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nsw-2016-storm-impact
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Chris Leaman 6 years ago
parent 6ffdd2611d
commit 66c7b25cc4
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7
notebooks/01_exploration.ipynb
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@ -90,8 +90,7 @@
"# Note that the forecasted data sets should be in the same order for impacts and twls\n", "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
"impacts = {\n", "impacts = {\n",
" 'forecasted': {\n", " 'forecasted': {\n",
" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n", " 'postintertidal_slope_sto06': df_from_csv('impacts_forecasted_postintertidal_slope_sto06.csv', index_col=[0]),\n",
" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
" },\n", " },\n",
" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n", " 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
" }\n", " }\n",
@ -99,8 +98,7 @@
"\n", "\n",
"twls = {\n", "twls = {\n",
" 'forecasted': {\n", " 'forecasted': {\n",
" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n", " 'postintertidal_slope_sto06':df_from_csv('twl_postintertidal_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", " }\n",
"}\n", "}\n",
"print('Done!')" "print('Done!')"
@ -260,6 +258,7 @@
" title='Bed Profiles',\n", " title='Bed Profiles',\n",
" height=300,\n", " height=300,\n",
" legend=dict(font={'size': 10}),\n", " legend=dict(font={'size': 10}),\n",
" showlegend=False,\n",
" margin=dict(t=50, b=50, l=50, r=20),\n", " margin=dict(t=50, b=50, l=50, r=20),\n",
" xaxis=dict(\n", " xaxis=dict(\n",
" title='x (m)',\n", " title='x (m)',\n",

534
notebooks/07_evaluate_model_performance.ipynb
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@ -10,9 +10,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"heading_collapsed": true
},
"source": [ "source": [
"## Setup notebook\n", "## Setup notebook\n",
"Import our required packages and set default plotting options." "Import our required packages and set default plotting options."
@ -21,9 +19,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"hidden": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Enable autoreloading of our modules. \n", "# Enable autoreloading of our modules. \n",
@ -37,9 +33,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"hidden": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from IPython.core.debugger import set_trace\n", "from IPython.core.debugger import set_trace\n",
@ -66,6 +60,7 @@
"from scipy import stats\n", "from scipy import stats\n",
"from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import confusion_matrix\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import MultipleLocator\n",
"from matplotlib.lines import Line2D\n", "from matplotlib.lines import Line2D\n",
"from cycler import cycler\n", "from cycler import cycler\n",
"from scipy.interpolate import interp1d\n", "from scipy.interpolate import interp1d\n",
@ -75,9 +70,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"hidden": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Matplot lib default settings\n", "# Matplot lib default settings\n",
@ -128,11 +121,22 @@
"# Note that the forecasted data sets should be in the same order for impacts and twls\n", "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
"impacts = {\n", "impacts = {\n",
" 'forecasted': {\n", " 'forecasted': {\n",
" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv',index_col=[0]),\n", " 'postintertidal_slope_hol86': df_from_csv('impacts_forecasted_postintertidal_slope_hol86.csv', index_col=[0]),\n",
" 'mean_slope_hol86': df_from_csv('impacts_forecasted_mean_slope_hol86.csv',index_col=[0]),\n", " 'postintertidal_slope_nie91': df_from_csv('impacts_forecasted_postintertidal_slope_nie91.csv', index_col=[0]),\n",
" 'mean_slope_nie91': df_from_csv('impacts_forecasted_mean_slope_nie91.csv',index_col=[0]),\n", " 'postintertidal_slope_pow18': df_from_csv('impacts_forecasted_postintertidal_slope_pow18.csv', index_col=[0]),\n",
