Migrated from github ComputationalModelling/demo

Old repo: https://github.com/TomorrowsCities/ComputationalModelling/tree/main/demo

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+
+#USER root
+RUN apt update
+RUN apt -y install gdal-bin libgdal-dev
+
+USER user
+
+ENV HOME=/home/user \
+        PATH=/home/user/.local/bin:$PATH
+
+COPY --chown=user . $HOME/app
+
+WORKDIR $HOME/app
+
+RUN (cd demo & pip install -e .)
+
+CMD ["solara", "run", "--theme-variant", "dark", "demo.pages",  "--host", "0.0.0.0", "--port", "7860"]

LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2022
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

demo/__init__.py

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+"""Example Solara app as python packages"""
+__title__ = "Solara example app"
+__version__ = "0.0.1"

demo/assets/custom.css

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+header {
+    background-image: url('/static/public/tomorrows-cities-logo-header.png'); /* Replace 'your-image.jpg' with the path to your image file */
+    background-position: center; /* Center the background image */
+    background-repeat: no-repeat; /* Prevent image from repeating */
+    height: 200px; /* Set a specific height for the header */
+}
+
+.v-application--wrap > div:nth-child(2) > div:nth-child(2){
+    display: none !important;
+}
+
+.theme--light.v-sheet {
+	background-color: #EBEBEB;
+}
+
+.v-navigation-drawer__content {
+    background-color: #EBEBEB;
+}

