaling: instances: 1 resources: cpu: 1 memory_gb: 4 disk_size_gb: 20 ================================================ FILE: utils/google_utils.py ================================================ # Google utils: https://cloud.google.com/storage/docs/reference/libraries import os import platform import subprocess import time from pathlib import Path import requests import torch def gsutil_getsize(url=''): # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') return eval(s.split(' ')[0]) if len(s) else 0 # bytes def attempt_download(file, repo='ultralytics/yolov5'): # Attempt file download if does not exist file = Path(str(file).strip().replace("'", '')) if not file.exists(): try: response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] tag = response['tag_name'] # i.e. 'v1.0' except: # fallback plan assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt'] try: tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] except: tag = 'v5.0' # current release name = file.name if name in assets: msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' redundant = False # second download option try: # GitHub url = f'https://github.com/{repo}/releases/download/{tag}/{name}' print(f'Downloading {url} to {file}...') torch.hub.download_url_to_file(url, file) assert file.exists() and file.stat().st_size > 1E6 # check except Exception as e: # GCP print(f'Download error: {e}') assert redundant, 'No secondary mirror' url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' print(f'Downloading {url} to {file}...') os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) finally: if not file.exists() or file.stat().st_size < 1E6: # check file.unlink(missing_ok=True) # remove partial downloads print(f'ERROR: Download failure: {msg}') print('') return def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download() t = time.time() file = Path(file) cookie = Path('cookie') # gdrive cookie print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') file.unlink(missing_ok=True) # remove existing file cookie.unlink(missing_ok=True) # remove existing cookie # Attempt file download out = "NUL" if platform.system() == "Windows" else "/dev/null" os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') if os.path.exists('cookie'): # large file s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' else: # small file s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' r = os.system(s) # execute, capture return cookie.unlink(missing_ok=True) # remove existing cookie # Error check if r != 0: file.unlink(missing_ok=True) # remove partial print('Download error ') # raise Exception('Download error') return r # Unzip if archive if file.suffix == '.zip': print('unzipping... ', end='') os.system(f'unzip -q {file}') # unzip file.unlink() # remove zip to free space print(f'Done ({time.time() - t:.1f}s)') return r def get_token(cookie="./cookie"): with open(cookie) as f: for line in f: if "download" in line: return line.split()[-1] return "" # def upload_blob(bucket_name, source_file_name, destination_blob_name): # # Uploads a file to a bucket # # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python # # storage_client = storage.Client() # bucket = storage_client.get_bucket(bucket_name) # blob = bucket.blob(destination_blob_name) # # blob.upload_from_filename(source_file_name) # # print('File {} uploaded to {}.'.format( # source_file_name, # destination_blob_name)) # # # def download_blob(bucket_name, source_blob_name, destination_file_name): # # Uploads a blob from a bucket # storage_client = storage.Client() # bucket = storage_client.get_bucket(bucket_name) # blob = bucket.blob(source_blob_name) # # blob.download_to_filename(destination_file_name) # # print('Blob {} downloaded to {}.'.format( # source_blob_name, # destination_file_name)) ================================================ FILE: utils/gradcam.py ================================================ #!/usr/bin/env python # -*- coding:utf-8 -*- # Author: Richard Fang import torch from torch.autograd import Variable from torch.autograd import Function from torchvision import models from torchvision import utils import cv2 import sys import numpy as np import argparse import warnings warnings.filterwarnings("ignore") def preprocess_image(img): means=[0.485, 0.456, 0.406] stds=[0.229, 0.224, 0.225] preprocessed_img = img.copy()[:, :, ::-1] for i in range(3): preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i] preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i] preprocessed_img = np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1))) preprocessed_img = torch.from_numpy(preprocessed_img) preprocessed_img.unsqueeze_(0) input = Variable(preprocessed_img, requires_grad = True) return input def show_cam_on_image(img, mask, epoch, layer): heatmap = cv2.applyColorMap(np.uint8(255*mask), cv2.COLORMAP_JET) heatmap = np.float32(heatmap) / 255 # img = cv2.flip(img, 1) cam = heatmap + np.float32(img) cam = cam / np.max(cam) print(type(cam)) cv2.imwrite("./cam%d_%d.jpg" %(epoch, layer), np.uint8(255 * cam)) def calcGradCam(imgpath, feature, epoch, layer): """ :param feature: 特征图 :param grad_val: 对应的特征图的梯度 :return """ # ------------ Image Preprocess --------------- image_path = imgpath img = cv2.imread(image_path, 1) # print(np.shape(img)) img = np.float32(cv2.resize(img, (640, 640))) / 255 input = preprocess_image(img) # ------------ GradCam ------------------------- feature = feature.cpu().data.numpy() print("feature shape", feature.shape) cam = np.zeros(feature.shape[1:], dtype=np.float32) for i in range(feature.shape[0]): cam += feature[i, :, :] cam = np.maximum(cam, 0) # 比较cam的元素与0的大小，<0的都置0 # min = np.min(cam) # cam -= min cam = cv2.resize(cam, (640, 640)) # 归一化 cam cam = cam - np.min(cam) cam = cam / np.max(cam) print(cam.shape) # ---------- Show ------------- show_cam_on_image(img, cam, epoch, layer) return cam ================================================ FILE: utils/loss.py ================================================ # Loss functions import torch import torch.nn as nn import numpy as np from utils.general import bbox_iou from utils.torch_utils import is_parallel from utils.plots import plot_samples from torch.autograd import Variable from descriptor.LSS import denseLSS from descriptor.CFOG import denseCFOG def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 # return positive, negative label smoothing BCE targets return 1.0 - 0.5 * eps, 0.5 * eps class BCEBlurWithLogitsLoss(nn.Module): # BCEwithLogitLoss() with reduced missing label effects. def __init__(self, alpha=0.05): super(BCEBlurWithLogitsLoss, self).__init__() self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() self.alpha = alpha def forward(self, pred, true): loss = self.loss_fcn(pred, true) pred = torch.sigmoid(pred) # prob from logits dx = pred - true # reduce only missing label effects # dx = (pred - true).abs() # reduce missing label and false label effects alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) loss *= alpha_factor return loss.mean() class FocalLoss(nn.Module): # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): super(FocalLoss, self).