From 2cfe20606d3db44e00f1a8aa6ab1716a460436b8 Mon Sep 17 00:00:00 2001
From: Chris Leaman
Date: Mon, 26 Nov 2018 09:55:33 +1100
Subject: [PATCH] Update README.md, notebook and qgis
---
README.md | 65 ++++++++--------------
notebooks/01_exploration.ipynb | 97 ++++++++++++++-------------------
notebooks/qgis.qgz | Bin 12721 -> 22181 bytes
3 files changed, 65 insertions(+), 97 deletions(-)
diff --git a/README.md b/README.md
index 261f86b..341ff13 100644
--- a/README.md
+++ b/README.md
@@ -1,66 +1,47 @@
# 2016 Narrabeen Storm EWS Performance
-This repository investigates whether the storm impacts (i.e. Sallenger, 2000) of the June 2016 Narrabeen Storm could
-have been forecasted in advance.
+This repository investigates whether the storm impacts (i.e. Sallenger, 2000) f the June 2016 Narrabeen Storm could have been forecasted in advance.
## Repository and analysis format
This repository follows the [Cookiecutter Data Science](https://drivendata.github.io/cookiecutter-data-science/)
-structure where possible. The analysis is done in python (look at the `/src/` folder) with some interactive,
-exploratory notebooks located at `/notebooks`.
+structure where possible. The analysis is done in python (look at the `/src/` folder) with some interactive,exploratory notebooks located at `/notebooks`.
-Development is conducted using a [gitflow](https://www.atlassian
-.com/git/tutorials/comparing-workflows/gitflow-workflow) approach - mainly the `master` branch stores the official
-release history and the `develop` branch serves as an integration branch for features. Other `hotfix` and `feature`
-branches should be created and merged as necessary.
+Development is conducted using a [gitflow](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow) approach. The `master` branch stores the officialrelease history and the `develop` branch serves as an integration branch for features. Other `hotfix` and `feature` branches should be created and merged as necessary.
## Where to start?
Check .env
Uses pipenv
1. Clone this repository.
2. Pull data from WRL coastal J drive with `make pull-data`
-3. Check out jupyter notebook `./notebooks/01_exploration.ipynb` which has an example of how to import the data and
-some interactive widgets.
+3. Check out jupyter notebook `./notebooks/01_exploration.ipynb` which has an example of how to import the data and some interactive widgets.
## Requirements
The following requirements are needed to run various bits:
-- [Python 3.6+](https://conda.io/docs/user-guide/install/windows.html): Used for processing and analysing data.
-Jupyter notebooks are used for exploratory analyis and communication.
-- [QGIS](https://www.qgis.org/en/site/forusers/download): Used for looking at raw LIDAR pre/post storm surveys and
-extracting dune crests/toes
-- [rclone](https://rclone.org/downloads/): Data is not tracked by this repository, but is backed up to a remote
-Chris Leaman working directory located on the WRL coastal drive. Rclone is used to sync local and remote copies.
-Ensure rclone.exe is located on your `PATH` environment.
-- [gnuMake](http://gnuwin32.sourceforge.net/packages/make.htm): A list of commands for processing data is provided in
- the `./Makefile`. Use gnuMake to launch these commands. Ensure make.exe is located on your `PATH` environment.
+- [Python 3.6+](https://conda.io/docs/user-guide/install/windows.html): Used for processing and analysing data. Jupyter notebooks are used for exploratory analyis and communication.
+- [QGIS](https://www.qgis.org/en/site/forusers/download): Used for looking at raw LIDAR pre/post storm surveys and extracting dune crests/toes
+- [rclone](https://rclone.org/downloads/): Data is not tracked by this repository, but is backed up to a remote Chris Leaman working directory located on the WRL coastal drive. Rclone is used to sync local and remote copies. Ensure rclone.exe is located on your `PATH` environment.
+- [gnuMake](http://gnuwin32.sourceforge.net/packages/make.htm): A list of commands for processing data is provided in the `./Makefile`. Use gnuMake to launch these commands. Ensure make.exe is located on your `PATH` environment.
## Available data
-Raw, interim and processed data used in this analysis is kept in the `/data/` folder. Data is not tracked in the
-repository due to size constraints, but stored locally. A mirror is kept of the coastal folder J drive which you can
+Raw, interim and processed data used in this analysis is kept in the `/data/` folder. Data is not tracked in the repository due to size constraints, but stored locally. A mirror is kept of the coastal folder J drive which you can
use to push/pull to, using rclone. In order to get the data, run `make pull-data`.
List of data:
-- `/data/raw/processed_shorelines`: This data was recieved from Tom Beuzen in October 2018. It consists of pre/post
-storm profiles at every 100 m sections along beaches ranging from Dee Why to Nambucca . Profiles are based on raw
-aerial LIDAR and were processed by Mitch Harley. Tides and waves (10 m contour and reverse shoaled deepwater) for
-each individual 100 m section is also provided.
-- `/data/raw/raw_lidar`: This is the raw pre/post storm aerial LIDAR which was taken for the June 2016 storm. `.las`
-files are the raw files which have been processed into `.tiff` files using `PDAL`. Note that these files have not
-been corrected for systematic errors, so actual elevations should be taken from the `processed_shorelines` folder.
-Obtained November 2018 from Mitch Harley from the black external HDD labeled "UNSW LIDAR".
-- `/data/raw/profile_features`: Dune toe and crest locations based on prestorm LIDAR. Refer to `/notebooks/qgis.qgz`
-as this shows how they were manually extracted. Note that the shapefiles only show the location (lat/lon) of the dune
- crest and toe. For actual elevations, these locations need to related to the processed shorelines.
+- `/data/raw/processed_shorelines`: This data was recieved from Tom Beuzen in October 2018. It consists of pre/poststorm profiles at every 100 m sections along beaches ranging from Dee Why to Nambucca . Profiles are based on raw
+aerial LIDAR and were processed by Mitch Harley. Tides and waves (10 m contour and reverse shoaled deepwater) for each individual 100 m section is also provided.
+- `/data/raw/raw_lidar`: This is the raw pre/post storm aerial LIDAR which was taken for the June 2016 storm. `.las` files are the raw files which have been processed into `.tiff` files using `PDAL`. Note that these files have not
+been corrected for systematic errors, so actual elevations should be taken from the `processed_shorelines` folder. Obtained November 2018 from Mitch Harley from the black external HDD labeled "UNSW LIDAR".
+- `/data/raw/profile_features`: Dune toe and crest locations based on prestorm LIDAR. Refer to `/notebooks/qgis.qgz` as this shows how they were manually extracted. Note that the shapefiles only show the location (lat/lon) of the dune crest and toe. For actual elevations, these locations need to related to the processed shorelines.
## Notebooks
-- `/notebooks/01_exploration.ipynb`: Shows how to import processed shorelines, waves and tides. An interactive widget
- plots the location and cross sections.
-- `/notebooks/qgis.qgz`: A QGIS file which is used to explore the aerial LIDAR data in `/data/raw/raw_lidar`. By
-examining the pre-strom lidar, dune crest and dune toe lines are manually extracted. These are stored in the
-`/data/profile_features/`.
+- `/notebooks/01_exploration.ipynb`: Shows how to import processed shorelines, waves and tides. An interactive widget plots the location and cross sections.