demo/pages/__init__.py

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+#%%
+css = """
+
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url('/static/public/open-sans.regular.ttf') format('truetype');
+}
+
+@font-face {
+    font-family: 'Museo 900';
+    src: url('/static/public/Museo900-Regular.woff2') format('woff2'),
+        url('/static/public/Museo900-Regular.woff') format('woff');
+    font-weight: 900;
+    font-style: normal;
+    font-display: swap;
+}
+
+
+@font-face {
+    font-family: 'Museo 700';
+    src: url('/static/public/Museo700-Regular.woff2') format('woff2')
+}
+
+
+.v-application {
+    font-family: 'Open Sans'
+}
+
+.v-card__title {
+    padding: 2px;
+    font-family: 'Museo 900'
+}
+
+.v-application .ma-2 {
+    margin: 2px!important;
+}
+
+.leaflet-control-attribution {
+    display: none;
+}
+.v-btn__content {
+    font-size: 12px;
+}
+
+.v-input__control {
+    height: 30px
+}
+
+.jupyter-widgets.leaflet-widgets {
+    height: 300px;
+    overflow: hidden;
+}
+
+.col {
+    overflow: hidden;
+}
+
+.v-card__title {
+    font-size: 20px;
+    text-color: "orange";
+}
+
+label.v-label.theme--dark {
+    font-size: 14px;
+    line-height: 100%;
+}
+
+.metricdesc {
+    font-size: 15px;
+    margin-top: -30px;
+    text-align: center;
+}
+.infobox {
+    font-size: 14px;
+    line-height: 110%;
+}
+
+.infoitem {
+    font-size: 12px;
+    line-height: 110%;
+}
+
+.maintitle {
+    font-family: 'Museo 900';
+    font-size: 30px;
+    text-align: center;
+}
+
+"""
+
+import solara
+from lorem_text import lorem
+#import solara.express as px
+import plotly.express as px
+import pandas as pd
+import geopandas as gpd
+import plotly.graph_objects as go
+import ipyleaflet
+from random import uniform
+import random
+import numpy as np
+from branca.colormap import linear
+import json
+import math
+import matplotlib.pyplot as plt
+from plotly.subplots import make_subplots
+from matplotlib.figure import Figure
+from .engine import run_engine
+
+languages = ["EN","TR"]
+lang = solara.reactive("EN")
+
+lbl_land_use_btn = [{'EN': 'school and hospital oriented',
+                     'TR': 'okul ve hastane odaklı'},
+                     {'EN': 'population increase oriented',
+                     'TR': 'nüfus artışı odaklı'},
+                     {'EN': 'green area oriented',
+                     'TR': 'Yeşil çevre odaklı'},]
+lbl_maintitle = {'EN': 'Tomorrow\'s Cities Decision Making Simulation','TR': 'Yarının Şehirleri için Karar Verme Simülasyonu'}
+lbl_earthquake= {'EN': 'Earthquake', 'TR': 'Deprem'}
+lbl_flood = {'EN': 'Flood', 'TR': 'Sel'}
+lbl_debris = {'EN': 'Landslide', 'TR': 'Heyelan'}
+lbl_reset = {'EN': 'Reset Simulation', 'TR': 'Simülasyonu Sıfırla'}
+lbl_language = {'EN': 'Language', 'TR': 'Dil'}
+lbl_luf_res_high = {'EN': 'Residential (High Density)', 'TR': 'Yerlesim Alani (Yuksek Yogunluk)'}
+lbl_tv0_map_title = {'EN': '1. Today\'s land use', 'TR': '1. Mevcut plan'}
+lbl_density_map_title = {'EN': '2. Select the potential hazard', 'TR': '2. Olası tehlikeyi seçin'}
+lbl_policy_card_title = {'EN': '4. Select three policies to make your city resillient', 'TR': '4. Şehrinizi dayanıklı hale getirmek için üç politika seçin'}
+lbl_too_many_policy_selected_error = {'EN': 'You cannot select more than three policies', 
+                              'TR': 'Üçten fazla politika seçemezsiniz.'}
+lbl_infobox = {'EN': f'''
+               Our decisions directly influence our future. It is critical for a 
+               resilient city life where the decisions at the city scale  reduces the disaster risk in the future.
+               In this simulation, we calculate possible disaster impacts calculated with Tomorrow's Cities Decision 
+               Support System (TCDSE). The simulation shows how earthquake, flood and landslide hazards that may occur 
+               in an imaginary urban environment affect different urban scenarios. To use the simulation 1) Select the 
+               possible hazard 2) Select the possible city scenarios for the future 3) In order to reduce the impact 
+               against potential hazards; choose policies.
+               ''', 
+               "TR": f'''
+            Aldığımız kararlar, geleceğimizi doğrudan etkilemektedir. Kent ölçeğinde alınan kararların, gelecekte afet riskini azaltacak 
+            nitelikte olması dayanıklı bir şehir hayatı için kritik öneme sahiptir.
+            Bu simülasyonda, Yarının Şehirleri Karar Destek Sistemi (TCDSE) ile hesaplanan olası afet etkilerini hesaplıyoruz. Simülasyon, 
+            hayali bir kent ortamında meydana gelebilecek deprem, sel ve heyelan tehlikelerinin, farklı kent senaryolarını nasıl etkilediğini gösteriyor. 
+            Simülasyonu kullanmak için 1) Olası tehlikeyi seçin 2) Geleceğe dair olası şehir senaryolarını seçin 3) Bu şehrin olası tehlikeler karşısında en 
+            az etkiye maruz kalması için; hangi politikalarla yönetileceğini seçin.'''}
+lbl_infobox_title = {'EN': '', 'TR': ''}
+lbl_landuse_plans_title = {'EN': '3. Future land uses', 'TR': '3. Geleceğin alternatif yerleşim planlarından birini seçin'}
+lbl_run_btn = {'EN': 'RUN', 'TR': 'ÇALIŞTIR'}
+lbl_results_card_title = {'EN': '5. Impact metrics', 'TR': '5. Etki metrikleri'}
+lbl_info1 = {'EN': 'Number of individuals<br>39058', 'TR': 'Kişi Sayısı<br>39058'}
+lbl_info2 = {'EN': 'Number of households<br>10397',  'TR': 'Hane Sayısı<br>10397'}
+lbl_info3 = {'EN': 'Number of buildings<br>4810', 'TR': 'Bina Sayısı<br>4810'}
+lbl_info4 = {'EN': 'Number of schools<br>4', 'TR': 'Okul Sayısı<br>4'}
+lbl_info5 = {'EN': 'Number of hospitals<br>1', 'TR': 'Hastane Sayısı<br>1'}
+#lbl_info6 = {'EN': 'Number of green field<br>', 'TR': 'Yesil Alan Sayisi<br>'}
+
+hazard_types = ["earthquake", "flood", "debris"]
+hazard_type_default = hazard_types[0]
+hazard_type = solara.reactive("earthquake")
+
+flood_df = gpd.read_file('hazard_flood.geojson')
+debris_df = gpd.read_file('hazard_debris.geojson')
+earthquake_df = gpd.read_file('hazard_earthquake.geojson')
+
+earthquake_df_as_geo = gpd.read_file('hazard_earthquake.geojson')
+print('bir',earthquake_df_as_geo.crs)
+earthquake_df_as_geo = earthquake_df_as_geo.set_crs("EPSG:4326",allow_override=True)
+print('iki',earthquake_df_as_geo.crs)
+earthquake_df_as_geo = earthquake_df_as_geo.to_crs(32645)
+print('uc',earthquake_df_as_geo.crs)
+
+
+debris_df_as_geo = gpd.read_file('hazard_debris.geojson')
+debris_df_as_geo = debris_df_as_geo.set_crs("EPSG:4326",allow_override=True)
+debris_df_as_geo = debris_df_as_geo.to_crs("EPSG:32645")
+
+flood_df_as_geo = gpd.read_file('hazard_flood.geojson')
+flood_df_as_geo = flood_df_as_geo.set_crs("EPSG:4326",allow_override=True)
+flood_df_as_geo = flood_df_as_geo.to_crs("EPSG:32645")
+
+landuse_tv0_df = gpd.read_file('landuse_tv0.geojson')
+landuse_tv50_1_df = gpd.read_file('landuse_tv50_1.geojson')
+landuse_tv50_2_df = gpd.read_file('landuse_tv50_2.geojson')
+landuse_tv50_3_df = gpd.read_file('landuse_tv50_3.geojson')
+n_landuses = 3
+
+luf_types = pd.unique(landuse_tv0_df['luf'])
+
+luf_colors = dict()
+for luf_label in luf_types:
+    if luf_label == 'RESIDENTIAL (HIGH DENSITY)':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#A0522D', # sienna
+        }    
+    elif luf_label == 'HISTORICAL PRESERVATION AREA':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#673147', # plum
+        }    
+    elif luf_label == 'RESIDENTIAL (MODERATE DENSITY)':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#cd853f', # peru
+        }   
+    elif luf_label == 'COMMERCIAL AND RESIDENTIAL':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': 'red',
+        }   
+    elif luf_label == 'CITY CENTER':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#E6E6FA', # lavender
+        }   
+    elif luf_label == 'INDUSTRY':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': 'grey',
+        }   
+    elif luf_label == 'RESIDENTIAL (LOW DENSITY)':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#D2B48C', # tan
+        }   
+    elif luf_label == 'RESIDENTIAL (GATED NEIGHBORHOOD)':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': 'orange',
+        }   
+    elif luf_label == 'AGRICULTURE':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': 'yellow',
+        }   
+    elif luf_label == 'FOREST':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': 'green',
+        }   
+    elif luf_label == 'VACANT ZONE':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#90EE90', # lightgreen
+        }   
