### Install sentinelhub manually

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/README.md

Install the sentinelhub package manually by cloning the repository and running pip install.

```bash
pip install .
```

--------------------------------

### Install sentinelhub with pip

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/install.md

Use this command to install the base sentinelhub package using pip.

```bash
pip install sentinelhub
```

--------------------------------

### Install latest development version

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/install.md

Clone the GitHub repository and install the latest development version of sentinelhub-py in editable mode.

```bash
pip install -e . --upgrade
```

--------------------------------

### Install sentinelhub with AWS extension

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/install.md

Install sentinelhub with extra dependencies for interacting with Amazon Web Services.

```bash
pip install sentinelhub[AWS]
```

--------------------------------

### Install sentinelhub with pip

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/README.md

Install the sentinelhub package using pip. Use the `[AWS]` tag for additional AWS functionalities.

```bash
pip install sentinelhub
```

```bash
pip install sentinelhub[AWS]
```

--------------------------------

### Install sentinelhub with Conda

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/install.md

Install the sentinelhub package from the conda-forge channel using Conda.

```bash
conda install -c conda-forge sentinelhub
```

--------------------------------

### Start Batch Request Job

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Triggers the execution of a batch request job on the server. This starts the actual data processing.

```python
client.start_job(batch_request)
```

--------------------------------

### Start Batch Statistical Job

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_statistical.ipynb

Starts the execution of a batch statistical job using the `start_job` method of the Batch Statistical client. This method takes the request object as an argument.

```python
client.start_job(request)
```

--------------------------------

### Import Sentinel Hub and Utility Libraries

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities_cdse.ipynb

Imports necessary libraries for Sentinel Hub operations, including data handling, geometry manipulation, and request clients. Ensure these are installed.

```python
%matplotlib inline
# ruff: noqa: I001
import itertools
import tempfile
from pathlib import Path

import numpy as np
from shapely.geometry import MultiLineString, MultiPolygon, Polygon, box, shape

from sentinelhub import (
    CRS,
    BBox,
    BBoxSplitter,
    CustomGridSplitter,
    DataCollection,
    MimeType,
    MosaickingOrder,
    OsmSplitter,
    SentinelHubDownloadClient,
    SentinelHubRequest,
    TileSplitter,
    UtmGridSplitter,
    UtmZoneSplitter,
    read_data,
)
```

--------------------------------

### Initialize SentinelHubCatalog and Get Service Info

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Initializes the SentinelHubCatalog and retrieves basic service information. This is the first step to interact with the Sentinel Hub STAC catalog.

```python
from sentinelhub import SentinelHubCatalog

catalog = SentinelHubCatalog(config=config)

catalog.get_info()
```

--------------------------------

### Start Batch Request Analysis

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Initiates the analysis phase for a batch request on the server-side. This calculates information about tiles and processing units.

```python
client.start_analysis(batch_request)
```

--------------------------------

### Import necessary libraries for Sentinel Hub and data utilities

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Imports common libraries for data manipulation, plotting, and Sentinel Hub functionalities. Ensure these are installed before running.

```python
# Configure plots for inline use in Jupyter Notebook
%matplotlib inline

import datetime as dt

# Utilities
import boto3
import dateutil
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd

# Sentinel Hub
from sentinelhub import (
    CRS,
    BBox,
    ByocCollection,
    ByocCollectionAdditionalData,
    ByocCollectionBand,
    ByocTile,
    DataCollection,
    DownloadFailedException,
    MimeType,
    SentinelHubBYOC,
    SentinelHubRequest,
    SHConfig,
    bbox_to_dimensions,
)
```

--------------------------------

### Initialize WMS Request for Multiple Acquisitions in Time Window

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request_cdse.ipynb

Request all Sentinel-2 acquisitions within a specified date range by providing a tuple of start and end dates to the 'time' argument.

```python
wms_true_color_request = WmsRequest(
    data_collection=DataCollection.SENTINEL2_L1C.define_from("s2l1c", service_url=config.sh_base_url),
    layer="TRUE-COLOR-S2-L1C",
    bbox=betsiboka_bbox,
    time=("2017-12-01", "2017-12-31"),
    width=512,
    height=856,
    config=config,
)
```

--------------------------------

### Search Catalog Collections

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search.ipynb

Example of a search query for catalog collections, showing results with 'id' and 'properties' including 'datetime' and 'eo:cloud_cover'.

```python
search_results = data_collection.search( 
    bbox=BBox( (24.9, 49.9, 25.1, 50.1), crs=CRS.WGS84 ), 
    datetime=TimeInterval( '2020-12-11', '2020-12-12' ), 
    query= { 
        "eo:cloud_cover": { 
            "lt": 10 
        } 
    } 
)

for item in search_results:
    print(item['id'], item['properties']['datetime'], item['properties']['eo:cloud_cover'])

# Example output:
# S2B_MSIL2A_20201211T073720Z 2020-12-11T07:37:20Z 0.6
# S2B_MSIL2A_20201211T073219_N0214_R049_T39TVL_20201211T090759 2020-12-11T07:37:06Z 0.0
```

