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zuhqzE6aiue)aKgwf_!iXh3N(ZwN@uWFtm)l$p0G2KVw?T-UcbiqxrMgqo(TT6*7W5 zpvpxEvJgF>$+BmcYbUXc_Le{ zjO&GnRnsAHvP-JH__a=}k=QQ$3oCs&k*N5>R3Jp4GKrJ)V>C^~!e(bQ5^mQ=$VsfJ zCnubppz(jPtDwAmCV>KRqKSS~ohJ%9D70DVHNp9*Y^<}Bdc8IeHzw%0(_=Y|xenB^ z|J%Gyst5-tjdVO7>yIg3JD(GW`~oUdM@rGo`fG9y_$NTvmMZITi6s@oKq#|F@fr5U z6i)hg>qv0t+pOHo;h Date: Wed, 30 Aug 2023 22:04:33 -0600 Subject: [PATCH 03/18] Updated paper after review --- manuscript/paper.md | 119 +++++++++++++++++++------------------------- 1 file changed, 51 insertions(+), 68 deletions(-) diff --git a/manuscript/paper.md b/manuscript/paper.md index 4205244..188daf8 100644 --- a/manuscript/paper.md +++ b/manuscript/paper.md @@ -11,117 +11,100 @@ authors: equal-contrib: true affiliation: "1, 2" - name: Nathan J. Neeteson - orcid: equal-contrib: true affiliation: "1, 2" - name: Kathryn S. Stok - orcid: + orcid: 0000-0002-0522-4180 affiliation: 3 - name: Andrew J. Burghardt - orcid: + orcid: 0000-0002-6343-4944 affiliation: 4 - name: Michelle A.E. Espinosa - orcid: - affiliation: "5, 6" - - name: Andrew K.O. Wong - orcid: - affiliation: "5, 6" - - name: Serena Bonaretti - orcid: + orcid: 0000-0002-8279-9842 + affiliation: "3, 5, 6" + - name: Jared Vicory + orcid: 0000-0001-6041-5580 affiliation: 7 - - name: Big - orcid: - affiliation: 3 - name: Justin J. Tse - orcid: - affiliation: "2, 8" - - name: Jared Vicory - orcid: + orcid: 0000-0002-5107-5884 + affiliation: "1, 8" + - name: Pholpat Durongbhan + orcid: 0000-0002-5164-3482 + affiliation: 3 + - name: Serena Bonaretti + orcid: 0000-0003-4264-1773 affiliation: 9 + - name: Andy Kin On Wong + orcid: 0000-0001-8540-8854 + affiliation: "6, 10" - name: Steven K. Boyd - orcid: - affiliation: "1, 2, 8" + orcid: 0000-0002-2930-5997 + affiliation: "1, 8" - name: Sarah L. Manske - orcid: + orcid: 0000-0003-0888-9556 corresponding: true - affiliation: "1, 2, 8" + affiliation: "1, 8" affiliations: - - name: Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Canada + - name: McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Canada index: 1 - - name: McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Canada + - name: Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada index: 2 - name: Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia index: 3 -- name: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States +- name: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA index: 4 - - name: Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada + - name: i. Rehabilitation Sciences Institute, The University of Toronto, Toronto, Canada index: 5 - - name: Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada + - name: Joint Department of Medical Imaging, University Health Network, Toronto, Canada index: 6 - - name: Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada + - name: Kitware, Inc., Carrboro, North Carolina, USA index: 7 - name: Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada index: 8 - - name: Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada + - name: Swiss Center for Musculoskeletal Imaging, Balgrist Campus, Zurich, Switzerland index: 9 -date: 30 November 2022 + - name: Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada + index: 10 +date: 30 August 2023 bibliography: paper.bib --- 1 – Statement of Need: - -High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique that is used to assess bone microarchitecture and density with a nominal isotropic voxel size of 61um. HR-pQCT has a wide range of applications, including improving our understanding of bone diseases [1](https://doi.org/10.1186/s12880-020-00437-8), assessment of age-related changes [2](https://doi.org/10.1002/jbmr.2873), and evaluating the effects of pharmaceutical interventions [3](https://doi.org/10.1080/03009742.2020.1869303). Processing HR-pQCT images has largely been limited to the scanner manufacturer’s scripting language (Image Processing Language, IPL, Scanco Medical). By expanding image processing workflows outside of the scanner manufacturer’s environment, users can apply more advanced mathematical techniques to improve image processing. The ORMIR_XCT Python package was developed to first reimplement some existing IPL workflows and to provide an open and reproducible package to allow for the development of more advanced image processing workflows for HR-pQCT data. - -The development of this package began during the Jupyter Community Workshop in Maastricht, Netherlands in June 2022, hosted by the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) group. During this workshop, the conceptualization and initial development of the ORMIR_XCT package began with support from Kitware. +High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique with a nominal isotropic voxel size of 61µm capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases [1](https://doi.org/10.1186/s12880-020-00437-8), assess epidemiological associations [2](https://doi.org/10.1002/jbmr.2873), and evaluate the effects of pharmaceutical interventions [3](https://doi.org/10.1080/03009742.2020.1869303). Processing HR-pQCT images has largely been supported using the scanner manufacturer’s scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer’s software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The ORMIR_XCT Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows. 2 – Package Summary: +The development of this package began during the Jupyter Community Workshop in Maastricht, Netherlands in June 2022, hosted by the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) group. During this workshop, the conceptualization and initial development of the ORMIR_XCT package began with support from Kitware. The ORMIR_XCT package is currently being used by members of the ORMIR community to assess bone mineral density (BMD) and joint space width (JSW) changes in HR-pQCT scans of patients with osteoarthritis or rheumatoid arthritis. -Image processing of HR-pQCT scans has largely been limited to the use of the scanner manufacturer's processing language (Image Processing Language, IPL, Scanco Medical). To expand on existing IPL workflows developed for HR-pQCT data, the ORMIR_XCT package was created. This package reimplements existing IPL scripts and workflows in Python and adds additional functionality to improve current image processing techniques applied to HR-pQCT data. - -The ORMIR_XCT package contains several modules for processing HR-pQCT data of bones and joints, including joint space analysis, bone mineral density calculations, automatic contouring of the periosteal surface, and segmentation of trabecular bone. Analyses have been performed to compare results generated using the ORMIR_XCT package to results generated using IPL. Jupyter Notebook examples are provided to describe some of the workflows implemented in ORMIR_XCT package. - - -3 – Workflows: - -The ORMIR_XCT package contains four IPL workflows reimplemented in Python: -1. Automatic Periosteal Contouring -2. Joint Space Width Analysis -3. Bone Mineral Density -4. Trabecular Bone Segmentation - - -4 – Comparison to IPL: - -Autocontour: -Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 62). Binarized images were obtained using the IPL standard method for automatic periosteal contouring and using the ORMIR_XCT package. Segmentations were compared by computing the DICE coefficient and Jaccard index between images. The IPL method for automatic periosteal contouring was assumed to be the ground truth. Results of the segmentation comparison are shown in \autoref{fig:autocontour}. - -![Figure 1: DICE coefficients and Jaccard indices between IPL and ORMIR_XCT automatic comntouring implementations.\label{fig: autocontour}](autocontour_comparison.png) +The ORMIR_XCT package contains four modules for processing HR-pQCT data of bones and joints: 1) automatic contouring of the periosteal surface, 2) JSW analysis, 3) BMD calculation, and 4) segmentation of trabecular bone . Analyses have been performed to compare outputs from the ORMIR_XCT package to results generated using IPL. Jupyter Notebook examples are provided to describe the workflows implemented in the ORMIR_XCT package. +3 – Comparison to IPL: +3.1 - Autocontour: +Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59 ). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [4](https://doi.org/10.1016/j.bone.2010.05.034) and the ORMIR_XCT package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images . The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in Table 1\autoref{fig:Table1}. -Thickness: -The ORMIR_XCT package contains an open-source implementation of Hildebrand et al.'s algorithm for model-independent thickness estimation [4](https://doi.org/10.1046/j.1365-2818.1997.1340694.x). Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise. +![Table 1: DICE coefficients, Jaccard indices, and Hausdorff distances (voxels) between IPL and ORMIR_XCT automatic contouring implementations . All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. DIP2: 2nd distal interphalangeal joint, DIP3: 3rd distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table1}](Table1.png) -Hildebrand et al. note an optimization for the algorithm where first the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We have implemented this optimized version of the algorithm with the NumPy and Numba Python packages. Finally, we have noted, and compensated for, a discrepancy between Hildebrand et al.'s definition of the distance map and the outputs of many common open-source distance map algorithms (including those available in SimpleITK and scikit-image). Hildebrand et al. define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure." In contrast, most distance transforms will compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. The discrepancy is visualized in Figure 2 \autoref{fig: oversampling}. +3.2 - Thickness: +The ORMIR_XCT package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand et al [5](https://doi.org/10.1046/j.1365-2818.1997.1340694.x). This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise . -![Figure 2: .\label{fig: oversampling}](oversampling.png) +Hildebrand et al. note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with the NumPy (v1.23.3) [6](https://doi.org/10.1038/s41586-020-2649-2) and Numba (v0.56.3) [7](http://dx.doi.org/10.1145/2833157.2833162) Python packages . Finally, we have noted and compensated for a discrepancy between the definition of the distance map by Hildebrand et al and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [8](https://doi.org/10.3389/fninf.2013.00045) and scikit-image (v0.19.2) [9](https://doi.org/10.7717/peerj.453). Hildebrand et al. define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. -To compensate for this, we have developed an oversampling distance transform function. In this function, an input mask is upsampled to double the resolution, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned to be background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is downsampled back to the original resolution. +To compensate for this, we developed an oversampling distance transform function (Figure 1\autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. -The user is given a choice, via a boolean flag parameter in the compute_local_thickness_from_mask and calc_structure_thickness_statistics functions, whether they would like to use the oversampling distance transform for thickness estimation. The choice is given because different users may have differing definitions of the bounds of their structures (whether they are contained within the mask voxels or may extend to the center of the neighbouring background voxels) and because using the normal distance transform will provide better congruence with thickness calculations produced using IPL. +![Figure 1: Examples of the thickness estimation algorithm implemented in the ORMIR_XCT package. Top row: thickness calculation on a 1 voxel thick plate demonstrating how thickness estimation is improved using the oversampling distance transform function (thickness = 1 voxel using oversampling, thickness = 2 voxels without oversampling). Bottom row: thickness calculation applied to a sample finger joint.\label{fig: DT_Figure}](DT_Figure.png) -To evaluate the thickness computation implemented in the ORMIR_XCT package, a set of synthetic shapes were generated. These shapes include solid spheres, hollow spheres, filled cylinders, hollow cylinders, and plates of varying thicknesses [5](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [6](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: ridge_epsilon = 0.9, assign_epsilon = 1.8, suppress_boundary = 0, and version = 3. This set of parameters is currently used for JSW analysis of in vivo finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms and are shown in Figure 3 \autoref{fig:thickness}. +The user is given a choice, via a Boolean flag parameter in the compute_local_thickness_from_mask and calc_structure_thickness_statistics functions, to use the oversampling distance transform for thickness estimation. The choice is given because different users may have variable preferences in applying alternate definitions regarding the bounds of their structures (whether they be contained within the mask voxels or extend to the center of the neighbouring background voxels) and because using the normal (i.e., not oversampled) distance transform will provide better congruence with thickness calculations produced using IPL. -![Figure 3: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the ORMIR_XCT package.\label{fig: thickness}](JSW_Mean_Thickness.png) +To evaluate the thickness computation implemented in the ORMIR_XCT package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [10](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [11](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: ridge_epsilon = 0.9, assign_epsilon = 1.8, suppress_boundary = 0, and version = 3. This set of parameters is currently used for JSW analysis of in vivo finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (Figure 2\autoref{fig:IPL_vs_ORMIR_XCT-DT}). -Bone Mineral Density: -Images obtained from HR-pQCT scanners are saved using the manufacturer’s proprietary file format (.AIM). To process HR-pQCT images using Python, these files need to be converted to a file format that be interpreted by common image processing Python libraries (e.g., SimpleITK). The ITKIOScanco module is used in the ORMIR_XCT package to convert HR-pQCT images to other common medical image file types. However, the ITKIOScanco module converts HR-pQCT images to Hounsfield Units (HU) by default. Measures of bone mineral density (BMD) require the image units to be density units (mg of Hydroxyapatite per cm3). Several image unit conversions have been included in the ORMIR_XCT package to allow for conversion between HR-pQCT native units (termed Scanco units), HU, density units, and linear attenuation units. +![Figure 2: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the ORMIR_XCT package . Top row: black lines indicate mean difference and red dotted line shows the limits of agreement. Bottom row: solid black line indicates the linear regression between IPL and ORMIR_XCT computed thickness and red dotted lines indicate a line of unity.\label{fig: IPL_vs_ORMIR_XCT-DT}](IPL_vs_ORMIR_XCT-DT.png) -A sample dataset of HR-pQCT images of second and third metacarpal phalangeal (MCP) joints (n = 292) was used to compare BMD results between IPL and the ORMIR_XCT package. BMD is reported for the distal (DST) and proximal (PRX) segments of each joint. Results of this comparison are shown in Figure 4 \autoref{fig:bmd}. +3.3 - Bone Mineral Density: +Images obtained from HR-pQCT scanners are saved using the manufacturer’s proprietary file format (AIM). To process HR-pQCT images using Python, these files need to be converted to a file format that can be interpreted by common image processing Python libraries. The ITKIOScanco module is used in the ORMIR_XCT package to convert HR-pQCT images to other common medical image file types. However, the ITKIOScanco module converts HR-pQCT images to Hounsfield Units (HU) by default. Measures of BMD require the image to be calibrated in density units (mg of hydroxyapatite per cm3). Several image unit conversions have been included in the ORMIR_XCT package to allow for conversion between HR-pQCT native units (termed Scanco units) and each of HU, density, and linear attenuation units. A sample dataset of HR-pQCT images of second and third metacarpal phalangeal (MCP) joints (n = 292) was used to compare BMD results between IPL and the ORMIR_XCT package. BMD was reported separately for the distal (DST) and proximal (PRX) segments of each joint (Figure 3\autoref{fig:IPL_vs_ORMIR_XCT-BMD}). -![Figure 4: Bone mineral density (mg HA/ccm) computed using IPL and the ORMIR_XCT package.\label{fig: bmd}](bmd_comparison.png) +![Figure 3: Bone mineral density (mg HA/cm3) linear regression and Bland-Altman plots comparing IPL and the ORMIR_XCT computed bone mineral density (BMD ). All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. MCP2: 2nd metacarpal phalangeal joint, MCP3: 3rd metacarpal phalangeal joint, DST: distal, PRX: proximal.\label{fig: IPL_vs_ORMIR_XCT-BMD}](IPL_vs_ORMIR_XCT-BMD.png) -Trabecular Segmentation: -A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a threshold-based image segmentation. The gauss_seg function in IPL has been reimplemented in the ORMIR_XCT package and the similarity of IPL and ORMIR_XCT trabecular segmentations were computed using DICE coefficients and Jaccard indices \autoref{fig:trab_seg}. +3.4 - Trabecular Segmentation: +A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a global threshold-based image segmentation. The gauss_seg function in IPL has been reimplemented in the ORMIR_XCT package and the similarity of IPL and ORMIR_XCT trabecular segmentations were computed using DICE coefficients, Jaccard indices, and Hausdorff distances (Table 2\autoref{fig:Table2}). The same dataset used to test the automatic contouring module was used to compare IPL and ORMIR_XCT trabecular segmentation. -![Figure 5: DICE scores and Jaccard indices for trabecular segmentation performed using IPL and the ORMIR_XCT package.\label{fig: trab_seg}](trabecular_segmentation_comparison.png) +![Table 2: DICE scores, Jaccard indices, and Hausdorff distances (voxels) for comparison of trabecular segmentation performed using IPL versus the ORMIR_XCT package. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. For each algorithm, Gaussian smoothing was performed with sigma = 0.5. Subsequently, segmentation was performed with lower and upper thresholds of 1,170 and 10,000 HU, respectively. DIP2: second distal interphalangeal joint, DIP3: third distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table2}](Table2.png) \ No newline at end of file From db4c42a5f1f03f879f1ba9a9cba940842eb3ea75 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 30 Aug 2023 22:04:54 -0600 Subject: [PATCH 04/18] initial commit --- .../notebooks/JOSS_manuscript_Figure1.ipynb | 445 ++++++ .../notebooks/JOSS_manuscript_Figure2_3.ipynb | 192 +++ .../notebooks/JOSS_manuscript_Table1_2.ipynb | 806 +++++++++++ manuscript/notebooks/LinearRegression.py | 39 + .../notebooks/csv/F_CYL_JSW_THICKNESS_IPL.csv | 1001 +++++++++++++ .../F_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv | 1001 +++++++++++++ .../notebooks/csv/F_SPH_JSW_THICKNESS_IPL.csv | 51 + .../F_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv | 51 + .../notebooks/csv/H_CYL_JSW_THICKNESS_IPL.csv | 901 ++++++++++++ .../H_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv | 901 ++++++++++++ .../notebooks/csv/H_SPH_JSW_THICKNESS_IPL.csv | 1225 ++++++++++++++++ .../H_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv | 1225 ++++++++++++++++ .../notebooks/csv/PLATE_JSW_THICKNESS_IPL.csv | 1251 +++++++++++++++++ .../PLATE_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv | 1251 +++++++++++++++++ manuscript/notebooks/csv/autocontour.csv | 21 + manuscript/notebooks/csv/bmd_ipl.csv | 74 + manuscript/notebooks/csv/bmd_py.csv | 74 + manuscript/notebooks/csv/ipl_seg.csv | 21 + manuscript/notebooks/hausdorff.py | 111 ++ manuscript/notebooks/images/TEST_JOINT.nii | Bin 0 -> 38961904 bytes manuscript/notebooks/joss_plots.py | 202 +++ 21 files changed, 10843 insertions(+) create mode 100644 manuscript/notebooks/JOSS_manuscript_Figure1.ipynb create mode 100644 manuscript/notebooks/JOSS_manuscript_Figure2_3.ipynb create mode 100644 manuscript/notebooks/JOSS_manuscript_Table1_2.ipynb create mode 100644 manuscript/notebooks/LinearRegression.py create mode 100644 manuscript/notebooks/csv/F_CYL_JSW_THICKNESS_IPL.csv create mode 100644 manuscript/notebooks/csv/F_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv create mode 100644 manuscript/notebooks/csv/F_SPH_JSW_THICKNESS_IPL.csv create mode 100644 manuscript/notebooks/csv/F_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv create mode 100644 manuscript/notebooks/csv/H_CYL_JSW_THICKNESS_IPL.csv create mode 100644 manuscript/notebooks/csv/H_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv create mode 100644 manuscript/notebooks/csv/H_SPH_JSW_THICKNESS_IPL.csv create mode 100644 manuscript/notebooks/csv/H_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv create mode 100644 manuscript/notebooks/csv/PLATE_JSW_THICKNESS_IPL.csv create mode 100644 manuscript/notebooks/csv/PLATE_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv create mode 100644 manuscript/notebooks/csv/autocontour.csv create mode 100644 manuscript/notebooks/csv/bmd_ipl.csv create mode 100644 manuscript/notebooks/csv/bmd_py.csv create mode 100644 manuscript/notebooks/csv/ipl_seg.csv create mode 100644 manuscript/notebooks/hausdorff.py create mode 100644 manuscript/notebooks/images/TEST_JOINT.nii create mode 100644 manuscript/notebooks/joss_plots.py diff --git a/manuscript/notebooks/JOSS_manuscript_Figure1.ipynb b/manuscript/notebooks/JOSS_manuscript_Figure1.ipynb new file mode 100644 index 0000000..a1b5a16 --- /dev/null +++ b/manuscript/notebooks/JOSS_manuscript_Figure1.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1e340c6", + "metadata": {}, + "source": [ + "# Joint Space Analysis With and Without Oversampling\n", + "\n", + "This Jupyter Notebook demonstrates the effect of the oversampling distance transform function on joint space thickness calculations. An exampe is provided using a 1-voxel thick plate and a sample finger joint." + ] + }, + { + "cell_type": "markdown", + "id": "f1866dfc", + "metadata": {}, + "source": [ + "## Step 1: Imports\n", + "\n", + "Import libraries and load test image." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "816a3861", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import SimpleITK as sitk\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from matplotlib import colors\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", + "from ormir_xct.util.hildebrand_thickness import calc_structure_thickness_statistics\n", + "\n", + "from scipy.ndimage import (\n", + " binary_erosion,\n", + " binary_dilation,\n", + " distance_transform_edt,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8a813c5c", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the image as a NumPy array\n", + "img = sitk.ReadImage(os.path.join(\"images\", \"TEST_JOINT.nii\"))\n", + "arr = sitk.GetArrayFromImage(img)\n", + "\n", + "# Some default parameters used in the IPL JSW workflow\n", + "MISC2 = 27\n", + "CALC = MISC2 + 8" + ] + }, + { + "cell_type": "markdown", + "id": "b023b6dd", + "metadata": {}, + "source": [ + "## Step 2: Plate\n", + "\n", + "Generate the test plate (1-voxel thick) and compute the thickness using the oversampling distance transform function. Then, compute the thickness without oversampling. Print the results to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "fba274ed", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a plate for testing\n", + "# Voxel size: 1x1x1, thickness = 1\n", + "voxel_widths = (1,1,1)\n", + "thickness = 1\n", + "shape = (5,5,5)\n", + "\n", + "center = (voxel_widths[0] * (shape[0] // 2), voxel_widths[1] * (shape[1] // 2), voxel_widths[2] * (shape[2] // 2))\n", + "x, y, z = np.meshgrid(*[voxel_widths[i] * np.arange(0, shape[i]) for i in range(3)], indexing=\"ij\")\n", + "\n", + "mask = (np.abs(x - center[0]) < thickness / 2).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "3a8b7b8f", + "metadata": {}, + "outputs": [], + "source": [ + "# Run thickness calculation (PY)\n", + "plate_oversample = calc_structure_thickness_statistics(mask, 1, 0, mask, oversample=True, skeletonize=False)\n", + "plate_oversample_mask = plate_oversample[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "82ca70dd", + "metadata": {}, + "outputs": [], + "source": [ + "# Run thickness calculation (IPL)\n", + "plate_no_oversample = calc_structure_thickness_statistics(mask, 1, 0, mask, oversample=False, skeletonize=False)\n", + "plate_no_oversample_mask = plate_no_oversample[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "4c73a47e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " JSW.mean (mm) JSW.std (mm) JSW.min (mm) JSW.max (mm)\n", + "Oversampling=True 1.0 0.0 1.0 1.0\n", + "Oversampling=False 2.0 0.0 2.0 2.0\n" + ] + } + ], + "source": [ + "# Print the results\n", + "overample_data = [plate_oversample[0:4], plate_no_oversample[0:4]]\n", + "df = pd.DataFrame(overample_data, columns=[\"JSW.mean (mm)\", \"JSW.std (mm)\", \"JSW.min (mm)\", \"JSW.max (mm)\"])\n", + "df.index= [\"Oversampling=True\", \"Oversampling=False\"]\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "6124fcc3", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the oversampled image\n", + "shape = [2 * s - 1 for s in mask.shape]\n", + "upsampled_mask = np.zeros(shape, dtype=mask.dtype)\n", + "upsampled_mask[tuple([slice(None, None, 2)] * len(mask.shape))] = mask\n", + "upsampled_mask = binary_dilation(upsampled_mask, structure=np.ones(tuple([3] * len(mask.shape)))).astype(int)\n", + "upsampled_mask = binary_erosion(\n", + " np.pad(upsampled_mask, 1, mode=\"edge\"),\n", + " structure=np.ones(tuple([3] * len(mask.shape))),\n", + ").astype(int)[tuple([slice(1, -1)] * len(mask.shape))]\n", + "\n", + "# Run the distance transform on oversampled mask\n", + "oversampled_mask = upsampled_mask * distance_transform_edt(upsampled_mask, 1 / 2)" + ] + }, + { + "cell_type": "markdown", + "id": "5be0a79c", + "metadata": {}, + "source": [ + "## Step 3: JSW Analysis\n", + "\n", + "Next, compute the joint space thickness of the sample finger joint. Compute thickness with and without using the oversampling distance transform function." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "60e3f6f0", + "metadata": {}, + "outputs": [], + "source": [ + "# JSW Analysis Step 1: Image Padding\n", + "pad_image = sitk.ConstantPad(img, [MISC2, MISC2, 0], [MISC2, MISC2, 0], 0)\n", + "pad_image = sitk.BinaryThreshold(pad_image, 1, 127, 60, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "2d88418b", + "metadata": {}, + "outputs": [], + "source": [ + "# JSW Analysis Step 2: Image Dilation\n", + "image = sitk.BinaryThreshold(pad_image, 1, 127, 60, 0)\n", + "dilated_image = sitk.BinaryDilate(image, [MISC2, MISC2, MISC2], sitk.sitkBall, 0, 60)\n", + "\n", + "# Run connected components, sort the components by size, then take only the largest component\n", + "connected_image = sitk.ConnectedComponent(dilated_image, True)\n", + "\n", + "# Sort the components by size\n", + "relabel_image = sitk.RelabelComponent(connected_image)\n", + "\n", + "# Take only the largest component, set it's value to 127\n", + "first_component = sitk.BinaryThreshold(relabel_image, 1, 1, 127, 0)\n", + "filled_holes = sitk.BinaryFillhole(first_component, True, 127)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "f89b546e", + "metadata": {}, + "outputs": [], + "source": [ + "# JSW Analysis Step 3: Image Erosion\n", + "eroded_image = sitk.BinaryErode(filled_holes, [CALC, CALC, CALC], sitk.sitkBall, 0, 127)\n", + "eroded_image = sitk.BinaryThreshold(eroded_image, 127, 127, 30, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "6c368597", + "metadata": {}, + "outputs": [], + "source": [ + "# JSW Analysis Step 4: Extract Joint Space Mask\n", + "\n", + "# Add the eroded image (value = 30) and joint image (value = 60) together.\n", + "# Then threshold out JS image (value = 30)\n", + "add_image = sitk.Add(eroded_image, pad_image)\n", + "add_image = sitk.BinaryThreshold(add_image, 30, 30, 127, 0)\n", + "\n", + "connected_image = sitk.ConnectedComponent(add_image, False)\n", + "relabel_image = sitk.RelabelComponent(connected_image)\n", + "js_mask = sitk.BinaryThreshold(relabel_image, 1, 1, 1, 0)\n", + "\n", + "dilated_js_mask = sitk.BinaryDilate(js_mask, [CALC, CALC, CALC], sitk.sitkBall, 0, 1)\n", + "dilated_js_mask = sitk.Add(dilated_js_mask, pad_image)\n", + "dilated_js_mask = sitk.BinaryThreshold(dilated_js_mask, 1, 1, 1, 0)\n", + "\n", + "mask_np = sitk.GetArrayFromImage(dilated_js_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "389e9a37", + "metadata": {}, + "outputs": [], + "source": [ + "# JSW Analysis Step 5: Get Statistics\n", + "\n", + "# Oversampling = True\n", + "stats_oversample = calc_structure_thickness_statistics(\n", + " mask_np, dilated_js_mask.GetSpacing()[0], 0, \n", + " sitk.GetArrayFromImage(dilated_js_mask), oversample=True, skeletonize=False\n", + ")\n", + "dt_oversample = stats_oversample[4]\n", + "dt_oversample_masked = dt_oversample * (sitk.GetArrayFromImage(js_mask) != 0)\n", + "\n", + "\n", + "# Oversampling = False\n", + "stats_no_oversample = calc_structure_thickness_statistics(\n", + " mask_np, dilated_js_mask.GetSpacing()[0], 0, \n", + " sitk.GetArrayFromImage(dilated_js_mask), oversample=False, skeletonize=False\n", + ")\n", + "dt_no_oversample = stats_no_oversample[4]\n", + "dt_no_oversample_masked = dt_no_oversample * (sitk.GetArrayFromImage(js_mask) != 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a849f4e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " JSW.mean (mm) JSW.std (mm) JSW.min (mm) JSW.max (mm)\n", + "Oversampling=True 2.023954 0.350331 0.060700 2.350131\n", + "Oversampling=False 1.944500 0.448652 0.121401 2.443142\n" + ] + } + ], + "source": [ + "# Print the results\n", + "overample_data = [stats_oversample[0:4], stats_no_oversample[0:4]]\n", + "df = pd.DataFrame(overample_data, columns=[\"JSW.mean (mm)\", \"JSW.std (mm)\", \"JSW.min (mm)\", \"JSW.max (mm)\"])\n", + "df.index= [\"Oversampling=True\", \"Oversampling=False\"]\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "id": "3fd522d8", + "metadata": {}, + "source": [ + "## Step 4: Plotting\n", + "\n", + "Now plot the results. On the top row, we will plot a cross-section of the 1-voxel thick plate, the oversampled plate, and the results of the thickness computation with and without oversampling. On the bottom row, we will plot a cross-section of the sample finger joint, the dilated and oversampled joint space mask that is generated, and the results of the thickness computation with and without oversampling." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "811475e9", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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YWebVaS0qfzTkcma2zd261xUPNquqi5P8oBseWsL6iEWWt1TbcV32w7OAuboL+ds5hq1r/T4DPDPJPWvth+0BV2zvx9I+xyu6Y6iqs5J8CfinJM+lJYFnuoOY8ZuZ+ixF/Om6CjkG+I8kt6J16/Girux/oPVT/Q9VtWag/psz97ZbKrsuMGxUseRPtMT2jecYd5MRlSlJkpaY3T5IkjRiSa4LfIL2vfsfC0y3UXdL8xW65OLPaS3vZm6Jnkkez040zLTiWqvVVpInAtuvV+WbQ7tlv3J2P5gz/XBW1eW0dbxvkrvPXkCSzZIs1NfxhrqMlvy54tym66/yheu7wHX4PObzWWCXJA8bWObMrenQkmET1XUl8QPgLrQHVn1r1iRH0B669URawnL2+Lmcz2iSYKPavz/X/d1n1vCn0PU9O6Tv0X6AeBgtsTrXttyje/1kgS4+Zsx3nK+rddkPf0nrumLzgWl3pj1Ibq76bTOrL96FvKmb5z1Jdhwc0S3jrbQk82vn6OLkI7SuMp5AS75+rIs5AFTVTPcOT0xylSRp1y/vvH34Dkx3rTnW5wRaK+GZz2HO/ZCWHB7ltdW9ktxi5k2SLYG9aQ9+PGbeuTZAVV0GfBm4d9ddyUzZmwNPG0WZkiRp6dnyV5KkpXXHJBfRkgDb0G4B/0fg6sCzF2mVthVwSpJPAT+mPcjqtsBTga93D8aCdgt5AS9Nsg0tMXEkrV/PnwNvTnIDWjcLd6Albn6zvitUVScmeTnwBuD7ST4B/Jn2ILCbAffuJn0pLYH41SQfpCUkNqe1EHtEtx3WrG89FvF/wL8CX0vy4a7cR7FhyZhhP4/5vLGrw8eSvJP2GTwAuB/wzsGHN03Y4bT+VLcBZndl8V1asusutITlWSzuB8DjkrwS+AVwflV9dgnqOar9+9gkHwCenGQL2gPCbkZrhTr0cqvqwiRHAXfuBs1u2XsE7RgB+NQQy/tjkt/THnb2C9ox99uqOnLYOnXWZT/8H1or+sOSfITW5+uzaAnQ2Q9N/AHtYWBv7boHubyqPr7A+pyY5NHAJ4Fjk7yf1u/wtbr67U5rYf+2OWb/LK2P57fSfnj5yBzTPJPWYvjoJP/bLXsb4Oa02HMLrmzlPZ8nAs/tjvlf0a6VHk2LBR/tpvk2rUXsh5K8o6vXPWgPevvzIsvfED8FvtGVeS6tRf4NgMfOeujfUvt34L7AmoH1fTxX/jgxqpb+kiRpiZj8lSRpaT2le11Ku0D/JfAu4L1V9atF5r2A1rflvWjJmc1oDwR6Ay2BA0BV/TrJs2kt995LS4Y8uaoOSPIAWn+ozwM2obVGvActabLequqNSX5DS7D+W7d+v6Ila2am+XOSOwAvo7V+fAItUfBb4O3ATzakDovU71tJHgO8HFhNS9R+krZ91jfJOtTnsUCd/pzkzsDraNtia1ri7QW0z2haHE77zGBWwrKqzkvyE1rSe9gHlL0UuA5tPbeidSexwcnfrg/ikezfwNOBP9ASavei/XBxP9pnvS4OpyV/fznHjwPfobWevhrDb8vH0/qcfTOtn9sP0n7oGdq67IdVtSbJs2itWP+LFr+eQ0ugzk7+rgZ26er4XFpL2HmTv93yP5/klsBLaAnZ5wB/oXWr8I9Vdeg8813YJWSfABxfVT+eY5pfJ7kdLT49nJa0Pqtbh1fRPt/FfJOWhP5H4Hq0GHD8YN26bij26tb/5bQfR74B7Nn9HZXP0WLuS2jdkfwGeHxVfXTBuTZQVR2X5J60ffBltNj6AdpndgitL2tJkjTF0p5rIEmSJEmaJl23G7+ldYfxbxOuzhWS/CstIbxjVZ066fpIkqT52eevJEmSJGlOg31Ad++vQWtZ/XMTv5IkTT+7fZAkSZIkzecXSQ6i9bl9XVr3G39L6x9akiRNOZO/kiRJkqT5fI72YMXtaQ94+wnwwqr6v0lWSpIkDcc+fyVJkiRJkiSph+zzV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSesjkryRJkiRJkiT1kMlfSZIkSZIkSeohk7+SJEmSJEmS1EMmfyVJkiRJkiSph0z+SpIkSZIkSVIPmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5uwwkeWySr0yg3Lsk+WWS85M8dB3nXZPkqSOq2lzlvT7JPkNOe2KSe80zbo8kPx9iGU9KcsQ6VlPrKcmeSU4eeP/9JDefZJ20sm1IjFvfeccdd5LcLMlRSTLEtPslOXCB8ccl2XOI5VSSXdatplpfg9+HSZ6b5I2TrtNKNO5zpgXq4XG8AWafqwwx/XZJfpZk81HWa9olOSDJa7r/hzoP75skt0rynUnXYyUw3vaD8Xb99DHeLsUxneTNSZ65VHWaVssu+Zvkn7uL0fOTnJbki0nuOul6LZUkO3dBfOOZYVX1kaq6zwSq82rgHVW1ZVV9evbI7qLxwu6z+GMXTLZc10IWSsYOOf92wBOA93SJ8vO714VJLh94f/5iy6qqb1XVrutbl+UsyXO6Y+uvSQ6YdH0WsZq2f2oKdEnJnya5IMkfkvxPkm0mXa/lqLsYuKSLWWcn+U6SO63ncua9qBjSfwCrq6oG42gXVy8ceP/YxRZUVTevqjUbWJ9lJ8ktknw5yRlJatL1WcR7gccmue6kK9JH03DOtNKP4+67qpL816zhD+mGHzDmKr0EOKCqLhyoywPTfuD+S5I/J/lIklVjrtfETNt5+IYeM8Oqqp8AZyd50FItcyUz3k6e8Xb6TVu8hascuzOvHcZQ9GrgZUmuPoayJmZZJX+T/CvwVuB1wN8A1wfeBTxknuk3nmu4hnYD4LhFpnlQVW0J3A7YHfi3kdfqqp4EfKGqLuwS5Vt2dbofcOrM+26Y5ncq8Brg/ZOuyBA+A/xDkutNuiIrXZIXAG8EXghcE7gjLXZ8dam/QFdQTP9EF6+2A44ADk0Wb327lJJsD/wD8GmAWXH093Sxv3t9ZJx1W2YuAQ4CnjLpiiymqi4Cvkj7MVWjMdFzJo9jAH4NPHLW98kTgV+MsxJJNu3KPXBg2D8BH6Vd62wL3Bz4K3BEkmstcfkr5ft0gwx7zCzR9vwI8PQlWI4a4+3kGW8x3q6HwWNly6o6ddQFVtVpwM+AB4+6rElaNsnfJNektfR7dlUdWlV/qapLquqzVfXCbpr9khyc5MAk5wJPSrJDks8kOTPJr5I8bWCZt09r6Xhu96vgW7rhm3XL+HNay6sfJPmbeeq1S5JvJjmna9nziYFxf5fkq13ZP0/yyIFxm6c1L/9dN+8RabchHN5Ncnb3S8edMutW3yR37up0Tvf3zgPj1iT5jyTfTnJekq8k2XaB7fq0bruc2W2nHbrhvwb+FvhsV49NF/p8quoU2kXjLeYo40ZJvt5tzzO6X9W26cZ9mJbEnynnRd3wO6a1eDs7yY+z8G0v9wO+uVD95nCbJD/ptuEnkmzWlTu7e4Gdkhya5E9d/d8x18KS/Gf3GV5z5vNKsjrJWUl+m+R+A9NeM8n70lqun5LkNUk26sbNuT+l+a8kp3f760+TXGVbb4juuPo08OeFpkuyafe53GJg2HZpv9Jdt3s/3371P0kOGZjvjUm+1q3fpt02+33a8fjuzHNrTpekOBq474avudZXkq2BVwHPraovdTH5ROCRwM7A49Ji8IVJrj0w3227/XuT7v2/JDmhO16+nOQGA9NWkmcn+SXwy4WOhSQPSPLDbvhJSfYbWM7MXRVP7sadleQZSf6+iwVnDx7f3XH87STv6I7HnyW55wLbYqF1uHc3/zldGUMlcqvqEuCDwPWA68xR5tu6dTk3ydFJ9uiG7wW8DHhUWlz9cTd83tgzh3sDx3TH2rCunuRDad89xyXZfaCuV7SeSbJRkpcl+XU37dFJdppj/e7ard+e3fvqPrNfdp/XO5Mrk+LzfQaL7DP3T3J8V49Tkuy7Duu7qKr6eVW9j8V/SJ2Jj6tnDfu/tB++SXLTtO/4s7vt++Bu+J2742mn7v2tu23wd937Byb5Ua5sSX6rBaqxBnjA+q2thjXhc6bF9P04/gPwU7rzh7TvpjvTflQerPcn0+5kOSfJ4RnoamrY8pI8r5turpZkdwDOrqqTZ9YPeDPwmqr6aNeg4Q/AU4Hzgf+X4c6/5j3eu8/vxUl+Avwlycbd+1O6dfl5uu+5tGuk73bLOS3tu/DqA8uqJM/qPsfz0q49btSVeW6Sg2amT3du3e0vZ3T1mLPlY656Hn5ikn0zxzl7N/5FXf1OTfLUjOkW+IF1enGSPwAfyBxdMw3WJ4uf564B7plFrrm0boy3xluMt8s93l4ryefS8jFndf/P2UI765mb66yh7+fAVbUsXsBewKXAxgtMsx+tlc1DaYntmWTqu4DNgNsAfwLu0U3/XeDx3f9bAnfs/n868FlgC2AjYDdg63nK/Bjw8q68zYC7dsOvAZwEPBnYGLgtcAZws278O2k72I5dGXcGNqUlTGpwPWktW4/o/r82cBbw+G65j+neX6cbv4b2K9tNuvVfA7xhnrrfo6vT7bqy3w4cPjD+ROBeC2zvK8YDO9Eubv9joB5P7f7fhZZI2JTWku1w4K3zldNtkz8D9++2672799vNU48/AX8/x/A9gZPnqff3gR267XkC8IzZ83Sfy4+B/+o+z8HP90m0FnlXo90q+2Vgi4FxlwBP65bxTFqr2nTjPwW8p1vmdbu6PH2R/em+tGTnNrTE0U2B7efZHu8Czp7n9ZMhjrXX0G6LWWia9wOvHXj/bOBLi+1XtGPqF9022qObblU37r9oJwPXBraiHYOvn++zBP4beMukY9NKfrFAXKYlLT/W/f914GkD4/4TeHf3/0OAX3X79Ma0lhnfGZi2gK92+8XmCx0L3X5yy+74uRXwR+Ch3bidu2W9uzu27gNcRGvZel1a3DkduHs3/ZO6dft/wCbAo4BzgGt349dwZYybdx1orQnOA/6pW87/65b71Hm26X7Agd3/m3bb6vcDdTpiYNrH0ZLCGwMvoJ1kbzZ7OQPTzxt75qjHfwLvnGfcicz6bujKu4gWtzcCXg98b655aK3Efwrs2n2Gt+bK77CifWfsRfsOvf2sfeFz3Wd/fVrs32uIz2ChfeY0YI/u/2sBt5tnne/K/HH1bLpYvcCxsgtQi0xzt26dM1CfC2nfVZt06/cy4Oq0OHsesGs37Wtpx9nm3bZ9Tjf8trT9+g7d5/LE7rPYdK7Pkha3z5x0bOnjiyk5Z5pvOd2w/ej3cfwk2rnbP9PusAB4Fi0urnXuA/wL7VxkU1rLsB8NjJuzPNY+h3wFcMx8nwPtvOnzA+//rts2N5xj2lcB3+3+X+j8a5jj/Ue0/W/z7rM7CdihG78zcKPu/91od/Js3A0/Adhn1uf4f8DWXNli7mu0hiPXBI4HnjiwXS4F3tJtz7sDf+HK+HUALQmz1jYcqPN85+x70b73bk47vzywq9cu82zzDT0/PpEr9/+ZdXpjt06bM+s7evBY6P6f9zx3YPpzgVtNOl4t9xfGW+Pt2nUx3i6TeMvcx8p1gH/syt0K+CTw6YHxa7jymF6v3Fw3zcNpDV8mHsNG9Zp4BYauKDwW+MMi0+zH2snLnYDLgK0Ghr2eLtjQAvyrgG1nLedfgO8wxJcv8CFgf7oE1sDwRwHfmjXsPcAru53xQuDWcyxvZxZO/j4e+P6seb4LPKn7fw3wbwPjnkUXpOYo633Amwbeb0lLWu7cvb/KwTdr/hNpv46dDfyuO8g3H6jHfMmNhwI/nLWcwS/WFwMfnjXPl+mC2hzLuwT4uzmG78n8yd/HDbx/E1cmoq6YB7gT7UtyrsTWk4AjgU8AhwBXnzXuVwPvt+g+0+vRuiv568x26sY/BvjGIvvTPWhJ0zsCVxvxsTZM8vdewK8H3n8beMKQ+9UdgDO7feYx3bDQvphuNDDfnYDfzvdZ0pId7x/ltvC16L7yOOaJy8AbgK92/z8V+PrAZ30ScLfu/ReBpwzMdzXgAuAG3fui+8Guez/0sUA7gfyv7v+du2XtODD+z8CjBt4fQney1R3HV/xo0w37Plf+YLiGK0805l0H2i30gyfzAU5m4eTvxbS4ejotobfbQJ2OWGB9z6L7XmFW8pdFYs8cy3ov8/9weCJzX8QcNvD+ZsCFc80D/Bx4yDzLLuCltPhwiznG3XXg/UHAS4b4DObdZ2i3Yj6deX7gXcJjZZjkb7r6zBwbT+PK42YP2kn31Qam/xiwX/f/JrQLtZ8CX+LKBPL/0F3wDsz3c678kWOtzxK4MXDZKLfFSn0xJedM8y2nG9br45grkxGb034cvCbwPeAuLHDuQ0uAFHDNhcqjnaucQrvoPmJm+nmW+XLg4wPv79qVsdkc0z4D+GX3/0LnX8Mc7/8yMG4X2vfMvYBNFtl2+wCfmvU53mXg/dHAiwfev5kuicaVyYhrzPrc/737/wAWTkbMd87+fgaSp936FPMkIzb0xVWTvxcPfl4skPxlkfPcgWGn0H0H+Nrgz8p4a7ydmdZ4u0ziLWsfu2czkOQdmOY2wFkD79dw5TXZOufmBt7fG/jNUq7PtL2WTbcPtIv0bbN4nyknDfy/A60Fy3kDw35H+9UOWh98NwF+ltZ9wgO74R+mBfKPd83a35Rkk7QnIs50PD1zC+eLaF/o30+7XeNfuuE3AO7QNd8/O8nZtAT29WgtwTajtdBdVzt06zBocJ2gXSDOuICWfFt0WVV1Pm077zjP9HN5aFVtU1U3qKpn1UAn6jOS/E2Sj3e3OZxL+6Vo3q4oaNvuEbO23V2B7eeZ/izar0DrYphttBPwu6q6dJ5l7EL71fRVVXXxfMuvqgu6f7ekrdsmwGkD6/YeWis8mGd/qqqvA++gtRg/Pcn+abfcT8o3gC2S3CHJzrQg/Klu3IL7VVUdCfyGtp4HdZNtR0uSHz2wXb7UDZ/PVrQvBU3OGcwfl7fvxkNLqt4prR/ZuwGXA9/qxt0AeNvA534mbd8YjENXxPWFjoVuf/xGd1vQObQTuNmx5o8D/184x/vBWHBKdWcDnd/R9u/ZFlqHHWbVv1j7e2ouB3Vx9bpVdY+qOnquibpbs07obm06m3ZiPV9sXSz2zLYUcXWzefaNnVj4+28f2jY4dogyZj6veT+DReLnP9Ja3fyuu01snR+ut1S6fePjtKQ8tNYyM/0C7gCcVFWXD8xyxXd/tS5CDqDd1vrmgf32BsALZn2f7sTc+zG0z/ycJVkhzWUazpkW0/vjuNvun6e1dLtOVX17cHzaLdZvSLvF+lzaxShc+TksVN42wN60i+SFjqXZMXbm+3Kuz23w+3Sh869hjvfB76Nf0T6n/Wjb9OO5spuum3S31v6h2wavY8O+T8+qqr8MvJ/v+3Qu8+0va32/svh361L7Uw3fNdKw57me2y4d4+3cZRhvjbcLmYZ4O3PsblNVD02yRZL3pHWXei6tAec2mbvruvXJzc3offxdTsnf79JaLT10kekGL9RPBa6dZPBgvz7tVyKq6pdV9Rjaxe8bgYOTXKNav5Wvqqqb0bpjeCDtV55v1ZUdT9+8W8YfquppVbUD7Vepd6X1fXIS8M2BHXebbr5n0gLKRcCNFqn/XE6l7byDrlindbTWspJcg9asfn2WtZDX0dbrllW1Na214GCfl7PX+STar6qD2+4aVfWGeZb/E1oSf6mdBFx/ni9haLdCPBn4YpJhn5R5Em0/3nZg3bYeYn+iqv67qnaj/TJ8E9otQFeR1ofY+fO8Fu13chhVdRktcfuY7vW5gR9ZFtyvkjybdhvKqbQADe2YuBC4+cB2uWYt/JC+m9K65dDkzMTlhw8OTHui8v1otyRRVWcBX6H96vrPtF/fZ477k2hdDwwe75tX1XcGFrlWjFjgWPgo7ZbKnarqmrQuHjbkQWk7Jms9aO36tP12toXW4TTaySBwRT9jV+mnbV2l9e/7Ilr/yteqqm1oSbuZ+s4VV+eNPXMYVVydqctc338zHgE8NMnz13GZ8+5H8+0zVfWDqnoI7Tzg01z5g9RasvaPv3O99liHui7kY8A/pfW7dwfaDyfQ9rudkgyet13x3Z9kR9qdRR8A3pwr+4w8iXbL4uB22aKqPjZP+cbVyRv1OdNSWXbH8SwfonWXc+Ac4/6Z9uP+vWg/qu3cDc8Q5Z1Fu274QJK7LFD+7Bj7c9pdIY8YnKg75v+RK79PFzr/GuZ4n/19+tGquivtvK1o10PQWrX9DLhxtx++jA37Pr1Wdz44Y77v03VxGjDY7+OC360jOD+efSz+hZbgnSlvMKmw6HluF8evTtsXNB7GW+PtFYy3C5pkvH0BrduMO3Tb524zxcyecD1zczN6fw68bJK/1X7NeQXwziQzvwBskuR+Sd40zzwn0bpveH3aQ9xuRWvteyBAkscl2a5aS5qzu9kuT/IPSW7Z/ZpwLu2W9cuvWgIkeUSu7HD6LNqBfDmtf52bJHl8V89N0h4sdNOuvPcDb0l7GNJGaQ9225TWzcDltD5c5vKFbrn/nNZx+KNoQflzw23JtXwMeHKS23Rlvw44stoDm5bSVrTm++d0Jzazk5Z/ZO31PRB4UJL7dttms7QOyefs2Ju2Te6+xHWGdov3acAbklyjq8daXyxdgH8ZcFiShb6UZ6Y/jZYEe3OSrZNcLa3D9rvD/PtTt+/cIe0BWX+h/Xgw5z5ZVc+otZ+QOfiaL9FDtz9tRuu3aGa7L9TS/qO0ZN5ju/9nzLtfJbkJ7Vafx9G6MHlRktt0x8R7gf/KlZ3o75hkzge6dfXcjdYXrCaki8uvAt6eZK8uzu1MO1E6mXYXxYyP0rpA+CfW3l/eDbw03cMd0h5KttbJ2KBFjoWtaHd7XJTk9rQTyg1xXeB53Xo9gnZS8IU5pltoHT4P3DzJw7vj6Xms/Svz+tqKdlvXn4CNk7yC1hfYjD8CO88kCxeLPXP4KnC7DDzoYQn9L/AfSW6c5lZJBh9odypwT+D5SZ459yKuYt7PYL59JsnVkzw2yTWrtZw9l/nj6rcWiKtbVtW35pqvW7/NaBf0dHF13of5VNUPaUmC/wW+XFVnd6OOpLXAeFG3P+4JPIh2h1JorX7fRzvHOQ34j26+9wLP6NY/3XfZA7L2j+KD7k67FVSTM+pzpqWy7I7jWb5Ju8Xz7XOM24r2Y9mfacm8182MGKa8qlpDOzc6tPsumsv3aS2XZlrvF7Av8G/dOf5macnD/6XF9v8amHe+8691Ot6T7JrkHl1MuoiWnBz8Pj0XOD/t4ZHDfoYLeVW3/fagJWw+uYHLO4h2vnnTJFsA/77QxOt7frwOfkz7vr9NF/f3Gyh7mPPcu9O6+vnrEtRFwzHeGm+Nt8OZZLzdira9zk57aOAr55sw65GbG5i99+fAyyb5C1BVbwb+lXbbwJ9oGfzn0H4Fms9jaL8gnUprpv/KqjqsG7cXcFyS84G3AY+udmvC9YCDaQfhCbSA9WHm9vfAkd0yPgM8v6p+0/0qdB/g0V3Zf+DKhwJACzg/BX5Au83ijbQ+dS6g9WX67bQm6XectQ3+TDuAX0ALki8CHlhVZ7COuu3w77SWRafRflF89LouZwivoj1E5hxaIuTQWeNfTwu+ZyfZt0vaP4SWVJ35nF/I/Pvrh4D7Z+0n5m6w7te+B9G6d/g9LZn1qDmm+yDwauDraYmvxTyBlgg4nhaUDubK207m3J9oX0Tv7ab/He2z/8/1Xbd5/BstsL6Elpy9sBs2p2rdN/yFdhvIFweGz7lfpSW+DgTeWFU/rqpf0j7jD3dfhC+mPXjge2m3dBxG+5VvLg8C1lTVhv6KqQ1UVW+ifY6raTHzSNoxe89ZFzCfofUn+oeq+vHA/J+ixb+Pd5/7sbRWw/NZ6Fh4FvDqJOfRfiwcpjXCQo7s6nwGLS7/UxeD17LQOnSx+RG0PpD/3C3v27OXsR6+TLtl9Be07XARa9+CNXOS9+ckx3T/LxR7Zq/TH2n9DT9kCeo621ton81XaPvM+2h9wg2W/3vahcxLkjx1sQUush8ttM88Hjixm+cZtJP7pXQDWiydaeVwIYu36voorQXMFRcZ1boWehBtnc6g9V/4hKr6Ge0HhevS+nMr2h0pT06yR1UdRes7+B209f8VrR++q+iSFfenPaxRkzPqc6alsqyP42q+VlVnzjH6Q10Zp9Di5fdmjV+0vKr6Ku0ZIp9Ncrs5xl9M+9HmcQPDPtEt+/9163c8bZveZfC7Z4Hzr6GP986mtO+mM2jXKdel9R0K7Trln2kPlnwv7RkXG+IPXZ1OpXVn84wufq23qvoi7eG/36A7f+xGTSR5WlW/oF0PHAb8ktYP6aDFznMfS0vIaXyMt8Zb4+0QJhxv30r7bM7oyv3SAtOuV24urWvCm7FwXnHZm3kgiLSsJXkdcHpVvXXSddHoJTmS9pCCufq2kjZYkifRHh5w10nXZVKS3IyWCLx9ebLQe0meS+sy5UWLTixpgyXZjtb//W1rjv5I+yLtToUDq2qkrSO7FlzHApvW/M/rmEppd6e+p6om1u+81GfG2yUvZ9nG27kkeTPt4X7vmnRdRsnkryRJs5j8lSRpw40yGZHkYbTumLag/Vh5eVU9dKnLkaTlwHirhSyrbh8kSZIkSaI90Od04NfAZSxNX5nLWtd/6feT/DjtafevmnSdJPWC8XYeyyXu2vJXkiRJkqRlLkmAa1TV+d0Du46g9Xs5uy9VSdISWC5xd+NJV0CSJEmSJG2Yro/+87u3m3QvW3tJ0ogsl7g7VcnfLbbYoi68sJ/9b2+//facdtppk67GyLh+y1vf1w+gqjLpOkyjccbdce9nlmd5lje58sC4u676ch7cl3MK12O69GU9YO3YuNdee9UZZ5wx9LxHH330ccBFA4P2r6r9B6dJshFwNLAL8M6qOnLDatxfxt3p4npMl76sBxh3AaiqqXmtWrWqaBny3r1Wr1498Tq4fq7fSl0/aD/I+Zps3B33fmZ5lmd5kysPjLvTHI/7tq+5Hq7HcnoNHve77bZbrQvgqGFjCrAN8A3gFpOOb9P6Mu5O18v1mK5XX9YDjLtV5QPfJEmSJEnqk6o6m5aE2GvCVZGkFWGa467JX0mSJEmSlrkk2yXZpvt/c+DewM8mWilJ6rHlEnenqs9fSZIkSZK0XrYHPtj1P3k14KCq+tyE6yRJfbYs4q7JX0mSJEmSlrmq+glw20nXQ5JWiuUSd+32QZIkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSeiTJ85Mcm+S4JPtMuj6SJEmSJGlyTP5KUk8kuQXwNOD2wK2BBybZZbK1kiRJkiRJk2LyV5L646bAkVV1QVVdCnwTePiE6yRJkiRJkiZk40lXQJK0ZI4FXpvkOsCFwP2Bo2ZPlGRvYG+AbbfdltWrV4+lcqtWrRpbWZZneZY32fL23XffsZUlSZIkaX4mfyWpJ6rqhCRvBL4C/AX4EXDZHNPtD+wPsNNOO9W4kjSrV68ea0LI8izP8iZXniRJkqTpYLcPktQjVfW+qtqtqu4GnAX8YtJ1kiRJkiRJk2HLX0nqkSTXrarTk1yf1t/vHSddJ0mSJEmSNBkmfyWpXw7p+vy9BHh2VZ094fpIkiRJkqQJMfkrST1SVXtMug6SJEmSJGk62OevJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSemjkyd8kGyX5YZLPjbosSVrpkvy/JMclOTbJx5JsNuk6SdJK5XmwJI2XcVeSrmocLX+fD5wwhnIkaUVLsiPwPGD3qroFsBHw6MnWSpJWNM+DJWm8jLuSNMtIk79JVgEPAP53lOVIkq6wMbB5ko2BLYBTJ1wfSVqRPA+WpPEy7krS3FJVo1t4cjDwemArYN+qeuAc0+wN7A2w7bbb7vaSl7xkZPWZpFWrVnHyySdPuhoj4/otb31fv3333ZeqyqTrMQ5Jng+8FrgQ+EpVPXaOaSYSd8e9n1me5Vne5MpbSXF3Piv1PLgv5xSux3Tpy3rMjo277757HXXUUUPPn+Toqtp9JJXrAePu8uZ6TJe+rIdxt1NVI3kBDwTe1f2/J/C5xeZZtWpVAb18rV69euJ1cP1cv5W6fkCNKtZN0wu4FvB1YDtgE+DTwOOmJe6Oez+zPMuzvMmVBysj7i4Qj1fseXBfzilcj+l69WU9YO3YuNtuu9W6AI6adHyb1hfG3WX/cj2m69WX9QDjblWNtNuHuwAPTnIi8HHgHkkOHGF5krTS3Qv4bVX9qaouAQ4F7jzhOknSSuR5sCSNl3FXkuYxsuRvVb20qlZV1c60Bw59vaoeN6ryJEn8Hrhjki2SBLgnPvBCksbO82BJGi/jriTNb6QPfJMkjU9VHQkcDBwD/JQW4/efaKUkSZIkSdLEbDyOQqpqDbBmHGVJ0kpWVa8EXjnpekiSGs+DJWm8jLuStDZb/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSesjkryRJkiRJkiT1kMlfSZIkSZIkSeohk7+SJEmSJEmS1EMmfyVJkiRJkiSph0z+SpIkSZIkSVIPmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KUk8k2TXJjwZe5ybZZ9L1kiRJkiRJk7HxpCsgSVoaVfVz4DYASTYCTgE+Nck6SZIkSZKkybHlryT10z2BX1fV7yZdEUmSJEmSNBm2/JWkfno08LG5RiTZG9gbYNttt2X16tVjqdCqVavGVpblWZ7lTba8fffdd2xlSZIkSZqfyV9J6pkkVwceDLx0rvFVtT+wP8BOO+1U40rSrF69eqwJIcuzPMubXHmSJEmSpoPdPkhS/9wPOKaq/jjpikiSJEmSpMkx+StJ/fMY5unyQZIkSZIkrRwmfyWpR5JcA7g3cOik6yJJkiRJkibL5K8k9UhV/aWqrlNV50y6LpIkSRqfJDsl+UaS45Mcl+T5k66TJPXZcom76/TAt65F2UVVddmI6iNJkiRJktbdpcALquqYJFsBRyf5alUdP98MXuNL0gZZFnF3wZa/Sa6W5J+TfD7J6cDPgNO6jPZ/JtllPNWUJEmSJEnzqarTquqY7v/zgBOAHQen8RpfkpbOcom7i7X8/QZwGPBS4NiquhwgybWBfwDemORTVXXgaKspSZIkSVJ/nHrqqbzqVa9al1m2TXLUwPv9q2r/uSZMsjNwW+DIWaO8xpe0Yq3UuLtY8vdeVXXJ7IFVdSZwCHBIkk1GUjNJkiRJkjTjjKrafbGJkmxJu17fp6rOnTXaa3xJGl4v4u6C3T7MVC7JjZJs2v2/Z5LnJdlmcBpJkiRJkjQ5XQLhEOAjVXXo7PFe40vS0loOcXfB5O+AQ4DLun4o9gd2Aj46slpJkiRJkqShJQnwPuCEqnrLIpN7jS9JG2i5xN1hk7+XV9WlwMOAt1fVC4HtR1ctSZIkSZK0Du4CPB64R5Ifda/7zzOt1/iStOGWRdxdrM/fGZckeQzwROBB3TD7AZIkSZIkaQpU1RFAhpzca3xJ2kDLJe4O2/L3ycCdgNdW1W+T3BD48OiqJUmSJEmSRsRrfEkar4nF3aFa/lbV8cDzBt7/FnjjqColSZIkSZJGw2t8SRqvScbdBZO/SX4K1FyjgKqqW42kVpIkSZIkaUl5jS9J4zUNcXexlr8PHHUFJEmSJEnSWHiNL0njNfG4u2Dyt6p+N/N/khsAN66qw5Jsvti8kiRJkiRpeniNL0njNQ1xd6gHviV5GnAw8J5u0Crg0yOqkyRJkiRJGhGv8SVpvCYZd4dK/gLPBu4CnAtQVb8ErjuqSkmSJEmSpJHxGl+SxmticXfY5O9fq+rimTdJNmbuzoolSZIkSdJ08xpfksZrYnF32OTvN5O8DNg8yb2BTwKfXWiGJJsl+X6SHyc5LsmrNrSykqSFJdkmycFJfpbkhCR3mnSdJGml8TxY0jKwztf408y4K2kZmFjcHTb5+xLgT8BPgacDX6iqly8yz1+Be1TVrYHbAHslueP6VlSSNJS3AV+qqr8Dbg2cMOH6SNJK5HmwpGm3Ptf408y4K2naTSzuDvtUuXtU1XuB984MSPLEqvrgfDNUVQHnd2836V4LNmfeYostaLP1z5o1a3q7buD6LXd9X7/dd9990lUYiyTXBO4GPAmgu6Xk4oXmkSQtvfU5D95kk03Yb7/9Rlyz0dthhx1cjyniekyX97znPYtPND7rfI0/zVZy/qEv13Kux3Tpy3pMWS5gYnE3w3yYSQ4HjgNeAGwF/C+tr4p/WmS+jYCjgV2Ad1bVi+eYZm9gb4Dttttut4MOOmhd12FZOP/889lyyy0nXY2Rcf2Wt76v37777stRRx2VSddj1JLcBtgfOJ7W6vdo4PlV9ZdZ010Rd7fddtvdXvKSl4ylfqtWreLkk08eS1mWZ3mWN9ny9t13X6qq93F3Iet6Hrztttvu9t///d/jreQIbLrppvz1r3+ddDU2mOsxXfqyHi94wQs49dRTr4iNO+ywQz396U8fev799tvv6KpakkzG+l7jT7OVmn/oy7Wc6zFd+rIes3MBKzXuDtvy9+60yv24e/+KqvrYYjNV1WXAbZJsA3wqyS2q6thZ0+xPS1aw66671p577jlklZaXNWvW0Nd1A9dvuev7+q0gGwO3A55bVUcmeRvt1pJ/H5xoMO7utNNOte+++46lcqtXr2ZcZVme5VneZMvTup8H/+3f/m394he/GH9Fl9hNbnITXI/p4XpoAet1jT/NVmr+oS/Xcq7HdOnLekyZicXdYfv8vRZwe+DXtL50bpBk6NYcVXU28A1gr3WtoCRpaCcDJ1fVkd37g2nJYEnShHgeLGlKbdA1/jQz7kqaUhOLu8Mmf79He4DQXsDfAzsA315ohiTbdb+4kWRz4N7Az9a/qpKkhVTVH4CTkuzaDbonrQsISdIYeR4saRlY52v8aWbclbQMTCzuDtvtw72q6vcAVXUh8Lwkd1tknu2BD3b97lwNOKiqPrf+VZUkDeG5wEeSXB34DfDkCddHklYiz4MlTbv1ucafZsZdSdNuYnF32OTvOUneQuufooBvAq9eaIaq+glw2w2rniRpXVTVj4CpeqSpJK00ngdLWgbW+Rp/mhl3JS0DE4u7w3b78H7gPOARwCOBc4EPjKpSkiRJkiRpZLzGl6TxmljcHbbl742q6h8H3r8qyY9GUB9JkiRJkjRaXuNL0nhNLO4O2/L3wiR3nXmT5C7AhaOpkiRJkiRJGiGv8SVpvCYWd4dt+ftMWufp1+zenwU8cTRVkiRJkiRJI+Q1viSN18Ti7rDJ359W1a2TbA1QVeeOsE6SJEmSJGl0vMaXpPGaWNwdNvn72yRfAj4BfH2E9ZEkjdEWW2xBVY2lrDVr1oytLMuzPMubbHm777772MqSJK0Xr/ElabwmFneH7fP374DDgGfTKvuOwX4qJEmSJEnSsuE1viSN18Ti7lDJ36q6oKoOqqqHA7cFtga+OdKaSZIkSZKkJec1viSN1yTj7rAtf0ly9yTvAo4GNgMeObJaSZIkSZKkkfEaX5LGa1Jxd6g+f5OcCPwQOAh4YVX9ZZSVkiRJkiRJo+E1viSN1yTj7rAPfLuVT/+UJEmSJKkXvMaXpPGaWNxdsNuHJP+W5NrzVS7JPZI8cDRVkyRJkiRJS8VrfEkar2mIu4u1/P0p8NkkFwHHAH+i9UlxY+A2tKfUvW6UFZQkSZIkSUvCa3xJGq+Jx90Fk79V9X/A/yW5MXAXYHvgXOBAYO+qunCUlZMkSZIkSUvDa3xJGq9piLtD9flbVb8EfjniukiSJEmSpBHzGl+SxmuScXfBPn8lSZIkSZIkScvTUC1/JUnLQ5ITgfOAy4BLq2r3ydZIkiRJkiRNislfSeqff6iqMyZdCUmSJEmSNFlDdfuQ5E1Jtk6ySZKvJflTkseNunKSJEmSJGlpeY0vSeM1ybg7bMvf+1TVi5I8DDgReDhwOO3JdJKk6VHAV5IU8J6q2n/2BEn2BvYG2G677VizZs1YKnb++eePrSzLszzLm2x5kqSp5zW+JI3XxOLusMnfmekeAHyyqs5JMqIqSZI2wF2r6pQk1wW+muRnVXX44ARdQnh/gF133bX23HPPsVRszZo1jKssy7M8y5tseZKkqec1viSN18Ti7lDdPgCfS/IzYDfga0m2Ay4aXbUkSeujqk7p/p4OfAq4/WRrJEmSpCnkNb4kjdfE4u5Qyd+qeglwZ2D3qroE+AvwkFFWTJK0bpJcI8lWM/8D9wGOnWytJEmSNG28xpek8Zpk3B32gW+PAC6pqsuS/ButP4odRlozSdK6+hvgiCQ/Br4PfL6qvjThOkmSJGnKeI0vSeM1ybg7bLcP/15V5yW5K3Av4H3A/4yuWpKkdVVVv6mqW3evm1fVayddJ0mSJE0lr/ElabwmFneHTf5e1v19ALB/VX0euPpoqiRJkiRJkkbIa3xJGq+Jxd1hk7+nJHkP8CjgC0k2XYd5JUmSJEnS9PAaX5LGa2Jxd9hCHgl8GbhvVZ0NXBt44agqJUmSJEmSRsZrfEkar4nF3aGSv1V1AXA6cNdu0KXAL0dVKUmSJEmSNBpe40vSeE0y7g6V/E3ySuDFwEu7QZvQnkonSZIkSZKWEa/xJWm8Jhl3h+324WHAg4G/AFTVqcBWo6qUJEmSJEkaGa/xJWm8JhZ3h03+XlxVBRRAkmuMrkqSJEmSJGmEvMaXpPGaWNwdNvl7UPdEum2SPA04DHjv6KolSRqHCy64gCRjeR199NFjK8vyLM/yJl+eJGmqeY0vSeM1sbi78TATVdXqJPcGzgV2BV5RVV8dac0kSZIkSdKS8xpfksZrknF3qOQvQFchvwwkSZIkSVrmvMaXpPGaVNwdqtuHJA9P8ssk5yQ5N8l5Sc4ddeUkSZIkSdLS8hpfksZrknF32Ja/bwIeVFUnjLIykiRJkiRp5LzGl6TxmljcHfaBb3/0S0GSJEmSpF7wGl+SxmticXfYlr9HJfkE8GngrzMDq+rQUVRKkiRJkiSNjNf4kjReE4u7wyZ/twYuAO4zMKwAvxgkSZIkSVpevMaXpPGaWNwdNvn7gqo6c3BAkhuOoD6SJEmSJGm0vMaXpPGaWNwdts/fzybZeuZNkpsCn11ohiQ7JflGkuOTHJfk+RtSUUnScJJslOSHST436bpI0krkebCkZWCdr/GnmXFX0jIwsbg7bPL3dbRKbplkN+Bg4HGLzHMpLat9M+COwLOT3Gz9qypJGtLzAR/gIUmT43mwpGm3Ptf408y4K2naTSzuDtXtQ1V9PskmwFeArYCHVdUvFpnnNOC07v/zkpwA7Agcv2FVliTNJ8kq4AHAa4F/nXB1JGlF8jxY0rRbn2v8aWbclTTtJhl3U1Xzj0zeTut8eMY9gV8DJwJU1fOGKiTZGTgcuEVVnTtr3N7A3gDbbrvtbi95yUuGr/0ysmrVKk4++eRJV2NkXL/lre/rt++++1JVmXQ9xiHJwcDraV8m+1bVA+eYZiJxd9z7meVZnuVNrryVFHcXs9LOg/tyTuF6TJe+rMfs2LjDDjvU05/+9KHn32+//Y6uqt03pA5LdY0/zYy7y5PrMV36sh7G3Waxlr9HzXp/9LoWkGRL4BBgn9mBF6Cq9gf2B9hpp51q3333XdciloXVq1fT13UD12+56/v6rRRJHgicXlVHJ9lzvukmFXfHvZ9ZnuVZ3uTKU7MSz4P7sq+5HtOlL+sxJTb4Gn+aGXeXL9djuvRlPabExOPugsnfqvogQJJrABdV1WXd+42ATRdbeNec+RDgI1V16IZXV5K0gLsAD05yf2AzYOskB1bVcu6/TZKWJc+DJU2jDb3Gn2bGXUnTaBri7rAPfPsasPnA+82BwxaaIUmA9wEnVNVb1q96kqRhVdVLq2pVVe0MPBr4uolfSRo/z4MlLQPrfI0/zYy7kpaBicXdYZO/m1XV+TNvuv+3WGSeuwCPB+6R5Efd6/7rWU9JkiRpufA8WNK0W59r/Glm3JU07SYWdxfr83fGX5LcrqqOAUiyG3DhQjNU1RGAD/qQpAmoqjXAmglXQ5JWJM+DJS0D63yNP82Mu5KWgYnF3WGTv/sAn0xyKi2gXg941KgqJUmSJEmSRmYfvMaXpHHahwnF3aGSv1X1gyR/B+zaDfp5VV0yumpJkiRJkqRR8BpfksZrknF3weRvkntU1deTPHzWqJskwSdoSpIkSZK0PHiNL0njNQ1xd7GWv3cHvg48aI5xBfjFIEmSJEnS8uA1viSN18Tj7oLJ36p6Zff3yaOuiCRJkiRJGh2v8SVpvKYh7g7V52+STYF/BHYenKeqXj2aakmSJEmSpFHwGl+SxmuScXeo5C/wf8A5wNHAX0dXHUmSJEmSNGJe40vSeE0s7g6b/F1VVXuNtCaSJEmSJGkcvMaXpPGaWNy92pDTfSfJLUdaE0mSJEmStF6SvD/J6UmOHWJyr/ElaQMtl7i7YMvfrvKXd9M9OclvaE2TA1RV3Wr0VZQkSZIkSYs4AHgH8KH5JvAaX5KW1AEsg7i7WLcPOwK3GXUlJEmSJEnS+quqw5PsvMhkXuNL0hJZLnF3seTvb6vqd2OpiSRJkiRJK8Rpp53Gfvvtty6zbJvkqIH3+1fV/utYrNf4klaslRp3F0v+XjfJv843sqressT1kSRJkiRJV3VGVe2+gcvwGl+ShteLuLtY8ncjYEtaPxSSJEmSJGn58hpfksZr4nF3seTvaVX16rHURJIkSZIkjZLX+JI0XhOPu1dbZLy/BkqSJEmSNOWSfAz4LrBrkpOTPGWuycZcLUnqreUSdxdr+XvPsdRCkrTBkmwGHA5sSovvB1fVKydbK0mSJI1DVT1miMm8xpekJbJc4u6Cyd+qOnNcFZEkbbC/AveoqvOTbAIckeSLVfW9SVdMkiRJk+c1viSN1zTE3cVa/kqSlomqKuD87u0m3asmVyNJkiRJkjRJJn8lqUeSbAQcDewCvLOqjpxjmr2BvQG23XZbVq9ePZa6rVq1amxlWZ7lWd5ky9t3333HVpYkSZKk+Zn8laQeqarLgNsk2Qb4VJJbVNWxs6bZH9gfYKeddqpxJWlWr1491oSQ5Vme5U2uPEmSJEnT4WqTroAkaelV1dnAN4C9JlwVSZIkSZI0ISZ/JaknkmzXtfglyebAvYGfTbRSkiRJkiRpYuz2QZL6Y3vgg12/v1cDDqqqz024TpIkSZIkaUJM/kpST1TVT4DbTroekiRJkiRpOtjtgyRJkiRJkiT1kMlfSZIkSZIkSeohk7+SJEmSJEmS1EMmfyVJkiRJkiSph0z+SpIkSZIkSVIPmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknpoZMnfJO9PcnqSY0dVhiTpSkl2SvKNJMcnOS7J8yddJ0laqTwXlqTxMu5K0txG2fL3AGCvES5fkrS2S4EXVNXNgDsCz05yswnXSZJWqgPwXFiSxukAjLuSdBUjS/5W1eHAmaNaviRpbVV1WlUd0/1/HnACsONkayVJK5PnwpI0XsZdSZpbqmp0C092Bj5XVbdYYJq9gb0Btt12291e8pKXjKw+k7Rq1SpOPvnkSVdjZFy/5a3v67fvvvtSVZl0Pcapi7+HA7eoqnNnjZtI3B33fmZ5lmd5kytvJcbduSx2LtzH8+C+nFO4HtOlL+sxOzYmWdeL8aOravclrlavGHeXL9djuvRlPYy7naoa2QvYGTh22OlXrVpVQC9fq1evnngdXD/Xb6WuH1CjjHXT9gK2BI4GHj5NcXfc+5nlWZ7lTa48WFlxd4F4vDNDngv35Ty4L+cUrsd0vfqyHrB2bFyP+Y+adFyb9hfG3WX7cj2m69WX9QDjblWNtM9fSdKYJdkEOAT4SFUdOun6SJIkSZKkyTH5K0k9kSTA+4ATquotk66PJEmSJEmarJElf5N8DPgusGuSk5M8ZVRlSZIAuAvweOAeSX7Uve4/6UpJ0krkubAkjZdxV5LmtvGoFlxVjxnVsiVJV1VVRwAr/gFLkjQNPBeWpPEy7krS3Oz2QZIkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSeiLJ+5OcnuTYSddFkiRJkiRNnslfSeqPA4C9Jl0JSZIkSZI0HUz+SlJPVNXhwJmTrockSZIkSZoOG0+6ApKk8UqyN7A3wLbbbsvq1avHUu6qVavGVpblWZ7lTba8fffdd2xlSZIkSZqfyV9JWmGqan9gf4CddtqpxpWkWb169VgTQpZneZY3ufIkSZIkTQe7fZAkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXknoiyceA7wK7Jjk5yVMmXSdJkiRJkjQ59vkrST1RVY+ZdB0kSZIkSdL0sOWvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSemikyd8keyX5eZJfJXnJKMuSJBl3JWlaGI8lTcJKjj0red0lTc5yiD0jS/4m2Qh4J3A/4GbAY5LcbFTlSdJKZ9yVpOlgPJY0CSs59qzkdZc0Ocsl9oyy5e/tgV9V1W+q6mLg48BDRlieJK10xl1Jmg7GY0mTsJJjz0ped0mTsyxiT6pqNAtO/gnYq6qe2r1/PHCHqnrOrOn2Bvbu3t4COHYkFZq8bYEzJl2JEXL9lre+r98Nqmq7SVdi1JZB3B33fmZ5lmd5kytvRcTd+SyDeDxKfTmncD2mS1/WY63YmORLtHUb1mbARQPv96+q/QeWN1Ts6SPjbi+OD9djuvRlPYy7wMaTrkC30fYHSHJUVe0+4SqNRJ/XDVy/5a7v66e1TSrujns/szzLs7zJlafh9PE82PWYLq7HdKuqvSZdh5XGuDu9XI/p0pf1mG2lxt1RdvtwCrDTwPtV3TBJ0mgYdyVpOhiPJU3CSo49K3ndJU3Osog9o0z+/gC4cZIbJrk68GjgMyMsT5JWOuOuJE0H47GkSVjJsWclr7ukyVkWsWdk3T5U1aVJngN8GdgIeH9VHbfIbPsvMn456/O6geu33PV9/VaEZRB3x72fWZ7lWd7kylvRlkE8HiXXY7q4HivIesaeXjDu9oLrMV36sh4jtVzi7sge+CZJkiRJkiRJmpxRdvsgSZIkSZIkSZoQk7+SJEmSJEmS1ENTkfxNsleSnyf5VZKXTLo+SynJ+5OcnuTYSddlFJLslOQbSY5PclyS50+6TkspyWZJvp/kx936vWrSdVpqSTZK8sMkn5t0XTQ+44y7446D445Lk4oT4zx2k5yY5KdJfpTkqDGUt02Sg5P8LMkJSe40wrJ27dZr5nVukn1GWN7/6/aTY5N8LMlmoyqrK+/5XVnHjXK9tGH6cC7cl3Pevpzb9ukc1nNVjYJxd3oYd6ePcbd/Jt7nb5KNgF8A9wZOpj0p7zFVdfxEK7ZEktwNOB/4UFXdYtL1WWpJtge2r6pjkmwFHA08tEefX4BrVNX5STYBjgCeX1Xfm3DVlkySfwV2B7auqgdOuj4avXHH3XHHwXHHpUnFiXEeu0lOBHavqjNGWc5AeR8EvlVV/5v21NwtqursMZS7EXAKcIeq+t0Ilr8jbf+4WVVdmOQg4AtVdcBSl9WVdwvg48DtgYuBLwHPqKpfjaI8rZ++nAv35Zy3L+e2fTqH9VxVS824O12Mu9PHuNs/09Dy9/bAr6rqN1V1Me0i5SETrtOSqarDgTMnXY9RqarTquqY7v/zgBOAHSdbq6VTzfnd2026V2+ekphkFfAA4H8nXReN1Vjj7rjj4Ljj0iTiRJ+P3STXBO4GvA+gqi4eR+K3c0/g16NI/A7YGNg8ycbAFsCpIyzrpsCRVXVBVV0KfBN4+AjL0/rpxblwX855+3Ju25dz2D5/32mijLtTxLg7XYy7/TQNyd8dgZMG3p/MMjzQBUl2Bm4LHDnhqiyp7paHHwGnA1+tqj6t31uBFwGXT7geGq8VE3fHFZcmECfeyniP3QK+kuToJHuPuKwbAn8CPtDdbva/Sa4x4jJnPBr42KgWXlWnAKuB3wOnAedU1VdGVR5wLLBHkusk2QK4P7DTCMvT+lkxMXm5We7ntj05h30rnqtq6Rl3p5Rxdyq8FeNu70xD8lc9kGRL4BBgn6o6d9L1WUpVdVlV3QZYBdy+u4122UvyQOD0qjp60nWRRmGccWmccWJCx+5dq+p2wP2AZ3e3GY7KxsDtgP+pqtsCfwFG3hdf173Eg4FPjrCMa9FaFt0Q2AG4RpLHjaq8qjoBeCPwFVqXDz8CLhtVeVKf9OHcdrmfw3quKq0sxt3JM+721zQkf09h7VYoq7phWia6/mwOAT5SVYdOuj6j0t12/A1grwlXZancBXhw15fnx4F7JDlwslXSmPQ+7k4qLo0pToz92O1arFJVpwOfot0uOSonAycPtJQ4mJYMHrX7AcdU1R9HWMa9gN9W1Z+q6hLgUODOIyyPqnpfVe1WVXcDzqL1cajp0vuYvNz07dx2GZ/Deq6qUTHuThnj7tQw7vbUNCR/fwDcOMkNu1Y3jwY+M+E6aUhdp+bvA06oqrdMuj5LLcl2Sbbp/t+c9lCAn020Ukukql5aVauqamfacff1qhpZCzRNlV7H3XHHpXHHiXEfu0mu0T18g677hfvQuhMYiar6A3BSkl27QfcExvHAj8cwwi4fOr8H7phki24/vSetX7uRSXLd7u/1af39fnSU5Wm99DomLzd9Obftwzms56oaIePuFDHuTg/jbn9NPPnbPYDkOcCXaRdAB1XVcZOt1dJJ8jHgu8CuSU5O8pRJ12mJ3QV4PO0XoR91r/tPulJLaHvgG0l+QjtJ+GpVfW7CdZI2yLjj7gTi4LjjUt/jxN8ARyT5MfB94PNV9aURl/lc4CPdNr0N8LpRFtYlte9Na4k7Ml1r5oOBY4Cf0s7D9h9lmcAhSY4HPgs8e4wPz9OQ+nIu3KNz3r6c2/b9u0lab8bdqWPclUYsVcvu4YOSJEmSJEmSpEVMvOWvJEmSJEmSJGnpmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSesjkryRJkiRJkiT1kMlfSZIkSZIkSeohk7+SJEmSJEmS1EMmfyVJkiRJkiSph0z+SpIkSZIkSVIPmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSesjkryRJkiRJkiT1kMlfSZIkSZIkSeohk7+SJEmSJEmS1EMmfyVJkiRJkiSph0z+SpIkSZIkSVIPmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/JUkSZIkSZKkHjL5K0mSJEmSJEk9ZPJXkiRJkiRJknrI5K8kSZIkSZIk9ZDJX0mSJEmSJEnqIZO/kiRJkiRJktRDJn8lSZIkSZIkqYdM/kqSJEmSJElSD5n8lSRJkiRJkqQeMvkrSZIkSZIkST1k8leSJEmSJEmSesjkryRJkiRJkiT1kMlfSZIkSZIkSeohk7+SJEmSJEmS1EMmfyVJkiRJkiSph0z+SpIkSZIkSVIPmfyVJEmSJEmSpB4y+StJkiRJkiRJPWTyV5IkSZIkSZJ6yOSvJEmSJEmSJPWQyV9JkiRJkiRJ6iGTv5IkSZIkSZLUQyZ/JUmSJEmSJKmHTP5KkiRJkiRJUg+Z/NXYJXlSkiPGUM5xSfYcdTlLIcnOSSrJxpOuiyRJkiRJkvrB5G/PJDkxyYVJzh94vWPS9VoqSQ5I8pphpq2qm1fVmiGXe2KSey0wfs8uOfupWcNv3Q0fqhxJkiRJkiRpXGxl2E8PqqrDJl2JHvoTcKck16mqP3fDngj8YoJ1kiRJkiRJkuZky98VJMn/JDlk4P0bk3wtzbWSfC7Jn5Kc1f2/amDaNUlek+Q7XWvizya5TpKPJDk3yQ+S7DwwfSV5XpLfJDkjyX8mmXN/S/J3Sb6a5MwkP0/yyHVYpwd33Tuc3dXxpgPjrmjNm2S/JAcl+VCS87p5du/GfRi4PvDZbt1eNE9xFwOfBh7dzbcR8CjgI7Pq9LYkJ3Xb5egkewyMu32So7pxf0zylnnW6x+7+t9i2G0hSZIkSZIkDTL5u7K8ALhl1+fuHsBTgCdWVdH2hQ8AN6AlQi8EZncX8Wjg8cCOwI2A73bzXBs4AXjlrOkfBuwO3A54CPAvsyuU5BrAV4GPAtftynhXkpsttjJJbgJ8DNgH2A74Ai2Be/V5Znkw8HFgG+AzM+tXVY8Hfk9rMb1lVb1pgWI/BDyh+/++wLHAqbOm+QFwG9p2+SjwySSbdePeBrytqrambcOD5livJwNvBO5VVccuUBdJkiRJkiRpXiZ/++nTXUvYmdfTAKrqAlry9i3AgcBzq+rkbtyfq+qQqrqgqs4DXgvcfdZyP1BVv66qc4AvAr+uqsOq6lLgk8BtZ03/xqo6s6p+D7wVeMwcdX0gcGJVfaCqLq2qHwKHAI8YYj0fBXy+qr5aVZcAq4HNgTvPM/0RVfWFqroM+DBw6yHKWEtVfQe4dpJdaUngD80xzYHd9ry0qt4MbArs2o2+BNglybZVdX5VfW/W7PsALwT2rKpfrWv9JEmSJEmSpBkmf/vpoVW1zcDrvTMjqupI4DdAGGh1mmSLJO9J8rsk5wKHA9t0XRvM+OPA/xfO8X7LWfU4aeD/3wE7zFHXGwB3GExWA48FrjfEeu7QLXdm3S7vytxxnun/MPD/BcBmSdan3+sPA88B/gH41OyRSfZNckKSc7r1uSawbTf6KcBNgJ91XWU8cNbsLwTeOZOUlyRJkiRJktaXD3xbYZI8m9YS9VTgRcDru1EvoLVOvUNV/SHJbYAf0pLE62sn4Lju/+tz1e4RoCVrv1lV916P5Z8K3HLmTZJ0ZZ6yHsuqdZj2w8CvgA9V1QWt2CvqsAdtu94TOK6qLk9yFt12rKpfAo/p+j9+OHBwkusMLPs+wJeS/KGqDkGSJEmSJElaT7b8XUG6PnJfAzyO1v3Di7okL8BWtNa7Zye5Nlftv3d9vLB7kNxOwPOBT8wxzeeAmyR5fJJNutffDz64bQEHAQ9Ics8km9AS2H8FvrMedf0j8LfDTFhVv6V1ifHyOUZvBVwK/AnYOMkrgK1nRiZ5XJLtulbKZ3eDLx+Y/zhgL+CdSR68rishSZIkSZIkzTD520+fTXL+wOtTXfcGB9L64f1x1wL1ZcCHk2xK65N3c+AM4HvAl5agHv8HHA38CPg88L7ZE3T9C9+H9qC3U2ldM7yR1jp5PtXN+3NaIvvtXb0fRHto28XrUdfXA//WdT2x72ITV9URVTVXS+Yv07bdL2hdUlzE2t1f7AUcl+R82sPfHl1VF85a9o9pfSG/N8n91mNdJEmSJEmSJFK1Lne7S8NJUsCNl/qhZUkOBQ6vqrcu5XIlSZIkSZKkvrHlr5aNJDsCdwWOmnRdJEmSJEmSpGln8lfLQpJn0R5A94GqOmLS9ZEkSZIkSZKmnd0+SJIkSZIkSVIP2fJXkiRJkiRJknrI5K8krSBJ3p3k37v/90xy8hjLriS7LMFyjkuy54bXSABJ1iR56jpM//ok+4ywSlMvyc7d/rxx9/6LSZ446XqNW5JDktxv0vXouyTnJ/nbMZU1b5xO8tgkXxliGfslOXDpa7d8Jblvkk9Puh6TluTEJPfq/n9Zkv+ddJ3GLclzk7xx0vXQ3Iy3y5/xtuljvF2Ka8kk309y86Wq03Ky8aQrMCjJ1PVBsdtuu026CtKydfTRR0+6Cleoqky6DqOW5ETgb4BLgcuA44EPAftX1eUAVfWM9Vz2k4CnVtVdl6SyV13+AcDJVfVvi01bVUN/YXfb5KlVddgC07wMeBqwHXA28O2qetSwZYxatw47ADtU1RkDw38I3Aa4YVWdOKa6bAc8AdhlYNg2wOuBhwFbA78G3lJVHxhHnaZBVU1NArTbn1/Wvd0Y2AS4sHv/u3U5fobwRuB/gC8u4TJXjCRrgAOrasELsqrach2WWcCNq+pXc4zboH2jqj4CfGTYukyjCcbT1wLPGSgvwL7A3sAq4E+0bbtfVf11BOVPnap63aTrMCPJHlwZxwJsAfxlYJKbVdXvl6i49wK/SvLmqjp9iZapRRhvx894Oz2mKd5Ca0QB/Ja14+yvq+rWYyh+NfBq4B/HUNZUmark7zQ66qijJl0Fadlq37UaswdV1WFJrgncHXgbcAfgyZOt1nTqWms+HrhXVf06yfWAB0+4WnP5LfAY4O0ASW5JuzgdtycBX6iqC7t6XB04DDgduBNwMnBP4INJrlVVb1nKwpNsXFWXLuUy+6Y7wX8dLPyjTXcxlJkfhtazrO8n2TrJ7lXlCdOUG3bfWAHGGk+T/D1wzar63sDg/wb2ov2Y9gNgV+ADwM2Ahyxx+cbNRVTVt4AtYa2kxDZzbbcN3Z5VdVGSL9I++9XruxxNN+PtFYy3WsiccXbEPgO8O8n1quoPYy57ouz2QZJ6qKrOqarPAI8CnpjkFtBa2CZ5zVzzJHlJkl8nOS/J8Uke1g2/KfBu4E7d7XBnd8M3TbI6ye+T/LHrUmLzgeW9MMlpSU5N8i/rUv8kD+66dzi765bgpgPjBm9j2i/JQUk+1NX7uCS7d+M+DFwf+GxX7xfNUdTfA1+uql932+0PVbX/QFlr0ro5+H6Sc5P8X5JrD4z/ZJI/JDknyeGDtxEl2TzJm5P8rht/xMz2SXLHJN/p1u/HWbwbiw/TTlpnPJHWqntwmz0gyQ+7ep6UZL+BcZslOTDJn7syf5Dkb+bY7tsn+UmSF85Tj/sB3xx4/3jaNn5EVf22qi6pqi8BzwNe3SUGX5zk4FnlvC3Jf3f/XzPJ+7p95ZQkr0myUTfuSUm+neS/kvwZ2C/JLkm+2W3TM5J8YtZyT+q2wdFprblmxu3XfV4HdvvKT5PcJMlLk5zezXefgekX/Oxnrc8VXWd0dT6iOzbOSvLbDHSNkOSG3b5yXpLDkrwzY7pls6vna5N8G7gA+NvB46mbZq1bSIfYV9cADxhD9XstydOS/CrJmUk+k2SHgXFX3OaYFsPfmeTz3T50ZJIbdeMO72b5cVrMW987GO6V5JfdZ/7OpP2SO7NvD9Tr5km+2tX5j2kt3Wav1yZJPpbWRcjVs0DM7qbfoZv2T92x87yBcbdPclR3PP4xyVu64UPFtwEbGk937j6TvdO+305Lsu8C5a0VN5PcGHgW8Niq+m5VXVpVx9FaIe2V5B5J7pD23bLRwHwPS/KT7v+r5crv7D932/Tas+r3lCS/B76+0DZK8uQkJ3Sfx2+SPH2gzD2TnJzkRWlx8rQkD01y/yS/6D77lw1Mv1+Sg5N8olveMUnmbMk1GGsG6vzEtHOKM5K8fGDazZN8MC2mntDVZyzdVg2s04FJzgWelFnnUpnVjdZC+3FnDcbNiYnxdmZ6463xdtri7e2TfLfbbqcleUdaQ5O5pr1/2vXqeWnXD/sOjHtgkh91y/lOklvNjKuqi4CjgfuOfo2mi8lfSeqxqvo+rTXmHotNS7tdfw/gmsCrgAOTbF9VJwDPAL5bVVtW1Tbd9G8AbkK7dWsXYEfgFQBJ9qLdYnVv4MbAvRhSkpsAHwP2oXXF8AVaAnfOL39aS92PA9vQfs19B0BVPR74Pa019JZV9aY55v0e8IS0RPXugyd+A54A/AuwPa1Ljf8eGPfFbv2uCxzD2rforQZ2A+4MXBt4EXB5kh2BzwOv6YbvCxyS1qXCfL4HbJ3kpl0dHw3MThj+pavrNrSLymcmeWg37om0z3Un4Dq0z/PCwZmT3JB2wvyOqvrPeepxS+DnA+/vDXyxqv4ya7pDgM1orYE/Dtw/yVZdORsBjwQ+2k17AG277gLcFrgPMNgH8R2A39C6NHkt8B/AV4Br0W7fe/vAtD+g7Y/X7pb/ySSbDYx/EO1C5FrAD4Ev086FdqTdAvaeWeux0Ge/kDvQttO2wJuA9yVX3ArxUeD7tM9hP1oCfU5Jrt+duM73+uch6zPo8bRbH7cCfrfQhEPuqycA47hNr7eS3IPWdcojafva72jHzXweTYvR1wJ+RTsuqKq7deNv3cW8T8wz/2IeSPth7FZdna5ygdQdz4cBX6Ld1rsL8LVZ02wOfBr4K/DIqrq4GzVnzE5yNeCzwI9px+Q9gX2SzJT/NuBtVbU1cCPgoG74ovFtlg2NpzP+gRb/7wO8OAM/oswyO27ek9bN0fcHJ6qqk7q63buqjuzqcI+BSf6ZK+Pmc4GH0u7w2QE4C3jnrHLvDtyU9vkttI1Op33mW9PuEvqvJLcbWM71aPF85jv+vcDjaN9vewD/3n1/zHgI8EmujMOfTrLJPNtmtrvSWuXdE3hFrvzh95XAzsDf0r53HrfQQtJ+xJwvbr5ryLoMeghwMG1/WPBW/CH2YzBuTozx1ngLxtvONMbby4D/Rzt/vlNXt2fNM+37gKdX1VbALYCvd/W5LfB+4Om07f8e4DNJNh2Yd0XGYJO/ktR/p9JOChZUVZ+sqlOr6vLuJPaXwO3nmrZLZO0N/L+qOrOqzqPd3vbobpJHAh+oqmO7xOB+61DfRwGfr6qvVtUltCTq5rQk6lyOqKovVNVltMTe0F/mVXUg7aTuvrTE5+lJXjxrsg8PrMe/A4+cSRJX1fur6ryuz7D9gFuntWS9Gi1p+PyqOqWqLquq73TTPY7WdcIXum39VeAo4P6LVHem9cS9aSctp8xalzVV9dNumT+hJdDv3o2+hHYCtEtXl6Or6tyB2W8GfAN45WDL5zlsA5w38H5b4LTZE3W3cJ0BbFtVv6Mlxh/Wjb4HcEFVfS+tJcT9gX2q6i9d/4f/xZX7EcCpVfX2rrXGhd263IDWh9xFVXXEQLkHVtWfu2nfDGxKO7Gd8a2q+nJXv0/Sflx4Q7effRzYOa0P4xnzfvaL+F1VvbfbJz9Iu8D8myTXp13kvaKqLu7q/pn5FlJVv6+qbRZ4fXS+eRdwQFUd122jSxaZdph99TzafqH191jg/VV1TBcjXkq702Lneab/VFV9v9uPP0L7wWMpvaGqzq7Wx+k35ln+A4E/VNWbu+PwvO4CesbWtETFr4End8fCjPli9t8D21XVq7vj4ze0C9+ZeHAJsEuSbavq/Lrytt7F4ttcNiSeznhVF7d+SruF+DHzlLUNQ8TNzmndeLoyHwNXJH/u3w2Dlkx4eVWdPPD980/pHkLZ2a+r30zcnHMbVdXnq+rX1XyT9uPa4A/GlwCvHYiT29KSQudVa0F3PGt/7x5dVQd307+Flsi44zzrO9urqurCqvoxLSk1s9xHAq+rqrOq6mQW+SGuqm61QNycL5GwkO9W1ae7/WGhRBcsvh9D2x+uuR710IYz3jbGW+PtNMTbMwYSxft22+p71c5RT6QlbmfvCzMuAW6WZOuursd0w/cG3lNVR3bb/4O0H0UGt8uKPHc1+StJ/bcjcOZiEyV5wsAtMmfTfkXddp7Jt6P12XX0wPRf6oZD+2X8pIHpF2xhOMsOg9NX65P0pG495jLYX9MFwGazTsgWVFUfqap70U4CngH8x6wWOrPXYxNg2yQbJXlD2m1g5wIndtNs2702o52Iz3YD4BGDv4zTfn3ffpGqfpjWEuFJzLplDiDttrVvpN2+d063LtsOzPtl4ONpt829aVbLgMfSTsYPZmFn0VqMzjhjrnp323/bbjy01ggzJ+qDrSluQNuepw1si/fQWlLPGNz+0FpQB/h+2i2MV3QpkmTftFvUzumWdU3W3of/OPD/hcAZAxdJMxf0gw97mfOzn72+c7hin6yqCwaWuwNw5sCwudZv1NalvGH21a1oD0rU+psd884H/szwMW/oBxQNaZjl78Tc8W3GHWkt2d5QVbMf6DxfzL4BsMOs/e1ltFb/AE+h3W3ys7TbaB/YDV8svs1lQ+LpjNnxYQfmNlTc7GzP2nHz4V1rpYcDx1T7MQ3atvrUwHY6gdZiavD268H6zbuNktwvyffSbik+m5b0GFzXP88RJ2fH0jnjZvf9fTLzb5vZ5tv3Zp9TTHvcXGg/hrY/nLOE9dPwjLfG2xnG2ytNKt5uO5AoXp3WHdvn0rrhOJfWsGi+8+5/pG2/36V1B3enbvgNgBfM2rd3Yu3tsiLPXU3+LuCqsVOSlpe0Bx/sCByxyHQ3oP3i/xzgOtW6djiWlmQDmB0Qz6CdgNx84Ev7mnXlU5JPo33Rzrj+OlT7VNoX90zd0i3rlHnnmN/Qgbxaf7WfBH5CS3zPmL0el9DW/59pt1vdi5Zk3Hmmyt34i2i3ys12Eq1F6eAv49eoqjcsUr/f0R6ccX/g0Dkm+SitFelOVXVNWj/NGVi3V1XVzWgtqB/I2n2w7dfV+aOLtGz9Ce1iYMZhwP2SXGPWdP9I+5V9pqXIJ4E9k6yitQCeSf6e1E03ePK3da39FOy1PsNq/TI/rap2oN3S9a60foD3oCWGHwlcq9uHz+HKfXh9zPfZr6/TgGsnGXzYyU7zTZzW7cP5C7weux51mH1M/IW1H75yvYH/h9lXb0prMaL1NzvmXYPWamh9Yt64nES7JXQ+X6HdWv21LNwf5Oxl/nbW/rZVVd0foKp+WVWPof049Ebg4CTXGCK+XcWGxNMBs+PDqfMUNztufh3YKclad9Yk2YmWxPlaV8fjaUmO+7H2j2bQttX9Zm2rzapqcJ+54lifbxt1iY5DaHfY/E0XN78wx7quiyu2S9pdMKuYf9sM67RuOVcpYy7dD4Pzxc13r0f56xo3592PO8bNyTHeXrlM463xdi6TjLf/A/wMuHG1Lkdexjzbp6p+UFUPoe2jn+bKrklOorWeHvy8tqiqjw3MviJjsMlfSeqhtAdtPZB2y9CB1W6TWsg1aCcuf+rmfzJrJ0D/CKxK1+9u9+vye2l9VV23m2fHXNli9iDaQ1Fu1iW6XrkO1T8IeECSe3a/lL+AliD8zjosY7De854wpz1Q4wFJtkp7oMP9gJsDg7fTPW5gPV4NHNz9Kr9VV68/0y4CXzczQ7d93g+8Je2BGhsluVN34ncg8KAk9+2Gb5b2kIfBE635PAW4R121j126+pxZ7Unit6edvM6s5z8kuWWX2D2XlsS8fGDeS4BH0PaDD3UnkHP5AmvffvVhWiuDT6Y9QGKTbh/4b9otcOd02+NPtAfcfIB2sXFCN/w02kXLm7t99mpJbpRkvlu8SPKIgW11Fm2/vbxb/0tp+/DGSV5BuxVyQ8z32a+X7gLoKNqD667etVJ40ALT/75aX4LzvRbse3JIPwIe3X12uwP/NDBumH317rS+r7X+PgY8OcltuhjxOuDIarc8rqsFY94S+hywfZJ90h7+uVWSOwxOUK2f9Y/SEhLDtJj/PnBe2kMiN+/2uVt0P2KS5HFJtuvi69ndPJcPEd/ms17xdMC/J9ki7UGfTwbm6/NzrbhZVb+gJTc+kvZAxY26ZRwCHFZVhw3M+1Hg+cDdaD+izXg38Nruh1uSbJdk3qfWL7CNrk7rHudPwKXdd+B95lvOkHZL8vC01oX7sPYPgevrIOClSa6V1hf5cxaauKpuvkDcfMYG1gVa3Lx/kmsnuR5tPWcsuB93jJuTY7xtjLfG2/lMMt5uRdtm5yf5O+CZc03UnUM/Nsk1q3V5cS5X7ofvBZ6R1qI8Sa6R7lqvm3czWh/KX13Hui17Jn8lqV8+m+Q82q+eL6f1//TkxWbqfvF+M/Bd2snsLYFvD0zydeA44A9JZlo+vpj28Ivvpd2acxhd/6pV9UXgrd18v+r+LlqNbt6f0/oafTutleWDaA9tu3iBeefzeuDf0vUlNcf4c2m/Kv+ednL7JuCZNdCPLC3BeQDt9qjNgJmnIX+I1krgFFofXLNPtvYFfkp7CNmZtJYTV6v2kImHdOX+ifZZvZAhvpOr9RN21DyjnwW8uvv8X8GVv4BDa5V0cLe+J9D6N/7wrGVfTLvV7W+A98+TAP4Q7YJ3826ev9JaPp9ES5ifS9vnXl5XfWjcR7tpZ/dT+wTaCfHxtGTuwSzcBcbfA0cmOZ/WUuT51fqq+zKt65Ff0D6Xi9jwW9Xm++w3xGNpD7H4M+1Bap+gnaxPyr/TWqifRXuozRWfz2L7aneReH7NepCK1kl1F5//TrsYPY32eTx6wbnmtx/wwS7mPXJpqnhV1fp5vzctPv+B1kf8P8wx3X/QWuQclu7p6Ass8zJaC6nb0FqJnQH8L1f2jboXcFx37L8NeHS1/hUXjW/zlLe+8XTGN2nfb18DVlfVV+Yp5xjgnFnJmud063YgcD4tdq2h3TUxaKbvy69X1eBdB2+jxb+vdHX8Hu1Bk/OZcxt1n+PzuvU7i5Z0mbcf8iH9H63v/rNoD5h8eC3ev/hiXk37ofG3tHONg5ls3PwwrdXYibQfMK9IRC22H3eJh/vT+oPXeBlvr5zWeGu8nc8k4+2+tO1yHi2Ju9CDFB8PnNhdgz6Ddn5Nt589jfZww7No+82TBuZ7ELCmqja0hfSyk5qirg2STE9lsNsHaUMlG3IXy9KqqumpjK4iyaHA4VX11knXZVCSNbSW0/876bpMiySvA06fts9qqY3rs0/yCeBnVbUureOnQpJDgPdV1RcmXZflKMkxwKur6tOTrouGl/ZgqN8Cm1R7CNQw89wHeFZVPXSEVZu4JPvRHnK04NPhl6CcZ9KSUfPeJTKtkjyXdnv7iyZdl5XEeLs8GW/nZ7xdP0mOBJ5SVcdOui7jNvQDcSRJGoXulqK70lqMaspV1csmXYflrGsteybtYuY+tJa1C/b3PK2qanarGQ2pu+30psAPJ10XjV7XSm3OlmpaXJLtabfXfxe4Ma07qHdMtFLrqarePuk6rDTG25XFeLth+hRv51JVC7Xa7jW7fZiHrX4lafSSPIt2Mv6BWV0tSH11PdrthufT+kZ+ZlWtuAvSJDulPd37+LSHhTx/jmn2THJOkh91r1dMoq5LLckbaRemL64rnyYuaX5XB95DuxX467Rbnd810RotQysx7hpvpXVmvF1C0xR37fZhHtO0XaTlym4fJElz6VqWbF9Vx3QP4TgaeGjX//jMNHsC+1bVAydTS0nqD+OuJI3XNMVdW/5KkiRprKrqtO7BLDMPszkB2HGytZKk/jLuStJ4TVPctc/fOdjqV9JKMU13XEiaHoN3S9z3vreqM/583jrNf8zRJx4HXDQwaP+q2n+uabsHutwWOHKO0XdK8mPgVFqriOPWqSJTyLgraS7G3dEx7kqay0qKuyZ/JWnF22jSFZA0VS5b690ZZ5zLd458+TotYbNN9r6oqnZfbLokWwKHAPtU1bmzRh8D3KCqzk9yf+DTtIeP9IBxV9Ig4+7oGXclDVpZcdfkryRJkuZVXMall52/5MtNsgntRPgjVXXoVcodODmuqi8keVeSbavqjCWvjCRNEeOuJI1X3+OuyV9JkiTNr4q67KLFp1sHaU8EfR9wQlW9ZZ5prgf8saoqye1pz6r485JWRJKmkXFXksar53HX5K+kkXn729/Oc5/73ElXQ5K0QS6nLrtwqRd6F+DxwE+T/Kgb9jLg+gBV9W7gn4BnJrkUuBB4dPlgBkkrgnFXksar33HX5K+kkXnOc55j8leSlrmqy5e8JURVHQFkkWneAbxjSQuWpGXAuCtJ49X3uGvydxZ/2JQkSRpQBUt8MixJWoBxV5LGq+dx1+SvJEmS5pW6jFy89A/AkCTNzbgrSePV97hr8leSJEnzqyKX/XXStZCklcO4K0nj1fO4a/JXkiRJC7icXNrfk2FJmj7GXUkar37HXZO/kiRJml9Vr0+GJWnqGHclabx6HndN/kqSJGl+VeTSiyddC0laOYy7kjRePY+7Jn8lSZI0r9TlvT4ZlqRpY9yVpPHqe9w1+StppKqKJJOuhiRpfVWRiy+cdC0kaeUw7krSePU87pr8lSRJ0vyqyKWXTLoWkrRyGHclabx6HndN/kqSJGkBRS7r78mwJE0f464kjVe/467JX0mSJM2vCnrcEkKSpo5xV5LGq+dx1+SvpJGz319JWsZ6fhucJE0d464kjVfP467JX0mSJM2vLoeL+/v0Y0maOsZdSRqvnsddk7+SJEmaV3reEkKSpo1xV5LGq+9x92qTroAkSZKmWAGXXrpur0Uk2SnJN5Icn+S4JM+fY5ok+e8kv0rykyS3G8XqSdLUMe5K0nj1PO7a8leSJEnzq4JLL1vqpV4KvKCqjkmyFXB0kq9W1fED09wPuHH3ugPwP93ftSRZBTwa2APYAbgQOBb4PPDFqrp8qSsvSSNl3JWk8ep53DX5K0mSpAXUUK0b1mmJVacBp3X/n5fkBGBHYPBk+CHAh6qqgO8l2SbJ9t28ACT5QDff54A3AqcDmwE3AfYCXp7kJVV1+JKugCSNlHFXksar33HX5K8kSZLmVwWXrPPJ8LZJjhp4v39V7T/XhEl2Bm4LHDlr1I7ASQPvT+6GnTYw7M1Vdewciz0WODTJ1YHrr2PdJWmyjLuSNF49j7smfwfssMMOk66CJEnSdCng0nW+g/eMqtp9sYmSbAkcAuxTVeeuc9XmPhEeHH8x8Kt1Xa4kTZRxV5LGq+dx1+TvgFNOOWXSVZAkSZoqVVCX1pIvN8kmtBPhj1TVoXNMcgqw08D7Vd2wuZb1QOA/gBvQzm8DVFVtvaSVlqQxMO5K0nj1Pe5ebR3rLUmSpJWkaI+rWJfXIpIEeB9wQlW9ZZ7JPgM8oXsK8h2Bcwb7P5vlrcATgetU1dZVtZUJCEnLlnFXksar53HXlr+SJEmaX0FdlqVe6l2AxwM/TfKjbtjL6Porq6p3A18A7k+7je0C4MkLLO8k4NjuYRmStLwZdyVpvHoed03+SpIkaX4FdfHS3ixWVUfQblVbaJoCnj3kIl8EfCHJN4G/DixjvlYWkjS9jLuSNF49j7smfyWNXLvbQZK0PAUum/qewl4LnA9sBlx9wnWRpA1k3JWk8ep33DX5K2mkTPxK0jJXUJduNOlaLGaHqrrFpCshSUvCuCtJ49XzuDv1aW1JkiRNUIW67Grr9JqALyS5zyQKlqQlZ9yVpPHqedy15a+kkbHVryQtf7U8WkI8E9g3yV+BS2j9q5VPnpe0HBl3JWm8+h53Tf5KkiRpAZlU64ahVdVWk66DJC0d464kjVe/467JX0kjYatfSeqJCpdfMv2njEluBezMwPltVR06sQpJ0voy7krSePU87k7/mkmSJGlylsFtcEneD9wKOA64vBtcgEkIScuPcVeSxqvncdfkr6QlZ6tfSeqPqlCXTffJMPD/27vvMDmqM9/j31PdkzRCEggBEkpEgUzO0UYEk2QwNl6DbWwwGExYwPZ6A9jY67R7vdz1tbFExiQbMCZHAcZkARKILEBCJIEA5TC5qt77R3XP9Ix6cndXd83v8zz9SB2m6u0J79T86tQ5+5jZ1LiLEBEpBPVdEZHSSnrfVfibwzmHmcVdhkhFU/ArIpI8YZnPgQbMds5NNbM34i5ERKQQ1HdFREoryX1X4a/EbsmSJYwbN25AH6uwvrx4Xtk3SxER6S9zZX8ZHHA90QHxJ0ALHasf7xRvWSIiA6C+KyJSWgnvuwp/pWSKMSK0t20qHC4djfgVKR3naplefzJel5+7sEvPa58IKufxkPWFZqy2Zp5rur7AlUoSmDkCv+wPGa8GTgJeJf+3ucigqO9KKanvikR996j6k9vvd/3bPiTqpf35i3+N+q50I+l9t+zfmVS+OEPB7L4VAheXgl+RYnOctNFZHDthBQD1Va0c/K2rO78k99d/6LDQi/4NPAg9LIzmsTLfw6zLKP3Q0bh0Q+585pROD5tFP9vnv/sQza0fZR8t4PuSSmHlfxncUjO7O+4iJEnUdyVe6rsy9Di+seGZHDdhFQB1VW0c+vVrcV4Yjcg0h4Vu/f4apAjb0tFz1vPfpY0rR3D3c9/N+5z6riS57yr8lYIrxyBQIXDxlOPXWyQJJg0/lA3DMfxu5wZ223cu1aOvJTWiufOLQtceFODREUR4hiPE8DL/AmEKlwoywUSXnXlG3ehVnHDUrPaHcg+gj/50NEE4hnVrNuBrT44mJGQlS/is4fkivHMpN2aOsPwXwJjnnPsLcA/RZXAAmJlWnZc+U9+VcqG+K0NFbt/dff85VI++Fm9Ey3on10hb9JifgjDEmcN8w1IOzCdV10LQVBP1UQDLBMRdAuFhG67hhCMeyluL+u7QlvS+q/BXCqJSAsDcOhUED16lfN1FKsmFE85kk9o2Tjr8YYZP+UfPL/YMF5I/iIBopAQezgw86zggzsN5BtbxGucZEN3fYNPlAIzcdDnPbPMeYeCxZNFEbnn9TD5q8rhm2YxBvGMpe1YRC2DUER0EfzHnMQMUQkiv1Hel7KjvSsJdMP4sxta1ctJRDzJsu46+6zywkKi3QtRfPcOFBs6BF0AI5qdwziDnSovUsGYsMwLYzMNlRgeHvtfriGCgve+OGru0ve9+8u4Ebn5NfXdISHjfVfgrA5KE0K/re1AY3D9J+B4QKRd11RM5uPoorjj2aUbv/Ce8ura+f3B3QYRnELooiAgdLh1grf3/te88iw7CLbstDy8VMnbyYs6fvJjWphrO+uAwlq0bwQmvvYURsrpJC38nTaEPhp1z1wDTgc/MbIc8zx8E3AW8m3nodjP7RXfbM7NTuntOJB/1XSl36ruSNLl9d+Ndr8LVR5dEuC7f6p3uZ8NgiHqvD+ZF/ZXMiTMDXDYtrvKjUcGhh/keeIbnoqkhOo0CdpbZV56TdJne6wHjtn6f8yd9REtjLWd9eBgrGjbghNfeIbA29d0ESnLfVfgrfTZp0iQ++OCDuMsommyYud122zF//vyYqylfe+65J3Pnzo27DJFEcK6afxl3Gmfu8zzjD7py4BvqGkTkeZ6gsCdsXDrA/BRVta1M2vpdJppj/tQ0fks1v/z72dzT+BJLGp4u6D4lHkVaAONa4I9EqxZ350kzm97TRpxzPwFmmtmKbp4/GBhmZvcOtFBJFvVdqQTqu5IkzlXzg81O4+x95zBh2pWQXj/wBaITabmPh9HNeZkZeEMg3REAg+VMuQOEXvvJM+eFUdrl0556WZgGZ5mrLGj/N5eFLicYjlJnr8qnZngjW0x5h8nmeGtnh99axS8fPJs71qnvJkXS+67C3y5qampoaWnp/YVDwPbbb8/HH3/MmjVr4i6lpN588832IHjUqFFssskmOOd48803Y64sXvX19TQ2NsZdhkgipFMbcuLIb/Dve7/C1odfiUsVeJHsLqPQslw6wNoKMwot2k8ImbmxXOZgOl3Tys+P/AenvDeJT9aewI3vbMSd6/5MEK4e/PuSeJgr+EgIM3vCOTe5AJt6FbjHOdcMvAgsBWqBbYBdgEeA3xRgP1Lh1HeloqjvSgKkUxvy9RHf4N/2eo0pR2b6btfg1+vyL12eyzzuMkGw+XQKgHOn2smOEM4NgC2zAecZXjp/3+8xBLaOaXksjPouHlTVtvKfx8zi1Hcm88FK9d1ESHjfdeV0qbtzrmyKKafPS6mNGDGCtWvXxl1GWRqK3xdJmd7Buh2aM7RFfbfsJ7bvs+r0Zjjn0dL2cdyl5FVTNY6vDv8yvz/+IUZs/z6uqsDhQ+5CRGHOY6EHQTQfWthUk38xg+yqyT3MT5ldAddCD8usuGyZbVkYnS23bA2Z7YRhiuXLNuLfnp7CY2330dy6uGBvV4ol6NQzd9xwuN01bed+bWGrO555H1iW89AVZnZF7msyB8P39nAZ3G3AYuBj4F/M7PXu9uec2wbYHxgLNAHzgSfMrKlfhZdA0vpuuVPfVd+tDOq7xaS+W1o1VeM4rv7L/OH4hxk59b2o72aC3E7B70CGIvqZADgk00Oj3md+KuqXYccOsv3SfA+zzDQQ5mWS5BzWOfDL9uTcaSIs9KJgMLP93D8tw8CjtbFOfbfiDK2+q5G/3XDOKeiT9WQ/P0Phe+Pcc8/lkksuibsMkT6rrR7PXqnDWeXW8UrbLXGX04Xjnzc7i29vt4CdjrkSl4ouU6s4OSMg+vwhzth4zHKuOnY2T7/2Be7+cBR/XnkZEBSnRik8cwT9HwmxzMz2GMReXwQmmdk659xRwJ1EoxvyMrMFwIJB7E8SR31XfbeCqe9KRXJ8f5OzOGX7hexyXGakb77Qt+v0Dv2R+7HZheGyV130pcV1DX6zj+UEwJ3mAe46+pcwml7CWXsA7JxRVdvCmE2WcvWXl/Pc6wdw94ejuXa5+m5FSXjfVfgrgELf/kp6CKzvB6k8jolVuzKuqoaxVsMn9XvxWcPzcRcFwNb1R3PiRhO54PvX4tW1Dvxgty9y55jssgJ9ITkvbB951h97b/cGu06u4YfrpvGN5+p5q+GuIlQ3cM5Vs92wo6kP65nbdGPc5ZQNA8KwmN+4efZptibn//c752Y65zY2s2U9fZwIqO/mKve+K/mp70qlmTz8cL4xait+8r0bSI9qjHqjBy43ceppioeByPZfzzJzA3dM/ZCPyzPtg/k580pY94U5Lwp9jfX/Tnae4REQeCkIYO/Pvc5u26b5lzX787UnRzO/4Y5+vzUpvaT3XYW/PRgKo38V8g1OEkNgfU9IJfLcMA6sm4iX+fbdpW0XHnYvYdYaa1171H2LB77xLCO2fyjWOrLyzWlWaukqn5Ej1nDd7mnuXHQW//vJ7bT6n8RdFlvWH8nn3CS2GJ6iOYAPvPI5gRA7cwRhaS+Xdc5tBnxqZuac24voT8XlJS1CKpL67vrKte9KD9R3pYLsMuxE7v/Ky4ze6R+Qts7Bb18C365/f/byt7Xz6DHkzb+Lbj7AczmjfHsOgNffZs7oX8/wUiFB5iRdVZXPhqNX8pd9q7j1zbO4WH23/CW87yr8HcIU8hVOpZ8o0PeCVLovDvsWG1R13J86soq/N9cRxBT+1lVP5In9tmPKHn+nZlz5DJjpaV7JUnKesfFGKzhtoxV8aflu3P7OZP606mlWNL4cRzXsXXcSJ46todprA9oAeLdhFx5G4S9EIyECv7AHw865m4CDgI2dc4uBnwFVAGZ2GXA8cKZzziea0+wEq+RftFJ06rs9K6++K71R35VKUFs9nof23IUddn2G+slLooXYsou6ZZOmvmSpua8p0NUTufP1Aph5+QPgLot0ZmVP3PWnh3upgNB1/NyaeWyy6WecufEyjvl4F/66YEuuXa2+W66S3ncV/vai0kO9fE488URuvvnmuMtInEodBazgVyrdiNopTK5PU91ldNV2tYfyeuNtJa+nvmYrnth/C3aY/o+S77sSbTp6GWeOXsZ3W0Zw2mPfYXbTdSXb97j6A/l81Y58Y8tleK5jnYTQHPuMHs1jrWNo85eWrJ5yZeYICnwZnJmd2MvzfwT+2NftOed+C/yK6MD5QWAn4Admpvk7hgD13f6Js+9K36jvSrkbVjOZB/fYgT2PeByvrgW8nOB3MHP6DlTo8ga12cXeCF3+Nf9yPyZn1K91s72eOBd9DrILdToXYubhpUI22+xTzhmzjO82jOTUx7/Nc03X92vbUnxJ77ul/pGsSEkKx5xzCn6LzDnXftt///3jLiev3BpFKllVegzf3fBQxtYF1KfDTrf96zcraS3OVfObLc7gtS+NYofpj5d030lQU9PCJft8zC07fZMNarfBudqi7m+T+r24dNtxnL/TIjYZvoaN69e230bXr2Pa5h+zUc1WRa2hkgSh169bDL6YmTdtOvAesDXw4zgKkdJR3x2cUvdd6R/1XSlHzlVz4YQzmXvIePY84nFcTVtmxK8Vfk7ffMI8Uz5kQ9rMz0F21K/5qSj4zTxmQXS/061EV2ekvBDnjLphTfxhr0+54XPfVt8tQ0nuuxr5O0Qo5IvHM888U1ajx/V9IEmzXfVBbFidfxXd4WmjvmYrGlreKXodn687lW9MNE4+9bpoQYhykKcMK9ClTFakg51RI1ez+8jVPD9hE+5642AueO9GQmso6D5qq8dzWPV0frzzYkaP+LjTc2aO0BxmjmE1KVJUdbOVocWs9AtgDED2mPZo4FYzW63fecmmvlsYpei70n/qu1KO9q37DieMr+K0r9+CV9eClw1+nWXm+h3ghoM+/q2cL/QNM/0xM8VDe/AbeoRBKhoRnJmH18Iw7xQP0ZM9F2+h1z6/b39kR/86z3BeiDPH6A1XsteItcyeMJa73jyYn76vvlsOkt53Ff72UTkFeP1RXV1NW1tb3GUMeXFPCXH88cdz222lv/xdpNgOGzmGtMsf/o6uCdjJO4DZFDf8PWzY6dx88kPUTx4CizgM4KB3IDzPOHbqazSHJ/GfH1xTsIX7nKvlym0OZv/t5uZ93swRBtHBvZlHLSMLst/KV/jL4IrgXufcm0SXwZ3pnBsDNMdckxSJ+m7hFavvykCp70p5mVZ3Gjed8BQjt/wIV+Xjqv1oUTfXscAbZKZ8KIaQ7ucDzjNFQ9iWjqZ8aA9+HV42/eo692+X4LfTtnLnDQ5d++ji7mSnfOjKOWv/13mGZwGplOPY7ebTHHybXy2+Wn03dsnuuwp/+6ESA2AFv+UlrhBYwa8k0bCayQyvMmpT3f88VRXx19ym9ftwSPVuXP3DG6K5zvoqBPMcjiL0gR4uXett9JkFqR4vfYtj0aKvTX2dIyYdzH/N24KngmdZ3jhvgFtKsW/dtzh/G5/9d3ip/QC8K8sZNWLmuH7XDTngqQHuMkHMwA9Ku/pxf5nZv2fmQVttZoFzrgE4Nu66pLDUd4uvcH1XBkN9V8rFJvV7MS29B1eddStVI9d1Dn2hU/BbNDnBr4Ud9y1wUQ/OM+rX2tKEfk5h5gh9Dy8d9jjKN9t3c/tv+6jfLsFg15HAYS8/s54zAjo+d6lUgJnj+O3nc9QWh/Df8ybzdPg8yxpe6HE7UhxJ77sKfxNMl92UL+ccv/71r7nggguKvh+RpDqs+kg2q+35DPk+G9Xw7Keb0eoXdnTYiNopzD7CZ/zBl0cP+C5zUEiPQUAnoYsOmAuo/SA0O6AhW0vQy5xmvSxqYZ0OrHMOfIt8dtw5o662mYv2XMDbSz/HM0v344+fXI7h92s7Pxx7BhccMwuXyj9KfD2hh99aRV11P8KlBDMcQS+XQ8bNOfc14MHMgfBPgN2IFsQYAkNDhwb13ezHVUbflcFR35VysEHtNjx6YC1bH/Sn9tAXWP8kem7GGhZw9G8Po32zI2zNj06imZ/qmO6hpaoj+M0NaDMBsMv8Hsj+2zno7WHUb2/ldjPqNx8vFRJkThCmvJBhtU1ctOcCFi2bwpOf7cMl6rsll/S+q/C3nyph9K8Cv8pw4YUXcuGFFxbt+0nfB5JkztWyaa3HqOoWst/qnjO8LqO6xtQ2MXzlpqwoYPi70bCdeePrLYzeL2c0VNogNKzFg76MLAvJv+LwYIRdAogMC7320Wd5R5mFjrCt/4cDuSPasiNli2nbMZ+w7ZhP+NLEY7j13bE80PQ6i9c91uPHTBp+KKdtvDXnHX8HXnXfL6WzIAWrhw+y4mSpgMvgfmpmtzrnDgAOBf4HuBTYO96ypBDUdzPbrYC+K4WjvitxGlk3lRePrGfzfZ7HpYPOo32h40Ravm/TsJvH872uH4+3j/r1M3P9ZgPfnH+Dlqqov3bXH81FI4adYaHDebZ+j+5y4i0MvB5H/VroCINU3ikf2hecy6wr0ZVz1n5Lp3222XQJ22y6hOkTj+G6BWN5oOUpVjS+nP+9SMElue+W/TsrR1dccUXcJeT14IMPKvCrQM659tvLLw+usS9fvrx9WyJJNr5+P44cv5zh1a1sUN3CqJomNqprZOP6devd0q6mYPsdV38g809oZvTRH0FdGqpc9JvUA5cGVxO23+9NNGKtQD+r+UKAzCgIa0t1jCAbQACRb/RZdmRD130W6xJlzzM8z0ilAnbb9WV+f84NvPH9t/h/W3+PDWq3We/1o+p24P9t/T1e++eX+bcfXUntpM+oHruq59umq6nedDVVG60t+MjASmfmKmH14+yw7qOBK8zsPqA6jkKksNR3y6fvPnXU6qLsS9anvitx2rh+d2ZP24jx+72Mq+4m+O2t7/a2DmfYwy2P3ODXgkzgG0Qn2ixIEbRU4zfWErZW9W2udMtMERF47f9vv9Ex1UNvwW/2ftDah77ew33oHARvOmoF/7rn68zYasfe34sURNL7rkb+DsAZZ5zB6aefHncZnSjsS4Zddtml2+d6GyGs7wEZSlJUUZtqI50KSbmQdOZy/uqUT8oL8TIrv3uesZltwWc8P+h9jqybyoIfvUbVIRtD8wioaYWmRmj1oTWAwHBpgDAaiebR84Fvdg7KkMGFjWHHpc9ty0bQsmIkDz29Hz9btDqzm44iwjwFhV0XvQCOrt+aHx3wXPvB7ajNluKlotdlD4Jz58ftr75+TCoVUlPbQG1dMzXDG6kbs5LqSdH7qvIaOHvqnzj2/p3Z4/5dMUJWN7/H6LopzPtyE5sc9qfMVvqwryAzgtAnmvArw1MI3K7cL4MDPnLOXQ4cBvwf51wNGuRQ8dR3y6vvrn5ai2CWkvquxKG+ZiueO9xj/H7PR1dY0GV+X1j/q9xlpK+FmaOv7kYA9xYM52yn/fWZKX4scLR+uiENn23Eg8/tza/eX0ZImLev9uao+q354b5z2qd/yPZdM5d3cbfuTr75rVW9v5fsXMTW81UbXafUWN2q8ymllOS+q/B3gCph+gdJltxwt+v3noJfGWr2SW9FOrWEtBdQlQpIewHpVEAqFZLKBMDZy6eu2KOKfZ4Y3P62rD+SV857Hu+4bQkAr7oG/DY8z4MaHxoaoNnvFETQ6jDPFTeIyAQQbz2wHy8vnsRN79Vxf8PlwKKBvM12M9bNYkbOOpE/nXAmG9e2cczO8xix8coeA4jeRtb1dply7vOeF1Bb10ztyLXUbbqC9CYNmScA5yDtGH/MK3zyZQ9afB674hgO+sEs8BzQy4hvP4DQIMx8gRzRYlCtHV8HN4A/JJLIcGW/AAbwT8ARwMVmtso5Nxb4ccw1ySCo75ZX31145z7s+eib/XlLMgjquxKHcfUH8sRhLYw/YN76i7r1Jk8AnH28uzmALadXO6/z/fZt5oS+bz64P/MWT+Iv79XwUOMVwOB60sx1s5h5Z8f9C8afxegany/vPI+RY1b0+LEWOgI/TdiW/+fUckLEMPDWm/Khu77stU8BETDn3a04Z+EdfXszMmhJ77sKfyucQr+hSV93GdpSHDEu+mM05YWkvYCUF3YKfquq2vC8EC8VUpX2ca4Ws+Z+78m5Wh7Y42g+P/1h3DHbtz8e1tbjwgALQ1xzY+bRzkGEYdDSh5/VTBBB4KKD7D4cYAdrarG2NL+79gQe+8yYY8+wqunxfr+/vvrlh5cCcNnHx7FRuBNVeFx1+Kukq9uA6NK4mmFNmfeT/z1b6DovWtSNfAfDqSofr9qPAh4PSLlMwAukU9GtOs1BP3qIPo30zX5caERraYTrnzP3FPxmmUFQ5LlFB8vMGp1znwEHAAuIvrIL4q1KBkJ9N1JOfdd8+OPcXWj1nx7gu5H+Ut+VUnKumsumfJMjd57Hpvu+jkvlOS7qy2FRT1M29GK91/gOf9UwrC3N/974Tzz2WcALNptVTY/1oZCB+c3imQBc9clxjAqnUkOaa456meqajnUjqmpbCAOv/aSbmddpsIB1GTkaBtH0ALmjfrMn8cw6jwT2XDTVTjrtY+a45b16/GBl0d6vdJb0vqvwt4IpABSRocplFnerSbflDX5T6YBUOro/dtwSLt7y2/zonf7N155OjWb2gbuz4/8swtLbr/e8eSnC4SOjQaiQOWptgqa2jlEOznofhQbtz3cKI/J45c5prG2u45RXW3i/4XHMrqBPCx0VyPyGjtEH29zV+TK0n038Ll8Y9zHbbvlu+2O5I9X6szBRmDkojuY9y/nkZUf8eg48LwpwvUH8LvRcJgR20UhgD/AMr8pvvwRQIuV+GZxz7mfAHsAU4E9AFXAjsH+cdUn/qO+urxz67hu3f54Zn142kPJlENR3pRRS3kj+tvORHH3yDbha6zR9TqdANvt4b4u05TsZ1d0xVW6fyvn4l+6ZxrrmOk59vSnTdy8nrr671e3V5CbhP5t4Mp8fu4QpW3Vc7dE18M0K/BRhznQPHa/v3J9Dc+3T1qXTPql0wLNvbcc96y4v4LuSvkhy31X4OwhxTv2g4FdEhjrPi+b6rapqw3PW6YCpqqqNdFUb1fXNVNU38c3PP8F1S77OK4239Gnb/7zZ2Zy3zxw2/9lqrIc59DsFEb4P1X40F2X2ADVt0JY9qHW9jy7LDSMy981PsfTFKfz8kQO4buVfymYEgFlrp/s/f/8yNl62O9PeOJDfHPQCqXSAlwqw0CPsx4II3R1AA5ngN/P/wQa/WZ4DL3OJlx/g6sFb5QMw86WpwD8Gv48KZzj8Ii0qVUDHAbsCLwKY2cfOuQ3iLUn666E9D2CH332ivtuNOPquNcNPZ09BvbC01HelVG783JeYfs5N0VVV6Uw81BqtpeHM2gNfy53WoetcvnmC306hbuDWH0nsZ65MCKNF16wtzccvTeFXT+7JDStvJAjLY4HJ7vruIfMP5L8Pe4502m+fnz0rOzK4a/CbHfUbhb8eYegIQ2+94NdLBVzyTopSBt6S/L6r8LfCJD30TaVS+L5fkG09+uijHHLIIQXZloiUn+hAyaipbsPzgvaDpXQ6oKa+keoNGqnZZCWpcT7Dxhgv7Pc39jzvRF5qvKnbbW5ZfyTf3GgyP732PoKNNu1THealsOoaGFaPC8NoBGlDS/4X9yWIAAhhybM7cPuLe3DhB7Pww49p82f0qZ44LWt4gVt5gbvu2wznPC7d+ouMqG5h+3GLqanp5nOSR7ej1TyisLa6qjDBb6dte9HXJh3i0tEfPY80Dm4Oz6Qwyn8kBNBqZuYywzedc/VxFyT9d/arHi8/O5dgnz16fJ36boei913fcX8PvzelONR3pVR+umglx7w7kurdgo4Ha0NobetYHyEwXOZP9PZuut78vF2C38yJNJedRit7os1PQTYEbUuz+MXtuf3Vnfn5R/fhB+/gB4NfpLnYljW8wC28wB13Rn33im0PZVR1C9tPeJ/q2pb8I33bQ9+O4DfIzC/rnOF52bl+o0/0c81/i+W9DWVJ77sKfytIkoPfYoygPvjggzttN8mfP5GhzLmQVDqgtr6RVDogXd1G1QYNVI9ZTWpyCkZuglXXYFO35blf3Mb9fz6Bf3r177T5S9u34bl6/rrTMRz9T3fDUSsI6Fvwm2XVtbjWFqiuhpbmaPREmFmEyDcsdx7a7MFxN2FE8+IxXHzrsVyy7ElWNV3Z309HWWj1PwHgu/OvBeDYRWcyffNmpm33eo8fFwTp9lEQXu6cu7mXYxcj+IWOKSAKc/4xWSpgDjTgr5nVj0c5574HfBeozB+gIWx+wx1M++Ep/OPi6wn26yUAVt/tpFh998HrvkIY3l6UmqUH6rtSIgsb7uOg/3sST150G94eG+P8tmgx3HQrtLZGAXD26orujpG6CX4JHRZGYS9kFqwMPSzwaPhoDP9z/xe5bPnfWdNcmdMbZPvud16/HoAvLzyTozdv5uCpr+GcEQZRkJgb+gIEQXR1RmiufYG37ECWdFUbtz23L2avxfOmhrKE912FvxUiicFlqafM6Lq/JH5ORYayVDqgalgz1SPWUbXRWryJVTByFOalosv6vRR24HYcOW0hDU+t5MbLTuYXHy7hB2Mn8P2zr8MOXIB5Owxo3+alCGvr8Pw2XDodhYhtfvscsgTRZV8u9w/r3APl0GPV/Mlc9uhB/HHZHJY1XDrIz0Z5uWvtpdy3YDQjP5jIAal9OHfqEsZtvBTPs/Z5JqHzHGi5IyacZ1EQUYzQN5fXsZDcTQ8dyruNfy7u/ipEdBlceY+EMLOLnXOHAWuI5kG7yMwejrksGYCnm/7Ezv96HK/MuAd/5927fZ36bs8K1XevWTAa01mxklPflVKa03QDO//qWF69+F5s6pbgt+E8L5pI3ctMe9DqRyfY6DIXcJ7gl9BhLVXtC5u1Px+kWP7WJC57ej8uXzGb5Y0zi/7eSunOtZdyb5e+O3b0svY55c1ce+ibT1V1G6lqnzs/cuq7MUh631X4WwGSFlLGNU9yV/nqSNrnWmQo8VIB6dpW0iMa8TYx2GBEJvj1sOwcZl4K81IEn9+dEz8/nxMBWEXIboUoIJo6IJXOhIisd0ncequuhw7M8fy9B3PQ7McJw+QuqOMHy1neuJy7mMddzznO2vQspm26ku3HLSbIc6CVTnVcfug8w9X6makZityn0ylwIctaqjBrLu6+KoQBfoFHQjjnrgGmA5+Z2XpnXVz0C/n3wFFAI3Cymb3YY53Rwa+ChwSY33AH+515Ek/98hbCQ3bt/oXquz0qSN+VWKjvSqm91XAX+/3wWzx10e24AyaA53VcP5GdXsczzCdn8bc8wS9gbelomoMg1ek1zzz8BQ6f8whh+FJp3lQMuvbd08ecxbTNVjN17OL1XuvaR/1a+7zBXirAQ5lEHJLed8s71h7inHOJCSPNrP1WzrI1brfddnGXIiLdCrh2UTeLAbnMfK211VEgAO3/mud1LOxVDNk5KLMj0HoTOlqXjWL23YdyxNzXCMO1xaut7BgzP53Bd9+ew1mPb8OKtSNoba0mDFPtNzOH8wyvKoi+rqUY+Zux6q1J/O6zp0qyr0pgBkHo9evWB9cCR/Tw/JHANpnb6UCPwzKdc19xzi1wzq12zq1xzq11zq3p41uUMjSn6Qb2uOhY0q+/1P2L1Hf7of99d87fvsiDLffEXfiQpL4rcZjbdCN7/vJovAXvYOmqnP6azpxoc7g832qdgl8/Rdia7jTVQ/OKETw5axrHzHthyPXdK5bO4IwFc/jnJ7dq77td5wMG2qd+eGruHjzeendcBQ9pSe+7Cn8HqVjhbBJC39NPP70iAt985s+fXzGBtchQ9G7qw7690POiA9cS6JhewotuzkWjI7quGhs6rC3FZ/OmsMuluzBt9n00trxXkhrLzdrmBTzRdDXTZt/Hla9v0+lS5KzspXJ9WrCpQJob6ljVpLnWcgXm+nXrjZk9Aazo4SXHAtdb5Fmiuc3G9vD63wLHmNlIMxthZhuY2Yh+vUkpO6823spupx+De/jlvM+r7/Zff/rukjUjaW5df7SalIb6rsTh1cZb2e2c4wifWAzpqij4TaWhOt35JHzOAmbtUz34KaylCshcdRE6lry+NbtfP4Uj5txOU+sH8bypmK1pfqtT380u9Gbm2qeAyPbdTxuHtc8lLKWX5L6r8LfM7LjjjhUX/KZSKc477zzOPffcToHp5ZdX5sTt+WTf05lnnhl3KSKS0cezrZFijvjtSZgdDZHp64FH2FLFT2Z8h0PvncCihgeAoMdNDA0BVy69nHNnT+CFjybg+3m+XiU8YvnZI/uVbmcVwOjfgXDmYHhj59zcnNvp/dzt5kDuWZ7Fmce686mZze/nPqQCvNp4K3+/ZTou7GOvVN/toz70XYmN+q7E6dXGW/n77UdhnhedZMvVdXqdbPAbelhbqn26h7Cliv+46kSOfGRj3ls3i+ii+qEu6rvnzN6cOR9OJgyjz2120U2JV9L7rub8LYDcsHago0SvvvpqTjvttEKVVBJDcUTszJkzmTlzZsUF9CJJ8966h7jrvbP4zufejLuU/MIwunYIoiAi9CDwaFk6ivOuO45rl89EB8GdGT5PN/2JZ96sZYsPpnHVjhsyaVhTyetoeHpDbm98qOT7LWcGtOUZHdiLZWa2RxHK6c5c59wtwJ1AS/ZBM7u9hDVIkXxp3iwW/mYKY3/Sw4vUd/utXPqurE99V+J2zLwHeeviqUw6vy0a/WvV0by/vgEWnWTzXXuvtezI3yBF87KRnH/z0dywQn23K8NndtN1PPtmLVt9cAiX7lDN2A2XEwYeZl6fRpNKcSS97yr8LbC+BsHpdJogqMxRB0Mx9O0q+zlQCCwSF6Mt7P1VsQgzhYWAn+kRoWPWX7/EjYtGc+vqGbGVVgnMmlnU8ABnvnos0z/Ykx8f/mjJ9t3wzCj2vWpP1jbfUbJ9VgIz4vhj5CNgQs798ZnHujOCaKGML+Y8ZoBCiAQIw7Uc/eAE5l3w4fqj0KIXZP5FfXcA4uy7kp/6rsQtDNcy/ZHNeOVfVuG8/KNS24Nfv2PE7wN3HcFf3h3F7WvUd3ti1szChvs4+7UvcdjwHTlnj5cI/BRtGgEcm6T3XYW/ReSc6zYoVfCbDAqBReJz+bK/cOyaadQPa+j9xWGAAywMijYFhAsD8NuiECK09kuPrTXNHX/+Kt96/R6CcHVR9p1EbzXcxdsNaZrvP4Nz95rHFhOXFXeHofHs0/swv+G24u6nQsVwMHw3cI5z7mZgb2C1mS3p4fU/MrNOc6o557YoZoFSWm823ses047ji9e81/6Y+m5hde27Ei/1XYnb240PcP+5X+Ho3z6Da23tWEw5e6ItG/wGHmFrmr/dOZ1T37xTfbcf3m64hwUNaVY9cwYnb/vxQEaeSgElue8q/C2yJIWCCn67pxBYpPT8YCVtQcfZcVfCBcHyys5HGYbgB1grLH5iF25+cTd+8t7VGH689VUgw2fGpzO4/P4NeXrNoex2xuPF21lzK9NffKx4269gBgQF/vFyzt0EHEQ0V9pi4GdAFYCZXQbcDxwFLCQa4XBKL5u8xzl3pJmtyWx/e+BWYIfCVi5xMWvl0rdHcdTLL+DvvHv0oPpuweX23aOH7Rx3OUOW+q6UA7NWLn1rBEe//RFM3Cha8M2sY45fP0XYluad53bi1jem8qsPr1TfHQDD588rZ3DTnA3Yt+b4uMsZspLedxX+Sp8o+O0bM1MALFJCv3hjGDf0NCV+V2GAC4P8lw0PVhjgwo65KFbO25q9Z4Usa0jO4pdx8YOVTHv2LZ6u2p+pRz2BN7467pKGFAP8Ao+EMLMTe3negLP7scnfEB0QHw1MAa4HvjnwCqUcPdhwOU2vjaUqm0mq7xaNH6zkrrWXxl3GkKW+K+XiocYraHhzIsMnZuYDD8ECh7VUEbalWfLKNhz05CpWNKpfDFYYruXppj/FXcaQlfS+q/BXeqXgt38UAIuUzsvBo7z2wQHsstXC7l8Uhh2XqUHHFBAFDIBdGOD8tmgUWuCz9KGt2Om2OpY1vFCwfQx1jS3vsesj77H97ON45rsvMPzA5XGXNHSYwy/zyxDN7D7nXBXwELABcJyZvR1zWVIE9z54CF85MVrsU31XEkt9V8rIvY8exAlHzIoWfAvBWtNY4PHOczvx+SeWs6rptbhLFBm8hPddhb+S1yWXXMI555wTdxkVKxuYf+ELX+CJJ56IuRqR5GpuXcxdH4xm5y3fwbn8J6qc34ZV13SEvlD4eX/91sxlxz4Njw1j79s3Y1mDFswphvkNd7DfNcfx4iaPkJ5SuFX/rEEnOrsTjYSIu4r8nHOX0Hkp8ZHAO0Tzp2Fm58ZTmRTL9xfO4fg1bYTDhqvvSmKp70o5Oeud2ZzQsA4Aa3ZYW5qPX5rCYU+1KPiVxEh631X4K52cdNJJXH/99XGXkRiPPx7NT6mRwCLFc92Ky9jihe9xzhEP9/2D/FZcurogo3+d39p+2XHjfQGTL9+QVU0KIIppfsMdfO6/j2TeGS8zbJ/CLCpy9s+/jx9oZep8ojnQyvb32Nwu9zXsM+HWNL/Fb07+Pv9x81OA+q4kk/qulJO1zQu48Adn8ev/upKwsZoPnt+RPR5eytrmBXGXJlIwSe+7Cn+lnaZ3KB5NBSFSTAH/veRezg5r8z8dhjjfx9LpnCkfvIIEwM5vbb/suPXelex15d6sarpnwNuTvlvU8AB/vvdUvrfPzQXZXnNQkM0kVqEXwCgUM7sOwDlXDzSbWZC5nwJq4qxNiqfR99R3JfHUd6WcNAUOmkI+mL0Th/yjWsGvJFKS+67X+0tkKFDwW3z6HIsUT3PrR3z/li/StHxU5yeyCwGFQaf/ty8Q5LdG8/WG/Uv+ojl+o+DXW/AWa65tY8IfNuHtBgUQpXTuO7fzymX7xl1G4mVXP+7PLQZ/B+py7tcBj8RSiRTdxUtu4P5zdlbflcRS35VyM+Oz65l58SnsOmspi9c9Fnc5IgWX9L6rkb9DnALJ0sp+vjUKWKTQjNvWzOSUl7/GYVPf7fxUJuh1fhuWrooWf+syAhgv1adF4FwYZILkTIAcBjx/5WHs/+T9xXlb0iM/WMnej89jbmovPve95we8nZUPjePJ1h4WDRzizKCtcNMrF0utma3L3jGzdc65YXEWJMUThms5bl5hRv2LlCP1XSk3YbiW8xZcFXcZIkWT9L6rkb9DmILf+OhzL1IcJ7+5iDVvTcz/ZBhmpmjoPAI4G+Lit+JamzJz+Abr31qbotf4be0f472zkGNf/LR0b1DW4wfLmfnSVFjbPOBtvPbaVN5bN6uAVSVLhYyEaHDO7Za945zbHWiKpRIRkUFS3xURKa2k912N/B1inHNcd911nHTSSXGXMuRpHmCRwlvW8AJ73nQ4L1T5jDi2reOJMMyM+A3XGwEMmRG92VG/YYh5HdNAtE8RAe2vB3C+zzOXHcGyhnuL+p6kd1csncGmPz+Ti/7rGqju56HN6mZuemd8cQpLkHKdAy3H+cCtzrmPAQdsBnw91opERAZBfVdEpLSS3HcV/g4hGm1afhQAixTee+tmsfv1h/PmxCfwdh/T8UR3AXD78znBbu4G88wH7Hwft2oFP5o3kug8scTtlx9ewfCLTuWHF10Dw/qw7kFoEIY0LxzNlUsvK36BFcwMgjK/DM7M5jjntgOmZB56y8zaevoYEZFypb4rIlJaSe+7Cn+HCAW/5UsBsEjhvbduFlv/6iDmn/0GNYfWdjzRJQAGohAYug2CO30s0dzBbsnHnP+DU5nbNLNYb0H6LeDfFl3JrjOPY9r53czB7AdR6AsQGNYMR/zhaOBPJauyEhngl+lhhHPuYDN71Dn3lS5Pbeucw8xuj6UwEZFBUN8VESmtpPddhb9DgILf8qcAWKTwFq97jL3+eBxfv3lTLrzino7F3MLOp3SzITC9LPYWLfQWfWzTvJHM+HRGoUuWQTMueHUY/3h1A2rGrVj/2cDDWtMQOsKWKp59YBovBn+Poc7KU8aXwX0BeBT4Up7nDFAIISIVSX1XRKS0ktx3Ff4mnILfyqEAWKTw5jfcwW/9yfz46UaqDtyg85M5o4Db7/cm85p9ZxwA3FbYYqUg5jbdyD6//yoPfOkdRk34FFyXr6tFI7zb1tXx54XjaWq9K4YqK4sBbWF5Hk+Y2c8y/54Sdy0iIoWivisiUlpJ77sKfxNMwW/lUQAsUniNLe/x62vO4ucH5lmYrS+Bbx4r3CeDrEqK6fXG2/jjU2dx7heeIl3T2uk5Cx1+SzXvLx7P1cs0ersvLL4VjfvMOVcDfBWYTM7xrZn9Iq6aREQGSn1XRKS0kt53Ff6KlBkFwCKFd/Ent3P2tWMYc/LqQW/rL+dN45OGPxegKimm3316K9vOO5q9Jr673nPL14zk5FcbY6iqMhlGUP4nlO8CVgMvAC0x1yIiMijquyIipZX0vqvwN4H+8z//k4suuijuMmQQFACLFFar/wmN6ybhwvXnge2vjxprMfwCVCXF1OYv5bxFT3L/hlM7Pb60YTj/unAV7zc8ElNllanQB8POuSOA3wMp4Coz++8uz58M/A/wUeahP5rZVT1scryZHVHQIkVEYqS+KyJSWknuuwp/E0ZTPSSHAmCRwvraw1sy5ztvQv3wuEuREmlseZ8/vHY4X56wjlRm7t+5y0ewqOGmmCurLEZhD4adcylgBnAYsBiY45y728ze6PLSW8zsnD5u9hnn3I5m9mrBChURiYn6rohIaSW97yr8TRAFv8mT/ZoqBBYZvJeb7uD963di0pkf9f7ibtibn/LMMv08VgrD57Y1M/lg0UkcMnoEHzTATat6Opku+RjQZgObH7sbewELzWwRgHPuZuBYoOvBcK+cc68BIdEx7SnOuUVEl8E5wMxsp4JVLSJSIuq7IiKllfS+q/A3IRT8JptGAYsMnlkzu89ax8vVm7D5qZ8NaBufzv4c9627rMCVSbHNbbqJ9PJv8nzLnZi19v4B0olh+PT7OGNj59zcnPtXmNkVmf9vDnyY89xiYO882/iqc+7zwNvAD8zswzyv2RzYpb/FiYiUM/VdEZHSSnrfVfibAAp+hwaNAhYZvNVNb/CXOWfw4xNu1vQPQ4jhM7vpeuj/AZ0AGAT9HwmxzMz2GMRe7wFuMrMW59wZwHXAwXle966ZvT+I/YiIlB/1XRGR0kp431X4KyIiQ8oF717ORv/xXU69+B6oru79A8IQWpqhsYmfP/Jl4OVilyhFoeB3oAzwKehlcB8BE3Luj6djoYton2bLc+5eBfy2m21t4pz7YXc7MrP/HWiRIiJxUd8VESmtpPddhb8V7p577om7BCmxc889lz/84Q9xlyFS0X67eAnffMpRu+Un0W/C7kbUB4b5QKsjWFfDMROWcfWyUlYqEj/DCAp7MDwH2MY5twXRQfAJwDdyX+CcG2tmSzJ3jwHmd7OtFDCcaM4zEZFEUN8VESmtpPddhb8Vbvr06XGXICX2+9//XuGvyCAtaniAL/7hO9z2tTmMnPgJeHlGhYaOMEgRtlRhgUfQlqYm7Ze+WJGYmTPaXFvhtmfmO+fOAWYRHcxeY2avO+d+Acw1s7uBc51zxwA+sAI4uZvNLTGzXxSsOBGRMqC+KyJSWknvuwp/RSqQFoATGbzZTdfxyvyvskddS7evsWwAHHiEfho/9PC8DQjDtSWsVCR+vgsKuj0zux+4v8tjF+X8/z+A/+jDpvTLUEQSSX1XRKS0ktx3Ff5WMC30NrQpABYZvGPmPc3t4YHsMuWtvM9b6AhCDws9zBxTJ7/LiYu+zZ9XzihxpSLxiVY/LttR74fEXYCISKGp74qIlFbS+67C3wql4FdEZPBa/U848fWXuKZtT3aevKjTc2Ze3o85ccvP+Nsr42hp+7gUJYrEzjACV54Hw2a2Iu4aREQKTX1XRKS0kt53Ff6KVDCN/hUZvLXNCzjhtc/49doT2G3MUjYdFf1uNVv/Z6utrYoRtU1MqN6VhQp/ZcgwAgo3B5qIiPRGfVdEpLSS3XcV/lYgjfqVXAqARQYvCFfz7+9eztafHc3Mz21EfU3zeq8xc7T6aQLz+HzdOBY2xFCoSAzK/DI4EZHEUd8VESmtpPddhb8iCaAAWKQwFjbcx6lvHMyPNtuKLUespjrVMem/GTQHVby9egQtAVr4TYaM6GC4+4URRUSksNR3RURKK+l9V+GvSEIoABYpjA/XPcr5Cx9lz7qT2GfkCHbdMBriGwItQYplLWkCMyYM25v31z0Sb7EiJWAJvwxORKTcqO+KiJRW0vuuwt8KoykfRERKY07TDbzQsgEbrJ7AV+unUZ8GB6xuhaWtrYy2TfhQo39lSDACS+7BsIhI+VHfFREprWT3XYW/Igmi0b8ihRWGa1nd9AbXNL3B6GG7MtqNp9W1srGNocFrAAvjLlGk6CzhB8MiIuVGfVdEpLSS3ncV/ookjAJgkeJY3jiP5cwD4ENvJAChNcZZkkhpmBEm+GBYRKTsqO+KiJRWwvuuwl8REZF+CsLVcZcgUjJGiG/JXQBDRKTcqO+KiJRW0vuuwt8Kovl+pa80+ldERArHCM2PuwgRkSFEfVdEpLSS3XcV/ooklAJgEREpBMMIw+ReBiciUm7Ud0VESivpfVfhb4X42te+FncJUoEUAIuIyKAZiR4JISJSdtR3RURKK+F9V+FvhfjrX/8adwkiIiIyBBnJXgBDRKTcqO+KiJRW0vuuwl+RhNPoXxERGZyQ0FrjLkJEZAhR3xURKa1k910v7gJEpPi++tWvxl2CiIhUMvP7d+uFc+4I59xbzrmFzrl/z/N8jXPulszzzznnJhfjbYmIlC31XRGR0kpw31X4KzIETJ8+Pe4SRESkYhmG369bT5xzKWAGcCQwFTjROTe1y8tOBVaa2dbA74D/U4Q3JiJSptR3RURKK9l9V+GvyBBw8sknx12CiIhULMMs7NetF3sBC81skZm1AjcDx3Z5zbHAdZn//w04xGkOIxEZMtR3RURKK9l9V3P+igwRmvtXurEMgvfjLkJEysqkLvdngb9xP7dR65ybm3P/CjO7IvP/zYEPc55bDOzd5ePbX2NmvnNuNTAaWNbPOsqR+q6IdKW+W1zquyLS1ZDquwp/K4CZxV2CJIQCYOnKzMbEXYOIlDczOyLuGpJEfVdEeqO+W1jquyLSm6T3XU37IDLE6GSCiIjE7CNgQs798ZnH8r7GOZcGRgLLS1KdiEjyqO+KiJRWWfVdhb8iIiIiUkpzgG2cc1s456qBE4C7u7zmbuA7mf8fDzxqOnspIjJQ6rsiIqVVVn1X0z6IDEGa/kFEROKSmdPsHGAWkAKuMbPXnXO/AOaa2d3A1cANzrmFwAqiA2YRERkA9V0RkdIqt77ryulknnOufIopI+X0NZJkGUoBsJkNnTcrIiIiIiIiIoKmfRAZ0oZS+CsiIiIiIiIiMtQo/BUZwsIwjLsEEREREREREREpEoW/ZU5TPkix6XtMRERERERERCSZFP6KiAJgEREREREREZEEUvgrIoACYBERERERERGRpFH4KyIiIiIiIiIiIpJACn9FREREREREREREEkjhr4iIiIiIiIiIiEgCpeMuoItlwPtxF1FOnHNxlyCSBJPiLkBEREREREREpNScFnkSERERERERERERSR5N+yAiIiIiIiIiIiKSQAp/RURERERERERERBJI4a+IiIiIiIiIiIhIAin8FREREREREREREUkghb8iIiIiIiIiIiIiCfT/ASDQ/wVmb/trAAAAAElFTkSuQmCC", + "text/plain": [ + "

