diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb index 81e42ab..18c5b6f 100644 --- a/notebooks/01_exploration.ipynb +++ b/notebooks/01_exploration.ipynb @@ -29,10 +29,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 232, "metadata": { "ExecuteTime": { - "start_time": "2018-11-22T22:48:17.826Z" + "end_time": "2018-11-23T01:45:39.791501Z", + "start_time": "2018-11-23T01:45:39.780471Z" }, "scrolled": true }, @@ -48,25 +49,50 @@ "import plotly.graph_objs as go\n", "import plotly.plotly as py\n", "import plotly.tools as tls\n", + "import plotly.figure_factory as ff\n", "\n", + "import matplotlib\n", + "from matplotlib import cm\n", "import colorlover as cl\n", "\n", "from ipywidgets import widgets, Output\n", - "from IPython.display import display, clear_output, Image, HTML" + "from IPython.display import display, clear_output, Image, HTML\n", + "\n", + "from sklearn.metrics import confusion_matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import our data into pandas Dataframes for the analysis. Data files are `.csv` files which are stored in the `./data/interim/` folder." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 273, "metadata": { "ExecuteTime": { - "start_time": "2018-11-22T22:48:17.829Z" + "end_time": "2018-11-23T02:59:30.467706Z", + "start_time": "2018-11-23T02:59:17.099702Z" }, "pixiedust": { "displayParams": {} - } + }, + "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:472: FutureWarning:\n", + "\n", + "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + "\n" + ] + } + ], "source": [ "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", @@ -96,106 +122,293 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2018-11-22T22:48:17.832Z" - } - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "tables = [Output() for x in range(len(impacts['forecasted']) + 1)]\n", - "tables" + "The following interactive data explorer displays information on a per `site_id` basis. It can be used to examine pre/post storm cross-sections, water level time series and observed/predicted storm impacts." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 276, "metadata": { "ExecuteTime": { - "start_time": "2018-11-22T22:48:17.835Z" + "end_time": "2018-11-23T03:07:46.410476Z", + "start_time": "2018-11-23T03:07:43.154124Z" }, "code_folding": [ 408 ], + "hide_input": false, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cc4ab9cdf0cb422f8870d6ba906e538a", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

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'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'),)))), HTML(value=''))), HBox(children=(FigureWidget({\n", + " 'data': [{'line': {'color': 'rgb(51,160,44)', 'width': 2},\n", + " 'name': 'Pre Storm Profile',\n", + " 'type': 'scatter',\n", + " 'uid': '2618ab16-1e96-4273-8141-bd4b6bdc9c83',\n", + " 'x': [0],\n", + " 'y': [0]},\n", + " {'line': {'color': 'rgb(255,127,0)', 'width': 2},\n", + " 'name': 'Post Storm Profile',\n", + " 'type': 'scatter',\n", + " 'uid': '3872fe91-a2e2-4762-aa61-c0e01b271cdd',\n", + " 'x': [0],\n", + " 'y': [0]},\n", + " {'marker': {'color': 'rgba(255,255,255,0)', 'line': {'color': 'rgba(106,61,154, 1)', 'width': 2}, 'size': 10},\n", + " 'mode': 'markers',\n", + " 'name': 'Pre-storm dune crest',\n", + " 'type': 'scatter',\n", + " 'uid': '93c17c28-b6f2-4c12-8a4b-b7683362ece4',\n", + " 'x': [0],\n", + " 'y': [0]},\n", + " {'marker': {'color': 'rgba(255,255,255,0)', 'line': {'color': 'rgba(202,178,214,1)', 'width': 2}, 'size': 10},\n", + " 'mode': 'markers',\n", + " 'name': 'Pre-storm dune toe',\n", + " 'type': 'scatter',\n", + " 'uid': '9b2cb05f-3f32-4a07-84fb-24e79e745a47',\n", + " 'x': [0],\n", + " 'y': [0]},\n", + " {'line': {'color': 