+- `/notebooks/qgis.qgz`: A QGIS file which is used to explore the aerial LIDAR data in `/data/raw/raw_lidar`. Byexamining the pre-strom lidar, dune crest and dune toe lines are manually extracted. These are stored in the `/data/profile_features/`.
## TODO
-- [ ] Setup precomit hook for automatic code formatting using [black](https://ljvmiranda921.github.io/notebook/2018/06/21/precommits-using-black-and-flake8/).
-- [ ] Raw tide WL's are interpolated based on location from tide gauges. This probably isn't the most accurate method, but should have a small effect since surge elevation was low during this event. Need to assess the effect of this method.
-- [ ] Estimate max TWL from elevation where pre storm and post storm profiles are the same.
- [ ] Mitch updated the raw profiles.mat to include more information about the survey time. Our data scripts should be updated to parse this new information and include it in our dataframes.
-- [ ] Implement (bayesian change detection algorithm)[https://github.com/hildensia/bayesian_changepoint_detection] to help detect dune crests and toes from profiles
-- [ ] Implement dune impact calculations
\ No newline at end of file
+- [ ] Setup precomit hook for automatic code formatting using [black](https://ljvmiranda921.github.io/notebook/2018/06/21/precommits-using-black-and-flake8/). Low priority as can run black using the command `make format`.
+- [ ] Raw tide WL's are interpolated based on location from tide gauges. This probably isn't the most accurate method, but should have a small effect since surge elevation was low during this event. Need to assess the effect of this method.
+- [ ] Estimate max TWL from elevation where pre storm and post storm profiles are the same. Need to think more about this as runup impacting dune toe will move the dune face back, incorrectly raising the observed twl. Perhaps this estimation of max TWL is only useful for the swash regime.
+- [ ] Implement (bayesian change detection algorithm)[https://github.com/hildensia/bayesian_changepoint_detection] to help detect dune crests and toes from profiles. Probably low priority at the moment since we are doing manual detection.
+- [ ] Implement dune impact calculations as per Palmsten & Holman. Calculation should be done in a new dataframe.
+- [ ] Implement data/interim/*.csv file checking using py.test. Check for correct columns, number of nans etc. Testing of code is probably a lower priority than just checking the interim data files at the moment.
+- [ ] Investigate using [modin](https://github.com/modin-project/modin) to help speed up analysis.
\ No newline at end of file
diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb
index 18c5b6f..03189fd 100644
--- a/notebooks/01_exploration.ipynb
+++ b/notebooks/01_exploration.ipynb
@@ -13,8 +13,8 @@
"execution_count": 1,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-22T22:48:17.973982Z",
- "start_time": "2018-11-22T22:48:17.825797Z"
+ "end_time": "2018-11-25T21:56:41.533322Z",
+ "start_time": "2018-11-25T21:56:41.362045Z"
}
},
"outputs": [],
@@ -29,11 +29,11 @@
},
{
"cell_type": "code",
- "execution_count": 232,
+ "execution_count": 2,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-23T01:45:39.791501Z",
- "start_time": "2018-11-23T01:45:39.780471Z"
+ "end_time": "2018-11-25T21:57:10.456785Z",
+ "start_time": "2018-11-25T21:56:41.534324Z"
},
"scrolled": true
},
@@ -70,11 +70,11 @@
},
{
"cell_type": "code",
- "execution_count": 273,
+ "execution_count": 3,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-23T02:59:30.467706Z",
- "start_time": "2018-11-23T02:59:17.099702Z"
+ "end_time": "2018-11-25T21:57:33.969096Z",
+ "start_time": "2018-11-25T21:57:10.456785Z"
},
"pixiedust": {
"displayParams": {}
@@ -130,11 +130,11 @@
},
{
"cell_type": "code",
- "execution_count": 276,
+ "execution_count": 8,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-23T03:07:46.410476Z",
- "start_time": "2018-11-23T03:07:43.154124Z"
+ "end_time": "2018-11-25T22:27:09.754717Z",
+ "start_time": "2018-11-25T22:27:06.633214Z"
},
"code_folding": [
408
@@ -146,7 +146,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "cc4ab9cdf0cb422f8870d6ba906e538a",
+ "model_id": "9bf50bbca81147ee9d37120a32bed4ea",
"version_major": 2,
"version_minor": 0
},
@@ -166,45 +166,45 @@
"
\n"
],
"text/plain": [