+    elif luf_label == 'RECREATION AREA':
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': '#32CD32', #lime
+        }   
+    else:
+        luf_colors[luf_label] = {
+        'color': 'black',
+        'fillColor': random.choice(['red', 'yellow', 'green', 'orange','blue']),
+        } 
+
+#
+plt.switch_backend("agg")
+plt.rcParams['axes.facecolor'] = '#1e1e1e'
+plt.rcParams['axes.edgecolor'] = '#1e1e1e'
+#plt.switch_backend('macosx')
+#%matplotlib inline
+for j in range(1, n_landuses+1):
+    df = eval(f'landuse_tv50_{j}_df')
+    title = f'tv50_{j}'
+    for i, row in df.iterrows():
+        df.loc[i, 'color'] = luf_colors[row['luf']]['fillColor']
+    fig, ax = plt.subplots(nrows=1, ncols=1,figsize=(4*1.3, 5*1.3))
+    fig.set_tight_layout(True)
+    fig.patch.set_facecolor('white')
+    #fig.patch.set_alpha(0.7)
+    df.plot(color=df['color'],ax=ax)
+    #ax.set_title(f'{title}')
+    ax.set_xticks([])
+    ax.set_yticks([])
+    plt.savefig(f'landuse_{title}_selected.png')
+
+    fig, ax = plt.subplots(nrows=1, ncols=1,figsize=(4*1.3,5*1.3))
+    fig.set_tight_layout(True)
+    #fig.patch.set_facecolor('black')
+    fig.patch.set_alpha(0.0)
+    df.plot(color=df['color'],ax=ax)
+    #ax.set_title(f'{title}')
+    ax.set_xticks([])
+    ax.set_yticks([])
+    plt.savefig(f'landuse_{title}.png')
+
+landuse_tv0_json = json.loads(landuse_tv0_df.to_json())
+luf_filtered_data = solara.reactive(landuse_tv0_json)
+
+luf_selected = solara.reactive(luf_types)
+luf_type_selected = dict()
+for luf_type in luf_types:
+    luf_type_selected[luf_type] = solara.reactive(True)
+
+lbl_luf = dict()
+for luf_label in luf_types:
+    if luf_label == 'RESIDENTIAL (HIGH DENSITY)':
+        lbl_luf[luf_label] = {'EN': 'Residential (High Density)', 'TR': 'Konut Alanı (Yuksek Yoğunluk)'}
+    elif luf_label == 'HISTORICAL PRESERVATION AREA':
+        lbl_luf[luf_label] = {'EN': 'Historical Preservation', 'TR': 'Tarihi Sit Alanı'}
+    elif luf_label == 'RESIDENTIAL (MODERATE DENSITY)':
+        lbl_luf[luf_label] = {'EN': 'Residential (Moderate Density)', 'TR': 'Konut Alanı (Orta Yoğunluk)'}
+    elif luf_label == 'COMMERCIAL AND RESIDENTIAL':
+        lbl_luf[luf_label] = {'EN': 'Commercial and Residential', 'TR': 'Konut ve Ticari'}
+    elif luf_label == 'CITY CENTER':
+        lbl_luf[luf_label] = {'EN': 'City Center', 'TR': 'Şehir Merkezi'}
+    elif luf_label == 'INDUSTRY':
+        lbl_luf[luf_label] = {'EN': 'Industry', 'TR': 'Sanayi'}
+    elif luf_label == 'RESIDENTIAL (LOW DENSITY)':
+        lbl_luf[luf_label] = {'EN': 'Residential (Low Density)', 'TR': 'Konut Alanı (Düşük Yoğunluk)'}
+    elif luf_label == 'RESIDENTIAL (GATED NEIGHBORHOOD)':
+        lbl_luf[luf_label] = {'EN': 'Residential (Gated Community)', 'TR': 'Konut Alanı (Site)'}
+    elif luf_label == 'AGRICULTURE':
+        lbl_luf[luf_label] = {'EN': 'Agriculture', 'TR': 'Tarım'}
+    elif luf_label == 'FOREST':
+        lbl_luf[luf_label] = {'EN': 'Forest', 'TR': 'Orman'}
+    elif luf_label == 'VACANT ZONE':
+        lbl_luf[luf_label] = {'EN': 'Vacant Zone', 'TR': 'Boş Alan'}
+    elif luf_label == 'RECREATION AREA':
+        lbl_luf[luf_label] = {'EN': 'Recreation Area', 'TR': 'Park'}
+    else:
+        lbl_luf[luf_label] = {'EN': luf_label, 'TR': '(TR)'+luf_label}
+
+policies = [1,2,3,4,5,6]
+lbl_policy = dict()
+lbl_policy[1] = {'EN': 'Loans for reconstruction for minor to moderate damages', 
+                 'TR': 'Küçük ila orta dereceli hasarlar için yeniden yapılanma kredileri'}
+lbl_policy[2] = {'EN': 'Knowledge sharing about disaster risk reduction in public and private schools', 
+                 'TR': 'Devlet ve özel okullarda afet risklerinin azaltılması hakkında bilgi paylaşımı'}
+lbl_policy[3] = {'EN': 'Cat-bond agreement for education and health facilities', 
+                 'TR': 'Eğitim ve sağlık tesisleri için afet bonosu'}
+lbl_policy[4] = {'EN': 'Repair loan assistance for flooding', 
+                 'TR': 'Sel için onarım kredisi yardımı'}
+lbl_policy[5] = {'EN': 'Technical assistance for debris removal in education facilities', 
+                 'TR': 'Eğitim tesislerinde enkaz kaldırma için teknik yardım'}
+lbl_policy[6] = {'EN': 'Compulsory content insurance for schools and hospitals', 
+                 'TR': 'Okullar ve hastaneler için zorunlu eşya sigortası'}
+#lbl_policy[7] = {'EN': 'Green resilient infrastructure (prevention) & financial support for business recovery', 'TR': 'Politika 7'}
+#lbl_policy[8] = {'EN': 'Green resilient infrastructure (prevention), reinforcement of schools, hospitals & shelters, financial support for affected business & household', 'TR': 'Politika 8'}
+policy_selected = dict()
+for policy in policies:
+    policy_selected[policy] = solara.reactive(False)
+last_selected_policies = solara.reactive(set())
+
+earthquake_locs = np.array([earthquake_df.geometry.y.to_list(), earthquake_df.geometry.x.to_list(), earthquake_df.im.to_list()]).transpose().tolist()
+flood_locs = np.array([flood_df.geometry.y.to_list(), flood_df.geometry.x.to_list(), flood_df.im.to_list()]).transpose().tolist()
+debris_locs = np.array([debris_df.geometry.y.to_list(), debris_df.geometry.x.to_list(), debris_df.im.to_list()]).transpose().tolist()
+
+hazard_intensities = {
+    "earthquake": earthquake_locs,
+    "flood": flood_locs,
+    "debris": debris_locs
+}
+
+hazard_df = {  
+    "earthquake": earthquake_df,
+    "flood": flood_df,
+    "debris": debris_df  
+}
+
+hazard_df_as_geo = {  
+    "earthquake": earthquake_df_as_geo,
+    "flood": flood_df_as_geo,
+    "debris": debris_df_as_geo  
+}
+
+zoom_default = 13
+tv0_zoom = solara.reactive(zoom_default)
+
+tv0_center_default = (flood_df.geometry.y.mean(), flood_df.geometry.x.mean())
+tv0_center = solara.reactive(tv0_center_default)
+print(tv0_center)
+def random_color(feature):
+    return luf_colors[feature['properties']['luf']]
+
+@solara.component
+def TV0MAP():
+    selected_lufs = set()
+    for luf_type in luf_types:
+        if luf_type_selected[luf_type].value:
+            selected_lufs.add(luf_type)
+    x = {'type':landuse_tv0_json['type'] }
+    x['features'] = []
+    for f in landuse_tv0_json['features']:
+        if f['properties']['luf'] in selected_lufs:
+            x['features'].append(f)
+    ipyleaflet.Map.element(
+        zoom=tv0_zoom.value,
+        on_zoom=tv0_zoom.set,
+        max_zoom=zoom_default,
+        min_zoom=zoom_default,
+        center=tv0_center.value,
+        on_center=tv0_center.set,
+        scroll_wheel_zoom=False,
+        dragging=False,
+        double_click_zoom=False,
+        touch_zoom=False,
+        box_zoom=False,
+        keyboard=False,
+        zoom_control=False,
+        layers=[
+            ipyleaflet.basemap_to_tiles(ipyleaflet.basemaps.Esri.WorldImagery),
+            ipyleaflet.GeoJSON(
+                data=x,
+                style={
+                    'opacity': 1, 'dashArray': '9', 'fillOpacity': 0.5, 'weight': 1
+                },
+                hover_style={
+                    'color': 'white', 'dashArray': '0', 'fillOpacity': 0.5
+                },
+                style_callback=random_color)
+        ],
+    )
+
+@solara.component
+def DensityMap(hazard_type):
+    locs = hazard_intensities[hazard_type]
+    ipyleaflet.Map.element(
+        zoom=tv0_zoom.value,
+        max_zoom=zoom_default,
+        min_zoom=zoom_default,
+        on_zoom=tv0_zoom.set,
+        center=tv0_center.value,
+        on_center=tv0_center.set,
+        scroll_wheel_zoom=False,
+        dragging=False,
+        double_click_zoom=False,
+        touch_zoom=False,
+        box_zoom=False,
+        keyboard=False,
+        zoom_control=False,
+        layers=[
+            ipyleaflet.basemap_to_tiles(ipyleaflet.basemaps.Esri.WorldTopoMap),
+            ipyleaflet.Heatmap(
+                name=hazard_type,
+                locations=locs,
+                radius=10
+            ),
+        ],
+    )
+
+
+run_is_allowed = solara.reactive(True)
+
+@solara.component
+def PolicyValidation():
+    npolicies_selected = 0
+    selected_policies = set()
+    for p in policies:
+        if policy_selected[p].value:
+            selected_policies.add(p)
+            npolicies_selected += 1
+    if npolicies_selected > 3:
+        print("roll-back")
+        solara.Warning(label=lbl_too_many_policy_selected_error[lang.value])
+        run_is_allowed.value = False
+        # Roll-back to last selection
+        #for p in policies:
+        #    if p in last_selected_policies.value:
