def update_map(df, df_ngb, coords=C.SEATTLE, zoom=C.INIT_ZOOM):
if not(df.empty):
coords = (df.coordY[0], df.coordX[0])
zoom = 18
new_map = folium.Map(location=coords, zoom_start=zoom)
if not(df.empty):
locations = np.asarray(df[pd.Index(['coordY', 'coordX'])])
# 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>Not Eligible</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> Here are some pre-approved AADU plans to consider."
value += "<h5> For a DADU, this home is " + "<b>" + \
str(df.iloc[0]["adu_eligible"]) + "</b></h5>"
if (str(df.iloc[0]["adu_eligible"]) == "Eligible"):
value += "Here are some pre-approved DADU plans to consider."
if (df.iloc[0]["zone_ind"] == 1 and str(df.iloc[0]["adu_eligible"]) == "Eligible" and pd.isna(df.iloc[0]["ADU"])):
value += "<br>This home is eligible to build both an ADU AND a DADU!."
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]["lot_dim_indic"] == "no"):
value += "<br> This lot's dimensions are 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"
if (df.iloc[0]["zone_ind"] == 1):
value += "<h5><i>Potential considerations of concern for ADUs and DADUs: </i></h5>"
if (df.iloc[0]["treecanopy_prct"] > 30):
value += "Your home may have a significant tree canopy percentage that may restrict your ability to build a DADU"
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 be prone to floods. 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"
if (df.iloc[0]["side_sewer_conflict"] == 1):
value += "<br> Your home has a conflicting side sewer crossing You may have a conflicting side sewer with a neighbor or may need additional side sewer construction"
else:
if (df.iloc[0]["intersecting_sewer"] == 1):
value += "<br> Your home has a side sewer that crosses another lot. You may need to reroute or construct a new side sewer for a DADU"
if (df.iloc[0]["landlocked_parcel"] == 1):
value += "<br> Being a landlocked parcel, you may have to run a new side sewer through another's lot while constructing an ADU."
value += "<br><br>More details on the eligibility criteria and your home's eligibility below"
value += "<br>Check for neighborhood covenants"
return value
folium.Polygon(locations=locations, color='blue', weight=6,
fill_opacity=0.5, fill=True,
popup=folium.Popup(output(), max_width=2000, show=True),
tooltip='Click me!',).add_to(new_map)
neighbor = folium.map.FeatureGroup(name="neighbor",
overlay=True, control=True, show=True,)
for i in range(0, len(df_ngb)):
folium.Marker(location=[df_ngb.iloc[i]['latitude'], df_ngb.iloc[i]['longitude']],
popup=folium.Popup("Type: " + df_ngb.iloc[i]["ADU_type"] + "<br>"
"Permitted in: " +
str(int(df_ngb.iloc[i]["year_issue"])) + "<br>"
+ "Address: " + df_ngb.iloc[i]["address"], max_width=2000)
).add_to(neighbor)
neighbor.add_to(new_map)
folium.LayerControl().add_to(new_map)
new_map.save("map.html")
return open("map.html", "r").read()
scores = 0
for label in grids_met:
g = board.find_grid(label)
scores += g.congestion_ratio
return scores / N
if __name__ == '__main__':
school_locations = pd.read_csv(SCHOOL_FILE)
bps_sensor = pd.read_csv(BPS_FILE)
reg = Region(42.70, -71.50, 42.10, -70.75)
reg.add_ratio()
bps_sensor = clean_data(bps_sensor)
scores = [(pnts, score_route(reg, pnts)) for pnts in grainby(bps_sensor)]
base_map = folium.Map(location=(42.30, -71.05), zoom_start=13)
idling_route = min(scores, key=lambda x: x[1])
busy_route = max(scores, key=lambda x: x[1])
folium.Polygon(idling_route[0],
color='blue',
popup='most idling',
opacity=1).add_to(base_map)
folium.Polygon(busy_route[0], color='red', popup='most busy',
opacity=1).add_to(base_map)
base_map.save('q3.html')
import folium
import pandas as pd
newList = []
def switchLatLng(geos):
for geo in geos:
if isinstance(geo[0],list):
switchLatLng(geo)
else:
newList.append([geo[1], geo[0]])
return newList
myMap = folium.Map([22.73444963475145, 120.28458595275877], zoom_start=6)
kaoVill = folium.FeatureGroup()
features = pd.read_json('../../dist/mapdata/KaoVillageRange.json')["features"]
for feature in features:
villpopup = '這裡是<h3>'+feature['properties']['TV_ALL']+'</h3>總面積為'+feature['properties']['AREA']+'平方公尺'
coors = [switchLatLng(feature['geometry']['coordinates'])]
newList = []
villRange = folium.Polygon(coors,
popup = villpopup,
fill = True).add_to(kaoVill)
kaoVill.add_to(myMap)
myMap.fit_bounds(kaoVill.get_bounds())
myMap.save('Folium_KaoVillpopup.html')
bounds = []
for site in sitesR.values:
name = site[2]
#建立locations陣列，儲存各範圍polygon所有座標點位。
#換一個範圍就要先清空，所以建在這邊
locations = []
# 每一點位的polygon座標list。因為有的位置有兩個以上的範圍，這邊要先將每個範圍獨立開來。
for rs in site[1]:
siteLocations = rs.split(',')
for siteLatLng in siteLocations:
latlng = siteLatLng.split(' ')
local = [float(latlng[0]), float(latlng[1])]
locations.append(local)
bounds.append(local)
#抓到該範圍所有點後，建立polygon
#需folium 0.6.0以上才會有polygon可以使用
folium.Polygon(
locations=locations,
color='blue',
weight=1,
fill_color='#ffa',
fill_opacity=0.1,
fill=True,
popup=name,
tooltip='取得工程資訊',
).add_to(kaoMap)
#將bounds建立polygon並利用.get_bounds()取得視圖範圍，進而設定地圖的縮放範圍
bLayer = folium.Polygon(locations=bounds)
kaoMap.fit_bounds(bLayer.get_bounds())
#將地圖存成html檔
kaoMap.save('PWBsites.html')
circle = i['circle']
center = circle['center']