- "VBox(children=(VBox(children=(HTML(value='Filter by observed and predicted impacts:'), HBox(children=(VBox(children=(HTML(value='Observed Impacts'), SelectMultiple(index=(0, 1), options=('swash', 'collision'), value=('swash', 'collision')))), VBox(children=(HTML(value='Forecasted: foreshore_slope_sto06'), SelectMultiple(index=(0, 1, 2), options=('collision', 'swash', 'overwash'), value=('collision', 'swash', 'overwash')))), VBox(children=(HTML(value='Forecasted: mean_slope_sto06'), SelectMultiple(index=(0, 1, 2), options=('collision', 'swash', 'overwash'), value=('collision', 'swash', 'overwash')))))))), HBox(children=(VBox(children=(HTML(value='Filter by site_id:'), HBox(children=(Dropdown(description='site_id: ', index=942, options=('AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', 'AVOCAn0004', 'AVOCAn0005', 'AVOCAn0006', 'AVOCAn0007', 'AVOCAn0008', 'AVOCAn0009', 'AVOCAs0002', 'AVOCAs0003', 'AVOCAs0004', 'AVOCAs0005', 'AVOCAs0006', 'AVOCAs0007', 'AVOCAs0008', 'BILG0001', 'BILG0002', 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'GRANTSn0004', 'GRANTSn0005', 'GRANTSn0006', 'GRANTSn0007', 'GRANTSn0008', 'GRANTSn0009', 'GRANTSn0010', 'GRANTSn0011', 'GRANTSn0012', 'GRANTSn0013', 'GRANTSn0014', 'GRANTSn0015', 'GRANTSn0016', 'GRANTSn0017', 'GRANTSn0018', 'GRANTSn0019', 'GRANTSn0020', 'GRANTSn0021', 'GRANTSn0022', 'GRANTSn0023', 'GRANTSn0024', 'GRANTSs0001', 'GRANTSs0002', 'GRANTSs0003', 'GRANTSs0004', 'GRANTSs0005', 'GRANTSs0006', 'GRANTSs0007', 'GRANTSs0008', 'GRANTSs0009', 'GRANTSs0010', 'GRANTSs0011', 'GRANTSs0012', 'GRANTSs0013', 'GRANTSs0014', 'HARGn0001', 'HARGn0002', 'HARGn0003', 'HARGn0004', 'HARGn0005', 'HARGn0006', 'HARGn0007', 'HARGs0001', 'HARGs0002', 'HARGs0003', 'HARGs0004', 'HARGs0005', 'HARGs0006', 'HARGs0007', 'HARR0001', 'HARR0002', 'HARR0003', 'HARR0004', 'HARR0005', 'HARR0006', 'HARR0007', 'HARR0008', 'HARR0009', 'HARR0010', 'HARR0011', 'HARR0012', 'HARR0013', 'HARR0014', 'HARR0015', 'HARR0016', 'HARR0017', 'HARR0018', 'HARR0019', 'HARR0020', 'HARR0021', 'HARR0022', 'HARR0023', 'HARR0024', 'HARR0025', 'HARR0026', 'HARR0027', 'HARR0028', 'HARR0029', 'HARR0030', 'HARR0031', 'HARR0032', 'HARR0033', 'HARR0034', 'HARR0035', 'HARR0036', 'HARR0037', 'HARR0038', 'HARR0039', 'HARR0040', 'HARR0041', 'HARR0042', 'HARR0043', 'HARR0044', 'HARR0045', 'HARR0046', 'HARR0047', 'HARR0048', 'HARR0049', 'HARR0050', 'HARR0051', 'HARR0052', 'HARR0053', 'HARR0054', 'HARR0055', 'HARR0056', 'LHOUSE0001', 'LHOUSE0002', 'LHOUSE0003', 'LHOUSE0004', 'LHOUSE0005', 'LHOUSE0006', 'LHOUSE0007', 'LHOUSE0008', 'LHOUSE0009', 'LHOUSE0010', 'LHOUSE0011', 'LHOUSE0012', 'LHOUSE0013', 'LHOUSEn0001', 'LHOUSEn0002', 'LHOUSEn0003', 'LHOUSEn0004', 'LHOUSEn0005', 'LHOUSEn0006', 'LHOUSEn0007', 'LHOUSEn0008', 'LHOUSEn0009', 'LHOUSEn0010', 'LHOUSEn0011', 'LHOUSEn0012', 'LHOUSEn0013', 'LHOUSEn0014', 'LHOUSEn0015', 'LHOUSEn0016', 'LHOUSEn0017', 'LHOUSEn0018', 'LHOUSEn0019', 'LHOUSEn0020', 'LHOUSEn0021', 'LHOUSEn0022', 'LHOUSEn0023', 'LHOUSEn0024', 'LHOUSEn0025', 'LHOUSEn0026', 'LHOUSEn0027', 'LHOUSEn0028', 'LHOUSEn0029', 'LHOUSEn0030', 'LHOUSEn0031', 'LHOUSEn0032', 'LHOUSEn0033', 'LHOUSEn0034', 'LHOUSEn0035', 'LHOUSEn0036', 'LHOUSEn0037', 'LHOUSEn0038', 'LHOUSEn0039', 'LHOUSEn0040', 'LHOUSEn0041', 'LHOUSEn0042', 'LHOUSEn0043', 'LHOUSEn0044', 'LHOUSEn0045', 'LHOUSEn0046', 'LHOUSEn0047', 'LHOUSEn0048', 'LHOUSEn0049', 'LHOUSEn0050', 'LHOUSEn0051', 'LHOUSEn0052', 'LHOUSEn0053', 'LHOUSEn0054', 'LHOUSEn0055', 'LHOUSEn0056', 'LHOUSEn0057', 'LHOUSEn0058', 'LHOUSEn0059', 'LHOUSEn0060', 'LHOUSEn0061', 'LHOUSEn0062', 'LHOUSEn0063', 'LHOUSEn0064', 'LHOUSEn0065', 'LHOUSEn0066', 'LHOUSEn0067', 'LHOUSEn0068', 'LHOUSEn0069', 'LHOUSEn0070', 'LHOUSEn0071', 'LHOUSEn0072', 'LHOUSEn0073', 'LHOUSEn0074', 'LHOUSEn0075', 'LHOUSEn0076', 'LHOUSEn0077', 'LHOUSEn0078', 'LHOUSEn0079', 'LHOUSEn0080', 'LHOUSEn0081', 'LHOUSEn0082', 'LHOUSEn0083', 'LHOUSEn0084', 'LHOUSEn0085', 'LHOUSEn0086', 'LHOUSEn0087', 'LHOUSEn0088', 'LHOUSEn0089', 'LHOUSEn0090', 'LHOUSEn0091', 'LHOUSEn0092', 'LHOUSEn0093', 'LHOUSEs0001', 'LHOUSEs0002', 'LHOUSEs0003', 'LHOUSEs0004', 'LHOUSEs0005', 'LHOUSEs0006', 'LHOUSEs0007', 'LHOUSEs0008', 'LHOUSEs0009', 'LHOUSEs0010', 'LHOUSEs0011', 'LHOUSEs0012', 'LHOUSEs0013', 'LHOUSEs0014', 'LHOUSEs0015', 'LHOUSEs0016', 'LHOUSEs0017', 'LHOUSEs0018', 'LHOUSEs0019', 'LHOUSEs0020', 'LHOUSEs0021', 'LHOUSEs0022', 'LHOUSEs0023', 'LHOUSEs0024', 'LHOUSEs0025', 'LHOUSEs0026', 'LHOUSEs0027', 'LHOUSEs0028', 'LHOUSEs0029', 'LHOUSEs0030', 'LHOUSEs0031', 'LHOUSEs0032', 'MACM0001', 'MACM0002', 'MACM0003', 'MACM0004', 'MACM0005', 'MACM0006', 'MACM0007', 'MACM0008', 'MACM0009', 