+        #        policy_selected[p].value = True
+        #    else:
+        #        policy_selected[p].value = False
+    else:
+        last_selected_policies.value = selected_policies
+        run_is_allowed.value = True
+
+
+landuseplan_selected = solara.reactive(1)
+
+lbl_metric1 = {"EN": "Number of workers unemployed", "TR": "İşini kaybedenlerin sayısı"}
+lbl_metric2 = {"EN": "Number of children with no access to education", "TR": "Okula gidemeyen çocuk sayısı"}
+lbl_metric3 = {"EN": "Number of households with no access to hospital", "TR": "Hastane erişimi olmayan hane sayısı"}
+lbl_metric4 = {"EN": "Number of individuals with no access to hospital", "TR": "Hastane erişimi olmayan kişi sayısı"}
+lbl_metric5 = {"EN": "Number of homeless households", "TR": "İşlevsiz kalan hane sayısı"}
+lbl_metric6 = {"EN": "Number of homeless individuals", "TR": "Evsiz kalan sayısı"}
+lbl_metric7 = {"EN": "Population displacement", "TR": "Yer değiştirmek zorunda kalan  kişi sayısı"}
+
+metrics =  {"metric1": {"desc": lbl_metric1, "value": solara.reactive(0), "max_value": solara.reactive(0)},
+            "metric2": {"desc": lbl_metric2, "value": solara.reactive(0), "max_value": solara.reactive(0)},
+            "metric3": {"desc": lbl_metric3, "value": solara.reactive(0), "max_value": solara.reactive(0)},
+            "metric4": {"desc": lbl_metric4, "value": solara.reactive(0), "max_value": solara.reactive(0)},
+            "metric5": {"desc": lbl_metric5, "value": solara.reactive(0), "max_value": solara.reactive(0)},
+            "metric6": {"desc": lbl_metric6, "value": solara.reactive(0), "max_value": solara.reactive(0)},
+            "metric7": {"desc": lbl_metric7, "value": solara.reactive(0), "max_value": solara.reactive(0)}}
+
+
+@solara.component
+def DialWidget(desc, value, max_value=10000):
+    if max_value == 0:
+        max_value = 10000
+    fig = Figure(tight_layout=True,dpi=50, edgecolor="dimgray",frameon=False)
+    fig.set_size_inches(2.5, 1)
+    ax = fig.subplots()
+    ax.axis('equal')
+    #ax.axis('off')
+
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+    t = np.linspace(0, math.pi, 100)
+
+    cos = np.cos(t)
+    sin = np.sin(t)
+    
+    ax.plot(cos,sin, linewidth=2)
+    value_t = math.pi * (1 - (value / max_value))
+
+    fill_color = 'red'
+    if value_t >  math.pi / 2  :
+        x1 = np.linspace(-1,np.cos(value_t),100)
+        y1 = np.sqrt(1 - x1**2)
+        ax.fill_between(x1,y1,color=fill_color)
+        x1 = np.linspace(np.cos(value_t),0,100)
+        y1 = np.tan(value_t) * x1
+        ax.fill_between(x1,y1,color=fill_color)
+    else:
+        x = np.linspace(-1, np.cos(value_t),100)
+        y1 = np.sqrt(1-x**2)
+        y2a = np.zeros(100)
+        y2b = np.tan(value_t) * x 
+        y2 = np.maximum(y2a,y2b)
+        ax.fill_between(x,y1,y2,color=fill_color)
+
+    ax.plot([0,0.9*np.cos(value_t)],[0, 0.9*np.sin(value_t)], linewidth=2)
+    ax.scatter([0],[0],color='white',s=50)
+    ax.text(-1.1,0,0,fontdict={'fontsize':12},verticalalignment="center",
+        horizontalalignment="right",color="white")    
+    ax.text(1.1,0,max_value,fontdict={'fontsize':12},verticalalignment="center",
+        horizontalalignment="left",color="white")
+    ax.plot([-0.9,-1.1],[0,0],color="white")
+    ax.plot([0.9,1.1],[0,0],color="white")
+    ax.plot([0,0],[0.9,1.1],color="white")
+    horizontalalignment = "right" if value_t > math.pi/2 else "left"
+    ax.text(1.1*np.cos(value_t),1.1*np.sin(value_t),value,fontdict={'fontsize':12},verticalalignment="center",
+        horizontalalignment=horizontalalignment,color="white")
+    ax.set_xlim(-1.7,1.7)
+    ax.set_ylim(-0.2,1.2)
+    #ax.text(0, -0.1, desc,fontdict={'fontsize':20},verticalalignment="center",
+    #    horizontalalignment='center',color="white")
+    solara.FigureMatplotlib(fig)
+
+#@solara.component
+#def MetricWidget(desc, value, max_value=10000):
+#    DialWidget(desc, value, max_value)
+
+@solara.component
+def MetricWidget(name, description, value, max_value=10000):
+    value, set_value = solara.use_state_or_update(value)
+    max_value, set_max_value = solara.use_state_or_update(max_value)
+    options = {
+        "series": [ {
+                "type": 'gauge',
+                "min": 0,
+                "name": description,
+                "max": max(1,max_value), # workaround when max_value = 0
+                "startAngle": 180,
+                "endAngle": 0,
+                "progress": {"show": True, "width": 8, "itemStyle": {"color": 'red'}},
+                "pointer": { "show": False},
+                "axisLine": {"lineStyle": {"width": 8}},
+                "axisTick": {"show": False},
+                "splitLine": {"show": False},
+                "axisLabel": {"show": False},
+                "anchor": {"show": False},
+                "title": {"show": False},
+                "detail": {
+                    "valueAnimation": True,
+                    "offsetCenter": [0, '-15%'],
+                    "fontSize": 14,
+                    "color": 'white'},
+                "title": {"fontSize": 12},
+                "data": [{"value": value, "name": name}]}]}
+    print(f'value/max_value {value}:{max_value}')
+
+
+    solara.FigureEcharts(option=options, attributes={ "style": "height: 100px; width: 100px" })
+
+
+
+
+@solara.component 
+def MetricsPanel(metrics):
+    '''
+        with solara.Columns([33,33,33]):
+            with solara.Column(align="center", gap="2px"):
+                for key in ["metric1","metric2"]:
+                    MetricWidget(key, metrics[key]["desc"], metrics[key]["value"].value,
+                            max_value=metrics[key]["max_value"].value)
+                    solara.HTML(unsafe_innerHTML=metrics[key]["desc"][lang.value], classes=["metricdesc"])
+            with solara.Column(align="center", gap="2px"):
+                for key in ["metric3","metric4"]:
+                    MetricWidget(key, metrics[key]["desc"], metrics[key]["value"].value,
+                            max_value=metrics[key]["max_value"].value)
+                    solara.HTML(unsafe_innerHTML=metrics[key]["desc"][lang.value], classes=["metricdesc"])
+            with solara.Column(align="center", gap="2px"):
+                for key in ["metric6","metric7"]:
+                    MetricWidget(key, metrics[key]["desc"], metrics[key]["value"].value,
+                            max_value=metrics[key]["max_value"].value)
+                    solara.HTML(unsafe_innerHTML=metrics[key]["desc"][lang.value], classes=["metricdesc"])
+    '''
+    with solara.GridFixed(columns=3):
+        for key in ["metric1","metric2","metric3"]:
+            with solara.Row(justify="center"):
+                MetricWidget(key, metrics[key]["desc"], metrics[key]["value"].value,
+                    max_value=metrics[key]["max_value"].value)
+        for key in ["metric1","metric2","metric3"]:
+            with solara.Row(justify="center"):
+                solara.HTML(unsafe_innerHTML=metrics[key]["desc"][lang.value], classes=["metricdesc"])
+        for key in ["metric4","metric6","metric7"]:
+            with solara.Row(justify="center"):
+                MetricWidget(key,metrics[key]["desc"], metrics[key]["value"].value,
+                    max_value=metrics[key]["max_value"].value)
+        for key in ["metric4","metric6","metric7"]:
+            with solara.Row(justify="center"):
+                solara.HTML(unsafe_innerHTML=metrics[key]["desc"][lang.value], classes=["metricdesc"])
+            
+
+
+def run_simulation():
+    print("Running simulation")
+    print(f"Selected landuse plans: {landuseplan_selected.value}")
+    selected_policies = set()
+    for p in policies:
+        if policy_selected[p].value:
+            selected_policies.add(p)
+    print(f"Selected policies: {selected_policies}")
+ 
+    new_metrics = run_engine(hazard_type.value, 
+                             landuseplan_selected.value, 
+                             hazard_df_as_geo[hazard_type.value],
+                             list(selected_policies))
+    for key in new_metrics.keys():
+        metrics[key]["value"].value = new_metrics[key]["value"]
+        metrics[key]["max_value"].value = new_metrics[key]["max_value"]
+
+    print(metrics)
+  
+@solara.component
+def LandUsePlans():
+    for p in policies:
+        if policy_selected[p].value:
+            print(p, 'selected')
+
+    for p in policies:
+        if policy_selected[p].value:
+            print(p, 'selected')
+
