[SOLVED] Contour plot using folium in pycharm

Contour plot using folium in pycharm

I have a data with latitude, longitude and temp. I need to draw a contour plot for temp based on the latitude and longitude in folium. I tried this one ..but nothing works not producing a map at all. Please anybody help me to get a solution.

sample_data=pd.read_csv('cont.csv',delim_whitespace=True)
plot=sample_data[["lat","lon","Temp"]]
map=folium.Map(location=[plot.lat.mean(),plot.lon.mean()],
                   zoom_start=14,control_scale=True)

Thanks in advance I'm also adding the next code I tried List=sample_data["Temp"].tolist() m=folium.Map(Location=[plot.lat.mean(),plot.lon.mean()],zoom_start=6) HeatMap(list).add_to(m) m.save("out.html")

But nothing works .. It also giving an error float not subscriptable'

Solution

have used earthquake data, as it has latitude, longitude and magnitude
originally understood that a heatmap was requested, so included
have reused a previous answer: https://stackoverflow.com/a/70632936/3437504
have realised there is an issue with it. meshgrid() needs to use linear to interpolate points https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html#scipy.interpolate.griddata
have also included actual points
now contour lines make sense. I would expect that temperature data will work equally well. Hence reason I've used constants for columns in data frame
further demonstrated it works with temperature data. Data is from UK weather stations and contour line geometry is within scope of UK

import pandas as pd
import numpy as np
import folium
from folium.plugins import HeatMap, HeatMapWithTime
from scipy.interpolate import griddata
from matplotlib import pyplot as plt
import geojsoncontour
import geopandas as gpd
import json, requests, io

earthquakes = False
if earthquakes:
    df = pd.read_csv(
        "https://earthquake.usgs.gov/fdsnws/event/1/query.csv?starttime=2021-09-18%2000:00:00&endtime=2022-01-17%2023:59:59&minmagnitude=2.5&orderby=time"
    )
    df["time"] = pd.to_datetime(df["time"])
    # df = df.loc[df["time"].dt.date.eq(df.groupby(df["time"].dt.to_period("D")).size().idxmax().to_timestamp())]

    MEASURE="mag"
    LON="longitude"
    LAT="latitude"
else:
    df = pd.read_csv(io.StringIO("""lat,lon,T
60.749,-0.854,7.4
60.139,-1.183,6.8
59.527,-1.628,7.6
58.288,-4.442,2.2
56.867,-4.708,3.7
54.721,-6.814,1.5
52.242,-2.885,-0.8
52.48,-1.689,2.7
52.225,-0.464,3.1
51.86,-1.692,1.3
54.803,-4.008,5.5
55.627,-3.735,4.0
54.572,-2.413,0.5
55.285,-2.279,0.9
55.02,-1.88,3.9
55.1604,-6.9492,3.2
51.201,-1.805,2.3
53.811,-1.865,1.7
53.031,-0.502,4.3
53.088,0.274,0.9
52.148,-2.04,-0.3
54.614,-3.157,1.2
53.497,-3.056,3.1
52.757,-3.464,3.1
52.949,1.127,3.8
57.077,-2.836,-1.8
56.852,-2.264,3.7
57.725,-4.896,-0.4
50.89,0.319,4.2
54.707,-7.577,1.9
53.12755,-1.97993,2.6
50.354,-4.121,4.0
54.237,-6.502,2.1
52.358,-1.33,0.8
53.093,-3.941,0.6
58.954,-2.9,7.3
56.326,-3.729,5.8
50.7366,-3.40458,0.8"""))
    MEASURE="T"
    LON="lon"
    LAT="lat"

# basic map..
m = folium.Map(location=[df[LAT].mean(), df[LON].mean()], zoom_start=2)


# create a heatmap of where measures are
HeatMap(
    df.loc[:, [LAT, LON]].values, name="heatmap"
).add_to(m)


temp = df[MEASURE]
lon = df[LON]
lat = df[LAT]

x = np.linspace(min(lon), max(lon), 100)
y = np.linspace(min(lat), max(lat), 100)

X, Y = np.meshgrid(x, y)
Z = griddata((lon, lat), temp, (X, Y), method="linear")
contour = plt.contour(X, Y, Z)

# plot contour lines on folium
gdf = gpd.GeoDataFrame.from_features(
    json.loads(
        geojsoncontour.contour_to_geojson(
            contour=contour, min_angle_deg=3.0, ndigits=5, stroke_width=1
        )
    )
).set_crs("EPSG:4326")
m = gdf.explore(m=m, color=gdf["stroke"], name="contour")

# add to folium where measures are...
m = gpd.GeoDataFrame(
    geometry=gpd.points_from_xy(df[LON], df[LAT]),
    data=df[MEASURE],
    crs="epsg:4326",
).explore(m=m, column=MEASURE, cmap="hot", name="locations")

folium.LayerControl().add_to(m)
m

output (clipped)

UIK temperature data