def test_createTable():
conn = adusql.Connection()
try:
conn.createTable(DATA, TABLE)
except:
assert(False)
conn.drop(TABLE)
def test_getFuncs():
conn = adusql.Connection()
for func in [conn.getParcelCoords,
conn.getZipcode]:
data = func(PIN)
assert(isinstance(data, pd.DataFrame))
assert(len(data) > 0)
def test_update_map():
PIN = 3626039263
adunit = ads.Connection("adunits.db")
df = adunit.getParcelCoords(PIN)
neighbors = adunit.getNeighbors(df)
result = update_map.update_map(df, neighbors)
assert(isinstance(result, object))
def test_insert_select():
conn = adusql.Connection()
try:
conn.createTable(DATA, TABLE)
conn.insert(DATA, TABLE)
except:
assert(False)
data = conn.select(TABLE)
conn.drop(TABLE)
assert(len(data) == 2*SIZE)
def master_callback(value):
df = pd.DataFrame()
neighbors = pd.DataFrame()
global zipc
zipc = None
if value != None:
adunit = ads.Connection("adunits.db")
df = adunit.getParcelCoords(value)
neighbors = adunit.getNeighbors(df)
zipc = df.iloc[0]["zipcode"]
return [
update_map(df, neighbors, coords=SEATTLE, zoom=INIT_ZOOM),
update_criteria_details(df),
update_details(df),
neighbor_adu(value, df, neighbors),
]
def update_map(value, coords=C.SEATTLE, zoom=C.INIT_ZOOM):
if value != None:
adunit = ads.Connection("adunits.db")
df = adunit.getParcelCoords(value)
df.to_csv("df.csv")
# coords = (newCoords.latitude[0], newCoords.longitude[0])
coords = (df.coordY[0], df.coordX[0])
# coords = (df.iloc[0]["coordY"]["coordX"])
# float max digits is not long enough
zoom = 18
# def df_to_geojson(df, properties, lat='latitude', lon='longitude'):
# geojson = {'type': 'FeatureCollection', 'features': []}
# feature = {'type': 'Feature',
# 'properties': {},
# 'geometry': {'type': 'Polygon',
# 'coordinates': []}}
# for _, row in df.iterrows():
# feature['geometry']['coordinates'].append([row[lon], row[lat]])
# for prop in properties:
# feature['properties'][prop] = row[prop]
# geojson['features'] = feature
# return geojson
# cols = ['adu_eligible', 's_hood', 'zone_ind', 'sqftlot',
# 'ls_indic', 'lotcov_indic', 'lotcoverage', 'sm_lotcov_ind', 'sm_lotcov',
# 'yrbuilt', 'daylightbasement', 'sqftfinbasement', 'shoreline_ind',
# 'parcel_flood', 'parcel_landf', 'parcel_peat',
# 'parcel_poteslide', 'parcel_riparian', 'parcel_steepslope',
# ]
# geojson = df_to_geojson(df, cols, lat='coordX', lon='coordY')
new_map = folium.Map(location=coords, zoom_start=zoom)
# with open('myfile.geojson', 'w') as f:
# json.dump(geojson, f)
# with open('myfile2.geojson', 'w') as f2:
# geojson.write(f2)
# f2.close()
if value != None:
# parcel = folium.map.FeatureGroup(name="parcel",
# overlay=True, control=True, show=True,)
# for i in range(0, len(geojson["features"])):
# print(len(geojson["features"]))
# print(i)
# print(geojson["features"][i]["geometry"])
# feature = folium.features.GeoJson(geojson["features"][i]["geometry"],
# name=(geojson["features"][i]["properties"]["sqftlot"]),
# style_function=style_function,
# highlight_function=highlight_function,)
# folium.Popup(
# "Square feet of lot: " + geojson["features"][i]["properties"]["sqftlot"], max_width=200).add_to(feature)
# parcel.add_child(feature)
# parcel = folium.features.GeoJson(geojson, style_function=style_function, highlight_function=highlight_function) #### Anag