'rgb(44,127,184)', 'width': 4},\n", + " 'mode': 'lines',\n", + " 'name': 'Peak R_high: foreshore_slope_sto06',\n", + " 'type': 'scatter',\n", + " 'uid': '1f12592c-7294-4735-8bda-b9bdf30f32c4',\n", + " 'x': [0],\n", + " 'y': [0]},\n", + " {'line': {'color': 'rgb(127,205,187)', 'width': 4},\n", + " 'mode': 'lines',\n", + " 'name': 'Peak R_high: mean_slope_sto06',\n", + " 'type': 'scatter',\n", + " 'uid': '711f34f8-0dd7-4d38-9bcc-67636d7ed2a5',\n", + " 'x': [0],\n", + " 'y': [0]}],\n", + " 'layout': {'height': 300,\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", + " 'showgrid': True,\n", + " 'showline': True,\n", + " 'title': 'x (m)',\n", + " 'zeroline': True},\n", + " 'yaxis': {'autorange': False,\n", + " 'range': [-1, 20],\n", + " 'showgrid': True,\n", + " 'showline': True,\n", + " 'title': 'z (m)',\n", + " 'zeroline': True}}\n", + "}), FigureWidget({\n", + " 'data': [{'lat': array([-33.46381539, -33.46301835, -33.46221051, ..., -33.4279646 ,\n", + " -33.42732743, -33.42671036]),\n", + " 'lon': array([151.43639576, 151.43690633, 151.43738179, ..., 151.4501613 ,\n", + " 151.45092222, 151.45170635]),\n", + " 'marker': {'size': 10},\n", + " 'mode': 'markers',\n", + " 'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n", + " 'WAMBE0027'], dtype='Filter by observed and predicted impacts:\",\n", - ")\n", + " value=\"Filter by observed and predicted impacts:\", )\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", + "selectboxes = [\n", + " 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", "\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(\n", - " ).unique().tolist(),\n", - " value=impacts['forecasted'][forecast].storm_regime.dropna(\n", - " ).unique().tolist(),\n", + " options=impacts['forecasted'][\n", + " forecast].storm_regime.dropna().unique().tolist(),\n", + " value=impacts['forecasted'][forecast].storm_regime.dropna()\n", + " .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(\n", - " children=[title, box]) for title, box in zip(titles, selectboxes)])\n", + "children = widgets.HBox(children=[\n", + " widgets.VBox(children=[title, box])\n", + " for title, box in zip(titles, selectboxes)\n", + "])\n", "filter_container = widgets.VBox(children=[filter_title, children])\n", "\n", - "\n", "# Create widgets for selecting site_id\n", - "site_id_title = widgets.HTML(\n", - " value=\"Filter by site_id:\",\n", - ")\n", + "site_id_title = widgets.HTML(value=\"Filter by site_id:\", )\n", "\n", "site_id_select = widgets.Dropdown(\n", " description='site_id: ',\n", " value='NARRA0001',\n", - " options=df_profiles.index.get_level_values(\n", - " 'site_id').unique().sort_values().tolist()\n", - ")\n", + " options=df_profiles.index.get_level_values('site_id').unique()\n", + " .sort_values().tolist())\n", "\n", - "site_id_impacts = widgets.HTML(\n", - " value=\"\",\n", - ")\n", + "site_id_impacts = widgets.HTML(value=\"\", )\n", "\n", "site_id_container = widgets.HBox(children=[\n", - " widgets.VBox(children=[site_id_title, widgets.HBox(children=[site_id_select])]),\n", - " site_id_impacts\n", + " widgets.VBox(\n", + " children=[site_id_title,\n", + " widgets.HBox(children=[site_id_select])]), site_id_impacts\n", "])\n", "\n", - "\n", - "\n", - "\n", "# Build colors for each of our forecasts\n", - "colors = list(reversed(cl.scales[str(max(len(impacts['forecasted']), 3))]['seq']['YlGnBu']))\n", + "colors = list(\n", + " reversed(cl.scales[str(max(len(impacts['forecasted']),\n", + " 3))]['seq']['YlGnBu']))\n", "\n", "# Add panel for pre/post storm profiles\n", "trace1 = go.Scatter(\n", " x=[0],\n", " y=[0],\n", " name='Pre Storm Profile',\n", - " line=dict(\n", - " color=('rgb(51,160,44)'),\n", - " width=2)\n", - ")\n", + " line=dict(color=('rgb(51,160,44)'), width=2))\n", "trace2 = go.Scatter(\n", " x=[0],\n", " y=[0],\n", " name='Post Storm Profile',\n", - " line=dict(\n", - " color=('rgb(255,127,0)'),\n", - " width=2)\n", - ")\n", + " line=dict(color=('rgb(255,127,0)'), width=2))\n", "trace3 = go.Scatter(\n", " x=[0],\n", " y=[0],\n", @@ -204,11 +417,7 @@ " marker=dict(\n", " color='rgba(255,255,255,0)',\n", " size=10,\n", - " line=dict(\n", - " color='rgba(106,61,154, 1)',\n", - " width=2\n", - " )\n", - " ),\n", + " line=dict(color='rgba(106,61,154, 1)', width=2)),\n", ")\n", "trace4 = go.Scatter(\n", " x=[0],\n", @@ -218,25 +427,21 @@ " marker=dict(\n", " color='rgba(255,255,255,0)',\n", " size=10,\n", - " line=dict(\n", - " color='rgba(202,178,214,1)',\n", - " width=2\n", - " )\n", - " ),\n", + " line=dict(color='rgba(202,178,214,1)', width=2)),\n", ")\n", "\n", "forecast_traces = []\n", "for forecast, color in zip(impacts['forecasted'], colors):\n", - " forecast_traces.append(go.Scatter(\n", - " x=[0],\n", - " y=[0],\n", - " name='Peak R_high: {}'.format(forecast),\n", - " mode='lines',\n", - " line=dict(\n", - " color=color,\n", - " width=4,)\n", - "\n", - " ))\n", + " forecast_traces.append(\n", + " go.Scatter(\n", + " x=[0],\n", + " y=[0],\n", + " name='Peak R_high: {}'.format(forecast),\n", + " mode='lines',\n", + " line=dict(\n", + " color=color,\n", + " width=4,\n", + " )))\n", "\n", "layout = go.Layout(\n", " title='Bed Profiles',\n", @@ -249,21 +454,17 @@ " showgrid=True,\n", " zeroline=True,\n", " showline=True,\n", - " range=[0, 200]\n", - " ),\n", + " range=[0, 200]),\n", " yaxis=dict(\n", " title='z (m)',\n", " autorange=False,\n", " showgrid=True,\n", " zeroline=True,\n", " showline=True,\n", - " range=[-1, 20]\n", - " )\n", - ")\n", - "\n", - "g_profiles = go.FigureWidget(data=[trace1, trace2, trace3, trace4]+forecast_traces,\n", - " layout=layout)\n", + " range=[-1, 20]))\n", "\n", + "g_profiles = go.FigureWidget(\n", + " data=[trace1, trace2, trace3, trace4] + forecast_traces, layout=layout)\n", "\n", "# Add panel for google maps\n", "mapbox_access_token = 'pk.eyJ1IjoiY2hyaXNsZWFtYW4iLCJhIjoiY2pvNTY1MzZpMDc2OTN2bmw5MGsycHp5bCJ9.U2dwFg2c7RFjUNSayERUiw'\n", @@ -273,9 +474,7 @@ " lat=df_sites['lat'],\n", " lon=df_sites['lon'],\n", " mode='markers',\n", - " marker=dict(\n", - " size=10\n", - " ),\n", + " marker=dict(size=10),\n", " text=df_sites.index.get_level_values('site_id'),\n", " ),\n", " go.Scattermapbox(\n", @@ -300,20 +499,15 @@ " mapbox=dict(\n", " accesstoken=mapbox_access_token,\n", " bearing=0,\n", - " center=dict(\n", - " lat=-33.7,\n", - " lon=151.3\n", - " ),\n", + " center=dict(lat=-33.7, lon=151.3),\n", " pitch=0,\n", " zoom=12,\n", - " style='satellite-streets'\n", - " ),\n", + " style='satellite-streets'),\n", ")\n", "\n", "fig = dict(data=data, layout=layout)\n", "g_map = go.FigureWidget(data=data, layout=layout)\n", "\n", - "\n", "subplot = tls.make_subplots(3, 1, print_grid=False, shared_xaxes=True)\n", "g_timeseries = go.FigureWidget(subplot)\n", "\n", @@ -346,12 +540,9 @@ " y=[0, 3],\n", " name='Dune Crest',\n", " mode='lines',\n", - " line=dict(\n", - " color=('rgb(214, 117, 14)'),\n", - " width=2,\n", - " dash='dot')\n", - " ), row=1, col=1)\n", - "\n", + " line=dict(color=('rgb(214, 117, 14)'), width=2, dash='dot')),\n", + " row=1,\n", + " col=1)\n", "\n", "g_timeseries.add_trace(\n", " go.Scatter(\n", @@ -359,22 +550,18 @@ " y=[0, 3],\n", " name='Dune Toe',\n", " mode='lines',\n", - " line=dict(\n", - " color=('rgb(142, 77, 8)'),\n", - " width=2,\n", - " dash='dash')\n", - " ), row=1, col=1)\n", + " line=dict(color=('rgb(142, 77, 