'MACM0010', 'MACM0011', 'MACM0012', 'MACM0013', 'MACM0014', 'MACM0015', 'MACM0016', 'MANNING0001', 'MANNING0002', 'MANNING0003', 'MANNING0004', 'MANNING0005', 'MANNING0006', 'MANNING0007', 'MANNING0008', 'MANNING0009', 'MANNING0010', 'MANNING0011', 'MANNING0012', 'MANNING0013', 'MANNING0014', 'MANNING0015', 'MANNING0016', 'MANNING0017', 'MANNING0018', 'MANNING0019', 'MANNING0020', 'MANNING0021', 'MANNING0022', 'MANNING0023', 'MANNING0024', 'MANNING0025', 'MANNING0026', 'MANNING0027', 'MANNING0028', 'MANNING0029', 'MANNING0030', 'MANNING0031', 'MANNING0032', 'MANNING0033', 'MANNING0034', 'MANNING0035', 'MANNING0036', 'MANNING0037', 'MANNING0038', 'MANNING0039', 'MANNING0040', 'MANNING0041', 'MANNING0042', 'MANNING0043', 'MANNING0044', 'MANNING0045', 'MANNING0046', 'MANNING0047', 'MANNING0048', 'MANNING0049', 'MANNING0050', 'MANNING0051', 'MANNING0052', 'MANNING0053', 'MANNING0054', 'MANNING0055', 'MANNING0056', 'MANNING0057', 'MANNING0058', 'MANNING0059', 'MANNING0060', 'MANNING0061', 'MANNING0062', 'MANNING0063', 'MANNING0064', 'MANNING0065', 'MANNING0066', 'MANNING0067', 'MANNING0068', 'MANNING0069', 'MANNING0070', 'MANNING0071', 'MANNING0072', 'MANNING0073', 'MANNING0074', 'MANNING0075', 'MANNING0076', 'MANNING0077', 'MANNING0078', 'MANNING0079', 'MANNING0080', 'MANNING0081', 'MANNING0082', 'MANNING0083', 'MANNING0084', 'MANNING0085', 'MANNING0086', 'MANNING0087', 'MANNING0088', 'MANNING0089', 'MANNING0090', 'MANNING0091', 'MANNING0092', 'MANNING0093', 'MANNING0094', 'MANNING0095', 'MANNING0096', 'MANNING0097', 'MANNING0098', 'MANNING0099', 'MANNING0100', 'MANNING0101', 'MANNING0102', 'MANNING0103', 'MANNING0104', 'MANNING0105', 'MANNING0106', 'MANNING0107', 'MANNING0108', 'MANNING0109', 'MANNING0110', 'MANNING0111', 'MANNING0112', 'MANNING0113', 'MANNING0114', 'MANNING0115', 'MANNING0116', 'MANNING0117', 'MANNING0118', 'MANNING0119', 'MANNING0120', 'MANNING0121', 'MANNING0122', 'MANNING0123', 'MANNING0124', 'MANNING0125', 'MANNING0126', 'MANNING0127', 'MONA0001', 'MONA0002', 'MONA0003', 'MONA0004', 'MONA0005', 'MONA0006', 'MONA0007', 'MONA0008', 'MONA0009', 'MONA0010', 'MONA0011', 'MONA0012', 'MONA0013', 'MONA0014', 'MONA0015', 'MONA0016', 'MONA0017', 'MONA0018', 'MONA0019', 'MONA0020', 'MONA0021', 'NAMB0001', 'NAMB0002', 'NAMB0003', 'NAMB0004', 'NAMB0005', 'NAMB0006', 'NAMB0007', 'NAMB0008', 'NAMB0009', 'NAMB0010', 'NAMB0011', 'NAMB0012', 'NAMB0013', 'NAMB0014', 'NAMB0015', 'NAMB0016', 'NAMB0017', 'NAMB0018', 'NAMB0019', 'NAMB0020', 'NAMB0021', 'NAMB0022', 'NAMB0023', 'NAMB0024', 'NAMB0025', 'NAMB0026', 'NAMB0027', 'NAMB0028', 'NAMB0029', 'NAMB0030', 'NAMB0031', 'NAMB0032', 'NAMB0033', 'NAMB0034', 'NAMB0035', 'NAMB0036', 'NAMB0037', 'NAMB0038', 'NAMB0039', 'NAMB0040', 'NAMB0041', 'NAMB0042', 'NAMB0043', 'NAMB0044', 'NAMB0045', 'NAMB0046', 'NAMB0047', 'NAMB0048', 'NAMB0049', 'NAMB0050', 'NAMB0051', 'NAMB0052', 'NAMB0053', 'NAMB0054', 'NAMB0055', 'NAMB0056', 'NAMB0057', 'NAMB0058', 'NAMB0059', 'NAMB0060', 'NAMB0061', 'NAMB0062', 'NAMB0063', 'NAMB0064', 'NAMB0065', 'NAMB0066', 'NAMB0067', 'NAMB0068', 'NAMB0069', 'NAMB0070', 'NAMB0071', 'NAMB0072', 'NAMB0073', 'NARRA0001', 'NARRA0002', 'NARRA0003', 'NARRA0004', 'NARRA0005', 'NARRA0006', 'NARRA0007', 'NARRA0008', 'NARRA0009', 'NARRA0010', 'NARRA0011', 'NARRA0012', 'NARRA0013', 'NARRA0014', 'NARRA0015', 'NARRA0016', 'NARRA0017', 'NARRA0018', 'NARRA0019', 'NARRA0020', 'NARRA0021', 'NARRA0022', 'NARRA0023', 'NARRA0024', 'NARRA0025', 'NARRA0026', 'NARRA0027', 'NARRA0028', 'NARRA0029', 'NARRA0030', 'NARRA0031', 'NARRA0032', 'NARRA0033', 'NARRA0034', 'NARRA0035', 'NARRA0036', 'NINEMn0001', 'NINEMn0002', 'NINEMn0003', 'NINEMn0004', 'NINEMn0005', 'NINEMn0006', 'NINEMn0007', 'NINEMn0008', 'NINEMn0009', 'NINEMn0010', 'NINEMn0011', 'NINEMn0012', 'NINEMn0013', 'NINEMn0014', 'NINEMn0015', 'NINEMn0016', 'NINEMn0017', 'NINEMn0018', 'NINEMn0019', 'NINEMn0020', 'NINEMn0021', 'NINEMn0022', 'NINEMn0023', 'NINEMn0024', 'NINEMn0025', 'NINEMn0026', 'NINEMn0027', 'NINEMn0028', 'NINEMn0029', 'NINEMn0030', 'NINEMn0031', 'NINEMn0032', 'NINEMn0033', 'NINEMn0034', 'NINEMn0035', 'NINEMn0036', 'NINEMn0037', 'NINEMn0038', 'NINEMn0039', 'NINEMn0040', 'NINEMn0041', 'NINEMn0042', 'NINEMn0043', 'NINEMn0044', 'NINEMn0045', 'NINEMn0046', 'NINEMn0047', 'NINEMn0048', 'NINEMn0049', 'NINEMn0050', 'NINEMn0051', 'NINEMn0052', 'NINEMn0053', 'NINEMn0054', 'NINEMs0001', 'NINEMs0002', 'NINEMs0003', 'NINEMs0004', 'NINEMs0005', 'NINEMs0006', 'NINEMs0007', 'NINEMs0008', 'NINEMs0009', 