--------------------------------

### Import necessary libraries

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/statistical_request_cdse.ipynb

Imports required Python packages for data manipulation, plotting, and Sentinel Hub functionalities. Ensure 'geopandas', 'matplotlib', and 'seaborn' are installed.

```python
%matplotlib inline

import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

from sentinelhub import (
    CRS,
    BBox,
    DataCollection,
    Geometry,
    SentinelHubStatistical,
    SentinelHubStatisticalDownloadClient,
    SHConfig,
    parse_time,
)
```

--------------------------------

### Create Data Collection from Existing

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_collections.ipynb

Define a new data collection based on an existing one, modifying parameters such as the `service_url`. This example creates a Sentinel-2 L2A collection pointing to the MUNDI deployment.

```python
from sentinelhub import DataCollection

s2_l2a_mundi = DataCollection.define_from(
    DataCollection.SENTINEL2_L2A, "SENTINEL2_L2A_MUNDI", service_url="https://shservices.mundiwebservices.com"
)

s2_l2a_mundi
```

--------------------------------

### Import Visualization and Data Handling Libraries

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities_cdse.ipynb

Imports additional libraries required for data visualization and advanced handling, such as GeoPandas, Matplotlib, and Xarray. These might require separate installation.

```python
import geopandas as gpd
import matplotlib.pyplot as plt
import rioxarray  # noqa: F401 # Its necesary for xarray.open_mfdataset() with engine `rasterio`
import xarray as xr  # It may need Dask library https://docs.dask.org/en/stable/install.html
from matplotlib.patches import Polygon as PltPolygon
from mpl_toolkits.basemap import Basemap  # Available here: https://github.com/matplotlib/basemap
```

--------------------------------

### WMS Request with Custom Evalscript

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

This example shows how to define a custom evalscript in Python and use it in a WMS request via the CustomUrlParam.EVALSCRIPT parameter. This allows for custom image processing directly on the Sentinel Hub.

```python
# by Braaten, Cohen, Yang 2015
my_evalscript = """
var bRatio = (B01 - 0.175) / (0.39 - 0.175);
var NGDR = (B01 - B02) / (B01 + B02);

function clip(a) {
    return a>0 ? (a<1 ? a : 1) : 0;
}

if (bRatio > 1) {
    var v = 0.5*(bRatio - 1);
    return [0.5*clip(B04), 0.5*clip(B03), 0.5*clip(B02) + v];
}

if (bRatio > 0 && NGDR > 0) {
    var v = 5 * Math.sqrt(bRatio * NGDR);
    return [0.5 * clip(B04) + v, 0.5 * clip(B03), 0.5 * clip(B02)];
}

return [2*B04, 2*B03, 2*B02];
"""

evalscript_wms_request = WmsRequest(
    data_collection=DataCollection.SENTINEL2_L1C,
    layer="TRUE-COLOR-S2-L1C",  # Layer parameter can be any existing Sentinel-2 L1C layer
    bbox=betsiboka_bbox,
    time="2017-12-20",
    width=512,
    custom_url_params={CustomUrlParam.EVALSCRIPT: my_evalscript},
    config=config,
)

evalscript_wms_data = evalscript_wms_request.get_data()
plot_image(evalscript_wms_data[0])
```

--------------------------------

### Obtain Tile Paths from AWS

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Use the previously defined function to get a list of tile paths from a specific S3 bucket and date range. Ensure the 'config' object is properly initialized.

```python
tiles_path = list_objects_path(
    bucket="sentinel-s2-l2a-mosaic-120", year_count=1, month_count=1, day_count=1, config=config
)
```

--------------------------------

### WMS Request to Read Data from Disk

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

Create a new WMS request, pointing to the same `data_folder` where data was previously saved. This setup allows the library to load data from disk instead of re-downloading.

```python
wms_bands_request_from_disk = WmsRequest(
    data_collection=DataCollection.SENTINEL2_L1C,
    data_folder="test_dir",
    layer="BANDS-S2-L1C",
    bbox=betsiboka_bbox,
    time="2017-12-15",
    width=512,
    height=856,
    image_format=MimeType.TIFF,
    config=config,
)
```