" 'mean_slope_pow18': df_from_csv('impacts_forecasted_mean_slope_pow18.csv',index_col=[0]),\n", " 'postintertidal_slope_sto06': df_from_csv('impacts_forecasted_postintertidal_slope_sto06.csv', index_col=[0]),\n",
" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv',index_col=[0]),\n", " 'postmean_slope_hol86': df_from_csv('impacts_forecasted_postmean_slope_hol86.csv', index_col=[0]),\n",
" 'postmean_slope_nie91': df_from_csv('impacts_forecasted_postmean_slope_nie91.csv', index_col=[0]),\n",
" 'postmean_slope_pow18': df_from_csv('impacts_forecasted_postmean_slope_pow18.csv', index_col=[0]),\n",
" 'postmean_slope_sto06': df_from_csv('impacts_forecasted_postmean_slope_sto06.csv', index_col=[0]),\n",
" 'preintertidal_slope_hol86': df_from_csv('impacts_forecasted_preintertidal_slope_hol86.csv', index_col=[0]),\n",
" 'preintertidal_slope_nie91': df_from_csv('impacts_forecasted_preintertidal_slope_nie91.csv', index_col=[0]),\n",
" 'preintertidal_slope_pow18': df_from_csv('impacts_forecasted_preintertidal_slope_pow18.csv', index_col=[0]),\n",
" 'preintertidal_slope_sto06': df_from_csv('impacts_forecasted_preintertidal_slope_sto06.csv', index_col=[0]),\n",
" 'premean_slope_hol86': df_from_csv('impacts_forecasted_premean_slope_hol86.csv', index_col=[0]),\n",
" 'premean_slope_nie91': df_from_csv('impacts_forecasted_premean_slope_nie91.csv', index_col=[0]),\n",
" 'premean_slope_pow18': df_from_csv('impacts_forecasted_premean_slope_pow18.csv', index_col=[0]),\n",
" 'premean_slope_sto06': df_from_csv('impacts_forecasted_premean_slope_sto06.csv', index_col=[0]),\n",
" },\n", " },\n",
" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n", " 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
" }\n", " }\n",
@ -140,11 +144,22 @@
"\n", "\n",
"twls = {\n", "twls = {\n",
" 'forecasted': {\n", " 'forecasted': {\n",
" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv',index_col=[0,1]),\n", " 'postintertidal_slope_hol86': df_from_csv('twl_postintertidal_slope_hol86.csv', index_col=[0,1]),\n",
" 'mean_slope_hol86': df_from_csv('twl_mean_slope_hol86.csv',index_col=[0,1]),\n", " 'postintertidal_slope_nie91': df_from_csv('twl_postintertidal_slope_nie91.csv', index_col=[0,1]),\n",
" 'mean_slope_nie91': df_from_csv('twl_mean_slope_nie91.csv',index_col=[0,1]),\n", " 'postintertidal_slope_pow18': df_from_csv('twl_postintertidal_slope_pow18.csv', index_col=[0,1]),\n",
" 'mean_slope_pow18': df_from_csv('twl_mean_slope_pow18.csv',index_col=[0,1]),\n", " 'postintertidal_slope_sto06': df_from_csv('twl_postintertidal_slope_sto06.csv', index_col=[0,1]),\n",
" 'mean_slope_sto06': df_from_csv('twl_mean_slope_sto06.csv',index_col=[0,1]),\n", " 'postmean_slope_hol86': df_from_csv('twl_postmean_slope_hol86.csv', index_col=[0,1]),\n",
" 'postmean_slope_nie91': df_from_csv('twl_postmean_slope_nie91.csv', index_col=[0,1]),\n",
" 'postmean_slope_pow18': df_from_csv('twl_postmean_slope_pow18.csv', index_col=[0,1]),\n",
" 'postmean_slope_sto06': df_from_csv('twl_postmean_slope_sto06.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_hol86': df_from_csv('twl_preintertidal_slope_hol86.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_nie91': df_from_csv('twl_preintertidal_slope_nie91.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_pow18': df_from_csv('twl_preintertidal_slope_pow18.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_sto06': df_from_csv('twl_preintertidal_slope_sto06.csv', index_col=[0,1]),\n",
" 'premean_slope_hol86': df_from_csv('twl_premean_slope_hol86.csv', index_col=[0,1]),\n",
" 'premean_slope_nie91': df_from_csv('twl_premean_slope_nie91.csv', index_col=[0,1]),\n",
" 'premean_slope_pow18': df_from_csv('twl_premean_slope_pow18.csv', index_col=[0,1]),\n",
" 'premean_slope_sto06': df_from_csv('twl_premean_slope_sto06.csv', index_col=[0,1]),\n",
" }\n", " }\n",
"}\n", "}\n",
"print('Done!')" "print('Done!')"