radius = circle['radius']
folium.Circle(
radius=radius,
location=center,
fill=True,
tooltip="Obstacle",
opacity=0.2,
color=ORANGE
).add_to(my_map)
# Create Polygon
folium.Polygon(
locations=boundaryArr,
fill=False,
tooltip="GeoFence",
color=RED).add_to(my_map)
# Create Waypath
for i in range(len(waypoint)-1):
if not hitCircle(linePath[i]):
folium.PolyLine(
locations=waypoint[i:i+2],
fill=False,
tooltip=f"Waypath {i}",
color=GREEN
).add_to(my_map)
# Create autopilot route
route = []
populationFemale, literacy, sexRatio,
childPopulation)
iframe = branca.element.IFrame(html=html, width=400, height=250)
popup = folium.Popup(iframe, parse_html=True)
folium.Marker(location=[lat, lan],
popup=popup,
tooltip='<strong>Click to view demography of ' +
str(place),
icon=folium.Icon(color=colorDecide(event),
icon_color='white',
icon='cloud')).add_to(subgroup)
subgroup.add_to(m)
folium.Polygon(locations_43,
popup=None,
color=findcolor(dic['Shivaji Nagar']),
weight=4,
opacity=0.8,
fill_color=findcolor(dic['Shivaji Nagar'])).add_to(m)
folium.Polygon(locations_71,
popup=None,
color=findcolor(dic['Chembur']),
weight=4,
opacity=0.8,
fill_color=findcolor(dic['Chembur'])).add_to(m)
folium.Polygon(locations_74,
popup=None,
color=findcolor(dic['Mahul']),
weight=4,
opacity=0.8,
fill_color=findcolor(dic['Mahul'])).add_to(m)
folium.Polygon(locations_88,
def update_map(df, df_ngb, coords=C.SEATTLE, zoom=C.INIT_ZOOM):
if not(df.empty):
coords = (df.coordY[0], df.coordX[0])
zoom = 18
new_map = folium.Map(location=coords, zoom_start=zoom)
if not(df.empty):
locations = np.asarray(df[pd.Index(['coordY', 'coordX'])])
# re implement this using switch case
def output():
value = "<center><h4>Your home's Eligibility for an ADU</h4></center>"
if (df.iloc[0]["zone_ind"] == 0):
value += "<h5> For an AADU, this home is <b>not eligible</b></h5> because it is not located in a single-family zone"
elif (df.iloc[0]["zone_ind"] == 1):
value += "<br><h5> For an AADU, this home is <b>eligible</b></h5> Here are some pre-approved AADU plans to consider."
value += "<h5> For a DADU, this home is " + "<b>" + \
str(df.iloc[0]["adu_eligible"]) + "</b></h5>"
if (str(df.iloc[0]["adu_eligible"]) == "Eligible"):
value += "Here are some pre-approved DADU plans to consider."
if (df.iloc[0]["zone_ind"] == 1 and str(df.iloc[0]["adu_eligible"]) == "Eligible" and pd.isna(df.iloc[0]["ADU"])):
value += "<br>This property is eligible to build both an AADU and a DADU!"
if (df.iloc[0]["adu_eligible"] == "Ineligible"):
value += "<h5><i>Potential Challenges for DADUs</i></h5>"
if (df.iloc[0]["zone_ind"] == 0):
value += "<br> Your property is not located on a single-family zone"
if (df.iloc[0]["ls_indic"] == 0):
value += "<br> Your property does not meet the minimum lot size for a DADU"
if (df.iloc[0]["lot_dim_indic"] == "no"):
value += "<br> Your lot's dimensions are not large enough to house a DADU"
if (df.iloc[0]["lotcov_indic"] == 0):
value += "<br> Given lot coverage limits, your property appears not to have sufficient available lot area to build a DADU"
if (not pd.isna(df.iloc[0]["ADU"])):
value += "<br> At least one ADU already exists on your property"
if (df.iloc[0]["shoreline_ind"] == 1):
value += "<br> Your property is located within 200 feet of a designated shoreline (i.e., the Shoreline District). \
DADUs are not allowed in the Shoreline District"
if (df.iloc[0]["zone_ind"] == 1):
value += "<h5><i>Potential considerations of concern for AADUs and DADUs: </i></h5>"
if (df.iloc[0]["yrbuilt"] < 1959):
value += "Given the age of your home, you converting existing space to an AADU could require substantial changes or upgrades to meet current building codes"
if (df.iloc[0]["treecanopy_prct"] > 30):
value += "Your home may have a significant tree canopy percentage that may restrict your ability to build a DADU"
if (df.iloc[0]["parcel_steepslope"] == 1):
value += "<br> Your property appears to have some steep areas that may make it more costly to permit and build an ADU and/or limit where you can site a DADU"
if (df.iloc[0]["parcel_flood"] == 1):
value += "<br> Your property appears to be located in a floods-prone area. This may make it more costly or difficult to design, permit, and build an ADU"
if (df.iloc[0]["parcel_poteslide"] == 1):
value += "<br> Your property appears to be located in a potential slide area. This may make it more costly or difficult to design, permit, and build an ADU"
if (df.iloc[0]["parcel_landf"] == 1):
value += "<br> Your property appears to be located on a former landfill. This may make it more costly or difficult to design, permit, and build an ADU"
if (df.iloc[0]["parcel_peat"] == 1):
value += "<br> Your property appears to be located in an area prone to peat settlement. This may make it more costly or difficult to design, permit, and build an ADU"
if (df.iloc[0]["parcel_riparian"] == 1):
value += "<br> Your property appears to be located within a riparian corridor. This may make it more costly or difficult to design, permit, and build an ADU"
if (df.iloc[0]["side_sewer_conflict"] == 1):
value += "<br> Your property appears to have a side sewer conflict. This could affect or limit the size or location of an ADU on your lot."