--------------------------------

### Get Sentinel-2 Acquisitions in Time Window (True Color)

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

Retrieves all Sentinel-2 L1C true color images acquired within a specified date range. The `time` argument accepts a tuple of start and end dates. The number of available images and their acquisition dates can be accessed via `get_data()` and `get_dates()` respectively.

```python
wms_true_color_request = WmsRequest(
    data_collection=DataCollection.SENTINEL2_L1C,
    layer="TRUE-COLOR-S2-L1C",
    bbox=betsiboka_bbox,
    time=("2017-12-01", "2017-12-31"),
    width=512,
    height=856,
    config=config,
)
```

```python
wms_true_color_img = wms_true_color_request.get_data()
```

```python
print("There are %d Sentinel-2 images available for December 2017." % len(wms_true_color_img))
```

```python
plot_image(wms_true_color_img[2])
```

```python
print("These %d images were taken on the following dates:" % len(wms_true_color_img))
for index, date in enumerate(wms_true_color_request.get_dates()):
    print(" - image %d was taken on %s" % (index, date))
```

--------------------------------

### Initialize Sentinel Hub Client and Configuration

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/session_sharing.ipynb

Initializes the Sentinel Hub client with configuration, asserting the minimum package version. Ensure your OAuth client ID and secret are provided in the SHConfig.

```python
from sentinelhub import SentinelHubDownloadClient, SentinelHubSession, SHConfig, __version__

assert __version__ >= "3.6.0", "The minimal required package version for this tutorial is 3.6.0"

config = SHConfig()
# config.sh_client_id = ""
# config.sh_client_secret = ""

if not config.sh_client_id or not config.sh_client_secret:
    print("Please provide the credentials (OAuth client ID and client secret).")

# The following endpoint can be accessed only if a user is authenticated:
EXAMPLE_URL = "https://services.sentinel-hub.com/oauth/tokeninfo"
```

--------------------------------

### Initialize WebFeatureService

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Initializes the `WebFeatureService` client for interacting with WFS services. Requires the `sentinelhub` library.

```python
from sentinelhub import WebFeatureService
```

--------------------------------

### Configure Sentinel Hub Instance

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

Initializes the Sentinel Hub configuration. A warning is printed if the instance ID is not set, which is required for OGC functionality.

```python
from sentinelhub import SHConfig

config = SHConfig()

if config.instance_id == "":
    print("Warning! To use OGC functionality of Sentinel Hub, please configure the `instance_id`.")
```

--------------------------------

### Initialize UtmGridSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Instantiate `UtmGridSplitter` with a list of areas, a base CRS, and desired bounding box dimensions in meters. This splitter also enforces consistent bounding box sizes in meters.

```python
utm_grid_splitter = UtmGridSplitter([hawaii_area], CRS.WGS84, (50000, 50000))
```

--------------------------------

### Configure Sentinel Hub Instance

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Sets up the configuration for Sentinel Hub, allowing for an optional instance ID to be specified. This is a prerequisite for many Sentinel Hub operations.

```python
INSTANCE_ID = ""

config = SHConfig()
if INSTANCE_ID:
    config.instance_id = INSTANCE_ID
```

--------------------------------

### Initialize SHConfig

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/configure.md

Instantiate the SHConfig class to use default configuration values. The output shows the default fields and their initial empty or default values.

```python
from sentinelhub import SHConfig

config = SHConfig()

config
```

--------------------------------

### View Sentinel Hub Configuration Options

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/configure.md

This command displays available configuration options for Sentinel Hub. Use it to explore and understand the various settings you can adjust.

```bash
$ sentinelhub.config --help
```

--------------------------------

### Get All Collections and Filter BYOC/Batch

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Retrieves all available data collections from the catalog and filters out BYOC and batch collections. Use this to get a list of standard satellite data collections.

```python
collections = catalog.get_collections()

collections = [collection for collection in collections if not collection["id"].startswith(("byoc", "batch"))]

collections
```

--------------------------------

### Configure Sentinel Hub credentials

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Sets up Sentinel Hub configuration, including client ID and secret. It's recommended to store these in a config.toml file, but they can also be provided directly.

```python
# Insert your credentials here in case you don't already have them in the config.toml file:
SH_CLIENT_ID = ""
SH_CLIENT_SECRET = ""

config = SHConfig()

if SH_CLIENT_ID and SH_CLIENT_SECRET:
    config.sh_client_id = SH_CLIENT_ID
    config.sh_client_secret = SH_CLIENT_SECRET

if not config.sh_client_id or not config.sh_client_secret:
    print("Warning! To use Sentinel Hub BYOC API, please provide the credentials (client ID and client secret).")
```

--------------------------------

### Iterate Over BYOC Collections

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Get an iterator for all BYOC collections. This is efficient for handling a large number of collections.

```python
collections_iterator = byoc.iter_collections()
```

--------------------------------

### Configure Sentinel Hub Configuration

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Loads Sentinel Hub configuration. It's important to configure the `instance_id` for WFS functionality.

```python
from sentinelhub import SHConfig

config = SHConfig()

if config.instance_id == "":
    print("Warning! To use WFS functionality, please configure the `instance_id`.")
```

