diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb
index fb37d8d..bb0e806 100644
--- a/notebooks/01_exploration.ipynb
+++ b/notebooks/01_exploration.ipynb
@@ -10,11 +10,11 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 6,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-19T00:22:35.172482Z",
- "start_time": "2018-11-19T00:22:35.000206Z"
+ "end_time": "2018-11-21T05:09:17.883914Z",
+ "start_time": "2018-11-21T05:09:16.981157Z"
}
},
"outputs": [],
@@ -29,11 +29,11 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 7,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-19T00:22:50.594936Z",
- "start_time": "2018-11-19T00:22:35.173486Z"
+ "end_time": "2018-11-21T05:09:17.891936Z",
+ "start_time": "2018-11-21T05:09:17.884916Z"
},
"scrolled": true
},
@@ -49,17 +49,17 @@
"import plotly.graph_objs as go\n",
"import plotly.plotly as py\n",
"\n",
- "from ipywidgets import widgets\n",
+ "from ipywidgets import widgets, Output\n",
"from IPython.display import display, clear_output, Image"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 22,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-19T00:51:58.002082Z",
- "start_time": "2018-11-19T00:51:45.127794Z"
+ "end_time": "2018-11-21T05:26:30.254784Z",
+ "start_time": "2018-11-21T05:26:13.523566Z"
},
"pixiedust": {
"displayParams": {}
@@ -78,491 +78,61 @@
}
],
"source": [
- "data_folder = '../data/interim'\n",
- "df_waves = pd.read_csv(os.path.join(data_folder, 'waves.csv'), index_col=[0,1])\n",
- "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])\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])"
+ "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
+ " return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
+ "\n",
+ "df_waves = df_from_csv('waves.csv', index_col=[0, 1])\n",
+ "df_tides = df_from_csv('tides.csv', index_col=[0, 1])\n",
+ "df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
+ "df_sites = df_from_csv('sites.csv', index_col=[0])\n",
+ "df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
+ "\n",
+ "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
+ "impacts = {\n",
+ " 'forecasted': {\n",
+ " 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
+ " 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
+ " },\n",
+ " 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
+ " }\n",
+ "\n",
+ "\n",
+ "twls = {\n",
+ " 'forecasted': {\n",
+ " 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
+ " 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
+ " }\n",
+ "}"
]
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": null,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-19T01:46:34.068613Z",
- "start_time": "2018-11-19T01:46:34.021932Z"
+ "end_time": "2018-11-21T04:02:25.614132Z",
+ "start_time": "2018-11-21T04:02:25.609119Z"
}
},
- "outputs": [
- {
- "data": {
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- " 0.9038070077671668,\n",
- " 0.9029920317337962,\n",
- " 0.9168944419741138,\n",
- " 0.9060133253634725,\n",
- " 0.9234356237212368,\n",
- " 0.9636884966733456,\n",
- " 0.9755737029064364,\n",
- " 0.9702829562843946,\n",
- " 0.9718671879151456,\n",
- " 0.9828543680939154,\n",
- " 0.983976532687876,\n",