@ -152,9 +167,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"heading_collapsed": true
},
"source": [ "source": [
"## Generate longshore plots for each beach" "## Generate longshore plots for each beach"
] ]
@ -163,128 +176,152 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"hidden": true "code_folding": []
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"beach = 'NARRA'\n", "beaches = list(\n",
"\n", " set([\n",
"df_obs_impacts = impacts['observed'].loc[impacts['observed'].index.str.\n", " x[:-4] for x in df_profiles.index.get_level_values('site_id').unique()\n",
" contains(beach)]\n", " ]))\n",
"\n", "\n",
"# Get index for each site on the beach\n", "for beach in beaches:\n",
"n = [x for x in range(len(df_obs_impacts))][::-1]\n", " \n",
"n_sites = [x for x in df_obs_impacts.index][::-1]\n", " df_obs_impacts = impacts['observed'].loc[impacts['observed'].index.str.\n",
"\n", " contains(beach)]\n",
"# Convert storm regimes to categorical datatype\n", "\n",
"cat_type = CategoricalDtype(\n", " # Get index for each site on the beach\n",
" categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n", " n = [x for x in range(len(df_obs_impacts))][::-1]\n",
"df_obs_impacts.storm_regime = df_obs_impacts.storm_regime.astype(cat_type)\n", " n_sites = [x for x in df_obs_impacts.index][::-1]\n",
"\n", "\n",
"# Create figure\n", " # Convert storm regimes to categorical datatype\n",
"f, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8) = plt.subplots(\n", " cat_type = CategoricalDtype(\n",
" 1,\n", " categories=['swash', 'collision', 'overwash', 'inundation'],\n",
" 8,\n", " ordered=True)\n",
" sharey=True,\n", " df_obs_impacts.storm_regime = df_obs_impacts.storm_regime.astype(cat_type)\n",
" figsize=(18, 8),\n", "\n",
" gridspec_kw={'width_ratios': [4, 4, 2, 2, 2, 2, 2, 2]})\n", " # Create figure\n",
"\n", " \n",
"# ax1: Impacts\n", " # Determine the height of the figure, based on the number of sites.\n",
"\n", " fig_height = max(6, 0.18 * len(n_sites))\n",
"# Define colors for storm regime\n", " f, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8) = plt.subplots(\n",
"cmap = {'swash': '#1a9850', 'collision': '#fee08b', 'overwash': '#d73027'}\n", " 1,\n",
"\n", " 8,\n",
"# Common marker style\n", " sharey=True,\n",
"marker_style = {\n", " figsize=(18, fig_height),\n",
" 's': 60,\n", " gridspec_kw={'width_ratios': [4, 4, 2, 2, 2, 2, 2, 2]})\n",
" 'linewidths': 0.7,\n", "\n",
" 'alpha': 1,\n", " # ax1: Impacts\n",
" 'edgecolors': 'k',\n", "\n",
" 'marker': 'o',\n", " # Define colors for storm regime\n",
"}\n", " cmap = {'swash': '#1a9850', 'collision': '#fee08b', 'overwash': '#d73027'}\n",
"\n", "\n",
"# Plot observed impacts\n", " # Common marker style\n",
"colors = [cmap.get(x) for x in df_obs_impacts.storm_regime]\n", " marker_style = {\n",
"colors = ['#d73027' if c is None else c for c in colors]\n", " 's': 60,\n",
"ax1.scatter([0 for x in n], n, color=colors, **marker_style)\n", " 'linewidths': 0.7,\n",
"\n", " 'alpha': 1,\n",
"# Plot model impacts\n", " 'edgecolors': 'k',\n",
"for i, model in enumerate(impacts['forecasted']):\n", " 'marker': 'o',\n",
"\n", " }\n",
" # Only get model results for this beach\n",
" df_model = impacts['forecasted'][model].loc[impacts['forecasted'][model].\n",
" index.str.contains(beach)]\n",
"\n",
" # Recast storm regimes as categorical data\n",
" df_model.storm_regime = df_model.storm_regime.astype(cat_type)\n",
"\n", "\n",
" # Assign colors\n", " # Plot observed impacts\n",
" colors = [cmap.get(x) for x in df_model.storm_regime]\n", " colors = [cmap.get(x) for x in df_obs_impacts.storm_regime]\n",
" colors = ['#aaaaaa' if c is None else c for c in colors]\n", " colors = ['#aaaaaa' if c is None else c for c in colors]\n",