__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = 'none' # required to apply FL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) # p_t = torch.exp(-loss) # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py pred_prob = torch.sigmoid(pred) # prob from logits p_t = true * pred_prob + (1 - true) * (1 - pred_prob) alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = (1.0 - p_t) ** self.gamma loss *= alpha_factor * modulating_factor if self.reduction == 'mean': return loss.mean() elif self.reduction == 'sum': return loss.sum() else: # 'none' return loss class QFocalLoss(nn.Module): # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): super(QFocalLoss, self).__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = 'none' # required to apply FL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) pred_prob = torch.sigmoid(pred) # prob from logits alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = torch.abs(true - pred_prob) ** self.gamma loss *= alpha_factor * modulating_factor if self.reduction == 'mean': return loss.mean() elif self.reduction == 'sum': return loss.sum() else: # 'none' return loss # varifocal loss class VFLoss(nn.Module): def __init__(self, loss_fcn, gamma=2.0, alpha=0.25): super(VFLoss, self).__init__() # 传递 nn.BCEWithLogitsLoss() 损失函数 must be nn.BCEWithLogitsLoss() self.loss_fcn = loss_fcn # self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = 'none' # required to apply VFL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) pred_prob = torch.sigmoid(pred) # prob from logits focal_weight = true * (true > 0.0).float() + self.alpha * (pred_prob - true).abs().pow(self.gamma) * (true <= 0.0).float() loss *= focal_weight if self.reduction == 'mean': return loss.mean() elif self.reduction == 'sum': return loss.sum() else: return loss # Ranking Loss class RankingLoss2(nn.Module): def __init__(self, threshold): super().__init__() self.threshold = threshold self.sigmoid = nn.Sigmoid() def forward(self, pred, true): loss = 0 bs, c, h, w = pred.shape for x, y in zip(pred, true): x = self.sigmoid(x) loss -= y * torch.log(1 - (y - x) + 1e-7) + (1 - y) * torch.log(torch.where(self.threshold - (1 - x) > 0, 1 - self.threshold + (1 - x) + 1e-7, torch.ones([1], dtype=x.dtype, device='cuda'))) return loss.sum() / (bs * c * h * w) # Ranking Loss class RankingLoss(nn.Module): def __init__(self, gamma): super().__init__() self.gamma = gamma self.sigmoid = nn.Sigmoid() def forward(self, pred, true): loss = 0.0 bs, c, h, w = pred.shape for pred_i, true_i in zip(pred, true): for x, y in zip(pred_i, true_i): x = self.sigmoid(x) mask_negative = y < 0.3 mask_positive = y > 0.5 s = x[mask_positive] if len(s) == 0: val = 0 else: pos_pred = x[mask_positive].min() neg_pred = x[mask_negative].max() val = torch.exp(neg_pred - pos_pred) #val = (((1 + neg_pred - pos_pred) / 2) ** self.gamma) * torch.exp(neg_pred - pos_pred) #val = torch.log(1 - (1 + neg_pred - pos_pred) / 2.0 + 1e-7) ''' pos_pred = x[mask_positive].mean() neg_pred = x[~mask_positive].mean() if (pos_pred - neg_pred).item() >= 0.7: val = 0 else: val = torch.exp(neg_pred - pos_pred) ''' loss += val return loss / (bs * c) # Similarity Loss class SimLoss(nn.Module): def __init__(self, gamma): super().__init__() self.gamma = gamma self.sigmoid = nn.Sigmoid() def des_SSD(self, i, j, descriptor): mask_i = torch.ge(i.squeeze(0).squeeze(0), 1) mask_i = torch.tensor(mask_i, dtype=torch.float32) mask_j = torch.ge(j.squeeze(0).squeeze(0), 1) mask_j = torch.tensor(mask_j, dtype=torch.float32) mask = torch.mul(mask_i, mask_j) num = mask[mask.ge(1)].size()[0] if descriptor == 'CFOG': des_i = denseCFOG(i) des_j = denseCFOG(j) elif descriptor == 'LSS': des_i = denseLSS(i) des_j = denseLSS(j) des_i = torch.mul(des_i, mask) des_j = torch.mul(des_j, mask) SSD_loss = nn.MSELoss(reduction='sum') loss = SSD_loss(des_i, des_j) / num return loss def des_NCC(self, i, j, descriptor): mask_i = torch.ge(i.squeeze(0).squeeze(0), 1) mask_i = torch.tensor(mask_i, dtype=torch.float32) mask_j = torch.ge(j.squeeze(0).squeeze(0), 1) mask_j = torch.tensor(mask_j, dtype=torch.float32) mask = torch.mul(mask_i, mask_j) num = mask[mask.ge(1)].size()[0] if descriptor == 'CFOG': des_i = denseCFOG(i) des_j = denseCFOG(j) elif descriptor == 'LSS': des_i = denseLSS(i) des_j = denseLSS(j) des_i = torch.mul(des_i, mask) des_j = torch.mul(des_j, mask) loss = self.gncc_loss(des_i, des_j) * 512 * 512 / num return loss def gradient_loss(self, s, penalty='l2'): dy = torch.abs(s[:, :, 1:, :] - s[:, :, :-1, :]) dx = torch.abs(s[:, :, :, 1:] - s[:, :, :, :-1]) if (penalty == 'l2'): dy = dy * dy dx = dx * dx d = torch.mean(dx) + torch.mean(dy) return d / 2.0 def mse_loss(self, x, y): return torch.mean((x - y) ** 2) def DSC(self, pred, target): smooth = 1e-5 m1 = pred.flatten() m2 = target.flatten() intersection = (m1 * m2).sum() return (2. * intersection + smooth) / (m1.sum() + m2.sum() + smooth) def gncc_loss(self, I, J, eps=1e-5): I2 = I.pow(2) J2 = J.pow(2) IJ = I * J I_ave, J_ave = I.mean(), J.mean() I2_ave, J2_ave = I2.mean(), J2.mean() IJ_ave = IJ.mean() cross = IJ_ave - I_ave * J_ave I_var = I2_ave - I_ave.pow(2) J_var = J2_ave - J_ave.pow(2) cc = cross / (I_var.sqrt() * J_var.sqrt() + eps) # 1e-5 return -1.0 * cc + 1 def compute_local_sums(self, I, J, filt, stride, padding, win): I2, J2, IJ = I * I, J * J, I * J I_sum = nn.functional.conv2d(I, filt, stride=stride, padding=padding) J_sum = nn.functional.conv2d(J, filt, stride=stride, padding=padding) I2_sum = nn.functional.conv2d(I2, filt, stride=stride, padding=padding) J2_sum = nn.functional.conv2d(J2, filt, stride=stride, padding=padding) IJ_sum = nn.functional.conv2d(IJ, filt, stride=stride, padding=padding) win_size = np.prod(win) u_I = I_sum / win_size u_J = J_sum / win_size cross = IJ_sum - u_J * I_sum - u_I * J_sum + u_I * u_J * win_size I_var = I2_sum - 2 * u_I * I_sum + u_I * u_I * win_size J_var = J2_sum - 2 * u_J * J_sum + u_J * u_J * win_size return I_var, J_var, cross def cc_loss(self, x, y): dim = [2, 3, 4] mean_x = torch.mean(x, dim, keepdim=True) mean_y = torch.mean(y, dim, keepdim=True) mean_x2 = torch.mean(x ** 2, dim, keepdim=True) mean_y2 = torch.mean(y ** 2, dim, keepdim=True) stddev_x = torch.sum(torch.sqrt(mean_x2 - mean_x ** 2), dim, keepdim=True) stddev_y = torch.sum(torch.sqrt(mean_y2 - mean_y ** 2), dim, keepdim=True) return -torch.mean((x - mean_x) * (y - mean_y) / (stddev_x * stddev_y)) def Get_Ja(self, flow): D_y = (flow[:, 1:, :-1, :-1, :] - flow[:, :-1, :-1, :-1, :]) D_x = (flow[:, :-1, 1:, :-1, :] - flow[:, :-1, :-1, :-1, :]) D_z = (flow[:, :-1, :-1, 1:, :] - flow[:, :-1, :-1, :-1, :]) D1 = (D_x[..., 0] + 1) * ((D_y[..., 1] + 1) * (D_z[..., 2] + 1) - D_z[..., 1] * D_y[..., 2]) D2 = (D_x[..., 1]) * (D_y[..., 0] * (D_z[..., 2] + 1) - D_y[..., 2] * D_x[..., 0]) D3 = (D_x[..., 2]) * (D_y[..., 0] * D_z[..., 1] - (D_y[..., 1] + 1) * D_z[..., 0]) return D1 - D2 + D3 def NJ_loss(self, ypred): Neg_Jac = 0.5 * (torch.abs(self.Get_Ja(ypred)) - self.Get_Ja(ypred)) return torch.sum(Neg_Jac) def lncc_loss(self, i, j, win=[9, 9], eps=1e-5): I = i J = j I2 = I.pow(2) J2 = J.pow(2) IJ = I * J filters = Variable(torch.ones(1, 1, win[0], win[1])).cuda() padding = (win[0] // 2, win[1] // 2) I_sum = nn.functional.conv2d(I, filters, stride=1, padding=padding) J_sum = nn.functional.conv2d(J, filters, stride=1, padding=padding) I2_sum = nn.functional.conv2d(I2, filters, stride=1, padding=padding) J2_sum = nn.functional.conv2d(J2, filters, stride=1, padding=padding) IJ_sum = nn.functional.conv2d(IJ, filters, stride=1, padding=padding) win_size = win[0] * win[1] u_I = I_sum / win_size u_J = J_sum / win_size cross = IJ_sum - u_J * I_sum - u_I * J_sum + u_I * u_J * win_size I_var = I2_sum - 2 * u_I * I_sum + u_I * u_I * win_size J_var = J2_sum - 2 * u_J * J_sum + u_J * u_J * win_size cc = cross * cross / (I_var * J_var + eps) lcc = -1.0 * torch.mean(cc) + 1 return lcc def forward(self, reference, sensed_tran, sensed, reference_inv_tran, descriptor, similarity): if similarity == 'SSD': # Similarity: SSD or NCC based on descriptors loss1 = Variable(self.des_SSD(reference, sensed_tran, descriptor), requires_grad=True) loss2 = Variable(self.des_SSD(sensed, reference_inv_tran, descriptor), requires_grad=True) elif similarity == 'NCC': loss1 = Variable(self.des_NCC(reference, sensed_tran, descriptor), requires_grad=True) loss2 = Variable(self.des_NCC(sensed, reference_inv_tran, descriptor), requires_grad=True) loss = (loss1 + loss2) * 0.5 return loss class ComputeLoss: # Compute losses def __init__(self, model, autobalance=False): super(ComputeLoss, self).__init__() self.sort_obj_iou = True # 在计算objectness时是否对ciou进行排序 device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters # Define criteria BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) #BCEobj = VFLoss(BCEobj) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets # Focal loss g = h['fl_gamma'] # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance self.RKobj = RankingLoss(2.0) for k in 'na', 'nc', 'nl', 'anchors': setattr(self, k, getattr(det, k)) def __call__(self, p, targets): # predictions, targets, model device = targets.device lcls, lbox, lobj, lrk = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) tcls, tbox, indices, anchors, offsets = self.build_targets(p, targets) # targets #plot_samples(batch_index, imgs, path, tcls, tbox, indices, anchors, offsets, targets) # Losses for i, pi in enumerate(p): # layer index, layer predictions b, a, gj, gi = indices[i] # image, anchor, gridy, gridx tobj = torch.zeros_like(pi[..., 0], device=device) # target obj n = b.shape[0] # number of targets if n: ps = pi[b, a, gj, gi] # prediction subset corresponding to targets # Regression pxy = ps[:, :2].sigmoid() * 2. - 0.5 pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] pbox = torch.cat((pxy, pwh), 1) # predicted box iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) lbox += (1.0 - iou).mean() # iou loss # Objectness score_iou = iou.detach().clamp(0).type(tobj.dtype) if self.sort_obj_iou: sort_id = torch.argsort(score_iou) # 将iou按照从小到大进行排序，返回下标索引 b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id] tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou # 根据model.gr设置真实框的标签值 # Classification if self.nc > 1: # cls loss (only if multiple classes) t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets t[range(n), tcls[i]] = self.cp lcls += self.BCEcls(ps[:, 5:], t) # BCE # Ranking #lrk += self.RKobj(pi[..., 4], tobj) obji = self.BCEobj(pi[..., 4], tobj) lobj += obji * self.balance[i] # obj loss if self.autobalance: self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() if self.autobalance: self.balance = [x / self.balance[self.ssi] for x in self.balance] lbox *= self.hyp['box'] lobj *= self.hyp['obj'] lcls *= self.hyp['cls'] lrk *= 0.1 bs = tobj.shape[0] # batch size loss = lbox + lobj + lcls + lrk return loss * bs, torch.cat((lbox, lobj, lcls, lrk)).detach() def build_targets(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch, offset = [], [], [], [], [] gain = torch.ones(7, device=targets.device) # normalized to gridspace gain ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices g = 0.5 # bias off = torch.tensor([[0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm ], device=targets.device).float() * g # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain if nt: # Matches r = t[:, :, 4:6] / anchors[:, None] # wh ratio j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse j, k = ((gxy % 1. < g) & (gxy > 1.)).T l, m = ((gxi % 1. < g) & (gxi > 1.)).T j = torch.stack((torch.ones_like(j), j, k, l, m)) t = t.repeat((5, 1, 1))[j] offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] else: t = targets[0] offsets = 0 # Define b, c = t[:, :2].long().T # image, class gxy = t[:, 2:4] # grid xy gwh = t[:, 4:6] # grid wh gij = (gxy - offsets).long() gi, gj = gij.T # grid xy indices # Append a = t[:, 6].long() # anchor indices indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid indices tbox.append(torch.cat((gxy - gij, gwh), 1)) # box anch.append(anchors[a]) # anchors tcls.append(c) # class offset.append(offsets) return tcls, tbox, indices, anch, offset ================================================ FILE: utils/metrics.py ================================================ # Model validation metrics from pathlib import Path import matplotlib.pyplot as plt import numpy as np import torch from . import general def fitness(x): # Model fitness as a weighted combination of metrics w = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0] # weights for [tp[0], fp[0], fn[0], f1[0], mp, mr, map50, map] return (x[:, :8] * w).sum(1) def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): """ Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (nparray, nx1 or nx10). conf: Objectness value from 0-1 (nparray). pred_cls: Predicted object classes (nparray). target_cls: True object classes (nparray). plot: Plot precision-recall curve at mAP@0.5 save_dir: Plot save directory # Returns The average precision as computed in py-faster-rcnn. """ # Sort by objectness i = np.argsort(-conf) tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] # Find unique classes unique_classes = np.unique(target_cls) nc = unique_classes.shape[0] # number of classes, number of detections # Create Precision-Recall curve and compute AP for each class px, py = np.linspace(0, 1, 1000), [] # for plotting ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) for ci, c in enumerate(unique_classes): i = pred_cls == c n_l = (target_cls == c).sum() # number of labels n_p = i.sum() # number of predictions if n_p == 0 or n_l == 0: continue else: # Accumulate FPs and TPs fpc = (1 - tp[i]).cumsum(0) tpc = tp[i].cumsum(0) # Recall recall = tpc / (n_l + 1e-16) # recall curve r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases # Precision precision = tpc / (tpc + fpc) # precision curve p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score # AP from recall-precision curve for j in range(tp.shape[1]): ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) if plot and j == 0: py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 # Compute F1 (harmonic mean of precision and recall) f1 = 2 * p * r / (p + r + 1e-16) if plot: plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') i = f1.mean(0).argmax() # max F1 index tp = (r * n_l).round() # true positives fn = n_l - tp fp = (tp / (p + 1e-16) - tp).round() # false positives return tp[:, i], fp[:, i], fn[:, i], p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') def compute_ap(recall, precision): """ Compute the average precision, given the recall and precision curves # Arguments recall: The recall curve (list) precision: The precision curve (list) # Returns