8)'), width=2, dash='dash')),\n", + " row=1,\n", + " col=1)\n", "\n", "g_timeseries.add_trace(\n", " go.Scatter(\n", " x=[0, 3],\n", " y=[0, 3],\n", " name='Tide+Surge WL',\n", - " line=dict(\n", - " color=('rgb(8,51,137)'),\n", - " width=2,\n", - " dash='dot')\n", - " ), row=1, col=1)\n", + " line=dict(color=('rgb(8,51,137)'), width=2, dash='dot')),\n", + " row=1,\n", + " col=1)\n", "\n", "for forecast, color in zip(twls['forecasted'], colors):\n", " g_timeseries.add_trace(\n", @@ -382,26 +569,22 @@ " x=[0],\n", " y=[0],\n", " name='R_high: {}'.format(forecast),\n", - " line=dict(\n", - " color=color,\n", - " width=3)), row=1, col=1)\n", - "\n", + " line=dict(color=color, width=3)),\n", + " row=1,\n", + " col=1)\n", "\n", "# Add trace for each forecasted beta term\n", - "for forecast, color in zip(impacts['forecasted'],colors):\n", + "for forecast, color in zip(impacts['forecasted'], colors):\n", " g_timeseries.add_trace(\n", " go.Scatter(\n", " x=[0, 1],\n", " y=[0, 1],\n", " name='Beta: {}'.format(forecast),\n", - " line=dict(\n", - " color=color,\n", - " width=3)),\n", + " line=dict(color=color, width=3)),\n", " row=2,\n", " col=1,\n", " )\n", "\n", - "\n", "# Create axis for Tp on same plot as Hs\n", "g_timeseries['layout']['yaxis4'] = {'overlaying': 'y3', 'side': 'right'}\n", "g_timeseries.data[1]['yaxis'] = 'y4'\n", @@ -417,33 +600,38 @@ "g_timeseries['layout'].update(height=400, legend=dict(font={'size': 10}))\n", "g_timeseries['layout'].update(margin=dict(t=20, l=50, r=20, b=100))\n", "\n", - "\n", - "\n", "# Add panel for some tables\n", "titles = ['observed'] + [forecast for forecast in impacts['forecasted']]\n", "titles = [widgets.HTML(value=\"{}\".format(title)) for title in titles]\n", "\n", + "\n", "def get_observed_impacts_table(site_id):\n", - " display(impacts['observed'].query(\"site_id=='{}'\".format(site_id)).T)\n", + " display(impacts['observed'].query(\"site_id=='{}'\".format(site_id)).T)\n", "\n", "\n", - "def get_forecasted_impacts_table(site_id,forecast):\n", - " display(impacts['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id)).T)\n", + "def get_forecasted_impacts_table(site_id, forecast):\n", + " display(impacts['forecasted'][forecast].query(\n", + " \"site_id=='{}'\".format(site_id)).T)\n", "\n", - " \n", - "impacts_table_observed = widgets.interactive_output(get_observed_impacts_table, {'site_id': site_id_select})\n", + "\n", + "impacts_table_observed = widgets.interactive_output(\n", + " get_observed_impacts_table, {'site_id': site_id_select})\n", "forecasted_impacts_tables = []\n", "for forecast, title in zip(impacts['forecasted'], titles[1:]):\n", - " forecasted_impacts_tables.append(widgets.interactive_output(get_forecasted_impacts_table, {'site_id': site_id_select, 'forecast':title}))\n", + " forecasted_impacts_tables.append(\n", + " widgets.interactive_output(get_forecasted_impacts_table, {\n", + " 'site_id': site_id_select,\n", + " 'forecast': title\n", + " }))\n", "\n", "tables = [impacts_table_observed] + forecasted_impacts_tables\n", "\n", - "title_tables=[widgets.VBox(children=[title,table]) for title,table in zip(titles,tables)]\n", - " \n", - "tables_container= widgets.HBox(children=[*title_tables])\n", - "\n", - "\n", + "title_tables = [\n", + " widgets.VBox(children=[title, table])\n", + " for title, table in zip(titles, tables)\n", + "]\n", "\n", + "tables_container = widgets.HBox(children=[*title_tables])\n", "\n", "\n", "def update_profile(change):\n", @@ -484,8 +672,8 @@ " for n, forecast in enumerate(impacts['forecasted']):\n", " R_high = max(impacts['forecasted'][forecast].query(\n", " \"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", + " 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", @@ -505,6 +693,12 @@ " 