From e7d6aa8761ee80c89fbdbe33ff54bcda4ff1fbe3 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Thu, 15 Nov 2018 12:40:34 +1100 Subject: [PATCH 1/7] Replace zeros with nans in beach profile In raw beach profile data, the end of some cross-sections can be padded with zero values. This probably shouldn't be the case - if we wanted to assume a zero elevation at these locations, we should do that in whatever calculation, not in the raw data. The added function will detect these padded zero values and replace them with nans. --- src/data/mat_to_csv.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/src/data/mat_to_csv.py b/src/data/mat_to_csv.py index d4dbbe2..0033ea9 100644 --- a/src/data/mat_to_csv.py +++ b/src/data/mat_to_csv.py @@ -7,6 +7,7 @@ from datetime import datetime, timedelta import pandas as pd from mat4py import loadmat +import numpy as np logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False) logger = logging.getLogger(__name__) @@ -152,6 +153,25 @@ def parse_profiles(profiles_mat): df = pd.DataFrame(rows) return df +def remove_zeros(df_profiles): + """ + When parsing the pre/post storm profiles, the end of some profiles have constant values of zero. Let's change + these to NaNs for consistancy. Didn't use pandas fillnan because 0 may still be a valid value. + :param df: + :return: + """ + + df_profiles = df_profiles.sort_index() + groups = df_profiles.groupby(level=['site_id','profile_type']) + for key, _ in groups: + logger.debug('Removing zeros from {} profile at {}'.format(key[1], key[0])) + idx_site = (df_profiles.index.get_level_values('site_id') == key[0]) & \ + (df_profiles.index.get_level_values('profile_type') == key[1]) + df_profile = df_profiles[idx_site] + x_last_ele = df_profile[df_profile.z!=0].index.get_level_values('x')[-1] + df_profiles.loc[idx_site & (df_profiles.index.get_level_values('x')>x_last_ele), 'z'] = np.nan + + return df_profiles def matlab_datenum_to_datetime(matlab_datenum): # https://stackoverflow.com/a/13965852 @@ -228,6 +248,9 @@ def main(): df_tides.set_index(['site_id', 'datetime'], inplace=True) df_sites.set_index(['site_id'], inplace=True) + logger.info('Nanning profile zero elevations') + df_profiles = remove_zeros(df_profiles) + logger.info('Outputting .csv files') df_profiles.to_csv('./data/interim/profiles.csv') df_tides.to_csv('./data/interim/tides.csv') From 1b521a052424886bf87a236a279b47639275ac09 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Thu, 15 Nov 2018 12:40:58 +1100 Subject: [PATCH 2/7] Add module for parsing observed storm impacts --- src/analysis/observed_storm_impacts.py | 138 +++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 src/analysis/observed_storm_impacts.py diff --git a/src/analysis/observed_storm_impacts.py b/src/analysis/observed_storm_impacts.py new file mode 100644 index 0000000..1b501ed --- /dev/null +++ b/src/analysis/observed_storm_impacts.py @@ -0,0 +1,138 @@ +import logging.config +import os + +import numpy as np +import pandas as pd +from scipy.integrate import simps + +logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False) +logger = logging.getLogger(__name__) + + +def return_first_or_nan(l): + """ + Returns the first value of a list if empty or returns nan. Used for getting dune/toe and crest values. + :param l: + :return: + """ + if len(l) == 0: + return np.nan + else: + return l[0] + + +def volume_change(df_profiles, df_profile_features, zone): + logger.info('Calculating change in beach volume in {} zone'.format(zone)) + + df_vol_changes = pd.DataFrame(index=df_profile_features.index) + df_profiles = df_profiles.sort_index() + sites = df_profiles.groupby(level=['site_id']) + + for site_id, df_site in sites: + logger.debug('Calculating change in beach volume at {} in {} zone'.format(site_id, zone)) + prestorm_dune_toe_x = df_profile_features.loc[df_profile_features.index == site_id].dune_toe_x.tolist() + prestorm_dune_crest_x = df_profile_features.loc[df_profile_features.index == site_id].dune_crest_x.tolist() + + # We may not have a dune toe or crest defined, or there may be multiple defined. + prestorm_dune_crest_x = return_first_or_nan(prestorm_dune_crest_x) + prestorm_dune_toe_x = return_first_or_nan(prestorm_dune_toe_x) + + # If no dune to has been defined, Dlow = Dhigh. Refer to Sallenger (2000). + if np.isnan(prestorm_dune_toe_x): + prestorm_dune_toe_x = prestorm_dune_crest_x + + # Find last x coordinate where we have both prestorm and poststorm measurements. If we don't do this, + # the prestorm and poststorm values are going to be calculated over different lengths. + df_zone = df_site.dropna(subset=['z']) + x_last_obs = min([max(df_zone.query("profile_type == '{}'".format(profile_type)).index.get_level_values('x')) + for profile_type in ['prestorm', 'poststorm']]) + + # Where we want to measure pre and post storm volume is dependant on the zone selected + if zone == 'swash': + x_min = prestorm_dune_toe_x + x_max = x_last_obs + elif zone == 'dune_face': + x_min = prestorm_dune_crest_x + x_max = prestorm_dune_toe_x + else: + logger.warning('Zone argument not properly specified. Please check') + x_min = None + x_max = None + + # Now, compute the volume of sand between the x-coordinates prestorm_dune_toe_x and x_swash_last for both prestorm + # and post storm profiles. + prestorm_vol = beach_volume(x=df_zone.query("profile_type=='prestorm'").index.get_level_values('x'), + z=df_zone.query("profile_type=='prestorm'").z, + x_min=x_min, + x_max=x_max) + poststorm_vol = beach_volume(x=df_zone.query("profile_type=='poststorm'").index.get_level_values('x'), + z=df_zone.query("profile_type=='poststorm'").z, + x_min=x_min, + x_max=x_max) + + df_vol_changes.loc[site_id, 'prestorm_{}_vol'.format(zone)] = prestorm_vol + df_vol_changes.loc[site_id, 'poststorm_{}_vol'.format(zone)] = poststorm_vol + df_vol_changes.loc[site_id, '{}_vol_change'.format(zone)] = prestorm_vol - poststorm_vol + + return df_vol_changes + + +def beach_volume(x, z, x_min=np.NINF, x_max=np.inf): + """ + Returns the beach volume of a profile, calculated with Simpsons rule + :param x: x-coordinates of beach profile + :param z: z-coordinates of beach profile + :param x_min: Minimum x-coordinate to consider when calculating volume + :param x_max: Maximum x-coordinate to consider when calculating volume + :return: + """ + profile_mask = [True if x_min < x_coord < x_max else False for x_coord in x] + x_masked = np.array(x)[profile_mask] + z_masked = np.array(z)[profile_mask] + + if len(x_masked) == 0 or len(z_masked) == 0: + return np.nan + else: + return simps(z_masked, x_masked) + + +def impacts_from_profiles(df_profiles, df_profile_features): + # Impacts should be per site, so use the profile_features as the base index. + df_observed_impacts = pd.DataFrame(index=df_profile_features.index) + + # Swash zone volume change + prestorm_swash_vol, poststorm_swash_vol = volume_change(df_profiles, df_profile_features, zone='swash') + + # Dune volume change + + # If no dune volume change, then swash zone + + # + pass + + +def storm_regime(df_observed_impacts): + logger.info('Getting observed storm regimes') + df_observed_impacts.loc[df_observed_impacts.swash_vol_change < 3,'storm_regime'] = 'swash' + df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change > 3, 'storm_regime'] = 'collision' + return df_observed_impacts + +if __name__ == '__main__': + logger.info('Importing existing data') + data_folder = './data/interim' + df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2]) + df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'), index_col=[0]) + + logger.info('Creating new dataframe for observed impacts') + df_observed_impacts = pd.DataFrame(index=df_profile_features.index) + + logger.info('Getting pre/post storm volumes') + df_swash_vol_changes = volume_change(df_profiles, df_profile_features, zone='swash') + df_dune_face_vol_changes = volume_change(df_profiles, df_profile_features, zone='dune_face') + df_observed_impacts = df_observed_impacts.join([df_swash_vol_changes, df_dune_face_vol_changes ]) + + # TODO Classify regime based on volume changes + df_observed_impacts = storm_regime(df_observed_impacts) + + # TODO Save dataframe to csv + df_observed_impacts.to_csv(os.path.join(data_folder, 'impacts_observed.csv')) From d05ded9c4476cf093961de38992218429c04c16e Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Thu, 15 Nov 2018 12:48:59 +1100 Subject: [PATCH 3/7] Fix line breaks and add information about gitflow model --- README.md | 52 +++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 39 insertions(+), 13 deletions(-) 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/`. From 36bbb8390f29d307fa77412cd51a4d2847e263d5 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Mon, 19 Nov 2018 15:14:28 +1100 Subject: [PATCH 4/7] Add module to compare forecasted and observed storm impacts --- src/analysis/compare_impacts.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 src/analysis/compare_impacts.py diff --git a/src/analysis/compare_impacts.py b/src/analysis/compare_impacts.py new file mode 100644 index 0000000..a74e8c5 --- /dev/null +++ b/src/analysis/compare_impacts.py @@ -0,0 +1,32 @@ +""" +Compares forecasted and observed impacts, putting them into one data frame and exporting the results. +""" + +import logging.config +import os + +import pandas as pd + +logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False) +logger = logging.getLogger(__name__) + + +def compare_impacts(df_forecasted, df_observed): + """ + Merge forecasted and observed storm impacts + :param df_forecasted: + :param df_observed: + :return: + """ + df_compared = df_forecasted.merge(df_observed, left_index=True, right_index=True, + suffixes=['_forecasted', '_observed']) + return df_compared + + +if __name__ == '__main__': + logger.info('Importing existing data') + data_folder = './data/interim' + df_forecasted = pd.read_csv(os.path.join(data_folder, 'impacts_forecasted_mean_slope_sto06.csv'), index_col=[0]) + df_observed = pd.read_csv(os.path.join(data_folder, 'impacts_observed.csv'), index_col=[0]) + df_compared = compare_impacts(df_forecasted, df_observed) + df_compared.to_csv(os.path.join(data_folder, 'impacts_observed_vs_forecasted_mean_slope_sto06.csv')) From 995b01172fe5c06fbafc37b061f17d7da0cae10d Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Mon, 19 Nov 2018 15:38:23 +1100 Subject: [PATCH 5/7] Add function to extract the forecasted storm impacts --- src/analysis/forecasted_storm_impacts.py | 73 ++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 src/analysis/forecasted_storm_impacts.py diff --git a/src/analysis/forecasted_storm_impacts.py b/src/analysis/forecasted_storm_impacts.py new file mode 100644 index 0000000..edeb8ff --- /dev/null +++ b/src/analysis/forecasted_storm_impacts.py @@ -0,0 +1,73 @@ +""" +Estimates the forecasted storm impacts based on the forecasted water level and dune crest/toe. +""" + +import logging.config +import os + +import pandas as pd + +logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False) +logger = logging.getLogger(__name__) + + +def forecasted_impacts(df_profile_features, df_forecasted_twl): + """ + Combines our profile features (containing dune toes and crests) with water levels, to get the forecasted storm + impacts. + :param df_profile_features: + :param df_forecasted_twl: + :return: + """ + logger.info('Getting forecasted storm regimes') + + df_forecasted_impacts = pd.DataFrame(index=df_profile_features.index) + + # For each site, find the maximum R_high value and the corresponding R_low value. + idx = df_forecasted_twl.groupby(level=['site_id'])['R_high'].idxmax().dropna() + df_r_vals = df_forecasted_twl.loc[idx, ['R_high', 'R_low']].reset_index(['datetime']) + df_forecasted_impacts = df_forecasted_impacts.merge(df_r_vals, how='left', left_index=True, right_index=True) + + # Join with df_profile features to find dune toe and crest elevations + df_forecasted_impacts = df_forecasted_impacts.merge(df_profile_features[['dune_toe_z', 'dune_crest_z']], + how='left', + left_index=True, + right_index=True) + + # Compare R_high and R_low wirth dune crest and toe elevations + df_forecasted_impacts = storm_regime(df_forecasted_impacts) + + return df_forecasted_impacts + + +def storm_regime(df_forecasted_impacts): + """ + Returns the dataframe with an additional column of storm impacts based on the Storm Impact Scale. Refer to + Sallenger (2000) for details. + :param df_forecasted_impacts: + :return: + """ + logger.info('Getting forecasted storm regimes') + df_forecasted_impacts.loc[ + df_forecasted_impacts.R_high <= df_forecasted_impacts.dune_toe_z, 'storm_regime'] = 'swash' + df_forecasted_impacts.loc[ + df_forecasted_impacts.dune_toe_z <= df_forecasted_impacts.R_high, 'storm_regime'] = 'collision' + df_forecasted_impacts.loc[(df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high) & + (df_forecasted_impacts.R_low <= df_forecasted_impacts.dune_crest_z), + 'storm_regime'] = 'overwash' + df_forecasted_impacts.loc[(df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_low) & + (df_forecasted_impacts.dune_crest_z <= df_forecasted_impacts.R_high), + 'storm_regime'] = 'inundation' + + return df_forecasted_impacts + + +if __name__ == '__main__': + logger.info('Importing existing data') + data_folder = './data/interim' + df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2]) + df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'), index_col=[0]) + df_forecasted_twl = pd.read_csv(os.path.join(data_folder, 'twl_mean_slope_sto06.csv'), index_col=[0, 1]) + + df_forecasted_impacts = forecasted_impacts(df_profile_features, df_forecasted_twl) + df_forecasted_impacts.to_csv(os.path.join(data_folder, 'impacts_forecasted_mean_slope_sto06.csv')) From 6c7f17cebe134f81a26a342507c58e4c4ba6ee45 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Mon, 19 Nov 2018 15:40:43 +1100 Subject: [PATCH 6/7] Tidy up observed storm impacts --- src/analysis/observed_storm_impacts.py | 37 +++++++++++++------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/src/analysis/observed_storm_impacts.py b/src/analysis/observed_storm_impacts.py index 1b501ed..be48505 100644 --- a/src/analysis/observed_storm_impacts.py +++ b/src/analysis/observed_storm_impacts.py @@ -22,6 +22,13 @@ def return_first_or_nan(l): def volume_change(df_profiles, df_profile_features, zone): + """ + Calculates how much the volume change there is between prestrom and post storm profiles. + :param df_profiles: + :param df_profile_features: + :param zone: Either 'swash' or 'dune_face' + :return: + """ logger.info('Calculating change in beach volume in {} zone'.format(zone)) df_vol_changes = pd.DataFrame(index=df_profile_features.index) @@ -96,27 +103,19 @@ def beach_volume(x, z, x_min=np.NINF, x_max=np.inf): return simps(z_masked, x_masked) -def impacts_from_profiles(df_profiles, df_profile_features): - # Impacts should be per site, so use the profile_features as the base index. - df_observed_impacts = pd.DataFrame(index=df_profile_features.index) - - # Swash zone volume change - prestorm_swash_vol, poststorm_swash_vol = volume_change(df_profiles, df_profile_features, zone='swash') - - # Dune volume change - - # If no dune volume change, then swash zone - - # - pass - - def storm_regime(df_observed_impacts): + """ + Returns the dataframe with an additional column of storm impacts based on the Storm Impact Scale. Refer to + Sallenger (2000) for details. + :param df_observed_impacts: + :return: + """ logger.info('Getting observed storm