- " 1.0192056294910876,\n",
- " 1.02304570647853,\n",
- " 1.0275918776272523,\n",
- " 1.02350472267319,\n",
- " 1.0363437201913752,\n",
- " 1.0228960581536888,\n",
- " 1.031773040065624,\n",
- " 0.9961028819855686,\n",
- " 0.9799407193704522,\n",
- " 0.9639080227074568,\n",
- " 0.9526042230966292,\n",
- " 0.9513024537542358,\n",
- " 0.9556460082467124,\n",
- " 0.9286465597857544,\n",
- " 0.9110140301695694,\n",
- " 0.8984138010448336,\n",
- " 0.910361202178044]"
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_twl.query(\"site_id=='NARRA0018'\").Hs0.tolist()"
- ]
+ "outputs": [],
+ "source": []
},
{
"cell_type": "code",
- "execution_count": 184,
+ "execution_count": 60,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-19T04:00:27.734848Z",
- "start_time": "2018-11-19T04:00:26.097751Z"
+ "end_time": "2018-11-21T06:07:17.024328Z",
+ "start_time": "2018-11-21T06:07:14.488829Z"
},
- "code_folding": [
- 277
- ],
+ "code_folding": [],
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3790264e7f4a4fb6b5838d18c50957dc",
+ "model_id": "e4413b11f1694a8fb8da0c001aa18344",
"version_major": 2,
"version_minor": 0
},
@@ -582,34 +152,44 @@
"\n"
],
"text/plain": [
- "VBox(children=(VBox(children=(HTML(value='Filter by observed and predicted impacts:'), HBox(children=(SelectMultiple(description='Forecasted Impacts', index=(0, 1, 2), options=('overwash', 'collision', 'swash'), value=('overwash', 'collision', 'swash')), SelectMultiple(description='Observed Impacts', index=(0, 1), options=('swash', 'collision'), value=('swash', 'collision')))))), VBox(children=(HTML(value='Filter by site_id:'), HBox(children=(Dropdown(description='site_id: ', index=943, options=('AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', 'AVOCAn0004', 'AVOCAn0005', 'AVOCAn0006', 'AVOCAn0007', 'AVOCAn0008', 'AVOCAn0009', 'AVOCAs0001', 'AVOCAs0002', 'AVOCAs0003', 'AVOCAs0004', 'AVOCAs0005', 'AVOCAs0006', 'AVOCAs0007', 'AVOCAs0008', 'BILG0001', 'BILG0002', 'BILG0003', 'BILG0004', 'BILG0005', 'BLUEYS0001', 'BLUEYS0002', 'BLUEYS0003', 'BLUEYS0004', 'BLUEYS0005', 'BLUEYS0006', 'BOAT0001', 'BOAT0002', 'BOAT0003', 'BOAT0004', 'BOAT0005', 'BOOM0001', 'BOOM0002', 'BOOM0003', 'BOOM0004', 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'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', 'DUNBn0057', 'DUNBn0058', 'DUNBn0059', 'DUNBn0060', 'DUNBn0061', 'DUNBn0062', 'DUNBn0063', 'DUNBn0064', 'DUNBn0065', 'DUNBn0066', 'DUNBn0067', 'DUNBn0068', 'DUNBn0069', 'DUNBn0070', 'DUNBn0071', 'DUNBn0072', 'DUNBn0073', 'DUNBn0074', 'DUNBs0001', 'DUNBs0002', 'DUNBs0003', 'DUNBs0004', 'DUNBs0005', 'DUNBs0006', 'DUNBs0007', 'DUNBs0008', 'DUNBs0009', 'DUNBs0010', 'DUNBs0011', 'ELIZA0001', 'ELIZA0002', 'ELIZA0003', 'ELIZA0004', 'ELIZA0005', 'ELIZA0006', 'ELIZA0007', 'ENTRA0001', 'ENTRA0002', 'ENTRA0003', 'ENTRA0004', 'ENTRA0005', 'ENTRA0006', 'ENTRA0007', 'ENTRA0008', 'ENTRA0009', 'ENTRA0010', 'ENTRA0011', 'ENTRA0012', 'ENTRA0013', 'ENTRA0014', 'ENTRA0015', 'ENTRA0016', 'ENTRA0017', 'ENTRA0018', 'ENTRA0019', 'ENTRA0020', 'ENTRA0021', 'ENTRA0022', 'ENTRA0023', 'ENTRA0024', 'ENTRA0025', 'ENTRA0026', 'ENTRA0027', 'ENTRA0028', 'ENTRA0029', 'ENTRA0030', 'ENTRA0031', 'ENTRA0032', 'ENTRA0033', 'ENTRA0034', 'ENTRA0035', 'ENTRA0036', 'ENTRA0037', 'ENTRA0038', 'ENTRA0039', 'ENTRA0040', 'ENTRA0041', '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': 'a7ef1527-c36f-4f59-9d62-64928b7b924f',\n",
