rasterio | Rasterio reads and writes geospatial raster datasets

>>> from dask_rasterio import read_raster

>>> array = read_raster('tests/data/RGB.byte.tif')
>>> array
dask.array

>>> array.mean()
dask.array
>>> array.mean().compute()
40.858976977533935

>>> f  

from __future__ import print_function
import rasterio

def parse_sns(event):
    sns_message = json.loads(event['Records'][0]['Sns']['Message'])
    s3_event = sns_message['Records'][0]['s3']
    bucket = s3_event['bucket']['name']
    key = s3_event  

import numpy as np
import pandas as pd
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import rasterio
from rasterio.crs import CRS

# define interpolation inputs
points = list(zip(df.longitude,df.latitude))
values =

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

# Get limit points
l = gpd.read_file('param_geo/sabah.shp')['geometry'].bounds
lat_min,lat_max,lon_min,lon_max  = l['miny'].iloc[0], l['maxy'].iloc[0], l['minx'].iloc[0], l['maxx'].iloc[0]
xmin,xmax = ax.get_xlim()
ymin,ymax = ax.get_ylim(

########## MAYAVI 

# xcb plugin 
RUN apt-get install -y --no-install-recommends xvfb libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 && \
    apt-get clean -y
# Try

# for a gridded dataset with 2-D arrays lats, lons
# and a list of shapely polygons/multipolygons all_shapes

XX = lons.ravel()
YY = lats.ravel()

single_multipolygon = shapely.ops.unary_union(all_shapes)

in_any_shape = shapely.vectorized