From 131b3d814c4dd21fcd603d642b24c5b21a791f79 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Thu, 22 Nov 2018 12:27:35 +1100 Subject: [PATCH] Improve site_id widgets for data exploration --- notebooks/01_exploration.ipynb | 1097 +++++++++++++++++++++++++------- 1 file changed, 860 insertions(+), 237 deletions(-) diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb index bb0e806..bc63cf3 100644 --- a/notebooks/01_exploration.ipynb +++ b/notebooks/01_exploration.ipynb @@ -29,11 +29,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 61, "metadata": { "ExecuteTime": { - "end_time": "2018-11-21T05:09:17.891936Z", - "start_time": "2018-11-21T05:09:17.884916Z" + "end_time": "2018-11-21T22:54:03.111350Z", + "start_time": "2018-11-21T22:54:03.091796Z" }, "scrolled": true }, @@ -48,6 +48,7 @@ "import plotly\n", "import plotly.graph_objs as go\n", "import plotly.plotly as py\n", + "import plotly.tools as tls\n", "\n", "from ipywidgets import widgets, Output\n", "from IPython.display import display, clear_output, Image" @@ -119,20 +120,23 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 157, "metadata": { "ExecuteTime": { - "end_time": "2018-11-21T06:07:17.024328Z", - "start_time": "2018-11-21T06:07:14.488829Z" + "end_time": "2018-11-22T01:22:23.730687Z", + "start_time": "2018-11-22T01:22:19.346920Z" }, - "code_folding": [], + "code_folding": [ + 335, + 430 + ], "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e4413b11f1694a8fb8da0c001aa18344", + "model_id": "237c6a47bf2d418eb98f0ada3a9b00a2", "version_major": 2, "version_minor": 0 }, @@ -155,36 +159,36 @@ "VBox(children=(VBox(children=(HTML(value='Filter by observed and predicted impacts:'), HBox(children=(VBox(children=(HTML(value='Observed Impacts'), SelectMultiple(index=(0, 1), options=('swash', 'collision'), value=('swash', 'collision')))), VBox(children=(HTML(value='Forecasted: foreshore_slope_sto06'), SelectMultiple(index=(0, 1, 2), options=('collision', 'swash', 'overwash'), value=('collision', 'swash', 'overwash')))), VBox(children=(HTML(value='Forecasted: mean_slope_sto06'), SelectMultiple(index=(0, 1, 2), options=('collision', 'swash', 'overwash'), value=('collision', 'swash', 'overwash')))))))), VBox(children=(HTML(value='Filter by site_id:'), HBox(children=(Dropdown(description='site_id: ', index=942, options=('AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', 'AVOCAn0004', 'AVOCAn0005', 'AVOCAn0006', 'AVOCAn0007', 'AVOCAn0008', 'AVOCAn0009', 'AVOCAs0002', 'AVOCAs0003', 'AVOCAs0004', 'AVOCAs0005', 'AVOCAs0006', 'AVOCAs0007', 'AVOCAs0008', 'BILG0001', 'BILG0002', 'BILG0003', 'BILG0004', 'BILG0005', 'BLUEYS0001', 'BLUEYS0002', 'BLUEYS0003', 'BLUEYS0004', 'BLUEYS0005', 'BLUEYS0006', 'BOAT0001', 'BOAT0002', 'BOAT0003', 'BOAT0004', 'BOAT0005', 'BOOM0001', 'BOOM0002', 'BOOM0003', 'BOOM0004', 'BOOM0005', 'BOOM0006', 'BOOM0007', 'BOOM0008', 'BOOM0009', 'BOOM0010', 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'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': '391027af-be5a-4413-863d-53385a59391d',\n", + " 'uid': '534bf20e-d5c0-49b0-8f3a-247fc1db8b29',\n", " 'x': [0],\n", " 'y': [0]},\n", " {'name': 'Post Storm Profile',\n", " 'type': 'scatter',\n", - " 'uid': 'a5d7d2e9-d3af-462c-9466-51c2b6f09aa4',\n", + " 'uid': '4da21238-7eab-464a-94e3-374d731a3797',\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': '2eba8b17-45a7-4bcd-b988-31bbb487fb1f',\n", + " 'uid': '1bcb6e22-4d7a-487d-a63f-23086513084e',\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': '022f5c93-fc10-4d1d-ac97-e30b8efd28d1',\n", + " 'uid': '39507d76-af86-4685-812d-03a96960aac9',\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", + " 