else:
if (df.iloc[0]["intersecting_sewer"] == 1):
value += "<br> Your property has a side sewer that crosses another lot. You may need to reroute or construct a new side sewer for a DADU"
if (df.iloc[0]["landlocked_parcel"] == 1):
value += "<br> Your lot appears to be landlocked. Your ADU may require a new side sewer through a neighboring lot."
value += "<br><br>More details on the eligibility criteria and your home's eligibility below"
value += "<br>Check for neighborhood covenants"
return value
folium.Polygon(locations=locations, color='blue', weight=6,
fill_opacity=0.5, fill=True,
popup=folium.Popup(output(), max_width=2000, show=True),
tooltip='Click me!',).add_to(new_map)
neighbor = folium.map.FeatureGroup(name="neighbor",
overlay=True, control=True, show=True,)
for i in range(0, len(df_ngb)):
folium.Marker(location=[df_ngb.iloc[i]['latitude'], df_ngb.iloc[i]['longitude']],
popup=folium.Popup("Type: " + df_ngb.iloc[i]["ADU_type"] + "<br>"
"Permitted in: " +
str(int(df_ngb.iloc[i]["year_issue"])) + "<br>"
+ "Address: " + df_ngb.iloc[i]["address"], max_width=2000)
).add_to(neighbor)
neighbor.add_to(new_map)
folium.LayerControl().add_to(new_map)
new_map.save("map.html")
return open("map.html", "r").read()
def map(
self,
width='100%',
height='100%',
tiles='Cartodb Positron',
ignore=[],
**kwargs,
):
''' Plot overview map with folium
Parameters:
----------
tiles: str
tiles used, see `folium.Map?``
- "OpenStreetMap"
- "Mapbox Bright" (Limited levels of zoom for free tiles)
- "Mapbox Control Room" (Limited levels of zoom for free tiles)
- "Stamen" (Terrain, Toner, and Watercolor)
- "Cloudmade" (Must pass API key)
- "Mapbox" (Must pass API key)
- "CartoDB" (positron and dark_matter)
'''
if ignore == 'all':
ignore = self._units
m = folium.Map(
location=[self.lat_mid, self.lon_mid],
width=width,
height=height,
zoom_start=11,
tiles=tiles,
)
# bathymetric contours
contours_geojson = load_bathy_contours()
tooltip = folium.GeoJsonTooltip(
fields=['title'],
aliases=['depth'],
)
popup = folium.GeoJsonPopup(
fields=['title'],
aliases=['depth'],
)
#colorscale = branca.colormap.linear.Greys_03.scale(levels[-1],levels[0])
def style_func(feature):
return {
'color': feature['properties']
['stroke'], #colorscale(feature['properties']['level-value']),
'weight': 3, #x['properties']['stroke-width'],
#'fillColor': x['properties']['fill'],
'opacity': 1.,
#'popup': feature['properties']['title'],
}
folium.GeoJson(
contours_geojson,
name='geojson',
style_function=style_func,
tooltip=tooltip,
popup=popup,
).add_to(m)
# campaign details
for uname, u in self.items():
if uname not in ignore:
for d in u:
folium.Polygon([(d.start.lat, d.start.lon),
(d.end.lat, d.end.lon)],
tooltip=uname + ' ' + d.label + '<br>' +
str(d.start.time) + '<br>' +
str(d.end.time),
color=cnames[u['color']],
dash_array='10 20',
opacity=.5).add_to(m)
folium.Circle(
(d.start.lat, d.start.lon),
tooltip=uname + ' ' + d.label + '<br>' +
str(d.start.time),
radius=2 * 1e2,
color=cnames[u['color']],
).add_to(m)
folium.Circle(
(d.end.lat, d.end.lon),
tooltip=uname + ' ' + d.label + '<br>' +
str(d.end.time),
radius=1e2,
color=cnames[u['color']],
).add_to(m)
# useful plugins
MeasureControl().add_to(m)
fmtr_lon = "function(dec) {var min= (dec-Math.round(dec))*60; " \
+"direction = (dec < 0) ? 'W' : 'E'; " \
+"return L.Util.formatNum(dec, 0) + direction + L.Util.formatNum(min, 2);};"
fmtr_lat = "function(dec) {var min= (dec-Math.round(dec))*60; " \
+"direction = (dec < 0) ? 'S' : 'N'; " \
+"return L.Util.formatNum(dec, 0) + direction + L.Util.formatNum(min, 2);};"
MousePosition(lat_formatter=fmtr_lon, lng_formatter=fmtr_lat).add_to(m)
return m
if found: all_subs.append(route)
return all_subs
if __name__ == '__main__':
f = open('../csv/GoogleAPIKey.txt', 'r')
API_key = f.readline()
f.close()
school_locations = pd.read_csv(SCHOOL_FILE)
bps_sensor = pd.read_csv(BPS_FILE)
reg = Region(42.70, -71.50, 42.10, -70.75)
reg.add_ratio()
bps_sensor = clean_data(bps_sensor)
for pnts in grainby(bps_sensor):
sts = trace_street(API_key, reg, pnts)
bps_points = list()
for st in sts:
bps_points.append(st.begin_ptn.get_pair())
if not (st.begin_ptn == st.end_ptn):
bps_points.append(st.end_ptn.get_pair())
base_map = folium.Map(location=(42.30, -71.05), zoom_start=13)