" ax1.scatter([i + 1 for x in n], n, color=colors, **marker_style)\n", " ax1.scatter([0 for x in n], n, color=colors, **marker_style)\n",
"\n", "\n",
"# Add model names to each impact on x axis\n", " # Plot model impacts\n",
"ax1.set_xticks(range(len(impacts['forecasted']) + 1))\n", " for i, model in enumerate(impacts['forecasted']):\n",
"ax1.set_xticklabels(['observed'] +\n", "\n",
" [x.replace('_', '\\_') for x in impacts['forecasted']])\n", " # Only get model results for this beach\n",
"ax1.xaxis.set_tick_params(rotation=90)\n", " df_model = impacts['forecasted'][model].loc[\n",
"\n", " impacts['forecasted'][model].index.str.contains(beach)]\n",
"# Add title\n", "\n",
"ax1.set_title('Storm regime')\n", " # Recast storm regimes as categorical data\n",
"\n", " df_model.storm_regime = df_model.storm_regime.astype(cat_type)\n",
"# Create custom legend\n", "\n",
"legend_elements = [\n", " # Assign colors\n",
" Line2D([0], [0],\n", " colors = [cmap.get(x) for x in df_model.storm_regime]\n",
" marker='o',\n", " colors = ['#aaaaaa' if c is None else c for c in colors]\n",
" color='w',\n", "\n",
" label='Swash',\n", " # Only plot markers which are different to the observed storm regime. \n",
" markerfacecolor='#1a9850',\n", " # This makes it easier to find where model predictions differ\n",
" markersize=8,\n", " y_coords = []\n",
" markeredgewidth=1.0,\n", " for obs_impact, for_impact in zip(df_model.storm_regime,\n",
" markeredgecolor='k'),\n", " df_obs_impacts.storm_regime):\n",
" Line2D([0], [0],\n", " if obs_impact == for_impact:\n",
" marker='o',\n", " y_coords.append(None)\n",
" color='w',\n", " else:\n",
" label='Collision',\n", " y_coords.append(i + 1)\n",
" markerfacecolor='#fee08b',\n", "\n",
" markersize=8,\n", " ax1.scatter(y_coords, n, color=colors, **marker_style)\n",
" markeredgewidth=1.0,\n", "\n",
" markeredgecolor='k'),\n", " # Add model names to each impact on x axis\n",
" Line2D([0], [0],\n", " ax1.set_xticks(range(len(impacts['forecasted']) + 1))\n",
" marker='o',\n", " ax1.set_xticklabels(['observed'] +\n",
" color='w',\n", " [x.replace('_', '\\_') for x in impacts['forecasted']])\n",
" label='Overwash',\n", " ax1.xaxis.set_tick_params(rotation=90)\n",
" markerfacecolor='#d73027',\n", "\n",
" markersize=8,\n", " # Add title\n",
" markeredgewidth=1.0,\n", " ax1.set_title('Storm regime')\n",
" markeredgecolor='k'),\n", "\n",
"]\n", " # Create custom legend\n",
"ax1.legend(\n", " legend_elements = [\n",
" handles=legend_elements, loc='lower center', bbox_to_anchor=(0.5, 1.1))\n", " Line2D([0], [0],\n",
"\n", " marker='o',\n",
"# Replace yticks with site_ids\n", " color='w',\n",
"yticks = ax1.get_yticks().tolist()\n", " label='Swash',\n",
"yticks = [n_sites[int(y)] if 0 <= y <= len(n_sites) else y for y in yticks]\n", " markerfacecolor='#1a9850',\n",
"ax1.set_yticklabels(yticks)\n", " markersize=8,\n",
"\n", " markeredgewidth=1.0,\n",
"# ax2: elevations\n", " markeredgecolor='k'),\n",
"\n", " Line2D([0], [0],\n",
"# Dune elevations\n", " marker='o',\n",
"df_feats = df_profile_features_crest_toes.xs(['prestorm'],\n", " color='w',\n",
" level=['profile_type'])\n", " label='Collision',\n",
"df_feats = df_feats.loc[df_feats.index.str.contains(beach)]\n", " markerfacecolor='#fee08b',\n",
"\n", " markersize=8,\n",
"ax2.plot(df_feats.dune_crest_z, n, color='#fdae61')\n", " markeredgewidth=1.0,\n",
"ax2.plot(df_feats.dune_toe_z, n, color='#fdae61')\n", " markeredgecolor='k'),\n",
"ax2.fill_betweenx(\n", " Line2D([0], [0],\n",
" n,\n", " marker='o',\n",
" df_feats.dune_toe_z,\n", " color='w',\n",
" df_feats.dune_crest_z,\n", " label='Overwash',\n",
" alpha=0.2,\n", " markerfacecolor='#d73027',\n",