      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(figsize=(20,10), nrows=2, ncols=4)\n", + "fig.tight_layout(pad=4.0, rect=[0, 0.03, 1, 0.95])\n", + "\n", + "fig.suptitle(\n", + " \"Distance Transform With and Without Oversampling\",\n", + " fontsize=\"xx-large\")\n", + "\n", + "# Create a custom colormap for the plate\n", + "# Thicknesses of 1 voxel = gray, thicknesses of 2 voxels = white\n", + "cmap = colors.ListedColormap([\"black\", \"gray\", \"white\"])\n", + "bounds=[0,1,2,3]\n", + "norm = colors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "ax1 = axs[0][0]\n", + "ax2 = axs[0][1]\n", + "ax3 = axs[0][2]\n", + "ax4 = axs[0][3]\n", + "ax5 = axs[1][0]\n", + "ax6 = axs[1][1]\n", + "ax7 = axs[1][2]\n", + "ax8 = axs[1][3]\n", + "\n", + "# Plot the top row (plate images)\n", + "view_slice = (slice(None), mask.shape[1]//2, slice(None))\n", + "im1 = ax1.imshow(mask[view_slice], extent=[0, 5, 0, 5], cmap=\"gray\", vmin=0, vmax=1, aspect=\"auto\")\n", + "ax1.grid()\n", + "ax1.set_ylabel(\"Thickness (voxels)\")\n", + "ax1.set_title(\"Cross-section of Plate (Thickness = 1 voxel)\", wrap=True)\n", + "ax1.set_yticks(np.arange(0, 5, step=1))\n", + "ax1.set_xticks(np.arange(0, 5, step=1))\n", + "\n", + "im2 = ax2.imshow(oversampled_mask[view_slice], extent=[0, 10, 0, 10], cmap=\"gray\", vmin=0, vmax=0.5, aspect=\"auto\")\n", + "ax2.grid()\n", + "ax2.set_title(\"Oversampled Plate (Thickness = 1 voxel)\", wrap=True)\n", + "ax2.set_yticks(np.arange(0, 10, step=1))\n", + "ax2.set_xticks(np.arange(0, 10, step=1))\n", + "\n", + "im3 = ax3.imshow(plate_oversample_mask[view_slice], extent=[0, 5, 0, 5], cmap=cmap, norm=norm, aspect=\"equal\")\n", + "ax3.grid()\n", + "ax3.set_title(\"Plate Thickness Mask (Oversampling = True)\", wrap=True)\n", + "ax3.set_yticks(np.arange(0, 5, step=1))\n", + "ax3.set_xticks(np.arange(0, 5, step=1))\n", + "divider3 = make_axes_locatable(ax3)\n", + "cax3 = divider3.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "cbar3 = plt.colorbar(im3, cax=cax3, label=\"Thickness (voxels)\")\n", + "\n", + "im4 = ax4.imshow(plate_no_oversample_mask[view_slice], extent=[0, 5, 0, 5], cmap=cmap, norm=norm, aspect=\"equal\")\n", + "ax4.grid()\n", + "ax4.set_title(\"Plate Thickness Mask (Oversampling = False)\", wrap=True)\n", + "ax4.set_yticks(np.arange(0, 5, step=1))\n", + "ax4.set_xticks(np.arange(0, 5, step=1))\n", + "divider4 = make_axes_locatable(ax4)\n", + "cax4 = divider4.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "cbar4 = plt.colorbar(im4, cax=cax4, label=\"Thickness (voxels)\")\n", + "\n", + "# Plot the bottom row (joint images)\n", + "view_slice = (slice(None), local_thickness.shape[1]//2, slice(None))\n", + "im5 = ax5.imshow(arr[view_slice], cmap=\"gray\", vmin=0, vmax=1, aspect=\"equal\")\n", + "ax5.set_title(\"Example Joint Mask\")\n", + "ax5.get_xaxis().set_visible(False)\n", + "ax5.get_yaxis().set_visible(False)\n", + "\n", + "im6 = ax6.imshow(dt_oversample[view_slice], cmap=\"inferno\", vmin=0, vmax=2.5, aspect=\"equal\")\n", + "ax6.set_title(\"Dilated Joint Space Mask (Oversampling = True)\", wrap=True)\n", + "divider6 = make_axes_locatable(ax6)\n", + "cax6 = divider6.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "cbar6 = plt.colorbar(im6, cax=cax6, label=\"Thickness (mm)\")\n", + "ax6.get_xaxis().set_visible(False)\n", + "ax6.get_yaxis().set_visible(False)\n", + "\n", + "im7 = ax7.imshow(dt_oversample_masked[view_slice], cmap=\"inferno\", vmin=0, vmax=2.5, aspect=\"equal\")\n", + "ax7.set_title(\"Joint Thickness Map (Oversampling = True)\", wrap=True)\n", + "divider7 = make_axes_locatable(ax7)\n", + "cax7 = divider7.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "cbar7 = plt.colorbar(im7, cax=cax7, label=\"Thickness (mm)\")\n", + "ax7.get_xaxis().set_visible(False)\n", + "ax7.get_yaxis().set_visible(False)\n", + "\n", + "im8 = ax8.imshow(dt_no_oversample_masked[view_slice], cmap=\"inferno\", vmin=0, vmax=2.5, aspect=\"equal\")\n", + "ax8.set_title(\"Joint Thickness Map (Oversampling = False)\", wrap=True)\n", + "divider8 = make_axes_locatable(ax8)\n", + "cax8 = divider8.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "cbar8 = plt.colorbar(im8, cax=cax8, label=\"Thickness (mm)\")\n", + "ax8.get_xaxis().set_visible(False)\n", + "ax8.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "503126da", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/manuscript/notebooks/JOSS_manuscript_Figure2_3.ipynb b/manuscript/notebooks/JOSS_manuscript_Figure2_3.ipynb new file mode 100644 index 0000000..a97b7e2 --- /dev/null +++ b/manuscript/notebooks/JOSS_manuscript_Figure2_3.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "31c4dc1e", + "metadata": {}, + "source": [ + "# Comparing IPL and ORMIR_XCT Calculated Thicknesses and BMD\n", + "\n", + "To compare the IPL and ORMIR_XCT algorithms for thickness estimation and bone mineral density (BMD) calculation, Bland-Altman and linear regression plots were generated using data from simulated shapes. The shapes used are freely available on Zenodo (https://doi.org/10.5281/zenodo.8155738) and include:\n", + "- Hollow spheres\n", + "- Solid speheres\n", + "- Hollow cylinders\n", + "- Solid cylinders\n", + "- Plates\n", + "\n", + "CSV files containing the calculated thickness of each shape using IPL and ORMIR_XCT are provided in the csv direcotry." + ] + }, + { + "cell_type": "markdown", + "id": "60fdeaf8", + "metadata": {}, + "source": [ + "## Step 1: Imports\n", + "\n", + "Import libraries and create dictionaries for the CSV filepaths. Functions to plot the data are provided in the current directory." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "709d90a5", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import pandas\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "\n", + "from joss_plots import bmd_plots, thickness_plots, get_bmd_list, get_thickness_list\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.linear_model import LinearRegression as sklearnLinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75d6912d", + "metadata": {}, + "outputs": [], + "source": [ + "thickness_files = {\n", + " \"Filled Spheres\": {\n", + " \"IPL\": os.path.join(\"csv\", \"F_SPH_JSW_THICKNESS_IPL.csv\"),\n", + " \"PY\": os.path.join(\"csv\", \"F_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv\")\n", + " },\n", + " \"Hollow Spheres\": {\n", + " \"IPL\": os.path.join(\"csv\", \"H_SPH_JSW_THICKNESS_IPL.csv\"),\n", + " \"PY\": os.path.join(\"csv\", \"H_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv\")\n", + " },\n", + " \"Filled Cylinders\": {\n", + " \"IPL\": os.path.join(\"csv\", \"F_CYL_JSW_THICKNESS_IPL.csv\"),\n", + " \"PY\": os.path.join(\"csv\", \"F_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv\")\n", + " },\n", + " \"Hollow Cylinders\": {\n", + " \"IPL\": os.path.join(\"csv\", \"H_CYL_JSW_THICKNESS_IPL.csv\"),\n", + " \"PY\": os.path.join(\"csv\", \"H_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv\")\n", + " },\n", + " \"Plates\": {\n", + " \"IPL\": os.path.join(\"csv\", \"PLATE_JSW_THICKNESS_IPL.csv\"),\n", + " \"PY\": os.path.join(\"csv\", \"PLATE_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv\")\n", + " }\n", + "}\n", + "\n", + "bmd_files = {\n", + " \"IPL\": os.path.join(\"csv\", \"bmd_ipl.csv\"),\n", + " \"PY\": os.path.join(\"csv\", \"bmd_py.csv\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2ba653d4", + "metadata": {}, + "source": [ + "## Step 2: Read CSV Files\n", + "\n", + "Read the data in the CSV files using Pandas and save the thickness data" + ] + }, + { + "cell_type": "markdown", + "id": "4d6ba998", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2369b04c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bmd_plots(bmd_files)" + ] + }, + { + "cell_type": "markdown", + "id": "e343cbfd", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f2fc046f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing data for Filled Spheres\n", + "Processing data for Hollow Spheres\n", + "Processing data for Filled Cylinders\n", + "Processing data for Hollow Cylinders\n", + "Processing data for Plates\n" + ] + }, + { + "data": { + "image/png": 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      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "thickness_plots(thickness_files)" + ] + }, + { + "cell_type": "markdown", + "id": "119ef2b6", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/manuscript/notebooks/JOSS_manuscript_Table1_2.ipynb b/manuscript/notebooks/JOSS_manuscript_Table1_2.ipynb new file mode 100644 index 0000000..9a22af8 --- /dev/null +++ b/manuscript/notebooks/JOSS_manuscript_Table1_2.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d33a5b74", + "metadata": {}, + "source": [ + "# Comparison of IPL and ORMIR_XCT Segmentations\n", + "\n", + "This notebook outputs tables of DICE coefficients, Jaccard indicies, and Hausdorff distances between IPL and ORMIR_XCT generated segmentations. In particular, automatic periosteal segmentation and trabecular segmentation are compared.\n", + "\n", + "CSV files of DICE coefficients, Jaccard indicies, and Hausdorff distances (mean and maximum) are provided in the csv directory and will be loaded using Pandas." + ] + }, + { + "cell_type": "markdown", + "id": "4e9e748b", + "metadata": {}, + "source": [ + "# Step 1: Imports\n", + "\n", + "Import libraries and load the CSV files." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "5db94b02", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "bfc3a708", + "metadata": {}, + "source": [ + "## Step 2: Generate the Tables" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "fe8d744b", + "metadata": {}, + "outputs": [], + "source": [ + "# Set table default options\n", + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('colheader_justify', 'center')" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "98106c6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
       DIP2 DICEDIP2 JACCARDDIP2 Mean Hausdorff (voxels)DIP2 Max Hausdorff (voxels)DIP3 DICEDIP3 JACCARDDIP3 Mean Hausdorff (voxels)DIP3 Max Hausdorff (voxels)TMC DICETMC JACCARDTMC Mean Hausdorff (voxels)TMC Max Hausdorff (voxels)
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      \n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "autocontour_seg_path = os.path.join(\"csv\", \"autocontour.csv\")\n", + "\n", + "df_autocontour = pd.read_csv(autocontour_seg_path)\n", + "\n", + "# Remove Hausdorff distance columns (in mm)\n", + "df_autocontour = df_autocontour.drop(columns=[\"Scan\", \"DIP2 Mean Hausdorff (mm)\", \"DIP2 Max Hausdorff (mm)\", \n", + " \"DIP3 Mean Hausdorff (mm)\", \"DIP3 Max Hausdorff (mm)\", \n", + " \"TMC Mean Hausdorff (mm)\", \"TMC Max Hausdorff (mm)\"])\n", + "\n", + "# Fill NaN values with the string 'No Seg'\n", + "df_autocontour.fillna(\"No Seg\", inplace=True)\n", + "\n", + "# Display the table\n", + "df_autocontour.style.format(precision=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41c44b75", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/manuscript/notebooks/LinearRegression.py b/manuscript/notebooks/LinearRegression.py new file mode 100644 index 0000000..547328c --- /dev/null +++ b/manuscript/notebooks/LinearRegression.py @@ -0,0 +1,39 @@ +import numpy as np +from sklearn.linear_model import LinearRegression as sklearnLinearRegression + +class LinearRegression: + def __init__(self): + self.model = None + self.slope = 0 + self.intercept = 0 + self.r_sq = 0 + self.x_new = None + self.y_new = None + + def get_model(self): + return self.model + + def get_slope(self): + return self.slope + + def get_intercept(self): + return self.intercept + + def get_r_sq(self): + return self.r_sq + + def get_x_new(self): + return self.x_new + + def get_y_new(self): + return self.y_new + + def fit_model(self, x_values, y_values, max_thickness): + self.model = sklearnLinearRegression() + self.model.fit(x_values, y_values) + self.r_sq = self.model.score(x_values, y_values) + self.intercept = self.model.intercept_ + self.slope = self.model.coef_ + + self.x_new = np.linspace(0, max_thickness, 1000) + self.y_new = self.model.predict(self.x_new[:, np.newaxis]) \ No newline at end of file diff --git a/manuscript/notebooks/csv/F_CYL_JSW_THICKNESS_IPL.csv b/manuscript/notebooks/csv/F_CYL_JSW_THICKNESS_IPL.csv new file mode 100644 index 0000000..ca94c82 --- /dev/null +++ b/manuscript/notebooks/csv/F_CYL_JSW_THICKNESS_IPL.csv @@ -0,0 +1,1001 @@ +Filename,Version,BG Th V1,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos,Version,BG Th V2,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos,Version,BG Th V3,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos 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+F_CYL_OUTR_10_LEN_22.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_32.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_42.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_52.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_62.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_72.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_82.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_10_LEN_92.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_11_LEN_102.LOG,1,9.996354,0.060274,10,-16.468122,269.226318,2,9.996354,0.060274,10,-16.468122,269.226318,3,9.996354,0.060274,10,-16.468122,269.226318 +F_CYL_OUTR_11_LEN_112.LOG,1,9.99668,0.057525,10,-17.266823,296.17041,2,9.99668,0.057525,10,-17.266823,296.17041,3,9.99668,0.057525,10,-17.266823,296.17041 +F_CYL_OUTR_11_LEN_12.LOG,1,9.968532,0.174657,10,-5.360486,26.758194,2,9.968532,0.174657,10,-5.360486,26.758194,3,9.968532,0.174657,10,-5.360486,26.758194 +F_CYL_OUTR_11_LEN_122.LOG,1,9.996953,0.05512,10,-18.030188,323.114838,2,9.996953,0.05512,10,-18.030188,323.114838,3,9.996953,0.05512,10,-18.030188,323.114838 +F_CYL_OUTR_11_LEN_132.LOG,1,9.997184,0.052993,10,-18.762487,350.058441,2,9.997184,0.052993,10,-18.762487,350.058441,3,9.997184,0.052993,10,-18.762487,350.058441 +F_CYL_OUTR_11_LEN_142.LOG,1,9.997382,0.051096,10,-19.467268,377.002289,2,9.997382,0.051096,10,-19.467268,377.002289,3,9.997382,0.051096,10,-19.467268,377.002289 +F_CYL_OUTR_11_LEN_152.LOG,1,9.997555,0.049388,10,-20.147434,403.946777,2,9.997555,0.049388,10,-20.147434,403.946777,3,9.997555,0.049388,10,-20.147434,403.946777 +F_CYL_OUTR_11_LEN_162.LOG,1,9.997706,0.047841,10,-20.805401,430.891937,2,9.997706,0.047841,10,-20.805401,430.891937,3,9.997706,0.047841,10,-20.805401,430.891937 +F_CYL_OUTR_11_LEN_172.LOG,1,9.99784,0.046431,10,-21.443161,457.836304,2,9.99784,0.046431,10,-21.443161,457.836304,3,9.99784,0.046431,10,-21.443161,457.836304 +F_CYL_OUTR_11_LEN_182.LOG,1,9.997958,0.045138,10,-22.062452,484.77951,2,9.997958,0.045138,10,-22.062452,484.77951,3,9.997958,0.045138,10,-22.062452,484.77951 +F_CYL_OUTR_11_LEN_192.LOG,1,9.998065,0.043948,10,-22.664883,511.724426,2,9.998065,0.043948,10,-22.664883,511.724426,3,9.998065,0.043948,10,-22.664883,511.724426 +F_CYL_OUTR_11_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_11_LEN_22.LOG,1,9.98297,0.129411,10,-7.460585,53.68578,2,9.98297,0.129411,10,-7.460585,53.68578,3,9.98297,0.129411,10,-7.460585,53.68578 +F_CYL_OUTR_11_LEN_32.LOG,1,9.988327,0.107427,10,-9.088354,80.624283,2,9.988327,0.107427,10,-9.088354,80.624283,3,9.988327,0.107427,10,-9.088354,80.624283 +F_CYL_OUTR_11_LEN_42.LOG,1,9.99112,0.093827,10,-10.466101,107.565704,2,9.99112,0.093827,10,-10.466101,107.565704,3,9.99112,0.093827,10,-10.466101,107.565704 +F_CYL_OUTR_11_LEN_52.LOG,1,9.992834,0.084354,10,-11.682509,134.507874,2,9.992834,0.084354,10,-11.682509,134.507874,3,9.992834,0.084354,10,-11.682509,134.507874 +F_CYL_OUTR_11_LEN_62.LOG,1,9.993994,0.077272,10,-12.783737,161.450974,2,9.993994,0.077272,10,-12.783737,161.450974,3,9.993994,0.077272,10,-12.783737,161.450974 +F_CYL_OUTR_11_LEN_72.LOG,1,9.99483,0.071718,10,-13.797358,188.394302,2,9.99483,0.071718,10,-13.797358,188.394302,3,9.99483,0.071718,10,-13.797358,188.394302 +F_CYL_OUTR_11_LEN_82.LOG,1,9.995462,0.067212,10,-14.741474,215.338257,2,9.995462,0.067212,10,-14.741474,215.338257,3,9.995462,0.067212,10,-14.741474,215.338257 +F_CYL_OUTR_11_LEN_92.LOG,1,9.995956,0.06346,10,-15.628641,242.281845,2,9.995956,0.06346,10,-15.628641,242.281845,3,9.995956,0.06346,10,-15.628641,242.281845 +F_CYL_OUTR_12_LEN_102.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_112.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_12.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_122.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_132.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_142.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_152.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_162.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_172.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_182.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_192.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_12_LEN_22.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_32.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_42.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_52.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_62.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_72.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_82.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_12_LEN_92.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_CYL_OUTR_13_LEN_102.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_112.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_12.LOG,1,11,0,11,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_122.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_132.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_142.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_152.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_162.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_172.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_182.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_192.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_13_LEN_22.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_32.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_42.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_52.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_62.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_72.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_82.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_13_LEN_92.LOG,1,12,0,12,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_14_LEN_102.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_112.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_12.LOG,1,11,0,11,0,0,2,11.990698,0.096027,12,-10.214075,102.386818,3,11.990698,0.096027,12,-10.214075,102.386818 +F_CYL_OUTR_14_LEN_122.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_132.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_142.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_152.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_162.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_172.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_182.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_192.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_14_LEN_22.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_32.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_42.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_52.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_62.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_72.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_82.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_14_LEN_92.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_CYL_OUTR_15_LEN_102.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_112.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_12.LOG,1,11,0,11,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_15_LEN_122.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_132.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_142.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_152.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_162.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_172.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_182.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_192.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_15_LEN_22.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_32.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_42.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_52.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_62.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_72.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_82.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_15_LEN_92.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_CYL_OUTR_16_LEN_102.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_112.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_12.LOG,1,11,0,11,0,0,2,11.994746,0.072309,12,-13.678186,185.173813,3,11.994746,0.072309,12,-13.678186,185.173813 +F_CYL_OUTR_16_LEN_122.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_132.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_142.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_152.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_162.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_172.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_182.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_192.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_16_LEN_22.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_32.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_42.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_52.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_62.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_72.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_82.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_16_LEN_92.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_CYL_OUTR_17_LEN_102.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_112.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_12.LOG,1,11,0,11,0,0,2,12,0,12,0,0,3,12,0,12,0,0 +F_CYL_OUTR_17_LEN_122.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_132.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_142.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_152.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_162.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_172.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_182.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_192.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_17_LEN_22.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_32.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_42.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_52.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_62.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_72.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_82.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_17_LEN_92.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_CYL_OUTR_18_LEN_102.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_112.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_12.LOG,1,11,0,11,0,0,2,11.993271,0.081771,12,-12.060685,143.508331,3,11.993271,0.081771,12,-12.060685,143.508331 +F_CYL_OUTR_18_LEN_122.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_132.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_142.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_152.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_162.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_172.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_182.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_192.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_18_LEN_22.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_32.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_42.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_52.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_62.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_72.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_82.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_18_LEN_92.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_CYL_OUTR_19_LEN_102.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_112.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_12.LOG,1,11,0,11,0,0,2,11.996572,0.058462,12,-16.983019,286.5047,3,11.996572,0.058462,12,-16.983019,286.5047 +F_CYL_OUTR_19_LEN_122.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_132.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_142.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_152.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_162.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_172.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_182.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_192.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_19_LEN_22.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_32.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_42.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_52.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_62.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_72.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_82.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_19_LEN_92.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_CYL_OUTR_2_LEN_102.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_112.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_12.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_122.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_132.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_142.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_152.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_162.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_172.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_182.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_192.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_2.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_22.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_32.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_42.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_52.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_62.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_72.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_82.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_2_LEN_92.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_CYL_OUTR_20_LEN_102.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_112.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_12.LOG,1,11,0,11,0,0,2,11.994499,0.073972,12,-13.366294,176.706482,3,11.994499,0.073972,12,-13.366294,176.706482 +F_CYL_OUTR_20_LEN_122.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_132.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_142.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_152.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_162.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_172.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_182.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_192.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_20_LEN_22.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_32.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_42.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_52.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_62.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_72.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_82.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_20_LEN_92.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_CYL_OUTR_21_LEN_102.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_112.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_12.LOG,1,11,0,11,0,0,2,11.997124,0.053561,12,-18.558607,342.503937,3,11.997124,0.053561,12,-18.558607,342.503937 +F_CYL_OUTR_21_LEN_122.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_132.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_142.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_152.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_162.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_172.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_182.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_192.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_21_LEN_22.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_32.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_42.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_52.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_62.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_72.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_82.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_21_LEN_92.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_CYL_OUTR_22_LEN_102.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_112.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_12.LOG,1,11,0,11,0,0,2,11.995516,0.066822,12,-14.827564,217.905472,3,11.995516,0.066822,12,-14.827564,217.905472 +F_CYL_OUTR_22_LEN_122.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_132.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_142.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_152.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_162.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_172.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_182.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_192.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_22_LEN_22.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_32.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_42.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_52.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_62.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_72.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_82.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_22_LEN_92.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_CYL_OUTR_23_LEN_102.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_112.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_12.LOG,1,11,0,11,0,0,2,11.997617,0.048761,12,-20.406372,414.502594,3,11.997617,0.048761,12,-20.406372,414.502594 +F_CYL_OUTR_23_LEN_122.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_132.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_142.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_152.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_162.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_172.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_182.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_192.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_23_LEN_22.LOG,1,21,0,21,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_32.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_42.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_52.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_62.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_72.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_82.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_23_LEN_92.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_24_LEN_102.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_112.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_12.LOG,1,11,0,11,0,0,2,11.996175,0.061739,12,-16.070349,256.304962,3,11.996175,0.061739,12,-16.070349,256.304962 +F_CYL_OUTR_24_LEN_122.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_132.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_142.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_152.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_162.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_172.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_182.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_192.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_24_LEN_22.LOG,1,21,0,21,0,0,2,21.991915,0.089558,22,-10.984165,118.665642,3,21.991915,0.089558,22,-10.984165,118.665642 +F_CYL_OUTR_24_LEN_32.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_42.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_52.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_62.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_72.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_82.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_24_LEN_92.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_CYL_OUTR_25_LEN_102.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_112.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_12.LOG,1,11,0,11,0,0,2,11.99795,0.045241,12,-22.00956,482.503021,3,11.99795,0.045241,12,-22.00956,482.503021 +F_CYL_OUTR_25_LEN_122.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_132.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_142.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_152.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_162.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_172.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_182.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_192.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_25_LEN_22.LOG,1,21,0,21,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_25_LEN_32.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_42.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_52.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_62.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_72.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_82.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_25_LEN_92.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_CYL_OUTR_26_LEN_102.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_112.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_12.LOG,1,11,0,11,0,0,2,11.996768,0.056764,12,-17.500139,304.303986,3,11.996768,0.056764,12,-17.500139,304.303986 +F_CYL_OUTR_26_LEN_122.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_132.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_142.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_152.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_162.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_172.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_182.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_192.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_26_LEN_22.LOG,1,21,0,21,0,0,2,21.99571,0.065356,22,-15.168156,228.095322,3,21.99571,0.065356,22,-15.168156,228.095322 +F_CYL_OUTR_26_LEN_32.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_42.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_52.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_62.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_72.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_82.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_26_LEN_92.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_CYL_OUTR_27_LEN_102.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_112.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_12.LOG,1,11,0,11,0,0,2,11.998259,0.041694,12,-23.897396,569.168396,3,11.998259,0.041694,12,-23.897396,569.168396 +F_CYL_OUTR_27_LEN_122.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_132.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_142.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_152.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_162.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_172.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_182.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_192.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_27_LEN_22.LOG,1,21,0,21,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_27_LEN_32.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_42.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_52.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_62.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_72.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_82.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_27_LEN_92.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_CYL_OUTR_28_LEN_102.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_112.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_12.LOG,1,11,0,11,0,0,2,11.996161,0.061851,12,-16.041416,255.361877,3,11.996161,0.061851,12,-16.041416,255.361877 +F_CYL_OUTR_28_LEN_122.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_132.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_142.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_152.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_162.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_172.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_182.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_192.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_28_LEN_22.LOG,1,21,0,21,0,0,2,21.994478,0.074109,22,-13.343571,176.065018,3,21.994478,0.074109,22,-13.343571,176.065018 +F_CYL_OUTR_28_LEN_32.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_42.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_52.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_62.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_72.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_82.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_28_LEN_92.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_CYL_OUTR_29_LEN_102.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_112.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_12.LOG,1,11,0,11,0,0,2,11.998493,0.03879,12,-25.698626,658.502014,3,11.998493,0.03879,12,-25.698626,658.502014 +F_CYL_OUTR_29_LEN_122.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_132.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_142.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_152.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_162.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_172.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_182.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_192.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_29_LEN_22.LOG,1,21,0,21,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_29_LEN_32.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_42.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_52.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_62.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_72.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_82.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_29_LEN_92.LOG,1,28,0,28,0,0,2,28,0,28,0,0,3,28,0,28,0,0 +F_CYL_OUTR_3_LEN_102.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_112.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_12.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_122.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_132.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_142.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_152.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_162.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_172.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_182.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_192.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_3_LEN_22.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_32.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_42.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_52.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_62.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_72.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_82.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_3_LEN_92.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_CYL_OUTR_30_LEN_102.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_112.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_12.LOG,1,11,0,11,0,0,2,11.996639,0.057876,12,-17.16008,292.503571,3,11.996639,0.057876,12,-17.16008,292.503571 +F_CYL_OUTR_30_LEN_122.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_132.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_142.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_152.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_162.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_172.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_182.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_192.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_30_LEN_22.LOG,1,21,0,21,0,0,2,21.99518,0.069266,22,-14.296973,202.417496,3,21.99518,0.069266,22,-14.296973,202.417496 +F_CYL_OUTR_30_LEN_32.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_42.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_52.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_62.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_72.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_82.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_30_LEN_92.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_CYL_OUTR_31_LEN_102.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_112.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_12.LOG,1,11,0,11,0,0,2,11.998663,0.036549,12,-27.284082,742.503235,3,11.998663,0.036549,12,-27.284082,742.503235 +F_CYL_OUTR_31_LEN_122.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_132.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_142.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_152.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_162.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_172.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_182.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_192.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_CYL_OUTR_31_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_31_LEN_22.LOG,1,21,0,21,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_CYL_OUTR_31_LEN_32.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 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+F_CYL_OUTR_33_LEN_102.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_112.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_12.LOG,1,11,0,11,0,0,2,11.998061,0.043991,12,-22.641834,510.702209,3,11.998061,0.043991,12,-22.641834,510.702209 +F_CYL_OUTR_33_LEN_122.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_132.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_142.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_152.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_162.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_172.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_182.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_192.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_33_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 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+F_CYL_OUTR_38_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_38_LEN_22.LOG,1,21,0,21,0,0,2,21.996058,0.062663,22,-15.831774,248.655899,3,21.996058,0.062663,22,-15.831774,248.655899 +F_CYL_OUTR_38_LEN_32.LOG,1,31,0,31,0,0,2,31.99564,0.065888,32,-15.044448,224.342651,3,31.99564,0.065888,32,-15.044448,224.342651 +F_CYL_OUTR_38_LEN_42.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_CYL_OUTR_38_LEN_52.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_CYL_OUTR_38_LEN_62.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_CYL_OUTR_38_LEN_72.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_CYL_OUTR_38_LEN_82.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_CYL_OUTR_38_LEN_92.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_CYL_OUTR_39_LEN_102.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_112.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_12.LOG,1,11,0,11,0,0,2,11.99861,0.037248,12,-26.770342,714.701111,3,11.99861,0.037248,12,-26.770342,714.701111 +F_CYL_OUTR_39_LEN_122.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_132.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_142.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_152.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_162.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_172.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_182.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_192.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_39_LEN_22.LOG,1,21,0,21,0,0,2,21.998236,0.041968,22,-23.742514,561.72937,3,21.998236,0.041968,22,-23.742514,561.72937 +F_CYL_OUTR_39_LEN_32.LOG,1,31,0,31,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_CYL_OUTR_39_LEN_42.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_CYL_OUTR_39_LEN_52.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 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+F_CYL_OUTR_7_LEN_52.LOG,1,6,0,6,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +F_CYL_OUTR_7_LEN_62.LOG,1,6,0,6,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +F_CYL_OUTR_7_LEN_72.LOG,1,6,0,6,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +F_CYL_OUTR_7_LEN_82.LOG,1,6,0,6,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +F_CYL_OUTR_7_LEN_92.LOG,1,6,0,6,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +F_CYL_OUTR_8_LEN_102.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_112.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_12.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_122.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_132.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_142.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_152.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_162.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_172.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_182.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_192.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_8_LEN_22.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_32.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_42.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_52.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_62.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_72.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_82.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_8_LEN_92.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_CYL_OUTR_9_LEN_102.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_112.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_12.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_122.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_132.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_142.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_152.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_162.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_172.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_182.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_192.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_2.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +F_CYL_OUTR_9_LEN_22.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_32.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_42.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_52.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_62.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_72.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_82.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_CYL_OUTR_9_LEN_92.