folium.Polygon(bps_points, color='red').add_to(base_map)
base_map.save('route_tracer.html')
break
def map_hotspot_scale(hscale,
lat=None,
lng=None,
R=8,
geo_range=1.0,
outputfile='hotspot_probs.html',
bbox=None):
"""
:param hscale: list of hotspot probabilities
:param lat: map center latitude
:param lng: map center longitude
:param outputfile:
:param bbox: lat upper left, long upper left, lat upper right, long upper right
:return:
"""
hs = load_hotspots()
h_by_addr = dict()
for h in hs:
h_by_addr[h['address']] = h
if not bbox:
bbox = [
lat + geo_range, lng - geo_range, lat - geo_range, lng + geo_range
]
else:
lat = (bbox[0] + bbox[2]) / 2
lng = (bbox[1] + bbox[3]) / 2
tiles = 'http://{s}.tiles.mapbox.com/v4/wtgeographer.2fb7fc73/{z}/{x}/{y}.png?access_token=pk.eyJ1IjoiY2Fybml2ZXJvdXMxOSIsImEiOiJja2U5Y3RyeGsxejd1MnBxZ2RiZXUxNHE2In0.S_Ql9KARjRdzgh1ZaJ-_Hw'
my_map = folium.Map(
location=[lat, lng],
zoom_start=6,
#tiles=tiles,
#API_key='pk.eyJ1IjoiY2Fybml2ZXJvdXMxOSIsImEiOiJja2U5Y3RyeGsxejd1MnBxZ2RiZXUxNHE2In0.S_Ql9KARjRdzgh1ZaJ-_Hw'
#attr='Mapbox'
)
vals = [x['odds'] for x in hscale]
avg = np.mean(vals)
#vals = np.array(vals)/avg
print(f"{np.max(vals)}")
print(f"average scaling factor = {avg}")
cnorm = mpl.colors.TwoSlopeNorm(vcenter=avg,
vmin=np.min(vals),
vmax=np.max(vals) * 1.2)
colmap = cm.ScalarMappable(norm=cnorm, cmap='RdYlGn')
idc = np.argsort(vals)
hexs = set([]) # store hex's where odds < 1.0 for displaying
hex_parent = set([])
hex_gparent = set([])
hex_ggparent = set([])
for idx in idc[::-1]:
hp = hscale[idx]
hlat = h_by_addr[hp['addr']]['lat'] #+ (random.random()-0.5)*.0000
hlng = h_by_addr[hp['addr']]['lng'] #+ (random.random()-0.5)*.0000
if not (bbox[2] < hlat < bbox[0] and bbox[1] < hlng < bbox[3]):
continue
color = "#" + ''.join(
[f"{x:02x}" for x in colmap.to_rgba(hp['odds'], bytes=True)[:3]])
folium.CircleMarker(
(hlat, hlng),
color='black',
fill_color=color,
popup=f"{h_by_addr[hp['addr']]['name']} [{hp['odds']:.2f}]",
fill=True,
fill_opacity=0.9,
number_of_sides=8,
radius=11,
opacity=.35,
weight=2,
z_index_offset=2 - int(hp['odds'])).add_to(my_map)
hexs.add(h3.h3_to_parent(h_by_addr[hp['addr']]['location'], R))
hex_parent.add(
h3.h3_to_parent(h_by_addr[hp['addr']]['location'], R - 1))
hex_gparent.add(
h3.h3_to_parent(h_by_addr[hp['addr']]['location'], R - 2))
hex_ggparent.add(
h3.h3_to_parent(h_by_addr[hp['addr']]['location'], R - 3))
print(f"drawing {len(hexs)} target hexs")
for hex in hexs:
hex_points = list(h3.h3_to_geo_boundary(hex, False))
hex_points.append(hex_points[0])
folium.PolyLine(hex_points, weight=1.5, color='black',
opacity=.45).add_to(my_map)
folium.Polygon(hex_points)
print(f"drawing {len(hex_parent)} parent hexs")
for hex in hex_parent:
hex_points = list(h3.h3_to_geo_boundary(hex, False))
hex_points.append(hex_points[0])
folium.PolyLine(hex_points, weight=1.5, opacity=.65).add_to(my_map)
print(f"drawing {len(hex_gparent)} grandparent hexs")
for hex in hex_gparent:
hex_points = list(h3.h3_to_geo_boundary(hex, False))
hex_points.append(hex_points[0])
folium.PolyLine(hex_points, weight=1.5, color='white',
opacity=.65).add_to(my_map)
print(f"drawing {len(hex_gparent)} great grandparent hexs")
for hex in hex_ggparent:
hex_points = list(h3.h3_to_geo_boundary(hex, False))
hex_points.append(hex_points[0])
folium.PolyLine(hex_points, weight=1.5, color='pink',
opacity=.65).add_to(my_map)
my_map.save(outputfile)
linewidths=.5,
cbar_kws={"shrink": .5})
#Map to show stationary stations position
######################
import folium
map_osm = folium.Map(
location=[13.088279, 80.181568], #"13.088279,80.181568")
zoom_start=12)
# for point in range(0, len(locationlist)):
folium.Marker([13.088279, 80.181568], popup='sta').add_to(map_osm)
folium.Marker([13.1046, 80.1710], popup='sta2').add_to(map_osm)
folium.Marker([13.005, 80.2398], popup='sta3').add_to(map_osm)
folium.Marker([13.16, 80.260], popup='sta4').add_to(map_osm)
folium.Polygon([[13.15, 80.250], [13.18, 80.280], [13.20, 80.270],
[13.20, 80.290]])
map_osm
colors = ["windows blue", "amber", "faded green", "dusty purple"]
sns.set(
rc={
"figure.figsize": (15, 10),
"axes.titlesize": 18,
"axes.labelsize": 12,
"xtick.labelsize": 14,
"ytick.labelsize": 14
})
#seasonal decompose to detect stationarity trends and seasonality
