New answers tagged rasterio
2
Here's a very naive way using rasterio.shutil.copy to generate VRT XML for each source raster, combine them and output a stacked VRT (equivalent to gdalbuildvrt -separate).
By naive, I mean it does not check any of the properties and assumes rasters are all the same shape, extent, pixel size, data type, projection etc.
import xml.etree.ElementTree as ET
...
0
If you're looking for a QGIS solution, you can check the r.to.vect GRASS function under the QGIS Processing Toolbox.
These links below might help:
Converting raster to vector by generating center lines?
https://www.youtube.com/watch?v=TQ1sD4NSpHo
5
You're writing a floating point array as an unsigned 8 bit integer. Try
src = rasterio.open('raster2.tif')
profile = src.profile
with rio.open('test.tif', 'w', **profile) as dst:
dst.write(new_raster, 1) # No need to cast to int, as profile is already float32
or if that doesn't work, enforce the casting using .astype(rio.float32) instead of your ...
5
If you're ultimately working toward a polygon (circle) intersection with your raster, there are built-in methods for calculating a numpy mask, given a polygon.
You can use this question to see how Shapely can be used to get a circle of defined radius from a centre point (you will need to convert to a projected coordinate system).
Then, use rasterio.features....
0
So, I got it working using rasterio. The problem was that the Affine pivot parameter is actually in pixel coordinates, and I was passing real world coordinates. So few lines of code later, this is rotating as exected!
#!/usr/bin/python
from optparse import OptionParser
import rasterio
from affine import Affine # For easly manipulation of affine matrix
from ...
3
Here's a non geocube way using rasterio and fiona (could also use the higher level geopandas which uses fiona under the hood). This
loops through small chunks (aka a rasterio.windows.Window) and the features in your polygon dataset,
rasterizes the features just in the small chunk area,
sums the chunks,
writes each summed chunk to the appropriate part of ...
0
# Look at this simple code:
import os
import gdal
path = r'inputpath_directory'
# for Loop
bands = []
for i in os.listdir(path):
bands.append(i)
print(bands) # a list of bands
# To save a raster using gdal:
gdal_array.SaveArray(bands,
r'outputpath_name',
"GTiff",
...
0
# Look At this code:
import rasterio
import numpy as np
import matplotlib.pyplot as plt
# open the bands:
B4 = rasterio.open(inputpath_name)
B5 = rasterio.open(inputpath_name)
# First calculate your NDVI with this function:
def ndvi(red, nir):
red = B4.read(1)
nir = B5.read(1)
red = red.astype('float64')
nir = nir.astype('float64')
...
1
This should work with what you require. Read about rasterio write here
d = ['Blue', 'Red', 'Green']
test = 'test.tif'
folder = Path('Path/to/My/Folder')
with rasterio.open("test.tif") as src:
for f in folder.glob("*.tif"):
if any(color in f.name for color in d):
with rasterio.open(f) as med:
new_raster = ...
0
I would recommend using @radouxju approach to calculating NDVI in this answer. Here is an untested example based on the link you provided and radouxju's NDVI approach:
import os
import rasterio
import numpy as np
# Filepath
dataset = r'C:\HY-DATA\HENTENKA\CSC\Data\Helsinki_masked_p188r018_7t20020529_z34__LV-FIN.tif'
# Outfile path
outpath = r'C:\path\to\...
1
rioxarray can simplify that:
https://corteva.github.io/rioxarray/stable/examples/reproject_match.html
https://www.earthdatascience.org/courses/use-data-open-source-python/intro-raster-data-python/raster-data-processing/subtract-rasters-in-python/
https://carpentries-incubator.github.io/geospatial-python/07-raster-calculations/index.html
1
The rasterio documentation for the mask function is not very clear here. The shape parameter must be an iterable of geometries, not a simple geometrie.
The documentation of the shape parameter (i.e. "The values must be a GeoJSON-like dict or an object that implements the Python geo interface protocol...") describes the nature of the values ...
1
You can iterate all over array, if that helps?
def reclassify(arr, rows, cols):
for r in range(rows):
for c in range(cols):
temp_value = arr[r][c]
if temp_value < 20:
temp_value = 4
elif temp_value >=80:
temp_value = 1
elif (temp_value >=50) and (...
0
from osgeo import gdal
gdal.Warp(destNameOrDestDS = outputpath, # directory output
srcDSOrSrcDSTab = intputpath, # directoy intput
cutlineDSName = shapefile or geojson, # vector file
cropToCutline = True, # Select True
copyMetadata = True, # optional
dstNodata = 0) # if you have values ...
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