# print(geojson["features"][0]["geometry"])
# folium.Popup()
# folium.Marker(coords, popup=folium.Popup("<b><h4>Is this home ADU eligible? </h4></b>" +
# str(df.iloc[0]["adu_eligible"]) + "<br><h5><i>Details</i></h5>" +
# "<br>Neighborhood: " + str(df.iloc[0]["s_hood"]) +
# "<br>Is this a Single Family zoned home? " + str(df.iloc[0]["zone_ind"]) +
# "<br>Square feet of lot: " + str(df.iloc[0]["sqftlot"]) +
# "<br> ls_indic " + str(df.iloc[0]["ls_indic"]) +
# "<br> lotcov_indic " + str(df.iloc[0]["lotcov_indic"]) +
# "<br> lotcoverage " + str(df.iloc[0]["lotcoverage"]) +
# "<br> sm_lotcov_ind " + str(df.iloc[0]["sm_lotcov_ind"]) +
# "<br> sm_lotcov " + str(df.iloc[0]["sm_lotcov"]) +
# "<br> Year House Built " + str(df.iloc[0]["yrbuilt"]) +
# "<br> Does this home have a daylight basement? " + str(df.iloc[0]["daylightbasement"]) +
# "<br> Square foot in basement " + str(df.iloc[0]["sqftfinbasement"]) +
# "<br> Does this lot border a shoreline? " + str(df.iloc[0]["shoreline_ind"]) +
# "<br><i>Environmentally Critical Areas assessment</i>" +
# "<br>Is this parcel on a steep slope? " + str(df.iloc[0]["parcel_steepslope"]) +
# "<br>Is this parcel on a previously flooded area? " + str(df.iloc[0]["parcel_flood"]) +
# "<br>Is this parcel on a potential slide area? " + str(df.iloc[0]["parcel_poteslide"]),
# max_width=2000)
# ).add_to(new_map)
# locations = geojson["features"]["geometry"]["coordinates"]
locations = np.asarray(df[pd.Index(['coordY', 'coordX'])])
# print(locations)
#re implement this using switch case
def output():
value = "<center><h4>Your home's ADU Eligibility</h4></center>"
if (df.iloc[0]["zone_ind"] == 0):
value += "<h5> For an AADU, this home is <b>Ineligible</b></h5> This is not a single family zoned home"
elif (df.iloc[0]["zone_ind"] == 1):
value += "<br><h5> For an AADU, this home is <b>Eligible</b></h5>"
value += "<h5> For a DADU, this home is " + "<b>" + str(
df.iloc[0]["adu_eligible"]) + "</b></h5>"
if (df.iloc[0]["adu_eligible"] == "Ineligible"):
value += "<h5><i>Potential Problems for DADUs</i></h5>"
if (df.iloc[0]["zone_ind"] == 0):
value += "<br> This is not a single family zoned home"
if (df.iloc[0]["ls_indic"] == 0):
value += "<br> This lot is not large enough to house a DADU"
if (df.iloc[0]["lotcov_indic"] == 0):
value += "<br> There is insufficient lot coverage on this parcel to build a DADU"
if (not pd.isna(df.iloc[0]["ADU"])):
value += "<br> There is already at least one existing ADU on this property"
if (df.iloc[0]["shoreline_ind"] == 1):
value += "<br> This home is next to the shoreline. DADUs cannot be built by shorelines"
value += "<h5><i>Potential considerations of concern for ADUs and DADUs: </i></h5>"
if (df.iloc[0]["parcel_steepslope"] == 1):
value += "<br> Your home may have some steep areas that may make it more costly to permit and build an ADU"
if (df.iloc[0]["parcel_flood"] == 1):
value += "<br> Your home may have been flooded in the past. This may make it more costly to permit and build an ADU"
if (df.iloc[0]["parcel_poteslide"] == 1):
value += "<br> Your home may be a potential slide area. This may make it more costly to permit and build an ADU"
if (df.iloc[0]["parcel_landf"] == 1):
