@ -12,8 +12,8 @@
"execution_count": 1,
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"start_time": "2018-12-03T23:04:57.006071 Z"
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"outputs": [],
@ -28,8 +28,8 @@
"execution_count": 2,
"metadata": {
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"outputs": [],
@ -61,8 +61,8 @@
"execution_count": 3,
"metadata": {
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"end_time": "2018-12-03T03:38:53.297184 Z",
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"start_time": "2018-12-03T23:04:58.751721 Z"
}
},
"outputs": [
@ -77,7 +77,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:47 2: FutureWarning:\n",
"C:\\Users\\z5189959\\Desktop\\nsw-2016-storm-impact\\.venv\\lib\\site-packages\\numpy\\lib\\arraysetops.py:52 2: FutureWarning:\n",
"\n",
"elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
"\n"
@ -127,162 +127,158 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare underpredicted cases"
"### Compare predicted R_high with D_low\n",
"Let's see what the distribution of R_high is compared with D_low. How far off are the predicted water levels compared with the dune toes?"
]
},
{
"cell_type": "code",
"execution_count": 3 9,
"execution_count": 2 9,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-03T04:05:30.984007Z",
"start_time": "2018-12-03T04:05:30.805508Z"
"end_time": "2018-12-04T02:20:58.446500Z",
"start_time": "2018-12-04T02:20:58.439480Z"
}
},
"outputs": [],
"source": [
"def get_site_ids(df_forecasted, df_observed, forecasted_regime, observed_regime):\n",
" \"\"\"\n",
" Returns list of site_ids which match the given forecasted and observed regime\n",
" \"\"\"\n",
" set1 = set(df_forecasted.query(\"storm_regime == '{}'\".format(\n",
" forecasted_regime)).index.get_level_values('site_id'))\n",
" set2 = set(df_observed.query(\"storm_regime == '{}'\".format(\n",
" observed_regime)).index.get_level_values('site_id'))\n",
" return sorted(list(set1.intersection(set2)))\n",
"\n",
"\n",
"def get_R_high_D_low_diff(site_ids, df_profile_features, df_twls):\n",
" \"\"\"\n",
" Returns a dataframe of the difference between the R_high and D_low differences. \n",
" Positive values indicate R_high is larger than D_low.\n",
" \"\"\"\n",
" # Get dune toes at these sites and predicted max R_high\n",
" df_toes = df_profile_features.loc[site_ids].query(\n",
" 'profile_type==\"prestorm\"').dune_toe_z\n",
" df_R_highs = df_twls.loc[site_ids].groupby('site_id')['R_high'].max()\n",
"\n",
" # Join into one dataframe\n",
" df_twl_toes = pd.concat([df_toes, df_R_highs], axis=1, sort=True)\n",
" df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
" return df_twl_toes['diff']\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-04T03:55:51.858020Z",
"start_time": "2018-12-04T03:55:50.879155Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dune_toe_z</th>\n",
" <th>R_high</th>\n",
" <th>diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AVOCAn0005</th>\n",
" <td>3.306</td>\n",
" <td>3.260440</td>\n",
" <td>-0.045560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AVOCAn0008</th>\n",
" <td>3.507</td>\n",
" <td>3.220084</td>\n",
" <td>-0.286916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BILG0005</th>\n",
" <td>4.807</td>\n",
" <td>3.293445</td>\n",
" <td>-1.513555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BLUEYS0001</th>\n",
" <td>3.064</td>\n",
" <td>2.800144</td>\n",
" <td>-0.263856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BLUEYS0002</th>\n",
" <td>2.929</td>\n",
" <td>2.470641</td>\n",
" <td>-0.458359</td>\n",
" </tr>\n",
" </tbody>\n",
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"text/plain": [
" dune_toe_z R_high diff\n",
"AVOCAn0005 3.306 3.260440 -0.045560\n",
"AVOCAn0008 3.507 3.220084 -0.286916\n",
"BILG0005 4.807 3.293445 -1.513555\n",
"BLUEYS0001 3.064 2.800144 -0.263856\n",
"BLUEYS0002 2.929 2.470641 -0.458359"
"FigureWidget({\n",
" 'data': [{'marker': {'color': '#ef8a62'},\n",
" 'name': 'Overpredicted',\n",
" …"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result "
"output_type": "display_data"
}
],
"source": [
"# Find site_ids where the forecast has been underpredicted\n",
"set1 = set(impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime == 'swash'\").index.get_level_values('site_id'))\n",
"set2 = set(impacts['observed'].query(\"storm_regime == 'collision'\").index.get_level_values('site_id'))\n",
"site_ids = list(set1.intersection(set2))\n",
"\n",
"# Get dune toes at these sites and predicted max R_high\n",
"df_toes = df_profile_features.loc[site_ids].query('profile_type==\"prestorm\"').dune_toe_z\n",
"df_R_highs = twls['forecasted']['mean_slope_sto06'].loc[site_ids].groupby('site_id')['R_high'].max()\n",
"\n",
"# Join into one dataframe\n",
"df_twl_toes = pd.concat([df_toes, df_R_highs],axis=1,sort=True)\n",
"df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
"df_twl_toes.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's plot the comparison between our R_high TWL values and the dune toes to see how far off they were."