+    #with solara.Column(gap="2px"):
+    #    with solara.Columns([2,32,32,32,2]):
+    #        solara.HTML(unsafe_innerHTML="")
+    #        for i in range(1, n_landuses+1):
+    #            if landuseplan_selected.value == i: 
+    #                solara.Image(f"landuse_tv50_{i}_selected.png", width="230px")
+    #            else:
+    #                solara.Image(f"landuse_tv50_{i}.png",width="230px")    
+    #        solara.HTML(unsafe_innerHTML="")
+    #    with solara.Row():
+    #        with solara.ToggleButtonsSingle(value=landuseplan_selected):
+    #            for i in range(0, n_landuses):
+    #                solara.Button(lbl_land_use_btn[i][lang.value], value=i+1, text=True, style="width: 320px; height: 26px")
+
+        
+    def landuse_1():
+        landuseplan_selected.value = 1
+    
+    def landuse_2():
+        landuseplan_selected.value = 2
+
+    def landuse_3():
+        landuseplan_selected.value = 3
+
+    funcs = {1: landuse_1, 2: landuse_2, 3: landuse_3}
+
+    butons = []
+    with solara.Columns([32,32,32]):
+        for i in range(0, n_landuses):
+            with solara.Column():
+                if landuseplan_selected.value == i + 1: 
+                    solara.Button(lbl_land_use_btn[i][lang.value], value=i+1, 
+                              text=True, on_click=funcs[i+1], style="background-color: #545454; height: 26px")
+                    with solara.Row(justify="center"):
+                        solara.Image(f"landuse_tv50_{i+1}_selected.png", width="200px")
+
+                else:
+                    solara.Button(lbl_land_use_btn[i][lang.value], value=i+1, 
+                              text=True, on_click=funcs[i+1], style="height: 26px")
+                    with solara.Row(justify="center"):
+                        solara.Image(f"landuse_tv50_{i+1}.png", width="200px")    
+                
+
+
+@solara.component
+def Page():
+    def reset_view():
+        tv0_zoom.value = zoom_default
+        tv0_center.value = tv0_center_default
+        hazard_type.value = hazard_type_default
+        for luf_type in luf_types:
+            luf_type_selected[luf_type].value = True
+        landuseplan_selected.value = 1
+
+    solara.Style(css)
+    with solara.Columns([1,98,1]):
+        solara.HTML(unsafe_innerHTML="")
+        with solara.Column():
+            with solara.Columns(([10,80,10])):
+                solara.Button(label=lbl_reset[lang.value], on_click=reset_view, style="height: 26px;")
+                solara.HTML(unsafe_innerHTML=lbl_maintitle[lang.value],classes=["maintitle"])
+                with solara.ToggleButtonsSingle(value=lang, style="align-items: center;"):
+                    solara.Button(label="EN", value="EN", text=True,classes=["langbutton"],style="height: 26px;")
+                    solara.Button(label="TR", value="TR", text=True,classes=["langbutton"],style="height: 26px;")
+            with solara.Columns([50,50]):
+                with solara.Column(gap="2px"):
+                    with solara.Card(title=lbl_tv0_map_title[lang.value]):
+                        with solara.Columns([35,35,30], classes=["mycol"]):
+                            TV0MAP()
+                            with solara.Column(gap="0px"):
+                                for luf_type in luf_types:
+                                    with solara.Row():
+                                        #solara.Button(label="", 
+                                        #          color=luf_colors[luf_type]['fillColor'],
+                                        #          classes=["lufbox"])
+                                        solara.HTML(unsafe_innerHTML="&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;", 
+                                                    style=f"margin: 1px; background-color: {luf_colors[luf_type]['fillColor']};")
+                                        solara.HTML(unsafe_innerHTML=lbl_luf[luf_type][lang.value], 
+                                                    style="font-size: 12px; line-height: 100%; display: flex; justify-content: center; align-items: center;")
+                                        #solara.Checkbox(label=lbl_luf[luf_type][lang.value],value=luf_type_selected[luf_type], 
+                                        #            style="margin-top: 0px; line-height: 80%;")
+                            with solara.Column(gap="20px"):
+                                solara.HTML(unsafe_innerHTML=lbl_info1[lang.value], classes=["infoitem"])
+                                solara.HTML(unsafe_innerHTML=lbl_info2[lang.value], classes=["infoitem"])
+                                solara.HTML(unsafe_innerHTML=lbl_info3[lang.value], classes=["infoitem"])
+                                solara.HTML(unsafe_innerHTML=lbl_info4[lang.value], classes=["infoitem"])
+                                solara.HTML(unsafe_innerHTML=lbl_info5[lang.value], classes=["infoitem"])
+                    with solara.Card(title=lbl_infobox_title[lang.value]):
+                        solara.HTML(unsafe_innerHTML=lbl_infobox[lang.value], classes=["infobox"])
+                    with solara.Card(title=lbl_density_map_title[lang.value]):
+                        with solara.Columns([30,30,30]):
+                            with solara.Column():
+                                if hazard_type.value == "earthquake":
+                                    bgcolor = "background-color: #545454;"
+                                else:
+                                    bgcolor = ""
+                                with solara.Row(justify="center"):
+                                    solara.Button(lbl_earthquake[lang.value], value="earthquake", text=True, on_click=lambda: hazard_type.set("earthquake"), style=f"{bgcolor} width: 200px; height: 26px")
+                                DensityMap("earthquake")
+                            with solara.Column():
+                                if hazard_type.value == "flood":
+                                    bgcolor = "background-color: #545454;"
+                                else:
+                                    bgcolor = ""
+                                with solara.Row(justify="center"):
+                                    solara.Button(lbl_flood[lang.value], value="flood", text=True,  on_click=lambda: hazard_type.set("flood"), style=f"{bgcolor} width: 200px; height: 26px")    
+                                DensityMap("flood")
+                            with solara.Column():
+                                if hazard_type.value == "debris":
+                                    bgcolor = "background-color: #545454;"
+                                else:
+                                    bgcolor = ""
+                                with solara.Row(justify="center"):
+                                    solara.Button(lbl_debris[lang.value], value="debris", text=True, on_click=lambda: hazard_type.set("debris"), style=f"{bgcolor} width: 200px; height: 26px")  
+                                DensityMap("debris")
+                        #with solara.Columns([30,30,30]):
+                        #    DensityMap("earthquake")
+                        #    DensityMap("flood")
+                        #    DensityMap("debris")
+                with solara.Column(gap="2px"):
+                    with solara.Card(title=lbl_landuse_plans_title[lang.value]):
+                        LandUsePlans()
+                    with solara.Card(lbl_policy_card_title[lang.value]):
+                        with solara.GridFixed(columns=3,row_gap="2px", column_gap="2px"):
+                            for p in policies:
+                                solara.Checkbox(label=lbl_policy[p][lang.value],value=policy_selected[p], style="font-size: 15px")
+                    solara.Button(label=lbl_run_btn[lang.value],on_click=run_simulation, disabled=not run_is_allowed.value, style="height: 50px")
+                    PolicyValidation()
+                    with solara.Card(title=lbl_results_card_title[lang.value]):
+                        MetricsPanel(metrics)
+        solara.HTML(unsafe_innerHTML="")
+
+
+
+@solara.component
+def Layout(children):
+    # if you need the normal applayout
+    # layout = solara.AppLayout(children=children, style)
+    layout = solara.Div(children=children, style={"width": "100%", "min-height": "100vh"})
+
+    def log(*args):
+        print("stop doing that!")
+
+    #solara.v.use_event(layout, "contextmenu.prevent", log)
+    return layout
+
+Page()