value += "<br> Your home may be located on (or close to a?) a landfill. This may make it more costly to permit and build an ADU"
if (df.iloc[0]["parcel_peat"] == 1):
value += "<br> Your home may be a peat settlement. This may make it more costly to permit and build an ADU"
if (df.iloc[0]["parcel_riparian"] == 1):
value += "<br> Your home may be on a riparian corridor. This may make it more costly to permit and build an ADU"
value += "<br><br><a href=" ">More details on the eligibility criteria and your home's eligibility here</a>"
return value
folium.Polygon(
locations=locations,
color='blue',
weight=6,
fill_color='red',
fill_opacity=0.5,
fill=True,
popup=folium.Popup(output(),
max_width=2000,
fill_color="green",
show=True),
# popup=folium.Popup("<h5> For a DADU, this home is " + "<b>" +
# str(df.iloc[0]["adu_eligible"]) + "</b></h5>" + "<h5> For an AADU, this home is <b>Eligible</b></h5>" + "<h5><i>Essential Criteria</i></h5>" +
# " Is this a Single Family zoned home? " + str(df.iloc[0]["zone_ind"]) +
# "<br> Is this lot large enough to house a DADU? " + str(df.iloc[0]["ls_indic"]) +
# "<br> Is lot coverage sufficient for a DADU? " + str(df.iloc[0]["lotcov_indic"]) +
# "<br> Existing ADUs or DADUs on this property " + str(df.iloc[0]["ADU"]) +
# "<br>Potential considerations of concern: <b>None</b>" +
# "<br><br><a href="">More details on the eligibility criteria and your home's eligibility here</a>",
# max_width=2000),
tooltip='Click me!',
).add_to(new_map)
# feature = folium.features.GeoJson(geojson["features"]["geometry"],
# name=None, style_function=style_function, highlight_function=highlight_function,)
# folium.Popup("Square feet of lot: " + str(geojson["features"]["properties"]["sqftlot"]), max_width=300).add_to(feature)
# parcel.add_child(feature)
# feature.add_to(new_map)
# parcel.add_to(new_map)
new_map.save("map.html")
return open("map.html", "r").read()
def test_getAddresses():
conn = adusql.Connection()
data = conn.getAddresses()
assert(isinstance(data, pd.DataFrame))
assert(len(data) > 0)
def test_getNeighbors():
conn = adusql.Connection()
df = conn.getParcelCoords(PIN)
data = conn.getNeighbors(df)
assert(isinstance(data, pd.DataFrame))
assert(len(data) > 0)
def test_drop():
conn = adusql.Connection()
conn.drop(TABLE)
def test_connect():
conn = adusql.Connection()
conn.connect()
assert(conn.connected)
def test_constructor():
conn = adusql.Connection()
assert(not conn.connected)
dbc.NavItem(dbc.NavLink("Neighborhood View", href="/neighborhood")),
],
# brand="ADUniverse",
brand_href="/",
brand_external_link=True,
color="primary",
dark=True,
fluid=True,
id="navbar",
style={
}
)
# Address addressDropdown
adunit = ads.Connection()
addresses = adunit.getAddresses()
addresses = adunit.getAddresses(sqftlot=C.SQFTLOT)
AddressDropdown = dcc.Dropdown(
id='addressDropdown',
options=[
{'label': i, 'value': j} for i, j in zip(addresses.address, addresses.PIN)
],
placeholder='Type your house address here...',
style={'width': '100%', 'display': 'inline-block', 'vertical-align': 'top'}
)
# create empty map zoomed in on Seattle
Map(location=SEATTLE, zoom_start=INIT_ZOOM, control_scale=True).save("map.html")
MapBlock = html.Iframe(id='map', srcDoc=open("map.html", "r").read(),