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 \ No newline at end of file diff --git a/manuscript/notebooks/csv/F_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv b/manuscript/notebooks/csv/F_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv new file mode 100644 index 0000000..af7de08 --- /dev/null +++ b/manuscript/notebooks/csv/F_CYL_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv @@ -0,0 +1,1001 @@ +Filename,main_th,main_th_sd,main_th_min,main_th_max +F_CYL_OUTR_1_LEN_102_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_112_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_12_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_122_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_132_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_142_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_152_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_162_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_172_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_182_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_192_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_22_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_32_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_42_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_52_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_62_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_72_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_82_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_1_LEN_92_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_10_LEN_102_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_112_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_12_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_122_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_132_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_142_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_152_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_162_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_172_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_182_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_192_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_10_LEN_22_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_32_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_42_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_52_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_62_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_72_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_82_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_10_LEN_92_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_11_LEN_102_OVERSAMPLE_SKEL,9.88948565,0.229504622,3,9.899494937 +F_CYL_OUTR_11_LEN_112_OVERSAMPLE_SKEL,9.89040891,0.218682967,3,9.899494937 +F_CYL_OUTR_11_LEN_12_OVERSAMPLE_SKEL,9.782400998,0.77694937,3,9.899494937 +F_CYL_OUTR_11_LEN_122_OVERSAMPLE_SKEL,9.891176231,0.209260614,3,9.899494937 +F_CYL_OUTR_11_LEN_132_OVERSAMPLE_SKEL,9.891824043,0.200959861,3,9.899494937 +F_CYL_OUTR_11_LEN_142_OVERSAMPLE_SKEL,9.892378249,0.193574505,3,9.899494937 +F_CYL_OUTR_11_LEN_152_OVERSAMPLE_SKEL,9.89285777,0.18694779,3,9.899494937 +F_CYL_OUTR_11_LEN_162_OVERSAMPLE_SKEL,9.89327675,0.180958137,3,9.899494937 +F_CYL_OUTR_11_LEN_172_OVERSAMPLE_SKEL,9.893645974,0.175509607,3,9.899494937 +F_CYL_OUTR_11_LEN_182_OVERSAMPLE_SKEL,9.893973808,0.17052534,3,9.899494937 +F_CYL_OUTR_11_LEN_192_OVERSAMPLE_SKEL,9.894266842,0.16594294,3,9.899494937 +F_CYL_OUTR_11_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_11_LEN_22_OVERSAMPLE_SKEL,9.84599625,0.528395861,3,9.899494937 +F_CYL_OUTR_11_LEN_32_OVERSAMPLE_SKEL,9.864825614,0.426130455,3,9.899494937 +F_CYL_OUTR_11_LEN_42_OVERSAMPLE_SKEL,9.873851175,0.36680447,3,9.899494937 +F_CYL_OUTR_11_LEN_52_OVERSAMPLE_SKEL,9.879148119,0.326897137,3,9.899494937 +F_CYL_OUTR_11_LEN_62_OVERSAMPLE_SKEL,9.882631426,0.297701379,3,9.899494937 +F_CYL_OUTR_11_LEN_72_OVERSAMPLE_SKEL,9.885096405,0.275148892,3,9.899494937 +F_CYL_OUTR_11_LEN_82_OVERSAMPLE_SKEL,9.886932663,0.257050598,3,9.899494937 +F_CYL_OUTR_11_LEN_92_OVERSAMPLE_SKEL,9.888353536,0.242110176,3,9.899494937 +F_CYL_OUTR_12_LEN_102_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_112_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_12_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_122_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_132_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_142_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_152_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_162_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_172_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_182_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_192_OVERSAMPLE_SKEL,11,0,11,11 +F_CYL_OUTR_12_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 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+F_CYL_OUTR_6_LEN_112_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_12_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_122_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_132_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_142_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_152_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_162_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_172_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_182_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_192_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_6_LEN_22_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_32_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_42_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_52_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_62_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_72_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_82_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_6_LEN_92_OVERSAMPLE_SKEL,5,0,5,5 +F_CYL_OUTR_7_LEN_102_OVERSAMPLE_SKEL,5.821765464,0.161002964,3,5.830951895 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+F_CYL_OUTR_7_LEN_42_OVERSAMPLE_SKEL,5.80796717,0.254047932,3,5.830951895 +F_CYL_OUTR_7_LEN_52_OVERSAMPLE_SKEL,5.81256909,0.227382734,3,5.830951895 +F_CYL_OUTR_7_LEN_62_OVERSAMPLE_SKEL,5.815635654,0.207665487,3,5.830951895 +F_CYL_OUTR_7_LEN_72_OVERSAMPLE_SKEL,5.817825379,0.192323241,3,5.830951895 +F_CYL_OUTR_7_LEN_82_OVERSAMPLE_SKEL,5.819467303,0.179945634,3,5.830951895 +F_CYL_OUTR_7_LEN_92_OVERSAMPLE_SKEL,5.820744136,0.169686408,3,5.830951895 +F_CYL_OUTR_8_LEN_102_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_112_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_12_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_122_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_132_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_142_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_152_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_162_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_172_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_182_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_192_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_8_LEN_22_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_32_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_42_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_52_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_62_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_72_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_82_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_8_LEN_92_OVERSAMPLE_SKEL,7,0,7,7 +F_CYL_OUTR_9_LEN_102_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_112_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_12_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_122_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_132_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_142_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_152_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_162_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_172_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_182_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_192_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_2_OVERSAMPLE_SKEL,1,0,1,1 +F_CYL_OUTR_9_LEN_22_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_32_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_42_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_52_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_62_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_72_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_82_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_CYL_OUTR_9_LEN_92_OVERSAMPLE_SKEL,8.602325267,3.55E-15,8.602325267,8.602325267 \ No newline at end of file diff --git a/manuscript/notebooks/csv/F_SPH_JSW_THICKNESS_IPL.csv b/manuscript/notebooks/csv/F_SPH_JSW_THICKNESS_IPL.csv new file mode 100644 index 0000000..7d87439 --- /dev/null +++ b/manuscript/notebooks/csv/F_SPH_JSW_THICKNESS_IPL.csv @@ -0,0 +1,51 @@ +Filename,Version,BG Th V1,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos,Version,BG Th V2,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos,Version,BG Th V3,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos +F_SPH_OUTR_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_SPH_OUTR_10_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +F_SPH_OUTR_11_DT_JSW_B1.LOG,1,10,0,10,0,0,2,10,0,10,0,0,3,10,0,10,0,0 +F_SPH_OUTR_12_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_SPH_OUTR_13_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +F_SPH_OUTR_14_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +F_SPH_OUTR_15_DT_JSW_B1.LOG,1,14,0,14,0,0,2,14,0,14,0,0,3,14,0,14,0,0 +F_SPH_OUTR_16_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +F_SPH_OUTR_17_DT_JSW_B1.LOG,1,16,0,16,0,0,2,16,0,16,0,0,3,16,0,16,0,0 +F_SPH_OUTR_18_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +F_SPH_OUTR_19_DT_JSW_B1.LOG,1,18,0,18,0,0,2,18,0,18,0,0,3,18,0,18,0,0 +F_SPH_OUTR_2_DT_JSW_B1.LOG,1,1,0,1,0,0,2,1,0,1,0,0,3,2,0,2,0,0 +F_SPH_OUTR_20_DT_JSW_B1.LOG,1,19,0,19,0,0,2,19,0,19,0,0,3,19,0,19,0,0 +F_SPH_OUTR_21_DT_JSW_B1.LOG,1,20,0,20,0,0,2,20,0,20,0,0,3,20,0,20,0,0 +F_SPH_OUTR_22_DT_JSW_B1.LOG,1,21,0,21,0,0,2,21,0,21,0,0,3,21,0,21,0,0 +F_SPH_OUTR_23_DT_JSW_B1.LOG,1,22,0,22,0,0,2,22,0,22,0,0,3,22,0,22,0,0 +F_SPH_OUTR_24_DT_JSW_B1.LOG,1,23,0,23,0,0,2,23,0,23,0,0,3,23,0,23,0,0 +F_SPH_OUTR_25_DT_JSW_B1.LOG,1,24,0,24,0,0,2,24,0,24,0,0,3,24,0,24,0,0 +F_SPH_OUTR_26_DT_JSW_B1.LOG,1,25,0,25,0,0,2,25,0,25,0,0,3,25,0,25,0,0 +F_SPH_OUTR_27_DT_JSW_B1.LOG,1,26,0,26,0,0,2,26,0,26,0,0,3,26,0,26,0,0 +F_SPH_OUTR_28_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_SPH_OUTR_29_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +F_SPH_OUTR_3_DT_JSW_B1.LOG,1,2,0,2,0,0,2,2,0,2,0,0,3,3,0,3,0,0 +F_SPH_OUTR_30_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +F_SPH_OUTR_31_DT_JSW_B1.LOG,1,30,0,30,0,0,2,30,0,30,0,0,3,30,0,30,0,0 +F_SPH_OUTR_32_DT_JSW_B1.LOG,1,31,0,31,0,0,2,31,0,31,0,0,3,31,0,31,0,0 +F_SPH_OUTR_33_DT_JSW_B1.LOG,1,32,0,32,0,0,2,32,0,32,0,0,3,32,0,32,0,0 +F_SPH_OUTR_34_DT_JSW_B1.LOG,1,33,0,33,0,0,2,33,0,33,0,0,3,33,0,33,0,0 +F_SPH_OUTR_35_DT_JSW_B1.LOG,1,34,0,34,0,0,2,34,0,34,0,0,3,34,0,34,0,0 +F_SPH_OUTR_36_DT_JSW_B1.LOG,1,35,0,35,0,0,2,35,0,35,0,0,3,35,0,35,0,0 +F_SPH_OUTR_37_DT_JSW_B1.LOG,1,36,0,36,0,0,2,36,0,36,0,0,3,36,0,36,0,0 +F_SPH_OUTR_38_DT_JSW_B1.LOG,1,37,0,37,0,0,2,37,0,37,0,0,3,37,0,37,0,0 +F_SPH_OUTR_39_DT_JSW_B1.LOG,1,38,0,38,0,0,2,38,0,38,0,0,3,38,0,38,0,0 +F_SPH_OUTR_4_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +F_SPH_OUTR_40_DT_JSW_B1.LOG,1,39,0,39,0,0,2,39,0,39,0,0,3,39,0,39,0,0 +F_SPH_OUTR_41_DT_JSW_B1.LOG,1,40,0,40,0,0,2,40,0,40,0,0,3,40,0,40,0,0 +F_SPH_OUTR_42_DT_JSW_B1.LOG,1,41,0,41,0,0,2,41,0,41,0,0,3,41,0,41,0,0 +F_SPH_OUTR_43_DT_JSW_B1.LOG,1,41,0,41,0,0,2,41,0,41,0,0,3,41,0,41,0,0 +F_SPH_OUTR_44_DT_JSW_B1.LOG,1,43,0,43,0,0,2,43,0,43,0,0,3,43,0,43,0,0 +F_SPH_OUTR_45_DT_JSW_B1.LOG,1,43,0,43,0,0,2,43,0,43,0,0,3,43,0,43,0,0 +F_SPH_OUTR_46_DT_JSW_B1.LOG,1,45,0,45,0,0,2,45,0,45,0,0,3,45,0,45,0,0 +F_SPH_OUTR_47_DT_JSW_B1.LOG,1,46,0,46,0,0,2,46,0,46,0,0,3,46,0,46,0,0 +F_SPH_OUTR_48_DT_JSW_B1.LOG,1,47,0,47,0,0,2,47,0,47,0,0,3,47,0,47,0,0 +F_SPH_OUTR_49_DT_JSW_B1.LOG,1,48,0,48,0,0,2,48,0,48,0,0,3,48,0,48,0,0 +F_SPH_OUTR_5_DT_JSW_B1.LOG,1,4,0,4,0,0,2,4,0,4,0,0,3,4,0,4,0,0 +F_SPH_OUTR_50_DT_JSW_B1.LOG,1,49,0,49,0,0,2,49,0,49,0,0,3,49,0,49,0,0 +F_SPH_OUTR_6_DT_JSW_B1.LOG,1,5,0,5,0,0,2,5,0,5,0,0,3,5,0,5,0,0 +F_SPH_OUTR_7_DT_JSW_B1.LOG,1,6,0,6,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +F_SPH_OUTR_8_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +F_SPH_OUTR_9_DT_JSW_B1.LOG,1,8,0,8,0,0,2,8,0,8,0,0,3,8,0,8,0,0 \ No newline at end of file diff --git a/manuscript/notebooks/csv/F_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv b/manuscript/notebooks/csv/F_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv new file mode 100644 index 0000000..513b662 --- /dev/null +++ b/manuscript/notebooks/csv/F_SPH_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv @@ -0,0 +1,51 @@ +Filename,main_th,main_th_sd,main_th_min,main_th_max +F_SPH_OUTR_1_OVERSAMPLE_SKEL,1,0,1,1 +F_SPH_OUTR_10_OVERSAMPLE_SKEL,8.602325267,1.78E-15,8.602325267,8.602325267 +F_SPH_OUTR_11_OVERSAMPLE_SKEL,9.860702469,0.254933224,8.185352772,9.899494937 +F_SPH_OUTR_12_OVERSAMPLE_SKEL,10.32991358,0.137261407,9,10.34408043 +F_SPH_OUTR_13_OVERSAMPLE_SKEL,11.38415672,0.41963734,8.185352772,11.44552314 +F_SPH_OUTR_14_OVERSAMPLE_SKEL,12.4498996,1.78E-15,12.4498996,12.4498996 +F_SPH_OUTR_15_OVERSAMPLE_SKEL,13.31825396,0.53212689,8.185352772,13.37908816 +F_SPH_OUTR_16_OVERSAMPLE_SKEL,14.52583905,7.11E-15,14.52583905,14.52583905 +F_SPH_OUTR_17_OVERSAMPLE_SKEL,15.57733918,0.135400057,13.92838828,15.58845727 +F_SPH_OUTR_18_OVERSAMPLE_SKEL,16.33130407,0.116336661,14.79864859,16.34013464 +F_SPH_OUTR_19_OVERSAMPLE_SKEL,17.74823935,3.55E-15,17.74823935,17.74823935 +F_SPH_OUTR_2_OVERSAMPLE_SKEL,1,0,1,1 +F_SPH_OUTR_20_OVERSAMPLE_SKEL,18.41195264,3.55E-15,18.41195264,18.41195264 +F_SPH_OUTR_21_OVERSAMPLE_SKEL,19.66321589,0.164248904,16.09347694,19.67231557 +F_SPH_OUTR_22_OVERSAMPLE_SKEL,20.26186399,0.126528547,18.84144368,20.27313493 +F_SPH_OUTR_23_OVERSAMPLE_SKEL,21.40665536,0.232764993,16.09347694,21.42428529 +F_SPH_OUTR_24_OVERSAMPLE_SKEL,22.32771678,0.124820728,20.85665361,22.3383079 +F_SPH_OUTR_25_OVERSAMPLE_SKEL,23.54900268,0.245079499,16.09347694,23.55843798 +F_SPH_OUTR_26_OVERSAMPLE_SKEL,24.39104301,0.051768168,22.69361144,24.39262184 +F_SPH_OUTR_27_OVERSAMPLE_SKEL,25.49352001,0.375479613,16.55294536,25.51470164 +F_SPH_OUTR_28_OVERSAMPLE_SKEL,26.4360698,0.066030109,24.71841419,26.43860813 +F_SPH_OUTR_29_OVERSAMPLE_SKEL,27.30137787,0.418841913,16.55294536,27.33130074 +F_SPH_OUTR_3_OVERSAMPLE_SKEL,1.732050808,2.22E-16,1.732050808,1.732050808 +F_SPH_OUTR_30_OVERSAMPLE_SKEL,28.33532102,0.055549397,26.73948391,28.33725463 +F_SPH_OUTR_31_OVERSAMPLE_SKEL,29.42463619,0.435167173,16.55294536,29.44486373 +F_SPH_OUTR_32_OVERSAMPLE_SKEL,30.37693612,0.080269596,28.75760769,30.38091506 +F_SPH_OUTR_33_OVERSAMPLE_SKEL,31.40301059,0.242892828,24.06241883,31.41655614 +F_SPH_OUTR_34_OVERSAMPLE_SKEL,32.28987672,0.092432329,30.77336511,32.29551052 +F_SPH_OUTR_35_OVERSAMPLE_SKEL,33.37923534,0.271489725,24.06241883,33.39161571 +F_SPH_OUTR_36_OVERSAMPLE_SKEL,34.33067189,0.095408208,32.78719262,34.33656943 +F_SPH_OUTR_37_OVERSAMPLE_SKEL,35.36473786,0.084250817,33.86738844,35.36947837 +F_SPH_OUTR_38_OVERSAMPLE_SKEL,36.37011035,0.070599617,34.68429039,36.37306696 +F_SPH_OUTR_39_OVERSAMPLE_SKEL,37.34306853,0.100523831,35.81898938,37.34969879 +F_SPH_OUTR_4_OVERSAMPLE_SKEL,3,0,3,3 +F_SPH_OUTR_40_OVERSAMPLE_SKEL,38.40015858,0.094104379,36.81032464,38.40572874 +F_SPH_OUTR_41_OVERSAMPLE_SKEL,39.32005738,0.204853466,34.10278581,39.33192088 +F_SPH_OUTR_42_OVERSAMPLE_SKEL,40.3336491,0.062794422,38.71692137,40.33608806 +F_SPH_OUTR_43_OVERSAMPLE_SKEL,41.40514802,0.124185374,37.98683983,41.41255848 +F_SPH_OUTR_44_OVERSAMPLE_SKEL,42.26583392,0.119532349,38.92300091,42.27292278 +F_SPH_OUTR_45_OVERSAMPLE_SKEL,43.29476709,0.114118358,39.9374511,43.30127019 +F_SPH_OUTR_46_OVERSAMPLE_SKEL,44.39325375,0.06664361,42.74342055,44.39594576 +F_SPH_OUTR_47_OVERSAMPLE_SKEL,45.37114857,0.105492266,41.89272013,45.37620522 +F_SPH_OUTR_48_OVERSAMPLE_SKEL,46.33036589,0.10966016,42.93017587,46.33573135 +F_SPH_OUTR_49_OVERSAMPLE_SKEL,47.35471194,0.108590695,43.8520239,47.36032094 +F_SPH_OUTR_5_OVERSAMPLE_SKEL,4.242640687,8.88E-16,4.242640687,4.242640687 +F_SPH_OUTR_50_OVERSAMPLE_SKEL,48.27407524,0.097921816,46.7653718,48.28043082 +F_SPH_OUTR_6_OVERSAMPLE_SKEL,4.358898944,0,4.358898944,4.358898944 +F_SPH_OUTR_7_OVERSAMPLE_SKEL,5.830951895,1.78E-15,5.830951895,5.830951895 +F_SPH_OUTR_8_OVERSAMPLE_SKEL,6.557438524,8.88E-16,6.557438524,6.557438524 +F_SPH_OUTR_9_OVERSAMPLE_SKEL,7.681145748,1.78E-15,7.681145748,7.681145748 \ No newline at end of file diff --git a/manuscript/notebooks/csv/H_CYL_JSW_THICKNESS_IPL.csv b/manuscript/notebooks/csv/H_CYL_JSW_THICKNESS_IPL.csv new file mode 100644 index 0000000..a0e2f87 --- /dev/null +++ b/manuscript/notebooks/csv/H_CYL_JSW_THICKNESS_IPL.csv @@ -0,0 +1,901 @@ +Filename,Version,BG Th V1,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos,Version,BG Th V2,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos,Version,BG Th V3,BG Th.Sd,BG Th.Max,BG Th.Skew,BG Th.Kurtos +H_CYL_OUTR_11_INR_1_LEN_102.LOG,1,5,0,5,0,0,2,5,0,5,0,0,3,5,0,5,0,0 +H_CYL_OUTR_11_INR_1_LEN_112.LOG,1,5,0,5,0,0,2,5,0,5,0,0,3,5,0,5,0,0 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+PLATE_WIDTH_11_LEN_21_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_23_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_25_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_27_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_29_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_11_LEN_31_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_33_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_35_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_37_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_39_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_41_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_43_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_45_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_47_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_49_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_11_LEN_7_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_11_LEN_9_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_12_LEN_11_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_13_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_15_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_17_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_19_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_21_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_23_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_25_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_27_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_29_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_12_LEN_31_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_33_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_35_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_37_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_39_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_41_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_43_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_45_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_47_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_49_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_12_LEN_7_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_12_LEN_9_DT_JSW_B1.LOG,1,11,0,11,0,0,2,11,0,11,0,0,3,11,0,11,0,0 +PLATE_WIDTH_13_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_13_LEN_11_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_13_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_15_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_17_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_19_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_21_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_23_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_25_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_27_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_29_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_13_LEN_31_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_33_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_35_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_37_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_39_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_41_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_43_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_45_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_47_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_49_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_13_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_13_LEN_9_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_14_LEN_11_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_13_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_15_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_17_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_19_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_21_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_23_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_25_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_27_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_29_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_14_LEN_31_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_33_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_35_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_37_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_39_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_41_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_43_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_45_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_47_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_49_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_14_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_14_LEN_9_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13,0,13,0,0,3,13,0,13,0,0 +PLATE_WIDTH_15_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_15_LEN_11_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_13_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_15_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_17_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_19_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_21_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_23_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_25_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_27_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_29_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_15_LEN_31_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_33_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_35_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_37_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_39_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_41_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_43_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_45_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_47_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_49_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_15_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_15_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13.975436,0.154823,14,-6.139227,35.704231,3,13.975436,0.154823,14,-6.139227,35.704231 +PLATE_WIDTH_15_LEN_9_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_16_LEN_11_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_13_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_15_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_17_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_19_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_21_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_23_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_25_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_27_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_29_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_16_LEN_31_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_33_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_35_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_37_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_39_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_41_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_43_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_45_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_47_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_49_DT_JSW_B1.LOG,1,15,0,15,0,0,2,15,0,15,0,0,3,15,0,15,0,0 +PLATE_WIDTH_16_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 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+PLATE_WIDTH_17_LEN_29_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_17_LEN_31_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_33_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_35_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_37_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_39_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_41_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_43_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_45_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_47_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_17_LEN_49_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 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+PLATE_WIDTH_18_LEN_25_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_27_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_29_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_18_LEN_31_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_33_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_35_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_37_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_39_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_41_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_43_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 +PLATE_WIDTH_18_LEN_45_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17,0,17,0,0,3,17,0,17,0,0 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+PLATE_WIDTH_27_LEN_43_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_27_LEN_45_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_27_LEN_47_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_27_LEN_49_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_27_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_27_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13.975436,0.154823,14,-6.139227,35.704231,3,13.975436,0.154823,14,-6.139227,35.704231 +PLATE_WIDTH_27_LEN_9_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17.967573,0.177146,18,-5.27789,25.861355,3,17.967573,0.177146,18,-5.27789,25.861355 +PLATE_WIDTH_28_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_28_LEN_11_DT_JSW_B1.LOG,1,21,0,21,0,0,2,21.965879,0.181548,22,-5.13169,24.337107,3,21.965879,0.181548,22,-5.13169,24.337107 +PLATE_WIDTH_28_LEN_13_DT_JSW_B1.LOG,1,25,0,25,0,0,2,25.975555,0.15443,26,-6.158383,35.928356,3,25.975555,0.15443,26,-6.158383,35.928356 +PLATE_WIDTH_28_LEN_15_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_17_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_19_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_21_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_23_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_25_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_27_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_29_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_28_LEN_31_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_33_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_35_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_37_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_39_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_41_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_43_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_45_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_47_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_49_DT_JSW_B1.LOG,1,27,0,27,0,0,2,27,0,27,0,0,3,27,0,27,0,0 +PLATE_WIDTH_28_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_28_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13.975436,0.154823,14,-6.139227,35.704231,3,13.975436,0.154823,14,-6.139227,35.704231 +PLATE_WIDTH_28_LEN_9_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17.967573,0.177146,18,-5.27789,25.861355,3,17.967573,0.177146,18,-5.27789,25.861355 +PLATE_WIDTH_29_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_29_LEN_11_DT_JSW_B1.LOG,1,21,0,21,0,0,2,21.965879,0.181548,22,-5.13169,24.337107,3,21.965879,0.181548,22,-5.13169,24.337107 +PLATE_WIDTH_29_LEN_13_DT_JSW_B1.LOG,1,25,0,25,0,0,2,25.975555,0.15443,26,-6.158383,35.928356,3,25.975555,0.15443,26,-6.158383,35.928356 +PLATE_WIDTH_29_LEN_15_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_17_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_19_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_21_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_23_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_25_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_27_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_29_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_29_LEN_31_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_33_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_35_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_37_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_39_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_41_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_43_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_45_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_47_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_49_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_29_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_29_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13.975436,0.154823,14,-6.139227,35.704231,3,13.975436,0.154823,14,-6.139227,35.704231 +PLATE_WIDTH_29_LEN_9_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17.967573,0.177146,18,-5.27789,25.861355,3,17.967573,0.177146,18,-5.27789,25.861355 +PLATE_WIDTH_3_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_3_LEN_11_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_13_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_15_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_17_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_19_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_21_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_23_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_25_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_27_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_29_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_3_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_31_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_33_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_35_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_37_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_39_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_41_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_43_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_45_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_47_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_49_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_5_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_7_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_3_LEN_9_DT_JSW_B1.LOG,1,3,0,3,0,0,2,3,0,3,0,0,3,3,0,3,0,0 +PLATE_WIDTH_30_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_30_LEN_11_DT_JSW_B1.LOG,1,21,0,21,0,0,2,21.965879,0.181548,22,-5.13169,24.337107,3,21.965879,0.181548,22,-5.13169,24.337107 +PLATE_WIDTH_30_LEN_13_DT_JSW_B1.LOG,1,25,0,25,0,0,2,25.975555,0.15443,26,-6.158383,35.928356,3,25.975555,0.15443,26,-6.158383,35.928356 +PLATE_WIDTH_30_LEN_15_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_17_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_19_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_21_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_23_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_25_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_27_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_29_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_30_LEN_31_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_33_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_35_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_37_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_39_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_41_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_43_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_45_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_47_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_49_DT_JSW_B1.LOG,1,29,0,29,0,0,2,29,0,29,0,0,3,29,0,29,0,0 +PLATE_WIDTH_30_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9.983573,0.127176,10,-7.596947,55.770851,3,9.983573,0.127176,10,-7.596947,55.770851 +PLATE_WIDTH_30_LEN_7_DT_JSW_B1.LOG,1,13,0,13,0,0,2,13.975436,0.154823,14,-6.139227,35.704231,3,13.975436,0.154823,14,-6.139227,35.704231 +PLATE_WIDTH_30_LEN_9_DT_JSW_B1.LOG,1,17,0,17,0,0,2,17.967573,0.177146,18,-5.27789,25.861355,3,17.967573,0.177146,18,-5.27789,25.861355 +PLATE_WIDTH_31_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_31_LEN_11_DT_JSW_B1.LOG,1,21,0,21,0,0,2,21.965879,0.181548,22,-5.13169,24.337107,3,21.965879,0.181548,22,-5.13169,24.337107 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+PLATE_WIDTH_8_LEN_41_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_43_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_45_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_47_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_49_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_5_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_7_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_8_LEN_9_DT_JSW_B1.LOG,1,7,0,7,0,0,2,7,0,7,0,0,3,7,0,7,0,0 +PLATE_WIDTH_9_LEN_1_DT_JSW_B1.LOG,1,1,0,1,0,0,2,2,0,2,0,0,3,2,0,2,0,0 +PLATE_WIDTH_9_LEN_11_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_13_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_15_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_17_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_19_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_21_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_23_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_25_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_27_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_29_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_3_DT_JSW_B1.LOG,1,5,0,5,0,0,2,6,0,6,0,0,3,6,0,6,0,0 +PLATE_WIDTH_9_LEN_31_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_33_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_35_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_37_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_39_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_41_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_43_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_45_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_47_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_49_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_5_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_7_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 +PLATE_WIDTH_9_LEN_9_DT_JSW_B1.LOG,1,9,0,9,0,0,2,9,0,9,0,0,3,9,0,9,0,0 diff --git a/manuscript/notebooks/csv/PLATE_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv b/manuscript/notebooks/csv/PLATE_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv new file mode 100644 index 0000000..980d449 --- /dev/null +++ b/manuscript/notebooks/csv/PLATE_OVERSAMPLE_SKEL_DT_THICKNESS_PY.csv @@ -0,0 +1,1251 @@ +Filename,main_th,main_th_sd,main_th_min,main_th_max +PLATE_WIDTH_1_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_11_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_13_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_15_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_17_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_19_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_21_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_23_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_25_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_27_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_29_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_3_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_31_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_33_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_35_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_37_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_39_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_41_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_43_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_45_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_47_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_49_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_5_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_7_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_1_LEN_9_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_10_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_10_LEN_11_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_13_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_15_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_17_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_19_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_21_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_23_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_25_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_27_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_29_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_10_LEN_31_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_33_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_35_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_37_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_39_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_41_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_43_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_45_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_47_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_49_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_7_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_10_LEN_9_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_11_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_11_LEN_11_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_13_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_15_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_17_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_19_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_21_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_23_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_25_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_27_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_29_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_11_LEN_31_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_33_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_35_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_37_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_39_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_41_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_43_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_45_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_47_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_49_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_11_LEN_7_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_11_LEN_9_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_12_LEN_11_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_13_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_15_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_17_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_19_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_21_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_23_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_25_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_27_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_29_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_12_LEN_31_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_33_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_35_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_37_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_39_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_41_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_43_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_45_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_47_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_49_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_12_LEN_7_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_12_LEN_9_OVERSAMPLE_SKEL,11,0,11,11 +PLATE_WIDTH_13_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_13_LEN_11_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_13_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_15_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_17_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_19_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_21_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_23_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_25_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_27_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_29_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_13_LEN_31_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_33_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_35_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_37_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_39_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_41_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_43_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_45_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_47_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_49_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_13_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_13_LEN_9_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_14_LEN_11_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_13_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_15_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_17_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_19_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_21_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_23_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_25_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_27_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_29_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_14_LEN_31_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_33_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_35_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_37_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_39_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_41_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_43_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_45_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_47_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_49_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_14_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_14_LEN_9_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_15_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_15_LEN_11_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_13_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_15_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_17_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_19_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_21_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_23_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_25_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_27_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_29_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_15_LEN_31_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_33_OVERSAMPLE_SKEL,15,0,15,15 +PLATE_WIDTH_15_LEN_35_OVERSAMPLE_SKEL,15,0,15,15 