Average precision, precision curve, recall curve """ # Append sentinel values to beginning and end mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) mpre = np.concatenate(([1.], precision, [0.])) # Compute the precision envelope mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) # Integrate area under curve method = 'interp' # methods: 'continuous', 'interp' if method == 'interp': x = np.linspace(0, 1, 101) # 101-point interp (COCO) ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate else: # 'continuous' i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve return ap, mpre, mrec class ConfusionMatrix: # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix def __init__(self, nc, conf=0.25, iou_thres=0.45): self.matrix = np.zeros((nc + 1, nc + 1)) self.nc = nc # number of classes self.conf = conf self.iou_thres = iou_thres def process_batch(self, detections, labels): """ Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: detections (Array[N, 6]), x1, y1, x2, y2, conf, class labels (Array[M, 5]), class, x1, y1, x2, y2 Returns: None, updates confusion matrix accordingly """ detections = detections[detections[:, 4] > self.conf] gt_classes = labels[:, 0].int() detection_classes = detections[:, 5].int() iou = general.box_iou(labels[:, 1:], detections[:, :4]) x = torch.where(iou > self.iou_thres) if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] else: matches = np.zeros((0, 3)) n = matches.shape[0] > 0 m0, m1, _ = matches.transpose().astype(np.int16) for i, gc in enumerate(gt_classes): j = m0 == i if n and sum(j) == 1: self.matrix[detection_classes[m1[j]], gc] += 1 # correct else: self.matrix[self.nc, gc] += 1 # background FP if n: for i, dc in enumerate(detection_classes): if not any(m1 == i): self.matrix[dc, self.nc] += 1 # background FN def matrix(self): return self.matrix def plot(self, save_dir='', names=()): try: import seaborn as sn array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) fig = plt.figure(figsize=(12, 9), tight_layout=True) sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, xticklabels=names + ['background FP'] if labels else "auto", yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) fig.axes[0].set_xlabel('True') fig.axes[0].set_ylabel('Predicted') fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) except Exception as e: pass def print(self): for i in range(self.nc + 1): print(' '.join(map(str, self.matrix[i]))) # Plots ---------------------------------------------------------------------------------------------------------------- def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): # Precision-recall curve fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) py = np.stack(py, axis=1) if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py.T): ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) else: ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) ax.set_xlabel('Recall') ax.set_ylabel('Precision') ax.set_xlim(0, 1) ax.set_ylim(0, 1) plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") fig.savefig(Path(save_dir), dpi=250) def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): # Metric-confidence curve fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py): ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) else: ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) y = py.mean(0) ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_xlim(0, 1) ax.set_ylim(0, 1) plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") fig.savefig(Path(save_dir), dpi=250) ================================================ FILE: utils/plots.py ================================================ # Plotting utils import glob import math import os import random from copy import copy from pathlib import Path import cv2 import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import torch import yaml from PIL import Image, ImageDraw, ImageFont from scipy.signal import butter, filtfilt from utils.general import xywh2xyxy, xyxy2xywh, xywhn2xyxy from utils.metrics import fitness # Settings matplotlib.rc('font', **{'size': 11}) matplotlib.use('Agg') # for writing to files only class Colors: # Ultralytics color palette https://ultralytics.com/ def __init__(self): self.palette = [self.hex2rgb(c) for c in matplotlib.colors.TABLEAU_COLORS.values()] self.n = len(self.palette) def __call__(self, i, bgr=False): c = self.palette[int(i) % self.n] return (c[2], c[1], c[0]) if bgr else c @staticmethod def hex2rgb(h): # rgb order (PIL) return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) colors = Colors() # create instance for 'from utils.plots import colors' def hist2d(x, y, n=100): # 2d histogram used in labels.png and evolve.png xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) return np.log(hist[xidx, yidx]) def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy def butter_lowpass(cutoff, fs, order): nyq = 0.5 * fs normal_cutoff = cutoff / nyq return butter(order, normal_cutoff, btype='low', analog=False) b, a = butter_lowpass(cutoff, fs, order=order) return filtfilt(b, a, data) # forward-backward filter def plot_one_box(x, im, color=None, label=None, line_thickness=3): # Plots one bounding box on image 'im' using OpenCV assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.' tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1 # line/font thickness # print(tl) color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) # print(c1, c2) cv2.rectangle(im, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) def plot_one_box_PIL(box, im, color=None, label=None, line_thickness=None): # Plots one bounding box on image 'im' using PIL im = Image.fromarray(im) draw = ImageDraw.Draw(im) line_thickness = line_thickness or max(int(min(im.size) / 200), 2) draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot if label: fontsize = max(round(max(im.size) / 40), 12) font = ImageFont.truetype("Arial.ttf", fontsize) txt_width, txt_height = font.getsize(label) draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) return np.asarray(im) def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() # Compares the two methods for width-height anchor multiplication # https://github.com/ultralytics/yolov3/issues/168 x = np.arange(-4.0, 4.0, .1) ya = np.exp(x) yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 fig = plt.figure(figsize=(6, 3), tight_layout=True) plt.plot(x, ya, '.