"swash_overpredicted_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
" df_observed=impacts['observed'],\n",
" forecasted_regime='collision',\n",
" observed_regime='swash')\n",
"swash_overpredicted_diffs = get_R_high_D_low_diff(site_ids=swash_overpredicted_site_ids,\n",
" df_profile_features=df_profile_features,\n",
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
"\n",
"swash_correct_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
" df_observed=impacts['observed'],\n",
" forecasted_regime='swash',\n",
" observed_regime='swash')\n",
"swash_correct_diffs = get_R_high_D_low_diff(site_ids=swash_correct_site_ids,\n",
" df_profile_features=df_profile_features,\n",
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
"\n",
"\n",
"trace1 = go.Histogram(y=swash_correct_diffs.tolist(),\n",
" opacity=0.75,\n",
" name='Correctly predicted',\n",
" marker=dict(\n",
" color='#67a9cf',\n",
" ),\n",
" ybins=dict(\n",
" size=0.1\n",
"),)\n",
"trace2 = go.Histogram(y=swash_overpredicted_diffs.tolist(),\n",
" opacity=0.75,\n",
" name='Overpredicted',\n",
" marker=dict(\n",
" color='#ef8a62',\n",
"),\n",
" ybins=dict(\n",
" size=0.1\n",
"),)\n",
"\n",
"layout = go.Layout(\n",
" title='R_high - D_low<br>Swash Regime',\n",
" barmode='overlay',\n",
" yaxis=dict(\n",
" title='z (m AHD)'\n",
" ),\n",
" xaxis=dict(\n",
" title='Count'\n",
" ),\n",
" bargap=0.2,\n",
" bargroupgap=0.1,\n",
" legend=dict(x=.6, y=1)\n",
")\n",
"\n",
"g_plot_swash = go.FigureWidget(data=[trace2, trace1], layout=layout)\n",
"\n",
"# To output to file\n",
"img_bytes = pio.write_image(g_plot_swash, 'g_plot_swash.png',format='png', width=600, height=400, scale=5)\n",
"\n",
"g_plot_swash\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": 5 4,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-03T04:08:15.732169Z",
"start_time": "2018-12-03T04:08:15.656966Z"
"end_time": "2018-12-04T04:10:47.339268 Z",
"start_time": "2018-12-04T04:10:45.796887 Z"
}
},
"outputs": [
{
"data": {
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"model_id": "3933da9295fe446f9413bca8842100c 2",
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"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>FigureWidget</code>.</p>\n",
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" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
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"text/plain": [
"FigureWidget({\n",
" 'data': [{'type': 'histogram',\n",
" 'uid': '75f0d11f-9242-4fc7-b433-1f04e1e37ba6',\n",
" 'y': [-0.045560088746212646, -0.28691603912686325,\n",
" -1.5135547360075963, ..., -0.4613631587476821,\n",
" -0.5212332930925054, -0.3948507473332721]}],\n",
" 'layout': {'bargap': 0.2,\n",
" 'bargroupgap': 0.1,\n",
" 'title': 'D_low - R_high<br>Observed Collision, Forecasted Swash',\n",
" 'xaxis': {'title': 'Count'},\n",
" 'yaxis': {'title': 'z (m AHD)'}}\n",
"})"
" 'data': [{'marker': {'color': '#ef8a62'},\n",
" 'name': 'Underpredicted',\n",
" …"
]
},
"metadata": {},
@ -290,10 +286,45 @@
}
],
"source": [
"trace1 = go.Histogram(y=df_twl_toes['diff'].tolist())\n",
"collision_underpredicted_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
" df_observed=impacts['observed'],\n",
" forecasted_regime='swash',\n",
" observed_regime='collision')\n",
"collision_underpredicted_diffs = get_R_high_D_low_diff(site_ids=collision_underpredicted_site_ids,\n",
" df_profile_features=df_profile_features,\n",
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
"\n",
"collision_correct_site_ids = get_site_ids(df_forecasted=impacts['forecasted']['mean_slope_sto06'],\n",
" df_observed=impacts['observed'],\n",