+ " 'uid': '391027af-be5a-4413-863d-53385a59391d',\n",
" 'x': [0],\n",
" 'y': [0]},\n",
" {'name': 'Post Storm Profile',\n",
" 'type': 'scatter',\n",
- " 'uid': '8f93d4ab-7ef5-4798-b76d-ea742faf88a5',\n",
+ " 'uid': 'a5d7d2e9-d3af-462c-9466-51c2b6f09aa4',\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': 'fd8574f3-f280-4ef6-9792-d1c14df1eb55',\n",
+ " 'uid': '2eba8b17-45a7-4bcd-b988-31bbb487fb1f',\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': '4e6d2022-7ff2-42e6-9334-d76411906808',\n",
+ " 'uid': '022f5c93-fc10-4d1d-ac97-e30b8efd28d1',\n",
+ " 'x': [0],\n",
+ " 'y': [0]},\n",
+ " {'name': 'Peak R_high: foreshore_slope_sto06',\n",
+ " 'type': 'scatter',\n",
+ " 'uid': '0c93ba4e-2213-4db9-9b13-1227d17f5074',\n",
+ " 'x': [0],\n",
+ " 'y': [0]},\n",
+ " {'name': 'Peak R_high: mean_slope_sto06',\n",
+ " 'type': 'scatter',\n",
+ " 'uid': 'dceb5ce7-eb4c-4af6-9a79-cf0b2291e801',\n",
" 'x': [0],\n",
" 'y': [0]}],\n",
" 'layout': {'height': 300,\n",
- " 'legend': {'x': 0.5, 'y': 1},\n",
- " 'margin': {'b': 50, 'l': 20, 'r': 20, 't': 50},\n",
+ " 'legend': {'font': {'size': 10}},\n",
+ " 'margin': {'b': 50, 'l': 50, 'r': 20, 't': 50},\n",
" 'title': 'Bed Profiles',\n",
" 'xaxis': {'autorange': True,\n",
" 'range': [0, 200],\n",
@@ -633,7 +213,7 @@
" '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",
+ "titles = ['Observed Impacts']\n",
+ "selectboxes = [widgets.SelectMultiple(\n",
+ " options=impacts['observed'].storm_regime.dropna().unique().tolist(),\n",
+ " value=impacts['observed'].storm_regime.dropna().unique().tolist(),\n",
+ " disabled=False)]\n",
+ "\n",
+ "# Iterate through each of our forecasted impacts \n",
+ "for forecast in impacts['forecasted']:\n",
+ " selectboxes.append(\n",
+ " widgets.SelectMultiple(\n",
+ " options=impacts['forecasted'][forecast].storm_regime.dropna().unique().tolist(),\n",
+ " value=impacts['forecasted'][forecast].storm_regime.dropna().unique().tolist(),\n",
+ " disabled=False))\n",
+ " titles.append('Forecasted: {}'.format(forecast))\n",
+ "\n",
+ "titles = [widgets.HTML(value=title) for title in titles]\n",
+ " \n",
+ "children = widgets.HBox(children=[widgets.VBox(children=[title, box]) for title, box in zip(titles, selectboxes)])\n",
+ "filter_container = widgets.VBox(children=[filter_title,children])\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",
@@ -788,11 +375,19 @@
" ),\n",
")\n",
"\n",
+ "forecast_traces = []\n",
+ "for forecast in impacts['forecasted']:\n",
+ " forecast_traces.append(go.Scatter(\n",
+ " x = [0],\n",
+ " y = [0],\n",
+ " name = 'Peak R_high: {}'.format(forecast)\n",
+ " ))\n",
+ " \n",
"layout = go.Layout(\n",
" title = 'Bed Profiles',\n",
" height=300,\n",
- " legend=dict(x=0.5, y=1),\n",
- " margin=dict(t=50,b=50,l=20,r=20),\n",
+ " legend=dict(font={'size':10}),\n",
+ " margin=dict(t=50,b=50,l=50,r=20),\n",
" xaxis=dict(\n",
" title = 'x (m)',\n",
" autorange=True,\n",
@@ -811,7 +406,7 @@
" )\n",
")\n",
"\n",