###########################
from statsmodels.tsa.seasonal import seasonal_decompose
tooltip="Faubourg de l'Arche",
color="green",
weight=5,
opacity=5).add_to(quartier)
f.Polygon(Quartier_hotel,
tooltip="Hotel de ville",
color="lightblue",
weight=5,
opacity=5).add_to(quartier)
f.Polygon(Quartier_Marceau,
tooltip="Marceau",
color="orange",
weight=4,
opacity=5).add_to(quartier)
Courbevoie_map.add_child(quartier)
Affichage_Quartier_Courbevoie()
limite_ville = f.FeatureGroup(name='Délimitation Courbevoie')
f.Polygon(lcourbevoie, color="blue", weight=7.5,
opacity=8).add_to(limite_ville)
Courbevoie_map.add_child(limite_ville)
f.LayerControl(collapsed=False).add_to(Courbevoie_map)
url = 'templates/courbevoie/CarteCourbevoieVille.html'
Courbevoie_map.save('maCarteCourbevoie.html')
webbrowser.open(os.getcwd() + "/maCarteCourbevoie.html")
#display(Courbevoie_map)
def CreaMap(adresse,nom):
Courbevoie_map= f.Map(location=[48.8967,2.2567],zoom_start=15)
icon_market=f.Icon(icon='shopping-cart', prefix='fa',markerColor='orange')
icon_pharmacie=f.Icon(icon='',prefix='fa',marketColor='green')
icon_medecin=f.Icon(icon='medkit',prefix='fa')
icon_pharmacie=f.Icon(icon='ambulance',prefix='fa',markerColor='green')
icon=icon_maison=f.Icon(icon='home',markerColor='red')
icon1=f.Icon(marketColor='green')
f.Marker([48.8967, 2.2567], popup="Monoprix",tooltip="Monoprix",icon=icon_pharmacie).add_to(Courbevoie_map)
iti_sup=f.FeatureGroup(name='Itinéraire Supérette la plus proche')
iti=f.FeatureGroup(name='Itinéraire Pharmacie la plus proche')
zone_sport=f.FeatureGroup(name='Zone de sport')
Courbevoie_map = Market_adresse(adresse,Courbevoie_map)
Courbevoie_map = Affichage_client(adresse, Courbevoie_map,icon_maison)
#on crée la map
#on peut rajouter des marqueurs definis par leur longitude et latitude
#On cherche à créer des icon
#info-sign
#cloud
#home
#arrow-right
#file="communes-93-seine-saint-denis.geojson"
#f.GeoJson(file,name="Courbevoie").add_to(Courbevoie_map)
#Traiter les données d'un fichier csv pas aboutis car données eronnées
#lperi=[]
#lperifinale=[]
#g=open(r'C:\Users\u\Downloads\BAN_odbl_92026.csv', newline='')
#reader=csv.reader(g)
#for row in reader:
#l=[]
#if (row[14]!='lat' and row[15]!="long"):
#a=float(row[14])
#b=float(row[15])
#l.append(a)
#l.append(b)
#lperi.append(tuple(l))
#print(lperi)
#for i in range(100):
#lperifinale.append(lperi[i])
#f.Polygon(lperifinale, color="blue", weight=7.5, opacity=8).add_to(Courbevoie_map)
#Trouver les données de Courbevoie :
lcourbevoie=[( 48.906294552742324,2.266398166116926), (48.902283797738605,2.284270341799327), (48.898400020270955,2.271065300760704),(48.896657,2.264661), (48.88688989505178,2.253756950262125), (48.89567616526661,2.2337852355532384), (48.900653602651886,2.2316248960533365), (48.90607328944114,2.2582158432572785), (48.906294552742324,2.266398166116926)]
#f.Polygon(lcourbevoie, color="blue", weight=7.5, opacity=8).add_to(Courbevoie_map)
#Mettre un bouton
#a=f.LayerControl(position='bottomright', collapsed=True,autoZIndex=True).add_to(Courbevoie_map)
#a.add_child(name="blabla")
tooltip="Clique pour voir ce magasin"
#popup1=f.Popup(print("blabla"))
#f.Marker([48.90,2.26],popup=f.Popup(max_width=450).add_child(f.Marker([48.95,2.26]).add_to(Courbevoie_map)).add_to(Courbevoie_map))
#le popup ce qui apparait
#le tooltip ce qui nous fais cliquer
#Pour changer les graphiques de l'affichage
#f.raster_layers.TileLayer('Stamen Terrain').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('Stamen Toner').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('Stamen Watercolor').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('CartoDB Positron').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('CartoDB Dark_Matter',name="map sombre").add_to(Courbevoie_map)
#Le par defaut est le mieux dans notre cas
# using ipywidgets
# plot location with marker
Courbevoie_map = Affichage_supérette(Courbevoie_map)
Courbevoie_map = Affichage_pharmacie(Courbevoie_map)
iti = Itinéraire_Pharmacie_la_plus_proche(Convertisseur_adresse_gps(adresse),Convertisseur_adresse_gps(Adresse_Pharmacie_la_plus_proche(adresse)),iti)
iti_sup= Itinéraire_Supérette_la_plus_proche(Convertisseur_adresse_gps(adresse),Coordonnée_Superette_la_plus_proche(adresse),iti_sup)
Courbevoie_map.add_child(iti)