" forecasted_regime='collision',\n",
" observed_regime='collision')\n",
"collision_correct_diffs = get_R_high_D_low_diff(site_ids=collision_correct_site_ids,\n",
" df_profile_features=df_profile_features,\n",
" df_twls=twls['forecasted']['mean_slope_sto06'])\n",
"\n",
"\n",
"trace1 = go.Histogram(y=collision_correct_diffs.tolist(),\n",
" opacity=0.75,\n",
" name='Correctly predicted',\n",
" marker=dict(\n",
" color='#67a9cf',\n",
" ),\n",
" ybins=dict(\n",
" size=0.1\n",
"),)\n",
"trace2 = go.Histogram(y=collision_underpredicted_diffs.tolist(),\n",
" opacity=0.75,\n",
" name='Underpredicted',\n",
" marker=dict(\n",
" color='#ef8a62',\n",
" ),\n",
" ybins=dict(\n",
" size=0.1\n",
"),)\n",
"\n",
"layout = go.Layout(\n",
" title='D_low - R_high<br>Observed Collision, Forecasted Swash',\n",
" title='R_high - D_low<br>Collision Regime',\n",
" barmode='overlay',\n",
" yaxis=dict(\n",
" title='z (m AHD)'\n",
" ),\n",
@ -301,172 +332,267 @@
" title='Count'\n",
" ),\n",
" bargap=0.2,\n",
" bargroupgap=0.1\n",
" bargroupgap=0.1,\n",
" legend=dict(x=.6, y=1)\n",
")\n",
"\n",
"g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
"g_plot"
"g_plot_collision = go.FigureWidget(data=[trace2, trace1], layout=layout)\n",
"\n",
"# To output to file\n",
"img_bytes = pio.write_image(g_plot_collision, 'g_plot_collision.png',format='png', width=600, height=400, scale=5)\n",
"\n",
"g_plot_collision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above plot shows that the R_high value for most of the incorrectly forecasted collision regimes, was typically underpredicted by less than 0.5 m. "
"### Does dune toe lower?\n "
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### What do over predicted and underpredicted profiles look like?"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"### Compare overpredicted cases"
"Define a function for getting the average beach profile for a number of given site_ids: "
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 156 ,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-03T04:08:56.128806Z",
"start_time": "2018-12-03T04:08:55.894182Z"
}
"end_time": "2018-12-04T23:11:08.853877Z",
"start_time": "2018-12-04T23:11:08.846876Z"
},
"hidden": true
},
"outputs": [],
"source": [
"def get_avg_profile(site_ids, debug=False):\n",
" rows = []\n",
" for n,site_id in enumerate(site_ids):\n",
" profile = df_profiles.query(\"site_id == '{}' and profile_type == 'prestorm'\".format(site_id))\n",
" profile_z = np.array(profile.z.tolist())\n",
" profile_x = np.array(profile.index.get_level_values('x').tolist())\n",
" \n",
" # Let's center the profile based on the z=0 location\n",
" idx_last_z_val = max(np.argwhere(~np.isnan(profile_z)==True))[0]\n",
" x_last_val = profile_x[idx_last_z_val]\n",
" profile_x = [x - x_last_val for x in profile_x]\n",
" \n",
" # Put values into a dictionary\n",
" for x,z in zip(profile_x, profile_z):\n",
" rows.append({'x':x, 'z': z})\n",
"\n",
" # Return early for debugging\n",
" if debug and n>3:\n",
" break\n",
" \n",
" # Create dataframe from rows\n",
" df = pd.DataFrame(rows)\n",
" avg_profile = df.groupby('x').agg({'z': [np.nanmean, np.nanstd]}).reset_index()\n",
"\n",
" return {\n",
" 'x': avg_profile.x.tolist(),\n",
" 'z': avg_profile.z.nanmean.tolist(),\n",
" 'std': avg_profile.z.nanstd.tolist(),\n",
" 'n': n+1 # number of profiles\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Now, let's look at whether there is a difference between the average beach profile of correctly forecasted site_ids and incorrectly forecasted site_ids. First, looking at sites where we observed swash regime."