- "g1 = go.FigureWidget(data=[trace1, trace2, trace3, trace4],\n",
+ "g_profiles = go.FigureWidget(data=[trace1, trace2, trace3, trace4]+forecast_traces,\n",
" layout=layout)\n",
"\n",
"\n",
@@ -861,7 +456,7 @@
")\n",
"\n",
"fig = dict(data=data, layout=layout)\n",
- "g2 = go.FigureWidget(data=data,layout=layout)\n",
+ "g_map = go.FigureWidget(data=data,layout=layout)\n",
"\n",
"\n",
"# Add panel for time series\n",
@@ -877,13 +472,18 @@
" 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",
+ "forecast_traces = []\n",
+ "for forecast in impacts['forecasted']:\n",
+ " forecast_traces.append(go.Scatter(\n",
+ " x = [0],\n",
+ " y = [0],\n",
+ " name = 'Beta: {}'.format(forecast),\n",
+ " yaxis='y3'\n",
+ " ))\n",
+ "\n",
+ " \n",
+ "data=[trace_Hs0, trace_Tp] + forecast_traces\n",
"\n",
"layout = go.Layout(\n",
" title = 'Hydro/Morpho Parameters',\n",
@@ -910,27 +510,10 @@
" )\n",
")\n",
"\n",
- "g3 = go.FigureWidget(data=data, layout=layout)\n",
+ "g_params = 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",
@@ -959,7 +542,15 @@
" dash = 'dot')\n",
")\n",
"\n",
- "data=[trace_R_high, trace_R_low, trace_dune_crest, trace_dune_toe,trace_tide]\n",
+ "forecast_traces = []\n",
+ "for forecast in twls['forecasted']:\n",
+ " forecast_traces.append(go.Scatter(\n",
+ " x = [0],\n",
+ " y = [0],\n",
+ " name = 'R_high: {}'.format(forecast),\n",
+ " ))\n",
+ " \n",
+ "data=[trace_dune_crest, trace_dune_toe,trace_tide] + forecast_traces\n",
"\n",
"layout = go.Layout(\n",
" title = 'Water Level & Dune Toe/Crest',\n",
@@ -975,12 +566,17 @@
" ),\n",
")\n",
"\n",
- "g4 = go.FigureWidget(data=data, layout=layout)\n",
+ "g_twls = go.FigureWidget(data=data, layout=layout)\n",
+ "\n",
"\n",
"\n",
"def update_profile(change):\n",
" \n",
" site_id = site_id_select.value\n",
+ " \n",
+ " if site_id is None:\n",
+ " return \n",
+ " \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",
@@ -998,95 +594,128 @@
" 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",
- " g1.data[1].x = poststorm_x\n",
- " g1.data[1].y = poststorm_z\n",
- " g1.data[2].x = dune_crest_x\n",
- " g1.data[2].y = dune_crest_z\n",
- " g1.data[3].x = dune_toe_x\n",
- " g1.data[3].y = dune_toe_z\n",
+ " with g_profiles.batch_update():\n",
+ " g_profiles.data[0].x = prestorm_x\n",
+ " g_profiles.data[0].y = prestorm_z\n",
+ " g_profiles.data[1].x = poststorm_x\n",
+ " g_profiles.data[1].y = poststorm_z\n",
+ " g_profiles.data[2].x = dune_crest_x\n",
+ " g_profiles.data[2].y = dune_crest_z\n",
+ " g_profiles.data[3].x = dune_toe_x\n",
+ " g_profiles.data[3].y = dune_toe_z\n",
+ " \n",
+ " for n, forecast in enumerate(impacts['forecasted']):\n",
+ " R_high = max(impacts['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id)).R_high)\n",
+ " g_profiles.data[4+n].x=[200,400]\n",
+ " g_profiles.data[4+n].y=[R_high, R_high]\n",
" \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",
+ " with g_map.batch_update():\n",
+ " g_map.layout.mapbox['center'] = {\n",
" 'lat': site_coords['lat'].values[0],\n",
" 'lon': site_coords['lon'].values[0]\n",
" }\n",
- " g2.layout.mapbox['zoom'] = 15\n",
- " 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",
+ " g_map.layout.mapbox['zoom'] = 15\n",
