diff --git a/README.md b/README.md index 35ed418..fd87bb7 100644 --- a/README.md +++ b/README.md @@ -1,30 +1,56 @@ # 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) of 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. ## Where to start? 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 use to push/pull to, using rclone. In order to get the data, run `make pull-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 +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/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. ## 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/`. \ No newline at end of file +- `/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/`. diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb index a4ee9d2..fb37d8d 100644 --- a/notebooks/01_exploration.ipynb +++ b/notebooks/01_exploration.ipynb @@ -13,8 +13,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2018-11-11T22:32:56.827980Z", - "start_time": "2018-11-11T22:32:56.532764Z" + "end_time": "2018-11-19T00:22:35.172482Z", + "start_time": "2018-11-19T00:22:35.000206Z" } }, "outputs": [], @@ -32,8 +32,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2018-11-11T22:33:22.254342Z", - "start_time": "2018-11-11T22:32:56.828981Z" + "end_time": "2018-11-19T00:22:50.594936Z", + "start_time": "2018-11-19T00:22:35.173486Z" }, "scrolled": true }, @@ -55,11 +55,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2018-11-12T06:21:34.344883Z", - "start_time": "2018-11-12T06:21:23.543798Z" + "end_time": "2018-11-19T00:51:58.002082Z", + "start_time": "2018-11-19T00:51:45.127794Z" }, "pixiedust": { "displayParams": {} @@ -83,24 +83,486 @@ "df_tides = pd.read_csv(os.path.join(data_folder, 'tides.csv'), index_col=[0,1])\n", "df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0,1,2])\n", "df_sites = pd.read_csv(os.path.join(data_folder, 'sites.csv'),index_col=[0])\n", - "df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'),index_col=[0])" + "df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'),index_col=[0])\n", + "df_impacts_compared = pd.read_csv(os.path.join(data_folder,'impacts_observed_vs_forecasted_mean_slope_sto06.csv'),index_col=[0])\n", + "df_twl = pd.read_csv(os.path.join(data_folder,'twl_mean_slope_sto06.csv'),index_col=[0,1])" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 60, "metadata": { "ExecuteTime": { - "end_time": "2018-11-12T06:29:07.451994Z", - "start_time": "2018-11-12T06:29:06.845896Z" + "end_time": "2018-11-19T01:46:34.068613Z", + "start_time": "2018-11-19T01:46:34.021932Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.2632991506198927,\n", + " 1.393768803168096,\n", + " 1.4898137015209056,\n", + " 1.4536075884721669,\n", + " 1.4108238472203196,\n", + " 1.3456902382958191,\n", + " 1.3190526770579034,\n", + " 1.291134623539095,\n", + " 1.2716049325008096,\n", + " 1.234754724771738,\n", + " 1.1825435278076464,\n", + " 1.2252064606390358,\n", + " 1.2640989420800277,\n", + " 1.2757496999030895,\n", + " 1.2958669929936903,\n", + " 1.2951053747668917,\n", + " 1.2997067012745651,\n", + " 1.2927882315939971,\n", + " 