Courbevoie_map.add_child(iti_sup)
#Affichage Zone de Sport
loc=Convertisseur_adresse_gps(adresse)
f.CircleMarker(location=loc,tooltip="Zone de sport",radius=300,color='black').add_to(zone_sport)
Courbevoie_map.add_child(zone_sport)
#On fait afficher les featureGroup
all_subgroups = f.FeatureGroup(name='Contour Courbevoie')
f.Polygon(lcourbevoie, color="blue", weight=7.5, opacity=8).add_to(all_subgroups)
Courbevoie_map.add_child(all_subgroups)
# add layer control to map (allows layers to be turned on or off)
f.LayerControl(collapsed=False).add_to(Courbevoie_map)
#Mesure Control
# measure control
measure_control = plugins.MeasureControl(position='bottomright',
active_color='red',
completed_color='red',
primary_length_unit='meter')
# add measure control to map
Courbevoie_map.add_child(measure_control)
url = 'courbevoie/templates/courbevoie/maCarteCourbevoie.html'
Courbevoie_map.save(url)
def _new_polygon(locations, hotspot):
return folium.Polygon(locations=locations,
fill_color='red' if hotspot else 'green',
fill_opacity=0.2,
color='black',
weight=1)
school_locations = pd.read_csv(SCHOOL_FILE)
bps_sensor = pd.read_csv(BPS_FILE)
reg = Region(42.70, -71.50, 42.10, -70.75)
reg.add_ratio()
bps_sensor = clean_data(bps_sensor)
routes = grainby(bps_sensor)
for index, origin_route in enumerate(routes):
if len(origin_route) > 300 or len(origin_route) < 15: continue
base_map = folium.Map(location=(42.30, -71.05), zoom_start=13)
folium.Polygon(origin_route,
popup='bps route',
color='blue',
opacity=1).add_to(base_map)
sts = trace_street(API_key, reg, origin_route)
sts = [st for st in sts if not (st.begin_ptn == st.end_ptn)
] # filter out single point street
csts = [st for st in sts if st.has_congestion]
subs_routes = substitue_route(csts)
if len(subs_routes) == 0: continue
add_school_markers(base_map, school_locations)
for route in subs_routes:
folium.Polygon(route,
popup='optimized route',
def show_map():
'''
Function to render a map around the specified line
'''
# import pdb
# pdb.set_trace()
if request.method == 'GET':
name = request.values.get('name')
coords_list = ast.literal_eval(request.values.get('coords'))
geom_type = request.values.get('geom_type')
else:
name = request.form.get('name')
coords_list = ast.literal_eval(request.form.get('coords'))
geom_type = request.form.get('geom_type')
if geom_type == "MultiPolygon":
poly_points = []
total_points = [] #used to calculate the average coordinates of the area of interest
for a_polygon_coords in coords_list[0]: # may have more than one polygon
points = []
for coords in a_polygon_coords:
points.append(tuple([coords[1], coords[0]])) # swap x & y for mapping
total_points.append(tuple([coords[1], coords[0]]))
poly_points.append(points)
ave_lat = sum(p[0] for p in total_points) / len(total_points)
ave_lon = sum(p[1] for p in total_points) / len(total_points)
lon_diff = max(p[1] for p in total_points) - min(p[1] for p in total_points)
lat_diff = max(p[0] for p in total_points) - min(p[0] for p in total_points)
ave_diff = (lon_diff + lat_diff)/2
if ave_diff > 0.47:
zoom_start_level = 8
elif ave_diff > 0.3:
zoom_start_level = 9
elif ave_diff > 0.2:
zoom_start_level = 10
elif ave_diff > 0.1:
zoom_start_level = 11
elif ave_diff > 0.02:
zoom_start_level = 12
elif ave_diff > 0.01:
zoom_start_level = 13
elif ave_diff > 0.007:
zoom_start_level = 14
else:
zoom_start_level = 15
# create a new map object
folium_map = folium.Map(location=[ave_lat, ave_lon], zoom_start=zoom_start_level)
tooltip = 'Click for more information'
for a_poly_point in poly_points:
folium.Polygon(a_poly_point, color="red", weight=2.5, opacity=1, popup=name, tooltip=tooltip).add_to(folium_map)
elif geom_type == 'MultiLine': #polyline
for a_line_coords in coords_list[0]:
points = []
for coords in a_line_coords:
points.append(tuple([coords[1], coords[0]]))
ave_lat = sum(p[0] for p in points) / len(points)
ave_lon = sum(p[1] for p in points) / len(points)
# create a new map object
folium_map = folium.Map(location=[ave_lat, ave_lon], zoom_start=13)
tooltip = 'Click for more information'
folium.PolyLine(points, color="red", weight=2.5, opacity=1, popup = name, tooltip=tooltip).add_to(folium_map)
else: #point
# create a new map object
lat = coords_list[1]
lon = coords_list[0]
