hkristen

"""Example: TiledInferenceCallback for large-scale geospatial inference.

This script demonstrates how to use TorchGeo's TiledInferenceCallback to run
inference on large rasters that don't fit in memory. It handles:
- Tiled processing with configurable patch size and overlap
- Weighted blending at patch boundaries (cosine/linear)
- Automatic geospatial metadata preservation
- Chunked GeoTIFF output for large results

Usage:

calebrob6

calebrob6

calebrob6

MarcSkovMadsen

import pandas as pd
import hvplot.pandas
import panel as pn

pn.extension(sizing_mode="stretch_width")

PALETTE = [
    "#ff6f69",
    "#ffcc5c",
    "#88d8b0",

ashleve

from pytorch_lightning import LightningDataModule
from torch_geometric.datasets import TUDataset
from torch_geometric.data import DataLoader
from sklearn.model_selection import KFold


class ProteinsKFoldDataModule(LightningDataModule):
    def __init__(
            self,
            data_dir: str = "data/",

abelcallejo

Installing GDAL 3.2.1 on Amazon Linux 2

As of this day, this is probably the only and fastest way of installing it.

Package requirements

Based from the GDAL and PROJ build requirements, here is the full list of required packages to install:

tvst

"""Hack to add per-session state to Streamlit.

Usage
-----

>>> import SessionState
>>>
>>> session_state = SessionState.get(user_name='', favorite_color='black')
>>> session_state.user_name
''

hunterjm

# Change this path to your config directory
CONFIG_DIR="/home/homeassistant/.homeassistant"

cd /tmp

# Clone the latest code from GitHub
git clone --depth 1 https://github.com/tensorflow/models.git tensorflow-models

# download protobuf 3.4
curl -OL https://github.com/google/protobuf/releases/download/v3.4.0/protoc-3.4.0-linux-x86_64.zip

leon-sleepinglion

from flask import Flask
from flask_restful import Api, Resource, reqparse

app = Flask(__name__)
api = Api(app)

users = [
    {
        "name": "Nicholas",
        "age": 42,