+ " g_map.data[1].lat = [site_coords['lat'].values[0]]\n",
+ " g_map.data[1].lon = [site_coords['lon'].values[0]]\n",
+ " g_map.data[1].text = site_coords['lon'].index.get_level_values('site_id').tolist()\n",
"\n",
" # Update time series plots \n",
- " df_timeseries = df_twl.query(\"site_id=='{}'\".format(site_id))\n",
- " times = df_timeseries.index.get_level_values('datetime').tolist()\n",
- " with g3.batch_update():\n",
- " g3.data[0].x = times\n",
- " g3.data[1].x = times\n",
- " g3.data[2].x = times\n",
- " g3.data[0].y = df_timeseries.Hs0.tolist()\n",
- " g3.data[1].y = df_timeseries.Tp.tolist()\n",
- " g3.data[2].y = df_timeseries.beta.tolist()\n",
+ " df_waves_site = df_waves.query(\"site_id=='{}'\".format(site_id))\n",
+ " times = df_waves_site.index.get_level_values('datetime').tolist()\n",
+ " Hs0s = df_waves_site.Hs0.tolist()\n",
+ " Tps = df_waves_site.Tp.tolist()\n",
+ " with g_params.batch_update():\n",
+ " g_params.data[0].x = times\n",
+ " g_params.data[0].y = Hs0s\n",
+ " g_params.data[1].x = times\n",
+ " g_params.data[1].y = Tps\n",
" \n",
+ " for n, forecast in enumerate(twls['forecasted']):\n",
+ " df_twl = twls['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id))\n",
+ " times = df_twl.index.get_level_values('datetime').tolist()\n",
+ " beta = df_twl.beta.tolist()\n",
+ " g_params.data[2+n].x= times\n",
+ " g_params.data[2+n].y= beta\n",
+ "\n",
+ "\n",
" # Update water levels plot\n",
- " df_timeseries = df_twl.query(\"site_id=='{}'\".format(site_id))\n",
- " with g4.batch_update():\n",
- " g4.data[0].x = times\n",
- " g4.data[1].x = times\n",
- " g4.data[2].x = [min(times), max(times)]\n",
- " g4.data[3].x = [min(times), max(times)]\n",
- " g4.data[4].x = times\n",
- " g4.data[0].y = df_timeseries.R_high.tolist()\n",
- " g4.data[1].y = df_timeseries.R_low.tolist()\n",
- " g4.data[2].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[0],\n",
- " g4.data[3].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[0],\n",
- " g4.data[4].y = df_timeseries.tide.tolist()\n",
+ " df_tide_site = df_tides.query(\"site_id=='{}'\".format(site_id))\n",
+ " mask = (df_tide_site.index.get_level_values('datetime') >= min(times)) & (df_tide_site.index.get_level_values('datetime') <= max(times))\n",
+ " df_tide_site = df_tide_site.loc[mask]\n",
+ "\n",
+ " with g_twls.batch_update():\n",
+ " g_twls.data[0].x = [min(times), max(times)]\n",
+ " g_twls.data[1].x = [min(times), max(times)]\n",
+ " g_twls.data[2].x = df_tide_site.index.get_level_values('datetime')\n",
+ " g_twls.data[0].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[0],\n",
+ " g_twls.data[1].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[0],\n",
+ " g_twls.data[2].y = df_tide_site.tide.tolist()\n",
+ " \n",
+ " for n, forecast in enumerate(twls['forecasted']):\n",
+ " df_twl = twls['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id))\n",
+ " times = df_twl.index.get_level_values('datetime').tolist()\n",
+ " R_high = df_twl.R_high.tolist()\n",
+ " g_twls.data[3+n].x= times\n",
+ " g_twls.data[3+n].y= R_high\n",
+ " \n",
" \n",
+ "site_id_select.observe(update_profile, names=\"value\")\n",
+ " \n",
+ " \n",
" \n",