demo/pages/engine.py

@@ -0,0 +1,334 @@
+#%%
+import warnings
+import json
+import sys
+import argparse
+import io
+import os
+import pandas as pd
+import psycopg2
+import geopandas as gpd
+import numpy as np
+from scipy.stats import norm
+from scipy.interpolate import interp1d
+
+# Damage States
+DS_NO = 1
+DS_SLIGHT = 2
+DS_MODERATE = 3
+DS_EXTENSIZE = 4
+DS_COLLAPSED = 5
+
+# Hazard Types 
+HAZARD_EARTHQUAKE = "earthquake"
+HAZARD_FLOOD = "flood"
+HAZARD_DEBRIS = "debris"
+
+weights = {
+    "earthquake": {"metric1": 0.2,
+                   "metric2": 0.2,
+                   "metric3": 0.2,
+                   "metric4": 0.2,
+                   "metric5": 0.2,
+                   "metric6": 0.2,
+                   "metric7": 0.2,
+                   },
+    "flood": {"metric1": 20,
+                   "metric2": 20,
+                   "metric3": 20,
+                   "metric4": 20,
+                   "metric5": 20,
+                   "metric6": 20,
+                   "metric7": 20,
+                   },
+    "debris": {"metric1": 200,
+                   "metric2": 200,
+                   "metric3": 200,
+                   "metric4": 200,
+                   "metric5": 200,
+                   "metric6": 200,
+                   "metric7": 200,
+                   }
+}
+
+def run_engine(hazard_type, scenario, gdf_intensity, policies=[]):
+
+    building_file = f"building_tv50_{scenario}.geojson"
+    household_file = f"household_tv50_{scenario}.json"
+    individual_file = f"individual_tv50_{scenario}.json"
+   
+    threshold = 1
+    threshold_flood = 0.2
+    threshold_flood_distance = 40
+    epsg = 32645 # katmandu
+
+    df_buildings = gpd.read_file(building_file)
+    df_household = pd.read_json(household_file)
+    df_individual = pd.read_json(individual_file)
+    #gdf_intensity = intensity_df.copy()
+
+    # Read vulnerability from this table if hazard is earthquake
+    if hazard_type == HAZARD_EARTHQUAKE:
+        df_eq = pd.read_csv('earthquake_fragility.csv')
+
+    elif hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+        df_flood = pd.read_csv('flood_vulnerability.csv')
+
+    # TODO: Fix the confusion geometry/geograhy etc
+    gdf_buildings = gpd.GeoDataFrame(df_buildings, 
+        geometry=gpd.points_from_xy(df_buildings.xcoord, df_buildings.ycoord))
+
+    #
+    # We asssume all input is EPSG:4326
+    gdf_buildings = gdf_buildings.set_crs("EPSG:4326",allow_override=True)
+    #gdf_intensity = gdf_intensity.set_crs("EPSG:4326",allow_override=True)
+
+    # Convert both to the same target coordinate system
+    print(epsg)
+    gdf_buildings = gdf_buildings.to_crs(f"EPSG:{epsg}")
+    #gdf_intensity = gdf_intensity.to_crs(f"EPSG:{epsg}")
+
+    #%%
+    gdf_building_intensity = gpd.sjoin_nearest(gdf_buildings,gdf_intensity, 
+                how='left', rsuffix='intensity',distance_col='distance')
+    gdf_building_intensity = gdf_building_intensity.drop_duplicates(subset=['bldid'], keep='first')
+
+    # TODO: Check if the logic makes sense
+    if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+        away_from_flood = gdf_building_intensity['distance'] > threshold_flood_distance
+        print('threshold_flood_distance',threshold_flood_distance)
+        print('number of distant buildings', len(gdf_building_intensity.loc[away_from_flood, 'im']))
+        gdf_building_intensity.loc[away_from_flood, 'im'] = 0
+
+    #%%
+    gdf_building_intensity[['material','code_level','storeys','occupancy']] =  \
+        gdf_building_intensity['expstr'].str.split('+',expand=True)
+    gdf_building_intensity['height'] = gdf_building_intensity['storeys'].str.extract(r'([0-9]+)s').astype('int')
+    #%%
+    lr = (gdf_building_intensity['height'] <= 4)
+    mr = (gdf_building_intensity['height'] >= 5) & (gdf_building_intensity['height'] <= 8)
+    hr = (gdf_building_intensity['height'] >= 9)
+    gdf_building_intensity.loc[lr, 'height_level'] = 'LR'
+    gdf_building_intensity.loc[mr, 'height_level'] = 'MR'
+    gdf_building_intensity.loc[hr, 'height_level'] = 'HR'
+
+    #%%
+    gdf_building_intensity['vulnstreq'] = \
+        gdf_building_intensity[['material','code_level','height_level']] \
+            .agg('+'.join,axis=1)
+
+    #%%
+    if hazard_type == HAZARD_EARTHQUAKE:
+        bld_eq = gdf_building_intensity.merge(df_eq, on='vulnstreq', how='left')
+        nulls = bld_eq['muds1_g'].isna()
+        bld_eq.loc[nulls, ['muds1_g','muds2_g','muds3_g','muds4_g']] = [0.048,0.203,0.313,0.314]
+        bld_eq.loc[nulls, ['sigmads1','sigmads2','sigmads3','sigmads4']] = [0.301,0.276,0.252,0.253]
+        bld_eq['logim'] = np.log(bld_eq['im']/9.81)
+        for m in ['muds1_g','muds2_g','muds3_g','muds4_g']:
+            bld_eq[m] = np.log(bld_eq[m])
+
+        for i in [1,2,3,4]: 
+            bld_eq[f'prob_ds{i}'] = norm.cdf(bld_eq['logim'],bld_eq[f'muds{i}_g'],bld_eq[f'sigmads{i}'])
+        bld_eq[['prob_ds0','prob_ds5']] = [1,0]
+        for i in [1,2,3,4,5]:
+            bld_eq[f'ds_{i}'] = np.abs(bld_eq[f'prob_ds{i-1}'] - bld_eq[f'prob_ds{i}'])
+        df_ds = bld_eq[['ds_1','ds_2','ds_3','ds_4','ds_5']]
+        bld_eq['eq_ds'] = df_ds.idxmax(axis='columns').str.extract(r'ds_([0-9]+)').astype('int')
+
+        # Create a simplified building-hazard relation
+        bld_hazard = bld_eq[['bldid','occupancy','eq_ds']]
+        bld_hazard = bld_hazard.rename(columns={'eq_ds':'ds'})
+
+        ds_str = {1: 'No Damage',2:'Low',3:'Medium',4:'High',5:'Collapsed'}
+
+    elif hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+        bld_flood = gdf_building_intensity.merge(df_flood, on='expstr', how='left')
+        x = np.array([0,0.5,1,1.5,2,3,4,5,6])
+        y = bld_flood[['hw0','hw0.5','hw1','hw1.5','hw2','hw3','hw4','hw5','hw6']].to_numpy()
+        xnew = bld_flood['im'].to_numpy()
+        flood_mapping = interp1d(x,y,axis=1,kind='linear',bounds_error=False, fill_value=(0,1))
+        # TODO: find another way for vectorized interpolate
+        bld_flood['fl_prob'] = np.diag(flood_mapping(xnew))
+        bld_flood['fl_ds'] = 0
+        bld_flood.loc[bld_flood['fl_prob'] > threshold_flood,'fl_ds'] = 1
+
+        # Create a simplified building-hazard relation
+        bld_hazard = bld_flood[['bldid','occupancy','fl_ds']]
+        bld_hazard = bld_hazard.rename(columns={'fl_ds':'ds'})
+
+        ds_str = {0: 'No Damage',1:'Flooded'}
+
+    bld_hazard['occupancy'] = pd.Categorical(bld_hazard['occupancy'])
+    for key, value in ds_str.items():
+        bld_hazard.loc[bld_hazard['ds'] == key,'damage_level'] = value
+    bld_hazard['damage_level'] = pd.Categorical(bld_hazard['damage_level'], list(ds_str.values()))
+
+    #%% Find the damage state of the building that the household is in
+    df_household_bld = df_household.merge(bld_hazard[['bldid','ds']], on='bldid', how='left',validate='many_to_one')
+    #%% find the damage state of the hospital that the household is associated with
+    df_hospitals = df_household.merge(bld_hazard[['bldid','damage_level', 'ds']], 
+            how='left', left_on='commfacid', right_on='bldid', suffixes=['','_comm'],
+            validate='many_to_one')
+    #%%
+    df_workers = df_individual.merge(bld_hazard[['bldid','damage_level', 'ds']], 
+        how='left', left_on='indivfacid_2', right_on='bldid', 
+        suffixes=['_l','_r'],validate='many_to_one')
+
+    #%%
+    df_students = df_individual.merge(bld_hazard[['bldid','damage_level', 'ds']], 
+        how='left', left_on='indivfacid_1', right_on='bldid', 
+        suffixes=['_l','_r'],validate='many_to_one')
+    #%%
+    df_indiv_hosp = df_individual.merge(df_hospitals[['hhid','ds','bldid']], 
+                    how='left', on='hhid', validate='many_to_one')
+    #%%
+    # get the ds of household that individual lives in
+    df_indiv_household = df_individual[['hhid','individ']].merge(df_household_bld[['hhid','ds']])
+
+    df_displaced_indiv = df_indiv_hosp.rename(columns={'ds':'ds_hospital'})\
+        .merge(df_workers[['individ','ds']].rename(columns={'ds':'ds_workplace'}),on='individ', how='inner')\
+        .merge(df_students[['individ','ds']].rename(columns={'ds':'ds_school'}), on='individ', how='inner')\
+        .merge(df_indiv_household[['individ','ds']].rename(columns={'ds':'ds_household'}), on='individ',how='left')
+    #%%
+    if hazard_type == HAZARD_EARTHQUAKE:
+    # Effect of policies on thresholds
+    # First get the global threshold
+        thresholds = {f'metric{id}': threshold for id in range(8)}
+    else:
+        # Default thresholds for flood and debris
+        # For flood, there are only two states: 0 or 1. 
+        # So threshold is set to 0. 
+        thresholds = {f'metric{id}': 0 for id in range(8)}
+
+    # Policy 6 is valid for all three hazard types 
+    # Policy-6: Compulsory content insurance for schools and hospitals
+    # increases threshold for loss of edu/health in all hazard-types from minor to moderate
+    # slight to moderate
+    if 6 in policies and thresholds['metric3'] == DS_NO:
+        thresholds['metric3'] = DS_SLIGHT
+    if 6 in policies and thresholds['metric2'] == DS_NO:
+        thresholds['metric2'] = DS_SLIGHT
+
+    if hazard_type == HAZARD_EARTHQUAKE:
+        # Policy-1: Loans for reconstruction for minor to moderate damages
+        # Changes: Damage state thresholds for “displacement”
+        # Increase thresholds from “slight to moderate” as fewer people will be displaced.
+        if 1 in policies and thresholds['metric7'] == DS_NO:
+            thresholds['metric7'] = DS_SLIGHT
+
+        # Policy-3: Cat-bond agreement for education and health facilities
+        # Changes: Damage state thresholds for “loss of access to hospitals” and “loss of access to schools”
+        # Increase thresholds from “slight to moderate” as fewer people will be displaced.
+        if 3 in policies and thresholds['metric3'] == DS_NO:
+            thresholds['metric3'] = DS_SLIGHT
+        if 3 in policies and thresholds['metric2'] == DS_NO:
+            thresholds['metric2'] = DS_SLIGHT
+
+        # Policy-2: Knowledge sharing about DRR in public and private schools
+        # Changes: Damage state thresholds for “loss of school access”
+        # Increase thresholds loss of school access to beyond current scale. So that the impact will be downgraded to “0”.
+        if 2 in policies:
+            thresholds['metric2'] = DS_COLLAPSED
+
+    if hazard_type == HAZARD_FLOOD:
+        # Polcy-4: Repair loan assistance for flooding
+        # we have only two states 0/1. So if this policy
+        # is effective, increase it to 1 meaning that
+        # population displacement is solved
+        if 4 in policies:
+            thresholds['metric6'] = 1
+    
+    if hazard_type == HAZARD_FLOOD or hazard_type == HAZARD_DEBRIS:
+        # Policy-5: Technical assistance for debris removal in education facilities
+        # loss of education is solved via this policy. For both flood and debris
+        # loss of education metric is fixed.
+        if 5 in policies:
+            thresholds['metric2'] = 1
+
+    #%% metric 1 number of unemployed workers in each building
+    df_workers_per_building = df_workers[df_workers['ds'] > thresholds['metric1']].groupby('bldid',as_index=False).agg({'individ':'count'})
+    df_metric1 = bld_hazard.merge(df_workers_per_building,how='left',left_on='bldid',right_on = 'bldid')[['bldid','individ']]
+    df_metric1.rename(columns={'individ':'metric1'}, inplace=True)
+    df_metric1['metric1'] = df_metric1['metric1'].fillna(0).astype(int)
+
+    #%% metric 2 number of students in each building with no access to schools
+    df_students_per_building = df_students[df_students['ds'] > thresholds['metric2']].groupby('bldid',as_index=False).agg({'individ':'count'})
+    df_metric2 = bld_hazard.merge(df_students_per_building,how='left',left_on='bldid',right_on = 'bldid')[['bldid','individ']]
+    df_metric2.rename(columns={'individ':'metric2'}, inplace=True)
+    df_metric2['metric2'] = df_metric2['metric2'].fillna(0).astype(int)
+
+    #%% metric 3 number of households in each building with no access to hospitals
+    df_hospitals_per_household = df_hospitals[df_hospitals['ds'] > thresholds['metric3']].groupby('bldid',as_index=False).agg({'hhid':'count'})
+    df_metric3 = bld_hazard.merge(df_hospitals_per_household,how='left',left_on='bldid',right_on='bldid')[['bldid','hhid']]
+    df_metric3.rename(columns={'hhid':'metric3'}, inplace=True)
+    df_metric3['metric3'] = df_metric3['metric3'].fillna(0).astype(int)
+
+    #%% metric 4 number of individuals in each building with no access to hospitals
+    df_hospitals_per_individual = df_hospitals[df_hospitals['ds'] > thresholds['metric4']].groupby('bldid',as_index=False).agg({'nind':'sum'})
+    df_metric4 = bld_hazard.merge(df_hospitals_per_individual,how='left',left_on='bldid',right_on='bldid')[['bldid','nind']]
+    df_metric4.rename(columns={'nind':'metric4'}, inplace=True)
+    df_metric4['metric4'] = df_metric4['metric4'].fillna(0).astype(int)
+
+    #%% metric 5 number of damaged households in each building
+    df_homeless_households = df_household_bld[df_household_bld['ds'] > thresholds['metric5']].groupby('bldid',as_index=False).agg({'hhid':'count'})
+    df_metric5 = bld_hazard.merge(df_homeless_households,how='left',left_on='bldid',right_on='bldid')[['bldid','hhid']]
+    df_metric5.rename(columns={'hhid':'metric5'}, inplace=True)
+    df_metric5['metric5'] = df_metric5['metric5'].fillna(0).astype(int)
+
+    #%% metric 6 number of homeless individuals in each building
+    df_homeless_individuals = df_household_bld[df_household_bld['ds'] > thresholds['metric6']].groupby('bldid',as_index=False).agg({'nind':'sum'})
+    df_metric6 = bld_hazard.merge(df_homeless_individuals,how='left',left_on='bldid',right_on='bldid')[['bldid','nind']]
+    df_metric6.rename(columns={'nind':'metric6'}, inplace=True)
+    df_metric6['metric6'] = df_metric6['metric6'].fillna(0).astype(int)
+
+    #%% metric 7 the number of displaced individuals in each building
+    # more info: an individual is displaced if at least of the conditions below hold
+    df_disp_per_bld = df_displaced_indiv[(df_displaced_indiv['ds_household'] > thresholds['metric6']) |
+                                        (df_displaced_indiv['ds_school'] > thresholds['metric7']) |
+                                        (df_displaced_indiv['ds_workplace'] > thresholds['metric7']) |
+                                        (df_displaced_indiv['ds_hospital'] > thresholds['metric7'])].groupby('bldid',as_index=False).agg({'individ':'count'})
+    df_metric7 = bld_hazard.merge(df_disp_per_bld,how='left',left_on='bldid',right_on='bldid')[['bldid','individ']]
+    df_metric7.rename(columns={'individ':'metric7'}, inplace=True)
+    df_metric7['metric7'] = df_metric7['metric7'].fillna(0).astype(int)
+
+    df_metrics = {'metric1': df_metric1,
+                'metric2': df_metric2,
+                'metric3': df_metric3,
+                'metric4': df_metric4,
+                'metric5': df_metric5,
+                'metric6': df_metric6,
+                'metric7': df_metric7}
+
+    #%%
+    number_of_workers = len(df_workers.loc[df_workers['indivfacid_2'] > 0])
+    print('number of workers', number_of_workers)
+
+    number_of_students = len(df_workers.loc[df_students['indivfacid_1'] > 0])
+    print('number of students', number_of_students)
+
+    number_of_households = len(df_household)
+    print('number of households', number_of_households)
+
+    number_of_individuals = len(df_individual)
+    print('number of individuals', number_of_individuals)
+    metrics = {"metric1": {"desc": "Number of workers unemployed", "value": 0, "max_value": 20_000},
+                "metric2": {"desc": "Number of children with no access to education", "value": 0, "max_value": 9_000},
+                "metric3": {"desc": "Number of households with no access to hospital", "value": 0, "max_value": 20_000},
+                "metric4": {"desc": "Number of individuals with no access to hospital", "value": 0, "max_value": 75_000},
+                "metric5": {"desc": "Number of homeless households", "value": 0, "max_value": number_of_households},
+                "metric6": {"desc": "Number of homeless individuals", "value": 0, "max_value": 75_000},
+                "metric7": {"desc": "Population displacement", "value": 0, "max_value": 75_000},}
+    metrics["metric1"]["value"] = int(df_metric1['metric1'].sum())
+    metrics["metric2"]["value"] = int(df_metric2['metric2'].sum())
+    metrics["metric3"]["value"] = int(df_metric3['metric3'].sum())
+    metrics["metric4"]["value"] = int(df_metric4['metric4'].sum())
+    metrics["metric5"]["value"] = int(df_metric5['metric5'].sum())
+    metrics["metric6"]["value"] = int(df_metric6['metric6'].sum())
+    metrics["metric7"]["value"] = int(df_metric7['metric7'].sum())
+
+    for key in metrics.keys():
+        metrics[key]["value"] = int(metrics[key]["value"] * weights[hazard_type][key])
+
+    # return to WSG
+    return metrics
+