folium_map = folium.Map(location=[lat, lon], zoom_start=15)
tooltip = 'Click for more information'
folium.Marker([lat, lon], popup=name, tooltip=tooltip).add_to(folium_map)
# if len(coords_list) == 1: #polyline or polygon
# # swap x & y for mapping
# points = []
# if len(coords_list[0]) == 1: #polygon
# for coords in coords_list[0][0]:
# points.append(tuple([coords[1], coords[0]]))
# ave_lat = sum(p[0] for p in points) / len(points)
# ave_lon = sum(p[1] for p in points) / len(points)
# # create a new map object
# folium_map = folium.Map(location=[ave_lat, ave_lon], zoom_start=12)
# tooltip = 'Click for more information'
# folium.Polygon(points, color="red", weight=2.5, opacity=1, popup=name, tooltip=tooltip).add_to(folium_map)
# else: #polyline
# for coords in coords_list[0]:
# points.append(tuple([coords[1], coords[0]]))
# ave_lat = sum(p[0] for p in points) / len(points)
# ave_lon = sum(p[1] for p in points) / len(points)
# # create a new map object
# folium_map = folium.Map(location=[ave_lat, ave_lon], zoom_start=13)
# tooltip = 'Click for more information'
# folium.PolyLine(points, color="red", weight=2.5, opacity=1, popup = name, tooltip=tooltip).add_to(folium_map)
# else: #point
# # create a new map object
# lat = coords_list[1]
# lon = coords_list[0]
# folium_map = folium.Map(location=[lat, lon], zoom_start=15)
# tooltip = 'Click for more information'
# folium.Marker([lat, lon], popup=name, tooltip=tooltip).add_to(folium_map)
return folium_map.get_root().render()
target_list = [{"target_name": "hedef_x", "coordinates": [5.0, 2.0]},
{"target_name": "hedef_y", "coordinates": [17.0, 1.5]},
{"target_name": "hedef_z", "coordinates": [9.0, 22.0]},
{"target_name": "hedef_t", "coordinates": [1.0, 3.0]}]
target_list.insert(0, {"target_name": "start", "coordinates": [0.0, 0.0]})
target_list.append({"target_name": "return", "coordinates": [0.0, 0.0]})
geomap = folium.Map([0, 0], zoom_start=5)
geopath_polygon = []
for arr in polys:
geopath_polygon.append([[coord.x, coord.y] for coord in arr])
polypath = polys
for i in geopath_polygon:
folium.Polygon(i, fill=True, color='red').add_to(geomap)
file = open("output.txt", "w")
file.write("[")
for i in range(len(target_list)):
if i != len(target_list) - 1:
shortest_path = g.shortest_path(vg.Point(target_list[i]["coordinates"][0], target_list[i]["coordinates"][1]),
vg.Point(target_list[i + 1]["coordinates"][0], target_list[i + 1]["coordinates"][1]))
geopath = [[point.x, point.y] for point in shortest_path]
file.write(f'({target_list[i+1]["target_name"]}, {geopath}),\n')
print(geopath)
for point in geopath:
folium.Marker(point, popup=str(point)).add_to(geomap)
folium.PolyLine(geopath).add_to(geomap)
folium.Marker(geopath[0], icon=folium.Icon(color='red')).add_to(geomap)
folium.Marker(geopath[-1], icon=folium.Icon(color='red')).add_to(geomap)
import folium
import csv
import pyproj
with open('gps surface.csv') as csvfile:
gps_data = [i[1:3] for i in csv.reader(csvfile, delimiter=',')][2:]
inProj = pyproj.Proj(init='epsg:28992')
outProj = pyproj.Proj(init='epsg:4326')
reproj = [pyproj.transform(inProj,outProj,i[0],i[1]) for i in gps_data]
reproj = [(i[1],i[0]) for i in reproj]
m = folium.Map(location=reproj[0], tiles='cartodbdark_matter', zoom_start=15)
reproj.append(reproj[0])
folium.Polygon(reproj,fill=True).add_to(m)
for i in enumerate(reproj):
folium.CircleMarker(location=i[1],radius=0.7, popup=str(i)).add_to(m)
m.save('roof_map.html')
"locations_71": 0,
"locations_74": 2,
"locations_88": 1,
"locations_89": 4,
"locations_94": 5
}
def findcolor(loc):
col = colorEval(data.iloc[diction[loc]]["AQI"])
return col
folium.Polygon(locations_43,
popup=None,
color=findcolor("locations_43"),
weight=4,
opacity=0.8,
fill_color=findcolor("locations_43")).add_to(m)
folium.Polygon(locations_71,
popup=None,
color=findcolor("locations_71"),
weight=4,
opacity=0.8,
fill_color=findcolor("locations_71")).add_to(m)
folium.Polygon(locations_74,
popup=None,
color=findcolor("locations_74"),
weight=4,
opacity=0.8,
fill_color=findcolor("locations_74")).add_to(m)
folium.Polygon(locations_88,
def visualize_hexagons(hexagons_df,
legends=True,
join_muiltiindex_cols=True,
polygon_conf_dict={},
folium_map=None,
folium_map_config={}):
"""
Creates a Folium map with hexagons and pop-up legend.