" color='#fdae61',\n", " markersize=8,\n",
" label='$D_{low}$ to $D_{high}$')\n", " markeredgewidth=1.0,\n",
"\n", " markeredgecolor='k'),\n",
"model_colors = [\n", " ]\n",
" ax1.legend(\n",
" handles=legend_elements, loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
"\n",
" # Replace axis ticks with names of site ids\n",
" ytick_labels = ax1.get_yticks().tolist()\n",
" yticks = [\n",
" n_sites[int(y)] if all([y >= 0, y < len(n_sites)]) else ''\n",
" for y in ytick_labels\n",
" ]\n",
" yticks = [x.replace('_', '\\_') for x in yticks]\n",
" ax1.set_yticklabels(yticks)\n",
"\n",
" # ax2: elevations\n",
"\n",
" # Dune elevations\n",
" df_feats = df_profile_features_crest_toes.xs(['prestorm'],\n",
" level=['profile_type'])\n",
" df_feats = df_feats.loc[df_feats.index.str.contains(beach)]\n",
"\n",
" ax2.plot(df_feats.dune_crest_z, n, color='#fdae61')\n",
" ax2.plot(df_feats.dune_toe_z, n, color='#fdae61')\n",
" ax2.fill_betweenx(\n",
" n,\n",
" df_feats.dune_toe_z,\n",
" df_feats.dune_crest_z,\n",
" alpha=0.2,\n",
" color='#fdae61',\n",
" label='$D_{low}$ to $D_{high}$')\n",
"\n",
" model_colors = [\n",
" '#1f78b4',\n", " '#1f78b4',\n",
" '#33a02c',\n", " '#33a02c',\n",
" '#e31a1c',\n", " '#e31a1c',\n",
@ -296,93 +333,149 @@
" '#ffff99',\n", " '#ffff99',\n",
" ]\n", " ]\n",
"\n", "\n",
"# Define colors to cycle through for our R_high\n", " # Define colors to cycle through for our R_high\n",
"ax2.set_prop_cycle(cycler('color', model_colors))\n", " ax2.set_prop_cycle(cycler('color', model_colors))\n",
"\n",
"# Plot R_high values\n",
"for model in impacts['forecasted']:\n",
"\n",
" # Only get model results for this beach\n",
" df_model = impacts['forecasted'][model].loc[impacts['forecasted'][model].\n",
" index.str.contains(beach)]\n",
"\n",
" # Recast storm regimes as categorical data\n",
" ax2.plot(df_model.R_high, n, label=model.replace('_', '\\_'))\n",
"\n",
"# Set title, legend and labels\n",
"ax2.set_title('TWL \\& dune\\nelevations')\n",
"ax2.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
"ax2.set_xlabel('Elevation (m AHD)')\n",
"\n",
"\n",
"# ax3: Plot R_high - D_low\n",
"\n",
"# Define colors to cycle through for our R_high\n",
"ax3.set_prop_cycle(cycler('color', model_colors))\n",
"\n",
"# Plot R_high values\n",
"for model in impacts['forecasted']:\n",
" \n",
" df_model = impacts['forecasted'][model].loc[impacts['forecasted'][model].\n",
" index.str.contains(beach)]\n",
" # R_high - D_low\n",
" ax3.plot(df_model.R_high - df_feats.dune_toe_z, n, label=model.replace('_', '\\_'))\n",
"\n",
"ax3.axvline(x=0,color='black',linestyle=':')\n",
"ax3.set_title('$R_{high}$ - $D_{low}$')\n",
"ax3.set_xlabel('Height (m)')\n",
"ax3.set_xlim([-2,2])\n",
"\n",
"\n", "\n",
" # For TWL elevations, Rhigh-Dlow and R2 axis, only plot a few models\n",
" models_to_plot = [\n",
" 'premean_slope_hol86',\n",
" 'premean_slope_sto06',\n",
" 'preintertidal_slope_hol86',\n",
" 'preintertidal_slope_sto06',\n",
" ]\n",
" models_linewidth = 0.8\n",
"\n", "\n",
"# Define colors to cycle through for our R2\n", " # Plot R_high values\n",
"ax4.set_prop_cycle(cycler('color', model_colors))\n", " for model in models_to_plot:\n",
"\n", "\n",
"# R_high - D_low\n", " # Only get model results for this beach\n",
"for model in impacts['forecasted']:\n", " df_model = impacts['forecasted'][model].loc[\n",
" df_R2 = impacts['forecasted'][model].merge(twls['forecasted'][model],on=['site_id','datetime'])\n", " impacts['forecasted'][model].index.str.contains(beach)]\n",
" df_R2 = df_R2.loc[df_R2.index.str.contains(beach)]\n",
" ax4.plot(df_R2.R2, n, label=model.replace('_', '\\_'))\n",
"\n", "\n",
"ax4.set_title(r'$R_{2\\%}$')\n", " # Recast storm regimes as categorical data\n",