earthquake_fragility.csv

@@ -0,0 +1,12 @@
+vulnstreq,muds1_g,muds2_g,muds3_g,muds4_g,sigmads1,sigmads2,sigmads3,sigmads4
+Adb+LC+LR,0.399,0.861,1.238,1.577,0.586,0.586,0.586,0.586
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+BrCfl+LC+LR,0.057,0.119,0.214,0.361,0.451,0.349,0.286,0.247
+BrCri+LC+LR,0.124,0.175,0.295,0.445,0.326,0.3,0.254,0.254
+StMin+LC+LR,0.057,0.098,0.147,0.223,0.406,0.404,0.358,0.310
+RCi+LC+LR,0.180,0.677,4.915,5.773,0.223,0.223,0.223,0.223
+RCi+MC+LR,0.137,0.633,1.577,2.016,0.223,0.223,0.223,0.223
+RCi+HC+LR,0.109,0.255,0.578,0.689,0.228,0.228,0.218,0.217
+RCi+LC+MR,0.039,0.322,4.027,5.352,0.259,0.259,0.259,0.259
+RCi+MC+MR,0.031,0.268,0.793,1.036,0.268,0.268,0.268,0.268
+RCi+HC+MR,0.048,0.203,0.313,0.314,0.301,0.276,0.252,0.253