'NINEMs0010', 'NINEMs0011', 'NINEMs0012', 'NINEMs0013', 'NINEMs0014', 'NINEMs0015', 'NINEMs0016', 'NINEMs0017', 'NINEMs0018', 'NINEMs0019', 'NINEMs0020', 'NINEMs0021', 'NINEMs0022', 'NINEMs0023', 'NINEMs0024', 'NINEMs0025', 'NINEMs0026', 'NINEMs0027', 'NINEMs0028', 'NINEMs0029', 'NINEMs0030', 'NINEMs0031', 'NINEMs0032', 'NINEMs0033', 'NINEMs0034', 'NINEMs0035', 'NINEMs0036', 'NINEMs0037', 'NINEMs0038', 'NINEMs0039', 'NINEMs0040', 'NINEMs0041', 'NINEMs0042', 'NINEMs0043', 'NINEMs0044', 'NINEMs0045', 'NINEMs0046', 'NINEMs0047', 'NINEMs0048', 'NINEMs0049', 'NINEMs0050', 'NINEMs0051', 'NINEMs0052', 'NINEMs0053', 'NINEMs0054', 'NINEMs0055', 'NINEMs0056', 'NINEMs0057', 'NINEMs0058', 'NINEMs0059', 'NINEMs0060', 'NSHORE_n0001', 'NSHORE_n0002', 'NSHORE_n0003', 'NSHORE_n0004', 'NSHORE_n0005', 'NSHORE_n0006', 'NSHORE_n0007', 'NSHORE_n0008', 'NSHORE_n0009', 'NSHORE_n0010', 'NSHORE_n0011', 'NSHORE_n0012', 'NSHORE_n0013', 'NSHORE_n0014', 'NSHORE_n0015', 'NSHORE_n0016', 'NSHORE_n0017', 'NSHORE_n0018', 'NSHORE_n0019', 'NSHORE_n0020', 'NSHORE_n0021', 'NSHORE_n0022', 'NSHORE_n0023', 'NSHORE_n0024', 'NSHORE_n0025', 'NSHORE_n0026', 'NSHORE_n0027', 'NSHORE_n0028', 'NSHORE_n0029', 'NSHORE_n0030', 'NSHORE_n0031', 'NSHORE_n0032', 'NSHORE_n0033', 'NSHORE_n0034', 'NSHORE_n0035', 'NSHORE_n0036', 'NSHORE_n0037', 'NSHORE_n0038', 'NSHORE_n0039', 'NSHORE_n0040', 'NSHORE_n0041', 'NSHORE_n0042', 'NSHORE_n0043', 'NSHORE_n0044', 'NSHORE_n0045', 'NSHORE_n0046', 'NSHORE_n0047', 'NSHORE_n0048', 'NSHORE_n0049', 'NSHORE_n0050', 'NSHORE_n0051', 'NSHORE_n0052', 'NSHORE_n0053', 'NSHORE_n0054', 'NSHORE_n0055', 'NSHORE_n0056', 'NSHORE_n0057', 'NSHORE_n0058', 'NSHORE_n0059', 'NSHORE_n0060', 'NSHORE_n0061', 'NSHORE_n0062', 'NSHORE_n0063', 'NSHORE_n0064', 'NSHORE_n0065', 'NSHORE_n0066', 'NSHORE_n0067', 'NSHORE_n0068', 'NSHORE_n0069', 'NSHORE_n0070', 'NSHORE_n0071', 'NSHORE_n0072', 'NSHORE_n0073', 'NSHORE_n0074', 'NSHORE_n0075', 'NSHORE_n0076', 'NSHORE_n0077', 'NSHORE_n0078', 'NSHORE_n0079', 'NSHORE_n0080', 'NSHORE_n0081', 'NSHORE_n0082', 'NSHORE_s0001', 'NSHORE_s0002', 'NSHORE_s0003', 'NSHORE_s0004', 'NSHORE_s0005', 'NSHORE_s0006', 'NSHORE_s0007', 'NSHORE_s0008', 'NSHORE_s0009', 'NSHORE_s0010', 'NSHORE_s0011', 'NSHORE_s0012', 'NSHORE_s0013', 'NSHORE_s0014', 'NSHORE_s0015', 'NSHORE_s0016', 'NSHORE_s0017', 'NSHORE_s0018', 'NSHORE_s0019', 'NSHORE_s0020', 'NSHORE_s0021', 'NSHORE_s0022', 'NSHORE_s0023', 'NSHORE_s0024', 'NSHORE_s0025', 'NSHORE_s0026', 'NSHORE_s0027', 'NSHORE_s0028', 'NSHORE_s0029', 'NSHORE_s0030', 'NSHORE_s0031', 'NSHORE_s0032', 'NSHORE_s0033', 'NSHORE_s0034', 'NSHORE_s0035', 'NSHORE_s0036', 'NSHORE_s0037', 'NSHORE_s0038', 'NSHORE_s0039', 'NSHORE_s0040', 'NSHORE_s0041', 'NSHORE_s0042', 'NSHORE_s0043', 'NSHORE_s0044', 'OLDBAR0001', 'OLDBAR0002', 'OLDBAR0003', 'OLDBAR0004', 'OLDBAR0005', 'OLDBAR0006', 'OLDBAR0007', 'OLDBAR0008', 'OLDBAR0009', 'OLDBAR0010', 'OLDBAR0011', 'OLDBAR0012', 'OLDBAR0013', 'OLDBAR0014', 'OLDBAR0015', 'OLDBAR0016', 'OLDBAR0017', 'OLDBAR0018', 'OLDBAR0019', 'OLDBAR0020', 'OLDBAR0021', 'OLDBAR0022', 'OLDBAR0023', 'OLDBAR0024', 'OLDBAR0025', 'OLDBAR0026', 'OLDBAR0027', 'OLDBAR0028', 'OLDBAR0029', 'OLDBAR0030', 'OLDBAR0031', 'OLDBAR0032', 'OLDBAR0033', 'OLDBAR0034', 'OLDBAR0035', 'OLDBAR0036', 'ONEMILE0001', 'ONEMILE0002', 'ONEMILE0003', 'ONEMILE0004', 'ONEMILE0005', 'ONEMILE0006', 'ONEMILE0007', 'ONEMILE0008', 'ONEMILE0009', 'ONEMILE0010', 'ONEMILE0011', 'ONEMILE0012', 'ONEMILE0013', 'PEARLn0001', 'PEARLn0002', 'PEARLn0003', 'PEARLn0004', 'PEARLn0005', 'PEARLs0001', 'PEARLs0002', 'PEARLs0003', 'PEARLs0004', 'PEARLs0005', 'SCOT0001', 'SCOT0002', 'SCOT0003', 'SCOT0004', 'SCOT0005', 'SCOT0006', 'SCOT0007', 'SCOT0008', 'SCOT0009', 'SCOT0010', 'SCOT0011', 'SCOT0012', 'STOCNn0001', 'STOCNn0002', 'STOCNn0003', 'STOCNn0004', 'STOCNn0005', 'STOCNn0006', 'STOCNn0007', 'STOCNn0008', 'STOCNn0009', 'STOCNn0010', 'STOCNn0011', 'STOCNn0012', 'STOCNn0013', 'STOCNn0014', 'STOCNn0015', 'STOCNn0016', 'STOCNn0017', 'STOCNn0018', 'STOCNn0019', 'STOCNn0020', 'STOCNn0021', 'STOCNn0022', 'STOCNn0023', 'STOCNn0024', 'STOCNn0025', 'STOCNn0026', 'STOCNn0027', 'STOCNn0028', 'STOCNn0029', 'STOCNn0030', 'STOCNn0031', 'STOCNn0032', 'STOCNn0033', 'STOCNn0034', 'STOCNn0035', 'STOCNn0036', 'STOCNn0037', 'STOCNn0038', 'STOCNn0039', 'STOCNn0040', 'STOCNn0041', 'STOCNn0042', 'STOCNn0043', 'STOCNn0044', 'STOCNn0045', 'STOCNn0046', 'STOCNn0047', 'STOCNn0048', 'STOCNn0049', 'STOCNn0050', 'STOCNn0051', 'STOCNn0052', 'STOCNn0053', 'STOCNn0054', 