"ax4.set_xlabel('Height (m)')\n", " ax2.plot(\n",
" df_model.R_high,\n",
" n,\n",
" label=model.replace('_', '\\_'),\n",
" linewidth=models_linewidth)\n",
"\n", "\n",
"# Need to chose a model to extract environmental parameters at maximum R_high time\n", " # Set title, legend and labels\n",
"model = 'mean_slope_sto06'\n", " ax2.set_title('TWL \\& dune\\nelevations')\n",
"df_beach = impacts['forecasted'][model].merge(twls['forecasted'][model], on=['site_id','datetime'])\n", " ax2.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
"df_beach = df_beach.loc[df_beach.index.str.contains(beach)]\n", " ax2.set_xlabel('Elevation (m AHD)')\n",
"# ax2.set_xlim([0, max(df_feats.dune_crest_z)])\n",
"\n", "\n",
"# Wave height, wave period, beach slope\n", " # ax3: Plot R_high - D_low\n",
"ax5.plot(df_beach.beta, n,color='#4daf4a')\n",
"ax5.set_title(r'$\\beta$')\n",
"ax5.set_xlabel('Mean prestorm\\nbeach slope')\n",
"ax5.set_xlim([0,0.15])\n",
"\n", "\n",
"ax6.plot(df_beach.Hs0, n,color='#999999')\n", " # Define colors to cycle through for our R_high\n",
"ax6.set_title('$H_{s0}$')\n", " ax3.set_prop_cycle(cycler('color', model_colors))\n",
"ax6.set_xlabel('Sig. wave height (m)')\n",
"ax6.set_xlim([3,5])\n",
"\n", "\n",
"ax7.plot(df_beach.Tp, n,color='#999999')\n", " # Plot R_high values\n",
"ax7.set_title('$T_{p}$')\n", " for model in models_to_plot:\n",
"ax7.set_xlabel('Peak wave period (s)')\n",
"ax7.set_xlim([8,14])\n",
"\n", "\n",
"ax8.plot(df_beach.tide, n,color='#999999')\n", " df_model = impacts['forecasted'][model].loc[\n",
"ax8.set_title('Tide \\& surge')\n", " impacts['forecasted'][model].index.str.contains(beach)]\n",
"ax8.set_xlabel('Elevation (m AHD)')\n", " # R_high - D_low\n",
"ax8.set_xlim([0,2])\n", " ax3.plot(\n",
" df_model.R_high - df_feats.dune_toe_z,\n",
" n,\n",
" label=model.replace('_', '\\_'),\n",
" linewidth=models_linewidth)\n",
"\n", "\n",
" ax3.axvline(x=0, color='black', linestyle=':')\n",
" ax3.set_title('$R_{high}$ - $D_{low}$')\n",
" ax3.set_xlabel('Height (m)')\n",
"# ax3.set_xlim([-2, 2])\n",
"\n", "\n",
"plt.tight_layout()\n", " # Define colors to cycle through for our R2\n",
"f.subplots_adjust(top=0.88)\n", " ax4.set_prop_cycle(cycler('color', model_colors))\n",
"f.suptitle(beach)\n",
"\n", "\n",
"# # Print to figure\n", " # R_high - D_low\n",
"plt.savefig('07_{}.png'.format(beach), dpi=600, bbox_inches='tight')\n", " for model in models_to_plot:\n",
" df_R2 = impacts['forecasted'][model].merge(\n",
" twls['forecasted'][model], on=['site_id', 'datetime'], how='left')\n",
" df_R2 = df_R2.loc[df_R2.index.str.contains(beach)]\n",
" ax4.plot(\n",
" df_R2.R2,\n",
" n,\n",
" label=model.replace('_', '\\_'),\n",
" linewidth=models_linewidth)\n",
"\n",
" ax4.set_title(r'$R_{2\\%}$')\n",
" ax4.set_xlabel('Height (m)')\n",
"# ax4.set_xlim([0, 10])\n",
"\n",
" # Beach slope\n",
" slope_colors = [\n",
" '#bebada',\n",
" '#bc80bd',\n",
" '#ffed6f',\n",
" '#fdb462',\n",
" ]\n",
" ax5.set_prop_cycle(cycler('color', slope_colors))\n",
" slope_models = {\n",
" 'prestorm mean': 'premean_slope_sto06',\n",
" 'poststorm mean': 'postmean_slope_sto06',\n",
" 'prestorm intertidal': 'preintertidal_slope_sto06',\n",
" 'poststorm intertidal': 'postintertidal_slope_sto06',\n",
" }\n",
"\n", "\n",
"plt.show()\n", " for label in slope_models:\n",
"plt.close()" " model = slope_models[label]\n",
" df_beta = impacts['forecasted'][model].merge(\n",
" twls['forecasted'][model], on=['site_id', 'datetime'], how='left')\n",
" df_beta = df_beta.loc[df_beta.index.str.contains(beach)]\n",
" ax5.plot(df_beta.beta, n, label=label, linewidth=models_linewidth)\n",
"\n",
" ax5.set_title(r'$\\beta$')\n",
" ax5.set_xlabel('Beach slope')\n",