1.3309337732401414,\n", + " 1.3193355176891717,\n", + " 1.2844322857877195,\n", + " 1.2628576180893467,\n", + " 1.2097343104491254,\n", + " 1.2013077303201378,\n", + " 1.1993481704538602,\n", + " 1.2198569961855183,\n", + " 1.2984481427280574,\n", + " 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'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', 'STUART0053', 'STUART0054', 'STUART0055', 'STUART0056', 'STUART0057', 'STUART0058', 'STUART0059', 'STUART0060', 'STUART0061', 'STUART0062', 'STUART0063', 'STUART0064', 'STUART0065', 'STUART0066', 'STUART0067', 'STUART0068', 'STUART0069', 'STUART0070', 'STUART0071', 'STUART0072', 'STUART0073', 'STUART0074', 'STUART0075', 'STUART0076', 'STUART0077', 'STUART0078', 'STUART0079', 'STUART0080', 'STUART0081', 'STUART0082', 'STUART0083', 'STUART0084', 'STUART0085', 'STUART0086', 'STUART0087', 'STUART0088', 'STUART0089', 'SWRO0001', 'SWRO0002', 'SWRO0003', 'SWRO0004', 'SWRO0005', 'SWRO0006', 'SWRO0007', 'SWRO0008', 'SWRO0009', 'SWRO0010', 'SWRO0011', 'SWRO0012', 'SWRO0013', 'SWRO0014', 'SWRO0015', 'SWRO0016', 'SWRO0017', 'SWRO0018', 'SWRO0019', 'SWRO0020', 'SWRO0021', 'SWRO0022', 'SWRO0023', 'SWRO0024', 'SWRO0025', 'SWRO0026', 'TREACH0001', 'TREACH0002', 'TREACH0003', 'TREACH0004', 'TREACH0005', 'TREACH0006', 'TREACH0007', 'TREACH0008', 'TREACH0009', 'TREACH0010', 'TREACH0011', 'TREACH0012', 'TREACH0013', 'TREACH0014', 'TREACH0015', 'TREACH0016', 'WAMBE0001', 'WAMBE0002', 'WAMBE0003', 'WAMBE0004', 'WAMBE0005', 'WAMBE0006', 'WAMBE0007', 'WAMBE0008', 'WAMBE0009', 'WAMBE0010', 'WAMBE0011', 'WAMBE0012', 'WAMBE0013', 'WAMBE0014', 'WAMBE0015', 'WAMBE0016', 'WAMBE0017', 'WAMBE0018', 'WAMBE0019', 'WAMBE0020', 'WAMBE0021', 'WAMBE0022', 'WAMBE0023', 'WAMBE0024', 'WAMBE0025', 'WAMBE0026', 'WAMBE0027'), value='NARRA0001'),)))), HBox(children=(FigureWidget({\n", " 'data': [{'name': 'Pre Storm Profile',\n", " 'type': 'scatter',\n", - " 'uid': 'a4b395ae-dd30-4b11-8943-b57902ef3fbc',\n", + " 'uid': 'a7ef1527-c36f-4f59-9d62-64928b7b924f',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'name': 'Post Storm Profile',\n", " 'type': 'scatter',\n", - " 'uid': 'ca7b4abf-cfd0-4527-b57e-7f23ef048d19',\n", + " 'uid': '8f93d4ab-7ef5-4798-b76d-ea742faf88a5',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'marker': {'color': 'rgb(17, 157, 255)', 'size': 20},\n", " 'mode': 'markers',\n", " 'name': 'Pre-storm dune crest',\n", " 'type': 'scatter',\n", - " 'uid': 'f14b6086-8986-43e0-bfb5-d4a9d08735c9',\n", + " 'uid': 'fd8574f3-f280-4ef6-9792-d1c14df1eb55',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'marker': {'color': 'rgb(231, 99, 250)', 'size': 20},\n", " 'mode': 'markers',\n", " 'name': 'Pre-storm dune toe',\n", " 'type': 'scatter',\n", - " 'uid': 'fbc7b977-16fd-453e-b7a5-67a4337ca75c',\n", + " 'uid': '4e6d2022-7ff2-42e6-9334-d76411906808',\n", " 'x': [0],\n", " 'y': [0]}],\n", - " 'layout': {'legend': {'x': 0, 'y': 1},\n", + " 'layout': {'height': 300,\n", + " 'legend': {'x': 0.5, 'y': 1},\n", " 'margin': {'b': 50, 'l': 20, 'r': 20, 't': 50},\n", " 'title': 'Bed Profiles',\n", " 'xaxis': {'autorange': True,\n", @@ -170,7 +633,7 @@ " 'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n", " 'WAMBE0027'], dtype='Filter by observed and predicted impacts:\",\n", + ")\n", + "\n", + "observed_impact_select = widgets.SelectMultiple(\n", + " options=df_impacts_compared.storm_regime_observed.dropna().unique(),\n", + " value=df_impacts_compared.storm_regime_observed.dropna().unique().tolist(),\n", + " description='Observed Impacts',\n", + " disabled=False\n", + ")\n", + "\n", + "forecasted_impact_select = widgets.SelectMultiple(\n", + " options=df_impacts_compared.storm_regime_forecasted.dropna().unique(),\n", + " value=df_impacts_compared.storm_regime_forecasted.dropna().unique().tolist(),\n", + " description='Forecasted Impacts',\n", + " disabled=False\n", + ")\n", + "\n", + "filter_container = widgets.VBox(children=[filter_title,widgets.HBox(children=[forecasted_impact_select,observed_impact_select])])\n", + "\n", + "\n", + "# Create widgets for selecting site_id\n", + "\n", + "site_id_title = widgets.HTML(\n", + " value=\"Filter by site_id:\",\n", + ")\n", + "\n", + "site_id_select = widgets.Dropdown(\n", " description='site_id: ',\n", " value='NARRA0001',\n", " options=df_profiles.index.get_level_values('site_id').unique().sort_values().tolist()\n", ")\n", - "container = widgets.HBox(children=[textbox])\n", + "site_id_container = widgets.VBox(children=[site_id_title,widgets.HBox(children=[site_id_select])])\n", + "\n", "\n", "\n", "# Add panel for pre/post storm profiles\n", @@ -240,7 +790,8 @@ "\n", "layout = go.Layout(\n", " title = 'Bed Profiles',\n", - " legend=dict(x=0, y=1),\n", + " height=300,\n", + " legend=dict(x=0.5, y=1),\n", " margin=dict(t=50,b=50,l=20,r=20),\n", " xaxis=dict(\n", " title = 'x (m)',\n", @@ -292,6 +843,7 @@ "\n", "layout = go.Layout(\n", " autosize=True,\n", + " height=300,\n", " hovermode='closest',\n", " showlegend=False,\n", " margin=dict(t=50,b=50,l=20,r=20),\n", @@ -309,13 +861,126 @@ ")\n", "\n", "fig = dict(data=data, layout=layout)\n", + "g2 = go.FigureWidget(data=data,layout=layout)\n", + "\n", + "\n", + "# Add panel for time series\n", + "\n", + "trace_Hs0 = go.Scatter(\n", + " x = [0,1],\n", + " y = [0,1],\n", + " name='Hs0'\n", + ")\n", + "trace_Tp = go.Scatter(\n", + " x = [0,2],\n", + " y = [0,2],\n", + " name='Tp',\n", + " yaxis='y2'\n", + ")\n", + "trace_beta = go.Scatter(\n", + " x = [0,3],\n", + " y = [0,3],\n", + " name='beta',\n", + " yaxis='y3'\n", + ")\n", + "data=[trace_Hs0, trace_Tp, trace_beta]\n", + "\n", + "layout = go.Layout(\n", + " title = 'Hydro/Morpho Parameters',\n", + " height=200,\n", + " margin=dict(t=50,b=50,l=50,r=50),\n", + " xaxis=dict(\n", + " title='time',\n", + " domain=[0.0, 0.9],\n", + " zeroline=False,\n", + " ),\n", + " yaxis=dict(\n", + " title = 'Hs0 (m)',\n", + " ),\n", + " yaxis2=dict(\n", + " title='Tp (s)',\n", + " overlaying='y',\n", + " side='right'\n", + " ),\n", + " yaxis3=dict(\n", + " title='beta (-)',\n", + " overlaying='y',\n", + " side='right',\n", + " position=0.97\n", + " )\n", + ")\n", + "\n", + "g3 = go.FigureWidget(data=data, layout=layout)\n", + "\n", + "\n", + "# Add