--------------------------------

### Get a BYOC Collection

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Retrieve a specific BYOC collection by its ID. Ensure you have the collection ID available.

```python
my_collection = byoc.get_collection(created_collection["id"])
```

--------------------------------

### Initialize Batch Statistical Client

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_statistical.ipynb

Initializes a Batch Statistical client instance, which is used to manage batch statistical requests. The client requires a configuration object.

```python
client = SentinelHubBatchStatistical(config)
```

--------------------------------

### Plot BYOC tiles on a map

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Visualize the spatial distribution of BYOC tiles using `geopandas.plot`. This requires matplotlib to be installed.

```python
fig, ax = plt.subplots(figsize=(17, 8))
tiles_gdf.plot(ax=ax);
```

--------------------------------

### Initialize and use OsmSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities_cdse.ipynb

Initializes an OsmSplitter with a given area, CRS, and zoom level. It then retrieves and prints the first bounding box and info list.

```python
osm_splitter = OsmSplitter([hawaii_area], CRS.WGS84, zoom_level=10)

print(repr(osm_splitter.get_bbox_list()[0]))
print(osm_splitter.get_info_list()[0])
```

--------------------------------

### Create a new BYOC Tile object

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Instantiate a ByocTile object with the path to the tile on an S3 bucket and its sensing time. The path should include a '(BAND)' placeholder if band names vary.

```python
new_tile = ByocTile(path="2019/11/27/28V/(BAND).tif", sensing_time=dt.datetime(2019, 11, 27))
```

--------------------------------

### Iterate over BYOC tiles

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Use `byoc.iter_tiles` to get an iterator for BYOC tiles within a collection. This is useful for processing tiles in batches.

```python
tile_iterator = byoc.iter_tiles(created_collection)
```

--------------------------------

### Create Shared Session for Multiprocessing

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/session_sharing.ipynb

Create a SentinelHubSession that will be shared with all worker processes in a multiprocessing setup. This session is the source for the shared token.

```python
# This will create a session that will be shared with all workers
session = SentinelHubSession(config)
```

--------------------------------

### Initialize and Use BBoxSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Initializes BBoxSplitter with an area, CRS, and grid dimensions. It then retrieves and prints the area's bounding box and a sample of the split bounding boxes with their associated information.

```python
bbox_splitter = BBoxSplitter(
    [hawaii_area], CRS.WGS84, (5, 4)
)  # bounding box will be split into grid of 5x4 bounding boxes

print("Area bounding box: {}\n".format(bbox_splitter.get_area_bbox().__repr__()))

bbox_list = bbox_splitter.get_bbox_list()
info_list = bbox_splitter.get_info_list()

print(
    "Each bounding box also has some info how it was created.\nExample:\nbbox: {}\ninfo: {}\n".format(
        bbox_list[0].__repr__(), info_list[0]
    )
)
```

--------------------------------

### Configure and Create Statistical Requests

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/statistical_request_cdse.ipynb

Sets up the aggregation parameters and defines the statistical calculations for the requests. It then iterates through provided geographic shapes to create individual SentinelHubStatistical requests.

```python
aggregation = SentinelHubStatistical.aggregation(
    evalscript=features_evalscript, time_interval=yearly_time_interval, aggregation_interval="P1D", resolution=(10, 10)
)

calculations = {"default": {"statistics": {"default": {"percentiles": {"k": [5, 50, 95]}}}}}}

features_requests = []
for geo_shape in polygons_gdf.geometry.values:
    request = SentinelHubStatistical(
        aggregation=aggregation,
        input_data=[
            SentinelHubStatistical.input_data(
                DataCollection.SENTINEL2_L2A.define_from("s2l2a", service_url=config.sh_base_url)
            )
        ],
        geometry=Geometry(geo_shape, crs=CRS(polygons_gdf.crs)),
        calculations=calculations,
        config=config,
    )

    features_requests.append(request)
```

--------------------------------

### Get the Next BYOC Collection from Iterator

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Retrieve the next BYOC collection from an iterator. Useful when you only need to process collections one by one.

```python
my_collection_using_next = next(collections_iterator)
print("name:", my_collection_using_next["name"])
print("id:", my_collection_using_next["id"]) 
```

--------------------------------

### Get and Print TileSplitter Information

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Retrieves and prints the number of bounding boxes, the first bounding box representation, and the information list for the TileSplitter.

```python
tile_bbox_list = tile_splitter.get_bbox_list()

print(len(tile_bbox_list))
print(tile_bbox_list[0].__repr__())
print(tile_splitter.get_info_list()[0])
```

--------------------------------

### Get Failed Statistical Requests

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_statistical.ipynb

Retrieves a list of any failed requests from the batch statistical processing. An empty list indicates all requests were successful.

```python
failed_requests = get_failed_statistical_requests(results)
failed_requests
```