" ax5.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))\n",
" # ax5.set_xlim([0, 0.15])\n",
"\n",
" # Need to chose a model to extract environmental parameters at maximum R_high time\n",
" model = 'premean_slope_sto06'\n",
" df_beach = impacts['forecasted'][model].merge(\n",
" twls['forecasted'][model], on=['site_id', 'datetime'], how='left')\n",
" df_beach = df_beach.loc[df_beach.index.str.contains(beach)]\n",
"\n",
" # Wave height, wave period\n",
" ax6.plot(df_beach.Hs0, n, color='#999999')\n",
" ax6.set_title('$H_{s0}$')\n",
" ax6.set_xlabel('Sig. wave height (m)')\n",
" ax6.set_xlim([3, 5])\n",
"\n",
" ax7.plot(df_beach.Tp, n, color='#999999')\n",
" ax7.set_title('$T_{p}$')\n",
" ax7.set_xlabel('Peak wave period (s)')\n",
" ax7.set_xlim([8, 14])\n",
"\n",
" ax8.plot(df_beach.tide, n, color='#999999')\n",
" ax8.set_title('Tide \\& surge')\n",
" ax8.set_xlabel('Elevation (m AHD)')\n",
" ax8.set_xlim([0, 2])\n",
"\n",
" plt.tight_layout()\n",
" f.subplots_adjust(top=0.88)\n",
" f.suptitle(beach.replace('_', '\\_'))\n",
"\n",
" # Set minor axis ticks on each plot\n",
" ax1.yaxis.set_minor_locator(MultipleLocator(1))\n",
" ax1.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax2.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax3.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax4.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax5.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax6.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax7.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
" ax8.yaxis.grid(True, which='minor', linestyle='--', alpha=0.1)\n",
"\n",
" # # Print to figure\n",
" plt.savefig('07_{}.png'.format(beach), dpi=600, bbox_inches='tight')\n",
"\n",
"# plt.show()\n",
" plt.close()\n",
" print('Done: {}'.format(beach))\n",
"print('Done!')"
] ]
}, },
{ {
@ -393,6 +486,13 @@
"Use sklearn metrics to generate classification reports for each forecasting model." "Use sklearn metrics to generate classification reports for each forecasting model."
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -407,7 +507,7 @@
"# Convert storm regimes to categorical datatype\n", "# Convert storm regimes to categorical datatype\n",
"cat_type = CategoricalDtype(\n", "cat_type = CategoricalDtype(\n",
" categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n", " categories=['swash', 'collision', 'overwash', 'inundation'], ordered=True)\n",
"df_obs.storm_regime = df_obs_impacts.storm_regime.astype(cat_type)\n", "df_obs.storm_regime = df_obs.storm_regime.astype(cat_type)\n",
"\n", "\n",
"for model in impacts['forecasted']:\n", "for model in impacts['forecasted']:\n",
" df_for = impacts['forecasted'][model]\n", " df_for = impacts['forecasted'][model]\n",

40
notebooks/09_superseded_run_comparison.ipynb
Unescape Escape View File

@ -118,10 +118,22 @@
"# Note that the forecasted data sets should be in the same order for impacts and twls\n", "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
"impacts = {\n", "impacts = {\n",
" 'forecasted': {\n", " 'forecasted': {\n",
" 'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n", " 'postintertidal_slope_hol86': df_from_csv('impacts_forecasted_postintertidal_slope_hol86.csv', index_col=[0]),\n",
" 'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n", " 'postintertidal_slope_nie91': df_from_csv('impacts_forecasted_postintertidal_slope_nie91.csv', index_col=[0]),\n",
" 'mean_slope_nie91': df_from_csv('impacts_forecasted_mean_slope_nie91.csv', index_col=[0]),\n", " 'postintertidal_slope_pow18': df_from_csv('impacts_forecasted_postintertidal_slope_pow18.csv', index_col=[0]),\n",