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", + "\n", + " df_tide_site = df_tides.query(\"site_id=='{}'\".format(site_id))\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_timeseries.batch_update():\n", " g_timeseries.data[0].x = times\n", " g_timeseries.data[0].y = Hs0s\n", @@ -512,8 +706,9 @@ " g_timeseries.data[1].y = Tps\n", "\n", " # Update beta values\n", - " idx_betas = [n for n, x in enumerate(\n", - " g_timeseries.data) if 'Beta' in x.name]\n", + " idx_betas = [\n", + " n for n, x in enumerate(g_timeseries.data) if 'Beta' in x.name\n", + " ]\n", " for i, forecast in zip(idx_betas, twls['forecasted']):\n", " df_twl = twls['forecasted'][forecast].query(\n", " \"site_id=='{}'\".format(site_id))\n", @@ -522,26 +717,20 @@ " g_timeseries.data[i].x = times\n", " g_timeseries.data[i].y = beta\n", "\n", - " # Update water levels plot\n", - " df_tide_site = df_tides.query(\"site_id=='{}'\".format(site_id))\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_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(\n", " 'datetime')\n", - " g_timeseries.data[2].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[\n", - " 0],\n", + " g_timeseries.data[2].y = dune_crest_z.tolist()[\n", + " 0], dune_crest_z.tolist()[0],\n", " g_timeseries.data[3].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[\n", " 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(\n", - " g_timeseries.data) if 'R_high' in x.name]\n", + " idx_betas = [\n", + " n for n, x in enumerate(g_timeseries.data) if 'R_high' in x.name\n", + " ]\n", " for i, forecast in zip(idx_betas, twls['forecasted']):\n", " df_twl = twls['forecasted'][forecast].query(\n", " \"site_id=='{}'\".format(site_id))\n", @@ -551,15 +740,18 @@ " g_timeseries.data[i].y = R_high\n", "\n", " # Update site id impacts\n", - " observed_regime = impacts['observed'].query(\"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n", - " site_id_impacts.value = \"Observed: {}
\".format(observed_regime)\n", - " \n", + " observed_regime = impacts['observed'].query(\n", + " \"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n", + " site_id_impacts.value = \"Observed: {}
\".format(\n", + " observed_regime)\n", + "\n", " for forecast in impacts['forecasted']:\n", - " regime = impacts['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n", - " site_id_impacts.value += '{}: {}
'.format(forecast, regime)\n", - " \n", - " # Update our tables\n", - " \n", + " regime = impacts['forecasted'][forecast].query(\n", + " \"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n", + " site_id_impacts.value += '{}: {}
'.format(\n", + " forecast, regime)\n", + "\n", + "\n", "site_id_select.observe(update_profile, names=\"value\")\n", "\n", "\n", @@ -567,12 +759,13 @@ "\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]\n", - " for key in impacts['forecasted']]\n", + " dfs = [impacts['observed']\n", + " ] + [impacts['forecasted'][key] for key in impacts['forecasted']]\n", "\n", " for box, df in zip(selectboxes, dfs):\n", - " valid_site_ids = list(set(valid_site_ids).intersection(\n", - " set(df[df.storm_regime.isin(box.value)].index.tolist())))\n", + " valid_site_ids = list(\n", + " 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", @@ -582,42 +775,465 @@ "for box in selectboxes:\n", " box.observe(update_filter, names=\"value\")\n", "\n", - " \n", - "\n", - "\n", "# Display our widgets!