"def update_filter(change):\n",
" \n",
- " # Get filtered impacts\n",
- " observed_impacts = observed_impact_select.value\n",
- " forecasted_impacts = forecasted_impact_select.value\n",
+ " # Iterate through each box, only keeping site_ids which are not filtered out by each box\n",
+ " valid_site_ids = impacts['observed'].index.tolist()\n",
+ " dfs = [impacts['observed']] + [impacts['forecasted'][key] for key in impacts['forecasted']]\n",
" \n",
- " # Get sites with these impacts \n",
- " site_id_select.options = df_impacts_compared.loc[df_impacts_compared.storm_regime_forecasted.isin(forecasted_impacts) & \n",
- " df_impacts_compared.storm_regime_observed.isin(observed_impacts),].index.tolist()\n",
+ " for box, df in zip(selectboxes, dfs):\n",
+ " valid_site_ids = list(set(valid_site_ids).intersection(set(df[df.storm_regime.isin(box.value)].index.tolist())))\n",
+ " site_id_select.options = sorted(valid_site_ids)\n",
+ "\n",
+ " # TODO Update options in selectboxes with number of observations?\n",
" \n",
+ "# Update the filter if any of the boxes changes\n",
+ "for box in selectboxes:\n",
+ " box.observe(update_filter, names=\"value\")\n",
" \n",
- "site_id_select.observe(update_profile, names=\"value\")\n",
- "observed_impact_select.observe(update_filter, names=\"value\")\n",
- "forecasted_impact_select.observe(update_filter, names=\"value\")\n",
+ "# Display our widgets!\n",
+ "widgets.VBox([filter_container,site_id_container,widgets.HBox([g_profiles,g_map]),g_twls,g_params])\n",
+ "\n",
"\n",
- "widgets.VBox([filter_container,site_id_container,widgets.HBox([g1,g2]),g3,g4])"
+ "\n",
+ "# For table\n",
+ "# out = Output()\n",
+ "# with out:\n",
+ "# display(df_waves.head(3))\n",
+ "\n",
+ "# widgets.VBox([filter_container,site_id_container, out])\n"
]
},
{
"cell_type": "code",
- "execution_count": 178,
+ "execution_count": 55,
"metadata": {
"ExecuteTime": {
- "end_time": "2018-11-19T03:23:37.582663Z",
- "start_time": "2018-11-19T03:23:37.577662Z"
+ "end_time": "2018-11-21T06:04:27.672922Z",
+ "start_time": "2018-11-21T06:04:26.098269Z"
}
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "([3.111490610630103, 3.111490610630103],)"
- ]
- },
- "execution_count": 178,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "g4.data[2].y"
+ "# print(max(impacts['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018')).R_high))\n",
+ "# print(max(impacts['forecasted']['mean_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018')).R_high))\n",
+ "\n",
+ "# df_twl = twls['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018'))\n",
+ "\n",
+ "df_waves_site = df_waves.query(\"site_id=='{}'\".format(\"NARRA0016\"))\n",
+ "times = df_waves_site.index.get_level_values('datetime').tolist()\n",
+ "\n",
+ "\n",
+ "df_tides_site = df_tides.query(\"{} <= datetime <= {}\".format(min(times), max(times)))\n",
+ "mask = (df_tides.index.get_level_values('datetime') >= min(times)) & (df_waves_site.index.get_level_values('datetime') <= max(times))"
]
}
],
@@ -1110,7 +739,10 @@
},
"toc": {
"base_numbering": 1,
- "nav_menu": {},
+ "nav_menu": {
+ "height": "47px",
+ "width": "262px"
+ },
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,