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-05T02:00:36.853374Z",
"start_time": "2018-12-05T01:58:21.839366Z"
},
"code_folding": [],
"hidden": true
},
"outputs": [
{
"data": {
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" <tbody>\n",
" <tr>\n",
" <th>AVOCAn0004</th>\n",
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" <td>3.416988</td>\n",
" <td>0.238988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOOM0004</th>\n",
" <td>3.065</td>\n",
" <td>3.074980</td>\n",
" <td>0.009980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOOM0011</th>\n",
" <td>2.771</td>\n",
" <td>6.491824</td>\n",
" <td>3.720824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOOM0012</th>\n",
" <td>2.796</td>\n",
" <td>3.148087</td>\n",
" <td>0.352087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CATHIE0001</th>\n",
" <td>2.780</td>\n",
" <td>3.522792</td>\n",
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" dune_toe_z R_high diff\n",
"AVOCAn0004 3.178 3.416988 0.238988\n",
"BOOM0004 3.065 3.074980 0.009980\n",
"BOOM0011 2.771 6.491824 3.720824\n",
"BOOM0012 2.796 3.148087 0.352087\n",
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"FigureWidget({\n",
" 'data': [{'line': {'color': 'rgb(205, 0, 0)', 'width': 2},\n",
" 'mode': 'lines',\n",
" …"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result "
"output_type": "display_data"
}
],
"source": [
"# Find site_ids where the forecast has been overpredicted\n",
"set1 = set(impacts['forecasted']['mean_slope_sto06'].query(\"storm_regime == 'collision'\").index.get_level_values('site_id'))\n",
"set2 = set(impacts['observed'].query(\"storm_regime == 'swash'\").index.get_level_values('site_id'))\n",
"site_ids = list(set1.intersection(set2))\n",
"\n",
"# Get dune toes at these sites and predicted max R_high\n",
"df_toes = df_profile_features.loc[site_ids].query('profile_type==\"prestorm\"').dune_toe_z\n",
"df_R_highs = twls['forecasted']['mean_slope_sto06'].loc[site_ids].groupby('site_id')['R_high'].max()\n",
"\n",
"# Join into one dataframe\n",
"df_twl_toes = pd.concat([df_toes, df_R_highs],axis=1,sort=True)\n",
"df_twl_toes['diff'] = df_twl_toes['R_high'] - df_twl_toes['dune_toe_z']\n",
"df_twl_toes.head()\n"
"overpredicted = get_avg_profile(swash_overpredicted_site_ids)\n",
"correct = get_avg_profile(swash_correct_site_ids)\n",
"\n",
"# Add mean profile\n",
"trace_overpredicted_mean = go.Scatter(\n",
" x=overpredicted['x'],\n",
" y=overpredicted['z'],\n",
" opacity=1,\n",
" mode='lines',\n",
" name='Mean overpredicted profile (n={})'.format(overpredicted['n']),\n",
" line=dict(\n",
" color=('rgb(205, 0, 0)'),\n",
" width=2)\n",
")\n",
"\n",
"trace_overpredited_std_top = go.Scatter(\n",
" x=overpredicted['x'],\n",
" y=np.add(overpredicted['z'], overpredicted['std']),\n",
" opacity=1,\n",
" hoverinfo='none',\n",
" showlegend=False,\n",
" mode='lines',\n",
" line=dict(\n",
" color=('rgb(205, 0, 0)'),\n",
" width=0.5,\n",
" dash='dash')\n",
")\n",
"\n",
"trace_overpredited_std_btm = go.Scatter(\n",
" x=overpredicted['x'],\n",
" y=np.subtract(overpredicted['z'], overpredicted['std']),\n",
" opacity=1,\n",
" hoverinfo='none',\n",
" mode='lines',\n",
" showlegend=False,\n",
" line=dict(\n",
" color=('rgb(205, 0, 0)'),\n",
" width=0.5,\n",
" dash='dash')\n",
")\n",
"\n",
"trace_correct_mean = go.Scatter(\n",
" x=avg_correct_x,\n",
" y=avg_correct_z,\n",
" opacity=1,\n",
" mode='lines',\n",
" name='Mean correct profile (n={})'.format(correct['n']),\n",