:param hexagons_df: DataFrame with the H3 index has index.
:param legends: If 'True', each hexagon will have a pop-up with all the values of its row.
:param join_muiltiindex_cols: Joins the 'hexagons_df' columns in a single columns, if MultiIndex
:param polygon_conf_dict: a dict to config the folium.Polygon obj. Ex.:
{
# fill color is True by default, it has to be explicit turned off
"color": {"col":"lost_tours", "big_is_better": False, "colormap_legend": "legend", "fill_color": True},
"color": {"val":"green"}, # Color can also just be a single value
"weight": {"col":"n_bookings", "max": 6, "min":1},
"opacity": {"val":0.3},
"fill_opacity": {"val":0.15}
}
default: opacity: 0.3; fill_opacity: 0.15; weight: 0.5; color: green
:param folium_map: The folium map obj to had the Hexagons to. If None a new map is created
:param folium_map_config: default = {"zoom_start": 11,"tiles": 'cartodbpositron', "location": avg_hexagon_location}
location: The initial lat, long to start the map at. If None tha Avg center of the hexagons will be used.
zoom_start: the 'folium.Map()' zoom_start value. default 11
tiles: the 'folium.Map()' tiles value. default 'cartodbpositron'
:return: Folium map obj
"""
hexagons_df = hexagons_df.copy()
hexagons = hexagons_df.index.values
n_hexs = len(hexagons)
add_color_map_to_map = False
# Hexagons popup Legends
if legends is None or legends is False:
legends = np.array(n_hexs * [None])
elif legends is True:
if join_muiltiindex_cols and type(
hexagons_df.columns) == pd.MultiIndex:
hexagons_df.columns = ["-".join(c) for c in hexagons_df.columns]
hexagons_dict = hexagons_df.to_dict("index")
legends = [f"{idx}: {hexagons_dict[idx]}" for idx in hexagons]
# processing Polygon Propreties
# Adding default Polygon configs
polygon_conf_dict.setdefault("opacity", {"val": 0.3})
polygon_conf_dict.setdefault("fill_opacity", {"val": 0.15})
polygon_conf_dict.setdefault("weight", {"val": 0.5})
polygon_conf_dict.setdefault("color", {"val": "green"})
all_poly_props_df = pd.DataFrame()
for col_name, conf in polygon_conf_dict.items():
if "col" in conf:
poly_prop_values = hexagons_df[conf["col"]]
# Normalize
if set(("min", "max")).issubset(conf):
poly_prop_values = (conf["min"] + norm_col(poly_prop_values) *
(conf["max"] - conf["min"])).values
elif "val" in conf:
poly_prop_values = len(hexagons_df) * [conf["val"]]
# else:
# raise Exception("No 'col' or 'val' key found! :(")
# Processing colors
if col_name == "color":
if "col" in conf:
big_is_better = conf[
"big_is_better"] if "big_is_better" in conf else True
cm_legend = conf[
"colormap_legend"] if "colormap_legend" in conf else str(
conf["col"]).replace("'", "")
colormap = ColorMap(np.min(poly_prop_values),
np.max(poly_prop_values), cm_legend,
big_is_better)
poly_prop_values = [colormap(ci) for ci in poly_prop_values]
add_color_map_to_map = True
# Adds fill color by default, it has to be explicit turned off
if ("fill_color" in conf and conf["fill_color"]) or ("fill_color"
not in conf):
all_poly_props_df["fill_color"] = poly_prop_values
all_poly_props_df[col_name] = poly_prop_values
polys_config = list(all_poly_props_df.to_dict("index").values())
# Initial Location
if "location" not in folium_map_config:
location = np.mean([h3.h3_to_geo(h3_id) for h3_id in hexagons], axis=0)
folium_map_config["location"] = location
# Creates Folium map
if folium_map is None:
folium_map_config.setdefault("zoom_start", 11)
folium_map_config.setdefault("tiles", 'cartodbpositron')
m = folium.Map(**folium_map_config)
else:
m = folium_map
# adding polygons
for hex_id, leg, poly_conf in zip(hexagons, legends, polys_config):
locations = h3.h3_to_geo_boundary(hex_id)
folium.Polygon(locations, popup=leg, **poly_conf).add_to(m)
# adds the colormap legend to the map
if add_color_map_to_map:
m.add_child(colormap.colormap)
colormap.colormap.add_to(m)
return m
height=300).interactive())
vega = folium.features.VegaLite(base, width=700, height=50)
graph_popup = folium.Popup() # Create a popup
vega.add_to(graph_popup) # Add graph to popup
marker = folium.features.Marker([df_cd.iloc[5, 0], df_cd.iloc[5, 1]],
icon=folium.Icon(color='white',
icon='bicycle',
icon_color="orange",
prefix='fa')).add_to(m)
graph_popup.add_to(marker)
marker.add_to(m)
# Linking markers
my_Polygone = folium.Polygon(locations=df_cd,
weight=4,
color='yellow',
opacity=1)
m.add_child(my_Polygone)
folium.TileLayer(tiles='Stamen Terrain', name='Stamen Terrain',
show=True).add_to(m)
folium.TileLayer(tiles='cartodb positron', name='cartodb positron',
show=True).add_to(m)
folium.TileLayer(tiles="stamentonerbackground",
name="stamentoner background",
show=True).add_to(m)
# Add a title
title_html = '''
<h3 align="center" style="color:purple;background-color:#2AAE67;font-size:210%"> <b>Montpellier's bicycle counters</b> </h3>