" 'mean_slope_hol86': df_from_csv('impacts_forecasted_mean_slope_hol86.csv', index_col=[0]),\n", " 'postintertidal_slope_sto06': df_from_csv('impacts_forecasted_postintertidal_slope_sto06.csv', index_col=[0]),\n",
" 'postmean_slope_hol86': df_from_csv('impacts_forecasted_postmean_slope_hol86.csv', index_col=[0]),\n",
" 'postmean_slope_nie91': df_from_csv('impacts_forecasted_postmean_slope_nie91.csv', index_col=[0]),\n",
" 'postmean_slope_pow18': df_from_csv('impacts_forecasted_postmean_slope_pow18.csv', index_col=[0]),\n",
" 'postmean_slope_sto06': df_from_csv('impacts_forecasted_postmean_slope_sto06.csv', index_col=[0]),\n",
" 'preintertidal_slope_hol86': df_from_csv('impacts_forecasted_preintertidal_slope_hol86.csv', index_col=[0]),\n",
" 'preintertidal_slope_nie91': df_from_csv('impacts_forecasted_preintertidal_slope_nie91.csv', index_col=[0]),\n",
" 'preintertidal_slope_pow18': df_from_csv('impacts_forecasted_preintertidal_slope_pow18.csv', index_col=[0]),\n",
" 'preintertidal_slope_sto06': df_from_csv('impacts_forecasted_preintertidal_slope_sto06.csv', index_col=[0]),\n",
" 'premean_slope_hol86': df_from_csv('impacts_forecasted_premean_slope_hol86.csv', index_col=[0]),\n",
" 'premean_slope_nie91': df_from_csv('impacts_forecasted_premean_slope_nie91.csv', index_col=[0]),\n",
" 'premean_slope_pow18': df_from_csv('impacts_forecasted_premean_slope_pow18.csv', index_col=[0]),\n",
" 'premean_slope_sto06': df_from_csv('impacts_forecasted_premean_slope_sto06.csv', index_col=[0]),\n",
" },\n", " },\n",
" 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n", " 'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
" }\n", " }\n",
@ -129,10 +141,22 @@
"\n", "\n",
"twls = {\n", "twls = {\n",
" 'forecasted': {\n", " 'forecasted': {\n",
" 'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n", " 'postintertidal_slope_hol86.csv': df_from_csv('twl_postintertidal_slope_hol86.csv', index_col=[0,1]),\n",
" 'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n", " 'postintertidal_slope_nie91.csv': df_from_csv('twl_postintertidal_slope_nie91.csv', index_col=[0,1]),\n",
" 'mean_slope_nie91':df_from_csv('twl_mean_slope_nie91.csv', index_col=[0, 1]),\n", " 'postintertidal_slope_pow18.csv': df_from_csv('twl_postintertidal_slope_pow18.csv', index_col=[0,1]),\n",
" 'mean_slope_hol86':df_from_csv('twl_mean_slope_hol86.csv', index_col=[0, 1]),\n", " 'postintertidal_slope_sto06.csv': df_from_csv('twl_postintertidal_slope_sto06.csv', index_col=[0,1]),\n",
" 'postmean_slope_hol86.csv': df_from_csv('twl_postmean_slope_hol86.csv', index_col=[0,1]),\n",
" 'postmean_slope_nie91.csv': df_from_csv('twl_postmean_slope_nie91.csv', index_col=[0,1]),\n",
" 'postmean_slope_pow18.csv': df_from_csv('twl_postmean_slope_pow18.csv', index_col=[0,1]),\n",
" 'postmean_slope_sto06.csv': df_from_csv('twl_postmean_slope_sto06.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_hol86.csv': df_from_csv('twl_preintertidal_slope_hol86.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_nie91.csv': df_from_csv('twl_preintertidal_slope_nie91.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_pow18.csv': df_from_csv('twl_preintertidal_slope_pow18.csv', index_col=[0,1]),\n",
" 'preintertidal_slope_sto06.csv': df_from_csv('twl_preintertidal_slope_sto06.csv', index_col=[0,1]),\n",
" 'premean_slope_hol86.csv': df_from_csv('twl_premean_slope_hol86.csv', index_col=[0,1]),\n",
" 'premean_slope_nie91.csv': df_from_csv('twl_premean_slope_nie91.csv', index_col=[0,1]),\n",
" 'premean_slope_pow18.csv': df_from_csv('twl_premean_slope_pow18.csv', index_col=[0,1]),\n",
" 'premean_slope_sto06.csv': df_from_csv('twl_premean_slope_sto06.csv', index_col=[0,1]),\n",
" }\n", " }\n",
"}\n", "}\n",
"print('Done!')" "print('Done!')"

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