--------------------------------

### Configure and Create Statistical Requests

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/statistical_request.ipynb

Sets up aggregation parameters and defines calculations for statistical requests. It then iterates through provided polygons to create individual requests.

```python
aggregation = SentinelHubStatistical.aggregation(
    evalscript=features_evalscript, time_interval=yearly_time_interval, aggregation_interval="P1D", resolution=(10, 10)
)

calculations = {"default": {"statistics": {"default": {"percentiles": {"k": [5, 50, 95]}}}}}}

features_requests = []
for geo_shape in polygons_gdf.geometry.values:
    request = SentinelHubStatistical(
        aggregation=aggregation,
        input_data=[SentinelHubStatistical.input_data(DataCollection.SENTINEL2_L2A)],
        geometry=Geometry(geo_shape, crs=CRS(polygons_gdf.crs)),
        calculations=calculations,
        config=config,
    )

    features_requests.append(request)
```

--------------------------------

### Configure Sentinel Hub credentials

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_statistical.ipynb

Initializes Sentinel Hub configuration. If client ID or secret are not set, a warning is printed, as they are required for using the Batch Statistical API.

```python
config = SHConfig()

if not config.sh_client_id or not config.sh_client_secret:
    print("Warning! To use Batch Statistical API, please provide the credentials (OAuth client ID and client secret).")
```

--------------------------------

### Get Bounding Boxes from UtmZoneSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Retrieve a list of `BBox` objects representing the split UTM zones. Each `BBox` is in the CRS of its corresponding UTM zone.

```python
utm_zone_splitter.get_bbox_list()
```

--------------------------------

### Get Specific Tiling Grid Definition

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Fetches the definition of a specific tiling grid by its ID. Replace `GRID_ID` with the desired grid's identifier.

```python
# Specify grid ID here:
GRID_ID = 1

client.get_tiling_grid(GRID_ID)
```

--------------------------------

### Display First Result

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Prints the first result obtained from the WFS search. This shows the structure and available properties for a single data product.

```python
results[0]
```

--------------------------------

### Initialize UtmZoneSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Instantiate `UtmZoneSplitter` with a list of areas, a base CRS, and desired bounding box dimensions in meters. The `reduce_bbox_sizes` option is not available for this splitter.

```python
utm_zone_splitter = UtmZoneSplitter([hawaii_area], CRS.WGS84, (50000, 50000))
```

--------------------------------

### Configure S3 Input and Output Paths

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_statistical.ipynb

Defines the S3 bucket paths for input data and output storage. Ensure the IAM user has the necessary permissions to access these locations.

```python
INPUT_PATH = "s3://<my-bucket>/<path-to-relevant-subfolder>/reprojected-geoms.gpkg"
OUTPUT_PATH = "s3://<my-bucket>/<path-to-relevant-subfolder>/output/"
```

--------------------------------

### Create Pandas DataFrame from BYOC Collections

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Convert all BYOC collections into a pandas DataFrame for easier data manipulation and analysis. This requires the pandas library to be installed.

```python
my_collections_df = pd.DataFrame(data=list(byoc.iter_collections()))
my_collections_df[["id", "name", "created"]].head()
```

--------------------------------

### Get Shape of a Specific Sentinel-2 Band

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

Access and print the shape of a specific band (e.g., the 13th band, which is B12) from the downloaded Sentinel-2 image data.

```python
wms_bands_img[-1][:, :, 12].shape
```

--------------------------------

### Configure BYOC Collection with Manual Band Settings

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Use this when you need to explicitly define bands for ingestion, such as for true color TIFFs. Ensure the `ByocCollectionAdditionalData` and `ByocCollectionBand` objects are correctly populated with source, band index, bit depth, and sample format.

```python
band_config = ByocCollectionAdditionalData(
    bands={
        # source should be the string inside (BAND) placeholder
        "R": ByocCollectionBand(source="RGB", band_index=1, bit_depth=8, sample_format="UINT"),
        "G": ByocCollectionBand(source="RGB", band_index=2, bit_depth=8, sample_format="UINT"),
        "B": ByocCollectionBand(source="RGB", band_index=3, bit_depth=8, sample_format="UINT"),
    }
)
byoc_manual_config = ByocCollection(
    name="byoc-manual-config", s3_bucket="byoc-config-demo", additional_data=band_config
)

byoc_manual_config_collection = byoc.create_collection(byoc_manual_config)
```

```python
byoc_manual_config_collection["isConfigured"]
```

```python
byoc_manual_config_collection["additionalData"]
```

```python
added_tile = byoc.create_tile(byoc_manual_config_collection, example_tile)
added_tile
```

```python
added_tile_info = byoc.get_tile(collection=byoc_manual_config_collection["id"], tile=added_tile["id"])
added_tile_info["status"]
```