" line=dict(\n",
" color=('rgb(0, 205, 0)'),\n",
" width=2)\n",
")\n",
"\n",
"trace_correct_std_top = go.Scatter(\n",
" x=avg_correct_x,\n",
" y=np.add(avg_correct_z, avg_correct_std),\n",
" opacity=1,\n",
" hoverinfo='none',\n",
" showlegend=False,\n",
" mode='lines',\n",
" line=dict(\n",
" color=('rgb(0, 205, 0)'),\n",
" width=0.5,\n",
" dash='dash')\n",
")\n",
"\n",
"trace_correct_std_btm = go.Scatter(\n",
" x=avg_correct_x,\n",
" y=np.subtract(avg_correct_z, avg_correct_std),\n",
" opacity=1,\n",
" hoverinfo='none',\n",
" mode='lines',\n",
" showlegend=False,\n",
" line=dict(\n",
" color=('rgb(0, 205, 0)'),\n",
" width=0.5,\n",
" dash='dash')\n",
")\n",
"\n",
"layout = dict(showlegend=True,\n",
" title='Observed Swash Impact Regime',\n",
" legend=dict(x=.6, y=1),\n",
" xaxis=dict(\n",
" range=[-150, 0]),\n",
" yaxis=dict(\n",
" range=[0, 10]))\n",
"\n",
"fig = go.FigureWidget(data=[trace_overpredicted_mean,\n",
" trace_overpredited_std_top,\n",
" trace_overpredited_std_btm,\n",
" trace_correct_mean,\n",
" trace_correct_std_top,\n",
" trace_correct_std_btm],\n",
" layout=layout)\n",
"\n",
"# To output to file\n",
"img_bytes = pio.write_image(\n",
" fig, 'mean_profiles_swash.png', format='png', width=600, height=600, scale=5)\n",
"\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We can see that the difference is pretty minimal. For cases where we predicted collision, but observed swash (overprediction), we see that overpredicted profiles are slightly more concave than correctly predicted sites."
]
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 162 ,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-03T04:14:46.601092Z",
"start_time": "2018-12-03T04:14:46.522883Z"
}
"end_time": "2018-12-05T02:03:38.394415Z",
"start_time": "2018-12-05T02:00:37.335377Z"
},
"hidden": true
},
"outputs": [
{
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"model_id": "1255bccc024e4690b4b8ff4ccc8e9e35 ",
"version_major": 2,
"version_minor": 0
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"text/html": [
"<p>Failed to display Jupyter Widget of type <code>FigureWidget</code>.</p>\n",
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" Widgets Documentation</a> for setup instructions.\n",
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"text/plain": [
"FigureWidget({\n",
" 'data': [{'type': 'histogram',\n",
" 'uid': '4a284474-2be1-4fd7-87d5-25364cc78df4',\n",
" 'y': [0.23898814460475037, 0.009980312001434566, 3.720823710344608,\n",
" ..., 1.5720238663972683, 0.912998680585452, 1.1419977620500927]}],\n",
" 'layout': {'bargap': 0.2,\n",
" 'bargroupgap': 0.1,\n",
" 'title': 'D_low - R_high<br>Observed Swash, Forecasted Collision',\n",
" 'xaxis': {'title': 'Count'},\n",
" 'yaxis': {'title': 'z (m AHD)'}}\n",
"})"
" 'data': [{'line': {'color': 'rgb(205, 0, 0)', 'width': 2},\n",
" 'mode': 'lines',\n",
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},
"metadata": {},
@ -474,29 +600,114 @@
}
],
"source": [
"trace1 = go.Histogram(y=df_twl_toes['diff'].tolist())\n",
"underpredicted = get_avg_profile(collision_underpredicted_site_ids)\n",
"correct = get_avg_profile(collision_correct_site_ids)\n",
"\n",
"layout = go.Layout(\n",
" title='D_low - R_high<br>Observed Swash, Forecasted Collision',\n",
" yaxis=dict(\n",
" title='z (m AHD)'\n",
" ),\n",
" xaxis=dict(\n",
" title='Count'\n",
" ),\n",
" bargap=0.2,\n",
" bargroupgap=0.1\n",
"# Add mean profile\n",
"trace_underpredicted_mean = go.Scatter(\n",