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+PLATE_WIDTH_18_LEN_47_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_18_LEN_49_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_18_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_18_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_18_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_19_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_19_LEN_11_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_13_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_15_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_17_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_19_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_21_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_23_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_25_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_27_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_29_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_19_LEN_31_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_33_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_35_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_37_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_39_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_41_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_43_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_45_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_47_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_49_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_19_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_19_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_19_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_2_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_11_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_13_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_15_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_17_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_19_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_21_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_23_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_25_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_27_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_29_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_3_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_31_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_33_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_35_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_37_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_39_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_41_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_43_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_45_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_47_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_49_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_5_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_7_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_2_LEN_9_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_20_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_20_LEN_11_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_13_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_15_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_17_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_19_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_21_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_23_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_25_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_27_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_29_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_20_LEN_31_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_33_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_35_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_37_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_39_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_41_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_43_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_45_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_47_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_49_OVERSAMPLE_SKEL,19,0,19,19 +PLATE_WIDTH_20_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_20_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_20_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_21_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_21_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_13_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_15_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_17_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_19_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_21_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_23_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_25_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_27_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_29_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_21_LEN_31_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_33_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_35_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_37_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_39_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_41_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_43_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_45_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_47_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_49_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_21_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_21_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_21_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_22_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_22_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_13_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_15_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_17_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_19_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_21_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_23_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_25_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_27_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_29_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_22_LEN_31_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_33_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_35_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_37_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_39_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_41_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_43_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_45_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_47_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_49_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_22_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_22_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_22_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_23_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_23_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_23_LEN_13_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_15_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_17_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_19_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_21_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_23_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_25_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_27_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_29_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_23_LEN_31_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_33_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_35_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_37_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_39_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_41_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_43_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_45_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_47_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_49_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_23_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_23_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_23_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_24_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_24_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_24_LEN_13_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_15_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_17_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_19_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_21_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_23_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_25_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_27_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_29_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_24_LEN_31_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_33_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_35_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_37_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_39_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_41_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_43_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_45_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_47_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_49_OVERSAMPLE_SKEL,23,0,23,23 +PLATE_WIDTH_24_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_24_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_24_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_25_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_25_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_25_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_15_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_17_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_19_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_21_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_23_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_25_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_27_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_29_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_25_LEN_31_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_33_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_35_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_37_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_39_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_41_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_43_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_45_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_47_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_49_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_25_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_25_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_25_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_26_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_26_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_26_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_15_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_17_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_19_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_21_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_23_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_25_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_27_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_29_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_26_LEN_31_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_33_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_35_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_37_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_39_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_41_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_43_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_45_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_47_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_49_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_26_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_26_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_26_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_27_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_27_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_27_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_27_LEN_15_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_17_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_19_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_21_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_23_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_25_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_27_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_29_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_27_LEN_31_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_33_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_35_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_37_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_39_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_41_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_43_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_45_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_47_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_49_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_27_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_27_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_27_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_28_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_28_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_28_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_28_LEN_15_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_17_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_19_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_21_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_23_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_25_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_27_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_29_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_28_LEN_31_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_33_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_35_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_37_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_39_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_41_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_43_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_45_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_47_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_49_OVERSAMPLE_SKEL,27,0,27,27 +PLATE_WIDTH_28_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_28_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_28_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_29_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_29_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_29_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_29_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_17_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_19_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_21_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_23_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_25_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_27_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_29_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_29_LEN_31_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_33_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_35_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_37_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_39_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_41_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_43_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_45_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_47_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_49_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_29_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_29_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_29_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_3_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_3_LEN_11_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_13_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_15_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_17_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_19_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_21_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_23_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_25_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_27_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_29_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_3_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_31_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_33_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_35_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_37_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_39_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_41_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_43_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_45_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_47_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_49_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_5_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_7_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_3_LEN_9_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_30_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_30_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_30_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_30_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_17_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_19_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_21_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_23_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_25_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_27_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_29_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_30_LEN_31_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_33_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_35_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_37_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_39_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_41_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_43_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_45_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_47_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_49_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_30_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_30_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_30_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_31_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_31_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_31_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_31_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_31_LEN_17_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_19_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_21_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_23_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_25_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_27_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_29_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_31_LEN_31_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_33_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_35_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_37_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_39_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_41_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_43_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_45_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_47_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_49_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_31_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_31_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_31_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_32_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_32_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_32_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_32_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_32_LEN_17_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_19_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_21_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_23_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_25_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_27_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_29_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_32_LEN_31_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_33_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_35_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_37_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_39_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_41_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_43_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_45_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_47_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_49_OVERSAMPLE_SKEL,31,0,31,31 +PLATE_WIDTH_32_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_32_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_32_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_33_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_33_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_33_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_33_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_33_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_19_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_21_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_23_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_25_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_27_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_29_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_33_LEN_31_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_33_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_35_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_37_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_39_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_41_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_43_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_45_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_47_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_49_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_33_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_33_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_33_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_34_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_34_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_34_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_34_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_34_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_19_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_21_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_23_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_25_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_27_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_29_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_34_LEN_31_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_33_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_35_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_37_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_39_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_41_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_43_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_45_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_47_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_49_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_34_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_34_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_34_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_35_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_35_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_35_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_35_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_35_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_35_LEN_19_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_21_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_23_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_25_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_27_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_29_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_35_LEN_31_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_33_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_35_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_37_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_39_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_41_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_43_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_45_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_47_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_49_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_35_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_35_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_35_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_36_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_36_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_36_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_36_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_36_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_36_LEN_19_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_21_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_23_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_25_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_27_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_29_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_36_LEN_31_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_33_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_35_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_37_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_39_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_41_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_43_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_45_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_47_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_49_OVERSAMPLE_SKEL,35,0,35,35 +PLATE_WIDTH_36_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_36_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_36_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_37_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_37_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_37_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_37_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_37_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_37_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_21_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_23_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_25_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_27_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_29_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_37_LEN_31_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_33_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_35_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_37_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_39_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_41_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_43_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_45_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_47_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_49_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_37_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_37_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_37_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_38_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_38_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_38_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_38_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_38_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_38_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_21_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_23_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_25_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_27_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_29_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_38_LEN_31_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_33_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_35_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_37_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_39_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_41_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_43_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_45_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_47_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_49_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_38_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_38_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_38_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_39_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_39_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_39_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_39_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_39_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_39_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_39_LEN_21_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_23_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_25_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_27_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_29_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_39_LEN_31_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_33_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_35_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_37_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_39_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_41_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_43_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_45_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_47_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_49_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_39_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_39_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_39_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_4_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_4_LEN_11_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_13_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_15_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_17_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_19_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_21_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_23_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_25_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_27_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_29_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_3_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_31_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_33_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_35_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_37_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_39_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_41_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_43_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_45_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_47_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_49_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_5_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_7_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_4_LEN_9_OVERSAMPLE_SKEL,3,0,3,3 +PLATE_WIDTH_40_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_40_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_40_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_40_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_40_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_40_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_40_LEN_21_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_23_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_25_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_27_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_29_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_40_LEN_31_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_33_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_35_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_37_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_39_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_41_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_43_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_45_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_47_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_49_OVERSAMPLE_SKEL,39,0,39,39 +PLATE_WIDTH_40_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_40_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_40_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_41_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_41_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_41_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_41_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_41_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_41_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_41_LEN_21_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_23_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_25_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_27_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_29_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_41_LEN_31_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_33_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_35_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_37_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_39_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_41_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_43_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_45_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_47_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_49_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_41_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_41_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_41_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_42_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_42_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_42_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_42_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_42_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_42_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_42_LEN_21_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_23_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_25_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_27_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_29_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_42_LEN_31_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_33_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_35_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_37_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_39_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_41_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_43_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_45_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_47_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_49_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_42_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_42_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_42_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_43_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_43_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_43_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_43_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_43_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_43_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_43_LEN_21_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_43_LEN_23_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_25_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_27_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_29_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_43_LEN_31_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_33_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_35_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_37_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_39_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_41_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_43_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_45_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_47_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_49_OVERSAMPLE_SKEL,43,0,43,43 +PLATE_WIDTH_43_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_43_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 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+PLATE_WIDTH_48_LEN_45_OVERSAMPLE_SKEL,47,0,47,47 +PLATE_WIDTH_48_LEN_47_OVERSAMPLE_SKEL,47,0,47,47 +PLATE_WIDTH_48_LEN_49_OVERSAMPLE_SKEL,47,0,47,47 +PLATE_WIDTH_48_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_48_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_48_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_49_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_49_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_49_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_49_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_49_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_49_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_49_LEN_21_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_49_LEN_23_OVERSAMPLE_SKEL,45,0,45,45 +PLATE_WIDTH_49_LEN_25_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_27_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_29_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_49_LEN_31_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_33_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_35_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_37_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_39_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_41_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_43_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_45_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_47_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_49_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_49_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_49_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_49_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_5_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_5_LEN_11_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_13_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_15_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_17_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_19_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_21_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_23_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_25_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_27_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_29_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_31_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_33_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_35_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_37_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_39_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_41_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_43_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_45_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_47_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_49_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_5_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_7_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_5_LEN_9_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_50_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_50_LEN_11_OVERSAMPLE_SKEL,21,0,21,21 +PLATE_WIDTH_50_LEN_13_OVERSAMPLE_SKEL,25,0,25,25 +PLATE_WIDTH_50_LEN_15_OVERSAMPLE_SKEL,29,0,29,29 +PLATE_WIDTH_50_LEN_17_OVERSAMPLE_SKEL,33,0,33,33 +PLATE_WIDTH_50_LEN_19_OVERSAMPLE_SKEL,37,0,37,37 +PLATE_WIDTH_50_LEN_21_OVERSAMPLE_SKEL,41,0,41,41 +PLATE_WIDTH_50_LEN_23_OVERSAMPLE_SKEL,45,0,45,45 +PLATE_WIDTH_50_LEN_25_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_27_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_29_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_50_LEN_31_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_33_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_35_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_37_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_39_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_41_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_43_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_45_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_47_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_49_OVERSAMPLE_SKEL,49,0,49,49 +PLATE_WIDTH_50_LEN_5_OVERSAMPLE_SKEL,9,0,9,9 +PLATE_WIDTH_50_LEN_7_OVERSAMPLE_SKEL,13,0,13,13 +PLATE_WIDTH_50_LEN_9_OVERSAMPLE_SKEL,17,0,17,17 +PLATE_WIDTH_6_LEN_1_OVERSAMPLE_SKEL,1,0,1,1 +PLATE_WIDTH_6_LEN_11_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_13_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_15_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_17_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_19_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_21_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_23_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_25_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_27_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_29_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_3_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_31_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_33_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_35_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_37_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_39_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_41_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_43_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_45_OVERSAMPLE_SKEL,5,0,5,5 +PLATE_WIDTH_6_LEN_47_OVERSAMPLE_SKEL,5,0,5,5 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+import os +import argparse + +from scipy.spatial import distance + +def hausdorff_distance(ref, image): + d = sitk.HausdorffDistanceImageFilter() + d.Execute(ref, image) + + return (d.GetAverageHausdorffDistance(), d.GetHausdorffDistance()) + +def get_image_pairs(directory): + ormir_masks = [] + ipl_masks = [] + + for filename in os.scandir(directory): + if filename.is_file(): + if filename.name.startswith("."): + continue + elif ("DST" in filename.path) or ("PRX" in filename.path): + continue + elif filename.path.endswith("_MASK.nii"): + ormir_masks.append(filename.path) + elif filename.path.endswith("_JOINT.nii"): + ipl_masks.append(filename.path) + + ipl_masks.sort() + ormir_masks.sort() + + return (ipl_masks, ormir_masks) + +def resample_to_image(img_path, ref_path): + resampled_path = os.path.splitext(img_path)[0] + resampled_path = resampled_path + "_RESAMPLED.nii" + + image = sitk.ReadImage(img_path) + imageSize = image.GetSize() + imageOrigin = image.GetOrigin() + imageSpacing = image.GetSpacing() + imageDirection = image.GetDirection() + imageDataType = image.GetPixelID() + + refImage = sitk.ReadImage(ref_path) + refImageSize = refImage.GetSize() + refImageOrigin = refImage.GetOrigin() + refImageSpacing = refImage.GetSpacing() + refImageDirection = refImage.GetDirection() + refImageDataType = refImage.GetPixelID() + + # Check optional arguements + sitkInterpolator = sitk.sitkLinear + + # Resample the image + print("** Resampling...") + resampleFilter = sitk.ResampleImageFilter() + resampleFilter.SetReferenceImage(refImage) + resampleFilter.SetInterpolator(sitkInterpolator) + resampleFilter.SetOutputPixelType(refImageDataType) + + # Print resampling progress + resampleFilter.AddCommand( + sitk.sitkProgressEvent, + lambda: print( + "\rProgress: {0:03.1f}%...".format(100 * resampleFilter.GetProgress()), end="" + ), + ) + resampleFilter.AddCommand(sitk.sitkProgressEvent, lambda: sys.stdout.flush()) + + resampledImage = resampleFilter.Execute(image) + + print("Done!") + + # Write out the resampled image + print("Writing resampled image to: " + str(resampled_path)) + sitk.WriteImage(resampledImage, resampled_path) + return resampledImage + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("directory", type=str) + args = parser.parse_args() + + directory = args.directory + + seg_pairs = get_image_pairs(directory) + + results = [] + + for i in range(len(seg_pairs[0])): + print(seg_pairs[0][i]) + print(seg_pairs[1][i]) + ref = sitk.ReadImage(seg_pairs[0][i], sitk.sitkInt16) + image = sitk.ReadImage(seg_pairs[1][i], sitk.sitkInt16) + + resampled_image = resample_to_image(seg_pairs[1][i], seg_pairs[0][i]) + mean, maximum = hausdorff_distance(ref, resampled_image) + + # try: + # mean, maximum = hausdorff_distance(ref, image) + # except: + # resampled_image = resample_to_image(seg_pairs[1][i], seg_pairs[0][i]) + # mean, maximum = hausdorff_distance(ref, resampled_image) + + print(f"Average: {mean}, Maximum: {maximum}") + results.append((mean, maximum)) + print("Done") + + # print(results) \ No newline at end of file diff --git a/manuscript/notebooks/images/TEST_JOINT.nii b/manuscript/notebooks/images/TEST_JOINT.nii new file mode 100644 index 0000000000000000000000000000000000000000..7073c98aa877e224a92e931ef2d56192294d9303 GIT binary patch literal 38961904 zcmeF)JDM%smLB9?Km+IpmKvZ(hPr`|hk+$;%cl>3iAe|49ZcGQvVLXd%5{nBoDt*l z{>RvvDEHjq|Hps&_y7C<`Jev%?