-', label='YOLOv3') plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2') plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6') plt.xlim(left=-4, right=4) plt.ylim(bottom=0, top=6) plt.xlabel('input') plt.ylabel('output') plt.grid() plt.legend() fig.savefig('comparison.png', dpi=200) def output_to_target(output): # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] targets = [] for i, o in enumerate(output): for *box, conf, cls in o.cpu().numpy(): targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) return np.array(targets) def plot_samples(batch_index, images, path, tcls, tbox, indices, anchors, offsets, targets): if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(tbox, torch.Tensor): tbox = tbox.cpu().numpy() bs, _, h, w = images.shape slide = [8, 16, 32] if np.max(images[0]) <= 1: images *= 255 file_path = str(path) + '/samples_visual' if not os.path.exists(file_path): os.makedirs(file_path) for j in range(bs): # image img = images[j] img = img.transpose(1, 2, 0) img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for target in targets: if target[0] == j: t = target[2:6].unsqueeze(0) t = xywhn2xyxy(t, w=640, h=640) cv2.rectangle(img, (int(t[0][0]), int(t[0][1])), (int(t[0][2]), int(t[0][3])), (255, 255, 255), thickness=2) else: continue for i in range(len(indices)): # layer indice = indices[i] image_indice = (indice[0]==j).nonzero() anchor = anchors[i][image_indice].squeeze(1) offset = offsets[i][image_indice].squeeze(1) gridy, gridx = indice[2][image_indice], indice[3][image_indice] grid = torch.cat([gridx, gridy], dim=1) + offset anchor_boxs = torch.cat([grid, anchor], dim=1) * slide[i] for anchor_box in anchor_boxs: bbox = xywh2xyxy(anchor_box.unsqueeze(0)) cv2.rectangle(img, (int(bbox[0][0]), int(bbox[0][1])), (int(bbox[0][2]), int(bbox[0][3])), (255, 0, 0), thickness=1) save_path = file_path+'/'+'image'+str(batch_index)+'_'+str(j)+'.jpg' cv2.imwrite(save_path, img) def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): # Plot image grid with labels if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): targets = targets.cpu().numpy() # un-normalise if np.max(images[0]) <= 1: images *= 255 tl = 3 # line thickness tf = max(tl - 1, 1) # font thickness bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images ns = np.ceil(bs ** 0.5) # number of subplots (square) # Check if we should resize scale_factor = max_size / max(h, w) if scale_factor < 1: h = math.ceil(scale_factor * h) w = math.ceil(scale_factor * w) mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init for i, img in enumerate(images): if i == max_subplots: # if last batch has fewer images than we expect break block_x = int(w * (i // ns)) block_y = int(h * (i % ns)) img = img.transpose(1, 2, 0) if scale_factor < 1: img = cv2.resize(img, (w, h)) mosaic[block_y:block_y + h, block_x:block_x + w, :] = img if len(targets) > 0: image_targets = targets[targets[:, 0] == i] boxes = xywh2xyxy(image_targets[:, 2:6]).T classes = image_targets[:, 1].astype('int') labels = image_targets.shape[1] == 6 # labels if no conf column conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) if boxes.shape[1]: if boxes.max() <= 1.01: # if normalized with tolerance 0.01 boxes[[0, 2]] *= w # scale to pixels boxes[[1, 3]] *= h elif scale_factor < 1: # absolute coords need scale if image scales boxes *= scale_factor boxes[[0, 2]] += block_x boxes[[1, 3]] += block_y for j, box in enumerate(boxes.T): cls = int(classes[j]) color = colors(cls) cls = names[cls] if names else cls if labels or conf[j] > 0.25: # 0.25 conf thresh label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) # Draw image filename labels if paths: label = Path(paths[i]).name[:40] # trim to 40 char t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, lineType=cv2.LINE_AA) # Image border cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) if fname: r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save Image.fromarray(mosaic).save(fname) # PIL save return mosaic def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): # Plot LR simulating training for full epochs optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals y = [] for _ in range(epochs): scheduler.step() y.append(optimizer.param_groups[0]['lr']) plt.plot(y, '.-', label='LR') plt.xlabel('epoch') plt.ylabel('LR') plt.grid() plt.xlim(0, epochs) plt.ylim(0) plt.savefig(Path(save_dir) / 'LR.png', dpi=200) plt.close() def plot_test_txt(): # from utils.plots import *; plot_test() # Plot test.txt histograms x = np.loadtxt('test.txt', dtype=np.float32) box = xyxy2xywh(x[:, :4]) cx, cy = box[:, 0], box[:, 1] fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) ax.set_aspect('equal') plt.savefig('hist2d.png', dpi=300) fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) ax[0].hist(cx, bins=600) ax[1].hist(cy, bins=600) plt.savefig('hist1d.png', dpi=200) def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() # Plot targets.txt histograms x = np.loadtxt('targets.txt', dtype=np.float32).T s = ['x targets', 'y targets', 'width targets', 'height targets'] fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) ax = ax.ravel() for i in range(4): ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) ax[i].legend() ax[i].set_title(s[i]) plt.savefig('targets.jpg', dpi=200) def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() # Plot study.txt generated by test.py fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) # ax = ax.ravel() fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: for f in sorted(Path(path).glob('study*.txt')): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] # for i in range(7): # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) # ax[i].set_title(s[i]) j = y[3].argmax() + 1 ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') ax2.grid(alpha=0.2) ax2.set_yticks(np.arange(20, 60, 5)) ax2.set_xlim(0, 57) ax2.set_ylim(30, 55) ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_ylabel('COCO AP val') ax2.legend(loc='lower right') plt.savefig(str(Path(path).name) + '.png', dpi=300) def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): # plot dataset labels print('Plotting labels... ') c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes nc = int(c.max() + 1) # number of classes x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) # seaborn correlogram sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) plt.close() # matplotlib labels matplotlib.use('svg') # faster ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) ax[0].set_ylabel('instances') if 0 < len(names) < 30: ax[0].set_xticks(range(len(names))) ax[0].set_xticklabels(names, rotation=90, fontsize=10) else: ax[0].set_xlabel('classes') sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) # rectangles labels[:, 1:3] = 0.5 # center labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) for cls, *box in labels[:1000]: ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot ax[1].imshow(img) ax[1].axis('off') for a in [0, 1, 2, 3]: for s in ['top', 'right', 'left', 'bottom']: ax[a].spines[s].set_visible(False) plt.savefig(save_dir / 'labels.jpg', dpi=200) matplotlib.use('Agg') plt.close() # loggers for k, v in loggers.items() or {}: if k == 'wandb' and v: v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() # Plot hyperparameter evolution results in evolve.txt with open(yaml_file) as f: hyp = yaml.safe_load(f) x = np.loadtxt('evolve.txt', ndmin=2) f = fitness(x) # weights = (f - f.min()) ** 2 # for weighted results plt.figure(figsize=(10, 12), tight_layout=True) matplotlib.rc('font', **{'size': 8}) for i, (k, v) in enumerate(hyp.items()): y = x[:, i + 7] # mu = (y * weights).sum() / weights.sum() # best weighted result mu = y[f.argmax()] # best single result plt.subplot(6, 5, i + 1) plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') plt.plot(mu, f.max(), 'k+', markersize=15) plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters if i % 5 != 0: plt.yticks([]) print('%15s: %.3g' % (k, mu)) plt.savefig('evolve.png', dpi=200) print('\nPlot saved as evolve.png') def profile_idetection(start=0, stop=0, labels=(), save_dir=''): # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] files = list(Path(save_dir).glob('frames*.txt')) for