--------------------------------

### Get Tile Sensing Time

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Retrieves the sensing time of a tile, either by parsing a string or directly from a dataclass object. This is often needed for time-interval filtering.

```python
tile_time = dateutil.parser.parse(tiles[0]["sensingTime"])
```

```python
tile_time = tile_dataclass.sensing_time
```

--------------------------------

### Import Sentinel Hub and Utility Libraries

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Imports necessary libraries for Sentinel Hub operations, including data handling, bounding box splitting, and request management. Also imports auxiliary libraries for visualization and data manipulation.

```python
%matplotlib inline
# ruff: noqa: I001
import itertools
import tempfile
from pathlib import Path

import numpy as np
from shapely.geometry import MultiLineString, MultiPolygon, Polygon, box, shape

from sentinelhub import (
    CRS,
    BBox,
    BBoxSplitter,
    CustomGridSplitter,
    DataCollection,
    MimeType,
    MosaickingOrder,
    OsmSplitter,
    SentinelHubDownloadClient,
    SentinelHubRequest,
    TileSplitter,
    UtmGridSplitter,
    UtmZoneSplitter,
    read_data,
)
```

```python
import geopandas as gpd
import matplotlib.pyplot as plt
import rioxarray  # noqa: F401 # Its necesary for xarray.open_mfdataset() with engine `rasterio`
import xarray as xr  # It may need Dask library https://docs.dask.org/en/stable/install.html
from matplotlib.patches import Polygon as PltPolygon
from mpl_toolkits.basemap import Basemap  # Available here: https://github.com/matplotlib/basemap
```

--------------------------------

### Get Collection Information

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Retrieve detailed information about a specific data collection, such as Sentinel-1 GRD. This method is useful for understanding the properties and available parameters of a dataset.

```python
catalog.get_collection(DataCollection.SENTINEL1_EW)
```

--------------------------------

### Execute Raw Dictionary Request

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/process_request.ipynb

Create and execute a download request using a raw dictionary. Ensure the 'content-type' header is set to 'application/json' for POST requests.

```python
# create request
download_request = DownloadRequest(
    request_type="POST",
    url="https://services.sentinel-hub.com/api/v1/process",
    post_values=request_raw_dict,
    data_type=MimeType.TIFF,
    headers={"content-type": "application/json"},
    use_session=True,
)

# execute request
client = SentinelHubDownloadClient(config=config)
img = client.download(download_request)
```

--------------------------------

### Get a specific feature by ID

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search.ipynb

Use `catalog.get_feature` to retrieve information about a specific satellite tile when you have its ID. This requires the data collection and the feature ID as arguments.

```python
catalog.get_feature(DataCollection.SENTINEL2_L2A, "S2A_MSIL2A_20210125T073201_N0214_R049_T39TWK_20210125T105105")
```

--------------------------------

### Configure Sentinel Hub Catalog API Credentials

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Configures the Sentinel Hub client with credentials. Prints a warning if client ID or secret are missing, which are required for Catalog API usage.

```python
config = SHConfig()

if config.sh_client_id == "" or config.sh_client_secret == "":
    print("Warning! To use Sentinel Hub Catalog API, please provide the credentials (client ID and client secret).")
```

--------------------------------

### Get Tile Ingestion Status

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Retrieves information about a specific tile within a BYOC collection to check its ingestion status. The status will change from 'WAITING' as the ingestion progresses.

```python
added_tile_info = byoc.get_tile(collection=byoc_auto_config_collection["id"], tile=added_tile["id"])
added_tile_info["status"]
```

--------------------------------

### Configure Sentinel Hub Credentials

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/process_request.ipynb

Initializes Sentinel Hub configuration. Ensure your OAuth client ID and client secret are provided for Process API access.

```python
from sentinelhub import SHConfig

config = SHConfig()

if not config.sh_client_id or not config.sh_client_secret:
    print("Warning! To use Process API, please provide the credentials (OAuth client ID and client secret).")
```

--------------------------------

### Configure SHConfig with a Profile

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/docs/source/configure.md

Load configuration from a specific profile in the TOML file. If no profile is specified, the default profile is used.

```python
from sentinelhub import SHConfig

config = SHConfig("myprofile")
```

--------------------------------

### Get the first tile from a BYOC collection iterator

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Use `next()` with `byoc.iter_tiles` to efficiently retrieve only the first tile from a large collection, avoiding loading all tile information.

```python
tile = next(byoc.iter_tiles(created_collection))
```

--------------------------------

### Get a specific feature by ID

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search_cdse.ipynb

Use this method to retrieve information about a specific satellite tile when its ID is known. This requires the data collection and the unique feature ID.

```python
catalog.get_feature(
    DataCollection.SENTINEL2_L2A,
    "S2A_MSIL2A_20210125T073201_N0500_R049_T39TWK_20230528T024029.SAFE",
)
```