|Z&}`+xr9pFjWkKm51P|GU@g*M9PQ^?#lH zzx>zP|I3Ie{@4HM-;FrN83YIrAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N z0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+ z009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBly zK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF z5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk z1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs z0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C7ZWj3a 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numpy as np +import matplotlib as mpl + +from LinearRegression import LinearRegression + +from matplotlib import pyplot as plt + +def get_thickness_list(csv, ipl=True): + # Assume CSV files have already been sorted by filename (alphabetically) + if ipl: + # We have an IPL generated CSV file + ipl_csv = pandas.read_csv(csv) + filenames = ipl_csv["Filename"].values.tolist() + thickness = ipl_csv["BG Th V3"].values.astype(float).tolist() + else: + # We have a Python generated CSV file + py_csv = pandas.read_csv(csv) + filenames = py_csv["Filename"].values.tolist() + thickness = py_csv["main_th"].values.astype(float).tolist() + + return thickness, filenames + +def get_bmd_list(csv): + csv = pandas.read_csv(csv) + filenames = csv["Filename"].values.tolist() + mcp2_dst_bmd = csv["MCP2 DST Mean BMD (mgHA/ccm)"].values.astype(float).tolist() + mcp2_prx_bmd = csv["MCP2 PRX Mean BMD (mgHA/ccm)"].values.astype(float).tolist() + mcp3_dst_bmd = csv["MCP3 DST Mean BMD (mgHA/ccm)"].values.astype(float).tolist() + mcp3_prx_bmd = csv["MCP3 PRX Mean BMD (mgHA/ccm)"].values.astype(float).tolist() + + bmd_dict = { + "Filenames": filenames, + "MCP2_DST_BMD": mcp2_dst_bmd, + "MCP2_PRX_BMD": mcp2_prx_bmd, + "MCP3_DST_BMD": mcp3_dst_bmd, + "MCP3_PRX_BMD": mcp3_prx_bmd + } + + return bmd_dict + +def bmd_plots(bmd_files): + fig = plt.figure(constrained_layout=True, figsize=(20,10)) + fig.suptitle( + "Bone Mineral Density (BMD) - IPL vs. ORMIR_XCT", + fontsize="xx-large", + ) + + subfigs = fig.subfigures(nrows=2, ncols=1) + subfigs[0].suptitle( + "Bland-Altman Plots of BMD - IPL vs. ORMIR_XCT", fontsize="x-large" + ) + subfigs[1].suptitle( + "Linear Regression Plots of BMD - IPL vs. ORMIR_XCT", fontsize="x-large" + ) + + axs1 = subfigs[0].subplots(nrows=1, ncols=4) + axs2 = subfigs[1].subplots(nrows=1, ncols=4) + + ipl_bmd_dict = get_bmd_list(bmd_files["IPL"]) + py_bmd_dict = get_bmd_list(bmd_files["PY"]) + + bmd = ((ipl_bmd_dict["MCP2_DST_BMD"], py_bmd_dict["MCP2_DST_BMD"]), + (ipl_bmd_dict["MCP2_PRX_BMD"], py_bmd_dict["MCP2_PRX_BMD"]), + (ipl_bmd_dict["MCP3_DST_BMD"], py_bmd_dict["MCP3_DST_BMD"]), + (ipl_bmd_dict["MCP3_PRX_BMD"], py_bmd_dict["MCP3_PRX_BMD"])) + + subplot = 0 + + for bmd_list in bmd: + bmd_ipl = bmd_list[0] + bmd_py = bmd_list[1] + + bmd_ipl = np.array([bmd_ipl]).T + bmd_py = np.array([bmd_py]).T + + diff = np.subtract(bmd_ipl, bmd_py) + mean = np.mean([bmd_ipl, bmd_py], axis=0) + sd = np.std(diff, axis=0) + md = np.mean(diff) + + axs1[subplot].scatter(mean, diff, color="blue", marker=".") + axs1[subplot].axhline(md, color="black", linestyle="-", label='MeanDiff') + axs1[subplot].axhline(md + 1.96 * sd, color="red", linestyle="--", label='MeanDiff +/- 1.96 * sd') + axs1[subplot].axhline(md - 1.96 * sd, color="red", linestyle="--") + axs1[subplot].grid() + axs1[subplot].set_title(list(ipl_bmd_dict.keys())[subplot+1]) + axs1[subplot].set_xlabel("Mean BMD (mg HA/cm^3)") + + max_thickness = max(np.max(bmd_ipl), np.max(bmd_py)) + regression = LinearRegression() + regression.fit_model(bmd_ipl, bmd_py, max_thickness) + model_r_sq = float(regression.get_r_sq()) + model_slope = float(regression.get_slope()) + model_intercept = float(regression.get_intercept()) + x_new = regression.get_x_new() + y_new = regression.get_y_new() + + axs2[subplot].scatter(bmd_ipl, bmd_py, color="blue", marker=".") + axs2[subplot].plot( + x_new, + y_new, + color="k", + linestyle="-", + label="y={:.2f}x+{:.2f}\nR^2={:.2f}".format( + model_slope, model_intercept, model_r_sq + ), + ) + axs2[subplot].set_title(list(ipl_bmd_dict.keys())[subplot+1]) + axs2[subplot].set_xlabel("IPL Calculated BMD (mg HA/cm^3)") + axs2[subplot].grid() + axs2[subplot].legend(loc="lower right") + + if subplot == 0: + axs1[subplot].set_ylabel("Difference (IPL - ORMIR_XCT) (mg HA/cm^3)") + axs2[subplot].set_ylabel("ORMIR_XCT Calculated BMD (mg HA/cm^3)") + elif subplot == 3: + axs1[subplot].legend(loc="lower right") + + subplot += 1 + + plt.show() + +def thickness_plots(thickness_files): + fig = plt.figure(constrained_layout=True, figsize=(20,10)) + fig.suptitle( + "Mean Thickness - IPL vs. ORMIR_XCT (Oversampling=True, Skeletonization=True)", + fontsize="xx-large", + ) + + subfigs = fig.subfigures(nrows=2, ncols=1) + subfigs[0].suptitle( + "Bland-Altman Plots of Mean Thickness - IPL vs. ORMIR_XCT", fontsize="x-large" + ) + subfigs[1].suptitle( + "Linear Regression Plots of Mean Thickness - IPL vs. ORMIR_XCT", fontsize="x-large" + ) + + axs1 = subfigs[0].subplots(nrows=1, ncols=5) + axs2 = subfigs[1].subplots(nrows=1, ncols=5) + + subplot = 0 + + for file_name in thickness_files: + print(f"Processing data for {file_name}") + + thickness_ipl, filename_ipl = get_thickness_list( + thickness_files[file_name]["IPL"], ipl=True + ) + thickness_py, filename_py = get_thickness_list(thickness_files[file_name]["PY"], ipl=False) + + thickness_ipl = np.array([thickness_ipl]).T + thickness_py = np.array([thickness_py]).T + + diff = np.subtract(thickness_ipl, thickness_py) + mean = np.mean([thickness_ipl, thickness_py], axis=0) + sd = np.std(diff, axis=0) + md = np.mean(diff) + + axs1[subplot].scatter(mean, diff, color="blue", marker=".") + axs1[subplot].axhline(md, color="black", linestyle="-", label='MeanDiff') + axs1[subplot].axhline(md + 1.96 * sd, color="red", linestyle="--", label='MeanDiff +/- 1.96 * sd') + axs1[subplot].axhline(md - 1.96 * sd, color="red", linestyle="--") + axs1[subplot].grid() + axs1[subplot].set_title(file_name) + axs1[subplot].set_xlabel("Mean (IPL, ORMIR_XCT)") + + max_thickness = max(np.max(thickness_ipl), np.max(thickness_py)) + regression = LinearRegression() + regression.fit_model(thickness_ipl, thickness_py, max_thickness) + model_r_sq = float(regression.get_r_sq()) + model_slope = float(regression.get_slope()) + model_intercept = float(regression.get_intercept()) + x_new = regression.get_x_new() + y_new = regression.get_y_new() + + axs2[subplot].scatter(thickness_ipl, thickness_py, color="blue", marker=".") + axs2[subplot].plot( + x_new, + y_new, + color="k", + linestyle="-", + label="y={:.2f}x+{:.2f}\nR^2={:.2f}".format( + model_slope, model_intercept, model_r_sq + ), + ) + axs2[subplot].plot((0,max_thickness), (0,max_thickness), color="red", linestyle="--") + axs2[subplot].set_title(file_name) + axs2[subplot].set_xlabel("IPL Calculated Thickness") + axs2[subplot].grid() + axs2[subplot].legend(loc="lower right") + + if subplot == 0: + axs1[subplot].set_ylabel("Difference (IPL - ORMIR_XCT)") + axs2[subplot].set_ylabel("ORMIR_XCT Calculated Thickness") + elif subplot == 4: + axs1[subplot].legend(loc="lower right") + + subplot += 1 + + plt.show() \ No newline at end of file From 5fbf2ab00445e79a1c736e2bf25f9651ac04e0ad Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Thu, 31 Aug 2023 20:38:22 -0600 Subject: [PATCH 05/18] Update paper.md Fixed formatting --- manuscript/paper.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/manuscript/paper.md b/manuscript/paper.md index 188daf8..6aaeabd 100644 --- a/manuscript/paper.md +++ b/manuscript/paper.md @@ -51,9 +51,9 @@ affiliations: index: 2 - name: Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia index: 3 -- name: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA + - name: Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA index: 4 - - name: i. Rehabilitation Sciences Institute, The University of Toronto, Toronto, Canada + - name: Rehabilitation Sciences Institute, The University of Toronto, Toronto, Canada index: 5 - name: Joint Department of Medical Imaging, University Health Network, Toronto, Canada index: 6 @@ -69,42 +69,42 @@ date: 30 August 2023 bibliography: paper.bib --- -1 – Statement of Need: -High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique with a nominal isotropic voxel size of 61µm capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases [1](https://doi.org/10.1186/s12880-020-00437-8), assess epidemiological associations [2](https://doi.org/10.1002/jbmr.2873), and evaluate the effects of pharmaceutical interventions [3](https://doi.org/10.1080/03009742.2020.1869303). Processing HR-pQCT images has largely been supported using the scanner manufacturer’s scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer’s software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The ORMIR_XCT Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows. +# 1 – Statement of Need: +High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique with a nominal isotropic voxel size of 61µm capable of imaging trabecular bone *in-vivo*. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases [1](https://doi.org/10.1186/s12880-020-00437-8), assess epidemiological associations [2](https://doi.org/10.1002/jbmr.2873), and evaluate the effects of pharmaceutical interventions [3](https://doi.org/10.1080/03009742.2020.1869303). Processing HR-pQCT images has largely been supported using the scanner manufacturer’s scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer’s software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The `ORMIR_XCT` Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows. -2 – Package Summary: -The development of this package began during the Jupyter Community Workshop in Maastricht, Netherlands in June 2022, hosted by the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) group. During this workshop, the conceptualization and initial development of the ORMIR_XCT package began with support from Kitware. The ORMIR_XCT package is currently being used by members of the ORMIR community to assess bone mineral density (BMD) and joint space width (JSW) changes in HR-pQCT scans of patients with osteoarthritis or rheumatoid arthritis. +# 2 – Package Summary: +The development of this package began during the Jupyter Community Workshop in Maastricht, Netherlands in June 2022, hosted by the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) group. During this workshop, the conceptualization and initial development of the `ORMIR_XCT` package began with support from Kitware. The `ORMIR_XCT` package is currently being used by members of the ORMIR community to assess bone mineral density (BMD) and joint space width (JSW) changes in HR-pQCT scans of patients with osteoarthritis or rheumatoid arthritis. -The ORMIR_XCT package contains four modules for processing HR-pQCT data of bones and joints: 1) automatic contouring of the periosteal surface, 2) JSW analysis, 3) BMD calculation, and 4) segmentation of trabecular bone . Analyses have been performed to compare outputs from the ORMIR_XCT package to results generated using IPL. Jupyter Notebook examples are provided to describe the workflows implemented in the ORMIR_XCT package. +The `ORMIR_XCT` package contains four modules for processing HR-pQCT data of bones and joints: 1) automatic contouring of the periosteal surface, 2) JSW analysis, 3) BMD calculation, and 4) segmentation of trabecular bone. Analyses have been performed to compare outputs from the `ORMIR_XCT` package to results generated using IPL. Jupyter Notebook examples are provided to describe the workflows implemented in the `ORMIR_XCT` package. -3 – Comparison to IPL: -3.1 - Autocontour: -Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59 ). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [4](https://doi.org/10.1016/j.bone.2010.05.034) and the ORMIR_XCT package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images . The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in Table 1\autoref{fig:Table1}. +# 3 – Comparison to IPL: +## 3.1 - Autocontour: +Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [4](https://doi.org/10.1016/j.bone.2010.05.034) and the `ORMIR_XCT` package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images. The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in Table 1 \autoref{fig:Table1}. -![Table 1: DICE coefficients, Jaccard indices, and Hausdorff distances (voxels) between IPL and ORMIR_XCT automatic contouring implementations . All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. DIP2: 2nd distal interphalangeal joint, DIP3: 3rd distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table1}](Table1.png) +![Table 1: DICE coefficients, Jaccard indices, and Hausdorff distances (voxels) between IPL and `ORMIR_XCT` automatic contouring implementations. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. DIP2: 2nd distal interphalangeal joint, DIP3: 3rd distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table1}](figures/Table1.png) -3.2 - Thickness: -The ORMIR_XCT package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand et al [5](https://doi.org/10.1046/j.1365-2818.1997.1340694.x). This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise . +## 3.2 - Thickness: +The `ORMIR_XCT` package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand *et al* [5](https://doi.org/10.1046/j.1365-2818.1997.1340694.x). This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise. -Hildebrand et al. note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with the NumPy (v1.23.3) [6](https://doi.org/10.1038/s41586-020-2649-2) and Numba (v0.56.3) [7](http://dx.doi.org/10.1145/2833157.2833162) Python packages . Finally, we have noted and compensated for a discrepancy between the definition of the distance map by Hildebrand et al and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [8](https://doi.org/10.3389/fninf.2013.00045) and scikit-image (v0.19.2) [9](https://doi.org/10.7717/peerj.453). Hildebrand et al. define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. +Hildebrand *et al.* note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with NumPy (v1.23.3) [6](https://doi.org/10.1038/s41586-020-2649-2) and Numba (v0.56.3) [7](http://dx.doi.org/10.1145/2833157.2833162). Finally, we have noted and compensated for a discrepancy between the definition of the distance map by Hildebrand *et al.* and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [8](https://doi.org/10.3389/fninf.2013.00045) and scikit-image (v0.19.2) [9](https://doi.org/10.7717/peerj.453). Hildebrand *et al.* define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. -To compensate for this, we developed an oversampling distance transform function (Figure 1\autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. +To compensate for this, we developed an oversampling distance transform function (Figure 1 \autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. -![Figure 1: Examples of the thickness estimation algorithm implemented in the ORMIR_XCT package. Top row: thickness calculation on a 1 voxel thick plate demonstrating how thickness estimation is improved using the oversampling distance transform function (thickness = 1 voxel using oversampling, thickness = 2 voxels without oversampling). Bottom row: thickness calculation applied to a sample finger joint.\label{fig: DT_Figure}](DT_Figure.png) +![Figure 1: Examples of the thickness estimation algorithm implemented in the `ORMIR_XCT` package. Top row: thickness calculation on a 1 voxel thick plate demonstrating how thickness estimation is improved using the oversampling distance transform function (thickness = 1 voxel using oversampling, thickness = 2 voxels without oversampling). Bottom row: thickness calculation applied to a sample finger joint.\label{fig: DT_Figure}](figures/DT_Figure.png) -The user is given a choice, via a Boolean flag parameter in the compute_local_thickness_from_mask and calc_structure_thickness_statistics functions, to use the oversampling distance transform for thickness estimation. The choice is given because different users may have variable preferences in applying alternate definitions regarding the bounds of their structures (whether they be contained within the mask voxels or extend to the center of the neighbouring background voxels) and because using the normal (i.e., not oversampled) distance transform will provide better congruence with thickness calculations produced using IPL. +The user is given a choice, via a Boolean flag parameter in the compute_local_thickness_from_mask and calc_structure_thickness_statistics functions, to use the oversampling distance transform for thickness estimation. The choice is given because different users may have variable preferences in applying alternate definitions regarding the bounds of their structures (whether they be contained within the mask voxels or extend to the center of the neighbouring background voxels) and because using the normal (*i.e.*, not oversampled) distance transform will provide better congruence with thickness calculations produced using IPL. -To evaluate the thickness computation implemented in the ORMIR_XCT package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [10](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [11](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: ridge_epsilon = 0.9, assign_epsilon = 1.8, suppress_boundary = 0, and version = 3. This set of parameters is currently used for JSW analysis of in vivo finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (Figure 2\autoref{fig:IPL_vs_ORMIR_XCT-DT}). +To evaluate the thickness computation implemented in the `ORMIR_XCT` package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [10](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [11](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: `ridge_epsilon = 0.9`, `assign_epsilon = 1.8`, `suppress_boundary = 0`, and `version = 3`. This set of parameters is currently used for JSW analysis of *in vivo* finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (Figure 2 \autoref{fig:IPL_vs_ORMIR_XCT-DT}). -![Figure 2: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the ORMIR_XCT package . Top row: black lines indicate mean difference and red dotted line shows the limits of agreement. Bottom row: solid black line indicates the linear regression between IPL and ORMIR_XCT computed thickness and red dotted lines indicate a line of unity.\label{fig: IPL_vs_ORMIR_XCT-DT}](IPL_vs_ORMIR_XCT-DT.png) +![Figure 2: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the `ORMIR_XCT` package . Top row: black lines indicate mean difference and red dotted line shows the limits of agreement. Bottom row: solid black line indicates the linear regression between IPL and ORMIR_XCT computed thickness and red dotted lines indicate a line of unity.\label{fig: IPL_vs_ORMIR_XCT-DT}](figures/IPL_vs_ORMIR_XCT-DT.png) -3.3 - Bone Mineral Density: -Images obtained from HR-pQCT scanners are saved using the manufacturer’s proprietary file format (AIM). To process HR-pQCT images using Python, these files need to be converted to a file format that can be interpreted by common image processing Python libraries. The ITKIOScanco module is used in the ORMIR_XCT package to convert HR-pQCT images to other common medical image file types. However, the ITKIOScanco module converts HR-pQCT images to Hounsfield Units (HU) by default. Measures of BMD require the image to be calibrated in density units (mg of hydroxyapatite per cm3). Several image unit conversions have been included in the ORMIR_XCT package to allow for conversion between HR-pQCT native units (termed Scanco units) and each of HU, density, and linear attenuation units. A sample dataset of HR-pQCT images of second and third metacarpal phalangeal (MCP) joints (n = 292) was used to compare BMD results between IPL and the ORMIR_XCT package. BMD was reported separately for the distal (DST) and proximal (PRX) segments of each joint (Figure 3\autoref{fig:IPL_vs_ORMIR_XCT-BMD}). +## 3.3 - Bone Mineral Density: +Images obtained from HR-pQCT scanners are saved using the manufacturer’s proprietary file format (AIM). To process HR-pQCT images using Python, these files need to be converted to a file format that can be interpreted by common image processing Python libraries. The `ITKIOScanco` module is used in the `ORMIR_XCT` package to convert HR-pQCT images to other common medical image file types. However, the `ITKIOScanco` module converts HR-pQCT images to Hounsfield Units (HU) by default. Measures of BMD require the image to be calibrated in density units (mg of hydroxyapatite per cm3). Several image unit conversions have been included in the `ORMIR_XCT` package to allow for conversion between HR-pQCT native units (termed Scanco units) and each of HU, density, and linear attenuation units. A sample dataset of HR-pQCT images of second and third metacarpal phalangeal (MCP) joints (n = 292) was used to compare BMD results between IPL and the `ORMIR_XCT` package. BMD was reported separately for the distal (DST) and proximal (PRX) segments of each joint (Figure 3 \autoref{fig:IPL_vs_ORMIR_XCT-BMD}). -![Figure 3: Bone mineral density (mg HA/cm3) linear regression and Bland-Altman plots comparing IPL and the ORMIR_XCT computed bone mineral density (BMD ). All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. MCP2: 2nd metacarpal phalangeal joint, MCP3: 3rd metacarpal phalangeal joint, DST: distal, PRX: proximal.\label{fig: IPL_vs_ORMIR_XCT-BMD}](IPL_vs_ORMIR_XCT-BMD.png) +![Figure 3: Bone mineral density (mg HA/cm3) linear regression and Bland-Altman plots comparing IPL and the ORMIR_XCT computed bone mineral density (BMD ). All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. MCP2: 2nd metacarpal phalangeal joint, MCP3: 3rd metacarpal phalangeal joint, DST: distal, PRX: proximal.\label{fig: IPL_vs_ORMIR_XCT-BMD}](figures/IPL_vs_ORMIR_XCT-BMD.png) -3.4 - Trabecular Segmentation: -A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a global threshold-based image segmentation. The gauss_seg function in IPL has been reimplemented in the ORMIR_XCT package and the similarity of IPL and ORMIR_XCT trabecular segmentations were computed using DICE coefficients, Jaccard indices, and Hausdorff distances (Table 2\autoref{fig:Table2}). The same dataset used to test the automatic contouring module was used to compare IPL and ORMIR_XCT trabecular segmentation. +## 3.4 - Trabecular Segmentation: +A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a global threshold-based image segmentation. The `gauss_seg` function in IPL has been reimplemented in the `ORMIR_XCT` package and the similarity of IPL and `ORMIR_XCT` trabecular segmentations were computed using DICE coefficients, Jaccard indices, and Hausdorff distances (Table 2 \autoref{fig:Table2}). The same dataset used to test the automatic contouring module was used to compare IPL and `ORMIR_XCT` trabecular segmentation. -![Table 2: DICE scores, Jaccard indices, and Hausdorff distances (voxels) for comparison of trabecular segmentation performed using IPL versus the ORMIR_XCT package. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. For each algorithm, Gaussian smoothing was performed with sigma = 0.5. Subsequently, segmentation was performed with lower and upper thresholds of 1,170 and 10,000 HU, respectively. DIP2: second distal interphalangeal joint, DIP3: third distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table2}](Table2.png) \ No newline at end of file +![Table 2: DICE scores, Jaccard indices, and Hausdorff distances (voxels) for comparison of trabecular segmentation performed using IPL versus the `ORMIR_XCT` package. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. For each algorithm, Gaussian smoothing was performed with sigma = 0.5. Subsequently, segmentation was performed with lower and upper thresholds of 1,170 and 10,000 HU, respectively. DIP2: second distal interphalangeal joint, DIP3: third distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table2}](figures/Table2.png) From 42e6318898afd57e420f87d2d0aa940efb1ad49b Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Thu, 31 Aug 2023 20:44:57 -0600 Subject: [PATCH 06/18] Update paper.md Fixed autoref of figures --- manuscript/paper.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/manuscript/paper.md b/manuscript/paper.md index 6aaeabd..828438f 100644 --- a/manuscript/paper.md +++ b/manuscript/paper.md @@ -80,31 +80,31 @@ The `ORMIR_XCT` package contains four modules for processing HR-pQCT data of bon # 3 – Comparison to IPL: ## 3.1 - Autocontour: -Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [4](https://doi.org/10.1016/j.bone.2010.05.034) and the `ORMIR_XCT` package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images. The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in Table 1 \autoref{fig:Table1}. +Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [4](https://doi.org/10.1016/j.bone.2010.05.034) and the `ORMIR_XCT` package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images. The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in \autoref{fig:Table1}. -![Table 1: DICE coefficients, Jaccard indices, and Hausdorff distances (voxels) between IPL and `ORMIR_XCT` automatic contouring implementations. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. DIP2: 2nd distal interphalangeal joint, DIP3: 3rd distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table1}](figures/Table1.png) +![Table 1: DICE coefficients, Jaccard indices, and Hausdorff distances (voxels) between IPL and `ORMIR_XCT` automatic contouring implementations. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. DIP2: 2nd distal interphalangeal joint, DIP3: 3rd distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig:Table1}](figures/Table1.png) ## 3.2 - Thickness: The `ORMIR_XCT` package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand *et al* [5](https://doi.org/10.1046/j.1365-2818.1997.1340694.x). This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise. Hildebrand *et al.* note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with NumPy (v1.23.3) [6](https://doi.org/10.1038/s41586-020-2649-2) and Numba (v0.56.3) [7](http://dx.doi.org/10.1145/2833157.2833162). Finally, we have noted and compensated for a discrepancy between the definition of the distance map by Hildebrand *et al.* and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [8](https://doi.org/10.3389/fninf.2013.00045) and scikit-image (v0.19.2) [9](https://doi.org/10.7717/peerj.453). Hildebrand *et al.* define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. -To compensate for this, we developed an oversampling distance transform function (Figure 1 \autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. +To compensate for this, we developed an oversampling distance transform function (\autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. -![Figure 1: Examples of the thickness estimation algorithm implemented in the `ORMIR_XCT` package. Top row: thickness calculation on a 1 voxel thick plate demonstrating how thickness estimation is improved using the oversampling distance transform function (thickness = 1 voxel using oversampling, thickness = 2 voxels without oversampling). Bottom row: thickness calculation applied to a sample finger joint.\label{fig: DT_Figure}](figures/DT_Figure.png) +![Figure 1: Examples of the thickness estimation algorithm implemented in the `ORMIR_XCT` package. Top row: thickness calculation on a 1 voxel thick plate demonstrating how thickness estimation is improved using the oversampling distance transform function (thickness = 1 voxel using oversampling, thickness = 2 voxels without oversampling). Bottom row: thickness calculation applied to a sample finger joint.\label{fig:DT_Figure}](figures/DT_Figure.png) The user is given a choice, via a Boolean flag parameter in the compute_local_thickness_from_mask and calc_structure_thickness_statistics functions, to use the oversampling distance transform for thickness estimation. The choice is given because different users may have variable preferences in applying alternate definitions regarding the bounds of their structures (whether they be contained within the mask voxels or extend to the center of the neighbouring background voxels) and because using the normal (*i.e.*, not oversampled) distance transform will provide better congruence with thickness calculations produced using IPL. -To evaluate the thickness computation implemented in the `ORMIR_XCT` package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [10](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [11](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: `ridge_epsilon = 0.9`, `assign_epsilon = 1.8`, `suppress_boundary = 0`, and `version = 3`. This set of parameters is currently used for JSW analysis of *in vivo* finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (Figure 2 \autoref{fig:IPL_vs_ORMIR_XCT-DT}). +To evaluate the thickness computation implemented in the `ORMIR_XCT` package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [10](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [11](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: `ridge_epsilon = 0.9`, `assign_epsilon = 1.8`, `suppress_boundary = 0`, and `version = 3`. This set of parameters is currently used for JSW analysis of *in vivo* finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (\autoref{fig:IPL_vs_ORMIR_XCT-DT}). -![Figure 2: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the `ORMIR_XCT` package . Top row: black lines indicate mean difference and red dotted line shows the limits of agreement. Bottom row: solid black line indicates the linear regression between IPL and ORMIR_XCT computed thickness and red dotted lines indicate a line of unity.\label{fig: IPL_vs_ORMIR_XCT-DT}](figures/IPL_vs_ORMIR_XCT-DT.png) +![Figure 2: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the `ORMIR_XCT` package . Top row: black lines indicate mean difference and red dotted line shows the limits of agreement. Bottom row: solid black line indicates the linear regression between IPL and ORMIR_XCT computed thickness and red dotted lines indicate a line of unity.\label{fig:IPL_vs_ORMIR_XCT-DT}](figures/IPL_vs_ORMIR_XCT-DT.png) ## 3.3 - Bone Mineral Density: -Images obtained from HR-pQCT scanners are saved using the manufacturer’s proprietary file format (AIM). To process HR-pQCT images using Python, these files need to be converted to a file format that can be interpreted by common image processing Python libraries. The `ITKIOScanco` module is used in the `ORMIR_XCT` package to convert HR-pQCT images to other common medical image file types. However, the `ITKIOScanco` module converts HR-pQCT images to Hounsfield Units (HU) by default. Measures of BMD require the image to be calibrated in density units (mg of hydroxyapatite per cm3). Several image unit conversions have been included in the `ORMIR_XCT` package to allow for conversion between HR-pQCT native units (termed Scanco units) and each of HU, density, and linear attenuation units. A sample dataset of HR-pQCT images of second and third metacarpal phalangeal (MCP) joints (n = 292) was used to compare BMD results between IPL and the `ORMIR_XCT` package. BMD was reported separately for the distal (DST) and proximal (PRX) segments of each joint (Figure 3 \autoref{fig:IPL_vs_ORMIR_XCT-BMD}). +Images obtained from HR-pQCT scanners are saved using the manufacturer’s proprietary file format (AIM). To process HR-pQCT images using Python, these files need to be converted to a file format that can be interpreted by common image processing Python libraries. The `ITKIOScanco` module is used in the `ORMIR_XCT` package to convert HR-pQCT images to other common medical image file types. However, the `ITKIOScanco` module converts HR-pQCT images to Hounsfield Units (HU) by default. Measures of BMD require the image to be calibrated in density units (mg of hydroxyapatite per cm3). Several image unit conversions have been included in the `ORMIR_XCT` package to allow for conversion between HR-pQCT native units (termed Scanco units) and each of HU, density, and linear attenuation units. A sample dataset of HR-pQCT images of second and third metacarpal phalangeal (MCP) joints (n = 292) was used to compare BMD results between IPL and the `ORMIR_XCT` package. BMD was reported separately for the distal (DST) and proximal (PRX) segments of each joint (\autoref{fig:IPL_vs_ORMIR_XCT-BMD}). -![Figure 3: Bone mineral density (mg HA/cm3) linear regression and Bland-Altman plots comparing IPL and the ORMIR_XCT computed bone mineral density (BMD ). All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. MCP2: 2nd metacarpal phalangeal joint, MCP3: 3rd metacarpal phalangeal joint, DST: distal, PRX: proximal.\label{fig: IPL_vs_ORMIR_XCT-BMD}](figures/IPL_vs_ORMIR_XCT-BMD.png) +![Figure 3: Bone mineral density (mg HA/cm3) linear regression and Bland-Altman plots comparing IPL and the ORMIR_XCT computed bone mineral density (BMD ). All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. MCP2: 2nd metacarpal phalangeal joint, MCP3: 3rd metacarpal phalangeal joint, DST: distal, PRX: proximal.\label{fig:IPL_vs_ORMIR_XCT-BMD}](figures/IPL_vs_ORMIR_XCT-BMD.png) ## 3.4 - Trabecular Segmentation: -A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a global threshold-based image segmentation. The `gauss_seg` function in IPL has been reimplemented in the `ORMIR_XCT` package and the similarity of IPL and `ORMIR_XCT` trabecular segmentations were computed using DICE coefficients, Jaccard indices, and Hausdorff distances (Table 2 \autoref{fig:Table2}). The same dataset used to test the automatic contouring module was used to compare IPL and `ORMIR_XCT` trabecular segmentation. +A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a global threshold-based image segmentation. The `gauss_seg` function in IPL has been reimplemented in the `ORMIR_XCT` package and the similarity of IPL and `ORMIR_XCT` trabecular segmentations were computed using DICE coefficients, Jaccard indices, and Hausdorff distances (\autoref{fig:Table2}). The same dataset used to test the automatic contouring module was used to compare IPL and `ORMIR_XCT` trabecular segmentation. -![Table 2: DICE scores, Jaccard indices, and Hausdorff distances (voxels) for comparison of trabecular segmentation performed using IPL versus the `ORMIR_XCT` package. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. For each algorithm, Gaussian smoothing was performed with sigma = 0.5. Subsequently, segmentation was performed with lower and upper thresholds of 1,170 and 10,000 HU, respectively. DIP2: second distal interphalangeal joint, DIP3: third distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig: Table2}](figures/Table2.png) +![Table 2: DICE scores, Jaccard indices, and Hausdorff distances (voxels) for comparison of trabecular segmentation performed using IPL versus the `ORMIR_XCT` package. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. For each algorithm, Gaussian smoothing was performed with sigma = 0.5. Subsequently, segmentation was performed with lower and upper thresholds of 1,170 and 10,000 HU, respectively. DIP2: second distal interphalangeal joint, DIP3: third distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig:Table2}](figures/Table2.png) From 94bd12a499ef5221e17331e3cac4ab512ea162f7 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Fri, 1 Sep 2023 07:51:20 -0600 Subject: [PATCH 07/18] Mask thickness to get more accurate stats --- ormir_xct/util/hildebrand_thickness.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ormir_xct/util/hildebrand_thickness.py b/ormir_xct/util/hildebrand_thickness.py index 64c66da..24ef185 100644 --- a/ormir_xct/util/hildebrand_thickness.py +++ b/ormir_xct/util/hildebrand_thickness.py @@ -277,6 +277,7 @@ def calc_structure_thickness_statistics( local_thickness = compute_local_thickness_from_mask( mask, voxel_width, oversample, skeletonize ) + local_thickness *= sub_mask else: warnings.warn( "cannot find structure thickness statistics for binary mask with no positive voxels" From f59eb9c2d4386d05630b7505626ed511f9711471 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Fri, 1 Sep 2023 08:05:33 -0600 Subject: [PATCH 08/18] added extra function parameters --- ormir_xct/joint_space_analysis/jsw_main.py | 2 +- ormir_xct/joint_space_analysis/jsw_morphometry.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ormir_xct/joint_space_analysis/jsw_main.py b/ormir_xct/joint_space_analysis/jsw_main.py index 19a83de..ac00244 100644 --- a/ormir_xct/joint_space_analysis/jsw_main.py +++ b/ormir_xct/joint_space_analysis/jsw_main.py @@ -61,7 +61,7 @@ def main(joint_seg_path, output_path): # Compute JS parameters print("Computing thickness...") dt_img, jsw_params = jsw_parameters( - pad_image, dilated_js_mask, output_path, basename, 0.0607, js_mask + pad_image, dilated_js_mask, basename, output_path, js_mask, 0.0607, True, True ) # Copy the origin and spacing information from the original image so the diff --git a/ormir_xct/joint_space_analysis/jsw_morphometry.py b/ormir_xct/joint_space_analysis/jsw_morphometry.py index 5b3e771..976f0ff 100644 --- a/ormir_xct/joint_space_analysis/jsw_morphometry.py +++ b/ormir_xct/joint_space_analysis/jsw_morphometry.py @@ -142,7 +142,7 @@ def jsw_erode(dilated_image, pad_image): def jsw_parameters( - pad_image, dilated_js_mask, output_path, filename, voxel_size=0.0607, js_mask=None + pad_image, dilated_js_mask, filename, output_path, js_mask=None, voxel_size=0.0607, oversamp=True, skel=True, minimum=0.0 ): """ Computes the following JSW parameters: @@ -174,7 +174,7 @@ def jsw_parameters( # Needs to be fixed for masks with JS minimum < 1 voxel result = calc_structure_thickness_statistics( - dilated_mask, voxel_size, 0, mask, oversample=False, skeletonize=True + dilated_mask, voxel_size, minimum, mask, oversample=oversamp, skeletonize=skel ) mean_thickness = result[0] From 05a643b223b71e192da2fcc37918b02c30f09c10 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Fri, 1 Sep 2023 09:36:37 -0600 Subject: [PATCH 09/18] Updated readme with install instructions --- README.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f602823..3b9bb5d 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,9 @@ # ORMIR_2022 -This project was partially developed during the Jupyter Community Workshop “Building the Jupyter Community in Musculoskeletal Imaging Research”. +ORMIR_XCT is a Python package for processing high resolution peripheral computed tomography (HR-pQCT) scans. Development of this project began during the “Building the Jupyter Community in Musculoskeletal Imaging Research” workshop hosted by the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) group. + +## Installation +### Step 1: Install the ORMIR_XCT Anaconda Environment: +`conda env create -f environment.yml` + +### Step 2: Install the Package: +`pip install -e .` From 14f84716d1212f18403881bebd2a9407c5e335f2 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Fri, 1 Sep 2023 10:35:47 -0600 Subject: [PATCH 10/18] Added sitk Hausdorff distance --- ormir_xct/util/segmentation_evaluation.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/ormir_xct/util/segmentation_evaluation.py b/ormir_xct/util/segmentation_evaluation.py index 58d6a3c..fbd3ccf 100644 --- a/ormir_xct/util/segmentation_evaluation.py +++ b/ormir_xct/util/segmentation_evaluation.py @@ -26,7 +26,7 @@ def binarize_numpy_array(arr): numpy.array Binarized image as an array """ - return (np.abs(arr) > 0).astype(np.int) + return (np.abs(arr) > 0).astype(np.int64) def get_distance_map_and_surface(mask): @@ -152,3 +152,23 @@ def calculate_surface_distance_measures(ref, seg, spacing): ssd_measures["std"] = np.std(all_dist_list) return ssd_measures + +def hausdorff_sitk(ref, seg): + """ + Compute the Hausdorff distance using SimpleITK. + + Parameters + ---------- + ref : sitk.Image + + seg : sitk.Image + + Returns + ------- + ssd_measures : tuple + A tuple containing the mean and max Hausdorff distance. + """ + d = sitk.HausdorffDistanceImageFilter() + d.Execute(ref, seg) + + return (d.GetAverageHausdorffDistance(), d.GetHausdorffDistance()) From 5202469e3be4ba45936f66f505127f389f932f3c Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Tue, 5 Sep 2023 10:27:21 -0600 Subject: [PATCH 11/18] updated tables for manuscript --- manuscript/figures/Table1.png | Bin 160697 -> 83515 bytes manuscript/figures/Table2.png | Bin 174803 -> 79087 bytes 2 files changed, 0 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be2b961bf067fdc21eb17de40a7e4ff258cf9e02 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 6 Sep 2023 13:25:34 -0600 Subject: [PATCH 12/18] initial commit --- .github/workflows/draft-pdf.yml | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 .github/workflows/draft-pdf.yml diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 0000000..09e2467 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,19 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: ORMIR_XCT Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + paper-path: manuscript/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: ORMIR_XCT Paper Draft + path: manuscript/paper.pdf \ No newline at end of file From 73b1ab0f35198b4bad9b07c1d4354e35b80d6aff Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 6 Sep 2023 14:07:15 -0600 Subject: [PATCH 13/18] updated paper and references --- manuscript/paper.bib | 64 ++++++++++++++++++++++++++++++++++++++++++-- manuscript/paper.md | 12 +++++---- 2 files changed, 69 insertions(+), 7 deletions(-) diff --git a/manuscript/paper.bib b/manuscript/paper.bib index bc90adc..209629a 100644 --- a/manuscript/paper.bib +++ b/manuscript/paper.bib @@ -34,6 +34,18 @@ @article{Brunet:2021 Year = 2021 } +@article{Burghardt:2010, + url = {https://doi.org/10.1016/j.bone.2010.05.034}, + Archiveprefix = {}, + Author = {{Burghardt}, A.J., {Buie}, H.R., {Laib}, A., {Majumdar}, S., {Boyd}, S.K.}, + Eprint = {}, + Journal = {Bone}, + Keywords = {HR-pQCT, Cortical bone, Porosity, Segmentation, Osteoporosis, Microcomputed tomography, MicroCT, Reproducibility}, + Month = sept, + Title = {{Reproducibility of direct quantitative measures of cortical bone microarchitecture of the distal radius and tibia by HR-pQCT}}, + Year = 2010 +} + @article{Hildebrand:1996, url = {https://doi.org/10.1046/j.1365-2818.1997.1340694.x}, Archiveprefix = {}, @@ -46,6 +58,54 @@ @article{Hildebrand:1996 Year = 1996 } +@article{Harris:2020, + url = {https://doi.org/10.1038/s41586-020-2649-2}, + Archiveprefix = {}, + Author = {{Harris}, C.R., {Millman}, K.J., {van der Walt}, S.J., {Gommers}, R., {Virtanen}, P., {Cournapeau}, D., {Wieser}, E., {Taylor}, J., {Berg}, S., {Smith}, N.J., {Kern}, R., {Picus}, M., {Hoyer}, S., {van Kerkwijk} M.H., {Brett}, M., {Haldane}, A., {del Río} J.F., {Wiebe}, M., {Peterson}, P., {Gérard-Marchant}, P., {Sheppard}, K., {Reddy}, T., {Weckesser}, W., {Abbasi}, H., {Gohlke}, C., {Oliphant}, T.E.}, + Eprint = {}, + Journal = {Nature}, + Keywords = {}, + Month = sept, + Title = {{Array programming with NumPy}}, + Year = 2020 +} + +@article{Lam:2015, + url = {http://dx.doi.org/10.1145/2833157.2833162}, + Archiveprefix = {}, + Author = {{Lam}, S.K., {Pitrou}, A., {Seibert}, S}, + Eprint = {}, + Journal = {Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC}, + Keywords = {LLVM, Python, Compiler}, + Month = nov, + Title = {{Numba: A llvm-based python jit compiler}}, + Year = 2015 +} + +@article{Lowekamp:2013, + url = {https://doi.org/10.3389/fninf.2013.00045}, + Archiveprefix = {}, + Author = {{Lowekamp}, B.C., {Chen}, D.T., {Ibáñez}, L., {Blezek}, D.}, + Eprint = {}, + Journal = {Frontiers in neuroinformatics}, + Keywords = {software design, Insight Toolkit, segmentation, software development, image processing software, image processing and analysis}, + Month = dec, + Title = {{The Design of SimpleITK}}, + Year = 2013 +} + +@article{Van der Walt:2014, + url = {https://doi.org/10.7717/peerj.453}, + Archiveprefix = {}, + Author = {{Van der Walt}, S., {Schönberger}, J.L., {Nunez-Iglesias}, J., {Boulogne}, F., {Warner}, J.D., {Yager}, N., {Gouillart}, E. {Yu}, T.}, + Eprint = {}, + Journal = {PeerJ}, + Keywords = {Image processing, Reproducible research, Education, Visualization, Open source, Python, Scientific programming}, + Month = june, + Title = {{scikit-image: image processing in Python}}, + Year = 2014 +} + @article{Stok:2020, url = {http://dx.doi.org/10.21037/qims.2019.12.11}, Archiveprefix = {}, @@ -58,9 +118,9 @@ @article{Stok:2020 Year = 2020 } -@misc{ORMIR_XCT_Shapes, +@misc{ORMIR_XCT_Shapes:2023, author = {M. T. Kuczynski}, - title = {Synthetic Shapes for ORMIR_XCT Thickness Measurement Validation }, + title = {Synthetic Shapes for ORMIR_XCT Thickness Measurement Validation}, year = {2023}, publisher = {Zenodo}, journal = {Zenodo}, diff --git a/manuscript/paper.md b/manuscript/paper.md index 828438f..4be35ed 100644 --- a/manuscript/paper.md +++ b/manuscript/paper.md @@ -70,7 +70,7 @@ bibliography: paper.bib --- # 1 – Statement of Need: -High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique with a nominal isotropic voxel size of 61µm capable of imaging trabecular bone *in-vivo*. