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"""Three ways of computing stats of a rasterio dataset.""" | |
import dask.array | |
import numpy | |
import rasterio | |
from rasterio.windows import Window | |
# Compute mean of entire dataset read into memory. Easiest, but doesn't | |
# scale for very large datasets. | |
with rasterio.open( |
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View cfrw.py
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"""Concurrent read-process-write example""" | |
import concurrent.futures | |
from itertools import islice | |
from time import sleep | |
import rasterio | |
CHUNK = 100 |
View advanced_rasterio_features.ipynb
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View coutwildrnp.jsonseq
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{"geometry": {"coordinates": [[[-111.73527526855469, 41.995094299316406], [-111.65931701660156, 41.99627685546875], [-111.6587142944336, 41.9921875], [-111.65888977050781, 41.95676803588867], [-111.67082977294922, 41.91230010986328], [-111.67332458496094, 41.905494689941406], [-111.67088317871094, 41.90049362182617], [-111.66474914550781, 41.893211364746094], [-111.6506576538086, 41.875465393066406], [-111.64759826660156, 41.87091827392578], [-111.64640808105469, 41.86273956298828], [-111.64334869384766, 41.858192443847656], [-111.63720703125, 41.85499572753906], [-111.633544921875, 41.847267150878906], [-111.63053894042969, 41.83409118652344], [-111.6330337524414, 41.82728576660156], [-111.63983154296875, 41.8227653503418], [-111.6484603881836, 41.82188034057617], [-111.66077423095703, 41.82327651977539], [-111.6712417602539, 41.82330322265625], [-111.67618560791016, 41.82013702392578], [-111.68803405761719, 41.78792953491211], [-111.69361114501953, 41.77931594848633], [-111.70162200927734, 41.77797317504883 |
View latency.txt
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Latency Comparison Numbers | |
-------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
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View benchmark.py
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# Comparing serialization of complex GeoJSON geometries using: | |
# | |
# - standard lib json, marshal, pickle, cPickle | |
# - umsgpack | |
# - shapely.wkb | |
# - geobuf (protobuf) | |
# | |
# The test case is a nearly circular polygon with 128 vertices. | |
# | |
# Python 2.7 because geobuf isn't possible on Python 3 (because |
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