--------------------------------

### Create BBox Object and Calculate Image Dimensions

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/process_request.ipynb

Initializes a BBox object with WGS84 coordinates and calculates the desired output image dimensions in pixels based on a specified resolution in meters.

```python
resolution = 60
betsiboka_bbox = BBox(bbox=betsiboka_coords_wgs84, crs=CRS.WGS84)
betsiboka_size = bbox_to_dimensions(betsiboka_bbox, resolution=resolution)

print(f"Image shape at {resolution} m resolution: {betsiboka_size} pixels")
```

--------------------------------

### List Available Tiling Grids

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Retrieves and lists all available tiling grids supported by the Batch API. This helps in selecting an appropriate grid for batch processing tasks.

```python
list(client.iter_tiling_grids())
```

--------------------------------

### Get a specific tile from a BYOC collection

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/byoc_request.ipynb

Use this method to retrieve a single tile by providing its collection ID and tile ID. This is useful when you know the exact tile you need.

```python
tile = byoc.get_tile(collection=created_collection["id"], tile=created_tile["id"])
```

```python
tile
```

--------------------------------

### Initialize and use TileSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities_cdse.ipynb

Initializes a TileSplitter with a given area, CRS, time interval, and data collection. It then prints the number of bounding boxes and details of the first bounding box and info.

```python
tile_splitter = TileSplitter(
    [hawaii_area],
    CRS.WGS84,
    ("2017-10-01", "2017-11-01"),
    data_collection=DataCollection.SENTINEL2_L1C.define_from("s2l1c", service_url=config.sh_base_url),
    config=config,
)

tile_bbox_list = tile_splitter.get_bbox_list()

print(len(tile_bbox_list))
print(tile_bbox_list[0].__repr__())
print(tile_splitter.get_info_list()[0])
```

--------------------------------

### Get Geometries from Split Bounding Boxes

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Retrieves a list of geometries, where each geometry is the intersection of a split bounding box with the original area of interest. This is useful for processing only the relevant parts of each tile.

```python
geometry_list = bbox_splitter.get_geometry_list()

geometry_list[0]
```

--------------------------------

### WMS Request with Evalscript URL

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

This example demonstrates how to use a remote evalscript by providing its URL to the CustomUrlParam.EVALSCRIPTURL parameter in a WMS request. This is useful for utilizing pre-existing, complex processing scripts.

```python
my_url = "https://raw.githubusercontent.com/sentinel-hub/custom-scripts/master/sentinel-2/ndmi_special/script.js"

evalscripturl_wms_request = WmsRequest(
    data_collection=DataCollection.SENTINEL2_L1C,
    layer="TRUE-COLOR-S2-L1C",  # Layer parameter can be any existing Sentinel-2 L1C layer
    bbox=betsiboka_bbox,
    time="2017-12-20",
    width=512,
    custom_url_params={CustomUrlParam.EVALSCRIPTURL: my_url},
    config=config,
)

evalscripturl_wms_data = evalscripturl_wms_request.get_data()
plot_image(evalscripturl_wms_data[0])
```

--------------------------------

### Configure S3 Bucket for Batch Output

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Sets up the S3 bucket path and IAM role ARN for the Batch Process to write output data. Follow the provided instructions for setting up AWS IAM Assume Role Workflow.

```python
BUCKET_PATH = "???"

ROLE_ARN = "???”
```

--------------------------------

### Configure Sentinel Hub credentials

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Initializes Sentinel Hub configuration. It includes a check to ensure that client ID and client secret are provided, which are necessary for using the Sentinel Hub Process API.

```python
config = SHConfig()

if config.sh_client_id == "" or config.sh_client_secret == "":
    print("Warning! To use Sentinel Hub Process API, please provide the credentials (client ID and client secret).")
```

--------------------------------

### NDVI Evalscript

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

An example evalscript for calculating the Normalized Difference Vegetation Index (NDVI) using Sentinel-2 Level 2A data. It requires B04 (Red) and B08 (NIR) bands.

```javascript
//VERSION=3

function evaluatePixel(samples) {
    let val = index(samples.B08, samples.B04);
    return [val];
}

function setup() {
  return {
    input: [{
      bands: [
        "B04",
        "B08",
        "dataMask"
      ]
    }],
    output: {
      bands: 1
    }
  }
}
```

--------------------------------

### Initialize SentinelHubSession from Token

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/session_sharing.ipynb

Create a SentinelHubSession object from an existing token. By default, sessions created this way are non-refreshing.

```python
session = SentinelHubSession.from_token(token)

session
```