regimes') - df_observed_impacts.loc[df_observed_impacts.swash_vol_change < 3,'storm_regime'] = 'swash' + df_observed_impacts.loc[df_observed_impacts.swash_vol_change < 3, 'storm_regime'] = 'swash' df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change > 3, 'storm_regime'] = 'collision' return df_observed_impacts + if __name__ == '__main__': logger.info('Importing existing data') data_folder = './data/interim' @@ -129,10 +128,10 @@ if __name__ == '__main__': logger.info('Getting pre/post storm volumes') df_swash_vol_changes = volume_change(df_profiles, df_profile_features, zone='swash') df_dune_face_vol_changes = volume_change(df_profiles, df_profile_features, zone='dune_face') - df_observed_impacts = df_observed_impacts.join([df_swash_vol_changes, df_dune_face_vol_changes ]) + df_observed_impacts = df_observed_impacts.join([df_swash_vol_changes, df_dune_face_vol_changes]) - # TODO Classify regime based on volume changes + # Classify regime based on volume changes df_observed_impacts = storm_regime(df_observed_impacts) - # TODO Save dataframe to csv + # Save dataframe to csv df_observed_impacts.to_csv(os.path.join(data_folder, 'impacts_observed.csv')) From a3b07f20e862947321b8f85daee93b58a19df013 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Mon, 19 Nov 2018 15:41:04 +1100 Subject: [PATCH 7/7] Add additional widgets for exploring data --- notebooks/01_exploration.ipynb | 1640 +++++++++++++++----------------- 1 file changed, 742 insertions(+), 898 deletions(-) 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", + " 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+ ], + "source": [ + "df_twl.query(\"site_id=='NARRA0018'\").Hs0.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "ExecuteTime": { + "end_time": "2018-11-19T04:00:27.734848Z", + "start_time": "2018-11-19T04:00:26.097751Z" }, + "code_folding": [ + 277 + ], "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d5226c9774bd4353aac2cc8f5732b182", + "model_id": "3790264e7f4a4fb6b5838d18c50957dc", "version_major": 2, "version_minor": 0 }, @@ -120,32 +582,33 @@ "

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'DIAMONDn0032', 'DIAMONDn0033', 'DIAMONDn0034', 'DIAMONDn0035', 'DIAMONDn0036', 'DIAMONDn0037', 'DIAMONDn0038', 'DIAMONDn0039', 'DIAMONDn0040', 'DIAMONDn0041', 'DIAMONDs0001', 'DIAMONDs0002', 'DIAMONDs0003', 'DIAMONDs0004', 'DIAMONDs0005', 'DIAMONDs0006', 'DIAMONDs0007', 'DUNBn0001', 'DUNBn0002', 'DUNBn0003', 'DUNBn0004', 'DUNBn0005', 'DUNBn0006', 'DUNBn0007', 'DUNBn0008', 'DUNBn0009', 'DUNBn0010', 'DUNBn0011', 'DUNBn0012', 'DUNBn0013', 'DUNBn0014', 'DUNBn0015', 'DUNBn0016', 'DUNBn0017', 'DUNBn0018', 'DUNBn0019', 'DUNBn0020', 'DUNBn0021', 'DUNBn0022', 'DUNBn0023', 'DUNBn0024', 'DUNBn0025', 'DUNBn0026', 'DUNBn0027', 'DUNBn0028', 'DUNBn0029', 'DUNBn0030', 'DUNBn0031', 'DUNBn0032', 'DUNBn0033', 'DUNBn0034', 'DUNBn0035', 'DUNBn0036', 'DUNBn0037', 'DUNBn0038', 'DUNBn0039', 'DUNBn0040', 'DUNBn0041', 'DUNBn0042', 'DUNBn0043', 'DUNBn0044', 'DUNBn0045', 'DUNBn0046', 'DUNBn0047', 'DUNBn0048', 'DUNBn0049', 'DUNBn0050', 'DUNBn0051', 'DUNBn0052', 'DUNBn0053', 'DUNBn0054', 'DUNBn0055', 'DUNBn0056', 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'ENTRA0042', 'ENTRA0043', 'ENTRA0044', 'ENTRA0045', 'ENTRA0046', 'ENTRA0047', 'ENTRA0048', 'ENTRA0049', 'ENTRA0050', 'ENTRA0051', 'ENTRA0052', 'ENTRA0053', 'ENTRA0054', 'ENTRA0055', 'ENTRA0056', 'ENTRA0057', 'ENTRA0058', 'ENTRA0059', 'ENTRA0060', 'ENTRA0061', 'ENTRA0062', 'ENTRA0063', 'ENTRA0064', 'ENTRA0065', 'ENTRA0066', 'ENTRA0067', 'ENTRA0068', 'ENTRA0069', 'ENTRA0070', 'ENTRA0071', 'ENTRA0072', 'ENTRA0073', 'ENTRA0074', 'ENTRA0075', 'ENTRA0076', 'ENTRA0077', 'ENTRA0078', 'ENTRA0079', 'FOST0001', 'FOST0002', 'FOST0003', 'FOST0004', 'FOST0005', 'FOST0006', 'GRANTSn0001', 'GRANTSn0002', 'GRANTSn0003', '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', '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 = $('