" x = underpredicted['x'],\n",
" y= underpredicted['z'],\n",
" opacity = 1,\n",
" mode='lines',\n",
" name='Mean underpredicted profile (n={})'.format(underpredicted['n']),\n",
" line = dict(\n",
" color = ('rgb(205, 0, 0)'),\n",
" width = 2)\n",
")\n",
"\n",
"trace_underpredicted_std_top = go.Scatter(\n",
" x = underpredicted['x'],\n",
" y= np.add(underpredicted['z'],underpredicted['std']),\n",
" opacity = 1,\n",
" hoverinfo='none',\n",
" showlegend=False,\n",
" mode='lines',\n",
" line = dict(\n",
" color = ('rgb(205, 0, 0)'),\n",
" width = 0.5,\n",
" dash = 'dash')\n",
") \n",
"\n",
"trace_underpredicted_std_btm = go.Scatter(\n",
" x = underpredicted['x'],\n",
" y= np.subtract(underpredicted['z'],underpredicted['std']),\n",
" opacity = 1,\n",
" hoverinfo='none',\n",
" mode='lines',\n",
" showlegend=False,\n",
" line = dict(\n",
" color = ('rgb(205, 0, 0)'),\n",
" width = 0.5,\n",
" dash = 'dash')\n",
") \n",
"\n",
"trace_correct_mean = go.Scatter(\n",
" x = avg_correct_x,\n",
" y= avg_correct_z,\n",
" opacity = 1,\n",
" mode='lines',\n",
" name='Mean correct profile (n={})'.format(correct['n']),\n",
" line = dict(\n",
" color = ('rgb(0, 205, 0)'),\n",
" width = 2)\n",
")\n",
"\n",
"g_plot = go.FigureWidget(data=[trace1], layout=layout)\n",
"g_plot"
"trace_correct_std_top = go.Scatter(\n",
" x = avg_correct_x,\n",
" y= np.add(avg_correct_z, avg_correct_std),\n",
" opacity = 1,\n",
" hoverinfo='none',\n",
" showlegend=False,\n",
" mode='lines',\n",
" line = dict(\n",
" color = ('rgb(0, 205, 0)'),\n",
" width = 0.5,\n",
" dash = 'dash')\n",
") \n",
"\n",
"trace_correct_std_btm = go.Scatter(\n",
" x = avg_correct_x,\n",
" y= np.subtract(avg_correct_z, avg_correct_std),\n",
" opacity = 1,\n",
" hoverinfo='none',\n",
" mode='lines',\n",
" showlegend=False,\n",
" line = dict(\n",
" color = ('rgb(0, 205, 0)'),\n",
" width = 0.5,\n",
" dash = 'dash')\n",
") \n",
" \n",
"layout = dict(showlegend=True,\n",
" title='Observed Collision Impact Regime',\n",
" legend=dict(x=.6, y=1),\n",
" xaxis=dict(\n",
" range=[-150,0]),\n",
" yaxis=dict(\n",
" range=[0,10]))\n",
" \n",
"fig=go.FigureWidget(data=[trace_underpredicted_mean, \n",
" trace_underpredicted_std_top,\n",
" trace_underpredicted_std_btm, \n",
" trace_correct_mean, \n",
" trace_correct_std_top, \n",
" trace_correct_std_btm], \n",
" layout=layout)\n",
"\n",
"# To output to file\n",
"img_bytes = pio.write_image(fig, 'mean_profiles_collision.png',format='png', width=600, height=600, scale=5)\n",
"\n",
"fig\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hidden": true
},
"source": [
"The errors when we forecast collision but observe swash are much greater than we we forecast swash and observe collision. For this case, errors in excess of 1.0 m common. Why is this?"
"This plot is a bit more interesting. It shows that we are correctly forecasting collision when the profile is more accreted/convex, but when the profile is more eroded/concave, the water level is underpredicted. Why is this? "
]
}
],
@ -506,6 +717,18 @@
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
@ -515,9 +738,14 @@
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
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"toc_position": {},
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
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},
"toc_section_display": true,
"toc_window_display": false
"toc_window_display": tru e
},
"varInspector": {
"cols": {
Chris Leaman
7 years ago