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import numpy as np | |
import matplotlib.pyplot as plt | |
import seaborn as sns | |
from multiprocessing import Pool | |
from scipy.stats import t | |
from tqdm import tqdm | |
class FracND: | |
class Worker: | |
def __call__(self, window): |
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''' | |
A workflow for classifying Landsat remote sensing imagery | |
involves utilizing soft voting with SVM and Gradient Boosting classifiers. | |
The Isolation Forest algorithm is applied to identify outliers in the | |
training data. Feature selection is performed through a Recursive Feature | |
Elimination approach. The outcome consists of classification and classification | |
probability raster images saved in TIF format. | |
Author: Fabian Löw | |
Date: June 2023 |
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import random | |
import sys | |
import noise | |
import numpy as np | |
import matplotlib.pyplot | |
if "--seed" in sys.argv: | |
try: | |
seed = int(sys.argv[sys.argv.index("--seed")+1]) |
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# Blender 質感・マテリアル設定実践テクニック | |
# 3-04-01.blend ドロドロ汚れの作り方 | |
import bpy | |
NOT_MODIFIED = False | |
object = bpy.data.objects['export3dcoat'] # シーンに配置済みのオブジェクト | |
slot = object.material_slots[0] # そのマテリアルスロット | |
material = slot.material # に設定されているマテリアル | |
node_tree = material.node_tree # のノードネットワーク | |
nodes = node_tree.nodes # にある各ノード | |
def delete(): |
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############################################################################################# | |
# title : Perform a Mann-Kendall trend test of satellite image time series | |
# purpose : Mann-Kendall test of monotonic trend and associated statistical significance | |
# author : Abdulhakim Abdi (@HakimAbdi) | |
# input : Raster stack/brick comprising a time series of observations | |
# output : Raster object of monotoic trend (Kendall's tau), significance or both (i.e. brick) | |
# data : Test data: https://1drv.ms/u/s!AsHsKb_qtbkwg4ti8pJCxvUer9rMqg?e=BGcH1K | |
# notes : Depending on the time series, it is might be useful prewhiten the data to remove serial correlation | |
# : prior to extracting the trend. See blog post on www.hakimabdi.com for more information. | |
############################################################################################# |
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