fi, f in enumerate(files): try: results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows n = results.shape[1] # number of rows x = np.arange(start, min(stop, n) if stop else n) results = results[:, x] t = (results[0] - results[0].min()) # set t0=0s results[0] = x for i, a in enumerate(ax): if i < len(results): label = labels[fi] if len(labels) else f.stem.replace('frames_', '') a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) a.set_title(s[i]) a.set_xlabel('time (s)') # if fi == len(files) - 1: # a.set_ylim(bottom=0) for side in ['top', 'right']: a.spines[side].set_visible(False) else: a.remove() except Exception as e: print('Warning: Plotting error for %s; %s' % (f, e)) ax[1].legend() plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() # Plot training 'results*.txt', overlaying train and val losses s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T n = results.shape[1] # number of rows x = range(start, min(stop, n) if stop else n) fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) ax = ax.ravel() for i in range(5): for j in [i, i + 5]: y = results[j, x] ax[i].plot(x, y, marker='.', label=s[j]) # y_smooth = butter_lowpass_filtfilt(y) # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) ax[i].set_title(t[i]) ax[i].legend() ax[i].set_ylabel(f) if i == 0 else None # add filename fig.savefig(f.replace('.txt', '.png'), dpi=200) def plot_results(file='path/to/results.csv', dir=''): # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(4, 5, figsize=(12, 6), tight_layout=True) ax = ax.ravel() files = list(save_dir.glob('results*.csv')) assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' for fi, f in enumerate(files): try: data = pd.read_csv(f) s = [x.strip() for x in data.columns] x = data.values[:, 0] for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]): y = data.values[:, j] # y[y == 0] = np.nan # don't show zero values ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) ax[i].set_title(s[j], fontsize=12) # if j in [8, 9, 10]: # share train and val loss y axes # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) except Exception as e: print(f'Warning: Plotting error for {f}: {e}') ax[1].legend() fig.savefig(save_dir / 'results.png', dpi=200) plt.close() ================================================ FILE: utils/torch_utils.py ================================================ # YOLOv5 PyTorch utils import datetime import logging import math import os import platform import subprocess import time from contextlib import contextmanager from copy import deepcopy from pathlib import Path import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.nn.functional as F import torchvision try: import thop # for FLOPS computation except ImportError: thop = None logger = logging.getLogger(__name__) @contextmanager def torch_distributed_zero_first(local_rank: int): """ Decorator to make all processes in distributed training wait for each local_master to do something. """ if local_rank not in [-1, 0]: torch.distributed.barrier() yield if local_rank == 0: torch.distributed.barrier() def init_torch_seeds(seed=0): # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html torch.manual_seed(seed) if seed == 0: # slower, more reproducible cudnn.benchmark, cudnn.deterministic = False, True else: # faster, less reproducible cudnn.benchmark, cudnn.deterministic = True, False def date_modified(path=__file__): # return human-readable file modification date, i.e. '2021-3-26' t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) return f'{t.year}-{t.month}-{t.day}' def git_describe(path=Path(__file__).parent): # path must be a directory # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe s = f'git -C {path} describe --tags --long --always' try: return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] except subprocess.CalledProcessError as e: return '' # not a git repository def select_device(device='', batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string cpu = device.lower() == 'cpu' if cpu: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False elif device: # non-cpu device requested os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability cuda = not cpu and torch.cuda.is_available() if cuda: n = torch.cuda.device_count() if n > 1 and batch_size: # check that batch_size is compatible with device_count assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' space = ' ' * len(s) for i, d in enumerate(device.split(',') if device else range(n)): p = torch.cuda.get_device_properties(i) s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB else: s += 'CPU\n' logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe return torch.device('cuda:0' if cuda else 'cpu') def time_synchronized(): # pytorch-accurate time if torch.cuda.is_available(): torch.cuda.synchronize() return time.time() def profile(x, ops, n=100, device=None): # profile a pytorch module or list of modules. Example usage: # x = torch.randn(16, 3, 640, 640) # input # m1 = lambda x: x * torch.sigmoid(x) # m2 = nn.SiLU() # profile(x, [m1, m2], n=100) # profile speed over 100 iterations device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') x = x.to(device) x.requires_grad = True print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") for m in ops if isinstance(ops, list) else [ops]: m = m.to(device) if hasattr(m, 'to') else m # device m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward try: flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS except: flops = 0 for _ in range(n): t[0] = time_synchronized() y = m(x) t[1] = time_synchronized() try: _ = y.sum().backward() t[2] = time_synchronized() except: # no backward method t[2] = float('nan') dtf += (t[1] - t[0]) * 1000 / n # ms per op forward dtb += (t[2] - t[1]) * 1000 / n # ms per op backward s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') def is_parallel(model): return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) def intersect_dicts(da, db, exclude=()): # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} def initialize_weights(model): for m in model.modules(): t = type(m) if t is nn.Conv2d: pass #nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif t is nn.BatchNorm2d: m.eps = 1e-3 m.momentum = 0.03 elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: m.inplace = True def find_modules(model, mclass=nn.Conv2d): # Finds layer indices matching module class 'mclass' return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] def sparsity(model): # Return global model sparsity a, b = 0., 0. for p in model.parameters(): a += p.numel() b += (p == 0).sum() return b / a def prune(model, amount=0.3): # Prune model to requested global sparsity import torch.nn.utils.prune as prune print('Pruning model... ', end='') for name, m in model.named_modules(): if isinstance(m, nn.Conv2d): prune.l1_unstructured(m, name='weight', amount=amount) # prune prune.remove(m, 'weight') # make permanent print(' %.3g global sparsity' % sparsity(model)) def fuse_conv_and_bn(conv, bn): # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ fusedconv = nn.Conv2d(conv.in_channels, conv.out_channels, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding, groups=conv.groups, bias=True).requires_grad_(False).to(conv.weight.device) # prepare filters w_conv = conv.weight.clone().view(conv.out_channels, -1) w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) # prepare spatial bias b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) return fusedconv def model_info(model, verbose=False, img_size=640): # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients if verbose: print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) for i, (name, p) in enumerate(model.named_parameters()): name = name.replace('module_list.', '') print('%5g %40s %9s %12g %20s %10.3g %10.3g' % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) # try: # FLOPS # from thop import profile # stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device) # input # flops = profile(deepcopy(model), inputs=img, verbose=False)[0] / 1E9 # stride GFLOPS # img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float # fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS # except (ImportError, Exception): # fs = '' try: # FLOPS from thop import profile img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float img = torch.zeros((1, 3, img_size[0], img_size[1]), device=next(model.parameters()).device) # input flops = profile(deepcopy(model), inputs=img, verbose=False)[0] / 1E9 # stride GFLOPS fs = '%.1f GFLOPS' % (flops) # 640x640 GFLOPS except (ImportError, Exception): fs = '' logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients, {fs}") def load_classifier(name='resnet101', n=2): # Loads a pretrained model reshaped to n-class output model = torchvision.models.__dict__[name](pretrained=True) # ResNet model properties # input_size = [3, 224, 224] # input_space = 'RGB' # input_range = [0, 1] # mean = [0.485, 0.456, 0.406] # std = [0.229, 0.224, 0.225] # Reshape output to n classes filters = model.fc.weight.shape[1] model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) model.fc.out_features = n return model def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) # scales img(bs,3,y,x) by ratio constrained to gs-multiple if ratio == 1.0: return img else: h, w = img.shape[2:] s = (int(h * ratio), int(w * ratio)) # new size img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize if not same_shape: # pad/crop img h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean def copy_attr(a, b, include=(), exclude=()): # Copy attributes from b to a, options to only include [...] and to exclude [...] for k, v in b.__dict__.items(): if (len(include) and k not in include) or k.startswith('_') or k in exclude: continue else: setattr(a, k, v) class ModelEMA: """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models Keep a moving average of everything in the model state_dict (parameters and buffers). This is intended to allow functionality like https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage A smoothed version of the weights is necessary for some training schemes to perform well. This class is sensitive where it is initialized in the sequence of model init, GPU assignment and distributed training wrappers. """ def __init__(self, model, decay=0.9999, updates=0): # Create EMA self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA # if next(model.parameters()).device.type != 'cpu': # self.ema.half() # FP16 EMA self.updates = updates # number of EMA updates self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) for p in self.ema.parameters(): p.requires_grad_(False) def update(self, model): # Update EMA parameters with torch.no_grad(): self.updates += 1 d = self.decay(self.updates) msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict for k, v in self.ema.state_dict().items(): if v.dtype.is_floating_point: v *= d v += (1. - d) * msd[k].detach() def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): # Update EMA attributes copy_attr(self.ema, model, include, exclude) ================================================ FILE: utils/wandb_logging/__init__.py ================================================ ================================================ FILE: utils/wandb_logging/log_dataset.py ================================================ import argparse import yaml from wandb_utils import WandbLogger WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' def create_dataset_artifact(opt): with open(opt.data) as f: data = yaml.safe_load(f) # data dict logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') opt = parser.parse_args() opt.resume = False # Explicitly disallow resume check for dataset upload job create_dataset_artifact(opt) ================================================ FILE: utils/wandb_logging/wandb_utils.py ================================================ import json import sys from pathlib import Path import torch import yaml from tqdm import tqdm sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path from utils.datasets import LoadImagesAndLabels from utils.datasets import img2label_paths from utils.general import colorstr, xywh2xyxy, check_dataset try: import wandb from wandb import init, finish except ImportError: wandb = None WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): return from_string[len(prefix):] def check_wandb_config_file(data_config_file): wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path if Path(wandb_config).is_file(): return wandb_config return data_config_file def get_run_info(run_path): run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) run_id = run_path.stem project = run_path.parent.stem model_artifact_name = 'run_' + run_id + '_model' return run_id, project, model_artifact_name def check_wandb_resume(opt): process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None if isinstance(opt.resume, str): if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): if opt.global_rank not in [-1, 0]: # For resuming DDP runs run_id, project, model_artifact_name = get_run_info(opt.resume) api = wandb.Api() artifact = api.artifact(project + '/' + model_artifact_name + ':latest') modeldir = artifact.download() opt.weights = str(Path(modeldir) / "last.pt") return True return None def process_wandb_config_ddp_mode(opt): with open(opt.data) as f: data_dict = yaml.safe_load(f) # data dict train_dir, val_dir = None, None if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): api = wandb.Api() train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) train_dir = train_artifact.download() train_path = Path(train_dir) / 'data/images/' data_dict['train'] = str(train_path) if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): api = wandb.Api() val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) val_dir = val_artifact.download() val_path = Path(val_dir) / 'data/images/' data_dict['val'] = str(val_path) if train_dir or val_dir: ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') with open(ddp_data_path, 'w') as f: yaml.safe_dump(data_dict, f) opt.data = ddp_data_path class WandbLogger(): def __init__(self, opt, name, run_id, data_dict, job_type='Training'): # Pre-training routine -- self.job_type = job_type self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call if isinstance(opt.resume, str): # checks resume from artifact if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): run_id, project, model_artifact_name = get_run_info(opt.resume) model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name assert wandb, 'install