'uid': '64fa5f96-580e-4188-a9f1-95a453763277',\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", + " 'uid': '65603ac3-721e-428a-8dc3-c94a500fdeeb',\n", " 'x': [0],\n", " 'y': [0]}],\n", " 'layout': {'height': 300,\n", @@ -213,7 +217,7 @@ " 'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n", " 'WAMBE0027'], dtype='= min(times)) & (df_tide_site.index.get_level_values('datetime') <= max(times))\n", + " mask = (df_tide_site.index.get_level_values('datetime') >= min(times)) & (\n", + " 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", + " with g_timeseries.batch_update():\n", + " g_timeseries.data[2].x = [min(times), max(times)]\n", + " g_timeseries.data[3].x = [min(times), max(times)]\n", + " g_timeseries.data[4].x = df_tide_site.index.get_level_values('datetime')\n", + " g_timeseries.data[2].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[0],\n", + " g_timeseries.data[3].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[0],\n", + " g_timeseries.data[4].y = df_tide_site.tide.tolist()\n", + "\n", + " # Update rhigh values\n", + " idx_betas = [n for n, x in enumerate(g_timeseries.data) if 'R_high' in x.name]\n", + " for i, forecast in zip(idx_betas,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", + " g_timeseries.data[5+n].x = times\n", + " g_timeseries.data[5+n].y = R_high\n", + "\n", + "\n", "site_id_select.observe(update_profile, names=\"value\")\n", - " \n", - " \n", - " \n", + "\n", + "\n", "def update_filter(change):\n", - " \n", + "\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", + " dfs = [impacts['observed']] + [impacts['forecasted'][key]\n", + " for key in impacts['forecasted']]\n", + "\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", + " valid_site_ids = list(set(valid_site_ids).intersection(\n", + " 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", + "\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", - "# Display our widgets!\n", - "widgets.VBox([filter_container,site_id_container,widgets.HBox([g_profiles,g_map]),g_twls,g_params])\n", "\n", + "# Display our widgets!ore forecast i\n", + "widgets.VBox([filter_container, site_id_container,\n", + " widgets.HBox([g_profiles, g_map]), g_timeseries])\n", + "# widgets.VBox([filter_container,site_id_container,widgets.HBox([g_profiles,g_map]),g_twls,g_params])\n", "\n", "\n", "# For table\n", @@ -691,31 +878,467 @@ "# with out:\n", "# display(df_waves.head(3))\n", "\n", - "# widgets.VBox([filter_container,site_id_container, out])\n" + "# widgets.VBox([filter_container,site_id_container, out])" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 164, "metadata": { "ExecuteTime": { - "end_time": "2018-11-21T06:04:27.672922Z", - "start_time": "2018-11-21T06:04:26.098269Z" + "end_time": "2018-11-22T01:26:00.955120Z", + "start_time": "2018-11-22T01:26:00.904958Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['2016-05-28 00:00:00',\n", + " '2016-05-28 01:00:00',\n", + " '2016-05-28 02:00:00',\n", + " '2016-05-28 03:00:00',\n", + " '2016-05-28 04:00:00',\n", + " '2016-05-28 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13:00:00',\n", + " '2016-06-14 14:00:00',\n", + " '2016-06-14 15:00:00',\n", + " '2016-06-14 16:00:00',\n", + " '2016-06-14 17:00:00',\n", + " '2016-06-14 18:00:00',\n", + " '2016-06-14 19:00:00',\n", + " '2016-06-14 20:00:00',\n", + " '2016-06-14 21:00:00',\n", + " '2016-06-14 22:00:00',\n", + " '2016-06-14 23:00:00',\n", + " '2016-06-15 00:00:00']" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 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))" + "g_timeseries.data\n", + "df_twl = twls['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018'))\n", + "times = df_twl.index.get_level_values('datetime').tolist()\n", + "times" ] } ],