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases [1](https://doi.org/10.1186/s12880-020-00437-8), assess epidemiological associations [2](https://doi.org/10.1002/jbmr.2873), and evaluate the effects of pharmaceutical interventions [3](https://doi.org/10.1080/03009742.2020.1869303). Processing HR-pQCT images has largely been supported using the scanner manufacturer’s scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer’s software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The `ORMIR_XCT` Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows. +High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique with a nominal isotropic voxel size of 61µm capable of imaging trabecular bone *in-vivo*. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases [@Brunet:2020], assess epidemiological associations [@Burt:2016], and evaluate the effects of pharmaceutical interventions [@Brunet:2021]. Processing HR-pQCT images has largely been supported using the scanner manufacturer’s scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer’s software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The `ORMIR_XCT` Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows. # 2 – Package Summary: @@ -80,14 +80,14 @@ The `ORMIR_XCT` package contains four modules for processing HR-pQCT data of bon # 3 – Comparison to IPL: ## 3.1 - Autocontour: -Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [4](https://doi.org/10.1016/j.bone.2010.05.034) and the `ORMIR_XCT` package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images. The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in \autoref{fig:Table1}. +Automatic periosteal contouring (autocontour) was performed on a sample dataset of HR-pQCT images of the 2nd and 3rd distal interphalangeal (DIP) and trapeziometacarpal (TMC) joints (n = 59). The images came from 10 participants in an osteoarthritic group and 10 participants in a control group. This dataset is representative of typical data that is collected using HR-pQCT. Binarized images were obtained using both the IPL standard method for automatic periosteal contouring [@Burghardt:2010] and the `ORMIR_XCT` package. Segmentation were compared by computing DICE coefficients, Jaccard indices, and Hausdorff distances between images. The IPL method for automatic periosteal contouring was taken as the ground truth. Results of the segmentation comparison are shown in \autoref{fig:Table1}. ![Table 1: DICE coefficients, Jaccard indices, and Hausdorff distances (voxels) between IPL and `ORMIR_XCT` automatic contouring implementations. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. DIP2: 2nd distal interphalangeal joint, DIP3: 3rd distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig:Table1}](figures/Table1.png) ## 3.2 - Thickness: -The `ORMIR_XCT` package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand *et al* [5](https://doi.org/10.1046/j.1365-2818.1997.1340694.x). This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise. +The `ORMIR_XCT` package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand *et al* [@Hildebrand:1996]. This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise. -Hildebrand *et al.* note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with NumPy (v1.23.3) [6](https://doi.org/10.1038/s41586-020-2649-2) and Numba (v0.56.3) [7](http://dx.doi.org/10.1145/2833157.2833162). Finally, we have noted and compensated for a discrepancy between the definition of the distance map by Hildebrand *et al.* and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [8](https://doi.org/10.3389/fninf.2013.00045) and scikit-image (v0.19.2) [9](https://doi.org/10.7717/peerj.453). Hildebrand *et al.* define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. +@Hildebrand:1996 note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with NumPy (v1.23.3) [@Harris:2020] and Numba (v0.56.3) [@Lam:2015]. Finally, we have noted and compensated for a discrepancy between the definition of the distance map by @Hildebrand:1996 and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [@Lowekamp:2013] and scikit-image (v0.19.2) [@Van der Walt:2014]). @Hildebrand:1996 define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. To compensate for this, we developed an oversampling distance transform function (\autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. @@ -95,7 +95,7 @@ To compensate for this, we developed an oversampling distance transform function The user is given a choice, via a Boolean flag parameter in the compute_local_thickness_from_mask and calc_structure_thickness_statistics functions, to use the oversampling distance transform for thickness estimation. The choice is given because different users may have variable preferences in applying alternate definitions regarding the bounds of their structures (whether they be contained within the mask voxels or extend to the center of the neighbouring background voxels) and because using the normal (*i.e.*, not oversampled) distance transform will provide better congruence with thickness calculations produced using IPL. -To evaluate the thickness computation implemented in the `ORMIR_XCT` package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [10](https://doi.org/10.5281/zenodo.8155738). The standard IPL joint space width (JSW) analysis workflow [11](http://dx.doi.org/10.21037/qims.2019.12.11) was performed on the synthetic shape dataset using the following parameters: `ridge_epsilon = 0.9`, `assign_epsilon = 1.8`, `suppress_boundary = 0`, and `version = 3`. This set of parameters is currently used for JSW analysis of *in vivo* finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (\autoref{fig:IPL_vs_ORMIR_XCT-DT}). +To evaluate the thickness computation implemented in the `ORMIR_XCT` package, a set of synthetic shapes were generated to simulate bones of varying sizes. These shapes include solid and hollow spheres, solid and hollow cylinders, and plates of varying thicknesses [@ORMIR_XCT_Shapes:2023]. The standard IPL joint space width (JSW) analysis workflow [@Stok:2020] was performed on the synthetic shape dataset using the following parameters: `ridge_epsilon = 0.9`, `assign_epsilon = 1.8`, `suppress_boundary = 0`, and `version = 3`. This set of parameters is currently used for JSW analysis of *in vivo* finger joints. ORMIR_XCT thickness was calculated using the oversampling distance transform function described above. Bland-Altman plots were generated to compare thickness results between algorithms (\autoref{fig:IPL_vs_ORMIR_XCT-DT}). ![Figure 2: Bland-Altman and regression plots of mean joint space thickness computed using IPL and the `ORMIR_XCT` package . Top row: black lines indicate mean difference and red dotted line shows the limits of agreement. Bottom row: solid black line indicates the linear regression between IPL and ORMIR_XCT computed thickness and red dotted lines indicate a line of unity.\label{fig:IPL_vs_ORMIR_XCT-DT}](figures/IPL_vs_ORMIR_XCT-DT.png) @@ -108,3 +108,5 @@ Images obtained from HR-pQCT scanners are saved using the manufacturer’s propr A common operation performed in IPL is the application of a Gaussian smoothing filter followed by a global threshold-based image segmentation. The `gauss_seg` function in IPL has been reimplemented in the `ORMIR_XCT` package and the similarity of IPL and `ORMIR_XCT` trabecular segmentations were computed using DICE coefficients, Jaccard indices, and Hausdorff distances (\autoref{fig:Table2}). The same dataset used to test the automatic contouring module was used to compare IPL and `ORMIR_XCT` trabecular segmentation. ![Table 2: DICE scores, Jaccard indices, and Hausdorff distances (voxels) for comparison of trabecular segmentation performed using IPL versus the `ORMIR_XCT` package. All scans were obtained from a second-generation HR-pQCT scanner (XtremeCT2, Scanco Medical) with an isotropic voxel size of 61µm. For each algorithm, Gaussian smoothing was performed with sigma = 0.5. Subsequently, segmentation was performed with lower and upper thresholds of 1,170 and 10,000 HU, respectively. DIP2: second distal interphalangeal joint, DIP3: third distal interphalangeal joint, TMC: trapeziometacarpal joint.\label{fig:Table2}](figures/Table2.png) + +# References: From 0f0ed6d346636c180cf12881e7667b5fab2d1864 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 6 Sep 2023 14:08:01 -0600 Subject: [PATCH 14/18] updated oversample and skeletonization flags --- ormir_xct/joint_space_analysis/jsw_main.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/ormir_xct/joint_space_analysis/jsw_main.py b/ormir_xct/joint_space_analysis/jsw_main.py index ac00244..68ab7a3 100644 --- a/ormir_xct/joint_space_analysis/jsw_main.py +++ b/ormir_xct/joint_space_analysis/jsw_main.py @@ -43,25 +43,25 @@ def main(joint_seg_path, output_path): print("Dilating image...") dilated_image = jsw_dilate(pad_image) sitk.WriteImage( - dilated_image, os.path.join(output_path, str(basename) + "_DILATE.mha") + dilated_image, os.path.join(output_path, str(basename) + "_DILATE.nii") ) # Erode image print("Eroding image...") eroded_image, js_mask, dilated_js_mask = jsw_erode(dilated_image, pad_image) sitk.WriteImage( - eroded_image, os.path.join(output_path, str(basename) + "_ERODE.mha") + eroded_image, os.path.join(output_path, str(basename) + "_ERODE.nii") ) - sitk.WriteImage(js_mask, os.path.join(output_path, str(basename) + "_JS_MASK.mha")) + sitk.WriteImage(js_mask, os.path.join(output_path, str(basename) + "_JS_MASK.nii")) sitk.WriteImage( dilated_js_mask, - os.path.join(output_path, str(basename) + "_DILATED_JS_MASK.mha"), + os.path.join(output_path, str(basename) + "_DILATED_JS_MASK.nii"), ) # Compute JS parameters print("Computing thickness...") dt_img, jsw_params = jsw_parameters( - pad_image, dilated_js_mask, basename, output_path, js_mask, 0.0607, True, True + pad_image, dilated_js_mask, basename, output_path, js_mask, 0.0607, True, False ) # Copy the origin and spacing information from the original image so the @@ -73,7 +73,7 @@ def main(joint_seg_path, output_path): # Mask the thickness map with the original joint space mask dt_img = sitk.Mask(dt_img, js_mask) - sitk.WriteImage(dt_img, os.path.join(output_path, str(basename) + "_DT.mha")) + sitk.WriteImage(dt_img, os.path.join(output_path, str(basename) + "_DT.nii")) if __name__ == "__main__": From d05c3de67fc909dfd68cbc9ba30eaa29fbb878be Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 6 Sep 2023 14:13:13 -0600 Subject: [PATCH 15/18] fixed issue with spaces in author name --- manuscript/paper.bib | 2 +- manuscript/paper.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/manuscript/paper.bib b/manuscript/paper.bib index 209629a..b64aa51 100644 --- a/manuscript/paper.bib +++ b/manuscript/paper.bib @@ -94,7 +94,7 @@ @article{Lowekamp:2013 Year = 2013 } -@article{Van der Walt:2014, +@article{Van-der-Walt:2014, url = {https://doi.org/10.7717/peerj.453}, Archiveprefix = {}, Author = {{Van der Walt}, S., {Schönberger}, J.L., {Nunez-Iglesias}, J., {Boulogne}, F., {Warner}, J.D., {Yager}, N., {Gouillart}, E. {Yu}, T.}, diff --git a/manuscript/paper.md b/manuscript/paper.md index 4be35ed..c0e8415 100644 --- a/manuscript/paper.md +++ b/manuscript/paper.md @@ -87,7 +87,7 @@ Automatic periosteal contouring (autocontour) was performed on a sample dataset ## 3.2 - Thickness: The `ORMIR_XCT` package contains an open-source implementation of the algorithm for model-independent thickness estimation by Hildebrand *et al* [@Hildebrand:1996]. This thickness estimation is used to compute several morphological measurements from HR-pQCT images, including trabecular thickness, trabecular separation, and cortical thickness. Given a binary mask describing a three-dimensional object as input, the algorithm first computes the distance map (via a distance transform), describing at each voxel the radius of the largest sphere centered at that voxel that is entirely within the object. Next, the local thickness map is computed by iterating through all voxels in the mask and assigning them a local thickness value that is the diameter of the largest sphere in the distance map that contains that voxel. The thickness of a structure can then be computed as the volume-weighted mean of the local thickness map, with optional adjustments for a minimum thickness value if desired to reduce the effect of surface noise. -@Hildebrand:1996 note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with NumPy (v1.23.3) [@Harris:2020] and Numba (v0.56.3) [@Lam:2015]. Finally, we have noted and compensated for a discrepancy between the definition of the distance map by @Hildebrand:1996 and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [@Lowekamp:2013] and scikit-image (v0.19.2) [@Van der Walt:2014]). @Hildebrand:1996 define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. +@Hildebrand:1996 note an optimization for the algorithm where first, the distance ridge (also known as skeletonization or medial axis) is computed and only the distance map values on the distance ridge are used for local thickness map computation. We implemented this optimized version of the algorithm with NumPy (v1.23.3) [@Harris:2020] and Numba (v0.56.3) [@Lam:2015]. Finally, we have noted and compensated for a discrepancy between the definition of the distance map by @Hildebrand:1996 and the outputs of many common open-source distance map algorithms (including those available in SimpleITK (v2.0.2) [@Lowekamp:2013] and scikit-image (v0.19.2) [@Van-der-Walt:2014]). @Hildebrand:1996 define their distance map as "the radius of the largest sphere centered at the point and still completely inside the structure". In contrast, most other distance transforms compute the distance from the center of a voxel inside the mask to the nearest voxel center outside the mask. To compensate for this, we developed an oversampling distance transform function (\autoref{fig:DT_Figure}). In this function, an input mask is up-sampled to double the voxel size, and a sequence of morphological filters are applied to ensure that the newly introduced voxels on the boundary between the structure and the background are assigned as background voxels. A distance transform is applied to this mask, resulting in a distance map where the spheres fit entirely within the structure defined by the mask, and the resulting distance map is down-sampled to the original voxel size. From 7e40934ec84ebdbba4a3bfb6fb583b435c441fd7 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 6 Sep 2023 15:28:39 -0600 Subject: [PATCH 16/18] initial commit --- examples/finger_joint_comparison.ipynb | 332 +++++++++++++++++++++ examples/images/IPL_JSW_DT_IMAGE.png | Bin 0 -> 300511 bytes examples/images/IPL_vs_ORMIR_XCT_JOINT.nii | Bin 0 -> 38961904 bytes examples/thickness_of_shapes.ipynb | 257 ++++++++++++++++ 4 files changed, 589 insertions(+) create mode 100644 examples/finger_joint_comparison.ipynb create mode 100644 examples/images/IPL_JSW_DT_IMAGE.png create mode 100644 examples/images/IPL_vs_ORMIR_XCT_JOINT.nii create mode 100644 examples/thickness_of_shapes.ipynb diff --git a/examples/finger_joint_comparison.ipynb b/examples/finger_joint_comparison.ipynb new file mode 100644 index 0000000..23156e9 --- /dev/null +++ b/examples/finger_joint_comparison.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "78149392", + "metadata": {}, + "source": [ + "# Finger Joint Comparison\n", + "\n", + "This notebook compares results from the IPL joint space width (JSW) analysis workflow to the results from the ORMIR_XCT package. To provide a more direct comparison, the same joint segmentation was used as input to the JSW analysis (generated using IPL). ORMIR_XCT results are shown with all combonations of oversampling and skeletonization.\n", + "\n", + "## IPL JSW Results\n", + "\n", + "The following distance transform image was generated in IPL:\n", + "\n", + "\n", + "The following JSW results were obtained from the above distance transform image:\n", + "\n", + "| JSW Parameter | Value |\n", + "|:--------:|:--------:|\n", + "| JS Volume (mm^3) | 78.2068 |\n", + "| Mean JS Width (mm) | 1.8472 |\n", + "| JS Max (mm) | 2.6101 |\n", + "| JS Min (mm) | 1.3354 |\n", + "| JS Asymmetry (JS Max / JS Min) | 1.9545 |" + ] + }, + { + "cell_type": "markdown", + "id": "ebee8652", + "metadata": {}, + "source": [ + "# Step 1: Imports\n", + "\n", + "Import modules/packages and set the input image path. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "93e98f00", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import SimpleITK as sitk\n", + "\n", + "from ormir_xct.joint_space_analysis.jsw_morphometry import jsw_pad, jsw_dilate, jsw_erode, jsw_parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7b4af5a3", + "metadata": {}, + "outputs": [], + "source": [ + "joint_seg_path = os.path.join(\"images\", \"IPL_vs_ORMIR_XCT_JOINT.nii\")\n", + "output_path = \"images\"" + ] + }, + { + "cell_type": "markdown", + "id": "79816632", + "metadata": {}, + "source": [ + "# Step 2: Run the JSW Workflow\n", + "\n", + "1. **Padding:** Pad the binary joint image with black space (zeros) to ensure the outside space is greater than the inside (joint) space.\n", + "2. **Dilation:** Next we dilate the binary image by a fixed constant (see `jsw_morphometry.py`) that is taken from the original IPL script. Here we use the SimpleITK Ball structural element for dilation which should be similar to the Euclidean metric used in IPL. Once dilation is complete, remove any islands and fill any holes in the dilated mask.\n", + "3. **Erosion:** Next erode the image and set the image's value to 30. Then, add the erosion and dilation images together. Areas with overlap between images will have a value of 90, and the joint space will have a value of 30. We can then use a binary threshold to extract the joint space. The joint space mask is then dilated to ensure correct distances are measured at the edges of the joint space.\n", + "4. **JSW Parameters:** Now we will compute JS volume, JSW mean, standard deviation, minimum, maximum, and JS asymmetry (JSW.Max / JSW.Min). Results are output to a CSV file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f460183e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Padding image...\n", + "Dilating image...\n", + "Eroding image...\n" + ] + } + ], + "source": [ + "filename = os.path.basename(joint_seg_path)\n", + "basename = os.path.splitext(filename)[0]\n", + "img = sitk.ReadImage(joint_seg_path, sitk.sitkUInt8)\n", + "\n", + "# 1. Padding\n", + "print(\"Padding image...\")\n", + "pad_image = jsw_pad(img)\n", + "\n", + "# 2. Dilation\n", + "print(\"Dilating image...\")\n", + "dilated_image = jsw_dilate(pad_image)\n", + "sitk.WriteImage(dilated_image, os.path.join(output_path, str(basename) + \"_DILATE.nii\"))\n", + "\n", + "# 3. Erosion\n", + "print(\"Eroding image...\")\n", + "eroded_image, js_mask, dilated_js_mask = jsw_erode(dilated_image, pad_image)\n", + "sitk.WriteImage(eroded_image, os.path.join(output_path, str(basename) + \"_ERODE.nii\"))\n", + "sitk.WriteImage(js_mask, os.path.join(output_path, str(basename) + \"_JS_MASK.nii\"))\n", + "sitk.WriteImage(dilated_js_mask, os.path.join(output_path, str(basename) + \"_DILATED_JS_MASK.nii\"))" + ] + }, + { + "cell_type": "markdown", + "id": "44fc2d09", + "metadata": {}, + "source": [ + "# Step 3: Compute the JSW Parameters\n", + "\n", + "All combinations of oversampling and skeletonization will be run through the distance transform algorithm to see which set of parameters best matches results from IPL." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bfbb96f0", + "metadata": {}, + "outputs": [], + "source": [ + "# 4. JSW Parameters\n", + "\n", + "# Oversampling=False, Skeletonization=False\n", + "dt_img1, jsw_params1 = jsw_parameters(pad_image, dilated_js_mask, basename,\n", + " output_path, js_mask, pad_image.GetSpacing()[0], \n", + " oversamp=False, skel=False)\n", + "sitk.WriteImage(dt_img1, os.path.join(output_path, str(basename) + \"_DT1.nii\"))\n", + "\n", + "# Oversampling=True, Skeletonization=False\n", + "dt_img2, jsw_params2 = jsw_parameters(pad_image, dilated_js_mask, basename,\n", + " output_path, js_mask, pad_image.GetSpacing()[0], \n", + " oversamp=True, skel=False)\n", + "sitk.WriteImage(dt_img1, os.path.join(output_path, str(basename) + \"_DT2.nii\"))\n", + "\n", + "# Oversampling=False, Skeletonization=True\n", + "dt_img3, jsw_params3 = jsw_parameters(pad_image, dilated_js_mask, basename,\n", + " output_path, js_mask, pad_image.GetSpacing()[0], \n", + " oversamp=False, skel=True)\n", + "sitk.WriteImage(dt_img1, os.path.join(output_path, str(basename) + \"_DT3.nii\"))\n", + "\n", + "# Oversampling=True, Skeletonization=True\n", + "dt_img4, jsw_params4 = jsw_parameters(pad_image, dilated_js_mask, basename,\n", + " output_path, js_mask, pad_image.GetSpacing()[0], \n", + " oversamp=True, skel=True)\n", + "sitk.WriteImage(dt_img1, os.path.join(output_path, str(basename) + \"_DT4.nii\"))" + ] + }, + { + "cell_type": "markdown", + "id": "68b9841c", + "metadata": {}, + "source": [ + "# Step 4: Display Results\n", + "\n", + "Use Pandas to print a table of the results we generated." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f3789891", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
       ipl_resultsoversamp=False, skel=Falseoversamp=True, skel=Falseoversamp=False, skel=Trueoversamp=True, skel=True
      JS Volume (mm^3)78.2180.4780.4780.4780.47
      Mean JS Width (mm)1.851.921.921.951.73
      JS Max (mm)2.612.442.352.412.31
      JS Min (mm)1.341.321.350.240.19
      JS Asymmetry (JS Max / JS Min)1.951.851.759.9412.05
      \n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the JS Volume\n", + "shape_stats = sitk.LabelShapeStatisticsImageFilter()\n", + "shape_stats.ComputeOrientedBoundingBoxOn()\n", + "shape_stats.Execute(js_mask)\n", + "\n", + "stats_list = [\n", + " (\n", + " shape_stats.GetPhysicalSize(i),\n", + " shape_stats.GetNumberOfPixels(i),\n", + " )\n", + " for i in shape_stats.GetLabels()\n", + "]\n", + "\n", + "jsv = stats_list[0][0]\n", + "\n", + "# Oversampling=False, Skeletonization=False\n", + "js_mean1 = jsw_params1[0][3]\n", + "js_max1 = jsw_params1[0][7]\n", + "js_min1 = jsw_params1[0][5]\n", + "js_as1 = js_max1 / js_min1\n", + "\n", + "# Oversampling=True, Skeletonization=False\n", + "js_mean2 = jsw_params2[0][3]\n", + "js_max2 = jsw_params2[0][7]\n", + "js_min2 = jsw_params2[0][5]\n", + "js_as2 = js_max2 / js_min2\n", + "\n", + "# Oversampling=False, Skeletonization=True\n", + "js_mean3 = jsw_params3[0][3]\n", + "js_max3 = jsw_params3[0][7]\n", + "js_min3 = jsw_params3[0][5]\n", + "js_as3 = js_max3 / js_min3\n", + "\n", + "# Oversampling=True, Skeletonization=True\n", + "js_mean4 = jsw_params4[0][3]\n", + "js_max4 = jsw_params4[0][7]\n", + "js_min4 = jsw_params4[0][5]\n", + "js_as4 = js_max4 / js_min4\n", + "\n", + "\n", + "# Display a table with the IPL and ORMIR_XCT calculated JS parameters\n", + "data = {\"ipl_results\": [78.2068, 1.8472, 2.6101, 1.3354, 1.9545],\n", + " \"oversamp=False, skel=False\": [jsv, js_mean1, js_max1, js_min1, js_as1],\n", + " \"oversamp=True, skel=False\": [jsv, js_mean1, js_max2, js_min2, js_as2],\n", + " \"oversamp=False, skel=True\": [jsv, js_mean2, js_max3, js_min3, js_as3],\n", + " \"oversamp=True, skel=True\": [jsv, js_mean3, js_max4, js_min4, js_as4],\n", + " }\n", + "df = pd.DataFrame(data, index=[\"JS Volume (mm^3)\", \"Mean JS Width (mm)\", \"JS Max (mm)\", \n", + " \"JS Min (mm)\", \"JS Asymmetry (JS Max / JS Min)\"])\n", + "\n", + "# Set table default options\n", + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('colheader_justify', 'center')\n", + "\n", + "df.style.format(precision=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21bce143", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/images/IPL_JSW_DT_IMAGE.png b/examples/images/IPL_JSW_DT_IMAGE.png new file mode 100644 index 0000000000000000000000000000000000000000..ee7ac04a3319c8e007a7ed0b5ee2a4c593ac655d GIT binary patch literal 300511 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"markdown", + "id": "58484de9", + "metadata": {}, + "source": [ + "# Thickness of Shapes\n", + "\n", + "Investigate the local thickness of structures in Python using the ORMIR_XCT package." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5ecb644c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import SimpleITK as sitk\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from ormir_xct.util.hildebrand_thickness import calc_structure_thickness_statistics" + ] + }, + { + "cell_type": "markdown", + "id": "86bddd4f", + "metadata": {}, + "source": [ + "Synthetic shape mask functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f9ccefd6", + "metadata": {}, + "outputs": [], + "source": [ + "def create_shape(shape, voxel_widths, thickness, shape_type=\"sphere\"):\n", + " center = (\n", + " voxel_widths[0] * (shape[0] // 2), \n", + " voxel_widths[1] * (shape[1] // 2), \n", + " voxel_widths[2] * (shape[2] // 2)\n", + " )\n", + " x, y, z = np.meshgrid(*[voxel_widths[i] * np.arange(0, shape[i]) for i in range(3)], indexing=\"ij\")\n", + " if shape_type == \"sphere\":\n", + " mask = (((x-center[0])**2 + (y-center[1])**2 + (z-center[2])**2) < (thickness/2)**2).astype(int)\n", + " elif shape_type == \"cylinder\":\n", + " mask = (((x-center[0])**2 + (y-center[1])**2) < (thickness/2)**2).astype(int)\n", + " elif shape_type == \"plate\":\n", + " mask = (np.abs(x-center[0]) < thickness/2).astype(int)\n", + " else:\n", + " raise ValueError(f\"`shape_type` can be `sphere`, `cylinder`, `plate`; got {shape_type}\")\n", + " return mask\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "40c64774", + "metadata": {}, + "source": [ + "Generate a synthetic sphere, cylinder, and plate:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "667a40b5", + "metadata": {}, + "outputs": [], + "source": [ + "shape = tuple([50]*3)\n", + "voxel_widths = tuple([0.0607]*3)\n", + "radius = 1\n", + "\n", + "sphere = create_shape(shape, voxel_widths, radius, shape_type=\"sphere\")\n", + "cylinder = create_shape(shape, voxel_widths, radius, shape_type=\"cylinder\")\n", + "plate = create_shape(shape, voxel_widths, radius, shape_type=\"plate\")" + ] + }, + { + "cell_type": "markdown", + "id": "9383b9fa", + "metadata": {}, + "source": [ + "Check their estimated mean thicknesses:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a6255985", + "metadata": {}, + "outputs": [], + "source": [ + "sphere_thickness_stats = calc_structure_thickness_statistics(sphere, voxel_widths, 0)\n", + "cylinder_thickness_stats = calc_structure_thickness_statistics(cylinder, voxel_widths, 0)\n", + "plate_thickness_stats = calc_structure_thickness_statistics(plate, voxel_widths, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "23a3f3fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sphere thickness is 0.898 +/- 0.000\n", + "Cylinder thickness is 0.929 +/- 0.000\n", + "Plate thickness is 1.032 +/- 0.000\n" + ] + } + ], + "source": [ + "print(f\"Sphere thickness is {sphere_thickness_stats[0]:0.3f} +/- {sphere_thickness_stats[1]:0.3f}\")\n", + "print(f\"Cylinder thickness is {cylinder_thickness_stats[0]:0.3f} +/- {cylinder_thickness_stats[1]:0.3f}\")\n", + "print(f\"Plate thickness is {plate_thickness_stats[0]:0.3f} +/- {plate_thickness_stats[1]:0.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b4a59b98", + "metadata": {}, + "source": [ + "This checks out because we set `radius = 1` when we generated these shapes." + ] + }, + { + "cell_type": "markdown", + "id": "6ed9be8f", + "metadata": {}, + "source": [ + "Now we can do a sweep over spheres, cylinders, and plates of various thicknesses to see how well we can estimate the thickness of these shapes at various resolutions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ae232e64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "thickness: 5\n", + "thickness: 10\n", + "thickness: 15\n", + "thickness: 20\n", + "thickness: 25\n", + "thickness: 30\n", + "thickness: 35\n", + "thickness: 40\n", + "thickness: 45\n" + ] + } + ], + "source": [ + "shape = (100, 100, 100)\n", + "voxel_widths = (1, 1, 1)\n", + "\n", + "true_thicknesses = np.arange(5, 50, 5)\n", + "\n", + "est_sph_thicknesses = []\n", + "est_cyl_thicknesses = []\n", + "est_plt_thicknesses = []\n", + "\n", + "for thickness in true_thicknesses:\n", + " print(f\"thickness: {thickness}\")\n", + " \n", + " sphere = create_shape(shape, voxel_widths, thickness, shape_type=\"sphere\")\n", + " est_sph_thicknesses.append(calc_structure_thickness_statistics(sphere, voxel_widths, 0)[0])\n", + " \n", + " cylinder = create_shape(shape, voxel_widths, thickness, shape_type=\"cylinder\")\n", + " est_cyl_thicknesses.append(calc_structure_thickness_statistics(cylinder, voxel_widths, 0)[0])\n", + " \n", + " plate = create_shape(shape, voxel_widths, thickness, shape_type=\"plate\")\n", + " est_plt_thicknesses.append(calc_structure_thickness_statistics(plate, voxel_widths, 0)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dc5e48e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_size = max(shape)//2\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", + "\n", + "axs[0].plot(true_thicknesses, est_sph_thicknesses, \"k-o\")\n", + "axs[0].plot([0, max_size], [0, max_size], \"r-\")\n", + "axs[0].grid()\n", + "\n", + "axs[0].set_xlabel(\"true sphere radius [vox]\")\n", + "axs[0].set_ylabel(\"estimated sphere radius [vox]\")\n", + "\n", + "axs[1].plot(true_thicknesses, est_cyl_thicknesses, \"k-o\")\n", + "axs[1].plot([0, max_size], [0, max_size], \"r-\")\n", + "axs[1].grid()\n", + "\n", + "axs[1].set_xlabel(\"true cylinder radius [vox]\")\n", + "axs[1].set_ylabel(\"estimated cylinder radius [vox]\")\n", + "\n", + "axs[2].plot(true_thicknesses, est_plt_thicknesses, \"k-o\")\n", + "axs[2].plot([0, max_size], [0, max_size], \"r-\", linewidth=2)\n", + "axs[2].grid()\n", + "\n", + "axs[2].set_xlabel(\"true plate thickness [vox]\")\n", + "axs[2].set_ylabel(\"estimated plate thickness [vox]\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5664988045b447106ab23d2cce4d1f4e8026a186 Mon Sep 17 00:00:00 2001 From: Michael Kuczynski Date: Wed, 6 Sep 2023 15:29:07 -0600 Subject: [PATCH 17/18] moved files --- examples/hildebrand_notebook.ipynb | 530 ----------------------------- examples/jsw_nathan.ipynb | 355 ------------------- examples/jsw_notebook.ipynb | 341 ------------------- manuscript/notebooks/hausdorff.py | 111 ------ 4 files changed, 1337 deletions(-) delete mode 100644 examples/hildebrand_notebook.ipynb delete mode 100644 examples/jsw_nathan.ipynb delete mode 100644 examples/jsw_notebook.ipynb delete mode 100644 manuscript/notebooks/hausdorff.py diff --git a/examples/hildebrand_notebook.ipynb b/examples/hildebrand_notebook.ipynb deleted file mode 100644 index 2bc6646..0000000 --- a/examples/hildebrand_notebook.ipynb +++ /dev/null @@ -1,530 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "58484de9", - "metadata": {}, - "source": [ - "# Hildebrand Local Thickness Algorithm Notebook\n", - "\n", - "Investigate the local thickness of structures in Python." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5ecb644c", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import SimpleITK as sitk\n", - "\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from ormir_xct.util.hildebrand_thickness import calc_structure_thickness_statistics\n", - "from ormir_xct.segmentation.ipl_seg import ipl_seg" - ] - }, - { - "cell_type": "markdown", - "id": "86bddd4f", - "metadata": {}, - "source": [ - "Synthetic shape mask functions:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f9ccefd6", - "metadata": {}, - "outputs": [], - "source": [ - "def create_shape(shape, voxel_widths, thickness, shape_type=\"sphere\"):\n", - " center = (\n", - " voxel_widths[0] * (shape[0] // 2), \n", - " voxel_widths[1] * (shape[1] // 2), \n", - " voxel_widths[2] * (shape[2] // 2)\n", - " )\n", - " x, y, z = np.meshgrid(*[voxel_widths[i] * np.arange(0, shape[i]) for i in range(3)], indexing=\"ij\")\n", - " if shape_type == \"sphere\":\n", - " mask = (((x-center[0])**2 + (y-center[1])**2 + (z-center[2])**2) < (thickness/2)**2).astype(int)\n", - " elif shape_type == \"cylinder\":\n", - " mask = (((x-center[0])**2 + (y-center[1])**2) < (thickness/2)**2).astype(int)\n", - " elif shape_type == \"plate\":\n", - " mask = (np.abs(x-center[0]) < thickness/2).astype(int)\n", - " else:\n", - " raise ValueError(f\"`shape_type` can be `sphere`, `cylinder`, `plate`; got {shape_type}\")\n", - " return mask\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "40c64774", - "metadata": {}, - "source": [ - "Generate a synthetic sphere, cylinder, and plate:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "667a40b5", - "metadata": {}, - "outputs": [], - "source": [ - "shape = tuple([50]*3)\n", - "voxel_widths = tuple([0.0607]*3)\n", - "radius = 1\n", - "\n", - "sphere = create_shape(shape, voxel_widths, radius, shape_type=\"sphere\")\n", - "cylinder = create_shape(shape, voxel_widths, radius, shape_type=\"cylinder\")\n", - "plate = create_shape(shape, voxel_widths, radius, shape_type=\"plate\")\n", - "\n", - "#sitk.compare(sphere, cylinder, link_cmap=True, axes=True, rotate=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0977246a", - "metadata": {}, - "outputs": [], - "source": [ - "#compare(sphere, plate, link_cmap=True, axes=True, rotate=True)" - ] - }, - { - "cell_type": "markdown", - "id": "9383b9fa", - "metadata": {}, - "source": [ - "Check their estimated mean thicknesses:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a6255985", - "metadata": {}, - "outputs": [], - "source": [ - "sphere_thickness_stats = calc_structure_thickness_statistics(sphere, voxel_widths, 0)\n", - "cylinder_thickness_stats = calc_structure_thickness_statistics(cylinder, voxel_widths, 0)\n", - "plate_thickness_stats = calc_structure_thickness_statistics(plate, voxel_widths, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "23a3f3fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sphere thickness is 0.898 +/- 0.000\n", - "Cylinder thickness is 0.929 +/- 0.000\n", - "Plate thickness is 1.032 +/- 0.000\n" - ] - } - ], - "source": [ - "print(f\"Sphere thickness is {sphere_thickness_stats[0]:0.3f} +/- {sphere_thickness_stats[1]:0.3f}\")\n", - "print(f\"Cylinder thickness is {cylinder_thickness_stats[0]:0.3f} +/- {cylinder_thickness_stats[1]:0.3f}\")\n", - "print(f\"Plate thickness is {plate_thickness_stats[0]:0.3f} +/- {plate_thickness_stats[1]:0.3f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "b4a59b98", - "metadata": {}, - "source": [ - "This checks out because we set `radius = 1` when we generated these shapes." - ] - }, - { - "cell_type": "markdown", - "id": "6ed9be8f", - "metadata": {}, - "source": [ - "Now we can do a sweep over spheres, cylinders, and plates of various thicknesses to see how well we can estimate the thickness of these shapes at various resolutions." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ae232e64", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "thickness: 5\n", - "thickness: 10\n", - "thickness: 15\n", - "thickness: 20\n", - "thickness: 25\n", - "thickness: 30\n", - "thickness: 35\n", - "thickness: 40\n", - "thickness: 45\n" - ] - } - ], - "source": [ - "shape = (100, 100, 100)\n", - "voxel_widths = (1, 1, 1)\n", - "\n", - "true_thicknesses = np.arange(5, 50, 5)\n", - "\n", - "est_sph_thicknesses = []\n", - "est_cyl_thicknesses = []\n", - "est_plt_thicknesses = []\n", - "\n", - "for thickness in true_thicknesses:\n", - " print(f\"thickness: {thickness}\")\n", - " \n", - " sphere = create_shape(shape, voxel_widths, thickness, shape_type=\"sphere\")\n", - " est_sph_thicknesses.append(calc_structure_thickness_statistics(sphere, voxel_widths, 0)[0])\n", - " \n", - " cylinder = create_shape(shape, voxel_widths, thickness, shape_type=\"cylinder\")\n", - " est_cyl_thicknesses.append(calc_structure_thickness_statistics(cylinder, voxel_widths, 0)[0])\n", - " \n", - " plate = create_shape(shape, voxel_widths, thickness, shape_type=\"plate\")\n", - " est_plt_thicknesses.append(calc_structure_thickness_statistics(plate, voxel_widths, 0)[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dc5e48e9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "max_size = max(shape)//2\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", - "\n", - "axs[0].plot(true_thicknesses, est_sph_thicknesses, \"k-o\")\n", - "axs[0].plot([0, max_size], [0, max_size], \"r-\")\n", - "axs[0].grid()\n", - "\n", - "axs[0].set_xlabel(\"true sphere radius [vox]\")\n", - "axs[0].set_ylabel(\"estimated sphere radius [vox]\")\n", - "\n", - "axs[1].plot(true_thicknesses, est_cyl_thicknesses, \"k-o\")\n", - "axs[1].plot([0, max_size], [0, max_size], \"r-\")\n", - "axs[1].grid()\n", - "\n", - "axs[1].set_xlabel(\"true cylinder radius [vox]\")\n", - "axs[1].set_ylabel(\"estimated cylinder radius [vox]\")\n", - "\n", - "axs[2].plot(true_thicknesses, est_plt_thicknesses, \"k-o\")\n", - "axs[2].plot([0, max_size], [0, max_size], \"r-\", linewidth=2)\n", - "axs[2].grid()\n", - "\n", - "axs[2].set_xlabel(\"true plate thickness [vox]\")\n", - "axs[2].set_ylabel(\"estimated plate thickness [vox]\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d3ab4736", - "metadata": {}, - "source": [ - "If you have run the JSW example notebook, there will be a saved image that has the joint pace mask for a finger joint. We can also investigate that as an example here..." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9b1ee20b", - "metadata": {}, - "outputs": [], - "source": [ - "data_fn = \"../tests/data/JS_MASK.mha\"\n", - "js_mask = sitk.GetArrayFromImage(sitk.ReadImage(data_fn)) > 0\n", - "voxel_widths = (0.0607, 0.0607, 0.0607)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2fe83308", - "metadata": {}, - "outputs": [], - "source": [ - "js_stats = calc_structure_thickness_statistics(js_mask, voxel_widths, 2*0.0607)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cab6f5a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JSV : 65.246 mm^3\n", - "JSW.mean: 1.359 mm\n", - "JSW.std : 0.200 mm\n", - "JSW.min : 0.121 mm\n", - "JSW.max : 1.623 mm\n" - ] - } - ], - "source": [ - "jsv = js_mask.sum()*voxel_widths[0]*voxel_widths[1]*voxel_widths[2]\n", - "print(f\"JSV : {jsv:0.3f} mm^3\")\n", - "print(f\"JSW.mean: {js_stats[0]:0.3f} mm\")\n", - "print(f\"JSW.std : {js_stats[1]:0.3f} mm\")\n", - "print(f\"JSW.min : {js_stats[2]:0.3f} mm\")\n", - "print(f\"JSW.max : {js_stats[3]:0.3f} mm\")" - ] - }, - { - "cell_type": "markdown", - "id": "379af9e3", - "metadata": {}, - "source": [ - "We can also attempt to do some microarchitectural analysis. Load a preprocessed sample HR-pQCT tibia image:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "db61a7e5", - "metadata": {}, - "outputs": [], - "source": [ - "data_fn = \"/Users/nathanneeteson/Documents/Data/Images/UNet/NORMXTII_0051_TL.npz\"\n", - "with np.load(data_fn) as data:\n", - " image = data[\"image\"]\n", - " cort_mask = data[\"cort_mask\"]\n", - " trab_mask = data[\"trab_mask\"]\n", - "\n", - "cort_thresh = 450\n", - "trab_thresh = 320\n", - "\n", - "cort_bone_mask = cort_mask & (\n", - " sitk.GetArrayFromImage(\n", - " ipl_seg(\n", - " sitk.GetImageFromArray(image),\n", - " cort_thresh,\n", - " 1e10, # crazy high number because there's no upper thresh\n", - " voxel_size=1,\n", - " sigma=0.8\n", - " )\n", - " )\n", - " == 127\n", - ")\n", - "\n", - "trab_bone_mask = trab_mask & (\n", - " sitk.GetArrayFromImage(\n", - " ipl_seg(\n", - " sitk.GetImageFromArray(image),\n", - " trab_thresh,\n", - " 1e10, # crazy high number because there's no upper thresh\n", - " voxel_size=1,\n", - " sigma=0.8\n", - " )\n", - " )\n", - " == 127\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d79592e3", - "metadata": {}, - "source": [ - "Plot a slice with `pyplot` quickly to make sure the data loaded correctly:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bd611d06", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1,5, figsize=(25,5))\n", - "\n", - "axs[0].imshow(image[:,:,168//2])\n", - "axs[0].set_title(\"image\")\n", - "\n", - "axs[1].imshow(cort_mask[:,:,168//2])\n", - "axs[1].set_title(\"cort mask\")\n", - "\n", - "axs[2].imshow(cort_bone_mask[:,:,168//2])\n", - "axs[2].set_title(\"cort bone\")\n", - "\n", - "axs[3].imshow(trab_mask[:,:,168//2])\n", - "axs[3].set_title(\"trab mask\")\n", - "\n", - "axs[4].imshow(trab_bone_mask[:,:,168//2])\n", - "axs[4].set_title(\"trab bone\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c5b5665e", - "metadata": {}, - "source": [ - "Get the thickness stats from the cortical mask to calculate `Ct.Th`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d942e0e9", - "metadata": {}, - "outputs": [], - "source": [ - "cort_thickness_stats = calc_structure_thickness_statistics(cort_mask, (0.0607, 0.0607, 0.0607), 0.0607)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c26ae1b", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Ct.Th: {cort_thickness_stats[0]:0.3f} mm\")" - ] - }, - { - "cell_type": "markdown", - "id": "ee995dee", - "metadata": {}, - "source": [ - "The reference value is 1.379 mm." - ] - }, - { - "cell_type": "markdown", - "id": "f0a91712", - "metadata": {}, - "source": [ - "Now try calculating `Tb.Th`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf9b7bf7", - "metadata": {}, - "outputs": [], - "source": [ - "trab_bone_thickness_stats = calc_structure_thickness_statistics(trab_bone_mask, (0.0607, 0.0607, 0.0607), 0.0607)" - ] - }, - { - "cell_type": "markdown", - "id": "fc2d75e9", - "metadata": {}, - "source": [ - "The reference value is 0.271 mm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3231eb00", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Tb.Th: {trab_bone_thickness_stats[0]:0.3f} mm\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53364957", - "metadata": {}, - "outputs": [], - "source": [ - "print(\" | Python | Reference (IPL)\")\n", - "print(\"----------------------------------------\")\n", - "print(f\"Ct.Th [mm] | {cort_thickness_stats[0]:0.3f} | 1.379 \")\n", - "print(f\"Tb.Th [mm] | {trab_bone_thickness_stats[0]:0.3f} | 0.271 \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cedeb6b5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3eb79b69", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/jsw_nathan.ipynb b/examples/jsw_nathan.ipynb deleted file mode 100644 index bbbf57d..0000000 --- a/examples/jsw_nathan.