wandb to resume wandb runs' # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') opt.resume = model_artifact_name elif self.wandb: self.wandb_run = wandb.init(config=opt, resume="allow", project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, name=name, job_type=job_type, id=run_id) if not wandb.run else wandb.run if self.wandb_run: if self.job_type == 'Training': if not opt.resume: wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict # Info useful for resuming from artifacts self.wandb_run.config.opt = vars(opt) self.wandb_run.config.data_dict = wandb_data_dict self.data_dict = self.setup_training(opt, data_dict) if self.job_type == 'Dataset Creation': self.data_dict = self.check_and_upload_dataset(opt) else: prefix = colorstr('wandb: ') print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") def check_and_upload_dataset(self, opt): assert wandb, 'Install wandb to upload dataset' check_dataset(self.data_dict) config_path = self.log_dataset_artifact(opt.data, opt.single_cls, 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) print("Created dataset config file ", config_path) with open(config_path) as f: wandb_data_dict = yaml.safe_load(f) return wandb_data_dict def setup_training(self, opt, data_dict): self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants self.bbox_interval = opt.bbox_interval if isinstance(opt.resume, str): modeldir, _ = self.download_model_artifact(opt) if modeldir: self.weights = Path(modeldir) / "last.pt" config = self.wandb_run.config opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ config.opt['hyp'] data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), opt.artifact_alias) self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), opt.artifact_alias) self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None if self.train_artifact_path is not None: train_path = Path(self.train_artifact_path) / 'data/images/' data_dict['train'] = str(train_path) if self.val_artifact_path is not None: val_path = Path(self.val_artifact_path) / 'data/images/' data_dict['val'] = str(val_path) self.val_table = self.val_artifact.get("val") self.map_val_table_path() if self.val_artifact is not None: self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) if opt.bbox_interval == -1: self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 return data_dict def download_dataset_artifact(self, path, alias): if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" datadir = dataset_artifact.download() return datadir, dataset_artifact return None, None def download_model_artifact(self, opt): if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' modeldir = model_artifact.download() epochs_trained = model_artifact.metadata.get('epochs_trained') total_epochs = model_artifact.metadata.get('total_epochs') assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( total_epochs) return modeldir, model_artifact return None, None def log_model(self, path, opt, epoch, fitness_score, best_model=False): model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ 'original_url': str(path), 'epochs_trained': epoch + 1, 'save period': opt.save_period, 'project': opt.project, 'total_epochs': opt.epochs, 'fitness_score': fitness_score }) model_artifact.add_file(str(path / 'last.pt'), name='last.pt') wandb.log_artifact(model_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) print("Saving model artifact on epoch ", epoch + 1) def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): with open(data_file) as f: data = yaml.safe_load(f) # data dict nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) names = {k: v for k, v in enumerate(names)} # to index dictionary self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( data['train']), names, name='train') if data.get('train') else None self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( data['val']), names, name='val') if data.get('val') else None if data.get('train'): data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') if data.get('val'): data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path data.pop('download', None) with open(path, 'w') as f: yaml.safe_dump(data, f) if self.job_type == 'Training': # builds correct artifact pipeline graph self.wandb_run.use_artifact(self.val_artifact) self.wandb_run.use_artifact(self.train_artifact) self.val_artifact.wait() self.val_table = self.val_artifact.get('val') self.map_val_table_path() else: self.wandb_run.log_artifact(self.train_artifact) self.wandb_run.log_artifact(self.val_artifact) return path def map_val_table_path(self): self.val_table_map = {} print("Mapping dataset") for i, data in enumerate(tqdm(self.val_table.data)): self.val_table_map[data[3]] = data[0] def create_dataset_table(self, dataset, class_to_id, name='dataset'): # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging artifact = wandb.Artifact(name=name, type="dataset") img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None img_files = tqdm(dataset.img_files) if not img_files else img_files for img_file in img_files: if Path(img_file).is_dir(): artifact.add_dir(img_file, name='data/images') labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) artifact.add_dir(labels_path, name='data/labels') else: artifact.add_file(img_file, name='data/images/' + Path(img_file).name) label_file = Path(img2label_paths([img_file])[0]) artifact.add_file(str(label_file), name='data/labels/' + label_file.name) if label_file.exists() else None table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): height, width = shapes[0] labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) box_data, img_classes = [], {} for cls, *xyxy in labels[:, 1:].tolist(): cls = int(cls) box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": cls, "box_caption": "%s" % (class_to_id[cls]), "scores": {"acc": 1}, "domain": "pixel"}) img_classes[cls] = class_to_id[cls] boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), Path(paths).name) artifact.add(table, name) return artifact def log_training_progress(self, predn, path, names): if self.val_table and self.result_table: class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) box_data = [] total_conf = 0 for *xyxy, conf, cls in predn.tolist(): if conf >= 0.25: box_data.append( {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": int(cls), "box_caption": "%s %.3f" % (names[cls], conf), "scores": {"class_score": conf}, "domain": "pixel"}) total_conf = total_conf + conf boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space id = self.val_table_map[Path(path).name] self.result_table.add_data(self.current_epoch, id, wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), total_conf / max(1, len(box_data)) ) def log(self, log_dict): if self.wandb_run: for key, value in log_dict.items(): self.log_dict[key] = value def end_epoch(self, best_result=False): if self.wandb_run: wandb.log(self.log_dict) self.log_dict = {} if self.result_artifact: train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") self.result_artifact.add(train_results, 'result') wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), ('best' if best_result else '')]) self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") def finish_run(self): if self.wandb_run: if self.log_dict: wandb.log(self.log_dict) wandb.run.finish()