panel for water level\n", + "\n", + "trace_R_high = go.Scatter(\n", + " x = [0,1],\n", + " y = [0,1],\n", + " name='R High',\n", + " line = dict(\n", + " color = ('rgb(91,220,229)'),\n", + " width = 2)\n", + ")\n", + "trace_R_low = go.Scatter(\n", + " x = [0,2],\n", + " y = [0,2],\n", + " name='R Low',\n", + " line = dict(\n", + " color = ('rgb(13,174,186)'),\n", + " width = 2)\n", + ")\n", + "trace_dune_crest = go.Scatter(\n", + " x = [0,3],\n", + " y = [0,3],\n", + " name='Dune Crest',\n", + " line = dict(\n", + " color = ('rgb(214, 117, 14)'),\n", + " width = 2,\n", + " dash = 'dot')\n", + ")\n", + "trace_dune_toe = go.Scatter(\n", + " x = [0,3],\n", + " y = [0,3],\n", + " name='Dune Toe',\n", + " line = dict(\n", + " color = ('rgb(142, 77, 8)'),\n", + " width = 2,\n", + " dash = 'dash')\n", + ")\n", + "trace_tide = go.Scatter(\n", + " x = [0,4],\n", + " y = [0,4],\n", + " name='Tide+Surge WL',\n", + " line = dict(\n", + " color = ('rgb(8,51,137)'),\n", + " width = 2,\n", + " dash = 'dot')\n", + ")\n", + "\n", + "data=[trace_R_high, trace_R_low, trace_dune_crest, trace_dune_toe,trace_tide]\n", + "\n", + "layout = go.Layout(\n", + " title = 'Water Level & Dune Toe/Crest',\n", + " height=200,\n", + " margin=dict(t=50,b=50,l=50,r=50),\n", + " xaxis=dict(\n", + " title='time',\n", + " domain=[0.0, 0.95],\n", + " zeroline=False,\n", + " ),\n", + " yaxis=dict(\n", + " title = 'Water Level (m)',\n", + " ),\n", + ")\n", + "\n", + "g4 = go.FigureWidget(data=data, layout=layout)\n", "\n", - "g2 = go.FigureWidget(data=data,\n", - " layout=layout)\n", "\n", - "def response(change):\n", + "def update_profile(change):\n", " \n", - " site_id = textbox.value\n", + " site_id = site_id_select.value\n", " site_profile = df_profiles.query('site_id == \"{}\"'.format(site_id))\n", " prestorm_profile = site_profile.query('profile_type == \"prestorm\"')\n", " poststorm_profile = site_profile.query('profile_type == \"poststorm\"')\n", @@ -332,6 +997,7 @@ " dune_toe_x = site_features.dune_toe_x\n", " dune_toe_z = site_features.dune_toe_z\n", " \n", + " # Update beach profile section plots\n", " with g1.batch_update():\n", " g1.data[0].x = prestorm_x\n", " g1.data[0].y = prestorm_z\n", @@ -342,7 +1008,7 @@ " g1.data[3].x = dune_toe_x\n", " g1.data[3].y = dune_toe_z\n", " \n", - " # Update \n", + " # Relocate plan of satellite imagery\n", " site_coords = df_sites.query('site_id == \"{}\"'.format(site_id))\n", " with g2.batch_update():\n", " g2.layout.mapbox['center'] = {\n", @@ -353,896 +1019,74 @@ " g2.data[1].lat = [site_coords['lat'].values[0]]\n", " g2.data[1].lon = [site_coords['lon'].values[0]]\n", " g2.data[1].text = site_coords['lon'].index.get_level_values('site_id').tolist()\n", - " \n", - "textbox.observe(response, names=\"value\")\n", - "widgets.VBox([container,widgets.HBox([g1,g2])])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2018-11-11T22:35:30.219167Z", - "start_time": "2018-11-11T22:35:30.045086Z" - } - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " fig.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('