'STOCNn0055', 'STOCNn0056', 'STOCNn0057', 'STOCNn0058', 'STOCNn0059', 'STOCNn0060', 'STOCNn0061', 'STOCNn0062', 'STOCNn0063', 'STOCNn0064', 'STOCNn0065', 'STOCNs0001', 'STOCNs0002', 'STOCNs0003', 'STOCNs0004', 'STOCNs0005', 'STOCNs0006', 'STOCNs0007', 'STOCNs0008', 'STOCNs0009', 'STOCNs0010', 'STOCNs0011', 'STOCNs0012', 'STOCNs0013', 'STOCNs0014', 'STOCNs0015', 'STOCNs0016', 'STOCNs0017', 'STOCNs0018', 'STOCNs0019', 'STOCNs0020', 'STOCNs0021', 'STOCNs0022', 'STOCNs0023', 'STOCNs0024', 'STOCNs0025', 'STOCNs0026', 'STOCNs0027', 'STOCNs0028', 'STOCNs0029', 'STOCNs0030', 'STOCNs0031', 'STOCNs0032', 'STOCNs0033', 'STOCNs0034', 'STOCNs0035', 'STOCNs0036', 'STOCNs0037', 'STOCNs0038', 'STOCNs0039', 'STOCNs0040', 'STOCNs0041', 'STOCNs0042', 'STOCNs0043', 'STOCNs0044', 'STOCNs0045', 'STOCNs0046', 'STOCNs0047', 'STOCNs0048', 'STOCNs0049', 'STOCNs0050', 'STOCNs0051', 'STOCNs0052', 'STOCNs0053', 'STOCNs0054', 'STOCNs0055', 'STOCNs0056', 'STOCNs0057', 'STOCNs0058', 'STOCNs0059', 'STOCNs0060', 'STOCNs0061', 'STOCNs0062', 'STOCNs0063', 'STOCNs0064', 'STOCNs0065', 'STOCNs0066', 'STOCNs0067', 'STOCNs0068', 'STOCNs0069', 'STOCNs0070', 'STOCNs0071', 'STOCNs0072', 'STOCNs0073', 'STOCNs0074', 'STOCNs0075', 'STOCNs0076', 'STOCNs0077', 'STOCNs0078', 'STOCNs0079', 'STOCNs0080', 'STOCNs0081', 'STOCNs0082', 'STOCNs0083', 'STOCNs0084', 'STOCNs0085', 'STOCNs0086', 'STOCNs0087', 'STOCNs0088', 'STOCNs0089', 'STOCNs0090', 'STOCNs0091', 'STOCNs0092', 'STOCNs0093', 'STOCNs0094', 'STOCNs0095', 'STOCNs0096', 'STOCNs0097', 'STOCNs0098', 'STOCNs0099', 'STOCNs0100', 'STOCNs0101', 'STOCNs0102', 'STOCNs0103', 'STOCNs0104', 'STOCNs0105', 'STOCNs0106', 'STOCNs0107', 'STOCNs0108', 'STOCNs0109', 'STOCNs0110', 'STOCNs0111', 'STOCNs0112', 'STOCNs0113', 'STOCNs0114', 'STOCNs0115', 'STOCNs0116', 'STOCNs0117', 'STOCNs0118', 'STOCNs0119', 'STOCNs0120', 'STOCNs0121', 'STOCNs0122', 'STOCNs0123', 'STOCNs0124', 'STOCNs0125', 'STOCNs0126', 'STOCNs0127', 'STOCNs0128', 'STOCNs0129', 'STOCNs0130', 'STOCNs0131', 'STOCNs0132', 'STOCNs0133', 'STOCNs0134', 'STOCNs0135', 'STOCNs0136', 'STOCNs0137', 'STOCNs0138', 'STOCNs0139', 'STOCNs0140', 'STOCNs0141', 'STOCNs0142', 'STOCNs0143', 'STOCNs0144', 'STOCNs0145', 'STOCNs0146', 'STOCNs0147', 'STOCNs0148', 'STOCNs0149', 'STOCNs0150', 'STOCNs0151', 'STOCNs0152', 'STOCNs0153', 'STOCNs0154', 'STOCNs0155', 'STOCNs0156', 'STOCNs0157', 'STOCNs0158', 'STOCNs0159', 'STOCNs0160', 'STOCNs0161', 'STOCNs0162', 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'NINEMs0004', 'NINEMs0005', 'NINEMs0006', 'NINEMs0007', 'NINEMs0008', 'NINEMs0009', 'NINEMs0010', 'NINEMs0011', 'NINEMs0012', 'NINEMs0013', 'NINEMs0014', 'NINEMs0015', 'NINEMs0016', 'NINEMs0017', 'NINEMs0018', 'NINEMs0019', 'NINEMs0020', 'NINEMs0021', 'NINEMs0022', 'NINEMs0023', 'NINEMs0024', 'NINEMs0025', 'NINEMs0026', 'NINEMs0027', 'NINEMs0028', 'NINEMs0029', 'NINEMs0030', 'NINEMs0031', 'NINEMs0032', 'NINEMs0033', 'NINEMs0034', 'NINEMs0035', 'NINEMs0036', 'NINEMs0037', 'NINEMs0038', 'NINEMs0039', 'NINEMs0040', 'NINEMs0041', 'NINEMs0042', 'NINEMs0043', 'NINEMs0044', 'NINEMs0045', 'NINEMs0046', 'NINEMs0047', 'NINEMs0048', 'NINEMs0049', 'NINEMs0050', 'NINEMs0051', 'NINEMs0052', 'NINEMs0053', 'NINEMs0054', 'NINEMs0055', 'NINEMs0056', 'NINEMs0057', 'NINEMs0058', 'NINEMs0059', 'NINEMs0060', 'NSHORE_n0001', 'NSHORE_n0002', 'NSHORE_n0003', 'NSHORE_n0004', 'NSHORE_n0005', 'NSHORE_n0006', 'NSHORE_n0007', 'NSHORE_n0008', 'NSHORE_n0009', 'NSHORE_n0010', 'NSHORE_n0011', 'NSHORE_n0012', 'NSHORE_n0013', 'NSHORE_n0014', 'NSHORE_n0015', 'NSHORE_n0016', 'NSHORE_n0017', 'NSHORE_n0018', 'NSHORE_n0019', 'NSHORE_n0020', 'NSHORE_n0021', 'NSHORE_n0022', 'NSHORE_n0023', 'NSHORE_n0024', 'NSHORE_n0025', 'NSHORE_n0026', 'NSHORE_n0027', 'NSHORE_n0028', 'NSHORE_n0029', 'NSHORE_n0030', 'NSHORE_n0031', 'NSHORE_n0032', 'NSHORE_n0033', 'NSHORE_n0034', 'NSHORE_n0035', 'NSHORE_n0036', 'NSHORE_n0037', 'NSHORE_n0038', 'NSHORE_n0039', 'NSHORE_n0040', 'NSHORE_n0041', 'NSHORE_n0042', 'NSHORE_n0043', 'NSHORE_n0044', 'NSHORE_n0045', 'NSHORE_n0046', 'NSHORE_n0047', 'NSHORE_n0048', 'NSHORE_n0049', 'NSHORE_n0050', 'NSHORE_n0051', 'NSHORE_n0052', 'NSHORE_n0053', 'NSHORE_n0054', 'NSHORE_n0055', 'NSHORE_n0056', 'NSHORE_n0057', 'NSHORE_n0058', 'NSHORE_n0059', 'NSHORE_n0060', 'NSHORE_n0061', 'NSHORE_n0062', 'NSHORE_n0063', 'NSHORE_n0064', 'NSHORE_n0065', 'NSHORE_n0066', 'NSHORE_n0067', 'NSHORE_n0068', 'NSHORE_n0069', 'NSHORE_n0070', 'NSHORE_n0071', 'NSHORE_n0072', 'NSHORE_n0073', 'NSHORE_n0074', 