\n", - "widgets.VBox([filter_container, site_id_container,\n", - " widgets.HBox([g_profiles, g_map]), g_timeseries, tables_container])\n", - "\n" + "widgets.VBox([\n", + " filter_container, site_id_container,\n", + " widgets.HBox([g_profiles, g_map]), g_timeseries, tables_container\n", + "])" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "ExecuteTime": { - "start_time": "2018-11-22T22:48:17.837Z" + "end_time": "2018-11-22T22:52:36.039701Z", + "start_time": "2018-11-22T22:52:36.035189Z" }, "scrolled": true }, - "outputs": [], "source": [ - "titles[0].observe" + "This visualization looks at how well the storm impact predictions performed. " + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "ExecuteTime": { + "end_time": "2018-11-23T01:57:05.476410Z", + "start_time": "2018-11-23T01:57:04.925795Z" + }, + "code_folding": [], + "hide_input": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1dfce0ba4b744e9190093562d5c339d3", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

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'y'},\n", + " {'font': {'color': '#FFFFFF'},\n", + " 'showarrow': False,\n", + " 'text': '0.21',\n", + " 'x': 'inundation',\n", + " 'xref': 'x',\n", + " 'y': 'collision',\n", + " 'yref': 'y'},\n", + " {'font': {'color': '#FFFFFF'},\n", + " 'showarrow': False,\n", + " 'text': '0.6',\n", + " 'x': 'swash',\n", + " 'xref': 'x',\n", + " 'y': 'swash',\n", + " 'yref': 'y'},\n", + " {'font': {'color': '#FFFFFF'},\n", + " 'showarrow': False,\n", + " 'text': '0.4',\n", + " 'x': 'collision',\n", + " 'xref': 'x',\n", + " 'y': 'swash',\n", + " 'yref': 'y'},\n", + " {'font': {'color': '#FFFFFF'},\n", + " 'showarrow': False,\n", + " 'text': '0.2',\n", + " 'x': 'overwash',\n", + " 'xref': 'x',\n", + " 'y': 'swash',\n", + " 'yref': 'y'},\n", + " {'font': {'color': '#FFFFFF'},\n", + " 'showarrow': False,\n", + " 'text': '3',\n", + " 'x': 'inundation',\n", + " 'xref': 'x',\n", + " 'y': 'swash',\n", + " 'yref': 'y'}],\n", + " 'height': 200,\n", + " 'margin': {'b': 40, 'l': 100, 'pad': 0, 'r': 100, 't': 40},\n", + " 'xaxis': {'dtick': 1, 'gridcolor': 'rgb(0, 0, 0)', 'side': 'top', 'ticks': '', 'title': 'Predicted'},\n", + " 'yaxis': {'dtick': 1, 'ticks': '', 'ticksuffix': ' ', 'title': 'Observed'}}\n", + "})))))))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create colorscale\n", + "rdylgr_cmap = matplotlib.cm.get_cmap('RdYlGn')\n", + "\n", + "norm = matplotlib.colors.Normalize(vmin=0, vmax=255)\n", + "\n", + "def matplotlib_to_plotly(cmap, pl_entries):\n", + " h = 1.0/(pl_entries-1)\n", + " pl_colorscale = []\n", + "\n", + " for k in range(pl_entries):\n", + " C = list(map(np.uint8, np.array(cmap(k*h)[:3])*255))\n", + " pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])\n", + "\n", + " return pl_colorscale\n", + "rdylgr = matplotlib_to_plotly(magma_cmap, 255)\n", + "\n", + "\n", + "\n", + "# Create widget for list of beaches.\n", + "beaches = df_sites.beach.unique().tolist()\n", + "\n", + "beach_title = widgets.HTML(value=\"Filter by beach:\", )\n", + "\n", + "beach_select = widgets.SelectMultiple(\n", + " options=beaches, value=beaches, disabled=False)\n", + "\n", + "beach_container = widgets.VBox([beach_title, beach_select])\n", + "\n", + "# Create confusion matrix for each forecasted impact data set\n", + "heatmaps = []\n", + "for forecast in impacts['forecasted']:\n", + "\n", + " z = [[.1, .3, .5, 2], [1.0, .8, .6, 1], [1.4, .28, 1.6, .21],\n", + " [.6, .4, .2, 3]]\n", + "\n", + " x = ['swash', 'collision', 'overwash', 'inundation']\n", + " y = list(reversed(x))\n", + "\n", + " z_text = z\n", + "\n", + " fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale=rdylgr)\n", + " heatmap = go.FigureWidget(data=fig.data, layout=fig.layout)\n", + "\n", + " heatmap.layout.update(\n", + " height=200, margin=go.layout.Margin(l=100, r=100, b=40, t=40, pad=0))\n", + " heatmap.layout.xaxis.update(title='Predicted')\n", + " heatmap.layout.yaxis.update(title='Observed')\n", + " heatmap_title = widgets.HTML(value=\"{}\".format(forecast) )\n", + " heatmaps.append(widgets.VBox([heatmap_title, heatmap]))\n", + "\n", + " \n", + "def update_heatmaps(change):\n", + " \n", + " for forecast, heatmap in zip(impacts['forecasted'], heatmaps):\n", + " selected_site_ids = df_sites[df_sites.beach.isin(beach_select.value)].index.tolist()\n", + "\n", + " df_ob = impacts['observed']\n", + " df_fo = impacts['forecasted'][forecast]\n", + "\n", + " observed_regimes = df_ob[df_ob.index.isin(selected_site_ids)].storm_regime.dropna().rename(\"observed_regime\")\n", + " forecasted_regimes = df_fo[df_fo.index.isin(selected_site_ids)].storm_regime.dropna().rename(\"forecasted_regime\")\n", + "\n", + " if any([observed_regimes.empty, forecasted_regimes.empty]):\n", + " return\n", + " \n", + " df_compare = pd.concat([observed_regimes, forecasted_regimes], axis='columns', names=['a','b'], sort=True)\n", + " df_compare.dropna(axis='index',inplace=True)\n", + "\n", + " z = confusion_matrix(df_compare.observed_regime.tolist(), df_compare.forecasted_regime.tolist(), labels = ['swash','collision','overwash','inundation'])\n", + " z = np.flip(z,axis=0)\n", + " z_list = list(reversed(z.tolist()))\n", + " \n", + " # Make incorrect values negative, so they get assigned a different color.\n", + " # Better for visualization\n", + " z_neg_incorrect = np.flip(np.identity(4),axis=0)\n", + " z_neg_incorrect[z_neg_incorrect==0]= -1\n", + " z_neg_incorrect = (z * z_neg_incorrect).tolist()\n", + " \n", + " fig = ff.create_annotated_heatmap(z_neg_incorrect, x=x, y=y, annotation_text=z)\n", + " heatmap.children[1].data[0].z = z_neg_incorrect\n", + " heatmap.children[1].layout.annotations = fig.layout.annotations\n", + "\n", + "# Hook changes to beach filter to update confusion heatmaps\n", + "beach_select.observe(update_heatmaps, names=\"value\")\n", + "\n", + "# Display our widgets\n", + "widgets.VBox([beach_container, widgets.VBox(heatmaps)])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 224, "metadata": { "ExecuteTime": { - "start_time": "2018-11-22T22:48:17.840Z" + "end_time": "2018-11-23T01:32:36.864786Z", + "start_time": "2018-11-23T01:32:36.833701Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[[-0.0, -0.0, -0.0, 0.0],\n", + " [-0.0, -0.0, 0.0, -0.0],\n", + " [-11.0, 9.0, -0.0, -0.0],\n", + " [12.0, -0.0, -2.0, -0.0]]" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# impacts['observed'].query(\"site_id=='{}'\".format(\"NARRA0018\")).T\n", - "impacts['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format(\"NARRA0018\")).T\n", - "\n" + "selected_site_ids = df_sites[df_sites.beach.isin(beach_select.value)].index.tolist()\n", + "\n", + "df_ob = impacts['observed']\n", + "df_fo = impacts['forecasted']['foreshore_slope_sto06']\n", + "\n", + "observed_regimes = df_ob[df_ob.index.isin(selected_site_ids)].storm_regime.dropna().rename(\"observed_regime\")\n", + "forecasted_regimes = df_fo[df_fo.index.isin(selected_site_ids)].storm_regime.dropna().rename(\"forecasted_regime\")\n", + "\n", + "df_compare = pd.concat([observed_regimes, forecasted_regimes], axis='columns', names=['a','b'], sort=True)\n", + "df_compare.dropna(axis='index',inplace=True)\n", + "\n", + "z = confusion_matrix(df_compare.observed_regime.tolist(), df_compare.forecasted_regime.tolist(), labels = ['swash','collision','overwash','inundation'])\n", + "z = np.flip(z,axis=0)\n", + "z_list = list(reversed(z.tolist()))\n", + "\n", + "# Make incorrect values negative, so they get assigned a different color.\n", + "# Better for visualization\n", + "z_neg_incorrect = np.flip(np.identity(4),axis=0)\n", + "z_neg_incorrect[z_neg_incorrect==0]= -1\n", + "z_neg_incorrect = (z * z_neg_incorrect).tolist()\n", + "z_neg_incorrect" ] } ],