tastatham

def list_tifs(path):

    import glob

    tifs = []
    for p in glob.glob(f"{path}/*/**/*.tif", recursive=True):
        t = p
        if "_HS_" not in t:
            tifs.append(t)
    return tifs

tastatham

def _h3fy_updated(gdf, resolution):
    """
    h3_radius values calculated using;
    ```python
    import numpy as np
    import pandas as pd
    
    df = pd.read_html("https://h3geo.org/docs/core-library/restable/")[0]
    y = df["Average Hexagon Area (km2)"].to_numpy()

tastatham

import geopandas
import dask_geopandas

path = "https://geodata.ucdavis.edu/gadm/gadm4.0/gpkg/gadm40_USA.gpkg"

gdf = geopandas.read_file(path, layer="ADM_1")
gdf.head()

# Filter and rename cols
gdf = gdf[["NAME_1", "geometry"]].rename(columns={"NAME_1": "State"})

tastatham

def spatial_shuffle(ddf, by="hilbert", column=None, npartitions=20, p=10, **kwargs):
    
    """
    A function that spatially shuffles a Dask-GeoSeries object by a method
    or a user-defined column

    Parameters
    ----------
    by : str
        partitioning method or column

tastatham

import pandas
import geopandas
import rioxarray
from shapely.geometry import box

def _polygonize(rioxarray_obj):
    
    """"""
    
    poly_ls = []

tastatham

import pandas as pd
import geopandas as gpd

def multi_ring_buffers(gdf, number, distance):
    
    """
    Apply a function to a GeoDataFrame containing Point data and compute constant multi-ring buffers
    
    Parameters
    ----------

tastatham

def compute_zonal_stats(raster, vector, fid, col, statistics,tile,mode):

    """
    Calculate zonal statistics using dask
    
   
    Parameters
    ----------
    
    raster             : A GeoDataFrame to create multiple copies based on the list of density thresholds

tastatham

import dask
import dask_geomodeling
request = {
    "mode": "vals",
    "bbox": (138000, 480000, 139000, 481000),
    "projection": "epsg:28992",
    "width": 256,
    "height": 256
}
graph, name = add.get_compute_graph(**request) 

tastatham

# Import modules 
from dask_geomodeling.raster import RasterBlock
from dask_geomodeling.raster import base
from dask_geomodeling.geometry import base

# Define paths 
arg_rast_path = 'raster.tif'
arg_vec_path = 'vector.shp'

"""

tastatham

# Load pkgs and data
import geopandas as gpd
data = gpd.read_file("https://gist.githubusercontent.com/HTenkanen/456ec4611a943955823a65729c9cf2aa/raw/be56f5e1e5c06c33cd51e89f823a7d770d8769b5/ykr_basegrid.geojson")
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Subset argentina as single ply
argentina = world[world['name'] == 'Argentina' ]
                  
# Define engine
engine = create_engine("postgresql+psycopg2://[user]:[password]@localhost:5432/[my_db]")