'NSHORE_n0075', 'NSHORE_n0076', 'NSHORE_n0077', 'NSHORE_n0078', 'NSHORE_n0079', 'NSHORE_n0080', 'NSHORE_n0081', 'NSHORE_n0082', 'NSHORE_s0001', 'NSHORE_s0002', 'NSHORE_s0003', 'NSHORE_s0004', 'NSHORE_s0005', 'NSHORE_s0006', 'NSHORE_s0007', 'NSHORE_s0008', 'NSHORE_s0009', 'NSHORE_s0010', 'NSHORE_s0011', 'NSHORE_s0012', 'NSHORE_s0013', 'NSHORE_s0014', 'NSHORE_s0015', 'NSHORE_s0016', 'NSHORE_s0017', 'NSHORE_s0018', 'NSHORE_s0019', 'NSHORE_s0020', 'NSHORE_s0021', 'NSHORE_s0022', 'NSHORE_s0023', 'NSHORE_s0024', 'NSHORE_s0025', 'NSHORE_s0026', 'NSHORE_s0027', 'NSHORE_s0028', 'NSHORE_s0029', 'NSHORE_s0030', 'NSHORE_s0031', 'NSHORE_s0032', 'NSHORE_s0033', 'NSHORE_s0034', 'NSHORE_s0035', 'NSHORE_s0036', 'NSHORE_s0037', 'NSHORE_s0038', 'NSHORE_s0039', 'NSHORE_s0040', 'NSHORE_s0041', 'NSHORE_s0042', 'NSHORE_s0043', 'NSHORE_s0044', 'OLDBAR0001', 'OLDBAR0002', 'OLDBAR0003', 'OLDBAR0004', 'OLDBAR0005', 'OLDBAR0006', 'OLDBAR0007', 'OLDBAR0008', 'OLDBAR0009', 'OLDBAR0010', 'OLDBAR0011', 'OLDBAR0012', 'OLDBAR0013', 'OLDBAR0014', 'OLDBAR0015', 'OLDBAR0016', 'OLDBAR0017', 'OLDBAR0018', 'OLDBAR0019', 'OLDBAR0020', 'OLDBAR0021', 'OLDBAR0022', 'OLDBAR0023', 'OLDBAR0024', 'OLDBAR0025', 'OLDBAR0026', 'OLDBAR0027', 'OLDBAR0028', 'OLDBAR0029', 'OLDBAR0030', 'OLDBAR0031', 'OLDBAR0032', 'OLDBAR0033', 'OLDBAR0034', 'OLDBAR0035', 'OLDBAR0036', 'ONEMILE0001', 'ONEMILE0002', 'ONEMILE0003', 'ONEMILE0004', 'ONEMILE0005', 'ONEMILE0006', 'ONEMILE0007', 'ONEMILE0008', 'ONEMILE0009', 'ONEMILE0010', 'ONEMILE0011', 'ONEMILE0012', 'ONEMILE0013', 'PEARLn0001', 'PEARLn0002', 'PEARLn0003', 'PEARLn0004', 'PEARLn0005', 'PEARLs0001', 'PEARLs0002', 'PEARLs0003', 'PEARLs0004', 'PEARLs0005', 'SCOT0001', 'SCOT0002', 'SCOT0003', 'SCOT0004', 'SCOT0005', 'SCOT0006', 'SCOT0007', 'SCOT0008', 'SCOT0009', 'SCOT0010', 'SCOT0011', 'SCOT0012', 'STOCNn0001', 'STOCNn0002', 'STOCNn0003', 'STOCNn0004', 'STOCNn0005', 'STOCNn0006', 'STOCNn0007', 'STOCNn0008', 'STOCNn0009', 'STOCNn0010', 'STOCNn0011', 'STOCNn0012', 'STOCNn0013', 'STOCNn0014', 'STOCNn0015', 'STOCNn0016', 'STOCNn0017', 'STOCNn0018', 'STOCNn0019', 'STOCNn0020', 'STOCNn0021', 'STOCNn0022', 'STOCNn0023', 'STOCNn0024', 'STOCNn0025', 'STOCNn0026', 'STOCNn0027', 'STOCNn0028', 'STOCNn0029', 'STOCNn0030', 'STOCNn0031', 'STOCNn0032', 'STOCNn0033', 'STOCNn0034', 'STOCNn0035', 'STOCNn0036', 'STOCNn0037', 'STOCNn0038', 'STOCNn0039', 'STOCNn0040', 'STOCNn0041', 'STOCNn0042', 'STOCNn0043', 'STOCNn0044', 'STOCNn0045', 'STOCNn0046', 'STOCNn0047', 'STOCNn0048', 'STOCNn0049', 'STOCNn0050', 'STOCNn0051', 'STOCNn0052', 'STOCNn0053', 'STOCNn0054', 'STOCNn0055', 'STOCNn0056', 'STOCNn0057', 'STOCNn0058', 'STOCNn0059', 'STOCNn0060', 'STOCNn0061', 'STOCNn0062', 'STOCNn0063', 'STOCNn0064', 'STOCNn0065', 'STOCNs0001', 'STOCNs0002', 'STOCNs0003', 'STOCNs0004', 'STOCNs0005', 'STOCNs0006', 'STOCNs0007', 'STOCNs0008', 'STOCNs0009', 'STOCNs0010', 'STOCNs0011', 'STOCNs0012', 'STOCNs0013', 'STOCNs0014', 'STOCNs0015', 'STOCNs0016', 'STOCNs0017', 'STOCNs0018', 'STOCNs0019', 'STOCNs0020', 'STOCNs0021', 'STOCNs0022', 'STOCNs0023', 'STOCNs0024', 'STOCNs0025', 'STOCNs0026', 'STOCNs0027', 'STOCNs0028', 'STOCNs0029', 'STOCNs0030', 'STOCNs0031', 'STOCNs0032', 'STOCNs0033', 'STOCNs0034', 'STOCNs0035', 'STOCNs0036', 'STOCNs0037', 'STOCNs0038', 'STOCNs0039', 'STOCNs0040', 'STOCNs0041', 'STOCNs0042', 'STOCNs0043', 'STOCNs0044', 'STOCNs0045', 'STOCNs0046', 'STOCNs0047', 'STOCNs0048', 'STOCNs0049', 'STOCNs0050', 'STOCNs0051', 'STOCNs0052', 'STOCNs0053', 'STOCNs0054', 'STOCNs0055', 'STOCNs0056', 'STOCNs0057', 'STOCNs0058', 'STOCNs0059', 'STOCNs0060', 'STOCNs0061', 'STOCNs0062', 'STOCNs0063', 'STOCNs0064', 'STOCNs0065', 'STOCNs0066', 'STOCNs0067', 'STOCNs0068', 'STOCNs0069', 'STOCNs0070', 'STOCNs0071', 'STOCNs0072', 'STOCNs0073', 'STOCNs0074', 'STOCNs0075', 'STOCNs0076', 'STOCNs0077', 'STOCNs0078', 'STOCNs0079', 'STOCNs0080', 'STOCNs0081', 'STOCNs0082', 'STOCNs0083', 'STOCNs0084', 'STOCNs0085', 'STOCNs0086', 'STOCNs0087', 'STOCNs0088', 'STOCNs0089', 'STOCNs0090', 'STOCNs0091', 'STOCNs0092', 'STOCNs0093', 'STOCNs0094', 'STOCNs0095', 'STOCNs0096', 'STOCNs0097', 'STOCNs0098', 'STOCNs0099', 'STOCNs0100', 'STOCNs0101', 'STOCNs0102', 'STOCNs0103', 'STOCNs0104', 'STOCNs0105', 'STOCNs0106', 'STOCNs0107', 'STOCNs0108', 'STOCNs0109', 'STOCNs0110', 'STOCNs0111', 'STOCNs0112', 'STOCNs0113', 'STOCNs0114', 'STOCNs0115', 'STOCNs0116', 'STOCNs0117', 'STOCNs0118', 'STOCNs0119', 'STOCNs0120', 'STOCNs0121', 'STOCNs0122', 'STOCNs0123', 'STOCNs0124', 'STOCNs0125', 