shapely | Manipulation and analysis of geometric objects

import sqlite3
conn = sqlite3.connect("places.db")
# Enable SpatialLite extension
conn.enable_load_extension(True)
conn.load_extension("/usr/local/lib/mod_spatialite.dylib")
# Create the masic countries table
conn.execute("select InitSpatialMetadata(  

import geopandas as gpd
import numpy as np
import shapely
import matplotlib.pyplot as plt

states = (
    gpd.read_file(
        "https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_1_states_province

gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'

some_map = gpd.read_file(fr'some_map.kml', driver='KML')

def getRandomPoint(map: gpd.geodataframe.GeoDataFrame) -> Tuple[float, float]:
    
    def generateRandomPointOfBox

from shapely.geometry import LineString, CAP_STYLE
from pyproj import Transformer
from shapely.ops import transform
import simplekml

# pick 2 end points of a line with longitude and latitude
pt1 = [-0.14062046656000524, 51.501870264040775

df = pd.DataFrame({
    'latitude': lat_grid.reshape(-1),
    'longitude': lon_grid.reshape(-1),
    'value': grid_cubic.reshape(-1)
    });

import matplotlib.pyplot as plt
plt.pcolor(np.array(df['longitude']).resh

data = np.array(data)
center = data.mean(0)
angle = np.arctan2(*(data - center).T[::-1])
index = np.argsort(angle)
polygon2 = Polygon(data[index])

fig, ax = plt.subplots(1, 2)

ax[0].set_title("Original")
convex_hu

from pathlib import Path
import pandas as pd
import geopandas as gpd
import shapely
import folium

# downloaded data
df = pd.read_csv(
    Path.home().joinpath("Downloads/SaobracajBeograd.csv"),
    names=["ID", "Date,Time", "E", "N", "Out

%timeit gdf["within"] = gdf.apply(within, args=(gdf,), axis=1)
print(f"""number of polygons: {len(gdf)}
number kept: {len(gdf.loc[lambda d: ~d["within"]])}
""")

2.37 s ± 118 ms per loop (mean ± std. dev. of 7 runs,

xres = lon_vec[1]-lon_vec[0]
yres = lat_vec[1]-lat_vec[0]

from rasterio.transform import Affine

transform = Affine.translation(lon_vec[0] - xres / 2, lat_vec[0] - yres / 2) * Affine.scale(xres, yres)

with rasterio.open(
    '/tmp/new.ti