--------------------------------

### Import Sentinel Hub and Utilities

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/process_request.ipynb

Imports necessary libraries for Sentinel Hub requests, including data handling, CRS, bounding boxes, and plotting utilities.

```python
import datetime
import os

import matplotlib.pyplot as plt
import numpy as np

from sentinelhub import (
    CRS,
    BBox,
    DataCollection,
    DownloadRequest,
    MimeType,
    MosaickingOrder,
    SentinelHubDownloadClient,
    SentinelHubRequest,
    bbox_to_dimensions,
)

# The following is not a package. It is a file utils.py which should be in the same folder as this notebook.
from utils import plot_image
```

--------------------------------

### Plotting Data by Identifier

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_statistical.ipynb

Initializes a matplotlib figure and axes for plotting. It then iterates through groups of data, where each group is defined by a unique 'identifier'. This setup is typically followed by plotting logic within the loop.

```python
fig, ax = plt.subplots(figsize=(15, 8))

for _, group in dataframe.groupby("identifier"):
```

--------------------------------

### NDVI Evalscript for Sentinel Hub

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities_cdse.ipynb

An example evalscript for Sentinel Hub to calculate NDVI using Sentinel-2 L2A bands B04 and B08. It specifies input bands and output band count.

```python
ndvi_eval = """
//VERSION=3

function evaluatePixel(samples) {
    let val = index(samples.B08, samples.B04);
    return [val];
}

function setup() {
  return {
    input: [{
      bands: [
        "B04",
        "B08",
        "dataMask"
      ]
    }],
    output: {
      bands: 1
    }
  }
}
"""
```

--------------------------------

### Set up IPython Environment

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/process_request.ipynb

Configures the IPython environment for automatic reloading of modules and inline display of matplotlib plots.

```python
%reload_ext autoreload
%autoreload 2
%matplotlib inline
```

--------------------------------

### Define bounding box and dimensions

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/reading_pu_from_headers.ipynb

Sets up the geographical bounding box and desired output dimensions for the Sentinel Hub request using WGS84 coordinates.

```python
betsiboka_coords_wgs84 = (46.16, -16.15, 46.51, -15.58)
resolution = 60
betsiboka_bbox = BBox(bbox=betsiboka_coords_wgs84, crs=CRS.WGS84)
betsiboka_size = bbox_to_dimensions(betsiboka_bbox, resolution=resolution)
```

--------------------------------

### Search Sentinel-2 L2A with WFS

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/data_search.ipynb

Searches for Sentinel-2 L2A data using the Web Feature Service (WFS) with specified bounding box, time interval, and maximum cloud cover. Requires SHConfig setup.

```python
from sentinelhub import BBox, CRS, DataCollection, SHConfig, WebFeatureService

caspian_sea_bbox = BBox((49.9604, 44.7176, 51.0481, 45.2324), crs=CRS.WGS84)
time_interval = "2020-12-10", "2021-02-01"

wfs_iterator = WebFeatureService(
    caspian_sea_bbox, time_interval, data_collection=DataCollection.SENTINEL2_L2A, maxcc=0.05, config=config
)


results = list(wfs_iterator)
print("Total number of results:", len(results))

results[0]
```

--------------------------------

### Initialize Batch Process Client

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/batch_processing.ipynb

Initializes the BatchProcessClient for interacting with the Sentinel Hub Batch API (Version 2). Ensure your configuration object contains valid credentials and service URLs.

```python
client = BatchProcessClient(config=config)
```

--------------------------------

### WCS Request with Specified Resolution

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/ogc_request.ipynb

Use `WcsRequest` to specify spatial resolution in meters using `resx` and `resy` arguments, instead of image dimensions. This example requests a true color image with a resolution of 60m x 60m.

```python
wcs_true_color_request = WcsRequest(
    data_collection=DataCollection.SENTINEL2_L1C,
    layer="TRUE-COLOR-S2-L1C",
    bbox=betsiboka_bbox,
    time="2017-12-15",
    resx="60m",
    resy="60m",
    config=config,
)

wcs_true_color_img = wcs_true_color_request.get_data()
```

```python
print(
    "Single element in the list is of type = {} and has shape {}".format(
        type(wcs_true_color_img[-1]), wcs_true_color_img[-1].shape
    )
)
```

```python
plot_image(wcs_true_color_img[-1])
```

--------------------------------

### Initialize TileSplitter

Source: https://github.com/sentinel-hub/sentinelhub-py/blob/master/examples/large_area_utilities.ipynb

Initializes a TileSplitter for splitting areas into satellite tiles. Requires area, CRS, time interval, data collection, and configuration.

```python
tile_splitter = TileSplitter(
    [hawaii_area], CRS.WGS84, ("2017-10-01", "2017-11-01"), data_collection=DataCollection.SENTINEL2_L1C, config=config
)
```