'STOCNs0126', 'STOCNs0127', 'STOCNs0128', 'STOCNs0129', 'STOCNs0130', 'STOCNs0131', 'STOCNs0132', 'STOCNs0133', 'STOCNs0134', 'STOCNs0135', 'STOCNs0136', 'STOCNs0137', 'STOCNs0138', 'STOCNs0139', 'STOCNs0140', 'STOCNs0141', 'STOCNs0142', 'STOCNs0143', 'STOCNs0144', 'STOCNs0145', 'STOCNs0146', 'STOCNs0147', 'STOCNs0148', 'STOCNs0149', 'STOCNs0150', 'STOCNs0151', 'STOCNs0152', 'STOCNs0153', 'STOCNs0154', 'STOCNs0155', 'STOCNs0156', 'STOCNs0157', 'STOCNs0158', 'STOCNs0159', 'STOCNs0160', 'STOCNs0161', 'STOCNs0162', 'STOCNs0163', 'STOCNs0164', 'STOCNs0165', 'STOCNs0166', 'STOCNs0167', 'STOCNs0168', 'STOCNs0169', 'STOCNs0170', 'STOCNs0171', 'STOCNs0172', 'STOCNs0173', 'STOCNs0174', 'STOCNs0175', 'STOCNs0176', 'STOCNs0177', 'STOCNs0178', 'STOCNs0179', 'STOCNs0180', 'STOCNs0181', 'STOCNs0182', 'STOCNs0183', 'STOCNs0184', 'STOCNs0185', 'STOCNs0186', 'STOCNs0187', 'STOCNs0188', 'STOCNs0189', 'STOCNs0190', 'STOCNs0191', 'STOCNs0192', 'STOCNs0193', 'STOCNs0194', 'STOCNs0195', 'STOCNs0196', 'STOCNs0197', 'STOCNs0198', 'STOCNs0199', 'STOCNs0200', 'STOCNs0201', 'STOCNs0202', 'STOCNs0203', 'STOCNs0204', 'STOCNs0205', 'STOCNs0206', 'STOCNs0207', 'STOCNs0208', 'STOCNs0209', 'STOCS0001', 'STOCS0002', 'STOCS0003', 'STOCS0004', 'STOCS0005', 'STOCS0006', 'STOCS0007', 'STOCS0008', 'STOCS0009', 'STOCS0010', 'STOCS0011', 'STOCS0012', 'STOCS0013', 'STOCS0014', 'STOCS0015', 'STOCS0016', 'STOCS0017', 'STOCS0018', 'STOCS0019', 'STOCS0020', 'STOCS0021', 'STOCS0022', 'STOCS0023', 'STOCS0024', 'STOCS0025', 'STOCS0026', 'STOCS0027', 'STOCS0028', 'STOCS0029', 'STOCS0030', 'STOCS0031', 'STOCS0032', 'STOCS0033', 'STOCS0034', 'STOCS0035', 'STOCS0036', 'STOCS0037', 'STOCS0038', 'STOCS0039', 'STOCS0040', 'STOCS0041', 'STOCS0042', 'STOCS0043', 'STOCS0044', 'STOCS0045', 'STOCS0046', 'STUART0001', 'STUART0002', 'STUART0003', 'STUART0004', 'STUART0005', 'STUART0006', 'STUART0007', 'STUART0008', 'STUART0009', 'STUART0010', 'STUART0011', 'STUART0012', 'STUART0013', 'STUART0014', 'STUART0015', 'STUART0016', 'STUART0017', 'STUART0018', 'STUART0019', 'STUART0020', 'STUART0021', 'STUART0022', 'STUART0023', 'STUART0024', 'STUART0025', 'STUART0026', 'STUART0027', 'STUART0028', 'STUART0029', 'STUART0030', 'STUART0031', 'STUART0032', 'STUART0033', 'STUART0034', 'STUART0035', 'STUART0036', 'STUART0037', 'STUART0038', 'STUART0039', 'STUART0040', 'STUART0041', 'STUART0042', 'STUART0043', 'STUART0044', 'STUART0045', 'STUART0046', 'STUART0047', 'STUART0048', 'STUART0049', 'STUART0050', 'STUART0051', 'STUART0052', 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" 'data': [{'line': {'color': 'rgb(51,160,44)', 'width': 2},\n",
" 'name': 'Pre Storm Profile',\n",
" 'type': 'scatter',\n",
- " 'uid': '2618ab16-1e96-4273-8141-bd4b6bdc9c83',\n",
+ " 'uid': 'c8ea5f67-a68e-492f-a718-c6d2b6a1a3ab',\n",
" 'x': [0],\n",
" 'y': [0]},\n",
" {'line': {'color': 'rgb(255,127,0)', 'width': 2},\n",
" 'name': 'Post Storm Profile',\n",
" 'type': 'scatter',\n",
- " 'uid': '3872fe91-a2e2-4762-aa61-c0e01b271cdd',\n",
+ " 'uid': 'ac860cf0-1e87-4a16-b5a1-ef914032c675',\n",
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" 'y': [0]},\n",
" {'marker': {'color': 'rgba(255,255,255,0)', 'line': {'color': 'rgba(106,61,154, 1)', 'width': 2}, 'size': 10},\n",
" 'mode': 'markers',\n",
" 'name': 'Pre-storm dune crest',\n",
" 'type': 'scatter',\n",
- " 'uid': '93c17c28-b6f2-4c12-8a4b-b7683362ece4',\n",
+ " 'uid': '6d629f5f-ecdb-4a38-b5da-9390e53902c4',\n",
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" 'y': [0]},\n",
" {'marker': {'color': 'rgba(255,255,255,0)', 'line': {'color': 'rgba(202,178,214,1)', 'width': 2}, 'size': 10},\n",
" 'mode': 'markers',\n",
" 'name': 'Pre-storm dune toe',\n",
" 'type': 'scatter',\n",
- " 'uid': '9b2cb05f-3f32-4a07-84fb-24e79e745a47',\n",
+ " 'uid': '7caff243-2d24-4b71-9eea-a037a121f7ff',\n",
" 'x': [0],\n",
" 'y': [0]},\n",
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" 'mode': 'lines',\n",
" 'name': 'Peak R_high: foreshore_slope_sto06',\n",
" 'type': 'scatter',\n",
- " 'uid': '1f12592c-7294-4735-8bda-b9bdf30f32c4',\n",
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" 'y': [0]},\n",
" {'line': {'color': 'rgb(127,205,187)', 'width': 4},\n",
" 'mode': 'lines',\n",
" 'name': 'Peak R_high: mean_slope_sto06',\n",
" 'type': 'scatter',\n",
- " 'uid': '711f34f8-0dd7-4d38-9bcc-67636d7ed2a5',\n",
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@@ -233,7 +233,7 @@
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