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "id": "71095eda", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import itk\n", - "import datetime\n", - "import numpy as np\n", - "import SimpleITK as sitk\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from ormir_xct.util.hildebrand_thickness import (\n", - " compute_local_thickness_from_mask,\n", - " calc_structure_thickness_statistics\n", - ")\n", - "\n", - "\n", - "# Define a global variable for erosion and dilation\n", - "# These values are taken directly from the JSW IPL scripts\n", - "MISC2 = 27\n", - "CALC = MISC2 + 8\n", - "\n", - "# Set the image path\n", - "filename = 'J0010803_JOINT.nii'\n", - "image_dir = os.path.dirname(os.getcwd())\n", - "image_dir = os.path.join(image_dir, 'tests')\n", - "image_dir = os.path.join(image_dir, 'data')\n", - "seg_path = os.path.join(image_dir, filename)\n", - "\n", - "# Read the image using SimpleITK\n", - "seg_image = sitk.ReadImage(seg_path)\n", - "seg_image_np = sitk.GetArrayFromImage(seg_image)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82ebc3bf", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "da192d8d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "view_slice = (slice(None), seg_image_np.shape[1]//2, slice(None))\n", - "\n", - "fig = plt.figure(figsize=(10,10))\n", - "\n", - "plt.imshow(sitk.GetArrayFromImage(seg_image)[view_slice])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5f3875bf", - "metadata": {}, - "outputs": [], - "source": [ - "# Image padding only in the X and Y direction\n", - "pad_image = sitk.ConstantPad(seg_image, [MISC2, MISC2, 0], [MISC2, MISC2, 0], 0)\n", - "\n", - "# Use a binary threshold filter to set the segmentation image's value to 60 instead of the default 127\n", - "# We will use the difference in binary image values when thresholding out the joint space in later steps\n", - "pad_image = sitk.BinaryThreshold(pad_image, 1, 127, 60, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8dfc0f48", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "view_slice = (slice(None), seg_image_np.shape[1]//2, slice(None))\n", - "\n", - "fig = plt.figure(figsize=(10,10))\n", - "\n", - "plt.imshow(sitk.GetArrayFromImage(pad_image)[view_slice])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7d419608", - "metadata": {}, - "outputs": [], - "source": [ - "# Binary image dilation using a sphere structural element\n", - "dilated_image = sitk.BinaryDilate(pad_image, [MISC2, MISC2, MISC2], sitk.sitkBall, 0, 60)\n", - "\n", - "# Run connected components, sort the components by size, then take only the largest component\n", - "connected_image = sitk.ConnectedComponent(dilated_image, True)\n", - "\n", - "# Sort the components by size\n", - "relabel_image = sitk.RelabelComponent(connected_image)\n", - "\n", - "# Take only the largest component, set it's value to 127\n", - "first_component = sitk.BinaryThreshold(relabel_image, 1, 1, 127, 0)\n", - "filled_holes = sitk.BinaryFillhole(first_component, True, 127)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b5a287d3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 1, figsize=(10,10))\n", - "\n", - "axs[0].imshow(sitk.GetArrayFromImage(seg_image)[view_slice])\n", - "axs[1].imshow(sitk.GetArrayFromImage(filled_holes)[view_slice])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1bfffb9c", - "metadata": {}, - "outputs": [], - "source": [ - "# Erode the image, set the eroded mask's value to 30\n", - "eroded_image = sitk.BinaryErode(filled_holes, [CALC, CALC, CALC], sitk.sitkBall, 0, 127)\n", - "eroded_image = sitk.BinaryThreshold(eroded_image, 127, 127, 30, 0)\n", - "\n", - "# Add the eroded image (value = 30) and joint image (value = 60) together. \n", - "# Then threshold out JS image (value = 30)\n", - "add_image = sitk.Add(eroded_image, pad_image)\n", - "add_image = sitk.BinaryThreshold(add_image, 30, 30, 127, 0)\n", - "\n", - "connected_image = sitk.ConnectedComponent(add_image, False)\n", - "relabel_image = sitk.RelabelComponent(connected_image)\n", - "js_mask = sitk.BinaryThreshold(relabel_image, 1, 1, 1, 0)\n", - "dilated_js_mask = sitk.BinaryDilate(js_mask, [CALC, CALC, CALC], sitk.sitkBall, 0, 1)\n", - "\n", - "dilated_js_mask = sitk.Add(dilated_js_mask, pad_image)\n", - "dilated_js_mask = sitk.BinaryThreshold(dilated_js_mask, 1, 1, 1, 0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "67bb4942", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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L9nnn3se0zK0l9SCd9lRgZl4CXLJB8zparl5JqjEvNr/E3PWvDmyeP3lPctZsduXZEqtSV+lz/JOsBC64bCJ7v+vr7NF7C54gHLET8ehassEHFyRw5nWp+nXh9OSTFryLlW9pHaJmd93JVTVGn383pz/0j8y85IrNbnvLbdM4/v3n0Ov/ZnVBZVL18xXpUpXb7d9ncMy5kzv9PLvf8FFWH++kj2qxw5X38o693sI7DxhPQ256RN20n32HJZ88sosqk6qbwUqqctmwnrq1zZ12/GOmTObt75vIPl9bTPPaLRtXo+4vGxtpXrmSpmef46T3fZg/b+JncFBdP7553vd49DuHdWGFUnXyVqDUA/1xLZx3+RQARkx/iOaVK32PnzYq7nyA8792Po39YLu3/ZU/HXDj67YZv20T/XdeWUJ1UnUxWEk9yMeXjGX6wjGs+usAxnyt5Sm/zrsWpu5k6OUtPy8vLD2CCduewC9H/6bkiqTqZLCSeoArX9yJJnrxf987lJ2n3ll2Oaph2//kTlYuPoSpl+0MwKSBi6gLR5VILzNYSTUgsmUqhC2dvHFdNrA2X725d8NhY2hasYIhGKrUcfXTZ3LDPi2vgX3Xwr8wrH4A67KBxsa6kiuTymewkmpA79/O4MxDTuWWWb/dou33vvk8xky599WGdAJHda5D/vNCdv2KwV0yWEm1onnzE1qNP3sSfRc+zz7PP0ZTB96qIG2pScedzUk3zyAS8GdOMlhJ3cG6bOCYi/+ewXc9SNPq1WWXox6kaf4Cfn7xO9jlL8/4/kgJg5VU0777wnC+fPtJRHMw+id30ewVA5Vg25vuMVRJFQYrqUZdt3IQX/3tuxjz8bvKLkWSVOEzslINWtCwii9deSZ7Gqokqap4xUqqQZMmXcjOt91RdhmSpA1s9opVRFwVEcsi4uFWbYMj4taIeLTyOajVuosjYn5EzIuIEzqrcKmnOunoCfT+3f1llyFJasOW3Ar8EXDiBm0XAdMzczQwvbJMROwLnAHsV9nn8ohwxjipg/LIAxn+qzUc98FJND7xFDQ7VFiSqtFmg1Vm/hFYvkHzqcDVle9XAxNatV+XmesycwEwHxhXTKlSz9V7yQvc8csD6X3bTEOVJFWx9g5e3zEzlwBUPodW2ocDT7XablGlTVIHNC54khH/4ZgqSap2RQ9ejzba2pxYJyImA5MBtqFfwWVIkiR1vfZesVoaEcMAKp/LKu2LgF1abTcCWNzWATJzamaOzcyxvenbzjIkSZKqR3uD1c3AxMr3icBNrdrPiIi+ETEKGA3c07ESJUmSasNmbwVGxLXAscCQiFgEXAJcCkyLiEnAQuA0gMycHRHTgDlAI3BeZjrSVpIk9QibDVaZeeZGVo3fyPZfAr7UkaIkSZJqka+0kSRJKojBSpIkqSAGK0mSpIIYrCRJkgpisJIkSSqIwUqSJKkgBitJkqSCGKwkSZIKYrCSJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSpIkqSAGK0mSpIJsNlhFxFURsSwiHm7V9tWIeCQiHoyIX0TE9q3WXRwR8yNiXkSc0El1S5IkVZ0tuWL1I+DEDdpuBd6UmQcAfwEuBoiIfYEzgP0q+1weEXWFVStJklTFNhusMvOPwPIN2n6bmY2VxbuAEZXvpwLXZea6zFwAzAfGFVivJElS1SpijNWHgf+tfB8OPNVq3aJK2+tExOSImBERMxpYV0AZkiRJ5epQsIqIzwGNwDUvN7WxWba1b2ZOzcyxmTm2N307UoYkSVJVqG/vjhExETgZGJ+ZL4enRcAurTYbASxuf3mSJEm1o11XrCLiROAzwCmZuabVqpuBMyKib0SMAkYD93S8TEmSpOq32StWEXEtcCwwJCIWAZfQ8hRgX+DWiAC4KzM/lpmzI2IaMIeWW4TnZWZTZxUvSZJUTeLVu3jlGRiD87AYX3YZkiRJm3VbXj8zM8e2tc6Z1yVJkgpisJIkSSqIwUqSJKkgBitJkqSCGKwkSZIKYrCSJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSpIkqSAGK0mSpIIYrCRJkgpisJIkSSrIZoNVRFwVEcsi4uE21n0qIjIihrRquzgi5kfEvIg4oeiCJUmSqtWWXLH6EXDiho0RsQtwPLCwVdu+wBnAfpV9Lo+IukIqlSRJqnKbDVaZ+UdgeRurvgl8GshWbacC12XmusxcAMwHxhVRqCRJUrVr1xiriDgFeDozH9hg1XDgqVbLiyptkiRJ3V791u4QEf2AzwFvb2t1G23ZRhsRMRmYDLAN/ba2DEmSpKrTnitWewCjgAci4glgBHBfROxEyxWqXVptOwJY3NZBMnNqZo7NzLG96duOMiRJkqrLVgerzHwoM4dm5sjMHElLmHpzZv4VuBk4IyL6RsQoYDRwT6EVS5IkVaktmW7hWuBOYK+IWBQRkza2bWbOBqYBc4BfA+dlZlNRxUqSJFWzzY6xyswzN7N+5AbLXwK+1LGyJEmSao8zr0uSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBNhusIuKqiFgWEQ9v0H5BRMyLiNkR8ZVW7RdHxPzKuhM6o2hJkqRqVL8F2/wIuAz48csNEfFW4FTggMxcFxFDK+37AmcA+wE7A7dFxJjMbCq6cEmSpGqz2StWmflHYPkGzecCl2bmuso2yyrtpwLXZea6zFwAzAfGFVivJElS1WrvGKsxwFsi4u6I+ENEHFppHw481Wq7RZW214mIyRExIyJmNLCunWVIkiRVjy25Fbix/QYBhwOHAtMiYncg2tg22zpAZk4FpgIMjMFtbiNJklRL2nvFahFwY7a4B2gGhlTad2m13QhgccdKlCRJqg3tDVa/BI4DiIgxQB/gWeBm4IyI6BsRo4DRwD0F1ClJklT1NnsrMCKuBY4FhkTEIuAS4CrgqsoUDOuBiZmZwOyImAbMARqB83wiUJIk9RTRkofKNTAG52ExvuwyJEmSNuu2vH5mZo5ta50zr0uSJBXEYCVJklQQg5UkSVJBqmKMVUQ8A6ym5clCdS9DsF+7I/u1e7Jfuyf7tXi7ZeYOba2oimAFEBEzNjYQTLXLfu2e7NfuyX7tnuzXruWtQEmSpIIYrCRJkgpSTcFqatkFqFPYr92T/do92a/dk/3ahapmjJUkSVKtq6YrVpIkSTXNYCVJklSQ0oNVRJwYEfMiYn5EXFR2PdpyEXFVRCyrvIz75bbBEXFrRDxa+RzUat3FlX6eFxEnlFO1NicidomI30fE3IiYHREXVtrt2xoWEdtExD0R8UClX79Yabdfu4GIqIuIWRHxq8qy/VqSUoNVRNQB3wHeAewLnBkR+5ZZk7bKj4ATN2i7CJiemaOB6ZVlKv16BrBfZZ/LK/2v6tMI/ENm7gMcDpxX6T/7tratA47LzAOBg4ATI+Jw7Nfu4kJgbqtl+7UkZV+xGgfMz8zHM3M9cB1wask1aQtl5h+B5Rs0nwpcXfl+NTChVft1mbkuMxcA82npf1WZzFySmfdVvq+k5X/Ww7Fva1q2WFVZ7F35k9ivNS8iRgAnAT9o1Wy/lqTsYDUceKrV8qJKm2rXjpm5BFp+QQNDK+32dQ2KiJHAwcDd2Lc1r3K76H5gGXBrZtqv3cO3gE8Dza3a7NeSlB2soo0253/onuzrGhMRA4AbgI9n5opNbdpGm31bhTKzKTMPAkYA4yLiTZvY3H6tARFxMrAsM2du6S5ttNmvBSo7WC0Cdmm1PAJYXFItKsbSiBgGUPlcVmm3r2tIRPSmJVRdk5k3Vprt224iM18AbqdljI39WtuOAk6JiCdoGU5zXET8FPu1NGUHq3uB0RExKiL60DKg7uaSa1LH3AxMrHyfCNzUqv2MiOgbEaOA0cA9JdSnzYiIAK4E5mbmN1qtsm9rWETsEBHbV75vC7wNeAT7taZl5sWZOSIzR9LyO/R3mXkW9mtp6ss8eWY2RsT5wG+AOuCqzJxdZk3achFxLXAsMCQiFgGXAJcC0yJiErAQOA0gM2dHxDRgDi1PnZ2XmU2lFK7NOQo4G3ioMh4H4LPYt7VuGHB15QmwXsC0zPxVRNyJ/dod+fe1JL7SRpIkqSBl3wqUJEnqNgxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBWk04JVRJwYEfMiYn5EXNRZ55EkSaoWkZnFHzSiDvgLcDywCLgXODMz5xR+MkmSpCrRWVesxgHzM/PxzFwPXAec2knnkiRJqgr1nXTc4cBTrZYXAYe13iAiJgOTAeqoO6QfAzupFEmSpOKs5PlnM3OHttZ1VrCKNtpec88xM6cCUwEGxuA8LMZ3UimSJEnFuS2vf3Jj6zrrVuAiYJdWyyOAxZ10LkmSpKrQWcHqXmB0RIyKiD7AGcDNnXQuSZKkqtAptwIzszEizgd+A9QBV2Xm7M44lyRJUrXorDFWZOYtwC2ddXxJkqRq48zrkiRJBTFYSZIkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFaTdwSoidomI30fE3IiYHREXVtoHR8StEfFo5XNQceVKkiRVr45csWoE/iEz9wEOB86LiH2Bi4DpmTkamF5ZliRJ6vbaHawyc0lm3lf5vhKYCwwHTgWurmx2NTChgzVKkiTVhELGWEXESOBg4G5gx8xcAi3hCxhaxDkkSZKqXYeDVUQMAG4APp6ZK7Ziv8kRMSMiZjSwrqNlSJIkla5DwSoietMSqq7JzBsrzUsjYlhl/TBgWVv7ZubUzBybmWN707cjZUiSJFWFjjwVGMCVwNzM/EarVTcDEyvfJwI3tb88SZKk2lHfgX2PAs4GHoqI+yttnwUuBaZFxCRgIXBahyqUJEmqEe0OVpn5JyA2snp8e48rSZJUq5x5XZIkqSAGK0mSpIIYrCRJkgpisJIkSSqIwUqSJKkgBitJkqSCGKwkSZIKYrCSJEkqiMFKkiSpIAYrSZKkghisJEmSCmKwkiRJKojBSpIkqSAGK0mSpILUl12AJEmbE/Utv66yqQkyS65G2rgOX7GKiLqImBURv6osD46IWyPi0crnoI6XKUnqsXrV8eMFf+DGJ+7guY8cXnY10iYVcSvwQmBuq+WLgOmZORqYXlmWJKnd+kUd/Xr14bKLL2P+NwxXql4dClYRMQI4CfhBq+ZTgasr368GJnTkHJIkvezwbeq45J3X8+h3Diu7FKlNHb1i9S3g00Bzq7YdM3MJQOVzaFs7RsTkiJgRETMaWNfBMiRJPcUHBz7Lt97+ExZ/+siyS5Fep93BKiJOBpZl5sz27J+ZUzNzbGaO7U3f9pYhSerGom9fGt96EL02+HV1Sv81/Pb8r9D01jfT9NY3UzdwYEkVSq/VkacCjwJOiYh3AtsAAyPip8DSiBiWmUsiYhiwrIhCJUk9T6/dRjD9J1cCfV63blj9AG675ioA3nLeR+l/y/3kOu+AqFztvmKVmRdn5ojMHAmcAfwuM88CbgYmVjabCNzU4SolSdqE//vO93jy4kPKLkPqlAlCLwWOj4hHgeMry5IkdarfffgrfHnB3Rz5wPqyS1EPVsgEoZl5O3B75ftzwPgijitJ0pYaVj+AYfUwYvC9zPvzyTz3lhXQ3FR2WephfKWNJKlbGVLXn6tH3sZfLjuEuh12KLsc9TAGK0lSt9M76lgwYSpPf2A0Kz5wODH2TWWXpB7CdwVKkrqtBz5zOQB7/O4c9v7MzjQ+vbjkitTdecVKktTtPXbcD9ntl8shouxS1M0ZrCRJPcJ/7XwHn3tsVtllqJszWEmSqtJLE8Zx+s3/V9jx6qIXR/Rt4uTZz9PrDW8o7LhSawYrSVJVWveGOj40sNiXd/SOOi4Y9CRR568/dQ5/siRJPc6T5+5H/bCdyi5D3ZDBSpLU48y+4HKWnjyK+p12LLsUdTNOtyBJ7dSrX7+qecos1zeQDb7KZWvM+OIV7LvTFHb9ygu+vFmFMVhJUjt95P6HmdD/hbLLAGDM7yex51k+8ba15px7OQceciY7TZhbdinqJgxWktSGuu2346L7/rDJbY7o20Rd1HVRRZt2599cxiOP939N2xcmT6L3bTNLqqh29IosuwR1IwYr9RjPfeQIRpz9OM+81J8BJz5edjnqAivPOJyh5y5o17596ho5ZpvNbVUdoQpgaF1/hm5QzsFfnsVjq14dQ7S+uY7m45eRjY1dXF11++abpvHRn5/FyPc/WHYp6gYMVuoRnvu7I9j5rAX8cvRveLZpNUd85VPs8U/3Oa6iG1h78jgWH9N2wBl+0BJ+Ofo3XVxR9fj6sPtes9yUzex16RSiuWVc2NAZyYBpd5VRWlU5dttmfjbuB5x56YWMuujOsstRjetQsIqI7YEfAG8CEvgwMA/4OTASeAI4PTOf78h5pI4acNoSbh79a6DlzfePnnUFR8z6GIN+PY+m5/3xrHZx8H6s2a1/m+uWfeAlHj36J11cUW2qi17M/8B3X1k+Yv/38tL6cfRqSPr+z70lVla+Q/r24d6zvsHpFx8J6a1BtV9Hr1h9G/h1Zr4vIvoA/YDPAtMz89KIuAi4CPhMB88jtVvdjkPp1/v1T0vd+fXv8pZ1H2XArx+iec2aEipTa3VvHExs0/a9twWfC+YcObWLK+r+7jzwBrgcFjSs4vwjTwegaWnPvVVYR1A/vPKiZsOV2imynT88ETEQeADYPVsdJCLmAcdm5pKIGAbcnpl7bepYA2NwHhbj21WHtDnnPjqfCf1XbXT9fpdNYcS/39GFFaktjbftyvR9by67jB7vuA9Wz4D3F84+gru/fEWXn/ekoyfQ+PgTXX5e1Y7b8vqZmTm2rXUduWK1O/AM8MOIOBCYCVwI7JiZSwAq4WpoB84hdbo7pnydNR9r4scvHszv9m/7dpOK9fyHjuCmf/nqa9oG1/UFepdTkF4x7Yf/SUMmb585mZ3fPafscqSa05GZ1+uBNwNXZObBwGpabvttkYiYHBEzImJGAw4gVvGivp5DZjXztm2f3eR22/XalmH1A5i8/QMccF9UzYSP3UXd9ttx5APrX/PnU5/9GcPqB7zmT98wVFWDIXX9GVY/gGkH/4Dm6buUXU4pxt/8IOveeWjZZahGdeRW4E7AXZk5srL8FlqC1Z54K1BVIOrruenJO7fqF3ZDNrH37z/CXp9YRNMzz3Ridd3b4k8dyZoDXwKgrncTfznmxyVXpPZ4rGEVb//jBQDs9amnaVpa7AuRN6du3zE8OvGNPHp2198OPPKTH+MN1/nEpNrWKbcCM/OvEfFUROyVmfOA8cCcyp+JwKWVz5vaew6pq/WOOh477ofs95EpjPx5f8dZbMTyDx9BU5+Nrx9/5j18a9iMritInWKP3gN4bPwPAdhv0hT6Lt+DNz78Er3+dH+XnL9pzl8YfeUecHaXnE4qREefCrwAuKbyRODjwDm03F6cFhGTgIXAaR08h7TVoncfOGA0vbi7XfvPvuByDlg/hV1+tpbGJX8tuLraUr/bLjRvP+A1bT/+56+zT59+JVWkMsw+/3IA9rz9Q4xeuc8r7c0PPtItn6BbtXMvBg3bqcf//dfWa/etwCJ5K1BFq9tvL2659ecdPs7uv/goYy6c2XMeP48g6l9763Txf+/Bg+OuLakgVbt3HjCe5hdXko0NnRKw6sbswS2331D4cbfE7td/lNF/375/nKl766ynAqVu79EJV/C3B72N549aXnYpXWLV+8bxu29d9pq2eu6iY8+5qDu76YHf0kwzY791ITt/1WlLJIOVtAl10Ysrdv0V//nAWO44cBODimrYofc3MbZ/y/v0dqqb6dN52iq9ow6o45rzvsHjk4cwbdmhPHeUbzNQz2WwkjZjUF0/Th54P3cwruxS2m35r8bQv8/rZ58H+Ic3/oxBdS+Pl6qelwqrthzQZxsO6LOKN+/y/zj9lg+x3Tvnl11Sh110/P/j0m+fwugLfTpQW85gJW2BHevW8+QXj2Tkv80gG9oOKNVi+TlHsHJkq7m4Iplx0DcZ0Kvt18W0vIlKKsau9QP43QE/483/ciFksPt1z9A099Gyy2qXydst5vax83iu7EJUUwxW6nbqd9qRZeMGF3rMEfUDeOTvLueY+ybT/3dzaV65stDjt1f9sJ1Yc8CI17QdOmUWlw/f8F/YGwtVUvH69erDIx9pmXtq38Yp7HjPdvRdvo6896GtOk7djkN57nBf3qHaYrBSt7Psnbtz7791zoSCf7xiKm/98N/R9/cPkuu6/o0BdUPe+JrlRe/fnQc+fXmX1yFtqTnnXg7nwllPHMvyU1p+fpue3bJrQM+9fQ/uvrTrJweVOsJgJW2l31/1fQ74+hSGfb1rn4Cq22EHbnng1g1ap3dpDVJ7/XTk7fAgNGUzJ48+muY1a8ouSeoUBiupHW77+Fc54qDz2fPsWZ12juMeWs27B97/ynIdCQzY6PZ6rf0um8Ju3y9mbM/+v32GL+94fyHH6unqohffnnsrf/+eyeTM2WWXs1k/2O03XP/Izlyz94jNbyxhsJLaZWhdf6456gf89N4j21z/Pw/sz5iPbN0rXda+axz7//MDryxP3v4OBtX171Cd3d3e35/CTnc3tLlu5OynaCzofY+zzj+IY7fb+Et5F76/icePv6qQc/UEY3r35+gfzuR//uNYBl7b9hN3iy4+kgvOLv+NaP169WHvvksAg5W2jMFKaqfDt6nj8OFtz8r81u3m8qnvnrFVx9tt92Vc9prj+bTey45+8D08M3PH17XvMe05mmbPa3OfIufKjz/fT99NrB+17hD2WnTuFh3rlrO+yh69vfL4+SGPcMfk3Zn7N+Pou7Se3S5pubU+/5uH07xtM+8bdycf2/7pkquUtp7BSuoE7x2wgveeMrXsMmreiY+cxLqmepquHsrIa+983fqmEmpqS/30mYzcwuFuJ485l522X/Gatl6R/HqfX1Qm2+w5btnrFtgLblg1kC8v+FsAbnvPVxll8FQNM1hJqhoLGlYxp2HIK8u9Jqykz4oV9OHJEqsq1q6ntT3lwDWPDGOH+hVtrnv7tqu7deh674AVvPffX376z1Cl2mawklSqhnz1utNx//tJxnzsnlZr2w4a3dG1e+8M7Nzmuu0fv49xfVv+O/UiqAvf3ShVK4OVpNKsaV7Pe/d6K9nYMiJqTMPMkiuqTv+6zxGvfH/htIO56yvfLbEaSZtisJLUZcZeci5DZrx6FSoyaV49p8SKakPryWgH3zyHE2f/LfSC62+6chOvKpJUhg4Fq4j4BPARIIGHgHNoeZTp58BI4Ang9Mz0VefqMjv8+Rn2+d4U5n7UGcmrwbNNqzn+0n8EYOdbnqDx6cWvrMuyiqphTStWwKyW+Z+O+dInyF4t74Vce+xK5h71kzJLk0QHglVEDAf+Htg3M1+KiGnAGcC+wPTMvDQiLgIuAj5TSLXSFmiaN59R19fBR8uupOc6/fHx3LdwFwCa1tYz5jstj9IXOQWCYIcrXn1S8qVF49iz4UOvLM885gq267VtCVVJPVtHbwXWA9tGRAMtV6oWAxcDx1bWXw3cjsFK6hG+unwPmrMXi/5rNHv8vO2JH9U5tr3pHvZoNZ/mp+5+G3v2WwbAnn2X8t4BPedBAKlM7Q5Wmfl0RHwNWAi8BPw2M38bETtm5pLKNksiwleTS93YumxgedM6GoDpB7+RbFjPGzBUlW3hYatZSMvM/b846wMc+e9fA2BYvdMZSJ2pI7cCBwGnAqOAF4D/joiztmL/ycBkgG2cYVqqWafOm0Ae9/IM2etLrUVt2+6nd/Ghnx5N1Ndz4xN30K9Xn7JLkrqtjtwKfBuwIDOfAYiIG4EjgaURMaxytWoYsKytnTNzKjAVYGAMdgyrVGMOu+hchvxuIb3Wrq2aGdC1adnYyGmHvxuAxquD3+zzq5IrkrqfjgSrhcDhEdGPlluB44EZwGpgInBp5bP8t2iqx8knFnHUxz/G7d/8TreesbqrPd+0hnd9/BMAvPFPj9G4tM1/N6mKNS5qubqY/3QgRw/7KMv3rmPOeT5BKxWlI2Os7o6I64H7aHnYZxYtV6AGANMiYhIt4eu0IgqVtkbz6tW84cYZNH+zGTBYddTnl+3PddOPIhqC3W9oeRLNq1S1Le54gP7AwNG7s+fgjwEw8/3f9ElCqYMis/y7cANjcB4W48suQ91M1Ndz05N30jd6l11KzTr/6cP469o3MHv6GHb9wh1ll6NOtv7W3XjjNqs5Z9ifOKnf2rLLqQqz17/ER+aexcB3PFZ2Kaoit+X1MzNzbFvrnHld3dqda/ty1DYN3g7cQi82v8Tc9a8ObJ4/eU9y1mx25dkSq1JX6XP8k6wELrhsInu/6+vs0dsnCL/09DsNVdoqBit1W9nYyH/scQDnPjqfCf1XlV1OTZi04F2sfEvrEDW7tFpUntHn383pD/0jMy+5ouxSpJrjK9IlAbD7DR9l9fEGULXY4cp7ecdeb+GdB4ynIR1RJ20pg5W6ve+eOYEPLHhr2WVUrWOmTObt75vIPl9bTPNax9WoRTY20rxyJU3PPsdJ7/swf17bXHZJUk3wVqC6vZzxMItXjyy7jKryx7Vw3uVTABgx/SGaV670PX7aqLjzAc7/2vk09oPt3vZX/nTAjWWX1CVOmHsyL141gu18k4C2gsFKPcLSP+/M54fuz78NfajsUkr18SVjmb5wDKv+OoAxX2t5ys/rENoSQy9v+Xl5YekRTNj2BH45+jclV9T5Hp09nNHXGKq0dQxW6hF2/eId/Hf90fzbpJ4ZrK58cSea6MX/fe9Qdp56Z9nlqIZt/5M7Wbn4EKZetjMAkwYuoi6636iSX6/pS99nfZpYW89gpR6jV1Owpnl9j3hP2rpsYG2+enPvhsPG0LRiBUMwVKnj6qfP5IZ9hgLwroV/6ZYvdv6Xz5/Drtc5d5u2nsFKPcauX7iDd957Hrd///tll9Lp9r75PMZMuffVhlxRXjGS1IN0v+u30iZsO/0hTphwdtlldJrxZ0/inX/zHvb5p8cg89U/UieZdNzZfOeFXcouQ6oaBiv1KM1r11L35NKyyyjcumzgsIvOpc9dj9D06OM0Pftc2SWph2iav4CfX/wOJjx6QtmlFOagS6cw6M+Lyi5DNcpgpR4nV61m1C8ns6q59uds+u4Lwxn1y8ns84vz2f4nd9G8enXZJakH2vame5izeKeyy+iwddnAqJsms/M1j9D4lMFK7WOwUo/TvHo1Y6bcwwcffxeLGmt3pvHrVg7iq799F2Om3MPo8+/2lp9KFY/348oXaztcrc1Gxky5l6bnlpddimqYwUo91upjnuH7zx9WdhntsqBhFV+68kz2/Lhz7Kg6jPzcnXzn2+/msYba/MdKQzbx8Pq+ZZehbsBgJdWgSZMuZOev+Ci4qssO372T8953btlltMuPVwznX3Z/s1d+1WGbDVYRcVVELIuIh1u1DY6IWyPi0crnoFbrLo6I+RExLyK6z2hGdTufe/x+Pj/kwbLL2GonHT2B3r+7v+wypDblzDmcNO4kmrJ25vQfN+s0bjhir7LLUDexJVesfgScuEHbRcD0zBwNTK8sExH7AmcA+1X2uTwinLpWVWlk/Sp619CP56LGVRz3wUk0PvEUNDeVXY7UtuYmmpc/X3YVW6WhsY6mF14suwx1E5sNVpn5R2DDkXynAldXvl8NTGjVfl1mrsvMBcB8YFwxpUo90zkL38I+353CCVd8mt63zTRUqerl+vW86Xvns6AGxlsdfv/72Pba7csuQ91Ie8dY7ZiZSwAqn0Mr7cOBp1ptt6jS9joRMTkiZkTEjAbWtbMMqXv7+JKx3Pfz/dn1X+5gxH84pkq1IRsb2fVf7uDEu6Zw19rq/YfAGQuOo9fVQ3jDdT4EouIUPXg92mhrcyRgZk7NzLGZObY3PokhbejXa/py99fGstM3DVSqTSPf/yB/98DZzF2/puxSXueXqwfw/D+M4A0/N1SpWO0NVksjYhhA5XNZpX0R0PrdBiOAxe0vT+qZ1mUD/3nqBAZe6//0Vdt2fvccTv7FJ8su4zXWNK9n6qGHwF219/CKql97g9XNwMTK94nATa3az4iIvhExChgN3NOxEqWeZV02MGHMsTTNnld2KVK3M3v9S7xn5JEOVlenqd/cBhFxLXAsMCQiFgGXAJcC0yJiErAQOA0gM2dHxDRgDtAInJeZ1XuDXaoipz8+npUf24HIpHm1oUrdx16XPs5BC6Zw/0WXl10K0DIGTOoskVUwGdrAGJyHxfiyy1APs/ycI/jHi3/G6QPK/5frAfecyeCpA+h7y71llyJ1ivrddmHh6bvw0CfKC1dTX9yZH3z5VAb96M7SalD3cFtePzMzx7a1zpnX1WMN/uGdzH5pRNllcNSD72HgTwcaqtStNT75FLv+d7kvNr5/1a6GKnU6g5VUon97dm/6fWk7+l9/d9mlSJ1v3Xo+v2z/mpqVXdpaBiupJAsbV3HH8bvS6/9mlV2K1CUal/yVew+uZ0Xz2i4/96rmtTy7rn+Xn1c9j8FKKsGixlX83a5H07R02eY3ltRh+//PBbx49HNll6EeYLNPBUoq1leX78Hvx+/Bq9O/Sep02db81VLxvGKlHu2Ovx/HsQ9P6LLzvXX2qfzm/L/xSpUkdVMGK/Vovf4wi6eWDuqScx163+msu3IYdbff1yXnk9Ri/7s/wIjfesVKXcNbgVIXOGfhW9jmykH0+4WvqJG62rY3bUe/G51mQV3DYCV1sttf6sVfJw+n34NOqSB1tT+vbab+pfInwlbPYbBSj5cZNGUzdVH8nfF12cCX93sLzWsfKfzYUk3qhL9nG9OUzXzpHafzhnleKVbXcYyVerwxkx7mkC+fX/hx/7gWJuzxFprXdv2cPVI1qt9tF6Yt/BOD6vp1+rnWNK/nXfscS9O8+Z1+Lqk1r1ipx8uG9UTB72T96KIjWDh5FM1r5xR7YKmWRbBdr2277HTNa9Z02bmkl3nFSgKG/eE59vrhuYUdb+lLA2m+31AllWHmuvUc+eWPk01NZZeiHshgJQFNs+cx6voVhRzr88v2Z97v9yjkWFJ3UbfnKB778PBOP88Nqwbyvv89nx3/6w5IB62r6202WEXEVRGxLCIebtX21Yh4JCIejIhfRMT2rdZdHBHzI2JeRJzQSXVLheu1dj3fen5kh45xw6qB/PK6t7DrF+4opiipm3jxzTvyyEeu6PTzfOPxtzFmyj2dfh5pY7bkitWPgBM3aLsVeFNmHgD8BbgYICL2Bc4A9qvsc3lE1BVWrdSJmuY+yq/fvCNN2dyu/Z9vWsO3PnMmwy81VEllWNO8npfW9y67DPVwmw1WmflHYPkGbb/NzJeH+94FjKh8PxW4LjPXZeYCYD4wrsB6pap11tHvp98vnKtKKsshP/g4O5wyr+wy1MMVMcbqw8D/Vr4PB55qtW5RpU2qCbluHaccNYE/r93yq1bPN63hpKNOpXHhok6sTNKmHPGpjzHqGw9vfkOpk3UoWEXE54BG4JqXm9rYrM3RgxExOSJmRMSMBtZ1pAypUI1PLOTiT36Mzyw9aLPb/nL1AE7+5CdoXPCkA2WljVh6wZG89bN/7rTjH/r5cxl062M0rSjmARSpI9odrCJiInAy8LeZr/xGWQTs0mqzEcDitvbPzKmZOTYzx/amb3vLkDrFtr+8h9kvDtvkNv/+7F589scfZMB/e/tP2pSVuzfzb0MfKvy4Lza/xO7Xf5Qh1z1A0zPPFH58qT3aFawi4kTgM8Apmdl6BrabgTMiom9EjAJGAz6eoZo0e84uXLdy0EbX//Qvh7LLvzpQXSrDgoZVnD7vNEb//d1OBKqqsiXTLVwL3AnsFRGLImIScBnwBuDWiLg/Ir4LkJmzgWnAHODXwHmZ6QxtqkljptzDF647k780rH7duoWNq1i7yiutUldb0LCKmevW848LJ8B4xzWq+kRWwbiQgTE4D4vxZZchtWnNuw/j/77zvde0HfD1KQz7ulerpC0x/5uH89j7v1vIsQ754rkM+d6dhRxLaq/b8vqZmTm2rXXOvC5tRr+bZvDO8ae9sjz+7Ens/G3vcEtd7e3vm8gOV95bdhnSJvkSZmlzmptofnQBx595DgB9ZjxCc2PBb22W1Ka71jbxT+d8BID6++b6d09Vz2AlbYFsbKTXH2YB0L552SVtra8u34Nrpp7Ajn9oue3u3z3VAoOVJKlqHP3ge1hTeS3N6ocGM/I/Hcuo2mKwkiSV5rGGVfz5pZGvLA+cuIr+S5cBsENJNUkdYbCSJHW6pmzmpVz/uvYT/nQBe/ztrFYty7quKKkTGKwkSZ3uzAXH8+IxL7yufY+8v8trkTqTwUqS1Kn2+tKjrN52W2h+ruxSpE5nsJIkdaqmZw1U6jmcIFSSJKkgBitJkqSCGKwkSZIKYrCSJEkqiMFKkiSpIAYrSZKkgmw2WEXEVRGxLCIebmPdpyIiI2JIq7aLI2J+RMyLiBOKLliSJKlabckVqx8BJ27YGBG7AMcDC1u17QucAexX2efyiKgrpFJJkqQqt9lglZl/BJa3seqbwKeBbNV2KnBdZq7LzAXAfGBcEYVKkiRVu3aNsYqIU4CnM/OBDVYNB55qtbyo0tbWMSZHxIyImNHAuvaUIUmSVFW2+pU2EdEP+Bzw9rZWt9GWbbSRmVOBqQADY3Cb20iSJNWS9rwrcA9gFPBARACMAO6LiHG0XKHapdW2I4DFHS1SkiSpFmz1rcDMfCgzh2bmyMwcSUuYenNm/hW4GTgjIvpGxChgNHBPoRVLkiRVqS2ZbuFa4E5gr4hYFBGTNrZtZs4GpgFzgF8D52VmU1HFSpIkVbPN3grMzDM3s37kBstfAr7UsbIkSZJqjzOvS5IkFcRgJUmSVBCDlSRJUkEMVpIkSQUxWEmSJBXEYCVJklQQg5UkSVJBDFaSJEkFMVhJkiQVxGAlSZJUEIOVJElSQQxWkiRJBTFYSZIkFcRgJUmSVJDNBquIuCoilkXEwxu0XxAR8yJidkR8pVX7xRExv7LuhM4oWpIkqRrVb8E2PwIuA378ckNEvBU4FTggM9dFxNBK+77AGcB+wM7AbRExJjObii5ckiSp2mz2ilVm/hFYvkHzucClmbmuss2ySvupwHWZuS4zFwDzgXEF1itJklS12jvGagzwloi4OyL+EBGHVtqHA0+12m5RpU2SJKnb25JbgRvbbxBwOHAoMC0idgeijW2zrQNExGRgMsA29GtnGZIkSdWjvVesFgE3Zot7gGZgSKV9l1bbjQAWt3WAzJyamWMzc2xv+razDEmSpOrR3mD1S+A4gIgYA/QBngVuBs6IiL4RMQoYDdxTQJ2SJElVb7O3AiPiWuBYYEhELAIuAa4CrqpMwbAemJiZCcyOiGnAHKAROM8nAiVJUk8RLXmoXANjcB4W48suQ5IkabNuy+tnZubYttY587okSVJBDFaSJEkFMVhJkiQVxGAlSZJUkKoYvB4RzwCraZmyQd3LEOzX7sh+7Z7s1+7Jfi3ebpm5Q1srqiJYAUTEjI2NsFftsl+7J/u1e7Jfuyf7tWt5K1CSJKkgBitJkqSCVFOwmlp2AeoU9mv3ZL92T/Zr92S/dqGqGWMlSZJU66rpipUkSVJNKz1YRcSJETEvIuZHxEVl16MtFxFXRcSyysu4X24bHBG3RsSjlc9BrdZdXOnneRFxQjlVa3MiYpeI+H1EzI2I2RFxYaXdvq1hEbFNRNwTEQ9U+vWLlXb7tRuIiLqImBURv6os268lKTVYRUQd8B3gHcC+wJkRsW+ZNWmr/Ag4cYO2i4DpmTkamF5ZptKvZwD7Vfa5vNL/qj6NwD9k5j7A4cB5lf6zb2vbOuC4zDwQOAg4MSIOx37tLi4E5rZatl9LUvYVq3HA/Mx8PDPXA9cBp5Zck7ZQZv4RWL5B86nA1ZXvVwMTWrVfl5nrMnMBMJ+W/leVycwlmXlf5ftKWv5nPRz7tqZli1WVxd6VP4n9WvMiYgRwEvCDVs32a0nKDlbDgadaLS+qtKl27ZiZS6DlFzQwtNJuX9egiBgJHAzcjX1b8yq3i+4HlgG3Zqb92j18C/g00NyqzX4tSdnBKtpo8zHF7sm+rjERMQC4Afh4Zq7Y1KZttNm3VSgzmzLzIGAEMC4i3rSJze3XGhARJwPLMnPmlu7SRpv9WqCyg9UiYJdWyyOAxSXVomIsjYhhAJXPZZV2+7qGRERvWkLVNZl5Y6XZvu0mMvMF4HZaxtjYr7XtKOCUiHiCluE0x0XET7FfS1N2sLoXGB0RoyKiDy0D6m4uuSZ1zM3AxMr3icBNrdrPiIi+ETEKGA3cU0J92oyICOBKYG5mfqPVKvu2hkXEDhGxfeX7tsDbgEewX2taZl6cmSMycyQtv0N/l5lnYb+Wpr7Mk2dmY0ScD/wGqAOuyszZZdakLRcR1wLHAkMiYhFwCXApMC0iJgELgdMAMnN2REwD5tDy1Nl5mdlUSuHanKOAs4GHKuNxAD6LfVvrhgFXV54A6wVMy8xfRcSd2K/dkX9fS+LM65IkSQUp+1agJElSt2GwkiRJKojBSpIkqSAGK0mSpIIYrCRJkgpisJIkSSqIwUqSJKkgBitJkqSC/H+dJE+J1w3UIwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 1, figsize=(10,10))\n", - "\n", - "axs[0].imshow(sitk.GetArrayFromImage(js_mask)[view_slice])\n", - "axs[1].imshow(sitk.GetArrayFromImage(dilated_js_mask)[view_slice])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1f589513", - "metadata": {}, - "outputs": [], - "source": [ - "local_thickness = compute_local_thickness_from_mask(\n", - " sitk.GetArrayFromImage(dilated_js_mask),\n", - " 0.0607\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6d6651fd", - "metadata": {}, - "outputs": [], - "source": [ - "local_thickness_masked = local_thickness * (sitk.GetArrayFromImage(js_mask) != 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "331645f0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
      " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(2, 1, figsize=(10,10))\n", - "\n", - "axs[0].imshow(local_thickness[view_slice])\n", - "axs[1].imshow(local_thickness_masked[view_slice])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "dc069bee", - "metadata": {}, - "outputs": [], - "source": [ - "lts = local_thickness_masked[local_thickness_masked>0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1b16e326", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "JSW.mean : 1.881 mm\n", - "JSW.std : 0.464 mm\n", - "JSW.min : 0.865 mm\n", - "JSW.max : 2.417 mm\n" - ] - } - ], - "source": [ - "print(f\"JSW.mean : {lts.mean():0.3f} mm\")\n", - "print(f\"JSW.std : {lts.std():0.3f} mm\")\n", - "print(f\"JSW.min : {lts.min():0.3f} mm\")\n", - "print(f\"JSW.max : {lts.max():0.3f} mm\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6edc8f50", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9913467", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d4a8283", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/jsw_notebook.ipynb b/examples/jsw_notebook.ipynb deleted file mode 100644 index 9e851d1..0000000 --- a/examples/jsw_notebook.ipynb +++ /dev/null @@ -1,341 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9cee3924", - "metadata": {}, - "source": [ - "# Joint Space Width Jupyter Notebook\n", - "\n", - "Run the Scanco IPL Joint Space Width (JSW) analysis in Python." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7a02fc93", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f22a6b85d82f4274974b3cb14e864a6f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a8bba1a8", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9d31c66bd91e4c0e8bf2434f334e6ed6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Viewer(axes=True, geometries=[], gradient_opacity=0.9, point_sets=[], rendered_image= Date: Wed, 6 Sep 2023 15:35:27 -0600 Subject: [PATCH 18/18] Ran black --- manuscript/notebooks/LinearRegression.py | 3 +- manuscript/notebooks/joss_plots.py | 49 ++++++++----- .../joint_space_analysis/jsw_morphometry.py | 10 ++- ormir_xct/util/hildebrand_thickness.py | 2 +- ormir_xct/util/process_py_log.py | 1 - ormir_xct/util/segmentation_evaluation.py | 1 + setup.py | 2 +- tests/autocontour/test_autocontour.py | 2 +- tests/autocontour/test_autocontour_gobj.py | 1 + .../test_bone_mineral_density.py | 31 +++++---- .../test_bone_mineral_density_masked.py | 69 ++++++++++++++----- .../test_bone_connectivity.py | 6 +- tests/joint_space_analysis/test_jsw_main.py | 6 +- .../test_jsw_morphometry.py | 9 ++- tests/util/test_file_converter.py | 2 +- tests/util/test_file_reader.py | 1 - tests/util/test_hildebrand_thickness.py | 22 +++--- tests/util/test_scanco_rescale.py | 32 ++++++--- tests/util/test_vtk_sitk_conversion.py | 4 +- 19 files changed, 168 insertions(+), 85 deletions(-) diff --git a/manuscript/notebooks/LinearRegression.py b/manuscript/notebooks/LinearRegression.py index 547328c..f9bf10d 100644 --- a/manuscript/notebooks/LinearRegression.py +++ b/manuscript/notebooks/LinearRegression.py @@ -1,6 +1,7 @@ import numpy as np from sklearn.linear_model import LinearRegression as sklearnLinearRegression + class LinearRegression: def __init__(self): self.model = None @@ -36,4 +37,4 @@ def fit_model(self, x_values, y_values, max_thickness): self.slope = self.model.coef_ self.x_new = np.linspace(0, max_thickness, 1000) - self.y_new = self.model.predict(self.x_new[:, np.newaxis]) \ No newline at end of file + self.y_new = self.model.predict(self.x_new[:, np.newaxis]) diff --git a/manuscript/notebooks/joss_plots.py b/manuscript/notebooks/joss_plots.py index 2b6d201..ba78364 100644 --- a/manuscript/notebooks/joss_plots.py +++ b/manuscript/notebooks/joss_plots.py @@ -7,6 +7,7 @@ from matplotlib import pyplot as plt + def get_thickness_list(csv, ipl=True): # Assume CSV files have already been sorted by filename (alphabetically) if ipl: @@ -22,6 +23,7 @@ def get_thickness_list(csv, ipl=True): return thickness, filenames + def get_bmd_list(csv): csv = pandas.read_csv(csv) filenames = csv["Filename"].values.tolist() @@ -35,13 +37,14 @@ def get_bmd_list(csv): "MCP2_DST_BMD": mcp2_dst_bmd, "MCP2_PRX_BMD": mcp2_prx_bmd, "MCP3_DST_BMD": mcp3_dst_bmd, - "MCP3_PRX_BMD": mcp3_prx_bmd + "MCP3_PRX_BMD": mcp3_prx_bmd, } return bmd_dict + def bmd_plots(bmd_files): - fig = plt.figure(constrained_layout=True, figsize=(20,10)) + fig = plt.figure(constrained_layout=True, figsize=(20, 10)) fig.suptitle( "Bone Mineral Density (BMD) - IPL vs. ORMIR_XCT", fontsize="xx-large", @@ -61,10 +64,12 @@ def bmd_plots(bmd_files): ipl_bmd_dict = get_bmd_list(bmd_files["IPL"]) py_bmd_dict = get_bmd_list(bmd_files["PY"]) - bmd = ((ipl_bmd_dict["MCP2_DST_BMD"], py_bmd_dict["MCP2_DST_BMD"]), - (ipl_bmd_dict["MCP2_PRX_BMD"], py_bmd_dict["MCP2_PRX_BMD"]), - (ipl_bmd_dict["MCP3_DST_BMD"], py_bmd_dict["MCP3_DST_BMD"]), - (ipl_bmd_dict["MCP3_PRX_BMD"], py_bmd_dict["MCP3_PRX_BMD"])) + bmd = ( + (ipl_bmd_dict["MCP2_DST_BMD"], py_bmd_dict["MCP2_DST_BMD"]), + (ipl_bmd_dict["MCP2_PRX_BMD"], py_bmd_dict["MCP2_PRX_BMD"]), + (ipl_bmd_dict["MCP3_DST_BMD"], py_bmd_dict["MCP3_DST_BMD"]), + (ipl_bmd_dict["MCP3_PRX_BMD"], py_bmd_dict["MCP3_PRX_BMD"]), + ) subplot = 0 @@ -81,11 +86,13 @@ def bmd_plots(bmd_files): md = np.mean(diff) axs1[subplot].scatter(mean, diff, color="blue", marker=".") - axs1[subplot].axhline(md, color="black", linestyle="-", label='MeanDiff') - axs1[subplot].axhline(md + 1.96 * sd, color="red", linestyle="--", label='MeanDiff +/- 1.96 * sd') + axs1[subplot].axhline(md, color="black", linestyle="-", label="MeanDiff") + axs1[subplot].axhline( + md + 1.96 * sd, color="red", linestyle="--", label="MeanDiff +/- 1.96 * sd" + ) axs1[subplot].axhline(md - 1.96 * sd, color="red", linestyle="--") axs1[subplot].grid() - axs1[subplot].set_title(list(ipl_bmd_dict.keys())[subplot+1]) + axs1[subplot].set_title(list(ipl_bmd_dict.keys())[subplot + 1]) axs1[subplot].set_xlabel("Mean BMD (mg HA/cm^3)") max_thickness = max(np.max(bmd_ipl), np.max(bmd_py)) @@ -107,7 +114,7 @@ def bmd_plots(bmd_files): model_slope, model_intercept, model_r_sq ), ) - axs2[subplot].set_title(list(ipl_bmd_dict.keys())[subplot+1]) + axs2[subplot].set_title(list(ipl_bmd_dict.keys())[subplot + 1]) axs2[subplot].set_xlabel("IPL Calculated BMD (mg HA/cm^3)") axs2[subplot].grid() axs2[subplot].legend(loc="lower right") @@ -122,8 +129,9 @@ def bmd_plots(bmd_files): plt.show() + def thickness_plots(thickness_files): - fig = plt.figure(constrained_layout=True, figsize=(20,10)) + fig = plt.figure(constrained_layout=True, figsize=(20, 10)) fig.suptitle( "Mean Thickness - IPL vs. ORMIR_XCT (Oversampling=True, Skeletonization=True)", fontsize="xx-large", @@ -134,7 +142,8 @@ def thickness_plots(thickness_files): "Bland-Altman Plots of Mean Thickness - IPL vs. ORMIR_XCT", fontsize="x-large" ) subfigs[1].suptitle( - "Linear Regression Plots of Mean Thickness - IPL vs. ORMIR_XCT", fontsize="x-large" + "Linear Regression Plots of Mean Thickness - IPL vs. ORMIR_XCT", + fontsize="x-large", ) axs1 = subfigs[0].subplots(nrows=1, ncols=5) @@ -148,7 +157,9 @@ def thickness_plots(thickness_files): thickness_ipl, filename_ipl = get_thickness_list( thickness_files[file_name]["IPL"], ipl=True ) - thickness_py, filename_py = get_thickness_list(thickness_files[file_name]["PY"], ipl=False) + thickness_py, filename_py = get_thickness_list( + thickness_files[file_name]["PY"], ipl=False + ) thickness_ipl = np.array([thickness_ipl]).T thickness_py = np.array([thickness_py]).T @@ -159,8 +170,10 @@ def thickness_plots(thickness_files): md = np.mean(diff) axs1[subplot].scatter(mean, diff, color="blue", marker=".") - axs1[subplot].axhline(md, color="black", linestyle="-", label='MeanDiff') - axs1[subplot].axhline(md + 1.96 * sd, color="red", linestyle="--", label='MeanDiff +/- 1.96 * sd') + axs1[subplot].axhline(md, color="black", linestyle="-", label="MeanDiff") + axs1[subplot].axhline( + md + 1.96 * sd, color="red", linestyle="--", label="MeanDiff +/- 1.96 * sd" + ) axs1[subplot].axhline(md - 1.96 * sd, color="red", linestyle="--") axs1[subplot].grid() axs1[subplot].set_title(file_name) @@ -185,7 +198,9 @@ def thickness_plots(thickness_files): model_slope, model_intercept, model_r_sq ), ) - axs2[subplot].plot((0,max_thickness), (0,max_thickness), color="red", linestyle="--") + axs2[subplot].plot( + (0, max_thickness), (0, max_thickness), color="red", linestyle="--" + ) axs2[subplot].set_title(file_name) axs2[subplot].set_xlabel("IPL Calculated Thickness") axs2[subplot].grid() @@ -199,4 +214,4 @@ def thickness_plots(thickness_files): subplot += 1 - plt.show() \ No newline at end of file + plt.show() diff --git a/ormir_xct/joint_space_analysis/jsw_morphometry.py b/ormir_xct/joint_space_analysis/jsw_morphometry.py index 976f0ff..c5b4701 100644 --- a/ormir_xct/joint_space_analysis/jsw_morphometry.py +++ b/ormir_xct/joint_space_analysis/jsw_morphometry.py @@ -142,7 +142,15 @@ def jsw_erode(dilated_image, pad_image): def jsw_parameters( - pad_image, dilated_js_mask, filename, output_path, js_mask=None, voxel_size=0.0607, oversamp=True, skel=True, minimum=0.0 + pad_image, + dilated_js_mask, + filename, + output_path, + js_mask=None, + voxel_size=0.0607, + oversamp=True, + skel=True, + minimum=0.0, ): """ Computes the following JSW parameters: diff --git a/ormir_xct/util/hildebrand_thickness.py b/ormir_xct/util/hildebrand_thickness.py index 24ef185..416d5b6 100644 --- a/ormir_xct/util/hildebrand_thickness.py +++ b/ormir_xct/util/hildebrand_thickness.py @@ -70,7 +70,7 @@ def compute_local_thickness_from_sorted_distances( The local thickness field. """ - for (rd, (ri, rj, rk)) in zip(sorted_dists, sorted_dists_indices): + for rd, (ri, rj, rk) in zip(sorted_dists, sorted_dists_indices): rd_vox = rd / voxel_width di_min = np.maximum(np.floor(ri - rd_vox[0]) - 1, 0) di_max = np.minimum(np.ceil(ri + rd_vox[0]) + 2, local_thickness.shape[0]) diff --git a/ormir_xct/util/process_py_log.py b/ormir_xct/util/process_py_log.py index eb94099..7c77e08 100644 --- a/ormir_xct/util/process_py_log.py +++ b/ormir_xct/util/process_py_log.py @@ -85,7 +85,6 @@ def process_py_log(log_dir, output_dir): ] for shape in shape_list: - for log_file in shape[0]: # Read the log (text) file basename = os.path.basename(log_file) diff --git a/ormir_xct/util/segmentation_evaluation.py b/ormir_xct/util/segmentation_evaluation.py index fbd3ccf..883a3ec 100644 --- a/ormir_xct/util/segmentation_evaluation.py +++ b/ormir_xct/util/segmentation_evaluation.py @@ -153,6 +153,7 @@ def calculate_surface_distance_measures(ref, seg, spacing): return ssd_measures + def hausdorff_sitk(ref, seg): """ Compute the Hausdorff distance using SimpleITK. diff --git a/setup.py b/setup.py index aa2d8a0..0d1cea9 100644 --- a/setup.py +++ b/setup.py @@ -3,6 +3,6 @@ from setuptools import setup setup( - setup_requires=['pbr'], + setup_requires=["pbr"], pbr=True, ) diff --git a/tests/autocontour/test_autocontour.py b/tests/autocontour/test_autocontour.py index 66d2d2a..6033bcb 100644 --- a/tests/autocontour/test_autocontour.py +++ b/tests/autocontour/test_autocontour.py @@ -16,8 +16,8 @@ from ormir_xct.autocontour.autocontour import autocontour -class TestAutocontour(unittest.TestCase): +class TestAutocontour(unittest.TestCase): def setUp(self): self.true_mask_filename = "test_joint_mask.nii" self.true_image_filename = "test_joint.nii" diff --git a/tests/autocontour/test_autocontour_gobj.py b/tests/autocontour/test_autocontour_gobj.py index ef2a059..e65cb8a 100644 --- a/tests/autocontour/test_autocontour_gobj.py +++ b/tests/autocontour/test_autocontour_gobj.py @@ -14,6 +14,7 @@ from ormir_xct.autocontour.autocontour_gobj import autocontour_gobj from ormir_xct.autocontour.AutocontourKnee import AutocontourKnee + class TestAutocontour(unittest.TestCase): def test_autocontour(self): pass diff --git a/tests/bone_mineral_density/test_bone_mineral_density.py b/tests/bone_mineral_density/test_bone_mineral_density.py index 86b2a47..6d3999d 100644 --- a/tests/bone_mineral_density/test_bone_mineral_density.py +++ b/tests/bone_mineral_density/test_bone_mineral_density.py @@ -10,16 +10,17 @@ import unittest import numpy as np import SimpleITK as sitk -from ormir_xct.bone_mineral_density.bmd import bmd +from ormir_xct.bone_mineral_density.bmd import bmd from ormir_xct.util.scanco_rescale import ( - convert_scanco_to_bmd, convert_linear_attenuation_to_bmd, - convert_hu_to_bmd - ) + convert_scanco_to_bmd, + convert_linear_attenuation_to_bmd, + convert_hu_to_bmd, +) -class TestBoneMineralDensity(unittest.TestCase): +class TestBoneMineralDensity(unittest.TestCase): def test_convert_to_bmd_from_bmd(self): - array = np.ones((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) mean = array.mean() std = array.std() @@ -28,33 +29,37 @@ def test_convert_to_bmd_from_bmd(self): self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) - + def test_convert_to_bmd_from_scanco(self): - array = np.ones((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) scanco_array = convert_scanco_to_bmd(array, 1, 1, 1) mean = scanco_array.mean() std = scanco_array.std() - result_mean, result_std = bmd(sitk.GetImageFromArray(array), "scanco", 1, 0, 1, 1) + result_mean, result_std = bmd( + sitk.GetImageFromArray(array), "scanco", 1, 0, 1, 1 + ) self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) def test_convert_to_bmd_from_attenuation(self): - array = np.ones((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) attenuation_array = convert_linear_attenuation_to_bmd(array, 1, 1) mean = attenuation_array.mean() std = attenuation_array.std() - result_mean, result_std = bmd(sitk.GetImageFromArray(array), "attenuation", 0, 0, 1, 1) + result_mean, result_std = bmd( + sitk.GetImageFromArray(array), "attenuation", 0, 0, 1, 1 + ) self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) def test_convert_to_bmd_from_hu(self): - array = np.ones((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) hu_array = convert_hu_to_bmd(array, 1, 1, 1) mean = hu_array.mean() @@ -63,4 +68,4 @@ def test_convert_to_bmd_from_hu(self): result_mean, result_std = bmd(sitk.GetImageFromArray(array), "hu", 0, 1, 1, 1) self.assertAlmostEqual(result_mean, mean, 8) - self.assertAlmostEqual(result_std, std, 8) \ No newline at end of file + self.assertAlmostEqual(result_std, std, 8) diff --git a/tests/bone_mineral_density/test_bone_mineral_density_masked.py b/tests/bone_mineral_density/test_bone_mineral_density_masked.py index 1cd0073..9d9b75d 100644 --- a/tests/bone_mineral_density/test_bone_mineral_density_masked.py +++ b/tests/bone_mineral_density/test_bone_mineral_density_masked.py @@ -11,30 +11,39 @@ import numpy as np import SimpleITK as sitk from ormir_xct.util.scanco_rescale import ( - convert_scanco_to_bmd, convert_linear_attenuation_to_bmd, - convert_hu_to_bmd - ) -from ormir_xct.bone_mineral_density.bmd_masked import bmd_masked + convert_scanco_to_bmd, + convert_linear_attenuation_to_bmd, + convert_hu_to_bmd, +) +from ormir_xct.bone_mineral_density.bmd_masked import bmd_masked -class TestBoneMineralDensityMasked(unittest.TestCase): +class TestBoneMineralDensityMasked(unittest.TestCase): def test_convert_to_bmd_from_bmd(self): - array = np.ones((5,5,5), dtype=float) - mask = np.zeros((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) + mask = np.zeros((5, 5, 5), dtype=float) mask[1:4, 1:4, 1:4] = 1 masked_array = array * mask mean = masked_array[masked_array > 0].mean() std = masked_array[masked_array > 0].std() - result_mean, result_std = bmd_masked(sitk.GetImageFromArray(array), sitk.GetImageFromArray(mask), "bmd", 0, 0, 0, 0) + result_mean, result_std = bmd_masked( + sitk.GetImageFromArray(array), + sitk.GetImageFromArray(mask), + "bmd", + 0, + 0, + 0, + 0, + ) self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) - + def test_convert_to_bmd_from_scanco(self): - array = np.ones((5,5,5), dtype=float) - mask = np.zeros((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) + mask = np.zeros((5, 5, 5), dtype=float) mask[1:4, 1:4, 1:4] = 1 scanco_array = convert_scanco_to_bmd(array, 1, 1, 1) @@ -42,14 +51,22 @@ def test_convert_to_bmd_from_scanco(self): mean = masked_array[masked_array > 0].mean() std = masked_array[masked_array > 0].std() - result_mean, result_std = bmd_masked(sitk.GetImageFromArray(array), sitk.GetImageFromArray(mask), "scanco", 1, 0, 1, 1) + result_mean, result_std = bmd_masked( + sitk.GetImageFromArray(array), + sitk.GetImageFromArray(mask), + "scanco", + 1, + 0, + 1, + 1, + ) self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) def test_convert_to_bmd_from_attenuation(self): - array = np.ones((5,5,5), dtype=float) - mask = np.zeros((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) + mask = np.zeros((5, 5, 5), dtype=float) mask[1:4, 1:4, 1:4] = 1 attenuation_array = convert_linear_attenuation_to_bmd(array, 1, 1) @@ -57,14 +74,22 @@ def test_convert_to_bmd_from_attenuation(self): mean = masked_array[masked_array > 0].mean() std = masked_array[masked_array > 0].std() - result_mean, result_std = bmd_masked(sitk.GetImageFromArray(array), sitk.GetImageFromArray(mask), "attenuation", 0, 0, 1, 1) + result_mean, result_std = bmd_masked( + sitk.GetImageFromArray(array), + sitk.GetImageFromArray(mask), + "attenuation", + 0, + 0, + 1, + 1, + ) self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) def test_convert_to_bmd_from_hu(self): - array = np.ones((5,5,5), dtype=float) - mask = np.zeros((5,5,5), dtype=float) + array = np.ones((5, 5, 5), dtype=float) + mask = np.zeros((5, 5, 5), dtype=float) mask[1:4, 1:4, 1:4] = 1 hu_array = convert_hu_to_bmd(array, 1, 1, 1) @@ -72,7 +97,15 @@ def test_convert_to_bmd_from_hu(self): mean = masked_array[masked_array > 0].mean() std = masked_array[masked_array > 0].std() - result_mean, result_std = bmd_masked(sitk.GetImageFromArray(array), sitk.GetImageFromArray(mask), "hu", 0, 1, 1, 1) + result_mean, result_std = bmd_masked( + sitk.GetImageFromArray(array), + sitk.GetImageFromArray(mask), + "hu", + 0, + 1, + 1, + 1, + ) self.assertAlmostEqual(result_mean, mean, 8) self.assertAlmostEqual(result_std, std, 8) diff --git a/tests/joint_space_analysis/test_bone_connectivity.py b/tests/joint_space_analysis/test_bone_connectivity.py index bd2690c..6798ae2 100644 --- a/tests/joint_space_analysis/test_bone_connectivity.py +++ b/tests/joint_space_analysis/test_bone_connectivity.py @@ -1,9 +1,9 @@ import unittest -from ormir_xct.joint_space_analysis.connected_check import connected_check +from ormir_xct.joint_space_analysis.connected_check import connected_check -class TestBoneConnectivityCheck(unittest.TestCase): +class TestBoneConnectivityCheck(unittest.TestCase): @unittest.skip("unimplemented") def test_placeholder(self): - pass \ No newline at end of file + pass diff --git a/tests/joint_space_analysis/test_jsw_main.py b/tests/joint_space_analysis/test_jsw_main.py index 239fba1..d0ab33c 100644 --- a/tests/joint_space_analysis/test_jsw_main.py +++ b/tests/joint_space_analysis/test_jsw_main.py @@ -1,9 +1,9 @@ import unittest -from ormir_xct.joint_space_analysis.jsw_main import main +from ormir_xct.joint_space_analysis.jsw_main import main -class TestJointSpaceWidthMain(unittest.TestCase): +class TestJointSpaceWidthMain(unittest.TestCase): @unittest.skip("unimplemented") def test_placeholder(self): - pass \ No newline at end of file + pass diff --git a/tests/joint_space_analysis/test_jsw_morphometry.py b/tests/joint_space_analysis/test_jsw_morphometry.py index 765d135..7f56f75 100644 --- a/tests/joint_space_analysis/test_jsw_morphometry.py +++ b/tests/joint_space_analysis/test_jsw_morphometry.py @@ -1,11 +1,14 @@ import unittest from ormir_xct.joint_space_analysis.jsw_morphometry import ( - jsw_pad, jsw_dilate, jsw_erode, jsw_parameters + jsw_pad, + jsw_dilate, + jsw_erode, + jsw_parameters, ) -class TestJointSpaceWidthMorphometry(unittest.TestCase): +class TestJointSpaceWidthMorphometry(unittest.TestCase): @unittest.skip("unimplemented") def test_placeholder(self): - pass \ No newline at end of file + pass diff --git a/tests/util/test_file_converter.py b/tests/util/test_file_converter.py index 9f485a5..24b835e 100644 --- a/tests/util/test_file_converter.py +++ b/tests/util/test_file_converter.py @@ -674,7 +674,7 @@ def test_dicom_to_isq(self): Test the conversion of DICOM to ISQ using ITKIOScanco and SimpleITK. """ extension = os.path.splitext(self.test_dcm_to_isq_temp)[1] - self.assertTrue(extension.lower() == '.isq') + self.assertTrue(extension.lower() == ".isq") file_converter(self.test_dcm_temp, self.test_dcm_to_isq_temp) diff --git a/tests/util/test_file_reader.py b/tests/util/test_file_reader.py index 2472f9b..07550fe 100644 --- a/tests/util/test_file_reader.py +++ b/tests/util/test_file_reader.py @@ -15,7 +15,6 @@ class TestFileReader(unittest.TestCase): - filenames = [ "test_aim", "test_isq", diff --git a/tests/util/test_hildebrand_thickness.py b/tests/util/test_hildebrand_thickness.py index 27e2d79..e6f5663 100644 --- a/tests/util/test_hildebrand_thickness.py +++ b/tests/util/test_hildebrand_thickness.py @@ -4,28 +4,34 @@ from ormir_xct.util.hildebrand_thickness import ( compute_local_thickness_from_sorted_distances, compute_local_thickness_from_mask, - calc_structure_thickness_statistics + calc_structure_thickness_statistics, ) def create_sphere_mask(shape, voxel_width, radius): - center = (voxel_width[0] * (shape[0] // 2), voxel_width[1] * (shape[1] // 2), voxel_width[2] * (shape[2] // 2)) - x, y, z = np.meshgrid(*[voxel_width[i] * np.arange(0, shape[i]) for i in range(3)], indexing="ij") + center = ( + voxel_width[0] * (shape[0] // 2), + voxel_width[1] * (shape[1] // 2), + voxel_width[2] * (shape[2] // 2), + ) + x, y, z = np.meshgrid( + *[voxel_width[i] * np.arange(0, shape[i]) for i in range(3)], indexing="ij" + ) mask = np.zeros(shape, dtype=bool) - mask[(x - center[0]) ** 2 + (y - center[1]) ** 2 + (z - center[2]) ** 2 <= radius ** 2] = 1 + mask[ + (x - center[0]) ** 2 + (y - center[1]) ** 2 + (z - center[2]) ** 2 + <= radius**2 + ] = 1 return mask - class TestComputeLocalThicknessFromMask(unittest.TestCase): - @unittest.skip("unimplemented") def test_placeholder(self): pass class TestCalcStructureThicknessStatistics(unittest.TestCase): - def test_get_sphere_thickness(self): voxel_width = (1, 1, 1) shape = (50, 50, 50) @@ -35,7 +41,7 @@ def test_get_sphere_thickness(self): s = calc_structure_thickness_statistics( sphere, voxel_width, min_thickness, pad_amount=10, oversample=False ) - self.assertAlmostEqual(2*radius, s[0], places=1) + self.assertAlmostEqual(2 * radius, s[0], places=1) if __name__ == "__main__": diff --git a/tests/util/test_scanco_rescale.py b/tests/util/test_scanco_rescale.py index 752c9f1..59de9c9 100644 --- a/tests/util/test_scanco_rescale.py +++ b/tests/util/test_scanco_rescale.py @@ -12,12 +12,17 @@ import SimpleITK as sitk from ormir_xct.util.scanco_rescale import ( - convert_scanco_to_linear_attenuation, convert_scanco_to_hu, - convert_scanco_to_bmd, convert_hu_to_linear_attenuation, - convert_hu_to_scanco, convert_hu_to_bmd, - convert_linear_attenuation_to_hu, convert_linear_attenuation_to_scanco, - convert_linear_attenuation_to_bmd -) + convert_scanco_to_linear_attenuation, + convert_scanco_to_hu, + convert_scanco_to_bmd, + convert_hu_to_linear_attenuation, + convert_hu_to_scanco, + convert_hu_to_bmd, + convert_linear_attenuation_to_hu, + convert_linear_attenuation_to_scanco, + convert_linear_attenuation_to_bmd, +) + def create_test_image(dimensions, value): """ @@ -30,7 +35,6 @@ def create_test_image(dimensions, value): class TestScancoRescale(unittest.TestCase): - def test_scanco_to_linear_attenuation(self): # LinearAttenuation = ScancoUnits / mu_scaling test_image = create_test_image((10, 10, 10), 8192) @@ -57,7 +61,9 @@ def test_scanco_to_bmd(self): mu_scaling = 8192 rescale_slope = 9 rescale_intercept = 1 - result_image = convert_scanco_to_bmd(test_image, mu_scaling, rescale_slope, rescale_intercept) + result_image = convert_scanco_to_bmd( + test_image, mu_scaling, rescale_slope, rescale_intercept + ) np.testing.assert_array_equal(expected_image, result_image) def test_hu_to_linear_attenuation(self): @@ -86,7 +92,9 @@ def test_hu_to_bmd(self): mu_water = 0.5 rescale_slope = 1 rescale_intercept = 1 - result_image = convert_hu_to_bmd(test_image, mu_water, rescale_slope, rescale_intercept) + result_image = convert_hu_to_bmd( + test_image, mu_water, rescale_slope, rescale_intercept + ) np.testing.assert_array_equal(expected_image, result_image) def test_linear_attenuation_to_hu(self): @@ -111,5 +119,7 @@ def test_linear_attenuation_to_bmd(self): expected_image = create_test_image((10, 10, 10), 8192) rescale_slope = 8191 rescale_intercept = 1 - result_image = convert_linear_attenuation_to_bmd(test_image, rescale_slope, rescale_intercept) - np.testing.assert_array_equal(expected_image, result_image) \ No newline at end of file + result_image = convert_linear_attenuation_to_bmd( + test_image, rescale_slope, rescale_intercept + ) + np.testing.assert_array_equal(expected_image, result_image) diff --git a/tests/util/test_vtk_sitk_conversion.py b/tests/util/test_vtk_sitk_conversion.py index 84fac40..c6c0169 100644 --- a/tests/util/test_vtk_sitk_conversion.py +++ b/tests/util/test_vtk_sitk_conversion.py @@ -23,6 +23,7 @@ def create_test_image(dimensions, value): array = np.full(dimensions, value, np.int32) return array + def create_vtk_image(dimensions, value): """ Creates a VTK image containing a single value of given dimension. @@ -33,13 +34,14 @@ def create_vtk_image(dimensions, value): flat_data_array = data.flatten() vtk_data = numpy_to_vtk(num_array=flat_data_array, deep=True, array_type=data_type) - + vtk_image = vtk.vtkImageData() vtk_image.GetPointData().SetScalars(vtk_data) vtk_image.SetDimensions(shape[0], shape[1], shape[2]) return vtk_image + def vtk_image_to_numpy(vtk_image): """ Converts a VTK image to a numpy array.

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