flood_vulnerability.csv

@@ -0,0 +1,1261 @@
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+BrCfl+LC+5s+Edu,0.000,0.152,0.216,0.264,0.304,0.352,0.376,0.392,0.400
+BrCfl+LC+6s+Edu,0.000,0.127,0.180,0.220,0.253,0.293,0.313,0.327,0.333
+BrCfl+LC+7s+Edu,0.000,0.109,0.154,0.189,0.217,0.251,0.269,0.280,0.286
+BrCfl+LC+8s+Edu,0.000,0.095,0.135,0.165,0.190,0.220,0.235,0.245,0.250
+BrCfl+LC+9s+Edu,0.000,0.084,0.120,0.147,0.169,0.196,0.209,0.218,0.222
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+RCi+HC+9s+Hea,0.000,0.051,0.072,0.088,0.101,0.117,0.125,0.131,0.133
+RCi+HC+10s+Hea,0.000,0.046,0.065,0.079,0.091,0.106,0.113,0.118,0.120
+StMin+LC+1s+Hea,0.000,0.456,0.600,0.600,0.600,0.600,0.600,0.600,0.600
+StMin+LC+2s+Hea,0.000,0.228,0.324,0.396,0.456,0.528,0.564,0.588,0.600
+StMin+LC+3s+Hea,0.000,0.152,0.216,0.264,0.304,0.352,0.376,0.392,0.400
+StMin+LC+4s+Hea,0.000,0.114,0.162,0.198,0.228,0.264,0.282,0.294,0.300
+StMin+LC+5s+Hea,0.000,0.091,0.130,0.158,0.182,0.211,0.226,0.235,0.240
+StMin+LC+6s+Hea,0.000,0.076,0.108,0.132,0.152,0.176,0.188,0.196,0.200
+StMin+LC+7s+Hea,0.000,0.065,0.093,0.113,0.130,0.151,0.161,0.168,0.171
+StMin+LC+8s+Hea,0.000,0.057,0.081,0.099,0.114,0.132,0.141,0.147,0.150
+StMin+LC+9s+Hea,0.000,0.051,0.072,0.088,0.101,0.117,0.125,0.131,0.133
+StMin+LC+10s+Hea,0.000,0.046,0.065,0.079,0.091,0.106,0.113,0.118,0.120
+StMin+MC+1s+Hea,0.000,0.456,0.600,0.600,0.600,0.600,0.600,0.600,0.600
+StMin+MC+2s+Hea,0.000,0.228,0.324,0.396,0.456,0.528,0.564,0.588,0.600
+StMin+MC+3s+Hea,0.000,0.152,0.216,0.264,0.304,0.352,0.376,0.392,0.400
+StMin+MC+4s+Hea,0.000,0.114,0.162,0.198,0.228,0.264,0.282,0.294,0.300
+StMin+MC+5s+Hea,0.000,0.091,0.130,0.158,0.182,0.211,0.226,0.235,0.240
+StMin+MC+6s+Hea,0.000,0.076,0.108,0.132,0.152,0.176,0.188,0.196,0.200
+StMin+MC+7s+Hea,0.000,0.065,0.093,0.113,0.130,0.151,0.161,0.168,0.171
+StMin+MC+8s+Hea,0.000,0.057,0.081,0.099,0.114,0.132,0.141,0.147,0.150
+StMin+MC+9s+Hea,0.000,0.051,0.072,0.088,0.101,0.117,0.125,0.131,0.133
+StMin+MC+10s+Hea,0.000,0.046,0.065,0.079,0.091,0.106,0.113,0.118,0.120
+StMin+HC+1s+Hea,0.000,0.456,0.600,0.600,0.600,0.600,0.600,0.600,0.600
+StMin+HC+2s+Hea,0.000,0.228,0.324,0.396,0.456,0.528,0.564,0.588,0.600
+StMin+HC+3s+Hea,0.000,0.152,0.216,0.264,0.304,0.352,0.376,0.392,0.400
+StMin+HC+4s+Hea,0.000,0.114,0.162,0.198,0.228,0.264,0.282,0.294,0.300
+StMin+HC+5s+Hea,0.000,0.091,0.130,0.158,0.182,0.211,0.226,0.235,0.240
+StMin+HC+6s+Hea,0.000,0.076,0.108,0.132,0.152,0.176,0.188,0.196,0.200
+StMin+HC+7s+Hea,0.000,0.065,0.093,0.113,0.130,0.151,0.161,0.168,0.171
+StMin+HC+8s+Hea,0.000,0.057,0.081,0.099,0.114,0.132,0.141,0.147,0.150
+StMin+HC+9s+Hea,0.000,0.051,0.072,0.088,0.101,0.117,0.125,0.131,0.133
+StMin+HC+10s+Hea,0.000,0.046,0.065,0.079,0.091,0.106,0.113,0.118,0.120

mypy.ini

@@ -0,0 +1,3 @@
+[mypy]
+check_untyped_defs = True
+ignore_missing_imports = True

pre_process.py

@@ -0,0 +1,27 @@
+#%%
+import pandas as pd
+import geopandas as gpd
+
+
+df = pd.read_excel('/Users/huseyin.kaya/Downloads/Tomorrowville_Dataset/TV_Others/VS2/_outputs/Layer_Building_VS2.xlsx')
+
+#df = pd.read_excel('/Users/huseyin.kaya/Downloads/Tomorrowville_Dataset/TV_Others/VS1/_outputs/Layer_Individual_VS1.xlsx')
+#df.to_json('individual_tv50_1.json')
+
+#df = df.rename(columns={'X':'x','Y':'y','IM':'im'})
+
+gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.xcoord, df.ycoord))
+gdf.set_crs(epsg=32645, inplace=True)
+gdf = gdf.to_crs(crs="EPSG:4326")
+
+gdf.xcoord = gdf.geometry.x
+gdf.ycoord = gdf.geometry.y
+
+#gdf = gdf.sample(n=1000)
+gdf = gdf.reset_index(drop=True)
+#gdf = gdf.drop(columns=['xcoord','ycoord'])
+#gdf = gdf.drop(columns=['geometry'])
+
+gdf.to_file('building_tv50_2.geojson', driver="GeoJSON")  
+#%%
+

pyproject.toml

@@ -0,0 +1,26 @@
+[build-system]
+requires = ["hatchling >=0.25"]
+build-backend = "hatchling.build"
+
+[project]
+name = "demo"
+license = {file = "LICENSE"}
+classifiers = ["License :: OSI Approved :: MIT License"]
+dynamic = ["version", "description"]
+dependencies = [
+    "solara",
+]
+
+[tool.hatch.version]
+path = "demo/__init__.py"
+
+
+
+[project.urls]
+Home = "https://www.github.com/widgetti/solara"
+
+[tool.black]
+line-length = 160
+
+[tool.isort]
+profile = "black"

requirements.txt

@@ -0,0 +1,8 @@
+solara
+geopandas
+ipyleaflet
+plotly
+lorem_text
+matplotlib
+psycopg2-binary 
+scipy